esa-sceva/satcom-chunk-collection huggingface dataset:
# Limit Equilibrium Models for Tunnel Face Stability in Composite Soft-Hard Strata
## 5 Analysis of Theoretical Model
### Solving for Limit Support Pressure  
The limit support pressure ratio of the tunnel face is mainly calculated based on the moment equilibrium at origin \(O\) (as shown in Figure 11), and the limit equilibrium equation is expressed as follows:  
\[M_{b}+M_{w}-M_{NT}-2M_{TS}-M_{p}-M_{b}=0 \tag{7}\]  
Figure 11: Force analysis of longitudinal section. where \(M_{v}\) represents the moment of the vertical earth pressure, \(\sigma_{v}\); \(M_{w}\) represents the moment of the weight, \(W\), in the log-spiral zone; \(M_{NT}\) represents the moment of the resistance to sliding on the log-spiral slip surface; \(M_{TS}\) represents the moment of the resistance to shear force, \(T_{s}\), on the lateral sliding surface; \(M_{p}\) represents the moment of the support pressure, \(P\), on the tunnel face; and \(M_{b}\) represents the moment of the resistance force on the bottom slip surface of the log-spiral zone. 1. Calculation of \(M_{v}\)  
In scenarios where the thickness of the overburden soil is less than (or equal to) the diameter of tunnel and the ground exhibits relatively low stiffness, the soil arching effect is typically neglected, and the vertical earth pressure is determined using the full-overburden theory [38]. Hence, the vertical earth pressure, \(\sigma_{v}\), exerted on the top of the tunnel can be described using the following equation:  
\[\sigma_{v}=\sigma_{s}+\gamma C \tag{8}\]  
where \(\sigma_{s}\) denotes the additional pressure of the surface; \(\gamma\) denotes soil density. Moment, \(M_{v}\), can be expressed as follows:  
\[M_{v}=\sigma_{v}BL_{2}(R_{a}\cos\phi-L_{2}/2) \tag{9}\]  
\[B=\frac{\pi D}{4} \tag{10}\]  
where \(B\) represents the equivalent width of the tunnel face. 2. Calculation of \(M_{w}\)  
The forces acting on the \(i\)th unit obtained from the log-spiral zone are illustrated in Figure 12. The weight of the \(i\)th unit can be expressed as:  
\[dW=[R(x)\cos(\phi+x)-L_{1}]\gamma Bdz \tag{11}\]  
where  
\[dz=R(x)\sin\theta dx \tag{12}\]  
\[\theta=\frac{\pi}{2}-x \tag{13}\]  
and \(\theta\) represents the angle between the tangent of log-spiral slip surface and the horizontal line. Figure 12: Force analysis of \(i\)th unit.
## 5 Analysis
### Security
#### 5.1.3 Authenticated Vehicular Communications  
To receive a service message in the form of \(M=\{pid_{di},sRSU_{j},msg,ts^{\prime},S_{j},ttl\}\) served by sRSUs on VANETs, \(V_{d}\) must be authenticated to a contact point \(RSU_{i}\) or carrier vehicle \(V_{c}\) as \(V_{d}\) is the valid destination vehicle of \(pid_{di}\) specified in the message \(M\). In message forwarding phase of Section 4.4, when \(V_{d}\) requests a message \(M\) kept by a contact point \(RSU_{i}\) or a carrying vehicle \(V_{c}\), \(V_{d}\) must present its id-based signature \(S^{\prime}_{d}\gets idSig(vsk_{di},req\,pid_{di})\) which is in turn verified under the given \(pid_{di}\) of the message \(M\). If we assume the security of an id-based signature scheme, any vehicle which does not know the private key corresponding to \(pid_{di}\) except \(V_{d}\) cannot forge the signature nor impersonate \(V_{d}\). Therefore, only the valid vehicle \(V_{d}\) authenticated under the \(pid_{di}\) can receive the message \(M\).  
In addition, when \(V_{d}\) receives a message \(M\), \(V_{d}\) also authenticates the message sender sRSU by verifying the attached id-based signature \(S_{j}\) under the sRUS's identity \(sRSU_{j}\) specified in \(M\). That is, \(V_{d}\) can be convinced that the message \(M\) was sent from the \(sRSU_{j}\), in which \(V_{d}\) is interested, if the signature \(S_{j}\) is verified as valid under the id of \(sRSU_{j}\).
## 4 Discussion
The difference between those values and those determined here could be related mainly to the different temperatures rather than to the medium used. Our results showed excellent agreement between the two experimental approaches, providing a valuable method to determine acidic constants values, in a wide range of acidity, with accuracy and in aqueous media (i.e., from pH 2 to pH 12). The acidic constants determined in this work can be considered significant values in the chemistry of these ligands, and they could be indispensable to understanding ligand behavior in biological systems. Of particular biomedical importance is the ability of these compounds to form stable complexes with biologically active metal ions. The studies in this area have focused on the enhanced curing ability in the case of vitamin C and metal ion deficiencies; on the development of therapeutic agents with potential antitumor, antibacterial, antioxidant and anti-inflammatory properties with enhanced potency with respect to the noncomplexed ligands; and on their use as synthetic models for metal containing complex biological systems [13; 18; 48]. Conceptualization: E.F. Investigation: L.M. Resources: E.F. Writing--original draft: L.M. and E.F. Writing--review and editing: E.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding. We thank the University of Calabria. Data Availability Statement: Data sharing not applicable. Conflicts of Interest: The authors declare no conflict of interest. Figure A1: \(Z_{\rm H}\) as a function of \(-\)log [H\({}^{+}\)] for quercetin (**a**), luteolin (**b**) and L-ascorbic acid (**c**), at 37 \({}^{\circ}\)C and in 0.16 M NaCl. Orange circles and orange crosses refer to C\({}_{\rm L}\) 0.5 \(\times\) 10\({}^{-3}\) M, while blue triangles refer to C\({}_{\rm L}\) 5 \(\times\) 10\({}^{-3}\) M.
## 4 Results and Discussions  
### Differences in Fire Monitoring  
[PERSON] et al. [56] found that it is difficult for MODIS to detect FRP when below 10 MW, while [PERSON] et al. [46] show that VIIRS is more sensitive for small fires. In order to quantify this difference, we compared the spatial distribution of FRP below 10 MW of VIIRS and MODIS Aqua during the study period. For better visualization, FRP is divided into two categories: less than 5 MW and between 5-10 MW. It can be clearly found from Figure 2 that the monitoring result FRP of MODIS Aqua below 5 MW is far worse than that of VIIRS. During the study period, a total number of 269,753 fires with FRP lower than 5 MW were detected by VIIRS, and 2624 fires with FRP lower than 5 MW were detected by MODIS Aqua. 83,900 fire points of FRP between 5-10 MW were detected by VIIRS, and 6387 fire points of FRP between 5-10 MW were detected by MODIS Aqua. Although the spatial resolution of VIIRS is higher than that of MODIS, which means that multiple VIIRS AF pixels may be represented by one MODIS AF pixel, the band feature that VIIRS is sensitive to small fires is the main reason for the higher number of fire pixels than MODIS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Land Cover Class** & **CO\({}_{2}\)** & **CO** & **PM\({}_{2.5}\)** & **BC (Black Carbon)** \\ \hline Temperate forest & 1572 & 106 & 13.8 & 0.56 \\ Shrubland & 1646 & 61 & 4.9 & 0.46 \\ Agriculture & 1308 & 92 & 8.3 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Emission factors (g kg\({}^{-1}\) dry matter residue) for various species emitted in southeast Australia.  
[PERSON] [55] and [PERSON] [56] also suggested that low FRP fires are the most common type of fire events at present. If this part is ignored, it may have a significant impact on the estimated results of fire emission pollutants. Obviously, the estimation of fire emission pollutants based on VIIIRS has more advantages than on MODIS.
# A Review of Medication Adherence Monitoring Technologies +
## 4 A Review of Medication Adherence Monitoring Systems
### Sensor-Based Systems
#### 4.1.2 Wearable Sensors
It consists of two wrist worn sensors and a mobile phone app, where each of the wearable sensors is embedding an accelerometer and a Bluetooth module. The researchers assume that the patient takes the medication within a known interval of a given time of the day. Based on this, the mobile App triggers the wrist sensors to operate over a given time window to collect acceleration as well as RSSI (Received Signal Strength Indicator) measurements. Once hand movement is detected within this interval, the acceleration and RSSI is compared with pre-defined threshold values. From this comparison, it can be concluded if each hand is in proximity of the other, which is the hands position associated with medication taking. Advantages of wearable sensors approaches include the ability of monitoring the user behavior in a free-living environment [111]. Another advantage is the accuracy of sensor-based systems. However, a main disadvantage that is pertained with wearable-based systems is the user acceptance and comfort, especially when considering old people [110; 119]. This is due to the requirement that the sensor should be attached to the user for possibly a long time and recharged frequently, as wearables are usually powered by small batteries.
## 3 Results  
### Calculation of Optimal Weighting Factors  
The SPEI indices of different time scales can reflect different types of droughts. Among these, vegetation drought typically refers to drought conditions with a shorter duration, generally lasting less than 12 months [59; 74; 75]. Consequently, we selected the SPEI across time scales of 1 to 12 months to investigate the optimal weighting values of VCI and TCI within the VHI. The growing season, which spans from April to September, was chosen for this study [13; 76].  
Figure 2 illustrates that the correlation between VHI and SPEI varies significantly with different weighting values. Specifically, when the weight of VCI is low, the correlation between VHI and SPEI across different time scales is high. However, this correlation tends to decrease as the VCI weight increases. This suggests that while both VCI and TCI provide valuable information on drought, an unbalanced weighting may negatively impact the efficacy of the VHI in drought monitoring. Among the various SPEI time scales, SPEI03 exhibited the strongest correlation with VHI. Furthermore, SPEI03 is more appropriate for the analysis of vegetation drought [6; 77]. Therefore, three months was selected as the optimal time scale for further optimizing VHI and determining the optimal weights.  
The contribution of VCI and TCI to VHI is significantly influenced by regional vegetation types [17]. As shown in Table 11, the optimal weights for VCI and TCI in VHI vary across different vegetation types. For forest areas, the correlation coefficient between SPEI03 and VHI was maximized with a VCI weight of 0.28, whereas the optimal VCI weights were 0.23, 0.45, and 0.28 for grasslands, croplands, and changing vegetation types, respectively. Considering all vegetation types together, the highest correlation coefficient between SPEI03 and VHI was 0.61 with a VCI weight of 0.29. Based on the above findings, a higher weight should be assigned to TCI in calculating VHI for the YRB to enhance the accuracy of agricultural drought monitoring.
## 3 Controller Design and Stability Analysis  
An adaptive passive fault-tolerant tracking controller is designed in this section for HSTs subject to uncertain parameters, unmeasurable additional disturbance, unknown actuator faults and actuator saturation simultaneously, so that good tracking performance and excellent robustness can be ensured. In view of the strong robustness to parametric uncertainties and disturbances, as well as the requirement to relax the system knowledge, variable-gain PID-type sliding mode surface is introduced and adaptive technology is adopted in the controller design. To facilitate controller design, we define position tracking error, velocity tracking error and acceleration tracking error as \(e=x-x_{d}\), \(\dot{e}=v-v_{d}\), and \(\ddot{e}=\dot{v}-\dot{v}_{d}\), where \(x_{d}\), \(v_{d}\) and \(\dot{v}_{d}\) are the desired position, velocity and acceleration, respectively. **Assumption 3**. : _It is assumed that the actual state variables \(x\), \(v\) and \(\dot{v}\) are measurable. In addition, the desired trajectories \(x_{d}\), \(v_{d}\), and \(\dot{v}_{d}\) are smooth, bounded, and computable._  
Traditional proportion-derivative (PD)-type sliding mode surface has slow response speed, while fixed-gain PID-type sliding mode surface may intensify the vibration of the control signal and induce the excitation of high-frequency unmodeled dynamics of system [29]. Thus, variable-gain PID-type sliding mode surface, which can offer fast transient response and small steady-state error, is adopted in this paper. According to the position and velocity tracking errors, PID-type sliding surface with variable gains is selected as follow [22; 23]:  
\[s=\dot{e}+\hat{\delta}_{1}e+\hat{\delta}_{2}\int_{0}^{t}e(\tau)d\tau, \tag{13}\]  
where \(\hat{\delta}_{1}\) and \(\hat{\delta}_{2}\) are two variable gains updated by adaptive laws to be designed later. Then the time derivative of Equation (13) is:  
\[\dot{s}=\ddot{e}+\hat{\delta}_{1}e+\hat{\delta}_{1}\dot{e}+\hat{\delta}_{2} \int_{0}^{t}e(\tau)d\tau+\hat{\delta}_{2}e. \tag{14}\]  
To deal with the actuator saturation, an auxiliary signal \(\varphi\) is introduced as follows:  
\[\dot{\varphi}=-\frac{\alpha}{\dot{m}}\varphi+\frac{\sigma(t)}{\dot{m}}, \tag{15}\]  
where \(\alpha\) is a positive constant chosen by the designers. Define \(\overline{s}=s-\varphi\). Then,  
\[\dot{\overline{s}}=\dot{s}-\dot{\varphi}. \tag{16}\]  
Substituting Equations (11), (14), and (15) into Equation (16), yields to  
\[m\dot{\overline{s}}=(1-\tau_{c})F+\sigma(t)-\partial\zeta^{T}(v)+P+m(\hat{ \delta}_{1}\dot{e}+\hat{\delta}_{2}e-\dot{v}_{d}+\dot{\hat{\delta}}_{1}e+\hat{ \delta}_{2}\int_{0}^{t}e(\tau)d\tau)-m\dot{\varphi}. \tag{17}\]  
**Theorem 1**. : _For the train dynamics described in Equations (1) and (11) subject to uncertain parameters, unmeasurable additional disturbance, unknown loss of actuator effectiveness faults and actuator saturation simultaneously, suppose that Assumptions 1-3 are satisfied.
## 5 Conclusions  
Based on the MC method, a simulation method IMC was established for underwater imaging. Firstly, the influence of laser divergence angle and initial spot radius on the transmission effect was clarified by analyzing the spatiotemporal broadening effect of the laser with different initialization parameters after unidirectional transmission. Then, we used the Mie theory to relate the scattering medium in the simulation to the real situation. In order to collect the reflected signal carrying the target information more effectively, the IMC method was optimized to a range-gated simulation model and performed an imaging simulation of specific targets. It showed that the optimized method reduced the influence of backscattering caused by the medium on detection. It is an effective method to overcome the backscattering of a medium, which can improve the contrast and signal-to-noise ratio of the detected image. Furthermore, the influence of roughness on imaging results were analyzed, and the applicability of IMC to targets with different roughness was verified. Finally, the applicability of the IMC method was demonstrated by well-matched quantitative comparison results under continuously changing AL, where the maximum error between the simulation and experimental results was less than 30%.  
In summary, the proposed IMC method establishes a channel simulation model based on the Mie theory, connects the scattering environment in the simulation with the real situation, and uses the time of flight (TOF) of photon to realize gated reception. Supplemented by modeling the detection process to simulate the whole imaging link, it can be considered that our proposed IMC simulation method has the ability to describe the transmission process of the Gaussian light field in complex underwater environments and the imaging process for two-dimensional simple targets. Therefore, the unidirectional transmission process of the IMC method could determine the optimal beam parameters in the pre-design period of the underwater communication system, and the complete bidirectional transmission IMC process lays a research foundation for the establishment of the imaging detection model for complex underwater targets.  
Conceptualization, W.C., Y.H. ([PERSON]) and Q.C.; methodology, S.G. and Y.H. ([PERSON]); software, S.G. and Y.C.; validation, S.G., Y.H. ([PERSON]), Q.C. and Y.C.; writing--original draft preparation, S.G.; writing--review and editing, Q.C. and Y.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (61991450; 61991453); Shanghai \"Science and Technology Innovation Action Plan\" Social Development Science and Technology Project (No. 21 DZ1205400).  
Not applicable.  
The data in this study can be requested from the corresponding author.  
We appreciate [PERSON] for her effective discussions.  
The authors declare no conflict of interest.
# Analysis of Inductive Displacement Sensors with Large Range and Nanoscale Resolution
## 3 Principle Analysis
### Fringing Effect Analysis and Armature Design Consideration  
There is a fringing effect [10] at the edge of the sensor probe, as shown in Figure 9. Therefore, the geometric dimension of the armature needs to be considered. Assume that the side length of the sensor core is \(a\), and the side length of the armature is \(\lambda a\). In the process of displacement sensing, the edge of the measured surface of the armature needs to exceed \((\lambda-1)a/2\) in the transverse direction out of the edge of the sensor probe. This can reduce the error caused by the transverse parasitic motion of the armature in practical application. The rule for selecting geometric dimensions of the armature is determined by considering the fringing effect. As shown in Figure 10, when \(\lambda\) approach to 2, the change trend of sensor inductance with \(\lambda\) slows down, thus we should select \(\lambda\geq 2\).  
Figure 10: Relationship between sensor inductance and armature size.  
Figure 9: Fringing effect and geometric dimension of armature.
###### Abstract  
Cross-database electrocardiogram (ECG) classification remains a critical challenge due to variations in patient populations, recording conditions, and annotation granularity. Existing methodologies for ECG arrhythmia classification have primarily utilized datasets with either fine-grained or coarse-grained labels, but seldom both simultaneously. Fine-grained labels provide beat-level annotations, whereas coarse-grained labels offer only record-level labels. In this study, we propose an innovative cross-database learning framework that utilizes both fine-grained and coarse-grained labels in tandem, thereby enhancing classification performance across heterogeneous datasets. Specifically, our approach begins with the pretraining of a CNN-based beat classifier that takes ECG signals as the input and predicts beat types on a finely labeled dataset, namely the MIT-BIH Arrhythmia Database (MITDB). The pretrained model is then fine-tuned using weakly supervised learning on two coarsely labeled datasets: the SPH one, which contains four rhythm classes, and the PTB-XL one, which involves binary classification between the sinus rhythm (SR) and atrial fibrillation (AFIB). Once the beat classifier is adapted to a new dataset, it generates a two-dimensional beat-score-map (BSM) representation from the input ECG signal. This 2D BSM is subsequently utilized as the input for arrhythmia rhythm classification. The proposed method achieves F1 scores of 0.9301 on the SPH dataset and 0.9267 on the PTB-XL dataset, corresponding to the multi-class and binary rhythm classification tasks described above. These results demonstrate a robust cross-database classification of complex cardiac arrhythmia rhythms. Furthermore, t-SNE visualizations of the 2D BSM representations, after adaptation to the coarsely labeled SPH and PTB-XL datasets, validate how our method significantly enhances the ability to differentiate between various arrhythmia rhythm types, thus highlighting its effectiveness in cross-database ECG analysis.  
Ecc; arrhythmia classification; cross-database; weakly supervised learning +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Methylene Blue Removal
#### 3.2.2 pH  
To observe the influence of pH on the uptake of MB of the different biomasses, solutions with varying pH levels (2-10) were tested for a 200 ppm MB dye solution (Figure 3). Initially, pH experiments for CH and HH were conducted for a broad range of pH levels (2-10). Figure 3 indicates that there was no substantial variation in MB removal rates for pH levels above 4. pH levels above 10 were not tested as studies [90] have shown that the optimum MB removal is below 10, while the optimal pH level for MB dye removal with biosorbents is around 5 [91]. With the pH of textile composite wastewater typically between 7 and 9 [92], the initial pH range of this study was set to 2-10 as it accurately reflects the pH levels encountered in industrial wastewater. As there was little variance in adsorption between pH 4-10, pH levels 5 and 7 were omitted for LD, RS, and WW.  
Figure 2: Effects of particle size of LD, RS, CH, HH, and WW on MB removal efficiency.  
There are two ways in which the interaction between a sorbate and a sorbent is affected by the pH of an aqueous [93]. As dyes are complex aromatic organic compounds that have varying functional groups and unsaturated bonds, they have different ionization potentials at different pH levels, resulting in the pH-dependent net charge on dye molecules. Furthermore, the sorbent surface contains numerous functional groups. Thus, the net charge on sorbents, which could be measured in the form of zeta potential or isoelectric point, is also pH dependent. Therefore, the interaction between dye molecules and biosorbents is a combination of charges on dye molecules and the surface of the sorbent. In this study (Figure 3), the biosorbents demonstrated relatively low adsorption capacities in strong acidic environments (pH \(<\) 2). This can be attributed to the decrease the attractive force between biosorbents and MB due to the formation of a positive charge on the surface of biosorbents with the excess H\({}_{3}\)O+ ions in the mixture in strong acidic environments [94,95]. Considering the MB dye is a cationic dye at pH \(>\) 3.4 (pKa = 3.8) [95], a higher pH is favoured for MB adsorption. MB dye removal remained at a constant level between the pH levels of 4 and 10 for all biosorbents (Figure 3), which is in accordance with previous studies [96,97]. This occurs as higher pH levels increase the presence of OH- ions, which in turn decrease the positively charged site on biosorbents and promotes the electrostatic attraction between the positively charged cationic dye and the negatively charged biosorbent surface [95,98]. In addition to the electrostatic interactions and adsorption at the pores, some other weak interactions such as cation exchange [99,100], hydrogen bonding [101,102], and dipole-dipole interaction [103] are also relatively significant in the MB removal processes. In conclusion, based on the outcomes of this study, a pH of 6 was selected for all further experiments as this was the pH level that provided optimal adsorptions for all biosorbents.