esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
### Collusion Attack  
Collusion attack can be defined as the execution of operations that have the ability to combine multiple copies of the media or other files together so as to produce a new copy. In a cloud storage situation, any revoked user can use such operations and can guess the private key easily to break into the system and access the files. Therefore, this problem has to be solved to increase the security of the cloud storage system, maintaining the integrity of the specifications using the BH-WABE algorithm.  
Without generality loss, assume \(J\) = [1,2]. Then, based on the encryption algorithm, the encrypted texts 'D' associated with policy \((N_{1},\rho_{1})\cap(N_{2},\rho_{2})\) is computed as follows:  
\[D_{0}=n\cdot\prod_{i=1,2}e(g,g)^{\alpha_{i}s_{i}}=ne(g,g)^{\alpha_{1}s_{1}+ \alpha_{2}s_{2}} \tag{2}\]  
Figure 9: Cost of cipher text. (**a**) Storage cost analysis; (**b**) Time cost analysis.  
\[X_{i}=\left\{g^{si},Y_{i}=f^{si},E_{i}=B_{i}^{si}\right\}_{j=1,2} \tag{3}\]  
\[D_{i,j}=\left\{(g^{x_{i}\lambda_{i,j}}z_{p_{i}(j)}^{-r_{i,j}},C_{i,j}=g^{r_{i,j}} )_{j=1,\ldots,l_{i}}\right\}_{i=1,2} \tag{4}\]  
Let us consider two users, P1 and P2, with identifiers p1 and p2 respectively. In addition, user P1 owns attribute set S1, whose elements are monitored by authority A1, and S1 only satisfies the access structure \((N_{1},\rho_{1})\) but not \((N_{2},\rho_{2})\). Likewise, the other user, P2, owns attribute set S2, whose elements are monitored by authority A2, and S2 only satisfies the access structure \((N_{2},\rho_{2})\) but not \((N_{1},\rho_{1})\). Both users can get their keys from A1 and A2. However, if a user is not in the encryption list, then he cannot obtain \((N_{2},\rho_{2})\) in the ciphertext. So, the decryption algorithm will fail. Specifically, only those intended recipients can decrypt this data. Therefore, users can only access the data they are allowed to and not the data they are not authorized to.
## 7 Effect of Zeolite Addition on WMA Properties
### Effect of Aging Process on the Properties WMA with Zeolites  
The asphalt binder aging processes significantly affects the performance of the pavements during their service life [57].  
[PERSON] et al. [58] analysed the effect of long-term aging on properties of WMA with zeolite addition. Analysed samples of mix asphalt with the addition of PG 64-22 asphalt originating from two different sources; using two types of aggregates: granite and marble. Aspha-Min zeolite was dosed at 0.3% with regard to mix asphalt mass. Addition of 1% hydrated lime was used as an adhesive agent. Reference HMA was mixed at the temperature of 150 \({}^{\circ}\)C, and WMA, at 120 \({}^{\circ}\)C. The compaction temperature was 5 \({}^{\circ}\)C lower than the production temperature. A resilient modulus (MR) test (ASTM D 4123 procedure) at three test temperatures: 5 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, a wheel-rutting resistance APA test, and water resistance test measured using TSR index were performed on samples produced using a gyratory compactor. The foregoing set of tests was repeated on samples aged artificially by conditioning the compacted samples in a forced draft oven for 120 h at a temperature of 85 \({}^{\circ}\)C. This procedure is designed to simulate the aging the compacted mixture will undergo during 7-10 years. Samples of WMA with zeolite addition, compared to HMA samples, presented significantly lower stiffness moduli at test temperatures of 25 \({}^{\circ}\)C and 40 \({}^{\circ}\)C whereas, at 5 \({}^{\circ}\)C, the results were similar. In case of the aged samples, the zeolite addition had no significant effects on the MR values. Resilient modulus values were comparable or lower than HMA. Zeolite addition had no significant effect on ITS of unaged samples. On the other hand, ITSR water resistance index was higher for WMA with zeolites, regardless of other factors (type of asphalt, aggregate, age of tested samples). By contrast, resistance to permanent deformations was measured using rut depth, deteriorated for WMA with zeolite, both in unaged and artificially aged samples.  
Physical and mechanical properties of mix asphalt, such as water sensitivity, stiffness modulus, resistance to cracking, and permanent deformation, depend on the type of tested mix, type of aggregates and binders being used, as well as their properties. Test procedures and conditions are also significant. All of this causes difficulty in terms of comparing study results presented in the literature. This article focuses on comparing WMA parameters with regard to HMA which are usually presented as a reference.  
Based on the performed literature review it is not possible to determine the effect of zeolite addition on mix asphalt properties. Result divergence indicates a need for comprehensive WMA studies, comparing zeolites with different structure types, and the development of research procedures dedicated to the technology in question.
## 3 Materials and Methods
### Performance Evaluation  
To evaluate the prediction performance of the proposed model and compare it with other models, eight evaluation indexes are measured on training datasets and independent datasets. These indexes include the accuracy (ACC), precision (PRE), sensitivity (SEN), specificity (SPE), F1 measure (F1), [PERSON]'s correlation coefficient (MCC), balanced accuracy (BACC), area under precision recall curve (AUPR) and area characteristic curve under the prediction receiver operator (AUC), which are defined as follows:  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN}. \tag{6}\]  
\[PRE=\frac{TP}{TP+FP}. \tag{7}\]  
\[SEN=\frac{TP}{TP+FN}. \tag{8}\]  
\[SPE=\frac{TN}{TN+FP}. \tag{9}\]  
Figure 3: The framework of the CnnRBP model. L is the length of the feature subset selected by the LightGBM algorithm.  
\[F1=\frac{2\times SEN\times PRE}{SEN+PRE}. \tag{10}\]  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}. \tag{11}\]  
\[BACC=\frac{(\frac{TP}{P}+\frac{TN}{N})}{2}. \tag{12}\]  
where \(P\) is the number of positive samples, \(N\) is the number of negative samples, \(TP\) is the number of positive samples that are correctly predicted, \(FP\) is the number of negative samples that are incorrectly predicted, \(TN\) is the number of positive samples that are incorrectly predicted.  
Considering the imbalance in testing datasets, the ACC cannot accurately evaluate the performance of classification models. Alternatively, the F1 measure, MCC and BACC can evaluate the overall performance of a model more appropriately. In addition, the ROC curve and PR curve are frequently used to assess the overall performance of binary classification models, so the AUC and AUPR are calculated to compare the performance of classification models more directly.
## 4 Issues of Opportunistic Routing in Asynchronous WSN
### Hidden Terminal Problem  
In the hidden terminal problem, when two senders are unknown to each other because they are out of each other's transmission range, while the receiver is in the range of both senders, there is a high probability that their packet transmission will collide with each other on the receiver end.  
In asynchronous duty-cycled WSNs, where nodes perform transmissions without prior knowledge, they are prone to collide with each other, whether it is a data packet or an acknowledgment packet. Hence, it is important to consider this issue in opportunistic routing with asynchronous WSNs, as this is a major issue that leads to unnecessary energy consumption.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 6 Evaluation
### Results and Discussion
#### 6.2.6 Impact of the PQC Algorithm on Performance in the Live-Streaming Use Case  
Throughput  
Figure 13 shows the total throughputs observed from the live-streaming session. As shown in Figure 13a, an almost constant total throughput was achieved with most of the PQC algorithms across all security levels compared to AES-256 (9.7 Mbps). BIKE-L3-F512 achieved the lowest throughput of 8.6 Mbps, while K512-F512 achieved a total throughput of 9.2 Mbps, slightly less than AES-256. The other algorithms recorded slightly higher total throughputs. Similarly, as shown in Figure 13b, total throughputs comparable to that of AES-256 were observed across all PQC algorithms, with BIKE-L1-F1024 (10.1 Mbps), HQC-128-F1024 (10.0 Mbps), and HQC-192-F1024 (10.3 Mbps) achieving total throughputs slightly higher than AES-256.  
Figure 12: Video streaming frame retransmission times: **(a)** PQC–AES-256 (with F512). **(b)** PQC–AES-256 (with F1024).  
Finally, we measured the retransmission rates of the live stream. According to the results shown in Figure 14a, most of the PQC algorithms in the hybrid protocols are suitable for the live-streaming use case and comparable to AES-256 performance. A lower retransmission rate of less than 0.1% was observed in most algorithms, except for HQC-256-F512 (0.16%) and K512-F512 (0.67%), compared to AES-256 (0.72%), while higher retransmission rates were observed for BIKE-L3-F512 (2.28%) and HQC-128-F512 (1.46%).  
Additionally, for KEM algorithms paired with F1024, Figure 14b shows the retransmission rates. BIKE-L1-F1024 (3.57%), BIKE-L3-F1024 (2.42%), and HQC-192-F1024 (3.06%) had higher retransmission rates compared to the other algorithms and AES-256. The lowest retransmission rate was observed for HQC-256-F1024 (0.1%). This suggests that BIKE-L1-F1024, BIKE-L3-F1024, and HQC-192-F1024 may not be suitable for real-time live streaming without freezing and buffering compared to the other algorithms.
## 4 Conclusions  
The oxidation time and the addition of CaSiO\({}_{3}\) were the key parameters for the growth of a greater amount of Hap, which was observed in the signals of the phosphate group in the FTIR analysis, in the activated carbon surface with agglomerated deposits of particles with a sphere-like morphology in the SEM microscopy, and in a higher amount of Ca and P in the EDS analysis. The ash content and XRF also confirmed that these parameters determined the HAp growth in the sample AC-2 hHAp, with the highest oxidation time and the addition of CaSiO\({}_{3}\) during the incubation process. The charge distributions showed that the oxidized materials had a low PZC compared to that of the AC, and the incubated/modified with HAp showed a higher PZC due to the presence of hydroxyapatite with a basic behavior.  
The MB adsorption capacity of the materials was better fitted to the Langmuir model, with a q\({}_{\text{max}}\) of 9.81 mg\(\cdot\)g\({}^{-1}\) for the AC-2h21. In addition, the MB adsorption kinetics followed the pseudo-second-order reaction model, with an average removal of 95.61% of the MB at a contact time of 180 min in a concentration of 1 mg L\({}^{-1}\). The results demonstrated good adsorption behavior from the synthesized composites for organic molecules, such as dyes, even with the modification with hydroxyapatite with a removal rate of 99.4% in 180 min for the AC-2 hHAp. The characteristics of the obtained materials with hydroxyapatite could be suitable for the adsorption process due to the ion exchange capacity of HAp and the surface of activated carbon.  
A.M.-S.: The student performed the experimental part and wrote the introduction and methods of the manuscript. J.C.R.-H.: designed the experimentation, followed up on the methodology, and wrote the results and discussion of the manuscript. S.E.F.-V.: supported in the experimentation and discussion of infrared spectroscopy characterizations. A.G.E.-G.: obtained the micrographs and discussed the results of the structural properties. J.Y.G.-C.: conducted the ash content experiments and supported the discussion of the results. F.P.L.-C.: supported the thermodynamic and kinetic experiments of methylene blue adsorption and analyzed the results together with the student. G.B.E.-I.: supported the experimental procedure for the obtention of the composites without a calcium silicate addition and the discussion of the results. All authors have read and agreed to the published version of the manuscript.  
[PERSON] received a grant from CONACyT. Grant number 1033316.  
Not applicable.  
To Consejo Nacional de Ciencia y Tecnologia (CONACyT) for the scholarship granted (grant number 1033316).  
We know of no conflicts of interest associated with this publication, and there has been no significant financial support for this work that could have influenced its outcome.
## 1 Introduction
Moreover, issues related to the definition of boundary conditions further constrain the use of SPH in tunnel and underground engineering, where its application is still in the early stages. This paper systematically summarizes and derives the key characteristics and fundamental equations of the SPH method, illustrating its application with an example based on the Navier-Stokes (N-S) equations. Furthermore, it provides an in-depth analysis of successful SPH applications across various fields. To enhance boundary representation and tensor stability, particularly in the context of tunneling, the SPH method is employed to simulate tunnel blasting construction. The simulation results show a strong correlation with actual monitoring data, offering robust theoretical guidance and practical insights for scholars in the field.
## 2 Theoretical Foundation  
Accurate signal detection is a critical necessity within wireless communication systems, as it directly influences the mitigation of symbol errors and the optimization of overall system throughput and spectral efficiency. In the context of SISO and MIMO systems, the transmission chain from a transmitter to a receiver undergoes distinct formulations.  
For the SISO scenario, Equation (1) encapsulates the relationship, where '\(y\)' represents the received signal, '\(x\)' signifies the transmitted signal, '\(H\)' denotes the channel matrix, and '\(n\)' symbolizes noise.  
\[y=Hx+n \tag{1}\]  
Meanwhile, in the case of MIMO systems, Equation (1) extends to encompass multiple antennas and transmissions, represented as Equation (2), where '\(h\)' denotes channel coefficients, '\(x\)' represents transmitted symbols, and '\(n\)' denotes noise.  
\[y=\begin{bmatrix}h_{11}&h_{12}\\ h_{21}&h_{22}\end{bmatrix}x+n \tag{2}\]  
\[y_{1}=h_{11}x_{1}+h_{21}x_{2}+n \tag{3}\]  
\[y_{2}=h_{12}x_{1}+h_{22}x_{2}+n \tag{4}\]  
The relations in (2)-(4) are represented in Figure 1.  
There are several challenges that hinder signal detection in wireless communication systems. Noise, an inherent element, introduces uncertainty into the received signals. Additionally, interference arises from multiple users sharing the same frequency band, causing signal overlap and distortion. Furthermore, fading effects due to multi-path propagation lead to time-varying channel conditions, further complicating the process of signal detection.  
The following sections delve deeper into these techniques, exploring their principles, mathematical foundations, strengths, limitations, and performance evaluation metrics. This theoretical foundation aims to provide insights into the capabilities and limitations of various techniques, paving the way for advanced DNN-based data detection models for massive MIMO systems.
# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 4 Discussion and Conclusions
However, when the proposed approach is applied in the real world, this issue becomes indispensable. When the density of the crowds is low, the skeleton tracking is effective. However, when the density is high, the problem occlusion of pedestrians should be considered. Therefore, one of the future works is to combine the prediction system with real-time detection of pedestrians via multiple vision sensors. (4) In terms of kinematic feasibility of the robots, we have used Pioneer 3-DX and Patrolbot, which are differential drive robots. However, other robots, like Summit XL or others with car-like models, might not execute these sharp turns particularly at relatively high speeds. The TEB planner provides local plans that are feasible for Ackermann drives and the planner supports car-like robots. The kinematic feasibility of the robots to execute the generated trajectories needs to be further modified and validated on car-like robot models. (5) The rapidly-exploring random tree (RRT) [31] algorithm is also a famous trajectory planning method. To compare with RRT and combine the RRT method with pedestrian trajectory prediction is one of the future works. Fourth, robots with multiple travel modes can navigate safely in a medium-density crowded unstructured environment. We used the following mode and probing mode to deal with the \"freezing robot problem\" (FRP) [18], which is simple and feasible compared with existing solutions, such as [7; 17; 18], avoiding complex robot-human interaction modeling and learning. However, the navigation efficiency is still relatively low in the case of medium or high density of population, and the validation is conducted in a simulated environment only, ignoring the cooperative navigation behavior between pedestrians and robots in the real world, which is the direction of our future research. More results for testing the real mobile bot (e.g., [32]) in more dense area experiments in real world are required in the future. In conclusion, the proposed navigation approach based on human trajectory prediction and multiple travel modes proves to be a novel and feasible solution in crowded, unstructured, and dynamic environments that is safe, efficient, and socially compliant. [PERSON] and proposed [PERSON] the idea of the paper; [PERSON] and [PERSON] conceived and designed the experiments. [PERSON] and [PERSON] performed the experiments. [PERSON], [PERSON] and [PERSON] analyzed the data and wrote the paper. All authors have read and approved the final manuscript. This work is financially supported by Shenzhen Key Laboratory Project (Grant No. ZDSYS201707271637577), in part by Shenzhen Peacock Plan (Grant No. KQTD2016113010571019), Shenzhen Fundamental Research Funds (Grant Nos. JCYJ20160608153218487 and JCYJ20160429190300857) and National Natural Science Foundation of China (Grant No. U1713221). The authors declare no conflict of interest.
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 2 Three-Phased Induction Motor
### Connection  
The connection to the network or to a drive of these machines passes through a terminal board protected by housing, the access of which is usually conducted by removing a waterproof cover located on the top. The cable entry is performed via a cable gland that is responsible for ensuring the tightness, as well as the mechanical retention, of the power cable. The six terminals of the stator windings of three-phase machines are always placed in the same way on the terminal board. This particular organization allows either star or delta connection, following a simple and standardized procedure, as presented in Figure 2. The three windings can either be connected in star (or Y) or in delta (D). To connect the windings of the motor in star, it is sufficient to connect the terminals U\({}_{2}\), V\({}_{2}\), and W\({}_{2}\) together, as shown in red in the example presented in Figure 2. To connect the windings of the motor in delta, it is sufficient to separately connect the terminals U\({}_{1}\) to W\({}_{2}\), V\({}_{1}\) to U\({}_{2}\), and W\({}_{1}\) to V\({}_{2}\), as shown in blue in the example presented in Figure 2.  
The key point to choose how to connect the terminals of the motor is solely the phase-to-phase voltage of the network. Indeed, in the winding of a motor marked 230/400 V, 50 Hz should be supplied with a 230 V, 50 Hz voltage. Then, such a motor can either be connected to a 230/400 V, 50 Hz network with a star (Y) connection to its windings, or to a 133/230 V, 50 Hz network with a delta (D) connection to its windings. In both situations, each motor winding is supplied by a 230 V, 50 Hz voltage.  
Three-phase induction motors can rotate in both directions of rotation. Whether they are star (Y) or delta (D) connected, the reversal of their rotational direction is carried out by simply swapping two supply conductors, such as, for example, U\({}_{1}\) and V\({}_{1}\) in Figure 2. Thus, a circular permutation of the three conductors does not reverse the direction of rotation.
## 3 Methodology
### SDR Module
#### 3.3.1 Reception and Processing of Data from the SDR  
The first part is based on GNU Radio, and its purpose is to capture signals from different frequency bands, calculate their power, and store them in text files, which will be used in the second block for detection. Figure 2 shows the structure followed in GNU Radio, consisting of four GNU Radio blocks. The first two blocks are native to the software, while the subsequent two have been created based on the project's requirements.  
Figure 2: GNU Radio schematic used to receive signals from the bladeRF SDR and process them for input to the detection system.  
The first GNU block, Osmocom Source, is responsible for receiving complex signals from the bladeRF SDR, where parameters such as the bandwidth, frequency, and gain of the receiving system are set. The second block, Selector, is used to block data transmission and is only employed when changing the system's frequency. If it is not blocked, there is a risk of data being stored incorrectly.  
The third block receives the complex signals from the SDR and calculates the power in dBm by applying a Hamming window. Finally, the fourth block stores the calculated power of that frequency band in a text file, creating a separate file for each analyzed band.  
Thus, the system first monitors a series of frequency bands with a bandwidth of 100 MHz, then calculates the power at 2048 points within that band, and, finally, stores those values in a text file that is updated each time values for that frequency band are received.  
For the tests conducted, three frequency bands commonly employed in UAV usage are analyzed: 950 MHz, 2.450 GHz, and 5.80 GHz. In this way, three different files are generated, containing various samples obtained from each of the frequencies. Each sample consists of 2048 power values, which, together, represent 100 MHz of that particular frequency band. These samples of 2048 power values are the data input to the autoencoders for detection.