esa-sceva/satcom-chunk-collection huggingface dataset:
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 2 Basics of BS Information and LTE Performance Metrics  
This section introduces the fundamentals of LTE physical-layer signals, which are crucial for identifying the BS from which the signal is transmitted. Additionally, we will cover the key performance metrics that assess the quality and strength of the received LTE signal, which will be utilized in the rainfall sensing study presented in this work.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Reference** & **Signal Frequency** & **Distance Between BS and User Terminal** & **Signal Features** & **Insights** \\ \hline
[19] & 3G (2100 MHz) & a few meters (LoS) WiFi (2.4 GHz, 5 GHz) & received level (RSL) & signal have more potential to detect regional heavy rainfall. \\ \hline
[20] & GSM (1.8 GHz) & 400 m & received strength & signal have largely discounted the impact of precipitation on GSM signals. \\ \hline
[21] & GSM (800 MHz-3 GHz) GPS & - & received strength & signal can when signal strength is quite low. \\ \hline
[15] & 2G & - & RSSI & The average of RSSI in the rain is lower than the average in the whole day, but such decrease is not as significant as expected from the theoretical analysis. \\ \hline
[22] & LTE & 200 m & received strength & signal level in rain is lower than that in no-rain cases. \\ \hline
[16] & LTE & 200 m (LoS) & RSL & The mean and variance of the probability density distributions in different rain conditions are different but not enough to distinguish different rain intensities. \\ \hline
[23] & LTE & - & RSRP, RSL, Signal-to-Noise Ratio (SNR), RSRQ, Cell ID & The separation of different rainfall classes is not linear on signal data and cell selection-related parameters. \\ \hline
[14] & LTE & - & proportions of RSRP & Changes to the proportion of the weak coverage samples could be impacted by the propagation losses including rainfall influence attenuation. \\ \hline
[24] & lower than 10 GHz & - & RXL, RSRP, RSRQ, SNR, quality & There is a strong correlation between RXL and rainfall level (\(-0.78\)). \\ \hline
[25] & LTE (2630 MHz) & 228 m (LoS) & RSSI & The precipitation induces a decline in signal power, and the signal power does not increase immediately after the rain stops. \\ \hline
[17] & CBRS private LTE network (3560 MHz, 3640 MHz) & distance not mentioned (LoS) & RSRP & There is an inverse relationship between rainfall and hourly sampled RSRP upon the least squares regression analysis based on signal data during two rain events. \\ \hline \end{tabular}
\end{table}
Table 1: Research investigating the impact of rainfall on signal data collected by the user terminal.
## 5 Analysis of Results and Field Testing
### Evaluation Indicators  
In this paper, we used the mean Intersection over Union (mIoU) and Mean Pixel Accuracy (MPA) as objective metrics to evaluate the performance of the network, which are commonly used in the field of semantic segmentation. Intersection over Union (IoU) represents the ratio of the intersection and union of two sets of pixels of a certain label type to the predicted value, and is a prerequisite for calculating the mean intersection ratio. Its mathematical expression is given by Equation (7):  
\[IoU_{i}=\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}+\sum_{j=0}^{k}p_{ji}-p_{ii}} \tag{7}\]  
The Equation (7) gives the Intersection over Union for each class of labels, and by taking the mean value of each class, \(mIoU\) can be obtained; this is calculated as Equation (8). The average pixel accuracy is obtained by averaging the pixel accuracy of each category, the mathematical expression is Equation (9).  
\[mIoU=\frac{1}{k+1}\sum_{i=0}^{k}IoU_{i} \tag{8}\]  
\[MPA=\frac{1}{k+1}\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}}, \tag{9}\]  
where \(IoU_{i}\) denotes the intersection ratio of category \(i\), \(k\) denotes the total number of categories minus 1, \(p_{ii}\) denotes the number of pixel true category \(i\) predicted to be category \(i\), \(p_{ij}\) denotes the number of pixel true category \(i\) predicted to be category \(j\), and \(p_{ji}\) denotes the number of pixel true category \(j\) predicted to be category \(i\).  
Figure 9: Sample from the dataset.  
In addition to mIoU and mPA, Params and GFLOPs are also commonly used performance metrics for models. Params refers to the number of parameters in the model, while GFLOPs refers to the number of floating-point operations required for inference. These metrics can be used to evaluate model complexity and computational efficiency. When selecting models, it is usually necessary to balance between model accuracy and computational efficiency. Smaller Params and GFLOPs usually mean faster inference speeds and fewer computational resources, but may lead to a decrease in model accuracy. Therefore, Params and GFLOPs should be considered together in the model selection and optimization process to achieve the best performance and efficiency balance.
# Genesis of the Baiyangping Cu-Co and Pb-Zn Mineralizations in Lamping Basin, SW China
## 2 Geology of the Baiyangping Deposit  
The Lamping Basin is located in the central part of the Sanjiang Tethys Terranes and is bound by the Changning-Menglian suture to the west, and the Jinshajiang-Ailaoshan suture to the east [24; 26; 28]. The prominent structure in the Lamping basin is the northerly trending thrust fault belt (Figure 1b). The thrusts in the western part of the belt dip \(\sim\)30\({}^{\circ}\) W, whereas those in the east dip \(\sim\)50-80\({}^{\circ}\) E. In the region under investigation, the northerly trending thrust faults can be further divided into those trending NNW with a west inclination and those trending NNE with an east inclination, both of which have dip-angles over 60\({}^{\circ}\). There are also ESE-trending strike-slip faults, which cut the above-mentioned faults. The Baiyangping deposit contains Pb-Zn and Cu-Co orebodies. The Cu orebodies are commonly hosted in Jurassic, Cretaceous, and Paleocene mudstones, whereas the Pb-Zn orebodies generally appear in Triassic limestones, Jurassic sandstones, and argillaceous limestones. The mineralization type of the deposit involves open-space filling and replacement of host rocks along different faults or within hydrothermal dissolution-collapse breccias. The ore minerals in the Baiyangping deposit include chalcogen, chalcopyrite, ten-nantite, galena, sphalerite, cobaltine, siegente, arsenopyrite, argentite, kongsbergite with trace bournonite, orpiment, realgar, cerussite, and hemimorphite. The gangue minerals include quartz, calcite, ankerite, dolomite, and barite, and the alteration-related minerals consist of silica, sericite, carbonates, smithsonite, and epidote.  
Two hydrothermal events have been identified in the area. The first one is characterized by early barren carbonate-quartz veins followed by Cu-Co mineralization within carbonate veins or as breccia cement (Figure 2a,b). This hydrothermal event is characterized by a variety of Cu and Co sulfides as well as sulfosalts including tennantite-tetrahedrite series, chalcopyrite, bornite, argentite, kongsbergite, cobaltine, siegenite, and Co-bearing arsenopyrite. Tennantite commonly replaces chalcopyrite along its margin (Figure 3a-c). The second hydrothermal event is dominated by Pb-Zn mineralization and is featured in carbonate veins with variable amounts of galena, sphalerite, tetrahedralite, bornite, and minor pyrite and pyrrhotite (Figures 2c,d and 3d). The chalcopyrite-tennantite-tetrahedrite-cobaltine stage is earlier than the sphalerite-galena stage.  
Figure 2: Macroscopic features of ore samples: (**a**) waryl calcite vein; (**b**) tennantite cementing brecciated carbonate (Cu-Co stage); (**c**) sphalerite–galena–dolomite mineral paragenesis (Pb–Zn stage);and **(d)** galena-calcite vein within the wall rock (Pb-Zn stage). Abbreviations: Cal = calcite, Dol = dolomite, Gn = galena, Tnt = tennantite, and Sp = sphalerite.
# Real-Time UAV Trash Monitoring System
## 4 Experimental Results
Figure 16: The result of the unmanned aerial vehicle (UAV) trash monitoring system for the Wanghai Xiang beach. (**a**) The UAV in the Wanghai Xiang beach. (**b**) Video streaming server: the detection result of a small area in the Wanghai Xiang beach. (**c**) Video streaming server: the detection result of a small area in the Wanghai Xiang beach. (**d**) Video streaming server: the detection result (a small area) in the Wanghai Xiang beach. (**e**) A UAV map to monitor the trash detection and the trash polluted area in the Wanghai Xiang beach. In auto mode, the altitude of the UAV was set to 10 m on the cruising path so that the camera could obtain a picture with 10 m length. In auto mode, the speed of UAV was 2 m/s, which means every 5 s, the UAV flew 10 m (equal to the length of one picture). The proposed trash detection system sent one processed picture of the coastal image every 5 s so that the picture would not have overlap. The result of auto mode is shown in Figure 15. In manual mode, as shown in other figures, the altitude was not fixed. In order to show a clear view of different size of trash, the UAV was controlled manually and the altitudes were different.
## 3 Method  
The proposed LQR framework focuses on three key aspects: dataset construction, query complexity identification, and adaptive multi-retrieval. The framework consists of four stages: dataset construction, query complexity identification, adaptive retrieval, and LLM inference, as illustrated in Figure 1. In the dataset construction stage, parallel dataset generation processes were carried out with the assistance of a state-of-the-art LLM GPT-4o to create training data for both the query complexity classifier and the relevant classifier for multi-retriever results. In the query complexity identification stage, we developed a query complexity classifier to determine whether the user query can be decomposed into multiple questions. In the adaptive retrieval stage, either a single-step strategy or a multi-step strategy is applied based on the complexity of the query for document retrieval. Finally, in the LLM inference stage, the input query and the retrieved documents are combined using a specifically designed prompt. Details of each stage are described in the following sections.
## 3 Materials and Methods
### Study Selection and Screening
#### 3.3.3 PRISMA-Based Selection Process  
To ensure a comprehensive and systematic identification of relevant studies, a rigorous multi-phase study selection process was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.  
Identification: An extensive search was performed across five databases (Google Scholar, ACM Digital Library, IEEE Xplore, Scopus, and Springer) using a predefined set of keywords related to technical debt in AI-based systems and competition platforms. Additionally, manual searches and snowballing techniques were employed to capture relevant studies not retrieved through database queries.  
Screening: Duplicate records were first removed. The remaining studies were screened by evaluating their titles and abstracts based on predefined inclusion and exclusion criteria. This initial filtering aimed to eliminate studies clearly unrelated to technical debt in AI or AI-based competitions.  
Eligibility: Full-text articles of potentially relevant studies were retrieved and assessed thoroughly against the eligibility criteria. Only studies that explicitly addressed technical debt in AI contexts were retained for inclusion.  
Inclusion: The final inclusion of studies was determined through consensus between the researchers. Each article was independently reviewed by the first author, who annotated key observations related to technical debt manifestations and mitigation strategies. The second author evaluated these annotations for accuracy and consistency. Any discrepancies between reviewers were resolved through discussion, ensuring methodological rigor and minimizing selection bias.  
Visualization: The PRISMA flow diagram (Figure 1) provides a visual summary of the study identification, screening, eligibility assessment, and inclusion process, including the number of records at each stage and reasons for exclusion where applicable.
## 4 Results
### SEM-EDS Analysis
In Figure 10b, the RCS proppant exhibited a substantial decrease in surface C concentration, dropping from 72.98% to 53.05% following diluted geothermal fluid exposure. This decline indicates significant resin degradation, consistent with SEM observations. Simultaneously, a 171% increase in Si content was observed, indicating the exposure of the silica-based core material as the resin layer deteriorated. Post-reaction spectra also revealed the emergence of new elemental species, including Na, chlorine (Cl), and Ca elements commonly associated with precipitation reactions driven by ion exchange between the resin matrix and ionic constituents of the Utah FORGE geothermal fluid (Table 3). Compared to the ULD proppant, the RCS proppant displayed a higher overall concentration of secondary species. Figure 11a,b presents EDS results for white and brown sand proppants, respectively, extending the elemental analysis to uncoated materials. Although SEM imaging (Figure 7) showed minimal morphological degradation in both sand proppants, EDS analysis revealed the presence of new elemental species on their surfaces post-exposure, indicating active surface-level geochemical interactions. The surface contribution of newly formed phases was estimated at 0.5% for ULD ceramic, 4.1% for RCS, 7.1% for white sand, and 15.2% for brown sand, highlighting increasing reactivity from coated to uncoated proppants. Notably, both proppants exhibited reductions in Si content, 10% for white sand and 27.53% for brown sand. In addition, magnesium (Mg), Na, and Cl, major constituents of the Utah FORGE geothermal fluid, were detected on the proppant surfaces post-reaction. These findings point to ion exchange processes and secondary mineral precipitation occurring at the solid-fluid interface.
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 2 Background and Problem
### Execution Time Metric  
Suppose that SimpleFlow is a long-running workflow service, where multiple executions can run simultaneously. Also suppose that the resources allocated to the two tasks in SimpleFlow are scalable at runtime (which is often the case in real-world SaaS systems). To manage the resource allocation, the workflow system needs to have access to a wide range of performance metrics, including the time metrics. Basic time metrics include the end-to-end time of the workflow and the task time (i.e., the time to run an individual task) in the workflow. But in order to provide more pertinent information about the workflow performance, it is also important to consider time metrics for trace components.  
Time metrics of those trace components can provide performance information to determine whether more computing resources should be allocated to the two tasks. Unlike the end-to-end time and task time, the time metrics for trace components cannot be measured directly. One generic technique is event stream processing, in which we can route the trace logs to a stream processing platform, filter the traces belonging to an interesting trace component and aggregate the time for those traces. But this technique is resource-consuming especially for high-throughput streams, because the telemetry needs to be streamed into a processing platform from (possibly) multiple sources (e.g., multiple service units), and because fragments of the streams need to remain in main memory for aggregation. In practice, the overhead of performance monitoring atop the operation cost of the applications should be minimised. Therefore, a light-weight trace profiling method, which can produce reasonably accurate time metrics for a variety of trace components, is desirable.
# Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11 Technology
## 3 Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11
### Wake-Up Radio Transmitter and Wake-up Radio Call
Alternatively, for longer WuC length and in order to avoid synchronization issues, we can force different IEEE 802.11n/ac symbols to have the same duration, applying an extension of 4 \(\upmu\)s to signals larger than 20 MHz by the usage of bit padding in the MAC payload. Figure 4: Example of two bits per symbol coding with four different signal bandwidths. A frame with 20 MHz of signal bandwidth representing bits “00”, a frame using 40 MHz represents bits “01”, 80 MHz corresponds to “11”, and 160 MHz to “10”. Figure 3: Example of one bit per symbol coding with two different signal bandwidths. A frame with 20 MHz of signal bandwidth represents bit “0”, and a frame using 40 MHz the bit “1”.
## 3 Results and Discussion
### Initial Water Distribution of the Slope  
By monitoring the change in soil water content, the study obtained data on the increase in soil water content in bare slopes and vegetated slopes with different slope ratios and depths. The results are presented in Figures 10 and 11. The figures demonstrate that, as the soil depth increases, the growth rate and peak increment of soil water content decrease for each slope. The maximum increments in soil water content (arrival time) for bare slopes with slope ratios of 1:1.5, 1:1.75, and 1:2 were 13.0% (90 min), 12.0% (140 min), and 11.8% (160 min), respectively. For vegetated slopes with slope ratios of 1:1.5, 1:1.75, and 1:2, the maximum increase in soil water content (arrival time) was 12.8% (130 min), 16.1% (210 min), and 21.3% (240 min), respectively. The rate and peak value of increase in shallow soil water content decrease with a greater slope ratio for bare slopes, while the difference in deep layers is not significant. In contrast, the change in soil water content for vegetated slopes is opposite to that of bare slopes. The soil water content of vegetated slopes increases faster with a greater slope ratio, but the peak value of soil water content increment is smaller. Additionally, the peak value of soil water content increment for shallow soil in vegetated slopes is greater than that of bare slopes at each slope ratio.  
Figure 10: The average increase in soil water content at different depths of the bare slope and how it varies with time: (**a**) slope ratio of 1:1.5, (**b**) slope ratio of 1:1.75, (**c**) slope ratio of 1:2.  
Figure 9: Variation in total solar radiation intensity during the meteorological monitoring stage.  
After the rainfall ended, the soil water content decreased the most when the slope ratio of the bare and vegetated slopes was 1:1.75. In the bare slope with a slope ratio of 1:1.75, the maximum water content reduction was observed in the 20 cm, 40 cm, 60 cm, and 100 cm soil layers, with decreases of 5.4% (240 min), 4.5% (240 min), 1% (300 min), and 1.2% (300 min), respectively. Similarly, in the slope with vegetation and a slope ratio of 1:1.75, the maximum reduction in water content at soil depths of 20 cm, 40 cm, 60 cm, and 100 cm (the time at which soil water began to decrease) was 10.5% (220 min), 6.3% (240 min), 2.8% (260 min), and 0.9% (300 min). These results indicate a gradual decrease in soil water content at different depths after the rainfall, with a gradual slowing down of the decline rate over time. Moreover, the soil water content in the vegetated slope decreases more significantly compared to the bare slope with the same slope ratio. As the soil depth increases, the rate of decline in soil water content gradually reduces and the onset of decline is delayed. By the eighth hour after the rainfall ended, the water content in the soil layer at each depth of the slope had stabilized. This time is therefore used as the initial stage of water distribution on slopes under the condition of natural evaporation.
# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 3 Construction Model of Power Fault Knowledge Graph Based on Deep Learning
### Entity Extraction Model Based on BBIGRU-GA-CRF
#### 3.2.2 BiGRU Layer  
Previous works have used BiLSTM networks, however this paper uses a BiGRU network which has simpler parameters, faster training speed, and an equivalent effect in extracting the global features. The input of the BiGRU module is each vector representation of the output of the BERT model.  
The calculation method of each state in the unit is as shown in Equations (16)-(19):  
\[r_{t}=\sigma(w_{r}C_{t}+h_{t-1}U_{r}) \tag{16}\]  
Figure 6: BERT multilayer bidirectional transformer encoder.  
\[z_{t}=\sigma(w_{z}C_{t}+h_{t-1}U_{z}) \tag{17}\]  
\[\widetilde{h_{t}}=tanh(w_{h}C_{t}+U_{h}[r_{t}\odot h_{t-1}]) \tag{18}\]  
\[h_{t}=(1-z_{t})\odot\widetilde{h_{t}}+h_{t-1}\odot z_{t} \tag{19}\]  
In the above formula: \(z_{t}\) is the update gate, \(C_{t}\) represents the input vector at time \(t\), \(r_{t}\) is the reset gate, \(\widetilde{h_{t}}\) Indicates the candidate hidden state, \(h_{t}\) represents the output of the time \(t\) unit. \(\odot\) represents the multiplication of corresponding position elements; \(w_{z}\), \(w_{r}\), \(w_{h}\) and \(U_{z}\), \(U_{r}\), \(U_{h}\) are Z respective \(z_{t}\), \(r_{t}\), and \(\widetilde{h_{t}}\). In the training process, the weight matrix is continuously updated.  
The BiGRU network is composed of a forward GRU network and backwards GRU network. The input sequence is sent to the two-layer GRU network with opposite direction for calculation. The two-layer GRU network is used to capture historical information and subsequent information. Finally, the output of the two-layer network is spliced according to their respective positions, and the result is the output of the final BiGRU network.
## 4 Hazards Analysis
### Desertification Hazard Assessment  
Desertification, as defined by the United Nations Convention to Combat Desertification (UNCCD) [40], refers to the degradation of land in arid, semi-arid, and dry sub-humid areas, where the ratio of annual precipitation to potential evapotranspiration falls between 0.05 and 0.65. This degradation results from various natural processes and human activities that reduce the productivity and ecological balance of affected areas.  
The current assessment of desertification on Lefkas Island follows the MEDALUS framework [31], which is based on static environmental parameters. While this method provides a useful spatial overview of land sensitivity, recent research highlights the potential of integrating dynamic, satellite-based indicators to improve the accuracy and responsiveness of land degradation models. For example, [PERSON] et al. (2024) [41] illustrate how deep learning methods applied to Sentinel-1 and Sentinel-2 data can track vegetation loss in near real-time, while [PERSON] et al. (2025) [42] demonstrate the benefits of combining semantic and contextual data layers for remote environmental monitoring. Similarly, [PERSON] and [PERSON] (2024) [43] show the effectiveness of long-term satellite-based land-use analysis in understanding flood-related landscape transformation, a method which could also be applied to desertification dynamics.  
These approaches underline the value of combining spatial and temporal data to identify ecological shifts that may not be visible through static models alone. Therefore, while this study offers a baseline spatial analysis, future work should explore the use of time-series satellite data and vegetation indices to support more dynamic, long-term desertification monitoring.  
In line with the approach used in this study, the MEDALUS methodology requires the evaluation of four main indices to produce the Desertification Hazard Map for Lefkas Island: