esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dual-Polarized Omnidirectional Rectenna Array for RF Energy Harvesting Harvesting
###### Abstract  
In this paper, a dual-polarized omnidirectional rectenna array using a hybrid power-combining scheme is proposed for the applications of RF energy harvesting. In the antenna design part, two omnidirectional antenna subarrays are created to receive horizontally polarized electromagnetic (EM) waves and a four-dipole subarray is produced to receive vertically polarized incoming EM waves. The two antenna subarrays of different polarizations are combined and optimized, so as to reduce the mutual influence between them. In this way, a dual-polarized omnidirectional antenna array is realized. In the rectifier design part, a half-wave rectifying structure is adopted for converting the RF energy into DC energy. Based on the Wilkinson power divider and 3-dB hybrid coupler structure, a power-combining network is designed to connect the whole antenna array and rectifiers. The proposed rectenna array is fabricated and measured under different RF energy harvesting scenarios. All simulated and measured results are in good agreement, which verifies the capabilities of the designed rectenna array.  
RF energy harvesting; dual-polarized; omnidirectional antenna; rectenna Article  
A Dual-Polarized Omnidirectional Rectenna Array for RF Energy Harvesting  
202320141071[[https://doi.org/10.3390/mil4051071](https://doi.org/10.3390/mil4051071)]([https://doi.org/10.3390/mil4051071](https://doi.org/10.3390/mil4051071))
## 4 Numerical Modeling
In the remaining sections, \(SRF_{QC}<SRF_{Q}\), and the difference depends on \(\sigma_{c}\). The _RMi_ system uses SL in its structure for the stress factor (Equation (13)), and its ratings are shown in Table 6. Comparing Table 6 with Table 2, SL is only applicable for the first three conditions of Table 2 where \(\sigma_{c}/\sigma_{1}>5\). Equation (13) shows that a high value of SL is favorable, but it is not applicable when \(\sigma_{c}/\sigma_{1}<5\), as shown in Table 2. The comparison of actual installed supports with those supports that are suggested by _RMi_ showed that the suggested support was heavier than the actual. The equations for the calculation of \(Q_{c}\) and \(G_{c}\) are rewritten in such a way that each equation is the function of the same parameter, and a relation is developed between the two, keeping in view the limitation of data. The extension of the two systems is based on Equations (22) and (23); therefore, these equations are used for determining the empirical supports of four rock units, using the \(Q\)-system support chart. The support application is numerically evaluated in terms of a decrease in the total displacement at different points along the tunnel periphery, and in the plastic zone. The maximum decrease in total displacement occurred at the tunnel crowns, with a value of 1.3 mm for the SS-2 rock unit of Project 6 (Table 12). According to plasticity theory, a plastic zone occurs around a tunnel after excavation when the induced stresses exceed the rock mass strength. The application of empirically recommended support not only reduced the number of yield elements, but also substantially decreased the extent of the plastic zone in all of the cases. With support, yield in tension was not observed, while yield in shear was either zero or substantially reduced. As can be seen from the capacity diagram in Figure 11, none of the cases cross the limit of FOS = 1.5. The capacity diagram for the liner shows that for the SS-2 rock unit of Project 6, the dominant failure mode was in compression; however, it too does not cross FOS = 1.5. In the case of the GN rock unit of Project 5, the dominant failure mode was in tension, but FOS > 1.5.
## 4 Experiment Settings
### Statistical Significance Test  
We assess the significance of our results--derived from multiple OOB bootstrap samples--using the Friedman test followed by the Nemenyi post-hoc test for pairwise comparisons.  
The Friedman test, a non-parametric repeated-measures method [21], ranks each algorithm's performance across bootstrap samples and compares their average ranks. Its statistic is computed as  
\[\chi_{F}^{2}=\frac{12n}{k(k+1)}\left[\sum_{j=1}^{k}R_{j}^{2}-\frac{k(k+1)^{2}}{4 }\right], \tag{44}\]  
where \(k\) is the number of algorithms, \(n\) is the number of bootstrap samples, and \(R_{j}\) is the sum of the ranks for the \(j\)th algorithm. The null hypothesis (\(H_{0}\)) asserts no significant differences among methods; if \(p<0.05\), \(H_{0}\) is rejected and we proceed with the Nemenyi test.  
The Nemenyi test computes a critical difference (CD) as  
\[CD=q\sqrt{\frac{k(k+1)}{6n}}, \tag{45}\]  
which indicates whether the difference in average ranks between any pair of methods is statistically significant. This Friedman-Nemenyi framework is robust against non-normal data and effectively compares multiple models while mitigating Type-I error risks.
# Chosen Plaintext Combined Attack against SM4 Algorithm
## 3 Methodologies  
It can be seen from Section 2.2 that chosen plaintext power analysis can only obtain one round key in each round of analysis. To recover the initial key of SM4, four rounds of analysis are needed to obtain four round keys. In order to improve the chosen plaintext power analysis, we utilize the differential characteristics of SM4 (see Section 2.3 for details). Thereby, it is only necessary to analyze the 2 nd and 4 th round of SM4 encryption to recover the whole initial key. The whole combined attack can be divided into two parts. Firstly, the intermediate value of the second and fourth round is obtained by round reduction chosen plaintext power analysis. Then the initial key is determined by differential analysis using S-box differential characteristics. The following two parts of the combined attack are introduced in turn.
# Novel Reliable and Energy-Efficient Routing Protocols for Wireless Sensor Networks
## 2 Related Work
Most of these protocols improve reliability by increasing the redundant data, which means wasted energy. These protocols, however, do not achieve optimal energy balancing, and do not take the reliability into consideration when selecting an energy-efficient path. As a result, further investigation is needed into developing packet forwarding mechanisms.
## 4 Results and Discussion  
### S-N Curves with Confidence Level  
Based on fatigue tests at \(-60\)\({}^{\circ}\)C, the test data of T- and cruciform welded joints were obtained. Then, the logarithmic S-N curve was confirmed based on data processing in Section 3.4, as shown in Table 5.  
The logarithmic S-N curves of the two welded joints at the 50% and 95% confidence levels are shown in Figure 10a,b.  
Figure 10: S–N curves of two welded joints at low temperature: (**a**) 50% confidence level and (**b**) 95% confidence level.  
\begin{table}
\begin{tabular}{c c} \hline \hline
50\% confidence level & \(\lg N=13.119-3\lg 5\) \\ \hline
95\% confidence level & \(\lg N=13.057-3\lg 5\) \\ \hline \hline
50\% confidence level & \(\lg N=13.119-3\lg 5\) \\ \hline
95\% confidence level & \(\lg N=13.057-3\lg 5\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Double logarithmic S–N curve with confidence level.  
At the logarithmic stress range of 2.50 (316 MPa), it could be observed that the logarithmic fatigue life of the different welded joints was different under the same logarithmic stress range. For example, under the logarithmic stress range of 2.50 (316 MPa), the logarithmic fatigue life at the 50% confidence level of the T-welded joints (lg\(N_{1}\)) was 5.632 (428,548 cycles), whereas that of the cruciform welded joints (lg\(N_{2}\)) was 5.623 (419,759 cycles). Under the logarithmic stress range of 2.50 (316 MPa), the logarithmic fatigue life at the 95% confidence level of the T-welded joints (lg\(N_{3}\)) was 5.587 (386,366 cycles), whereas that of the cruciform welded joints (lg\(N_{4}\)) was 5.557 (360,579 cycles). In summary, it could be observed that the fatigue life of the T-welded joints at the 50% and 90% confidence level was longer than that of the cruciform welded joints.
## 2 Methodology
### Hybrid Models  
This study aims to optimize the XGBoost model for predicting the quality classification of surrounding rock in underground engineering. To achieve this, ZOA and BO were employed to select the optimal hyperparameter combinations for XGBoost. These two optimization algorithms were chosen for their exceptional performance in addressing complex hyperparameter tuning problems and their distinct advantages. ZOA is a metaheuristic optimization algorithm inspired by the collective behavior of zebras, simulating their evasion behavior and group coordination mechanisms under predator threats. ZOA possesses global search capabilities and can avoid local optima, enabling it to find near-global optimal solutions in complex and high-dimensional search spaces. BO, on the other hand, employs an efficient exploration-exploitation balance strategy to identify better hyperparameter combinations within fewer iterations. Despite the different optimization philosophies of these algorithms, they follow similar steps when optimizing XGBoost, as illustrated in Figure 1. The specific process is as follows:  
1. Data Preparation: Collect rock mass classification data from underground engineering projects, randomly dividing it into training and test sets. Preprocess the data to eliminate the effects of class imbalance, scale, and magnitude. The same training set is used for both models, while the test set is reserved for evaluation.
2. Parameter Setting and Initialization: Define the hyperparameter optimization range for XGBoost. Set the population size and iteration number for ZOA and the same iteration number for BO. ZOA randomly generates a group of zebra individuals in the search space, while BO randomly selects several points within the search space.
3. Fitness Evaluation: Establish an appropriate fitness function for models and calculate the fitness value for each individual or point.
4. Iterative Update: ZOA updates positions using strategies to evade predators, retaining individuals with higher fitness for the next generation. BO selects the next evaluation point based on the surrogate model through the acquisition function and updates the surrogate model's parameters with new data points. In each iteration, the model evaluates the objective function.
5. Output Optimal Solution: Upon reaching the stopping condition, output the optimal hyperparameter combination.  
Figure 1: Overall construction process of the hybrid models.
## 2 Evaluation Study and Design
### Electromagnetic Simulations
#### 2.2.3 SAR Evaluation  
One of the notable characteristics of this technology is its ability to be used in the presence of humans, thereby effectuating real-time sanitation and reducing the risk of contagion in the event of an infected individual. To validate this approach, the Specific Absorption Rate (SAR) value was evaluated under these specific conditions and compared to the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines [27].  
Simulations were conducted to determine the maximum field amplitude at the edge of the nearest subject in two scenarios: the device placed on a table or mounted on a wall. The maximum field amplitude in the subject's head and trunk was estimated to be 0.48 V/m. Considering conductivity and muscle density, the SAR was calculated to be 0.002 W/kg under these conditions. According to ICNIRP regulations, the SAR should not exceed 2 W/kg for the head and trunk. As the calculated SAR value is significantly below this threshold, the device is compliant with the ICNIRP guidelines.
# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 3 Materials and Methods
### Data Collection and Preprocessing  
Video CaptureReal-time video data were captured using a web camera, accessed through the OpenCV library. OpenCV provided tools to read frames from the video feed sequentially, ensuring a continuous stream of image data for real-time processing. The use of OpenCV enabled the efficient extraction of each frame from the video stream, which is essential for real-time monitoring and fast drop detection.  
Image PreprocessingEach captured frame was resized to 960 \(\times\) 960 pixels using a letterbox function to preserve the aspect ratio and avoid distortion. The frames were then converted into tensors and normalized using the ToTensor function from the torchvision library. This step was essential to prepare the frames for input into the PyTorch model, which operates on tensors.  
Through this preprocessing, each video was correctly formatted for the YOLOv7-W6-Pose estimation model, enabling efficient real-time fall detection. Thus, the system can quickly and efficiently analyze images to detect possible falls in real time.
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### Machine Learning Algorithms
#### Instance Based  
Instance Based algorithms perform data classification based on the comparison of new test data, directly to the training instances. The comparison based on a similarity function and its output is fed into a classification function [56]. Instance based algorithms store training instances in their memory, therefore they can be pretty memory consuming as the data are increased.  
Between an FF-NN, and Regression Tree, the instance based algorithm _k_-Nearest Neighbor (_k_-NN) is used in [29] to make real-time autonomous accident detection as mentioned in Section 2.1.6, _k_-NN is an algorithm used for supervised classification and regression. The main principle of _k_-NN is that similar data have minimum distance with each other. _k_-NN is run multiple times on the testing data in order to select the ideal \(k\) factor that minimizes the errors while classifying data. We have already met _k_-NN in [35], where road surface abnormalities are detected using accelerometer data and applying _k_-Nearest Neighbour (_k_-NN), RF, and Support Vector Machine (SVM) algorithms. The parameter \(k\) of _k_-NN represents the number of the test sample's neighbours. It was adjusted empirically to optimize the results. All the classifiers presented an accuracy over 99% based on the CCR measurements. Finally, in [24], the problem of the automated detection of street elements dealt with the application of a DBN is shown. At the end of the DBN a classifier is used for the final element classification. When using _k_-NN as the selected classifier, a precision close to 90% was reached for street crossings, while a percentage of 80% was achieved for traffic lights and roundabouts.
###### Abstract  
_Haematococcus pluvialis_ accumulates astaxanthin, which is a high-value antioxidant, during the red cyst stage of its lifecycle. The development of a rigid cell wall in the cysts hinders the recovery of astaxanthin. We investigated morphological changes and cell disruption of mature _H. pluvialis_ cyst cells while using high-pressure homogenization for astaxanthin extraction. When treated with French-press-cell (pressure, 10,000-30,000 psi; passage, 1-3), the intact cyst cells were significantly broken or fully ruptured, releasing cytoplasmic components, thereby facilitating the separation of astaxanthin by ethyl acetate. Fluorescence microscopy observations using three different fluorescent dyes revealed that a greater degree of cell breakage caused greater external dispersion of astaxanthin, chlorophyll, lipids, proteins, and carbohydrates. The mechanical treatment resulted in a high cell disruption rate of up to 91% based on microscopic cell typing and Coulter methods. After the ethyl acetate extraction, the astaxanthin concentration significantly increased by 15.2 mg/L in proportion to the increase in cell disruption rate, which indicates that cell disruption is a critical factor for solvent-based astaxanthin recovery. Furthermore, this study recommends a synergistic combination of the fast instrumental particle-volume-distribution analysis and microscope-based morphologic phenotyping for the development of practical _H. pluvialis_ biorefinery processes that co-produce various biological products, including lipids, proteins, carbohydrates, chlorophyll, and astaxanthin.  
Haematococcus pluvialis; high-pressure homogenization; astaxanthin; cyst; cell disruption
## 3 The Proposed Intelligent Mixed Approach  
### Hypothesis of the Study  
The study was based on the hypothesis that the proposed intelligent mixed approach is capable of switching a common carrier signal among many frequency channels, using a lightweight and secure pseudo-random sequence of a finite period \(T\) which varies depending on the number of NB-IoT nodes deployed in the cell. The random sequence is made known to other NB-IoT nodes as well as to the Base Station (BS) through an initial broadcast of interest packets. Assuming that only static NB-IoT nodes are considered, this broadcast of interest messages is performed during the initial network deployment. Because of the adaptive nature of the proposed cluster head selection algorithm and the need, therefore, to swap CHs, the broadcasting of interest messages also happens periodically intending to reconfigure the network by selecting new CHs based on a residual energy and signal strength mixed gradient.  
The random sequence at a node must be known by neighboring nodes because of the multi-hop cooperative clustering aspect of the proposed method which dictates that some NB-IoT devices within the same cluster are selected to play a relay role to offload the BS. This is expected to reduce the transmission traffic, enhance the Up-link throughput and consequently the up-link data rate, mitigating the inter-cell interference, reducing the number of re-transmission attempts, therefore, enhancing network reliability and energy efficiency.  
It is also very important that the pseudo-random sequence generator algorithm is lightweight because of the limited computational, memory but also energy resources of the NB-IoT nodes [49]. Typically, this random number generator algorithm should be implementable on a basic 8-bit microcontroller and its hardware complexity in terms of its gate equivalence (GE) should prove its low complexity. This is a way to ensure that the algorithm is implementable on a small chip area as a single GE in this context is defined to be the silicon area occupied by a single NAND gate.  
In summary, this study was based on the hypothesis that the adaptive frequency hopping spread spectrum technique using a lightweight pseudo-random sequence generator, coupled with a cooperative adaptive clustering approach can result in an efficient way to offload the Base Station (BS), and therefore, allows for more NB-IoT nodes to be handled within a single cell. With an efficient way of grouping NB-IoT nodes within a cell and an appropriate cluster head selection based on a composite residual-energy aware and signal strength gradient, in smaller, there is a possibility for better handling of noise and interference mitigation within the cell. This is the hypothesis that this study verifies to primarily enhance network scalability of the NB-IoT network without degrading energy-efficiency performance.
# Monitoring and Data Analysis in Small-Scale Landslide Physical Model
## 2 Physical Model
### Soil Material Properties and Preparation of the Model  
Due to the relatively simple behavior of the infiltration process and the failure triggering, the fine (0-1.0 mm) sandy material was chosen for the initial test series. The grain size distribution of the selected Drava River sand is presented in Figure 2, while Table 1 presents its basic physical properties and initial conditions at the start of the tests. Friction angle and cohesion were determined in direct shear apparatus at the same relative density of 50% and at low normal stresses similar to those present in the slope model. Hydraulic conductivity at same conditions as in the slope model was determined by falling head test in oedometer.  
Figure 1: (**a**) Schematic view of the physical model with monitoring equipment: (1) tensiometers, (2) pore pressure transducers, (3) strain gauges, (4) accelerometers, (5) rainfall simulator (sprinkler system), (6) high speed cameras, (7) terrestrial laser scanner (TLS), (8) infrared camera; (**b**) photo of the physical model [31; 32; 33].  
Figure 2: Grain size distribution curve of the Drava River sand built-in in the model.  
The previously described Drava River sand was built-in in the flume in five layers each 6 cm thick, to the total height of 30 cm. Pre-prepared sandy material at water content of \(w=2\%\) was built-in using the under compaction method [34]. Each layer was compacted by the manual compactor to the medium dense conditions of relative density \(D_{r}=50\%\). Before the placing of a next layer, the previous ones' surface was raked and sprayed with water in order to maintain the initial moisture and to achieve the best possible connection between particles of each layer. To reach homogeneity of the material in the slope and relatively uniform conditions in built-up sandy material, the model was built in three segments--lower (L), middle (M), and upper (U) part--building up the material from the foot to the top of the slope. The details of the build-up layer dimensions and properties are presented in Table 2.
# Optimizing Energy Efficiency in Cloud Data Centers:
## 4 System Model
### Power Model and QoS Metrics
#### 4.1.3 Live Migration Cost  
Hot migration negatively affects the performance of applications running within a virtual machine. An experimental study was conducted to investigate the extent of this impact and to develop a modeling approach. It was found that performance degradation and downtime depend on the application's behavior, particularly how many memory pages are updated during its execution. For web applications, the average performance degradation and downtime are estimated to be around 10% of CPU utilization. This suggests that each migration could lead to an SLA violation. Therefore, it is crucial to minimize the number of virtual machine migrations. The duration of a hot migration depends on the total memory used by the virtual machine and the available network bandwidth. In our experiments, we define the performance degradation experienced by the VM using the following formulas.  
\[T_{i,m}=\frac{D_{i}}{B_{i}} \tag{8}\]  
\[\mathrm{U_{d,i}}=\int_{\mathrm{t_{0}}}^{\mathrm{t_{0}+T_{i,m}}}\mathrm{U_{i}(t)dt} \tag{9}\]  
where \(T_{i,m}\) is the time required to complete the migration, \(D_{i}\) is the amount of memory used by \(VM_{i}\) B\({}_{i}\) is the available network bandwidth, \(U_{d,i}\) is the total performance degradation by \(VM_{i}\), \(\mathrm{t_{0}}\) is the time when the migration begins, and \(U_{i}(t)\) is the CPU utilization by \(VM_{i}\).