esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Basic Work
### Determination of Cutoff Distance by Dichntomy  
Using the dichotomy method [30] to determine the cutoff distance \(d_{c}\) can solve the subjectivity of the traditional method by setting the truncation percentage \(p\), the main process is as follows:  
(1) Convert the distance list obtained by Euclidean distance or cosine distance into a distance matrix, and set the maximum value and minimum value of the distance matrix as \(d_{\max}\) and \(d_{\min}\), respectively;  
(2) The value of the initial truncation distance \(d_{c}\) is the average value of \(d_{\max}\) and \(d_{\min}\), namely \(d_{c}=\frac{d_{\max}+d_{\min}}{2}\);  
(3) Calculate the number \(n\_neigh\) less than \(d_{c}\) in the distance matrix, and calculate its ratio \(r\) to the total number;  
(4) If \(r<a_{1}\), assign the initialized \(d_{c}\) value to \(d_{\min}\), return to step (2), and continue the loop;  
(5) If \(r>a_{2}\), assign the initialized \(d_{c}\) value to \(d_{\max}\), return to step (2), and continue the cycle with the new \(d_{c}\) instead of the initial value;  
(6) If \(a_{1}\leq r\leq a_{2}\), or if \(d_{\max}-d_{\min}<0.0001\), then exit the loop;  
(7) Output \(d_{c}\).  
According to the value of truncation percentage \(p\) in the traditional DPC algorithm and in the results of many experiments, in this paper \(a_{1}\) and \(a_{2}\) are set as 0.01 and 0.04, respectively.
# Visual and Haptic Guidance for Enhancing Target Search Performance in Dual-Task Settings
## 3 Study 1: Visual Guidance Design and Evaluation
### Study Design
#### 3.1.1 Visual Guidance Interaction Design  
Visual guidance is a consistent and reliable tool that assists users across various digital devices such as HMDs and mobile screens. Numerous visual guidance techniques are employed to aid users in a variety of tasks. Among these techniques, the use of arrows has proven to be a straightforward and effective method for directing user attention [2]. Studies have demonstrated the efficacy of arrows as visual cues in various settings, including finding out-of-view objects [25], cueing targets in manual assembly assistance systems [2], performing 180-degree visual search tasks [5], and searching for locations on a digital map [3; 28].  
Therefore, in our first study, we opted to utilize the arrow as the visual guidance technique. Given that presenting the cue at the center of the screen could lead to information clutter [5], we positioned the arrow at a 20-degree angle from the current gaze to prevent it from obstructing the user's view in our cluttered target search and selection environments. The arrow appeared simultaneously as a target emerged behind the user, pointing in the direction of the target to guide the user to search for it behind them. To simplify target selection and avoid introducing more complex selection operations, we implemented the gaze-and-dwell selection confirmation technique. Users were merely required to maintain their gaze on the target for one second to affirm their selection.
# Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach
## 5 Problem Formulation and Algorithm
### Problem Decoupling  
To resolve the convex optimization problem with consensus algorithm, we need to decouple the global constraints. The Lagrangian multiplier method is a typical approach to transfer the equality constraints to the objective function. The corresponding Lagrangian function of original problem is given by:  
\[\begin{array}{ll}\Gamma(P(t),P^{hp}(t),H(t),\lambda(t),\mu(t))\\ =\sum\limits_{i\in V_{G}}C_{i}(t)+\sum\limits_{i\in V_{P}}I_{i}(t)+\sum\limits _{i\in V_{CHP}}F_{i}(H_{i}(t))\\ +\lambda(t)[\sum\limits_{i\in V_{P}}(P_{i}(t)+P_{i}^{hp}(t)-PV_{i}(t))-(\sum \limits_{i\in V_{G}}P_{i}(t)+\sum\limits_{i\in V_{CHP}}P_{i}(t))]\\ +\mu(t)[\sum\limits_{i\in V_{P}}(H_{i}(t)-P_{i}^{hp}(t)COP)-\sum\limits_{i\in V _{CHP}}H_{i}(t)]\end{array} \tag{20}\]  
where \(P(t)=[P_{1}(t),...,P_{N}(t)]^{\prime}\), \(H(t)=[H_{1}(t),...,H_{M}(t)]^{\prime}\) and \(P^{hp}(t)\)=\([P_{1}^{hp}(t),...,P_{N}^{hp}(t)]^{\prime}\) and \(\lambda(t)\), \(\mu(t)\) are the Lagrange multipliers at time \(t\) that are introduced to decouple the global power constraints.  
There exist different global constraints between electric network and thermal network and leading to the separation of them. We define \(\lambda_{i}(t)\) as incremental cost (incremental utility) for the energy sources (the demand) \(i\) in electric network as follows:  
\[\lambda_{i}(t)=\begin{cases}&C_{i}^{\prime}(t),\;i\in V_{G}\\ &0,\;i\in V_{CHP}\\ &\frac{\partial I_{i}(t)}{\partial P_{i}},\;i\in V_{P}\end{cases} \tag{21}\]  
Similarly, the incremental cost (incremental utility) \(\mu_{i}(t)\) for the energy sources (the demand) \(i\) of thermal network is given by:  
\[\mu_{i}=\left\{\begin{array}{ll}F^{\prime}(H_{i}(t))-\lambda_{i}(t)K_{CHP}, i\in V_{CHP}\\ &\frac{\partial I_{i}(t)}{\partial H_{i}},i\in V_{P}\end{array}\right. \tag{22}\]  
From Equations (20)-(22), we can decouple the primal problem into 3 sub-optimization problems only with local constraints.  
Prosumer subproblem (\(i\in V_{P}\)):  
\[\begin{array}{ll}min&I_{i}(t)+\lambda_{i}(t)(P_{i}(t)+P_{i}^{hp}(t)-PV_{i}(t) )\\ &+\mu_{i}(t)(H_{i}(t)-P_{i}^{hp}(t)COP)\end{array} \tag{23}\]\[\begin{array}{ll}s.t.&P_{i}^{\min}\leq P_{i}^{hp}(t)\leq P_{i}^{\max},\;i\in V_{P} \\ &P_{i}^{\min}\leq P_{i}(t)\leq P_{i}^{\max},\;i\in V_{P}\\ &H_{i}^{\min}\leq H_{i}(t)\leq H_{i}^{\max},\;i\in V_{P}\end{array} \tag{24}\]  
Utility grid subproblem (\(i\in V_{G}\)):  
\[\begin{array}{ll}min&C_{i}(t)-\lambda_{i}(t)P_{i}(t)\end{array} \tag{25}\]  
\[\begin{array}{ll}s.t.&P_{i}^{\min}\leq P_{i}\leq P_{i}^{\max}\end{array} \tag{26}\]  
CHP subproblem (\(i\in V_{CHP}\)):  
\[\begin{array}{ll}minF_{i}(H_{i}(t))-\lambda_{i}(t)P_{i}(t)-\mu_{i}(t)H_{i}( t)\end{array} \tag{27}\]  
\[\begin{array}{ll}s.t.&P_{i}^{\min}\leq P_{i}(t)\leq P_{i}^{\max}\\ &H_{i}^{\min}\leq H_{i}(t)\leq H_{i}^{\max}\end{array} \tag{28}\]  
Now, all the subproblems only have local constraints to be solved, which are appropriate to use consensus algorithm in a directed communication network.
## 5 Experimental Results
### Experiments with Intelligent Vehicle  
#### 5.2.1 Experimental Details  
We use an electric intelligent vehicle called Arrizo 5 to evaluate the proposed method (Figure 14). The intelligent vehicle is equipped with a monocular camera, an industrial personal computer (IPC), LRFs, INS, and a GPS receiver. It is worth noting that only the monocular camera and IPC are used in this test. The monocular camera is an industrial-grade gigabit camera with a high frame rate, which is equipped on the top of the vehicle (Figure 15). The camera is used to collect images of traffic signs. The IPC is used for data processing, which is at the core of the proposed method. The operating system of the IPC is Ubuntu18.04. The CPU is an IntelCore I7-7700T dual-card processor. The IPC is equipped on the right side of the intelligent vehicle trunk. In addition, the IPC is also equipped with a GTX2080 Ti Graphics Processing Unit (GPU), which can greatly increase the processing speed. The IPC and GPU are shown in Figure 16.  
The test route is selected in Zhenjiang, China. The test route includes a city expressway and an ordinary urban road, and the route covers different times and weather conditions. The total of route length is more than 20 km. The vehicle speed is in strict compliance with local regulations. We do not deliberately maintain the vehicle speed through the experiments. In this experiment, we use four state-of-the-art methods, namely SSD [23], YOLOv2 [22], YOLOv3 [24], and YOLOv5 [32] for comparison. The image display interface  
Figure 13: Images demonstrating the effect of improved Faster R-CNN detection (images on the **left** are the original images, and those on the **right** are the detection images).
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 2 Background  
This section introduces the data reduction concept and the metrics used to evaluate different data reduction techniques.  
Data reduction techniques are critical to managing information overload, providing strategic approaches to distill critical insights from massive datasets while containing storage costs [5]. Employing data reduction methodologies such as sampling, aggregation, and dimensionality reduction enables organizations to streamline data analysis processes, making information more accessible in a resource-efficient way. Beyond enhancing scalability, data reduction techniques play a critical role in machine learning, particularly through feature selection, which enables the identification of influential variables, streamlining predictive models for more efficient and accurate decision making. Moreover, reducing data volume addresses the challenge of handling large datasets and directly cuts cloud storage costs. Efficient data management leads to substantial savings, making operations more cost-effective in cloud-based storage solutions.  
The following metrics are used in the related work section to study and compare the different data reduction techniques:  
* Data size reduction is a percentage-based size comparison of the original and reduced data after applying the reduction techniques.
* Data accuracy quantifies the fidelity of the reduced data to the original dataset, typically expressed as a percentage. This metric measures the integrity and reliability of the reduced dataset, providing insight into the accuracy and fidelity of the information retained by the reduction process.
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 3 Examples of In Situ Neutron Radiography Investigations
### Hydrogen Diffusion at Temperatures between 900 and 1400 \({}^{\circ}\)C
#### 3.2.3 Results and Discussion  
A neutron radiography video of the reaction of the Zry-4 sample at 1000\({}^{\circ}\)C in steam is avaible online as the supplement S2. A summary of the hydrogen uptake during the oxidation of Zry-4 cladding tube segments in steam is given in Figure 10. The curve for 900 \({}^{\circ}\)C is not plotted in the diagram. No significant hydrogen uptake was determined during the whole time at this temperature. The reason for this is the monoclinic structure of the zirconium oxide at this temperature, resulting in a much lower oxidation rate, and with it the very low hydrogen partial pressure in the gas phase. At 1100 to 1500 \({}^{\circ}\)C, the oxide scale has a tetragonal structure. At 1000 \({}^{\circ}\)C, the oxidation starts with a tetragonal structure, which is stabilized by the maximal possible substoichiometry. When the oxygen vacancies are more and more filled, the oxide structure is transferred to the monoclinic one. Due to the differences of the atomic volumes between these two structures, strong stresses are induced by this phase transformation. A pronounced crack structure in the oxide layer is formed at this temperature, known as the breakaway effect.  
Generally, the initial hydrogen uptake is very fast as long as no protective oxide layer is formed. This finding is new; no other method has provided this information before. This stage of the hydrogen uptake is finished after about 20 s. Later, the hydrogen concentration decreases slightly. The hydrogen absorption increases with increasing temperature. At 1000 \({}^{\circ}\)C, the behavior differs after about 30 min. At this time, the crack formation in the oxide scale starts. Open cracks act as a \"hydrogen pump\": steam penetrates into the cracks and reaches the reaction zone close to the metal/oxide interface. The oxygen is consumed, and the hydrogen remains and becomes enriched in the cracks. The higher hydrogen partial pressure results in an enhanced hydrogen uptake according Sieverts' law. This additional hydrogen absorption has the consequence of a decrease in the total gas pressure in the cracks. New steam is sucked into the cracks to equalize the pressure, and the processes start again. A more detailed description of the tests and results for the E110 alloy and a deeper discussion are given in [18].
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## 3 Results  
### Comparison 1: LAI-RM to MODIS LAI (Mc5; Mc6) at 1.0 km  
#### 3.1.1 LAI-RM (1.0 km) Creation from In Situ LAI  
TRAC and DHP measurements were recorded and LAI calculated for all dates across all four research sites, then compared to Landsat NDVI (hardwood) and SR LAI (pine). The dates of in situ collection and Landsat imagery were coincident within a few days to a week except for two dates, one at \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **COOP Site** & **Elevation Diff (m) COOP-Site** & **Distance (km)** \\ \hline Appomatox & 51,011 & 77.4 & 15.8 \\ Hertford & 374,456 & 29.6 & 37.9 \\ Fairstone & 515,300 & -239 & 21 \\ Umstead & 377,079 & 27.9 & 8.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cooperative Observer Program (COOP) sites and proximity to the four LAI research sites. Hertford and the other at Fairystone (Table 4). All four research sites, across all dates, exhibited poor relationships (r\({}^{2}\) < 0.30) between NDVI and in situ LAI for hardwood, therefore in situ hardwood LAI was selected for upscaling to 1.0 km. A comparison SR LAI and in situ LAI in the pine LC (thinned and unthinned) showed no difference in the means except for Umstead (24 October--unthinned) and Appomattox (5 March--unthinned) sites. In both cases, in situ LAI rather than SR LAI was used for upscaling. The four research sites (Table 5) varied in LC homogeneity over the 1.0 km with Appomattox exhibiting the most diverse 'within' LC types (i.e., natural pine, unthinned planted pine, pine thinned every few rows) and 'between' LC types (i.e., pine, hardwood, other vegetation). Fairystone was at the other end of the spectrum, with one LC type, hardwood, accounting for 100% of the 1.0 km area (Table 5). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **In Situ** & **Landsat** & **MODIS** \\ \hline \multirow{2}{*}{Appomattox} & 5 March & 5 March & 5 March & 5 March \\  & 23 May & 24 May & 23 May & 23 May \\ \hline Hertford & 5 March & 18 February & 5 March \\ \hline Fairystone & 1 May & 29 April & 30 April \\ \hline \multirow{4}{*}{Umstead} & 25 June & 25 June & 25 Jun \\  & 4 September & 6 October & 6 September \\  & 9 May & 1 May & 9 May \\  & 21 October & 23 October & 24 October \\ \hline \hline \end{tabular}
\end{table} 
Table 4: In situ LAI, Landsat NDVI, MODIS collection dates.
# BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning
## 3 Algorithm Design
### DRL-SAR Algorithm
The loss of the actor network can be simply understood as follows: the greater the value of the action obtained, the smaller the network loss. Therefore, the loss function of the actor network can be defined as: Figure 6:5 (46)  
The loss functions \(J(\theta)\) and \(J(\omega)\) use gradient direction propagation to update the neural network parameters. At the same time, they balance the exploration of new actions and the use of known actions, increase the randomness of the learning process, and improve the generalization ability of the DRL-SAR. We add random noise \(\xi\) to action \(a_{t}\) obtained by the actor network in state \(s_{t}\), which is given by:  
Figure 6:6  
In summary, the DRL-SAR is shown as Algorithm 1. ```
0:\(\omega\), \(\theta\), \(\mathbf{S}=\{s_{1},s_{2},\cdots,s_{t}\}\)
0:Optimization result Begin
1: Initialize the actor network \(\pi_{\omega}(s_{t}|a_{t})\) and critic network \(\widetilde{Q}_{\pi}(s_{t},a_{t},\theta)\) with weights \(\omega\) and \(\theta\)
2: Initialize the target actor network \(\pi_{\omega^{\prime}}(s_{t}|a_{t})\) and target critic network \(\widetilde{Q}_{\pi}(s_{t},a_{t},\theta^{\prime})\) with weights \(\omega^{\prime}\) and \(\theta^{\prime}\) with initial weights \(\omega^{\prime}=\omega\) and \(\theta^{\prime}=\theta\)
3: Initialize the parameters and initial states of \(n\) short-message satellites and \(m\) short-message terminals
4: Initialize the replay memories \(D\), weight update interval, and random noise \(\xi\)
5: Initialize the state space \(\mathbf{S}=\{s_{1},s_{2},\cdots,s_{t}\}\) and \(s_{t}=\{\mathbf{L}_{t},\mathbf{A}_{n\times n_{t}}^{r},\mathbf{r}_{t}\}\), and get the graph tensor \(s_{1},s_{2},\cdots,s_{t}\)
6: for\(i\) in range(\(T_{max}\)):
7: Get \(a_{t}=\pi_{\omega}(s_{t})+\tilde{\zeta}\) in \(\mathbf{S}=\{s_{1},s_{2},\cdots,s_{t}\}\). 8: Get \(s_{t+1}\) and \(r(s_{t},a_{t})\) by \(a_{t}\). 9: Store transition \(\langle s_{t},s_{t+1},a_{t},r(s_{t},a_{t})\rangle\) in replay memories \(D\)
10:if\(len(D)>z\):
11: Randomly sample a batch of experiences \(\widetilde{D}\) from \(D\)
12: Calculate \(\widetilde{Q}_{\pi}(s_{t+1},a_{t+1},\theta^{\prime})\)
13: Update \(\theta\) according to Equation (43)
14: Update \(\omega\) according to Equation (44)
15:endif
16:if\(T_{max}\)mod \(n_{0}==1\):
17:\(\theta^{\prime}=\mu\theta+(1-\mu)\theta^{\prime}\)
18:\(\omega^{\prime}=\mu\omega+(1-\mu)\omega^{\prime}\)
19:endif
20:endfor
21: The model iteration ends and the optimization result is returned
22 End
```  
**Algorithm 1** DRL-SAR
# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 4 Results and Discussion
### Grad-CAM Explainability Analysis for Breast Cancer Identification  
Gradient-weighted Class Activation Mapping (Grad-CAM) was applied in this research to visualize the part of input images that contributed the most to model classification decisions. These visualizations can improve the interpretability of models and provide insight into the inner states of CNNs and are of high importance in high-stakes applications like breast cancer diagnosis. Grad-CAM is helpful in giving an account of whether the model is attending to clinically relevant regions, which in a way promotes model transparency and confidence in AI-aided diagnosis systems.  
Grad-CAM was used to estimate the spatial focus of the optimized MobileNets models wrapped in three imaging modalities, i.e., in mammography, in ultrasound, and in a combined fusion of both. In the mammographic case of MIAS (Figure 18), the FLA-optimized MobileNet model was quite accurate in the localization of a dense and irregular area that is often related to malignancy. Likewise, the presence of a heatmap in the ultrasound image (Figure 19) indicated a significant focus in proximity around the lesion boundary and educetxture within the internal area. In the fusion dataset (Figure 20), the DOA-optimized model exhibited the sharp activation on the tumor area with an apparent inhibition on the background noise, which suggests that the model can exploit the multimodal information in a very effective manner.  
Figure 19: High-resolution Grad-CAM visualization on a Breast Ultrasound image using the Optimized MobileNet-FLA model. The left panel displays the original ultrasound image, and the right panel shows the Grad-CAM heatmap, emphasizing areas with the highest contribution to malignancy prediction.  
Figure 18: Grad-CAM visualization using Optimized MobileNet-FLA highlights clinically relevant lesion regions in a MIAS mammogram, confirming malignancy-focused model attention.  
Although such visualizations indicate that the model focuses on clinically relevant areas, we admit that consideration of expert radiologist validation was beyond the scope of the given research, which weakens the overall findings concerning the matching of diagnoses. In subsequent studies, we intend to use qualitative scores and markings in certified radiologists to prove the relevance of the areas of interest in terms of clinical significance. The integration will enhance the claims of interpretability and put the model decision-making process in line with determined diagnostic standards.
# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 5 Results and Discussion  
### Dataset  
The dataset for the different acoustic scenes is taken from TAU Urban Acoustic Scenes 2020 Mobile development dataset [7], which is also used in the DCASE challenge. The 10 acoustic scenes included in the dataset are: airport, shopping_mall, metro_station, street_pedestrian, public_square, street_traffic, tram, bus, metro, and park. The dataset contains data from 10 European cities with a total duration of 64 h. 9 recording devices used in this dataset include 3 real devices and 6 simulated devices. 46 h of data are recorded with real devices, and smaller amounts are from simulated devices where the simulation is done using recorded audio and impulse responses of real devices with additional dynamic range compression. A cross-validation setup is provided for the balanced split in terms of scene classes and devices. The foreground speech dataset is randomly selected from LibriSpeech dataset [29]. To generate scenarios with foreground speech, a mixing dataset is created by adding different clean speech on top of background signal segments with SBRs randomly chosen from \(-\)5 dB, 0 dB, 5 dB, 10 dB, 15 dB, and 20 dB. The background ASC dataset and mixing dataset are concatenated to form the training dataset and are commonly used to train all proposed ResNet systems. For evaluation, the system performance is evaluated on the testing dataset with and without foreground speech. We make sure there is no overlap between the speaker set used for mixing the training data and the speaker set used on the evaluation data.  
To measure the reliability of classification accuracy, different cross-validation setups are used and the accuracy is presented in terms of 90% confidence intervals (CI).
## 3 Cluster Formation
### Failure Management
#### 3.3.2 Transient-Fault Management
At this state, the nodes in the range of neighbouring CHs join the nearest CH; the rest of cluster members, not being in communication range of any other CHs, compete for the CH position based on the connectivity factor (the ratio of the number of in-cluster neighbor of the node to the size of cluster) and energy factor. On the other hand, there is another possibility that the generated _unable-list_ does not exceed the threshold value. This means that the CH is not faulty, but due to some interference, the communication links of a few nodes are faulty. In this situation, nodes having a faulty medium with a CH join a neighbouring CH or if there is no neighbouring CH in range, they accept an in-cluster neighbouring node having healthy connections with their in-cluster CH as relay hop. CMATO is a distributed algorithm that properly and cooperatively can address the sudden crashes of multiple CHs at running time, and provides fault-tolerance for partial and transient CH failures. However, due to the hostile environment of implementation and inherent unreliability of wireless medium, it is highly likely that temporal disconnection of nodes is interpreted as permanent failure by the algorithm and this leads to frequent CH alternation of cluster members and instability of clusters, accordingly.