esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Bit Error Rate for Different Bias Sensitivity
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Pixel** & **A = 13** & **A = 24** & **A = 37** & **A = 50** & **A = 63** & **A = 75** \\ \hline p1 & \(<\)1.25 \(\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) \\ p2 & \(2.38\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) & \(7.16\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) \\ p3 & \(2.39\times\)\(10^{-4}\) & \(7.16\times\)\(10^{-4}\) & \(2.38\times\)\(10^{-3}\) & \(<\)1.25 \(\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) \\ p4 & \(7.16\times\)\(10^{-4}\) & \(1.43\times\)\(10^{-3}\) & \(1.00\times\)\(10^{-2}\) & \(<\)1.25 \(\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) & \(<\)1.25 \(\times\)\(10^{-4}\) \\ \hline \end{tabular}
\end{table} 
Table 3: The bit error rate is presented for different transmitter amplitudes, maintaining a constant distance of 200 cm between the transmitter and receiver. The transmitter operated at a frequency of 200 [Hz]. For the n-pulse modulation, a duty cycle (\(D\)) of 0.4 was applied, with the maximum number of pulses (\(n_{\text{max}}\)) set to 4. Additionally, the receiver bias sensitivity was configured to “high”. The abovementioned issue, which may appear as a problem, can also be viewed as an opportunity. Within the new paradigm of joint sensing and communication (JC&S), the receiver can dynamically adjust the bias sensitivity value based on the number of events gathered (sensing) to enhance the performance of the OCC link (communication). In noisy environments, where the number of events increases rapidly, the receiver could set lower bias sensitivity values or higher values in low-noise environments, to improve OCC link performance.
###### Abstract  
An enhanced SSDI (synchronized switch damping on inductor) approach is proposed to suppress the vibration of bladed disks in aero-engines. Different from the authors' former work (MSSP, 2017; JIMSS, 2018) where a local SSDI circuit is shunted to the piezoelectric materials at each blade sector, in this work two blade sectors are interconnected by a shared SSDI circuit. In this way, the switching action of SSDI is triggered by the relative displacement between two blade sectors. The feasibility of the dual-connected SSDI is numerically examined by a 2-DOF (degree-of-freedom) mechanical system, and further experimentally validated on a single-beam and a double-beam system. Results show that the damping performance increases with the amplitude of relative displacement. This feature is especially favorable for the application of blisks where the blade normally vibrates in different amplitudes and phases. Eventually, we conduct numerical simulation on the forced response of mistuned bladed disk undergoing travelling wave excitation. Results show that the dual-connected configuration can reduce at least half the number of switching shunts while maintain nearly the same performance as the conventional (local) SSDI.  
bladed disk; vibration control; synchronized switch damping on inductor; dual-connected configuration; multi-harmonic balance method; experimental validation
# A Psychoacoustic-Based Multiple Audio Object Coding Approach via Intra-Object Sparsity
###### Abstract  
Rendering spatial sound scenes via audio objects has become popular in recent years, since it can provide more flexibility for different auditory scenarios, such as 3D movies, spatial audio communication and virtual classrooms. To facilitate high-quality bitrate-efficient distribution for spatial audio objects, an encoding scheme based on intra-object sparsity (approximate \(k\)-sparsity of the audio object itself) is proposed in this paper. The statistical analysis is presented to validate the notion that the audio object has a stronger sparseness in the Modified Discrete Cosine Transform (MDCT) domain than in the Short Time Fourier Transform (STFT) domain. By exploiting intra-object sparsity in the MDCT domain, multiple simultaneously occurring audio objects are compressed into a mono downmix signal with side information. To ensure a balanced perception quality of audio objects, a Psychoacoustic-based time-frequency instants sorting algorithm and an energy equalized Number of Preserved Time-Frequency Bins (NPTF) allocation strategy are proposed, which are employed in the underlying compression framework. The downmix signal can be further encoded via Scalar Quantized Vector Huffman Coding (SQVH) technique at a desirable bitrate, and the side information is transmitted in a lossless manner. Both objective and subjective evaluations show that the proposed encoding scheme outperforms the Sparsity Analysis (SPA) approach and Spatial Audio Object Coding (SAOC) in cases where eight objects were jointly encoded.  
audio object coding; sparsity; psychoacoustic model; multi-channel audio coding Article
## 2 Performance Degradation Analysis of Space TWTA
### Analysis of Helix Current Degradation Characteristic  
The helix current of TWTA is the current that is not collected by the collector but is intercepted by the helix. Its long-term stability is affected by the life of the cathode, the reliability of electron gun assembly, the change of heater current, the change characteristics of the internal vacuum, the gain stability of TWTA, the sensitivity of the whole TWTA to ambient temperature and other parameters, and so on. In addition, the adaptability of the EPC to the performance of the TWT also affects the subtle changes in the helix current, especially the stability of the output voltage of the TWTA and the adjustment ability influence of the anode voltage on the cathode emission current of the TWTA within the lifecycle. The helix current is a key comprehensive index that characterizes the functional properties of the TWTA and shows a non-monotonic change.
# Volumetric Modulated Arc Therapy for Radiosurgery of Brain Metastases: A Single-Center Study
## 3 Results
### Demographic Characteristics
\begin{table}
\begin{tabular}{c c c c} \hline Age, years & Median (IQR) & 63 & (17) \\ \hline Sex, \(n\) (\%) & Female & 47 & (38.8) \\  & Male & 74 & (61.1) \\ \hline Primary tumor, \(n\) (\%) & Total cohort & 121 & (100) \\ \hline  & Total lung cohort & 82 & (67.8) \\ Lung & NSCLC adenocarcinoma & 56 & (46.3) \\  & NSCLC Non adenocarcinoma & 18 & (14.9) \\  & SCLC & 8 & (6.6) \\ \hline  & Total breast cohort & 16 & (13.2) \\  & HER2+/HR+ & 7 & (5.8) \\  & HER2+/HR- & 3 & (2.5) \\ Breast & HER2\(-/\)HR+ & 2 & (1.7) \\  & HER2\(-/\)HR\(-\) & 3 & (2.5) \\  & Sarcoma & 1 & (0.8) \\ \hline Gastrointestinal & & 8 & (6.6) \\ \hline Genitourinary & & 7 & (5.8) \\ \hline  & Total & 3 & (2.5) \\ Melanoma & BRAF negative/unknown & 1 & (0.8) \\  & BRAF positive & 2 & (1.7) \\ \hline  & Prostate & 2 & (1.7) \\ Remainder & Chorioocarcinoma & 1 & (0.8) \\  & Oropharynx & 1 & (0.8) \\  & Unknown & 1 & (0.8) \\ \hline  & 100–90 & 26 & (21.5) \\ KPS score, \(n\) (\%) & 80–70 & 53 & (43.8) \\  & \(<\)70 & 42 & (34.7) \\ \hline  & 0–1 & 38 & (31.4) \\ DS-GPA Class, \(n\) (\%) & 1.5–2.0 & 43 & (35.5) \\  & 2.5–3.0 & 32 & (26.4) \\  & 3.5–4.0 & 8 & (6.6) \\ \hline Epilepsy related to BMs, \(n\) (\%) & Yes & 37 & (30.6) \\  & No & 84 & (69.4) \\ \hline Overall survival, months & Median (IQR) & 7.72 & (0.905) \\ \hline Extracranial metastases, \(n\) (\%) & Yes & 70 & (57.9) \\  & No & 51 & (42.1) \\ \hline  & Total BMs treated & 229 & (100) \\ Radiosurgery treatment, \(n\) (\%) & SRS & 206 & (90) \\  & Fractionated SRS (fSRS) & 23 & (10) \\ \hline \end{tabular}
\end{table} 
Table 1: Clinical characteristics of patients treated by SRS/fSRS.
# A Deep-Learning-Based Approach to the Classification of Fire Types
## 4 Results
### Two-Level Classification  
Second, the work experimented with two levels of nested classifications: binary (fire vs. no fire), followed by a four-way classification of the four different types of classes, including solid-material, chemical-based, electric-based, and oil-based fires. Table 5 shows our experiments' results using two-level classification. First, the models performed a binary classification to determine whether there was a fire. For this task, it is obvious from the table that the InceptionV3 and Densenet121 attained our best results with an accuracy score of 98.16%, which is a relatively high score due to the simplicity of the task as compared to the second classification level. The literature (Table 1) shows close accuracy scores, although past results are not directly comparable to our findings due to a lack of access to the data used in previous work.  
For the second classification level, the models were trained to perform a four-way classification task in order to distinguish between four different types of fire. Specifically,  
Figure 7: The confusion matrix of MobileNetV2 on the flat, five-way classification.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Classification Algorithm** & **Accuracy** & **Avg. F1 Score** \\ \hline MobileNetV2 & 96.32\% & 0.963 \\ \hline InceptionV3 & **98.16\%** & **0.981** \\ \hline VGG-16 & 93.87\% & 0.938 \\ \hline EfficientNetV21 & 71.78\% & 0.718 \\ \hline VGG-19 & 87.73\% & 0.877 \\ \hline ResNet-50 & 79.14\% & 0.790 \\ \hline Densenet121 & **98.16\%** & **0.981** \\ \hline EfficientNet-b0 & 96.32\% & 0.963 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of using eight different classification algorithms for the binary classification of the fire vs. no-fire classes. Bold values denote the best performance in each column.  
the models were required to identify the type of fire, whether solid-material, electrical, oil-based, or chemical. Table 6 shows the results for this set of experiments, wherein the models--once the presence of fire was determined--classified the type of fire, whether a solid-material, electric-based, oil-based, or chemical fire. The best performance was achieved using the EfficientNet-b0 model, which had an accuracy score of 97.55%. This was followed by the DenseNet121 model, which also had an accuracy score of 95.09%. The lowest performance in this task was attained using EffecientNetV2I, which had an accuracy score of 54.60%.
## 4 The Proposed Method  
### Overview  
This paper proposes both a method to detect a malicious mobile app based on ViT and a method to extract information on malicious behavior using an attention map generated from ViT. This approach uses ViT's attention mechanism to extract regions of the images converted from the DEX that significantly influence the detection model's decision-making process. The extracted image regions are mapped to a DEX file to obtain DEX file data for these regions. Since DEX files contain the application's source code data, we can obtain the data that the model used to make decisions in the form of human-readable strings, such as class names and method names. Figure 1 shows the overall structure of our proposed approach. The pseudo-code of the algorithm for overall progress is shown in Algorithm 1.  
First, the DEX file format is parsed. A dex section data map is created with the offset value of the DEX file as the key and the DEX section's information that uses the offset's data as the value. Then, the app, which is represented as an image, is input into the detection model to obtain a prediction result. The next step is to create an attention map representing the most referenced regions for malware detection in the ViT model. To make high attention score regions, relatively high values are selected in the attention map. The dex section data and values are obtained by searching the dex section data map with the index of the highlighted area. Class and method information is extracted from the obtained data and provided to the user. The proposed method aims to detect malware and provide class and method names related to malicious behavior. The following sections sequentially describe each step of the proposed method.  
```
0:APK
0: Detection Result, List of Class and Method Name
1:\(DEX=extractDEXfromAPK(APK)\)
2:\(dex\_rgb\_image=conversionIntoImage(DEX)\)
3:\(dex\_section\_data\_map=DEXparse(DEX)\)
4:\(prediction=ViT(dex\_rgb\_image)\)
5:\(attention\_map,attention\_points=getAttentionMap(ViT,dex\_rgb\_image)\)
6:for\(attention\_points\)do
7:\(dex\_mm\_list=converToDexOffset(attention\_point)\)
8:endfor
9:\(class\_method\_names\_list=extractInformation(dex\_mm\_list,dex\_offset\_index\_ table)\)
10:return\(prediction,class\_method\_names\_list\)
```  
**Algorithm 1** Proposed overall progress Algorithm.
# Research on Narrow Linewidth External Cavity Semiconductor Lasers
## 3 Waveguide Structure of NLECSLs  
There are two types of waveguide structure of NLECSLs, including FBG and silicon-based waveguide. When the fiber grating is used as the feedback element of the external cavity laser, the linewidth performance is excellent and the tuning range is wider, but the volume is larger, the refractive index is smaller, the size is larger, and the absorption loss of material is larger [20]. Using the external low loss waveguide as the optical feedback element can effectively reduce the linewidth of the semiconductor laser and obtain low noise spectral characteristics. Due to its small size, low energy consumption, low loss and the ability to integrate with other optical components, NLECSLs based on silicon-based waveguides have become a competitive and attractive candidate laser in many coherent applications [21].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Central** & **Tuning** & **SMSR** & **Line Width** & **Output** & **Publication** \\
**Wavelength** & **Range** & & & **Power** & **Time** \\ \hline
2221 nm & 222 nm & 50 dB & 18 GHz & 8–24 mW & 2018 [17] \\ \hline
1550 nm & 106 nm & 55 dB & - & 30 mW & 2020 [18] \\ \hline
2.02 \(\upmu\)m & 110 nm & 53 dB & - & 7.1 mW & 2021 [19] \\ \hline \hline \end{tabular} Note: “-” denotes that the data are not available.  
\end{table}
Table 2: The performance of Littman structure of NLECSLs.  
Figure 2: Littman structure of NLECSLs.
## 2 Methodology
### FE Model Updating Using Cso
#### 2.2.1 Formulation of the Elemental Mass and Stiffness Matrices  
Semi-rigid boundary conditions are assumed to define the loosening of bolts at joints. At the boundary, the semi-rigidity is ensured by considering rotational springs for each of the elements. Figure 2b represents the deformed shape of the element. In between the internal and outer sides of the spring, a relative rotation is considered. On the internal side of the spring, the rotations are \(L\theta_{1}\), \(R\theta_{1}\) while on the outer face, these rotations are \(L\theta_{2}\), \(R\theta_{2}\), respectively. [PERSON] and [PERSON] (1963) [44] provide a relationship among the moment applied, rotational spring stiffness and internal and outer rotations:  
\[\frac{RM}{Rr_{c}}=R\theta_{2}-R\theta_{1}\text{ and }\frac{LM}{Lr_{c}}=L \theta_{2}-L\theta_{1} \tag{12}\]  
In Equation (12) given above, \(LM\), \(RM\), \(Lr_{c}\) and \(Rr_{c}\) are the moments applied at the spring and stiffness of rotational spring at the right and left end of each element. Element level stiffness-matrix and mass-matrix are then generated assuming the semi-rigid support conditions [45; 46]. In [44], the relationship between the fixity factors of rotational springs with stiffness is defined by  
\[\alpha_{Lr_{c}}=\frac{1}{1+\frac{3 EI/L}{Lr_{c}}}\text{ and }\alpha_{Rr_{c}}= \frac{1}{1+\frac{3 EI/L}{Rr_{c}}} \tag{13}\]  
In Equation (13), \(L\), \(E\) and \(I\) represent element-length, elastic modulus, and the moment of inertia, respectively. For a different types of connections, Eurocode 3, as given in the following Table 1, specifies the ranges of the fixity factor.  
After the numerical modeling, the model updating operation is performed by applying CSO to quantify the level of FFs at the joints. The model updating procedure for the study is presented in the following paragraph.
# GIS-Analysis for Active Tectonics Assessment of Wadi Al-Arish, Egypt
## 4 Results and Discussion  
### Morphotectonic Indices  
#### 4.1.1 Stream Length-Gradient Index (SL)  
Values of the stream length-gradient index over the entire study area, extracted from a 30-m resolution digital elevation model and geospatial analysis are illustrated in Figure 6. SL values were computed every 100 m over the main rivers and streams. Generally, in order to define and classify different values of the index related to rock strength, various types of rock resistance were recognized, from low rock resistance (silt, marl, sand, alluvial deposits, and consolidated conglomerate), moderate rock resistance (schist and fillite, calcareous sandstone, and traverting), to high rock resistance which are low level metamorphosis (marble) [6; 7].  
In this study, we observed that nearly all the lithology of the study area shows the same level of rock resistance [1; 5; 7]. Therefore, we assume that the role of rock strength is  
Figure 6: SL index along the drainage network of the study area. For lithological legend and catchments number, see Figures 3 and 5, respectively.  
negligible and that anomalies are related tectonic signatures. The SL index showed many tectonic anomalies, particularly along the channels running over shear zones in the middle part of the study area. Anomalies were observed due to high values over tectonic elements and low values over active spots (Figure 6). Values of the SL index had variable distribution over the whole study area. The highest and most anomalous values were observed along the Minsherah Abu Kandu shear zone in the middle zone of the proposed area. Various spots over the southern part of the study area had anomalously low SL index values over various trending fractures (Figure 6). In the northern part, the lowest values of the SL index, which were anomalously low for the area, occurred where the streams crossed the Libri anticline fold. Along the southern portion of the study area, the SL index tended to have relatively low values. An anomalous value of this index over a river parallel to Regabet El-Naam shear zone was recorded due high values over a stream running over a shear zone (Figure 6). Over the middle area, between the Minsherah Abu Kandu and Regabet El-Naam shear zones, the values of the SL index were mainly low. A few higher SL index values were related to tectonic signals. Along the Abu Kandu anticline fold, the SL index had anomalously low values crossing the fold.
## 3 Data and Methods
### Assessing Burned Trees and Woody Biomass Loss in the Fire Area  
The number of trees that were consumed by the fire (or which died following the fire) were calculated from the satellite-derived stand density and burn severity maps. Following in-field observations and observations reported by [PERSON] et al. 2009 [57], we applied assigned values of 22%, 54%, and 98% burned trees to areas that experienced low, moderate, and high burn severity, respectively. Average values of 10%, 25%, and 40% were applied on the AGB map to derive the woody biomass consumed by the fire in low, moderate, and high severity burned areas, respectively [57]. Higher values were reported in Mediterranean environments of 25%, 47%, and 65% [58], and 32%, 52%, and 85% [59] for low, moderate, and high burn severities. These values are generally within the range provided by [PERSON] et al. 2009 [57]. Nevertheless, we decided to use here the aforementioned averaged values provided by [PERSON] et al. 2009 [57], which give a more moderate and realistic estimation compared to the qualitative assessment which was undertaken during the field survey in our study area.
## 4 Conclusions
### Future Research Implications and Clinical Applications  
This line of research is fruitful for the development of specific biomarkers in both clinical conditions that can assist diagnostic processes as well as provide information about the overlapping neurodevelopmental origins of those conditions. Further research on \(mu\) rhythm suppression on schizophrenia and ASD will offer a more wholesome understanding of the factors that shape the aetiology of the disorders and will aid in the development and design of appropriate interventions. Priority should be given on disentangling core deficits from secondary effects related to attention, motivation, and symptom heterogeneity. Advances in neuroimaging and electrophysiological techniques, particularly when combined with eye-tracking and individualized symptom profiling, may help clarify whether the MNS anomalies reflect a distinct neurobiological marker or are contingent upon broader cognitive disruptions. Longitudinal and transdiagnostic studies are also essential to determine the developmental trajectories and shared versus divergent MNS mechanisms across the two conditions. Clinically, improved understanding of the MNS function may inform targeted interventions aimed at enhancing social cognition, such as neurofeedback protocols based on \(mu\) rhythm modulation or/and adjunctive treatments using neuromodulators, like oxytocin. However, translating these findings into practice will require rigorously controlled trials with standardised methodologies to ensure replicability and therapeutic efficacy across diverse clinical populations.  
Mirror neurons and \(mu\) rhythm suppression contribute to our understanding of the neural basis of communication and language, stipulating valuable insights for natural language processing. It is crucial to find the link and bridge the gap between artificial intelligence (AI) systems and neuroscientific evidence, in order to develop high-order models that mimic human communication, language and interaction in neurotypical and neurodivergent populations, such as ASD and schizophrenia. A deep understanding of the brain functions that define complex cognitive systems in individuals with neurodegenerative and neuropsychiatric disorders will enhance the advancement of innovative technological applications of AI systems on language impairments, and also improve language and communicational interventions and treatments [89; 90].  
Conceptualization, M.A., V.S. and E.P.; investigation, M.A., V.S. and E.P.; writing--original draft preparation, M.A., V.S. and E.P.; writing--review and editing, M.A., V.S. and E.P.; supervision, M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.