esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Analysis of Schemes
### Security Analysis
The submitted access policy \(\mathcal{T}\) must not be satisfiable by any of the attribute sets for which \(\mathcal{A}\) queried private keys in the previous phase. \(\mathcal{C}\) randomly selects a bit \(\mu\in\{0,1\}\), encrypts the plaintext \(M_{\mu}\) under the access policy \(\mathcal{T}\), and selects a random number \(s\in\mathbb{Z}_{p}\), setting \(c=s\). The challenge ciphertext \(CT^{*}\) under \(\mathcal{T}^{*}\) is then computed as \(C_{1}=M_{\mu}\cdot e^{\prime}(g,g)^{\alpha s}=M_{\mu}\cdot e(g,g)^{(\rho+ab)c}=M_{ \mu}\cdot e(g,g)^{\rho c}\cdot R\) and \(C_{2}=f^{s}=f^{c}\). The goal of \(\mathcal{A}\) in the subsequent phase is to determine the value of \(\mu\), thereby distinguishing whether the ciphertext corresponds to \(M_{0}\) or \(M_{1}\). 4. Phase 2 The phase is identical to Phase 1. 5. Guess \(\mathcal{A}\) outputs a guess \(\mu^{\prime}\in\{0,1\}\), attempting to determine whether the challenge ciphertext \(CT^{*}\) corresponds to the plaintext \(M_{0}\) or \(M_{1}\). \(\mathcal{C}\) uses \(\mathcal{A}\)'s guess \(\mu^{\prime}\) to decide whether the input tuple \((g,A,B,C,R)\) provided during the setup corresponds to a valid DBDH instance \((\mu=0)\) or a random tuple \((\mu=1)\). If \(\mu=0\), then the tuple \((g,A,B,C,R)=\left(g,g^{a},g^{b},g^{c},e(g,g)^{abc}\right)\) represents a valid DBDH instance, and \(CT^{*}\) is computed using \(M_{0}\). The probability that \(\mathcal{A}\) correctly identifies \(\mu=0\) is as follows:  
\[Pr\big{[}\mu^{\prime}=\mu\bigm{|}\mu=0\big{]}=\frac{1}{2}+\epsilon.\]  
If \(\mu=1\), then the tuple \((g,A,B,C,R)=\left(g,g^{a},g^{b},g^{c},R\right)\), where \(R\) is a random element in \(\mathbb{G}_{T}\) and does not correspond to a valid DBDH instance, and \(CT^{*}\) is computed using \(M_{1}\). The probability that \(\mathcal{A}\) correctly identifies \(\mu=1\) is as follows:  
\[Pr\big{[}\mu^{\prime}=\mu\bigm{|}\mu=1\big{]}=\frac{1}{2}+\epsilon.\]  
The overall probability of \(\mathcal{A}\) successfully guessing \(\mu\) is the average of these two cases:  
\[Pr\big{[}\mu^{\prime}=\mu\big{]}=\frac{1}{2}\big{(}Pr\big{[}\mu^{\prime}=\mu \bigm{|}\mu=0\big{]}+Pr\big{[}\mu^{\prime}=\mu\bigm{|}\mu=1\big{]})=\frac{1}{ 2}+\epsilon.\]  
The advantage of \(\mathcal{A}\) in distinguishing whether \(CT^{*}\) corresponds to \(M_{0}\) or \(M_{1}\) is defined as follows:  
\[Adv_{\mathcal{A}}=Pr\big{[}\mu^{\prime}=\mu\big{]}-\frac{1}{2}=\epsilon.\]  
If \(\mathcal{A}\) achieves a non-negligible advantage \(\epsilon\), then \(\mathcal{C}\) can use \(\mathcal{A}\)'s output to distinguish between a valid DBDH instance and a random tuple. The probability of \(\mathcal{C}\) successfully breaking the DBDH assumption is as follows:  
\[Adv_{\mathcal{C}}=\frac{Adv_{\mathcal{A}}}{2}=\frac{\epsilon}{2}.\]  
This concludes the proof that the proposed scheme is CPA-secure under the DBDH assumption.
## 3 Research Methodology  
The following section outlines the methodology applied to collect and analyze relevant works to address key research questions within the research field of this study and derive new insights from publications. Specifically, the research seeks to answer the following questions:  
**RQ1.**: _What are the objectives in multi-project scheduling problems?_
**RQ2.**: _What are the different types of solving methods in multi-project scheduling problems?_
**RQ3.**: _What types of instance sets are used in multi-project scheduling problems?_
**RQ4.**: _What are the potential future research directions in multi-project scheduling problems?_  
To find the answers to the above questions, [PERSON] et al. [19] was used as a reference paper. An extensive Systematic Literature Review (SLR) was conducted, sourcing relevant publications from academic databases such as Scopus, Web of Science, and Google Scholar. The SLR spanned studies published between 2013 and 2023 and applied specific keywords--such as \"multi-project scheduling problem\", \"uncertainty\", and \"flexibility\"--to capture a comprehensive view of developments in this field. After the initial identification of 826 records, a screening process yielded a refined selection of 108 studies that fit the established criteria. These included both qualitative studies lacking formalized models and calculations and quantitative studies that presented specific models and statistical analyses.  
The primary distinction between qualitative and quantitative research lies in data collection and analysis. Quantitative research focuses on numerical data and employs statistical analysis, whereas qualitative research provides descriptive insights, exploring concepts and experiences in depth.  
Given this study's emphasis on evaluating optimization models, the final selection prioritized quantitative studies. A total of 53 quantitative papers were chosen, since this research is based on a previous study and aims to evaluate optimization models, supplemented by 23 additional papers containing relevant insights published in 2024 for further review. This process resulted in a comprehensive pool of 76 studies (as shown in Figure 1).  
The design of this study is oriented toward analysis of the RCMPSP and related variants through the detailed study of each included paper in terms of problem characteristics, solution approaches, proposed benchmarks, and analysis through various statistics.  
This methodology thus enabled a robust foundation for responding to each research question. While RQ2 was only partially addressed in [PERSON] et al. [19], this study significantly expanded upon it, providing a thorough and detailed investigation into the diverse solution methods used within multi-project scheduling.
## 4 Computational Complexity  
Figure 2 illustrates the block diagram with the proposed structure of the MIMO-IM-OFDM receiver. In comparison to a conventional MIMO-OFDM receiver, the proposed receiver simplifies the implementation of the channel estimator by utilizing matrix-vector multiplication. Additionally, it offers lower-complexity nonlinear detection and mitigates ICI through the use of IM and OSIC detection techniques. The channel estimation required for data detection is only performed on carrier positions using the two-dimensional Slepian BEM estimator [reference] at the OFDM block level. The proposed OSIC-IM detection stage has a complexity of \(\mathcal{O}((\mathcal{BV})^{3})\), where \(\mathcal{V}=N_{d}/N_{c}\) represents the ratio of non-zero carriers per sub-block. As a result, the computational complexity of the proposed technique is lower than that of a conventional MIMO-OFDM system with linear or nonlinear detection. Table 2 provides a comparison of receiver complexities.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **OFDM-MMSE** & **OFDM-ML** & **OFDM-OSIC** & **Proposed** \\ \hline Demodulation & \(\mathcal{O}(N\log_{2}N)\) & N/A & \(\mathcal{O}(N_{d}\log_{2}N_{d})\) & \(\mathcal{O}(\mathcal{B}\log_{2}\mathcal{B})\) \\ CE & \(\mathcal{O}(N^{2})\) & \(\mathcal{O}(N^{2})\) & \(\mathcal{O}(N_{d}^{2})\) & \(\mathcal{O}((\mathcal{BV})^{2})\) \\ Detection & \(\mathcal{O}(N^{3})\) & \(\mathcal{O}(\Omega^{N}N)\) & \(\mathcal{O}(N_{d}^{3})\) & \(\mathcal{O}((\mathcal{BV})^{3})\) \\ \hline \end{tabular}
\end{table}
Table 2: Computational complexity in terms of complex products.
# An Improved Method for Evaluating Image Sharpness Based on Edge Information
## 4 Conclusions  
This paper proposes an improved method based on edge information for evaluating image sharpness. Firstly, the Canny edge detection algorithm based on the activation mechanism was used to obtain the edge position. Then, the edge direction of each edge point was determined by the eight-neighborhood grayscale difference method; the histogram of edge width was established afterwards. Finally, a distance factor was introduced into the weighted average edge width solving model to obtain the sharpness evaluation index. By comparing the image evaluation performance when three distance factors were applied, a comprehensive analysis showed that the type 3 distance factor possessed better accuracy and predictive monotonicity. In addition, to verify the superiority of the evaluation method proposed in this paper, three evaluation methods were compared on  
Figure 23: Real shooting images: (**a**) toy; (**b**) vehicle; (**c**) building; (**d**) cup; (**e**) indoor; (**f**) door.  
Figure 24: Evaluation values of real shooting images.  
the LIVE database. The experimental results showed that, compared with the traditional Tenggrad function evaluation method, the method proposed in this paper was greatly improved in performance and can meet the requirement of image content irrelevance and subjective-objective consistency.  
Conceptualization, Z.L. and Z.G.; methodology, Z.L.; software, Z.L.; validation, J.W.; formal analysis, Y.C.; investigation, Z.L.; resources, Z.L.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, Z.G.; supervision, H.H.; project administration, H.H.; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Laboratory Fund Project (Grant No. 6142003190302) and University Scientific Research Plan Project (Grant No. ZK22-19).  
The data presented in this study are available on request from the author.  
The authors declare no conflict of interest.
# A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations
###### Abstract  
A non-uniform constellation (NUC) can effectively reduce the gap between bit-interleaved coded modulation (BICM) capacity and Shannon capacity, which has been utilized in recent wireless broadcasting systems. However, the soft demapping algorithm needs a lot of Euclidean distance (ED) calculations and comparisons, which brings great demapping complexity to NUC. A universal low-complexity NUC demapping algorithm is proposed in this paper, which creates subsets based on the quadrant of the two-dimensional NUC (2D-NUC) received symbol or the sign of the in-phase (I)/quadrature (Q) component of the one-dimensional NUC (1D-NUC) received symbol. ED calculations and comparisons are only carried out on the constellation points contained in subsets. To further reduce the number of constellation points contained in subsets, the proposed algorithm takes advantage of the condensation property of NUC and regards a constellation cluster containing several constellation points as a virtual point. Analysis and simulation results show that, compared with the Max-Log-MAP algorithm, the proposed algorithm can greatly reduce the demapping complexity of NUC with negligible performance loss.  
broadcasting; non-uniform constellation; soft demapping algorithm; demapping complexity Article  
A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations  
[PERSON] _et al._ (2020), 12 [PERSON] _et al._ (2020), and [PERSON] _et al._ (2020),*
###### Abstract  
Advanced digital solutions are increasingly introduced into manufacturing systems to make them more intelligent. Intelligent Waste Management Systems in industries allow for data collection and analysis to make better-informed decisions, monitor and manage processes remotely, and improve waste management. In many industries, scrap is collected in large waste containers located on the factory floor, usually close to its source. In most cases, monitoring of waste containers' fill levels is either manually performed by visual inspection by the operators working in close proximity or by employing intrusive mechanical systems such as weight sensors. This work presents a computer vision system that uses Deep Learning (DL) and Convolutional Neural Network (CNN) for the automated estimation of the fill level in industrial waste containers of metal scrap. The training method and parameters as well as the classification performance of VGG16 CNN that was retrained upon images collected in the field, are presented in detail. The proposed method has been validated upon an industrial case study from the copper tube production industry in which the fill level of two waste containers is estimated. A total of 9772 images were captured for the first container and 11,234 images for the second container. The VGG16 model achieved an accuracy from 77.5% to 95% on the testing dataset. The industrial case study demonstrates that the proposed computer vision system has sufficient accuracy for classifying the fill levels of metal scrap containers which allows for the development of waste management applications in industrial environments.  
Industry 4.0; digitalization of industry; scrap management; convolutional neural networks +
Footnote †: journal: Applied
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 3 Proposed Method
### Spatiotemporal Feature Extraction and Place Classifier  
The coordinates of the bounding boxes that enclose flame, smoke, and non-fire objects were projected on the \(n\times n\times d\) activation map to extract the spatial features. Here, we extracted them in the last layer of ResNet152 which is a backbone of object detector (Here, \(d=1024\)) [3]. For detected object regions of SRoF the weighted global average pooling (GAP) was taken on the bounding boxes, but just GAP was taken on the whole image when  
Figure 4: Sample images for training Faster R-CNN; (a), (b), and (c) are fire, smoke, and non-fire images, respectively [3].  
Figure 3: Faster R-CNN structure for fire detection [26].  
there is no SRoF. The \(d\)-dimensional feature for successive frames were fed into LSTM to explore temporal behavior of video clip.  
As previously mentioned, the place classification for location identification shares the same feature maps of the last layer of ResNet152. Because the location could be captured in a whole image, however, we took GAP for each feature map and constructed a \(d\)-dimensional feature to classify the location of the fire, as shown in Figure 5. The SoftMax classifier was used for every frame and the average probability of a particular location among six categories was calculated for a given set of video frames to make a stable scene-level decision. In our Bayesian net, we did not use all six classes of locations, but classified them into fire-prone or fire-irrelevant locations. Note that the place classifier was constructed efficiently by sharing the feature maps of Faster R-CNN and could provide stable classification by taking ensemble averages over consecutive frames in a scene.
# Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism
## 4 Experimental Design and Results
### Experimental Results
Compared to the multimodal feature fusion approach of FAN-BD, CBMA-ViT is less effective in fault diagnosis based on vibration and current signals. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Normal** & **Inner Fault** & **Outer Fault** & **Ball Fault** \\ \hline FAN-BD Precision & 97.0\% & 97.5\% & 97.5\% & 97.0\% \\ CBMA-ViT Precision & 91.3\% & 92.2\% & 95.0\% & 91.9\% \\ ViT Precision & 93.0\% & 90.4\% & 91.0\% & 89.5\% \\ Swin Transformer Precision & 93.9\% & 93.5\% & 93.9\% & 92.1\% \\ ConvNeXt Precision & 92.0\% & 90.5\% & 91.1\% & 91.5\% \\ \hline FAN-BD Recall & 97.5\% & 97.5\% & 97.5\% & 98.0\% \\ CBMA-ViT Recall & 90.0\% & 94.5\% & 95.0\% & 90.6\% \\ ViT Recall & 93.0\% & 89.5\% & 91.0\% & 89.5\% \\ Swin Transformer Recall & 93.0\% & 93.5\% & 93.0\% & 93.0\% \\ ConvNeXt Recall & 92.0\% & 90.5\% & 92.9\% & 91.5\% \\ \hline FAN-BD F1-score  
Figure 10 visualizes the classification results of CBMA-ViT, ViT, Swin Transformer, and ConvNeXt when using the testing sets by mapping them into a two-dimensional space using t-SNE. Each subplot in Figure 10 corresponds to one algorithm, allowing for a direct comparison of their performance with the FAN-BD results shown in Figure 7. The t-SNE algorithm reduces the dimensionality of the data, enabling an intuitive observation of the classification results through the color and position of the points. As shown in the figure, FAN-BD achieved the highest accuracy, with fewer than ten misclassifications of six hundred samples. In contrast, CBMA-ViT and ConvNeXt misclassified more than thirty points, while both the ViT model and Swin Transformer misclassified at least fifty points each. This highlights the superior performance of FAN-BD compared to the other algorithms. Figure 8: Confusion Matrix Diagram of CBMA-ViT, ViT, Swin Transformer, and ConvNeXt. Figure 9: Comparison of loss functions between FAN-BD, ViT, Swin Transformer, and ConvNeXt.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 2 Preliminaries of Conventional Soft Decision Decoder
### Ordered Statistics Decoding (OSD)  
The ordered statistics decoding algorithm [12] first sorts the received signal sequence based on the bit reliability in a descending order and obtains a permutation. By such a permutation, the new systematic generator matrix will be derived via the sorted generator matrix. The associated received signal sequence and word are obtained. The test error patterns are then generated to produce the possible codewords. Finally, the best codeword is selected on a maximum likelihood basis. Figure 3 illustrates the steps in the OSD approach. Details of OSD(\(w\)) are described as follows:  
* Step 1: Obtain the signal sequence \(y\) and obtain the ordered index set of reliability \(idx\). \(idx=\{\eta_{0},\eta_{1},\ldots,\eta_{n-1}\}\) where \(|r_{\eta_{i}}|\geq\left|r_{\eta_{i}}\right|\), for \(k>l\).
* Step 2: Permute \(\mathbf{G}\) according to \(idx\) and perform Gaussian elimination as a systematic form \(\mathbf{G}^{{}^{\prime}}=\left[\mathbf{I}_{k}\middle|\overset{\sim}{\mathbf{P }}\right]\). If the form is unavailable, reorder \(idx\) based on the reliability.
* Step 3: Permute \(y\) and \(r\) as \(y^{\prime}\) and \(r^{\prime}\) according \(idx\). Let \(\mathbf{m}^{\prime}\) be the first \(k\) bits of \(r^{\prime}\).
* Step 4: Generate the test error patterns \(ep=\left[ep_{0},ep_{1},\ldots,ep_{k-1}\right]\) for \(\mathbf{m}^{\prime}\), where the Hamming weight of \(ep\) is less than or equal to \(w\). As a result, there are possible \(\sum_{i=0}^{w}\binom{k}{i}\) candidate messages. The candidate codewords \(dr\) will be obtained by \(\mathbf{G}^{\prime}\).
* Step 5: Evaluate \(D_{1}(dr)=\{i|r_{i}=dr_{i},0\leq i\leq n-1\}\) and compute the maximum likelihood metric \(\sum_{i\in D_{1}(dr)}|y_{i}|\) of the codeword \(dr\)
* Step 6: Output the best codeword as \(dc\) with the minimum value of \(\sum_{i\in D_{1}(dr)}|y_{i}|\).  
The Chase-II decoding algorithm is constructed on the HDD, which is invented based on the specific code itself. The plenty of test error patterns will be fed into the HDD iteratively. It also leads to longer decoding time. The OSD method is involved in the huge computation on the Gaussian elimination based on the received signal sequence. As a new signal sequence is received, the OSD decoder will re-sort the bit reliability and perform the Gaussian elimination again. Such an approach is computationally intensive.  
Figure 3: The OSD(\(w\)) Decoding Algorithm.
## 2 Materials and Methods
### Untargeted Metabonomic Analysis  
Herbal decoctions from the four treatments were subjected to the UHPLC-MS analysis. A Vanquish UHPLC System (Thermo Fisher Scientific, Waltham, MA, USA) and an ACQUITY UPLC\({}^{\text{\textregistered}}\) HSS T3 (150 \(\times\) 2.1 mm, 1.8 \(\upmu\)m) (Waters, Milford, MA, USA) were used. For LC-ESI (+)-MS analysis, the mobile phases consisted of (B2) 0.1% formic acid in acetonitrile (\(v/v\)) and (A2) 0.1% formic acid in water (\(v/v\)). Separation was conducted under the following gradient: 0-1 min, 2% B2; 1-9 min, 2%-50% B2; 9-12 min, 50%-98% B2; 12-13.5 min, 98% B2; 13.5-14 min, 98%-2% B2; 14-20 min, 2% B2. For LC-ESI (-)-MS analysis, the analytes were carried out with (B3) acetonitrile and (A3) ammonium formate (5 mM).  
Mass spectrometric detection of metabolites was performed on a Q Exactive Focus (Thermo Fisher Scientific, Waltham, MA, USA) with an ESI ion source. Simultaneous MS1 and MS/MS (Full MS-ddMS2 mode, data-dependent MS/MS) acquisition was used. The parameters were as follows: sheath gas pressure, 30 arb; aux gas flow, 10 arb; spray voltage, 3.50 kV and \(-\)2.50 kV for ESI (+) and ESI (-), respectively; capillary temperature, 325 \({}^{\circ}\)C; MS1 range, \(m/z\) 100-1000; MS1 resolving power, 70,000 FWHM; number of data-dependent scans per cycle, 3; MS/MS resolving power, 17,500 FWHM; normalized collision energy, 30 eV.
## 2 Background
### LLMs as Evaluators  
Studies have also explored the use of LLMs as evaluators. One study [14] assesses LLM-generated reading comprehension materials by comparing them to human-written counterparts through both automated and human evaluations. The results show that LLM-generated materials are rated higher than human written ones, with similar findings irrespective of the type of the evaluation. Another study [15] examines whether detailed prompting or simpler methods like model perplexity align better with human judgments in natural language generation tasks. This study introduces a taxonomy that encompasses four evaluation categories: content, relevance, integrity, and engagement--tested in multiple benchmark datasets. BIG-bench benchmark [16] evaluates large language models across 204 diverse and challenging tasks, with human expert raters providing a baseline for performance comparison. Additionally, study [17] compares the topic detection and naming capabilities of LLMs to human participants, revealing significant differences and emphasizing the need for more nuanced evaluation metrics. Study [18] tests the sensitivity of LLMs as evaluators and shows that LLM-based evaluators exhibit confusion between evaluation aspects, and in [19], researchers shed even more light on the limitations of LLMs as evaluators, demonstrating their bias towards their own generations. Finally, EvaluLLM [20] is an application that allows users to set up, run, and review evaluations using LLMs, while [21] introduces EvalGen, which is a tool that aids in evaluating LLM outputs by using LLMs to generate and suggest evaluation criteria and assertions.  
Recent efforts have also examined the use of LLMs as evaluators of other LLMs. [PERSON] et al. argue for human alignment in LLM evaluation pipelines, showing that model-generated evaluations can diverge from human judgment without careful design [22]. In follow-up studies, [PERSON] et al. and [PERSON] et al. emphasize how structured, context-aware evaluation frameworks, such as those used in ACME, a chatbot supporting migrant counseling in Europe, can improve both system transparency and user satisfaction [23; 24].  
These insights validate our methodological choice to compare human expert assessments with LLM self-evaluations under a shared rubric, a hybrid approach that enables scalable pre-screening while preserving human oversight. The present study introduces a human-machine evaluation framework tailored to Greek, a low-resource language, and rooted in migrant integration tasks. By applying a shared, expert-informed rubric to both human and LLM evaluators, the study not only confirms strong alignment between the two but also demonstrates how LLMs can reliably support preliminary evaluations in socially critical domains. This dual approach, grounded in realistic content, offers a model for ethically conscious LLM selection in under-represented language contexts.
# Reliability-Based View Synthesis for Free Viewpoint Video
## 3 Proposed Framework  
In this section, the proposed approach will be presented in detail. The framework of the proposed synthesis algorithm is illustrated in Figure 1. There are mainly four techniques proposed in this framework: depth refinement, background modeling, reliability-based blending, and depth map processing method. These approaches will be discussed in Section 3.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 5 Proposed Method
### Proposed Method for Satellite
#### 5.2.3 Computational Complexity of Satellite-Based Localization  
Table 5 gives the number of multiplications required for some elements.