esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Quantification of Bisphenol A in Dichloromethane Extracts and Food Simulants  
An LC-tandem mass spectrometer (Thermo Finnigan TSQ Quantum Ultra AM) was used for chromatographic separation and detection. The separation was carried out on a Restek Raptor Biphenyl column, 2.7 \(\upmu\)m, 50 \(\times\) 2 mm (injection volume 10 \(\upmu\)L, oven temperature 40 \({}^{\circ}\)C). The mobile phases consisted of ultrapure water (A) acetonitrile (LC-MS grade) (B). The constant flow rate was 500 \(\upmu\)L/min, and the gradient program was as follows: initial conditions at 75% (A), 25% (B); gradient up to 0% (A), 100% (B) until 2.5 min; 25% (A), 75% (B) until 5 min. The ionization was achieved using atmospheric pressure chemical ionization (APCI) in negative polarity mode (settings: spray-voltage: 7.0 eV; vaporizer temperature: 425 \({}^{\circ}\)C; shear gas flow: 15 arb; aux gas flow: 7 arb; capillary temperature: 275 \({}^{\circ}\)C; skinmer 0) with the multiple reaction mode (MRM) using the substance-specific mass transitions (parent ion (\(m/z\)): 227 daughter ion (\(m/z\)); 212 (quantifier); and 133 (qualifier).  
The data were collected and processed by Thermo XCalibur 2.2. The specified working range of the LC-MS method is between 1 ng/mL and 1000 ng/mL. The limit of quantification (the lowest measurable standard) is 1 ng/mL, while the detection limit is calculated using the lowest measurable standard and is 7 \(\upmu\)g/kg for a sample weight of approx. 3 g in the case of BPA in PET bottles and for approx. 50 g (according to 50 mL) 0.02 \(\upmu\)g/L for food simulants. The recovery rate of BPA inside PET is 100.83% (\(\pm\)2.23%, \(n=18\)) in the lower calibration range, 99.27% (\(\pm\)2.28%, \(n=18\)) in the middle range, and 98.24% (\(\pm\)1.78%, \(n=18\)) in the upper calibration range. The recovery rate of BPA in water is 99.79% (\(\pm\)3.0%, \(n=6\)). The suitability of the method for the determination of the substance was confirmed in validation measurements and independent inter-laboratory tests. The modified extraction method for the food simulants 20% ethanol, 10% ethanol, and 3% acetic acid was tested with spiked solutions. The recovery rate of BPA is 97.85% (2.59%, \(n=2\)) in 20% ethanol, 96.07% (\(\pm\)1.35%, \(n=2\)) in 10% ethanol, and 96.77% (\(\pm\)2.48%, \(n=2\)) in 3% acetic acid.
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 4 Slope-Based RAIM Availability Assessment
### Practical Meaning of the Ideal Slope Threshold  
Here, the specific example in Section 2.3 is used to intuitively explain the practical meaning of \(T_{Slope}\). The \(a_{v}\) value for this example is 4.944. Given \(P_{r}(\text{HMI})=1\times 10^{-7}\), \(P_{r}(\text{HMI},2F)=1.3\times 10^{-8}\), \(\text{VAL}=50\) m, \(P_{sat}=1\times 10^{-5}\) and \(\alpha=1\times 10^{-6}\), the ideal slope threshold can be numerically solved according to Equation (19), and \(T_{Slope}=5.390\). Figure 6 presents the \(\mu_{s}-lgP(\text{HMI}|1F)\) curves for \(Slope=6.060\) (PRN5), \(Slope=3.010\) (PRN6) and \(T_{Slope}=5.390\). \(P(\text{HMI}|1F)\) for \(T_{Slope}\) is calculated according to Equation (19). The \(\mu_{s}-lgP(\text{HMI}|1F)\) curve for \(T_{Slope}\) is tangent to the \(lgP_{r}(\text{HMI}|1F)\) line. The \(\mu_{s}-lgP(\text{HMI}|1F)\) curve for \(Slope=6.060\), which is larger than \(T_{Slope}\), is intersected with the \(lgP_{r}(\text{HMI}|1F)\) line. In contrast, the \(\mu_{s}-lgP(\text{HMI}|1F)\) curve for \(Slope=3.010\), which is smaller than \(T_{Slope}\), is separated from the \(lgP_{r}(\text{HMI}|1F)\) line.  
It can be deduced that the slope value determines the positional relationship between the \(\mu_{s}-lgP(\text{HMI}|1F)\) curve and the \(lgP_{r}(\text{HMI}|1F)\) line. \(T_{Slope}\) forms a tangent \(\mu_{s}-lgP(\text{HMI}|1F)\) curve to the \(lgP_{r}(\text{HMI}|1F)\) line. If the slope value of a faulty satellite is larger than \(T_{Slope}\), its \(\mu_{s}-lgP(\text{HMI}|1F)\) curve would be intersected with the \(lgP_{r}(\text{HMI}|1F)\) line, meaning that \(P(\text{HMI}|1F)\geq P_{r}(\text{HMI}|1F)\) can be satisfied for at least one possible measurement bias value. If the slope value of a faulty satellite was smaller than \(T_{Slope}\), its \(\mu_{s}-lgP(\text{HMI}|1F)\) curve would be separated from the \(lgP_{r}(\text{HMI}|1F)\) line, meaning that \(P(\text{HMI}|1F)<P_{r}(\text{HMI}|1F)\) can be ensured at an arbitrary measurement bias value. From the analysis of this specific example, the slope is an ideal test statistic for RAIM availability assessment, with the ideal threshold \(T_{Slope}\) calculated according to Equation (19). Therefore,  
Figure 5: \(T_{Slope}\) for different \(a_{v}\) values.  
for each observation epoch, the slope of a visible satellite can be considered as \"large slope\" if it was larger than \(T_{slope}\) and considered as \"small slope\" if it was smaller than \(T_{slope}\).
# Using Financial Instruments to Transfer the Information Security Risks
## 6 Design Specifications for Information Security Derivatives
### ISD Design Process
#### 6.2.3 Types of ISD  
The ISDs can be designed in at least four forms: (i) Information Security Options (ISO); (ii) Information Security Vanilla Options (ISVO); (iii) Information Security Futures (ISF); (iv) Information Security Swaps (ISS).  
_(i) Information Security Options:_ ISO are designed such that the buyer and seller agree to exchange a fixed payout linked to the occurrence of the underlying information security event or condition. In other words, the buyer of ISO is in money if the event occurs, and the seller is in money if the event does not occurs. Thus, there are only two possible outcomes (states). The payout received by the buyer and seller is same, however the price paid by the buyer and seller may be different. The difference in investment price depends on the event's probability estimate by the market forces.  
_(ii) Information Security Vanilla Options:_ ISVO are designed as a variable payout instrument. The payment by the seller to the buyer depend on how far the underlying event results are from the payout trigger. ISVO are different from ISO in a way that the buyer pays a premium to the seller in exchange of a variable payment. However, the seller only gets the premium and is not entitled for a reciprocal right to receive a payment from the buyer in case of non-occurrence of the underlying event.  
_(iii) Information Security Future:_ ISF is a contract between two counterparties. In ISF, one counterparty pays a variable amount to the other if the underlying event occurs. For the future contract the payout will vary with the degree by which the actual result falls short or surpasses the trigger. The payout varies like in case of the ISVO, however in case of a future contract a payment from the buyer to seller is required if the underlying event does not occurs.  
_(iv) Information Security Swaps:_ A traditional swap agreement between the counterparties fixes the obligation to exchange the predefined payments over a predefined time frame in future. Similarly, the ISS would require a payment(s) to the buyer if the underlying event occurs in exchange of a payment(s) from the buyer. The amount of the payment can be variable or fixed.  
_(v) Bundled Information Security Derivatives:_ This would be a group of fixed or variable payout information security derivatives, each of which represents a mutually exclusive range of states of the underlying event, and only some of the \"states\" of the underlying event can occur. Furthermore, a bundle of derivatives can be designed, where the occurrence of one or more states in one or more of derivatives in the bundle is conditional upon the occurrence of one or more states in the bundle contract.
## Materials and Methods
### Support Vector Machine Based on IFA
#### 2.5.2 The Pipeline Leak Recognition Method Based on SVM and IFA  
Based on above algorithms, a novel pipeline leak recognition method based on SVM and IFA is proposed, and the flow is shown as Figure 5.  
1. Data set acquisition. The data set is acquired and labeled, and the data set is divided into a training data set and a test data set.
2. Data preprocessing and feature extraction. The acquired data are preprocessed, and appropriate feature extraction methods are used to extract the features of the data.
3. Training and establishment of support vector machine model. Support vector machines are trained according to the extracted data features, and IFA is used to optimize the kernel function parameters of the SVM, and the optimal hyperplane of the corresponding data are found by calculating the training data.
4. Test data classification and result analysis. The test data are classified and tested by the support vector machine, and the test results are generated. The model is analyzed as to whether it can achieve a better classification effect.
# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 2 Related Work
### Transformer Adaptation Strategies for Limited Data  
Nevertheless, overfitting remains challenging when applying Transformer architectures to small-scale datasets. Recent studies propose multidimensional solutions: (1) pre-training and fine-tuning paradigm: leveraging BERT/GPT-like foundational models pre-trained on large-scale generic datasets, followed by target-domain fine-tuning to enhance generalization; (2) data augmentation techniques: expanding training data distributions through spatial operations like random cropping and rotation transformations; (3) regularization strategies: integrating dropout layers and weight decay parameters to control model complexity; (4) lightweight architectures and knowledge distillation: combining streamlined structures with model distillation techniques to reduce overfitting risks while maintaining computational efficiency; and (5) transfer learning: utilizing pre-trained parameters from related tasks for model initialization, followed by domain-adaptive fine-tuning for knowledge transfer. These methods collectively balance model capacity with data scale through synergistic interactions.  
With the widespread adoption of encryption technologies, evolution of attack techniques, and strengthening of privacy regulations, traditional traffic-classification methods have gradually become obsolete. Early machine-learning approaches were constrained by data-quality limitations, prompting the academic community to shift toward deep learning. While end-to-end architectures significantly enhanced classification performance, GNN encountered bottlenecks due to insufficient dynamic feature representation and high computational complexity. The introduction of Transformer architectures has brought breakthroughs through sequence modeling and self-attention mechanisms, with their advantages demonstrated in three key aspects: raw traffic processing, long-range dependency capture, and dynamic feature fusion. To address overfitting in small datasets, strategies like pre-training, data augmentation, and lightweight architectural designs have emerged as effective solutions. The field currently exhibits three major trends: the transition from manual feature engineering to deep automated learning, the evolution from static analysis to spatiotemporal dynamic modeling, and the systematic progression toward intelligent systems with enhanced robustness.
## 4 Discussion
The findings support the potential for personalized recovery protocols that can enhance performance and reduce injury risks [60]. Future research should continue to explore the integration of psychological variables and the impact of interpersonal dynamics on recovery and performance, further refining the predictive models and enhancing their applicability in real-world sports settings. Moreover, this study highlights the importance of personalized approaches in football training [61]. Each athlete responds uniquely to training loads and recovery processes, influenced by factors such as age, developmental stage, and individual physiology. ML models facilitate the creation of tailored recovery strategies that address these individual differences, thereby optimizing training adaptations and overall athletic performance [38, 39]. This study has several limitations that should be acknowledged. First, the sample size was limited to 60 sub-elite Portuguese young football players, which may restrict the generalizability of the findings to broader or more diverse populations. Second, the primary reliance on the sRPE as a subjective measure may introduce bias, as individual perceptions of effort can vary significantly. Additionally, the absence of objective biomechanical parameters, such as joint loads and muscle activation data, may have limited the accuracy of the ML models. The relatively low predictive accuracy observed suggests that incorporating a combination of subjective and objective metrics may improve model performance. Larger and more diverse samples, as well as the integration of objective biomechanical data, may enhance the robustness of the findings.
## 2 System Description  
### Quarter Car Suspension Model  
The quarter suspension model using a pneumatic actuator is displayed in Figure 1. The sprung mass \(m_{ch}\) denotes the total weight of the chassis structure and passenger while the unsprung mass \(m_{us}\) is composed of the assembly of the vehicle axis and wheel. The stiffness coefficient of the linear spring is defined by \(k_{sp}\) while the damping coefficient \(c_{d}\) of the damper is designed as passive components of the suspension system. The tire is modeled by a linear spring with a stiffness coefficient \(k_{dt}\) and the damping of the tire is represented by \(c_{dt}\). To create the active force \(F_{a}\), a pneumatic spring is used for the suspension, which is installed between the chassis and unsprung mass. Besides, the nonlinear stiffness coefficient \(k_{ap}\) of air bellow will contribute to the total stiffness of the system. The position of the chassis and unsprung mass will be determined by system state variables \(z_{ch}\) and \(z_{us}\) while road profile will be presented by \(z_{rp}\).  
The dynamic equations of the quarter car model are given as  
\[\begin{array}{l}m_{ch}\ddot{z}_{ch}=-F_{sp}(z_{ch},z_{us},t)-F_{da}(\dot{z}_{ ch},\dot{z}_{us},t)+F_{a}\\ m_{us}\ddot{z}_{us}=F_{sp}(z_{ch},z_{us},t)+F_{da}(\dot{z}_{ch},\dot{z}_{us},t)- F_{spt}(z_{us},z_{rp}t)-F_{dat}(\dot{z}_{us},\dot{z}_{rp},t)-F_{a}\end{array} \tag{1}\]  
These forces, which are created by the stiffness of pneumatic spring, mechanical springs, and tires, can be expressed as \(F_{sp}(z_{ch},z_{us},t)=(k_{sp}+k_{ap})(z_{ch}-z_{us})\), \(F_{da}(\dot{z}_{ch},\dot{z}_{us},t)=c_{d}(\dot{z}_{ch}-\dot{z}_{us})\), \(F_{spt}(z_{us},z_{rp}t)=k_{dt}(z_{us}-z_{rd})\),\(F_{dat}(z_{us},\dot{z}_{rp},t)=c_{dt}(\dot{z}_{us}-\dot{z}_{rd})\).  
The expression for the tire force depends on the road holding condition which is given by the following equation [45]  
\[F_{tr}=\left\{\begin{array}{ll}F_{spt}+F_{dat}&if\ F_{spt}+F_{dat}<(m_{ch}+m_{us} )g\\ 0&if\ F_{spt}+F_{dat}\geq(m_{ch}+m_{us})g\end{array}\right. \tag{2}\]  
where \(g\) is the acceleration of gravity.  
Define new state variables: \(x_{1}=z_{ch}\), \(x_{2}=\dot{z}_{ch}\), \(x_{3}=z_{us}\), \(x_{4}=\dot{z}_{us}\), dynamic equations of quarter vehicle suspension can be described by state space as follows  
\[\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=\frac{1}{m_{ch}}\big{[}-F_{sp}(x_{1},x_{3},t)-F_{dd}(x_{2},x_{4}, t)+F_{a}\big{]}\\ \dot{x}_{3}=x_{4}\\ \dot{x}_{4}=\frac{1}{m_{us}}\big{[}-F_{spt}(x_{3},z_{rp},t)-F_{dd}\big{(}x_{4 },\dot{z}_{rp},t\big{)}+F_{sp}(x_{1},x_{3},t)+F_{dd}(x_{2},x_{4},t)-F_{a}\big{]} \end{array} \tag{3}\]
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
###### Abstract  
Although the spectrum sensing algorithms based on deep learning have achieved remarkable detection performance, the sensing performance is easily affected by adversarial attacks due to the fragility of neural networks. Even slight adversarial perturbations lead to a sharp deterioration of the model detection performance. To enhance the defense capability of the spectrum sensing model against such attacks, an unsupervised learning-based spectrum sensing algorithm with defending adversarial attacks (USDAA) is proposed, which is divided into two stages: adversarial pre-training and fine-tuning. In the adversarial pre-training stage, encoders are used to extract the features of adversarial samples and clean samples, respectively, and then decoders are used to reconstruct the samples, and comparison loss and reconstruction loss are designed to optimize the network parameters. It can reduce the dependence of model training on labeled samples and improve the robustness of the model to attack perturbations. In the fine-tuning stage, a small number of adversarial samples are used to fine-tune the pre-trained encoder and classification layer to obtain the spectrum sensing defense model. The experimental results show that the USDAA algorithm is better than the denoising autoencoder and distillation defense algorithm (DAED) against FGSM and PGD adversarial attacks. The number of labeled samples used in USDAA is only \(11\%\) of the DAED. When the false alarm probability is \(0.1\) and the \(SNR\) is \(-10\) dB, the detection probability of the USDAA algorithm for the fast gradient sign method (FGSM) and the projected gradient descent (PGD) attack samples with random perturbations is above \(88\%\), while the detection probability of the DAED algorithm for both attack samples is lower than \(69\%\). Additionally, the USDAA algorithm has better robustness to attack with unknown perturbations.  
spectrum sensing; security; adversarial attacks and defense; unsupervised learning; contrast loss; reconstruction loss +
Footnote †: journal: Applied  
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# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 3 Materials and Methods
### Details of libFuzzer's Internal and Customization for Partial Coverage  
In libFuzzer, code coverage information is specifically referred to as Feature, which is a libFuzzer-specific terminology for measuring code coverage. libFuzzer converts code coverage information into multiple _features_, which include edge coverage, edge counters, value profile, indirect caller/callee pairs, etc. [18]. Each feature is represented as an unsigned 32-bit integer value based on an 8-bit counter array representing edges (possible execution branches between code). For partial coverage measurement customization, we change the granularity of the counter size to lower the precision/accuracy of the measured coverage. Specifically, we modify the 8-bit array counter for feature collection to 3 bits using counter buckets. Counters falling into each bucket 1, 2, 3, 4-7, 8-15, 16-31, 32-127, 128+ are mapped into corresponding integers ranging from 0 to 7. Also, we change the way libFuzzer handles input via the _ReducedInput_ option. The ReducedInput option allows for a new input to replace its parent input in case both inputs exhibit the same code coverage. In partial code coverage customization for lowering the precision, we discard the mutated test case with the same code coverage, even if it is shorter than its parent test case. These modifications give us a customized version of libFuzzer with less coverage feedback capability. We use such an intentionally poor-performing version of libFuzzer for comparison in our experiment.
# Simplified Energy Model and Multi-Objective Energy Consumption Optimization of a Residential House
## 2 Materials and Methods
### Mathematical Model
#### 2.1.2 Sunshine Irradiation Forecast  
For the irradiation forecast, a system from the European Union Science Hub called PVGIS (Photovoltaic Geographical Information System) was used [36]. It contains historical data on irradiation in different areas around the world measured for several years. The monthly irradiation data show a typical day's irradiation of a given month for a given place. In this case, Palermo (Italy) was chosen as the city of interest. Such an irradiation profile was then recalculated based on the expected cloudiness in the upcoming days by:  
\[\mathrm{I_{cloud}}=0.1\cdot\mathrm{I_{forecasted}}+0.9\cdot(1-\mathrm{C}/100) \cdot\mathrm{I_{forecasted}}, \tag{1}\]  
where  
\(\mathrm{I_{cloud}}\) is the irradiation with cloudiness taken into account, \(\mathrm{W/m^{2}}\);  
\(\mathrm{I_{forecasted}}\) is the typical irradiation on a clear day, \(\mathrm{W/m^{2}}\);  
\(\mathrm{C}\) is the level of cloudiness, %.  
Forecasted irradiation was calculated for each timestep (5 min) using linear interpolation for both the cloudiness forecast and the typical day's irradiation course.  
The attenuation of the irradiation by clouds is considered to be 90%. That means that even when the expected cloudiness level is 100% there is still some part of the irradiation that gets to the surface of the Earth.  
The main idea behind the use of (1) was to obtain an approximation of variation in the irradiation on the forecast horizon and to see how such variation would affect the model's output.
## 3 Results and Discussion
### Analysis of Factors Affecting Frost-Heave Crack Propagation
#### 3.3.1 Influence of Crack Width  
The average value of the strain curve after peak stabilization should be considered as the maximum generalized strain at the crack tip, as shown in Figure 11a. The relationship between the maximum generalized strain and the crack width of concrete specimens is given in Figure 15.  
A linear model fit was used in this study, as it could better fit the experimental data. The expression between the generalized strain and crack width is presented as follows:  
\[\varepsilon=1.1143d-0.283\ (0.2\ \leq\ d\ \leq\ 1) \tag{20}\]  
Propagation is prone to occur can be obtained, i.e., \(d>0.27\) mm. Therefore, corresponding maintenance measures should be undertaken for cracks with a width larger than 0.27 mm to avoid further crack propagation under frost-heave force and thus ensure the durability of slab-tracks.
## 3 Experiments  
### Dataset and Metrics  
In our experiments, the model was trained on the CelebA [44] dataset and evaluated on the CelebA and Helen [45] datasets along with the real face images. In the data preprocessing phase, images were simply cropped to a size of \(128\times 128\) based on their center point and then treated as the ground truth. Afterwards, \(16\times 16\) LR face images were obtained from the ground truth images with a /8 downscaling bicubic operation. It should be noted that the model does not require any additional facial landmarking on the dataset for training. Then, we trained the model on 18,000 face images from the CelebA dataset and evaluated its performance on 1000 face images from the same dataset along with 50 face images from the Helen dataset. Moreover, to prove the flexibility of the model, we directly evaluated the model on the Helen dataset and real face images with the model trained on CelebA. It should be noted that the proposed deep learning method cannot be trained with a single image. Thus, sufficient face images must be provided should anyone want to train it on their own dataset.  
To objectively evaluate the quality of the SR results, three image quality assessment metrics are employed here: the Peak Signal-to-Noise Ratio (PSNR) [58], Structural Similarity (SSIM) [59], and Learned Perceptual Image Patch Similarity (LPIPS) [60].