esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Chinese Domain Named Entity Recognition:
## 2 Related Work
### Lexicon-Based Chinese NER  
Chinese, unlike languages, such as English, that use spaces as separators, encounters the issue of word segmentation, where Chinese takes words as the basic semantic units. To better adapt to Chinese NER, recent studies have found that introducing Chinese lexicon-level features can enhance the performance of NER models. For example, [PERSON] et al. [24] proposed a lattice LSTM model for Chinese NER. This model is an extension of character-based NER models, incorporating words as input and additional gates to control information flow. The model explicitly utilizes word and word order information. However, due to the RNN structure adopted by the lattice LSTM model, it cannot capture long-distance dependencies, and the introduction of lexical information is lossy. To address these limitations, [PERSON] et al. [25] proposed the FLAT model for Chinese NER. This model is based on a transformer [26] to solve the lexical loss problem in lattice LSTM and uses relative position encoding to adapt a transformer to NER, enabling efficient GPU parallel computation. These methods, combined with pre-trained language models, explore Chinese NER and achieve SOTA performance on several Chinese benchmark datasets. However, [PERSON] et al. [27] and [PERSON] et al. [28] believe that existing lexicon-based models only conform to lexicon features through shallow and randomly initialized encoding layers without integrating them into the underlying layers of pre-trained language models to explore deep lexicon knowledge. To address this, they proposed methods to deeply integrate external lexicon features into the pre-trained language model BERT, achieving more effective fusion of entity boundaries and lexical information.  
All of these studies have explored the integration of Chinese lexicon-level features, but they did not address the practical issues faced by more detailed domains. Moreover, these methods rely on the quality of lexicons and have certain requirements for computational resources. In contrast, this paper focuses on extracting entities from the Chinese paleontology popular science domain, integrating Chinese lexicon-level features in a simple and effective manner while enhancing relative positional information. It provides a reference for addressing the practical issues faced by this domain.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 2 Threat Model and Security Guarantees
### Enhanced Security Guarantees and Key Verification  
Our framework provides the following enhanced security properties:  
1. Authenticated Encryption for Data in Transit: As previously defined, with the addition of _key verification_ to prevent key substitution attacks.
2. Integrity of FHE Public Keys: The protocol must ensure that the client uses a valid and correctly generated FHE key pair, preventing key substitution or corruption attacks by the server.
3. IND-CPA Security for Data in Use: As previously defined.
4. Computational Privacy with Operation Leakage: The operation types and parameters are transmitted in plaintext (protected only by AES). While this provides confidentiality against network attackers, it does leak information about computation patterns to the server. For applications requiring complete operational privacy, techniques like programmable bootstrapping or secure multi-party computation would be necessary.
## 5 Experimental Validation
### Binocular Camera Target Detection and Tracking
an average of 17.2 training images per person, whereas the test set contained 750 individuals with 19,732 images with an average of 26.3 test images per person. After partitioning the dataset, a feature extraction model was trained with 100 epochs. The tracking algorithms were evaluated using three metrics: multiple object tracking accuracy (MOTA), identification F1 (IDF1), and frame rate. The MOTA measures the performance of the tracker in object detection and trajectory maintenance, whereas IDF1 focuses more on the performance of the detector. IDF1 is used to assess the tracker's ability to accurately track the same object in a video over a long period by calculating the proportion of correct initial identity numbers to all identity numbers appearing in the video. The frame rate reflects the processing speed. The evaluation results of the DeepSORT algorithm are shown in Table 5.  
The evaluation results demonstrated that the tracking network could fulfill the tracking tasks in the experiments. Detection and tracking processes (a) Target detection using Yolo v5s: target detection was performed on each image using the Yolo v5s neural network to identify the location and class of each target. (b) Preprocessing the detection results: the detection results were converted to the format required by the DeepSORT algorithm; that is, they were converted to the center coordinates and bounding box size of each coordinate. (c) Running the DeepSORT algorithm: each target was tracked by the DeepSORT algorithm, which requires an initialized target list; thus, the ID of each target must be manually specified in the first frame. (d) Target-state update: for each new frame, the detection results were matched with the tracking results of the previous frame to update the state of each target, whereby the state estimation and prediction were generally based on the Kalman filter. (e) Output: the tracking results were output onto the image.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 6 Discussion
### Comparison with State-of-the-Art Methods  
#### 6.1.1 Detection Accuracy  
Adaptive-DecayRank consistently achieves higher detection accuracy across all datasets. On the **DARPA** dataset, it attains an average precision of **0.6455**, outperforming AnomRank (0.5665), DecayRank (0.572), DynAnom (0.5840), and SedanSpot (0.56). As shown in Table 4, this represents a notable **13.94%** improvement over AnomRank and a **12.86%** gain over DecayRank. Similarly, on the **CTU-13** dataset, Adaptive-DecayRank achieves an even higher precision of **0.6856**, improving over AnomRank (0.4118) by **66.49%** and DecayRank (0.6425) by **6.71%**.  
For the synthetic **RTM-10** dataset, which contains **10%** injected anomalies, our model achieves a precision of **0.6746**, significantly outperforming AnomRank (0.31) by **171.61%** and DecayRank (0.5865) by **15.02%**. The results are even more pronounced in the **RTM-30** dataset, where **30%** of nodes are anomalous. Here, our approach achieves **0.6976**, surpassing AnomRank (0.30) by **132.53%** and DecayRank (0.5699) by **22.41%**.  
Beyond precision, the AUC-ROC curve in Figure 5 highlights the model's ability to distinguish between normal and anomalous patterns. Adaptive-DecayRank attains the highest AUC score of **0.90** on DARPA, exceeding AnomRank (0.86) and DecayRank (0.83), reinforcing its capability to detect subtle structural deviations in dynamic graphs.  
Adaptive-DecayRank's superior performance stems from its ability to capture _short-term_ deviations and _micro-pattern_ changes in edge streams using an adaptive decay factor and Bayesian updates. Unlike static methods that treat all edges equally over time, our approach assigns higher weights to recent anomalies while gradually reducing the influence of older observations. This makes it particularly effective in dynamic environments where sudden structural changes or coordinated attacks occur.
# Review of Task-Scheduling Methods for Heterogeneous Chips
## 5 Research Status of Experiment
### Experimental Methods
#### 5.2.2 Software-in-Loop  
SIL simulation simulates heterogeneous chips through software that researchers can use to test the scheduling method without actual hardware. Common software-in-loop simulations include domain-specific system-on-chip simulation (DS3) [37], Multi2 Sim [46], Gem5 [47], and Simics[48]. The advantages of such platforms are their low cost, high  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Model-in-Loop** & **Software-in-Loop** & **Hardware-in-Loop** \\ \hline Experimental object & Task model & Software & Heterogeneous chip \\ \hline Precision & Low & Medium & High \\ \hline Number of test cases & High & Relatively High & Low \\ \hline Complexity & Simple & General & Complex \\ \hline Scenario & Simple & General & Complex \\ \hline Cost & Low & Low & High \\ \hline \multirow{4}{*}{Limitation} & Requires specialized & Limited hardware & Dependent on model \\  & equipment; high & interaction fidelity; & accuracy; may overlook \\  & implementation & potential timing & hardware-specific \\  & complexity and cost. & inaccuracies. & constraints. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of experimental methods.  
flexibility, and the ability to quickly iterate and test different scheduling methods. The main drawback, however, is that all of the features of the hardware may not be accurately reproduced.  
Figure 7 illustrates the DS3 lifecycle from job generation to task execution. First, the job generator injects the job into the job queue and loads the related tasks into the corresponding task queue. Next, the scheduler selects the task from the ready queue and assigns it to the idle processing unit for execution. In this process, the remaining tasks in the executable queue can be reloaded into the ready queue and scheduled again. Once the task is completed, it will be moved to the completed queue. DS3 can simulate benchmark applications and model them as directed acyclic graphs to evaluate the performance of different scheduling algorithms on target heterogeneous chip platforms. The list of the processing units and the computational cost and power consumption data of the application tasks are stored in the resource database. The job generator determines the average arrival interval of frames through a user-defined distribution and injects it into the simulation. The simulator calls the scheduler during each scheduling cycle, prepares the task list, and maps the tasks to the processing units based on the information in the resource database. Finally, the task is moved to the completed queue after completion.
## 3 Data Synthesis and Analysis  
This section covers the answers to the designed research questions stated in Section 3.1, with each subsection distinctly handling answers to each research question.  
RQ1. What Are the Various Nature-Inspired Meta-Heuristics Techniques That Have Been Hybridized with the K-Means Clustering Algorithm?  
Meta-heuristics techniques are developed for providing optimal solutions to optimization problems through iterative exploration and exploitation of the entire search space [20]. A number of these algorithms have been integrated with the traditional K-means algorithm to improve the process of data clustering. The following section presents the various nature-inspired meta-heuristics techniques that have been hybridized with the K-means clustering algorithm.
# Investigation of Reverse Swing and Magnus Effect on a Cricket Ball Using Particle Image Velocimetry
## 6 Conclusions  
The fluid dynamics around a double-sized scale model of a cricket ball, with and without backspin, were captured using particle image velocimetry measurements in an open-jet wind tunnel. Experiments were conducted at Reynolds numbers, degrees of surface roughness, and spin rates representative of match conditions. A laminar separation bubble was captured on the non-seam side of the ball over a range of Reynolds numbers corresponding to the critical flow regime. The laminar separation bubble was also present when the ball was subjected to backspin. This evidence reinforces an existing theory that a laminar separation bubble on the non-seam side is fundamental to the phenomenon of reverse swing. The conventional Magnus effect was also demonstrated, with the separation point on the upper (or retreating) side being delayed relative to the lower surface due to the reduced relative Reynolds number. The inverse Magnus effect was not observed at spin rates of up to \(\alpha=0.12\).  
Figure 12: Time-averaged velocity magnitude in the \(x\)-\(z\) plane at Re = \(1.94\times 10^{5}\) (U\({}_{\infty}\) = 20 m/s) showing (**a**) the symmetric flow field with no rotation and (**b**,**c**), the Magnus effect demonstrated at two different spin rates. Separation angles shown to nearest 5\({}^{\circ}\).  
**Author Contributions:** Conceptualization, J.A.S.; Data curation, E.H.; Formal analysis, R.W.J.; Investigation, E.H.; Methodology, R.W.J.; Project administration, G.D.L. and J.A.S.; Resources, J.A.S.; Supervision, J.A.S.; Writing-original draft, R.W.J.; Writing-review & editing, G.D.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
C\({}_{\rm y}\) non-dimensional side force  
\(d\) diameter [m]  
\(k\) size of roughness element [m]  
Re Reynolds number, \(\rm Re=\rho UL_{sc}d/\mu\)  
_U\({}_{sc}\)_ freestream velocity [m/s]  
\(V\) velocity magnitude [m/s]  
_x,y,z_ streamwise, spanwise and vertical axes  
_\(\alpha\)_ spin rate, \(\alpha=\Omega d/2U_{sc}\)  
_\(\beta\)_ angle between seam and freestream  
_\(\emptyset\)_ angle from the stagnation point  
_\(\emptyset_{\rm adv}\)_ separation angle on advancing surface  
_\(\emptyset_{\rm L}\)_ laminar separation angle  
_\(\emptyset_{\rm R}\)_ turbulent reattachment angle  
_\(\emptyset_{\rm net}\)_ separation angle on retreating surface  
_\(\emptyset_{\rm T}\)_ turbulent separation angle  
_\(\Omega\)_ rotational speed [rad/s]
## 4 Numerical Application
### Case Study 1  
This case study is useful to show the operation of the considered network without the support of distributed resources, that is DGs do not provide reactive power support and there is no DESS. In this case, the network is characterized by the presence of unbalances that sometimes exceed the imposed maximum limit (2%). As an example, Figure 4 shows the unbalance factor at each hour of a typical day of the summer season of the last year of the planning period (All of the figures reported hereinafter refer to the same typical day.). In this case it can be observed that the commercial line is characterized by quite high values of the unbalance factor which in two buses, in some hours of the day, even exceeds the limit. A similar behavior is observed also in one bus of the residential line.  
Line currents and bus voltages also show in some cases critical conditions. In Figure 5 the values of the line currents are reported in p.u. with reference to hour 21.00 showing a violation of the ampacity limits in some lines of the residential feeder. Figure 6 shows the voltage magnitude of the residential feeder at hour 21.00 of the typical day. Also in this case a violation of the limit can be observed. Critical conditions have been also observed in the other two feeders (here not reported for the sake of brevity) where, however, no violations are encountered.  
Figure 4: Case Study 1: Unbalance factor at each hour of a summer typical day.  
Figure 5: Case Study 1: Line currents of the residential feeder (phase no.1) at hour 21.00 of the typical day.
## 5 Results and Discussion  
### Baselines and Results Analysis  
We compared our proposed TS-CGANet with some state-of-the-art models. In addition to the four models introduced previously, we also selected three new full-band speech enhancement methods as the baseline models: GCRN, CTS-Net, and DeepFilterNet. In addition, we also selected an ultra-wideband speech enhancement method, S-DCCRN, for comparison.  
The GCRN proposes a novel approach to monaural speech enhancement using gated convolutional recurrent networks. The GCRN utilizes gated convolutions, which selectively allow or block information flow to effectively capture global and local spectral features of the speech signal. This is important for capturing complex spectral mappings that may be present in noisy and clean speech signals. The CTS-Net proposes a novel two-stage approach for enhancing monaural speech signals. The first stage of the approach involves learning a complex spectral mapping to clean speech using a deep neural network. The second stage then applies a non-linear mapping to further refine the spectral features of the enhanced speech. The DeepFilterNet utilizes a deep convolutional neural network (CNN) to learn a mapping function to clean speech, where the network is trained with a novel deep filtering loss function. The loss function allows the network to learn and optimize both the magnitude and phase components of the spectral features of the speech signal, thereby resulting in enhanced speech with improved quality. The S-DCCRN is a deep learning-based approach for speech enhancement. It is designed to enhance speech signals that are corrupted by noise in a wide frequency range. The model uses a dual-path complex convolutional recurrent neural network (DCCRN) architecture that includes both a sub-band processing path and a full-band processing path.  
Table 2 reveals several notable findings. Firstly, in all objective metrics, the TS-CGANet surpassed the compressed psychoacoustic excitation features. Secondly, the proposed spectrum splitting and sub-band fusion strategy were effective, as the TS-CGANet outperformed the one-stage baselines by a significant margin. Thirdly, the TS-CGANet exhibited superior performance compared to the SF-Net, which also utilizes spectrum splitting, particularly in the PESQ. The utilization of the proposed complex and real dual-path CGA network enabled better speech enhancement.
## 3 Latency-Classification-Based Deadline-Aware Task Offloading Algorithm (LCDA)  
As shown in Figure 3, we consider a typical MEC environment consisting of a user device, an edge cloud server, and a central cloud server. Typically, a user executes tasks on his/her device and offloads tasks to the edge server in accordance with an offloading policy.  
Figure 2: An illustrative example of the LCDA (latency classification based deadline aware task offloading algorithm).  
When a task running on a user device is offloaded to an edge cloud server due to a lack of resources, the LCDA performs a task classification process based on latency level. A task is either categorized as latency-sensitive or latency-tolerant. Next, by considering the weight values of tasks, we offload latency-tolerant tasks preferentially. The LCDA periodically checks the status of a given task and the utilization of server resources. The detailed descriptions of proposed algorithms' components are provided below.
# Mask-Wearing Detection in Complex Environments Based on Improved YOLOv7
## 4 Experiments and Results Analysis
### Experimental Design and Result Analysis
#### 4.4.3 Performance Comparison and Analysis with Mainstream Detection Models  
To quantitatively evaluate the performance of the improved model, this paper compares the improved model with Faster R-CNN, YOLOv5s, YOLOX, YOLOv7-tiny [30], the original YOLOv7 model, and the latest YOLOv8s model based on detection metrics. The comparative experimental results are shown in Figure 13 and Table 5. The mAP value of the improved model was higher than those of the other models, with a 6.5% improvement over Faster R-CNN, a 5.2% improvement over YOLOv7-tiny, and 1.7%, 1.6%, 1.4%, and 1.8% improvements over YOLOv5s, YOLOX, YOLOv7, and YOLOv8s, respectively. Compared with the original YOLOv7 model, the mAP values for each category were improved. Specifically, the mAP for faces without masks increased by 1.5%, that for faces with masks increased by 0.9%, and that for the mask category increased by 1.8%. Therefore, based on the comparison of mAP values, the detection performance of the improved model surpassed that of other mainstream detection models.  
Figure 12: Loss function convergence comparison.  
Figure 13: Comparison of mAP values for mainstream detection algorithms.
## 3 Results and Discussion
### Assessing the Presence of Contaminants in SDexF  
In the SDexF, the content of metals particularly harmful to health was determined due to their high toxicity. The levels of lead, cadmium, and mercury in the samples were below LOQ (Table 6). This means that SDexF does not contain any metals that may impair health and is safe for potential consumers. Additionally, SDexF was microbiologically tested (Table 7). According to the method used, _Salmonella_ spp. was not detected in 25 g of preparation. Tests were also performed to detect _Listeria monocytogenes_ and \(\beta\)-glucuronidase-positive _Escherichia coli_ in SDexF, resulting in both being \(<\) 10 CFU/g, which is a lower value than LOQ.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Citric Acid Content** & **Ethanol Content** \\ \hline SDexF1 & 0.006 \(\pm\) 0.003 a  & 4.16 \(\pm\) 0.02 a  \\ SDexF2 & 0.008 \(\pm\) 0.002 a  & 4.22 \(\pm\) 0.22 a  \\ SDexF3 & 0.007 \(\pm\) 0.002 a  & 4.65 \(\pm\) 0.09 b  \\ SDexF4 & 0.008 \(\pm\) 0.003 a  & 4.56 \(\pm\) 0.06 b  \\ \hline \hline \end{tabular}
\end{table}
Table 4: Citric acid and ethanol content [g/100 g] in SDexF1-SDexF4 batches.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tested Parameter** & **Result \(\pm\) Uncertainty** & **LOQ** \\ \hline \multirow{4}{*}{Energy value} & 295.66 kcal/100 g & n.a. (not applicable) \\  & 1238.56 kJ/100 g & 0.04 g/100 g \\  & Fat, including: & \(<\)LOQ & 0.04 g/100 g \\ - SFA & & \(<\)0.06 g/100 g & n.a. \\ - MUFA & & \(<\)0.06 g/100 g & n.a. \\ - PUFA & & \(<\)0.06 g/100 g & n.a. \\ - TFA & & \(<\)0.06 g/100 g & n.a. \\ - omega-3 & & \(<\)0.06 g/100 g & n.a. \\ - omega-6 & & \(<\)0.06 g/100 g & n.a. \\ Digestible carbohydrates, including: & 55.84 g/100 g & From calculations, based on the test results given in this study \\ - total sugars & 4.49 \(\pm\) 0.24 g/100 g & 0.43 g/100 g \\ \multirow{2}{*}{Satch Dietary fibre} & 29.06 \(\pm\) 2.53 g/100 g & n.a. \\  & 318.38 \(\pm\) 1.93/100 g & n.a. \\ Sodium chloride & 0.10 \(\pm\) 0.01 C/100 g & 0.05 g/100 g \\ Nitrogen & 0.013 \(\pm\) 0.00 g/100 g & 0.018 g/100 g \\ Protein & 0.08 \(\pm\) 0.00 g/100 g & n.a. \\ Ash & 0.39 \(\pm\) 0.01 g/100 g & 0.01 g/100 g \\ \hline \hline \end{tabular}
\end{table}
Table 5: Nutrient content and energy value of SDexF.