esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Limitations and Future Work
### Future Work  
1. Extend the method to 3D spatial and dynamic problems.
2. Incorporate anisotropic strength criteria for structural planes.
3. Conduct comprehensive mesh-dependency and large-deformation analyses.
4. Validate against field monitoring data for practical engineering applications.  
**Author Contributions:** Conceptualization, T.S.; methodology, T.S.; software, Y.P.; validation, B.H., J.W. and Y.P.; formal analysis, Y.P.; investigation, T.S.; resources, J.X.; data curation, J.W.; writing--original draft preparation, T.S.; writing--review and editing, J.X. and B.H.; visualization, Y.P.; supervision, J.X.; project administration, J.X.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Key Research and Development Program of China grant number [2022 YFC3005403]. And The APC was funded by Hohai University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Restrictions apply to the datasets: The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to [EMAIL_ADDRESS].  
**Conflicts of Interest:** Authors [PERSON], [PERSON] and [PERSON] were employed by the company Powerchina Huadong Engineering Corporation Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Case Study
### The FLEDS  
In this section, we will show how the FLEDS works and what components it consists of. There are two major functions in the FLEDS: FC1: Low fuel level warning and FC2: Fuel level estimation and display. FC1 is responsible for warning a driver that the fuel level is below a measurable limit of the tank capacity, even if the driver does not often check the fuel gauge. FC2 is to measure and present the fuel level in the instrument cluster to meet the need of the driver to know the current fuel level in the fuel tank. The estimated fuel level and the warning are both presented in the instrument cluster.  
Three electronic control unit (ECU) systems make up the FLEDS, which are engine management system, instrument cluster, and coordinator. Each ECU system contains several allocation elements (AEs) which are pieces of code responsible for realizing user functions. There are two AEs allocated in the coordinator ECU system (COO) used to estimate and display the fuel level: AE01 and AE02. Other parts of the FLEDS include a fuel level sensor (FLS) placed in the tank to estimate and check the fuel level, a parking brake switch (PBS) to provide parking brake status which is required for the purposes of refuel detection, and a battery to supply power to ECU systems. We show the components and functional structure of the FLEDS in Figure 5.  
* Engine management system ECU (EMS) is responsible for all the engine related functions.
* The function of instrument cluster ECU system (ICL) is to display indications to the driver, such as warning lamps.
* Coordinator ECU system (COO) is the core ECU of FLEDS and responsible for all functions related to the fuel level calculation. Three inputs are required by COO. They are:
* Fuel level signal from fuel level sensor (FLS)
* Fuel rate signal from EMS
* Parking brake status from parking brake switch (PBS)  
Figure 5: Functional structure composed of electronic control unit (ECU) systems of fuel level estimation and display system (FLEDS).  
AE01 is the allocation element responsible for the fuel level estimation. The fuel level measured by the FLS is first converted to a voltage value and then converted to a percentage of the total capacity. The result and the fuel consumption rate calculated by EMS are used as the input of Kalman filter algorithm, and the current fuel level is finally obtained. This result will be sent to ICL to display the current fuel level. Then AE02 compares the calculation results from AE01 with the tank capacity to determine whether to activate a low fuel level warning.
## 4 Results and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**1 h-Ahead nRMSE (\%)**} & \multicolumn{2}{c}{**24 h-Ahead nRMSE (\%)**} \\ \cline{3-6}
**Year** & **Month** & **DNN** & **GEP** & **DNN** & **GEP** \\ \hline \multirow{6}{*}{2020} & March & 6.77 & 7.10 & 11.5 & 15.7 \\  & April & 4.99 & 4.22 & 10.6 & 10.8 \\  & May & 5.88 & 5.83 & 16.2 & 14.8 \\  & June & 5.40 & 5.74 & 10.5 & 13.2 \\  & July & 4.82 & 5.40 & 7.86 & 11.5 \\  & August & 3.87 & 3.03 & 7.49 & 4.80 \\  & September & 4.93 & 5.38 & 8.72 & 12.9 \\  & October & 4.46 & 4.76 & 11.0 & 12.5 \\  & November & 6.33 & 6.50 & 20.5 & 17.6 \\  & December & 6.49 & 6.68 & 10.2 & 14.3 \\ \hline \multirow{6}{*}{2021} & January & 6.21 & 7.11 & 9.73 & 14.2 \\  & February & 6.40 & 6.91 & 9.91 & 13.5 \\  & March & 6.15 & 6.39 & 9.46 & 14.4 \\  & April & 5.79 & 5.82 & 9.39 & 12.3 \\  & May & 4.77 & 5.00 & 12.1 & 14.3 \\  & June & 4.89 & 5.35 & 10.1 & 13.9 \\  & July & 4.27 & 4.92 & 8.48 & 12.8 \\  & August & 4.27 & 5.03 & 7.63 & 11.6 \\  & September & 4.34 & 5.00 & 9.78 & 12.4 \\  & October & 6.80 & 7.00 & 26.0 & 21.8 \\  & November & 5.55 & 5.63 & 8.53 & 11.9 \\  & December & 6.35 & 6.51 & 8.75 & 12.5 \\ \hline \multirow{6}{*}{2022} & January & 7.06 & 7.77 & 10.9 & 16.1 \\  & February & 7.09 & 7.54 & 10.5 & 14.6 \\ \cline{1-1}  & March & 6.34 & 6.75 & 9.00 & 12.8 \\ \cline{1-1}  & April & 4.52 & 4.52 & 9.47 & 11.9 \\ \cline{1-1}  & May & 4.64 & 4.85 & 11.6 & 11.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Seasonal error metrics comparison of DNN and GEP. Figure 10: Illustration of DNN and GEP hour- and day-ahead forecast comparison for peak power.
# Multilayer Bolometric Structures for Efficient Wideband Communication Signal Reception
## 3 Experimental Setup and Measurements; Results and Discussion
### Results and Discussion
The total attenuation of the input line is based on the specific experiment but is typically \(-80\) dB so as to achieve quasi-single phone operation. The transmitted output signal from the waveguide port is amplified by a broadband low temperature amplifier with an average gain of \(30\) dB. The waveguide (Figure 5b) is typically placed in a well-shielded environment at the base temperature. Furthermore, our anti-reflection coating can serve as additional adsorbing layers in a bolometer based on the Josephson junction Al-AlOx-Al [39; 40]. Figure 5c shows a SEM image of a part of the test chip with such a Josephson junction. Our calculations affirm that the appropriate choice of the multilayer bolometric structure's thickness finds application in cutting-edge nano- and quantum technologies. However, this requires high-quality metal structures with high dielectric permittivity [61; 62]. Figure 5: (**a**) The proposed measurement scheme for mK temperatures. (**b**) Sketch of a cryogenic waveguide that will be used to test the results obtained at low temperatures. (**c**) An example of a key part (superconductor-insulator–superconductor tunnel contact) of a Josephson microwave photon detector whose sensitivity will be enhanced by the use of an anti-reflection coating designed for experimental conditions (with appropriate relatively high dielectric constant, appropriate dimensions, etc.). Here, we have two superconducting electrodes (absorption layers) separated in the overlap region by a thin “tunnel” dielectric layer; the absorption of microwave photons leads to a transition to a new (resistive) state of the entire nanostructure, and the proposed anti-reflection coating will increase the efficiency of the detector’s interaction with the field in this case as well.
###### Abstract  
Industrial control systems depend heavily on security and monitoring protocols. Several tools are available for this purpose, which scout vulnerabilities and take screenshots of various control panels for later analysis. However, they do not adequately classify images into specific control groups, which is crucial for security-based tasks performed by manual operators. To solve this problem, we propose a pipeline based on deep learning to classify snapshots of industrial control panels into three categories: internet technologies, operation technologies, and others. More specifically, we compare the use of transfer learning and fine-tuning in convolutional neural networks (CNNs) pre-trained on ImageNet to select the best CNN architecture for classifying the screenshots of industrial control systems. We propose the critical infrastructure dataset (CRINF-300), which is the first publicly available information technology (IT)/operational technology (OT) snapshot dataset, with 337 manually labeled images. We used the CRINF-300 to train and evaluate eighteen different pipelines, registering their performance under CPU and GPU environments. We found out that the Inception-ResNet-V2 and VGG16 architectures obtained the best results on transfer learning and fine-tuning, with F1-scores of 0.9832 and 0.9373, respectively. In systems where time is critical and the GPU is available, we recommend using the MobileNet-V1 architecture, with an average time of 0.03 s to process an image and with an F1-score of 0.9758.  
deep learning; image classification; transfer learning; industrial control system; fine-tuning
# Magnetoelectric Composites: Engineering for Tunable Filters and Energy Harvesting Applications
## 2 Materials and Methods  
Multiferroic ceramic composites, P-F, based on ferroelectric and magnetic materials were fabricated using a conventional solid-state reaction method and different ratios of ferroelectric and ferromagnetic components. The individual components of the composite were selected based on their high dielectric and ferroelectric properties (P) and good magnetic properties (F). The ferroelectric component was doped using a PZT solid solution with the chemical formula Pb(Zr\({}_{0.51}\)Ti\({}_{0.49}\))O\({}_{3}\) + 0.2%at. Bi\({}_{2}\)O\({}_{3}\) + 0.03%at. Nb\({}_{2}\)O\({}_{5}\) + 0.06%at. MnO\({}_{2}\) (P), while the magnetic component was a ferrite material with the chemical composition Ni\({}_{0.5}\)Zn\({}_{0.5}\)Fe\({}_{2}\)O\({}_{4}\) (F). The input ingredients to obtain the two components of the composite were high-purity simple and complex compounds. The detailed course of the technological process of the ferroelectric PZT-type material and ferrite material is given in a previous work [37].  
The synthesized composite components, i.e., P and F powders, were weighed in appropriate proportions (P/F ratios) and then mixed in a Fritsch Pulverisette-6 (IdarOberstein, Germany) planetary ball mill (wet milling in ethyl alcohol for 15 h). The powders mixtures were synthesized via calcination under the condition of 950 \({}^{\circ}\)C/2 h, while sintering of the multiferroic composite samples was carried out via the free sintering (pressureless) method (1250 \({}^{\circ}\)C/2 h). For electrical research, the surfaces of samples were polished, and silver electrodes were applied to them. The analysis of the results was performed on a series of multiferroic composite samples (P-F) with the following P/F percentages: 20/80 (P20-F80), 40/60 (P40-F60), 60/40 (P60-F40), 80/20 (P80-F20), 85/15 (P85-F15), and 90/10 (P90-F10).  
The observation of the surface morphology and the chemical composition tests of the multiferroic composite samples were recorded on a scanning electron microscope in combination with an energy dispersive spectrometer SEM-EDS (JEOL JSM-7100F TTL LV from Jeol Ltd., Tokyo, Japan). Two image capture techniques have been used: (i) the standard SB method, i.e., combining signals with the secondary and backscattered electron detectors, and (ii) the BSE method, i.e., signal from backscattered electron detector. The average grain size has been estimated using the ImageJ program. The X-ray diffraction patterns were tested at room temperature using the X'Pert Pro diffractometer (PANalytical, Eindhoven, the Netherlands) in the 20 range 10-60\({}^{\circ}\). Frequency measurements of the electric parameters were conducted on the 1260 A Frequency Response Analyzer, which is the cornerstone of FRA technology (AMETEK Scientific Instruments Berwyn, PA, USA) in a frequency range from 0.1 Hz to 10 MHz and at a temperature range from 30 to 400 \({}^{\circ}\)C.
# Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods
## 3 Materials and Methods  
This section describes the data set used in this study, the feature extraction processes with conventional and deep learning methods, the classifiers used, and our experimental evaluations. The experimental framework is illustrated in Figure 2. There are three main phases: image input, feature extraction, and classification.  
This paper presents a framework for lower-body detection using proposed anthropometric ratios, as illustrated in Figure 2. A person is captured or recorded by a camera, producing the input image. Then, the human body is detected and scaled using the proposed anthropometric ratios in combination with either traditional techniques (A-Traditional), such as the VJ and HOG-SVM algorithms, or the CNNs technique (A-CNNs). Finally, the lower-body area in the image is detected.  
For human body detection, there are two popular methods based on sliding windows for overall positioning and scaling in images [11; 12; 16]: traditional methods and deep learning methods.
## 3 Methodology
### Pre-Processing Techniques  
Pre-processing is crucial for refining Arabic text and enhancing the quality of topic modeling. Some of the techniques applied are detailed in the following:  
* Text Normalization: Standardizing text by removing diacritics, numbers, punctuation, and special characters and unifying character variations [17].
* Tokenization: Splitting text into individual words or tokens, considering Arabic-specific linguistic rules [12].
* Stop Word Removal: Eliminating common Arabic words that do not contribute significant meaning [20].
* Stemming and Lemmatization: Reducing words to their root forms using two different stemmers--ISRI and Tashaphyne.
* ISRI Stemmer: An Arabic stemmer based on the ISRI algorithm developed by the Information Science Research Institute. This stemmer removes prefixes, suffixes, and infixes from words and incorporates some degree of morphological analysis to accurately determine the roots of Arabic words. This approach generally enhances the accuracy of root identification [21].
* Tashaphyne Stemmer: Provided by the Tashaphyne library, this is a light Arabic stemmer that merges light stemming and root-based stemming principles. It segments Arabic words into roots and patterns while also removing common prefixes and suffixes without performing a full morphological analysis. The Tashaphyne stemmer has achieved remarkable results, outperforming other competitive stemmers (e.g., Khoja, ISRI, Motaz/Light10, FARASA, and Assem stemmers) in extracting Roots and Stems [22].
* \(n\)-Gram Construction: Creating combinations of adjacent words (\(n\)-grams) to capture more context within the text [23].
# Ensemble-Based Knowledge Distillation for Video Anomaly Detection
## 4 Experimental Evaluation
### Datasets  
In this study, we deploy UCF-Crime and RWF-2000 datasets for training and again evaluate the performance of the proposed approaches on these datasets. The UCF-Crime dataset includes 1900 videos from CCTV cameras, in which, 950 videos have anomaly (Abnormal) patterns in 13 different anomaly classes and the remaining 950 videos consist of only normal patterns. The training set of UCF-Crime includes 800 normal and 810 anomalous videos while the test set of UCF-Crime includes 150 normal and 140 anomalous videos. The RWF-2000 dataset includes 2000 videos. Each video of the RWF-2000 dataset is annotated with a class label of \"Violent\" or \"Non-Violent\"; 1000 videos have violent behavior patterns, while the other 1000 videos consist of solely non-violent behavior patterns in which both violent and non-violent video sets consist of 800 videos for training and 200 videos for testing.  
With respect to the scope of our study, the main task is the binary classification of video anomaly detection. Hence, we represent any video including an anomaly pattern belonging to one of 13 different anomaly classes as the \"Anomaly\" class video and videos belonging to the normal class as the \"Normal\" class video for UCF-Crime. Similarly again, we reconsider violent behavior patterns as a subset of anomaly class patterns; consequently, we set \"Violent\" class videos as videos containing anomaly patterns and \"Non-Violent\" class videos as videos including purely normal patterns for RWF-2000.
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 3 Results and Discussions
### Validation  
Analysis revealed that a significant majority of the building shapes perfectly aligned with the pixels classified as built-up areas on the LULC map (Figure 12). This agreement between both datasets provided evidence regarding the accuracy and dependability of the LULC map generated by utilizing the RF classifier. Validating the LULC map through comparison, with building shapes demonstrates the effectiveness of using this classification method. It provides confidence in the ability of the random forest classifier to accurately identify and classify land cover types, particularly built-up areas. This validation process contributes to the overall reliability of the LULC map and enhances its suitability for various applications, such as urban planning, resource management, and environmental monitoring in the Gelephu region.  
Figure 12: Validation maps. (**a**) South, (**b**) North-east. The colors represent the color scheme of LULC maps (Green: Vegetation; Blue: Water; Red: Built-up; Yellow: Barren land).
# Influence of Piano Key Vibration Level on Players' Perception and Performance in Piano Playing
## 2 Methods
### Experiment Design  
The target was to control the key vibration levels independent of the sound and to cover the level range of piano key vibrations of acoustic concert grand pianos. Independent control could not be achieved with the built-in vibrotactile feedback rendering system of the AvantGrand N3X; therefore, it was extended as illustrated in Figure 1.  
The mono audio output signal of the AvantGrand N3X was processed with a digital signal processor (DSP). Through a combined approach of vibrometer measurements and subjective evaluation by playing on the instrument, the DSP's filter stage was tuned to create vibration level \(V_{3}\) (see Figure 1), which approached the maximum vibration levels previously measured on acoustic grand pianos [7]. After implementation of \(V_{3}\), vibration levels \(V_{2}\) and \(V_{1}\) were created by attenuating the signal in steps of 6 dB. The no-vibration condition \(V_{0}\) completed the levels of the experiment. As shown in Figure 1, the vibration levels cover the range of acoustic grand pianos for notes A2, A3, and A4. For notes A0 and A1, the levels are more than 10 dB lower. The chosen music piece avoided the lowest notes. The deviations of the vibration level curves in Figure 1 are due to non-idealities of the excitation system.  
The experiment was created to study the influence of four vibration levels on players' personal judgment of the instrument quality and on the dynamics and timing of the players' performance of a music piece excerpt. The experiment was designed so that the participants were unaware of the independent variable, and the session was split into three parts to steer the pianists' attention to different instrument properties. Free verbalizations were used to assess the players' judgment, allowing for unrestrained and possibly unexpected answers. Since a small influence of the piano key vibrations on musical performance and the players' judgments was assumed--as natural levels are close to the threshold of vibration sensation [7; 8; 9]--numerous repetitions were included in the protocol, and participants with high levels of playing experience were selected.
## 4 Implementation and Performance Analysis of CAES-Based Image Encryption
### Visual Effect of Encrypted Images  
In this example, an image with a 256 \(\times\) 256 resolution was used as the plaintext data. Several of the plain and cipher images are shown below.  
From Figure 10, it can be observed that when the AES-ECB was used to encrypt the data, the intervals with the same color in the background would be encrypted into the same cipher text. This was a drawback of AES-ECB for image encryption. The encrypted picture looked confusing enough when AES-CBC was used to encrypt the image. When using the proposed improved CAES for pictures with a size of 256 \(\times\) 256 and 49,152 dynamic keys, as the original AES already had a qualified security, it would be impossible to completely solve all of the keys in short time. Furthermore, because of the design of the dynamic key, the 49,152 keys were updated immediately in the next round of encryption and the old password had no effect. Therefore, in terms of security, CAES was greatly improved compared with the original AES-ECB and AES-CBC.  
Figure 9: Flow chart of decryption using CAES.
## 3 Results
### Analysis of Ionospheric DVTEC Disturbances
#### 3.2.2 Comparison with a Day without Storms
By calculating the average peak-to-peak DVTEC amplitude of the 14 stations for the reference day, we obtained an average peak-to-peak amplitude of 0.23 TECU. On the other hand, the average peak-to-peak amplitude for the AEA day was 0.67 TECU. Comparing both results, we can see that, on a stormy day, the average peak-to-peak amplitude increased by a factor of 2.91 with respect to the average peak-to-peak amplitude of the non-stormy day.