esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
## 2 Research Area and Data Pre-Processing
### Data Source and Pre-Processing  
#### 2.2.1 Data Introduction  
GF-3 (Gaofen-3) is the first high-resolution multi-polar SAR satellite in China, providing support for the development of multi-polar SAR remote sensing, solving the problem of lack of SAR image data sources, and providing important data support for flood disaster research. We selected GF-3 data covering the research area with an imaging time of 6-7 am on 1 August 2023 as the main data for extracting the flood range after the disaster. The data comes from the China Resources Satellite Center [17]. The data type is Single Look Complex (SLC) format, and the polarization method is dual polarization with a resolution of 10 m. The detailed parameters are provided in the Table 1.
# Echelle Grating Spectroscopic Technology for High-Resolution and Broadband Spectral Measurement
## 3 Optical Layout
### Collimating/Focusing Optics  
The standard scheme of the cross-dispersion for the echelle spectrometers evolved from the Czerny-Turner (CT) structure [38; 39]. As shown in Figure 7, it uses two concave spherical mirrors as the collimating mirror and the imaging mirror, respectively, at the two sides of the grating. In the traditional CT structure, the comas from the collimating and imaging mirrors cancel each other out, which effectively improves imaging quality [40]. However, in OQLM cross-dispersion optics, the rays do not enter and exit on the principal plane of the grating, so the astigmatism, coma, and spherical aberration generated by the two spherical mirrors are superimposed [41]. Therefore, compressing aberrations and improving imaging quality are the challenges for echelle spectrometers.  
To overcome the influence of aberrations on the spectral resolution, multiplex alterations of optical paths are applied on the standard scheme by integrating extra optical components to the optics or by replacing the spherical mirrors with other macroshape mirrors. The astigmatism is the key influencing the resolution in the standard scheme. [PERSON] and [PERSON] proposed an approach that suppressed astigmatism by rotating the ordered separation system to an orthogonal plane and prevented additional coma [42]. [PERSON] [43] and [PERSON] [44] both added a cylindrical lens between the imaging mirror and the image plane to compress astigmatism. [PERSON] proposed an aberration-corrected method for the echelle spectrometer based on a digital micromirror device and a photomultiplier, placing a lens group in front of the digital micromirror device [45]. Using parabolic mirrors as the collimating and imaging mirrors is another way to improve the system [46],  
Figure 6: Schematic of cross-dispersion.  
Figure 7: Three major cross-dispersion layouts. (**a**) Pre-dispersed by the prism; (**b**) Post-dispersed by the prism; (**c**) Twice-dispersed by the Prism.  
which eliminates all the aberrations at the central wavelength. The system has much better imaging quality within a certain wavelength range, but the further away the wavelength is from the image center, the worse the image quality is. The spectral resolution difference from the nonuniform spot size is big. To overcome the inhomogeneity of the spot size, [PERSON] [47] and [PERSON] [48] both replaced the single imaging mirror with the three-mirror anti-astigmatism optical system. All the methods above are beneficial to optimizing the cross-dispersed optical system.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 5 Three-Layer Architecture of the Developed System
### Navigation  
Once a CH is selected the UAV enters the next phase called navigation. In similar way, the CH and CM switch their phases from setup to aggregation and communication, respectively. In this phase, all active nodes will switch on their frequencies from \(F_{1}\) to \(F_{2}\) (433 MHz to 2.4 GHz).  
Only the CH will operate on both frequencies. CH will use \(F_{2}\) to collect data from CMs and \(F_{1}\) for UAV navigation. While the UAV approaches the CH and attains an agreed height \(D_{2}\) all CM nodes must transmit their data to the CH which will aggregate it and get ready to make a link with the UAV. As soon as the UAV approaches \(D_{2}\) and starts handshaking, the CH will switch off its \(F_{2}\) module and the navigation phase is over. The navigation diagram is elaborated in the diagram of Figure 7.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Benchmark Results
#### 5.4.2 MSR-VTT Dataset: Method Performance Overview
Transformer-based approaches: Transformer-based approaches have surged in popularity recently, driven by their adeptness at modeling long-range dependencies within sequences. TextKG [32], a notable transformer-based methodology, has achieved impressive scores on the MSR-VTT dataset, boasting a BLEU of 46.6, METEOR of 30.5, CIDEr of 60.8, and ROUGE-L of 64.8. Similarly, HMN [36] is another transformer-based method that has demonstrated promising outcomes. Nevertheless, certain transformer-based techniques, such as SnapCap [42] and UAT-FEGs [35], have yielded relatively lower scores when juxtaposed with encoder-decoder-based counterparts. This disparity underscores that while transformers undeniably possess potent architectural strengths, their efficacy in video description tasks, specifically on the MSR-VTT dataset, may be contingent upon nuanced implementation specifics and design decisions. Attention-based approaches: Attention-based methods have been extensively used in video descriptions to focus on pertinent parts of the video when generating captions. Nevertheless, the performance of attention-based methods on the MSR-VTT dataset exhibits considerable variation. IVRC [100] reports relatively high scores, with BLEU (43.7), METEOR (30.2), CIDEr (57.1), and ROUGE-L (63.0). This method may leverage sophisticated attention mechanisms to enhance caption generation. Conversely, some attention-based methods, like ADL [47] and T-DL [104], report lower scores. This discrepancy could be attributed to differences in the attention mechanisms employed or the overall architecture of the methods. The varied performance of attention-based approaches highlights the importance of careful design and implementation in achieving optimal results on the MSR-VTT dataset.
# HP-LSTM: Hawkes Process-LSTM-Based Detection of DDoS Attack for In-Vehicle Network
## 3 Preliminary
### SOME/IP Protocol
#### 3.1.2 Communication Pattern  
We will discuss three communication patterns in the SOME/IP protocol in this section. Figure 2 illustrates three communication patterns for SOME/IP.  
Request/Response:Request/Response is one of the most crucial communication patterns, in which one ECU sends a request and another ECU sends a response. In fact, Request/Response communication is an RPC that comprises a request and a response [28]. The response should be sent after the request, and the server must not send response messages to clients that have not sent requests.  
Fire&Forget:Requests without response message are called Fire&Forget [28]. This means that the client sends a request without the server providing any response. This type of request, known as \"REQUST NO RETURN\", differs from the request in Request/Response communication.  
Notification Events:This communication pattern requires the client to first send a subscription request to the server, and the server responds with a confirmation message. These mechanisms are implemented in SOME/IP-SD [28]. In certain scenarios, the server publishes events to the clients, typically containing updated values or previously occurred events. Additionally, it should be noted that the message type for the entire publication process is NOTIFICATION.  
\begin{table}
\begin{tabular}{l l} \hline \hline Message ID & Message ID is a 32-bit identifier used to identify either an RPC call to a method of an application or an event. \\ \hline Length & The Length field specifies the byte length from the Request ID/Client ID to the end of the SOME/IP message. \\ \hline Request ID & The Request ID enables differentiation between multiple concurrent uses of the same method, getter, or setter by a server and client. \\ \hline Protocol & The Protocol Version field, occupying eight bytes, identifies the used SOME/IP Header format. \\ \hline Interface & The Interface Version is an eight-bit field containing the Major Version of the Service Interface. \\ \hline Message Type & The Message Type field is utilized to distinguish between different types of messages. According to AUTOSAR, there are approximately ten commonly used message types. \\ \hline Return Code & The Return Code is used to indicate whether a request has been successfully processed. Additionally, each message should transmit the Return Code field. \\ \hline Payload & The Payload field represents the valid information content to be transmitted. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Some of the fields presented in the SOME/IP packet.  
Figure 1: SOME/IP header format.
## 2 Materials and Methods
### The Assessment Procedures  
This paper investigated the scenario in which the students are placed in an adequately equipped classroom, while the previously prepared educational multimedia material is being presented to them. Therefore, this study encompasses the situation when both models of separation (spatial and temporal) between students and instructors exist. Two teaching assistants were present during the multimedia lecture presentation and testing to provide technical support during the distance lecture and to influence students to participate in testing seriously. Before the experiment, the participants were instructed about the testing procedure, and the MOS scale for QoE testing was also described to them.  
Two groups of 20 students each, previously described in the subsection related to participants' characteristics and selection, participated in experimental testing. The first group participated in testing based on the S-LM and the second in the NS-LM methodology of design and presentation.  
Before the presentation of the multimedia materials based on the described methodologies, both groups of participants answered entry test questions aiming to estimate their prior knowledge level. The entry test consisted of five questions related to the content of teaching material that would be used in further testing, one question from each of the five sections. After a 5 min recess, students from the two testing groups participated in the next phase of the experiment consisting of an online multimedia presentation and question-answering based on the S-LM and NS-LM methodology described in Figure 5. Immediately after the testing by each of the two methodologies, the participants replied to questions related to the QoE assessment.  
In this study, we applied two measures of efficiency. The first one is the level of acquired knowledge represented by the number of correct answers to the test questions. The second one is the student's QoE represented by the level of the MOS. To assess and rate the QoE, we used the absolute category rating (ACR) test method and five-level MOS scale, as standardized by ITU-T Recommendation P.910. Additionally, the three experts from the relevant field examined the questionnaire and test questions to ensure the content validity.
## 3 Multichannel MAC Protocol with Directional Antenna for FANET  
In this section, we propose blind rendezvous algorithms with directional antenna for main lobe rendezvous of a pair of UAVs and a FANET under the circumstance of being without coordination or prior information, which are denoted as the BR-DA and BR-DA-FANET algorithm, respectively. Furthermore, the ND-LP protocol and ACI-LP protocol are proposed to further decrease delay.
# Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
## 2 Related Work
### Embedding-Based Approach  
Embedding-based methods capture the general paradigm of normal samples by constructing an embedded feature space and subsequently evaluating the features using different statistical models or distance metrics. The main ones include Gaussian density estimation, normalizing flows [24; 25], non-parametric kernel density estimation (KDE) [26], and K-nearest neighbors (KNN) [27], etc. SPADE [5] uses a memory bank composed of normal features extracted from a pre-trained backbone network and employs KNN for anomaly detection at both the image and pixel level. PatchCore [7] builds on this by extracting multi-level features to obtain a better feature representation and aggregateslocal neighborhood feature blocks to increase the receptive field size and robustness to small spatial deviations. Additionally, PatchCore reduces the memory bank size through core-set downsampling. Gaussian-AD [28] uses multivariate Gaussian distribution for modelling image-level features and employs Mahalanobis distance to compute anomaly scores. PaDiM [6] follows this paradigm and further extracts multi-level patch-level features instead of image-level features, employing multivariate Gaussian modelling at patch level for enhanced anomaly detection performance. Furthermore, one-class classification is inherently integrated within embedding-based methods as follows: OC-SVM [29] aims to achieve anomaly detection by using only positive samples to learn a hyperplane that describes the features of the positive samples and keeps negative samples as far away as possible from this hyperplane. The basic idea of SVDD [30] is to construct a minimal hypersphere to contain as much of the data as possible, with variants such as [31; 32] also derived from this concept.
# Evaluating Short-Wave Effects in Railway Track Using the Rail Surface Signal
## 2 Positioning Method
Although the described method works in principle, it is computationally expensive and time-consuming due to the large amount of data (one data point every 5 mm) and the high number of iterations (to catch outliers, all potential displacements \(\pm\)25 m must be covered leading to 2000 iterations per measurement run). For this reason, a process step is carried out upstream guaranteeing a positioning accuracy of \(\pm\)1 m and reducing the computing time by a multiple.
# Review on Application of Quaternary Ammonium Salts for Gas Hydrate Inhibition
## 3 Gas Hydrate Inhibition  
Gas hydrates can be inhibited by either mechanical methods, i.e., pipe insulation, dehydration, and depressurization, or by chemical methods through injecting special chemicals called as hydrate inhibitors into pipelines [30]. Mechanical methods are considered impractical since they show disadvantages, such as dehydration is impossible between the well and the dehydration units, pipe insulation is too difficult and expensive to be implemented in the deep sea, and depressurization results in reduced transportation capability [31]. Thus, the most practical and economical method is by injection of hydrate inhibitor. In general, there are two major classifications of hydrate inhibitors, which are thermodynamic hydrate inhibitors (THIs) and low dosage hydrate inhibitors (LDHIs).
## 4 Results and Discussion
### Robustness of Deep-Learning Models under Various Communication Systems
#### 4.3.2 Performance Analysis of DL Models over AWGN Channel
Performance analysis of EfficientNetV2-B0 over AWGN channel: In contrast to ResNet152V2, EfficientNetV2-B0 achieved 95% accuracy on the clean (original) images even without applying any augmentation techniques during training as shown in Figure 10(b), this is due to its better learnability, as discussed in Section 4.3.1. However, there is a drop in the accuracy of models on images across higher order M-QAM. At \(\text{E}_{\text{b}}/\text{N}_{0}\) 0 dB, the highest drop in accuracy of 6% was observed on images from 16-QAM (highest accuracy) to 512-QAM (lowest accuracy); however, the difference in accuracy across M-QAM systems becomes smaller with increase in \(\text{E}_{\text{b}}/\text{N}_{0}\). For \(\text{E}_{\text{b}}/\text{N}_{0}\) 20 dB the accuracy curve almost flattens (indicating no difference in accuracy across higher order M-QAM), and the accuracy was almost same as it was on the original images. This indicates that the model can generalize the noise on the images across all systems and provide better accuracy even for higher order M-QAM systems. Furthermore, applying augmentation techniques during training drastically improved the EfficientNetV2-B0 model and it was able to predict almost all the clean images correctly, achieving 99% accuracy. The difference in accuracy on images from higher order M-QAM is less than 2% with only little distinctions between M-QAMs with odd and even length of symbol. When applying augmentation, the lowest accuracy obtained was 97% on images from 512-QAM at 0 dB. However, beyond \(\text{E}_{\text{b}}/\text{N}_{0}\) 10 dB, the model accuracy on images from most of the systems was same as it performed on the original images (except 128-QAM at 10 dB, 512-QAM and 1024-QAM at 15 dB, and 1024-QAM at 20 dB). Throughout the experiment, the accuracy from system with different channel estimation had overlapping patterns and there were no clear indications that complex channel estimation (such as MMSE) improves the model accuracy. In comparison to the two DL models, EfficientNetV2-B0 outperformed ResNet152V2 across all image communication systems, regardless of whether augmentation was applied or not, this is due it its robustness as discussed in Section 4.3.1.
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 3 Method
### Loss Function  
Inspired by [12; 37], we adopt the \(L_{BecDice}\) loss [17] for the ISIC 2018 dataset, as defined in Equation (1). This loss combines Binary Cross-Entropy (BCE) and Dice loss in a weighted fashion, with \(\lambda_{1}\) controlling their relative contributions. The BCE component enhances pixel-wise precision by minimizing the error between predicted values and actual binary labels. In contrast, Dice loss evaluates the overall spatial overlap, enhancing the segmentation of target structures, particularly in the presence of class imbalance. This combination effectively balances both local precision and global consistency. For the Synapse and ACDC datasets, which involve multi-class segmentation with notable class imbalance, we use a hybrid loss function \(L_{CeDice}\)[38], defined in Equation (2). This function integrates the standard multi-class Cross-Entropy (CE) loss and Dice loss, with individual weighting coefficients \(\lambda_{2}\) and \(\lambda_{3}\). The CE loss promotes accurate classification across all categories by minimizing the softmax-normalized cross-entropy. Meanwhile, the Dice loss emphasizes per-class spatial overlap, improving performance on underrepresented or small structures. This hybrid approach ensures both categorical precision and structural integrity in the segmentation results.  
\[L_{BecDice}=\lambda_{1}L_{Bce}+(1-\lambda_{1})L_{Dice} \tag{1}\]  
\[L_{Bce}=-\frac{1}{N}\sum_{1}^{N}\left[y_{i}\log(\hat{y}_{i})+(1-y_{i})\log(1 -\hat{y}_{i})\right] \tag{2}\]  
where \(N\) denotes the total number of samples, \(\hat{y}_{i}\in[0,1]\) is the predicted probability, and \(y_{i}\in\{0,1\}\) is the ground truth label for the \(i\)-th pixel.  
\[L_{Dice}=1-\frac{2|X\cap Y|}{|X|+|Y|} \tag{3}\]  
where \(|X|\) and \(|Y|\) denote the number of positive pixels in the ground truth and prediction, respectively, and \(|X\cap Y|\) denotes their intersection. The weighting factor \(\lambda_{1}\in[0,1]\) controls the balance between the two components (default: 0.6).  
\[L_{CeDice}=\lambda_{2}L_{Ce}+\lambda_{3}L_{Dice} \tag{4}\]  
\[L_{Ce}=-\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}y_{i,c}\log(\hat{y}_{i,c}) \tag{5}\]  
where \(y_{i,c}\in\{0,1\}\) is an indicator (1 if pixel \(i\) belongs to class \(c\), 0 otherwise), and \(\hat{y}_{i,c}\in[0,1]\) is the softmax probability that pixel \(i\) belongs to class \(c\). \(C\) denotes the number of classes.
## 6 Conclusions  
This paper proposes a fuzzy based collaborative task offloading for MEC-enabled SCN. In our scheme, the proposed FCTO algorithm analyzed the under-utilized and over-utilized SBS-MEC servers and determined the target computational node among SBS-MEC servers that were lightly loaded to share computation resources with each other. It alleviated the load on the local SBS-MEC server and could easily compute the different service requests according to user demands. Moreover, a fuzzy edge orchestrator module acted as a coordination point of the system to collect information from all SBS-MEC servers and, based on the fuzzy rules, it decided the task handling location which was either the local SBS-MEC server or the neighboring SBS-MEC server. Extensive simulations were performed to evaluate the performance of our proposed task offloading scheme. Our proposal outperformed two reference schemes, namely, local SBS-MEC and workload orchestration based task offloading (WOTO) schemes in terms of reducing task failure rate and execution latency. Moreover, it also provided more server utilization. For future studies, we intend to use a machine learning approach for collaboration among SBS-MEC servers. Moreover, for more efficient reduction of task failure and time consumption, we expand the FCTO algorithm with user's mobility in small-cell networks.  
Figure 10: Performance analysis for different sizes of VMs and various number of mobile devices : (**a**) FCTO scheme; (**b**) WOTO scheme; (**c**) FCTO scheme (Effect of VMs for task failure rate); (**d**) FCTO scheme (Effect of VMs for server utilization).  
**Author Contributions:** This paper represents the results of collaborative teamwork. Methodology, M.D.H.; Project administration, E.-N.H.; Software, M.D.H. and T.S.; Supervision, E.-N.H.; Writing--original draft, M.D.H.; Writing--review and editing, M.D.H., T.S., V.N., W.u.R., T.D.T.N., L.N.T.H., and E.-N.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2017-0-00294, Service mobility support distributed cloud technology).  
**Conflicts of Interest:** The authors declare no conflict of interest.