esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analytical Solution of Stress and Displacement of Frozen Wall
### 4 Mechanical Model of Frozen Wall
The displacement of the outer edge of the frozen wall was always greater than that of the inner edge, which changed with temperature significantly more than that of the outer edge. When the average temperature was \(-20\)\({}^{\circ}\)C, the relationship between the inner edge displacement and the radius of the plastic zone calculated based on different strength criteria was obtained, as illustrated in Figure 5:0 The results show that the displacement of increased rapidly with the increase in the radius of the plastic zone. When the relative radius \(r\) of the plastic zone reached 1.8, the calculated displacement results based on the twin shear strength criterion, Tresca strength criterion, Mohr-Coulomb strength criterion, and Druker-Prager strength criterion were 134.5, 118.2, 98.6, and 98.0 cm, respectively, which all exceed the allowable displacement range of the frozen wall. When a part of the frozen wall entered the plastic state, the displacement of the whole frozen wall increased considerably. Therefore, the bearing capacity of the frozen wall should be improved by increasing the thickness of the frozen wall or reducing the average temperature of the frozen wall, which would minimize the chances of the frozen wall entering the plastic state. Figure 5:1 Variation of displacement of the inner edge and outer edge of the frozen wall. Figure 5:2 Variation of frozen wall displacement with plastic zone radius. The results obtained using the heterogeneous frozen wall displacement formula derived in this paper, considering frost heave characteristics, were compared with the results of the frozen wall displacement formula without considering frost heave (converted from Ref. [20]). Figures 14 and 15 illustrate the comparison. The results show that the displacement mainly comprised two parts: the displacement generated by the frozen wall in the elastic or elastic-plastic state under the action of external load, and the displacement caused by soil frost heave. When the frost heave property was not considered, the calculated displacement of the frozen wall was smaller. Further analysis showed that the displacement of the inner and outer edges of the frozen wall due to frost heave remained unchanged when the buried depth was 100-300 m. In the depth range of 500-600 m, the frozen wall entered the elastoplastic state, and the displacement of the inner edge of the frozen wall caused by frost heave increased with the increase in the plastic zone. Meanwhile, the displacement of the outer edge caused by frost heave decreased with the increase in the plastic zone. A comparison of the displacements of the inner and outer edges showed that the total displacement of the outer edge of the frozen wall was greater than that of the inner edge at the same burial depth. Moreover, the displacement of frost heave on the outer edge was greater than that on the inner edge. Figure 5:3 Comparison of the calculation results of the frozen wall inner
# Seismic Risk Analysis of Existing Link Slab Bridges Using Novel Fragility Functions
## 4 Failure Rate and Result Discussion
### Failure Rates Sensitivity to Different Bridge Geometries  
To conclude, the failure rate sensitivity to the different examined bridge geometries is assessed. Due to space constraints, it is not possible to maintain the previous level of detail in the results presentation of every case study. A comparison among the risk values obtained from different bridges is proposed in the charts of Figure 16. Different markers  
Figure 14: Fragility curves for different percentages of damaged piers, evaluated for two limit states: (**a**) damage, (**b**) collapse.  
Figure 15: Annual failure rates for different damage and collapse extent scenarios involving: (**a**) links, (**b**) bearings, (**c**) piers.  
identify different bridges, according to the legend in Figure 16f; colors identify the limit state (as before). The analysis of the outcomes leads to the following main comments:  
* Overall, single-element failure rates (Figure 16a) of different bridges do not vary significantly and the estimated MAFs remain relatively constant.
* A higher variability is observed on the annual rates characterizing different failure mechanism combinations (Figure 16b), which are however expected to change from case to case; in particular, this variability is higher at the damage LS and quite negligible at the collapse LS.
* Concerning the damage extent scenarios, it is worth noting that the failure rates on bearings (Figure 16c) are comparable among all the case studies, while some differences can be detected from the point of view of links (Figure 16d) and piers (Figure 16e).
* More specifically (Figure 16d), the longest bridge (L/B = 20) is the only one that shows a different trend of MAF values, and this trend testifies that the probability of link failure is higher but involves a limited number of elements; on the contrary, shorter bridges have a higher probability of more widespread links disruptions.
* Regarding the piers, all the bridges show proper safety margins against the collapse, while the damage extent scenarios at the damage limit state are characterized by MAF values with a certain degree of variability.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 4 Experiment
### Data set Configuration and Experimental Conditions
#### 4.1.2 Test Data Parameters  
The signal parameters of the independent test data are the same as those of the training dataset but are not involved in training at all. The different parameters are mainly the target SNR range of the test set. In the test set, SNR varies from 0 to 16 dB in 2 dB steps. From Figure 9. we can visualize the effect of SNR on the generated time-frequency diagram. With the reduction in SNR, noise has more and more influence on the definition of a time-frequency image, and it is more difficult to analyze the signal in the image through vision. The number of time-frequency images in the test set is 1000 for each SNR condition.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 4 Fall Detection with WiFi Signal Based on LSTM Model
### CSI Data Acquisition  
This part of the paper mainly studies fall detection under the occlusion relationship; hence, data collection is also established under the occlusion relationship. We formed a penetralium in a small room, using a wooden door between the WiFi router and the receiving computer to directly form a blocking relationship. In the Ubuntu14.04 LTS Linux operating system equipped with an Intel 5300 network card, the AP mode was used to collect data. The router had two antennae at the transmitting end and three antennae at the receiving end.
# Analysis of Machine Learning Techniques for Information Classification in Mobile Applications
## 4 Image and Video Classification Algorithms on Mobile Devices
### MobileNet  
MobileNet is a model that can be deployed on mobile devices to meet the intelligence needs of the market for mobile applications due to the low number of parameters required for training, low latency in processing inferences and low consumption of computing resources. Mobilenet consists of an architecture based on depth-separated convolutions, allowing the model to be quite light and efficient. It can be used for classification, detection, embedding and segmentation and is used in a similar way to other large-scale models [29; 30].  
* MobileNetV2 is the second version of the MobileNet model that greatly improves the accuracy and inference time of the model. This update contains a full convolution layer with 32 filters and 19 bottleneck layers and direct access connections [29; 31].
* MobileNetV3: This version of the model is based on the EfficientNet search method with specific parameter space targets required for use on mobile devices. It is a lightweight model that allows for image classification with low inference times and fits architectures with limited computational resources [32; 33].  
Among the areas in which this model has been used to improve and automate processes are botany, botanics [34; 35; 36; 37], medicine [38; 39; 40; 41; 42], manufacturing [43], zoology [44], digital forensics processes [45], etc.  
As discussed above, MobileNet is based on an architecture based on depth-separable convolutions. This convolution consists of two operations: depthwise convolution and pointwise convolution. Depthwise convolution applies separate convolutions to each channel of the input tensor, i.e., a traditional \(n\times m\) convolution on a colour image. Subsequently, the activation maps resulting from the convolution operations are concatenated on the depth axis. A traditional \(1\times 1\) convolution is then applied to the resulting tensor (pointwise convolution), which combines the channels of the concatenated activation maps [46].
## 4 Practical Multidirectional MTF Measurement Method  
### Rationality of Multidirectional MTF  
Based on the analysis conclusion in Section 3.1, it can be seen that the results of measuring MTF in the horizontal or vertical direction with 0-14.5 deg edge are effective and reliable. If multidirectional MTF needs to be measured, the edge ROI should be projected in the direction perpendicular to the edge. The effectiveness of multidirectional MTF measurement will make the application of the edge method no longer limited by STA edge. The MTF measured by a certain angle edge can further reflect the image quality degradation of the sampling imaging system in the whole field of view, but it needs a more reasonable sampling MTF compensation model. In addition, PSF parameters obtained by multidirectional MTF can be used for non-blind image restoration.
# Mixed Alternating Projections with Application to Hankel Low-Rank Approximation
## 4 Linearized Cadzow Iterations in the Signal-Estimation Problem  
In the next Section 5, we apply the approach proposed in Section 3 to solving the SLRA problem (3) to estimate signals in time series. Since this approach considers alternating projections to linear subspaces, we start with linearization of the Cadzow iteration (5). The Cadzow iteration is not linear because it uses the projection onto the nonconvex set \(\mathcal{M}_{r}\) of matrices of rank not larger than \(r\), which is not a closed subspace but is a smooth manifold [19].  
Let us sketch the way of linearization in the neighborhood of a time series \(\mathsf{S}_{0}\in\mathcal{D}_{r}\) of rank \(r\). First, we consider the set \(\mathcal{M}_{r}\) in the neighborhood of the point \(\mathsf{S}_{0}=\mathcal{T}_{L}(\mathsf{S}_{0})\). The projection in the neighborhood of this point is correctly defined if we solve the signal-estimation problem for a noisy time series of the form \(\mathsf{X}=\mathsf{S}_{0}+\xi\), where \(\xi\) is a random vector with a zero mean and an autocovariance matrix proportional to \(\mathbf{W}_{0}^{-1}=\mathbf{\Sigma}_{0}\), where the variances are small enough and \(\mathbf{W}_{0}\) is a given weight matrix. After that, we construct a linearized iteration by replacing the set \(\mathcal{M}_{r}\) with a tangent subspace at \(\mathsf{S}_{0}\). Then, we can reduce the linearized iteration to a multiplication by a real matrix and find its properties. As a result, we obtain asymptotic relations for the linearization which explains the yield of the full Cadzow iteration (5).
# Estimation of Methane Gas Production in Turkey Using Machine Learning Methods
## 2 Materials and Methods  
In this study, three different machine learning methods, logistic regression, artificial neural networks, and support vector regression, were used to predict methane gas production in Turkey. Models with eleven independent variables were designed to predict the dependent variable methane gas. The data were reduced to 0-1 using the min-max normalization technique. Cross-validation was used to increase the success of the study. R\({}^{2}\), MAE, and MSE statistical metrics were used to determine the success and error of the models.
# Advanced Financial Fraud Malware Detection Method
## 2 Related Work
### Hybrid Analysis-Based Malware Detection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Studies** & **Year** & **Feature(s)** & **Dataset(s)** & **Algorithm(s)** & **Performance Evaluation** \\ \hline \multirow{3}{*}{[PERSON] et al. [47]} & \multirow{3}{*}{2016} & Permissions, Sensitive API Call, Dynamic behavior Sequences & Google Play [22] and Contagio [24] & \begin{tabular}{c} StormDroid (SVM, MLP, C4.5, IBK, NB, and Bagging predictor) \\ \end{tabular} & Accuracy: 93.80\% \\ \cline{1-1} \cline{3-3}  & & \begin{tabular}{c} System calls, SMS, Critical \\ API, User Activity, App \\ Metadata \\ \end{tabular} & \begin{tabular}{c} MalGenome [39], Contagio [24], and \\ VirusShare [23] \\ \end{tabular} & \begin{tabular}{c} K-NN, QDC, LDC, \\ PABZC, MLP, and \\ RBF \\ \end{tabular} & Accuracy: 96.90\% \\ \hline \multirow{3}{*}{[PERSON] et al. [49]} & \multirow{3}{*}{2020} & API calls, Permissions, Intents, Network traffic, Java classes, Inter-process communication & Drebin [28], VirusShare [23], and AndroZoo [42] & \begin{tabular}{c} LR, Naive Bayes, RF, \\ KNN, SGC, \\ Adaboost, SVM, and \\ Ensemble \\ \end{tabular} & \begin{tabular}{c} Drebin: 100\%, \\ VirusShare 100\%, \\ AndroZoo: 91.8\% \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] et al. [50]} & \multirow{3}{*}{2020} & MalGenome [39], Drebin [28], and CICMalDroid 2020 [51] & Gradient boost (GB) & Accuracy: 99.36\% \\ \cline{1-1} \cline{3-3}  & & \
\begin{tabular}{c} Drebin [28], AMD [31], \\ AndroZoo [38], (Github [53], \\ and Google Play \\ \end{tabular} &
\begin{tabular}{c} Tree Augmented \\ Naive Bayes (TAN) \\ \end{tabular} & Accuracy: 97.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of related studies that utilized hybrid analysis. 99.36%. The authors highlighted that a hybrid analysis approach enhanced the detection rate by approximately 5%. In [52], a hybrid malware detection method was proposed using tree-augmented Naive Bayes (TAN) to analyze the conditional dependencies between key static and dynamic features, including API calls, permissions, and system calls required for application functionality. The approach involved training three ridge-regularized logistic regression classifiers to analyze API calls, permissions, and system calls, and employing TAN to model the relationships between these outputs to identify malicious applications. The proposed method achieved sustained detection accuracy of 97% over an extended period.
# Real-Time Path Planning for Strategic Missions
###### Abstract  
Robot navigation is still an open research topic, mainly regarding applications that require online planning. In this context, this manuscript presents an implementation of the Lifelong Planning A* (LPA*) search algorithm to guide a mobile robot in an environment that changes along time, by detecting obstacles and updating the current mapping information. Firstly, simulations validate the strategy, and later experiments confirm these results considering a real application. The result is that the LPA* algorithm is able to guide the mobile robot to its target in a changing environment. Obstacles are interactively included in the scene to force the route redesign and the seeking for a new optimal solution connecting the current robot position and its target position.  
mobile robots; path planning; shortest path problem; service robots +
Footnote †: journal: Applied Sciences
# Development of Patient Specific Conformal 3D-Printed Devices for Dose Verification in Radiotherapy
## 4 Conclusions  
We developed an algorithm for the conversion of patient's CT data to a 3D-printer friendly interface for the printing of highly conformal 3D structures (boluses, phantoms) used for dose verification in radiotherapy with a geometric accuracy (DCS) of 0.957. Application of 3D-printed boluses allowed us to reduce air gaps between the bolus and irregular skin surfaces of a patient (phantom) to a minimum: to 0.4-2.1 mm for 0.5 cm bolus and 0.6-2.2 mm for 1.0 cm bolus. It was found that boluses of both thicknesses (0.5 and 1.0 cm) have comparable effectiveness as dose escalating devices, significantly increasing dose coverage of the target volume for both single field and intensity modulated fields treatment plans. Performed investigation has shown promising results regarding potential application of cost effective, personalized, 3D-printed boluses in combination with 3D-printed patient-specific phantoms for the verification of radiation dose plans, especially when treating concaved and uneven surfaces such as superficial head and neck cancer, which cannot be fitted adequately with a commercial bolus. It should be noted that discussed boluses and phantoms made of polylactic acid or other suitable thermoplastic are recyclable and may be reused for the printing of new 3D devices following the workflow implemented in this article.  
Conceptualization, D.A. and A.J.; Methodology, A.J., L.K. and J.L.; Software, B.G.U.; Validation, A.J.; Formal Analysis, M.I.; Investigation, L.K. and J.L.; Writing--Original Draft Preparation, A.J.; Writing--Review and Editing, D.A.; Supervision, D.A. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the research grant Reg. Nr. P-LL-21-74 of Lithuanian Research Council.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: Dose volume histograms for three IMRT plans obtained for the patient-specific phantom: without bolus (blue), with 0.5 cm 3D bolus (red) and 1.0 cm 3D bolus (gray).
## 1 Introduction  
Marine sediments, serving as carriers of environmental evolution information, are extensively utilized in paleoclimate and paleoenvironmental studies [1, 2, 3, 4, 5]. In recent years, the Beibu Gulf, situated in the northwest of the South China Sea, has emerged as a focal area for marine geological research owing to its unique geographical position and topographical conditions [6, 7, 8, 9]. Since the last deglaciation, the Beibu Gulf has undergone significant changes in sedimentary environments due to the continuous rise in global sea levels. Notably, the opening of the Qiongzhou Strait during the early Holocene significantly altered the sedimentary system within the Beibu Gulf [10, 11]. To elucidate regional paleoenvironmental and paleoclimate changes, numerous studies have been conducted on the marine records of the Beibu Gulf, establishing modern sediment source-sink processes and Quaternary paleoclimate evolution based on mineralogical and geochemical analyses of Beibu Gulf sediments [12, 13, 14, 15, 16, 17]. However, compared to extensive research on modern sediment source-sink processes, studies on sediment provenance transitions since the Holocene remain relatively limited [18; 19], particularly concerning significant geological events such as the opening of the Qiongzhou Strait, which have profoundly impacted the source-sink dynamics in this region.  
The Qiongzhou Strait, as a critical channel for material exchange between the Beibu Gulf and the eastern sea area, directly influenced the sediment supply and circulation patterns within the Beibu Gulf [20; 21]. Previous studies have documented the thick Holocene sediments deposited at the adjacent region of western mouth of the Qiongzhou Strait [22], with contributions from the Qiongzhou Strait also observed in the middle and southern area of the Beibu Gulf [18; 19]. Despite its importance, the geological events associated with the opening of the Qiongzhou Strait have not received adequate attention. Consequently, understanding the provenance supply to the Beibu Gulf since the opening of the Qiongzhou Strait remains unclear, and there is a lack of geological evidence effectively reflecting the timing and evolution of this event, which limits our comprehension of Holocene sedimentary evolution and the \"source-sink\" process. This study provides geochemical characteristics of the sediments based on their major and trace element compositions, deciphering the sedimentary provenance and depositional environment of core SO-31 in the Beibu Gulf, elucidating changes in provenance contributions from the Qiongzhou Strait and other major sediment sources to the central region of the Beibu Gulf. The research findings will provide critical geochemical evidence for a better understanding of the formation and evolution of the Qiongzhou Strait, as well as valuable insights into the paleoenvironmental changes in the Beibu Gulf.
## 2 Distributed Fair Rate Allocation Algorithm
### Algorithmic Form  
The CDM can be summarized in algorithmic form as follows:  
**Algorithm**. Coupled-Decompositions Method.  
Start with \(k=0,\ \mathbf{\mu}^{0}=\mathbf{0}\) and repeat:  
_Dual Subproblems_  
Using \(\lambda^{k}_{j}=[\mathbf{A}^{T}]_{j}\mathbf{\mu}^{k}\), compute  
\[d_{j}(\lambda^{k}_{j})=\left\{\begin{array}{ll}\max\limits_{r_{j},y^{k}_{j}}& U_{j}(r_{j})-\lambda^{k}_{j}\,y^{k}_{j}\\ s.t.&m_{j}\leq r_{j}\leq M_{j}\\ &r_{j}\leq y^{k}_{j}\end{array}\right.\]  
and get \(y^{k}_{j}\) as the inner maximizer.  
_Primal Projection_  
Solve  
\[\begin{array}{ll}\min\limits_{\hat{\mathbf{y}}^{k}}&||(\mathbf{y}^{k}-\hat{\mathbf{y}}^ {k})||^{2}\\ s.t.&[\mathbf{A}]_{j}\hat{\mathbf{y}}^{k}\leq c_{j}&\forall j\,|\,\mu^{k}_{j}=0\\ &[\mathbf{A}]_{j}\hat{\mathbf{y}}^{k}=c_{j}&\forall j\,|\,\mu^{k}_{j}>0\end{array}\]  
distributedly as sketched in Section 2.4 in order to get \(\hat{y}^{k}_{j}\).  
_Primal Subproblems_  
Solve  
\[p_{j}(\hat{y}^{k}_{j})=\left\{\begin{array}{ll}\max\limits_{r_{j}}&U_{j}(r_{ j})\\ s.t.&m_{j}\leq r_{j}\leq M_{j}\\ &r_{j}\leq\hat{y}^{k}_{j}\end{array}\right.\]  
and get \(\hat{\lambda}^{k}_{j}\) as the dual multiplier associated with the constraint \(r_{j}\leq\hat{y}^{k}_{j}\).  
Furthermore, Figure 6 summarizes in a flowchart all the tasks that a sensor has to execute in order to obtain its optimal rate allocation.
# Preliminary Results on Tropospheric ZTD Estimation by Smartphone
## 3 Results  
In this section, the main results obtained after the data processing phase are shown. Firstly, a comparison between the TORI GNSS station and TOSM (Xiaomi Mi8 smartphone close to TORI) is presented, to show the different ZTD estimations obtained considering both CSRS-PPP and RTKLIB software and these two receivers. Particular attention is paid to the positioning solutions, to not only verify the tropospheric estimation; knowing that the implemented algorithms are based on Kalman filter, this choice has been made to exclude the possibility that a good ZTD estimation produces the detriment of positioning solutions. For this reason, the Up component of the positioning solution has also been considered for investigating this aspect. Considering these stations, the performances obtained using the RTKLIB software are presented, again making the same consideration on the positioning solutions as well as on the ZTD. Moreover, considering only the TOSM station, the obtained results with the two types of software have been compared to highlight the differences in ZTD estimations and make a statistical analysis of these differences. Finally, a second case study has been investigated over a longer time span, considering again both CSRS-PPP and RTKLIB.