esa-sceva/satcom-chunk-collection huggingface dataset:
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 4 The D2D Loosely Coupled Model
### Network Control Management  
The proposed solution assumes that each node is capable of deciding which links to establish with its neighbors depending on membership in the Gnc and Gc groups. This neighborhood classification is not obvious for a participating node to determine autonomously. Two solutions are possible:  
* Based on the server: the nodes send updates if there are changes in the topology and the Application Server maintains the global topology map of the entire network and transmits a notification (in broadcast message) to all participants.
* Based on peers: the directly connected nodes exchange the member list of their groups.  
We found the server-based approach to be more robust since it allows newly connected users to receive existing topology maps in a direct and transparent manner. Also, the idea of keeping a single copy of the topology on the server side allows for better network management while avoiding overloading the bandwidth of the D2D links. As illustrated in Figure 11, when a new user joins the network, a Topology Map Notification Message is broadcast to all users (using messages 1, 2, and 3). If User_B and User_C are in range, a direct connection will be created (initiated by one of the two). In this case, the user who initiated the link establishment is responsible for sending the Topology Map Update Message to the Application Server (using message 4). A notification message will subsequently be broadcast to all participants including User_A, who will therefore be notified about the topology update. Thus, server notifications are sent during two scenarios: when a new UE connects or when a modification of the topology is reported by one of the UEs.  
Figure 11: Example of a sequence diagram for topology map control messages between users and the Application Server.
# Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
## 2 Demand Side Management
### Requirements of Demand Side Management  
For demand side management to function properly, it needs to be implemented in a smart grid since the various techniques that can be implemented within DSM need good communication infrastructure, advanced monitoring and control equipment. In CVR, the main control elements are the tap-changing transformers used to control the voltage levels at various locations along the grid. In order for this process to run smoothly, monitoring of all controlled buses that contain loads must be accurate and the information collected must be transferred quickly through the communication system to the control center. DLC has control switches as its main control elements that are used to switch out loads when the algorithm demands it. DLC also requires good communication systems to transfer information collected about the power consumed at different loads. Most grids nowadays have decent communication systems with the implementation of power line communications (PLC) and supervisory control and data acquisition (SCADA) systems. New energy meters can also switch out customers by a single command issued at the control center, which can be very useful when implementing DLC.
# Probabilistic Analysis of Infinite Slope Stability Considering Variation in Soil Depth
## 3 Results and Discussion
### Discussion  
To identify soil depth variability within a slope, it is essential to estimate the continuous soil depth profile along the slope. However, direct investigation methods, such as excavation pits, auger drilling, and dynamic cone penetration tests, are not suitable for achieving this. In this study, seismic refraction survey results were used to estimate soil depth variability, with a reference P-wave velocity of 700 m/s for soil depth estimation. Seismic refraction surveys proved effective in estimating soil depth and identifying its variability. The variability of soil depth was found to have an average COV of 30%, indicating high variability. This is one of the key findings of this study. Although soil depth is known to be a highly variable parameter, its quantitative characterization has not been well established compared to other soil properties. The probabilistic analysis results highlight the importance of considering soil depth variability in slope stability analysis, and the sensitivity analysis reveals that soil depth variability can have a greater impact on the probability of slope failure than the shear strength parameters of soil.  
However, establishing a threshold for the P-wave velocity to distinguish between soil and bedrock remains an area for further discussion. Although the P-wave velocity used for estimating soil depth was determined based on the literature and national standards, it may vary depending on geological conditions. Therefore, field investigations are necessary to verify the accuracy of the estimated soil depth.
## 1 Introduction
Intercept probability is evaluated in this model, where two different cases are considered, which are the \(k^{th}\) nearest and \(k^{th}\) best eavesdropper. PLS was studied over cascaded \(\kappa\)-\(\mu\) fading channels over the main link only in [20]. To the best of the authors' knowledge, no prior works studied the physical-layer security (PLS) for a three-node wiretap system model over cascaded \(\kappa\)-\(\mu\) fading channels at the main and the wiretap links and under the threat of multiple eavesdroppers. Moreover, no previous research has investigated the difference between colluding and non-colluding eavesdroppers with multiple antennas. Therefore, in this work, we explore the PLS of a system model consisting of a transmitter interacting with a legitimate receiver in the presence of multiple eavesdroppers. In one scenario, these eavesdroppers are considered to be colluding to intercept the information effectively, whereas in another, the eavesdroppers are considered to be non-colluding and have random positions. In the case of colluding eavesdroppers, it is assumed that each has a single antenna. These eavesdroppers can be replaced by a single eavesdropper equipped with several antennas. This assumption is valid since these colluding eavesdroppers can perform cooperative processing on the gathered intercepted information by sending it to a centralized processor [21; 22; 23; 24; 25]. The PLS is investigated in terms of the secrecy outage probability \((OP_{sec})\), the probability of non-zero secrecy capacity \((P_{\text{nsc}})\), and the intercept probability \((P_{int})\). Exact and asymptotic equations for the \(OP_{sec}\) and the \(P_{\text{nsc}}\) are derived. The results demonstrate the influence of multiple eavesdroppers on information privacy. Moreover, the impact of the cascade levels of the main and the wiretap channels over the security is addressed. This paper also investigates the effect of distances on security. Finally, a comparison between the two scenarios of the eavesdroppers' capabilities on degrading the main channel's privacy is provided.
###### Abstract  
The high entropy alloy (HEA) filler used during the fabrication method determines the reliability of HEAs for steel-aluminum dissimilar alloy configuration_HEAs have a direct impact on the formation of intermetallic compounds (IMC) formed by the interaction of iron (Fe) and aluminum (Al), and influence the size of the joint's interaction zone. A novel welding process for Fe-Al alloy joints was developed to prevent the development of a brittle iron-aluminum interface. This research involved investigation of the possibility of using HEA powdered filler. Fe\({}_{5}\)Co\({}_{20}\)Ni\({}_{20}\)Mn\({}_{35}\)Cu\({}_{20}\) HEAs was used as a filler for the laser joining lap configuration joining hyper-duplex stainless steel UNS S33207 to aluminum alloy 6061. This HEA has unique properties, such as high strength, good ductility, and high resistance to corrosion and wear. A tiny portion of the stainless-steel area was melted by varying the welding parameters. The high-entropy alloy (HEA) with slow kinetic diffusion and large entropy was employed to aid in producing solid solution structures, impeding the blending of iron and aluminum particles and hindering the development of Fe-Al IMCs. The weld seam was created without the use of Fe-Al IMCs, The specimen broke at the HEAs/Al alloy interface with a tensile shear strength of 237 MPa. The tensile-shear strength achieved was 12.86% higher than for the base metal A 6061 and 72.55% lower than for the UNS S33207 hyper-duplex stainless steel.  
high entropy alloy; hyper-duplex stainless steel; laser welding; aluminum alloy +
Footnote †: journal: Applied Sciences
# Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
## 4 Model Development and Training
### Data Gathering  
* Thermal Camera (Flir Lepton 3.5): Captured images of strawberry plants at 15 min intervals (Figure 3). Each image was saved as an array of size 120 \(\times\) 160 (which matches the camera resolution) in a CSV file format. Each pixel measurement has a precision of 0.05 \({}^{\circ}\)C and can measure temperatures between \(-\)40 \({}^{\circ}\)C and +400 \({}^{\circ}\)C. This approach provided a comprehensive dataset capturing the temperature variations of the plants.
* Light Sensor (BH1750): Measured the intensity of light in the plant environment (with accuracy of \(\pm\)20% and measurement range of 0.11-65,535 lux). The collected light  
Figure 3: Data gathering location and spatial characteristics: (**a**) Front view of the greenhouse and (**b**) top-down view of the greenhouse.  
Figure 2: The process of model training.  
intensity data were crucial for understanding the photosynthetic activity and growth patterns of the strawberry plants.
* Temperature and Humidity Sensor (DHT11): Provided ambient temperature (with accuracy of \(\pm\)2 \({}^{\circ}\)C and measurement range of 0-50 \({}^{\circ}\)C) and relative humidity data (with accuracy of \(\pm\)5% RH and measurement range of 0-100% RH). This information was essential for understanding the microclimate conditions surrounding the strawberry plants.
* Capacitive Soil Water Content Sensor: Collected analog value data (with accuracy of \(\pm\)2% measurement range of 0-100% RH) on the soil moisture levels, a critical factor affecting plant growth and nutrient uptake.  
A total of 1072 thermal images and 2220 sensor measurements were gathered and used in further data preprocessing and model training. Also, an attempt to use other sensors (pH and soil temperature seen in Figure 3) did not produce usable and valid data during the specified time, so they were not included in the final model development. Only values that had matching timestamps in both datasets were used in the corresponding model training.
## 3 Materials and Methods
### MP-IDSSC: Matching and Pose Estimation under Inverse Depth and Structural Semantic Constraints
#### 3.3.1 Cross-Validation of Dynamic Adaptive Threshold  
To improve the efficiency and accuracy of the algorithm, this study adopted the cross-validation method of a dynamic adaptive threshold based on the selected initial value. The fast library for approximate nearest neighbors (FLANN) algorithm was used to seek the matching point pairs for screening, which generally included a first matching point with the closest Euclidean distance and a second matching point with the second closest Euclidean distance. It is generally believed that, for a certain matching point pair to be screened, the smaller the distance _dis_ of the closest matching point compared with the distance _dis'_ of the second closest matching point, the better the matching quality. Traditional algorithms generally use a fixed scale factor \(t\) as the threshold. In particular, when _dis_\(<t\cdot\)_dis'_ is satisfied, the matching point pair is used as a candidate. However, because of the radiation difference, the distribution range of the Euclidean distance difference between point features is unpredictable, and it is generally necessary to manually adjust the threshold \(t\) to select a better matching point pair. To solve this problem, we designed a cross-validation method for a dynamic adaptive threshold. First, the FLANN cross-nearest neighbor search was used to calculate the average distance difference between the closest and second closest matching point in the cross search.  
\[dis_{agg}=\frac{\left(\sum_{i=1}^{N}\left(dis^{{}^{\prime}}-dis\right) \right)}{N} \tag{7}\]  
When the distance difference between the closest and second closest points is less than \(dis_{agg}\), the matching point pair in the cross search is used as the initial candidate. Using the average distance difference as a comparison criterion can adapt to the distance between feature points, retain high-quality matching points, and improve the stability of the RANSAC algorithm.
## 3 Results and Discussion
### Fluorescence Calibration  
#### 3.3.1 Alexa 488 Dextran  
The fluorescence intensity recorded from the Alexa 488 Dextran in the calibration has been subject to a linear regression analysis as a function of the surface density \(\sigma\) given for both the wet and dry state (Figure 8). The density range mirrors the average fluorescence intensity recorded in the experimental results, and it became clear that the intensity from the dried specimen is lower compared to the wet state. One possible explanation comes from a difference in the quantum yield, where the fluorophore in the dehydrated state may quench the fluorescence emission [42]. The fluorescence intensity of the immobilised samples was all measured in the dry state and hence the calibration line for the \"dry state\" was applied to estimate the number of Alexa 488 dextran molecules on the sample surface. Thus, the observed fluorescence intensity from dextran bound to the immobilised Con A Figure 7) was  
Figure 7: Average fluorescent intensity for FITC and Alexa dextran 488 following Con A immobilisation and fluorophore incubation (sample ID 4, 5, 6 and 7, Table 1), following immersion with MOPS buffer containing glucose at increasing concentrations (\(c_{glu}\) from 0 to 40 mM), \(n=30\).  
then converted to the number of bound dextran molecules, in which the maximum number (2.1 \(\times\) 10\({}^{12}\) molecules per cm\({}^{2}\)) should correlate with the number of functional Con A molecules at the surface (irrespective of their tertiary form and assuming that each Con A monomer have one carbohydrate binding site). After incubation in a MOPS buffer containing \(c_{glu}\) = 40 mM, the number of Alexa dextran molecules remaining on the surface was reduced to 6.1 \(\times\) 10\({}^{11}\) molecules per cm\({}^{2}\).
## 5 Artificial Neural Network Architectures and Learning Algorithms
### Discussion of Input Data, Types of Artificial Neural Networks, and Types of Learning Algorithms  
The most common type of ANN in islanding detection is MPNN--Table 5 and Figure 8. In the literature review, MPNN is used continuously by the authors. The reason for this is the speed of detection and adequate accuracy of the method. Authors of [84,99] claim that MLP's main advantages are flexibility and capability of capturing system nonlinearity. The other most common types of ANN are PNN and ANFIS. PNNs generate accurately predicted target probability output and can be more accurate than MPNNs. ANFIS's advantage over MPNN is adaptation capability and rapid learning capacity. ANFIS takes more time to process detection due to the fuzzy layer than MPNN and PNN [53,62]. The advantage of ANFIS is high accuracy and very small NDZ. Also, in [55], RNN is used, which is a novelty in island detection. It has proven effective, and the authors compare it with SOM and MPNN.  
The back-propagation learning algorithm is the most widely used learning algorithm for MPNN. The back-propagation learning algorithm is a widely used and well-established algorithm for training MPNN, effective in handling complex and non-linear relationships  
Figure 8: Different types of ANNs used in the observed time period.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Learning Algorithm** & **Papers** \\ \hline Back-propagation & [46, 47, 48, 49, 52, 58, 60, 61, 63, 65, 66, 68, 69, 70, 74, 75, 76, 78, 79, 82, 83, 86, 87,  
in data, but may suffer from slow convergence [87]. The Boltzmann learning algorithm is suitable for training Boltzmann machines (a type of recurrent neural network) and it can capture dependencies between variables over time but it has low computational efficiency [85].  
PNNs can learn different algorithms, the most common of which are based on Bayesian function, differential evolution, etc. ANFIS is a combination of two learning algorithms: fuzzy-logic and back-propagation, from which they derive the best advantages of both algorithms. The learning algorithm can be optimized where the amount of input data is reduced, and the learning process is improved. The best example of learning optimization is grey wolf and PSO. The above algorithms are supervised learning algorithms, while vector quantization is an example of an unsupervised learning algorithm. Vector quantization is based on competitive learning, so it is related to the SOM.  
As shown in Figure 7, the input data in ANN are diverse. By reviewing the literature, most authors often use voltage amplitude, with or without more quantities like current, power, etc. Input data are taken from the relays or by dynamic analysis on a real-time simulator. The amount of input data is often huge, so it is transformed by reduction, most commonly by DWT. Figure 9 shows a chart of ANN types with the associated type of transformation of input data.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 7 The Role of AI in Cancer Genomics
### Therapeutic Innovation in Oncology
#### 7.5.2. AI in Molecular Generation and Preclinical Optimization  
Beyond target identification, AI revolutionizes the design of new anti-cancer molecules. Techniques like virtual screening powered by AI can rapidly evaluate millions of chemical compounds to identify those most likely to bind a given cancer target with high affinity. This computational screening dramatically filters the pool of potential drugs, focusing experimental efforts on the most promising candidates. In parallel, generative AI models are now used to create novel chemical entities de novo based on desired pharmacological profiles. These models propose structures that are optimized for properties like improved selectivity, reduced toxicity, or resistance evasion, which are essential for the design of next-generation cancer therapeutics [100]. Figure 4 illustrates how AI tools, such as CNNs, can support early drug discovery stages. While these methods have shown improved predictive accuracy in preclinical models, their impact on overall clinical success rates remains under investigation.  
Figure 4: Overview of AI-driven drug repurposing and discovery workflow. The figure illustrates how machine learning and deep learning algorithms, including convolutional neural networks (CNNs), can process multi-omic and chemical data to identify novel drug–target interactions. The ‘\(\surd\)’ symbol indicates successful clinical validation or a positive outcome, while the ‘\(\times\)’ symbol indicates unsuccessful clinical validation or a negative outcome.  
AI is also proving crucial in downstream phases of drug development. Predictive models trained on pharmacological data from past compounds can anticipate the ADMET profile (absorption, distribution, metabolism, excretion, toxicity) of new molecules, identifying potential safety issues before clinical testing. This allows chemists to iteratively refine compounds, increasing their likelihood of success in trials [100]. Additionally, AI can suggest optimal drug combinations by analyzing how different agents interact within cellular networks, pointing to multi-drug regimens that might be more effective than single agents. This is particularly relevant in cancer, where combinatorial treatments are often needed to prevent or overcome resistance.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 4 Discussion
### Hardware Failure of Information System and Its Causes  
#### 4.1.1 Line Fault and Its Causes  
In the practical application process of information system communication technology, the specific line is one of the core aspects. Reasonably arrange the production line to obtain the ideal value in actual operation, so as to ensure that its follow-up application value is reflected, and the problems in this line aspect must be a greater threat. Based on this kind of line fault analysis, it is mainly due to the obvious aging and damage of communication lines, which will inevitably affect the stability effect of power supply, and also have a more obvious threat and impact on data transmission [24]. There are many reasons for this kind of line damage. In addition to the erosion or damage caused by the unreasonable external environment, the human impact is also a more common aspect. Because of the instantaneous high voltage or low voltage in the power supply process, the stability of the line itself may also be affected, resulting in obvious line failure.
# Subpath Queries on Compressed Graphs: A Survey
## 3 Indexing Labeled Graphs and Regular Languages
### Indexing Labeled Trees
#### 3.5.1 The Prefix Array of a Labeled Tree  
Consider the list containing the tree's nodes sorted by the co-lexicographic order of the paths connecting them to the root. We call this array the _prefix array_ (PA) of the tree. See Figure 7 for an example: node 5 comes before node 8 in the ordering because the path connecting the root to 5, \"aac\", is co-lexicographically smaller than the path connecting the root to 8, \"bc\". This node ordering is uniquely defined if the tree is a trie (that is, if each node has at most one outgoing edge per label). Otherwise, the order of nodes reached by the same root-to-node path can be chosen arbitrarily.  
A nice property of the prefix array of a tree is that, together with the tree itself, it is already an index; in fact, it is a straightforward generalization of the suffix array SA introduced in Section 2 (in this case, we call it \"prefix array\" since we are sorting prefixes of root-to-leaves paths). Since nodes are sorted co-lexicographically, the list of nodes reached by a path labeled with a given pattern \(\Pi\) can be found by binary search. At each search step, we jump on the corresponding node on the tree and compare \(\Pi\) with the labels extracted on the path connecting the node with the root. This procedure allows counting all nodes reached by \(\Pi\), as well as subsequently reporting them in optimal constant time each, in \(O(|\Pi|\log n)\) time, \(n\) being the number of nodes.