esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fully Automatic DEXP Method for Gravity Data and Its Application on a Potash Salt Deposit
## 2 Methodology
### Theory of the Automatic DEXP Method
#### 2.2.2 The Adding Constant Method  
Here, we replace \(\mathfrak{R}_{mn}\) in Equation (3) with \(\mathfrak{R}_{nm}\), as defined as Equation (5):  
\[\mathfrak{R}_{mn}{}^{\prime}=\frac{f_{m}}{F_{n}}=\frac{f_{m}}{f_{n}+C} \tag{5}\]  
where this paper introduces the definition of the constant \(C\), as presented in Equation (6). Notably, constant \(C\) replaces the threshold value \(\varepsilon\) in Equation (4), which effectively mitigates the effects caused by the threshold value \(\varepsilon\).  
\[C=|\text{max}(f_{n})|+|min(f_{n})| \tag{6}\]  
Therefore, we substitute Equation (5) into Equation (3), which achieves the improved equation shown in Equation (7):  
\[W_{n}{}^{\prime}=z^{0.5(m-n)}\mathfrak{R}_{mn}{}^{\prime},(m>n) \tag{7}\]  
To give a better comparison among the different imaging results, normalization is applied to the result, as shown in Equation (8):  
\[W_{(i,j)}{}^{\prime}=\frac{W_{(i,j)}-min(W)}{max(W)-min(W)} \tag{8}\]  
where \(W_{(i,j)}\) is the value location at \(x=i\), \(y=j\).  
It is evident that the improved equation, Equation (7), maintains both clear mathematical and physical meaning. Additionally, the introduction of the constant \(C\) can be interpreted as a translation of the function \(f_{n}\) along the \(y\)-axis by a distance of \(C\) units. This manipulation ensures that the essential characteristics and trends of the results are preserved without alteration. The demonstration is as follows:  
It is clear that constant \(C\) satisfies Equation (9):  
\[\frac{f_{m}}{|k\cdot f_{n}|}<\frac{f_{m}}{|f_{n}+C|}<\frac{f_{m}}{|f_{n}|}, \exists k\in N^{+} \tag{9}\]  
where \(k\) is positive integer and \(z>0\). Thus, (Equation (10)) can be written as:  
\[z^{0.5(m-n)}\cdot\frac{f_{m}}{|k\cdot f_{n}|}<z^{0.5(m-n)}\cdot\frac{f_{m}}{| f_{n}+C|}<z^{0.5(m-n)}\cdot\frac{f_{m}}{|f_{n}|} \tag{10}\]  
Which can be simplified as Equation (11):  
\[\frac{1}{k}\cdot W_{n}<W_{n}{}^{\prime}<W_{n} \tag{11}\]  
The DEXP method utilizes the extreme value of the imaging results to reflect the location of the anomaly body. It is apparent that the improved DEXP method, represented by Equation (10), does not affect the stability of the imaging results.
# Experimental Study of Tool Wear in Electrochemical Discharge Machining
## 2 Experimental Settings
### Materials  
According to the previous research literature [19; 20; 21; 22], the tungsten carbide electrode was selected. According to the research of [PERSON] et al. [23], in wire electrochemical discharge machining (WECDM), when a spiral electrode was used, the axial updating speed of electrolyte was accelerated, and the uniformity of the slit width in the depth direction was enhanced. [PERSON] studied the machining locality of the spiral electrode in the WECDM process, and fabricated a series of kerfs with 138 \(\upmu\)m wide [19]. For this reason, the spiral electrode was selected. Secondly, tool wear is related to the cathode size. In order to better compare the tool wear, a spiral tungsten carbide electrode with a 400 \(\upmu\)m diameter was selected (Aoshi, Shenzhen, China). This spiral electrode has the shape of a two-edged drill. Table 1 shows the geometric dimensions and physical properties of the tool electrode. The workpiece is made of carbon fiber-reinforced polymer (CFRP) composite material (Hengjing Shenzhen, China), using the laminated plate and plain weave fabric. The fiber volume was 65%, the base material was epoxy resin, and the size was 25 mm \(\times\) 25 mm \(\times\) 1 mm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Geometric dimensions & 38,1040,01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric dimensions and the physical properties of the tool electrodes.  
Figure 2: ECDM experimental system: (**a**) the schematic diagram; and (**b**) the actual view.  
According to the principle of electrochemical discharge machining, as the power is turned on, the system will undergo an electrochemical reaction. Hydrogen and oxygen are generated at the cathode and anode, respectively. The chemical equations is:  
\[\text{Cathode}:\ 2H_{2}O+2e^{-}\to 2(OH)^{-}+H_{2}\ \uparrow \tag{1}\]  
\[\text{Anode}:\ 4(OH)^{-}-4e^{-}\to 2H_{2}O+O_{2}\ \uparrow \tag{2}\]  
In ECDM, alkaline solution is usually used as the electrolyte. In this research. The electrolyte used in the ECDM experiment was 5-15-25 wt% sodium hydroxide (Zhiyuan, Tianjin, China). In addition, \(Fe^{2+}\) in the solution may combine with other ions, or migrate to the cathode and deposit. When \(Fe^{2+}\) is combined with hydroxide ion, then:  
\[Fe^{2+}+2 OH^{-}\to Fe(OH)_{2}\ \downarrow \tag{3}\]  
\(Fe(OH)_{2}\) has little solubility in water, which will precipitate. In order to prevent the interference of other ions with the experimental reaction process, the electrolyte was prepared using the sodium hydroxide solute and deionized water in proportion. The auxiliary anode used in the experiments is cylindrical graphite of \(\mathcal{O}10\) mm.
## 4 Methodology
### Detailed Implementation of the Two Dataset
#### 4.3.1 Equilibrium  
The reconstruction losses are considered to be at equilibrium when the distributions of the reconstruction losses of real \(\mathcal{L}(\mathrm{x})\) and generated images \(\mathcal{L}(\mathrm{G}(\mathrm{z}))\) are equal:  
\[\mathbb{E}[\mathcal{L}(\mathrm{x})]=\mathbb{E}[\mathcal{L}(\mathrm{G}(\mathrm{ z}))] \tag{15}\]  
If equilibrium were reached, it would mean that \(D\) becomes incapable distinguishing generated samples from real ones. For the training to go smoothly, neither network should win over the other, so the strategy is to define a ratio \(\gamma\) between the two losses.  
\[\gamma=\frac{\mathbb{E}[\mathcal{L}(G(z))]}{\mathbb{E}[\mathcal{L}(x)]} \tag{16}\]  
\(\gamma\) is called the diversity ratio, and it should be either [0,1]. Lower values of \(\gamma\) lead to \(\mathbb{E}[\mathcal{L}(x)]\gg\mathbb{E}[\mathcal{L}(\mathrm{G}(\mathrm{z}))]\), which means \(D\) focuses more on its auto-encoding task, and \(G\) has an incentive to produce more realistic images, which may be at the cost of image diversity. On the other hand, a higher value of \(\gamma\) leads to \(\mathbb{E}[\mathcal{L}(x)]=\mathbb{E}[\mathcal{L}(\mathrm{G}(\mathrm{z}))]\) which means that \(D\) focuses more on its discrimination task, and \(G\) has an incentive to produce more realistic images, which may be at the cost of image diversity.  
Figure 4 from [23] shows the differences of the generated images when the hyperparameter \(\gamma\) is set to different value. Overall, the model worked well when the value is changed because it still maintains a degree of face diversity across the range of \(\gamma\) values. At low value (0.3), the generated faces look generally uniform. However, at the higher value of \(\gamma\), e.g., 0.7, the variety of the generated faces increase, but artifacts and noise also appear more frequently.
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
## 3 Proposed Method  
### Overview  
In this study, a combined sampling and optimization approach is adopted for local path planning of mobile robots, as depicted in Figure 2. The PSF algorithm consists of two stages: the exploration stage and the optimization stage. During the exploration stage, a coordinate transformation is performed on the mobile robot. Then, a set of path candidates is generated through lateral planning and diagonal planning. Subsequently, the candidate paths that do not collide with obstacles in the environment are retained and scored. The candidate path with the highest score is selected as the preliminary path. In the optimization stage, the preliminary path undergoes further optimization. Initially, a factor graph is constructed, with vertices representing the preliminary path and edges representing path deviation constraints, obstacle constraints, and velocity constraints. Next, the factor graph is solved using the general graphic optimization (G2O). Finally, the coordinates are inversely transformed to generate the optimal local path.  
Due to the typical navigation tasks of mobile robots, which involve global path planning in grid maps using algorithms such as [PERSON]'s and A*, the global path is inevitably composed of straight-line segments. Therefore, this study only considers local path planning under this general scenario, where the global path consists of straight-line segments.
# Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
## 3 Materials and Methods
### Visual Reading Scores  
The artifacts in the clean (reference) images, motion-corrupted images, MC-Net-predicted images, and L1-predicted images were assessed visually by three experienced imaging specialists who were blinded with respect to the correction method. The scores for image artifacts ranged from 0 to 3 (0 = \"no\"; 1 = \"mild\"; 2 = \"moderate\"; and 3 = \"severe\" motion artifacts). We also performed inter-rater evaluations of the three readers on the results of the UNet model trained with L1 only and the proposed MC-Net. Hence, we used the Pingouin open-source package [27] to calculate intraclass correlation coefficients (ICC) [28] and test-retest reliability (95% confidence intervals of ICC).  
For simulated motion experiments, a mid-axial slice of five subjects from the test group were selected for reading. Ten distinct motion trajectories with a wide range of summed standard deviations were selected from the test group. The clean image, motion-corrupted image, and MC-Net prediction of each possible pair of a subject and a motion trajectory were reviewed by three imaging specialists in a randomized order. L1-norm prediction and MC-Net prediction were reviewed by the imaging specialists in a randomized order.  
For the real (non-simulated) motion experiments, the motion-corrupted images, along with the MC-Net predictions of the five subjects in Dataset 4, were reviewed. Three slices were reviewed per subject, and motion artifacts were rated using the scale described above.  
The artifact scores of motion-corrupted images and MC-Net predictions were compared using the one-sided Wilcoxon signed-rank test after averaging the reader scores. For simulated motions, artifact scores were compared for three ranges of summed standard deviations: [0-5] (mm/\({}^{\circ}\)), (6-10) (mm/\({}^{\circ}\)), and (10-15) (mm/\({}^{\circ}\)).
# Inner-Loop Controllers for Grid-Forming Converters
### Inner-Loop Controllers  
The need for standards in microgrid control is related to the new grid codes that are expected to appear in the next future. In this sense, the ISA-95 standard [9] deals with the integration enterprise and control systems. Each level has the duty of the command level and provides supervisory control over lower-level systems. In this sense, it is necessary to ensure that the command and reference signals from one level to the lower levels will have a low impact on the system's stability and robustness performance. Thus, the bandwidth must be decreased when increasing the control level [10].  
In order to adapt ISA-95 to the control of a microgrid, zero to three levels can be adopted as follows [11]:  
* Level 3 (tertiary control): This energy production level controls the power flow between the microgrid and the grid.
* Level 2 (secondary control): This ensures that the electrical levels into the microgrid are inside the required values. In addition, it can include a synchronization control loop to seamlessly connect or disconnect the microgrid to the distribution system.  
Figure 3: Block diagram of a grid-forming inverter.  
* Level 1 (primary control): The Droop control method is often used in this level to emulate physical behaviors that makes the system stable and more damped. It can include a virtual impedance control loop to emulate physical output impedance.
* Level 0 (inner control loops): Regulation issues of each module are integrated in this level. Current and voltage, feedback and feedforward, and linear and nonlinear control loops can be performed to regulate the output voltage and to control the current, while maintaining the system stable.  
Various control methodologies are employed for inner-loop current regulation in inverters [12; 13; 14; 15; 16], including resonant controllers [17], Proportional-Integral (PI) controllers [18], repetitive controllers [19], dead-beat controllers [20], and Model Predictive Control (MPC) [21]. While dead-beat and MPC strategies are inherently discrete-time in nature, resonant, repetitive, and PI controllers can be designed in both continuous and discrete domains. Given the DSP-based implementation of control algorithms, adopting a discrete-time representation of the plant model is often advantageous.
# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
### Paper's Composition  
The proposed paper is organized as follows. After the Introduction, Section 2 presents the cloud computing environment and describes the concept of resource scheduling with all the details that are necessary for understanding the conducted research. The whole section is devoted to cloud computing as its principles and architecture are enabling efficient utilization of available resources over the communication network with the objective of satisfying clients' requests. The need for resource scheduling and provisioning increases with the rise of submitted requests. The notion of resource scheduling becomes an important aspect of the cloud computing paradigm, where the goal is to execute all the tasks within the least possible time interval. Section 2 also addresses the applications of swarm intelligence algorithms in cloud scheduling where many examples can be found in the literature.  
The mathematical formulation of the cloud computing resource scheduling models, that were employed in our simulations, is given in Section 3. The single-objective scheduling model that was used in simulations with the real data set is described, followed by the multi-objective model that was utilized with the artificial data set. In this section, basic terminology and performance metrics that are necessary for understanding the conducted simulations are also introduced.  
The original, as well as the proposed hybrid WOA metaheuristics, are described in Section 4. In this section the original WOA approach is presented and its deficiencies that were noticed during the empirical simulations, are addressed from the theoretical, as well as from the practical standpoint. Finally, the proposed hybrid WOA, which incorporates certain carefully chosen components from the ABC and FA swarm intelligence metaheuristics, is shown.  
Section 5 is the experimental section, where obtained simulation results of the proposed hybrid algorithm are presented. As stated above, to validate and to measure improvements of the hybrid WOA over the original WOA, first, tests were performed on a wide set of bound-constrained benchmarks. Side-by-side comparison was conducted between the basic and hybrid approaches. Moreover, in comparative analysis other state-of-the-art swarm algorithms have been included that were validated against the same benchmarks. A major part of this section shows results of conducted simulations for two cloud computing resource scheduling models. As in the case of unconstrained benchmarks, comparative analysis was also performed with the original WOA, as well as with other well-known heuristics and metaheuristics. Most of the simulation results are visualized.  
In the final Section 6, conclusions, regarding the performance of our proposed hybrid WOA, that have been derived from the results of conducted simulations, are summarized. Also, future potential research directions from the domain of bio-inspired metaheuristics, as well as from the sphere of cloud computing challenges, are provided.
# Security Scheme Based on Parameter Hiding Technic for Mobile Communication in a Secure Cyber World
## 2 LTE
### LTE Initial Attach for UE and Threats  
Initial attach is the first access process to the mobile network by the user subscribing the LTE network using UE. For this, eNB is selected in the initial state process; after matching synchronization, IMSI, which is an identification parameter, is transmitted to request that MME access the network in the ECM connection establishment process [21].  
IMSI refers to the unique ID requested of each user when the mobile network administrator registers the user to the service, and IMSI refers to the unique binary code of identifications saved in a Universal Subscriber Identity Module (USIM) [22].  
IMSI transmitted as plain text is transmitted to MME through thousands of eNBs and has the vulnerability that IMSI is leaked to attackers by malicious eNBs. In the LTE technical documentation, according to the \"Technical Specification Group Services and System Aspects; Rationale and track of  
\begin{table}
\begin{tabular}{c c} \hline
**UE** & **User Equipment (e.g., Smart Phone)** \\ \hline eNB & Evolved Node B \\ MME & Mobility Management Entity \\ HSS & Home Subscriber Server \\ IMSI & International Mobile Subscriber Identity \\ RNTI & Radio Network Temporary Identities \\ GUTI & Global Unique Temporary Identifier \\ PLMN ID & Public Land Mobile Network ID (MCC + MNC) \\ MCC & Mobile Country Code \\ MNC & Mobile Network Code \\ RN & Random Number \\ h0 & Hash Function \\ F & 4 n bits String by h0 \\ C & Challenge Bits \\ SSK & Secret Sharing Key \\ S-Box & Substitution-Box \\ \hline \end{tabular}
\end{table}
Table 1: The terms and symbols used in the proposed security scheme.  
security decisions in Long Term Evolved RAN/3 GPP System Architecture Evolution(SAE) (Release 9),\" there are vulnerabilities during the initial attach process for using the LTE mobile network, in which the UE identifying parameters are transmitted in plain text. Tracing and privacy issues easily occur with this vulnerability [11; 12; 21; 22].  
However, current LTE initial attach processes are executing EPS-AKA based mutual authentication and encrypted communication focusing on classes such as Non Access Stratum (NAS) security setup after completing ECM connection establishment processes between UE and eNB [23; 24]. In other words, because communication protocols that transmit identification parameters as plain texts are already used before mutual authentication and encrypted communication, related user tracing attacks, terminal tracing attacks, and privacy infringement attacks can occur using leaked IMSI [25; 26]. Additionally, RNTI, which is the only identification value sorting UE in eNB, and Global Unique Temporary Identifier (GUTI), which is used instead of IMSI after a series of processes, are transmitted as plain texts in various cases; therefore, vulnerabilities and attack threats of IMSI are equally likely to occur [27; 28].
## 2 Literature Review
### Service Quality Evaluation Model  
#### 2.3.1 Importance Performance Analysis  
[PERSON] and [PERSON] [25] proposed importance performance analysis (IPA) (see Figure 3). IPA is a technique that prioritizes the relevant attributes of a particular service product by measuring its importance and degree of performance [26]. The IPA framework was first put forward in an attribution study on the motorcycle industry by plotting the average scores of importance and degree of performance in a 2D matrix and proposing different manufacturing/service strategies, depending on the position of each element, to resolve the production process faced by the enterprise [27].
## 4 Discussion
### Biomechanics of the Articular Cartilage  
Both the IM and the maximum applied load at the center region of the articular cartilage covered by the meniscus significantly decreased in arthritis. Degenerative changes are manifested in softening and continuous thinning of the articular cartilage to complete bone baldness [4]. In particular, the central part of the articular cartilage, where local loads are greatest [35] is affected by arthritis [36]. This concerns in particular the uncovered cartilage but also the meniscus-covered regions [36]. With \(2.11\pm 0.58\) mm for arthritic articular cartilage and \(2.13\pm 0.42\) mm for arthritis-free articular cartilage, the cartilage thicknesses were very similar. The values measured for human cartilage tissue of the tibial plateau correspond to the values described in literature on human cartilage tissue of the tibial plateau. [PERSON] et al. [37] reported cartilage thicknesses between 3.2 and 3.9 mm for the meniscus-uncovered parts and between 1.7 and 2.1 mm for the meniscus-covered areas. [PERSON] et al. [38] found a clear difference in cartilage thickness between the different stages of osteoarthritis--cartilage thickness was greatly reduced in severe osteoarthritis. [PERSON] et al. [25] found a statistically significant (\(p<0.001\)) difference in cartilage thickness between arthritic articular cartilage of the tibial plateau with \(2.25\pm 0.11\) mm and arthritis-free articular cartilage of the tibial plateau with \(2.0\pm 0.07\) mm. In this study,  
Figure 6: Combined mapping of IM of joint cartilage and associated menisci.  
there was no significant difference in cartilage thickness between arthritic samples and arthritis-free samples. An important task of the meniscus is the protection of the articular cartilage beneath [5; 7; 39]. Due to this protection, the articular cartilage may not have been reduced in the meniscus-covered area even under arthritic conditions. The biomechanical properties of the meniscus-covered articular cartilage decrease under arthritis significantly, although there is no relevant loss of articular cartilage. This means that the cartilage thickness as a structural characteristic of osteoarthritis may be misleading with regard to the interpretation of the biomechanical properties. Previous studies have shown that early stage OA processes occur without macroscopic changes in the cartilage morphology [40; 41]. [PERSON] et al. [26] showed that visual and thickness assessments are much less sensitive in distinguishing early degenerated cartilage than mechanical assessments where the functional and structural properties of cartilage play important roles. There are existing non-invasive and non-contact methods to test the biomechanical properties by using ultrasound imaging technologies [42]. Both ultrasound elasticity (UEI) and ultrasound shear wave elasticity imaging (USWEI) have limitations [42]. Therefore, minimally invasive indentation tests could better provide intraoperative information about the severity of joint damage and thus contribute to the decision between joint replacement and joint-preserving therapy.