esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Outage Performance Analysis
### Outage Probability of \(\mathrm{D}_{2}\)  
Similar to the outage probability related to the signal \(x_{1}\), the outage probability of the signal \(x_{2}\) at the second vehicle can be expressed as  
\[P_{out\_D2}=\underbrace{\Pr\left\{P_{S}\big{|}h_{SR_{k}}\big{|}^{2}<\gamma \right\}}_{B_{1}}+\underbrace{\Pr\left\{P_{S}\big{|}h_{SR_{k}}\big{|}^{2}> \gamma,P_{2R}\big{|}h_{R_{k}D_{2}}\big{|}^{2}<\gamma\right\}}_{B_{2}}, \tag{23}\]  
where \(\gamma=\frac{\omega_{2}}{a_{2}}\). To determine the transmission power at RSU to vehicle \(D_{2}\), we consider the ability of successful detection at RSU for signal \(x_{2}\) as  
\[\frac{1}{2}\log\left(1+a_{2}P_{S}\left(1-\rho\right)\big{|}h_{SR_{k}}\big{|}^{ 2}\right)\geqslant R_{2} \tag{24}\]  
The power splitting factor \(\rho_{2}\leqslant 1-\frac{\omega_{2}}{a_{2}P_{S}\big{|}h_{SR_{k}}\big{|}^{2}}\), where \(\omega_{2}=2^{2R_{2}}-1\) and \(R_{2}\) denotes the target rate of the second vehicle \(\rho_{2,\max}\leqslant 1-\frac{\omega_{2}}{a_{2}P_{S}\big{|}h_{SR_{k}}\big{|}^{2}}\). Therefore, the harvested power can be obtained at the RSU after energy harvesting is given by  
\[P_{2R}^{\prime}=\eta P_{S}\big{|}h_{SR_{k}}\big{|}^{2}\rho_{2,\max}=\left\{ \begin{aligned} &\eta\left(P_{S}\big{|}h_{SR_{k}}\big{|}^{2}- \gamma\right),\;\;if\quad P_{S}\big{|}h_{SR_{k}}\big{|}^{2}>\gamma\\ & 0,\;\;\;otherwise.\end{aligned} \right. \tag{25}\]  
To keep cognitive radio network works, the transmission power at the RSU for the second link from the RSU to the second vehicle must be constrained by  
\[P_{2R}=\min\left\{P_{2R}^{\prime},\frac{I}{\big{|}h_{R_{k}P}\big{|}^{2}}\right\} \tag{26}\]  
We then continue to compute \(B_{1}\) as below:  
\[ \begin{split} B_{1}&=\left[1-\sum_{k=1}^{K}\left( \begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\exp\left(-\frac{\gamma k}{P\Omega_{SR}}\right) \right]\left(1-\exp\left(-\frac{I}{P\Omega_{SP}}\right)\right)\\ &+\exp\left(-\frac{I}{P\Omega_{SP}}\right)-\sum_{k=1}^{K}\left( \begin{array}{c}K\\ k\end{array}\right)\frac{I\Omega_{SR}(-1)^{k-1}}{I\Omega_{SR}+\gamma k\Omega _{SP}}\exp\left(-\frac{\gamma k\Omega_{SP}+I\Omega_{SR}}{P\Omega_{SR}\Omega_{ SP}}\right)\end{split}  \tag{27}\]In addition, \(B_{2}\) can be rewritten as  
\[ \begin{split} B_{2}&=\Pr\left\{P_{S}\big{|}h_{SR_{k}} \big{|}^{2}>\gamma,P_{2R}\big{|}h_{R_{k}D_{2}}\big{|}^{2}<\gamma\right\}\\ &=\Pr\left\{P_{S}\big{|}h_{SR_{k}}\big{|}^{2}>\gamma,P_{2r}^{{}^{ \prime}}\big{|}h_{R_{k}D_{2}}\big{|}^{2}<\gamma,P_{2R}^{{}^{\prime}}<\frac{I}{ \big{|}h_{R_{k}P}\big{|}^{2}}\right\}\\ &+\Pr\left\{P_{S}\big{|}h_{SR_{k}}\big{|}^{2}>\gamma,P_{2r}^{{}^{ \prime}}\big{|}h_{R_{k}D_{2}}\big{|}^{2}<\gamma,P_{2R}^{{}^{\prime}}>\frac{I}{ \big{|}h_{R_{k}P}\big{|}^{2}}\right\}\end{split}  \tag{28}\]  
To compute \(B_{2}\), it can be extended as  
\[
# Hierarchical Reinforcement Learning for Crude Oil Supply Chain Scheduling
## 4 Hierarchical RL Scheduling Scheme
### Lower-Level Agent
#### 4.3.2 Barrier Function  
The purpose of designing the barrier function is to convert the hard constraints on the auxiliary variables into penalty items in the objective function. Each auxiliary variable \(m_{i}^{k}\) representing the end-stage inventory volume of the material kind \(k\) in facility \(v_{i}\) corresponds to an expected inventory volume \(\tilde{m}_{i}^{k}\) given by the upper-level agent. We hope that \(m_{i}^{k}\) is as close to \(\tilde{m}_{i}^{k}\) as possible. The greater the deviation from \(\tilde{m}_{i}^{k}\), the greater the penalty. In addition, the problem requires that the storage \(m_{i}^{k}\) is within the physical inventory limit \(ph_{i}^{k}\) all the time, and preferably within the upper and lower safety inventory limit of \(u_{i}^{k}\) and \(l_{i}^{k}\); then, for \(m_{i}^{k}\), which deviates a lot from \(\tilde{m}_{i}^{k}\) and thus exceeds the upper and lower safety inventory limit or the physical inventory limit, a higher penalty should be given.  
Based on these considerations, we design the barrier function as a piece-wise linear function, as shown in Figure 3. The barrier function of \(m_{i}^{k}\) is expressed mathematically as Equation (19), where \(\beta_{1}<\beta_{2}<\beta_{3}\), \(\Delta_{1}=u_{i}^{k}-l_{i}^{k}\), \(\Delta_{2}=l_{i}^{k}-0\), \(\Delta_{3}=ph_{i}^{k}-u_{i}^{k}\).
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 2 Methods  
The two crucial requirements for an effective AMSIM method are initial matching value prediction and matching error elimination. Our work focused on these requirements, as shown in Figure 1. Initial matching value prediction includes two steps. In the first step, two or more object points within an overlapping area were projected onto both images, based on the RPCs or some other direct models. RPCs can be used to calculate the parameters of the resampling model, as the resolution of the ETM+ orthoimages is lower than the resolution of the ZY-1-02C images. In the second step, the resampled Zy-1-02C images were processed by blocking the overall phase correlation (OPC) with the ETM+ orthoimages to generate the translation parameters, providing the shift values for NCC. Then, image matching was conducted by NCC and LSM. Finally, according to the matched points, the LSV of the point pairs were obtained statistically to detect and eliminate the gross errors.  
Figure 1: Flowchart of the process used in this study.
## 6 Conclusions and Future Work
This may help produce deeper insights into how dataset size and the specific domain impact the performance of predicting student dropout models. In our research, we employed data based on the activity levels of the student in the LMS Moodle system. As a future line of research, it would be interesting to study the performance of our approaches in other LMS systems, since some studies have shown that student dropout and performance may also depend on the LMS system used [30], and it could be a new variable to increase the knowledge of our models
* Exploration of heterogeneous and multimodal data: Although this study focused on homogeneous data, future research could consider including heterogeneous data, where entities or datasets have different rather than identical attributes. An exploration of heterogeneous data could provide a broader, more accurate view of student behavior and characteristics. In this study, we used only attributes related to students' activities. The inclusion of multimodal attributes, encompassing text, video, and audio, obtained from sources such as forum postings and recordings, has the potential to enrich the predictive modeling process by capturing a broader range of student interactions and experiences. **Author Contributions:** Conceptualization, S.V. and C.R. ; data curation, J.M.P. ; formal analysis, J.A.L. ; funding acquisition, S.V. ; methodology, S.V. and C.R. ; project administration, S.V. and C.R. ; software, J.M.P. ; supervision, S.V. ; validation, S.V. and C.R. visualization, J.M.P. and J.A.L. ; writing--original draft, J.M.P., J.A.L., S.V. and C.R. ; writing--review and editing, J.M.P., J.A.L., S.V. and C.R. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was funded by the Project CTA-22/1085 entitled Learning Analytics for Improving the Quality of Teaching in Educational Settings, by the Project PID2020-115832 GB-100 project of Spanish Ministry of Science and Innovation and the European Regional Development Fund, and the ProyExcel-0069 project of the Andalusian University. **Data Availability Statement:** The data are not publicly available due to the regulations of the private company who provided the online courses. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Background on SSI
### Current Examples  
The SSI wallets selected for closer inspection in Section 3.1 support different implementations of SSI. The most common implementation is based on the SSI protocol, initially developed by Sovrin and continued as Hyperledger Indy and Aries by the Linux Foundation [14]. Hyperledger Indy contains an implementation of a permissioned distributed ledger that is usually run in a consortium. This represents technology on layer one of the architecture described in Section 2.2. This base layer is enhanced by Hyperledger Aries, which provides tools for generating and communicating verifiable credentials. It can be used to build applications for architecture layers two and three.  
Figure 1: Typical layers of SSI architectures adapted from [14].
# Mapping Recent Shoreline Changes Spanning the Lateral Collapse of Anak Krakatau Volcano, Indonesia
## 4 Results and Discussion
Figure 6: Shorelines map for the AK Island. Shorelines for the May 2018 and November 2018 period (**a**). Shorelines on 15 May 2018 and 16 November 2018 with extent of the hot volcanic material detected from thermal anomalies (**b**). Shorelines for the November 2018 and July 2019 period (**c**). Shorelines for the July 2019 and November 2019 period (**d**). The 16 th of November 2018 shorelines, the last before the collapse, are shown as black polyline in (**c**,**d**). The original contrast between the mantling of tephra on the east coast and the presence of lava deltas on the west coast (particularly evident in the pre-collapse shoreline) is a consequence of the growth of the AK vent on the steep wall of the basin formed by the caldera collapse in 1883. As Anak was constructed from 1927, erosion of the early cone resulted in a crater-wall structure open to the west. When the activity of AK became fully subaerial, this structure helped direct lava flows more towards the south and west [27], while tephra were deposited on the eastern flank, which also preserved the shape of the earlier tuff cone [27]. This erosional pattern also reflects the strong current that is generally running from SW to NE [41] and long period swell waves entering Sunda Strait from the Indian Ocean to the south west. The positional accuracy of the measurements obtained using the automated shoreline mapping algorithm were assessed through comparison with manually digitized shorelines for six of Sentinel-2 RGB images (two acquired before and four acquired after the flank collapse), specifically (Figure 7)--15 May 2018, 16 November 2018, 2 February 2019, 3 April 2019, 25 April 2019 and 8 August 2019. This assessment comprised analyzing the correlation coefficient between the areal extents derived using the two methods (Figure 7). The high correlation coefficient (R\({}^{2}\) = 0.68) with a maximum differences in the order of 0.13 km\({}^{2}\) (<5% of the AK island initial area) demonstrates that the shoreline data derived using the proposed algorithm is reliable for the shoreline change analysis.
# Revisiting the Detection of Lateral Movement through Sysmon
## 6 Methodology
### Exploitation of Remote Services
#### 6.1.4 Exploitation of BlueKeep  
To extend the conducted research, and as a proof of concept of the generic nature of the proposed data-driven methodology for the identification of Exploitation of Remote Services attacks, the server (Figure 1 and Table 2) was exposed to the BlueKeep (CVE-2019-0708) RCE vulnerability. BlueKeep is related to Windows RDP and RDS, revealing a similar behavior to the above-mentioned WannaCry exploitation of the EternalBlue vulnerability. The exploitation was conducted via Metasploit's CVE-2019-0708 dedicated module.  
_Discussion:_ With respect to the proposed rule-based Sysmon policy, 689 (or \(\approx\)4%) _\"ProcessCreation\"_ and _\"ProcessChanged\"_ related logs _(Event IDs 1 and 2)_ were identified as strongly correlated to suspicious LM behavior on RDP. The examined rules were exported during the first stage of the experiments presented in Section 6.1. Despite promising results, the in-depth reexamination of the collected logs revealed the absence of _\"NetworkConnection\"_ logging activity with _\"Event ID 3\"_, specialized enough to describe the core functions of the RDP and RDS Windows protocol and services, respectively. Sysmon was updated with rules, generic enough to alert in the case of RDP BlueKeep exploitation. Simply put, Windows services that utilize the same port as the RDP protocol, namely _Destination Port 3389_, were identified and imported as rules. Their complete description is presented as follows:  
* _mstsc.exe_: It is related to Microsoft Terminal Services Client (MSTSC) Windows shared library (DLL) and allows access to RDP clients to perform a remote desktop connection through command line or PowerShell. If leveraged by an adversary, the relevant DLL may allow the execution of arbitrary controversial code remotely without the mediation of GUI or a SOCKS proxy connection [23].
* _RTSApp.exe_: It is related to the Royal TS remote management software from _codetward_. Royal TS supports both Windows RDP and RDS, protocol and services. If not found in C: \ProgramFiles\codedward\royal  
process, adversaries can take advantage of the frequency with which it appears on the target system by leveraging Chrome's launcher. This is accomplished through the remote manipulation of the target's registry permissions to eventually grant RCE and RDP exploitation. The simultaneous presence of the two processes or the occurrence of the second in a subfolder of the user's profile folder, in conjunction with the utilization of TCP port 3389, should be examined as malicious [29; 30].  
Regarding the aforesaid Sysmon's networking logging activity, 456 \"Network Connection\" Event ID 3 logs were identified, with more than half of them (or \(\approx\)242 Symson logs) related to the executed LM attacks. For easy reference, Table 15 in Appendix A.1 summarizes the most important rule-based features of this Section 6.1 as they emerged from the four executed experimental variants of the Exploitation of Remote Services attack.
## 2 Theoretical and Methodological Considerations  
### Processing Types and Allocation of Cognitive Resources in Translators  
Translation engages multiple layers of cognitive effort, involving both intentional focus and more automatic mental functions. Scholars have approached the division and description of these resources in various ways. Translation researchers have described these cognitive demands in various ways. Traditionally, the translation workflow has been categorized into two main phases, comprehending the source text (ST) and generating the target text (TT), e.g., [12, 13, 14]. Recent theoretical and empirical developments, however, have introduced a third dimension, \"parallel processing\", highlighting how translators often switch between or simultaneously handle ST understanding and TT formulation [15, 16, 17, 18].  
Insights from neurolinguistics and cognitive psychology suggest that multitasking is fundamental to how translation unfolds. Rather than progressing linearly from reading to writing, translators frequently adopt overlapping strategies, integrating comprehension and production [19, 20, 21]. Evidence from keystroke logging and eye-tracking studies rein forces this view, showing that there is frequent simultaneous attention to both ST and TT, challenging linear models of the translation process [6; 22; 23; 24; 25; 26].  
To better capture this complexity, [PERSON] [12] introduced the concept of attention units (AUs), mapping this activity more precisely. These include (1) ST AUs\(-\)focused on interpreting the source material; (2) TT AUs\(-\)concerned with constructing the translation; and (3) parallel AUs\(-\)capturing moments when both tasks occur concurrently. These distinctions have enabled more precise measurements of attention distribution and cognitive effort during translation.  
Building on this framework, the present study employs four empirical indicators\(-\)Total Attentional Duration, AU count, AU duration, and pupil dilation\(-\)to quantify cognitive effort for different translation directions and processing types. By adopting [PERSON]'s AU categorization, we aim to capture the dynamic nature of attention allocation in translation and provide deeper insights into how cognitive resources are managed during ST comprehension, TT production, and their concurrent execution.
## 3 Results and Discussion
### Comparison of Grinding Characteristic between Untreated and Pre-treated Converter Steel Slag
#### 3.3.3 Particle Size Distributions of Ground Untreated and Pretreated Steel Slag  
The particle size distribution and median diameter of ground untreated and pretreated steel slag powder are shown in Table 5 and Figure 7, respectively.  
As shown in Table 5, with increasing grinding time (\(\leq 50\) min), the proportion of particles for more than 32 \(\upmu\)m (especially particles for more than 65 \(\upmu\)m) is gradually and obviously decreased, while the proportion of particles for less than 32 \(\upmu\)m shows a reverse tendency, which indicates that the particle size distribution of steel slag is optimized by mechanical grinding. However, when grinding time is more than 50 min, it shows a complex or chaotic tendency due to the agglomeration of fine particles. Compared with untreated steel slag, the proportion of particles for less than 32 \(\upmu\)m in pretreated steel slag powder is much higher after the same grinding time, indicating that the increasing tendency of fine particles for pretreated steel slag powder is faster.  
For median diameter (D50), with the increasing of grinding time, the median diameter of steel slag is firstly decreased before 50 min grinding time, and then is increased after that, which is similar to the variation tendency of particle proportion of more than 32 \(\upmu\)m. At 50 min grinding time, the median diameter of untreated steel slag reaches a minimum, 32.89 \(\upmu\)m, which is much larger than that of pretreated steel slag, 18.16 \(\upmu\)m. This shows that it is difficult for untreated steel slag to be finely ground.
## 5 Analysis of Tunnel Bottom Structure Damage and Countermeasures
### Analysis of Tunnel Bottom Structure Countermeasures  
The buried depth of the tunnel is about 100 m, and the cumulative length of inverted arch uplift is 73 m. Uplift has occurred one after another and gradually developed into the situation in Figure 34.  
Figure 34: Bottom heave of tunnel.  
Figure 32: Joint 45 degrees.  
Figure 33: Joint level.  
The tunnel invert floor heave is often the result of a variety of complex factors, which are mainly summarized as the following three kinds:  
(1) The shallow range is large. The final range of the surrounding rock loose circle is stable at about 8 m (as outlined in Section 3.4). A significant portion of the loose load from the surrounding rock is transferred to the lower strata, imposing a substantial burden on the lower surrounding rock and leading to its sliding instability.  
(2) The inverted arch structure is not applied. In the case of steeply inclined rock masses and tunnels lacking an inverted arch structure, the bending stiffness of such large-span structures is insufficient to support the load exerted by the lower surrounding rock (floor heave force). Under the combined effects of lateral and vertical pressures at the base, the rock layers are subjected to expansion, forming bending folds that protrude toward the tunnel, leading to instability (interlayer shear dislocation and crushing of the surrounding rock result in volume expansion). The shear and compressive forces acting on the rock layers cause bending failure at the inverted arch, ultimately resulting in flexural-tensile floor heave.  
(3) The locking bolt angle is too shallow and lacks sufficient design strength. The angle of the locking foot bolt is too shallow, and its design strength is inadequate. The upper structure of the tunnel transfers the surrounding rock load to the sides and lower rock strata via the arch foot, making the arch foot a critical point for reinforcement. Although a locking foot bolt has been included in this section, its shallow angle limits its ability to withstand the significant shear stress, making it prone to failure as the surrounding rock deforms. Additionally, the anchor rod at the arch foot is not sufficiently reinforced, preventing the transfer of the upper load to the deeper surrounding rock layers on either side. As a result, stress concentrations at the arch foot lead to failure, causing the loosening of the shallow surrounding rock to propagate outward.  
From the three perspectives discussed above, an inverted arch structure was added to enhance the number and strength of anchor rods. Figure 35 illustrates the comparison of the tunnel bottom structure before and after treatment.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 6 Validation of the Proposed Protocol
### Routing Protocol Requirements Analysis
#### 6.1.3 Optimality  
A path that minimizes the rank value between a particular pair of source and destination nodes, along with the sub-paths, is defined as an optimal path [73]. A routing protocol is optimal if the traffic for each pair of nodes in the network is routed along the best path [76]. As SMTrust utilizes the _path_calculation_(\(G,f\), \(sd\), \(sn\)) function for the calculation of the path cost from sender to receiver \(p\)<\(sd\), \(sn\)> and the best path is selected. The parent selection algorithms implied by SMTrust also indicate the selection of the best route towards the root node from a sender node, thus fulfilling the property of optimality.