esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background Theories
### Gradient Class Activation Mapping  
Grad-CAM is a technique used to interpret and highlight the regions of interest in input data that have the most impact on a model's predictions. It achieves this by visualizing the activation maps generated by a CNN model. By doing so, Grad-CAM provides valuable insights into the features and patterns present in the vibration data that indicate drill bit wear. Unlike class activation mapping (CAM), which requires a specific type of CNN with a global average pooling layer to generate feature maps, Grad-CAM is more versatile and can be applied to a wider range of CNN architectures [33; 34]. To obtain the class-specific localization map, which is a two-dimensional matrix representing the discriminative regions for a particular class, the process involves calculating the gradient of the class score (\(y^{c}\)) with respect to the activations (\(A^{k}\)) of a specific convolutional layer. This is expressed in Equation (3). These gradients are then globally averaged across both the width (\(i\)) and height (\(j\)) dimensions. This pooling operation results in the neuron importance weights.  
\[\alpha_{k}^{c}=\frac{1}{Z}\sum_{i}\sum_{j}\frac{\partial y^{c}}{\partial A_{ij} ^{k}} \tag{3}\]  
While computing \(\alpha_{k}^{c}\) during the backward pass, the process entails a sequence of matrix multiplications between weight matrices and the gradients with respect to activation functions. This computational procedure effectively creates a partial linearization of the deep network downstream from the activation map \(A^{k}\). The weight \(\alpha_{k}^{c}\) essentially quantifies the 'importance' of feature map \(k\) in relation to the target class \(c\). To generate the class-specific localization map \(L_{Grad-CAM}^{c}\), a weighted combination of the forward activation maps is performed. This combination is then followed by the application of a Rectified Linear Unit (ReLU) activation function. The resulting expression is presented as Equation (4):  
\[L_{Grad-CAM}^{c}=\text{ReLU}\left(\sum_{k}\alpha_{k}^{c}A^{k}\right) \tag{4}\]  
In this equation, the variable \(k\) encompasses all feature maps, while \(\alpha_{k}^{c}\) represents the weight assigned to feature map \(k\) for class \(c\). The ReLU operation ensures that any negative values are changed to zero, highlighting the regions that have a positive impact on the classification of class \(c\). By using this formulation, the Grad-CAM localization map can pinpoint the important areas in the image that aid in predicting the target class [35].
## Data and Methods
### Method
#### 2.2.2 Emergency Medical Vulnerability and Survival Rates by Emergency Case  
(1) Emergency situation cases  
Since the golden time is very important for emergency patients, their survival rate can increase if prompt and appropriate first aid is administered. For example, with myocardial infarction, the survival rate may double when someone (e.g., the primary respondent around the patient) promptly performs cardiopulmonary resuscitation (CPR) and may triple when an automated external defibrillator (AED) is used [47]. Since the golden time is just 5 to 10 min for cardiac arrest, the survival rate of emergency patients depends not only on the real-time road traffic flow speed but also appropriate first aid administered before or during transportation [48; 49; 50].  
We evaluated changes in vulnerable areas and survival rates of an emergency by setting up four emergency medical response cases considering the presence of first aid and dispatching of EMT-P (Table 2). Case 1 is when a patient moves to an emergency center in person without initial emergency treatment. This case simulates the same situation that has been used in previous assessments of vulnerable areas of EMS. Case 2 is when a person near the emergency patient performs first aid according to the first aid directions provided by a telephone operator (dispatcher) and then the EMT-P arrives on-site to transport the patient to an emergency center by ambulance. Case 3 is when an EMT-P arrives onsite, provides first aid, and then transports the patient to an emergency center by ambulance. Case 4 is when an EMT-P or a community health center healthcare provider who is nearest to the emergency patient arrives onsite and provides first aid first, and then the EMT-P transports the patient to an emergency center.  
(2) Vulnerable areas for emergency medical services (EMS)  
Based on the relevant laws and regulations about vulnerable areas in Korea, the vulnerable area of EMS is defined as an area where an emergency medical center cannot be accessed within 30 min. Although data in previous studies on vulnerability to EMS within 30 min have been analyzed based on the road network and legal speed limit of the roads, these analyses do not reflect the real-time traffic flow speed. We evaluated the accessible time every five minutes according to the real traffic speed based on the traffic speed information provided by the Traffic and Road Information Service.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} O \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of emergency cases considering first-aid and transfer systems.  
(3) Survival rates  
The survival rate of patients with myocardial infarction decreased by 3-4% each minute the patient did not receive CPR, and by 8-10% each minute CPR was not performed [51; 52]. Therefore, the survival rate was assumed to decrease by 3% for each minute first aid was not provided by a person near the emergency patient, an EMT-P, or a community health center worker, and by 10% each minute no first aid was provided.
## 3 Results and Discussion
### Thermomechanical Properties
In the present study, the effect of monovalent NaPO\({}_{3}\) on T\({}_{\rm g}\) was less dominant compared to those of divalent Mg(PO\({}_{3}\))\({}_{2}\) or trivalent Al(PO\({}_{3}\))\({}_{3}\) in (70-x)V\({}_{2}\)O\({}_{5}\)-5 ZnO-22 BaO-3B\({}_{2}\)O\({}_{3}\)-xM(PO\({}_{3}\))\({}_{n}\) (mol%) (where M = Al, Mg, Na, n = 3, 2, 1, respectively, mol%), as explained in Figure 3. In terms of the thermomechanical properties, the substitution of Al(PO\({}_{3}\))\({}_{3}\) in the current glass system led to a strong effect on CTE compared to Mg(PO\({}_{3}\))\({}_{2}\). As discussed in several studies, it indicates that the network bonding and connectivity is more strongly influenced by divalent Mg(PO\({}_{3}\))\({}_{2}\) or trivalent Al(PO\({}_{3}\))\({}_{3}\) than monovalent NaPO\({}_{3}\), and is consistent with the greater field strength and M-O bond strength of divalent Mg(PO\({}_{3}\))\({}_{2}\) or trivalent Al(PO\({}_{3}\))\({}_{3}\). However, the increase in CTE with increasing modifier content would appear to dispute this structural assertion, as thermal expansion is also strongly influenced by the strength of network bonding, its connectivity, and the interactions between cations and nonbridging oxygen [28]. The CTE can also be explained based on the density. It is well known that the decreasing trend of density causes an increase in volume expansivity, resulting in an increase in the CTE of glass. Accordingly, in the present study, the density of the glasses decreased with respect to metaphosphate content at various concentrations, causing an increase in the CTE (see Figures 2 and 5).
# Analysis of Inductive Displacement Sensors with Large Range and Nanoscale Resolution
## 6 Conclusions  
A novel analysis and design method for the miniaturized inductive sensor with large measuring range and nanoscale resolution is proposed. Firstly, the leakage inductance model that can help designers design the parameters of large range differential inductive sensors more quickly and accurately is established. Secondly, a design rule of armature size is proposed by considering the fringing effect. Then, the complex error terms present in the transfer characteristic of differential inductive sensors are analyzed and the methods to suppress them are illustrated. Finally, A design rule including selecting the optimal excitation frequency is proposed to meet the requirements of high \(Q\) value and bandwidth and minimize the impact of core loss resistance on the performance of the sensor. Validated by experiments, the proposed analysis and design method can effectively guide the design of the inductive sensor with measuring range of \(\pm\)0.5 mm. The fabricated inductive sensor  
Figure 24: Experimental results of the prototype differential inductive sensor (composed of A1.5N1000): (**a**) Output voltage vs. \(x\) characteristic; (**b**) Mapping error; (**c**) Peak to peak output noise.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Range (mm)**} & **Sensitivity at Zero** & **RMS Resolution at** & **RMS Resolution at** & **Maximum Mapping** \\  & **Position (mV/imm)** & **Zero Position (nm)** & **Full Range (nm)** & **Error (\%FSO)** \\ \hline \(\pm\)0.5 & 0.71 & 2.2 & 13 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the prototype differential inductive sensor (composed of A1.5N1000).  
prototypes realize the ideal miniaturization, and the overall size is limited to less than 6 mm \(\times\) 6 mm \(\times\) 3 mm. Combined with large measuring range, high resolution and miniaturization, such inductive sensors can be well suitable for the compact, large stroke nanopositioning system.  
Conceptualization, S.F., Q.H., N.C., D.F. and F.C.; data curation, Q.H.; formal analysis, Q.H.; investigation, Q.H., S.F. and R.T.; methodology, S.F., N.C., R.T., D.F. and F.C.; project administration, S.F.; supervision, S.F.; writing--original draft, Q.H.; writing--review and editing, S.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number No. 52075536.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Effect of Cutting Depth  
Studying the relationship between the MSE and the cutting depth is one of the basic bases for optimizing the design of PDC bits [20]. The influence of cutting depth on rock breaking with the 3-RDE cutter is shown in Figure 14. As a whole, the cutting forces increase with the increasing of cutting depth. In detail, when the back-rake angle is \(15^{\circ}\), the axial force \(\mathrm{F_{n}}\) increases from 206.32 N to 367.62 N, 546.79 N, 752.80 N, 943.26 N, and the tangential force \(\mathrm{F_{h}}\) varies from 353.82 N to 635.08 N, 957.55 N, 1317.90 N, 1684.60 N as the cutting depth shifts from 1 mm to 3 mm. But visibly, the tangential force and its growth rate are significantly larger than the axial force. Meanwhile, the fluctuation of cutting forces increases significantly with the increase of cutting depth, indicating that the cutting stability gradually deteriorates, and the risk of impact failure increases.  
The influence of cutting depth on rock breaking is not only on cutting forces, but also on the MSE. As shown in Figure 14c, the MSE gradually decreases with the increase of cutting depth, which means the rock breaking efficiency narrows down. This can be attributed to the fact that as the cutting depth increases, the rock breaking changes from ductile fracture with a small cutting depth to brittle fracture with a large cutting depth. The big chip formation finally forms, which reduces the repeated fracture of the rock and reduces the MSE [23]. Therefore, a proper cutting depth is of great significantly for the rock breaking efficiency of 3-RDE cutter.
## 5 Results and Discussion
### Evaluation Method 1: Heuristic Evaluation Results  
The HCI experts were given the task of installing the application on mobile phones, connecting it to the watches, and analyzing them for usability issues. A total of 20 usability principles were evaluated by each of the 10 experts independently. We compiled the usability issues encountered by each usability expert to create this heuristic evaluation report and present it in Tables 5 and 6. These tables show the number of criteria violated per severity rating for each heuristic listed. Three hundred and seven (307) usability issues were identified in all four watches, where one-hundred and nine (35.5%) were discovered in the Fitbit Charge 5 with twenty-two minor, nineteen major, and fifteen disastrous problems. However, only 46 (14.9%) usability issues were reported for the Samsung Galaxy Watch 5. Out of 307, 66 issues (21.4%) were reported in the Galaxy Watch 4, and the remaining 86 (28%) were reported in the Fitbit Versa 2. Most of the problems were located in H8 \"Ease of Use\", H9 \"Flexibility and Efficiency of use\", and H10 \"Features\". The Galaxy Watch 4 demonstrates huge advances in its hardware and software due to its Exynos W920 architecture, Samsung's first 5 nm wearable processor [61]. There are two Cortex-A55 cores on the Exynos W920, along with a Mali-G68 MP2 GPU, designed to deliver 20 percent faster CPU performance and 10 times faster GPU performance than the Exynos W9110. In addition to higher power efficiency, the 5 nm processor also results in longer smartwatch battery life. A dedicated low-power processor on the Exynos W920 \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Severity**} & \multicolumn{6}{c}{**Severity**} \\ \cline{3-10}
**ID** & **Heuristics** & \multicolumn{3}{c}{**Fitbit Charge 5**} & \multicolumn{3}{c}{**Frequency**} & \multicolumn{3}{c}{**Fitbit Versa 2**} & \multicolumn{3}{c}{**Frequency**} \\ \cline{3-10}  & **Cosmetic** & **Minor** & **Major** & **Disastrous** & & **CosmeticMinor** & **Major** & **Disastrous** & \\ \hline H1 & Self-Descritiveness & 4 & 1 & 0 & 0 & 5 & 3 & 0 & 0 & 0 & 3 \\ H2 & Consistency and Standards & 2 & 2 & 0 & 0 & 4 & 4 & 4 & 0 & 0 & 8 \\ H3 & Aesthetic and Minimalist Design & 4 & 2 & 1 & 1 & 8 & 2 & 1 & 0 & 3 & 6 \\ H4 & Reduce Short Term Memory Load & 6 & 2 & 5 & 1 & 14 & 3 & 3 & 1 & 0 & 7 \\ H5 & Match Between System and Real-world & 9 & 3 & 2 & 1 & 15 & 5 & 2 & 1 & 0 & 8 \\ H6 & Error Tolerance & 3 & 1 & 2 & 3 & 9 & 6 & 1 & 2 & 2 & 11 \\ H7 & Privacy & 2 & 1 & 0 & 0 & 3 & 2 & 0 & 0 & 0 & 2 \\ H8 & Ease of Use & 11 & 3 & 4 & 2 & 20 & 7 & 1 & 3 & 1 & 12 \\ H9 & Flexibility and Efficiency of Use & 1 & 2 & 1 & 4 & 8 & 3 & 2 & 2 & 3 & 10 \\ H10 & Features & 6 & 2 & 3 & 1 & 12 & 4 & 2 & 3 & 2 & 11 \\ H11 & Satisfaction & 5 & 3 & 1 & 2 & 11 & 4 & 1 & 2 & 2 & 8 \\  & Total & 53 & 22 & 19 & 15 & 109 & 43 & 17 & 14 & 13 & 86 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Usability issues encountered by evaluators with the severity for the Fitbit watches.
# Non-Invasive and Accurate Blood Glucose Detection Based on an Equivalent Bioimpedance Spectrum
## 6 Classification of Blood Glucose Parameters Based on the K-Proximity Method  
### Impedance Spectra Features of Different Blood Glucose Samples  
The equivalent bioelectrical impedance acquisition platform designed in this paper is used for impedance spectrum acquisition and data preprocessing. The preprocessed impedance spectra are classified into \"standard blood glucose samples\" and \"hyperglycemia samples\" according to the threshold of 7.0 mmol/L biochemical blood glucose. The impedance spectra of standard blood glucose samples and hyperglycemia samples are shown in Figures 7 and 8, respectively. For hyperglycemia samples, it is found that the impedance shows a decreasing trend at 25-30 MHz and fluctuates at 40-60 MHz. In contrast, for standard blood glucose samples, the impedance increases at 25-35 MHz, then gently changes in the higher frequency band without clear regularity.  
For samples near the 7.0 mmol/L threshold (6.7-7.3 mmol/L, named as near-threshold samples hereafter), their impedance spectra are shown in Figure 9. It is found that, for most near-threshold samples, the impedance changes smoothly at 25-35 MHz, and only samples of 7.3 mmol/L show a decreasing trend. All spectra fluctuate slightly at 45-60 MHz, and some of them decrease at the higher end of frequency. Compared to Figures 7 and 8, there is no distinct feature among the impedance spectra of near-threshold samples.  
Figure 6: Block diagram of the equivalent bioelectrical impedance detection platform.  
Figure 8: Impedance mapping of above normoglycemic samples (vertical coordinate impedance value/\(\Omega\)).  
Figure 7: Impedance mapping of normal blood glucose samples (vertical coordinate impedance value/\(\Omega\)).  
Figure 9: Impedance mapping of a blood glucose sample with a critical biochemical blood glucose of 7.0 mmol/L (vertical coordinate impedance value/\(\Omega\)).
# Improving Hard Rock Materials Cuttability by Hydraulic Fracturing at Mining Working Face
## 1 Introduction
### Hydraulic Fracturing Equipment and Process  
Generally, the key equipment used for a deep-hole hydraulic fracturing application (e.g., the first caving at a working face, the roof cutting nearby a roadway, the coal-bed methane extraction) includes directional (or tunnel) drilling rigs and high-pressure hydraulic pumps. The above equipment is expensive. In shallow-hole hydraulic fracturing applications, there is no need for directional drilling rigs or large-power high-pressure hydraulic pumps, and equipment suitable for the working face advancement shall be selected. Based on the hydraulic fracturing process at the 51207 working face, the air-leg rock drill is selected as the drilling tool. Secondly, the packer is selected for shallow-hole hydraulic fracturing. Finally, the emulsion pump is chosen to pressurize the water injection. Table 3 shows the equipment used for un-directional hydraulic fracturing.  
The normal operation of the emulsion pump and the air-leg rock drill needs to be checked first, and then the performance of the packer [46]. Two air-leg rock drills start working at the same time. The 42 mm drill bit is employed to drill a borehole according to a specified angle and length. After a borehole is drilled, the mud in the borehole is washed away with clean water. After the drilling process is completed, the pipes and accessories are connected, according to Figure 13, from the outlet of the emulsion pump to the packer. Each borehole is fractured once. During hydraulic fracturing, the end cap of the packer is adjusted to a suitable position, the pressure relief valve is turned off and the number change on the pressure gauge is monitored simultaneously. When the injection pressure exceeds 20 MPa, the water outflow discharging from the roof, the floor, the hard rock fault and the adjacent boreholes is observed.  
Figure 12: Borehole layouts: (**a**) borehole layout in scheme A; (**b**) borehole layout in scheme B; (**c**) borehole layout in scheme C.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Name** & **Type** & **Illustration** & **Function** \\ \hline \multirow{3}{*}{1} & Air-leg rock drill & \multirow{3}{*}{YT-28} & The drill bit diameter is & \multirow{3}{*}{Used to drill a borehole.} \\  & & & & is 3 m. \\ \cline{3-5}  & & & & The rated pressure & \\ \multirow{3}{*}{2} & Emulsion pump & \multirow{3}{*}{BPW550} & The rated pressure & & \\  & & & is 31.5 MPa. & & borehole. \\ \cline{3-5}  & & & & The capsule length is 0.9 m, & \\ \multirow{3}{*}{3} & Packer & ZFA-19 & \multirow{3}{*}{38 mm and the withstand} & & \\  & & & & voltage is 40 MPa. & \\ \cline{3-5}  & & & & The rated withstand pressure & \\ \multirow{3}{*}{4} & Pressure duct & KJ10-55 & \multirow{3}{*}{The rated withstand pressure is 55 MPa.} & & \\  & & & & is 55 MPa. & \\ \cline{3-5}  & & & & & packer. \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of equipment.
# Relational Positioning Method for 2D and 3D Ad Hoc Sensor Networks in Industry 4.0
## 3 Relational Positioning Method
### Two-Dimensional (2D) Localization
Then, for those vertices in the region of interest (that is, for \(w_{i,k}\
eq 0\)), their weights \(w_{i,k}\) are incremented according to \(w_{i,k}=w_{i,k}+1\), for those locations (\(j\), \(k\)) where \(\zeta_{i,m}\leq 1\). In this form, the weights are heavier for those nodes in closer proximity to \(AN_{i}\), and likelihood indexes \(\rho_{j,k}\) are accordingly modified, denoting the more likely location for \(\mathbf{n}_{0}\). With these likelihoods \(\rho_{j,k}\), we can use entropy to measure the uncertainty of a vertex, and thus, we can focus on those vertices with less uncertainty. Entropy for the area/space discretized according to \(L\) can be expressed in this case as follows:  
\[H_{L}\Big{(}\mathbf{v}_{j,k}\Big{)}=-\,\sum_{j,k}\rho_{j,k}\log\Big{(}\rho_{j,k} \Big{)} \tag{1}\]  
Note that the likelihood \(\rho_{j,k}\) assignment process is conducted a number of times given by the combinations \({}_{N}\)C\({}_{2}\) for all the pairs of ANs (where \(N\) is the number of available ANs). As explained before, the proposed position location method diminishes the uncertainty or entropy by modifying the location likelihood of the nodes in the feasibility space using distance relationships. In Figure 1(a), vertices increment their weights according to \(\zeta_{1,2}\leq 1\), in Figure 1(b) according to \(\zeta_{1,3}\leq 1\), and in Figure 1(c) according to \(\zeta_{3,2}<1\), which is the same as \(\zeta_{2,3}>1\), i.e., vertices closer to \(AN_{3}\) increase their weights. Up to this stage, we worked with three ANs in order to keep the infrastructure requirements as low as possible. However, note that the feasibility vertex set can be reduced if more ANs are available. In Figure 3, we increase the number of ANs in the scenario illustrated in Figure 2, and it is possible to see that by applying an additional relation as \(\zeta_{1,A}\leq 1\), the feasible vertex set is reduced. Recall that \(\rho_{j,k_{c}}\) is the probability (likelihood) of vertex \(\mathbf{v}_{j,k_{c}}\) to be the position of \(\mathbf{n}_{0}\), where vertex probabilities \(\rho_{j,k_{c}}\) change after every relationship index \(\zeta_{i,m}\) is considered for weight assignment; this implies that the entropy function \(H_{\text{L}}(\mathbf{v})\) also changes after every stage. In Figure 4, we can see how entropy behaves after every \(\zeta_{i,m}\) is applied for the representative example explained in Figures 2 and 3. Figure 3: Illustration of a feasible vertex set reduction when four ANs are available and \(\zeta_{1,A}\leq 1\) is applied. Figure 2: Weight assignment distribution for tree ANs and for relations (a) \(\zeta_{1,2}\leq 1\), (b) \(\zeta_{1,3}\leq 1\), and (c) \(\zeta_{2,3}>1\).
# A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
## 4 OA-UWSN MAC Protocol Simulation Environment Settings
### Acoustic Properties of Underwater Channels
#### 4.2.1 Propagation Loss  
A seawater medium is a non-ideal loss medium. During the propagation of acoustic waves, the intensity of the seawater is attenuated in the propagation direction. Propagation loss is often used to represent the loss of acoustic signal energy in the ocean [32]. It can be assumed that the propagation loss consists of two parts: Expansion loss and attenuation loss. The extended loss is the geometric effect where the sound intensity is regularly weakened when the acoustic signal propagates outward from the sound source. It is also called geometric loss. Attenuation losses include absorption and scattering, where absorption refers to the effect where part of the acoustic energy is lost due to conversion to thermal energy.  
The overall acoustic path loss can be estimated using the semi-empirical formula shown in Equation (10) [33]:  
\[A(l,f)=(l/l_{r})^{k}a(f)^{l-l_{r}}. \tag{10}\]  
In Equation (10), \((l/l_{r})^{k}\) is the expansion loss, which is related to the propagation mode and distance of sound waves; \(l\) indicates the distance traveled; \(l_{r}\) indicates the reference distance; \(k\) is the expansion loss index, according to different acoustic propagation conditions, whose value varies usually between 1 and 2, corresponding to cylinder expansion and spherical expansion; and \(a(f)^{l-l_{r}}\) is attenuation loss, where \(f\) is the signal frequency and \(a(f)\) is the absorption coefficient, which increases significantly with an increasing frequency of sound waves, in addition to temperature and depth.