esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results And Analysis
### Performance Analysis  
Performance analysis of the hash function is presented in terms of BER (bit error rate) and ER (entropy rate). BER and ER are defined below.  
* Bit Error Rate (BER): One of the widely used parameters for comparing a pair of hash codes is the BER. It is equal to the number of bits that differ between two hash codes, divided by the length of  
Figure 2: Example of replacement of a sample value from the number of iterations and Hamming weight.  
the hash sequence. The maximum value of BER is 1 and the minimum is 0. In the first case the two hash codes are completely different, while in the second case the codes are the same. The formula for obtaining the BER is: \[BER=\sum_{i=1}^{N}\frac{h_{1}\left(i\right)\oplus h_{2}\left(i\right)}{L},\] (5) where \(h_{1}\) and \(h_{2}\) are the hash codes of two different recordings, \(L\) is the length of the hash value, and \(\oplus\) is the XOR operator. In this study, with 500 different recordings (\(a\)), the number of BER values obtained with all available pairs is calculated using the binomial coefficient: \[\begin{pmatrix}a\\ b\end{pmatrix}=\frac{a!}{b!\left(a-b\right)!},\] (6) for \(a=500\), \(b=2\). Then, our result is \(500!/(2!498!)=500\times 499/2=124,750\). Having many BER values, they should follow a Gaussian distribution with \(\mu=0.5\) and \(\sigma=\sqrt{\mu\left(1-\mu\right)/L}\)[37]. Since \(a=500\), \(L=96\), the expected standard deviation value will be \(\sigma=\sqrt{0.5\left(0.5\right)/96}=51\times 10^{-3}\). As a result, Figure 3 shows the histogram of 499 BER values obtained by the comparison between a sample recording and the rest of the remaining 499 recordings. Table 3 shows the expected values and experimental values obtained from the 124,750 BER values. As can be seen in this Table, with the proposed hash function, the data distribution approximates the Gaussian distribution. This function facilitates resistance to collisions since both the mean and the dispersion of bits that differ between two hash codes of different recordings are close to the theoretical value. \(\bullet\) ER (entropy rate). The ER value allows you to compare the performance of hash functions and, unlike other evaluation parameters, does not depend on the length of the hash code. The higher the ER value, the better the performance. The ER is obtained by taking into account the probability of transition between two hash sequences, \(p\), using the following equation: \[ER=-p\log_{2}\left(p\right)-\left(1-p\right)\log_{2}\left(1-p\right),\] (7) where \(p=0.5\left(\sqrt{\sigma_{\varepsilon}^{2}-\sigma_{t}^{2}/\sigma_{\varepsilon} ^{2}+\sigma_{t}^{2}}+1\right)\). For our case, \(p=0.626\), and therefore \(ER=0.953\). These values are discussed in Section 4.5.  
Figure 3: Gaussian distribution of 499 BER (bit error rate) values: An example.
###### Abstract  
The effects of the quality and intensity of night interruption light (NIL) on the flowering and morphogenesis of kalanchoe (_Kalanchoe blossfeldiana_) 'Lipstick' and 'Spain' were investigated. Plants were raised in a closed-type plant factory under 250 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) PPFD white light emitting diodes (LEDs) with additional light treatments. These treatments were designated long day (LD, 16 h light, 8 h dark), short day (SD, 8 h light, 16 h dark), and SD with a 4 h night interruption (NI). The NIL was constructed from 10 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) or 20 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) PPFD blue (NI-B), red (NI-R), white (NI-W), or blue and white (NI-BW) LEDs. In 'Spain', the SPAD value, area and thickness of leaves and plant height increased in the NI treatment as compared to the SD treatment. In 'Lipstick', most morphogenetic characteristics in the NI treatment showed no significant difference to those in the SD treatment. For both cultivars, plants in SD were significantly shorter than those in other treatments. The flowering of Kalanchoe 'Lipstick' was not affected by the NIL quality, while Kalanchoe 'Spain' flowered when grown in SD and 10 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) PPFD NI-B. These results suggest that the NIL quality and intensity affect the morphogenesis and flowering of kalanchoe, and that different cultivars are affected differently. There is a need to further assess the effects of the NIL quality and intensity on the morphogenesis and flowering of short-day plants for practical NIL applications.  
light intensity; light quality; light spectrum; night break; photomorphogenesis
## 1 Introduction
### Design Consideration of HG-SIQBC Configuration
#### 2.5.2 Voltage Ripple of Capacitors for HG-SIQBC  
The voltage ripple of the capacitors \(C_{1}\), \(C_{2}\), \(C_{3}\), and \(C_{0}\) is calculated using the current given in Equations (3) and (6); substituting the values of \(I_{C1}\), \(I_{C2}\), \(I_{C3}\), and \(I_{C0}\), we get  
\[\left\{\begin{array}{l}\Delta V_{C_{1}}=\frac{I_{C_{1}}}{C_{1}}(1-D)T_{s}= \frac{V_{0}(3D^{2}+6D-4)}{R\left(2+1-2D\right)^{2}C_{1}f_{s}}\\ \Delta V_{C_{2}}=\frac{I_{C_{2}}}{C_{2}}DT_{s}=\frac{V_{0}D(3D^{2}+6D-4)}{R \left(2-1-D\right)^{2}(D-1)C_{2}f_{s}}\\ \Delta V_{C_{3}}=\frac{I_{C_{3}}}{C_{3}}(1-D)T_{s}=\frac{V_{0}D^{2}}{RC_{3}f_{ s}}\\ \Delta V_{C_{0}}=\frac{I_{C_{0}}}{C_{0}}DT_{s}=\frac{V_{0}D}{RC_{0}f_{s}}\end{array}.\right. \tag{40}\]
## 5 Conclusions  
This paper investigates the multi-stage lifelong pathfinding problem for multiple AGVs in complex manufacturing environments. The study focuses on key challenges such as multi-stage transportation tasks, AGV turning simulation, dynamic obstacles, real-time planning, and lifelong scheduling, proposing an efficient hybrid planning framework that combines centralized and distributed approaches.  
To accurately model factory floor and AGV movement patterns, we introduce a grid-based map representation combined with a directed node graph, where each AGV position includes directional attributes. Additionally, we refine the action space by separating rotation and movement actions, ensuring that turning penalties are properly reflected in the planning process. To handle dynamic obstacles, real-time scheduling, and large-scale AGV fleets, we propose the following macro-control and micro-autonomy pathfinding strategy:  
1. Macro-Level (Centralized Planning): The central system decomposes multi-stage transportation tasks and pre-planned paths for each subtask using an modified A* algorithm.
2. Micro-Level (Distributed Planning): Each AGV uses a deep reinforcement learning-based navigation strategy, trained with PER-D3 QN-EDAgger, to make real-time decisions by considering local obstacles, pre-planned paths, and neighboring AGVs.  
The centralized pre-planning ensures high-quality paths, while the distributed decision-making enhances adaptability to dynamic environments. To optimize the distributed AGV navigation model, we introduce PER-D3 QN-EDAgger, which integrates the following: Prioritized Experience Replay for faster learning; Double Dueling Deep Q-Networks for more stable Q-value estimation; and \(\varepsilon\)-DAgger Imitation Learning, which accelerates training by incorporating expert demonstrations (A* pre-planned paths).  
Our experiments demonstrate that PER-D3 QN-EDAgger significantly improves pathfinding performance in the following ways: fast convergence during training, effectively addressing the sparse reward issue in reinforcement learning-based MAPF; high adaptability, maintaining high task completion rates across various obstacle densities and AGV fleet sizes; low path costs, efficiently avoiding dynamic and static obstacles while minimizing travel distance; millisecond-level response times, ensuring real-time pathfinding and rapid obstacle avoidance. This strategy successfully meets the requirements of real-time scheduling and dynamic obstacle avoidance in lifelong, multi-stage, multi-AGV pathfinding scenarios.
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 4 Results and Discussion
### Packet Loss Ratio  
This section demonstrates the behavior of the neutral host network in terms of the packet loss of different clients. To measure the packet loss ratio, the user traffic generated by the iperf tool, in this case, was over UDP (user datagram protocol) since it does not recover from lost packets as TCP does. The throughput per user was selected based on the maximum feasible value that allowed us to keep all eight users connected. The loss of connection for high loads that was observed while conducting these tests (and even failure to connect in some cases) can be justified by limitations of the simulator and the available computational resources.  
Table 4 presents the packet loss ratio (PLR) experienced by the user connections with the maximum feasible throughput per user. Except for the fixed throughput per user and the use of UDP traffic, the configurations are similar to those used in Table 2.  
According to these results, the proposed neutral host architecture experienced a mean PLR 96.8% lower than the configuration with just one client (0.036%, on average, compared to 1.11%). To ensure a fair comparison, both configurations were tested with a throughput of 23.1 Mbps.  
Thus, similarly to what was observed with the throughput, the differentiation of processes for each slice leads to better CPU distribution in the neutral host CN VM, resulting in a positive impact on network performance in terms of packet loss per user compared to using only one slice.
## Materials and Methods
### Prediction Models
#### 2.3.2 Ordinary Kriging (OK) Model  
OK is widely applied in the spatial prediction of soil properties. The core theory of OK is that spatial autocorrelation is based on the spatial autocorrelation of soil properties. The soil attributes of unsampled points can be predicted by giving weights to the surrounding observation points [17]. This technique is optimal because it provides unbiased estimates with minimum and known errors [22]. The predicted model can be expressed as follows:  
Figure 2: Change of climate conditions in Honghu City from January to December, 2013–2016, (**a**): the maximum temperature, (**b**): the minimum temperature, (**c**): the mean temperature, (**d**): the maximum daily precipitation and (**e**): days with daily precipitation greater than 0.1 mm.  
\[Z^{*}(x_{0})=\sum_{i=0}^{n}\lambda_{i}*Z(x_{i}), \tag{3}\]  
where \(Z^{*}(x_{0})\) is the estimated SOC value of variable \(Z\) at location \(x_{0}\), \(Z(x_{i})\) is the measured SOC data, \(\lambda_{i}\) represents the weights combined with the measured values, and n is the number of measured values within a neighborhood of four or eight.  
In this study, the OK model was generated by using a spherical variogram model on ArcGIS 10.4. The equation of the spherical model can be defined as follows:  
\[\gamma(h)=\left\{\begin{array}{cc}0&h=0\\ C_{0}+\left(\frac{3h}{2n}-\frac{h^{3}}{2n^{3}}\right)&0<h<a\\ C_{0}+C&h>a\end{array}\right., \tag{4}\]  
where \(h\) is the spatial lag between two locations, a is the range, \(C_{0}\) is the nugget value, and \(C_{0}+C\) is the partial sill.
# Quantum Game Application to Recovery Problem in Mobile Database
## 4 Discussion
### Simulation Results  
#### 4.2.1 Experiment One  
Aim: The first set of experiments was designed to evaluate the performance of the suggested recovery model in terms of actual execution time as a function of log file size. In general, the cost of recovery is very low for any scheme, since the full log information is stored at the current base station. When the MH travels a great distance from the initial BS, the difficulty of the recovery method used to locate and transmit the log file rises. Increasing the file size results in an increase in the transmission cost.  
Main Results: A As shown in Table 5, the suggested game theory-based recovery model delivers a faster execution time while increasing file size in a variety of mobile settings, depending on the simulation's changing nature.  
Discussion: One reason for these results is that, since the proposed model is based on a knowledge base that was developed after the pre-implementation of each chosen recovery protocol in various simulated settings, it chooses the best appropriate recovery protocol for the present circumstance (variation of log file size). As a result, the proposed model requires less time to execute for numerous simulation runs.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
### Meta-Heuristic
#### 6.1.1 Evolutionary Algorithms
The MRP was evaluated based on its  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Algorithm** & **Optimization Methods** & **Objective** & **Optimization Process** & **Matrices** & **Simulation** \\ \hline \multirow{4}{*}{[70]} & \multirow{4}{*}{GATERP} & GA-based energy efficient threshold-sensitive protocol & \multirow{4}{*}{Network lifetime} & Focus on GA-based identification of the nearest & Energy & \\  & & GA-based protocol for CH selection with a novel & & & optimal path from each & consumption, network lifetime & \\  & & fitness function and efficient & & & & and stability & \\  & & encoding protocol & & & & \\  & & & & Employed the GA and & Average energy, & \\  & & & crossover to reduce the & & WSN & \\  & & & & number of active nodes per & heterogeneity, & \\  & & & & lifetime & iteration via switching some & stability period & \\  & & & & nodes into sleep or into & and network & \\  & & & & inactive modes & lifetime & \\  & & & &  
energy usage, deviation energy, average energy, and network service time on a network with 100-500 nodes. * GADA-LEACH: Genetic algorithm-based distance aware routing protocol  
A GA-based distance-aware routing protocol for CH selection in WSNs was discussed in ref. [73]. CH selection was based on the residual energy of the CH and its distances from the BS and its neighbors. The selected CHs then initiated communication with the BS through a relay node. Each node in the sensor network is represented as a gene in a chromosome, whereas the length of the chromosomes is taken as equivalent to the overall number of nodes. The fitness of each chromosome is evaluated after establishing the initial population based on various metrics, such as the node energy, CH energy, and CH distance from the surrounding nodes and the BS. This algorithm considered the residual energy when reducing the number of CHs to ensure the selection of the most appropriate CHs. It also considered the chromosome length, equivalent to the overall number of nodes, which caused slow algorithmic convergence but had no regard for network-level CH disruption. * ERP: A new evolutionary-based routing protocol  
The evolutionary-based clustered routing protocol (ERP) was developed by [PERSON]. [74] using a population set, divided into individuals, to increase the stability of cluster-based routing protocols, as it aims to select the optimal number of clusters. The protocol uses a binary tournament selection method where specific individuals are selected based on their fitness value from the population in the current generation. The ERP could extend the lifespan of the network and reduce energy usage, but it performed poorly in terms of stability awareness. The achieved protocol improved clustering by using evolutionary algorithms or meta-heuristics with modified fitness functions to confer compactness and take into account separation error.
# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
## 3 Proposed SC-SR Algorithm  
In this section, the image denoising model based on superpixel clustering and sparse representation is presented. We first utilized the SLIC algorithm to generate \(M\) superpixels that include similar pixels. Next, the NSS prior was further exploited by making use of the sparse subspace clustering algorithm with local features to cluster similar superpixels into a group. In the abnormal region of similar superpixels group, we extracted overlapping patches for training dictionary and sought similar patches to each local patch. Finally, a weighted sparse coding was adopted for better sparse representation. The following is a detailed introduction on the proposed algorithm.
# Study on Mechanical Behavior of a New Three-Dimensional Sensor Under Rock Rheology
## 2 Three-Dimensional borehole Full Stress Monitoring Theoretical Analysis  
The design of three-dimensional borehole total stress monitor is to realize the long-term monitoring of ground stress. The primary problem of this study is the relationship between stress, strain and time of rock mass material, that is, the rheological problem. A large number of laboratory studies and field monitoring show that there is a significant rheological phenomenon in soft rock under high stress conditions [25; 26; 27; 28; 29; 30], and this phenomenon is also more obvious in hard rock with more joint fissures and microcracks. Therefore, when the rock area is in a high in situ stress state, the rock mass around the borehole will shrink until it is closed due to rheological action and, with the increase in time, the rock stress will eventually return to the state before the borehole. Therefore, based on the rock rheological phenomenon, this paper studies and analyzes the underground stress change and surrounding rock stability in the process of regional rock rheological stress recovery. According to the theory of elasticity [31], the stress state at a certain point in the object can be expressed by six stress components: \(\sigma_{x}\), \(\sigma_{y}\), \(\sigma_{z}\), \(\tau_{xy}\), \(\tau_{yz}\) and \(\tau_{zx}\), which can be expressed as \(\sigma=\left[\sigma_{x}\quad\sigma_{y}\quad\sigma_{z}\quad\tau_{xy}\quad\tau_ {yz}\quad\tau_{zx}\right]^{T}\); similarly, the strain state of a certain point of the object can also be expressed by \(\varepsilon_{x}\), \(\varepsilon_{y}\), \(\varepsilon_{z}\), \(\gamma_{xy}\), \(\gamma_{yz}\) and \(\gamma_{zx}\), and the strain array is expressed as \(\varepsilon=\left[\varepsilon_{x}\quad\varepsilon_{y}\quad\varepsilon_{z} \quad\tau_{xy}\quad\gamma_{yz}\quad\gamma_{zx}\right]^{T}\). Each sensitive element of the stress meter can only measure a single normal stress. In order to measure the complete three-dimensional stress, firstly, on the basis of the geodetic co-ordinate system \(O_{xyz}\), the spatial co-ordinate system \(O_{xyz\prime z}\) is established for the stress meter element, where the \(z^{\prime}\) axis is the normal direction of the bottom surface of the borehole, which is consistent with the \(z\) axis. The \(x^{\prime}\) axis is the radial symmetry axis of the stress meter, which is orthogonal to the \(y^{\prime}\) axis.
## 2 Methodology
### Condition Assessment Equipment Maintenance
#### 2.2.1 Baseline Condition Assessment Maintenance Model  
The condition assessment of the observed object is carried out on-site or remotely with the help of equipment for remote inspection and monitoring of the system. With advances in communication and information technology, equipment for remote monitoring of technical systems has become available and affordable to regular businesses. An analysis of the funds invested in equipment for remote monitoring and an analysis of the opportunities and services obtained in this way confirms the profitability of such investments. Information and quantities essential for the operation and maintenance of the technical system must be collected frequently and sometimes continuously. If such quantities can be measured in a relatively simple way, it can be concluded that the system is suitable for continuous intelligent monitoring, i.e., continuous collection of information about the operation and condition of the system with the help of equipment for measuring, remote reading and transmitting collected data. Such information is collected, locally processed and formulated, and then transferred to the central part of the monitoring system where further processing of the collected data is performed. Complex methods and algorithms for the analysis, processing and storage of collected data are available in the central part of the system for monitoring and processing collected information [27; 28]. Figure 2 shows specific CBM steps.
# Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures
## 4 Numerical Results
### First Step of the Strategy  
This section presents the results for the two best cases associated with each mesh refinement. In Figure 7, the Monte Carlo responses for the best cases related to M1 are displayed. These cases correspond to S4 with \(\sigma_{L}(G_{IC})=20\times G_{IC}\) and S5 with \(\sigma_{L}(G_{IC})=30\times G_{IC}\). Additionally, Figure 8 illustrates the Monte Carlo responses for the best cases of M2, which are associated with S5 having \(\sigma_{L}(G_{IC})=30\times G_{IC}\) and S6 with \(\sigma_{L}(G_{IC})=40\times G_{IC}\). Finally, Figure 9 showcases the global responses for the best cases regarding M3, linked to S6 with \(\sigma_{L}(G_{IC})=40\times G_{IC}\) and S7 with \(\sigma_{L}(G_{IC})=50\times G_{IC}\).  
Figure 6: Comparison between experimental and numerical (for the three meshes) global curves related to the simulation (S1).  
The observation of Figures 7-9 leads to the following comments:  
1. The post-peak section of the curves exhibits the experimental global response consistently within the range of numerical responses.
2. Except for Mesh 1, the peak load in the numerical simulations are lower compared to those observed in the experimental test. Moreover, this difference increases from Mesh 2 to Mesh 3. Several reasons can explain this behavior: 1. The peak load is correlated with the process of macrocrack localization (initiation) rather than the macrocrack propagation, which the proposed model is specifically designed to address. It focuses on the softening behavior of the specimen's global response during macrocrack propagation, not the microcracking to macrocracking transition. 2. The numerical modeling of this macrocrack propagation employs linear volume elements, i.e., three-dimensional finite elements with linear interpolation functions. However, typically, strong stress concentrations at the narrow notch tip require special crack elements with different formulations or elements utilizing more complex interpolation functions, often nonlinear, to accurately represent such stress concentrations. 3. Using linear elements (as in this case) results in higher stress concentrations at the notch tip for Meshes 2 and 3 compared to Mesh 1, considering the same notch opening. Consequently, for Meshes 2 and 3, the tensile stresses at the notch tip are higher, leading to smaller notch openings and loads initiatingcracking (D = 1) in the first elements at the notch tip compared to Mesh 1. Thus, the mean number of cracked elements at the moment of maximum peak load increases with mesh refinement. Specifically, the mean number of fully cracked elements at peak load for the presented simulations in Figures 7-9 are: 6.6 (Mesh 1); 75.1 (Mesh 2); and 140.4 (Mesh 3) [16]. Consequently, the peak load for Meshes 2 and 3 is lower compared to Mesh 1 due to a higher number of cracked elements at the peak-load stage.