esa-sceva/satcom-chunk-collection huggingface dataset:
# Identifying the Key Hazards behind Website Drop-Offs by Solving a Survival Problem
## 5 Survival Models
### Cox-Proportional Model  
A Cox-proportional hazard model was employed to determine the significant hazards that contribute to survival on the studied website. The model sought to solve the problem as expressed in Equation (3) where survived referred to the event of a web visit viewing three or more webpages within the visit:  
\[\text{surv}(\text{sesstime, survived})\ \sim\] \[(\text{Hits}+\text{OperatingSystem}+\text{deviceCategory}+\text{ Browser}+\text{Dist}+\text{OrganicSearches}+ \tag{3}\] \[\text{daysSinceLastSession}+\text{H}+\text{CR}+\text{T}+\text{CU}+ \text{SC}+\text{AP}+\text{ET}+\text{EA}+\text{U}+\text{AC}+\text{CE}+\text{ AU}+\text{L}+\text{CL})\]  
Prior to computing the Cox-proportional hazard model, the categorical features (OperatingSystem, deviceCategory, and Browser) were transformed into indicator variables.  
The Cox-proportional hazard model was trained on 80% of the data and tested on 20% of the data for validation. The Cox model recorded a classification accuracy of 58%.  
Figure 4 depicts the features and the corresponding hazard level of significant as determined by the Cox-proportional model.  
According to the Cox-proportional hazard model, the features, \"Hits\", \"ET\", \"OrganicSearches\", \"Browser\", and \"DeviceCategory\" were significant hazards to survival on the studied website. Further descriptive analysis showed that visits that were very engaged with the website (number of hits per page) had a high likelihood of survival. By definition, a hit represents the number of clicks a visitor makes when on a webpage. The feature \"ET\" represents visits where the first webpage viewed was the \"Engineering-Trade\" webpage. According to the data, visits with the first page being the \"Engineering-Trade\" have been shown to have a high likelihood to drop-off early. This is suggestive that either such visits  
Figure 4: Cox-proportional hazard feature level of significance.  
get all the information they need from the webpage or are dissatisfied and warranted to drop-off.  
The data also informed that visits that originated from an organic search had a high likelihood of survival on the website, and conversely, those visits that were not organically originated have showed a high likelihood to drop-off prior to viewing three or more webpages on the studied website. The Cox-proportional model detected that visits from the browsers, \"other\", \"Samsung Internet\", and \"Safari\" had higher likelihoods of dropping off prior to three page views. This could be due to either a population trait of the users of such browsers (or devices associated with such browsers) or the visual presentation of the corporate website when using these browsers.
# A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services
## 1 Introduction
Therefore, achieving low-complexity interference avoidance solutions to promote coexistence between services is also a key issue to be addressed. To address these needs, this paper first presents an interference analysis method to calculate the interference received by satellite systems from IMT-2020. It models and analyzes the actual interference received by satellites from terrestrial IMT-2020, considering factors such as IMT-2020 antennas, satellite antennas, and satellite-ground channels. The calculated interference-to-noise ratio is compared with existing protection standards to suggest the possibility of coexistence between IMT-2020 and FSS services. Based on the results of aggregated interference calculations, an innovative optimization algorithm based on a zoning strategy is proposed. This algorithm divides the vast number of ground base stations into zones and virtualizes the base stations within each zone into a virtual macro base station, allowing for power control of this macro-base station. Furthermore, three intra-zone power control methods are considered: average power control, power control based on channel gain, and power control for maximum sum rate, enabling fine-grained optimization of power control within each zone. This approach effectively reduces the complexity of IMT-2020 base station power control while avoiding extensive signaling interactions. The structure of the remaining sections is as follows. In Section 2 of this article, the interference scenarios are introduced, and the methods for calculating the number of IMT-2020 base stations within the FSS coverage area, the antenna models, and the calculation of aggregate interference are clarified. In Section 3, three specific algorithms are presented. Section 4 analyzes and discusses the simulation results, comparing the performance of severalschemes. Section 5 provides the conclusion of this article.
# An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
## 4 Results  
### Performance of the Proposed Method  
The original dataset is three-fold. We used the three-fold dataset and split it into seven folds based on subject-level variability. As a result, the experiment is performed six times with various combinations of training data each time. We first train/validate our model using fold-1 data and test it using fold-7 data for final accuracy. We apply the same strategy to the other remaining five folds. Fold-7 is only used to test the efficiency of our proposed model. The dataset is divided based on subject-level instead of image level. Furthermore, we divided the training set into 20% for model validation. The validation dataset is usually used to tune the hyperparameters such as the learning rate, number of epochs, etc. In our experiment, we set the epochs to 50. Our model is simple, which typically reaches an optimal point on the epochs between 40 to 45, and then possibly it stops converging. We used a validation set to get the best model while training. For example, whenever the model achieves good accuracy on the validation set in the training phase, it saves the best model with the best parameters. Later, we test this model using testing data to come up with overall accuracy. In order to verify the robustness of the model, we use different folds to train the model and calculate the average accuracy and standard deviation for each fold. Finally, the six performance estimates are summed to get an overall assessment of the classifier.  
Table 4 present the results of each fold's performance for VGG16 with and without attention module. Our proposed VGG16 with the ECA module outperforms plain VGG16 by identifying the most pertinent information in an input image and aids in classification accuracy. Adding the ECA module helps the model increase the model performance (the attention module does not ignore the irrelevant features, but it just diminishes their importance). Tables 5 and 6 represent the evaluation matrices of VGG16 with and without attention module obtained from different folds. In ALL classification, we can see that fold-1 produces the best mean accuracy result for cancer and healthy cell images. We can presume that the sample selection for the training set can significantly affect the performance results. Figure 6 provided the receiver operating characteristic (ROC) curves
# Bearing Behavior of Cold-Formed Thick-Walled Steel Plates with a Single Bolt
###### Abstract  
This paper presents the results of an experimental and analytical study on the behavior of cold-formed thick-walled steel plates connected by a single bolt under double shear. Experimental studies were conducted on a total of 54 bolted connections fabricated from two thicknesses of cold-formed thick-walled steel plates, with nominal thicknesses of 6 mm and 10 mm. The effects of the end distance, edge distance, and steel plate thickness on the bolt bearing behavior were investigated. The codes of (modified) ANSI/AISC 360-22 and EN1993-1-8 were applied to predict the failure modes and bearing resistance of the connections, and the predictions were compared with those of the measurements. The studies show that fractures occurred on the shear planes of the specimens that failed because of the shear-out that occurred due to reduced ductility, which was caused by the cold forming process. The modified ANSI/AISC gives the most accurate predictions of the failure modes and bearing resistance. However, the bearing resistance will be overestimated if the reduction in the area of the shear planes caused by the occurrence of fractures is ignored in specimens with an end distance higher than 2.0\(d_{0}\), and a reduction factor is needed to calculate the bearing resistance of the connections.  
thick-walled steel; shear connection; bearing resistance +
Footnote †: journal: Applied Sciences
# Along-Track Multistatic Synthetic Aperture Radar Formations of Miniatellites
## 4 Control of Sensor Position
### Mitigation of System Performance Degradation
#### 4.2.2 Twin Antenna  
The second mitigation scheme consists of using a new concept: the _twin antenna_ (see Figure 6). Splitting the antenna consists in using just one half of the antenna as in the multichannel case and, if technologically feasible, enlarging the two halves using some kind of boom.  
The twin antenna concept may be exploited in two different ways:  
* Use both rear and front parts, as there were 2 N receivers,
* Use a proper combination of rear or front that assures the best recombination scheme.  
The general principle on which grounds the first possibility is doubling the number of receivers, which is expected to produce beneficial effects as shall be shown in Appendix A. However, it is costly since it requires more satellites or, even in a multichannel case, it generates a doubled data volume. In the second possibility, the use of the front or the rear part of the antenna has a relevant impact on the phase terms in the reconstruction matrix, allowing substantial change in its structure since \(L_{a}\) is comparable with the final azimuth resolution. The adoption of a proper combination of the first or of the second halves of the antenna of the various receivers may increase the figure of performance allowing for a better recombination. The advantage with respect to the first solution is to guarantee the same data volume at the cost of reducing to half the total antenna area. This method is expected to produce an improvement in case of displacement of sensors from their optimal positions.  
As for the PRF tuning, the pre-computation of the figure of performance for a formation for which we accurately know the sensors location is a feasible task to be done before a programmed acquisition and for all the possible combinations of rear-front of the receivers that are \(2^{N}\). The best combination, i.e., the one providing the highest \(\mathbf{F}\), is then programmed onboard.  
In a practical example, we considered a formation with the same parameters as in Section 4.2.1 but \(N=7\) and the sensors set in: \(\mathrm{dx}=[-200,\,-147.776,\,-91.902,\,-20.398,\,43.245,\,115.746,\,200]\)m. In Table 1, the performance of the two mitigation schemes used separately and jointly are compared. As can be seen, although the tuning of PRF produces already acceptable performance (second row), using both the mitigation scheme further improves the recombination gain (i.e., increasing of SNR), despite of half of the antenna area being used and a better reduction of the first azimuth ambiguity and an improved azimuth resolution.  
Adoption of a twin antenna further improves the system performance, as shown in Figure 5b, where the figure of performance with PRF tuning only and PRF tuning-twin antenna are compared for a large number of a random positions' formation.  
Figure 6: Concept scheme of the twin antenna.
# Prototypical Graph Contrastive Learning for Recommendation
## 4 Experiments
### Performance Comparison (RQ1)  
As shown in Table 2, the performance of all baseline methods was compared across three datasets. All CL-based models outperformed LightGCN because they capture richer and more uniform embeddings by learning the consistency of embeddings from different augmented views. Similar to the objective of CL, DirectAU achieved superior performance by directly optimizing the alignment and uniformity of embeddings, surpassing some CL methods (SGL, NCL). ProtoRec's performance was better than other prototype-based recommendation methods (ProtoAU, NCL) because it constructs feature augmented views through prototypes, enabling users and items to learn more effective preference information. The diversity of prototype information also guides more uniform distribution of embeddings. As a feature augmentation method, ProtoRec demonstrated a slight performance advantage over SimGCL while outperforming other graph structure-augmented contrastive learning methods (LightGCL, RecDCL, AdaGCL). This is likely because disrupting the integrity of interaction information can lead to insufficient training for certain nodes. Additionally, because feature augmentation does not require the extra reconstruction of large graphs, ProtoRec also achieved improvements in time efficiency. ProtoRec achieved the best performance across all three datasets, validating the effectiveness of the prototype-based feature augmentation.  
When facing issues such as data sparsity and noise, LightGCN, which relies solely on graph neural network methods, requires more iterations to fit the data. In contrast, contrastive learning methods like SGL and NCL achieve more stable performance by capturing  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Dataset** & **Metric** & **LightGCN** & **ProtoAU** & **NCL** & **SGL** & **LightGCL** & **DirectAU** & **RecDCL** & **SCCF** & **AdaGCL** & **SimGCL** & **ProtoRec** \\ \hline \multirow{4}{*}{Yelp2018} & recall@20 & 0.0592 & 0.0611 & 0.0669 & 0.0676 & 0.0684 & 0.0704 & 0.0690 & 0.0701 & 0.0710 & 0.0721 & 0.0722 \\  & rcdc@20 & 0.0482 & 0.0503 & 0.05  
embedding diversity. DirectAU's research on improving embedding uniformity further validates the relationship between mitigating data sparsity and enhancing embedding uniformity. LightGCL, by using a low-rank approach, can extract important information from sparse data, while methods like ProtoRec alleviate data sparsity by extracting preference prototypes that guide embedding learning of rich features. ProtoRec leverages extracted prototype information to guide users and items in learning latent preferences within the embedding space, optimizing the differentiation between positive and negative samples in contrastive learning. The application of the prototype filtering network (PFN) reduces the impact of noise, while the single-view contrastive scheme improves training efficiency, resulting in excellent recommendation performance.
# Asymmetric Measurement-Device-Independent Quantum Key Distribution through Advantage Distillation
## 4 Conclusions  
The AD method, a classical algorithm based on information theory, can be combined with QKD without changing the existing system structure. Specifically, the AD method can be combined with an asymmetric seven-intensity MDI-QKD to improve the robustness effectively, so as to distinguish and extract highly correlated bit pairs from the weakly correlated information as the final secret key. The AD method has a better performance for the asymmetric MDI-QKD protocol. The greater the degree of asymmetry, the better the improvement of the AD method. The AD method can also improve the performance of the asymmetric MDI-QKD protocol under various finite-size effects, and can be more adaptable with finite-size cases. Our work may play a role in measurement-device-independent networks.  
Conceptualization, J.L. and Q.W.; Methodology, K.Z., J.L., H.D., C.Z. and Q.W.; Software, K.Z., J.L., H.D. and C.Z.; Validation, K.Z., J.L., H.D., X.Z., C.Z. and Q.W.; Formal analysis, K.Z., J.L., X.Z. and C.Z.; Investigation, J.L. and C.Z.; Resources, Q.W.; Data curation, K.Z., J.L., X.Z. and C.Z.; Writing--original draft, K.Z. and X.Z.; Writing--review & editing, J.L., H.D., X.Z. and Q.W.; Visualization, K.Z. and J.L.; Supervision, X.Z. and Q.W.; Project administration, Q.W.; Funding acquisition, X.Z. and Q.W. All authors have read and agreed to the published version of the manuscript.  
We gratefully acknowledge the financial support from the National Natural Science Foundation of China (12074194, 12104240, 62101285); Industrial Prospect and Key Core Technology Projects of Jiangsu provincial key R&D Program (E2022071); Natural Science Foundation of Jiangsu Province (BK20192001, BK20210582); Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_0726); Natural Science Foundation of the Jiangsu Higher Education Institutions(21 KJBI40014), and NUPTSF (NY220122, NY220123).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
Figure 4: Comparison of the secret key generation rate versus the transmission loss when the value \(L_{\text{asy}}=12\) dB. The different colors represent the values \(N=10^{10}\), \(N=10^{11}\), and \(N=10^{12}\), respectively. The solid line represents the secret key without the AD method, and the dotted line represents the secret key with the AD method.
## 3 Case Study and Results
### Reliability Calculation  
Based on the IPSO-LSSVM model, the limit state functions (Equations (1), (3), and (4)) of the three failure modes of the concrete gravity dam were constructed. The MC method  
Figure 10: Comparison between the predicted and true value of \(P_{\rm max}\).  
Figure 9: Comparison between the predicted and true value of \(P_{\rm min}\).  
was used to calculate the \(P_{\rm f}\) of the typical concrete gravity dam. The MC method requires a sufficiently large sample to achieve high accuracy, and the quality of the calculated results is generally evaluated using the coefficient of variation of \(P_{\rm f}\). When the coefficient of variation of \(P_{\rm f}\) is less than 0.1, the results are considered to meet the accuracy requirements [31]. Under the most unfavorable conditions, the \(K_{\rm c}\) and \(P_{\rm min}\) of the concrete gravity dam meet the safety requirements, so this section mainly analyzes the probability of stress exceedance at the toe of the dam. The probability of stress exceedance at the toe of the dam was calculated 10 times for different sample sizes to obtain the mean value and the coefficient of variation of \(P_{\rm f}\). The results are shown in Table 6, and the curve of \(P_{\rm f}\) is shown in Figure 11.  
From the results, it can be seen that, when the number of samples exceeds \(10^{6}\), the coefficient of variation of \(P_{\rm f}\) is already less than 0.1, indicating the results meet the accuracy requirements. The \(P_{\rm f}\) calculated by using the IPSO-LSSVM model is stable at \(8.87\times 10^{-3}\). Since there is only one failure mode, the \(P_{\rm f}\) of the concrete gravity dam system is \(8.87\times 10^{-5}\), which is much smaller than the value specified in the specification, so the concrete gravity dam is safe and reliable.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 5 Conclusions and FUTRE Work  
The emergence of WSNs in supporting a wide variety of applications has motivated researchers and attracted considerable attention in recent years, which makes routing tasks in WSNs among the most active and challenging research areas. We present in this paper a survey of routing mechanisms and protocols proposed for WSNs following a new deep classification model. In fact, we classified the mainstream recent proposed protocols using our taxonomy into nine categories of protocols, namely: application type, delivery mode, initiator of communication, network architecture, path establishment (route discovery), network topology (structure), protocol operation, next-hop selection, and latency-aware and energy-efficient routing. A detailed classification was presented under all the categories with the mention of its recent routing protocols. Furthermore, a performance evaluation of the most representative protocols is conducted under the NS3 simulator. Obtained results show that the E-DEEC, multichain-PEGASIS and M-GEAR protocols improve the network lifetime compared to other protocol which led us to conclude that on the use of smart techniques enhances the network lifespan and guarantee better coverage in the sensing area.  
As future work, we will prepare an in-depth analysis of routing attacks and security mechanisms in WSNs with proved analysis by simulations to facilitate the conception of smart and secure routing protocols for researchers.  
R.Z. design, validation, data curation, review and supervise; A.K. theoretical study, analysis, findings, simulation scenarios and wrote the manuscript. All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 7: Variance versus rounds.
## 4 Production System Case Study
### Design of the Generic Case Study
#### 4.1.1 Structure of the Simulation Case Study: Production System Flow and Characteristics  
Firstly, this sub-chapter describes the general structure of the simulation models that were applied to all simulation models within the simulation case study. The structure was generated to provide the necessary production system flow and characteristics in order to answer the research question. Thus, as can be seen in Figure 3, the structure considered a production system within a supply chain of suppliers-production system-distributors-retailers/customers that served as a generic framework applicable for any sector. Moreover, the production system consisted of technical processes including the transportation, warehousing, and production of finished products as well as management processes, systems, and organizational structure from operational to strategic levels.  
One of the agents within the supply chain are the suppliers, which are represented by production plants delivering the raw materials to the production system. Furthermore, the production system considers one warehouse for the storage of raw materials, three production steps with their respective machine groups and work-in-progress (WIP) inventory, and a final warehouse within the factory with the finished products. In addition, the production system integrates management activities and human resource planning tasks related to production management and the control and execution of the selected production strategies and methods. Then, finished products are delivered to end-customers within the distribution network that consists of a central warehouse and the transportation activities to the retailers. The central warehouse receives all the goods produced by the production facility and stores them. These products are finished, packaged products at this stage, so distribution activities include only those related with the storage and transport of product. As a summary, all models have a set of suppliers, one raw-materials warehouse, three production processes, one finished-products warehouse, a set of distributors, and three retailers serving end-customers that each have a certain demand.  
While all simulation models maintained this structure over the simulation period, capacities, methods, systems, human resource structures, and technologies changed depending on the simulation model influencing the model behavior.