esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Consumption Analysis of ISO/IEC 29192-2 Standard Lightweight Ciphers
## 3 ISO/IEC 29192-2 Standard Lightweight Ciphers
### CLEFIA Algorithm  
CLEFIA is a block cipher encryption algorithm designed to operate on 128-bit data blocks with key length options of 128, 192, or 256-bits. It employs a Feistel network structure, a common design for symmetric key block ciphers, which divides the data block into two halves and processes them through multiple rounds of encryption. The number of rounds in CLEFIA depends on the key length: 18 rounds for 128-bit keys, 22 rounds for 192-bit keys, and 26 rounds for 256-bit keys. Each round applies a series of operations, including substitution, permutation, and mixing, to provide strong security against cryptanalysis. In general, lightweight algorithms that use the Feistel architecture have more rounds than those that use the SPN architecture. To boost encryption speed, the CLEFIA designers lowered the number of rounds while maintaining security using a strong diffusion layer. The CLEFIA encryption structure is given in Figure 1a. The substitution layers ensure non-linearity, enhancing resistance to differential and linear cryptanalysis. The diffusion layer, implemented using a carefully designed MDS (maximum distance separable) matrix, spreads input changes across the data block, maximizing security with minimal computational overhead. CLEFIA also features a dual pseudo-random function (F0 and F1) within each round, which operates on data halves to further strengthen the encryption process. These elements work together to ensure CLEFIA's efficiency while maintaining robust protection against cryptanalytic attacks.  
Figure 1: Structural representation of lightweight encryption algorithms standardized under ISO/IEC 29192-2. (**a**) CLEFIA: A 128-bit block cipher using a Feistel network architecture with variable key lengths (128, 192, 256 bits) and modular key mixing and diffusion layers. (**b**) PRESENT: A 64-bit block cipher based on an SPN architecture with 31 rounds, incorporating S-box substitution and bitwise permutation layers. (**c**) LEA: A 128-bit block cipher utilizing ARX (addition, rotation, XOR) operations for high-speed encryption, with 24, 28, or 32 rounds depending on the key size.
# Effect of Moderate-Intensity Endurance Exercise on Inflammatory Cytokines in Leukocytes of Dogs
###### Abstract  
This study aimed to investigate the effect of a treadmill exercise on hematological and serum biochemical parameters and the expression of immune-related cytokine genes in leukocytes. For the experiment, six healthy adult dogs were divided into exercise and control groups. The exercise group performed an endurance exercise three times a week for four weeks. Blood samples were collected before exercise, two weeks after exercise, and post-exercise, and hematological and serum biochemical analysis and cytokine gene analysis were conducted. In the exercise group, white blood cell count (WBC), aspartate aminotransferase, serum alkaline phosphatase, and glucose levels were significantly decreased, but there was no change in the control group. The mRNA expression of TNF-\(\alpha\), IFN-\(\gamma\), IL-\(\beta\), and IL-4 was significantly decreased in the exercise group compared to the control group. There was no difference in IL-6, IL-8, and IL-10 mRNA expression between groups. The results in the current study demonstrate that short-term moderate-intensity endurance exercise alters WBC levels and mRNA cytokine expression in leukocytes and may have a meaningful effect on immune health in dogs.  
dog; endurance exercise; cytokine; hematological analysis; serum biochemical analysis Article
## 4 Simulations
### Comparison of Evaluation Indicators  
In this subsection, to compare the commonly used metrics for cascading failures in Equation (5) with the metrics proposed in Equation (7), we use Equation (8) to define the cluster head node load, letting \(\sigma=0.2\). The node capacity is adjusted by the exponential adjustment parameter in Equation (9), letting \(\lambda=0.8\). The communication efficiency of the overall network is calculated by Equation (6). The comprehensive evaluation indicators in Equations (5) and (7) are compared. By simulation, the results are as follows.  
As shown in Table 2, the evaluation metrics \(M\) proposed in this paper are consistently lower than the commonly used \(G\). This is a more realistic phenomenon in hierarchical industrial wireless sensor networks. For example, when \(\beta=0.3\), there are a total of 17 cluster head nodes surviving, yet only 13 cluster head nodes can connect to the sink node. Moreover, for the network efficiency, even though the number of survivors is similar, \(M\) and \(G\) differ significantly. This is because \(M\) takes into account the variation in transmission distance. Compared with the original network, the transmission distance is greatly increased and the network efficiency is greatly reduced, because the path to the convergence node is destroyed, and the original evaluation index does not reflect the network-efficiency problem. Therefore, the reasonableness of \(M\) can be verified.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Exponential Adjustment**} & \multicolumn{2}{c}{**Number of Surviving Nodes**} & \multicolumn{2}{c}{**Communication Efficiency**} & \multicolumn{2}{c}{**Comprehensive**} \\ \multicolumn{2}{c}{**Parameter \(\beta\)**} & & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**Evaluation Indicators**} \\ \hline  & M & G & & M & G \\
0.1 & 0 & 0 & 0 & 0 & 0 \\
0.2 & 0 & 5 & 0 & 0 & 0.050 \\
0.3 & 13 & 17 & 4.91 & 0.017 & 0.170 \\
0.4 & 0 & 7 & 0 &
# Unmanned Aerial Vehicle Routing Problems: A Literature Review
## 5 Challenges in UAV Routing
### Weather Conditions  
Weather is a critical feature for UAV routing as it affects the travel speed of the UAV, and the temperature in the atmosphere affects the energy consumption [14; 17; 141] of batteries used in UAVs. Figure 8 presents the relationships between different factors linked to energy consumption of UAVs. Weather in various forms is critical for energy consumption as it affects the travel speed of the UAV, and the temperature in the atmosphere affects the energy capacity of batteries used in UAVs. Air density affects energy consumption, but that is a function of humidity, air pressure, and temperature. Flying with the wind could, for example, reduce energy consumption. Cold temperatures may adversely affect battery performance until the batteries warm up [14; 142; 11].  
The current state of research has not considered the weather factors and ignores the impact of weather on performance [84; 94; 68]. Interestingly, this seems to add another type of SVRP that should be considered as weather is changing over time in a stochastic manner [134]. Rarely has research focused on considering wind conditions on energy consumption while simultaneously using that information in routing of UAVs [14; 139; 137].
# Surface Roughness Analysis in the Hard Milling of JIS SKD61 Alloy Steel
## 4 Results and Discussion
### Model of Ra-Based RSM  
The quadratic mathematical model, in this research, based on the experimental data, is developed by means of the RSM method for predicting the \(Ra\) in the hard milling of JIS SKD61 as follows:  
\[\begin{split} Ra\;=\;0.337\;+\;0.466\times 10^{-3}\times V\;+\;32.505 \times f-0.604\times a-0.0211\times H-9.866\times 10^{-6}\times V^{2}+\\ 578.333\times f^{2}-0.191\times a^{2}\;+\;0.506\times 10^{-3} \times H^{2}-0.0502\times V\times f+\;0.0092\times V\times a-7.155\times 10^{-5}\\ \times V\times H\;+\;7.694\times f\times a-0.977\times f\times H\;+ \;0.0091\times a\times H\end{split} \tag{8}\]  
The differences between the predicted and the measured values are specified in Figure 6. It shows that there is a good correlation between experimental and predicted results. Thus, the established model is effectively used for predicting \(Ra\) in the hard milling of JIS SKD61 alloy steel with 95% confidence intervals.  
Figure 6: The difference between predicted and measured values of \(Ra\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **DF** & **SS** & **MS** & **F** & **\(p\)** & **PC (\%)** & **Remarks** \\ \hline Model & 14 & 0.395893 & 0.028278 & 23.32 & \textless{}0.0001 & 96.45 & Significant \\ \(V\) & 1 & 0.012220 & 0.012220 & 10.08 & 0.008 & 2.97 & Significant \\ \(f\) & 1 & 0.268156 & 0.268156 & 221.2 & \textless{}0.0001 & 65.33 & Significant \\ \(a\) & 1 & 0.052057 & 0.052057 & 42.93 & \textless{}0.0001 & 12.68 & Significant \\ \(H\) & 1 & 0.010952 & 0.010952 & 9.03 & 0.011 & 2.66 & Significant \\ _V_\({}^{2}\) & 1 & 0.000228 & 0.000228 & 0.19 & 0.672 & 0.05 & Insignificant \\ _f_\({}^{2}\) & 1 & 0.020068 & 0.020068 & 16.55 & 0.002 & 4.88 & Significant \\ _a_\({}^{2}\) & 1 & 0.000353 & 0.000353 & 0.29 & 0.600 & 0.08 & Insignificant \\ _H_\({}^{2}\) & 1 & 0.000963 & 0.000963 & 0.79 & 0.390 & 0.23 & Insignificant \\ _V_\({}^{2}\) & 1 & 0.000768 & 0.001419 & 1.17 & 0.301 & 0.18 & Insignificant \\ _V_\({}^{2}\) & 1 & 0.005720 & 0.019275 & 15.90 & 0.002 & 1.39 & Significant \\ _V_\({}^{2}\) & 1 & 0.00019 & 0.000720 & 0.59 & 0.456 & 0.004 & Insignificant \\ _f_\({}^{2}\) & 1 & 0.002131 & 0.002131 & 1.76 & 0.210 & 0.51 & Insignificant \\ _f_\({}^{2}\) & 1 & 0.021511 & 0.021511 & 17.74 & 0.001 & 5.24 & Significant \\ _a_\({}^{2}\) & 1 & 0.000747 & 0.000747 & 0.62 & 0.448 & 0.18 & Insignificant \\ Error & 12 & 0.014551 & 0.001213 & - & - & - & - \\ Total & 26 & 0.410444 & - & - & - & - & - \\ \hline \hline \end{tabular} \(R^{2}\) = 0.9645 and adjusted \(R^{2}\) = 0.9232.  
\end{table}
Table 5: Analysis of variance for \(Ra\).
## 2 Overview of IoV  
Simply put, IoV is a form of IoT that is dedicated to vehicles solely. While IoV itself is a vehicular ad hoc network (VANET) that has now been advanced and linked to the Internet [8; 44], IoV can be defined as an integrated network platform that enable vehicles and its environment to interact and exchange information towards an intelligent transportation system by means of a dedicated communication media. The need to transit into an intelligent transportation system (ITS) demanded that VANET infrastructures be upgraded to accommodate for the increasing vehicles added to the network [45; 46; 47]. Figure 2 illustrates how a typical IoV system is made up and how vehicles communicate in the network. In an IoV system, vehicles communicate with other vehicles by means of vehicle-to-vehicle (V2V) communication, and with RSUs aided by vehicle-to-RSU (V2R) communication [8; 20]. The IoV system enables real-time information sharing relating to traffic or road condition; safety warnings can be relayed, and a better traffic management is achieved [44]. Other supporting applications that have emanated from IoV include autonomous driving, infotainment, smart payment services, and vehicle forensic investigations purposes [8; 27; 48]. It is projected that there will be over 2 billion vehicles in the network by 2035 [45]. The main goal of IoV is to lower the rate of accidents occurrence, drastically reduce traffic congestion, and provide secure and smart information exchange services [49; 50].  
The first step in formation of IoV is by integrating vehicles into wireless networks. Some of the existing wireless access technologies includes WLANs, WiMAX, Cellular Wireless, and satellite communications [46; 47; 51]. Vehicular communications in IoV are broadly divided into two main channels: intra-vehicle communication and inter-vehicle communication [44]. Intra-vehicle communication takes place within the vehicle. It is often referred to as vehicle-to-sensor communication (V2S) [44]. It is the interaction between the vehicle and the electronic control units (ECUs) inside the vehicle. The network protocols that facilitate intra-vehicle communication include CAN Bus, Local Interconnect Network (LIN), and Flex Ray communication networks [44]. Inter-vehicle communication, on the other hand, involves the interaction between the vehicles and other road entities connected in the IoV system [44; 52]. Vehicular communications that fall under the inter-vehicle communication include vehicle-to-vehicle (V2V), vehicle-to-roadside-unit (V2R), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), vehicle-to-sensor (V2S), and vehicle-to-grid (V2G) [44]. The Dedicated Short-Range Communication (DSRC), the Long-Term Evolution Vehicular Network (LTE-V), and the 5 th Generation mobile networks (5G) are among the network protocols dedicated to inter-vehicle communications [44; 53; 54].  
Figure 2: Typical infrastructure of IoV network. Adapted from [8].
## 2 Experiments
### Surface Characterization  
#### 2.4.1 Surface Roughness  
The images of the surface morphology for all the surface conditions were obtained using a laser-scanning confocal microscope (NTUME Precision Metrology Lab, Brand: Keyence, Osaka, Japan, Controller: VK-X1000, Measuring Head: VK-X1100). Confocal,  
Figure 5: Schematic of laser-scanning path and groove spacing in the present study.  
Figure 6: Schematic of test surface conditions in pool boiling experiments.  
three-dimensional, and surface profile images are shown in Figures 7 and 8. Surface roughness is a critical factor that can significantly influence the boiling heat transfer performance. Many studies have focused on the roughness effect on boiling surfaces; however, very few studies have considered anisotropic surface roughness, which depends on the measurement direction in microgrooves. The measurement results obtained in the parallel and normal directions to the microgrooves are shown in Table 3. To provide greater detail for the morphology of the microgrooves, scanning electron microscopy (SEM) images of the microgrooved surfaces are presented in Figure 9.  
Figure 7: Confocal, three-dimensional (3D), and surface profile images for S1 and S2 conditions measured using the laser confocal microscope. (The blue arrow represents the measured direction is normal to the grooves, and the green arrow represents the measured direction is parallel to the grooves).  
Figure 8: Confocal, three-dimensional (3D), and surface profile images for S3 and S4 conditions measured using the laser confocal microscope. (The blue arrow represents the measured direction is normal to the grooves, and the green arrow represents the measured direction is parallel to the grooves.)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Ra (Parallel, μm)** & **Ra (Normal, μm)** \\ \hline S1 & 1.31 & 1.73 \\ S2 & 1.64 & 6.86 \\ S3 & 1.76 & 6.11 \\ S4 & 1.68 & 5.09 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surface roughness measured in parallel and normal directions on S1–S4 surfaces.
## 4 Discussion  
### Insights from A. tonsa Test  
As a primary consumer, _A. tonsa_ feeds on phytoplankton and serves as a key food source for fish species [7; 30]. Its population trends directly reflect the broader environmental conditions, such as nutrient availability, pollution levels, and impacts from climate change [45; 46; 66]. A decline in _A. tonsa_ populations could indicate problems like eutrophication or water quality degradation, signaling a need for stronger conservation measures [67; 68; 69; 66]. Conversely, stable populations suggest effective management of these issues.  
Monitoring _A. tonsa_ is essential for assessing the success of conservation strategies and ensuring the long-term sustainability of the Black Sea ecosystem.  
This study was considered acceptable as the mortality (10%) in the control _A. tonsa_ and the dissolved oxygen concentration at the end of the test (8.65-9.28 mg/L) were in the range stipulated by the acceptability criteria of the protocol.  
Figure 6: Effect (%) of reference toxicant concentrations for 24 and 48-h exposure of _Acartia tonsa_.  
Protocol acceptability criteria state that the percentage mortality of the controls should be less than or equal to 10%, the dissolved oxygen concentration at the end of the test should be greater than or equal to 4 mg/L, and the toxicity of the reference chemical should be within the documented range.  
Our study found that the sensitivity of copepods to 3,5-dichlorophenol is consistent with the range stipulated in ISO standard, specifically with an obtained value of 1.15 mg/L. This value aligns with the expected sensitivity thresholds, indicating that the copepods exhibit a measurable response to 3,5-dichlorophenol exposure at this concentration. Such findings are critical for environmental monitoring and risk assessment, as they validate the use of copepods as bioindicators for aquatic toxicity. The alignment with ISO standards also enhances the reliability of our results, suggesting that our methodological approach is robust and can be replicated in other studies for consistent outcomes.  
The data indicates that the environmental parameters (temperature, salinity, pH, dissolved oxygen (D.O.), and oxygen saturation) were in the protocol range across different treatments (Table S7). Any slight variations observed were within acceptable limits and did not impact the validity of the results. Over time, observed trends, such as a decrease in temperature and pH, were consistent across all treatments, including the control. This consistency suggests that any changes in the experiment's outcomes are likely due to the treatments rather than environmental fluctuations.  
Therefore, the results were validated as the environmental conditions were maintained within a narrow and acceptable range across different treatments and time points. Minor variations observed do not significantly impact the overall experiment, ensuring that the treatment effects are the primary variable influencing the outcomes. Maintaining these controlled conditions makes the experiment's findings reliable and can be attributed to the treatments rather than extraneous environmental factors.
## 4 Multi-Objective Optimization Design
### Design Results of MOOD Problem
As can be seen from the table, the geometrical parameters that meet the requirements of the multi-objective optimization are the maximum buried depth, the maximum curve angle and the maximum curve radius within the domain, with an expected value of 0.725 as a result. Comparing the results of the optimization at point A, the optimization at point A results in the maximum buried depth, the minimum curve angle and the maximum curve radius within the domain, while the optimization at point B results in the maximum buried depth, the maximum curve angle and the maximum curve radius within the domain, as shown in Table 10. Here, combined with the analysis in Tables 6 and 7, the F-value of the curve angle in the RSM analysis of point B is greater than the F-value of point A, and the \(p\)-value of the curve angle is smaller than the \(p\)-value of point A. This means that the RSM analysis at point B has a more significant effect on the MOOD results regarding the curve angles, and the data are also more reliable. Therefore, the multi-objective optimization results for point B are chosen, i.e., the geometrical parameters that meet the requirements of the MOOD are the maximum buried depth, the maximum curve angle and the maximum curve radius within the domain. To verify the accuracy of the Design-Expert optimization design results, the finite element simulations method (FEM) was carried out based on the geometry obtained from the results. A comparison of the optimized design results with the FEM results is shown in Table 11, where a comparison of the data shows that the error rate of the FEM simulation and RSM optimal solution prediction results regarding the displacement response is 2.63% and that regarding the acceleration response is 1.90%. It can be seen that the results are basically the same and, therefore, the optimization results can be considered valid. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Depth (m)** & **Angle (deg)** & **R (m)** & **S (m)** & **\(a\) (m/s2)** & **Expected Value** \\ \hline
20.000 & 60.000 & 349.999 & 0.113 & 8.315 & 0.725 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Design expert optimized design results for point B.  
Figure 10: Expectation function and design variable relation surface for point B.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 5 Federated Training of Edge Autoencoders
We explored two federated learning scenarios:  
* **Scenario 1 (Non-IID--Single Digit per Device):** Each device was assigned images of a single digit from MNIST--e.g., device 0 trained on digit '0', device 1 on digit '1', and so on--covering all ten digit classes. This setup represents an extreme case of non-IID data distribution. * **Scenario 2 (IID--Partitioned Dataset):*
* Each device received a distinct subset of the MNIST dataset containing all ten digit classes but with no overlap between subsets. This scenario investigates two aspects:
* Training on Distinct MNIST Subsets: We evaluated how well the global model learns when devices are trained on disjoint partitions of the dataset. * Robustness to Anomalous Training Data: Selected devices were provided with corrupted training samples instead of regular MNIST, allowing us to assess the global model's resilience in the presence of local data contamination. The following subsections provide detailed descriptions of these federated training scenarios.