esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Multi-Day Flexible EMU Routing Plan for High-Speed Rail Networks
## 4 Experiments and Computational Results
### Sensitivity Analysis of Weight Coefficients  
To further evaluate how the minimum train connection time and the minimum coupling/decoupling time affect the model's two main objectives--minimizing total connection time and deadheading mileage--we conducted a sensitivity analysis by adjusting the weight coefficients \(\alpha_{1}\) and \(\alpha_{2}\).  
Specifically, we selected four representative parameter settings and solved the model under each one. The performance was assessed based on total objective value, total connection time, the number of EMUs used, deadheading mileage, and solution time. The results are summarized in Table 6.  
As shown in the table, when \(\alpha_{1}=0.6\) and \(\alpha_{2}=0.4\), the model achieved a balanced trade-off between the number of EMUs (42 units), total train connection time (54,641 min), and deadheading mileage (35,741 km), with a relatively fast solving time (1248 s). In contrast, other parameter settings either slightly increased the number of EMUs or led to longer connection time or deadhead mileage.  
Figure 11 illustrates the convergence trajectories of the objective function under four representative weight settings. The trends observed validate the effectiveness and robustness of the proposed optimization algorithm. In all cases, the objective values exhibit a steep decline within the first few iterations, followed by convergence toward a stable solution, indicating that the algorithm efficiently reaches a near-optimal region in a relatively short time.  
More specifically, when the weight coefficients are set as \(\alpha_{1}=0.8,\alpha_{2}=0.2\), the objective function stabilizes after approximately 20 iterations, albeit at a relatively higher value compared to the other scenarios. As the weight assigned to deadheading mileage  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Weight Setting** & **Objective Value** & \
\begin{tabular}{c} **Total Connection** \\ **Time (min)** \\ \end{tabular} & **EMUs** &
\begin{tabular}{c} **Deadheading** \\ **Mileage (km)** \\ \end{tabular} & **Solving Time (s)** \\ \hline \(\alpha_{1}=0.8,\ \alpha_{2}=0.2\) & 51694 & 55157 & 42 & 37846 & 1452 \\ \(\alpha_{1}=0.7,\ \alpha_{2}=0.3\) & 49666 & 55589 & 42 & 35846 & 1375 \\ \(\alpha_{1}=0.6,\ \alpha_{2}=0.4\) & 47081 & 54641 & 42 & 35741 & 1248 \\ \\(\alpha_{2}\) increases, the final objective value decreases, reflecting an improved trade-off between total train connection time and empty-running mileage.  
Among the four configurations, the setting \(\alpha_{1}=0.6,\alpha_{2}=0.4\) achieves the lowest final objective value, which is consistent with the quantitative results summarized in Table 6. This outcome supports the selection of this particular weight configuration as the baseline for subsequent analysis, as it yields superior overall performance across key metrics, including EMU utilization, connection efficiency, and computational efficiency.
# Wireless Energy Harvesting with Cooperative Relaying under the Best Relay Selection Scheme
## 3 System Model  
The CR system model is shown in Figure 2. We consider downlink EH relaying, in which a source node \(S\) sends the data to the destination node \(D\), and the communication is applied with the help of EH relaying, \(R_{i}\), \(i\in\{1,2,\ldots,N\}\) with the PS protocol. We assume a direct path between \(S\) and \(D\) with SNR, \(\gamma_{sd}\) with channel coefficients, \(h_{sd}\), and distance, \(d_{3}\). On the other hand, the SNR between \(S\) and \(R_{i}\) is denoted by \(\gamma_{si}=|h_{si}|^{2}E_{s}/N_{0}\) with channel coefficients, \(h_{si}\) and distance, \(d_{1}\). The SNR between \(R_{i}\) and \(D\) is denoted by \(\gamma_{id}=|h_{id}|^{2}E_{s}/N_{0}\) with channel coefficients, \(h_{id}\), and distance, \(d_{2}\). All nodes are assumed to have a single antenna, and all nodes operate in a half-duplex mode. All links are assumed to undergo Rayleigh fading with perfect channel state information (CSI). In this work, the transmission process occurs in two time slots with duration \(T\). From the AF-EH relaying, each relay is assumed to harvest a portion of the signal received by the PS protocol. A portion of the signal received by each relay is divided for information processing by a value of \(\rho\). Thus, the rest of the signal will be represented by \(1-\rho\).
## Materials and Methods  
### Controllable Active Cooling Device and Measurements (iGlove-1)  
An in-house (University of Antwerp, Antwerp, Belgium) developed active localised cooling device (_iGlove-1_, Figure 2) was developed to investigate the possibility of localised active cooling of hand fingers [21]. The _iGlove-1_ device was developed in such way as to provide a stable localised low temperature (cooling) to the five fingers of the subject's left hand. The _iGlove-1_ was equipped with 20 _Peltier_ elements (two elements positioned on both sides of each finger) to apply localised cooling of the fingers. Each element had a size about \(12\times 12\times 5\) mm.  
The _iGlove-1_ device consisted of a wooden construction on which the subject's left hand can rest. The construction was equipped with five mechanically adjustable holders to ensure a good contact between the Peltier elements and the fingers of different hand sizes. The two Peltier elements located on each side of the finger (two on the fingertip and another below the first phalanx, Figure 2) were always electrically connected in series.  
The _iGlove-1_ device was equipped with five NTC sensors (with an accuracy of \(\pm 0.1\)\({}^{\circ}\)C) to continuously measure the temperatures of the left-hand fingers. Another 20 NTC sensors measured the temperature of the Peltier elements (one sensor for each element). A Laser Doppler blood flow meter (MoorVMS-LDF2(r) by Moor Instruments) was used to measure the changes in the blood flow underneath the nail bed. The blood flow measurements were performed on only two fingers, the index finger and the thumb, of the subject's left hand as a representation of the changes in the blood flow to the five fingers.  
The temperature sensors were positioned on the underside of each fingertip, while the probes of the Laser-Doppler blood flow meter were placed on the middle of the nails of the thumb and index fingers. The skin temperatures of the five fingers and that of the ambient air (in degrees Celsius, \({}^{\circ}\)C) were recorded, as well as the blood flow (in Perfusion Units, PU) of the thumb and the index finger, every second (for more information see [20; 21; 22]).  
Figure 2: The _iGlove-1_-controlled cooling device for the left hand. (1) Peltier cooling elements; (2) NTC temperature sensor to measure the finger skin temperature; (3) one probe of the blood flow Laser Doppler meter.
## 3 Results  
### Contact Angle Measurement and Surface Energy  
The result of contact angle with respect to NTAPP treatment time was shown in Figure 4a. The results showed the general trend of decreasing contact angle for all three liquids tested with increasing NTAPP treatment time (\(p<0.05\)). The result of contact angle with respect to time passed from NTAPP treatment is shown in Figure 4b. Generally, the contact angle increased with time passing from the point where the NTAPP treatment occurred (\(p<0.05\)) for all liquids used in the test.  
The surface energy was then calculated from each contact angle and the results showed that surface energy increased with increasing NTAPP treatment time (Figure 5a) (\(p<0.05\)), whereas the surface energy decreased with increasing time following the NTAPP treatment (Figure 5b) (\(p<0.05\)).  
Figure 4: The contact angle following use of distilled water (black square), diiodomethane (red circle), and resin monomer mixture (blue triangle) on the surface of epoxy resin as; (**a**) increasing treatment time of non-thermal atmospheric pressure nitrogen plasma; (**b**) time following the treatment to non-thermal atmospheric pressure nitrogen plasma. There was a statistically significant difference (\(p<0.05\)) between all test groups.  
Figure 5: The calculated surface energy on the surface of epoxy resin as; (**a**) increasing treatment time of non-thermal atmospheric pressure nitrogen plasma; (**b**) time following the treatment to non-thermal atmospheric pressure nitrogen plasma. There was a statistically significant difference (\(p<0.05\)) between all test groups.
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 1 Introduction  
Visible Light Communications (VLCs) represents nowadays a novel and promising wireless technique for sending and receiving information in short-range links [1, 2, 3]. VLC works by modulating the light intensity of a transmitting source, which typically is a Light Emitting Diode (LED). This technology has gained momentum by the recent diffusion on the market of white LED lamps designed for ambient lighting, which have proven effective as VLC transmitters [4].  
The performance of a VLC link is limited by the Signal-to-Noise Ratio (SNR) present at the receiver, which imposes a tradeoff between the achievable data rate and the distance between the transmitter (TX) and the receiver (RX). Research showed that it is possible to establish communication in the Gb/s range (for example [PERSON] et al. showed a 3.4 Gb/s link [5]), or in a vehicle-to-vehicle (V2V) communication link, the maximum transmission distance is approximately 72 m in clear weather and 26 m in fog [6].  
The information needs to be modulated before being transmitted through light. The highest performance in terms of efficiency in the use of bandwidth is obtained by applying complex modulations such as Orthogonal Frequency-Division Multiplexing (OFDM) plus Quadrature Amplitude Modulation (QAM) [7]. However, the implementation of these techniques requires electronics systems capable of a relatively high calculation power and TX/RX front-ends with elevated analog performance. On the other hand, in the On-Off Key (OOK) modulation [8], the bits are coded in only two different intensities of the light. This approach is very simple, since in TX the LED can be directly driven by digital devices [9], and in RX even a simple threshold receiver can work [10]. Despite the simplicity of this method, OOK modulation can achieve a relatively high performance [11].  
An important variation of the OOK modulation is the Manchester coding. In Manchester OOK coding, the bits '0' and '1' are transmitted though the symbols couples '01' and '10', respectively (or the opposite) [12]. This choice grants a transition for every bit, which is very useful to recover the clock at the receiver and permits a direct-current (DC) balance of the channel. The positive features of this approach are confirmed by its inclusion in important standards such as the IEEE 802.15.7 [13] for wireless communications, which applies, for example, to automotive VLC applications [14; 15].
###### Abstract  
Models constitute the primary approaches for predicting terrestrial ecosystem gross primary production (GPP) at regional and global scales. Many satellite-based GPP models have been developed due to the simple algorithms and the low requirements of model inputs. The performances of these models are well documented at the biome level. However, their performances among vegetation subtypes limited by different environmental stresses within a biome remains largely unexplored. Taking grasslands in northern China as an example, we compared the performance of eight satellite-based GPP models, including three light-use efficiency (LUE) models (vegetation photosynthesis model (VPM), modified VPM (MVPM), and moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP)) and five statistical models (temperature and greenness model (TG), greenness and radiation model (GR), vegetation index model (VI), alpine vegetation model (AVM), and photosynthetic capacity model (PCM)), between the water-limited temperate steppe and the temperature-limited alpine meadow based on 16 site-year GPP estimates at four eddy covariance (EC) flux towers. The results showed that all the GPP models performed better in the alpine meadow, particularly in the alpine shrub meadow (\(R^{2}\geq 0.84\)), than in the temperate steppe (\(R^{2}\leq 0.68\)). The performance varied greatly among the models in the temperate steppe, while slight intermodel differences existed in the alpine meadow. Overall, MVPM (of the LUE models) and VI (of the statistical models) were the two best-performing models in the temperate steppe due to their better representation of the effect of water stress on vegetation productivity. Additionally, we found that the relatively worse model performances in the temperate steppe were seriously exaggerated by drought events, which may occur more frequently in the future. This study highlights the varying performances of satellite-based GPP models among vegetation subtypes of a biome in different precipitation years and suggests priorities for improving the water stress variables of these models in future efforts.  
g gross primary productivity; light use efficiency model; satellite remote sensing; MODIS; EVI; eddy covariance; grassland ecosystem; temperate steppe; alpine meadow
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 1 Introduction
Moreover, we refer to the theoretical analysis on the interpretability of Transformers in information theory [24; 25; 26] and describe the interpretability of our equivariant self-supervised vector network, which is used as inspiration to explain the equivariance and masked self-supervised learning in the network. Our network can effectively learn the deep latent features of the original data samples even when trained on incomplete data. Furthermore, we provide visualizations of the features generated by the encoder, offering a more intuitive elucidation of the network's equivariance. On downstream tasks, we extend equivariant networks [27; 28; 29; 30; 31] to both rotation-invariant and rotation-equivariant tasks, and mainly focus on equivariant reconstruction and pose change estimation in rotation equivariant tasks. It can realize not only rotation-invariant classification and segmentation tasks but also rotation-equivariant reconstruction and pose change estimation tasks, which are more complex. At the same time, it has good performance in few-shot classification learning, which reflects the good generalization ability and inclusiveness of the network. In summary, our main contributions are as follows:  
* We propose an equivariant self-supervised vector network for point clouds. The network with a novel structure has better robustness to rotated data and can reconstruct the point cloud under arbitrary rotation in 3D space. * Our network can deal with more rotation-equivariant tasks like pose change estimation and rotation-invariant tasks like classification, segmentation, and few-shot learning. It means our network has high generalization ability. * We perform an interpretability analysis of equivariant and self-supervised learning architectures in the network and visualizations to demonstrate that it is equivariant.
###### Abstract  
A vehicular ad hoc network (VANET) is the major element of the intelligent transportation system (ITS). The purpose of ITS is to increase road safety and manage the movement of vehicles. ITS is known as one of the main components of smart cities. As a result, there are critical challenges such as routing in these networks. Recently, many scholars have worked on this challenge in VANET. They have used machine learning techniques to learn the routing proceeding in the networks adaptively and independently. In this paper, a Q-learning and fuzzy logic-based hierarchical routing protocol (QFHR) is proposed for VANETs. This hierarchical routing technique consists of three main phases: identifying traffic conditions, routing algorithm at the intersection level, and routing algorithm at the road level. In the first phase, each roadside unit (RSU) stores a traffic table, which includes information about the traffic conditions related to four road sections connected to the corresponding intersection. Then, RSUs use a Q-learning-based routing method to discover the best path between different intersections. Finally, vehicles in each road section use a fuzzy logic-based routing technique to choose the foremost relay node. The simulation of QFHR has been executed on the network simulator version 2 (NS2), and its results have been presented in comparison with IRQ. IV2 XQ, QGrid, and GPSR in two scenarios. The first scenario analyzes the result based on the packet sending rate (PSR). In this scenario, QFHR gets better the packet delivery rate by 2.74%, 6.67%, 22.35%, and 29.98% and decreases delay by 16.19%, 22.82%, 34.15%, and 59.51%, and lowers the number of hops by 6.74%, 20.09%, 2.68%, and 12.22% compared to IRQ, IV2 XQ, QGrid, and GPSR, respectively. However, it increases the overhead by approximately 9.36% and 11.34% compared to IRQ and IV2 XQ, respectively. Moreover, the second scenario evaluates the results with regard to the signal transmission radius (STR). In this scenario, QFHR increases PDR by 3.45%, 8%, 23.29%, and 26.17% and decreases delay by 19.86%, 34.26%, 44.09%, and 68.39% and reduces the number of hops by 14.13%, 32.58%, 7.71%, and 21.39% compared to IRQ, IV2 XQ, QGrid, and GPSR, respectively. However, it has higher overhead than IRQ (11.26%) and IV2 XQ (25%).  
Vehicular ad hoc networks (VANETs); intelligent transportation system (ITS); routing; reinforcement learning (RL); fuzzy logic; smart city
## 2 Algorithm Principle  
Since broadband LEO satellites are communication satellites, their clock bias can be eliminated through a full-duplex (FD) communication system based on ICN technology (ICNT) [19]. Therefore, the following algorithms do not consider the error between the LEO satellite and the receiver clock, and the specific method for eliminating the clock bias is not within the scope of our article.
# Siberian Pine and Larch Response to Warming-Drying Climate in the Southern Boundary of Their Range
## 3 Results
### The Satellite Data Analysis  
#### 3.4.1 The Landsat-Based Analysis along the On-Ground Transect  
During the study period (1988-2023), forest loss (Siberian pine mostly) occurred at low elevations (c. \(<\)1300 m), whereas forested area gain occurred at high (\(>\)2000 m) elevations. Meanwhile, the area of shrublands increased at both high and low elevations (Figure 11). The upper boundary of the closed forests shifted uphill from 2124 to 2153 m a.s.l., or by c. +30 m. For the shrublands, those shifts were from 2273 to 2283 m or by c. +10 m. Thus, the shrubs' boundary is located about 130 m ahead of the forests. The estimated rates of the uphill migration are 0.8 m/yr. for forests and 0.3 m/yr. for shrubs.  
Figure 11: Difference (\(\Delta\)) in areas of forests and shrublands (2023 minus 1988). The forested area increased at high (\(>\)2000 m) and decreased at low (\(<\)1300 m) elevations. The area of shrublands increased at both high and low elevations.  
Figure 10: The treeline ecotone. Since 1998, winter (November–March) winds and vapor pressure deficit (VPD) suppressed the growth of larch and Siberian pine, whereas in the previous period (1973–1997), those influences were insignificant. Meanwhile, regeneration (REG) growth of both species was not suppressed. Significant correlations at \(p<\) 0.05, and \(p<\) 0.01 are indicated by two (**), and three (***) asterisks, correspondingly. Study periods are 1972–1997 and 1998–2023.
## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Model for Prediction
#### 4.4.3 MLP  
Among the diverse types of ANN, MLP has gained widespread adoption in the field of predicting building energy consumption. As a pivotal branch of ANN, MLP is renowned for its formidable capability in approximating nonlinear functions, enabling it to simulate the dynamic behaviors of complex systems with high precision, all without relying on intricate explicit model construction [161]. The structure of MLP is illustrated in Figure 13.  
MLP consists of an input layer, hidden layers, and an output layer. The input layer receives input variables. Let \(u(t)\) represent the system's output at time \(t\), and \(\tau 1\), \(\tau 2\), \(\ldots\), \(\tau n\) denote the time constant. The input layer is connected to the hidden layers, and the hidden layers are connected to the output layer through weights. After initializing these weights, the information propagates forward through the neurons upon activation [162]. By adjusting its internal connections and weights, the MLP Neural Network is able to deeply mine the hidden patterns within input data, thereby facilitating precise learning and prediction and providing robust technical support for modeling complex systems and solving related problems.  
To accurately predict the cooling water load required by commercial buildings, [PERSON] et al. [127] developed a machine learning model based on the MLP. This model ingeniously integrates key input variables such as the weather forecast for the following day, the day of the week, and the building occupancy rate. Unlike traditional prediction methods, this model sets its prediction target as a specific range of water volumes, offering facility managers a set of practical and flexible reference options that facilitate decision-making based on actual conditions. Furthermore, [PERSON] et al. [31] leveraged this MLP Neural Network framework to further explore its potential in predicting both cooling and heating loads in buildings. Among the multitude of potential solutions, MLP was selected as the core algorithm for predicting building energy consumption. Experimental data has demonstrated that, compared to other methods, the MLP Neural Network exhibits superior prediction accuracy, ushering in new breakthroughs and possibilities in the field of building energy consumption prediction.
# Review of Automatic Estimation of Emotions in Speech
## 4 Models to Use Measures of Analysis for Speech Emotion Classification
### Embedding  
As described later, ANNs have diverse architectures, consisting of many varied layers of nodes, possibly with feedback, where each network layer usually has input and output vectors with fixed numbers of values. However, speech input data vary greatly in duration. For speech coding and ASR, speech is partitioned into fixed 10-25 ms frames for repeated, periodic processing. For SER and ASV, however, the output is only classification for a given section of speech, but their input has large differences in duration. This requires an embedding, which maps data of variable length to a fixed length set [55; 56].