esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Method for Determining Optimal Weights
Special attention is paid to the performance value when the module size is 8 bits. This anomalous value (which is clearly less than expected) is explained by the low computational complexity of the ACF calculation operation. However, as we can observe, the performance is still higher since, in the presence of optimal weights, the value of \(C(B_{i})\) is less. In the case of stage B, we can observe the following (Figure 1b). The performance ratio of the methods also tends to unity; however, we can observe that the graph line is broken. This can be explained by the fact that, although the number of moduli increases the computational complexity, it is not as fast as in the case of stage A. This is because in RNS, in most situations, only the number of addition operations in formulas depends on the number of moduli, which carries a lower computational load. In addition, as mentioned above, we use the Monte Carlo method based on random weights, which of course can also have a certain effect on the result. However, as mentioned above, the proposed method had the best performance throughout the study. As a result of the simulation, we obtained the following results, presented in Table 3.  
Let us consider the received data in more detail. The first column stores the number of moduli in the set, and the second column stores the size of one modulo in the set. These columns are necessary for the convenience of finding the required set of weights. The third column, the value of \(\max C(B)\), allows you to compare the basis on which the set of optimal weights was determined. The fourth column is a set of optimal weights. The fifth column displays the absolute error of converting a number from RNS to a positional system when using the resulting set of weights. Let us analyze the value of the absolute error. To do this, we constructed the graph in Figure 2.  
In this case, we can observe the following. Despite some deviations, in both cases, the value of the error on average increases with the growth of the computational complexity of the experiment, which is a logical result. However, the value of the error does not go beyond the previously indicated boundary, which is equal to \(0.1\cdot 10^{-2}\)16, which is a good result.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 2 One Way Unclonable Functions with Ternary States
### Error-Correcting Methods Versus Search Engines
#### 2.4.1 Error-Correcting Codes (ECC)  
As shown in Figure 1, the challenges transmitted by the server (**T**, _IDAccess_) allow the generation of the response **K'** from the PUF, which should be similar to the initial responses **K** extracted from the information stored in a secure database by the server during enrollment. ECC is needed to uncover the stream **K** from the potentially erratic stream **K'**, and to generate error-free cryptographic keys. ECC exploits data streams called \"helpers\" to correct the erratic bits. In certain applications, the data helpers need to be protected by encryption schemes to avoid leakages to the opponents [29]. A fuzzy extractor embedded in the client device reads **K'** and the data helper to find **K**. The computing power consumed by the fuzzy extractor could be prohibitive for power-constrained IoTs, making these devices vulnerable to side-channel analysis. ECC has been successfully implemented with PUF BERs below 10%.
## 2 Materials and Methods
### Procedures
All of this satellite navigation is possible thanks to the previous creation of the atomic clock, which is the device that allows the calculation of the time that it takes a radio signal to travel from the satellite to the GPS receiver on Earth [16]. Ultimately, satellites transmit signals to GPS receivers to determine the location, speed, and direction of the devices [17; 40].
## 3 Results
### Texture Profile of Corn-Based Crisps with Fruits
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fruit Amount** & **Screw Speed** & & & & \\
**[\%]** & **[rpm]** & **CF [N]** & **H [N]** & **CR [N]** & **FR [N]** \\ \hline \multirow{4}{*}{0} & 80 & \(8.93\pm 0.72\) & \(84.09\pm 4.42\) & \(11.12\pm 9.02\) & \(82.89\pm 5.71\) \\  & 100 & \(8.12\pm 0.98\) & \(106.87\pm 4.11\) & \(15.41\pm 15.34\) & \(105.19\pm 4.41\) \\  & 120 & \(7.44\pm 1.09\) & \(99.27\pm 4.03\) & \(4.03\pm 1.49\) & \(95.36\pm 2.12\) \\ \hline \multirow{4}{*}{5} & 80 & \(7.02\pm 0.87\) & \(61.93\pm 8.05\) & \(4.41\pm 2.84\) & \(59.45\pm 8.24\) \\  & 100 & \(6.21\pm 0.79\) & \(70.97\pm 6.21\) & \(8.37\pm 6.22\) & \(68.54\pm 3.92\) \\  & 120 & \(7.40\pm 0.74\) & \(88.87\pm 7.25\) & \(6.18\pm 4.12\) & \(85.20\pm 11.15\) \\ \hline \multirow{4}{*}{10} & 80 & \(14.54\pm 1.73\) & \(141.14\pm 18.90\) & \(107.65\pm 21.52\) & \(141.14\pm 18.90\) \\  & 100 & \(12.32\pm 1.53\) & \(143.08\pm 18.10\) & \(110.57\pm 12.44\) & \(143.08\pm 18.10\) \\  & 120 & \(14.70\pm 3.48\) & \(135.23\pm 5.24\) & \(94.26\pm 8.24\) & \(135.23\pm 5.24\) \\ \hline \multirow{4}{*}{15} & 80 & \(10.76\pm 2.14\) & \(232.64\pm 29.28\) & \(206.08\pm 17.27\) & \(232.64\pm 29.28\) \\  & 100 & \(14.22\pm 2.26\) & \(354.39\pm 37.41\) & \(324.37\pm 33.50\) & \(354.39\pm 37.41\) \\ \cline{1-1}  & 120 & \(10.24\pm 2.33\) & \(353.85\pm 43.04\) & \(324.34\pm 40.44\) & \(353.85\pm 43.04\) \\ \hline \multirow{4}{*}{20} & 80 & \(20.52\pm 3.85\) & \(165.96\pm 30.99\) & \(175.05\pm 27.38\) & \(165.96\pm 30.99\) \\ \cline{1-1}  & 100 & \(14.50\pm 1.75\) & \(196.99\pm 44.30\) & \(215.85\pm 17.60\) & \(196.99\pm 44.30\) \\ \cline{1-1}  & 120 & \(8.27\pm 1.06\) & \(109.95\pm 6.41\) & \(8.62\pm 8.39\) & \(91.57\pm 35.95\) \\ \hline \multirow{4}{*}{15} & 80 & \(7.08\pm 2.85\) & \(187.20\pm 21.47\) & \(176.21\pm 22.06\) & \(187.20\pm 21.47\) \\ \cline{1-1}  & 100 & \(6.20\pm 2.12\) & \(348.44\pm 40.70\) & \(324.37\pm 33.50\) & \(348.44\pm 40.70\) \\ \cline{1-1}  & 120 & \(8.49\pm 2.44\) & \(259.89\pm 18.22\) & \(238.25\pm 19.45\) & \(259.89\pm 18.22\) \\ \hline \multirow{4}{*}{20} & 80 & \(10.24\pm 3.44\) & \(273.40\pm 34.37\) & \(271.70\pm 30.02\) & \(273.40\pm 34.37\) \\ \cline{1-1}  & 100 & \(4.22\pm 1.33\) & \(256.04\pm 14.76\) & \(246.73\pm 12.47\) & \(256.04\pm 14.76\) \\ \cline{1-1}  & 120 & \(6.18\pm 2.30\) & \(273.08\pm 17.29\) & \(248.63\pm 11.53\) & \(273.08\pm 17.29\) \\ \hline \hline \end{tabular}  
* CF—cutting force; H—hardness; CR—crispness; FR—fractraurability; results are presented as mean \(\pm\) standard deviation.  
\end{table} 
Table 8: Textural characteristics of corn-based crisps supplemented with apple powder and control sample.
## 2 Materials and Methods
### Neural Network Architecture  
BP neural network [20] is the most basic neural network, which can learn and store a large number of input-output mapping relationships without revealing the mathematical equations in advance. The learning process consists of two parts: forward propagation of signals and backpropagation of errors. Its learning rule is to use the steepest descent algorithm to continuously adjust the weights and thresholds of the network through backpropagation to minimize the sum of square errors of the network. Due to the effective function approximation ability of the BP neural network, we built a BPNN to map the correlation between the five motion feature parameters of the mobility model defined in Section 2.2 and network performance. The basic structure of the BP neural network is shown in Figure 1. Our BPNN is composed of an input layer, three hidden layers, and an output layer. Each neuron algebraically sums the weighted input and the threshold (offset) vector to obtain its own output.  
The input layer of the neural network is \(x=(x_{0},x_{1},x_{2},x_{3},x_{4})\), where \(x_{0},\cdots,x_{4}\) are five motion metrics designed in Section 2.2. The number of hidden layers in the BP neural network used in our design is \(3\), and each layer contains \(32\), \(48\), and \(16\) neurons, respectively. Finally, the number of neurons in the output layer is \(8\), \(y=(y_{0},y_{1},y_{2},y_{3},y_{4},y_{5},y_{6},y_{7})\), where \(y_{i}\) represents different network performance levels. Relu [28] activation function is used between the input layer and the hidden layer, and LogSoftmax is used as the activation function between the last hidden layer and the output layer.  
For hidden layers, the input is  
\[i_{j}=\sum_{j=0}^{4}w_{ij}x_{i}+b_{j},j=0,\cdots n-1 \tag{14}\]where \(n\) is the number of neurons in the current layer, \(w_{ij}\) is the weight coefficient, and \(b_{j}\) is the bias coefficient. If the activation function is _f(x)_ = ReLU, the output of this hidden layer is  
\[o_{j}=f(i_{j}) \tag{15}\]  
Then \(o_{j}\) continues to be propagated forward as input to the next hidden layer.  
To avoid the overfitting problem of BPNN, different parameter combinations need to be used to test and screen the best network model. Due to the different ranges of input and output variables, it is necessary to standardize the values of the dataset by using the normalization method to process input and output data. In addition, to ensure an accurate evaluation of the classification ability of the constructed model, the dataset is randomly divided into the training set and the validation set. Owing to the random partitioning of the dataset, both the validation and training sets contain collected data under different node motion states. Among them, the training set accounts for 80%, and the validation set accounts for 20%.
# Optimization and Experimental Verification of Lower
## 3 Experimental Verification of Lower Articulation  
### Structural Test  
#### 3.1.1 Structural Test Method  
In order to verify the experimental validation of the optimized lower articulation, we produced one unit and conducted a structural test to measure the strain. The test conditions were established based on the vehicle manufacturer specifications, advanced standards, and regulations EN 12663-1 and EN 13749 [21; 22]. The structural tests were performed under ambient conditions at room temperature, using displacement control. The details of the test loads, speeds, and equipment used for each direction are provided in Table 4. Figure 9 illustrates the strain gauges attached around the stress-prone areas identified through the structural analysis. To facilitate the application of the loads in the test directions, a separate fixing device was fabricated and installed on the lower articulation.  
Figure 8: Sensitivity analyses on the stress and weight: (**a**) sensitivity to stress; (**b**) sensitivity to weight.  
Figure 7: Results of structural analysis for optimum model: (**a**) tensile case; (**b**) compression case.  
The structural tests were then conducted three times in each direction using this fixing device, as shown in Figure 10.
## 1 Introduction
The CoMP clustering method has also been recently explored in conjunction with affinity propagation (AP) [26] and capacitated affinity propagation (CAP) [27]. Both of the methods utilize message passing [28; 29; 30; 31] to achieve the optimal base station cluster. When compared to AP, CAP limits the maximum number of clusters in order to enable sophisticated control over the clusters. These methods provide decentralized clustering with low computational complexity. However, they also attempt to solve the cell clustering by minimizing the sum-distance problem instead of maximizing the sum-capacity problem. The different approaches by these methods have resulted in suboptimal performance. In this paper, we propose a distributed downlink JP-COMP clustering algorithm that is based on multi-layer message passing. Based on graphical models described in relation to the clustering problem, all base stations perform distributed optimization by exchanging a small-sized information known as a message. Message exchange occurs in multi-layer to find the best solution. In addition, the distributed nature of the proposed algorithm requires lower computational complexity and it incurs less backhaul overload. The main contributions in this paper are summarized, as follows. We propose a distributed cell clustering algorithm utilizing message passing in downlink JP-CoMP. The paper addresses sum capacity maximization in cell clustering through approximation into a linear objective function. Based on the approximation, the dynamic cell clustering problem is rendered into a multi-layer message passing problem. Each base station will exchange messages with the neighboring base stations and will choose an appropriate partner that is based on the optimum sum capacity in the first layer. The partner selected during the previous layer will exchange messages again with the other neighboring chosen partners and will choose an appropriate partner based on the closest distance in the second layer. Finally, partner BSs in all layers form a cooperating cluster in which they share CSI and user data for joint transmission. By enabling distributed control of the network, this proposed algorithm also reduces both the computational complexity and backhaul overload. The rest of this paper is organized, as follows. Section 2 describes the JP-CoMP system model. Section 3 explains message passing for JP-CoMP clustering. Section 4 presents the simulation results. Section 5 summarizes this paper.
## 5 AI-Empowered Communication with Perceptual Constraint
### AI-Empowered Semantic Communication
#### 5.2.3 Generative Modeling in Base Station  
As part of the radio Access Network (RAN), the BS possesses powerful Tx and Rx capabilities to communicate simultaneously with multiple UE. Generative models have been applied to the BS for channel estimation, channel state information (CSI) acquisition, and resource allocation.  
The BS features encoders and decoders to process both uplink (from UE to the BS) and downlink (from the BS to UE) data streams. Modern cellular networks typically operate in full-duplex mode [136], enabling simultaneous bidirectional communication between UE and the BS for concurrent transmission and reception. Antenna arrays in the BS are used to send and receive radio signals, employing sophisticated beamforming techniques. The BS can estimate the current state of the channel, thereby optimizing downlink data transmission. In frequency division duplexing systems, the CSI is fed back to the BS for beamforming. In [118], a GAN-based CSI feedback approach is proposed, where the encoder learns a compressed codeword of the CSI and passes it to the generator for reconstruction in the BS. In [119], a CSI-GPT framework is proposed to efficiently acquire downlink CSI information in massive MIMO systems. In [137], the generalization ability of multi-modal large models across diverse base station environments is explored for various CSI-based tasks. Intelligent algorithms [115; 116; 117] can be applied to resource allocation and scheduling to dynamically allocate frequency, time, and power resources, thereby meeting real-time user needs and maximizing overall system performance.
## 2 Materials and Methods
### Mechanism of Dielectric-Enhanced Shockwaves
utilized a combination of transient electrical and physical parameter diagnostics and high-speed imaging to study the ignition process of aluminum powder suspensions in electrical explosions [27]. They found that the ignition of the aluminum powder is influenced by the combined effects of near-field shockwaves and plasma radiation. The ignited aluminum powder transfers energy to the surrounding liquid medium, further enhancing the shockwave effect. [PERSON] et al. performed electrical explosions with copper and aluminum wires in aluminum powder suspensions and discovered that the intense radiation flux from the electrical explosion ignited the aluminum particles, achieving a combustion efficiency of 32-79% [28]. The exothermic reaction for the electrical explosion ignition of aluminum powder suspensions is illustrated in Equation (1) [28]. \[2\text{Al}_{\text{(solid)}}+3\text{H}_{2}\text{O}_{\text{(liquid)}}=\text{Al} _{2}\text{O}_{\text{3(solid)}}+3\text{H}_{2\text{(gas)}}+\text{Q} \tag{1}\]  
To investigate these influencing factors, chemically stable reducing agents, specifically aluminum (Al) and iron (Fe) microparticles, were used to prepare suspensions serving as dielectric media. Additionally, suspensions of common oxidizing agents, namely iron (III) oxide (Fe\({}_{2}\)O\({}_{3}\)) and copper (II) oxide (CuO) microparticles, were prepared as dielectric media. These metallic microparticles were selected for their high slurry detonation thresholds, excellent safety profiles, and non-toxic properties. The energy (E\({}_{\text{b}}\)) acting on the rock primarily originates from shock waves and bubble pulsation. Bubble pulsation occurs due to the metal wire undergoing a series of phase transitions and energy exchanges with the surrounding environment. This process vaporizes the medium around the metal wire, generating numerous bubbles. As these bubbles expand, the pressure difference between their interior and exterior increases significantly. When the internal and external pressures equilibrate and the bubble reaches its maximum diameter, it begins to contract, initiating pulsation [29]. After several pulsation cycles, the bubbles collapse, ejecting gas and reaction products outward, generating a considerable jet impact force. This jet force augments the explosive energy. Consequently, enhancing the bubble pulsation effect can significantly increase the shock wave energy. To investigate the bubble pulsation factor, CO\({}_{2}\) gas was introduced into the liquid medium. The high-temperature and high-pressure environment generated by the electrical explosion promotes the rapid release of dissolved gas in the form of bubbles, thereby enhancing the bubble pulsation effect. Simultaneously, the massive release of gas instantly increases the ambient pressure of the liquid medium. Consequently, this dielectric medium serves as a model system for studying the combined effects of both influencing factors.
## 4 Results
### Spatial-Temporal Dynamics of Wetland Vegetation in the Dongting Lake Area
The land cover that was transformed from vegetation to build-up area in dispersed study regions in the early years of the research duration has been detected by BEAST (Figure 13b). In addition, the most significant disturbance of trend was observed in 2011, when the clearing of poplar policy was carried out. The extensive deforestation of poplar also brought vegetation death and degeneration of ecosystem. Figure 13c shows that the greenhouse effect may benefit the growth of vegetation and crops, because the NDVI peak of the stable area occurred in the years after large areas of poplars have been removed since 2011. The maximum NDVI value was captured in 2011 in some areas, because the extensive deforestation of poplar led to the gradual decrease in NDVI since 2011. All of the results demonstrate that the vegetation changes vary spatially and temporally. Figure 11: The total number of seasonal and trend changepoints, and the slope of trend during the period 2000–2019 over the study area measured by the BEAST. (**a**) the total number of seasonal changepoints, (**b**) the total number of the changepoints in trend, (**c**) the average of estimated slope of trend (unit: 20 a\({}^{-1}\)). The proportion of pixels with abrupt changes and that with the maximum NDVI value are shown in Figure 14a. The annual average temperature and precipitation over the study area in the research duration were calculated (Figure 14b) to investigate the relation between vegetation variation and climate change. As Figure 14 shows, the climate changes are not the main reason for vegetation variation, though the annual average temperature and precipitation in recent years are slightly different from that of previous years. The seasonal and trend change both peak in 2008 (Figure 14a), when the frozen disaster happened and resulted in a lots of vegetation death [59]. The change of Dongting Lake wetland in the later period of the study duration is larger than that in the early period, and the change accelerated gradually, because of the environmental pollutions and rapid urbanization in recent years. The maximum NDVI value appears in 2011, because the poplar cleaning policy in the Dongting Lake area came into force. Most poplars were cut in 2011, and the effect of the policy lasted for several years  
Figure 12: (**a**) The proportion (%) of seasonal- and trend-changed pixels with single/multiple times abrupt change(s), (**b**) the proportion (%) of stable and changed pixels for each DEM range. Figure 13: The spatial and temporal variation of the Dongting Lake wetland during 2000–2019. (**a**) and (**b**) give the time of the most significant seasonal/trend disturbance detected by BEAST; (**c**) is the year of the maximum NDVI of Dongting Lake wetland vegetation during the last 20 years. (five years since 2011). In general, extreme weathers and policy incentives are the main reasons for the vegetation change in the last 20 years.
## 3 Results and Discussion  
### Characterization of the Datasets  
Table 3 shows the objective measures to characterize the datasets. Largest polyps are found in PICCOLO, but also with greater variability of sizes. Brightest and highest contrast images can be found in Kvasir-SEG, while CVC-EndoSceneStill and PICCOLO show similar appearance in these aspects.  
It is also important to remark that the PICCOLO dataset includes WL and NBI images, which results in lower values in the brightness channel at the same time that the contrast is increased (higher values for the histogram flatness measure and lower values for histogram spread).
# Dynamic Round Robin CPU Scheduling Algorithm Based on K-Means Clustering Technique
###### Abstract  
Minimizing time cost in time-shared operating system is the main aim of the researchers interested in CPU scheduling. CPU scheduling is the basic job within any operating system. Scheduling criteria (e.g., waiting time, turnaround time and number of context switches (NCS)) are used to compare CPU scheduling algorithms. Round robin (RR) is the most common preemptive scheduling policy used in time-shared operating systems. In this paper, a modified version of the RR algorithm is introduced to combine the advantageous of favor short process and low scheduling overhead of RR for the sake of minimizing average waiting time, turnaround time and NCS. The proposed work starts by clustering the processes into clusters where each cluster contains processes that are similar in attributes (e.g., CPU service period, weights and number of allocations to CPU). Every process in a cluster is assigned the same time slice depending on the weight of its cluster and its CPU service period. The authors performed comparative study of the proposed approach and popular scheduling algorithms on nine groups of processes vary in their attributes. The evaluation was measured in terms of waiting time, turnaround time, and NCS. The experiments showed that the proposed approach gives better results.  
CPU scheduling; round robin; waiting time; turnaround time; K-means +
Footnote †: journal: Applied