esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Photocatalytic Tests
Following the first hour of irradiation, there is an increase in the rate of CO\({}_{2}\) generation, which is most likely due to the mineralization of previously produced intermediates. In terms of ethanol mineralization, the La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) catalyst is less active than the platinum-modified catalyst by post-synthesis impregnation. As can be seen in Figure 18, the amount of H\({}_{2}\) generated was 0.48 \(\upmu\)moles for the bare LTA sample and 1 \(\upmu\)mole for the Pt-modified sample after a 3 h reaction. The photocatalytic reaction follows first-order kinetics according to the equation \"In(C/C\({}_{0}\)) = \(-\)kt\", where C and C\({}_{0}\) designate the ethanol concentration at time 0 and time t, and \"k\" is the apparent rate constant. Figure 19 shows the graph ln(C\({}_{0}\)/C) versus the catalyst tested for the photocatalytic degradation of ethanol in the gas phase under simulated solar light irradiation. After three hours of reaction time, the value of the apparent speed k follows the following sequence: LTA (0.0008 min\({}^{-1}\)) > LTA-Pt (0.0016 min\({}^{-1}\)). These results show the increasing rate of reaction after the addition of the noble metal, which is in line with Figure 15 showing the decrease in the PL signal for the LTA-Pt sample (relative to LTA) in the water/ethanol solution, meaning a slower recombination of the photogenerated charges and their use for the catalytic process (a higher photoactivity of the LTA-Pt sample). Figure 19: The reaction rate constant “k” (measured in min\({}^{-1}\)). Figure 18: Evolution of H\({}_{2}\) from oxidative degradation of ethanol on synthesized materials exposed to simulated sunlight.
## 5 Experimental Validation
### Comparative Experiments on a Sudden Load and Sudden Relief Load  
To validate the robustness of the new control method, a comparison experiment involving sudden load and sudden relief load is conducted. Different loads are applied to or removed from different motors at different times while the multi-motor speed control system is running at 300 r/min. The loads are applied or removed by the load motor group depicted in Figure 3. The results of the sudden load and sudden relief load comparison experiment are shown in Figure 6.  
Figure 6: Comparative experimental results of load addition and reduction. (**a**) Comparative curve of speed response. (**b**) Comparative curve of speed tracking error. (**c**) Comparative curve of speed synchronization error.  
When subjected to a sudden load disturbance, there is little difference in response time between the two methods, as both can restore the speed to the set 300 r/min within a short period. From Figure 6a, after the sudden load disturbance, the chattering amplitude of the new control method is around 3 r/min, while the chattering amplitude of the traditional DCC method is around 5 r/min. The recovery time of the traditional DCC method is 0.8 s, which is shorter than the 1.5 s recovery time of the new control method. At the moment of loading, from Figure 6b, the speed increases by around 4% with the new control method, while it increases by around 8% with the traditional DCC method. Additionally, from Figure 6c, when the speed of one motor changes, the speeds of other motors also adjust accordingly to reduce the system's synchronization error. After the sudden load, the synchronization error of the new control method is around 3 r/min, which is better than the synchronization error of approximately 5 r/min in the traditional DCC method. Therefore, the new control method can effectively guarantee the robustness of the system.
## 2 Materials and Methods
### Characterization  
The pore size and surface area distribution of the mesoporous carbon were measured using the Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH) methods. The surface morphologies and element dispersive mapping of the samples were determined using scanning electron micrographs (SEM; Su 70, Hitachi, Japan) and field-emission transmission electron micrographs (FE-TEM; JEM 2100F, JEOL, Japan). X-ray diffraction (XRD) patterns were measured using an Empyrean (Panalytical) instrument. X-ray diffraction (XRD, Panalytical Malvern, 40 kV, 15 mA) patterns were recorded using Cu K\(\alpha\) radiation at \(\lambda\) = 1.5406 A. Thermogravimetric analysis (TGA) data was used to collect the sulfur content of the samples using a Perkin-Elmer TGA 7 run to 600 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\) under a N\({}_{2}\) atmosphere. The valence states of the elements and surface compositions for the samples were evaluated by X-ray photoelectron spectrometry (XPS, Thermo Electron Corporation, K-Alpha+) using an Al K\(\alpha\) X-ray source (hv = 1486.6 eV) at a spot diameter of 400 mm with charge compensation. The emitted photoelectrons were detected using a multichannel detector at a take-off angle of 90\({}^{\circ}\) with respect to the sample surface. During the measurement, the base pressure in the turbo-pumped analysis chamber was maintained at \(1.2\times 10^{-9}\) mbar. The survey spectra were acquired at a pass energy of 200 eV and resolution of 1 eV, while the high-resolution spectra were acquired at a pass energy of 50 eV and resolution of 0.1 eV. All the binding energies were referenced to the adventitious carbon (C 1s) core-level peak at 284.6 eV.
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 4 Methods
### Models
#### 4.3.1 Convolutional Neural Network  
A convolutional neural network (CNN) is one of the neural networks used for analyzing image data. As it can preserve contextual information, currently CNN is also used for extracting features in general. CNNs consist of convolution layers and pooling layers, which respectively learn feature vectors using an activation function and decrease the size of the data, making it relatively representative.
## 1 Introduction
Additionally, many prediction formulas have been developed that incorporate the inelastic response of UHPC [12; 26; 27; 28; 29; 30]. These references have been fundamentally employed in the moment-curvature prediction. It involves the utilization of the tensile and compressive constitutive stress-strain models with experimental investigations, which are problematic for design purposes. For these purposes, the establishment of simplified prediction models for the ultimate moment is therefore of great importance. Significant research efforts have been devoted to structural elements developed by high- and ultra-high-performance reinforced concrete. Such studies are conducted to investigate the sectional stress and strain distributions, the physicochemical characteristics (i.e., tensile strength, shape, aspect ratio, etc. ), and content, dispersion, the bonding strength of fibers, and other factors impacting the tensile behavior of UHPC [30; 31; 32; 33; 34; 35; 36; 37; 38]. However, these investigations have only addressed the use of single-kind fibers, and very little information (e.g., [10]) is obtainable on the use of a hybrid system of fibers in UHPC. In the current research, the primary goal was to study the structural performance of shear-deficient UHPC hybrid fiber-reinforced beams and to develop a reliable prediction model for their ultimate moment strength. Thus, 12 beams with various longitudinal bar arrangements were developed with low-to-high reinforcement percentages (0%, 0.54%, 0.84%, 1.21%, 2.14%, and 3.35%). All beams were prepared using a UHPC mixture containing 2.58% (vol.) of a hybrid system of smooth-coated fibers with various lengths and a unified diameter (0.2 mm), and tested under four-point loading conditions. In this work, the observed structural response (load-deflection and moment-curvature curves, ductility, crack response, and failure patterns) of beams is presented and discussed. In addition, a step-by-step analytical model for the prediction of the UHPC beam's moment capacity is described.
## 3 Results
### Hydrothermal Treatment
#### 3.1.3 FTIR and XRD Analysis of HTB  
FTIR and XRD analyses of HTB prepared under different temperatures were thoroughly investigated, as shown in Figure 3. The FTIR signal assignments are ascribed according to published papers [39; 40; 41; 42; 43; 44; 45; 46; 47], as listed in Table S1. The intensified FTIR signals at 1514 (aromatic skeletal vibrations in lignin), and 1060 and 1030 cm\({}^{-1}\) (characteristic stretching in cellulose) after hydrothermal treatment (Figure 3a) elucidate that the substantial removal of hemicellulose facilitates the exposure of cellulose and lignin, which was demonstrated by lignin droplets in Figure 3b.  
XRD was employed to estimate crystallinity of HTB prepared under different hydrothermal temperatures. The characteristic diffraction peaks at 16.4\({}^{\circ}\), 22.5\({}^{\circ}\), and 34.5\({}^{\circ}\), corresponding to (101), (002), and (004) lattice planes, exhibit a typical cellulose I crystalline structure [48]. _CrI_ is related to the stiffness and strength of fibers; a higher _CrI_ implies a more crystalline and ordered structure [48]. As seen from the inset plots in Figure 3c, _CrI_ was gradually enhanced with the increase in hydrothermal temperature, highlighting that hydrothermal treatment is an efficient way to remove amorphous portions of bamboo, and _CrI_ increased dramatically when the temperature exceeded 190 \({}^{\circ}\)C, which is probably related to the further removal of amorphous lignin, as illustrated in Figure 2d.  
Figure 2: Effects of hydrothermal treatment (**a**) temperature, (**b**) time, and (**c**) solid-to-liquid ratio on xylose concentration in hydrolysate; effects of hydrothermal treatment (**d**) temperature, (**e**) time, and (**f**) solid-to-liquid ratio on the removal of hemicellulose and the retention of cellulose and lignin in HTB; effects of hydrothermal treatment (**g**) temperature, (**h**) time, and (**i**) solid-to-liquid ratio on hydrothermal severity and pH of hydrolysate.
## 2 Characteristics of the Structure of HAAS for Deep, Fully Mechanized Roadways
### Technical Characteristics of the Support  
Technical characteristics of the new type of HAAS for fully mechanized roadways are shown as follows.  
1. Novel structural design The new type of HAAS, which is controlled by the hydraulic system, is characterized by convenient operation and control, low inertia of motion of the whole structure and rapid reaction speed. In addition, the support is able to realize self-advance, perform other various actions and be easily automated. The movement mechanism of the support is arranged in the middle of the column. With such an arrangement, the front support and the rear support push and pull each other, thus avoiding disadvantages caused by the arrangements along the roof and the floor in mechanized roadways. Moreover, as the whole structural frame of the support is relatively large, the roadheader can conduct operations under the complete coverage from the support.
2. High adaptability The columns are hinged with the roof beam. In this way, the columns on both sides, by adjusting themselves to different heights, are applicable to the roadway with an inclined roof, whose maximum dip angle reaches 15\({}^{\circ}\). Since the telescopic columns of the support directly contact the roadway floor, the support is also suitable for roadways with an undulating floor. In addition, the maximum working resistance of the support is 1600 kN, enabling the support to provide greater supporting force and to serve roadway excavation with high roof loads.
3. High level of safety and reliability The new type of HAAS actively supports the roadway roof inside the empty roof area through the extension of the front probe beam. This indicates significant progress in safety compared with traditional passive-support methods, such as temporary supports with the capped one column or with the front probe beam. Moreover, the great support strength and working resistance of the support further enhance its safety and reliability. The designed length of the support ranges from 5500 mm to 7900 mm. In such an environment, with abundant working space and high safety, workers can set anchor bolts (cables) under the protection of the hydraulic support.  
High excavation efficiency  
The new type of HAAS adopts the hydraulic system, which endows it with the ability of rapid movement. This ability facilitates the advance of the working cycle during roadway excavation. Moreover, the long step distance of support movement improves the circulating footage and significantly reduces the time spent in temporary support, thereby remarkably promoting excavation efficiency. In addition, the advance support with a simple structural design can be run by only one person, while the traditional temporary support with front probe beam needs to be operated by at least three to four people. Consequently, the new type of HAAS saves working space and contributes to the improvement of the overall excavation efficiency.
## 4 Results and Discussion  
### The 1 LCM for the Vehicle with No Driver Assistance System, for the Homogeneous Road Surface  
Figure 4 shows the results of the simulation of the lane change maneuver for a vehicle with no driver assistance system on a homogeneous road surface for three surface types (dry, wet, ice) and three vehicle speeds (50, 90, and 140 km/h) in the form of time histories of the input applied, i.e., steering wheel angle \(\alpha_{\rm k}\), and of the response obtained, i.e., lateral displacement of vehicle's centre of mass (\(\rm y_{\rm OC}\)) and time derivative of the displacement (\(\rm y_{\rm OC}\)Prim \(\equiv\rm dy_{\rm OC}/dt\)).  
Figure 4: Lane change (1 LCM) simulation results. Road surface: (**a**) dry concrete, (**b**) wet concrete, (**c**) ice. Notation: \(\alpha_{\rm k}\)—input, i.e., steering wheel angle; \(\rm y_{\rm OC}\)—lateral displacement of vehicle’s centre of mass \(\rm O_{\rm C}\); \(\rm y_{\rm OC}\)Prim—lateral velocity of vehicle’s centre of mass \(\rm O_{\rm C}\).  
Satisfactory results were obtained for all three road surface types and the three vehicle speeds under consideration. Even for ice and 140 km/h, the assumed effect was obtained, and this is an extreme case (it is hard to imagine that there is someone who would drive on ice at such a speed).  
As mentioned before, the assumed lateral displacement of 3.5 m was obtained by iterating the value of steering wheel angle amplitude. The results for the dry and wet concrete pavement were similar to each other, which may indicate a lack of interesting results for the changing of lanes from dry to wet concrete and vice versa. However, they were achieved for different amplitudes of the sinusoidal input applied to the steering wheel.  
The results for the ice-covered surface differed from the other two for each of the vehicle speeds considered, especially for 140 km/h.  
The maximum values of lateral acceleration (the time histories of this quantity have not been provided here) were in the range of 1.47-2.55 m/s\({}^{2}\). This means that they corresponded to their variability ranges in real conditions (0.3-3.2 m/s\({}^{2}\)) given in [39].
## 1 Introduction
[15] employed on-site microseismic monitoring data, acoustic emission, electromagnetic radiation, and other monitoring data to propose a multi-parametric coupling method for identifying hazardous areas of rock burst. [PERSON] et al. [16] analyzed the characteristics of microseismic waveforms and spectra at various energy levels to establish prediction indices and set thresholds for rock burst. [PERSON] et al. [17] proposed a comprehensive warning system that integrates medium- and long-term dynamic warnings of mining-induced seismic activity with short-term spatial and temporal warnings of rock burst deformation energy. [PERSON] et al. [18] assessed the rock burst risk in tunnel boring by examining the deformation damage and energy evolution characteristics of the surrounding rock after boring. [PERSON] et al. [19] developed a quantitative rock burst risk assessment method using the plastic zone morphology coefficient as an index. [PERSON] et al. [20] combined motion thermogram data from laboratory rock burst monitoring with an advanced deep neural network approach employing a regional convolutional network (Mask R-CNN). [PERSON] et al. [21] noted that current empirical methods for rock burst prediction predominantly depend on the geomechanical parameters of rocks. These methods are crucial in understanding and predicting rock burst events. [PERSON] et al. [22] advanced this research by investigating additional factors and techniques that can enhance the accuracy and reliability of rock burst prediction. The above research findings have a guiding role in monitoring and preventing rock burst in coal mining activities. However, the inducing factors of rock burst in coal mines are complex and diverse. Most studies based on the analysis of the mechanism of rock burst occurrence, and energy dissipation characteristics cannot meet the requirements of the quantitative assessment of roadway rock burst risk under the influence of varied mechanical parameters in coal and rock mass. Based on the variation characteristics of coal mass parameters, this paper considers the essence of rock burst phenomena as an extreme manifestation driven by the stress difference gradient within continuous unit bodies in coal. The change rate of the stress difference gradient is employed to characterize its ability to resist stress-driven deformation, thereby quantitatively assessing the rock burst risk during roadway tunneling. The research findings could have theoretical and practical significance for the prevention and control of rock burst in coal roadways.
# Attacks on IoT: Side-Channel Power Acquisition Framework for Intrusion Detection
## 6 Discussions
While some devices can host a machine-learning-based intrusion-detection system, such as the Raspberry Pi, for instance, excess power usage cannot be allowed for most IoT devices. This means these models must be implemented on IoT gateways instead, with the devices communicating their monitored side-channel data to the gateway for inference. At a minimum, simple approaches to detecting these attacks must be deployed. With a clear normal class, simple techniques such as thresholds on the side-channel current draw can effectively detect an intrusion from the excess current draw over normal behaviour. If the normal class tends to infrequently surpass this threshold, simply having another threshold on the number of times the current draw can exceed a certain amount within a period would effectively reduce false positives. Excessive false positives can pose a significant problem. Similar to how an Intrusion Prevention System (IPS) works, a fully automated IDS will halt regular operation whenever an anomaly is detected, leading to too much interruption to normal behaviour. For a semi-automated IDS, with a human in the loop, while the human can discern if it is a false positive, too many false positives will lead to an excessive workload for the human. This will likely lead them to disable the IDS entirely. When intrusions do not exceed the normal behaviour's current draw, comparing a data segment's average current draw with a pre-defined baseline can effectively detect anomalies. While these approaches may seem simple, even this bare minimum to detect attacks is not currently being deployed to protect IoT devices in the field. This approach could save companies USD millions in the case of an attack. Where traditional packet-based detection approaches may fail, detecting a simple spike in the power draw of a critical piece of equipment could prevent catastrophic damage. An essential aspect of this work is its non-intrusive nature. This means that systems can be retrofitted with side-channel monitoring systems without affecting the system's original operation. Another key takeaway of this study is that, for most of those working in the security sector, this will be the first time they see attacks they are familiar with represented in anything other than packer captures. This study investigates a far-too-overlooked key piece of basic information which every device has, its power data. It demonstrates how security teams in the field can use side-channel power data.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 3 Experimental Validation
### Data Analysis
#### 3.3.2 Anomaly Detection
 \tag{11}\]  
where \(d_{2}\), \(D_{3}\), and \(D_{4}\) are used to create an unbiased estimate of the process standard deviation. These standard values depend on the sample size, which, in this case, is set to \(n=2\). For the MR, the values \(d_{2}=1.128\), \(D_{3}=0\) and \(D_{4}=3.267\) were used [43]. The results of applying SPC to the regression model's prediction of the dependence between 3D displacement and temperature over time are shown in Figure 12. Figure 13 shows the \(\varepsilon\)-MR chart for normalized estimation using the SPC method, with a control limit of \(\pm 3\) sigma. To save space within the graph, a common legend that applies to all subplots has been provided at the top of Figure 12. Less outlier behavior was observed in these control plots compared to the hanger-bending deformation plots. In detail, for the \(X\)-axis displacement, no outliers were found at either control limit, two outliers were found for the \(Y\)-axis, and, for the \(Z\)-axis, no outliers were found in the MR chart, while a relatively large number of outliers were detected at the SPC-based control limit set, but not at the \(\pm 3\sigma\) range. In other words, while some outliers are observed in both control charts, most of the data remain within the control limits and appear to be short-term phenomena rather than indications of structural defects. Specifically, these anomalies seem to be deflection events caused by sudden load changes (e.g., passing vehicles) and do not appear to indicate permanent damage or significant risks to the structure. Figure 12: GPR prediction: (**a**) difference predicted vs. observed X-displacement; (**b**) difference predicted vs. observed Y-displacement; (**c**) difference predicted vs. observed Z-displacement.