esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Fitting Description  
The model fitting process can be divided into three stages: pre-regression, regression, and post-regression steps [20]. The pre-regression stage mainly consists of the selection of a set of mathematical expressions to be fitted to the data. The regression stage consists of selecting the proper method for parameter estimation, while the post-regression stage consists of sets of activities necessary to evaluate the model.  
To illustrate the model fitting process, the following mathematical annotation is used:  
\[z=f(x,\beta)+\theta,\]where \(z\) is the response variable, \(x\) represents the input variables, \(\beta\) is the parameter of the model to be estimated, and \(\theta\) is the error. \(f\) is a mathematical function such as an exponential or logarithmic function (see Table 1 for more examples), whose expression depends on \(x\) and \(\beta\). For example, if \(f\) is the exponential function with the mathematical expression \(f(x)=b(x-x_{b})^{2}\), you have \(\beta_{f}=\{b,x_{b}\}\).  
The ability to identify and include the right variables in a model is equally important to the type of model chosen. We consider a group of \(m\) observations \(\{z_{i}\}\) on variables \(\{x_{i}\}\) and try to estimate parameter \(\beta\) by minimizing \(\theta\). The minimization of \(\theta\) follows the least-square curve-fitting procedure using the Levenberg-Marquardt (LM) algorithm. For a given equation \(f(x,\beta)\), parameters are estimated such that the sum of square of the deviations \(s(\beta)\) minimizes \(\theta\) via the following expression: \(s(\beta)=\sum\limits_{i=1}^{m}\left[z_{i}-f(x_{i}|\beta)\right]^{2}\).  
The LM algorithm combines the gradient descent and the Gauss-Newton methods. The gradient descent method reduces the sum of the squared errors by updating the parameters in the downhill direction (the direction opposite to the gradient of the equation), while the Gauss-Newton method reduces the sum of the squared errors by assuming the least-square function is locally quadratic with parameters near the optimum. The fitting procedure starts with an initial value of \(x_{0}\); then, \(x\) is adjusted by \(\Delta\) only for downhill steps verifying \(\left(\mathrm{J}^{\mathrm{T}}\mathrm{J}+\lambda\mathrm{I}\right)\Delta=\mathrm{ J}^{\mathrm{T}}\mathrm{r}\), where \(J\) is the Jacobian matrix of derivatives of the residuals for the parameters, \(\lambda\) is the damping parameter between the two steps, and \(r\) is the residual vector. A set of equations used in the study that can be selected by the user are presented in the Supplementary Materials.  
The selection of mathematical expressions for the fitting process is guided by the type of data available to fit, the knowledge of the system, and its boundaries conditions. The estimation of mathematical expression parameters is directly linked to the convergence of the fitting algorithm and the initial conditions [3].
## 6 Entropy Based on a Wavelet Transform and a Noise Model
Denoting \(h\) the information relative to a single wavelet coefficient, we define:  
\[H(X)=\sum_{j=1}^{l}\sum_{k=1}^{n_{j}}h(w_{j,k}) \tag{10}\]  
with the information of a wavelet coefficient, \(h(w_{j,k})=-\ln p(w_{j,k})\), ([PERSON]'s definition rather than [PERSON]'s). \(l\) is the number of scales, and \(n_{j}\) is the number of samples in wavelet band (scale) \(j\). For Gaussian noise, and recalling that wavelet coefficients at a given resolution scale are of zero mean, we get  
\[h(w_{j,k})=\frac{w_{j,k}^{2}}{2\sigma_{j}^{2}}+\text{constant} \tag{11}\]  
where \(\sigma_{j}\) is the noise at scale \(j\). When we use the information in a functional to be minimized (for filtering, deconvolution, thresholding, etc. ), the constant term has no effect and we can omit it. We see that the information is proportional to the energy of the wavelet coefficients. The larger the value of a normalized wavelet coefficient, then the lower will be its probability of being noise, and the higher will be the information furnished by this wavelet coefficient. In summary,  
* Entropy is closely related to energy, and as shown can be reduced to it, in the Gaussian context. * Using probability of wavelet coefficients is a very good way of addressing noise, but less good for non-trivial signal. * Entropy has been extended to take account of resolution scale. In this section we have been concerned with the following view of the data: \(f=g+\alpha+\epsilon\) where observed data \(f\) is comprised of original data \(g\), plus (possibly) background \(\alpha\) (flat signal, or stationary noise component), plus noise \(\epsilon\). The problem of discovering signal from noisy, observed data is important and highly relevant in practice but it has taken us some way from our goal of cluster or segmentation modeling of \(f\) - which could well have been cleaned and hence approximate well \(g\) prior to our analysis. An additional reason for discussing the work reported on in this section is the common processing platform provided by entropy. Often the entropy provides the optimization criterion used (see [13, 16, 26], and many other works besides). In keeping with entropy as having a key role in a common processing platform we instead want to use entropy for cross-model selection. Note that it complements other criteria used, e.g., ML, least squares, etc. We turn now towards a new way to define entropy for application across families of GMM analysis, wavelet transform based approaches, and other approaches besides, all with the aim of furnishing alternative segmentations.
## 2 Taxonomy of DDoS Attacks
### DDoS Attack Architecture
For a given time \(t>0\), which indicates the time at which legitimate traffic \(X_{t}\) is successfully transmitted, the steady-state throughput \(P\) of the legitimate traffic flow during an attack period \(T\) and the probability of a successful attack \(P_{uu}\) are expressed using (6) [39]:  
\[\left\{ \begin{array}{l}P=\lim\limits_{t\to\infty}\frac{X_{t}}{P\times C}\\ P_{uu}=P\end{array} \right. \tag{6}\]  
where \(C\) indicates the ideal burst magnitude for a successful attack to occur. The values of \(P_{uu}\) vary between 0 and 1. A lower \(P_{uu}\) indicates that the attack is very significant. The model described by [PERSON] and [PERSON] [38] was demonstrated on the CAIDA dataset to evaluate how the DDoS attack affected bandwidth usage. In this dataset, 104 distinct IP addresses were considered and split into 52 regular and 52 attacking packets. Using an attack period of 40 s for each IP, evaluation results showed that, during the DDoS attack, attacking packets utilize over 6.5 GB/s of bandwidth while regular traffic uses just 5.6 MB/s. This study supports the assertion that DDoS attacks have a considerable impact on the target server's bandwidth depletion. To avoid a server shutdown, the attack needs to be detected and mitigated. As can be observed in Figure 3, in DDoS, the use of botnets to launch attacks is evident. Today, botnets are not limited to personal computers. The attacker can further increase the traffic they produce by using handheld and IoT devices [39]. According to a report released by Akamai [40] (see Figure 4), the volume of DDoS attack traffic generated through IoT devices between 14 July and 16 December 2020 was more than 300 GB/s. The malware Mirai, XOR, and Spike were observed to have more than 300 GB/s. Attacks are increasingly significant, since hackers may use IoT devices such as Wi-Fi routers, security cameras, and smart TVs to launch attacks by taking advantage of their inherent weaknesses. These vulnerable devices can be exploited to flood target networks with traffic to take down their servers. IoT devices are susceptible to remote manipulation by attackers because of the open nature of the internet and poorly maintained firmware. Once infected, these devices are integrated into botnets and start to take over the targeted server or service [41]. The rapid expansion of unsecured IoT devices has provided an expanding pool of DDoS attack resources.
## 1 Introduction
Subsequently, we collected data by deploying network cameras at a mining site in Henan, China, followed by image data processing and annotation to construct a dedicated dataset for foreign object detection on molybdenum conveyor belts. After implementing the model improvements and preparing the dataset, we conducted a series of experiments based on the enhanced model. In the result section of this paper, we analyze the training process and compare the experimental outcomes. Ultimately, our findings demonstrate that the proposed improved anchor-free object detection method exhibits a significant accuracy advantage over other networks of comparable scale in the context of detecting foreign objects on molybdenum mine conveyor belts.
# Socioeconomic Impact on Urban Resilience against Flood Damage
## 3 Results
### Relationship between Urban Resilience and Socioeconomic Cost  
The urban resilience and socioeconomic costs are summarized in Table 5, and the correlation analysis based on the summarized data is presented in Table 6. All variables exhibited negative relationships. Specifically, the correlation between resilience and PL was \(-\)0.498, showing a stronger negative correlation than the \(-\)0.355 between resilience and TCC. This analysis indicates that higher resilience tends to reduce social costs due to flood damage. It implies that as resilience increases, the loss increase rate of PL is greater than that of TCC. This can be interpreted as a result of the nature of TCC, which is more affected by the network structure and the location of the occurrence, even when the same flood depth occurs, compared to PL. Urban resilience and socioeconomic cost had a roughly inverse relationship due to the characteristics of direct and indirect damages occurring from flooding (Figure 7). However, by breaking down into the attributes of resilience rather than overall resilience, it was possible to identify specific factors that socioeconomically impact the city. For instance, while the resilience of cities A and E was similar at approximately 0.35, there was a 35% difference in socio-economic costs (light gray color in Table 5). In the case of city A, it was identified that the \(\text{Si}_{\text{b}}\) is lower compared to city E, which leads to higher VPA. Similarly, cities F, P,  
Figure 5: PL of each city. City E experienced the highest PL followed by Cities A and G in terms of the severity of damage. Figure 6: TCC of each city. In the case of City L, the most significant damage occurred, while City E, which experienced the highest PL, suffered damage six times. and Q, despite having similar resilience levels of 0.5, showed significant differences in TCC, ranging from USD 0 to USD 7493 (dark gray color in Table 5). This is due to the characteristics of TCC, which are determined by which roads are flooded among the numerous flood scenarios. These cases imply that even if a rainfall of similar intensity occurs, interpreting urban damage from the perspective of social capabilities, such as RA or SR, can result in varying degrees of damage. Indeed, cities F, P, and Q had similar flood areas and VPA, but there was a significant difference in TCC. Additionally, while the resilience of cities G and J was similar (about 0.31-0.32), the socioeconomic damage in G is approximately 1.8 million dollars, which was 1.8 times greater than that in J (light blue color in Table 5). The RA (resilience ability) of G city was about eight times lower than that of J, suggesting that G city was expected to face greater challenges in evacuation despite having higher resilience compared to J.
## 2 Design of the Form Trimming Machine
### Knife System
#### 2.3.2. Principle of the Cutting Motion of Blades  
Each cutter unit contains one fixed blade and one moving blade. The moving blade swings back and forth to make a cutting function. Figure 5 shows the principle of the generation of reciprocating motion of the moving blade. The driving motor drives the inner gear with the eccentric wheel to rotate around the center (\(O_{1}\)) of the inner gear, and then, drives the moving blade to rotate around the pin (\(O\)). When the eccentric wheel rotates for one circle, the moving blade swings back and forth once.  
Figure 4: The knife system in different shapes: (**a**) Straight-line shape for trimming cones; (**b**) Arc shape for trimming balls.  
Figure 3: The knife system and the cutter unit.  
According to the kinematic relationship, the coordinates of the eccentric wheel's center (\(O_{2}\)) is given by  
\[\left\{\begin{array}{c}x_{O_{2}}=L_{OO_{2}}\sin\theta=e\sin\omega_{0}t\\ y_{O_{2}}=L_{OO_{2}}\cos\theta=h-e\cos\omega_{0}t\end{array}\right. \tag{2}\]  
where \(L_{OO_{2}}\) is the distance between \(O\) and \(O_{2}\) (mm), \(h\) is the length between point \(O_{1}\) and point \(O\) (mm), \(e\) is the eccentricity of the eccentric wheel (mm), \(\theta\) is the swing angle of the moving blade (rad), \(\omega_{0}\) is the angular speed of the driving motor (rad/s). The swing angle is an important parameter for the cutting blades. According to Equation (2), the swing angle meets the following Equation:  
\[\tan\theta=\frac{e\sin\omega_{0}t}{h-e\cos\omega_{0}t} \tag{3}\]  
Then, \(\theta\) can be obtained by  
\[\theta=\arctan\biggl{(}\frac{e\sin\omega_{0}t}{h-e\cos\omega_{0}t}\biggr{)} \tag{4}\]  
The swing angular speed of the moving blade \(\omega_{1}\) (rad/s) is determined by  
\[\omega_{1}=\frac{d\theta}{dt}=\frac{he\omega_{0}\cos\omega_{0}t-e^{2}\omega_{ 0}}{e^{2}+h^{2}-2 he\cos\omega_{0}t} \tag{5}\]  
The swing limit angle of the moving blade can be derived as  
\[\phi=2\times\theta_{\text{max}}=2\times\arcsin(\frac{e}{h})\times\frac{360}{2\pi} \tag{6}\]  
According to the parameters of the chosen blades, \(e\) = 6 mm, \(h\) = 35 mm, the maximum swing angle of the moving blade can be calculated as 19.74\({}^{\circ}\). This swing angle could permit that the moving blade sweeps the full cutting gap with respect to the fixed blade in one reciprocating cycle.  
Figure 5: Generation of reciprocating motion of the moving blade (\(O\) is the rotating center of the moving blade (green), \(O_{1}\) is the rotating center of the inner gear and the eccentric wheel (red), \(O_{2}\) is the center of the eccentric wheel, \(e\) is the eccentricity of the eccentric wheel, \(\omega_{0}\) is the angular speed of the driving motor, \(\omega_{1}\) is the swing angular speed of the moving blade, and \(\theta\) is the swing angle of the moving blade).
# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 2 Modeling the Stroke Possibility
### Procedure and Experimental Methodology
#### 2.3.5 Resampling the Data  
Resampling the data is an important step in dealing with imbalanced data and improving the performance of machine learning models. In our research, we used two resampling methods to address the imbalance in the dataset and achieve more accurate stroke risk prediction.  
The first resampling approach we used involved only upsampling the minority class. This approach involves creating new synthetic samples of the minority class to balance the dataset. This method is simple and easy to implement, but it may not always improve the performance of the machine learning model. In some cases, it may result in overfitting, where the model performs well on the training data but poorly on the test data.  
The second resampling approach we used involved both upsampling the minority class and downsampling the majority class. This approach helps balance the dataset by creating new synthetic samples of the minority class and removing some samples from the majority class. This method can improve the performance of the machine learning model by reducing the impact of the majority class on the training process.  
In our study, we found that the second resampling approach was more effective in improving the performance of our machine learning models. The balanced dataset produced by this approach resulted in higher accuracy, precision, and recall measures compared to the imbalanced dataset and the first resampling approach.  
However, resampling the data is not a perfect solution and may have some limitations. In some cases, resampling may result in loss of information or introduce bias in the dataset. Therefore, it is important to carefully evaluate the impact of resampling on the performance of the machine learning model and consider other approaches such as cost-sensitive learning or ensemble methods.  
In summary, resampling the data is a critical step in dealing with imbalanced data and improving the performance of machine learning models. Our study demonstrates the effectiveness of the second resampling approach in improving the accuracy, precision, and recall measures of our stroke risk prediction models. However, further research is needed to explore other approaches and evaluate the impact of resampling on different types of datasets and machine learning models.
# The Multiband over Spatial Division Multiplexing Sliceable Transceiver for Future Optical Networks
## 6 Experimental Assessment  
This section summarizes the main achieved results of the analysis and assessment of the proof-of-concept of the MB(oSDM) S-BVT. We have targeted two different scenarios/use cases to assess the different capabilities and operation modes of the proposed transceiver, for both MB and MBoSDM transmission. For MB transmission assessment, the generated MB (S+C+L) high-capacity flow is transmitted over different fibre spools of G.652D SSMF up to 75 km [45]. The SSMF, presents a CD coefficient of 17 ps/(nmkm) at 1550 nm and a mode field diameter (MFD) in the range of 8.6-9.2 um at 1310 nm. In this particular scenario of assessing MB transmission, the data generator module of Figure 5 is not activated. Additionally, a 19-cores MCF of 25.4 km is considered to assess the MBoSDM transmission. The MCF has a MFD \(>\)7.88 um, corresponding to the fibre core diameter. The MB signal (S+C+L) is transmitted over core #1, whereas the adjacent cores (# 2, 3, 4, 5, 6, and 7) are filled with alternative data signals generated to evaluate both the in-band and out-band XT, as it will be explained and detailed later in this section. The alternative data are created by enabling the data generator module of Figure 5 to fill the adjacent/neighbouring cores of the MCF with data to evaluate the XT effect. In the following experiments we consider  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline CP overhead & 4\% \\ TS overhead & 1.9\% \\ FEC overhead & 7\% \\ FEC type & HD-FEC \\ Target BER & \(4.62\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: DSP and performance parameters.  
Figure 6: (**a**) Transmitted MB high\(-\)capacity flow after MB(oSDM) S\(-\)BVT optical aggregator; (**b**) S-band SSB; (**c**) C-band SSB; (**d**) L-band DSB-transmitted spectra.  
that each fibre core can support the transmission of all bands and each band can convey different user data.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 2 Materials and Methods
### Physics-Informed Learning  
In Section 1, we presented the generalization of attack paths across high-dimensional environments as one of the key challenges of this research. To address this challenge, we introduce a Physics-Informed Graph Neural Network (PIGNN).  
Physics-informed neural networks (PINNs) [93], also known as Theory-Trained Neural Networks (TTNs) [94]--or, more generally, model-based deep learning [95]--are designated as an emerging research field of neural network architectures that integrates prior knowledge of physical laws and known interaction logic into the learning process.  
In the context of attack path prediction across high-dimensional environments, using PINNs instead of agnostic Deep Neural Networks presents two key advantages: **(1)** incorporating known interaction principles and properties into the training restricts the space of feasible learnable solutions and thereby enhances the generalizability of the function approximation, and **(2)** by embedding this prior knowledge, PINNs also effectively augment the information content of the available data, enabling convergence towards accurate solutions with a limited number of training samples.  
To include known interaction principles into the training, we crafted a custom \"physics-informed\" loss function. In our case, this loss function exploits known properties of graph theory to penalize the network when predictions are incoherent with domain knowledge, e.g., if predicted adjacency matrix describes a graph containing cycles or branches. The loss function is detailed in Section 2.5.
## 2 Materials and Methods
### Biopotential Readout Circuits with Synchronous PLI Filtering  
Two simplified biopotential readout circuit concepts are shown in Figure 1. Generally, they consist of an analog front end (AFE), two ADCs, and a digital part embedding the novel SF algorithm. AFE includes at least two amplification channels for the differential- and common-mode input signals and an optional driven reference electrode (DRE) circuit. It is worth noting that DRE is a generalization of the well-known driven-right-leg circuit coming from the standard 12-lead ECG setup where the reference electrode is placed on the right leg [44]. The number of differential channels can be arbitrarily increased. For example, in a 12-lead ECG system, the differential channels are 8 (6 precordial and 2 peripheral leads), but for simplicity only 1 differential channel is shown in Figure 1. The proposed SF algorithm can be embedded in biopotential acquisition configurations with and without DRE. In systems with DRE, the common-mode signal could be taken from the DRE electrode, as shown in Figure 1a. In ground-free systems shown in Figure 1b, the AFE common-mode input impedance should be low enough to provide flowing path for both bias and interference currents and to keep the amplifier inputs within their operating range. Both differential- and common-mode channels have appropriate gain coefficients, namely, \(\mathrm{A_{d}}\) and \(\mathrm{A_{cm}}\), respectively. They must also have appropriate bandwidths. For example, the diagnostic ECG bandwidth of the differential channel is broad from 0.05 Hz to 150 Hz, while the bandwidth of the common-mode channel could be considerably limited within a span of a few Hertz around the fundamental PLI harmonic. Since the amplitude of the common-mode signal is uncontrollable and can vary along with the input PLI level, the common-mode gain \(\mathrm{A_{cm}}\) must be controllable too. We have previously proposed such an option by means of a software automatic gain control (SAGC) [45], which could be embedded either as a variable gain amplifier (VGA) in AFE or as a digital multiplier in the digital part. The amplified differential- and common-mode analog signals are digitized by two ADCs, which could be either stand-alone integrated circuits or embedded into the digital processing part. Thus, the SF algorithm can input and synchronously process the samples of both differential- and common-mode signals. Generally, depending on the specific readout circuit design, the digital SF algorithm could be embedded either in a DSP, FPGA, or in the system MCU. For mains-powered medical instrumentation, AFE must be supplied from a medical-grade power supply. AFE provides safe and reliable patient isolation by limiting the safe level of the viable mains currents, usually through optical, magnetic, or wireless signal transmission to the non-isolated part.