esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Studies
### Gesture Interactions in TV Interfaces  
In the realm of home entertainment and television, mid-air gesture interactions serve as an alternative to traditional remote controls. Research in this area primarily aims to identify gesture sets that feel instinctive, allowing users to browse, select, and operate TV content from a certain distance. For instance, [PERSON] et al. recorded a wide range of hand motions in participants to determine the most intuitive gestures for essential TV operations such as channel switching and volume control [27]. Other studies have also focused on gesture elicitation techniques to create user-centric and accessible interaction methods for TVs. [PERSON] and [PERSON] conducted a gesture elicitation study focused on television interactions, evaluating the ease of performing and the remembering probability for free-hand gestures across plural TV control functions [28]. Similarly, [PERSON] et al. investigated how users naturally perform gestures for smart TV control, focusing on the importance of recallability and usability in developing a practical gesture system [29]. [PERSON] et al. carried out research into gesture elicitation to develop intuitive gestures for smart TV interactions, assessing their effectiveness in terms of their user-friendliness and retention and the ease of learning and memorizing gestures [30]. These studies suggest that the design of TV gestural controls should align with users' inherent mental models of natural physical actions to maximize their intuitiveness.  
Beyond this perspective, some studies have highlighted significant usability challenges for gesture-driven TV interaction methods. Notably, gesture-controlled TV interfaces may impose cognitive loads and mental stress on users compared to conventional remote controls. For instance, [PERSON] et al. examined mid-air gestures in comparison to remote control methods, finding that although gesture-based input led to increased mental effort and stress, users also perceived enhanced task effectiveness and adaptability [31]. Analogously, [PERSON] et al. examined how users responded to gesture-based control systems in smart environments, including smart devices like TVs, discovering that while gesture interactions offer novel and forward-thinking possibilities, interacting through mid-air gestures tends to elevate users' mental strain and cognitive load compared to conventional input techniques [32]. Additional concerns highlighted in the literature involve the precision of gesture detection and the commonly cited \"Gorilla Arm\" fatigue phenomenon: this refers to the physical strain that can occur when users hold their arms or hands in extended positions--such as maintaining a pointing gesture in mid-air--for an extended duration, which can cause muscle fatigue and negatively impact user comfort and the overall interaction usability [33]. Overall, research in the TV domain underscores that gesture-based TV control is pragmatically feasible and could be enjoyable to users, yet the properties of hand gestures should be planned to mitigate their cognitive load and physical strain.
# CNN for User Activity Detection Using Encrypted In-App Mobile Data
## 3 Proposed Methodology
### Unknown In-App Data Detection  
In a real-world setting, data traffic captured contains both previously known and unknown traffic flows. Previously known traffic is related to the in-app activities considered during model training whereas unknown traffic relates to the in-app activities not considered during model training. A major challenge to the robustness of the classifier's performance comes from previously unknown/unseen traffic. Identifying previously untrained in-app activities using the proposed method is one of the key contributions of this work.  
When the traffic flows are converted into images and input to the CNN model, they pass through hidden layers and reach the output layer. A SoftMax layer is added to the end of the CNN, which converts the output values into a probability distribution. Model's output layer has nodes that is equal to the number of classes. The SoftMax layer provides probabilities for each class label in the interval (0, 1). Usually for a given input sample, one of the classes will have a higher probability value than the rest of the classes. In normal traffic classification tasks, a class is assigned to a data point based on the highest probability. However, in this work instead of making the class with the highest probability be the final classification, a threshold approach is used to determine if the test sample is a known or unknown instance. Figure 4 shows the technique used to detect noise (unknown in-app data) generated by previously unknown traffic. _Pmax_ denotes the node with the highest predicted class probability. The decision for converting the predicted probability into a class label is dominated by a parameter known as the _threshold_. If _Pmax \(<\) threshold_, then the test sample is labeled as an unknown instance. Threshold set on a positive class determines whether the test sample belongs to one of the trained classes, which translates into a pre-trained in-app activity or not.  
To examine the impact of the threshold on model's performance, a range of threshold values were selected and tested. Setting a threshold too high results in an increase in false negatives, while setting it too low leads to an increase in false positive. Therefore, setting a balanced threshold value is challenging. In this work, a threshold value that contributes to achieving the highest classification accuracy was utilized, which was obtained empirically as 0.97.
## 5 Discussion  
Soundscape design should be based on the relationship between human perceptual construct and physical phenomenon of the acoustic environment. As shown in Figure 1, acoustic recording and reproduction techniques are essentially adopted through every stage of soundscape design process. In particular, soundscape recording and reproduction techniques play a more critical role in Stage 2 for proposing and evaluating the soundscape designs before their implementations in situ. In Stage 2, various soundscape design approaches might be applied to improve the existing poor acoustic conditions, and those approaches should be assessed through subjective tests based on human perception of acoustic environment. In this context, soundscape design needs to consider the  
Figure 8: (**Left**) AR setup with spatial audio from rendered virtual sound objects for in situ environments. (**Right**) A lab-based VR setup from omni-directional camera recordings and a reproduced acoustic environment using spatial audio from ambisonic microphone recordings and rendered virtual sound objects [97]. degree of ecological validity of the soundscape for reliable solutions, which largely depends on the adopted recording and reproduction techniques. Therefore, for clarity, the discussion will be focused on highlighting the degree of perceptual accuracy of the recording and reproduction techniques introduced in Sections 2 and 3 respectively, in representing the acoustic environment. As stated in ISO 12913-1:2014, any evaluation of soundscape is based upon the human perception of the acoustic environment (actual or simulated). The technique of constructing an audible acoustic environment, or Auralisation, is commonly used for soundscape evaluation in controlled environments. According to [PERSON] [81], the goal of auralisation also stems from the human perception of the acoustic environment, in which the synthesized acoustics only needs to be perceptually correct and not physically perfect. Perceptual accuracy, also called plausibility, of the virtual acoustic environment is defined by [PERSON] and [PERSON] as \"a simulation in agreement with the listener's expectation towards an equivalent real acoustic event\" [98]. The listener's expectation, however, is subjective and can vary depending on the intended tasks in the acoustic environment. Ultimately, the ecological validity of a virtual acoustic environment will be application specific. This task-specific criterion implies that there are different levels of perceptual accuracy that can create \"a suitable reproduction of all required quality features for a given application\" [87]. Out of the quality features suggested by [PERSON], emphasis will be directed to features, which are attributed to recording and reproduction techniques. Therefore, it would be beneficial to classify the characteristics of the acoustic environment, such that the appropriate techniques are selected to achieve the desired level of perceptual accuracy. The characteristics of the acoustic environment are summarized in Table 3.
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 4 The D2D Loosely Coupled Model
### Neighbor Classification  
To facilitate the management of the network, each selected user (SU) will detect neighbors in his coverage and classify them according to two possible group types: The first single group, named Gnc (Group of not connected), which includes all the neighbors who have no path connecting them. The second group type is named Gc (Group of connected). Depending on the topology, some Gc groups can be created to include all the SU neighbors who already have a path that connects them. If many groups are created, they will be named Gc1, Gc2, Gc3, etc. We mention that the path that connects two neighbors can allow either a direct link or indirect link between them by crossing other UEs that are not necessarily neighbors of the SU. In the example illustrated in Figure 7, the SU should create the following groups: Gnc = {3, 6}, Gc1 = {1, 2}, and Gc2 = {5, 7, 8}.
# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
## 3 Methods
### Location and Orientation of the Model  
The spectral power distribution (SPD) of light incident on the occupant's eye is determined by the average sky conditions and subsequent reflections within the surrounding environment. For this analysis, two distinct geographical locations were selected: Seville, Spain; and London, UK. These were selected as representative locations due to their contrasting daylight availability conditions. Seville, located in southern Spain, features a Mediterranean climate with high annual solar radiation levels and abundant daylight, especially during summer months. In contrast, London, situated in northern Europe, presents significantly lower daylight availability, characterized by frequent overcast conditions and shorter daylight hours, particularly in winter. These differences allow for the assessment of daylight metrics under diverse climatic scenarios, providing a more comprehensive comparison of their performance. The virtual model was oriented to both north and south in each location. This approach is fundamental for a comprehensive lighting study, as these orientations represent the extremes of natural light availability: the south maximizes direct solar exposure, while the north offers more consistent diffuse daylight and minimal direct solar impact, allowing for a thorough analysis of metric performance under diverse conditions.  
Figure 2 illustrates the average SPD for each location, displaying the spectral distribution of each sky as derived from statistical data on the annual percentage of overcast skies.  
Figure 1: Dimensions of the virtual model and the calculation point array. The fenestration dimension (X) varies as a function of the WWR. For a WWR of 20%, the fenestration dimension is 1.34 by 1.34 m; for a WWR of 30%, it is 1.64 by 1.64 m; and for a WWR of 40%, it is 1.90 by 1.90 m. The 50 grid points were utilized to calculate the spatial metrics. The red points delineate the section where non-spatial metrics have been analyzed.  
The analysis was conducted using DaySim 3.1, based on climate files derived from long-term meteorological data (typically spanning 5-10 years). The SPD presented does not correspond to a single theoretical sky type (e.g., those defined in ISO/CIE 15469:2004(E)/CIE S 011/E:2003 [33]), but rather to a weighted average between clear and overcast-sky conditions, reflecting their relative frequency in the local climate data. For CS calculations, the spectral input was obtained by combining typical SPDs for each sky condition. Therefore, the resulting SPD represents a location-specific annual average, not an idealized or fixed sky model [78], and the prevailing climatic conditions of each site. The standard CIE D65 spectral power distribution (SPD), commonly representing a typical sky vault, was included for comparative purposes. This D65 SPD curve closely resembles the spectral distribution observed under intermediate sky conditions.
## 5 Discussion
### Results Analysis
#### 5.1.3 The Result Analysis of Electricity Sale Price  
In addition to the supply-demand relationship, the electricity sale price is directly affected by CET and TGC prices, and its changing trends deeply reflect the complexity of the electricity market. The impact of TGC price on electricity sale price is mainly reflected in the continuous expansion of the market share of new energy generation. With the growth of renewable energy installed capacity, the adjustment of the TGC quota ratio directly affects the TGC price, thereby changing the cost structure of power enterprises and further promoting fluctuations in electricity sale price. CET price has a more significant impact on electricity sale price. The research shows that with the tightening of the carbon emission cap target, the CET price rises, pushing the electricity sale price to increase first, then slowly fall, and finally stabilize at 0.5 yuan/KWh. As strict carbon cap targets push up CET prices, the cost of carbon compliance for power enterprises increases accordingly, which in turn increases the cost of electricity production. This cost pressure has limited further declines in electricity sale price during the study period, preventing them from reaching a very economical price. With the development of clean energy, electricity production costs are gradually decreasing, ultimately stabilizing electricity sales prices. However, due to the transmission mechanism of carbon trading prices and the upfront investment in power generation facilities, even if the cost of renewable energy generation technology decreases, the electricity sale price cannot reach a low level in the short term. When the carbon emission cap target increases, the electricity sale price continues to decline, attributed to the dual effect of relaxing carbon emission constraints. On the one hand, the pressure on power companies to reduce carbon dioxide decreases, and they are unwilling to invest in the construction of renewable energy generation equipment. On the other hand, by leveraging the marginal cost advantage of traditional power generation equipment, the short-term electricity production cost is effectively reduced [49]. The adjustment of carbon emission cap targets directly affects the cost structure of electricity production, affecting the electricity sales price. Therefore, when setting carbon emission cap targets, policymakers need to comprehensively consider the cost structure of the electricity market (such as the number of power generation equipment, investment costs for renewable energy generation capacity, and operating years of power generation equipment) and price fluctuations to ensure the feasibility of policies and market stability.
# Optical Fiber Current Sensors Based on FBG and Magnetotstrictive Composite Materials
## 2 Working Principle of the Sensor
### Magnetic Circuit Sensing System  
#### 2.1.1 Magnetostrictive Material  
Magnetostrictive materials extend (or shorten) in size under the action of external magnetic field, and then return to their original size after the external magnetic field is eliminated. This phenomenon is called magnetostriction. Since this phenomenon was first discovered by Joule in Ni materials, it is also called the Joule phenomenon or Joule effect [7]. Magnetostriction refers to geometric deformation caused by externally applied magnetic field [8]. The magnitude of magnetostriction is expressed by the magnetostriction coefficient \(\lambda=\Delta L/L\), where \(\Delta L\) refers to the variation of material length and \(L\) refers to the overall length of material, as shown in Figure 1.  
The causes of magnetostriction are shown in Figure 2[9]. Magnetic materials can be divided into numerous magnetic domains microscopically. When external magnetic field intensity is 0, the magnetic moment directions of magnetic domains are different, but the vector sum is zero, and the magnetic moment of the whole object is zero, which indicates that the material is not magnetic in the macroscopic view, i.e., the material does not deform. When the magnetic domain is in an applied magnetic field, it will deviate from the original self-magnetization direction and deviate towards the direction of the applied magnetic field. When the magnetic domain deflects in the same direction, a magnetic field force will be generated in this direction, which will cause the ferromagnetic material to be deformed by the force in this direction, resulting in magnetostriction. Within a certain range, magnetostriction of ferromagnetic materials will increase with the increase of the applied magnetic field, but when the applied magnetic field exceeds a certain range, magnetostriction basically remains unchanged, i.e., the deformation (strain) of the material reaches saturation [10].
###### Abstract  
An unmanned aerial vehicle (UAV) swarm has emerged as a powerful tool for mission execution in a variety of applications supported by deep neural networks (DNNs). In the context of UAV swarms, conventional methods for efficient data processing involve transmitting data to cloud and edge servers. However, these methods often face limitations in adapting to real-time applications due to the low latency of cloud-based approaches and weak mobility of edge-based approaches. In this paper, a new system called deep reinforcement learning-based resilient layer distribution (DRL-RLD) for distributed inference is designed to minimize end-to-end latency in UAV swarm, considering the resource constraints of UAVs. The proposed system dynamically allocates CNN layers based on UAV-to-UAV and UAV-to-ground communication links to minimize end-to-end latency. It can also enhance resilience to maintain mission continuity by reallocating layers when inoperable UAVs occur. The performance of the proposed system was verified through simulations in terms of latency compared to the comparison baselines, and its robustness was demonstrated in the presence of inoperable UAVs.  
resource-constrained UAV swarm; distributed inference; resilient UAV system; deep reinforcement learning; end-to-end latency optimization. +
Footnote †: journal: applied sciences
# AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation
## 3 Preliminaries
### LightGCN  
The matrix factorization method described in the previous section has certain limitations, as it can only capture information from the direct neighbors of a given node in the graph. However, we aim to obtain information from multi-hop neighbor nodes, as this enhances the utilization of the graph structure. LightGCN [13] addresses this need.  
LightGCN is derived from graph collaborative filtering (GCF) [6], but it differentiates itself by eliminating nonlinear activations and matrix transformations that have been shown to be less effective. This modification is likely due to the fact that in recommendation systems, each graph node is constructed and trained using only the IDs of users or items, resulting in less rich information compared to that found in image data.  
LightGCN employs the embedding matrix from matrix factorization as the initial embedding and then completes the embedding training through three layers of embedding propagation. In each layer, each node updates its information by aggregating the embeddings of its neighbors, which can be conceptualized as a form of graph convolution [25]. The mathematical representation of this process is illustrated as shown in Figure 2.  
In this context, \(D\) represents a diagonal degree matrix, and \(\widetilde{A}=D^{-1/2}AD^{-1/2}\) is a diffusion matrix computed from the degree matrix \(D\) and the adjacency matrix \(A\), which only needs to be calculated once. This multi-layer embedding propagation process can be abstracted as follows:  
\[e_{i}^{(k+1)}=AGG(e_{i}^{(k)},e_{i}^{(k)},i\in N_{u}\big{\}}). \tag{2}\]  
Figure 1: Matrix factorization.  
Figure 2: Diffusion of LightGCN.  
The iterative aggregation formula for each layer is expressed as follows:  
\[e_{u}^{(l+1)}=\sum_{i\in N_{u}}\frac{1}{\sqrt{|N_{u}|}\sqrt{|N_{i}|}}e_{i}^{(l)} \,\quad e_{i}^{(l+1)}=\sum_{u\in N_{l}}\frac{1}{\sqrt{|N_{i}|}\sqrt{|N_{u}|}}e_{u} ^{(l)}. \tag{3}\]  
In this model, the only learnable parameters are the initial embeddings. The final embeddings are obtained by summing the embeddings from each layer as follows:  
\[e_{u}=\sum_{k=0}^{K}\alpha_{k}e_{u}^{k},\quad e_{i}=\sum_{k=0}^{K}\alpha_{k}e_ {i}^{k}. \tag{4}\]
## 2 Experimental Design and FE Modeling
### Experimental Apparatus and Methods  
EPS is a viscoelastic material, and its uniaxial compression stress-strain curve (SS curve) is shown in Figure 2. Because the EPS compressive strength is usually expressed as the stress (10% strain strength) at 10% strain in the case of 50 mm\({}^{3}\) samples [13; 14; 15; 16], 10% strain was set as the EPS reference strain level in this study. The elastic modulus was calculated from the compression SS curve by three methods: (1) as the slope of the initial proportional portion (initial tangent modulus and Young's modulus), (2) as the slope of the line segment connecting the 10% strain point on the curve (secant modulus), and (3) as the slope of the tangent line at the 10% strain point on the curve (tangent modulus).  
The EPS Poisson's ratio is the ratio of the horizontal strain to the vertical strain, for a uniaxially compressed test specimen under a compression test with a loading rate of 12.5 mm/min. To measure the horizontal displacement on both sides of a test specimen, a jig was designed, in which two linear variable differential transformers (LVDTs) were installed in a horizontal straight line facing each other (Figure 3). The SS curve was measured by placing a test specimen centrally between the two parallel plates of the compression-testing machine and compressing it to 80% of the original thickness at a compression rate of 10% of its original thickness per minute [13].  
The uniaxial compression creep test equipment was composed of a hardware system consisting of a dead load addition part, a linear displacement-measuring part, a specimen compression part, a load-measuring part, and a software system for continuously measuring and analyzing the specimen's displacement with time (Figure 4). During the creep test, five levels of applied stress were arbitrarily selected under a 10% strain strength, for each EPS density scenario. The creep test was conducted in two ways: the 12-h-long creep test was performed for analyzing the short-term creep response after loading, while the 7-day-long creep test was performed for validating the FEA results for the creep behavior of EPS-based cushioning systems.  
Figure 3: Universal testing machine, with a lateral strain-measuring device for EPS specimens.  
Figure 2: Typical SS curve and elastic modulus of EPS.