esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
## 2 Relate Research  
One of the main methods for detecting post-disaster damage using non-machine learning is damage detection based on synthetic aperture radar (SAR) [13]. [PERSON] et al. comprehensively used pre-disaster very high-resolution (VHR) optical and post-disaster SAR images to conduct a comparative test of determining whether a building is damaged [14]. Given the development of machine learning, some methods of shallow learning structure have been proposed. [PERSON] et al. used a systematic approach to evaluate object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery [15]. In addition, other machine learning methods, such as SVM, K-nearest neighbor, random forest, AdaBoost, and Naive Bayes, combine texture and geometric information to evaluate damages to buildings [16; 17; 18; 19].  
Compared with the traditional feature extraction method, CNN [20] does not need to extract specific manual features from the image for a specific task but simulate the human visual system to perform hierarchical abstraction processing on the original image to generate classification results. CNN has the advantages of strong applicability, simultaneous feature extraction and classification, strong generalization ability, and few global optimization training parameters [21]. Research has shown that the accuracy of classification can be improved by changing the network structure of CNN [22]. The scenario after a disaster is extremely confusing and has more complex features than normal scenarios [23]. Therefore, improving the classification accuracy is the key to obtaining the ideal detection accuracy. [PERSON] and [PERSON] used a CNN model to classify the post-disaster scene of Hurricane Harvey in 2017 and achieved good results [24].  
However, the samples for training may be insufficient because a post-disaster image is difficult to obtain. [PERSON] and [PERSON] synthesized a new sample from one image by overlaying another image that was randomly chosen from the training data [25]. Pre-training a model is also a good method of improving the effect [26]. The model pre-trained by [PERSON] et al. on the ImageNet database has good generalization ability [27]. To obtain an improved model, this method inspired us to pre-train our own models.  
The current study uses the SSD algorithms to identify and evaluate post-disaster buildings. The data augmentation method is adopted for solving the problem of having only a few training samples. The VGG16 convolutional encoder is trained to replace the trained weights with the VGG16 structure in SSD.
## Nomenclature  
PM, SM  
DOFs  
Degrees of Freedom  
PW  
Parallel Wrist  
SPM  
Spherical Parallel Manipulator  
LM, CD, GD  
Linkage Mechanism, Cable Drive, Gear Drive  
PA, HA, EM  
Pneumatic Actuator, Hydraulic Actuator, Electric Motor  
ROM  
Range of Motion  
F/E, AD/AB, I/E,  
Flexion/Extension, Adduction/Abduction, Internal/External Rotation,  
P/S, UD/RD  
Promation/Supination, Ulnar Deviation/Radial Deviation Motions  
SR, EW, WT  
Shoulder Joint, Elbow Joint, Wrist Joint  
TRL  
Technology Readiness Level  
BMA  
Biomimetic actuators  
R  
Revolute joint  
U\({}_{\text{i}}\)  
Universal joints  
P\({}_{\text{i}}\)  
Prismatic joints  
S\({}_{\text{i}}\)  
Spherical joints
## 3 Results and Discussions
### Emission Characteristics
#### 3.2.4 Smoke Emission  
Figure 11 presents smoke opacity for four different test fuels according to various engine loads. At 30 Nm, the smoke opacity of BP10, BP30 and BP50 decreases by 9.38%, 6.25% and \(-\)18.75% (increase) compared with that of BP 0, respectively. At 60 Nm, the smoke opacity of BP10, BP30 and BP50 decreases by 5.88%, 11.77% and 8.82% compared with that of BP0, respectively. At 90 Nm, the smoke opacity of BP10, BP30 and BP50 decreases by 9.88%, 11.11% and 6.17% compared with that of BP0, respectively. Except for B50, the smoke opacity under 30 Nm is increased, and the smoke opacity of most blend fuels is lower than that of diesel. This may be because the density and viscosity of palm oil with a higher mixing ratio are higher, which worsens the atomization effect and leads to incomplete combustion. The reason for the reduction of smoke opacity caused by the addition of palm oil may be mainly attributed to the oxygen content of palm oil. In addition, the formation of diesel smoke is not only related to incomplete combustion, but also related to the combustion of aromatic hydrocarbons [16]. Palm oil is a kind of vegetable oil, which does not contain sulfur and aromatic hydrocarbons. Therefore, adding palm oil to diesel reduces smoke emission. [PERSON] et al. [34] pointed out that oxygenated fuel can inhibit the formation of aromatic hydrocarbons and carbon black due to the strong bond combination of carbon atoms and oxygen atoms in oxygenated fuel. Moreover, the low cetane number of the fuel leads to longer ignition delay, which provides longer mixing time between fuel and air, thereby reducing smoke emission. On the other hand, with the increase of engine load, the average smoke opacity of all fuels first decreases slightly and then increases significantly. This is mainly because there are more fuel rich areas under lower and higher loads. The reason for the obvious increase of smoke opacity under 90 Nm may be mainly due to the increase of fuel injected into the cylinder, which increases the fuel droplet size and is not conducive to the formation of homogeneous mixture [31].
# A Survey on Cybersecurity in IoT
## 4 Network Security and Communication Protocols
### Advanced Security Mechanisms  
To address these vulnerabilities, several advanced security mechanisms have been developed and proposed. For MQTT, techniques such as payload encryption, token-based authentication, and the integration of zero-trust architectures are being explored to enhance security without significantly impacting performance. The concept of zero-trust, where every device is authenticated and authorized before it can communicate, is particularly relevant for IoT, where devices may be deployed in potentially hostile environments [183; 184; 185].  
In CoAP, the use of object security, where security is applied directly to the data being transmitted rather than the transport layer, has been proposed as a means to improve security without the overhead associated with DTLS [186]. This approach, combined with the implementation of secure key management protocols such as OSCORE (Object Security for Constrained RESTful Environments), can mitigate many of the security risks inherent in CoAP-based systems [187; 188].  
Zigbee's security can be enhanced through the implementation of stronger key management practices and the use of frequency hopping techniques to make it more difficult for attackers to intercept and analyze communications. Additionally, ongoing research into the use of quantum-resistant cryptography for IoT networks promises to provide long-term security solutions that can withstand the advances in computational power expected in the coming years [189; 190; 191].
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 5 Experimental Results
### Results and Discussion
Meanwhile, RNN has a lower AUC value (0.92) than LSTM and GRU due to RNN having a slightly higher FPR (12.8%), which negatively impacts the calculated AUC value. Overall, the LSTM classifier outperforms the GRU and RNN classifiers in evaluation performance, consistent with the fact that LSTM and GRU were designed to overcome RNN's vanishing gradient problem. As a result, LSTM is being used in research as a classifier to build a training model capable of detecting ICMPv6 flooding DDoS attacks with high detection accuracy and a low FPR. Lastly, the detection time was measured for all detection models to measure their performance in real-time network environments. The experiment was run thrice on a physical machine to ensure reliable results. The average detection time was calculated as tabulated in Table 11. The average detection time of RNN, LSTM, and GRU are 5.530, 8.337, and 8.110 s, respectively, on a Windows 10 workstation with an Intel Core i5-3570 3.40 GHz CPU and 16 GB RAM. The detection time can be reduced further by employing a high-end server.
## 3 Materials and Methods
### Data Processing  
The XRFS, RS, and LIBS data was pre-processed as described below. Subsequent chemometric data processing and analysis was undertaken using MATLAB(version 2019a. Partial least squares discriminant analysis (PLSDA) was used for both the instrument-specific and fusion classifiers. PLSDA is an extension of the partial least squares (PLS) regression technique commonly used in chemometrics to model the multivariate structural relationship between two matrices [54; 55]. In this study, PLSDA models were developed for the spectral data using open source software--the Pattern Recognition Toolbox for MATLAB ([[https://github.com/covartech/PRT](https://github.com/covartech/PRT)]([https://github.com/covartech/PRT](https://github.com/covartech/PRT))). The specific implementation of PLSDA was based on the SIMPLS algorithm [56].  
LIBS: Two preprocessing steps were applied to the LIBS spectra followed by normalization. First, sections of the LIBS spectra were removed to ensure that the presence of argon would not be used for classification. Three spectral windows were therefore used for subsequent data processing: 190-675, 765-771, and 776-779 nm. The presence of argon in the LIBS spectra is an effect of the collection system and its removal ensures that the presence of argon will not erroneously bias classification results. This process reduced the LIBS spectra from 23,431 data points to 14,823 data points. Second, the LIBS spectra were further preprocessed by applying a baseline correction algorithm that consisted of a two-stage process. A Hampel filter was first applied to remove the response peaks from the spectra [57]. For the filter, the window size was 2% of the spectrum length (296 data points). For each sample, if the response magnitude was more than three standard deviations from the window's median, it was replaced by the median. Once the response peaks were removed from the spectrum, the baseline of the spectrum was estimated by smoothing the residual with a median filter that was 0.5% of the spectrum length (74 data points). This smoothed baseline estimate was then subtracted from the original spectrum. Once the argon portions of the spectrum were removed and the baselinecorrected, the LIBS spectrum was scaled by subtracting the mean of the spectrum and dividing by its standard deviation.  
XRFS: While three different beam energies were used to collect XRFS spectra, preliminary testing indicated no increase in classification performance with the addition of the spectra from the 40 keV and 15 keV spectra; therefore, only the 50 keV spectra were used for classification. The XRFS spectra were truncated to be the same length for classification purposes and normalized using the same procedure described above for the LIBS spectra.  
RS: RS spectra were normalized as described for LIBS and XRFS spectra.
# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 1 Introduction
[16] developed a stacked classifier combining MRI and genetic data, enabling simultaneous identification of pathological brain regions and associated risk genes. Building on this paradigm, [PERSON] et al. [17] proposed PIDGN, a multimodal deep learning framework that employs a Transformer architecture and 3D ResNet18 for feature extraction, coupled with a dynamic gated attention mechanism to fuse multimodal features, achieving both accurate early diagnosis and model interpretability. Similarly, [PERSON] et al. [18] introduced the CERNNE method, which integrates fMRI and SNP data, using a clustering evolution random neural network ensemble to automatically select PD-related features, thereby improving patient identification and disease cause prediction accuracy. Together, these studies highlight the transformative potential of multimodal integration in unraveling the biological complexity of PD and advancing diagnostic precision. While existing methods have advanced PD diagnosis, two critical challenges remain unresolved and require optimization: persistent dependence on predefined genetic loci and ongoing limitations in static multimodal fusion strategies. To overcome these limitations, this study proposes the Residual Attention Contrastive Fusion (RACF) network, an interpretable deep learning framework designed to integrate Genome-Wide Association Study (GWAS)-prescreened SNPs with structural MRI features for PD diagnosis. The framework introduces two methodological innovations. First, it establishes a priori-agnostic SNP analysis paradigm that synergizes GWAS-based filtering with transformer architectures to model non-linear genetic interactions, eliminating dependency on predefined loci. Second, it implements dynamic cross-modal fusion through attention mechanisms and contrastive learning, enabling adaptive weighting of genetic-neuroanatomical relationships while mitigating static fusion limitations. The model incorporates dual interpretability pathways using SHapley Additive exPlanations (SHAP) values and Gradient-weighted Class Activation Mapping (Grad-CAM) to decode genetic epistasis effects and neuroimaging biomarkers. Validated against existing models, RACF demonstrates superior diagnostic accuracy. The primary contribution of this study lies in creating a novel unified framework that concurrently addresses three critical challenges in neurodegeneration diagnostics: disentangling genetic epistasis complexity, optimizing multimodal fusion dynamics, and bridging the clinical interpretability gap--thereby advancing both mechanistic understanding and translational applications.
# Experimental Identification of Characteristic Curves of
## 6 Conclusions  
In this research, the Total Evacuation Time (TET), which measures how quickly an ejector can reach the lowest minimum pressure, is predicted by developing accurate empirical models that incorporate key ejector performance metrics such as the characteristic \(m_{s}(p)\) curve and polytropic \(n(p)\) curve. Through refined experimental setups, it succeeded in capturing data that improved the precision of these models, minimizing deviations between experimental and predicted results.  
One finding of this work is the importance of the characteristic \(m_{s}(p)\) curve, which links the absorbed flow rate to the working pressure. The data and literature revealed a critical transition point, or breakpoint (bP), where the operation mode of the ejector shifts from supersonic to subsonic. This transition influences both the \(m_{s}(p)\) curve and the Predicted TET (PTET), and pinpointing this bP improved the accuracy of the mathematical models. The research demonstrated that with each refined setup, the PTET approached the actual experimental values more closely, reducing the error margin from 7.6% to just 1.4%in the most precise setup. The polytropic \(n(p)\) curve, which describes how the polytropic coefficient evolves as a function of working pressure, played an important role in forecasting the PTET. The experiments demonstrated that at higher pressures, the polytropic coefficient behaves adiabatically (1.4), while at lower pressures, it approaches isothermal conditions (1.0). Refinement of the experimental setup improved the fitting of data to the second-order polynomial trend, critically halving PTET error deviation from 2.9 % to 1.4 % in the most refined setup. The PTET deviation of 1.4% (2.6 s in absolute terms) demonstrates that the empirical model successfully matches the TET, confirming its validity.  
With the models validated, an optimized vacuum ejector design was tested, which was based on previous authors' research. This optimized ejector showed a 4% improvement in PTET (about 8 s), which exceeded the PTET error. The empirical models based on these curves proved highly effective in reducing the dependency on additional experimental data, allowing for faster comparison and optimization of different ejector designs. This underscores the potential for combining experimental data with simulations to design more efficient industrial components such as vacuum ejectors.
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 3 Methodology
### Problem Definition and Formulation
#### 3.2.3 Search Space and Constraints  
It is well known that constraints enhance the problem complexity. Hence, we either (i) applied constraints as penalty functions into the cost function, such as Equations (9)-(11), or (ii) applied constraints as search space limitations, as described below.  
The coordinates of the space are denoted as \(x\), \(y\), and \(z\); hence, the search space can be expressed as  
\[\{(x,y,z)\in\mathcal{R}|x_{min}\leq x\leq x_{max},y_{min}\leq y\leq y_{max},z_{ min}\leq z\leq z_{max}\}, \tag{12}\]  
where, \(x_{min},x_{max},y_{min},y_{max},z_{min}\), and \(z_{max}\) define the boundary of \(x\), \(y\), and \(z\), respectively. The boundaries of \(x\) and \(y\) can be defined according to the scale of the considered environment. The constraint of the permitted UAV flight height is applied to the boundaries of \(z\). Hence, \(z_{min}\) and \(z_{max}\) can be defined based on the UAV flight regulations in each region/country.  
Another constraint that needs to be taken into account is the maximum flight distance, \(L_{max}\), the maximum distance a drone can travel in a single flight. \(L_{max}\) is mainly limited due to physical constraints, such as battery and fuel restrictions. Therefore,  
\[L_{total}\leq L_{max}, \tag{13}\]  
where \(L_{max}\) for a battery-powered multirotor can be estimated as  
\[L_{max}=V_{avg}\times\frac{C_{battery}\times D_{battery}}{AAD}\ (\text{in km}), \tag{14}\]  
where \(V_{avg}\) is the drone's average speed in km/h, \(C_{battery}\) is the battery capacity in Ah, \(D_{battery}\) is the battery discharge ratio, and \(AAD\) is the drone's average ampere draw in amps. In this study, \(D_{battery}\) is considered 0.8, since it is common practice not to discharge LiPo batteries below 20% mAh during flight. The calculation of \(AAD\) depends on factors such as the drone's payload, the size of motors, and utilized hardware components. The details of \(AAD\) of the utilized drones in this study can be found in [50].
## 2 Device and Experimental Setup
### Terahertz Characterisation Setup  
The schematic of the free-space setup used to measure the response of the transistor under sub-THz radiation is given in Figure 2. It was already described in [19]. A commercial solid-state sub-THz two-tones source was employed. The emitted power by the source at its lowest frequency (0.15 THz) was of 3 mW, while at 0.3 THz the power was 6 mW. A calibrated pyroelectric detector was used to characterize the source prior to measurements.  
The transistor was first wire-bounded using 25 \(\upmu\)m-diameter pure gold wires on a DIP14 (dual in line package) as shown in Figure 3. The photovoltaic mode response of the transistor was measured using the locking technique (a Stanford Research SR830 (Stanford Research Systems, Sunnyvale, CA, USA) lock-in amplifier was used) with chopping frequency at 298 Hz. Measurements were done at room temperature.  
Figure 3: Left: The device under study, framed in a red rectangle for ease of viewing, glued and mounted on a DIP14 and wire-bounded to it. Right: Detailed view of the \(\Pi\)-gate transistor.  
Figure 2: **(a)** Schematic description of the experimental setup: a solid-state sub-THz source by RPG (RPG-Radiometer Physics GmbH, Meckenheim, Germany) (shown in the bottom left corner) generates two output frequencies 0.15 and 0.3 THz; **(b)** Detail of the schematic of the FET detector mounted on a x-y stage with both configurations and where the THz rays polarization is highlighted.
## 3 Time-Synchronization Algorithm of Heterogeneous Networks  
### Network-Synchronization Procedure via a Gateway  
A gateway is needed to convey messages from one type of network to another. It is unavoidable to have a processing delay to convert the messages, which impacts the time-synchronization performance. Figure 2 illustrates the time-synchronization process of the E2E method in a CAN-Ethernet network including a gateway with an Ethernet node as a master. t\({}_{\text{Eth2 CAN}}\) indicates the processing delay_required  
Figure 1: Time-synchronization mechanism of IEEE 1588 in (a) end-to-end and (b) peer-to-peer systems [13].  
to convert an Ethernet message into a CAN message, and \(\text{t}_{\text{CAN2 Eth}}\) denotes the processing delay_required to convert a CAN message into an Ethernet message. \(\text{t}_{\text{Eth2 CAN}}\) is calculated by measuring the time interval from the time instant the gateway receives the Ethernet message to the moment it starts to transmit the converted CAN message. Conversely, \(\text{t}_{\text{CAN2 Eth}}\) is obtained by measuring the time interval from the time instant the gateway receives the CAN message to the time it begins to send the converted Ethernet message. Both \(\text{t}_{\text{CAN2 Eth}}\) and \(\text{t}_{\text{Eth2 CAN}}\) may have different values because the time required for each network to receive and convert the message differs. Consequently, the network has an asymmetrical structure when a gateway is included, which can prevent precise synchronization. Equations (3) and (4) represent propagation delay and offset calculations, considering the processing delay that occurs when a gateway converts a message in a CAN-Ethernet network.  
\[delay=\frac{\left[(t_{4}-t_{1}-t_{\text{Eth2 CAN}}-t_{\text{CAN2 Eth}})-(t_{3}-t_ {2})\right]}{2} \tag{3}\]  
\[offset=t_{2}-t_{1}-t_{\text{Eth2 CAN}}-delay=\frac{\left[(t_{2}-t_{1}-t_{\text{ Eth2 CAN}})-(t_{4}-t_{3}-t_{\text{CAN2 Eth}})\right]}{2} \tag{4}\]  
To calculate the propagation delay and offset in a CAN-Ethernet network with a gateway, the slave must confirm the values of \(\text{t}_{\text{Eth2 CAN}}\) and \(\text{t}_{\text{CAN2 Eth}}\). For this purpose, the gateway can store the calculated value of \(\text{t}_{\text{Eth2 CAN}}\) in the synchronization message and transmit it to the slave, while \(\text{t}_{\text{CAN2 Eth}}\) can be stored in the delay-request message and transmitted to the master. The master learns \(\text{t}_{\text{CAN2 Eth}}\) at time \(\text{t}_{4}\) after receiving the delay-request message, stores the value obtained by subtracting \(\text{t}_{\text{CAN2 Eth}}\) from \(\text{t}_{4}\) in the delay-response message, and transmits it to the slave.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 3 Methodology  
Using the CAN-SEAT strategy, clients may need to provide a valid digital certificate to identify themselves. The client is represented as \(C=\{n,e,s,v\}\), where \(n\), \(e\), \(s\), and \(v\) represent the client's name,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protection** & **Symmetric** & \
\begin{tabular}{c} **Factoring** \\ **Modulus** \\ \end{tabular} &
\begin{tabular}{c} **Elliptic** \\ **Curve** \\ \end{tabular} & **Hash** \\ \hline Legacy standard level & & & & \\ Should not be used in new systems & 80 & 1024 & 160 & 160 \\ Near-term protection & 128 & 3072 & 256 & 256 \\ Security for at least 10 years (2020–2028) & 256 & 15360 & 512 & 512 \\ Security for 30 to 50 years (2020–2068) & 256 & 15360 & 512 & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The minimal key sizes for security (bits) [23].  
e-mail, issuer information, and the certificate validity data, respectively. This section describes two methods of client-aware negotiation based on renegotiation or redirection.