esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Field Test
### Monitoring Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Position** & **Maximum Value/mm** & **Average/mm** \\ \hline Left drift arch hance (3–3\({}^{\prime}\)) & 29.4–89.0 & 63.2 \\ Left drift arch feet (5–5\({}^{\prime}\)) & 33.5–108.8 & 75.2 \\ Middle drift arch hance (4–4\({}^{\prime}\)) & 15.4–30.2 & 20.1 \\ Middle drift arch feet (6–6\({}^{\prime}\)) & 23.6–47.0 & 29.3 \\ Main section arch hance (3–4) & \(-\)6.5—8.4 & \(-\)7.3 \\ Main section arch feet (5–6) & \(-\)7.9—15.2 & \(-\)10.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Monitoring results of convergence. the convergence value in each drift, it is evident that the left drift experiences the largest convergence, followed by the middle drift. Moreover, the convergence value of the main section is negative, which means that the arch structure of the tunnel has expanded outward after the temporary support is removed. This observation is made in the context of the double-side drift method. Figure 8 illustrates that the convergence at each monitoring point underwent three stages: rapid growth, slow deformation, and stabilization. The deformation rate of both side drifts decreased after the lower bench excavation, attributed to the improved condition of the surrounding rock at the lower level, enhancing the stability of the supporting structure. The convergence rate of the left drift increased significantly after excavating the right drift. This suggests that the drift excavation on the shallow side caused the unloading of the original rock load, resulting in an increase in horizontal stress on the deep side. During the excavation of the middle drift, the convergence value of the left drift decreased slightly, while the convergence value of the right drift remained relatively stable. This indicates that the horizontal stress on the support structure of the shallow side is not significant after excavation of the adjacent drift. Upon removal of the temporary support, the main section undergoes horizontal deformation due to the extrusion of the surrounding rock. The arch support structure quickly stabilizes the deformation. The stress distribution of the structure is analyzed in combination with the analysis of the arch settlement. Once the original load from the temporary support is removed, the arch support structure experiences settlement, and both sides expand outward according to a certain rule. The removal of the temporary support mainly affects the vertical load sharing.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 3 System Model
### Proposed Scheme-II (Key Recovery System Using Proxy Re-Encryption)
#### 3.3.1 Design Goals of Proposed Scheme-II  
The model of the key recovery system using proxy re-encryption is a key recovery system that is used when a device key is lost or corrupted as shown in Figure 3. The device requests key recovery from Med and sends KRF. The Med receiving the KRF verifies that it is a lawful device of KRF. If it is a lawful device, it generates a re-encryption key. Then, it requests key recovery from KRC and sends the obtained KRF and the re-encryption key. After receiving the KRF and re-encryption key, the KRA partially calculates KRF and sends the partially calculated KRF to KRA. After receiving the partial calculated KRF, KRA performs some calculations and sends partial calculated KRF to KRC. After receiving KRF, KRC sends it to the Med. The Med decrypts it, generates a session key, and sends it to the device.
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
###### Abstract  
The Long-Term Evolution (LTE) system was a result of the 3 rd-Generation Partnership Project (3 GPP) to assure Quality-of-Service (QoS) performance pertaining to non-real-time and real-time services. An effective design with regards to resource allocation scheduling involves core challenges to realising a satisfactory service in an LTE system, particularly with the growing demand for network applications. The continuous rise in terms of the number of network users has resulted in impacts on the performance of networks, which also creates resource allocation issues when performing downlink scheduling in an LTE network. This research paper puts forward a review of optimisation pertaining packet scheduling performance through the LTE downlink system by introducing a new downlink-scheduling algorithm for serving video application through LTE culler networks, and also accounts for QoS needs and channel conditions. A comparison of the recommended algorithms' performances was made with regards to delay, throughput, PLR, and fairness by utilising the LTE-SIM simulator for video flow. On the basis of the outcomes obtained, the algorithms recommended in this research work considerably enhance the efficacy of video streaming compared against familiar LTE algorithms.  
LTE network; downlink scheduling; video streaming; QoS; EXPRule; jitter 20221177. [[https://doi.org/10.3390/computers11050077](https://doi.org/10.3390/computers11050077)]([https://doi.org/10.3390/computers11050077](https://doi.org/10.3390/computers11050077))  
11 March 2022  
Accepted: 28 April 2022  
Published: 9 May 2022  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion
### Processing Patterns
For sample #2 with the high laser scanning speed, the cross pattern was observed clearly. Figure 2: The SEY results of untreated Mo, laser processed Mo samples #1, #2, #3, #4, #5, #6 and #7. Sample #1 (cross hatched pattern), #3 (line hatched pattern) and #6 (line hatched pattern) were processed with the same scanning speed of 100 mm s\({}^{-1}\). Sample #2 (cross hatched pattern), #5 (line hatched pattern) and #7 (line hatched pattern) were processed by the same scanning speed of 1000 mm s\({}^{-1}\). Samples #1, #2, #3, #4 and #5 were treated with the same laser power of 10 W, while samples #6 and sample #7 were processed by the same laser power of 13.3 W. The pitch spacings of sample #1, #2, #3, #5, #6 and #7 were all 15 \(\upmu\)m and the one of sample #4 was 20 \(\upmu\)m. The other laser ablation parameters of these seven laser treated samples are shown in Table 1.
# Narrowing Linewidth of Wavelength-Swept Active Mode Locking Laser Based on Cross Gain Modulation
## 2 Experiments and Results
The dynamic characteristics were evaluated based on a 6-dB roll-off of the point spread function (PSF), which was acquired through the Fourier transform of an interference signal by passing through a fiber-type Mach-Zehnder interferometer (MZI). Figure 5c shows the measured sweeping trace of a sawtooth wave drive function at 20 kHz. This 6-dB PSF parameter using the XGM was measured as 1.41 mm.
# Design of a Rotary Transformer for Installations on Large Shafts
## 4 Design of the Transformer  
After the preliminary numerical finite-element investigations, the optimization of the size of the core was performed. The goal was to obtain the required power transfer from the primary to secondary, trying to exploit the magnetic core as much as possible, within the magnetic/thermal limits of the considered materials and minimizing the weight of the device's active parts. To simplify the procedure, the optimization was split into three different steps: First, a brute-force grid-search optimization was performed to investigate many different geometrical configurations of the core and to identify the most promising. Then, a genetic algorithm was performed, starting from the latter, to refine the winding arrangement. Finally, a further weight reduction was achieved by removing some parts of the magnetic core and investigating their impact on the power transfer by 3D simulation. In all these runs, a conservative limit of \(B=0.2\) T was set for the magnetic flux density inside the core, which is well below the saturation limit of the ferrite, so as to avoid the risk of transformer overrating. Actually, a cautious threshold was required, since the analyses in FEMM were performed at the fundamental frequency only. For similar reasons, a maximum current density of \(j=10\) A/mm\({}^{2}\) was considered, in conjunction with a load resistance \(R_{\text{L}}\) of 20 \(\Omega\). For a fixed slot area, Joule losses are proportional to the square of the current density:  
Figure 7: Electromagnetic model of the rotary transformer in ANSYS Maxwell: (**a**) shows top view and (**b**) shows front view of a generic slice of the transformer, whereas (**c**) shows the complete 3D model.  
\[P_{\rm{Joule}}\propto j^{2} \tag{17}\]  
Therefore, the combination of a relatively low current density along with favourable air conditions (such as those of a typical helicopter de-icing application [11; 12]) should avoid the risk of transformer overheating. The device was supplied with a sinusoidal voltage that had a peak value of \(4/\pi\cdot 270\,\)V and \(f=20\,\)kHz. Namely, the first harmonic of a square wave with amplitude \(270\,\)V (which is equal to the DC bus voltage, as well) was considered. In principle, thermal analyses should be carried out, as well, during the design of the transformer (as reported in [27]); however, since no peculiar application was considered, the current density constraint should limit the Joule losses and therefore prevent overheating. It should be underlined that compensation through resonant capacitors was not considered during this early design stage.
## 4 Experiments
### Results
#### 4.5.2 Ablation Experiment of Each Loss  
To demonstrate the effectiveness of the four losses, we conducted ablation experiments. The low-light enhancement effects are shown in Figure 8, where (a) is the input low-light image; (b) is the low-light enhancement result, including four kinds of losses, where the brightness and color have reached the ideal effect; (c) is the result of removing the spatial consistency loss (\(L_{spa}\)) and the image contrast is significantly reduced, such as part of the character's clothes; (d) is the result of removing exposure control loss (\(L_{exp}\)), where the low-light area of the image has not been enhanced and is still dark; (e) is the result  
Figure 7: RGB channel curve parameter graph.  
of removing color constancy loss (\(L_{col}\)), where the image has obvious color loss and the overall color turns green; and (f) is the result of removing luminance smoothing loss (\(L_{traA}\)), where the image has obvious artifacts, seriously affecting the visual effect. Through the ablation experiments of each loss, it can be seen that the four different losses had different contributions to low-light enhancement; removing any one made the low-light enhancement effect worse.  
In order to prove the effectiveness of \(L_{spu}\) more objectively, the input original image, the image enhanced by including the four losses, and the image enhanced by removing \(L_{spu}\) were converted to double type and a mesh map luminance visualization was carried out using MATLAB. As shown in Figure 9, image (b) with \(L_{spu}\) included had a roughly similar luminance structure to the input original image (a), while image (c) with \(L_{spu}\) removed had a high overall luminance and a reduced contrast, which further demonstrated the importance of \(L_{spu}\) in preserving the differences in the neighboring regions between the input image and the enhanced image.
## 4 Discussion
#### Future Perspective  
A larger-scale, prospective, randomized trial would improve knowledge of the effect of this technology on long-term results, both in terms of quality of life, functional results and implant survival (long-term loosening rate). In addition, accelerometer assistance would allow navigation for the distal femoral cut as well, and the study of the combined precision of the tibial and distal femoral cuts would be relevant. On another side, the evolution towards reusable sensors would be preferable to single-use sensors; however the sterilization of electronic equipment is still difficult to achieve.
# Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV
## 6 Hardware Configuration and Experiment
### PIXHAWK PX4 Flight Controller  
The main component in this entire hardware setup known as brain of this underactuated quadrotor is the controller. The proposed controller in this hardware design is PIXHAWK PX4 version of flight controller. This will deal with all types of maneuvering operations. This controller has better capability to tackle the control of all BLDC motors, power distribution, radio transmission and above all the global positioning satellite (GPS). This is the controller with 32-bit microprocessor, 256 KB random access memory and 2 MB flash memory, along with gyroscopic sensor, barometer and 14-bit magnetometer, which make it a one of a kind and most suitable for our experiment. In addition to these features, it has 14 pulse width modulation (PWM) output channels along with autopilot facility and GPS.
## 1 Introduction
For instance, knowledge gaps comprising the collaborative effect of surface preparation/roughness and fiber reinforcement for different fiber types need to be addressed, and so does the distinction between the impact of fibers on ITZ quality (early-age damage prevention such as drying shrinkage 14) and the contribution to fracture mechanisms such as crack deviation, kinking, or trapping [11; 13; 16; 18]. The main objective of the work presented hereafter is to provide a contribution towards the understanding of complex mechanisms involved with the interfacial cracking in cement-based bi-material systems. The study is focused on testing and characterization of fracture in Mode-I for different types of fiber reinforced concretes (the repair materials) and, especially, their bond to an older, unreinforced concrete (the substrate). Beyond the plain, unreinforced control condition, volume fractions of 0.5% and 1% with three types of fibers were considered for the repair material, namely: (i) 8 mm long Poly-Vinyl-Alcohol (PVA) fibers, (ii) 12 mm long PVA fibers (to compare the effects of different fiber lengths), and (iii) 13 mm long steel fibers (to compare fibers with similar lengths but different materials, matrix bonding, and stiffness). Both PVA and steel fibers were selected because this reinforcement can be very effective at enhancing the mechanical performance of concrete and especially its fracture toughness, with the potential also for strain hardening behavior in tension. In addition, both PVA and steel fibers can be very good at preventing or mitigating cracking related to volume changes such as shrinkage, a crucial property in repair applications where optimum compatibility between old and new concrete needs to be achieved to prevent damage acceleration. Previous studies have shown that both PVA and steel fibers can enhance concrete-concrete bond [11; 14; 15; 18], although most of the information available is focused on shear bond and very limited information is available on progressive debonding in Mode I. Studies also indicate that steel fibers can be more effective than PVA fibers at lower volume fractions while, conversely, PVA fibers can be more effective at higher volume fractions. Moreover, regarding shear bond, PVA have proven more effective than steel fibers in applications with minimum interfacial roughness (i.e., smooth interface, no sandblasting) [18]. In this study, the Mode-I crack propagation was investigated through a Coutered Double Cantilever Beam (CDCB) test 15, supported by imaging techniques for stress-strain analysis. Fracture parameters were computed based on a classic Modified Linear Elastic Fracture Mechanics (MLEFM) approach. No bonding agent was applied but the substrates were sandblasted to enhance interfacial roughness before applying the repairs. Interfacial roughness and crack deviation were quantified through laser scanning; correlations between surface topography and fracture parameters were analyzed.
# Spreading-Based Voice Encryption by Means of OVSF Codes
###### Abstract  
This paper presents a new methodology to encrypt voice signals, in such a way that they simulate being a noise signal. The objective is to obtain a signal that does not generate suspicions about its content, while protecting the message. The process is based on the spread and scrambling of the signal through the use of OVSF (orthogonal variable spreading factor) codes. The security of the method is based mainly on the input value used for the randomization phase. From a computational cost point of view, the use of fixed-length codes allows for a shorter retrieval time compared to a similar method based on variable-length codes. Regarding the encrypted signal, its main characteristic is its high entropy (very close to the ideal value). Finally, in the recovery process, a signal identical to the original is obtained.  
Keywords:signal encryption; ciphered voice; spreading; privacy; noise Article
## 3 Materials and Methods
### Feature Selection
#### 3.3.1 Mutual Information (MI)  
It is a statistical measure used to quantify the level of dependence between two random variables [41]. Unlike other filtering methods, it can also catch non-linear dependencies [37]. Ranking criteria based on information theory assess the interdependence of two variables, with MI represented mathematically as \(I(X;Y)\), as shown in Equation (1).  
\[I(X;Y)=\sum_{x,y}p(x,y)\log\frac{p(x,y)}{p(x)p(y)} \tag{1}\]  
where the joint probability distribution function of \(X\) and \(Y\) is represented by \(p(x,y)\), while \(p(x)\) and \(p(y)\) indicate the marginal probability distributions of \(X\) and \(Y\), respectively. Moreover, MI can be reformulated using entropy, as shown in Equations (2)-(5).  
\[I(X;Y)=H(X)-H(X|Y) \tag{2}\]  
\[H(X)=-\sum_{x\in X}p(x)\text{log}(p(x)) \tag{3}\]  
\[H(X|Y)=-\sum_{y\in Y}p(y)\sum_{x\in X}p(x|y)\text{log}(p(x|y)) \tag{4}\]  
\[H(X,Y)=-\sum_{x,y}p(x,y)\text{log}(p(x,y)) \tag{5}\]where \(H(X)\) is the marginal entropy, \(H(X|Y)\) represents the conditional entropy, and \(H(X,Y)\) refers to the joint entropy of \(X\) and \(Y\). Marginal entropy \(H(X)\) quantifies the uncertainty associated with the random variable \(X\), while \(H(X|Y)\) measures the residual uncertainty in \(X\) after \(Y\) is known. This formulation underscores the essence of Mutual Information as the measure of information that one variable provides about the other, capturing both linear and non-linear dependencies [25,41].
## 5 Experiments and Results  
### Test Case 1--Six-Generator Test System with System Losses  
The modified bat algorithm was applied to a six-generator test system. For the first test case, the values of parameters \(d\) and \(e\) have been set to zero to simplify the problem into a convex optimization problem. Two simulations were carried out for a total demand of 700 MW and 800 MW. For all the experiments the maximum allowable error tolerance was set to 0.01 MW. The data for the test system is given in Tables 1-3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Generator** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Pmin (MW) & 10 & 10 & 35 & 35 & 130 & 125 \\ Pmax (MW) & 125 & 150 & 225 & 210 & 325 & 315 \\ \hline \end{tabular}
\end{table}
Table 1: Generator active power limits.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** \\ \hline \(A_{i}^{(0)}\) & 0.9 \\ \(r_{i}^{(0)}\) & 0.1 \\ \([\ \zeta\ \zeta\ ]\) & [0.97 0.95] \\ \([W_{min}\ \ W_{max}]\) & [0.4 0.9] \\ \([\ \ C_{1}\ \ C_{2}\ \ C_{3}\ \ C_{4}]\) & [u(0,3) u(0,2) u(0,1) u(0,1)] \\ \(p\) & 2000 \\ \([\ \ G\ H\ ]\) & [10 200] \\ \hline \end{tabular}
\end{table}
Table 3: Optimization parameters.  
Figure 1: (**a**) Effect of tuning parameter G; (**b**) Effect of tuning parameter H.  
Figure 2: (**a**) Bat Algorithm (**b**); Modified Bat Algorithm.  
For the purpose of comparison real coded GA was used with roulette wheel selection, arithmetic crossover and uniform mutation. The crossover and mutation probabilities were 0.9 and 0.02 respectively. Also for the purpose of comparison, PSO was implemented with inertia weight decreasing linearly form 0.9 to 0.4. Velocity constant was set to 2 for all the experiments. Values used for the tuning parameters while simulating test case 1 are in Table 3. The results are presented in Table 4.  
In the first experiment MBA attained better results than the original BA. The mean and standard deviation reduced by $0.41 and 16% respectively. However, PSO performed better that the both of them by achieving the lowest mean. The worst value achieved by PSO was $36927.72 which was far greater than $36916.93 (BA) and $36916.12 (MBA). This resulted in a higher standard deviation for PSO.  
In the next experiment the total system demand was increased from 700 MW to 800 MW. The system parameters remained the same. The results of the experiment are detailed in Table 5.  
In the second experiment the MBA again out performed BA. The mean and standard deviation reduced by $0.08 and 15% respectively. In this experiment also PSO achieved the lowest mean. It is important to note that in both the experiments MBA attained the lowest standard deviation.
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 3 Preliminaries
### Evaluation Metrics
#### 3.6.4 F1-Score  
F1-score, also called F-measure, is the harmonic mean between _precision_ and _recall_[56]. It is given by Equation (14).  
\[F1-score=2*\frac{precision\times recall}{precision+recall} \tag{14}\]  
Specifically, the F1-score with reference to ontology alignment is given by Equation (15) [56].  
\[f-measure(A)=\frac{recall(A)\text{x }precision(A)}{\text{x }recall(A)+(1- \alpha)\text{x }precision(A)} \tag{15}\]  
In this study, the relative weight of _recall_ and _precision_, denoted by \(\alpha\), has been set to 0.5. This default weight is usually set to 0.5 which gives the same value when the F-measure is calculated using Equation (12). In some cases, weight can be higher for recall and lower for _precision_. In such cases, one can determine the value of the weight for the _recall_ by applying Equation (13). The weight must be a decimal value in the range 0 and 1.
## 2 Theoretical Derivation  
### Stress Intensity Factor Correction Formula  
In the traditional pressure vessel crack growth model, assuming the initial crack angle is \(0^{\circ}\), the stress intensity factor \(K\) of the crack growth model [16] is:  
\[K=\frac{\sigma R}{h}F_{s}(\lambda_{a})\sqrt{\pi a} \tag{1}\]  
Breaking the assumption that the initial crack propagation angle is \(0^{\circ}\), we assume the stress intensity factor \(F\) is affected by the crack initiation angle \(\alpha\), where \(F\) is a coefficient variable. Therefore, we can express the crack growth model when the crack initiation angle is \(\alpha\) as Formula (2). The modified stress intensity factor \(K^{*}\) is:  
\[K^{*}=\frac{\sigma R}{h}F_{s}(\lambda_{a})\sqrt{\pi a}\times F \tag{2}\]  
For the above formulas, \(\sigma\) is the stress on the inner wall of the cylinder, \(R\) is the average radius of the curvature, \(h\) is the thickness of the pipe wall, \(a\) is the half-crack length, \(\mu\) is the Poisson's ratio, and \(\lambda_{a}\) is the shell parameter. \(\lambda_{a}\) can be calculated directly from the material parameters by Formula (3). \(F_{S}(\lambda_{a})\) is a constant equation, so \(F_{S}(\lambda_{a})\) can be directly calculated by \(\lambda_{a}\).  
\[\lambda_{a}=\frac{\left[12\big{(}1-\mu^{2}\big{)}\right]^{0.25}a}{\sqrt{Rh}} \tag{3}\]