esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper** & **Research Context** & **Used Technique** & **Accuracy** & **Reliability** & **Efficiency** & **Computation Time (Seconds)** \\ \hline \multirow{3}{*}{[16]} & Boost clothing image categorization for & LeNet and & & & & \\  & e-commerce. & ResNet & \(\times\) & \(\surd\) & \(\times\) & 1.2 s \\ \hline \multirow{3}{*}{[17]} & Utilization deep learning & & & & & \\  & for retail checking in & YOLOv5 & \(\times\) & \(\surd\) & \(\surd\) & 0.8 s \\  & reserved environments. & & & & & \\ \hline \multirow{3}{*}{[18]} & Elaborate a visitor & & & & & \\  & supervision system with & YOLO (You Only & & & & \\  & Raspberry Pi and & Look Once) & \(\times\) & \(\times\) & \(\surd\) & 1.5 s \\  & Telegram. & & & & & \\ \hline \multirow{3}{*}{[19]} & Research retail buying & RFID technology, & & & & & \\  & machine learning and & & machine learning & & & \\  & RFID. & algorithms & & & & \\ \hline \multirow{3}{*}{[20]} & Enhance product & Zero-shot & & & & \\  & detection in commerce & learning, deep & \(\times\) & \(\surd\) & \(\times\) & 2.0 s \\  & surroundings. & learning & & & & \\ \hline \multirow{3}{*}{[21]} & Real-time vehicle & & & & & \\  & tracking in dynamic & YOLOv5s\_DSC, & & & & \\  & traffic using optimized & DeepSORT & \(\surd\) & \(\times\) & \(\surd\) & 0.9 s \\  & object detection. & & & & & \\ \hline \multirow{3}{*}{[22]} & Systems for managing & YOLOv5 + Deep & & & & & \\  & traffic in real time. & SORT & \(\surd\) & \(\times\) & \(\surd\) & 0.0167 s \\ \hline \multirow{3}{*}{[23]} & Customer activity & SSD Mobilenet & & & & \\  & detection in Mexican & V2, & & & & \\  & retail stores for improved & YOLO-FastestV2, & \(\surd\) & \(\surd\) & \(\times\) & 1.1 s \\  & experience. & YOLOv5, & & & & \\  & YOLOxn & & & & & \\ \hline \multirow{3}{*}{Our Proposed & Real-time purchasing & & & & & \\  & tendency identification & & & & & \\  & in material stores & & & & & \\  & according to YOLOv5 & DeepSORT, & & & & \\  & and DeepSORT & & & & & \\  & algorithms in a & & & & & \\  & recommender system. & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the related works. primarily concentrates on discrete elements like security or classification accuracy. For example, prior research has shown good accuracy in controlled environments. Still, it frequently needs to pay more attention to the complexity of dynamic retail environments, including changing illumination and consumer occlusions. The computational efficiency of these models is also still an issue, because many of them are designed for specific tasks rather than real-time use. By using YOLOv5 and DeepSORT, which are particularly tailored for real-time consumer behavior tracking and analysis, our suggested study seeks to close these gaps. This strategy aims to improve operational efficiency and detection accuracy while addressing the demand for flexible analytics to optimize shop layouts and enhance consumer interaction.
## 3 Proposed Method
### Partial Block Scheme  
We propose the partial block scheme to address environmental changes such as partial occlusion, partial illumination variation and partial blurring. Partial blocks are computed for each frame from the whole block and divided into four parts. We can generate the partial blocks using the whole block and Equation (1).  
\[\begin{split} P^{m}&=W^{m}/d\\ P^{n}&=W^{n}/d\end{split} \tag{1}\]  
where \(W^{m}\) and \(W^{n}\) are the height and width of the whole block, \(P^{m}\) and \(P^{n}\) are the height and width of partial blocks and the sizes of partial blocks are identical. \(d\) is a factor that adjusts the partial blocks' size. The centers of partial blocks are obtain with:  
\[\begin{split} B_{k}(x_{c},y_{c})&=\left\{\begin{array} []{ll}W(x_{c},y_{c}),&k=0\\ W(x_{c},y_{c}-\omega P^{m}),&k=1\\ W(x_{c},y_{c}+\omega P^{m}),&k=2\\ W(x_{c}-\omega P^{n},y_{c}),&k=3\\ W(x_{c}+\omega P^{n},y_{c}),&k=4\end{array}\right.\end{split} \tag{2}\]  
In Equation (2), \(W(x_{c},y_{c})\) is the center position of the whole block, and \(B_{k}(x_{c},y_{c})\) is the center position of all blocks, which include the whole block and partial blocks. \(k\) means the index of blocks, and \(\omega\) means the factor that adjust the location for partial blocks. The indices of partial blocks are 0-4, and \(B_{0}\) means the whole block. \(B_{1}\), \(B_{2}\), \(B_{3}\) and \(B_{4}\) mean partial blocks, respectively. As shown Figure 3, the positions of partial blocks depend on the position of the whole block, and this can adjust the parameter \(\omega\). Furthermore, the sizes of the partial blocks are set to an identical size for the convenience of calculation by parameter \(d\). We proposed a partial block scheme to deal with the partial occlusion problem. Partial occlusion can occur in all blocks including the whole block. However, the proposed method is designed to track any block without partial occlusion.  
The whole block is small or unsuitable parameter \(d\) can produce too small partial blocks. The small size of partial blocks among the generated partial blocks can disturb object tracking. Thus, we employ excluding very small blocks using Equations (3) and (4).  
\[\begin{split} B_{k\in\{1,2\}}^{w}&=\left\{ \begin{array}{ll}0,&P^{m}\ <\ \tau\\ 1,&\text{otherwise}\end{array}\right.\\ \\ B_{k\in\{3A\}}^{w}&=\left\{\begin{array}{ll}0,&P^{n}\ <\ \tau\\ 1,&\text{otherwise}\end{array}\right.\end{split} \tag{3}\]  
where \(\tau\) is the threshold for the decision whether to exclude partial blocks. \(B_{k}^{w}\) means the weighting for partial blocks. If \(P^{m}\) is smaller than \(\tau\), \(B_{1}^{w}\) and \(B_{2}^{w}\) are excluded blocks. Furthermore, if \(P^{n}\) is smaller than \(\tau\), \(B_{3}^{w}\) and \(B_{4}^{w}\) are excluded blocks. All partial blocks are not large enough; we can only use whole blocks, and \(B_{0}^{w}\) is always 1.
## 2 Methods
### Pre-Process Analysis
#### 2.2.2 Time Offset Adjustments  
LOTRs have shown to exhibit different internal time delays between analog (AN) and photon-counting (PC) channels of the same module, which change for different LOTR versions. We found that each line needed separate adjustments, which can be achieved interactively with the online display of the LPP (see Figure 7), and, once found, fixed through correct settings in a configuration file. This procedure allows for a precise relative adjustment by means of adequate atmospheric features visible in more than one channel. We found time offsets between the AN and PC channels of the same LOTR of the order of \(\sim\)0.5 \(\upmu\)s for the TR-20 and \(\sim\)50 ns for the newer TR-40, with the AN signal arriving earlier. Between two LOTRs, time differences of the same order have been observed. Finally, an absolute time calibration needs to be performed for all channels synchronously, which is achieved through the diffuse backscatter signal of the laser light from the first dichroic guide mirror (see Figure 2), visible in some channels. This procedure will be modified for the final BRL, in which the stray light from diffuse reflection of laser light from the guiding mirrors will be isolated from the rest of the system.
# Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties
## 2 Problem Formulation
### MPC Control Structure  
Consider the time-varying linear system, which has multi-faceted uncertainty, as follows [16]:  
\[\begin{array}{l}\left\|u(k+i|k)\right\|_{2}\leq u_{max}\;\;i.k\geq 0\\ \left\|y(k+i|k)\right\|_{2}\leq y_{max}\;\;i.k\geq 0\end{array} \tag{14}\]  
The cost function in all forms of robust MPC is the square regulator cost function as follows:  
\[J_{0}^{p}(k)=\sum_{i=0}^{p}\Big{[}\left\|x(k+i|k)\right\|_{Q}^{2}+\left\|u(k+ i|k)\right\|_{R}^{2}\Big{]} \tag{15}\]  
where \(\mathbf{Q}\geq\mathbf{0}\) and \(\mathbf{R}\geq\mathbf{0}\) are symmetrical weighting matrices. This paper considers an infinite predictive horizon and an unlimited control horizon, similar to many research studies in the MPC field. MPC with a limited horizon exhibits weaker nominal stability compared to a state with an unlimited horizon. Moreover, by incorporating an unlimited horizon for the cost function, the conversion of the problem into the linear matrix inequality (LMI) form becomes more straightforward, requiring fewer computations to derive the associated conditions.  
In scenarios where the system exhibits uncertainty, optimizing a cost function that ensures robust performance at each \(K\) sampling time rather than relying on a cost function with nominal efficiency becomes essential. This particular cost function is expressed as:  
\[\min_{u(k+i|k),i=0.1,\ldots,m}(\max_{[A(k+i)B(k+i)]\in\Omega}J_{0}^{\infty}(k)) \tag{16}\]  
This cost function indicates a min-max optimization problem. Maximizing the pre-formation within the \(\Omega\) set involves the selection of the time-varying system \([A(k)B(k)]e\mathbf{\Omega}\). \(\mathbf{i}\geq\mathbf{0}\), which, if used for prediction, the largest value is obtained; in other words, the worst case of the objective function \(\mathbf{j_{0}^{\theta}}(\mathbf{k})\) is obtained from the systems in \(\Omega\). This is the worst-case scenario for the cost function using the current and future control signals, \(\mathbf{u}(k+i|\mathbf{k})\mathbf{i}>\mathbf{0}\).
# Effects of Caprolactam Wastewater on Algal Growth and Nutrients Removal by _Arthrospira platensis_
## 3 Results and Discussion
### Fed-Batch Supply of WCP Decreasing the Impact on A. platensis
#### 3.3.2 Effect of WCP Supply Modes on _A. platensis_ Photosynthesis Pigments  
The pigments of photosynthetic microalgae are important indicators of algal cell growth [38]. The photosynthetic pigments of _A. platensis_, including Chl-a, Car, and phycobilproteins, were measured during cultivation with different WCP supply modes (Table 3). The WCP supply modes significantly influenced the contents of Chl-a and PC, and the batch mode had the greatest impact. For example, the Chl-a and PC contents of fed-batch mode 1 in the second growth period (day 8) were 16.38 mg/g and 98.01 mg/g, respectively. However, the contents were 12.59 mg/g and 69.52 mg/g, respectively, in the batch mode. The WCP supply modes had little effect on the Car and APC contents.  
As the culture time increased, the Chl-a and PC contents in fed-batch mode 1 and fed-batch mode 2 showed a decreasing trend. In fed-batch mode 1, the Chl-a content decreased by 13.3%, 14.3%, 26.3%, and 24.6% on days 4, 8, 12, and 16, respectively, compared with the control. Moreover, the PC content decreased by 12.0%, 25.7%, 38.4%, and 36.8% on days 4, 8, 12, and 16, respectively, compared with the control. This phenomenon is consistent with the observation that the inhibition of algal growth was enhanced over time (Figure 7C).  
Figure 7: Effects of caprolactam wastewater supply modes on the growth of _Arthrospira platensis_. (**A**) Growth curves, (**B**) final biomass (g/L), and (**C**) Inhibition rate over time. Error bars represent standard deviation (\(n=3\)). Data with different letters indicate significant differences (\(p<0.05\)) between the control and treatments.
## 3 Measurement Setup

Table 3: Measured \(S_{21}\) for the complementary metareasonator sensor due to interaction with different OUT. Figure 6: Measurement environment (**a**) dragon lab 10–100 \(\upmu\)L adjustable single channel automatic pipette, (**b**) oil quantity measurement, (**c**) pouring the OUT into the Teflon container. Figure 8: The magnitude of transmission (\(S_{21}\)) coefficients for the complementary metaresonator sensor due to interaction with different OUT. Figure 7: Complementary metaresonator sensor loaded with the OUT. Figure 9: Dielectric constant of OUT versus the resonance frequency of the sensor due to interaction with the OUTs. In Figure 9, the slopes of the first (red curve), and second (blue curve) curves provide the fabricated meteresonator sensor's sensitivity, which can be measured mathematically using the following relation [48]:  
\[S=\frac{\partial f_{d}}{\partial\epsilon_{rd}}=\lim_{(\Delta\epsilon_{r2}- \Delta\epsilon_{r1})\to 0}\frac{\Delta f_{u}-\Delta f_{l}}{\Delta\epsilon_{r2}- \Delta\epsilon_{r1}}. \tag{4}\]  
Using (4), the sensitivity of the complementary meteresonator sensor is calculated and tabulated in Table 4. The sensitivity of second resonance is higher than the first resonance due to the higher resonance frequency for all the OUTs. The sensitivity of corn oil is maximum due to small dielectric constant. The sensitivity due to the first resonance frequency lies between 31.4% to 27% and that due to the second resonance is between 46.5% and 39.4%.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 4 Technical Discussion
### 4.1. Note Specifications and Applications
#### 4.1.5 Sensor Support  
Most of the commercially available sensor motes reviewed in this study do not offer all the basic built-in sensors. As an example, the Waspmote Plug & Sense! offers only two in-built sensors, namely the accelerometer and the temperature sensor. Instead, these commercial motes, such as the LOTUS, IRIS, MICAz and Waspmote Plug & Sense!, have external sensor boards with sockets to allow different sensors to be connected for flexibility. Some of the sensor node platforms also accommodate a range of GPIO ports. A notable number of sensor nodes from the recent research works provide built-in basic sensors.
## 4 Results and Discussions
### Strength Degradation  
The degradation coefficient (\(\eta\)) of the bearing capacity of the same displacement cycle is the ratio of the maximum loads of the last and first cycles. The degradation curves of the bearing capacity of the specimens are depicted in Figure 14. The values of \(\eta\) on the main stages are presented in Table 6. Figure 14 and Table 6 demonstrate the following:  
(1) The steel frame is a typical flexible structure with good deformation capacity, and its strength degradation is unclear before the peak load. In SFIRACSWs, the walls act as the first seismic fortification lines that resist most of the horizontal load. The concrete at the diagonal compressive strips is gradually crushed and dropped, and the bearing capacity of the structure decreases sharply through the continuous expansion of the cracks in the wall.  
(2) The degradation coefficients of the bearing capacity of SPE1, SPE2, and SPE3 are more than 0.97 when the horizontal drift angle is less than 0.01 rad. The degradation coefficients remain more than 0.80 when the horizontal drift angle is 0.02 rad, indicating that the specimens of infilled cast-in-place RACSWs also have a high safety reserve.  
(3) The degradation coefficients of the bearing capacity of SPE4 and SPE5 are close when the horizontal drift angle is less than 0.02 rad. The degradation law is consistent, denoting that the damage degree of the specimens of infilled prefabricated RACSWs is the same under the same drift angle. Furthermore, the connecting stiffness of BCJs slightly influences the strength degradation of the specimens of infilled prefabricated RACSWs. The degradation coefficients of the bearing capacity of the specimens of infilled prefabricated RACSWs are more than 0.85.  
Figure 13: Stiffness degradation curves.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Specimen**} & \multirow{3}{*}{\
\begin{tabular}{c} **\(\mathbf{K_{0}}\)** \\ **(kN-mm\({}^{-1}\))** \\ \end{tabular} } & \(\mathbf{\theta=0.001}\) & \(\mathbf{\theta=0.010}\) & \(\mathbf{\theta=0.015}\) & \(\mathbf{\theta=0.020}\) & \(\mathbf{\theta=0.025}\) \\  & & **rad** & **rad** & **rad** & **rad** & **rad** \\ \cline{2-7}  & \multirow{2}{*}{
\begin{tabular}{c} **(kN-mm\({}^{-1}\))** \\ \end{tabular} } & \(\mathbf{K_{1}}\) & \(\mathbf{K_{2}}\) & \(\mathbf{K_{3}}\) & \(\mathbf{K_{4}}\) & \(\mathbf{K_{5}}\) & \(\mathbf{K_{6}}\) \\  & & **(kN-mm\({}^{-1}\))**
# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 2 Clinical Experience  
Before we review the use of several data analysis methods in automatic AF detection, a short introduction to AF is given in this section. AF is medically defined as a supraventricular tachyarrhythmia, i.e., an irregular heart rhythm which is driven by non-organized atrial activity. Diagnosis criteria are then absence of the P-wave, the presence of an anarchic auriculogram (F wave), and an irregular heart rhythm, usually rapid with thin QRS complexes [16; 17]. Great progresses have been made in the AF management, but AF remains a major cause of cardiovascular mortality and morbidity, with examples such as heart failure, stroke, and sudden death. AF is independently associated with a 2-fold increased risk of all-cause mortality in women and a 1.5-fold increase in men. Up to 20-30% of patients that experienced an ischemic stroke were diagnosed with an AF before, during, or after the initial event. Moreover, AF is currently the most frequent arrhythmia (1-3% of population) and its occurrence is predicted to increase gradually in the coming years according to population ageing trends. It is evaluated that 1% of total healthcare spending is directly generated by AF. Only early and effective prevention and treatment will be able to prevent its rising cost. For these reasons, AF has to be diagnosed as soon as possible and the largest population should be screened [16; 17].  
Electrocardiogram (ECG) records of the heart rhythm are necessary for AF diagnosis, exhibiting the typical AF characteristics: irregular RR intervals that are often said to be anarchical and absence of P-waves. It is conventionally accepted that a 30 s lasting episode is necessary and sufficient to diagnose a clinically relevant AF. AF can be seen in symptomatic or asymptomatic ('silent AF') subjects and, in fact, AF patients often experience both symptomatic and asymptomatic episodes. As silent undiagnosed AF is common and associated with severe consequences such as stroke and death, quick ECG recording is an efficient and cost-effective way to screen people and diagnose chronic AF. The longer the duration of the ECG recording, the higher detection rate of paroxysmal AF. However, chronic AF detection rate is also increased by daily short-term ECG recordings, especially in elderly population.  
To succeed in this challenge, new technologies are developed to increase AF detection, such as patient-operated devices, skin patches, or even smartphones, smart watches, and blood pressure monitoring devices. However, massive data generated need to be automatically analyzed since a 24 h length recording is expected to show approximately 100,000 heartbeats that need to be read, making it especially time-consuming. Physician time-consumption and costs can be reduced by automatic analysis of heart rhythm, leading to concomitant developments of automatic AF arrhythmia detection algorithms.
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 3 Related Works
[3] proposed an NDP detection system. However, the proposed system achieves low performance. Another security model by [PERSON] et al. [27] detects covert network channels in IPv6. However, this model also achieves low performance. Overall, based on Table 1, none of the existing approaches investigated deep learning techniques. The following section underlines the proposed deep-learning-based approach. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Author(s)**} & \multirow{2}{*}{**Techniques**} & \multirow{2}{*}{**IPv6**} & \multicolumn{2}{c}{**Dataset**} & \multirow{2}{*}{**Detection**} & \multirow{2}{*}{**Limitations**} \\  & & & **Protocol** & & & **Type** & \\ \cline{2-7}  & **ML** & **DL** & & **IPv6** & **IPv4** & & \\ \hline [PERSON] et al. [3] & ✓ & ✗ & ✓ & ✓ & ✗ & 84.5\% & This approach achieves low performance. \\ [PERSON] et al. [5] & ✓ & ✗ & ✓ & ✓ & ✗ & 85.83\% & Achieves low detection accuracy. \\ [PERSON] et al. [10] & ✓ & ✗ & ✓ & ✓ & ✗ & 99\% and 91.17\% & Limited to detect NDP attacks. \\ [PERSON] et al. [11] & ✓ & ✗ & ✓ & ✗ & ✓ & - & No information about detection accuracy. \\  & & & & & & It was also evaluated with an IPv4 dataset. \\ [PERSON] et al. [12] & ✓ & ✗ & ✓ & ✓ & ✗ & 98.3\% & Designed to detect only ICMPv6 echo request attacks. \\ [PERSON] et al. [25] & ✓ & ✗ & ✓ & ✓ & ✗ & 98.55\% & Designed to detect only RA DDoS. \\ [PERSON] et al. [26] & ✓ & ✗ & ✓ & ✓ & ✗ & 99.95\% & Only detects RA DDoS attacks. \\ [PERSON] et al. [27] & ✓ & ✗ & ✓ & ✓ & ✗ & 96.46\% & Still, the performance needs to be improved. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A qualitative comparison of existing techniques with their limitations.
# Deep Representation of EEG Signals Using Spatio-Spectral Feature Images
## 4 Results
### Individual Subject Model  
The performance of each CNN model trained on data from each of the 25 individual subjects is shown in Figure 4. In addition to the random chance level performance (accuracy of \(1/3\)), the performance of a naive model that builds a prediction based on the majority class is shown using empty bars. A naive model produces a constant output as it always predicts the class that has the highest prior probability. Since the time duration taken by each subject for writing and resting activity varies, the number of feature vectors extracted from each segments also varies, producing different prior probabilities. In this experiment, writing segments are longer than listening and resting for all subjects; thus, the writing class constitutes the majority class and a naive model always predicts the writing class for each input.  
The test performance of every CNN model was found to be better than random chance. Compared to the naive model, the proposed CNN architecture performs well for all subjects except for subjects 16 and 17. It is interesting to observe that the same CNN architecture trained on different individuals performs differently. By optimizing the CNN architecture for individual subjects, the performance could have been improved. However, for comparative purposes, CNN models always followed the same architecture shown in Figure 3.  
Figure 5 shows the Receiver Operating Characteristics (ROC) curves plot for all the models, which results in an average Area Under the Curve (AUC) of 0.86. This indicates that on average, models were performing well.  
Figure 4: Performance of each CNN model trained and tested on the same individual subject. Empty bars represent the performance of the majority-based model and the black horizontal line represents the random chance-level performance, i.e., \(1/3\).  
Figure 5: ROC curve of all the 25 CNN models relating true positive rate (TPR) and false positive rate (FPR) values.