esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present context of cellular networks, device-to-device (D2D) communication stands out as a promising technique to enhance the spectrum efficiency. Simultaneously, RIS have gained considerable attention due to their ability to enhance the quality of dynamic wireless networks by maximizing the spectrum efficiency without increasing the power consumption. However, prevalent centralized D2D transmission schemes require global information, leading to a significant signaling overhead. Conversely, existing distributed schemes, while avoiding the need for global information, often demand frequent information exchange among D2D users, falling short of achieving global optimization. This paper introduces a framework comprising an outer loop and inner loop. In the outer loop, decentralized dynamic resource allocation optimization has been developed for self-organizing network communication aided by RIS. This is accomplished through the application of a multi-player multi-armed bandit approach, completing strategies for RIS and resource block selection. Notably, these strategies operate without requiring signal interaction during execution. Meanwhile, in the inner loop, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm has been adopted for cooperative learning with neural networks (NNs) to obtain optimal transmit power control and RIS phase shift control for multiple users, with a specified RIS and resource block selection policy from the outer loop. Through the utilization of optimization theory, distributed optimal resource allocation can be attained as the outer and inner reinforcement learning algorithms converge over time. Finally, a series of numerical simulations are presented to validate and illustrate the effectiveness of the proposed scheme.  
reconfigurable intelligent surfaces; ad-hoc network; multi-player multi-armed bandit; TD3; RIS selection; resource block selection; RIS phase shift; energy efficiency; reinforcement learning
# A Review of Distribution Grid Consumption Strategies Containing Distributed Photovoltaics
## 2 Principle of Photovoltaic Power Generation and Consumption Mode
### Photovoltaic Power Generation Systems and Their Consumption Methods  
#### 2.2.1 Introduction to Photovoltaic Power Generation System  
The importance of photovoltaic (PV) power systems in the renewable energy sector cannot be overstated. It is a zero-emission way of obtaining energy, which helps to reduce dependence on fossil fuels, lower greenhouse gas emissions, and combat climate change. In practice, PV power generation systems are not only widely used to generate power on the rooftops of residential and commercial buildings but are also utilized in the construction of large-scale PV power plants to provide large-scale clean energy for the public power grid. Photovoltaic power generation systems are mainly composed of photovoltaic modules, controllers, inverters, batteries, and other accessories (grid-connected does not require batteries). According to whether it depends on the public grid or not, it is divided into off-grid and grid-connected, in which the off-grid system is operated independently and does not need to depend on the grid [7]. The standalone system is shown as A in Figure 6, and the grid-connected system is shown as B in Figure 6.  
Figure 4: P–U characteristic curve with illumination.  
Figure 5: P–U characteristic curve with temperature.
## 4 Blackout Analysis
### Preconditions
#### 4.1.7 Lack of N-k Operating Reliability Criteria (PREC_7)  
The N-k security criteria have been used for many years to assess power system reliability. The power system is secure (N-k security) if a cascade is not started by the loss of any group of k elements at the same time. The majority of the TSOs are required to have at least N-1 security [77]. However, there are situations when the system cannot be kept within the N-1 boundaries due to unforeseen circumstances or faults. This criterion was not fulfilled in the case of the blackout on 31 March 2015 in Turkey. It is interesting to note that in Turkey, both Eastern and Western regions were independently N-1-secure, not N-1-compliant, emphasizing the need for close collaboration between operators of interconnected networks.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.1 Automatic Identification System (AIS)  
The AIS assists the OOW in navigating safely. It transmits three types of data (i.e., static, dynamic, and voyage-related) and safety-related messages to other vessels and stations on the shore (e.g., VTS stations, AIS base stations) [47]. According to the VesselFinder.com site, 612,974 AIS-equipped vessels exist worldwide as of 16 December 2021 [49]. The AIS essentially performs the following four functions [47]:  
* identifying ships;
* assisting in target tracking;
* exchanging information;
* providing additional information to assist situation awareness.  
Figure 1: INS composition.  
In addition to the functions listed above, the AIS is also used for Search and Rescue (SAR) operations by receiving messages from search and rescue locating devices [47]. The AIS Search and Rescue Transmitter (AIS-SART) is used for transmitting messages involving position, static, and safety information of a unit in distress [50]. Further, the AIS Emergency Position-Indicating Radio Beacon (AIS-EPIRB) and the AIS Man Overboard (AIS-MOB) function are also used for search and rescue purposes [51].
## 4 Global Path Planning Based on Network Analysis Method
### General Road Processing  
When the road shows its edge feature, GPS data of the start point and the end point are gained. But, more closely, GPS points are needed for an accurate navigation. So, some insertion points between the start point and the end point are added in this edge. Due to the fact that all the edge features are mapping to the straight line, all the insertion points are on this edge. Then, we get a series of detailed GPS points for the description this path.
## 2 Optimization Model for Reference Trajectory in ARP
### Feasibility Constraints Judgment for ARP  
ARP is usually required to meet the following conditions: the location of the parking garage must be known, the boundary lines and corner points of the garage should be distinctly discernible, and there are no obstacles inside the parking garage [20]. In addition, it is necessary to meet the feasibility constraints of the parking garage: the central point \(P_{1}\) of the parking initiating region should be in the permitted parking region \(\Omega_{C,1}\). The line segment connecting the central point \(P_{1}\) and the bottom midpoint \(P_{b1}\) of the initiating region is \(L_{b}\), and the angle between the line \(L_{b}\) and the line \(L_{d}\) of the garage bottom is defined as the initiating tilt angle \(\Delta\angle_{B}\), with its absolute value being less than the threshold value \(\Delta\angle_{B\max}\). The constraint equation that determines the feasibility of initiating the parking process in the garage is outlined as follows:  
\[\begin{array}{l}\forall P_{1}\in\Omega_{C,1}\\ |\angle_{B}|=|\angle(T(P_{1},P_{b1}),L_{d})|<\Delta\angle_{B\max}\end{array} \tag{1}\]  
According to the initiating feasibility limitations of the parking garage shown in Formula (1), the initiating tilt angle should be small enough.
# Predictive Migration Performance in Vehicular Edge Computing Environments
## 2 Related Work
They compare the probabilistic approach to more enhanced approaches, including k-nearest neighbours and decision trees, in the cases when prediction is based on global knowledge and per-user-only knowledge. Their results show that the handover events can be predicted with an accuracy between 57% and 90% depending on the employed technique. These results raise the question of how this accuracy affects the performances of the services when handover prediction mechanisms are employed to do proactive migration. Moreover, another important factor that needs to be considered is that the performances of most of the discussed schemes and analyses are presented using a simplified MEC scenario consisting of less than 30 MEC servers in a simple setting, which is usually presented as a straight 1 km two-way street with base stations positioned at regular intervals. The main goal of this paper is to analyse how various degrees of prediction inaccuracy affect the MEC service delay by investigating proactive service migration performance in a large-scale scenario using non-uniformly distributed base stations scattered in a realistic urban vehicular traffic setting. Firstly, in the next section, we introduce our proactive approach to MEC resource management. Secondly, we present the use case implemented in Section 5 to analyse a large-scale deployment of a vehicular network in a realistic urban scenario connected via 5G and supported with a number of distributed MEC servers. The use case for the analysis is a large-scale deployment of a vehicular network in a realistic urban scenario connected via 5G and supported with a number of distributed MEC servers. The simulation parameters are chosen to be aligned with the identified use cases and examples for autonomous driving with specific service-level requirements provided by the 5G Automotive Association (5 GAA) [17]. Our simulation results show that 20% of the error in the mobility prediction can introduce an additional delay of over 80% to services, which needs to be carefully considered and taken into account for ultra-low-latency, highly reliable services, such as autonomous driving.
## 2 Methodology
### Classifier for Distinguishing Forged Signatures  
The proposed signature generation model uses original signature images as training data, producing signatures that closely resemble the originals. However, in real-world scenarios, forged signatures present a significant challenge. Although forgeries can closely mimic authentic signatures, subtle differences, such as the pen's pressure, spacing, and layout, can be used to differentiate them. To ensure our model generates signatures that closely replicate even these subtle characteristics, we trained a classifier to distinguish between original and forged signature images. By integrating the classifier's output into the loss function, we aimed to enhance the similarity between the generated and original signatures from a classification perspective.  
Figure 3: Detailed structure of the blocks used in the noise prediction module.  
Figure 2: Overall structure of the noise prediction module used in the generation model.  
The classifier takes an image pair as input, which comprises either two original images or an original and a forged image, and classifies them accordingly. During this process, the signature image undergoes a wavelet transform to isolate the high-frequency components in the vertical, horizontal, and diagonal directions. After excluding the low-frequency band, the high-frequency components are combined and converted back into an image, which is then fed into the classifier. The original and forged signature images from the CEDAR dataset exhibit different background brightness levels. If used as training data without preprocessing, this background difference could impact classification results.  
While methods such as thresholding [28] or optical flow algorithms [29] can be used to reduce background variations, our proposed method employs a preprocessing step using the wavelet transform [30] to emphasize the structural features of the signature. This is because thresholding or optical flow algorithms can blur the endpoints or boundaries of the signature, resulting in the loss of detailed information and negatively affecting classification performance. By applying the wavelet transform to the signature image, we extract high-frequency components \(cH\), \(cV\), and \(cD\) in the vertical, horizontal, and diagonal directions, respectively, and create a normalized combined high-coefficient \(CHC_{norm}\) image. The process of creating \(CHC_{norm}\) is determined via the following equation:  
\[CHC=\sqrt{cH^{2},cV^{2},cD^{2}} \tag{13}\]  
\[CHC_{norm}=\frac{CHC-\min(CHC)}{\max(CHC)-\min(CHC)} \tag{14}\]  
The \(CHC_{norm}\) images generated for the original and forged signatures are passed to the classifier model as pairs of original signatures and pairs of original and forged signatures. The classifier determines whether the input image pairs are original signature pairs or not. In the convolution and dense layers, the activation function used is LeakyReLU, while the final layer employs a sigmoid function (Figure 4).
# Multi-Module Combination for Underwater Image Enhancement
## 4 Experiment
### Ablation Study  
We conducted ablation experiments to systematically validate the role of each module in the proposed methodology. We progressively removed or replaced key components of the model using the control variable method, including the following: (a) white balance and visibility restoration only (WBVR), (b) white balance and contrast enhancement only (WBCE), (c) white balance only (WB), (d) contrast enhancement only (CE), (e) visibility restoration only (VR), and (f) the complete method (UMMC). Figure 8 presents a visual comparison of these approaches. The experimental results indicate that CE significantly improves the brightness of the image, while WB effectively reduces color deviation; however, it also contributes to the overall darkening of the image. Both VR and WBVR tend to produce oversaturation, which can lead to a loss of detail. Additionally, under uneven lighting conditions, the visibility of dark details is enhanced, but the lighter areas become overexposed, resulting in color distortion. Black artifacts may appear at the junctions of visible content and darker regions, highlighting a limitation of the visibility restoration module (VR) when applied to non-uniformly illuminated images. WBVR corrects color bias to a certain extent compared to VR, underscoring the importance of WB. After fusing with contrast-enhanced images, UMMC compensates for the deficiencies of VR, further demonstrating the superiority of the UMMC full model. In contrast, WBCE enhances image  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Left Match Point** & **Right Match Point** \\ \hline Raw & 1110 & 1128 \\ UW-AAE & 2367 & 2341 \\ EUICCCLF & 4149 & 4216 \\ MLLE & 3132 & 3104 \\ PCDE & 4031 & 4054 \\ UWCNN & 258 & 233 \\ UIESS & 1637 & 1599 \\ BRUIE & 4260 & 4260 \\ UMMC & 3562 & 3474 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The image feature point matching results for each algorithm are presented. The best result is in red, whereas the second-best result is in green.  
Figure 7: Comparisons of keypoint matching.  
details without equally compensating for color accuracy. UMMC effectively integrates WBVR and WBCE, resulting in images characterized by uniform colors and rich details. The results in Table 4 demonstrate the effectiveness of UMMC.
## 3 Results and Discussion
### Modeling Average Airflow Velocity Measured by the Grid Anemometers  
In Figure 4, the values of the average airflow velocities in the experiment (represented by the black curve) and simulation (represented by the red curve) are shown. They were calculated as an arithmetic mean of the airflow velocities measured by the grid anemometers (AG1-5) for all the three ventilation modes measured at the considered time intervals by the grid anemometers. Relatively small differences between the experimental and simulation data can be observed. In Figures 5 and 6, absolute and relative differences between the average airflow velocities obtained by the experiment and the simulation are illustrated for all the three ventilation modes. The maximal values of the differences are shown in Table 3. The results indicate that the average velocities measured by the grid anemometers in the simulation differ from the experimental data by values lower (or in exceptional cases by relatively very close values) than the measurement accuracy of the grid anemometers (0.3 m/s). This evaluation indicates a relatively very good accuracy of average airflow velocity modeling using grid anemometers compared to the measurement accuracy of the grid anemometers.
# Imaging Top of Volcanic Mounds Using Seismic Time- and Depth-Domain Data Processing
## 3 Results of Seismic Data Processing  
Seismic data processing consists of time- and depth-domain stages; the latter includes MVA. The purpose of time-domain processing is to produce suitable subsurface images to carry out successful geological interpretation of CO\({}_{2}\) storage feasibility. Depth-domain processing aims to accurately refine the depth of the subsurface, which is useful in the drilling process and in the computation of storage volume. The following sections explain how each of the two processing steps is executed, including the input parameters and results of filter application.
## 3 Materials and Methods
### Thermal Parameters Defining the Performance of the Building Envelope  
The thermal performance of the building envelope is determined by a set of parameters that describe both steady-state and dynamic heat transfer phenomena. These indicators are essential for evaluating the energy efficiency, thermal comfort, and moisture behavior of opaque building components. The following parameters were assessed for the case study building components.  
Thermal transmittance, commonly referred to as the U-value [W/m\({}^{2}\)K], quantifies the steady-state rate of heat transfer through a building element per unit area and per unit temperature difference between the internal and external environments. It is calculated as the reciprocal of the total thermal resistance of the element, including both surface resistances. The total thermal resistance is the sum of the resistances of individual material layers, each computed as the ratio between layer thickness and its thermal conductivity, plus the internal and external surface resistances.  
The surface mass represents the mass per unit area of a multilayered building component. It is calculated by summing the products of the density and thickness of each individual layer, expressed as kg/m\({}^{2}\). This parameter is particularly significant for evaluating the thermal inertia of opaque envelope elements, as greater surface mass contributes to improved thermal lag and attenuation of daily temperature fluctuations.  
Periodic thermal transmittance, denoted as YIE, characterizes the dynamic behavior of a building envelope component under sinusoidal temperature variations, typical of day-night cycles. It is defined as the amplitude of the heat flow rate entering the internal environment per unit temperature variation on the external surface, under periodic conditions.  
The attenuation factor, indicated as f, measures the capacity of an opaque component to reduce the amplitude of thermal waves between the exterior and interior surfaces. It is defined as the ratio between the periodic thermal transmittance (YIE) and the steady-state transmittance (U-value). This dimensionless coefficient ranges from 0 to 1, where lower values denote a better ability to attenuate heat flow fluctuations.  
The thermal wave phase shift, or time lag (\(\Delta\tau\)), expressed in hours (h), quantifies the delay between the peak temperature on the external surface of a building element and the corresponding peak on the internal surface. It is obtained from the argument (phase angle) of the complex transfer function used in dynamic thermal analyses and is expressed in hours.  
Permeance expresses the capacity of a building component to transmit water vapor through its thickness under a given vapor pressure difference. It is calculated as the inverse of the total vapor resistance [kg/m\({}^{2}\)sPa].
## 5 Uncertainty
### Temperature Compensation during Operation  
Localization is performed by TDoA and therefore Equation (12) applies. It can be seen that there is no systematic error component with the exception of the temperature. For the best approach it is suggested that at least on receiver, already used within the calibration, is kept for the full system life time. The design of _LOSNUS_ actually favors such an approach as adding more receivers does not in any case reduce measurement frequency. In addition, the cost increase is minimal.  
If such a receiver R exists and we have three time differences the receiver can be located by the transmitters using a multilateration equation. Such a function requires four transmitter positions assumed without loss of generality as \(T_{1},T_{2},T_{3},T_{4}\), three time differences \(t_{12},t_{13},t_{14}\) and the temperature T for calculating speed of sound.  
\[R^{\prime}=multilat(T_{1},T_{2},T_{3},T_{4},t_{12},t_{13},t_{14},T) \tag{27}\]  
Assume for now that there is no error component in the time differences. What we know is that the receiver should still be at the same position obtained during calibration. If this is not the case then T must be wrong. Therefore, the correct temperature T is the one which minimizes  
\[c=\|R-R^{\prime}\|=\|R-multilat(T_{1},T_{2},T_{3},T_{4},t_{12},t_{13},t_{14},T)\| \tag{28}\]  
\[T=arg\left(min(\|R-R^{\prime}\|)\right) \tag{29}\]