esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 4 ANN-Based Placement Scheme Selection  
The main difference between the two algorithms mentioned before, is that the virtual centroid point \(p_{1}^{c}\in A\) given from the k-means where the UFBS will be placed, will be equidistant from all GMTs. Conversely, the medoid point \(p_{1}^{m}\in A\) given from the k-medoids will be a GMT location within the region of interest that will minimize the objective function (see (29)). Consequently, if the GMTs are spread equally in the area of interest, the \(p_{1}^{c}\) point provided by k means will improve the channel quality of GMTs, since the distances of the GMTs from the UFBS will be almost identical and the LoS probability will be significantly high. On the contrary, if a GMT is remote (outlier), the k-means algorithm will try to find the point \(p_{1}^{c}\) equidistant from every GMT, detaching it quite a bit from the majority of GMTs and thus increasing the GMTs' propagation losses. In contrast, the k-medoids through the proposed \(p_{1}^{m}\) point reduce the point-to-group-centroid distances, achieving higher A2G channel gains and increasing the QoS of the overall system.  
To better highlight the advantages of each algorithm, let us consider a toy network with GMTs located in the 2D plane as depicted in Figure 2. Focusing on Figure 2 on the right, the group of GMTs in the right form a cluster, while the rightmost GMT is an outlier. The \(p_{1}^{c}\in A\) point proposed by the k-means is greatly influenced by the outlier and thus cannot represent the correct cluster center. In contrast, the medoid point \(p_{1}^{m}\in A\) provided by k-medoids is robust to the outlier and correctly represents the cluster center. On the contrary, regarding Figure 2 on the left, we notice that there is no remote GMT, and everyone is close to each other, forming a cluster of GMTs. Consequently, the \(p_{1}^{c}\in A\) proposed from the k-means is equidistant from all GMTs, thus increasing the channel gain compared to the \(p_{1}^{m}\in A\), which is not equidistant from all GMTs offered from the k-medoids algorithm.  
Motivated by this observation, the PSS can be regarded as a supervised classification problem, where it can be approximated through the utilization of a fully connected artificial  
Figure 2: Comparisons between the k-means and k-medoids regarding the UFBS placement procedure.  
neural network (ANN) to enhance the overall system QoS. Since an ANN model learns how to efficiently match predictions to patterns seen during the training method, a data set containing various features that affect the A2G transmission should be created. To this end, this section presents the data set generation procedure, the date prepossessing, and the hyper-parameter tuning of the ANN model.
## 4 Sensitivity Analysis and Discussions
### The Impact Analysis of Brine Concentration on Injection Fluid  
Figure 27 shows that higher injection brine concentrations result in a narrower fluctuation range of outlet brine concentration in the first stage, starting at the injection concentration level. The pattern of brine concentration change, featuring an initial rapid increase followed by a gradual approach to saturation, remains similar across the different injection concentrations. Higher injection concentrations lead to higher overall outlet concentrations with smaller fluctuation ranges. With low injection concentrations, the outlet concentrations initially drop sharply at start of each stage, and then increase smoothly. This phenomenon will gradually disappear as the injection concentration rises. The cavern volume grows exponentially under various injection brine concentrations, mirroring the control group, as shown in Figure 28. Higher injection concentrations slow cavern volume expansion, with uniform increases in concentration leading to uniform decreases in the final volume. To maximize the cavern volume, injecting as much freshwater as possible, while minimizing the recycling of discharged brine, is advisable.  
Figure 26: Cavity’s final shapes in vertical section (X = 0 m) under different flow rates.  
Figure 25: First-stage cavern shapes in vertical section (X = 0 m) under different flow rates.  
As shown in Figure 29, the injection brine concentration significantly impacts cavern expansion and the leaching rate in the first stage. Higher concentrations result in reduced heights and lengths of both the dissolution and return cavities, with the cavern height decreasing more rapidly as the concentration increases uniformly. At a concentration of 200 g/L, the height of dissolution cavity was only half compared to that when using freshwater. The dissolution cavity contour remains relatively smooth under varying concentrations. Higher concentrations lead to slower leaching rates and cavern expansion, reducing the height of the dissolution cavities, as shown in Figure 30. The cavern shape remains largely unchanged with concentration adjustments at the injection point. The effect of injection concentration on expansion significantly surpasses the temperature influence, highlighting the substantial impact of both the injection flow rate and the brine concentration on cavern development.  
Figure 28: Cavern volume evolution with leaching time under different injection concentrations.  
Figure 27: Outlet brine concentration evolution with leaching time under different injection concentrations.  
Figure 29: First-stage cavern shapes in vertical section (X = 0 m) under different injection concentrations.
## 2 Challenges and Routing in 6G Networks
### Routing in 6G Networks
#### 2.2.2 Review of 6G Routing
proposed an approach named ONE, which was introduced to tackle the challenges of real-time user assignment, traffic routing, and Virtual Network Function (VNF) deployment, with the aim of improving the energy efficiency and user admission ratio of mobile networks. The suggested remedies are demonstrated to outclass the current best practices in energy efficiency, where ONE attains a maximum of 89% optimal energy efficiency while consuming as low as 90% less computational time, even in conditions with intense user traffic. Further development of the heuristic approaches used in the article and more comprehensive comparative analyses may provide more accurate and reliable results [87]. In 2022, [PERSON] et al. proposed a new routing strategy aimed at minimizing overall latency in a 6G wide area network (WAN). In this approach, they employ a hybrid fiber optic wireless framework for the WAN, featuring a grid network backbone composed of ultra-fast optical fibers and local wireless network access hubs. The initial and final routings are made via the wireless channel, and the intermediate points are made via the backbone optical network. In the theoretical and simulation results, the average end-to-end delay was found to be less than 1 ms. It would be useful to evaluate the proposed approach under different network topologies, dimensions, and traffic conditions to determine its effectiveness in different scenarios. While the proposed approach offers a new routing technique, it is unclear how it will be implemented in a real-world system. Therefore, future research may focus on the practical applicability of this approach and evaluate its performance in a real-world environment [58]. In 2023, [PERSON] et al. worked on a wireless intelligent router (WIR) in a network for efficient resource allocation and re-routing. Their purpose was to reduce both the power wasted and latency in the case of congestion. For this purpose, the reinforcement-based WIR, using the Q learning algorithm, monitors the network and enables users to re-route in case of congestion. It has been proven in simulation results that it reduces the average power consumption and latency. In addition, as the network grows, Q-table scalability can be a problem. The proposed model has been tested on a small network. The feasibility of a real-world version of the concept is not clear [88]. In 2023, [PERSON] et al. put forward a steady and effective routing strategy for autonomous vehicles within 6G networks. Their aim was to provide a protocol that enables automatic routing decision-making and optimizes information delivery and transmission durations using IoT technology. For this purpose, they used computational intelligence in the model they presented. An assessment of the performance of the proposed method using the NS-2 simulation environment demonstrated superior results regarding data transmission, reception, loss rate, latency, and energy efficiency compared to the currently available approaches. The study can be improved with real data and machine learning approaches [89].
## 1 Introduction
In addition, it is feasible and important for combining vehicles with cloud computing for the development of IOV, which will solve the communication decencies problem of the vehicle-to-everything (V2X) [17; 18]. V2X is a form of technology that allows vehicles to communicate with moving parts of the traffic system around them, including V2V and V2I. None of the routing protocols described above consider combing the wireless channel access constraint with the mobility associated with links to compute an optimized route. Our goal is to present an efficient cross-layer SDVN routing solution for message propagation based on previous works [11; 14; 15; 17]. The proposed protocol selects appropriate relay nodes by calculating several parameters including vehicle velocity, node position information, link stability, and wireless bandwidth allocation. The following are the key contributions of this paper. (i) An in-depth discussion about SDVN is described in terms of its main components and the benefits of introducing SDN technologies to vehicular networks. (ii) A novel cross-layer SDVN routing protocol is proposed to find a more robust route between source and destination. We intend to solve the problems of spectrum scarcity, intermittently connected networks, and high latency. The beauty of the proposed scheme is not only the efficiency of the route discovery process, but also the quality of the available paths. The rest of the paper is organized as follows. The existing SDVN architecture is described in Section 2.1. The proposed routing protocol is described in detail in Section 2.2 and followed by a performance analysis and simulation result in Section 3. Finally, in Section 4, the conclusion is given.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 2 System Design
### Design Considerations for Wi-Fi Hotspot Module  
The main design consideration of the Wi-Fi hotspot module is to choose a single-board computer with lower power consumption. The less power an access point consumes, the longer it can connect people to the internet when solar power is not available.  
The single-board computer is responsible for hosting the software access point, which broadcasts Wi-Fi signals for users to connect. The access point will act as a network layer router, forwarding user packets to the satellite terminal. The hotspot will run network address translation (NAT) to allow multiple users on the local area network to share the satellite connection. In addition, a dynamic host configuration protocol server (DHCP) will be hosted on the computer to automatically assign Internet protocol (IP) addresses to the local hosts.
# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
## 2 System Model
### Analysis  
The blockchain mainly reflected in the unique hash structure makes successful tampering or forgery impossible. In this work, the application of blockchain is mainly reflected as follows: Blockchain nodes differ according to their infrastructure and expected benefits. Most nodes participate in mining to obtain incentive and reputation from block generations. All nodes are responsible for verifying the validity of newly generated blocks. Some trusted nodes are responsible for off-chain storage of data and ledgers to obtain other rewards. It can be observed that the choice to join the blockchain depends on the resource redundancy invested in mining. In addition, rational users can quickly switch between different clusters (mainly based on the distance properties). Although some vehicles may have poor storage or computational capability, most nodes are only responsible for storing the latest transaction records of the current cluster, while trusted nodes are in charge of all ledgers. The flexibility of nodes demonstrates that their architecture mainly exists in the form of small clusters, and the task attributes are mainly related to the property. This also encourages miners to choose how to join the blockchain and how to obtain profits based on the capabilities of their infrastructure.  
In the considered blockchain-based system, miners have the right to synchronously choose to become involved in computing on any chain (main- or side-chain) for profit. The profit mainly refers to the utility income of miners joining in the blockchain, including incentive and reputation. The incentive is mainly the direct utility benefits of fixed reward, performance reward and participate reward, obtained by participation in mining (or a dividend reward obtained by joining in mining pools). Reputation mainly comes from behavioral income in the blockchain network, also known as indirect utility income, and mainly includes local subjective reputation, local objective reputation and cross-chain reputation, etc. In this work, incentive and reputation are positively correlated.
## 4 Experiments
### Dual-Branch Attention Mechanism  
In order to verify the effect of the proposed dual-branch structure in this manuscript, we added the dual-branch attention mechanism to different positions in the model. Specifically, we added dual branches at positions of different sizes on the feature map, including feature map sizes of 16 \(\times\) 16, 32 \(\times\) 32, 64 \(\times\) 64, and 128 \(\times\) 128, and compared the FID evaluation metric, which is shown in Table 2. It can be concluded from the experimental results that the dual-branch attention module can perform better in the middle and advanced feature maps. Among them, the FID value under the feature map size of 64 \(\times\) 64 is better than the FID value under the small size of 16 \(\times\) 16 and 32 \(\times\) 32. This is because the method can fully calculate the dual-branch attention of the model under the larger size of the feature map, and feeds back the correlation between the elements in the channel and spatial dimensions to the original feature map better.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Method**} & **Label0** & **Label1** & **Label2** & **Label3** \\ \cline{3-6}  & & **No Noise** & **No Noise** & **No Noise** & **Gaussian** \\ \hline \multirow{4}{*}{FID} & DCGAN & 346.51 & 376.96 & 278.52 & 272.33 \\  & WGAN & 217.52 & 182.23 & 233.06 & 202.71 \\ \cline{1-1}  & SAGAN & 127.54 & 133.48 & 139.05 & **118.14** \\ \cline{1-1}  & With-CAM & 182.88 & 169.93 & 219.58 & 178.01 \\ \cline{1-1}  & **CDBA-GAN** & **45.89** & **63.65** & **51.81** & 120.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of FID results in different models.
# Electro-Thermal and Aging Lithium-Ion Cell Modelling with Application to Optimal Battery Charging
## 2 Cell Modelling
### Whole Cell Model  
The electro-thermal aging model described in the previous paragraphs can be represented by the block diagram of Figure 1. In this figure, the sub-models denoted 'Anode' and 'Cathode' are deduced from the description performed in Section 2.1. The thermal model of Section 2.2 provides the cell temperature information from the current applied to the aging model described in Section 2.3. This model has been validated by several tests, as described in [6].  
Regarding the tuning of the parameters associated with this model, it can be partially accomplished using information provided by the battery manufacturer. Tests are required to tune the remaining parameters. The tuning methodology is described in [5], and needs to be applied for each new type of battery. As no feedback exists between the aging model and the electrochemical sub-models, the model can fit the behaviour of the battery only within a given aging range (this is enough to get the battery state estimation during a charge). The Model of Figure 1 must be adjusted (on board) periodically to take into account the impact of aging on some parameters of these sub-models (OCP functions, charge transfer resistance,... ).  
Further studies can be conducted (see [3]) to include the electrolyte dynamics in the anode and cathode electrochemical sub-models. Considering the ion diffusion phenomenon inside the electrolyte would increase the model accuracy in estimation of the terminal voltage, but would also increase the model's complexity.
# Cryptanalysis of a New Color Image Encryption Using Combination of the 1D Chaotic Map
## 4 Experiments and Analysis  
A series of simulation experiments have been carried out to verify our attacking scheme with an Intel(R) Core(TM) i5-5300 CPU 2.39 GHz and 8 GB memory capacity. MALTAB R2016b is used for the experiments. Five standard images with the size of \(256\times 256\) are chosen in the experiments including 'Baboon', 'Soccer', 'Pepper', 'Lena', and 'Monarch as shown in Figure 3a1-e1. The corresponding encrypted images are shown in Figure 3(a2,b2,c2,d2,e2). We attack the \(lp\) and diffusion using the steps in the first phase in Section 3. The retrieved permutation-diffusion images and retrieved permutation-only images are obtained as in Figure 3(a3,b3,c3,d3,e3) and Figure 3(a4,b4,c4,d4,e4), respectively. After attacking permutation in the second phase in Section 3, the restored images are obtained which have been shown in Figure 3(a5,b5,c5,d5,e5). Figure 3(a5,b5,c5,d5,e5) coincides with the original images, compared with Figure 2(a1,b1,c1,d1,e1). The recovery performance evaluation can also be made by root mean square error (RMSE). Denoting the original image I and the recovered image I', RMSE defined as below.  
\[RMSE=\sqrt{\sum_{j=1}^{n}\sum_{i=1}^{m}(I(i,j)-I\prime(i,j))^{2}} \tag{26}\]  
In all the experiments, the values of RMSE are all zeros which mean the recovered images are all the same as the corresponding original images. In other words, the original images are accurately recovered. Therefore, the attack results demonstrate the effectiveness of the cryptanalysis.  
The running times of the proposed scheme and [PERSON]'s scheme in terms of attacking \(lp\) and diffusion and attacking permutation are shown in Table 2. The images are chosen in different sizes including \(256\times 256\), \(512\times 512\) and \(1024\times 1024\). From Table 2, the average running time of attacking the amount of \(lp\) and diffusion in two schemes is almost the same. But the average running time of attacking permutation in the proposed method is less than that in [PERSON]'s scheme. The total average running time of the proposed method attacking the encrypted images of size \(256\times 256\) is 3.9151 s which is 1.33 s less than that of [PERSON]'s scheme. The difference values of attacking the encrypted the images with size \(512\times 512\) and \(1024\times 1024\) are about 2.80 s and 18.2 s, respectively. Therefore, the cryptanalysis speed of the proposed method is better than the existing scheme.  
Figure 2: Images corresponding to the three division plaintext sequences: (**a**) Image corresponding to \(S\), (**b**) Image corresponding to \(R\), (**c**) Image corresponding to \(U\).
# Assessment of Areal Methane Yields from Energy Crops in Ukraine, Best Practices
###### Abstract  
Growing and utilizing bioenergy crops as feeding substrates in biogas plants may aid the development of the biogas sector in Ukraine. Therefore, research was done on potential methane yields from 22 high-yield varieties of 7 different crops grown in Ukraine for their biogas production suitability. Annual crops (maize, soybean, sweet sorghum and sorghum hybrids) and perennials (miscanthus, paulownia and switchgrass) harvested at three different harvesting times (H1, H2 and H3) related to specific stages of phenological development were investigated. The perennial crops studied were from different vegetation years. The samples were analysed in Ukraine on their dry matter- and volatile solids contents, dry matter yield (DMY) and crop nitrogen (N) uptake. The 55 \({}^{\circ}\)C -dried samples were delivered to Germany for their analysis with the Hohenheim Biogas Yield Test (HBT) on their specific methane yield (SMY). Based on DMY and SMY, the areal methane yields (AMY) were calculated. The highest SMY and AMY were found for maize, sweet sorghum and miscanthus. The highest average SMY of 0.35 \(\pm\) 0.03 m\({}^{3}\)CH\({}_{4}\) kgVS\({}^{-1}\) was found for maize samples harvested at H2. Miscanthus \"Giganteus\" from the 8 th vegetation year harvested at H1 has shown the highest AMY of 7404.50 \(\pm\) 199.00 m\({}^{3}\)CH\({}_{4}\) ha\({}^{-1}\).  
anaerobic digestion; biogas; maize; soybean; sorghum; miscanthus; switchgrass; paulownia; BBCH-code; nitrogen use efficiency
# Geospatial Simulation System of Mountain Area Black Ice Accidents
## 2 Related Works
### Road Surface Temperature Forecast Considering the 3D Shape  
More recently, several studies have emphasized the impact of shade and topographic conditions on black ice formation. [PERSON] et al. [13] added spatial information and traffic information, such as latitude/longitude, altitude, shade, traffic volume, and characteristics of road materials, in addition to weather information. [PERSON] et al. [14] presented a remote-controlled black ice detection and warning system with detection sensors and a geographic information system to provide in-time warning signals during black ice emergencies. Visual functions were implemented in the system to help users locate the affected sites on a digital map. More recently, [PERSON] et al. [15] developed a frozen road estimation model and validated the model with the data in Montana Road Weather Information System (RWIS) during winter in 2020-2021. In addition to meteorological information, [PERSON] et al. [5] examined the effects of geometric and topographic conditions on the predictions by incorporating the slope angle. They developed an installation method for an anti-icing system based on a regional climate, solar radiation analysis, and dynamic vehicle simulation, which considered road geometry. Although a few studies have employed geographical features for black ice prediction, research that evaluates the risk of accidents caused by black ice in conjunction with the national road management system is still insufficient.