esa-sceva/satcom-chunk-collection huggingface dataset:
# The Development and Evaluation of a Training Monitoring System for Amateur Rugby Union
## 4 Discussion
### Match-Day Challenges  
The six S&C coaches interviewed spoke about the challenges associated with gathering TL data around matches, resulting in the theme of match-day challenges being highlighted. The consensus of the coaches was that getting pre-session measures before a match was impractical as players have their own routines and coaches do not want to highlight negative thoughts within the players. In addition, the coaches do not see the benefit of having these data as they are not going to act on it before the match. There is, however, evidence to suggest that a pre-match measure of wellness can be used to indicate reactions to external load and hence assist player rotation strategies [51]. This may be particularly valuable in amateur rugby as a congested competition schedule can cause increased strain on players [52]. Coaches also stated they felt uncomfortable asking players to submit their post-match data, particularly after a loss. Similarly, 18% of players found giving data around matches to be very or extremely burdensome compared to only 8% around training sessions (Figure 4). This is similar to the findings of [PERSON] and [PERSON] [53], who suggested low use of post-match sRPE may be due to the effect on sensitivity as a consequence of the psychological states of players and coaches in the competitive environment. Additionally, the match outcome (win/loss/draw) can have a moderate to large effect on the players' RPE [54]. Nonetheless, without post-match sRPE data, an S&C coach cannot accurately calculate weekly load and as a result training strategies and TL prescription based on these data will be limited [37]. Crucially, sRPE has been shown to be temporally robust for up to at least 24 h post-exercise [55] and a practical solution to alleviate the issue may be for practitioners to collect post-match sRPE the following day.
# Investigation of a Solar-Driven Organic Rankine Cycle with Reheating
## 2 Material and Methods
### The Developed Mathematical Formulation
#### 2.2.3 Yearly Analysis and Financial Evaluation  
The yearly electricity production (\(E_{el}\)) is calculated as below:  
\[E_{el}=\int_{year}P_{el}\ dt \tag{28}\]  
The yearly solar irradiation input (\(E_{sol}\)) is calculated as below:  
\[E_{sol}=\int_{year}Q_{sol}\ dt \tag{29}\]The yearly system efficiency (\(\eta_{sys,y}\)) is calculated as below:  
\[\eta_{sys,y}=\frac{E_{el}}{E_{sol}} \tag{30}\]  
The capital cost of the investment (\(C_{0}\)) is given below:  
\[C_{0}=K_{col}\cdot A_{col}+K_{tank}\cdot V+K_{ORC}\cdot P_{el,nom} \tag{31}\]  
The yearly cash flow of the system (\(CF\)) is given as follows:  
\[CF=K_{el}\cdot E_{el}-(O\&M) \tag{32}\]  
The yearly operation and maintenance costs (O\(\&M\)) are chosen to be 0.5% of the investment cost.  
The definition of the simple payback period (\(SPP\)) is given as follows:  
\[SPP=\frac{C_{0}}{CF} \tag{33}\]  
The definition of the net present value of the investment (\(NPV\)) is given as follows:  
\[NPV=-C_{0}+CF\cdot\sum_{i=1}^{N}\frac{1}{(1+d)^{i}} \tag{34}\]  
where (\(N\)) is the project lifetime and (\(d\)) the discount factor.
## 4 Analysis and Results
### Informal Security Analysis
#### 4.1.1 Resistance to Impersonation Attacks  
Impersonation attacks are a type of forgery where an adversary pretends to be or tries to impersonate a legitimate entity by computing legitimate messages leading to the exposure of an entity's identity. In the proposed scheme and during the registration phase, the identities of [PERSON] and B are shared to the AM in a secure channel; moreover, the AM generates alias identities (AID) for [PERSON] and B by applying XOR operation and hash function on the real identity and the secret number (SN), which is only known to the AM. Since the hash function is an irreversible operation, the adversary is unable to extract the real identity or the secret number (SN) from the alias identity. Let us assume that the adversary obtained the alias identity of [PERSON] (AID\({}_{\text{C}}\)) and communicates with [PERSON]. In this case, the adversary cannot generate the ciphers C1, C2, C3, and Hash2 correctly because it does not know the values used to generate these ciphers, such as SKAM_C and H (MID\({}_{\text{C}}\)). Figure 9 explains this scenario of the attack.
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 3 Operator's Dilemma in the Unlicensed Spectrum
### Infinite Repeated Game in the Unlicensed Spectrum
#### 3.4.4 Performance Analysis for Multi-RAT Systems  
Based on a potential collusion scenario and a harmonious coexistence scenario, we now investigate the performance of both LTE and WiFi networks to point out the importance of mandatory LBT. In performance analysis, we consider payoffs as the average channel utilization in the unlicensed spectrum [19]. LTE operators are assumed to utilize the 3 GPP category 4 LBT scheme to achieve fair coexistence with WiFi networks because its sharing mechanism operates as WiFi-like as possible among existing LBT schemes such as ETSI, ECCA, and ICCA [17]. Both LTE and WiFi have two different traffic models--File Transfer Protocol (FTP) traffic and Voice over Internet Protocol (VoIP) traffic--which indicate low load and high load, respectively [20]. We also evaluate the average channel utilization of both LTE and WiFi networks as the number of LTE operators increases.  
Figure 6 represents the average channel utilization when LTE and WiFi networks have low load. If LBT is not mandatory, LTE operators possibly collude with each other and then deceive two WiFi networks into thinking that the unlicensed spectrum is always busy. As shown in Figure 6a, as the number of LTE operators increases, they alternatively exploit the unlicensed spectrum in their own selfish manner, e.g., a round robin between LTE operators. In this case, WiFi networks cannot use the unlicensed spectrum at all. In Figure 6b, once LBT is mandated to induce reciprocal behavior from LTE operators, the performance of WiFi networks significantly improves as much as that of LTE operators. Therefore, a mandatory LBT enables both LTE operators and the WiFi network to achieve harmonious coexistence in the unlicensed spectrum.  
Figure 5: The results of the unlicensed spectrum game: LTE operators have strategic plans for the unlicensed spectrum.  
In a similar way, Figure 7 presents average channel utilization with high load. As the number of LTE operators increases, although LTE operators exploit the unlicensed spectrum based on their own cooperation, fair coexistence with WiFi networks hardly happens without a mandatory LBT in Figure 7. As shown in Figure 7, a mandatory LBT of LTE operators is essential for mutual benefits towards harmonious coexistence between LTE operators and WiFi networks.
###### Abstract  
In this paper, we experimentally investigate the turbulence mitigation methods in free-space optical communication systems based on orbital angular momentum (OAM) multiplexing. To study the outdoor atmospheric turbulence environment, we use an indoor turbulence emulator. Adaptive optics, channel coding, Huffman coding combined with low-density parity-check (LDPC) coding, and spatial offset are used for turbulence mitigation; while OAM multiplexing and wavelength-division multiplexing (WDM) are applied to boost channel capacity.  
free-space optical communications; orbital angular momentum; turbulence mitigation +
Footnote †: journal: Applied Physics Letters
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 6 Resource Scheduling
### Scheduling Based on RL and DRL
[95] proposed a scheduling approach based on DQN and aimed to guarantee low-latency requirements and maximize data transmission downlink time in 5G-RAN when spectrum resources are insufficient. The DQN agent used a DNN formed by three and two neurons in the input and output (using ReLU) layers and three hidden layers. The approach considered the space of states as the total spectrum requirement, low-latency data delay constraints, and available spectrum. The space of actions was defined to assign 5G spectral resources during time intervals. Ref. [96] presented a DQN-based video stream scheduling solution to maximize the long-term QoE satisfaction of drones running on a 5G network. The DNN agent activated the output layer of DNN using the Softmax function and modeled the space of states regarding packet arrival rate, packet service rate, service slot duration, startup delay, traffic intensity, and packet arrival probability. The space of actions was defined to reconfigure the packet prefetching strategy and the startup delay.
# BFRVSR: A Bidirectional Frame Recurrent Method for Video Super-Resolution
## 3 Methods
### Bidirectional Frame Recurrent Video Super-Resolution (BFRVSR)
#### 3.1.1 Flow Estimation  
The network structure of FlowNet is shown in the Figure 4. First, the network uses the optical flow estimation module to estimate the low-resolution image of the previous frame \(I_{t-1}^{LR}\) and the low-resolution image of the current frame \(I_{t}^{LR}\) to obtain a low-resolution motion vector diagram \(F_{t\to t-1}^{LR}\). Our method of FlowNet is similar to the method in FRVSR [6].  
\[F_{t\to t-1}^{LR}=FlowNet\Big{(}I_{t-1}^{LR}\oplus I_{t}^{LR}\Big{)}\in[-1,1]^{ H\times W\times 2} \tag{1}\]  
\(F_{t\to t-1}^{LR}\) shows the position information from the current image to the previous frame.  
Figure 4: Overview of FlowNet. \(2\times\) represents the linear superposition of two identical modules.  
Figure 3: Overview of training network framework (**left**). Trainable modules include FlowNet and SuperNet. Upsampling uses bilinear interpolation. Loss function used during training (**right**).
## 1 Introduction
The robustness of the panel detection procedure is also a major contribution that distinguishes this work from previous methods. A novel method that is based on image reprojection and function minimization is developed to detect the panels from the images. The proposed approach is based on a combination of adaptive thresholding and the correction of perspective distortion by considering the panel corner. Minimizing the distance from the reprojection on multiple adjacent panels ensures correct panel detection, regardless of orientation or camera position. This robust approach is verified in extensive tests. The rest of this work is organized, as follows. In Section 2, the main steps for image undistortion are summarized. Image undistortion is a key point when pre-processing the thermal images, because it can give a better representation of all of the elements captured by the thermal camera without optical aberrations. Aerial IRT considerations for PV applications are summarized in Section 3. This Section is complemented with Section 4, where the acquisition procedure to collect all of the thermal images is carried out. The automated image processing procedure is explained in Section 5, which consists of several algorithms to properly cope with the processing of the thermal images. Section 6 summarizes the main results when evaluating the proposed algorithm with the database. Finally, the conclusions are given in Section 7.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 4 Analysis of Evacuation Path Planning Model Experiment
### Experimental Analysis of Single-Robot Path Planning  
In order to verify the effectiveness of the Bloch Spherical Quantum Genetic Algorithm, the model is solved by simulation. The simulations of BQGA, ACA and GA were conducted for comparison tests, respectively. In order to reflect the fairness of the comparison results, the same population size and evolutionary generation were used for three algorithms. The parameters were set as follows: evolutionary generation \(maxgen=100\), number of populations \(popsize=100\), number of quantum bits \(n=2\), and 19 evacuation nodes. Then the performances of the three algorithms are verified in Figures 9-11.  
Figure 8: (**a**) Direction of movement; (**b**) demonstration of obstacle avoidance.  
Figure 7: (**a**) Plane segmentation of three-dimensional space; (**b**) two-dimensional display of three-dimensional space division.
# Implementation of MS Circle Map in Digital Image Encryption
## 3 Results and Discussion  
The implementation of Algorithm 1 for image encryption and Algorithm 2 for image decryption was carried out using the Python 3.13.3 programming language on a computer with the following specifications: i5-7200U Intel(R) Core (TM), @ 2.50 GHz 2.70 GHz CPU, 8.00 GB Memory (RAM), Windows 10 Home Operating System, and 64 bit Single Language. The results presented and analyzed in this section are examples of results obtained using one combination value of the five key parameters: \(x_{0}=0.9\), \(\lambda=2.1\), \(\Omega=0.5\), K = 1000, and r = 3.8. These key parameter values must be the same during the encryption and decryption processes. Furthermore, to test the resilience of encrypted and decrypted images against attacks, it is necessary to conduct analyses such as initial value sensitivity, uniformity, correlation, differential analysis, key space, image quality, and encryption-decryption process time.
## 4 Security Analysis of the Proposed Protocol
### Heuristic Analysis
#### 4.2.1 Mutual Authentication  
The proposed scheme can attain mutual authentication since \(U_{i}\) and \(GWN\) authenticate each other bidirectionally by checking if \(B_{2}^{*}=B_{2}\) and \(B_{12}^{*}=B_{12}\), respectively. Similarly, with \(MS_{j}\) checking whether \(B_{5}^{*}=B_{5}\) and \(GWN\) verifying that \(B_{8}^{*}=B_{8}\), \(GWN\) and \(MS_{j}\) can authenticate each other successfully.
###### Abstract  
Background: This study targets the development of an explainable deep learning methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI images. Deep learning is currently judged as non-transparent due to the model's complex non-linear structure, and thus, it is considered a -black box-, making it hard to gain a comprehensive understanding of its internal processes and explain its behavior. Existing explainable artificial intelligence tools can provide insights into the internal functionality of deep learning and especially of convolutional neural networks, allowing transparency and interpretation. Methods: This study seeks to address the identification of patients' CAD status (infarction, ischemia or normal) by developing an explainable deep learning pipeline in the form of a handcrafted convolutional neural network. The proposed RGB-CNN model utilizes various pre- and post-processing tools and deploys a state-of-the-art explainability tool to produce more interpretable predictions in decision making. The dataset includes cases from 625 patients as stress and rest representations, comprising 127 infarction, 241 ischemic, and 257 normal cases previously classified by a doctor. The imaging dataset was split into 20% for testing and 80% for training, of which 15% was further used for validation purposes. Data augmentation was employed to increase generalization. The efficacy of the well-known Grad-CAM-based color visualization approach was also evaluated in this research to provide predictions with interpretability in the detection of infarction and ischemia in SPECT MPI images, counterbalancing any lack of rationale in the results extracted by the CNNs. Results: The proposed model achieved 93.3% accuracy and 94.58% AUC, demonstrating efficient performance and stability. Grad-CAM has shown to be a valuable tool for explaining CNN-based judgments in SPECT MPI images, allowing nuclear physicians to make fast and confident judgments by using the visual explanations offered. Conclusions: Prediction results indicate a robust and efficient model based on the deep learning methodology which is proposed for CAD diagnosis in nuclear medicine.  
deep learning; convolutional neural network; explainable artificial intelligence; Grad-CAM +
Footnote †: journal: _Sciences_
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 3 Modeling of UWA Channel Problems and Challenges
### Channel Capacity  
The theoretical upper bound referred to as the channel capacity \(C\) according to the Shannon theorem is transmitted through an undistorted channel considering the white Gaussian noise is anticipated in [153],  
\[C=B\log_{2}(1+\frac{S}{N}) \tag{40}\]  
where, \(B\) represents the channel bandwidth measured in Hz and \(\frac{S}{N}\) is the channel SNR. Equation (40) is the basic Shannon relationship and is extended for the scenarios where frequency has a direct relation with noise and thus results in [153]:  
\[C=\int_{B}\log_{2}\biggl{(}1+\frac{S(f)}{N(f)}\biggr{)}df \tag{41}\]  
The total capacity for the time-invariant channel is calculated by adding the individual capacities of the sub-bands over a certain time interval considering the Gaussian noise. Each sub-band is centered on the bandwidth, i.e., transmission frequency with a width of \(\Delta f\). Equation (41) is further extended to achieve the channel capacity over a distance \(l\) along with the transmission power \(P(l)\) and is written as in [153]:  
\[C=\int_{B}\log_{2}\biggl{(}\frac{P(l)}{A(l,f)N(f)B(l)}\biggr{)}df \tag{42}\]  
The fundamental absorption coefficient model requires a dependence of the capacity on acidity, salinity, temperature, and depth.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### Proposed Data Packet Format
#### 4.3.3 Timer  
The timer also plays a vital role during the dissemination of the data packet in push-based data forwarding. The timer is used to forward the data packet for a specific amount of time. If the timer reaches the maximum time, the data packet will be dropped and forwarding will be stopped by the intermediate vehicles.
# Real-Time Vision through Haze Based on Polarization Imaging
## 4 Target Enhancement Algorithms Based on Polarization Information  
### Polarization Image Registration Algorithm Based on SURF  
The real-time optical sensing and detection system described in this study used four cameras aligned parallel to the optical axis, which can cause misalignment and rotation of pixel units because of different shooting positions and directions of the cameras. Because the solutions of intensity, polarization difference, and polarization degree images are based on pixel units, misalignment of pixel units between polarized azimuth images can result in false edges or blurring.  
In our study, by the adoption of the SURF-based real-time image registration algorithm for subpixel-precision registration of the acquired linear polarization degree images, the pixels between the polarization images were aligned via the procedure shown in Figure 4, which constituted three steps: feature detection and extraction, feature vector matching, and spatial transformation model parameter estimation [17; 18]. The detailed algorithm procedure is listed below.  
Step 1: Use the high-precision four-dimensional calibration platform to pre-calibrate the overlapping imaging areas of the adjacent cameras.  
Step 2: Use fast Hessian detectors to extract the feature points of the reference image and the image to be registered within the overlapping area, and generate SURF description vectors.  
Step 3: Use the fast approximate nearest neighbors (FANN) algorithm to obtain the initial matching point pair, and sort the Euclidean distance of the feature vectors of the pair.  
Figure 3: Four-axis calibration platform.  
Step 4: Use the progressive sample consensus (PROSAC) algorithm to perform spatial transformation model parameter estimation, which derives the geometric spatial transformation relationship between the reference image and the image to be registered.  
This algorithm is invariant to changes in image size, rotation, and illumination. The registration speed is within milliseconds, and the accuracy is at the level of 0.1 pixel.
# Direct Solar Thermal Water-Splitting Using Iron and Iron Oxides at High Temperatures: A Review
## 1 Introduction
The global size of the market for iron oxide is estimated to be valued at around USD 2629.4 million in 2023, with projections reaching USD 3.6 billion by the end of 2030 [9]. The thermocatalytic material used in the TSP is ceria [3,4]. This technology today yields low production of solar hydrogen due to technological problems [8]. Regarding production costs, it is estimated that hydrogen from solar photovoltaic energy and wind energy is between 4.0-9.0 USD/Kg H\({}_{2}\) and could be below 1.5 USD/Kg H\({}_{2}\), with the costs of photovoltaic solar electricity at 14 USD/MWh in 2030 [8]. Regarding thermal water-splitting using iron and iron oxides at high temperatures, the number of articles published has shown significant growth between the years 2007 and 2023, as shown in Figure 1, which illustrates the numbers of publications that contain the specific words, \"thermal water splitting using iron and oxides at high temperatures\" in Web of Science. This review is essential, as it provides the fundamental theoretical information necessary to understand the challenges associated with using pure iron or iron oxides as thermocatalytic materials in thermochemical cycles for hydrogen production. It is crucial to comprehend the mechanisms of these materials' reactions in the presence of water vapour from a thermodynamic perspective, identify the heterogeneous reactions that occur on non-catalytic surfaces through electrochemistry, and consider the oxidation mechanisms associated with reactions at high temperatures with steam.
## 5 Results and Analysis
### Simulation Results  
#### 5.2.1 Parameter Analysis  
Firstly, before comparing and validating the performance of the IF-DDPG algorithm, we should set two important deep neural network hyperparameters in the algorithm: the learning rate \(\beta\) for the policy network and the learning rate \(\theta\) for the value network. The experimental environment consists of 4 edge servers and 4 terminal devices, where the quantity of available CPU cores for terminal devices is \(K=4\). Table 2 shows 5 learning rate allocation schemes for the experimental settings, which are Scheme 1: \(\beta=0.01\) & \(\theta=0.02\), Scheme 2: \(\beta=0.001\) & \(\theta=0.002\), Scheme 3: \(\beta=0.0001\) & \(\theta=0.0002\), Scheme 4: \(\beta=0.00001\) & \(\theta=0.00002\), and Scheme 5: \(\beta=0.000001\) & \(\theta=0.000002\). Figures 4 and 5 show the convergence performance of 5 learning rate allocation schemes for the IF-DDPG algorithm in terms of reward values and total system energy consumption after 1500 iterations. From the figures, it can be observed that when Scheme 1 is employed, the IF-DDPG algorithm converges. However, it is observed that in the middle and later stages, the curve tends to flatten and converge to a local optimal solution. The reason for this is that a higher learning rate leads to larger update steps for the deep neural network. It is more prone to local convergence. When Scheme 5 is employed, the curve oscillates repeatedly and changes slowly, indicating that the IF-DDPG algorithm fails to converge currently.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Area & \(100\times 100\) m\({}^{2}\) & \(I\) & 2 Mbits \\ \hline \(B\) & 1 MHz & \(I^{a}\) & [60–80] Kbits \\ \hline \(\alpha_{0}\) & \(-50\) dB & \(f^{\min}\) & 0.1 GHz \\ \hline \(\delta^{2}\) & \(-100\) dBm & \(f^{\max}\) & 0.2 GHz \\ \hline \(w^{\min}\) & 1 & \(s\) & 1000 cycles/bit \\ \hline \(w^{\max}\) & 10 & \(\kappa_{u}\) & \(10^{-27}\) \\ \hline \(p_{\min}\) & 1 w & \(\gamma\) & 0.001 \\ \hline \(p_{\max}\) & 10 w & \(\tau\) & 0.01 \\ \hline \(T_{t}^{\max}\) & 100 ms & \(C\) & 100 \\ \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.