esa-sceva/satcom-chunk-collection huggingface dataset:
# Immersive Two-Channel Recordings Based on Personalized BRIRs and Their Applications in Industry
## 4 The Importance of Externalization
An interesting aspect of externalization is shown with neuroplasticity, as reviewed by [PERSON] et al. in Chapter 1 of [15]. Neuroplasticity in sound localization allows the brain to adapt and rewire itself in response to changes in sensory input, improving spatial processing. This ability, demonstrated through mechanisms like spatial cue remapping and reweighting, helps individuals adjust to altered binaural cues, such as hearing loss or changes in sound localization cues. Training and behavioral context further refine these adaptations, with cross-modal influences (e.g., vision) enhancing auditory spatial processing. These findings suggest that neuroplasticity plays a crucial role in externalizing sound sources by adapting auditory processing to new spatial conditions. This allows for listeners to adapt to different sets of HRIRs and suggests that personalization may not in fact be needed. However, since some listeners have been found to have a preferences for personalized BRIRs, and they are perfectly capable of externalization as well, the authors tried to focus on the solutions possible with their usage. Recording individual BRIR sets helps capture the ear's individual responses to acoustic waves and applies them later to the signal in two-channel audio. Signal processing using such a method under headphone listening causes some parts of the signal to be attenuated and others to be amplified, thus replacing the role of the human ear in the process of recording acoustic waves [39]. This is because the main essence of the HRIRs is to record information that people naturally use to locate sounds. By using binaural microphones to record sounds directly in people's ears, and later synthesizing 3D recordings with them, it is possible to achieve with headphones an immersive experience the same as in a listening room with speakers. The idea of preserving all of the impressions experienced by the listeners in the selected space in the created recordings, and using personalized BRIRs for this purpose, is fundamental in the skillful processing of 3D sound [40].
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 2 Background
### Description of Routing Protocols  
#### 2.3.1 The AODV Protocol  
The AODV protocol [14] is a reactive routing protocol, which implies that a route discovery process is initiated when a route to a specific destination is needed. Route discovery is accomplished by flooding the network with RREQ messages. Upon receiving RREP messages the route with the smallest hop count will be recorded in the routing table. The routing table on each node contains information about the next hop to the destination, the number of hops, and the expiration time of the routing table entry. Each time the route is used for forwarding data, its expiration time is updated. In the case of periodic communication, the routing table entry never expires.  
There are two ways of creating routes in the AODV protocol. Protocol's behavior depends on the flag called _destination only_. When the flag is set (further referenced as DFT), only the destination node can reply to a RREQ message. This implies that every time a new route is requested, a new original route from the source to the destination is created, without any influence of already existing routes. On the other hand, when the flag is not set (further referenced as DFF), any node that has a route to the destination can reply to the RREQ message. As a result, the routes that already exist influence the searching process. The existing routes are then used by many nodes and even though a better route exists, it is not discovered. The difference in the created routes can be seen in Figure 1. Nodes are depicted as circles with their IDs written up left from them. The node with \(ID=0\) is the sink. Grey links, although they exist, are not used in any route. The thicker the link is, the more routes are going through it. Topology in Figure 1a was created with the _destination only_ flag not set. Compared to Figure 1b, routes in Figure 1a were attached to the existing ones, and thus the topology has fewer \"branches\".  
AODV scales well to large networks, as shown in [20]. It is a widely accepted choice for reactive routing [21].  
Figure 1: Topologies created by AODV. **(a)**_Destination only_ flag is set to false (DFF), **(b)**_Destination only_ flag is set to true (DFT).
# 5G Network Deployment Planning Using Metaheuristic Approaches
## 5 Results and Analysis
### Result for Different Carrier Frequencies Using SA  
From Table 9 after SA optimization, the number of BSs remained constant, but notable improvements in coverage and capacity were observed. The final configuration maintained 115 BSs for 28 GHz and 147 for 3.6 GHz with increased coverage values of 0.9131 for 28 GHz and sustained 1 for 3.6 GHz. Capacities also improved, reaching 0.87152 for 28 GHz and 0.9201 for 3.6 GHz. Following the selective elimination of specific BSs, the final configuration showcased a reduction in the number of BSs to 55 for 28 GHz and 90 for 3.6 GHz. Despite a decrease in coverage for 28 GHz to 0.833, the coverage for 3.6 GHz remained at 1. Capacities saw a decline, reaching 0.7187 for 28 GHz and 0.6006 for 3.6 GHz.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{2}{l}{**Using GWO for Optimization**} \\ \hline  & **Carrier Freq** & **28 GHz** & **3.6 GHz** \\ \hline  & Initial BS No. & 115 & 147 \\ Initial Arrangement & Initial Cov & 0.8993 & 1 \\  & Initial Cap & 0.868 & 0.927 \\ \hline  & Final BS & 115 & 147 \\  & Final Cov & 0.9444 & 1 \\  & Final Cap & 0.9375 & 0.8194 \\ \hline  & Final BS & 65 & 115 \\  & Final Cov & 0.9201 & 0.993 \\  & Final Cap & 0.8576 & 0.7777 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results for different 5G Multi-Tier RAN planning scenarios using GWO.  
This reduction in the BS count, coupled with trade-offs in coverage and capacity, resulted an enhancement in the existing optimization scenarios by proposing the incorporation of a 2.6 GHz carrier frequency for RMa deployment alongside the existing UMa arrangement. This extension aims to achieve 100% coverage.  
Figure 10a illustrates the outcome of the SA optimization at 28 GHz after the elimination process. The configuration shows a reduced number of base stations (55 for 28 GHz) and likely highlights the refined deployment. Additionally, a Voronoi Plot--a graphical representation dividing space based on proximity to specific points--might be included to visualize the coverage areas and distribution of base stations in the 5G network.
## 4 Results and Discussion  
### Results of PFH Inversion  
The inversion results showed that the force induced by the Taimali landslide was initiated at about 09:31:34 and terminated at 09:34:28 (Figure 5). Among the three components of force, the largest force existed in the E-W direction, reaching \(3.7\times 10^{10}\) N. The maximum force in the N-S direction and the maximum vertical forces were \(1.6\times 10^{10}\) and \(1.2\times 10^{10}\) N, respectively.  
After the force-time function was obtained, the inverted force was applied to get the synthetic waveforms of each seismic station (Figure 6). Most synthetic waveforms were consistent with the station records: 47% of the synthetic waveforms had correlation coefficients (cc) values higher than 0.9, and 70% of the synthetic waveforms had cc values higher than 0.7. They both indicated that the inversion results were robust. The ANPB and CHGB stations both had strong ambient noise, which led to the low SNR and cc values. In addition, the poor results of the KMNB and PHUB stations might be attributable to the velocity model used in this study. Despite the poor results caused by the strong ambient noise and the influence of the velocity model, the high cc values suggested that the inversion had good performance and the results were highly reliable.  
Based on the analysis of satellite imagery and the results of waveform inversion, the Taimali landslide did toward the northeast at the beginning, which implied that the force would first head toward the southwest. However, the first pulse of force from 09:31:21 to 09:31:34 was toward the northeast. Thus, the first pulse of force was considered a precursory signal caused by Gibbs effects [7]. The landslide propagation began at 09:31:34, at which time the force headed toward the southwest. The force after the 250 th second was considered fluctuations of ambient noise, not the propagation of the Taimali landslide. The 174-s duration of the landslide was consistent with the waveform observation (Figure 6); therefore, the force-time function indicated that the Taimali landslide initiated at 09:31:34 and terminated at 09:34:28.  
Figure 5: The force–time function of the Taimali landslide by point force history (PFH) method. The time series began at 09:31 and ended at 09:36 UTC. The green zone represents the duration of the landslide propagation.  
Figure 6: Three components of the synthetic waveforms and the seismograms of each station. Z denots vertical direction, R denotes radial direction, and T denotes tangential direction. Correlation coefficients are represented by “cc”. Light-colored seismograms represent that the lower weighted coefficients were adopted to conduct the PFH inversion.
# XCO\({}_{2}\) Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker
###### Abstract  
In view of the urgent need for high coverage and high-resolution atmospheric CO\({}_{2}\) data in the study of carbon neutralization and global CO\({}_{2}\) change research, this study combines the Kriging interpolation and the Triple Collision (TC) algorithm to fuse three XCO\({}_{2}\) datasets, OCO-2, GOSAT, and CarbonTracker, to obtain a \(1^{\circ}\times 1^{\circ}\) half-monthly average XCO\({}_{2}\) dataset. Through a sub division of the Kriging interpolation, the average coverages of the OCO-2 and GOSAT XCO\({}_{2}\) interpolating datasets are increased by 53.65% and 48.5%, respectively. In order to evaluate the accuracy of the TC fusion dataset, this study used a reliable reference dataset, TCCON data, as the verification data. Through comparative analysis, the MAE of the fusion dataset is 0.6273 ppm, RMSE is 0.7683 ppm, and \(R^{2}\) is 0.8279. It can be seen that the combination of Kriging interpolation and TC algorithm can effectively improve the coverage and accuracy of the XCO\({}_{2}\) dataset.  
XCO\({}_{2}\); GOSAT; OCO-2; CarbonTracker; Kriging interpolation; triple collocation Article
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 2 Space Debris Capture  
Despite the growing concern about space debris, no autonomous capturing system has been officially used yet. The required technologies can be quite diverse and by 2025 we will see the launch of the first autonomous chaser satellites by ClearSpace to remove an ESA-owned item from orbit (ClearSpace-1 mission [39; 40]).  
ADR missions depend a lot on the targeted debris. The most commonly studied solution is to design one capturing system for one specific debris (one-to-one solution). Currently, voluminous and well-known satellites are the ones aimed to be targeted first. However, although these satellites are one of the main threats to generating more space debris, it is only one side of the problem. The new mega-constellations of CubeSats coming in the next decade in LEO (around the 500-700 km orbits) will increase the number of decommissioned satellites remaining in orbit. As a result, the urge to tackle the small satellites in LEO is and will be real.  
The capturing mechanism plays a key role in the success of an autonomous space debris removal mission, especially if it is designed to target a wide range of debris, as the one proposed in this paper. To that extent, to design such a system, it is of utmost  
Figure 1: Concept of a CubeSat-based system for capturing small debris.  
importance to know about the variety of objects in LEO, obtain knowledge of that data, and determine what range of debris our mechanism should target first. These parameters will impact the design of an autonomous ADR system.
## 3 Experiments  
### Implementation  
To evaluate the performance, we compare our proposed method with various methods such as CenterNet [14] (baseline), Faster R-CNN [5] and SSD [7]. The performance of the methods is measured in terms of mAP and detection time. For the CenterNet framework, training was conducted using a batch size of 2 for 140 epochs. We use HourglassNet-104 as feature extractor backbone pretrained on COCO dataset from ExtremeNet [18]. The initial learning rate is 1.25 \(\times\)10\({}^{-4}\) which decreases by a multiplication of 0.1 at 90 and 120 epochs. The Adam optimizer is used for network optimization. A spatial resolution 512 \(\times\) 512 is used for the input image. Faster R-CNN detector is trained with ResNet-50 backbone.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Output** & **Operation, Kernel Size,** \\
**Name** & **Dimension** & **output Channels, Stride** \\ \hline Layer1 & 128 \(\times\) 128 & Conv, 4 \(\times\) 4, 64, 2 \\ Layer2 & 64 \(\times\) 64 & Conv, 4 \(\times\) 4, 128, 2 \\ Layer3 & 32 \(\times\) 32 & Conv, 4 \(\times\) 4, 256, 2 \\ Layer4 & 16 \(\times\) 16 & Conv, 4 \(\times\) 4, 512, 2 \\ Layer5 & 4 \(\times\) 4 & Conv, 4 \(\times\) 4, 1, 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The design of the discriminator network.  
This model is trained for 50 epochs with batch size 4 and learning rate 0.001. SSD network is trained using VGG16 backbone with a batch size of 4 and initial learning rate of 0.001. The training is performed for 16,000 iterations. The experimental results are shown in percentage (%) of mAP value over all logo classes using Intersection of Union (IoU) value 0.5. Average inference time is given for one image. The inference time is calculated on our machine with Intel Core i7-8700 CPU, GeForce GTX 980 Ti GPU, Pytorch 0.4.1, CUDA 9.0 and CUDNN 7.1.
## 2 Background and Related Work
### Related Work
At the same time, interference mitigation techniques, such as enhanced inter-cell interference coordination (eICIC) and beamforming, can reduce the system outage without increasing the spectrum. Table 1 summarizes the weaknesses found in the state-of-the-art works analyzed. Based on the previous work presented in this section, the limitations identified in the state of the art can be summarized as follows:  
* The actual scenarios and the safety requirements of an underground mine are not considered when designing a sensor deployment strategy. * The analyzed papers do not consider the actual structure of the mines nor their characteristics, such as tunnel geometry, galleries, levels of exploitation, inclination or tilt inside the tunnel; the authors evaluated ideal tunnel structures, or sometimes geometries which were far from reality. * The analyzed papers do not use distance-based accurate coverage and connectivity parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Approach** & **Parameters Evaluated** & **Limitations** \\ \hline [PERSON], [PERSON] [42] & Enhancement of relay nodes & & Only consider relay node deployment \\ [PERSON], [PERSON] [42] & communication approach in WSN-IoT for underground & Coverage and connectivity & No simulations or experiments \\  & coal mines & & are conducted \\  & Multilevel sensor deployment & & Use mine structures far from reality \\  & approach in IIoT-based & Number of nodes deployed, & (many parallel roadways) \\ [PERSON] and [PERSON] [43] & environmental monitoring & network lifetime, & Uses sensing and communication range \\  & systems in underground & energy efficiency & values not supported with experiments \\  & coal mines & & or simulations \\  & & & Uses sensing and communication range \\  & A novel wireless sensor network & & values not supported with experiments \\ [PERSON], [PERSON], [PERSON] [45] & deployment scheme for & Coverage, energy hole problem & or simulations \\  & environmental monitoring in & & Focused on the longwall exploitation \\  & longwall coal mines & & technique, which represents a mine \\  & & & structure composed of a single tunnel \\  & Node deployment of band-type & & Uses sensing range value not supported \\  & wireless sensor network for & Sensing efficiency, coverage & with experiments or simulations \\  & underground coalmine tunnels & & Involves a mine structure composed of a \\  & Deployment strategies for the & & single tunnel \\ [PERSON] et al. [47] & industrial IoT: A case study based & Coverage and connectivity & Focused on open-pit mines \\  & on surface mines & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Resume of related work. * There needs to be evidence of articles that analyze the behavior of distribution functions used in random deployments to determine the geolocation of sensor nodes in underground mining scenarios.
## 3 Results and Discussion
### Characterization of PCB Extracted via Optimized Conditions  
PCB was characterized at 666 nm via HPLC, as shown in Figure 3. A comparison of standard PCB (PCB STD in Figure 3) and Arthospira-derived PCB extracts (PCB Sample in Figure 3) showed retention times of \(\sim\)4.77 min and \(\sim\)4.66 min, respectively, consistent with a previous PCB study [12]. The peak of \(\sim\)4.1 min retention time observed in PCB STD, although not identified in PCB Sample, is likely to be an adduct of methanol during methanol extraction, which may reduce the purity of the PCB extract [12, 32]. Thus, nuclear magnetic resonance analysis is essential for determining the exact purity of the PCB extract [12].  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{l}{**Variable**} & \multicolumn{2}{l}{**Yield**} \\ \cline{2-5}  & **Extraction Temperature (\({}^{\circ}\)C)** & **Extraction Time (h)** & **C-PC to Solvent Ratio (_w/v_)** & **(\%)** \\ \hline CCD (theoretical) & 68.81 & 14.91 & 1:95 & 29.18 \\ Validation (actual) & 69 & 14.91 & 1:95 & 29.67 \(\pm\) 1.33 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the numerical optimization and actual verification run.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Source** & \multicolumn{1}{l}{**Sum of**} & \multicolumn{1}{l}{**Df**} & \multicolumn{1}{l}{**Mean**} & \multicolumn{1}{l}{**F Value**} & \multicolumn{1}{l}{\(p\)**Value**} \\  & **Squares** & & **Squares** & **F Value** & **Value** \\ \hline Model & 1448.13 & 9 & 160.90 & 62.84 & \textless{}0.0001  \\ A: Extraction temperature & 160.21 & 1 & 160.21 & 62.57 & \textless{}0.0001  \\ B: Extraction time & 20.14 & 1 & 20.14 & 7.87 & 0.0186  \\ C: C-PC to solvent ratio & 828.43 & 1 & 828.43 & 323.56 & \textless{}0.0001  \\ AB & 23.15 & 1 & 23.15 & 9.04 & 0.0132  \\ AC & 3.34 & 1 & 3.34 & 1.30 & 0.2799  \\ BC & 6.35 & 1 & 6.35 & 2.48 & 0.1462  \\ A\({}^{2}\) & 334.44 & 1 & 334.44 & 130.63 & \textless{}0.0001  \\ B\({}^{2}\) & 44.12 & 1 & 44.12 & 17.23 & 0.0020  \\ C\({}^{2}\) & 146.60 & 1 & 146.60 & 57.26 & \textless{}0.0001  \\ \hline Residual & 25.60 & 10 & 2.56 & & \\ Lack-of-Fit & 20.46 & 5 & 4.09 & 3.98 & 0.0779  \\ Pure Error & 5.14 & 5 & 1.03 & & \\ \hline Cor Total & 1473.73 & 19 & & & \\ \hline R\({}^{2}\) & 0.9826 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of variance for the quadratic model.
## 2 Materials and Methods
### Methodology Design and Implementation
#### 2.2.5 Performance Metrics  
The predictions of the models developed in this study can generate four possible outcomes: True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN). Positive individuals in our study had hypertension, whereas negative patients had normal blood pressure. TP and TN are correct predictions. FP outcomes are positive predictions when they are actually negative. On the other hand, FN outcomes are predictions that are negative when they are actually positive. We evaluate the prediction models with the following performance metrics:* Sensitivity or recall or TP rate--This metric indicates the proportion of true positives predicted out of all positives in a dataset: \[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (1)
* Specificity or False Negative rate--This is the number of negative cases that are mistakenly identified as positive: \[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}\] (2)
* Accuracy--This is a metric used to determine how many correct predictions a model produced through the whole test dataset: \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\] (3)
* Precision--This indicates the correctness of the correct prediction: \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] (4)
* _F1_-score--This metric measures the accuracy of the model based on sensitivity and precision. A higher _F1_-score value indicates the model is more accurate: \[F1-\text{score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}\] (5)
* Misclassification rate or classification error--a performance indicator that indicates the proportion of incorrect predictions without differentiating between positive and negative predictions: \[\text{Misclassification rate}=1-\text{Accuracy}\] (6)
* Area Under the Receiver Operating Characteristic Curve (AUC) is used to assess the model's prediction accuracy. This statistic assesses the algorithm's ability to distinguish between hypertensive and normal individuals.
# Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning
## 5 Discussion
### The Effect of Information Transfer between Layers  
In this experiment, we conduct relevant experiments in 5-way 1-shot and 5-shot scenarios to validate the classification effectiveness of selecting and processing intermediate features at different layers in Branch 2. The results are presented in Table 6. The baseline model represents a dual-branch structure with a tutoring learning module. In this model, the shallowest layer of Branch 2 is obtained from the output of the first layer of the feature extractor. '' indicates that the second layer of the feature extractor is chosen to form the shallowest layer of Branch 2 while delaying its max-pooling layer. Compared to our baseline model, when selecting features from the second layer, our model demonstrates an improvement in classification performance by 0.94% and 0.14% for the 5-way 1-shot and 5-shot settings, respectively. These experimental findings indicate that further processing of shallow feature information contributes to enhanced classification accuracy and highlights the crucial impact of selecting shallow features as well. Additionally, Table 6 reveals that knowledge transfer between internal layers within the relation network module in Branch 2 also aids in improving accuracy (by approximately +0.41%) for the case of a 5-way 1-shot scenario; however, such knowledge transfer has a negative effect when dealing with a more data-rich setting such as a 5-shot scenario (i.e., having five labeled examples per class). Further analysis suggests that this occurs because under such settings, where models learn relatively robust prototypes, higher-level features carrying semantic information exert greater influence on final classification decisions. Therefore, the information transferred from shallower layers instead affects overall decision-making.