esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Properties and Freeze-thaw Durability of Basalt Fiber Reactive Powder Concrete
## 4 Conclusions  
In this paper, the mechanical properties and freeze-thaw durability of BFRPC with different basalt fiber contents were tested and compared with PRPC and SFRPC to investigate the effects of basalt fiber contents and fiber type on the mechanical properties and freeze-thaw durability of RPC. Besides, the freeze-thaw durability of RPC under two freeze-thaw conditions was tested to investigate the effects of freeze-thaw conditions on the freeze-thaw durability of RPC. Based on the results of the experiment, the conclusions are as follows:  
1. The compressive strength and flexural strength can be improved by adding basalt fiber into RPC, and the compressive and flexural strength of BFRPC increase as the basalt fiber content increases. Compared with PRPC, the compressive strength and flexural strength of BFRPC (12 kg/m\({}^{3}\) content) are increased by 6% and 18.5%, respectively; the improvement of basalt fiber on the flexural strength of RPC is greater than compressive strength.
2. The freeze-thaw durability of BFRPC increases as the basalt fiber content increases. After 800 freeze-thaw cycles, the mass loss rate and compressive strength loss rates of BFRPC with a basalt fiber content of 12 kg/m\({}^{3}\) are 0.85% and 19.17%, respectively, which are lower than that of PRPC. The addition of basalt fiber could significantly improve the freeze-thaw durability of RPC.
3. Compared with the fresh-water freeze-thaw cycle, the mass loss rate and compressive strength loss rate of RPC are higher under the chloride-salt freeze-thaw cycle, and the damage of the chloride-salt freeze-thaw cycle on RPC is great.
4. After 800 freeze-thaw cycles, the mass loss rate and compressive strength loss rates of SFRPC are greater than 2% and 20%, respectively, which are higher than that of BFRPC. Steel fiber is not as effective at enhancing the freeze-thaw durability of RPC as basalt fiber.  
Conceptualization, H.L. and X.G.; methodology, C.L. and X.G.; formal analysis, W.L. and X.L.; investigation, W.L., B.Z. and X.L.; writing--original draft preparation, W.L.; writing--review and editing, C.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Industrial Technology Research & Development Special Project of Jilin Province (2018C042-1); the Transportation Science and Technology Program of Jilin Province (2018-1-9); and the Science Technology Development Program of Jilin Province (20200403157 SF). The authors would like to express their appreciation to the anonymous reviewers for their constructive suggestions and comments on improving the quality of the paper. The authors declare no conflict of interest.  
Figure 9: Compressive strength and Compressive strength loss rate versus freeze–thaw cycles: (a) Compressive strength; (b) Compressive strength loss rate.
## 3 Results and Discussion
### Identification of Metallic/Bimetallic Nanoparticles
#### 3.1.1 UV-Visible Spectra  
The biosynthesized Ag, Ag-SeO\({}_{2}\), and Ag-TiO\({}_{2}\) NPs prepared using _Beta vulgaris_ L aqueous extract were characterized via UV-visible spectra (Figure 1). The _Beta vulgaris_ L extract revealed two distinctive peaks at \(\lambda_{\text{max}}\) ca. 554.0 nm (abs. 1.546) and \(\lambda_{\text{max}}\) ca. 246.0 nm (abs. 0.777); these peaks are related to the adsorption intensities of the pigment agreeing, respectively, to yellow betaxanthin and red-purple betain [47]. The color change in the used aqueous extract in the biosynthesis of metallic/bimetallic nanoparticles was indicated through physical observation. The reddish-brown color of the prepared extract was changed into a brown color in the case of silver nanoparticles, a yellow color in the case of Ag-selenium dioxide NPs, and a gray color in the case of the preparation of Ag-titanium dioxide NPs. The nanoparticle solution of silver (Ag-mNPs) revealed surface plasmon resonance peaks at \(\lambda_{\text{max}}\) ca. 598.0 nm (abs. 1.632) and \(\lambda_{\text{max}}\) ca. 246.0 nm (abs. 0.770). The three perceived intensity peaks were recorded at \(\lambda_{\text{max}}\) ca. 428.0 nm (abs. 0.558), 395.0 nm (abs. 0.529), and 245.0 nm (abs. 0.791) for the biosynthesized Ag-SeO\({}_{2}\) bimetallic nanoparticles. Otherwise, the gray solution of Ag-TiO2 bimetallic nanoparticles displayed two individual intensity peaks at \(\lambda_{\text{max}}\) ca. 675.0 nm (abs. 1.920) and 247.0 nm (abs. 0.756). The results verified the formation of metallic/bimetallic nanoparticles since the addition of the metal (Ag+, Ag+Se\({}^{2+}\), and Ag+-Ti\({}^{2+}\)) solutions into the aqueous plant extract led to an increase in the surface plasmon resonance peak at \(\lambda_{\text{max}}\) ca. 598.0 nm (Ag mNPs) and 675.0 nm (abs. 1.920) (Ag-TiO\({}_{2}\) bmNPs), and a decrease in the intensity peaks for Ag-SeO\({}_{2}\) bmNPs at \(\lambda_{\text{max}}\) ca. 428.0 nm (abs. 0.558) and 395.0 nm (abs. 0.529).
## 2 Materials and Methods
### Computational Techniques
#### 2.2.1 Development of Models Using ANN  
The feedforward backpropagated algorithm, using the scaled conjugate gradient method [69], is the type of ANN used in this work. Eighteen input variables incorporating the weight proportion and size of the micro/nano particles were considered as the input nodes in the input layer and five output variables--two tribological properties and three mechanical properties--were considered the output nodes in the output layer. The output layer is connected to the input layers by means of a hidden or a concealed layer that has to be iterated by the user. The configuration of ANN is signified by the 18-N-1 scheme, where 18 denotes the total input variables, N denotes the number of hidden layers, and 1 denotes each of the output variables. The linking of each layer is done by functions. A non-linear transfer function--the tan hyperbolic (tanh) function--links the input and hidden nodes (\(H_{j}\)), which is expressed in the Equation (2), comprising the weighted sum of the normalized inputs (\(x_{i}\)) and the connection weights (\(w_{ji}\)) that connect the input to the hidden layers.  
\[H_{j}=tanh\ \left(\sum w_{ji}x_{i}+b_{j}\right) \tag{2}\]  
The output node (\(Y\)) was then determined by the weighted sum of the outputs, and the connection weights (\(W_{j}\)) of the hidden-to-output layer by the linear function is expressed in Equation (3).  
\[Y=\left(\sum W_{j}H_{j}+b^{\prime}\right) \tag{3}\]  
Here, the bias values \(b_{j}\) and \(b^{\prime}\) correspond to the input-to-hidden and hidden-to-output layers, respectively. During the process of learning, the deviation from the targeted output to the real output is fed back to regulate the weights and bias values of the corresponding layers [36]. The above computation is explained in Figure 1. The developed models are used as objective functions for the optimization work. The entire ANN modelling was done by utilizing the Neural Network toolbox in MATLAB(r).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Low** & **High** & **Average** & **Normal** \\ \hline \multicolumn{5}{c}{**Input variables (for wear properties):**} \\ \hline Hardness of counteracting & & & & \\ material (kgf/mm\({}^{2}\)) & 67.5 & 1950 & 856.958 & 427.162 \\ Method & 0 & 1 & 0.161 & 0.368 \\ Speed of sliding (m/s) & 0.004 & 1.667 & 0.415 & 0.427 \\ Lubrication & 0 & 2 & 0.851 & 0.886 \\ Normal Load (N) & 3.9 & 140 & 38.283 & 29.472 \\ Sliding distance (m) & 5 & 10,000 & 959.481 & 1230.49 \\ \hline \multicolumn{5}{c}{**Output variables:**} \\ \hline Young’s modulus (MPa) & 272 & 974 & 617.825 & 194.356 \\ Ultimate tensile strength (MPa) & 14.2 & 77 & 32.726 & 10.675 \\ Hardness (MPa) & 38 & 120 & 61.568 & 17.884 \\ Specific wear rate (mm\({}^{3}\)/Nm) & \(8\times 10^{-9}\) & 0.00009 & \(1.98\times 10^{-5}\) & \(1.83\times 10^{-5}\) \\ Coefficient of friction (CoF) & 0.04 & 0.84 & 0.2036 & 0.203 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.
## 4 Results and Discussion
### FEM Results
#### 4.2.2 Steel Frame  
The effect of the value of the friction coefficient between the contact surfaces of steel plates of the slip-critical connections on the numerical cyclic behavior of the steel frame with slip-critical connection is presented in Figure 18. As can be seen in Figure 18, when the value of the friction coefficient was set to 0.2, the numerical result was in good agreement with the test result.  
Figure 18: The effects of the value of friction coefficient between the contact surfaces of steel plates in the slip-critical connections and the calibrated material models on the numerical cyclic behavior of steel frame [36; 46].
# Allocating Small Transporters to Large Jobs
## 6 The General Transporter Allocation Problem
### Heuristics to Compute the Pareto Frontier
#### 6.2.1 Extensions of the Saturated Algorithms  
Now, we extend the saturated greedy algorithms \((\text{SGJ})\), \((\text{SGR}i)\), and \((\text{SGR}c)\) to compute a Pareto frontier for \((\text{TAP})\). Note that every solution of the Pareto frontier must assign at least one transporter to each job, as otherwise, the makespan is infinite.  
It is straightforward to extend the _greedy algorithms by transporter_\((\text{SGR}i)\) and \((\text{SGR}c)\) to compute a Pareto frontier, since every iteration already provides a partial assignment using one more transporter. At every iteration, we generate a completed assignment by temporarily providing one transporter to each job that had not received any transporter yet. If we do not have enough transporters to complete the assignment, the algorithm stops and returns the computed Pareto frontier. We denote by \((\text{GR}i)\) and \((\text{GR}c)\) the extension of the greedy heuristics to the general case.  
For the _greedy algorithm by job_\((\text{SGJ})\), there is no simple way of creating a Pareto frontier. Our extension \((\text{GJ})\) of algorithm \((\text{SGJ})\) iteratively runs with different transporter counts, always choosing the ones with biggest capacity. For a fixed transporter count, when assign  
Figure 3: Minimum number of transporters in an assignment with a given makespan. A feasible solution needs at least one transporter per job. The solution minimizing the makespan does not necessarily require all \(N_{R}\) transporters.  
ing transporters to a job, \((\mathrm{GJ})\) must restrict the maximum number of transporters assigned to this job such that the next jobs can still receive at least one transporter. Since Algorithm \((\mathrm{GJ})\) allows jobs to not be saturated, it becomes more important to order the jobs in a meaningful sequence before assigning the transporters. We sort the jobs by decreasing values of completion time they would have if they were only assigned the smallest transporter. If we denote by \(\kappa_{\min}\) the minimum transporter filling time, by Equation (1), that means jobs are sorted by non-increasing value of \(\mathrm{C}^{\mathrm{sat}}(j)\cdot\frac{\delta(j)+\kappa_{\min}}{\kappa_{\min}}\), depending on the job return time \(\delta(j)\). In our simulations, we will only consider jobs with the same saturated processing time. In this case, our job order is equivalent to ordering the jobs by decreasing return times.
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 1 Introduction
In addressing the unique challenges posed by mmWave communication, such as limited propagation and physical obstructions, [16] outlines a novel mmWave-based strategy tailored for sensor data sharing among vehicles. This method is designed to bolster road safety and enrich driving experiences by nominating lead vehicles for data coordination, thereby significantly curtailing broadcasting delays by an estimated 30%. The criteria for selecting these lead vehicles include computational capacity and strategic positioning to diminish communication lags.
# Reliability Assessment of Steel-Lined and Prestressed FRC Slabs against Projectile Impact
## 7 Conclusions  
Based on the findings of the present reliability study, the following conclusions can be drawn:  
* When striking velocity exceeds the ballistic limit, failure is inevitable, but from a probabilistic perspective, it simply means that the probability of failure is high.
* Out of 35 specimens, only six specimens are as reliable as desired at the considered impact velocities, and all the rest have reliability indices below 3.0.
* Increasing impact velocity sharply decreases reliability. The reliability index is around 4 when the projectile strikes PFRC slabs at half of their ballistic limit (\(\sim\)0.5\(V_{P}\)). However, if the impact velocity (\(V_{0}\)) approaches 1.2 times the slab's ballistic limit (\(\sim 1.2V_{P}\)), the reliability index reduces to around 0.75, indicating very low reliability or a high failure probability. A \(V_{0}/V_{P}\) ratio of 0.7 or lesser results in a reliability of three and above.
* As a design guideline, PFRC slabs should have a ballistic limit of around \(V_{0}\) /0.7-1.4\(V_{0}\) to have the slab's desired reliability index of at least 3.0.
* Without fiber, the slab's reliability was substantially low. The reliability of slabs improved considerably after fibers were added. The maximum increase in reliability was observed for specimens with the highest amounts of steel fibers (1.2%). Second-highest reliability belongs to the specimen with the second-highest percentage of steel fibers.
* An FRC slab without prestressing and steel lining, subjected to a projectile strike velocity of 118.7 m/s, should have a steel fiber proportion of about 1.2% to attain the desired reliability index of 3.0.
* The steel lining on the back face makes the slab's reliability almost double when there is no lining. Additionally, steel lining makes the PFRC slabs as reliable as desired.
* A prestressed slab has a higher safety level. Even a minimal amount of prestressing makes a noticeable improvement in the reliability of PFRC slabs.  
Conceptualization, methodology, validation, formal analysis, writing --original draft, writing--review and editing, N.S. and H.A.; Writing--review and editing, Q.M.L.; funding acquisition, T.A., H.A., N.S.; Supervision, investigation, writing--review and editing, T.A. and Y.A.-S. All authors have read and agreed to the published version of the manuscript.  
This work is based on the Project funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (14-ADV1056-02-R).  
Figure 7: Effect of steel lining on the reliability of PFRC slabs.
## 2 Safety Analysis Framework for Complex Electromechanical Equipment System Based on STAMP
### Comparison between FTA, FMECA, HAZOP, and STAMP  
Traditional risk analysis theory considers hazards as the result of the action of a series of events. The considered events usually involve several types of component failures or human errors, mainly adopting the forward sequence method (such as failure mode and impact analysis) or the backward sequence method (Such as the fault tree directly describes the linear relationship between the failure and the influencing factors and is suitable for analyzing the hazard caused by the failure of a physical component or a simple system.) Three hazard analysis techniques are suggested in the functional safety standard (ISO26262): fault tree  
Figure 4: Multi-level control process.  
Figure 5: Basic causes of unsafe control. Reproduced with permission from [15], Elsevier, 2019 analysis (FTA), failure modes and effects analysis (FMEA), and hazard and operability (HAZOP) analysis. Table 2 summaries and compares three general hazard analysis techniques with STAMP.  
FTA, FMECA, and HAZOP are proposed before the 1980s in which industrial automation and intelligence are still not highly dependent. Only giant companies and some research institutes like the nuclear industry, aerospace, and universities have enough human and material resources to carry out relevant technology research. Although some detail can be found by these three methods, the limitation of methods shown in Table 2 is clear. These traditional safety analysis methods based on the event chain model are unable to do as much as they can when dealing with computer-oriented automated control system. However, due to the particularity understanding of safety in STAMP, the connotation of safety has been substantially improved from the introduction of concepts such as hierarchical models, process models, and safety constraints.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 4 Preliminaries of the Security Protocol
### The Ring-LWE Encryption Scheme
#### 4.1.4 Decryption  
Compute  
\[\tilde{m}=INTT(\tilde{c}_{1}*\tilde{r}_{2}+\tilde{c}_{2})\in R_{q}\]using the inverse NTT. Then, the original message \(m\) is recovered from \(\tilde{m}\) using a decoder.
# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 2 State of the Art
Without losing the generality of the previous classification, some emerging technologies can be highlighted that add value to these systems and show promise in the scientific field studying these topics:  
* Wearable and portable sensors: Compact and flexible sensors, such as those based on conductive polymers, enable continuous and comfortable monitoring [16; 17; 18]. * Advanced digital processing: Machine learning algorithms and adaptive filters could improve pattern recognition in EMG signals, reducing noise and identifying patients' movement intent. * Integration with smart controllers: Modern exoskeletons utilize control architectures based on EMG signals to provide personalized assistance, adjusting parameters such as force and speed in real-time. Typical EMG equipment features high-channel acquisition capabilities, with some systems supporting up to hundreds of channels for biomedical signals (including EMG and EEG). These systems commonly include wireless transmission of data to external computers, with battery life ranging from 4 to 8 h depending on the model. Maximum sampling rates typically range from 2 kSPS to 10.24 kSPS, though most systems are limited to around 3 kSPS, which may not fully capture the broad EMG bandwidth (0.01 Hz to 10 kHz). Some devices include inertial measurement units (IMUs) for additional data, and their sensors may use patch electrodes or mesh arrangements to capture signals. Data storage options, such as flash memory cards, are also available for longer-range recording (over 100 m). Despite these capabilities, all devices generally require connection to an external computer for signal processing, making them less portable and limiting their use in mobile applications, such as integration with exoskeletons. Moreover, aside from the need to be connected to a computer, if we combine the total size of all the devices necessary for capturing and processing the EMG signal in each individual system, they result in large systems that are hardly portable and, therefore, difficult to apply in exoskeletons.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 6 New Science Investigations
### Mesoscale Ocean Eddies  
In [137], researchers explored the feasibility of using GNSS-R data from CYGNSS to detect mesoscale ocean eddies, reporting on their findings for the first time. The study revealed evidence of normalized bistatic radar cross-sectional responses over the center or edges of the eddies. Analysis of GNSS-R observations along with ancillary data from the ECMWF Reanalysis-5 (ERA-5) revealed strong inverse correlations of the normalized bistatic radar cross section with sensible heat flux and surface stress under certain conditions.
## 5 Module I: Anonymity
### Communication Phase
#### 5.3.1 Transmission as a Sensor  
When SNi senses data, it needs to send a message hop-by-hop to the BS. The SNi only recognizes itself by its (PIDi), and the BS will recognize the source of the message by its PIDi as well. Thus, the PIDi of the source needs to be included in the message until the BS receives it. Consequently, the PID of a sensor will be updated after every transmission. The SNi needs to select one neighbor from Ni to forward the message to it. The selection process goes through a probabilistic protocol, which guarantees that SNi does not use one neighbor all the time when forwarding its data; first, for routing privacy, and second for increasing the lifetime of the WSN. SNi will form the message in the following format:  
\[\mathrm{M}_{\mathrm{i}\rightarrow\mathrm{j}}=\mathrm{OHPID}_{\mathrm{i} \leftrightarrow\mathrm{j}}\parallel\mathrm{E}_{\mathrm{ki}\rightarrow\mathrm{ j}}\mathrm{(PIDi\parallel E}_{\mathrm{ki}\rightarrow\mathrm{bs}}\mathrm{(Di))} \tag{9}\]  
where Di includes the sensed data. Once SNi knows that the message (Mi\(\rightarrow\)j) is delivered to the the neighbor, it needs to dispose of the current pseudonym PIDi and issue a new one for the next transmission as indicated in Equation (10):  
\[\mathrm{PIDi}=\mathrm{H}_{1}\mathrm{(PIDi\oplus a_{i})} \tag{10}\]  
In addition, both SNi and SNj will dispose of the current OHPIDi\(\leftrightarrow\)j and issue a new one for the next communication between the two neighbors according to Equation (11):  
\[\mathrm{OHPIDi\rightarrow\mathrm{j}}=\mathrm{H}_{1}\mathrm{(OHPIDi\to \mathrm{j}\oplus a_{i\rightarrow\mathrm{j}})} \tag{11}\]  
The message (M) will then be reformatted by the recipient [PERSON] and again forwarded to the next node, say SNr, and so on, until it gets to the BS. If [PERSON] was the BS, then the BS uses the shared one-hop key between the sensor and the BS, to decrypt the data and to get the PIDi, which the BS can use to recognize the source SNi. Only at this point of time, BS can update the value of PIDi of SNi. It also reads the data (Di) which the BS can decrypt using Ki\(\leftrightarrow\)bs.