esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### Malware Classification  
Traditional malware analysis is often divided into static and dynamic analysis. Static analysis only makes use of raw byte features or disassembled instruction sequences without really executing malware. These static features include byte N-Gram distribution or Term Frequency and Inversed Document Frequency (TF-IDF) [17; 18; 19; 20], Application Programming Interface (API) call sequence [21], opcode N-Gram [22], control flow graph(CFG) [2; 19], function length frequency(FLF) [23] and printable string information (PSI) [23; 24] etc. Static analysis features are cheap and fast to obtain, but they can be easily confused by variants of malware using encryption, packing, polymorphism, obfuscation, meta-morphism techniques [2]. Dynamic analysis methods collect behaviors of malware when executing in virtual environment that reflect their run-time intent [1; 2; 25]. Dynamic analysis can be more accurate but more time-consuming and costly than static analysis limiting their large-scale application. Dynamic techniques include function call monitoring, function parameter analysis, information flow tracking, instruction traces, autostart extensibility points and so on [1]. After analysis, common machine learning tools are utilized to build classification model including SVM, decision tree, Naive Bayes, Decision Tree, Boosted Tree, k-Nearest-Neighbor(kNN) etc. discussed in [25].  
Typically, [PERSON] et al. [17] used Common N-Gram Analysis (CNA) extracting distribution of N-Gram substrings of malware as input and kNN algorithm as classification method where only most frequent N-Gram substrings take effect. [PERSON] et al. [20] also extracted N-Gram statistic as input but made some changes when calculating sample-wise distance. [PERSON] et al. [26] chose to extract PE meta-data instead of raw-byte features and similarly used kNN to classify.
# Frequency Response Extension Method of MET Vector Hydrophone Based on Dynamic Feedback Network
## 3 Materials and Methods
### Calculation of Compensation Method for Low-Frequency Cutoff Frequency
\begin{split} W_{\text{sl}}W_{\text{fl}}&=\lim_{s \to 0}W_{\text{s}}W_{\text{f}}\\ &=\lim_{s\to 0}\frac{As}{s^{2}+Cs+B}\cdot\frac{C_{1}R_{2}s}{C_{1}R_{ 1}s+1}\cdot\frac{C_{2}R_{3}s+1}{C_{2}R_{2}s+C_{2}R_{3}s+1}\\ &=\lim_{s\to 0}\biggl{(}\frac{A}{s^{2}+Cs+B}s\biggr{)}\biggl{(} \frac{1}{\frac{R_{1}}{R_{2}}+\frac{1}{C_{1}R_{2}}s}\cdot\frac{1}{\frac{C_{2}R _{2}s}{C_{2}R_{3}+1}+1}\biggr{)}\\ &=\frac{A}{B}C_{1}R_{2}s^{2}\end{split}  \tag{17}\]  
The influence of feedback on the attenuation section is not significant, so the attenuation section of the open-loop response can be approximated as the closed-loop response. Let the attenuation section of the expected closed-loop transfer function be the same as the open-loop one. Thus, the expected closed-loop transfer function \(H_{\text{0l}}\) can be represented by Equation (18). \[H_{\text{0l}}=\frac{A}{B}C_{1}R_{2}s^{2} \tag{18}\]  
The ideal passband is flat, so the transfer function \(H_{\text{0h}}\) of the expected closed-loop passband part can be represented by a constant, as shown in Equation (19). \[H_{\text{0h}}=S \tag{19}\]  
where \(S\) is the gain of the expected closed-loop passband. To unify \(H_{\text{0l}}\) and \(H_{\text{0h}}\) into one transfer function, \(H_{\text{0}}\) is represented by Equation (20). \(H_{\text{0l}}\) is a second-order system, and \(H_{\text{0h}}\) is a zero-order system. Therefore, \(H_{\text{0}}\) is also designed as a second-order system. To optimize the frequency response of the system, \(H_{\text{0}}\) is designed as the optimal second-order system. \[H_{\text{0}}=\frac{Ss^{2}}{s^{2}+\sqrt{\frac{2S}{\phi}}s+\frac{S}{\phi}} \tag{20}\]  
where  
\[\phi=\frac{A}{B}C_{1}R_{2} \tag{21}\]  
Next, further derive the relationship between the feedback parameters and the cutoff frequency. In the optimal second-order system, the relationship between \(S\) and the cutoff angular frequency \(w_{\text{l}}\) can be represented by Equation (22) as follows:  
\[S=\phi w_{\text{l}}^{2} \tag{22}\]  
Equation (23) is used to convert the cutoff angular frequency \(w_{\text{l}}\) to the cutoff frequency \(f_{\text{l}}\):  
\[f_{\text{l}}=\frac{w_{\text{l}}}{2\pi} \tag{23}\]  
The relationship between the low-frequency cutoff frequency and the feedback parameters can be represented by Equations (24) and (25), where \(k_{\text{p0}}\) and \(k_{\text{d0}}\) are the proportional and differential parameters, respectively, and \(W_{\text{fb}}=k_{\text{p0}}+k_{\text{d0}}s\). \[k_{\text{p0}} =\frac{K_{\text{a}}}{A}\Bigg{(}\frac{\sqrt{2}B}{C_{1}R_{2}\pi f_{1}}- \frac{BC_{2}}{C_{1}}-\frac{BR_{1}}{R_{2}}-\frac{C}{C_{1}R_{2}}\Bigg{)} \tag{24}\] \[k_{\text{d0}} =\frac{K_{\text{a}}}{A}\Bigg{(}\frac{B}{4\pi^{2}C_{1}R_{2}f_{1}^{ 2}}+\frac{BC_{2}^{2}R_{3}}{C_{1}}-\frac{1}{C_{1}R_{2}}-\frac{CR_{1}}{R_{2}}-BC_ {2}R_{1}-\frac{CC_{2}}{C_{1}}\Bigg{)} \tag{25}\]  
At this point, the feedback parameters can be directly obtained from the results of the excitation coil self-test.
## 4 Results and Discussion  
Figure 2 presents the Pareto frontier obtained by applying the NNCM method. In the figure, the non-dominated points are shown, only. Points P1 (in red) and P121 (in green) are the so-called anchor points of the frontier, i.e., the best solutions in terms of technical performances (P1) and ergonomics (P121), respectively, while the other points balance the two divergent objective functions. Each point of the frontier corresponds to a specific RMS configuration.  
Focusing on the anchor points, the technical objective function values range from 4253.41 min, by optimizing the technical objective function, to 5563.71 min, by optimizing the ergonomic objective function. On the other hand, the ergonomic objective function values range from 255.89, by optimizing the technical objective function, to 226.92, by optimizing the ergonomic objective function. The smaller range of variation of the ergonomic objective function is due to the OCRA constraint (Equation (27)), which limits the OCRA index to assume values corresponding to a non-risk situation. Globally, the anchor point values and Pareto frontier trend show the existence of a significant trade-off between technical and ergonomic performances.  
Among the non-dominated points, the choice of the final RMS configuration structure is according to any suitable informal approach. In the numerical application, together with P1 and P121, i.e., the technical and ergonomic optima, point P40 (in blue in Figure 2) rises up, representing a break point, because it allows getting good technical performances without a relevant increase of the ergonomic indicator. In detail, this point is selected according to the criteria of the minimum Euclidean distance from the utopia point, i.e., the point with technical and ergonomic optima as coordinates [28]. Compared to the technical anchor point P1, the selected break point involves an improvement of the ergonomic performances of about 7.25%, accepting a worsening of the technical objective function of about 8.50%. On the other hand, compared to the ergonomic anchor point P2, the RMS  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**OCRA Parameters** & & & \\ \hline Auxiliary module type (m) & 1 & 2 & 3 \\ \hline \(FMDM_{mr}\) & 0.35 & 1 & 1 \\ \(PMDM_{mr}\) & 1 & 0.6 & 1 \\ \(RPMD_{mr}\) & 1 & 0.7 & 1 \\ \(AMDM_{mr}\) & 1 & 0.8 & 1 \\ \(NTD_{mr}\) & 15 & 59 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: OCRA parameters for the ergonomic risk assessment (disassembly tasks).  
configuration in P40 involves an improvement of the technical performances of about 16.75%, accepting a worsening of the ergonomic objective function of about 4.60%.
# An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features
## 5 Discussions and Conclusions  
### Discussion  
This paper developed a LRCN model tailored to recognize driver activity. Generally, for driver activity, recognition backgrounds are not necessary and less informative. In particular, a complex background can negatively influence the effectiveness of driver activity recognition. Thus, learning both the spatial and temporal features by implementing a proper LSTM becomes the key way to improve driver activity recognition performance. In this sense, we aimed to emphasize the use of the LSTM model for sequence inputs and introduced a new framework for robust driver activity recognition practice without performing any traditional unwanted major preprocessing measures.  
One of the limitations of developing a deep neural network is the need for large-scale datasets for training and validation. Thus, to handle this problem, we integrated transfer learning and fine tuning on a pre-trained VGG16 architecture for strong initialization of the unified LRCN model. Moreover, to improve the predictive capability of the model, the network hyperparameters were carefully analyzed and optimized. At this moment, the major challenge was that the hyperparameters are interlinked and several parameter combinations have different effects on the overall results of the model. Enhancing the parameters while optimizing the other predefined values may not result in the correct hyperparameter combination. Nevertheless, the final proposed model, which contains the optimal values of the hyperparameters, achieved superior prediction on the test datasets as well as in the real-time evaluation in comparison with the other experimented parameters.  
Although the pre-trained CNN architecture applied in this study was originally trained with only RGB images while utilizing transfer learning and fine tuning with our RGB and IR images based on the designed LRCN model, in almost all cases, the IR image results showed superior performance. An in-depth understanding of the image is mainly because thermal imaging or infrared cameras work by measuring and detecting the infrared radiation emanating from objects. In other words, it only senses the difference in heat within the temperature range inside the vehicle. At this moment, the IR image provides a clear posture of the driver regardless of the cluttered background environment and sunlight. On the other hand, RGB images are easily affected by sunlight and illumination. This creates high noise when training a deep learning model and can lead to reduced performance. In addition, the ultimate aim of developing a driver activity recognition model was to integrate it with a warning system to alert drivers from being distracted, regardless of which distracted activity is performed. As a result, merging all distraction activities as one class and executing binary classification led the classifier model to tremendous state-of-the-art performance.
## 2 Centralized Treatment Plants
### Industrial Wastewater Remediation
#### 2.3.4 Food and Beverage Industry
After RO treatment, the water was of excellent quality, meeting all the limits for drinking water. The MF permeate quality met the requirements for agriculture irrigation in terms of suspended solids but not the 10 CFU/100 mL limit for _E. coli_. It can be used in industrial services, but membrane pore obstruction was the main challenge in operating the ceramic MF membrane [99]. On the other hand, agro-industrial wastewater is in many cases suitable to be remediated by natural-based methods, as in the case of winery wastewater which was used as growth media, under mixotrophic and heterotrophic conditions, for the cyanobacteria _Arthospira maxima_ and the green microalgae _Scenedesmus obliquus_, _Auxenochlorella prototheoides_ and _Chlorella vulgaris_. Maximum removal values within 8 days of incubation were 92, 91, 49 and 40% for COD, TN, polyphenols and P-PO4, respectively, with 147.5 mg L\({}^{-1}\) d of biomass productivity. This biological treatment does not allow for reaching the legal limits for water release, and a UV LED/TiO2 treatment was applied for the removal of COD (40%) and total polyphenols (80%), allowing for release in superficial water bodies (COD < 150 mg L\({}^{-1}\)) [100]. A large-scale project was carried out using an integrated treatment system composed of anaerobic and aerobic units followed by submerged ceramic membrane bioreactor SMBR for reclaiming 200 m\({}^{3}\)/d of raw food wastewater for the purpose of irrigation. The WW was a mixed stream generated from different production lines (including fruit juice, seasoning, etc.) and cleaning wastewater (floors, machinery and pipes). Prior to the biological process, a DAF system was designed to decrease oil and grease content. Thereafter, the WW passed through an up-flow anaerobic sludge blanket (UASB) system and an aerobic tank, both added with zero-valent iron (ZVI) to enhance the organic matter degradation. In the following SMBR, every membrane module contained 400 pieces of polyethyleneimine (PEI)-modified Al\({}_{2}\)O\({}_{3}\)-ceramic membrane with an in situ auto-controlled brush cleaning system as an innovative mechanical cleaning for membrane fouling mitigation. The results of the 240-day operation indicated the technical feasibility of the treatment and the possibility to reuse the final effluent for irrigation: the COD content resulted around 120 mg/L and the BOD5 around 32 mg/L [101].
## 3 Experimental Result
### DC Tree Growth Characteristics of Different Samples  
The DC electrical treeing patterns in both the DC sample and the Cable sample at different time intervals as they grow towards breakdown are shown in Figure 7. From Figure 7a,d, it can be observed that when the voltage first reaches 25 kV, the electrical treeing in both materials is in its initial state, characterized by a small size and a dendritic structure. As shown in Figure 7b,e, with the increase in voltage application time, the length and width of the electrical trees gradually expand. Multiple branches emerge at the needle tip in both samples, with one primary branch growing longer than the other. As the applied voltage continues to be maintained, and the electrical treeing approaches breakdown; the electrical tree patterns inside the samples are depicted in Figure 7c,f. In this stage, the treeing extends toward the grounded electrode. Notably, in the Cable sample, the treeing primarily exhibits growth in the length direction, whereas in the DC sample, significant growth is also observed in the width direction. The width of the electrical tree in the DC sample is noticeably larger than that in the Cable sample.  
The variations in the length and width of electrical trees during the growth process in different XLEE samples are shown in Figure 8. It can be observed that, in the initial stage of constant voltage, the length and width of the electrical tree in the DC sample slowly increase from 0.25 mm to 0.75 mm within 3500 s. Subsequently, the size of the electrical tree rapidly expands. From 3500 to 4631 s, the length of the tree quickly grows from 0.75 mm to 2.5 mm, with even faster growth in the width direction, reaching 3.5 mm, followed by breakdown. In contrast, the Cable sample shows almost no growth in the early stage of constant voltage. However, starting from 2500 s, its electrical tree rapidly grows in both length and width, eventually leading to breakdown at 3143 s. It is noteworthy that the absence of data for the Cable sample's length growth from 1.75 mm to the 3 mm gap between the needle and plate is due to its direct breakdown following multiple discharges at 1.75 mm.  
From the above analysis, it can be concluded that although the DC sample exhibits a longer withstand time compared to the Cable sample, both materials display a similar evolutionary process in the lengthwise direction during the electrical treeing under a direct current field. Under the influence of the DC electric field, the electrical trees exhibit a dendritic structure and grow slowly at first, with the growth rate gradually accelerating after a certain period of applied voltage, eventually leading to breakdown. However, the growth in the width direction differs slightly between the two materials. In the latter stage of growth, the DC-XLEE sample shows a tendency for lateral expansion, while the Cable-XLEE sample does not.  
Figure 8: Tree sizes of different XLPE samples during DC tree aging.  
Figure 7: The tree image of different XLPE at different times.
# A Review on SIW and Its Applications to Microwave Components
## 13 SIW Sensors  
Sensors and wireless identification are modern technologies with a wide range of applications. Some popular utilisations include indoor and outdoor tracking, sensing, operation of tags attached objects, human bodies, etc. Sensing and wireless identification  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline Ref. & \(f_{\emptyset}\) (GHz) & Filtering Property & No. of Poles & No. of Bands & Fractional Bandwidth (\%) & Gain (dBi) & Polarisation in Both E- (dB) & Dimension (\(\lambda_{\omega}\times\lambda_{\omega}\)) \\ \hline
[84] & 9.6 & Yes & 2 & 1 & 2.53 & 5.8 & \textgreater{}20 & - \\ \hline
[85] & 5.365 & Yes & 4 & 1 & 7.64 & 5.3 & \textgreater{}20 & \(0.9\times 1.1\) \\ \hline
[86] & 11.8 & Yes & 3 & 1 & 11.84 & 5.0 & - & \(0.48\times 0.47\) \\ \hline
[87] & 3.59/4.11 & Yes & 4 & 2 & 2.3 & 4.84 & \textgreater{}16 & \(1.3\times 1.1\) \\ \hline
[92] & 27.5 & Yes & 3 & 1 & 25.8 & 8.9 & \textgreater{}29 & \(3.94\times 3.26\) \\ \hline
[93] & 25.5 & Yes & 2 & 1 & 15.6 & 10.05 & - & \(2.17\times 0.8\) \\ \hline
[94] & 7.25 & No & - & - & 1.52 & 3.16 & \textgreater{}12.5 & \(0.265\times 0.318\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of published works on SIW antennas.  
Figure 11: Image, simulation, and measurement responses of a filtering antenna [84].  
of people and physical objects has enabled them to become smartly connected. This has led to scientific breakthroughs in various fields of human endeavours including healthcare, health monitoring, disaster monitoring, logistics, social networking, smart environments, security services, etc.  
Substrate-integrated waveguides have been widely employed in achieving humidity sensors [95] and rotation sensors [96] due to their improved performance and accuracy. Microstrip-based sensors [97] normally exhibit poor quality factors and moderate sensitivity. This explains why their use is limited to testing of only medium to high-loss dielectric materials as explained in [98; 99; 100; 101]. The SIW sensor reported in [98] was excited using an external coupling topology incorporating a transition offset. The sensor exhibits yielding at high sensitivity of 20 MHz which is equivalent to 0.67% in terms of normalised sensitivity [98]. An SIW-based sensor with negative order resonance is reported in [102]. The sensor is reported to be \"compact dielectric-permittivity of liquid samples\". The sensor was implemented using SIW and achieved a compact footprint of (0.25 \(\times\) 0.42)\(\lambda_{\mathrm{o}}\), where \(\lambda_{\mathrm{o}}\) is the wavelength in free-space at the sensor's operating frequency. The image and experimental resonant curves of one of the designed sensors (that is, sensor A) for various samples is shown in Figure 12. A performance comparison of published works on SIW sensors is given in Table
## 4 Experimental Results
### Malware Families
#### 4.2.2 Shellcode  
It refers to a small piece of code executed by a target program following the exploitation of a vulnerability, enabling control over the target system. Attackers often embed malicious shellcodes within macros to execute unauthorized actions when the document is opened.
## 4 Application
### MS Location Examples
The upper left corner shows the misfit contour map of cross-correlation travel time L2 difference between synthetic and observed waveforms. The green and black stars separately indicate the MS-GSWI location and premeasured source location. (**c,d**) Schematic diagrams of typical sensitivity kernels from the MS-GSWI location to two far away sensors. (**e**) Fitting performance of SEM modeled waveforms excited from the MS-GSWI location (red curves) and premeasured source location (grey curves) to the observed waveforms (black curves), and red and grey numbers correspond to their cross-correlation travel time difference to observed waveforms. The cross-correlation coefficients between the DD-SVD-FD wavelet modeled waveforms and observed waveforms were displayed as black numbers. travel time misfit of the modeled waveforms excited from MS-GSWI source location is smaller than that of the premeasured source location, which may be caused by currently inaccurate velocity models. According to misfit distribution of the L2 norm difference of cross-correlation travel time (inserted map in the upper left corner of Figures 6b and 7b), the location determined by the MS-GSWI location is very close to the premeasured source location, and the location errors of blasting events 1 and 3 are 25.0 m and 14.0 m, respectively. The direction-dependent gradient and curvature characteristics of misfit function near MS-GSWI location point could indicate whether the location result along corresponding direction is well constrained.
# [PERSON] Glacier Status Analysis Based on Historical Data and Moving Detection Using RPAS
## 2 Project Aims
### Detection of Long-Term Changes in the Extent of the Glacier  
Another goal was to detect changes in the extent of the glacier based on the analysis of historical image data. The on-site measurement in 2019 was compared with historical image data. There are freely available data from satellites on the web, accessed via Google Earth, new free satellite data from the Copernicus system, and we also managed to get historical photographic aerial data from the Danish archive. The data can be processed in geographic information system (GIS), and the glacier extent changes over time can be modelled.
## 4 Experimental Results and Analyses  
### Evaluation Methods  
We use precision, recall, and the \(F_{\beta}\) score to evaluate our method's performance for shot boundary detection. Recall is the ratio of correctly identified shot boundaries to the number of ground-truth shot boundaries. Precision is the ratio of correctly identified shot boundaries to the total detected shot boundaries. The \(F_{\beta}\) score is used to balance the precision and recall and a higher \(\beta\) value will give more importance to high precision values. In the paper, the F1-score is used. The metrics are defined as follows:  
\[\text{Recall}=(\text{Correctly\_identified})/(\text{Correctly\_identified}+ \text{Missed\_identified}) \tag{1}\]  
\[\text{Precision}=(\text{Correctly\_identified})/(\text{Total\_detected}) \tag{2}\]  
\[\text{F}_{\beta}=((\beta^{2}+1)*\text{Precision}*\text{Recall})/(\beta^{2}* \text{Precision}+\text{Recall}) \tag{3}\]  
Figure 7: The results of the matching of image pairs under different feature descriptors. (**a**) The transforms from left to right: Horizontal flip; crop plus bright; Projection; (**b**) The transforms from left to right: picture add post; noise plus contrast; rotation plus crop plus noise.