esa-sceva/satcom-chunk-collection huggingface dataset:
# Search Space Reduction for Localization and Tracking of an Acoustic Source
## 3 Three-Dimensional Localization and Tracking
Figure 6: \(B(r_{s},z_{s},\phi)\) calculated with the KRAKEN model and \(N\times 2\)D modeling: (**a**) stationary source at 331 Hz, all 5 files; (**b**) stationary source at 171 Hz, file 1 (the shape is identical for the remaining files); (**c**) moving source at 170 Hz, even files only. Figure 7: Wing “motion” for the stationary source at 331 Hz, file 4: the Bartlett power moves from the left to the right, starting at 5370 m and ending at 540 m, with a step of 5 m. The highest power is obtained at \(\phi_{s}=92^{\circ}\) and \(r_{s}=5425\) m.  
The wing motion provides a new estimate of source position, given by \((r_{*},z_{s},\phi_{*})\); the adoption of this result as final depends on the desired degree of accuracy. However, further refinement can proceed inside the search space given by \([r_{*}\pm\delta r]\times[z_{s}\pm\delta z]\times[\phi_{*}\pm\delta\phi]\) with a new set of small parameters \((\delta r,\delta z,\delta\phi)\), with the advantage that the calculation of replicas inside this search space is not computationally demanding; this provides the final 3D estimate of source position \((r_{\odot},z_{\odot},\phi_{\odot})\). For instance, for the stationary source at 331 Hz and file 1, it was found that \((r_{\odot},z_{\odot})=(5435,65)\) m and \(\phi_{\odot}=93^{\circ}\) (see Figure 8); this 3D estimate corrects \(r_{s}\) by almost 60 m, has no effect on source depth, and provides a value for the source bearing close to the expected one. A summary of localization results at 331 Hz for all files is presented in Table 5; one can notice that all range and bearing estimates are now within the expected boundaries of range and bearing errors. Corresponding results for the other two cases are presented in Tables 6 and 7; source positions over the \((X,Y)\) plane are shown in Figure 9. Discretization values for range
# An Eccentricity Based Data Routing Protocol with Uniform Node Distribution in 3D WSN
## 4 Performance Evaluation
### Simulation Results
#### 4.2.5 Network Lifetime  
The nature of energy depletion of the constituted nodes in the protocols are distinctive. The energy consumption of the nodes in EDR is not equally distributed; rather, the elected RC nodes dissipate energy faster than the normal nodes until the defined \(T_{\mathrm{E}}\) level. Contrasting with EDR, energy depletion of the nodes tends to be distributed in 3D LEACH and 3D EADC. The energy consumption of the constituted nodes in 3D EADC is more equally distributed compared to 3D LEACH.  
The lifetime of a WSN is defined by a certain percentage of live nodes in EADC. Here, the network lifetime is defined as the time until 10% of nodes are alive. Figure 8a compares the network lifetime in uniform node distribution, considering the alive nodes in the network. The result shows that the steady state and network lifetime of EDR are longer than the 3D LEACH and the 3D EADC. The steady state of a network is defined as the time period of the first node die (FND) in the network. EDR outperforms on averages of 1.34 times, 1.09 times and 2.59 times, 1.26 times of the steady state and network lifetime of 3D LEACH and 3D EADC, respectively.  
To compare the network lifetime of uniform with nonuniform node distribution, we also simulated the protocols with nonuniform node distribution in the same network circumstance. Figure 8a,b clearly depict that the steady state and network lifetime of EDR are enhanced in uniform compared to nonuniform. It is worth noticing that there is no significant changes of the network lifetime of 3D LEACH and 3D EADC with different node distributions.  
Figure 7: Data packets received at the sink used 3-hops.
# Initial Experimentation of a Real-Time 5G mmWave Downlink Positioning Testbed +
## 5 Field Campaign
### Description  
The HOP-5G mmWave positioning testbed was deployed on demand at Airbus premises for experimentation purposes. The FR2 downlink transmission was established at 27 GHz with a static OAI gNB and a moving OAI UE in LoS. The 5G PRS configuration described in Section 2.1 was used to achieve a bandwidth of 92.16 MHz. As it is shown in Figure 4, the starting position of the UE was as close as possible to the gNB, and then the UE (mounted on a trolley) was moved linearly until a maximum coverage distance. The UE moved with a nearly linear trajectory at a nearly constant and low velocity, i.e., with variations between 0.25 m/s and 1 m/s, except at planned temporary stops to evaluate the static scenario.  
The field experiment was based on a five-step procedure repeated in each test to perform the AoA measurements:  
1. The OAI UE acquires and synchronizes to the 5G NR signals transmitted from the OAI gNB.
2. The GNSS receiver manager collects and disseminates the GNSS-based UE position solution in real time.
3. For each beam within [\(-45^{\circ}\), \(-41.5^{\circ}\), \(\ldots\), \(41.5^{\circ}\), \(45^{\circ}\)], the AoA algorithm enables the corresponding beam on the Sivers EVK, the OAI UE estimates the RSRP from the PRS subframes, and the AoA algorithm collects the RSRP measurement for the specific beam.
4. The AoA algorithm performs real-time AoA estimation from the collected RSRP measurements.
5. The AoA estimate is disseminated together with the latest GNSS position solution.  
The data measurements with the AoA, RSRP and GNSS solution were logged in real time and post-processed for performance analysis.  
Figure 4: Field deployment of the HOP-5G mmWave positioning testbed: (**a**) tests start at minimum distance between gNB and UE; (**b**) tests finalize at maximum coverage between gNB and UE.
# Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
## 3 Materials and Methods
### YOLOv8  
In a 2023 study by [PERSON] et al. [24], YOLOv8 was introduced as the newest iteration in the YOLO series for object detection, officially released on January 10 th. This edition is noted for its enhanced processing speed, crucial for real-time application scenarios without compromising the model's accuracy. A pivotal advancement in YOLOv8 was its shift towards an anchor-free framework, which simplified the training process on varied datasets and incorporated a sophisticated backbone network along with multi-scale prediction capabilities, thereby significantly improving the detection of miniature objects. Comparative assessments have demonstrated YOLOv8's superior accuracy over its predecessors, establishing its competitive stance among contemporary object detection models.  
A defining feature of YOLOv8, as underscored by [PERSON] [25], is its implementation of a single-pass detection methodology, which optimizes efficiency by amalgamating object detection and segmentation into a singular operation. This approach, alongside a convolutional neural network (CNN), pre-trained on an extensive dataset, enables the model to accurately predict the presence, location, size, and classification of objects within an image. YOLOv8's architectural innovations, including an advanced backbone and an anchorless split Ultralytics head, enhance its accuracy and detection efficiency. Furthermore, the model offers a variety of pre-trained configurations to suit different tasks and performance requirements. The YOLOv8 model architecture is shown in Figure 6.  
Figure 4: Image tagging preprocessing.  
Figure 5: Image Auto-Orient examples.  
Figure 3: Distribution of animal species in dataset for image processing.
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 6 Conclusions  
In this work, we have reviewed the state-of-the-art methods in spacecraft anomaly detection and highlighted the benefits and drawbacks of each method. We also compared their performance on benchmark datasets. In summary, the choice of spacecraft anomaly detection method depends highly on the nature of the spacecraft telemetry data (spatial,temporal, complex, small/large size, sequence length, etc.), the type of anomalies, technical constraints, and computational resources.  
We can argue that AI models have shown superior performance over traditional thresholding methods.  
Nevertheless, a larger number of publicly available labeled anomaly datasets is crucial to ensure the possibility of benchmarking models and the uptake of such techniques. In addition, real-time and onboard implementation of such complex models is an ongoing area of research.  
Conceptualization, A.F.; Formal analysis, A.F.; Funding acquisition, G.V.; Methodology, A.F. and G.V.; Project administration, G.V.; Resources, G.V.; Supervision, G.V. and R.C.; Validation, G.V. and R.C.; Writing--original draft, A.F.; Writing--review and editing, A.D., R.C. and G.V. All authors have read and agreed to the published version of the manuscript.  
Project ASTRA-AI Financed by Xjenza Malta through FUSION Space Upstream Programme.  
Not applicable.  
The authors express their gratitude to all reviewers and editors for their valuable suggestions in improving this paper.  
The authors declare no conflicts of interest.
## 4 Discussion  
### Occupants' Personal Control  
The current study analyzed data from 7441 occupants in 167 European modern office buildings, sustaining and reinforcing findings from previous studies, revealing the importance of the perceived personal control over indoor environment parameters. The overall occupants' comfort should be examined by investigating the role of every aspect of personal control. [PERSON] [16] highlighted three aspects of personal control; (i) available control, (ii) exercised control; (iii) perceived control. Available control can be described by the degree and type of control made available by the environment. It can be defined by 'the degree of manipulation of thermostats and other manual controls as well as the existence of operable windows, blinds, sunshades, ventilation vanes, doors and HVAC system components'. Exercised control is defined by 'the relative frequency in which occupants engage in several types of thermal-related behaviors in order to obtain thermal comfort when needed'. The occupants' perceived control is being produced as the interaction of the different degrees of available (building controls) and exercised control (frequency of use). OFFICAIR database does not include information about the occupants' exercise control so in that study this aspect was not examined. The main idea is that personal control, both available and perceived, works as a moderator affecting the occupants' satisfaction on IEQ parameters and on the overall comfort. [PERSON] [16] examined the perceived control over temperature, ventilation, shading from the sun, lighting and noise. The results showed that occupants have moderate or low control on these parameters with a significant group of occupants, who declared that they had no access to the control of their environment. This was also remarked by [PERSON] and [PERSON] [19], where almost 60% of the responders had no control access. Furthermore, around 30% of the participants were very dissatisfied with the level of control. An interesting relationship between the perceived productivity in offices and perceived control has emerged, where higher productivity levels were recorded when occupants perceived higher degrees of personal control. [PERSON] et al. [18] also pointed out the issue of productivity versus personal control, concluding in the same finding. In this analysis, only a limited number of available control types were found to be associated with the perceived control. Perceived control over temperature, ventilation, light and noise was found to be associated only with the presence of operable windows. The effect of operable windows in the indoor environment on occupants' comfort was also raised by [PERSON] et al. [47]. The scores of perceived temperature control differ significantly with the different types of solar shading devices. However, after importing these available control types along with various general building characteristics in the multilevel model, only operable windows remained significant. Similar findings were also
# The Impact of an Agent's Voice in Psychological Counseling: Session Evaluation and Counselor Rating
## 5 Results  
Correlation analysis of five dependent variables was conducted, and the analytic result is as follows (Table 2). As the correlation of dependent variables was significant, MANOVA was conducted. A Box's M test was performed to verify an assumption on the invariance of covariance. The test result showed that M = 59.836, F(45,17465.664) = 1.1186,and \(p\) = 0.184, indicating that the assumption was appropriate. The MANOVA results showed that [PERSON]'s \(\lambda\) = 0.495, F(15, 221.246) = 4.277, \(p\) < 0.01, and \(\eta^{2}\) = 0.209, confirming that dependent variables showed a significant difference according to the voice of the psychological counseling agent. Table 3 lists the mean and the standard deviation of dependent variables according to the voice of the psychological counseling agent.  
Before an analysis of variance (ANOVA) of dependent variables, the [PERSON]'s test was performed to verify an ANOVA assumption on the invariance of error variance. The test result showed that the five dependent variables did not violate the assumption (depth, F(3,84) = 0.49, \(p\) = 0.69; smoothness, F(3,84) = 1.11, \(p\) = 0.30; attractiveness, F(3,84) = 0.93, \(p\) = 0.43; expertness, F(3,84) = 1.11, \(p\) = 0.35; trustworthiness, F(3,84) = 2.49, \(p\) = 0.07). The ANOVAs were significant for depth (F(3,84) = 9.96, \(p\) < 0.01, \(\eta^{2}\) = 0.262), attractiveness (F(3,84) = 7.06 \(p\) < 0.01, \(\eta^{2}\) = 0.201), and expertness (F(3,84) = 8.90 \(p\) < 0.01, \(\eta^{2}\) = 0.241). The ANOVAs were not significant for smoothness (F(3,84) = 2.28 \(p\) = 0.085, \(\eta^{2}\) = 0.075) and trustworthiness (F(3,84) = 1.93 \(p\) = 0.132, \(\eta^{2}\) = 0.064).  
A post hoc analysis of the ANOVA results of depth, attractiveness, and expertness was conducted based on [PERSON]'s honestly significant difference (HSD) test. As for depth, subjects evaluated counseling time as more valuable when they experienced counseling with the agent using an older male or female voice compared to that using a younger male or female voice. Regarding attractiveness, subjects perceived that agent using a female voice had a higher level of attractiveness than those using a male voice, regardless of the voice age. In terms of expertness, subjects recognized that the agent using an older male or female voice had a higher level of expertness than those using a younger male or female voice.
## 3 Problem Definition and Formulation
### Constraints
#### 3.2.4 Computational Complexity Constraints  
During the task allocation process, the change in computational complexity should be maintained within an acceptable range, to prevent an excessive computational burden. Specifically, for each task \(j\) and satellite node \(i\), the change in computational complexity must adhere to the following constraint:  
\[|t_{ij}-t^{\prime}_{ij}|\leq C_{\text{max}},\quad\forall i=1,2,\ldots,n,\forall j =1,2,\ldots,m \tag{6}\]  
where \(t_{ij}\) and \(t^{\prime}_{ij}\) represent the original and adjusted resource allocations for task \(j\) at satellite node \(i\), and \(C_{\text{max}}\) is the maximum allowable change in computational complexity. This constraint ensures that the variation in computational resources during task scheduling does not exceed a certain threshold, helping to avoid an excessive computational burden and improving system stability and responsiveness.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 2 Materials and Methods
### Methods
#### 2.3.1 Calculation of GWS Changes
_Validation and Refinement_  
Estimated uncertainties are validated through comparison with formal solutions and empirical benchmarks across both global and basin scales. Platforms such as GravIS provide time series of uncertainty alongside regional TWS values. Through this comprehensive framework, GRACE delivers robust monthly uncertainty estimates that integrate multiple error sources:  
- A formal covariance matrix defines the baseline uncertainty, accounting for instrumental and model-based errors. - Spatial covariance models adapt these uncertainties to regional scales. - Filtering and leakage errors are combined quadratically into the total uncertainty. - Auxiliary model uncertainties (atmosphere, ocean, GIA) are integrated and corrected. - Final estimates are validated against simulations and observations. As a result, each GRACE-derived TWS or GWS map or time series includes explicit uncertainty quantification, strengthening its application in hydrological analysis, risk assessment, and evidence-based decision-making.
# Automated Assessment of Inferences Using Pre-Trained Language Models
## 2 Materials and Methods  
### Data Collection and Evaluation by Human Experts  
A dataset of 720 sentence-response pairs was collected as follows. The stimulus text in Korean consisted of 10 sentences taken from an elementary school reading textbook. The average number of words per sentence was 9.2. The think-aloud protocol was administered to 72 third- and fourth-grade elementary school students in public elementary schools in South Korea. The participant's verbal response to each sentence was recorded and transcribed.  
Among the total 720 sentence-response pairs, 58 pairs were removed because the reader failed to produce any meaningful response. The remaining 662 sentence-response pairs were used for further analysis. Three evaluators individually assessed each sentence-response pair according to the nine inference types defined in a previous study [21] (summarized in Table 1). Each evaluator first identified the type of inference and then determined whether an inference was made (labeled as 1) or not (labeled as 0). The evaluators' decisions agreed on 642 sentence-response pairs (97%). For the mismatched 20 pairs (3%), evaluators discussed until they reached the same conclusion. Among the 662 sentence-response pairs, 438 pairs were labeled as inference (1), and 224 were labeled as no inference (0).  
Table 2 shows typical examples of sentence-response pairs in which inferences were made. The evaluators agreed that, in the first example in Table 2, the inference was made because the reader tried to make a prediction about what would come next based on what had come before. In the second example in Table 2, the reader tried to connect the background knowledge (The plants seem to be inactive in general.) when explaining the given sentence. In the third example in Table 2, the reader tried to associate the information in the given sentence with their background knowledge (The Venus firetrap is a famous example of a plant that eats insects.). In the fourth example in Table 2, the reader's response was a simple paraphrase of the given sentence, and the sentence and response were almost identical in meaning. In the last example in Table 2, there was no meaningful response from the reader.  
Figure 1: Using the think-aloud protocol, the subject’s response to each sentence is collected (**A**). Human evaluators then assess the sentence–response pairs to determine whether an inference was drawn (**B**). The proposed method is used to classify a sentence–response pair as inferential or not, without having to involve human experts (**C**).
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 3 Methodology
### Framework  
Given the crucial role of transformers in handling sequence data and the effectiveness of GNNs in processing spatial data, we now present the location prediction model for wargaming that leverages transformers and GNNs as core modules. Figure 2 illustrates the architecture of the proposed ELP-Net for wargaming. In this framework, the input attribute vector is denoted as \(X:=\{X_{1},\cdots,X_{T}\}\in\mathbb{R}^{b\times T\times d_{x}}\), where \(T\) represents the length of the sequence, \(b\) is the batch size, and \(d_{x}\) denotes the dimension of the input feature. The spatial tensor is represented as \(S:=\{S_{1},\cdots,S_{T}\}\in\mathbb{R}^{b\times T\times P\times M}\), where \(P=25\) is the size of the spatial tensor, and \(M=66\times 51=3366\) corresponds to the total number of hexagonal grids on the map. The transformer module consists of linear layers, multi-head self-attention (MSA) mechanisms, and position-wise feed-forward (FFN) networks. The GNN module is structured with a Conv2D, a graph learning layer, temporal convolution (TC) modules, and graph convolution (GC) modules. After concatenating the transmissions of the spatial and attribute features, the network is divided into \(N\) blocks, each with the same architecture. To preserve temporal feature information, we utilize a gated recurrent unit (GRU), a variant of recurrent neural networks that is designed to manage information flow through update and reset gates. Finally, a fully connected layer of size \(M\), followed by a softmax layer, is used to generate the output tensor for each piece.  
In the following sections, we provide a detailed breakdown of each module to offer a comprehensive understanding of the proposed model. For reproducibility, a more detailed experimental setup is presented in Appendix A.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Sub-Features** & **Size** & **Description** \\ \hline \multirow{4}{*}{Attribute vectors} & Stage & 20 bits & Stage of the wargame. \\  & Occupation points & 4 bits & Status of occupation points. \\  & Our piece attributes & 23 bits & 138 bits total for 6 pieces. \\  & Enemy piece attributes & 23 bits & 138 bits total for 6 pieces. \\  & Result & 1 bit & Labeled data for model training. \\ \hline \multirow{4}{*}{Spatial tensors} & Player color & \(1\times 66\times 51\) & Red (0) or blue (1). \\  & Our piece location & \(6\times 66\times 51\) & Locations of 6 pieces. \\ \cline{1-1}  & Observed enemy locations & \(6\times 66\times 51\) & Locations of 6 enemy pieces. \\ \cline{1-1}  & Enemy vehicle vision & \(1\times 66\times 51\) & Vision range of enemy vehicles. \\ \cline{1-1}  & Enemy soldier vision & \(1\times 66\times 51\) & Vision range of enemy soldiers. \\ \cline{1-1}  & Map tensor & \(4\times 66\times 51\) & Elevation of the map. \\ \cline{1-1}  & Previous stage enemy locations & \(6\times 66\times 51\) & Labeled data for model training. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of data features.  
Figure 2: The framework of ELP-Net for wargaming.