esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Analyzing the mining, geological, and mining engineering conditions of the development of the Kazakhstan deposit shows that the deposit is characterized by complex geological and mining conditions. The presence of intensely disturbed sericite-chlorite-carbonate metastatic rocks in the bottom of the deposit and at its contacts significantly worsens the conditions for conducting and maintaining mining operations in these zones. These metamorphosed rocks require the development of special measures to prevent their interaction with the aggressive mine atmosphere, as well as the use of heavy types of fastening and advanced fastening systems. At the lower levels of the deposit, areas with elevated air and mass temperatures act as catalysts for the development of additional thermal stresses, metamorphic processes, and chemical reactions, all of which lead to rock collapse. Solid barite-polymetallic, copper-zinc, and disseminated copper-pyrite ores, along with some varieties of rocks (such as siliceous siltstones), exhibit high strength properties. Thus, during driving and supporting operations in these zones, it is essential to account for the likelihood of dynamic rock pressure and apply flexible types of support.  
The numerical modeling results obtained through RS2 were calibrated by comparing them with field measurements and observational data from similar mining conditions. Key input parameters, such as the cohesion, internal friction angle, and modulus of elasticity, were adjusted based on laboratory testing results and previous case studies from mines with comparable geological settings. To ensure accuracy, the model was validated by comparing simulated stress and displacement patterns with in situ measurements of the deformations observed in the mine workings.  
In the lower sections of the deposit, rocks and ores of stability categories III-IV and IV-V predominate. Exposure periods for outcrops (up to 10 m\({}^{2}\)) in such zones are limited: up to 3 days for unstable rocks and 1 day for very unstable rocks. Consequently, for workings driven through these zones, it is essential to minimize the time required to install temporary supports. Additionally, the use of various reinforcement strategies, including SZA anchors and multi-layer shotcrete, was modeled and compared with real-world performance to further refine the model parameters. The calibrated RS2 model enabled accurate predictions of stress redistribution and deformation over time, guiding the selection of optimal support systems for maintaining stability in challenging conditions, particularly in rocks of stability categories III-IV and IV-V. The applied reinforcement strategies demonstrated their effectiveness under dynamic stress conditions. However, in some areas of the mine, high lateral pressures resulted in sidewall collapse and floor heaving, necessitating adjustments to  
Figure 10: Changes in strength factor based on support technology.  
the reinforcement approach. Through careful calibration, the selected technologies ensure long-term stability of the workings, significantly enhancing operational safety.
# TARCS: A Topology Change Aware-Based Routing Protocol Choosing Scheme of FANETs
## 3 A Topology Change Aware Routing Protocol Choosing Scheme (TARCS)
### Periodically Topology Change Aware (PTCA)
The main method of node mobility mode discrimination of this paper is to compare the topology change perception result \(TCD_{nturk}\) and the topological change threshold reference value \(TCD_{TH}\).
## 4 Results and Discussion
### Comparison and Evaluation of Results  
For further comparison and evaluation, we simulate the ascending and descending LOS deformation maps to calculate the residuals by our fault geometric parameters and slip based on Okada model. The 3D simulated deformation components were all projected to the LOS direction and the common area was cut for convenient comparison.  
Figure 7 shows the measurements, models and residuals maps. The characteristics of simulated LOS deformation maps are well matched with the real LOS deformation maps, no matter based on the fault parameters and slip inversed from the LOS data sets or 3D data sets. The area where residuals exist of Figure 7c,f is smaller than the area of Figure 7i,l. The obvious residuals appear in the near field of Figure 7i,l.  
For further comparison, we derive the profile CD on deformation maps. Figure 8 shows the displacements along the profile CD. Figure 8 shows the displacements along the profile CD. Regard smoothness and tendency of curve as a comparison-reference. It is clear that the profiles in Figure 8a,b are in agreement mostly, while the profiles in Figure 8c,d are in disagreement relatively. The differences exist due to the fault parameters inversed based on the 3D and LOS coseismic deformation data sets.  
Figure 9 shows the residual distribution histograms. According to the Figure 9, the standard deviation (Std) of residuals in Figure 7c,f are 2.4 cm and 2.9 cm, respectively. The standard deviation of residuals in Figure 7i,l are 3.5 cm and 4.2 cm, respectively. The morphological characteristics of curves in Figure 9a,b are slimmer and higher than the curves in Figure 9c,d.  
Figure 6: Schematic view of the source mechanism. (**a,b**) are source mechanism based on the 3D deformation data sets and the line-of-sight (LOS) deformation data sets, respectively. The blue star represents the epicenter of the mainshock. The red solid squares represent the city Halabja and Sarpol-e-Zahab. The arrows in (**a,b**) are the slip vectors.  
Figure 7: The measurements, models and residuals maps. (**a**–**f**) derived from the 3D coseismic deformation data sets. (**g**–**l**) derived from the LOS coseismic deformation data sets. The black line is the profile CD.  
Figure 8: The displacements along the profile CD. (**a**,**b**) correspond the profile CD in Figure 7a,b and Figure 7d,e, respectively. (**c**,**d**) correspond the profile CD in Figure 7g,h and Figure 7j,k, respectively.  
The comparative analysis indicated that the residuals of inversion based on the 3D data sets are controlled better. We regard the Std as the comparative standard. Thus, the fault inversion using the 3D data sets improved nearly 30% relatively the use of the LOS data sets. These results are within a reasonable range as defined by other studies [1,7], but our analysis is unique due to the reconstruction of constrained 3D surface movements and the derivation of the fault's optimal focal plane. These results support existing seismological interpretations and contribute to a better understanding of the main fault's behavior.
# Self-Service Data Science in Healthcare with Automated Machine Learning
## 2 Overview of AutoML Methods
### Healthcare  
FLASH [10] and AutoPrognosis [11] have both been developed for healthcare or with funds for healthcare, but with different incentives. FLASH was developed to improve the efficiency of creating and evaluating pipelines. It proposes a two-layer Bayesian Network approach to improve search efficiency. AutoPrognosis has recently been developed with the practitioner in mind. It follows a principled Bayesian approach in all components. AutoPrognosis uses Bayesian model averaging for pipeline construction. Meta-learning is used to find similar groups of patients. Both clinical and statistical features of datasets are used for Warm-starting. FLASH is a black-box tool, as most AutoML tools are. In contrast, AutoPrognosis is the only AutoML method that contains an explainer to justify its recommendations to a clinician.  
We cannot compare the performances of both methods, as there is no benchmark test available featuring both methods at the time of writing. FLASH tested its performance on a medical dataset with the binary classification task of predicting drug non-responders. In this case, it outperformed other methods based on TPE and SMAC using error rate as the performance metric [10]. AutoPrognosis outperformed Auto-WEKA, Auto-Sklearn, and TPOT on multiple datasets in its own comparison with other methods [11].
# Effect of Target Size, Location, and Input Method on Interaction in Immersive Virtual Reality
## 4 Results
### Application of Fitts' Law  
The correlation between the TCT and the index of difficulty can be analyzed using Fitts' law. In order to calculate the ID, the distance (A) from the starting point to the target was substituted for the distance from the center of the current button to the center of the next button, and the size of the target (W) was substituted for the width of the button. Table 5 shows the average movement time according to the ID for each target selection method. Through this, a regression analysis was performed, and the results are shown in Table 6.  
The result showed that the values of R2 were 0.71 in the gaze-selection method and 0.90 in the manual-selection method. Therefore, it turns out that Fitts' law fits better when the target is selected by the manual-selection method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\(\cdot\)} & \multicolumn{1}{c}{\(\mathbf{R^{2}}\)} & \multicolumn{1}{c}{\(\mathbf{F}\)} & \multicolumn{1}{c}{\(\mathbf{df1}\)} & \multicolumn{1}{c}{\(\mathbf{df2}\)} & \multicolumn{1}{c}{\(\boldsymbol{p}\)-Value} & \multicolumn{1}{c}{\(\mathbf{a}\) (\(\boldsymbol{\beta_{0}}\))} & \multicolumn{1}{c}{\(\mathbf{b}\) (\(\boldsymbol{\beta_{1}}\))} \\ \hline Gaze-selection & 0.71 & 64.493 & 1 & 26 & \textless{}0.000 & 0.563 & 0.574 \\ \hline Manual-selection & 0.90 & 234.270 & 1 & 26 & \textless{}0.000 & \textless{}0.614 & 0.722 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Regression analysis between the index of difficulty and task completion time.  
Figure 11: VRSQ scores for selection method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{4}{c}{**Gaze-Selection**} & \multicolumn{5}{c}{**Manual-Selection**} \\ \hline
**ID** & **TCT** & **ID** & **TCT** & **ID** & **TCT** & **ID** & **TCT** \\ \hline
2.51 & 1.965 & 4.39 & 3.327 & 2.51 & 1.267 & 4.39 & 2.572 \\
2.94 & 2.208 & 4.46 & 3.329 & 2.94 & 1.351 &
# Neural Network-Based Identification of Cloud Types from Ground-Based Images of Cloud Layers
## 2 Data  
At present, the International Cloud Atlas (WMO) [23] classifies clouds into three families and ten genera according to their height and textures. In addition, according to the different reasons for cloud formation, they can be divided into cumulus, stratiformis, and undulatus, as shown in Table 1 and Figure 2, which enumerate each cloud sample in the datasets. Clouds generated by convective motion are cumulus, clouds formed by systematic vertical motion are stratiformis, and clouds formed by atmospheric fluctuations or atmospheric turbulence are undulatus. However, no meteorological office or research institute has a dedicated database of ground-based cloud maps yet. Therefore, this study will use CCSN [17] and the dataset from the Cloud Watching Contest [24] as a foundation to establish a better dataset and improve it under the supervision of the Macau Meteorological Society. We then removed poor-quality images from the dataset (e.g., images containinga certain proportion of buildings or trees). Specifically, we eliminated data samples with less than a certain proportion of clouds in a 480 \(\times\) 480-pixel image. Both the CCSN and the Cloud Watching Contest datasets were obtained from a single institution, indicating that all images in the same dataset were captured at the same location. Additionally, cloud production is influenced by geographic location, cold, and warm currents [25]. Therefore, if data were captured from the same location, the number of the same cloud genera in the dataset would be excessively high. To avoid having one category of data samples significantly outnumber the others and causing the model to overly focus on that category during feature extraction, thereby affecting the overall performance of the model, we additionally collected and labeled cloud genera images with a smaller sample size and added them to the SSC. This ensures that the data volume of each cloud genera in the dataset is roughly the same. The types of cloud genera in our dataset and the amount of data samples are shown in Table 1.
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
## 4 L2-CalSat Description
### L2-CalSat Design
#### 4.2.1 Payload, Communications and OBC  
The main payload of L2-CalSat is a multi-frequency variable source, covering the overall bandwidth of the particular CMB mission to be calibrated. Both, the signal frequency and power should be variable in order to characterise completely systematic errors of the instrumentation on-board CMB-Sat. The power level has to be optimised for the detectors to be working in their nominal range, avoiding saturation. Due to the wide frequency-range to be covered, it is necessary to implement a multi-frequency source, generating monochromatic amplitude- and frequency-variable signals and also broadband signals, covering several sub-bands determined by the commercial wave-guide standards. The calibration source is described in detail in the next subsection.  
Two additional subsystems are considered as secondary payload. The first one is a communication system to determine the distance between L2-CalSat and CMB-Sat, and also the relative position when calibrating. The second subsystem is an On-Board Computer (OBC), which must be able to control, as efficiently as possible, the main payload and the communication system.  
This mission requires very accurate knowledge of relative positioning and has very strict orientation requirements during calibration. In order to determine the distance of the telescope and the relative position with the desired accuracy, it is proposed the use of a radio-frequency communication module, recently established for the formation flying mode of the SULFRO [51] concept. In particular, it is proposed an inter-satellite communication system (like Swift RelNav) based on a pair of communication modules, each one with two sets of three orthogonal dipole antenna and a radio transceiver. Notice that one module is placed in L2-CalSat and another module must be placed on the main satellite. The configuration is depicted in Figure 7.  
In addition, an OBC and a Software Defined Radio (SDR) algorithm will be also required. The SDR algorithm is needed for distance measurement and orientation determination during calibration, to make sure that both satellites are maintaining their optimal configuration of relative position. On the other hand, the OBC is required to process the information coming from the array of sensors, and engage the reaction wheels accordingly. Besides, the OBC must be mounted onto a dock board in compliance with the PC104 standard.  
The OBC module NanoMind Z7000 [52] designed by GomSpace has been finally selected, because it is able to process a wide range of operations and calculations to determine the relative position and distance of the telescope, while fitting with the strategy of using cost-effective devices. In addition, this OBC has already been used in several CubeSat missions presenting good performance.  
Figure 7: Exterior view (**a**) and exploded view (**b**) of the preliminary design for L2-CalSat. ADCS is the Attitude Determination and Control System, referred as AOCS in the text. PDU is the Power Distribution Unit.
## 4 Assessment of Adjacent Building Damage with Ground Movements  
### DPI of the Building  
To accurately assess the damage to adjacent buildings, the ground movement behind the excavations can be obtained by the proposed simplified evaluation. Based on the ground movement and building information, such as the location and geometry of the building, the angular distortion, \(\beta\), and lateral extension strain, \(\epsilon_{\text{l}}\), can be obtained by the equations in Figure 1, and the DPI of the building can be determined using the methods proposed by [PERSON] et al. [30]. The level of building damage can then be assessed by interpreting the DPI results. The DPI is expressed as Equations (11)-(13) [16,30].  
\[\text{DPI}=\epsilon_{\text{p}}/(1/200)\times 100 \tag{11}\]  
\[\epsilon_{\text{p}}=[(\epsilon_{\text{h}})(\cos\theta_{\text{max}})^{2}+\beta (\sin\theta_{\text{max}})(\cos\theta_{\text{max}})] \tag{12}\]  
\[\theta_{\text{max}}=(1/2)\tan^{-1}(\beta/\epsilon_{\text{h}}) \tag{13}\]  
where \(\epsilon_{\text{p}}\) is the maximum principal tensile strain, and \(\theta_{\text{max}}\) denotes the direction of crack formation measured from the vertical plane.  
Figure 14: Comparison of predictions of surface settlement profiles with field observations: (**a**) Case 1, (**b**) Case 2, and (**c**) Case 3.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
###### Abstract  
Oriented object detection in aerial imagery presents unique challenges due to the arbitrary orientations, diverse scales, and limited availability of labeled data. In response to these issues, we propose RASST--a lightweight Rotationally Aware Semi-Supervised Transformer framework designed to achieve high-precision detection under fully and semi-supervised conditions. RASST integrates a hybrid Vision Transformer architecture augmented with rotationally aware patch embeddings, adaptive rotational convolutions, and a multi-scale feature fusion (MSFF) module that employs cross-scale attention to enhance detection across object sizes. To address the scarcity of labeled data, we introduce a novel Pseudo-Label Guided Learning (PGL) framework, which refines pseudo-labels through Rotation-Aware Adaptive Weighting (RAW) and Global Consistency (GC) losses, thereby improving generalization and robustness against noisy supervision. Despite its lightweight design, RASST achieves superior performance on the DOTA-v1.5 benchmark, outperforming existing state-of-the-art methods in supervised and semi-supervised settings. The proposed framework demonstrates high scalability, precise orientation sensitivity, and effective utilization of unlabeled data, establishing a new benchmark for efficient oriented object detection in remote sensing imagery.  
Keywords:lightweight vision transformer; oriented object detection; semi-supervised learning; rotational invariance; pseudo-labeling; adaptive rotational convolution; multi-scale feature fusion; global consistency loss +
Footnote †: journal: Applied sciences
## 10 Results and Discussion
### End-to-End Delay  
The time it takes for data packets to move from source to destination within the network is revealed by analyzing the end-to-end delay results for the GBG-RPL and BCPS-RPL methodologies. The relationship between the number of nodes and the end-to-end delay for both the BCPS-RPL and GBG-RPL methods is depicted in Figure 10.  
The relation between the number of nodes and the end-to-end latency for both the BCPS-RPL and GBG-RPL mechanisms in the CPS is shown in Figure 10. It proves that for both systems, the end-to-end delay grows with the number of nodes. The outcomes show that the GBG-RPL mechanism outperformed the BCPS-RPL mechanism in terms of end-to-end delay. Initially, the BCPS-RPL has a smaller end-to-end delay, which is due to the fact that the initial trust is being calculated in the device layer, and this trend continues until 15 min into the simulation. Similarly, the GBG-RPL displays a high end-to-end delay at the initialization until 15 min of simulation as the packets are traveling from the device layer to the fog layer. However, once the trust has been established and attacks are induced, the trends for both techniques change and the GBG-RPL starts performing better. The main reason is that in BCPS-RPL, the nodes are continuously performing trust calculations at the device layer, and repetitive trust calculations have induced congestion at the nodes in the form of longer packet queuing, processing delays, propagation delays, and transmission delays. The end-to-end delay is further increased once the attacks are induced, and packets are retransmitted frequently. In contrast, in the case of the GBG-RPL, once the trust has been established in the network, only trust parameters are being forwarded from the device layer to the fog layer, which reduces the network congestion at the device layer. Furthermore, once the attacks are induced in the network, the attackers are detected in a timely manner, and only information related to the malicious node is propagated from the fog layer to  
Figure 10: End-to-end delay.  
the device layer. Therefore, the GBG-RPL, in the longer run, reduces its end-to-end delay, which improves the network efficiency.
## 5 Proposed Protocol to Secure E-Commerce Transactions
### Procedures of Proposed Protocol
#### 5.3.1 Key Generation Procedure  
1. We generate 1024-bit \(K_{u}\) public and \(K_{r}\) private keys using parameters \(q\) and \(g\) were mentioned by the ELG algorithm in Section 5.1.1;
2. We divide the \(Ku\) public key into parts \(k_{1}\), \(k_{2}\), \(k_{3}\) and \(k_{4}\) with a key size of 256 bits that fits the key size of the FER algorithm, which is 256 bits;  
Figure 5: Purchase and payment requests.  
3. We perform XOR operations on keys such as \(k_{5}=k_{1}\oplus k_{2}\) and \(k_{6}=k_{3}\oplus k_{4}\), following which we obtain the final key, \(FK=k_{5}\oplus k_{6}\);
4. We add an \(ID\) to each final key and perform the \(FK_{i}=FK\oplus K_{ID}\) operation;
5. We use DLD to process the leakage probability (\(P_{i}\)) of a (\(D_{i}\)) data group from the guilty party to a group of \(\mathcal{M}\)s' agents (\(GM_{i}\));
6. We hide the keys of score (\(sco\))=\(PW\oplus K_{r}\) and \(FK_{e}=FK_{i}\oplus sco\oplus PW\). In the case of the following connection, we do not generate keys. However, we change the \(ID\) for each key (\(K_{ID}\)). This ID is associated with the key, and not the user (see Algorithm 1).  
Input: \(q\), \(g\) values and \(PW\), \(t\)-threshold  
Output: \(K_{r}\) and \(K_{u}\) keys with a 256-bit length  
```
1:Using ELG to generate \(K_{u}\) and \(K_{r}\) with 1024-bit length
2:Dividing \(K_{u}\) into four parts with a 256-bit length
3:Four sub-keys: \(k_{1}\), \(k_{2}\), \(k_{3}\) and \(k_{4}\)
4:Applying \(\oplus\) with sub-keys
5:Obtaining \(FK_{i}\) with 256-bit length
6:Adding \(ID\) for each key, Computing \(FK_{i}\longleftarrow FK\oplus ID\)
7:Computing \(P_{i}\longleftarrow GM_{i}/D_{i}\)
8:If \(P_{i}(D_{i})>t\) Declare as Info leakage
9:Else repeat step 7
10:Protecting \(K_{r}\) and \(FK_{i}\) on the device
11:Computing \(sco\longleftarrow PW\oplus K_{r}\)
12:Storing \(FK_{e}\longleftarrow FK_{i}\oplus sco\oplus PW\)
13:Next connection go to step 4 with changing \(Key-ID\)
```  
**Algorithm 1** Keys generation procedure.