esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mechanical Model and Solution
### Displacement and Stress of the Frozen Wall Elastic Zone  
To facilitate the solution, the mechanical model shown in Figure 3 is split into two basic models as shown in Figures 4 and 5.  
Figure 4: Unloading model.  
Figure 5: Mechanical model for the plastic zone of a frozen wall where the inner edge is partially unloaded in a radial manner.  
The analytical solutions of stress and displacement for the elastic zone of the frozen wall are equal to the solutions for the unloading model shown in Figure 3 superimposed over the initial stress and displacement [23; 24].  
The unloading amount of the radial stress at the inner edge of the frozen wall elastic zone is  
\[\Delta p_{p}=p_{p}-p_{0} \tag{11}\]  
According to the elastic thick-walled cylinder theory [25; 26] and combining Equations (7), (10) and (11), the stress and the displacement of the unloading model can be obtained. Based on the aforementioned superposition, analytical solutions of the stress and displacement for the frozen wall elastic zone and the surrounding unfrozen alluvium are obtained as follows:  
\[\sigma_{r}^{\text{II}}=-\Delta p_{p}\frac{\zeta-\zeta\frac{r_{p}^{2}}{r^{2}}- \frac{r_{p}^{2}}{r^{2}_{c}}+\frac{r_{p}^{2}}{r}}{\frac{r_{p}^{2}}{r_{c}^{2}}-1}+ p_{0} \tag{12}\]  
\[\sigma_{\theta}^{\text{II}}=-\Delta p_{p}\frac{\zeta+\zeta\frac{r_{p}^{2}}{r^{ 2}}-\frac{r_{p}^{2}}{r_{c}^{2}}-\frac{r_{p}^{2}}{r^{2}_{c}}}{\frac{r_{p}^{2}}{ r_{c}^{2}}-1}+p_{0} \tag{13}\]  
\[u^{\text{II}}=r\frac{\Delta p_{p}\big{(}1+\mu^{\text{II}}\big{)}}{E^{\text{II} }}\left[1-2\mu^{\text{II}}-\frac{1-\zeta}{1-\frac{r_{p}^{2}}{r_{c}^{2}}}\left( 1-2\mu^{\text{II}}+\frac{r_{p}^{2}}{r^{2}}\right)\right] \tag{14}\]  
\[\sigma_{r}^{\text{III}}=\Delta p_{p}\frac{\zeta\tau_{c}^{2}}{r^{2}}+p_{0} \tag{15}\]  
\[\sigma_{\theta}^{\text{III}}=-\Delta p_{p}\zeta\frac{r_{c}^{2}}{r^{2}}+p_{0} \tag{16}\]  
\[u^{\text{III}}=-\Delta p_{p}\zeta\frac{1+\mu^{\text{III}}}{E^{\text{III}}} \frac{r_{c}^{2}}{r} \tag{17}\]  
where \(\zeta=\frac{2}{\left[2+M\left(\frac{r_{c}^{2}}{r_{c}^{2}}-1\right)\right]}\); \(M=\left(\frac{E^{\text{II}}}{E^{\text{III}}}-1\right)\frac{1+\mu^{\text{III}} }{1-\left(\mu^{\text{II}}\right)^{2}}+2+\frac{\mu^{\text{III}}-\mu^{\text{II} }}{1-\left(\mu^{\text{II}}\right)^{2}}\); \(E\) and \(\mu\) are the elastic modulus and the Poisson's ratio, respectively; and superscripts II and III represent the frozen wall elastic zone and the surrounding unfrozen alluvium, respectively.  
From Equations (13) and (14), the solutions for the circumferential stress and the displacement at the inner edge of the frozen wall elastic zone are  
\[\sigma_{\theta}^{\text{II}}\Big{|}_{r=r_{p}}=-\Delta p_{p}\frac{2\zeta-\frac{ r_{p}^{2}}{r_{c}^{2}}-1}{\frac{r_{p}^{2}}{r_{c}^{2}}-1}+p_{0} \tag{18}\]  
\[u^{\text{II}}\Big{|}_{r=r_{p}}=r_{p}\frac{\Delta p_{p}\big{(}1+\mu^{\text{II}} \big{)}}{E^{\text{II}}}\left[1-2\mu^{\text{II}}-\frac{2(1-\zeta)\big{(}1-\mu^{ \text{II}}\big{)}}{1-\frac{r_{p}^{2}}{r_{c}^{2}}}\right] \tag{19}\]
## 6 Applications of RF Energy Harvesting  
RFEH has emerged as a transformative solution that enables electronic devices to operate independently by capturing ambient electromagnetic energy and converting it into usable electrical power. Its significance spans multiple industries, including smart cities, biomedical engineering, industrial IoT, agriculture, and even space communications, as shown in Figure 10. Recent advancements have further accelerated its integration into real-world applications, paving the way for sustainable, maintenance-free, and efficient device networks.
# Evaluation of the Effectiveness of Driver Training in the Use of Advanced Driver Assistance Systems
## 2 Research Procedure
### Test Drive  
In order to ensure a reliable evaluation of the training and the participant's progress, an assessment module was developed. Both objective and subjective measures were employed. Due to drivers' tendency to overestimate their skills, questionnaires were designed to assess driver behaviour from both the driver's and the instructor's perspectives. These questionnaires included detailed questions allowing for the evaluation of specific aspects of driving and driver competence.  
Objective assessment was conducted after the driving session, based on parameters recorded by the driving simulator.  
As with the practical training, the test scenario consisted of a drive along a high-speed route within a simulated environment. During the session, data were collected to assess the effectiveness of the training, identify driver behaviours requiring improvement, and determine which elements should be repeated and reinforced. The scenario included surrounding traffic, changing weather conditions, and three sudden situations requiring the driver to take manual control of the vehicle. Upon initiating the drive, the participant was instructed to accelerate to a speed of 100 km/h and activate the autonomous driving mode. The driver's task was to maintain the vehicle in the middle lane while monitoring the road situation. After a certain period, a request for a manual takeover occurred due to a blocked lane. After manoeuvring around the obstacle, the driver re-engaged the autonomous mode at 100 km/h. Subsequently, another hazardous situation arose, again requiring the driver's intervention. This sequence of events occurred three times throughout the scenario. However, each situation was characterised by a different traffic context, thereby necessitating a unique driver response in each case.  
The final stage of assessing the driver's knowledge involved a change in weather conditions, specifically the onset of fog, without a system-generated alert instructing a manual takeover. During training, participants were informed that the system would not function properly under such conditions, and the correct response was to immediately resume control of the vehicle--even in the absence of a system prompt. The total length of the test scenario was 9350 m. Obstacles were placed approximately every 2000 m, providing sufficient space to avoid the hazard, stabilise the driving trajectory, and safely re-engage the system. Figure 3 illustrates the emergency situations included in the test scenario.
# Algorithms for Optimal Power Flow Extended to Controllable Renewable Systems and Loads
## 2 Materials: Optimal Power Flows Considering Uncertainty Cost Functions
### Optimization Problem Statement
#### 2.1.2 Concept of Uncertainty Cost Functions from Previous Studies  
In order to calculate Uncertainty Costs Functions (UCFs), it is necessary to define underestimation and overestimation costs developed in previous studies [4; 11] :  
Uncertainty Cost Due to Underestimation  
Costs due to underestimation refer to the power that a renewable generation unit cannot deliver to the grid when the scheduled power value of the plant is smaller than the available generation power:  
\[P_{Sch}<P_{A\
u}, \tag{11}\]  
where \(P_{Sch}\) and \(P_{A\
u}\) are the scheduled power and the available power, respectively. In this case, penalty cost due to underestimation is given by:  
\[C_{sub}(P_{Sch},P_{A\
u})=\left\{\begin{aligned} c_{u}(P_{A\
u}-P_{ Sch})&& if&& P_{Sch}\leq P_{A\
u}\leq P_{max}\\ & 0&& Otherwise,\end{aligned}\right. \tag{12}\]  
where \(c_{u}\) is the penalty cost coefficient due to underestimation and \(P_{max}\) is the generator maximum output power.  
Now, because of the variability of the renewable power sources, the power generated by these sources has an associated Probability Density Function (PDF) \(f_{n}(P)\). The uncertainty cost due to underestimation is defined as the expected value of \(C_{sub}\) (developed from Expression (12)):  
\[E[C_{sub}(P_{Sch},P_{A\
u})]=\int\limits_{P_{Sch}}^{P_{max}}c_{u}(P_{A\
u}-P_ {Sch})f_{n}(P_{A\
u})dP_{A\
u}. \tag{13}\]  
Uncertainty Cost Due to Overestimation  
Costs due to overestimation refer to the power that cannot be supplied by a renewable generator because the available power is smaller than the previously scheduled power:  
\[P_{A\
u}<P_{Sch}. \tag{14}\]In this case, penalty cost due to overestimation is given by:  
\[C_{so}(P_{Sch},P_{Av})=\left\{\begin{aligned} & c_{o}(P_{Sch}-P_{Av})&& if \qquad P_{min}\leq P_{Av}\leq P_{Sch}\\ & 0&& Otherwise,\end{aligned}\right. \tag{15}\]  
where \(c_{o}\) is the penalty cost coefficient due to overestimation and \(P_{min}\) is the generator minimum output power.  
In the same way as the underestimation condition, and based on the stochastic nature of renewable sources, the uncertainty cost due to overestimation is given by the expected value of \(C_{so}\) (developed from Expression (15)):  
\[E[C_{so}(P_{Sch},P_{Av})]=\int\limits_{P_{min}}^{P_{Sch}}c_{o}(P_{Sch}-P_{Av}) f_{n}(P_{Av})dP_{Av}. \tag{16}\]  
Finally, the Uncertainty Cost Function (UCF) for a given renewable source is equal to the sum of underestimation and overestimation costs (developed from Expressions (13) and (16) respectively):  
\[UCF(P_{Sch},P_{Av})=E[C_{sub}(P_{Sch},P_{Av})]+E[C_{so}(P_{Sch},P_{Av})]. \tag{17}\]  
In [11], the authors of this paper presented the development of the Uncertainty Cost functions of PVG, WEG, PEV and RHG and the Formulation and application of marginal cost functions of PVG, WEG, PEV, and RHG. They are useful as target functions in this paper, and the marginal cost functions are used in the analytical algorithm applied in this paper.
# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 2 Related Work
### Limitations of Existing Methods  
Despite significant advances in infrared small-target detection, current techniques--both traditional and deep learning-based--remain challenged by several fundamental limitations:  
Low SNR and Extremely Small Target Size: Infrared targets are often just a few pixels and exhibit very low signal-to-noise ratios, making them vulnerable to suppression during both handcrafted filtering and deep feature downsampling.  
Supervision and Localization Gaps: Deep learning methods typically rely on box-level labels, providing weak supervision that fails to localize sub-pixel targets accurately. This leads to spatial misalignment and missed detections, as no mechanism enforces dense or pixel-precise guidance.  
Loss of Fine-Scale Texture: In CNN architectures, repeated pooling and convolution reduce the visibility of subtle spatial details. Shallow layers that retain this information are either underutilized or merged too late, resulting in lost texture essential for small target discrimination.  
Lack of Geometric Constraints: Most detection frameworks regress bounding boxes independently without explicit geometric consistency (size, aspect ratio). This absence of structure-aware regularization increases localization drift, especially under clutter and noise.  
Together, these limitations underscore the need for a hybrid architecture that integrates dense spatial supervision, fine-detail preservation, and global geometric regularization. PSHNet is designed to meet exactly this requirement.
## 4 Results
### Heuristic Evaluation Using Automated Tools
#### 4.1.2 Tool Performance  
Regarding the tool's performance, we can see from Table 8 that the tools' performance differed according to the system. The performances of the AChecker and HTML_CodeSniffer tools were similar; the difference between their total performance in terms of the number of detected errors did not exceed one and three errors for the Noor and Faris systems, respectively. Furthermore, the results for the HTML_CodeSniffer and SortSite tools were similar for the iEN system; the difference between their total performance scores did not exceed nine. Generally, the performances of the three tools AChecker, HTML_CodeSniffer, and SortSite were similar. However, the performance of the Total Validator tool had a big gap compared to the others; after we investigated this gap, we found that the tool calculated the redundancy of the error; this explained the huge numbers generated by the tool. Figure 6 illustrates the performance of the tools for the three systems. Moreover, returning to Table 7, we can see that the AChecker tool failed to detect the violations corresponding to the robust principle for the Noor system and the operable principle for the Faris and iEN systems. Furthermore, the HTML_CodeSniffer tool did not detect any errors regarding the operable principle in the iEN system. The observed discrepancies in the performances of different tools highlighted the importance of employing different tools to increase the effectiveness of the evaluation [36].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System** & **Page Name** & **AChecker** & **HTML\_CodeSniffer** & **SortSite** & **Total Validator** & **Total** \\ \hline \multirow{8}{*}{Noor} & Log In & 20 & 14 & 15 & 27 & 76 \\  & Home & 16 & 19 & 11 & 61 & 107 \\  & Resources & 16 & 17 & 13 & 49 & 95 \\  & Borrow & 14 & 15 & 10 & 41 & 80 \\  & Skills & 16 & 17 & 10 & 47 & 90 \\  & Marks & 14 & 15 & 10 & 41 & 80 \\  & Total & 96 & 97 & 69 & 266 & 528 \\ \hline \multirow{8}{*}{Faris} & Log In & 29 & 33 & 70 & 76 & 208 \\  & Home & 31 & 25 & 77 & 96 & 229 \\  & Vacations & 91 & 84 & 82 & 186 & 443 \\  & Salaries & 28 & 34 & 68 & 75 & 205 \\  & Total & 179 & 176 & 297 & 433 & 1085 \\ \hline \multirow{8}{*}{iEN} & Log In & 23 & 20 & 13 & 38 & 94 \\  & Home & 79 & 17 & 18 & 36 & 150 \\ \cline{1-1}  & Download & 79 & 17 & 14 & 35 & 145 \\ \cline{1-1}  & Course & 79 & 17 & 14 & 35 & 145 \\ \cline{1-1}  & Play Content & 79 & 17 & 14 & 35 & 145 \\ \cline{1-1}  & View Course & 89 & 15 & 18 & 40 & 162 \\ \cline{1-1}  & Total & 349 & 86 & 77 & 184 & 696 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Total number of errors based on the pages examined.
# A Channel Allocation Mechanism for Cellular Networks
## 3 Channel Allocation Mechanism in Cellular Networks
### The Number of Call Departures (\(D_{i}\))  
For estimating the number of Call Departure (\(D_{i}\)), two cases including (1) Call Arrival (in Figures 9 and 10); and (2) Handover In (in Figures 11 and 12) are discussed and analyzed. For the case of Call Arrival, a call is performed by a MS in Cell\({}_{i}\) at \(t_{1}\) and the call departure of the MS is performed in Cell\({}_{i}\) at \(t_{2}\) (in Figures 9 and 10). In Case 1, the number of Call Departure is expressed as \(D_{i}^{A}\). For the case of Handover In, a handover process is performed by a communicating MS in Cell\({}_{i}\) at \(t_{1}\) when the MS enters Cell\({}_{i}\), and the call departure of the MS is performed in Cell\({}_{i}\) at \(t_{2}\) (in Figures 11 and 12). In Case 2, the number of Call Departure is expressed as \(D_{i}^{I}\). Therefore, the number of Call Departure (\(D_{i}\)) is expressed as Formula (8). The scenario is called Call Departure derived from Handover In for Cell\({}_{i}\). Furthermore, \(O_{i}\) can be estimated by Formula (10) in accordance with Formula (7) and Formula (9):  
\[D_{i}=D_{i}^{A}+D_{i}^{I}, \tag{8}\]  
\[A_{i}+I_{i}=D_{i}+O_{i}, \tag{9}\]  
\[A_{i}=D_{i}=F_{i}\times\frac{1-e^{-\frac{\lambda l_{i}}{V_{i}}}}{\lambda}. \tag{10}\]  
Figure 8: The timing diagram for Handover Out the specific Cell\({}_{i}\) derived from Call Arrival [27; 28; 29; 30; 31; 32; 33].  
In the following sections, we describe the results of the algorithm.  
Figure 11: The timing diagram for Call Departure in the specific Cell\({}_{i}\) derived from Call Arrival [27; 28; 29; 30; 31; 32; 33].  
Figure 10: The timing diagram for call departure in the specific Cell\({}_{i}\) derived from call arrival [27; 28; 29; 30; 31; 32; 33].  
Figure 9: The scenario diagram for vehicle movement and call departure in the specific Cell\({}_{i}\) derived from call arrival [27; 28; 29; 30; 31; 32; 33].
## 5 User Preference Modeling
### User Preference Modeling for Social Tags
#### 5.2.1 Social Tag Representation of Video  
It is a fact that users are reluctant to label a video in mobile video recommendations because labeling a mobile video is time-consuming work, especially for those users who only want to watch the mobile videos for a few minutes. Meanwhile, tags labeled by the user always are invalid even when not related with the mobile video. We tried to validate our statements by collecting 100 mobile videos from the Watermelon App to analyze the relationship between video duration and number of comments. The results are demonstrated in Figure 4.  
From Figure 4, we can see that the number of comments reduces with the drop of the duration of videos. Therefore, it is impossible to expect users to label a tag for mobile videos. An available way of removing invalid tags is to recommend mobile videos based on the tags that have been processed and standardized. Therefore, the first thing that should be done for a video recommender system is to decide which words can be used as tags and then make a word dictionary. Here, we choose to represent the video labeled by experts with a vector.  
Firstly, the popularity of a social tag is calculated, which is an estimate of how common a social tag is in general usage. For instance, the social tag \"animal\" in mobile videos is very common, and social tag frequency tends to incorrectly emphasize videos with the more frequent social tag \"animal\" instead of more meaningful tags such as \"dog\" or \"cow\". The social tag \"animal\" is not a good keyword for distinguishing relevant and non-relevant videos and terms. Obviously, the less-common words \"dog\" and \"cow\" are suitable selections. As a result, the social tag popularity factor is incorporated to diminish the weight of social tags that occur very frequently in the video set and increase the weight of social tags that occur rarely. The social tag popularity \(p\) can be acquired by  
\[p\ =\ \log\Big{(}\frac{N}{1\ +\ |v\in V;t\in v\|}\Big{)} \tag{9}\]  
Figure 4: The relationship between time and number of comments.  
where \(N\) is the total number of videos in the database \(V\); and \(|\{v\in V\text{:}t\in v\}|\) is the number of videos where the social tag \(t\) appears. Finally, a video \(v\) with social tag popularity can be defined as:  
\[v\ =\ \left(w_{1},w_{2},\ldots,w_{i},\ldots,w_{|D|i}\right) \tag{10}\]  
\[w_{i}\ =\ f(t_{i},v)\ \times\ p_{i} \tag{11}\]  
where \(||D||\) is the size of the social tag dictionary, \(f(.)\) is equal to 1 when video \(v\) has the social tag \(t_{i}\), and the social tag weight in video \(v\) is the product of function \(f(.)\) and popularity \(p\).
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 7 Conclusions  
A public key-based authentication and en-route filtering scheme (PKAEF) is proposed in this paper. PKAEF integrates a mechanism for verifying the partial signatures and a substitution mechanism to substitute the malicious cluster nodes or CHs; thus, PKAEF can resist report disruption attacks. Additionally, PKAEF contains an effective report-forwarding protocol and an en-route filtering scheme, which makes it able to combat selective forwarding attacks and false data injection attacks. Moreover, PKAEF provides a trust value-based mechanism for detecting and punishing the malicious nodes, which can mitigate the impact of malicious nodes. Both theoretical analysis and simulation results demonstrate that, in most cases, PKAEF outperforms DSEDA and PDF in terms of security, filtering efficiency, and data availability.  
[PERSON] and [PERSON] developed the system and threat models and algorithms. The experimental simulations were conducted by [PERSON] and [PERSON]. [PERSON] analyzed the data. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant Nos. 61572263, 61502251, 61872197 and 61402225; the Key Project of Natural Science Research of Jiangsu University under the grant No. 14 KBJ520031; the National Natural Science Foundation of Jiangsu Province under grant Nos. BK201511 and BK20140832; the Postdoctoral Science Foundation of China under grant Nos. 2016M601859 and 2013M540447; the Postgraduate Research & Practice Innovation Program of Jiangsu Province under the grant No. KYZZ16_0259; and the Natural Research Foundation of Nanjing University of Posts and Telecommunications under the grant No. NY217119.  
The authors declare no conflict of interest.
## 1 Introduction
With the development of deep learning, recurrent neural networks (RNN) have become one of the most widely used models to solve time series problems for the reason that they can directly handle time series of varying length [20]. Long short-term memory (LSTM) and gated recurrent units (GRU) are the most popular RNN models for capturing long-term and short-term impacts in time series [21; 22]. Even though few studies focus on handling multivariate time series with massive missing data, there are still some outstanding works that have been proposed recently. GRU-D proposed by [PERSON] et al. [23] focuses on the missing pattern of multivariate time series. However, GRU-D treats multivariate time series as a whole and it does not consider the missing rate differences. [PERSON] et al. separate the multivariate time series into univariate time series and use an independent LSTM network for every variable to fully extract the single variable feature and its missing pattern at the cost of time consumption [24]. In this paper, we propose our method called variable sensitive GRU (VS-GRU), which has the following contributions:  
* In clinical time series, we believe the missing rate of variable is related to its characteristic. In addition to the missing mark, VS-GRU considers the missing rate of different variables, seeing it as another input of GRU. * VS-GRU processes variables separately at the same time in a simple structure. Variables can maintain its characteristics before being mixed with others, which increases robustness when dealing with time series with some variables that are almost completely missing. Figure 1: Example of clinical time series with six variables within 12 time steps. Red dots denote the observation marks. * VS-GRU considers the classification result at every time step, which decreases the probability of learning error from the whole time series. In Section 2, we present the related recent research in time series with missing values' classification problems and clinical time series analyses using deep learning. In Section 3, VS-GRU is presented in detail. We evaluate our method on two public clinical datasets and compare it with the state-of-the-art in Section 4. Finally, we make our conclusions in Section 5.
# Investigating IPTV Malware in the Wild
###### Abstract  
Technologies providing copyright-infringing IPTV content are commonly used as an illegal alternative to legal IPTV subscriptions and services, as they usually have lower monetary costs and can be more convenient for users who follow content from different sources. These infringing IPTV technologies may include websites, software, software add-ons, and physical set-top boxes. Due to the free or low cost of illegal IPTV technologies, illicit IPTV content providers will often resort to intrusive advertising, scams, and the distribution of malware to increase their revenue. We developed an automated solution for collecting and analysing malware from illegal IPTV technologies and used it to analyse a sample of illicit IPTV websites, application (app) stores, and software. Our results show that our IPTV Technologies Malware Analysis Framework (IITMAF) classified 32 of the 60 sample URLs tested as malicious compared to running the same test using publicly available online antivirus solutions, which only detected 23 of the 60 sample URLs as malicious. Moreover, the IITMAF also detected malicious URLs and files from 31 of the sample's websites, one of which had reported ransomware behaviour.  
malware analysis; cyber threat intelligence; IPTV; digital investigations Article