esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sample Description  
A total of 50 patients met the inclusion criteria; among them, 25 were men, and 25 were women. The mean age of the sample was 57.76 years (55.00-60.52) (Figure 2). The mean age for men was 59.20 years (54.73-63.67), whereas for women, it was 56.32 years (52.86-59.78). The difference was not statistically significant at \(p\) > 0.05 (Student's \(t\) test) (Figure 3).  
Among these fifty patients, four had well-controlled diabetes (glycoslated haemoglobin < 7%), and five had other systemic diseases. Furthermore, 10 patients were smokers.  
Forty-six patients reported discomfort 24 h after surgery, whereas four patients reported moderate to severe pain. In terms of inflammation, nine patients experienced inflammation according to their telephone follow-up.  
A total of ten patients required GBR via autologous bone obtained during the alveolus drilling.  
Of the fifty implants placed, four failed prior to placement of the prosthesis; in other words, the early success rate was 92%. All patients who experienced implant failure did so due to mobility observed during the placement of the healing abutments and impression abutments. None of these patients presented with pain or signs of infection; however, clinical mobility was evident. A digital periapical radiograph was taken before implant removal. This clarification has been added to the article.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Wachtel 2003 EHI 1–2 s**} \\ \hline \multirow{3}{*}{Full closure} & \(\bullet\) & Without fibrin \\ \cline{2-3}  & \(\bullet\) & Fibrin line \\ \cline{2-3}  & \(\bullet\) & Fibrin clot \\ \hline \multirow{3}{*}{Incomplete closure} & \(\bullet\) & Partial necrosis \\ \cline{2-3}  & \(\bullet\) & Total necrosis \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wachtel index: the presence of the five parameters above determine the value (0–4).  
The mean Wachtel healing index was 1.62 (1.39-1.54); most patients had a fine line between the edges of the incision with a small amount of fibrin interposed (Figures 4 and 5).  
Figure 3: Mean age distribution by sex. Women were on average younger than men; however, the median age for women was greater than that for men due to the presence of some outliers.  
Figure 2: Mean age.  
Figure 4: Histogram showing that the majority of patients (52%) had a Wachtel EHI of 1—that is, a fine line of fibrin was identified along the wound—followed by 28% of patients with an EHI of 2. Only 2% had an EHI of 0; that is, there was no fibrin line in the healing tissue.  
Figure 5: _Cont._  
None of the qualitative variables showed a statistically significant association with implant success.  
According to ANOVA, no statistically significant differences were found between short-term implant success and the healing variables recorded.
## 1 Introduction
presented a case study on igneous rock imaging through a strategy involving the joint use of a preferential filtering method and amplitude inversion affected by remanence in the Qikou depression in China [22]. [PERSON] et al. presented the interpretation of different magnetic analyses and a calculated 3D inversion model to provide important insights into the distribution of igneous rocks in central Iran [23]. [PERSON] et al. deeply studied the response characteristics of the gravity-magnetic-electric and physical properties of the Permian igneous rocks in the Sichuan Basin [24]. A 1445 km\({}^{2}\) high-resolution 3D seismic reflection dataset was used to analyze the Permian large igneous province in the subsurface of the Tazhong area in the central Tairm Basin in northwestern China [25]. [PERSON] et al. presented facies-controlled inversion for the prediction of volcanic rock and surrounding reservoir with near-offset seismic stack [26]. Notably, seismic detection techniques have been successfully applied to identify and characterize both igneous intrusion zones and thermally altered coal formations, providing critical insights into their spatial distribution and structural properties [27; 28; 29; 30; 31; 32; 33]. Using laboratory experiments and numerical models, the velocity of igneous intrusions and its relationship with rock fracture toughness were investigated [27]. [PERSON] et al. ocated the intrusion area of igneous rock using P-wave data and well logging data, combined with the impedance inversion method, seismic facies analysis, and the spectral decomposition technique [28]. [PERSON] et al. adopted the constrained sparse spike inverse modeling method to analyze and evaluate the intrusive area, the cooked area, and the gas-enriched area [29]. Particle swarm optimization wave impedance inversion improved the resolution of fine structures in igneous rocks [30]. A forward modeling intrusion of igneous rock was performed and seismic attributes in detecting the ranges of the coal seams intruded on by igneous rocks were theoretically analyzed [31]. Igneous intrusions can have profound effects on coal seams, influencing their physical and chemical properties, structural integrity, and gas content. Understanding these effects is crucial for both geological research and practical applications in coal mining and energy production. However, previous studies have provided limited comprehensive analyses regarding the overlying igneous rocks in coal mining. This paper aims to investigate the geological conditions associated with igneous rock occurrences in a coal mine situated within the Huaibei Coalfield, while systematically analyzing their potential hazards and impacts on coal mining.
# Cross-Entropy Method in Application to the SIRC Model
## 3 Description of the Numerical Results
### Proposed Optimization Methods in the Model Analysis  
The results will be presented for two moments in the model: at the beginning and in the middle of the epidemic. This is initialized with other initial parameters. For the beginning of the epidemic, they are as follows:  
\[S_{0}=1-I_{0},\,I_{0}=10^{-6},\,R_{0}=0,\,C_{0}=0.\]  
For the widespread epidemic:  
\[S_{0}=0.99,\,I_{0}=5*10^{-3},\,R_{0}=3*10^{-3},\,C_{0}=2*10^{-3}.\]  
The values in the model have been normalized for the entire population, and they add up to 1. The results for both beginning (v. Figure 1(a)) and during (v. Figure 1(b)) the epidemic were obtainedusing the Runge-Kutty method and shown here. The cost index in this case was in order 0.00799 and 0.00789 for the model of the beginning and the development of an epidemic.  
In Sections 3.3 and 3.4 two ways of calculating optimal functions \(u(t)\) and \(v(t)\) for the SIRC model will be considered. In the first version, the functions will be calculated separately. In the following, both \(u(t)\) and \(v(t)\) will be counted at the same time. All versions will be presented at two moments of the epidemic: when it began and when it has already spread. The results will be compared with those obtained in the article by [PERSON] et al. [31], where the problem was solved using the sequential quadratic programming method using a tool from Matlab. Unfortunately, the article does not specify the cost index value for solving the obtained sequential quadratic programming method. Therefore, in the paper, there were attempts to recreate the form of the function \(u(t)\) and \(v(t)\) and the cost index obtained for them (0.003308 for the first situation and 0.006489 for the second one) and the model solution.  
As can be seen in Figure 3 in the first situation, the control functions helped reduce peak infection, which was previously (without control Figure 2a) occurred between 2 and 4 months. The largest peak was around 20% occurred between 2 and 4 months. Previously, the largest peak was around 10%. In the case of the second situation, the peak also decreased, but not very clearly (it is also visible in the value of the cost index, which for the first situation is 0.003308, and for the other one 0.006489). It can be seen that timely control is also very important to reducing the harmful effects of an epidemic. For more conclusions on how to apply control properly in this model, see [31].
## 5 Discussion
From a defense perspective, this study suggests that static obfuscation, although intuitive and low-cost, is inadequate against adaptive DL adversaries. As SCAs continueto evolve, cryptographic security mechanisms must advance in parallel. Based on the findings of this study, developers of embedded systems and IoT devices should move beyond static obfuscation strategies and consider adopting dynamic obfuscation techniques that modify patterns in real time or at regular intervals [23]. For example, recent research has demonstrated that device-level hardening frameworks can employ Bayesian optimization to dynamically tune DL-based defenses on mobile processors, thereby minimizing side-channel leakage without incurring significant overhead [41]. Moreover, a novel line of research repurposes neural networks as inline circuit abstractions that obfuscate power signatures, effectively transforming ML models from offensive tools into embedded countermeasures [28]. In addition to technical considerations, this study raises important ethical implications, particularly in the context of CPS. These systems, which closely integrate computing components with physical processes, are increasingly deployed in safety-critical domains such as autonomous vehicles, industrial automation, smart grids, and healthcare [2,3,5]. The demonstrated ability of DL models to extract fine-grained operational details from side-channel data, even when obfuscation is applied, raises significant concerns regarding the confidentiality and integrity of CPS operations [6,17]. An attacker equipped with such techniques could infer internal control states or manipulate actuation patterns, potentially leading to physical disruption or safety violations [7]. Given the dual-use nature of the findings, it is essential to adopt a responsible research posture [1]. Future work should be guided by ethical frameworks that consider not only how to maximize technical robustness but also how to prevent misuse in real-world deployments. This includes responsible disclosure of vulnerabilities, the adoption of privacy-preserving architectural designs, and adherence to sector-specific safety standards [2,13,35]. Research into CPS security must, therefore, strike an appropriate balance between scientific advancement and practical safeguards to ensure the trustworthiness of these increasingly ubiquitous systems [5,7].
# Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis
## 4 Engineering Experiments
### Comparison of Prediction Models  
As a comparison experiment, the LSTM was used to build a prediction model, and the model was trained on the microseism dataset (including 8497 event records) of 103\(\mu\) working face without clustering process, and used to predict the location of high-energy mine earthquakes and the daily occurrence frequency corresponding to different energy levels. In the SVR model above, the microseism data used was distributed from April 23 to May 23, so the event samples in the test set of the LSTM prediction model were also set within this time period. The time series sets of microseism location information and energy frequency information were also processed respectively as shown in Figures 12 and 14 to obtain \(n=1320\), \(u=1310\), \(m=562\), \(v=559\). The Early Stopping method was used to prevent the overfitting issue. The specific method is to calculate the accuracy of validation data at the end of each epoch training. When the accuracy does not improve any more, the model training will be stopped to avoid overfitting. Similarly, the grid search method was used to optimize the hyperparameters of model, and the prediction of X coordinates of high-energy mine earthquakes and daily occurrence frequency corresponding to \(10^{4}\) J energy level events are shown in Figure 16:  
Figure 16: Prediction of LSTM model. (**a**) X coordinates of high-energy events. (**b**) Daily frequency corresponding to \(10^{4}\)J energy level events.  
Figure 15: Prediction of SVR with different kernel functions on daily frequency of \(10^{4}\) J level. (**a**) Linear kernel. (**b**) Sigmoid kernel. (**c**) Polynomial kernel. (**d**) RBF kernel.  
In the SVR model, the RBF kernel function has the best prediction effect. Therefore, the MSE and R\({}^{2}\) of the LSTM model in predicting the X coordinates of high-energy events and the daily frequency corresponding to 10\({}^{4}\) J energy level events are calculated and compared with the former. The results are shown in Table 7.  
Through comparative experiments, it can be concluded that the SVR model with RBF kernel function on the basis of clustering analysis is better than the LSTM model without clustering analysis, both for the prediction of the location of high-energy mine earthquakes and the prediction of daily frequency of high-energy mine earthquakes with different energy levels.
## 2 Materials and Methods
### Characterizations  
The crystal phases of the samples were investigated by X-ray diffraction (XRD) using a Rigaku Smartlab 9 kW X-ray diffractometer, equipped with a Cu-K\({}_{\alpha 1}\) radiation source (\(\lambda\) = 1.5406 A). The microstructure of the materials was visualized using scanning electron microscopy (SEM) by employing a JEOL 6490 microscope at an accelerating voltage of 20 kV, while the elemental analysis of the samples was examined by using an energy dispersive spectrometer (EDS), which is attached to the JEOL 6490 microscope. Atomic force microscopy (AFM) was utilized to survey the surface morphology of the samples, which was performed on a Bruker NanoScope 8 in tapping mode using a silicon cantilever with a tip radius of less than 10 nm and resonance frequency of 278 kHz. The mean particle size and the width of the particle distribution of the prepared TiO\({}_{2}\) nanoparticles dispersed in deionized water were determined by using a Malvern Mastersizer 3000 laser particle size analyzer. Fourier transform infrared spectroscopy (FT-IR) measurements were carried out to examine the atomic bonding in the prepared coating by using a Bruker Vertex-70 spectrometer, and the absorption spectra were collected in the range of 400-1400 cm\({}^{-1}\) applying a spectral resolution of 2 cm\({}^{-1}\). The surface wettability of the modified aerated brick was quantified by the contact angle, which was performed on a PowerEach JC2000D contact angle meter using the 5-point fitting method with a droplet volume of 2 \(\mu\)L.
###### Abstract  
This study considered the effect of pavement aggregate grain size on tyre-pavement contact interaction during the late stages of pavement skid resistance. First, hemispherical shells 7, 9, and 13 mm in diameter were used to simulate coarse pavement aggregates. Subsequently, a three-dimensional finite element tyre-pavement contact model developed using ABAQUS was employed to analyse the contact interaction between each simplified pavement type and the tyre under steady-state rolling and braking conditions. Finally, the concept of occlusal depth was proposed and applied to characterise pavement skid resistance. The results showed that under steady-state rolling conditions, the peak contact stress of the simplified pavement increased with the pavement mean texture depth, whereas the contact area decreased. Under steady-state braking conditions, the effect of the contact interaction between the tyre and simplified pavement aggregates was ranked in order of superiority as aggregate grain sizes of 9, 7, and 13 mm, indicating that aggregate grain size did not exhibit any correlation with tyre-pavement contact interaction. Additionally, the squares of linear correlation coefficients between the pavement cumulative occlusal depth and horizontal braking force reached 0.921, 0.941, 0.889, and 0.894 for vehicle speeds of 30, 60, 90, and 120 km/h, respectively, indicating that they could be used to assess pavement skid resistance.  
finite element model; aggregate grain size; tyre-pavement contact interaction; occlusal depth Article
## 5 Simulation Results and Discussion  
In this section, we critically analyze the achieved outcomes of the proposed model of SACFIR and SAMCFIR. The performance measuring parameters of network lifetime, stability, end-to-end delay and packet delivery ratio are considered most valuable to analyze. Extensive simulations of each experiment provide the average results of the proposed models in comparison with ATCEEC and MCEEC protocols. Conventional SDN controllers have flexibility issues in scalable wireless sensor networks, which is why recently-evolved solutions for SDN-WSNs have involved researchers proposing their own controllers according to the architecture and requirement of the sensor networks. A similar methodology has been adopted by this paper's contribution. This paper presents the SDN-enabled Iterative Reconfigurable WSNs (SDN-IRWSNs) hybrid network architecture for the deployment of sensor nodes. We develop our own SDN controller to execute the Network Topology Management Phase (NTMP), Network Settling Phase (NSP) and Network Forwarding Phase (NFP) of proposed SACFIR and SAMFIR routing protocols.  
In the general measurement process, we included specific network scenarios and omitted the other results due to space constraints. More specifically, the simulation scenarios included network areas of 100 m \(\times\) 100 m, 300 m \(\times\) 300 m and 450 m \(\times\) 450 m, in which 100, 200, and 300 sensor nodes are dispersed respectively at the infrastructure layer. These sensor nodes are connected with SDN controller resides at BS, where BS is located at the top of the network as shown in Figure 3. Other important simulation parameters are given in Table 2.
## 2 Data Processing and Analysis Method  
### Method of KPPP-estimated OTL Displacement  
The influence of OTL in the horizontal direction is about one-third of that in the vertical direction ([PERSON] and [PERSON], 2019). Therefore, in this paper, we mainly analyzed the OTL displacement in the vertical direction. According to the International Earth Rotation and Reference Systems Service (IERS) Convention 2010 ([PERSON] and [PERSON], 2018), the vertical OTL displacement of a station can be represented by four semidiurnal (M2, S2, N2 and K2) and four diurnal (K1, O1, P1 and Q1) tidal constituents, depending on the frequencies:  
\[\Delta c=\sum_{j=1}^{8}f_{j}A_{cj}\cos(\omega_{j}t+\chi_{j}(t_{0})+u_{j}-\phi_ {cj}) \tag{1}\]where \(A_{cj}\) and \(\phi_{cj}\) respectively represent the amplitude and phase of the \(j\)-th tidal constituent at the station; \(f_{j}\) and \(u_{j}\) denote the nodal modulation corrections; \(\omega_{j}\) is the angular frequency; and \(\chi_{j}(t_{0})\) is the astronomical argument at reference time \(t_{0}\). Table 1 lists the periods of the eight semidiurnal and diurnal tidal constituents. For a given location, except that the amplitude and phase of the \(j\)-th tidal constituent need to determined, the remaining parameters can be obtained according to the expression of these arguments in the IERS Convention 2010 [26].  
GPS kinematic precise point positioning (KPPP) is an absolute positioning method, which can directly determine the position of a receiver based on satellite constellations [27]. Therefore, an absolute displacement series in vertical direction with a high sampling rate can be obtained by the GPS KPPP solution. OTL displacements can usually be effectively estimated by using a 4-years of GPS observation [25], but simultaneous nodal correction is also needed. The OTL amplitude and phase of the semidiurnal and diurnal tidal constituents can be estimated from the KPPP coordinate series by using the classical harmonic analysis method [28].
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
## 3 Proposed Method
### Quantization  
In digital signal processing, quantization is mapping the co-domain of a signal into another set, the quantized co-domain, with fewer elements. While there are many methods of calculating quantization, this work employs uniform quantization. The distance between two quantization levels, the inter-level distance, is parametrized as a percentage of the sample standard deviation of the signal's constituent samples. As part of the proposed compression algorithm comprises the loadings and transform coefficient matrices resulting from PCA, quantization is applied to both matrices with individual inter-level distances.
## 1 Introduction
### Issues Regarding Vehicle Re-Identification  
Vehicle Re-Id is one of the interesting and demanding problems in the last two years in the computer vision research community. In order to track the vehicles from one frame of the camera to another, the frames of the different or same camera over a surveillance camera network is challenging. The reasons why vehicle tracking is challenging are discussed below.  
* **Inter-class similarity:** This issue arises with similar model vehicles or different manufacturers of vehicles having a visually similar appearance, like two different makes, models, and types of vehicles having a similar rear or front side as shown in Figure 3[11; 12].
* **Intra-class variability:** In intra-class variability, same vehicle looks visually different over the same or different surveillance camera network due to an inconsistent environment [11; 12].
* **Pose and viewpoint variations:** Vehicle pose and view angle varies due to the calibration of cameras on roadside. This is the reason for consistent changes in vehicle appearance and the vehicle looks totally different by changing view angle. In computer vision, sometimes two similar vehicles look more different in different viewpoints as compared to two different vehicles with the same view angle [8; 13], as shown in Figure 4.
* **Partial occlusions**: Some part of a targeted vehicle is covered by any other vehicle in a crowded scene or object, and as a result, the descriptor generated by an occluded image is corrupted [14; 15].
* **Illumination changes:** Vehicle illumination changes camera to camera [12] or even on the same camera in a different time because of the inconsistent environment [16]. Vehicles' head and rear lights in the night have an effect on illumination; as a result, vehicle appearance also changes with a period of time and different camera networks [13; 14; 17].  
Figure 4: Effect of viewpoint variation on same vehicle.  
Figure 3: Inter-class similarity.  
* **Resolutions variation:** Resolution changes in the set of same vehicle images, due to camera calibration and other factors, as many old cameras are installed on roadsides that capture low-resolution images. Due to this factor, it is hard to differentiate between vehicle images with low discriminatory details.
* **Deformation:** Changes in the vehicle body, or shape due to an accident or load.
* **Background clutter:** This issue arises due to background color and vehicle's color being the same in an image.
* **Changes in color response:** Color is one of the important parameters in vehicle Re-Id but a camera to camera color response varies due to camera features and settings [16] and this may affect vehicle appearance.
* **Lighting effects:** Shadows and the specular reflection of a vehicle body adds noise to an image descriptor [11; 18].
* **Long-term Re-Id:** With a large time and space between two views of the same vehicle there is a strong possibility that the vehicle may appear with some changes or be observed carrying some object/load through a different camera.
## 4 Experiment and Analysis
### Results and Analysis  
#### 4.3.1 Parameter Settings  
All experimental models are based on PyTorch framework. The dataset is divided into the training set, the validation set, and the testing set, according to the ratio of 8:1:1. During the model training period, the batch size is 64 and the epoch is 20. The learning rate is set to 1 \(\times\) 10\({}^{-3}\), the momentum is 0.9, and the weight decay parameter is 1 \(\times\) 10\({}^{-4}\).