esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Railway tunnels provide critical transport links for passengers and freight through terrain otherwise impassable to trains, facilitating time-efficient navigation through mountains, under waterbodies and bypassing human-made obstructions (e.g., buildings, utilities, mass-transit routes). As confined high-traffic subterranean infrastructure, tunnels are inherently hostile and dangerous environments, suffering perpetual degradation from both environmental and human factors (e.g., shifting landmass, extreme weather, above-ground construction) [1, 2, 3, 4, 5] which seed discernible damage to the intrados--the innermost surface of the tunnel arch [6]--surface and subsurface. In the UK, unlike comparatively modern highway and metro tunnels, railway tunnels frequently date back to the Victorian era. These historic masonry structures are inherently weaker than their modern concrete counterparts, meaning complex degradation can rapidly develop in the vicinity of seeded damage. Therefore, detection and hazard-level evaluation of all structural features (assets, defects) during Railway Tunnel Inspection (RTI) surveys is essential to inform targeted maintenance, ensuring continued safe and efficient operation. Use of Non-Destructive Inspection/Evaluation (NDI/E) techniques for Rail Tunnel SubSurface Inspection (RTSSI) is of paramount importance in modern surveys; however, they are not infallible due to accuracy and clarity limitations. Consequently, undetected seeding and growth of concealed subsurface defects can complicate or even scrub maintenance attempts, irrespectively posing serious safety risks that can endanger life. Timely reminders include undetected microfracture growth which caused catastrophic failure of the Gerrards Cross Tunnel (UK, 2005) [7,8] and two violent crown failures which partially collapsed 18 m of the Yangshang Tunnel (China, 2017) [9].  
Collectively these dangers highlight urgent need for a comprehensive, reliable, repeatable, time-efficient and clear RTSSI visualisation framework, based on NDI techniques, for accurate intrados subsurface feature detection and evaluation. In this work, we review the effectiveness of current RTSSI-relatable visualisation frameworks, focusing on the increasing capabilities of realistic 3D surveys and discussion of future research opportunities. Our aims are to highlight common critical limitations of current strategies and propose viable, pertinent improvements. Following an overview of research methodology (Section 2) we explore leading NDI methods in RTI for RTSSI (Section 3), before deliberating the issues of applying heuristic comparisons (Section 4). From this, we formulate criteria for effective RTSSI visualisation frameworks (Section 5), then appraise the scope of current connected research efforts (Section 6). A discussion of trends and identified findings is finally presented (Section 7).
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 3 Results
### Distribution of Operating Cost Components
#### 3.1.7. Comminution  
Figures 8 and 9 provide visual representations of the cost distribution across the various components contributing to crushing and milling costs, respectively.  
In terms of crushing, energy consumption is the largest contributor; it accounts for 56% of the total cost. Consumables and maintenance and repairs follow at 22% and 9%, respectively. In comparison, labor, as well as equipment depreciation, are amongst the smallest portions of the total crushing cost at 6% and 5%, respectively.  
From Figure 9, energy consumption is seen to account for 59.5% of the total milling cost. Consumables are a close second at 35.7%, followed by maintenance and repairs at 1.9%. Labor, as well as equipment depreciation, are the smallest cost components at 1.5% and 0.9%, respectively.  
Figure 8: Distribution of operating cost components associated with crushing in percent fraction.  
Figure 7: Distribution of operating cost components associated with blending in percent fraction.
## 6 Uncertainty Analysis  
Data measuring is associated with uncertainty and depends on the accuracy of the devices used in the experiments. The following equation can calculate the uncertainty values based on the root mean square error (RMSE) method:  
\[U=\sqrt{\frac{i}{0}\bigg{(}\frac{a_{i}}{x_{i}}\bigg{)}^{2}}\]  
where \(U\) is the uncertainty of the experiment, \(a\) is the accuracy of the devices provided by the manufacturers, and \(x_{i}\) represents the measured values. The accuracy of the measurement devices is shown in Table 7.  
Hence, the maximum uncertainty associated with the temperature measurements can be obtained as \(\pm\)2%.
## 3 Results and Discussion
### Hit-the-Ball
#### 3.2.1 Accuracy-Rate Analysis
After posterior comparison with the Mann-Whitney U test, the results showed a significant difference between the moderate-to-severe group and the normal group (\(p<0.05\)) in all three speed modes. There were also significant differences between the MCI and moderate-to-severe groups (\(p<0.05\)). This means that it is relatively easy to judge the ball type for the normal and MCI groups, while this same task will be difficult for the moderate-to-severe group. In the case of the time-limited mode, the performance of the three groups at the three game speeds was analyzed based on [PERSON]'s two-factor level variation. The results show that the groups have significant performance differences at different speeds (\(p<0.05\)). In addition, a Wilcoxon signed-rank test was used to perform a posterior comparison and the results showed that the speed score was significant (\(p<0.05\)) for the medium vs. fast  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Speed Levels of Game**} & \multicolumn{5}{c}{**Groups**} & \multicolumn{3}{c}{**Kruskal–Wallis Test**} \\ \cline{2-7}  & \multicolumn{1}{c}{**Normal**} & \multicolumn{1}{c}{**MCI**} & \multicolumn{1}{c}{**Moderate-to-Severe**} & \multirow{2}{*}{\(\mathbf{X^{2}}\)} & \multirow{2}{*}{\(\mathbf{p}\)**-Value**} \\  & \multicolumn{1}{c}{**Mean \(\pm\) SD 1 **} & \multicolumn{1}{c}{**Mean \(\pm\) SD 1 [FOOTNOTE:1]Footnote 1: SD indicates standard deviation. * \(p<0.05\). modes in every group as well as the slow vs. fast modes (\(p<0.05\)). This might indicate that the speed was too fast for all subjects, which is why they performed poorly. For the three game speeds under the time-unlimited mode, the Friedman test was used to observe whether the game speeds produced significant differences between the three groups. The results showed that only the moderate-to-severe groups had significant differences (\(p<0.05\)). In addition, the Wilcoxon signed-rank test was used to perform the posterior comparison, and the results showed that there were significant differences (\(p<0.05\)) between the speeds of the medium vs. fast and slow vs. fast modes in the moderate-to-severe group. Therefore, for the subjects in the moderate-to-severe group, there will be a large difference in the accuracy rates under high speed compared to other speeds. For the three groups, judgment ability was discriminated by the different speeds. The analysis was based on the Friedman test, and the MCI group presented significant (\(p<0.05\)) results at the different speeds. However, the results showed no significant difference (\(p>0.05\)) using the comparison with the Wilcoxon signed-rank test. Therefore, for the subjects in the moderate-to-severe group, there is a slight difference in judging the ball compared to the other two groups, but there is no large difference. The time-limited mode can, therefore, better classify the performance of the three groups than the time-unlimited mode. Table 9 shows the posterior comparisons of the results of different game speeds by test scores using the Friedman Test.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 2 Materials  
### BIRDS Satellite System  
The satellites of the BIRDS-3 and BIRDS-4 constellations are based on the 1U CubeSat standard. The external dimensions are (114.5 \(\times\) 107.8 \(\times\) 104) mm, and they have an average mass of 1.3 kg. The bus system consists of the front access board (FAB), the electrical power system (EPS), the onboard computer (OBC), the communication board (COM), and the rear access board (RAB). All printed circuit boards (PCBs) are connected to a backplane board via 50-pin connectors. Figure 1 shows the bus system configuration.  
The RAB is used for programming and monitoring each mission's microcontrollers. The FAB is used for programming and monitoring the Main PIC, FAB PIC, RESET PIC, and COM PIC microcontrollers. The details of the satellite system can be found in the BIRDS Open-Source Repository [15].  
In BIRDS-3, two available mission boards are utilized to space-qualify a low-powered long range (LoRa) modulation module, which has massive potential for future S&F missions. This mission is called a LoRa Demonstration Mission (LDM) [16].  
On the other hand, the BIRDS-4 constellation uses the mission boards for the nine satellite missions. The satellite structure was renewed by taking the BIRDS structure as a base to satisfy the requirements of the HNT mission [2,17]. The downlink frequency is 437.375 MHz, whereas APRS-DP and store-and-forward missions use the VHF band.  
Through the uplink signal sent by the ground station, the satellite receives, processes, and downlinks telemetry and mission data back to the ground station. The satellites use dipole antennas for communication. The antennas are deployed 30 min after satellite deployment. The uplink is performed through the BIRDS Ground Station Network, which has 13 participating countries worldwide, except in Europe and North America.  
Both constellations share the same EPS design and components. Solar panels placed on five sides of the CubeSat (+X, +Y, +Z, -X, -Z) are responsible for power generation. The solar panel consisting of two cells connected in series is shown in Figure 2.  
Figure 1: Bus system configuration.  
The specific solar cell (AZURESPACE 3G30A) has an ideal maximum power generation of 1.2 W. The BIRDS BUS uses Linear Technology Corporation's LTC3119 as the Battery Charge Regulator (BCR), due to its ability to control the maximum power point of solar cells. The energy generated is stored in six rechargeable Eneloop NiMH batteries with a capacity of 1900 mAh per battery arranged in a 3-series 2-parallel configuration. The total power generation per orbit is 2600 mWh, and the total power available to the satellite load is 1600 mWh. The power system has two 3.3 V lines, one 5 V line, and two unregulated lines. All of the EPS's vital components are illustrated in Figure 3.
## 4 Interference Characterization
### Signal to Interference and Noise Ratio (SINR)  
Data transmissions of a BSN incur interference to its neighboring BSNs, referred to as inter-user interference. Packet error rate (PER) is utilized to characterize the level of service degradation of BSNs due to inter-user interference. PER is defined as the ratio of the number of incorrectly received data packets to the total number of transmitted packets.  
PER can be derived from the signal-to-interference noise ratio (SINR) of a BSN. Note that not all the neighboring BSNs transmit at time \(t\). We denote \(\mathbb{1}_{j}\left(t\right)\) as the transmission status of BSN \(j\) at \(t\). \(\mathbb{1}_{j}\left(t\right)=1\) when BSN \(j\) transmits at time \(t\), and \(\mathbb{1}_{j}\left(t\right)=0\) when BSN \(j\) does not transmit at \(t\). It is noted that the probability of \(\mathbb{1}_{j}\left(t\right)=1\) also represents the duty cycle of BSN \(j\).  
The SINR of BSN \(i\) at time \(t\) is expressed as,  
\[P_{SINRi}(t)=\frac{\Omega(r_{i}(t))}{\sum_{j\
eq i}\mathbb{1}_{j}(t)\cdot \Omega(u_{i,j}(t))+\Omega_{n}} \tag{2}\]  
where \(r_{i}(t)\) is the distance between the transmitting sensor and the coordinator for the BSN \(i\) at time \(t\), \(u_{i,j}(t)\) is the distance between the transmitting node of BSN \(j\) and the coordinator of BSN \(i\) at \(t\), and \(\Omega_{n}\) is the background noise power [38].
# Extending Appearance Based Gait Recognition with Depth Data
## 5 Experimental Validation
### Experimental Setup
#### 5.1.2 Extraction of GEI and BGEI Features  
The silhouettes were extracted from cropped and normalized depth images which were provided with the dataset in raw format. From the extracted silhouettes of each recording, one GEI image and one BGEI image were constructed using the processing described before. Examples of resulting GEI and BGEI images of a person are depicted in Figure 9a,b, respectively.  
Overall, 3370 GEI images and the same number of BGEI images were created. The exact number of GEI and BGEI features per variation and type available for the experiments is depicted in Table 1. The resolution of the GEI and BGEI images used in the experiments is \(88\times 128\).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **GEI** & **BGEI** & **GEI** & **BGEI** \\ \hline _normal_ & 1830 & 1830 & _time normal_ & 192 & 192 \\ _backpack_ & 610 & 610 & _time backpack_ & 64 & 64 \\ _shoes_ & 610 & 610 & _time shoes_ & 64 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of created GEI and BGEI images used in the experiments.  
Figure 8: Example of RGB and depth cropped frames from TUM GAID dataset of the same walking person in six variations. Top-left to top-right: normal (**a**), carrying a backpack (**b**), and with silenced shoes (**c**). Bottom-left to bottom-right: time + normal (**d**), time + backpack (**e**), and time + silenced shoes (**f**).
# Satellite Image Compression Guided by Regions of Interest
## 2 Materials and Methods
### Scenarios
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image** & **Date** & **Time (UTC)** & **Platform** & **Instrument** & **Details** \\ \hline
0 & 2020-06-10 & 10:07:59 & 1061 & PS2 SD & no-clouds \\
1 & 2020-06-20 & 10:09:24 & 1066 & PS2 SD & no-clouds \\
2 & 2020-07-17 & 09:42:17 & 1040 & PS2 & no-clouds \\
3 & 2020-06-14 & 09:41:23 & 1003 & PS2 & no-clouds \\
4 & 2020-06-27 & 09:41:43 & 1027 & PS2 & no-clouds \\
5 & 2020-07-17 & 09:36:55 & 1034 & PS2 & no-clouds \\
6 & 2020-07-20 & 08:44:31 & 106d & PS2 SD & no-clouds \\
7 & 2020-07-24 & 08:45:15 & 106d & PS2 SD & no-clouds \\
8 & 2020-08-16 & 09:14:40 & 2235 & PSB.SD & no-clouds \\
9 & 2020-07-12 & 09:37:36 & 1005 & PS2 & clouds \\
10 & 2020-08-25 & 09:43:03 & 1034 & PS2 & clouds, no-vessels \\
11 & 2020-07-27 & 10:06:39 & 105a & PS2 SD & clouds \\
12 & 2020-07-24 & 10:09:45 & 1060 & PS2 SD & clouds \\
13 & 2020-07-09 & 09:13:40 & 2259 & PSB.SD & small clouds \\
14 & 2020-07-22 & 09:13:40 & 2277 & PSB.SD & small clouds \\
15 & 2020-08-17 & 09:45:32 & 1026 & PS2 & small clouds \\
16 & 2020-08-21 & 09:12:56 & 222b & PSB.SD & small clouds \\
17 & 2020-07-20 & 09:11:52 & 2235 & PSB.SD & fog \\
18 & 2020-08-07 & 09:39:37 & 0f17 & PS2 & fog \\
19 & 2020-08-07 & 09:37:09 & 0f15 & PS2 & fog \\
20 & 2020-08-30 & 09:39:13 & 0f34 & PS2 & fog \\
21 & 2018-03-16 & 09:24:33 & 0e16 & PS2 & snow, clouds, no-vessels \\
22 & 2019-02-20 & 09:34:19 & 1042 & PS2 & snow, clouds, no-vessels \\
23 & 2019-02-04 & 09:35:57 & 1018 & PS2 & snow, no-clouds, no-vessels \\
24 & 2019-02-06 & 09:30:34 & 1010 & PS2 & snow, no-clouds, no-vessels \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Images details. Figure 6: Images samples, RGB bands (truncated at 2000): (**a**) image 0; (**b**) image 1.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 2 Literature Review
### Positioning of This Study  
Based on the studies reviewed in Sections 2.1-2.3, we identify several gaps in the current literature. First, although fingerprinting-based methods have shown strong performance under NLoS conditions, most prior works have relied on a single type of signal feature--typically RSSI or AOA--limiting robustness and adaptability. Second, while UAVs have been successfully employed to improve spatial sensing, their trajectories are often predefined or optimized separately from the signal processing pipeline. This study aims to address these gaps by proposing a hybrid fingerprinting method that integrates RSSI, AOA, and EOA features to enhance localization accuracy. In addition, UAV flight paths are optimized based on the hybrid model to dynamically adapt to the environment and measurement conditions. The proposed framework builds upon previous efforts by [PERSON] [10] and [PERSON] [8] and extends them with both algorithmic innovation and practical simulation evaluation using ray-tracing data and real-world terrain modeling. By combining multidimensional signal features with environment-aware UAV trajectory optimization, this study offers a comprehensive solution to the challenges identified in prior work.
# En-Face Optical Coherence Tomography Angiography for Longitudinal Monitoring of Retinal Injury
## 2 Materials and Methods
Lesion Dynamics; B-Scan Measurements: Lesion growth measurements were compiled as an individual lesion by treatment group and gender. Measurements were recorded to capture the lesion growth dynamics; dimensions from the layers were a lateral line across the boundary of the outer nuclear layer and outer plexiform layer (Label: ONL). A line was drawn laterally across the Retinal Pigment Epithelium (Label: RPE) layer from edge to edge of visible disruptions. The height of the lesion (Label: Height) was measured as the maximum point of OPL-RPE disruption. The final measurement was the widest point across the Exterior Limiting Membrane to Inner Segment/Outer Segment (ELM-IS/OS) protrusion into the ONL (Label: PR). Retinal and Choroid Flatmounts: As traditional validation of CNV injury, choroidal flatmounts were prepared for both doses. The corresponding inner retinal was also extracted for flatmounts to show the vascular morphology. Retinas were prepared for confocal imaging by perfusion and/or direct staining with the appropriate fluorescent label. The vasculature was perfusion labeled with Fluorescein-labeled Concanavalin A (Con A) through the carotid artery [46]. All eyes were fixed in 4% PFA and micro-dissected for both retinal and choroid flatmounts. Flatmounts, that were not perfused were incubated with 594 AlexaFluor-labeled Isolectin B4 for 2 hours 1:500 dilution. All samples were stained with 1:200 DAPI solution for 30 minutes. The flatmounts were imaged on the confocal microscope to validate the CNV model. Statistics: For the in-vivo visualization with OCTA, there were 8 mice of mixed sexes and two groups of old and young for each condition. To prevent errors from variable group size effects, a constant 16 lesions that were verified imaged at all time points were used. For immunofluorescence staining, there were an additional 16 mice used to verify the in-vivo findings. The standard deviation \(\sigma\) and average \(\mu\) were computed for individual groups. Because the normalcy and repeated measures conditions were violated for the T-test and ANOVA analysis for significance, the percent change, and \(\mathrm{Cv}\) was calculated for both sex and age before pooling into the measurement tables. For dimensionality reduction in the longitudinal analysis, the first and second derivatives were computed for the change over time for each lesion and combined into the plotted data model. The significance of the percent change is \(>5\%\) from the initial value. The second derivative assesses for asymptotes, or steady state, where they are no further significant changes. The second derivative as an event detection model detects the trends to describe the multi-parametric longitudinal data. The CV within groups and between groups was assessed to ensure the trends were consistent within each condition.
## 3 Problem Definition and Formulation
### Constraints
#### 3.2.1 Resource Constraints  
Resource constraints reflect the limitations of resources at each node in the satellite network. In each time period, the total resource usage at a satellite node must not exceed its maximum resource capacity. For each satellite node \(i\), its available resources \(R_{i}\) must satisfy the following constraints:  
\[\sum_{j=1}^{m}t_{ij}\leq R_{i},\quad\forall i=1,2,\ldots,n \tag{3}\]  
where \(t_{ij}\) represents the resource allocation for task \(j\) at node \(i\), and \(R_{i}\) is the maximum resource capacity of satellite node \(i\). This constraint ensures that the resources at each satellite node are not overloaded, preventing excessive resource allocation and maintaining network stability.
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 5 Experimental Results in Area 2  
In the previous section, we validated our proposed short-term load forecasting method based on the data from region 1 and obtained good experimental results. However, relying on only one dataset to validate the experiments may be subject to chance, so in order to verify the accuracy as well as the generalizability of our proposed model, we conducted experiments using the dataset of Area 2 provided by the 9 th National Electricial Mathematical Modeling Competition in China. One of the EPT-VMD decomposition parts is described in Section 3.2. The ablation experimental result pairs of the EPT-VMD-TCN-TPA model proposed in this paper are shown in Figure 21. The error evaluation indexes and fitting effect data of each model are shown in Table 12. The classical comparison experimental results are shown in Figure 22 and the evaluation indexes of each model are shown in Table 13.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **SVR** & **LSTM** & **CNN-LSTM** & \begin{tabular}{c} **LSTM-** \\ **Attention** \\ \end{tabular} & **BiLSTM** & \
\begin{tabular}{c} **EPT-VMD-** \\ **TCN-TPA** \\ \end{tabular} &
\begin{tabular}{c} **EPT-VMD-** \\ **TCN-TPA** \\ \end{tabular} \\ \hline SVR & \(\begin{array}{c}\text{nan}\\ (-5.779)\\ \end{array}\) & \(\begin{array}{c}\text{0.0000}\\ (-5.828)\\ \end{array}\) & \(\begin{array}{c}\text{0.0000}\\ (-5.828)\\ \end{array}\) & \(\begin{array}{c}\text{0.0000}\\ (-6.444)\\ \end{array}\) & \(\begin{array}{c}\text{0.0000}\\ (-5.502)\\ \end{array}\) & \(\begin{array}  
Figure 21: Area 2 ablation experiment prediction results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MAPE (\%)** & **RMSE (MW)** & **MAE (MW)** & **R\({}^{2}\)** & **Time (s)** \\ \hline TCN & 3.02 & 279.9281 & 207.6085 & 0.9681 & 31.759 \\ VMD-TCN & 1.68 & 147.2106 & 116.8644 & 0.9912 & 356.854 \\ VMD-TCN-Attention & 1.63 & 144.1170 & 110.6246 & 0.9915 & 379.672 \\ VMD-TCN-TPA & 1.60 & 140.2134 & 109.3452 & 0.9919 & 411.754 \\
**EPT-VMD-TCN-TPA** & **1.58** & **137.6182** & **107.2617** & **0.9923** & **424.006** \\ EPT-VMD-FE-TCN-TPA & 2.02 & 170.3671 & 135.1109 & 0.9882 & 243.455 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Area 2 ablation model evaluation indicators.  
Figure 22: Area 2 prediction results for each model.  
As shown in Tables 12 and 13 and Figures 21 and 22, the MAPE, RMSE, and MAE of the EPT-VMD-TCN-TPA prediction model are 1.58%, 137.6182 MW, and 107.2617 MW, respectively. The R\({}^{2}\) reaches 0.9923. Compared with other models, our proposed EPT-VMD-TCN-TPA prediction model has the best prediction results. Therefore, it is evident that our proposed EPT-VMD-TCN-TPA model has some stability and scalability.