esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
## 3 Proposed Method
### Acoustic Signal Processing and Feature Vector Extraction  
To prepare the acoustic signals for model training, a preprocessing step was first applied. The raw audio data were resampled to 20 kHz, as most characteristic information of the signals lies below 10 kHz as shown in Figure 5.  
Figure 4: MFCC feature image.  
Figure 5: Acoustic data sampling rate normalization.  
This sampling rate ensures that the frequency components of interest are retained, in accordance with [PERSON]'s theorem. Next, the spectrograms were extracted using the short-time Fourier transform (STFT). A window length of 2048 samples (approximately 100 ms) with a 512-sample (about 25 ms) overlap was used. Given that each audio file contains up to 20,000 samples, the STFT generates a maximum of 40 time frames. The resulting spectrogram is a two-dimensional matrix representing the magnitude (in decibels) of each frequency band over time. To generate Mel-spectrograms and MFCCs, the spectrogram was processed using a Mel-filter bank with 40 bands. This step produces 40 Mel-spectrum values per time frame. MFCCs were computed by applying a discrete cosine transform (DCT) to the Mel-spectrogram, resulting in 40 coefficients.  
As the dataset includes audio segments of varying lengths, the number of time frames for each sample is inconsistent. To address this, zero-padding was applied to the beginning and end of each acoustic sample, as shown in Figure 6, so that all inputs had a uniform length of 40 frames.  
This ensures that the model receives inputs of consistent shape, improving training efficiency and performance. All feature vectors were saved as 160 \(\times\) 160-pixel PNG image files for use in the CNN training pipeline (see Figure 7).  
Figure 6: Zero-padding for acoustic data length normalization.  
Figure 7: This figure presents examples of feature vector extraction and visualization for data samples labeled as abnormal, chick, and normal (the third and fourth), displayed from left to right.
## 1 Introduction
Though the coordinate method, can theoretically find better results than the grid method with more flexible wind turbine positions, the grid method may narrow the gap of results by increasing the grid density. Given the strong dependence of the results on the optimization formulation, selected algorithm, etc., more in-depth studies are yet to be done by comparing the effectiveness of different wind farm design methods. However, the comparative research of wind farm design methods in literature has been rare. In reference [17], the impact of type of grid cell division on the wind farm optimization results is investigated for the square and circular shape wind farm. [PERSON] [18] employed a densified grid method (relative to the baseline grid method) for the layout optimization of a square shape wind farm, and an improvement of the optimized wind farm performance is achieved. [PERSON] et al. [19] carried out the comparative study of the grid and the coordinate methods, while the objective wind farm is also an ideal square shape. Above all, previous research on the comparison of different wind farm design methods are all targeting an ideal wind farm with regular boundary shape, based on which the drawn conclusions are probably not eligible for the real wind farm design. Hence, this paper aims to fill the research gap by studying a real two-dimensional wind farm layout optimization with an irregular boundary. In order to investigate the grid method more systematically, two grid densities (coarse 20 \(\times\) 30 and densified 40 \(\times\) 60 grid) are studied. When compared to previous research in the literature, the main contributions of this paper are: (1) the comparative study of different wind farm design methods is conducted for a real irregular-boundary wind farm for the first time; and, (2) Means of coping with the boundary constraints using different wind farm design methods are developed, which will shed light on a new perspective for tackling the wind farm optimization with other irregular boundary characteristics.
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 3 Case Study  
### Engineering Background  
The right-line tunnel between the Wenjing Road Station and Chalukou Station of Nanjing Metro Line 5 is about 1270 m long and buried at a depth of 16.4 to 25.5 m. The geological environment along the line is complex, and it passes through various strata such as silt, clay, rock, and composite strata. The geological profile is shown in Figure 5. During the shield tunnelling process, due to the variability and uncertainty of the strata, it is difficult to formulate a reasonable control strategy if the trend of tunnelling performance changes cannot be understood.
# Vehicle Collision Prediction under Reduced Visibility Conditions
## 5 Conclusions  
In this work, we proposed a Visibility-based Collision Warning System (ViCoWS) to predict future collision risks. In ViCoWS, the prediction horizons are adapted according to the different visibility conditions, such as foggy conditions. The velocity prediction model is based on back-propagation neural network (BPNN). The collision warning model is based on BPNN, which is used to predict the future collision warning levels for all time slots within the prediction horizon. ViCoWS alerts a driver when the collision warning level exceeds a threshold of 0.5. In non-congested traffic under low visibility conditions, ViCoWS can warn the driver by about 4.5 s prior to the collision (under visibility of 120 m) and 2.1 s prior to the collision (under visibility of 160 m). The FCPI method gives 0.6 s for the driver to react before collision. In congested traffic under low visibility conditions, ViCoWS can warn the driver by about 1.9 s prior to the collision, whereas the FCPI method gives 1.2 s for the driver to react before collision. In conclusion, irrespective of the traffic condition (non-congested or congested) and irrespective of the visibility conditions, the warning time by ViCoWS is always greater than the PRT thus it is safe; whereas that of FCPI fails to be safe in the case of congested traffic 160 m visibility. Further experiments all confirm the above claims.  
Figure 17: Comparison between ViCoWS and FCPI for 44 different visibilities (Set D).  
Figure 18: Comparison between ViCoWS and FCPI for 13 different visibilities (Set E).  
In the future, we can further consider other deceleration values of SV when a driver needs to slow down to avoid a collision. We can also consider to learn drivers' behavior, and change the threshold of collision warning level depending on different users. Moreover, with head-up display, ViCoWS can also be made to show the collision warning level in the form of green, yellow, or red light, which will be more intuitive. ViCoWS can also be applied to Cooperative Adaptive Cruise Control if it is combined with control methods that control vehicle velocity.  
[PERSON] was a Master Program student under the supervision of [PERSON]. [PERSON] was responsible for implementing the method, which was initially proposed by [PERSON]. [PERSON] performed the two experiments for non-congested traffic and congested traffic. [PERSON] revised the implementation and performed more experiments.  
This research was supported partially by a research project grant MOST 107-2221-E-194-001-MY3 from the Ministry of Science and Technology, Taiwan.  
The authors declare no conflicts of interest.
# Explainable Artificial Intelligence: Advancements and Limitations
## 4 Visual Explanations
### Activation Maximisation  
Visual explanations for CNN decisions can take the form of patterns that strongly activate a neuron, a feature map, an entire layer, or a predicted class--a process commonly referred to as activation maximisation. A notable methodology in this direction is random noise image optimisation. This approach starts with a random noise image, and the gradients of a specific unit are computed with respect to this image. The process identifies the patterns that most strongly activate the targeted unit, resulting in visionary pattern images that offer insights into the network's learnt representations [61, 70, 71, 72]. Activation maximisation with random image optimisation, while useful for understanding which input patterns maximise the response of specific neurons or layers in black-box models, comes with several limitations. One significant drawback is that the generated visualisations often lack interpretability and clarity, especially for higher-level neurons. The images produced tend to be highly abstract and sometimes visually unrealistic, making it difficult for humans to intuitively grasp what the model is focusing on. Additionally, they require numerous optimisation steps to generate meaningful activations, which makes them computationally expensive and hence their real-time or large-scale applications challenging.  
An alternative approach, which eliminates these drawbacks, outputs real images instead. This is achieved by feeding the entire training set to the trained model and selecting a group of images that highly activate a specific unit [61, 86]. However, this approach places a significant burden on end users, who must manually sift through highly activating images to identify common patterns--a process that is highly susceptible to human bias.  
In addition to the mentioned drawbacks, activation maximisation techniques are also prone to adversarial artefacts--small changes in the input can drastically change the output activation without corresponding to any meaningful difference in the input image, undermining the reliability of the explanations. Overall, while activation maximisation offers insights into model behaviour, its limitations in clarity, computational cost, and susceptibility to noise reduce its practical utility in explainability.  
Figure 2: An overview of XAI methodologies based on their modalities.
## 3 Results
### Machine Learning  
Recognition of Attachment, Self-Esteem, Self-Efficacy, and Stress Avoidance Using the VSG.  
Table 8 shows the results of machine learning models for each self-assessed psychological construct. All four variables (attachment, self-esteem, self-efficacy, and stress avoidance) have at least one subscale that was modelled with accuracy \(\geq\)0.8 and kappa \(\geq\)0.5 using data from the VSG.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**SCALE**} & \multirow{2}{*}{**MEAN**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**MEDIAN**} & \multicolumn{4}{c}{**CATEGORIZATION**} \\ \cline{5-8}  & & & & **Low** & **High** & **High/Total** \\ \hline Attachment subscale 1 & 37.76 & 8.85 & 37 & 31 & 28 & 0.47 \\ \hline Attachment subscale 2 & 29.92 & 6.97 & 29 & 32 & 27 & 0.46 \\ \hline Attachment subscale 3 & 37.63 & 4.72 & 38 & 25 & 34 & 0.58 \\ \hline Attachment subscale 3 & 19.52 & 3.91 & 19 & 33 & 26 & 0.44 \\ \hline Relationship(RQ) & & * & & 12 & 47 & 0.8 \\ \hline Self-Esteem & 75.29 & 12.26 & 76.5 & 37 & 23 & 0.38 \\ \hline Self-Efficacy & 29.71 & 5.9 & 28 & 28 & 32 & 0.53 \\ \hline BAS Subscale 1 & 10.78 & 2.06 & 11 & 28 & 30 & 0.52 \\ \hline BAS Subscale 2 & 11.26 & 2.51 & 11 & 30 & 28 & 0.48 \\ \hline BAS Subscale 3 & 15.12 & 3.96 & 16 & 22 & 36 & 0.62 \\ \hline BIS Subscale & 18.76 & 3.64 & 18 & 32 & 26 & 0.45 \\ \hline Brief Cope avoidant & 26.41 & 3.9 & 26.5 & 29 & 29 & 0.5 \\ \hline Brief Cope approach & 29.43 & 3.68 & 29 & 32 & 26 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Descriptive analysis of variables of interest and results of categorization. High/Total column shows balancing of both categories (perfect balancing would mean High/Total of 0.5). * Relationship questionnaire (RQ) was not numeric, so the number of subjects in each category (low for insecure, high for secure) is the original one.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & **Dataset** & **Model** & **Features (N)** & **Accuracy** & **Kappa** & **TPR** & **TNR** \\ \hline Attachment Subscale 1 & All (51 + 6) & GLMNet & 15 & 0.9 & 0.76 & 0.88 & 0.9 \\ \hline Attachment Subscale 2 & All (51 + 6) & GLMNet & 14 & 0.9 & 0.74 & 0.68 & 0.81 \\ \hline Attachment Subscale 3 & All (51 + 6) & GLMNet & 14 & 0.85 & 0.63 & 0.88 & 0.83 \\ \hline Attachment Subscale 4 & All (51 + 6) & GLMNet & 11 & 0.88 & 0.67 & 0.93 & 0.83 \\ \hline Relationship & All (51 + 6) & GLMNet & 11 & 0.91 & 0.58 & 0.65 & 0.98 \\ \hline \multirow{3}{*}{Self-esteem} & Decisions (42 + 6) & GLMNet & 10 & 0.77 & 0.46 & 0.64 & 0.88 \\ \cline{2-7}  & Self-reported (26 + 6) & SVM & 11 & 0.63 & 0 & 0.02 & 1 \\ \cline{1-1} \cline{2-7}  & Characters’ status (224 + 6) & GLMNet & 12 & 0.98 & 0.96 & 0.97 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Metrics of models fitted for each subscale. Dataset column reports datasets that produced the best results along with number of variables + number of demographic variables included.
# Tracking of Moving Targets Through Asynchronous Measures
## 3 Benchmark Tracking Algorithms  
In order evaluate and assess the performance of the algorithm illustrated in the previous paragraph, we have employed as benchmarks the Extended Kalman Filter and the Particle Filter.
# Reconfigurable Smart Contracts for Renewable Energy Exchange with Re-Use of Verification Rules
## 7 Tests  
As far as the pattern implementation validation is concerned, there have been prepared unit tests. The design of the pattern requires testing on two levels of detail: a separate verification rule and a complete smart contract. Dedicated test classes have been implemented. At the verification rule level, each test class corresponds to and tests one verification rule class, e.g., _TechnicalVR1 Test_ test class is for the _TechnicalVR1_ verification rule class. Each of the test classes comprises two tests that verify the _runRule()_ method, one positive (PASS) and one negative (FAIL). At the smart contract level, one test class is written. The _ExchangeEnergyContractTest_ class comprises four tests that verify the _checkSC()_ method. Each test checks the whole smart contract. One test is positive (PASS) and nine tests are negative (FAIL). In each negative test one, a different verification rule returns the _false_ Boolean value. Test classes are stored in _tests_ package within the pattern implementation project in Intellij IDEA. Listing 14 depicts the _TechnicalVR1 Test_ class with two tests, which check the _TechnicalVR1_ verification rule class. ```
packagepl.gdynia.amw.scdp.rules.exchangeEnergy; importorg.junit.jupiter.api.Test; importpl.gdynia.amw.scdp.Transaction; importsstaticorg.junit.jupiter.api.Assertions. *; classTechnicalVR1 Test{ TechnicalVR1 technicalVR1=newTechnicalVR1(); @Test voidrunRulePositive(){ Transactiontr=newTransaction(100,300,400,20,10,1001,1002,100,100); assertTrue(technicalVR1.runRule(tr));  @Test voidrunRuleNegative(){ Transactiontr=newTransaction(100,300,400,20,10,1001,1002,100,200); assertFalse(technicalVR1.runRule(tr)); }
```  
Listing 14: The source code of the TechnicalVR1 Test class. Similarly, Listing 15 depicts the _TechnicalVR1 ExtendedTest_ class with two tests, which checks the _TechnicalVR1 Extended_ verification rule class. ```
packagepl.gdynia.amw.scdp.rules.exchangeEnergy.crossCommunity; importstaticorg.junit.jupiter.api.Assertions.assertFalse; importstaticorg.junit.jupiter.api.Assertions.assertTrue; importpl.gdynia.amw.scdp.TransactionCross; classTechnicalVR1 ExtendedTest{ TechnicalVR1 ExtendedtechnicalVR1 Extended(); @org.junit.jupiter.api.Test voidrunRulePositive(){ TransactionCrosstr=newTransactionCross(100,300,400,20,10,1001,1002,100,1001,1002,100,101); assertTrue(technicalVR1 Extended.runRule(tr)); } @org.junit.jupiter.api.Test voidrunRuleNegative(){ TransactionCrosstr=newTransactionCross(100,300,400,20,10,1001,1002,100,100); assertFalse(technicalVR1 Extended.runRule(tr)); }
```  
Listing 16 depicts the _ExchangeEnergyContractTest_ class with 11 tests, which check both configurations of the _ExchangeEnergyContract_ concrete smart contract class. ``` packagepl.gdynia.amw.scdp.contracts; importstaticorg.junit.jupiter.api.Assertions.
# Recent Development of Mid-Infrared Supercontinuum Generation in Fluoroindate Glass Fibers
## 1 Introduction
Currently, the broadest SC spectra can be generated in ChG fibers with an extremely high nonlinear refractive index \(n_{2}\) that is approximately two orders of magnitude higher than that of fluoride fibers [65]. Although ChG fibers yield an extremely broad mid-IR SC spectrum, even exceeding 10 \(\upmu\)m [49; 50; 66], the overwhelming number of demonstrations to date concern light emission at the milliwatt level. Nonetheless, recent reports on SC generation in As\({}_{2}\)S\({}_{3}\) fibers show that output power scaling up to over 1 W with an LWE over 6 \(\upmu\)m is possible [67; 68]. Further upscaling of average output SC power is also expected, particularly because the damage threshold for arsenic sulfide fibers is rather high, reaching \(\sim\)2.9 GW/cm\({}^{2}\) for 2 ns pulses at 1.9 \(\upmu\)m [69] and over 1000 GW/cm\({}^{2}\) for 150 fs pulses at 3 \(\upmu\)m [70; 71]. It is well-known that to achieve efficient spectrum broadening, particularly extending toward the mid-IR region, it is necessary to pump a nonlinear medium in the anomalous dispersion region, relatively close to its ZDW [25; 30]. In the case of most available step-index ChG fibers, the material ZDWs are usually located at wavelengths longer than 4.8 \(\upmu\)m that are far away from the emission wavelengths of most available laser sources doped with Er\({}^{3+}\), Tm\({}^{3+}\), and Ho\({}^{3+}\) ions. Consequently, such pumping does not offer optimum broadband SC emission and usually requires the use of complex and expensive optical parametric oscillators (OPOs). In the case of fluoride fibers, the choice of pump sources is wider. Both fluorozirconate and fluoroindate step-index fibers exhibit a ZDW within the wavelength range of \(\sim\)1.5 to 2.1 \(\upmu\)m, covered by the widely available and powerful pulsed laser systems. The advantage of InF\({}_{3}\) fibers over ZBLANs is the wider transmission band that renders them ideal candidates for high-power SC generation in the 2-5 \(\upmu\)m atmospheric window.
# Virtual Gymnasium: Personalized Weight Perception Interface in Lifting Virtual Objects
## 4 Force Gauge Circular Graphical User Interface  
### System Overview  
The implementation of the proposed pseudo-weight perception method is described using system diagrams. Figure 2 illustrates the architecture of the proposed system. The human tracking system Kinect V2 tracks the 3D positions and movements of the left and right hands, elbows, and shoulders for data input through the input manager. The system calculates the average distance between joints in the calibration process. Then, the personalized movement trajectory is generated using the data and Equations (1) and (2). After calibration, the obtained data were integrated into the weight perception manager. The system checks the interaction between the user and virtual dumbbell using the Kinect device.  
When grasping occurs, collision detection is performed between the user's hands and virtual dumbbell. After confirming grasping occurrence, the pseudo-weight perception algorithm executes and attains the current lifting forces by calculating the current lifting force, as defined in Equation (8).  
This paper proposes a force gauge circular graphical user interface (FGCGUI) that allows users perceive personalized weight by controlling the lifting force of a virtual object. When a user grasps the virtual dumbbell at point A (Figure 3) and moves toward point B, the proposed algorithm begins to store the hand's position at every point between points A and B. During the lifting process, the user selects a standard joint such as the elbow, as shown in Figure 3. Thus, the movement trajectory of a dumbbell is generally represented  
Figure 2: System architecture.  
as a circular arc according to the length of the user's arm. Another consideration is that the lifting force generally changes according to the differences in the weight of virtual objects during the exercise process. If an appropriate lifting force for the virtual object and an appropriate lifting movement guideline are provided, users can feel an appropriate sense of weight when lifting the virtual object.
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 3 Methodology
### Complex Trajectory Gesture Recognition
#### 3.2.3 Neural Network Parameter Design  
The proposed bidirectional LSTM fusion neural network comprises two components: a three-dimensional convolutional neural network and a bidirectional LSTM neural network. The network parameter is outlined in Table 3.  
In the input layer, the input data cube is resampled into \(16\times 16\times 16\) distance, azimuth, and elevation data blocks, with a time series length of 10. Afterwards, the sequence folding module is used for processing, as a convolutional layer is required on the time series to extract high-dimensional features. Therefore, the folded sequence needs to be fed into a three-dimensional convolutional neural network for feature extraction.  
In a three-dimensional convolutional neural network, there are two convolutional pooling layers. In the first convolutional pooling layer, the kernel size is \(5\times 5\times 5\) with a step size of 1, using the same filling method to maintain the output dimension unchanged after convolution. The pooling layer size is \(2\times 2\times 2\) with a step size of 2, using the Max pooling method to perform feature scaling on the data size. In the second convolutional pooling layer, the kernel size is \(3\times 3\times 3\), with a step size of 1, using the same filling method. The pooling layer is the same as the previous layer, and the extracted features are a four-dimensional feature matrix of \(4\times 4\times 4\times 10\). After FC layers and sequence unfolding module, the features are input into a bidirectional LSTM neural network.  
Figure 15: Schematic diagram of bidirectional LSTM fusion neural network.  
In the bidirectional LSTM neural network, there are 128 LSTM cell units, and due to the bidirectional sequence output, the output dimension is 256. Finally, gesture classification prediction is completed through the FC layer and Softmax layer.