esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parallel Dot Product Matrix Calculation
### Performance Comparison  
The calculation amount of traditional matrix multiplication is \(O(N^{3})\), and the matrix multiplication algorithm running on parallel devices is designed in reference [16], which makes the calculation amount inversely proportional to the number of parallel devices. In the design method of this paper, Section 4.2 only describes the equivalent order reduction for the third and fourth joints, but since the structure of the robotic arm's homogeneous matrix is the same, and the distribution of zero values of the homogeneous matrix vectors of each joint is consistent, the calculation amount of the whole serial product is related to the number of equivalent order reduction matrices and the number of parallel dot product computing units, as shown in Figure 7.  
Figure 5: Example of the optimal order of the forward solution cascade multiplication of the homogeneous matrix.  
Figure 6: Task allocation of the matrix multiplication dot product computing unit group.  
In Figure 7, the dotted line is the calculation cycle of the forward kinematics solution with different numbers of equivalent reduced-order matrices under different parallel dot product computing units. The two black lines on the top and bottom represent the computing cycle under the parallel computation of a single computing unit and nine computing units in reference [16]. It can be seen that the algorithm in this paper is more flexible under the same degree of parallelism, which can effectively improve the computing performance and reduce the computing time by 90%.  
The design method in this paper is used in the experimental platform in [21], and the parallel dot product matrix calculation architecture in Section 4.1 is used to replace the traditional parallel matrix multiplication. The core calculation adopts Xilinx's XC5 VLX330T chip, which is used to complete the path planning motion of the entire seven-DOF robotic arm. Eight DPUs are used for parallel acceleration in the implementation, and the whole FPGA runs at 150 MHz. As shown in Table 1, the maximum operating frequency and logical Slice resource occupancy of the FPGA implementation based on the proposed design method are not much different from those in [21]. The DSP48E resource is increased by 20%, and the 18 Kb Block RAM resource is increased by 15%. In the conventional terminal line planning calculation mode, the calculation time of a single path planning is not more than 0.37 ms, which is 84% lower than that in [21].
## 5 Comparison of the Results Obtained for NSC with Available Data for HSC  
Based on the data obtained in the framework of the present study (see Section 4), the behavior of NSC, which corresponds to class C30 at relatively high loading rates, can be compared with that of HSC, which is described in [6]. The available data on HSC (class C90) includes the test results for three cylindrical specimens. Longitudinal deformations were measured using linear variable displacement transducers (LVDT) and transverse deformations measured by strain gauges. The dependences of these two types of deformations on stresses under uniaxial load are shown in Figure 13 and Figure 14, respectively. Corresponding data for the comparison of the elastic and nonelastic behavior of HSC and NSC are provided in Tables 7 and 8.  
Figure 14: Experimental \(\sigma_{c}\)-\(\varepsilon_{trans}\) dependences for HSC (following [6]).  
Figure 13: Experimental \(\sigma_{c}\)-\(\varepsilon_{c}\) dependences for HSC (following [6]).  
The limit transverse deformation of the HSC specimens corresponding to concrete elastic behavior is 0.15 %. In this case, the external load value is approximately half of the concrete strength, which corresponds to the structural phenomenon [13]. The average Poisson's ratio value is equal to 0.18. Further load increases are accompanied by the linear development of longitudinal deformations (Figure 13), while the transverse deformations begin to exhibit elastic-plastic behavior (Figure 14). Therefore, it is logical to analyze the ratio between the longitudinal and transverse deformations for further interpretation of the HSC behavior. Following Table 8, at ULS, this ratio is equal to 0.31, which is close to the results obtained in the frame of the present study (see Table 5). It should be mentioned that the relative transverse deformation growth, \(\frac{\epsilon_{trans1}/\epsilon_{c1}}{\
u}\), in both cases (for HSC and NSC) is equal to about 1.7.  
A comparison of the results obtained for the NSC and HSC (Tables 5 and 8) shows sufficient convergence of the standard deviations and the coefficients of variation for [PERSON]'s coefficient and \(\epsilon_{trans1}/\epsilon_{c1}\). The standard deviations are 0.024 and 0.031 and the coefficients of variation are 13.7% and 10.1% for [PERSON]'s coefficient and \(\epsilon_{trans1}/\epsilon_{c1}\), respectively. Thus, the obtained data confirm that it is possible to use experimental results for NSC specimens obtained at rather a high loading rate for the interpretation of HSC behavior. This, in turn, allows for the disclosure of the presence of the non-elastic behavior stage in compressed HSC, which is important for developing proper and effective design approaches.
## 4 Conclusions  
In this paper, we have investigated the impact of a tightly focused, circularly polarized laser pulse on the radiation characteristics of electrons with varying initial energies through scattering processes. Our findings reveal that the carrier envelope (CE) phase of the laser pulse significantly shapes the spatial distribution of Thomson scattering radiation, while its influence on the temporal spectrum and frequency spectrum is negligible. Notably, the pulse width of the radiation pulse exhibits a period-like variation with the phase, completing a cycle with a period of \(\pi\). Furthermore, the radiation is compressed into spikes lasting mere hundreds of zeptoseconds, significantly shorter in the time domain compared to processes in strong atomic fields.  
The initial energy of the electrons emerges as a critical factor in determining the characteristics of higher harmonic radiation. The initial energy of the electrons and the initial phase of the laser jointly influence the location of the maximum radiation. Meanwhile, electrons with higher initial energies exhibit more concentrated radiation, resulting in superior collimation. This enhanced collimation is particularly apparent in the radiation peak value, which is significantly influenced by the electron's initial energy. The higher the initial energy, the more focused and directional the resulting radiation becomes, offering potential advantages for applications that require precision targeting or beam shaping.  
These results may have profound implications for various applications. The ability to precisely control the spatial distribution of Thomson scattering radiation through the manipulation of the CE phase and the initial energy of the electrons could enable the development of advanced laser-based imaging techniques. Such techniques could find application in high-resolution microscopy, medical imaging, and materials science, where detailed spatial information is crucial.  
Conceptualization, Y.T.; Methodology, Y.Z.; Software, Y.Z., E.L. and Y.T.; Validation, Y.Z., E.L. and Y.T.; Formal analysis, Y.Z.; Data curation, Y.Z.; Writing--original draft, Y.Z.; Writing--review & editing, Y.Z. and E.L.; Visualization, Y.Z., E.L. and Y.T.; Funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the National Natural Sciences Foundation of China under grants No. 10947170/A05 and No. 11104291, the Natural Science Fund for Colleges and Universities in Jiangsu Province under grant No. 10 KJB140006, the Natural Sciences Foundation of Shanghai under grant No. 11 ZR1441300 and Colleges and Universities in Jiangsu Province under grant No. 10 KJB140006, and the Natural Science Foundation of Nanjing University of Posts and Telecommunications under grant No. 202310293146Y.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
The authors declare no conflict of interest.
# Enhancing Autonomous Driving Navigation Using Soft Actor-Critic
## 5 Experiments
### Training Setting  
We conducted extensive experiments in the CARLA simulator (version 0.9.10) with PyTorch for controlling autonomous driving within a challenging roundabout scenario. CARLA offers hyper-realistic physics to train and validate models in an environment close to the real world [37].  
Within this scenario, vehicles encountered numerous complexities, including maneuvering through multiple lanes, navigating through oncoming traffic, merging seamlessly with existing flows, and prioritizing pedestrian safety at designated crosswalks. The complexity of these challenges stems from the real-time decisions and good responses required for safe and efficient navigation.  
To ensure realism in our approach, we spawn diverse road participants in the traffic, including pedestrians, cars, bicycles, and motorcycles. This allows us to test and assess the performance of our autonomous driving algorithm across a wide range of traffic scenarios and obstacles.  
To recreate challenging traffic scenarios, we adjusted various factors like how fast vehicles move, the distance between them, and how often new vehicles enter the simulation. This helps us simulate situations on the road that closely resemble real-world driving scenarios. Then, we test our model in complex driving situations to achieve a robust evaluation.  
Furthermore, we integrated CARLA with Gym, an open-source Python library developed by OpenAI [38]. It was designed for reinforcement learning algorithm development and evaluation, and Gym served as an interface to facilitate communication between the DRL algorithm and the simulation environment.  
Figure 4 represents a roundabout where the AV must navigate smoothly while ensuring the safety of all road users, including cyclists, motorcycles, and other vehicles. This scenario involves the presence of 120 vehicles moving in randomly, including motorcycles, cyclists, and four-wheel vehicles, all controlled by the CARLA traffic manager.  
All vehicles except our AV are in autopilot mode. Each vehicle maintains a safe distance of 2.5 m from others and respects a speed limit of 30 ms. The objective is to facilitate the AV's entry into the roundabout without any collisions, thereby ensuring smooth and safe traffic flow.  
The simulator begins by capturing images at an initial size of \(800\times 600\) pixels. To minimize data overload, we choose to capture 5 frames per second (fps) during both the training and testing phases. Training episodes end based on the following conditions: (1) If a collision occurs between the AV and other road users; (2) If the AV successfully arrives at its destination; (3) If the number of episodes exceeds the maximum number of training steps, which is set at 1100.
###### Abstract  
As unmanned ground vehicles (UGV) continue to be adapted to new applications, an emerging area lacks proper guidance for global route optimization methodology. This area is surveillance. In autonomous surveillance applications, a UGV is equipped with a sensor that receives data within a specific range from the vehicle while it traverses the environment. In this paper, the ant colony optimization (ACO) algorithm was adapted to the UGV surveillance problem to solve for optimal paths within sub-areas. To do so, the problem was modeled as the covering salesman problem (CSP). This is one of the first applications using ACO to solve the CSP. Then, a genetic algorithm (GA) was used to schedule a fleet of UGVs to scan several sub-areas such that the total distance is minimized. Initially, these paths are infeasible because of the sharp turning angles. Thus, they are improved using two methods of path refinement (namely, the corner-cutting and Reeds-Shepp methods) such that the kinematic constraints of the vehicles are met. Several test case scenarios were developed for Goheung, South Korea, to validate the proposed methodology. The promising results presented in this article highlight the effectiveness of the proposed methodology for UGV surveillance applications.  
covering salesman problem; ant colony optimization; unmanned ground vehicle; surveillance; fleet scheduling; breakdown +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Temperature Rise with Excitation Frequencies in the 2.5 to 7 Hz Range
In addition, when the typical distance of 20 cm between camera lens and sample is increased to 3 m by using a telephoto lens, the \(2f\) signal from the sample remains unchanged. In total, the observed \(2f\) signal can be assigned to the hysteretic heating of the sample. The amplitude of the temperature oscillation at \(2f\) obtained from the spectra is not constant as a function of the induction frequency, as predicted in Equation (27), even if the signals are normalised to the square of the frequency-dependent induction current (Figure 6). The reason may be that the theory presented is not valid for the hysteretic regime. A reason for occurrence of a minimum in Figure 6 has probably no easy explanation due to the complexity of the magnetic phenomena involved.
# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 2 Methodology
### Segmentation  
The proposed consensus clustering method consists of a combination of traditional fuzzy sets and intuitionistic sets to not only increase the robustness of the noise but also use the neighborhood information when forming the clusters. To do so, we use the Robust Spatial Kernel FCM (RSKFCM) [28] and Generalized Spatial Kernel FCM (GSKFCM) [29] methods alongside the two variants of the Modified Intuitionistic Fuzzy C-Means [20] technique. Finally, we fuse the results of the clustering methods using a voting schema. The next subsections explain the employed clustering methods and the voting schema in detail.
## 5 Lactic Acid Bacteria
### Antimicrobial Compounds Produced by LAB
#### 5.3.1 Bacterios  
Bacteriocins are small antimicrobial peptides produced by various groups of bacteria, specifically LAB, to defend against other bacteria or pathogens. Bacterios are cationic molecules with 20 to 60 amino acids that present hydrophobic characteristics [17; 61]. Various classifications of bacterios have been proposed based on the first classification proposed by [71], which divided them into four classes. Generally, Class I, lantibiotics, are small thermo-stable peptides post-translationally modified, with uncommon amino acids, lanthionine, and methyl-lanthionine in their primary structure. Class II embraces heat-stable small amphiphilic helical peptides. Regarding class III, this comprises larger bacterios, thermo-labile and non-lytic, with complex activity and a complex protein structure. Finally, Class IV bacteriocins are considered more complex due to their lipid or carbohydrate fractions, and are sensitive to glycolytic or lipolytic enzymes. Bacteriocins have different mechanisms of action: after being synthetized and transported, they mainly act through the rupture of cell walls or the inhibition of the synthesis of target molecules [72].  
_Bifidobacterium animals_ is reported to produce the bacteriocin Bifidocinp-A, which showed antimicrobial activity against foodborne bacteria, supporting its use as a natural preservative in yogurt as defined in this paper [73]. Lactacin F from _Lactiplantibacillus plantarum_ has been employed in yogurt to improve its safety [74]. _Enterococcus_ species produce enterocins, which are bacteriocins that are highly effective against spore-forming bacteria of the _Bacillus_ and _Clostridium_ genus [17], such as _Bacillus cereus_, _Clostridium botulinum_, and _Clostridium perfringens_, which can lead to dairy product poisoning due to toxin production and can result in milk and dairy product spoilage [75].  
_Listeria monocytogenes_ is cold-tolerant, which implies that it can survive in yogurt stored at refrigerated temperatures. _Escherichia coli_ is very resistant to acid and can survive in yogurt [76]. _Staphylococcus aureus_ is another microbial culture that may also pose a risk in homemade milk and yogurt [77]. Several studies demonstrated that bacteriocins produced by LAB have inhibitory effects on these foodborne pathogens [78; 79]. Although best known for its inhibitory effect against bacteria, LAB bacteriocins have also been reported to exhibit antifungal properties. [PERSON] et al. [80] identified a new bacteriocin with activity against _Aspergillus parasiticus_ and _Aspergillus carbonarius_, produced by _Lacticaseibacillus paracasei_, that also inhibited mycotoxin excretion.  
Thus, the antimicrobial potential of bacteriocins from LAB makes them promising metabolites that can act as natural preservatives for yogurts. Consequently, it is notable that bacteriocins can be inserted in the clean label trend to increase food safety.
# A Review of Blockchain-Based Secure Sharing of Healthcare Data
## 4 Discussion of Potential Challenges  
Some challenges of blockchain-based healthcare data sharing.  
1. Blockchain capacity: Almost all models have adopted off-chain storage of original data, such as cloud and IPFS, and on-chain storage of data hashes to prevent data tampering. Increasing blockchain capacity and scalability in the future is a top priority.
2. Throughput: Throughput and latency are big factors that limit the development of blockchain. Bitcoin can only process seven transactions per second, and each transaction takes 1 h to determine. Ether has improved the throughput but still cannot fully meet the demand and needs further improvement.
3. Consensus algorithm: The consensus algorithm is an essential part of blockchain. A proper consensus algorithm can improve the security of blockchain and also reduce the transaction latency to increase the throughput. However, only a small number of models have improved the consensus algorithm. Optimizing the consensus algorithm according to the specific usage scenario can better improve the applicability of the model.
4. Anonymity: Anonymity is a double-edged sword. It protects the privacy of nodes but also brings additional risks to medical data. It makes it impossible to know the true identity of the nodes accessing the data. Especially in the public chain, it is unable to reject nodes trying to join.
5. Retrieval: Most of the models use encryption algorithms to improve security. However, ciphertext retrieval is more complex compared to plaintext retrieval. Almost all models ignore the workload of ciphertext retrieval. Only a very few papers have noticed and solved this problem.
6. Encryption algorithms: The encryption of medical data can increase the security of the data, but both encryption and decryption require a large amount of computing power. It is urgent to develop encryption algorithms with high security and low computational power.
## 4 Experimental Results
### Results  
#### 4.3.1 Comparison with RGB Image-Based Classification  
Table 4 summarizes the accuracy (TP/(TP+FP) [35], TP: True Positive, FP: False Positive) and the confusion matrix (ground truth vs. prediction) for Settings 1, 6, and 7. In Setting 1, two kinds of input are considered, including 2D skeleton plus depth and 3D skeleton, while the input is RGB image in Setting 6 and Setting 7. First, it shows that Setting 6 performs better than Setting 7, implying that body occlusion will degrade the classification accuracy. Second, in our proposed technique, even only four joints were used in consideration of blanket covering, the classification accuracies are 95.4% and 95.62%, respectively, for \"2D skeleton plus depth\" and \"3D skeleton\" input, which are higher than those in Setting 6 (RGB image-based, without blanket). To further improve the performances, the nose-centered coordinates for joints were used instead of the absolute image or camera coordinates, which indeed shows improvements in accuracy (up to 96.92% for \"3D skeleton\" in Table 4). This meets our statistics about the nose-distances (with respect to the camera) that a variation between left/right and supine postures for the volunteers (1399.5 mm for left-sided, 1404.2 mm for supine, and 1399.1 mm for right-sided) exists, and removal of this variation by subtracting nose coordinates from joint coordinates (thus, coordinates of the nose joint will become (0,0,0)) is helpful to the classification accuracy. The average nose-distances for the volunteer, male patient, and female patient are also analyzed as: 1400.9 mm, 1530.4 mm, and 1493.4 mm, respectively. There is an even larger variation in nose-distances between different target persons (obviously between the volunteers and the patients, in a range of 93 mm to 130 mm). The sources of variations are mostly due to difference in environment settings (such as the camera position and tilting angle). Although we try to make the environment in the laboratory and the hospital as similar as possible, they are always different in aspects of illumination, the height, and the orientation of the camera. In subsequent experiments, only the results based on nose-centered joint coordinates are shown so as to save space.
## 4 Results, Discussions and Achievements
### RMSE Analysis against Multiple Snapshots  
Based on Figures 4-6, the value of Root Mean Square Error (RMSE) is observed by increasing the Signal to Noise Ratio (SNR) from \(-30\) dB to \(0\) dB while in Figures 7-9 comparison of RMSE for FPA and PSO is performed with respect to a number of snapshots. In array signal processing, when direction of arrival estimation is performed multiple snapshots of the received signal are taken while we simulate the problem. If value of error reduced gradually when increasing the number of snapshots, it means that our algorithm works properly. Therefore, we perform the comparison of RMSE vs. snapshots for FPA and PSO.  
Figure 5: Robustness against noise for nine targets.  
Figure 6: Robustness against noise for eleven targets.
# Multi-Band Polarization Imaging in a Harsh Sea Fog Environment
## 2 Multi-Band Polarization Imaging Principle
### LCVR Phase Delay Calibration  
Liquid crystal phase variable retarder LCVR works in the 350 nm-700 nm wavelength range with a through-light aperture of 20 mm and a driving voltage of 0-8 V. The phase delay is dependent on the supply voltage. Calibration is essential in order to precisely determine the connection between the driving voltage and phase delay. In the system, the LCVR phase delay characteristics were calibrated using the light intensity technique, and the light intensity method calibration block diagram is shown in Figure 2.  
When the light is incident vertically, the LCVR drive voltage is continuously adjusted and the corresponding light intensity is measured using an optical power meter, and the amount of LCVR phase delay is obtained using Equation (6).  
\[M_{p}=\frac{1}{2}\begin{pmatrix}1&-1&0&0\\ -1&1&0&0\\ 0&0&0&0\\ 0&0&0&0\\ \end{pmatrix} \tag{15}\]  
Figure 2: Block diagram showing the concept of phase delay measuring.  
\[M_{LCVR}=\begin{pmatrix}1&0&0&0\\ 0&\cos\delta&0&-\sin\delta\\ 0&0&1&0\\ 0&\sin\delta&0&\cos\delta\end{pmatrix} \tag{16}\]  
\[S_{out}=M_{p1}\times M_{LCVR}\times M_{p2} \tag{17}\]  
From Equation (17), it is obtained that  
\[\delta=2n\pi\pm\cos^{-1}\left(\frac{2I}{I_{0}}-1\right),n=0,1,2,\cdots \tag{18}\]  
where \(I\) represents the intensity of the outgoing light, \(I_{0}\) represents the intensity of the light at the phase delay amount \(\delta=0\). Through the detector, the outgoing light is detected by the power meter, and the relationship between the driving voltage and the phase delay amount may be determined using Equation (18).  
The characteristic curves of the measured LCVR drive voltage versus the amount of phase delay are shown in Figure 3.