esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Medication Adherence Monitoring Technologies +
## 4 A Review of Medication Adherence Monitoring Systems
### Fusion-Based Systems
#### Sensor-App Systems  
Personal mobile device technology has witnessed a rapid progression in recent years. The services brought by mobile devices, such as the different means of communications and user applications, have enabled a host of possibilities. Thus, mobile applications' industry have been in race, including those for promoting healthcare of older patients [44; 142]. Specifically, many mobile and tablet based applications have been developed for medication adherence in the form of automated reminder systems [143; 144; 145].  
In this context, the sensor-app approach blends the use of sensor networks and mobile-app approaches for medication adherence tracking and monitoring. [PERSON] et al. [90] developed a pillbox containing multiple compartments with ambient light sensor fixed in each of them and a WiFi connection. Also, a mobile app has been developed that contains the medicine schedule. The pillbox and the mobile app are interconnected through an online data source. Hence, the mobile app generates alarms when it is the time of medication until the patient takes the medication from the pillbox or chooses to delay the action. In a recent study, [PERSON] and [PERSON] [91] proposed a pillbox-based system that uses the Arduino UNO WiFi and a load cell. Medication weight changes were reported to a server via the Internet. Also, a mobile application was developed that tracks the change in weight measurements and alerts the patient to take medication, if weight change is not detected. The system was tested for 160 times of medication taking and the accuracy of the mobile application notification functionally was 96.88%.
# A Review Paper on \"Graphene Field Emission for Electron Microscopy\"
## 3 Graphene-Based Cold Field Emitters  
Fast development of two-dimensional (2D) graphene in recent years has led to a variety of different applications for field emission based devices [20; 53]. Compared with other metallic or 1D nanoscale CFE electron sources, graphene is well known for possessing several desirable properties: (1) it has excellent thermal, mechanical, and electrical characteristics [17; 21]; (2) its work function can be significantly lowered both by direct contact with metals (effectively doping it), and through the application of intense electric fields [54; 55]; and (3) it is extremely flexible. Graphene CFE electron sources fall into two main categories: the single-tip cathode field nano-emitter (which includes point cathodes and ring cathodes) and graphene film cathodes, which consist of densely packed graphene nano-emitters. Only the former can be used for electron microscopy applications, as the latter produces a large array of defocused overlapping electron virtual sources that cannot be subsequently focused down to a single nano-sized probe.
## 4 Results
### Driving Risk Model
#### 4.2.2 Significant Difference Test of Driving Risk Points  
In order to further analyze the safety risk of driving in three types of curves, the significance analysis of the number of driving risk behavior points of vehicles in urban underground road curves under different types of curves and different radius ranges is carried out. The data do not obey the normal distribution, the non-parametric test analysis was adopted. The results are shown in Table 4. It can be found that at the 0.05 level, the number of driving risk points per unit length (m) of the independent curve is not significantly greater than that of the S curve (\(p\) = 0.96513).  
In addition, it can be observed that there are only two significant differences in the number of driving risk points per unit length of the curve under different radius ranges at the 0.05 level. Specifically, the average number of risk points per unit length  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Curve**} & \multirow{2}{*}{**Test Method**} & \multirow{2}{*}{**n**} & \multicolumn{3}{c}{**Number of Dangerous Points/m**} \\ \cline{3-5}  & & & **Null** & **Prob** \\ \cline{3-5}  & & & **Hypothesis** & \\ \hline Independent curve & [PERSON] & 160 & Median: & \\ S-curve & Whitney & 120 & independent curve \textgreater{} & 0.96513 \\ \hline Radius & & **n** & **Mean rank** & **Parameter** \\ \hline Extreme small radius curve & & 120 & 163.62 & E-G: \(p\) = 0.00194 \\ (R \(\leq\) 85 m) & Kruskal & & & Sig = 1 \\ \hline General small radius curve & Wallis & 80 & 123.76 & E-O: \(p\) = 0.00133 \\ (85 m \textless{} R \(\leq\) 150 m) & & & Sig = 1 \\ \hline Ordinary radius curve (R \textgreater{} 150 m) & 80 & 122.57 & Sig = 0 \\ \hline \hline \end{tabular} E stands for the extreme small radius curve; G stands for the general small radius curve; O stands for the ordinary radius curve. Sig = 1 indicates that the mean difference is significant at the 0.05 level; Sig = 0 indicates that the mean difference does not exist at the 0.05 level.  
\end{table}
Table 4: Analysis results of driving risk points variance.  
Figure 10: The G-G graph of curves.  
in extreme small radius curves is significantly higher than that in general small radius curves, and both are significantly higher than in ordinary radius curves. Therefore, the safety risk of driving on the three types of curves is ranked as follows: extreme small radius curves > general small radius curves > ordinary radius curves.
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 3 Methodology
### Data Preprocessing  
A variety of reasons, including satellite payload interference, the space environment, and other factors, may produce inaccuracies in constantly observed satellite data. As a precaution against such mistakes, we divided continuous observation data into fixed-length sliding windows (commonly known as \"sequences\"), which we then utilized as inputs to our proposed model. We also divided the data into sliding windows that were continuous but did not overlap because time series data are highly autocorrelated sequences. As a result, we carefully examined the difference between the first and final data points ordered by time in each time window to verify that the data were continuous. Time series windows with unreasonable time differences (i.e., gaps) were removed from the analysis.  
Subsequently, we reformulated the pre-earthquake ionospheric perturbation discrimination task as a multiclass multivariate time-series classification problem with the data marked as follows (Figure 3): the non-seismic-related data were marked as 0, seismic-related data were marked as 1, and data with a Kp index greater than 3.0, regardless of whether the data were related to an earthquake, were marked as 2, indicating density perturbations due to solar and magnetic activity [30]. It is known that the Earth's magnetic field undergoes temporal fluctuations and exhibits recognized patterns related to the movement of the poles, and time series data exhibit significant autocorrelation characteristics, which indicates that the field is highly variable. Therefore, to ensure that the overall Swarm dataset could be utilized effectively, it was carefully divided into two contiguous parts: the first 80 percent (in chronological order) of the data was used for model training (the training set), and the last 20 percent was used for model testing and final assessment (the test set).  
Figure 3: Sequence labeling after data segmentation using a sliding window. Consecutive observations of used parameters are segmented by non-overlapping sliding windows. T is the window’s length. Non-seismic data are marked as 0 (class 0), seismic data are marked as 1 (class 1), and data with a Kp index greater than 3 are marked as 2 (class 2), regardless of whether they are associated with an earthquake.
# A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
## 2 Methods
### Iterative Blind Deconvolution
#### 2.2.2 RL Deconvolution with SATV Regularization  
RL can effectively solve nuclear medical imaging problems, which is derived from the maximum likelihood of a Poisson distribution. The TV model was first proposed by [PERSON] et al. [24] in edge preserving image restoration, it is a popular choice of being the regularization term [25]. RL algorithm with total variation regularization (RLTV) was suggested in [26; 27].  
\[\overset{\wedge}{h}_{k+1}(\mathbf{s})=\overset{\wedge}{h}_{k}(\mathbf{s})[ \overset{\wedge}{f}_{k}(-\mathbf{s})*\frac{g(\mathbf{s})}{\overset{\wedge}{h} _{k}(\mathbf{s})*\overset{\wedge}{f}_{k}(\mathbf{s})}],\overset{TV}{h  
the term in the denominator provide an estimate of edges from an initial image \(f_{0}\) at scale \(\sigma\) using the Gaussian kernel \(G_{\sigma}\) filtered gradients, and \(\varepsilon\) is a parameter of adaptive function \(\lambda(\mathbf{s})\). The spatially adaptive function can help reduce noise without producing the staircase effect because it can self-adjust according to the smoothed gradient of the image. To summarize, the SATV regularization has two good features: (1) For edge and detail, because \(\lambda(\mathbf{s})\) is small which weakens the TV regularization, then the edge and texture will be well preserved; (2) for flat region, a large \(\lambda(\mathbf{s})\) leads to a large TV regularization strength, so the noise will be well suppressed.
## 3 Proposed CNN + DWFs Methodology and Dataset
### Signal Decomposition and 2-D Data Set Generation  
During the milling process, the impact of the cutting edge on the work piece is one of the main excitations of the spindle. As a complex mechanical system, the machine tool spindle's response to the cutting impact is complex and interfered by other excitations such as shaft eccentricity and mesh impact. The acquired vibration signal is a complex mixture, including harmonics of excitation frequencies, resonance of mechanical parts and broadband Gaussian noise. Although, the ability of deep neural networks to extract implicit features from input has been widely recognized. Using advanced signal processing knowledge to extract the informative sub-signal from the raw signal is undoubtedly beneficial for CNN to further extract effective features. Drawing on the experience of feature engineering in traditional fault diagnosis, the information of transient impact in vibration signals is often concentrated in certain frequency bands.  
Kurtosis is often used to select the proper demodulation band for further data processing and feature extraction since it can measure the impulsiveness of the signal [51; 52]. Figure 8 shows the normalized mean kurtosis map of 20 samples for each subset. The raw data samples were decomposed using the DWFs, the number of decomposition layers is set to 3. A subset of 20 samples was randomly selected for each set of cutting conditions for each tool wear state. It can be seen that the kurtosis of the reconstructed signals in different frequency bands was quite different. At the same time, for every subset, the kurtosis of the frequency band \(iwp_{1}^{2}\) (1600-4800 Hz) and \(iwp_{2}^{2}\)(800-2400 Hz) were higher than other frequency bands of the same layer. A wider frequency band may contain more feature information, but it also increases the computational complexity of the neural network. Therefore, \(iwp_{1}^{2}\) and \(iwp_{2}^{2}\) were utilized to create data sets in 1-D format, respectively. Compared to 1-D convolution, 2-D convolution creates a broader connection between the elements in the inputs. This means that for a particular frequency, 2-D CNN can learn more about its relationship to other frequencies. For the advantages of 2-D convolution, this paper folds the 1-D band spectrum into a 2-D matrix to create a 2-D data set.  
Transforming the temporal signal into the frequency domain and training the neural network with the spectral samples could eliminate the interference of the initial phase and reduce the difficulty of recognition. On the other hand, the length of the temporal signal sample was 12800 data points (1 s). Cutting the spectrum of the uninformative band would significantly reduce the size of the sample, further significantly reducing the computational time of the CNN.  
The spectral samples were then converted to logarithmic coordinates and normalized. The final step was to fold the sample into a 2-D matrix to accommodate the 2-D CNN format requirements for the input.
## 2 Materials and Methods
### Methods
#### 2.3.5 Extraction of Topographic Factors  
Slope and slope direction are two crucial parameters for depicting topographic and geomorphic features, capable of illustrating the undulation pattern of the terrain and its structural characteristics [30]. Initially, the ArcGIS slope and slope direction tools were employed to extract these parameters from the DEM in the study area. Subsequently, the reclassification tool in the spatial analysis toolbox of ArcGIS was used to categorize the slope values.  
Following the standards established by the International Geographical Union Commission on Geomorphological Surveys and Geomorphological Mapping (IGU CGSCGM) for the application of detailed geomorphological maps [31], and considering the specific conditions of the study area, the slope values were classified into six grades: 0\({}^{\circ}\)-5\({}^{\circ}\) (gentle slope); 5\({}^{\circ}\)-15\({}^{\circ}\) (slow slope); 15\({}^{\circ}\)-25\({}^{\circ}\) (incline slope); 25\({}^{\circ}\)-35\({}^{\circ}\) (steep slope); 35\({}^{\circ}\)-45\({}^{\circ}\) (slanted slope); and >45\({}^{\circ}\) (rapid steep slope) grades [32]. Additionally, the slope direction values were divided into nine directions, creating slope and slope direction maps for the study area.  
Detailed statistical analyses were conducted using Origin2022 software to gain a comprehensive understanding of the distribution of slope degree and slope direction grades and their respective area proportions.  
The degree of topographic relief is a macro-indicator describing the topographic characteristics of a region, which is used to indicate the height difference between the highest and lowest points in a specific area and can compare the regional geomorphology and delineate landform types [33]. The value range of the topographic relief degree in the study area is 0-173 m. According to the topographic relief degree division method [34] and combined with the actual situation of the study area, the ArcGIS reclassification tool was utilized to classify it into four types of terrain: plains (<30 m); plateaus (30-50 m); hills (50-100 m); and mountains (100-200 m). The expressions (Equation (6)) are as follows:  
\[RDLS=H_{max}-H_{min} \tag{6}\]  
where \(D\) denotes the degree of terrain undulation, \(H_{max}\) denotes the maximum elevation value, and \(H_{min}\) denotes the minimum elevation value.  
Surface roughness can effectively represent the degree to which the terrain within a particular area has been eroded and cut. The larger the value, the more complex the terrain and the more severe the erosion [35]. The natural interval point classification method divides the surface roughness reclassification into four classes, with values ranging from low to high. The expression (Equation (7)) is as follows:  
\[R=\frac{1}{cos(S)} \tag{7}\]  
where \(R\) denotes surface roughness and \(S\) denotes terrain slope.
## 2 Materials and Methods
### Slope Geometry and Boundary and Flow Conditions  
Figure 3 shows the geometry and dimensions of the evaluated slope. Regarding the slope geometry, a medium-high inclination angle of 45\({}^{\circ}\) was chosen [42; 43; 50]. Additionally, a slope height of 12 m was considered. This allows for the analysis of slope stability under conditions of high inclination and significant height.  
Figure 3: Slope geometry and dimensions.  
Figure 2: Soil suction curves for SP-SM and SW-SM.  
It is worth noting that, for the strength parameters used in the materials, a 45\({}^{\circ}\) slope represents an aggressive condition from a stability standpoint. However, the unsaturated condition of the soil--particularly due to matric suction--contributes significantly to maintaining the slope's stability in its initial state, prior to rainfall infiltration.  
For the analysis, control points were established at H = 12 [m], H = 9 [m], H = 6 [m], H = 3 [m], and H = 0 [m]. Figure 4 specifies the position of the points where the analysis for this research was conducted. This choice allows for a precise observation of variations in the soil properties and responses along the slope structure, improving the accuracy of the study on stability and material behavior.  
Additionally, the boundary conditions of the numerical model are shown. At the lower boundary of the model, complete deformation restriction is imposed, meaning the soil cannot deform in any direction. At the lateral boundaries, deformation is allowed only along the \(Y\)-axis. Meanwhile, at the upper boundaries of the model, full deformation is permitted in both the Y and X axes.  
Regarding flow conditions, it was determined that rainwater infiltration would occur at the upper boundary under the Infiltration condition in Plaxis 2D. In contrast, water flow would be allowed at both the lateral and lower boundaries under the Seepage condition.
# Treatment of Aqueous Bromate by Superparamagnetic BiOCl-Mediated Advanced Reduction Process
## 2 Results and Discussion
### Mechanism Clarification
Conditions: BiOCl-HAP\({}_{\rm SM}\) dose 0.5 g:\(\mathrm{L}^{-1}\), [DMPO]\({}_{0}\) = 10 mM, [BrO\({}_{3}^{-}\)]\({}_{0}\) = 6.6 uM, [Formate]\({}_{0}\) = 4.0, pH = 7.0, 25 \({}^{\circ}\)C.  
concentration evolution of formate in bulk solution was monitored in the BiOCl-HAP\({}_{\text{SM}}\)/formate system with and without competitors (Figure 8). Formate readily adsorbed onto BiOCl-HAP\({}_{\text{SM}}\) and the presence of 4 mM F\({}^{-}\) well inhibited formate's adsorption onto BiOCl-HAP\({}_{\text{SM}}\) (<4% for 20 min) (Figure 8a). Correspondingly, BrO\({}_{3}^{-}\) degradation readily decreased by about 11% when the adsorption of formate onto BiOCl-HAP\({}_{\text{SM}}\) was almost inhibited (Figure 8b). Given these results, one can conclude that the free formate acts as the source of CO\({}_{2}^{\bullet-}\) through HO-induced hydrogen abstraction and the adsorbed formate primarily serves as origin of reductive HCOO\({}^{\bullet-}\) (\(-\)2.1 V, [39]) which also contributes to the bromate degradation:  
\[\text{BiOCl}+hv\rightarrow\text{h}^{+}+\text{e}_{\text{CB}}^{-}, \tag{1}\]  
\[\text{h}^{+}+\text{H}_{2}\text{O}\rightarrow\text{H}^{+}+\text{HO-}_{\text{s}} \text{(diffusing to bulk solution to form free HO-}\left(\text{HO-}_{t}\right)), \tag{2}\]  
\[\text{HO-}_{t}+\text{HCOO}^{-}\rightarrow\text{CO}_{2}^{\bullet-}+\text{H}_ {2}\text{O}\ k=3.8\times 10^{9}\text{ M}^{-1}\text{-s}^{-1}, \tag{3}\]  
\[\text{BiOCl-HAP}_{\text{SM}}+\text{HCOO}^{-}(\text{Formate})\rightarrow\equiv \text{Bi-}^{-}\text{OOCH}(\text{Complex}), \tag{4}\]  
\[\equiv\text{Bi-}^{-}\text{OOCH}^{\text{electron injecting into the conducted band}}\text{HCOO}^{\bullet-}+\text{BiOCl-HAP}_{\text{SM}}, \tag{5}\]  
\[\text{h}^{+}+\equiv\text{Bi-}^{-}\text{OOCH}\rightarrow\text{HCOO}^{\bullet-}, \tag{6}\]  
\[\text{HCOO}^{\bullet-}\text{ electron injecting into the conducted band}\text{ CO}_{2}+\text{H}^{+}, \tag{7}\]  
\[\text{HCOO}^{\bullet-}\rightarrow\text{CO}_{2}+\text{H}\,. \tag{8}\]  
\[\text{H}\cdot+\text{HCOO}^{-}\rightarrow\text{H}_{2}+\text{CO}_{2}^{\bullet-} \ k=2.3\times 10^{8}\text{ M}^{-1}\text{-s}^{-1}. \tag{9}\]
# Reconstruction of Isolated Moving Objects by Motion-Induced Phase Shift Based on PSP
## 1 Introduction
As multiple objects are reconstructed, the double-frequency phase unwrapping method is employed. Instead of projecting multiple phase-shifted fringe patterns, the proposed method only projects two fringe patterns with different frequencies. The phase shift required by the PSP is introduced by the object motion. The model describing the motion-induced phase shift is presented. Then, the movement influence on the fringe pattern is analyzed. Through movement tracking, the phase information of the moving object in different frequencies is retrieved. Finally, the mismatch on the phase information between the two frequencies is compensated, and the isolated moving object is reconstructed. In Section 2 of the paper, the traditional PSP method is presented. Section 3 shows the principle of the motion-induced phase shift, and the influence caused by object movement is presented. In Section 4, experiments are presented to prove the effectiveness. Section 5 shows the conclusions of the paper.
# IpDFT-Tuned Estimation Algorithms for PMUs: Overview and Performance Comparison
## 5 Experimental Results in a Practical Case Study
The values of amplitude, phase, frequency and ROCOF evaluated at the reference times were estimated a posteriori through a multi-harmonic sine-fitting procedure [40]. The bar diagram in Figure 2 shows the maximum TVE, \(|\)FE\(|\) and \(|\)RFE\(|\) values computed over all data records and associated with the IpDFTc algorithm, the tuned TWLS estimator and the DCF technique using either the LSF or the STD filter, respectively. Given that, as shown in Sections 3 and 4, the best results as well as the full compliance of some algorithms are obtained over 4-cycle intervals in the _P Class_ case and over 8-cycle intervals in the _M Class_ case, respectively, the results obtained over 2-cycle and 6-cycle intervals are omitted here for the sake of brevity. Moreover, the TVE, \(|\)FE\(|\) and \(|\)RFE\(|\) values obtained over 2 cycle intervals are very large due to the large spectral leakage caused by the 2 nd and 3 rd harmonic. The results in Figure 2 confirm that, despite the presence of a variety of uncertainty contributions (most notably the limited spectral purity of the adopted waveform generator, the limited effective resolution of the DAQ and the uncertainty associated with the reconstruction of the reference values of amplitude, phase, frequency and ROCOF) the behavior of the estimation algorithms under test is consistent with the simulation results shown in Tables 4b and 5b. In particular, it is evident that:  
* the IpDFTc estimator exhibits the best accuracy both over 4-cycle and 8-cycle observation intervals, as expected since it is natively conceived to counteract the effect of the 2 nd harmonic;
* the tuned TWLS returns slightly worse results, but it is worth recalling that it behaves better in dynamic and transient testing conditions;
* the DCF technique is outperformed with either filter although the results obtained using a 4-cycle-long LSF are not much worse than those achieved with the IpDFTc and the tuned TWLS estimators. Note that the TVE values of the DCF technique with the LSF over 8-cycle intervals are quite worse than those over 4 cycle-intervals. However, this behavior is consistent with the corresponding simulation results shown in Tables 4b and 5b and this is probably due to the larger bandwidth of the LSF with an 8-cycle-long impulse response compared with the LSF with a 4-cycle-long impulse response. Indeed, while the latter filter is conceived for _P Class_ applications, the former one is optimized not to perturb stronger amplitude and phase oscillations in the _M Class_ case. Thus, the LSF with an 8-cycle-long impulse response makes the DCF technique more sensitive to noise (e.g., injected by the experimental tested).
# Analysis and Design of Functional Device for Vehicular Cloud Computing
## 3 System Model
### V2V Communication
#### 3.2.1 Direct Communication  
For the case of direct communication, the vehicle \(D\) receives a signal \(x(t)\) from vehicle \(S\) via a wireless channel. The receiving signal is written as  
\[y_{0}(t)=\sqrt{P_{s}}h_{0}x(t)+n_{0}(t). \tag{2}\]  
where \(h_{0}\) is an \(S\to D\) channel coefficient and \(n_{0}(t)\) is an additive white Gaussian noise (AWGN) term with a one-sided power spectral density (PSD) \(N_{0}\).  
The SNR related to a direct communication link at the receiving end can be calculated as  
\[\begin{split}\gamma_{0}&=\frac{P_{s}|h_{0}|^{2}}{| n_{0}(t)|^{2}}\\ &=\frac{P_{s}|h_{0}|^{2}}{N_{0}}.\end{split} \tag{3}\]