esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid-Based Analysis Impact on Ransomware Detection for Android Systems
## 4 Experiment Preparation and Implementation
### Ransomware Dataset Creation  
In this work, the Android ransomware dataset named \"CICAndMal2017\" was used [53]. It includes 10 ransomware families, where each family contains 9-11 samples of ransomware apps. In total, 100 samples were categorized based on the family as shown in Table 3.  
Before conducting the static and the dynamic experiments, all 100 ransomware samples were run using the MobSF tool to ensure their compatibility with the proposed platforms. However, 22% of Ransomware apps succeeded in running by MobSF. Therefore, all the confirmed samples were selected as a focus group for in-depth analysis and to be used as a comparison benchmark between the chosen platforms. On the other hand, the remaining samples, representing 78%, were successfully run and analyzed statically by MobSF. Consequently, they were only included in the static analysis comparison between VirusTotal and MobSF Static Analyzer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Ransomware Family** & **Sample Size** \\ \hline
1 & LockerPin & 10 \\ \hline
2 & Wannalocker & 11 \\ \hline
3 & Sypeng & 9 \\ \hline
4 & Simplocker & 10 \\ \hline
5 & RansomBO & 10 \\ \hline
6 & PornDroid & 10 \\ \hline
7 & Pletor & 10 \\ \hline
8 & Koler & 10 \\ \hline
9 & Jisut & 10 \\ \hline
10 & Charger & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of ransomware families used in the experiments.
## 5 Challenges and Future Directions
### Energy Use and Payload  
Efforts have been made to improve drone energy efficiency and extend flight times. Airframes composed of lightweight carbon fiber reduce power needs for lift [205]. Brushless motors also offer superior power-to-weight ratios at present to minimize energy draw. However, rotary drones cannot simply add more batteries to increase the endurance due to strict weight limits. The research shows an optimal mass exists, above which any added weight reduces the stability and flight times for rotorcrafts [206]. While incremental gains in efficiency continue being made, the fundamental constraints on rotary drone endurance remain difficult to overcome given the current battery technologies. Long-range missions may require fixed-wing platforms, advanced hybrid designs, or foundational innovations in energy storage.  
The limited payload capacity of lightweight UAVs also poses challenges. Their inability to carry heavier onboard equipment, such as cameras, multiple sensors, and scanning devices, restricts functionality. UAVs often need to be equipped with heavy sensor systems, including laser scanners, ultrasonic, RADAR, and LADAR. Additionally, cameras used on drones can be costly, yet have a low resolution. For instance, most thermal cameras range from 640x480 pixels in resolution and USD 2000-5000 in price. Thermal aerial imaging also faces factors impairing its quality, including the emitted/reflected radiation, distance, and atmospheric moisture. The compact size of UAVs constrains their ability to support multiple payloads, such as high-resolution cameras, scanning sensors, and other heavy gear. This limits the sensing and imaging capabilities, while onboard cameras remain expensive but have a low resolution. Thermal imaging also contends with environmental factors impacting the image quality. Enhancing the UAV payload capacity, sensors, and imaging continue to pose technology and cost challenges.
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 4 Experiments for LoRa PHY
### Path Loss Model  
The measurement data are then used for estimating a path loss model for analysis. Previous literature has studied various mathematical models for path loss estimation [26]. It is noted, however, that due to the complexity of signal propagation, it is impossible to derive a single model that accurately captures all path loss properties in different communication environments. Even developing a model that fits well to a specific environment can be achieved through a complex analytical model, such as the Indoor Dominant Path (IDP) model [27], or empirical measurement, which again fails to explain other communication environments. Therefore, it is often better to use a simple model that captures the core of signal propagation for a general analysis of various communication systems rather than develop a path loss model that approximates real-world channel status. Our analysis also follows this approach since the combined environment cannot be represented by a single model. To this end, this paper presents a simplified path loss model as a function of distance [28] as follows:  
Figure 6: The LoRa gateway used in experiments is installed on the roof of a five-story building about 15 m high. The gateway is placed on top of a reinforced concrete column exposed to the roof of the building, and the roof is surrounded by a parapet and a guardrail.  
\[P_{r}=P_{t}K\left[\frac{d_{0}}{d}\right]^{\gamma} \tag{2}\]  
The dB attenuation is rewritten as  
\[P_{r}\left[\mathrm{dBm}\right]=P_{t}\left[\mathrm{dBm}\right]+K-10\gamma log_{10 }\left[\frac{d}{d_{0}}\right], \tag{3}\]  
where \(P_{r}\) and \(P_{t}\) represent received and transmitted powers, respectively. \(K\) is a constant with no unit that depends on an antenna characteristic and average channel attenuation. \(d_{0}\) is a reference distance to the far-field of an antenna, and \(\gamma\) is a path loss exponent describing how rapidly a signal's power decreases with distance.  
Because of the complexity of the antenna near-field, Equation (3) is generally valid at a transmission distance \(d>d_{0}\), where \(d_{0}\) is typically assumed to be 1-10 m indoors and 10-100 m outdoors. The value of \(K\) is set to the free space path gain at distance \(d_{0}\), assuming the case of an omnidirectional antenna to approximate empirical measurements. The value of \(\gamma\) can be obtained in a way that minimizes the error with respect to empirical measurements. The experiments described in this section aim to (1) find \(\gamma\) so that it represents the combined environment and (2) then estimate the communication ranges.
## 3 Results and Discussion
### Compression Test  
Table 1 shows the room-temperature compression test results. The variation of 0.2% compressive yield strength (0.2% CYS), the ultimate compressive strength (UCS), and failure strain are shown in Figure 7. Increasing the vol% of Si\({}_{3}\)N\({}_{4}\) enhanced 0.2% CYS and UCS of magnesium. The 0.2% CYS of Mg increased from 63 MPa to 78 MPa, 101 MPa, and 118 MPa with the addition of 0.5, 1, and 1.5 vol% Si\({}_{3}\)N\({}_{4}\) nanoparticles, respectively. Similarly, the UCS increased with the increasing addition of Si\({}_{3}\)N\({}_{4}\) nanoparticles with 1.5 vol% Si\({}_{3}\)N\({}_{4}\) nanocomposites and a maximum UCS of 323 MPa, which is 41% higher than the pure Mg. The failure strain (FS) of the nanocomposites increased up to the inclusion of 1 vol% Si\({}_{3}\)N\({}_{4}\) nanoparticles, with a maximum FS of 21.2%, which is 30% greater than pure Mg. However, with the addition of 1.5 vol % Si\({}_{3}\)N\({}_{4}\) nanoparticles, the FS value was reduced to 18.2%. Nevertheless, all of the nanocomposites had a higher failure strain than pure Mg.  
Figure 5: Ignition temperatures of widely available magnesium alloys [29], in comparison to the current study.  
The significant improvement in the compressive yield strength and ultimate strength of the Mg/Si\({}_{3}\)N\({}_{4}\) nanocomposites may be due to the effective strengthening mechanisms, such as (i) the load bearing effect (efficient load transfer from soft matrix to hard Si\({}_{3}\)N\({}_{4}\) nanoparticles due to better interfacial integrity), (ii) Orowan strengthening (caused from  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **0.2\% CYS (MPa)** & **UCS (MPa)** & **Failure Strain (\%)** \\ \hline Pure Mg & \(63\pm 5\) & \(229\pm 7\) & \(16.3\pm 0.5\) \\ Mg-0.5 Si\({}_{3}\)N\({}_{4}\) & \(78\pm 4\) & \(252\pm 9\) & \(18.7\pm 0.8\) \\ Mg-1.0 Si\({}_{3}\)N\({}_{4}\) & \(101\pm 6\) & \(296\pm 11\) & \(21.2\pm 0.6\) \\ Mg-1.5 Si\({}_{3}\)N\({}_{4}\) & \(118\pm 8\) & \(323\pm 9\) & \(18.2\pm 0.8\) \\ \hline Mg-0.4 AlN [32] & \(72\pm 5\) & \(314\pm 20\) & \(17.5\pm 0.6\) \\ Mg-1.2 BN [26] & \(109\pm 4\) & \(307\pm 6\) & \(17.6\pm 2\) \\ Mg-2.5 TiO\({}_{2}\)[33] & \(101\pm 9\) & \(305.5\pm 11\) & \(22\pm 2\) \\ Mg-2 SiO\({}_{2}\)[34] & \(89\pm 1\) & \(207\pm 3\) & \(21.87\pm 2.1\) \\ Mg-1.5 CeO\({}_{2}\)[22] & \(111\pm 2\) & \(329\pm 7\) & \(19.7\pm 0.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compression test results of the samples.  
Figure 6: Thermal conductivity of pure Mg and Mg/Si\({}_{3}\)N\({}_{4}\) nanocomposites as a function of ignition temperature.  
Figure 7: The effect of Si\({}_{3}\)N\({}_{4}\) nanoparticles on compression properties.  
the presence of Si\({}_{3}\)N\({}_{4}\) nanoparticles), and (iii) dislocation strengthening (resulting from the increased dislocation density in the matrix and reinforcement interface due to the difference in the CTE and elastic modulus between the Mg matrix and Si\({}_{3}\)N\({}_{4}\) nanoparticles).
# Water Supply Systems for Settlements with Arsenic-Contaminated Groundwater--Making the Right Choice
## 3 Results and Discussion
### Drinking Water Treatment Options Based on Source Water Quality
#### 3.2.2 Treatment Options for the Municipality of Senta Using the Current Source  
The town of Senta is, by far, the largest in the municipality, with almost 19,000 inhabitants (approximately 80% of the total population in the municipality). Accordingly, Senta is  
Figure 5: Processes required for adequate water quality in Kanjiza, where the source contains CH\({}_{4}\), CO\({}_{2}\), NH\({}_{3}\), Fe, Mn, organic matter \(<\) 20 mg KMnO\({}_{4}\)/L, and As \(<\) 50 mg/L (Scheme D, Table 1).  
the largest water consumer, with 82% of the total consumption. However, the groundwater currently used in Senta is of poor quality and requires the even more complex water treatment Scheme F (Table 1, Figure 6) because of the high arsenic content and elevated NOM content. The possible treatment scheme includes (i) oxidation of As(III) into As(V) and partial removal of arsenic during deferrisation; (ii) addition of FeCl\({}_{3}\) coagulant, whereby most of the arsenic is removed by coprecipitation and adsorption at the formed FeOH\({}_{3}\) floccules; and (iii) removal of the remaining arsenic by the adsorbent.
# Image Magnification Based on Bicubic Approximation with Edge as Constraint
## 5 Experimental Results
### Visual Effect  
Then, we compare the visual effects of the magnified images. Experiment results show that most of the magnified images by the 8 methods have little difference in visual effects, but there are slight differences in the partially magnified images. The comparison results of visual effect of the 8 methods are given in Figures 4-8, where (a) is the HR image and (b), (c), (d), (e), (f), (g), (h), and (i) are magnified images by bicubic, DCCI, ICBI, SelfxSR, NARM, SISRRFI, LPRGSS, and the new method, respectively.  
In order to facilitate the comparison of visual effects, we have marked some visually distorted parts on the magnified image, as shown in the red box in Figure 4-8. Figures 4 and 5 show the results of the 8 methods for magnifying the two typical grayscale images. In Figure 4, it can be seen that the images (f) and (i) obtained by NARM and new method, respectively, are closest to the HR image (a); the images (c) and (d) obtained by DCCI and ICBI methods are relatively smooth at the edges and producing obvious distortion of lung tissue information; and the images (b), (g), and (h) obtained by the BiCubic, SISRRFI, and LPRGSS, respectively, have sawtooth problems at the edge. In Figure 5, the images (c), (d), and (f) obtained by DCCI, ICBI, and NARM methods, respectively, are relatively smooth at the edges, resulting in reduced detail information. The images (b), (e), (g), and (h) obtained by the bicubic, SelfxSR, SISRRFI, and LPRGSS methods, respectively, have different degrees of sawtooth problems at the edges and the edges are very rough. Overall, the image by the new method (i) is closest to the HR image (a).  
show that, for the aliasing images in AIS, the magnified images by the 8 methods have very small visual differences. In Figure 8, relatively speaking, the images (h) and (i) obtained by LPRGSS and the new method are closest to the HR image (a); the images (b), (e), (f), and (g) by bicubic, SelfxSR, NARM, and SISRRFI, respectively, are the second; and the images (c) and (d) by DCCI and ICBI are third. Obviously, in Figure 8, all eight images have the problem of losing details and distortion. Table 2, Figure 8 shows that, for the images in AIS, these 8 methods generally can not get good results; one of the main reasons is that the images obtained by downsampling may destroy the details of the original aliasing image. For example, Figure 9 is the downsampling of the aliasing image (a) in Figure 8, and these two images are obviously different.  
Figure 4: Chest images magnified by eight methods.  
Figure 5: Leena images magnified by eight methods.  
Figure 6: Branch images magnified by eight methods.  
Figure 7: Elderly images magnified by eight methods.  
Figure 8: Aliasing images magnified by eight methods.  
Figure 9: Downsampling of the aliasing image (a) in Figure 8.
## 4 Numerical Simulations
### Case 2: \(\delta=1.0\) rad  
From the analysis of Case 1, it is clear that, when the steering angle is 0.5 rad, the vehicle exhibits high lateral stability, with the SEC strategy prioritizing energy savings. As the steering angle increases from \(\delta=0.5\) rad to \(\delta=1.0\) rad, the evaluation index \(\rho\) ranges from 0.25 to 0.75, and the value of Type increases to 1, as shown in Figure 12d, which demonstrates the system's adaptive response to the higher steering angle. In this case, the energy-saving strategy remains the primary focus.  
Figure 11: Vehicle energy consumption at different SOC levels for a fixed \(\delta=0.5\) rad.  
Figure 12a shows that the adaptive coefficient \(z\) for the SEC-0.2 is larger than that of the SEC-0.8. Figure 12b reveals that the SEC-0.8 achieves a higher \(\rho\) value and faster dynamic response compared to the SEC-0.2. In Figure 12d, the absolute value of \(M_{z}^{\prime}\) for the SEC lies between those of the EC and SC, and the SEC-0.2 exhibits a lower value of \(M_{z}^{\prime}\) than the SEC-0.8. These observations highlight that the SEC-0.8 strategy maintains better stability under these conditions.  
Figure 13a,b compare the maximum yaw rates and side slip angles for different control strategies. The maximum yaw rates \(\gamma\) for the EC, SEC-0.2, SEC-0.8, and SC are 0.3218 rad/s, 0.2638 rad/s, 0.2576 rad/s, and 0.2358 rad/s, respectively. Similarly, the side slip angle \(\beta\) gradually decreases from 0.0236 rad (EC) to 0.0085 rad (SC). Figure 13c demonstrates that the stability of the EC, SEC-0.2, SEC-0.8, and SC improves sequentially, as shown in Figure 13d. Regarding stability, the boundary of the yaw rate is constrained by \(|\gamma|\leq\mu g/v_{x}\), which can reach a maximum value of 0.4165 rad/s, while the boundary of the side slip angle is approximately 0.1 rad. Since the yaw rate \(\gamma\) becomes closer to the boundary in the control process, it is considered a more critical factor influencing stability.  
In Figures 13 and 14, the lateral stability and energy consumption of the EC, SEC-0.8, and SC are compared. The SEC-0.8 can increase vehicle stability by 19.95% over the EC at the cost of a 21.79% increase in energy consumption, with these values computed as \(\max(\gamma_{\text{SEC-0.8}}-\gamma_{\text{EC}})/\gamma_{\text{EC}}\) and \(\max(E_{\text{SEC-0.8}}-E_{\text{EC}})/E_{\text{EC}}\), respectively. Compared to the SC, the SEC-0.8 reduces vehicle stability by 9.25% but achieves 30.51% energy savings. Under identical conditions, the SEC-0.2 reduces stability by 2.41% while improving energy savings by 4.34% compared to the SEC-0.8. Notably, the SEC with an SOC of 0.1 achieves approximately 8.44% greater energy savings than the SEC with an SOC of 1.0.  
Figure 12: Responses of stability and energy-saving controller under \(\delta=1.0\) rad: (**a**) adaptive coefficient \(z\), (**b**) evaluation index \(\rho\), (**c**) control method in the SEC, and (**d**) external yaw moment \(M_{z}^{\prime}\).
## 3 Data Collection and Labeling
### Data Labeling  
In this study, six researchers specializing in the field of SAL from Northeastern University participated in the labeling process. These experts included two associate professors, two doctoral candidates, and two master's degree learners. Each participant was tasked with evaluating the SAL abilities of learners based on the dataset we collected. This evaluation involved reviewing the collected data in conjunction with the scores of corresponding assignments within the course curriculum. The average scores from five distinct assignments were ultimately employed as the annotated indicators for gauging the SAL abilities of the learners.  
To facilitate the labeling of SAL task planning abilities, it is first necessary for each participant to analyze the learning process of learners in order to understand how these learners decompose their learning tasks. To ensure the validity of manual annotations, we required participants to answer specific questions given different types of interaction behaviors. This ensures a comprehensive understanding of the learner's learning process during analysis, as illustrated in Table 1. Additionally, participants can also refer to the learner's phased learning outcomes and final grades for labeling.  
The statistical outcomes of the capability labeling for task planning in the SAL context is illustrated in Figure 2. For the purpose of this research, the manually annotated SAL task planning abilities are classified into five distinct levels, ranging from 1 to 5. A score of 1 represents the lowest level of capability, while a score of 5 signifies the highest level of proficiency in SAL task planning.  
Figure 1: The distribution of subtasks in the learning assignments.
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
## 5 Future Recommendations
### Automated Calibration and Intelligent Operational Prognosis (ACIOP)  
Among the temperature sensor technologies, the OBS technique and FBG approach have the notable benefit of exhibiting linear sensitivity. However, they require calibration, because the surrounding ambient temperature and sensitivity of the FBG sensors slightly differ from each other. Although auto-calibration for the IRC technique is now available on the market, this feature has not been exploited or implemented for FBG and TSOP approaches. Machine learning could be incorporated to fill this gap, using the current ambient temperature and pre-trained data to compute FBG sensitivity to automate the calibration process. This could be a promising way to eliminate manual calibration. ACIOP, in this context, could use a deep learning algorithm to calibrate and predict each unit's junction temperature based on designated features such as the magnitude of the load across the power module, frequency of operation, usage, and components' aging. The relationship established by the deep-learning model from the highlighted features could further be used to evaluate component reliability, life cycle, and usage under various operation states. To integrate these features into FBG techniques in the future, adequate data acquisition and data-driven models would be necessary for the model to provide an acceptable prediction under various conditions.  
Figure 12: Schematic of distributed temperature sensing using FBG-based technique.
## 4 Experimental Result and Analysis
### Acceleration Response  
The acceleration response of the model is represented by the peak ratio of the acceleration of each floor to the acceleration of the table (acceleration amplification coefficient Ka). According to the data collected by each acceleration sensor, the change in the acceleration amplification coefficient Ka of the model under an earthquake is shown in Figure 10. Under the action of different seismic waves at the same level, the acceleration amplification coefficients of structures were different due to the differences in the spectral characteristics of seismic waves from different regions. However, the changing trend was basically consistent. The existence of steel truss structures caused sudden changes in the lateral stiffness of the structure along the height, resulting in a significant whip effect on the top structure and significant amplification of the top acceleration. Researchers found no significant changes in the X and Y directions under the action of three seismic waves by comparing the acceleration amplification coefficients of the model before and after the seismic wave delay and amplification. Furthermore, the maximum difference in the acceleration amplification coefficients of each floor was 5.48% (X direction) and 6.85% (Y direction), indicating that the seismic wave delay and amplification had no impact on the acceleration response of the structure.  
Figure 10: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Coalinga Wave**} & \multicolumn{2}{c}{**Morgan Hill Wave**} & \multicolumn{2}{c}{**Artificial Wave**} \\ \cline{3-7}  & **Sensor Location** & **X** & **Y** & **X** & **Y** & **X** & **Y** \\ \hline Moment of maximum peak & Top of the shaking table & 5.83 & 5.60 & 5.68 & 6.27 & 7.28 & 5.75 \\ acceleration of seismic waves (s) & Top of the simulated mountain & 6.27 & 5.71 & 6.03 & 6.36 & 7.61 & 5.81 \\ \hline Time Delay(s) & 0.44 & 0.11 & 0.35 & 0.09 & 0.33 & 0.06 \\ \hline Maximum peak acceleration of & Top of the shaking table & 2.19 & 1.87 & 2.13 & 1.90 & 2.06 & 1.85 \\ seismic waves (m/s\({}^{2}\)) & Top of the simulated mountain & 3.74 & 3.29 & 3.91 & 2.93 & 3.19 & 2.99 \\ \hline Acceleration amplification & 1.71 & 1.76 & 1.84 & 1.54 & 1.55 & 1.62 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model seismic wave delay and amplification.
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 3 Overview of the Discrete Analog Functional Blocks of ArduSiPM
### Peak Hold  
As previously mentioned, the typical SiPM pulse, amplified at the LNA stage, is extremely fast, with a rise time on the order of nanoseconds and a total duration of 20-40 ns, varying by SiPM model. Due to this rapid nature, using the microcontroller's built-in ADCs to capture the SiPM signal proved challenging; hence, we adopted an unconventional circuit solution for the sensor readout. The SiPM signal is about 100 times faster than the ADCs in cutting-edge microcontrollers. Since capturing the signal with multiple samples was impractical, we implemented a peak-hold circuit equipped with a programmable reset. This circuit extends the duration of the signal to the microsecond level, enabling capture by a 12-bit ADC at 2 Msamples/s. The programmable reset feature allows for fine-tuning the signal in accordance with the specific characteristics of the scintillator and the SiPM, ensuring correct bias restoration of the levels. While this solution increases the dead time on the order of microseconds--compared with the tens of nanoseconds typical of fast SiPM responses--it allows the circuit to handle event rates of up to hundreds of kHz without significant issues (refer to Figure 6).