esa-sceva/satcom-chunk-collection huggingface dataset:
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
###### Abstract  
The information bottleneck method is a generic clustering framework from the field of machine learning which allows compressing an observed quantity while retaining as much of the mutual information it shares with the quantity of primary relevance as possible. The framework was recently used to design message-passing decoders for low-density parity-check codes in which all the arithmetic operations on log-likelihood ratios are replaced by table lookups of unsigned integers. This paper presents, in detail, the application of the information bottleneck method to polar codes, where the framework is used to compress the virtual bit channels defined in the code structure and show that the benefits are twofold. On the one hand, the compression restricts the output alphabet of the bit channels to a manageable size. This facilitates computing the capacities of the bit channels in order to identify the ones with larger capacities. On the other hand, the intermediate steps of the compression process can be used to replace the log-likelihood ratio computations in the decoder with table lookups of unsigned integers. Hence, a single procedure produces a polar encoder as well as its tailored, quantized decoder. Moreover, we also use a technique called _message alignment_ to reduce the space complexity of the quantized decoder obtained using the information bottleneck framework.  
information bottleneck method; polar codes; quantized decoding; code construction +
Footnote †: journal:
## 5 Numerical Experiments
### Convergence and Sensitivity Analysis  
Although a good upper bound can be obtained by adopting the proposed heuristic lower bound in branch-and-bound algorithm, the convergence needs to be judged by the GAP between the upper bound and lower bound. In this section, we analyze the performance of the heuristic lower bound according to the results of Set 2 solved by branch-and-bound algorithm. Figure 17 shows the convergence of some representative cases where the blue solid line represents the lower bound, the yellow solid line represents the upper bound, and the green dotted line represents the optimal solution. For instances E-n22-k4-s6-17 and E-n22-k4-s12-16, the upper bound converges to optimal solution after nearly 1000 iterations, and the lower bound seems relatively loose and converges slowly. The lower bound is tight in instances E-n51-k5-s27-47 and E-n51-k5-s32-37, which equates to optimal solution at the beginning of iterations. For other instances, the lower bound convergence is close to the upper bound. In conclusion, we cannot simply say whether the lower bound is loose or tight, it is loose from the lower bound construction point of view because the capacity constraint is relaxed, but it has different performances in different instances.  
Figure 17: Convergence process of branch-and-bound algorithm.  
To analyze the influence of demand pattern, we solve the 54 instances in Set 4 with different demand input. As mentioned in Section 5.1.2, we design three demand generating rules corresponding to different scenarios, which are the Random Nodes rule (RN), The Equal Proportion rule (EP), and the Random Proportion rule (RP). The instances generated by RN are all feasible, and there are some infeasible cases when using EP and RP rules. Figure 18 compares the results of the feasible instances using three generating rules where we focus on the total transportation cost (the histogram) and the proportion of lateral-transshipments volume (the line chart). According to Figure 18, we can see that the lateral-transshipments volume is equal to zero for all instances except for instance 50-50 in RN, which indicates there is no need for lateral-transshipments when the demands of a station all originate from one warehouse. In the real-world scenario, if the station has massive demand for a warehouse, the decision maker will arrange a direct \"warehouse-station\" route, that is, to directly transport from the warehouse to the station without going through DCs. Figure 15 also shows that lateral-transshipments volume will make total transportation costs higher, and that the transportation costs under the RN rule are always the lowest, followed by RP, and the cost of EP is the highest for most instances. Moreover, lateral-transshipments are effective strategies to reduce transportation costs in certain demand pattern. For instances 50-46, proportions of lateral-transshipments volume under the EP and RP rules are more than 70%, and the value under EP even exceeds 80%.
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 4 Discussion
### Trust  
Users are more likely to adopt apps from trustworthy sources [79]. If an unknown or untrustworthy entity develops an app, then people may be reluctant to download and use it. Moreover, users distrusted the government's guarantee of privacy [63]. Participants did not like the idea of being tracked by the government [101] and lacked trust in the developers of the app [28], which could leak the information [115]. Throughout the pandemic, the advice provided by governments was often unclear due to the constantly changing guidelines and information. This lack of clarity resulted in a general lack of trust in both governments and software companies. Many people were hesitant to download or use certain apps due to a lack of confidence in the public and private institutions associated with them [50]. Nevertheless, some studies have reported that there was no relationship between trust and app adoption [44], while others found a significant positive correlation between trust on the contact-tracing app and adoption [32; 123]. However, no experimental studies have been published about trust in the digital contact-tracing context.  
**Recommendations for government**. Governments should collaborate with independent public health organisations and NGOs to develop and promote contact-tracing apps, ensuring a transparent development process. This is because collaborating with neutral,trusted organisations reduces perceptions of governmental overreach or misuse of surveillance data, thereby building trust. Governments should also consistently communicate the purpose, function, and benefits of the apps through clear and honest messaging and, where possible, avoid contradictions or frequent changes in guidelines. This is because they are responsible for delivering reliable information, thereby promoting credibility and trust, and ultimately driving adoption.  
**Recommendations for app developers**. App developers should collaborate with trusted third-party organisations to independently audit and certify the app's security and privacy practices and display certification prominently. This is because third-party validation enhances credibility, by addressing the lack of trust in app developers and governments. App developers should also regularly update the app with clear release notes, explaining new features and fixes to build trust in ongoing developer accountability. This is because regular, transparent updates show users that developers are committed to maintaining app quality and addressing concerns, thereby promoting trust.
# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 3 Datasets used in State-of-the-Art Techniques
### Presence of Nonverbal Cues  
Except for the AfeW and AfeW-VA datasets, all the other databases surveyed in this work were designed for the presence of nonverbal cues other than facial expressions in their samples. For example, EMOTIC and CAER/CAER-S focus on context; BoLD focuses on body language; GroupWalk focuses on gaits; and iMiGUE focuses on microexpressions of the body, hands, face, and the combination of them. However, it is possible to extract other nonverbal cues from these datasets, even if these were not the focus of their design. We list the presence and visibility of nonverbal cues in Table 4.  
Figure 4: Visualization of the continuous annotations for EMOTIC. An interactive visualization is available in the GitHub repository associated with this publication.  
Given how facial expressions are severely significant for emotion recognition, it is important to have good crops of the face available, which is the case for most of the samples from these datasets. For EMOTIC, however, it is more common to see images with severe facial occlusion, as shown in Figure 5. For the other datasets, since the data were obtained from television clips or movies, it is more common for people to be aligned with the camera. For GroupWalk, given how security cameras and handheld devices are also used to record the clips, it is common that sometimes the faces will be distant from the camera, making it difficult to explore this cue.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Dataset Name**} & \multicolumn{4}{c}{**Cue**} \\ \cline{2-5}  & **Facial Expression** & **Context or Background** & **Body Language** & **Others** \\ \hline EMOTIC [16; 53] & Somewhat present & Present & Somewhat present & N/A \\ CAER/CAER-S [17] & Present & Present & Somewhat present & N/A \\ iMiGUE [54] & Present & Missing & Annotated & Annotated microgestures \\ AfeW/AfeW-VA [51; 52] & Present & Somewhat present & Somewhat present & N/A \\ BoLD [55] & Present & Present & Annotated & N/A \\ GroupWalk [56] & Somewhat present & Somewhat present & Somewhat present & Gait is visible but not annotated \\ \hline \hline \end{tabular}
\end{table}
Table 4: Presence and visibility of nonverbal cues in the datasets currently employed by the state-of-the-art techniques. For each dataset (rows), we classify the cues (columns) as _missing, somewhat present, or present_, or _annotated_ for when the cue is not only visible but annotated by humans.  
Figure 5: Examples from the EMOTIC dataset [16; 53] with images with severe facial occlusion. For this dataset, it is expected that techniques can extract information from context. Therefore, this is not an issue but rather a characteristic. Each column contains samples from a subset. The images were padded to allow better visualization: emodb (**a**); ade20k (**b**); framesdb (**c**); mscoco (**d**).
# Light-Weighting of Planetary Gearbox Based on Multi-Strategy Optimization Sparrow Search Algorithm
## 3 Methodology
### Multi-Strategy Optimized Sparrow Search Algorithm
#### 3.2.3 Nonlinear Weight Factor Optimization Strategy  
It is pointed out that the nonlinear weight factor plays an important role in enhancing global search ability and escaping local optimum traps. In the MSOSSA, this strategy is incorporated into the position update method of scroungers, aiming to improve the overall performance of the algorithm. Specifically, in the early iterations of the algorithm, the nonlinear weight factor \(w\) is small and increases gradually. During this phase, scroungers eagerly pursue the food in the search space, demonstrating strong exploratory behavior. This behavior aids the algorithm in swiftly escaping from local optima traps, thereby enhancing the convergence speed of the algorithm. As iterations proceed, the weight factor \(w\) continues to increase. At this point, scroungers gradually increase their consideration of the distance between themselves and the food when navigating to high-quality food locations. A larger weight factor \(w\) leads scroungers to behave in a more exploitative manner. This enhanced local search capability enables the algorithm to conduct a more detailed search when approaching the optimal solution region. Thus, the solution's precision and the algorithm's overall optimization effectiveness are improved. Through this dynamic adjustment mechanism, the nonlinear weight factor effectively balances global search and local search capabilities, allowing the MSOSSA to perform optimally in each iteration stage.  
The beggar position update formula in MSOSSA is given in Equations (37) and (38).  
\[X_{i,j}^{t+1}=\begin{cases}Q\cdot exp\left(\frac{X_{\text{best}}^{t}-X_{i,j}^{ t}}{i^{2}}\right)&if\ i>N/2\\ X_{P}^{t+1}+w\cdot\left|X_{i,j}^{t}-X_{P}^{t+1}\right|\cdot A^{+}\cdot L& otherwise\end{cases} \tag{37}\]  
\[w=\lambda\left[\tan\left(\frac{\pi t}{3 iter_{max}}\cdot\frac{t^{2}}{{iter_{ max}}^{2}}\right)+1\right] \tag{38}\]  
\(\lambda\) is the adjustment coefficient. \(\lambda=0.35\).
# Integrating Text Classification into Topic Discovery Using Semantic Embedding Models
## 3 Proposed Approach  
This section presents the proposed approach for text classification and integrates it into topic discovery with semantic embedding models and three algorithms.  
The proposed approach for integrating text classification in the topic discovery process incorporates the following process:  
1. Pre-processing: The text sets are pre-processed; this consists of removing punctuation marks, converting to lowercase, and removing URL marks.
2. The semantic relationship extraction: Extracting semantic relationships from the English Wikipedia corpus is vital for constructing the proposed embedding models. It is necessary to extract the relations of synonymy, hyponymy, and hyperonymy using lexical-syntactic patterns extracted from the literature for these semantic relationships [3].
3. Development of embedding models: Each word pair of identified relationships is assigned a unique identifier for constructing semantic relationship embeddings [3].
4. Text classification from _20-Newsgroups_ and _Reuters_ Corpus with a convolutional neural network (CNN) [3].
5. Topic discovery for each class with the _latent Dirichlet analysis (LDA)_, _probabilistic latent semantic analysis (PLSA)_, and _latent semantic analysis (LSA)_ algorithms. An evaluation process based on normalized topic coherence using the top words is performed.  
The _LDA_, _PLSA_, and _LSA_ algorithms are chosen because they are the most used algorithms in the literature. It is possible to contrast with works that only use the input data with traditional preprocessing and discover topics against those works where they perform additional processing to the traditional one, such as text classification and community detection algorithms.  
Figure 1 shows the proposed approach. A total of 2 previously preprocessed corpora were classified, from which, 20 classes were obtained for the 20-Newsgroups corpus and 90 for the Reuters Corpus. The classes are the input data set to the LDA, PLSA, and LSA algorithms for topic discovery. The 20-Newsgroups corpus comprises 20,000 documents, organized into 20 classes, from which, 20, 50, and 100 topics are identified. On the other hand, the Reuters Corpus has fewer documents; 90 classes must be obtained, so it is impossible to extract 100 topics due to the corpus size.
# A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking
## 9 Conclusions  
We propose a producer mobility link service with which to enhance the producer mobility service under the NDN architecture. It attempts to extend the period in which the forwarding path to the producer is relevant by concealing link breakage due to the producer movement to the NDN system. The proposed scheme repairs the broken link by re-establishing a new connection between the NMP and the previous NAR, and then recovering the disrupted on-going transmission. In order to recover the disrupted on-going transmission between the NMP and the previous NAR, it treats the exchange of the <Interest, Data Packet> pair as a transaction. Please note that the proposed producer mobility scheme is transparent to the rest of NDN architecture, as it is in an NDN face. The simulation investigation results indicate that the proposed scheme is practical and effective for the NDN producer mobility solution.  
**Author Contributions:** [PERSON] has defined the dual-connectivity provider link service architecture and designed the proposed protocol. [PERSON]. ([PERSON]) has implemented the proposed protocol. [PERSON] has provided the guidelines and strategy to the performance evaluations. Finally, [PERSON]. ([PERSON]) performed the performance evaluations and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Korean MSIT (Ministry of Science and ICT), under the National Program for Excellence in SW (2015-0-00936), supervised by the IITP, and it was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1A6A1A03025526).  
**Acknowledgments:** The authors would like to thank all fundings and the support of the reviewers for their insightful comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: Required buffer size at NAR.
## 4 Results  
### Conclusions  
This paper presents a novel approach called the EEMD Granger causality test Dynamic Graph Attention Transformer Network (EG-DGATN) to overcome the challenge of water demand forecasting. Using more correlated sensor information, we used the EEMD-Granger causality testing method to establish a causal graph that spans multiple periods. Subsequently, we enhanced the position encoding of the Transformer model using Node2 Vec techniques to generate spatiotemporal embeddings across sensors and time granularities, which aids in modeling spatiotemporal synchronization. The input layer feeds water demand sensor data and is further used in the Encoder-Decoder architecture that includes stacked dynamic graph spatiotemporal attention layers to capture the causal spatiotemporal correlations between sequences.  
The experimental results reveal that compared to baseline models, the EG-DGATN improved the MAPE metrics by 2.12%, 4.33%, and 6.32% in forecasting intervals of 15 min, 45 min, and 90 min, respectively. Furthermore, compared to the 15-min forecasting scenario, longer forecasting granularities decreased the EG-DGATN's MAPE metrics by 6.46% and 7.68%, respectively. In contrast, ASTGCN decreased by 8.68% and 12.74%, GraphWave decreased by 7.78% and 11.45%, and GNNLSTM decreased by 7.72% and 9.75%, respectively. This demonstrates the stability of the EG-DGATN's performance across different prediction granularities.  
Moreover, through regression visualization experiments, we illustrated the good interpretability performance of the EG-DGATN in predicting results, further validating the effectiveness of our proposed method. Additionally, we replaced the EG-DGATN's EEMD-Granger graph construction approach with a spatial adjacency matrix derived from a random adjacency matrix. The effectiveness of the model using randomly generated adjacency matrices decreased by 4.63%, 6.51%, and 7.23% at different time granularities, while the model using traditional spatial adjacency matrices decreased by 4.88%, 5.56%, and 7.32% at different time granularities. These findings demonstrate the effectiveness of the EEMD-Granger causality testing method in boosting the model's predictive accuracy. Additionally, our method of shifting from the extraction of spatial information to that of causal information has proven successful.  
Figure 7: (**a**) \(R^{2}\) score visualization results of EG-DGATN on the test set (**b**) \(R^{2}\) score visualization results of ASTGCN on the test set (**c**) \(R^{2}\) score visualization results of STGCN on the test set (**d**) heatmap for comparison of GraphWave and EG-DGATN for generating dynamic graphs in different time granularities.
## 3 Treatment of Phases of Coherence Functions
### Separated Phase  
On the other hand, the phases in coherence matrix can be separated out to avoid participating in the Cholesky decomposition [36]. The coherence matrix \(\mathbf{\Gamma}(\omega)\) is first represented by its modulus matrix and phase matrix, i.e.,  
\[\mathbf{\Gamma}(\omega)=\mathbf{\Pi}(\omega)\otimes\mathbf{\Theta}(\omega) \tag{16}\]  
where \(\otimes\) denotes the multiplication operation between the corresponding elements of two matrices; \(\mathbf{\Pi}(\omega)\) = lagged coherence matrix and given by  
\[\mathbf{\Pi}(\omega)=\begin{bmatrix}1&|\gamma_{12}(\omega)|&\cdots&|\gamma_{1 n}(\omega)|\\ |\gamma_{21}(\omega)|&1&\cdots&|\gamma_{2n}(\omega)|\\ \vdots&\vdots&\ddots&\vdots\\ |\gamma_{n1}(\omega)|&|\gamma_{n2}(\omega)|&\cdots&1\end{bmatrix} \tag{17}\]  
and \(\mathbf{\Theta}(\omega)\) = phase matrix of \(\mathbf{\Gamma}(\omega)\) and given by  
\[\mathbf{\Theta}(\omega)=\begin{bmatrix}1&e^{i\theta_{12}(\omega)}&\cdots&e^{ i\theta_{1n}(\omega)}\\ e^{i\theta_{21}(\omega)}&1&\cdots&e^{i\theta_{2n}(\omega)}\\ \vdots&\vdots&\ddots&\vdots\\ e^{i\theta_{n1}(\omega)}&e^{i\theta_{n2}(\omega)}&\cdots&1\end{bmatrix} \tag{18}\]  
Because \(\mathbf{\Pi}(\omega)\) is also a Hermitian matrix with positive definite property, it can also be decomposed by the Cholesky decomposition, i.e.,  
\[\mathbf{\Pi}(\omega)=\mathbf{L}(\omega)\mathbf{L}^{\mathrm{T}}(\omega) \tag{19}\]  
where \(\mathbf{L}(\omega)\) = the decomposed lower triangular matrix. According to [36], the following two equations are true:  
\[\mathbf{L}(\omega)=\begin{bmatrix}|\beta_{11}(\omega)|&0&\cdots&0\\ |\beta_{21}(\omega)|&|\beta_{22}(\omega)|&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ |\beta_{n1}(\omega)|&|\beta_{n2}(\omega)|&\cdots&|\beta_{nm}(\omega)|\end{bmatrix} \tag{20}\]  
and  
\[\theta_{jk}(\omega)=\theta_{jk}(\omega) \tag{21}\]
# Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
## 4 Discussion
### Machine Learning Applications in Rockfall Hazard Prediction  
The application of machine learning techniques, particularly the Random Forest model, has yielded insightful results in predicting rockfall occurrences near highways. A comparison of the Random Forest model's variable importance with SHAP value analysis provided complementary perspectives on feature contributions, underscoring the critical role of various factors in predicting rockfall events.  
The variable importance analysis from the Random Forest model emphasized the predominant role of topographical and geographical features in predicting rockfalls. Variables such as slope_deg and dist_geo_sens_area ranked highest on the importance list, highlighting their substantial impact on the model's predictions. Other significant variables included dist_urban, curvature, and spi, which are also key geographical and topographical factors. In contrast, environmental factors exerted a lesser influence, while socioeconomic and geological variables had the least impact. Collectively, these findings illustrate the model's capability to integrate a diverse array of factors--geographical, topographical, environmental, geological, and socioeconomic--into accurate predictions.