esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis  
### Calculation Models  
This research includes three sets of calculation models: the calculation model for a plane containing a single roadway, the calculation model for a plane containing two  
Figure 1: Schematic diagram of stope layout at Level 650.  
Figure 2: Schematic cross-section of Stope 101 at Level 650.  
readways and the calculation model for a plane containing two rectangular openings. Following the sequence of the actual mining operations, the stress characteristics of a single roadway, the influence of the adjacent roadway on the existing roadway, and the optimal size parameters of the reserved pillars in Stope 101 were analyzed successively based on these calculation models.  
After the completion of the development of Level 650, according to the mining sequence, the design of Stope 101 as shown in Figure 1 was carried out first. All the recovery roadways in this level are tri-center arch roadways, with a cross-sectional dimension of 4.2 m \(\times\) 3.9 m and a span-to-rise ratio of 0.36. The calculation model of the surrounding rock mass of the roadway is shown in Figure 3a, where \(\sigma_{x}^{\infty}\) represents the far-field horizontal stress, and \(\sigma_{y}^{\infty}\) represents the far-field vertical stress.  
Upon the excavation of the adjacent roadway, there may be an impact on the stress state of the surrounding rock of the existing roadway. Therefore, in stope design, when two roadways are relatively close to each other, it is necessary to evaluate the mutual influence on the stress field between them. In the case studied in this research, the distance between the recovery roadways of two adjacent slopes is 15 m. Taking the recovery roadway of Stope 101 as the subject of study, the impact on the stress field around the recovery roadway of Stope 101 after the excavation of the recovery roadway in stope 102 was investigated. The stress field calculation model of the dual-roadway system is illustrated in Figure 3b.  
As seen in the stope's cross-sectional diagram (Figure 2), there is a gauge (waste rock) located at the position of 51 m into the stope, with a thickness of about 8 m. To improve the recovery rate, it was proposed to reserve the area where the gauge is located as a reserved pillar. To determine the optimal size of the pillar, the calculation model as shown in Figure 4 was established in this study, where \(w\) represents the width of the pillar. The ore body on both sides of the pillar is recovered through medium deep-hole blasting, forming rectangularly shaped openings. This calculation model aimed to determine the optimal size of the pillar by establishing the relationship between the pillar width and the safety coefficient of the pillar as well as the recovery rate.  
Figure 4: Schematic diagram of the stress calculation model for the plane containing a single roadway.  
Figure 3: Schematic diagram of the stress calculation model for the plane containing roadways. (**a**) A single roadway. (**b**) Two roadways.
## 4 Experiments, Results, and Analysis  
This section outlines the methodology used to train the DNN and the outcomes of deploying it for estimating GGD's parameters in composite signals. We suggest using a small deep neural network (S-DNN) that achieves performance comparable to the DNN with a sufficiently large number of SDR applications. A typical example is provided to demonstrate the use of the DNN estimates.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 3 BackCom: An Effective Solution
### Understanding BackCom  
Table 2 demonstrates the complete evolution of BackCom since its roots in 1945, with the concept of a spying device used by the Soviet Union to listen to conversations had at the US embassy [38]. Similarly, in the Second World War, BackCom was used to reflect back RADAR signals to identify \"foes\" or \"friends\" [39]. The idea of backscattering was officially published in 1948 by [PERSON] [40]. Afterwards, [PERSON] proposed a radio-communication-based Aloha system in 1970 for linking interactive computers and devices [41]. Later, electronic toll collection (ETC) based on RFID was introduced to collect tolls from vehicles [42; 43]. The hardware implementation was proposed for bridging physical things and the virtual world using electronic tags [44]. The signal flow diagram for BackCom shown in Figure 5a illustrates the carrier signals moving from a TV tower to a passive tag. The information-carrying backscattered modulated signals, moving from tag to reader, are demonstrated in Figure 5b. The backscatter radio system allows passive tags to consume sufficient energy from incident RF carrier signals, generated by different sources, while reflecting back part of the carrier signals to transmit the tag's data [45]. The working principle of BackCom [46], compared to traditional RFID, is as follows:  
1. Conventional RFID reader generates and transmits carrier waves to the RF-tags. In BackCom, the reader does not require carrier wave production. Instead, the tags receive the signals from nearby TV, cellular, or WiFi waves. 2. BackCom considers passive tags, similar to the RFID system. These passive tags capture electromagnetic (EM) waves to extract power. 3. Similar to RFID, the BackCom tag possesses a small dipole antenna that collects the energy as the varying potential difference, appearing across the arms of the dipole antenna. 4. A diode rectifies the potential in the arms of the dipole antenna, and the rectified voltages are then stored in the capacitor to supply continuous energy to the electronic circuit [47]. 5. The electronic component contains a micro-controller that can save the tag's information. 6. The tag's data is then modulated by reflecting incident RF signals. An on-off keying (OOK) scheme of amplitude modulation is usually considered. 7. Intentional impedance mismatching between the tag's load and the antenna causes the reflection of incident signals to modulate the tag's information. The increase or decrease in impedance causes the circuit to work as a transistor in an open switch state or a closed switch condition. 8. The RF reader in RFID and the corresponding BackCom reader in the tag decode the information of the received signals reflected by the tags. The above-mentioned eight points explain the BackCom working principle. The internal architecture of BackCom tag is elaborated below, in addition to the communication protocols for passive tags. Figure 5: Basics of BackCom. (**a**) Signal from reader to tag; (**b**) signal from tag to reader.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 3 IoT-WBAN with Energy Harvesting  
Energy harvesting is the process by which a SN can power itself [36; 37; 38; 39; 40; 41; 42] as shown in Figure 2. The SNs can be equipped with many types of techniques which help the node produce energy from the environment automatically. A summary of energy harvesting sources and the used mechanisms is shown in Table 1. Due to the small size of the SN, the available resources in it is also limited, such as small batteries having less available energy. The initial energy of the SN is 0.5 Joule which is very small. Due to this limitation, the energy available for the operation of the SN, such as transmission and reception of data, is limited [43; 44; 45; 46; 47; 48; 49; 50; 51]. In order to use a WBAN, practical efforts have been made worldwide to resolve problem of supplying energy to SNs through reliable and efficient techniques. The efficiency of power supply is ensured during the design of an integrated circuit; the capacitance provided is a critical aspect which is used for the regulation of operating clock frequency [44]. It has great effects on the energy depletion of the circuit. A high clock rate means more power usage. It is therefore recommended to design a low clock frequency circuit as shown in Equation (1).  
\[R=K\cdot V \tag{1}\]  
where, \(R\) represents the total quantity of charge, \(K\) represents the magnitude of capacitance, and \(V\) represents the current of the capacitor. The capacitor current is directly proportional to the quickness of the voltage change inside the capacitor. The voltage of the capacitor rises if the current is harvested during input mode. The integrated circuit (IC) should check the voltage more often in cases where the voltage of the capacitor changes rapidly.
## 3 Background Knowledge
### DNS-over-HTTPS Vulnerabilities in Enterprise Network  
Since DoH is a fast, lightweight, and encrypted communication channel, it can be exploited as a data exfiltration channel. This attack is a much stronger version of DNS tunneling, called DoH tunneling. DNS tunneling can be easily detected by capturing and analyzing the DNS traffic content. However, the DNS tunneling detection methods cannot  
Figure 1: DNS resolution process.  
Figure 2: DNS-over-HTTPS resolution.  
be applied to DoH tunneling due to the encryption and the massive HTTPS traffic. In [13], HTTP/2 is a recommended version of HTTP for DoH. The malware can utilize this HTTP/2 connection to send several DoH requests and responses to its server without creating a separate connection (or packet) for each request and response. Through this method, malware can hide the frequency of their DNS resolution and divide the exfiltrating data into small packets to hide their activities. This attack is hazardous for enterprise networks due to the share of network infrastructure, which can quickly spread the malware or bot in the network and steal information from shared databases. DoH tunneling may be slow but very efficient because it is unsusceptible to the traditional threat detection method. This work focuses on detecting DoH tunneling in an enterprise network.  
A typical DoH tunneling attack in an enterprise network is shown in Figure 3. Firstly, the attacker registers a domain and embeds it into the malware. The malware reads the exfiltrating data and divides it line by line. Each part of the exfiltrating data is added to the DNS query. The malware periodically sends a DNS query to the local DoH proxy to wrap the DNS query in HTTPS. The local DoH proxy executes a standard DoH process to locate the registered domain name of the attacker and direct the DNS query to that server. When the DNS query with the exfiltrating data arrives, the attacker can extract the needed data by analyzing the DNS query log. Then the attacker server sends back a DNS response to the new malware command-and-control encrypted in the resource record in DNS response format.
## 7 Conclusions  
This study aims to propose and evaluate a privacy-preserving deep learning framework that combines Instance Reduction Techniques with the Restricted Boltzmann Machine to preserve privacy in collaborative deep learning frameworks and overcome the limitations of other privacy-preserving solutions. In such a framework, each participating party applies a Restricted Boltzmann Machine model to their data, encoding it in a manner that retains the important features relevant for training. Subsequently, a reduction technique (specifically, ENN) is used to select a subset of the data to send to the central server. These reduced sets of encodings are transmitted without concerns about data privacy or computational and communication overheads. Experimental evaluations on MNIST [16], Fashion-MNIST [17], and EMNIST-Letters [18] datasets using CNN and MLP architectures compare the proposed framework with a conventional collaborative learning scenario. The results show that our framework preserves privacy with a little loss of accuracy and a large reduction in training time. The study highlights the framework's suitability for privacy-preserving collaborative deep learning in real-world applications, emphasizing its potential in sectors such as healthcare, finance, and smart security and surveillance systems, where both privacy and scalability are of utmost importance. For future work, we aim to address framework limitations, optimize reduction algorithms for diverse datasets, enhance model performance, and deploy the framework in real-world applications for comprehensive evaluation.  
Conceptualization, A.A. and K.E.H.; data curation, A.A. and K.E.H.; formal analysis, A.A. and K.E.H.; investigation, A.A. and K.E.H.; methodology, A.A. and K.E.H.; resources, A.A. and K.E.H.; software, A.A.; supervision, K.E.H.; validation, A.A. and K.E.H.; visualization, A.A.; writing--original draft, A.A. and K.E.H.; writing--review and editing, A.A. and K.E.H. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research and Innovation, \"Ministry of Education\" in Saudi Arabia for funding this research (IFKSUOR3-400).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Non-Contact and Real-Time Measurement of Kolsky Bar with Temporal Speckle Interferometry
###### Abstract  
In this paper, a new non-contact and real-time measurement system for Kolsky bars is presented. This system uses two sets of temporal speckle interferometry in-plane displacement measurement devices to replace two strain gauges of conventional Kolsky bars. The in-plane displacement measurement of the Kolsky bar is mainly intended to provide a new test method for the dynamic mechanical properties of small-size material samples with diameters below 2 mm. This method is non-contact, does not require any intermediate medium, and can make the Kolsky bar applicable to characterizing the dynamic mechanical properties of materials under higher strain rates and smaller size conditions. The measuring devices and principles are described. In addition, a preliminary experiment is carried out to demonstrate the performance of this new device.  
Kolsky bar; speckle; in-plane displacement measurement; wavelet transform; dynamic mechanical properties +
Footnote †: journal: Applied Sciences
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 5 Application of MIPs for the Detection of Different Classes of Contaminants
### Heavy Metals
#### 5.1.5 Nickel  
In nature, nickel is one of the most abundant metals, and has found many applications in industry, such as electroplating, batteries, and electronics [45]. Nickel is also one of the micronutrients essential for increasing hormonal activity and posturing lipid metabolism.  
Figure 5: Schematic illustration of the formation process of (**A**) the IIP and (**B**) the principle of PEC determination of Cr (VI) using IIP@ graphitic carbon nitride (F-g-C\({}_{3}\)N\({}_{4}\)) [53]. (Reproduced under permission from Elsevier, doi:10.1016/j.jhazmat.2016.03.046).  
On the other hand, excessive exposure to high levels of nickel may result in genotoxicity, haematotoxicity and carcinogenicity.  
A magnetic imprinted polymer was obtained based on precipitation polymerization of amine functionalized silica-capped iron oxide particles and 4-vinyl pyridine as a complexing agent, while methacrylic acid was the functional monomer. Inductively coupled plasma-optical emission spectrometry (ICP-OES) was used for the detection during all adsorption experiments and the polymer was found to be highly sensitive for Ni\({}^{2+}\) with pseudo-second-order kinetics and Langmuir isotherm. An adsorption capacity of 158.73 mg g\({}^{-1}\) was obtained with limit of detection, quantification, and the percent relative standard deviation of 0.58, 1.93, and 3.4%, respectively [55].  
Another nickel ion-imprinted polymers (IIPs) based on multi-walled carbon nanotubes (MWCNTs) were synthesized inverse emulsion system, using chitosan and acrylic acid as the functional monomers, Ni (II) as the template, and \(N^{\prime}N\)-methylene bis-acrylamide as the cross-linker. The polymer was used as modifier for carbon paste response and cyclic voltammetry was successfully applied showing cathodic and anodic peaks at 0.018 mA and \(-\)0.016 mA, while no peaks were shown for the bare. The chemisorptions process was in line with the pseudo-second-order adsorption kinetic model and Langmuir adsorption thermodynamic model [56].
# State-of-Charge Prediction Model for Ni-Cd Batteries Considering Temperature and Noise
###### Abstract  
The accurate prediction of the state of charge (SOC) of Ni-Cd batteries is critical for developing battery management systems for high-speed trains. To address the challenges of the large floating charge voltage of Ni-Cd batteries and the vulnerability of a battery's SOC to environmental factors such as temperature, this paper proposes an adaptive adjustment mechanism-based particle swarm optimization (APSO) generalized regression neural network (GRNN) model. The proposed model introduces the concept of the particle aggregation degree to quantify the convergence of the particle swarm optimization (PSO) algorithm. Furthermore, the speed weight of the particle swarm is adaptively adjusted using a comprehensive loss function to optimize the parameters of the GRNN model. To validate the proposed method, simulation experiments are conducted under test conditions using Ni-Cd batteries, and the prediction accuracies of various algorithms are compared. The experimental results demonstrate that the APCSO-GRNN model significantly reduces the model's prediction error. In addition, under the influence of different temperatures and noises, this method demonstrates strong robustness and high practical application value by accurately predicting the SOC, even with limited data samples.  
Ni-Cd battery; particle swarm optimization; GRNN; adaptive regulation; estimation of SOC
## 3 Results and Discussion
### Validation of the Developed Algorithm
#### 3.1.1 Validation of the Delamination Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Boundary** **Condition**} & \multirow{2}{*}{**Crack**} & \multirow{2}{*}{**Source**} & \multicolumn{6}{c}{**Frequency Mode**} \\ \cline{5-10}  & & & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{5}{*}{Free plate} & intact & [PERSON] et al. [40] & 73.31 & 202.59 & 243.37 & 264.90 & 306.34 & 444.80 \\  & & [PERSON] and & 89.68 & 201.42 & 259.61 & 263.38 & 327.58 & 478.18 \\  & & Prevent & 71.72 & 198.89 & 240.79 & 262.04 & 303.14 & 435.29 \\  & & [PERSON] et al. (1995) [40] & 72.94 & 202.07 & 241.18 & 258.51 & 304.29 & 421.99 \\  & & [PERSON] and & 89.66 & 200.27 & 248.93 & 256.67 & 317.72 & 442.25 \\  & & Prevent & 71.69 & 196.82 & 233.19 & 251.24 & 297.32 & 403.36 \\ \hline \multirow{5}{*}{Cantileever plate} & intact & [PERSON] et al. (1995) [40] & 41.35 & 60.66 & 221.52 & 258.72 & 286.78 & 407.98 \\  & & [PERSON] and & 41.07 & 68.17 & 226.26 & 257.49 & 299.74 & 435.58 \\  & & [PERSON] [41] & 40.99 & 60.03 & 217.87 & 256.68 & 284.18 & 401.93 \\  & & [PERSON] et al. (1995) [40] & 40.26 & 60.77 & 220.02 & 249.69 & 285.26 & 395.33 \\  & & [PERSON] and & 40.29 & 68.08 & 221.31 & 248.23 & 293.90 & 403.85 \\  & & Prevent & 39.68 & 59.89 & 213.21 & 241.40 & 279.46 & 372.20 \\ \hline \multirow{5}{*}{Simply supported plate} & intact & Ju et al. (1995) [40] & 164.37 & 404.38 & 492.29 & 658.40 & 854.17 & 1046.20 \\  & & [PERSON] and & 169.81 & 409.78 & 504.22 & 672.69 & 855.48 & 1044.71 \\  & & [PERSON] [41] & & 161.92 & 395.89 & 491.36 & 644.15 & 828.97 & 1004.49 \\  & & Prevent & 161.58 & 348.27 & 371.19 & 637.48 & 704.72 & 837.45 \\  & & [PERSON] and & 167.04 & 347.88 & 374.62 & 611.08 & 653.97 & 750.39 \\  & & [PERSON] [41] & & 158.03 & 319.12 & 330.15 & 563.77 & 605.42 & 692.47 \\ \hline \multirow{5}{*}{Clamped plate} & intact & Ju et al. (1995) [40] & 346.59 & 651.51 & 781.06 & 1017.20 & 1195.40 & 1487.00 \\  & & [PERSON] and & 346.81 & 643.44 & 777.93 & 982.16 & 1170.83 & 1412.03 \\ \cline{1-1}  & & Prevent & 340.45 & 630.83 & 765.18 & 963.91 & 1139.52 & 1390.43 \\ \cline{1-1}  & & Ju et al. (1995) [40] & 334.67 & 579.43 & 653.25 & 851.27 & 1074.07 & 1108.80 \\ \cline{1-1}  & & [PERSON] and & 316.88 & 529.34 & 554.81 & 783.80 & 893.91 & 947.52 \\ \cline{1-1}  & & Prevent & 298.14 & 497.36 & 507.74 & 772.32 & 825.85 & 894.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Natural frequencies of intact plate and plate with discrete single delamination of size a /2.
## 6 Evaluation and Analysis
### Simulation Results
#### 6.3.3 Results of Different Protocol Sets
In addition, the destination nodes have to reply to every RREQ packet received, which results in more route reply (RREP) packets as well. The DAAM-based protocol sets generate a similar amount of control packets. This is because with multiple available paths, the source node can always select another path, which can satisfy the priority requirement of the packet, to the destination node when the previous transmission fails. The route discovery procedure is performed and the associated routing overhead incurred only when none of the existing paths can be utilized for the transmission. In the simulations, the ACK frames are required for every frame transmission between a pair of nodes, where either a data or control packet from the NET layer is encapsulated into a frame. Therefore, the number of ACK frames is significantly higher than the total routing control packets. Table 5 presents the overall rankings for all the protocol sets based on the performance metrics. The protocol set with context-aware DR-MAC and context-aware DAAM (Set 9) shows the best overall result by ranking first in throughput, PDR, and delay; second in control overhead (only two ranks in this metric); and third in energy efficiency.
## 4 Effect of the Subsidence Width in the Numerical Model
### Analysis of the Soil Pressure  
Figure 9 shows the soil pressure distribution of the geotextile in the stable area for different subsidence widths. All of the curves have a peak value at the boundary between the stable area and the subsidence area, and then decreases rapidly to a smaller value. All of the curves have a second peak value of the soil pressure when the horizontal distance from the center is about 1.35-2 m. The peak value increases with the increase of the subsidence width. The maximum soil pressure value is 1485.27 kPa at the edge of the subsidence area when the anchorage ratio of \(L=2.22B\), and the maximum soil pressure value is 136.65 kPa in the stable area when the anchorage ratio of \(L=2.50B\). The soil pressure of each curve gradually decreases to be stable when the horizontal distance from the center is about 2.25 m. It indicates that the normal stress at the boundary between the stable area and the subsidence area increases significantly and the maximum soil pressure of the stable area does not fluctuate greatly with the increase of the subsidence width. This means that more soil loads in the subsidence area are transferred to the stable area.  
The subsidence width has less influence on the soil pressure curve of the stable area, while the influence is much greater in the area close to the subgrade collapse edge, and the larger the subsidence width is, the greater the influence of the subsidence width is.