esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection
## 2 Modeling Preparation  
### Problem Description  
As shown in Figure 1, this is a typical unsignalized intersection. Lane changing is not considered in this paper, mainly because it is generally prohibited for vehicles to change lanes at the entrance of intersections [13; 14]. In addition, readers can refer to the research on optimizing lane changing of CAVs [32]. Moreover, we only consider isolated intersections. Cooperative control can be implemented for intersections that are very close in distance [36]. Vehicle turns are not considered here to facilitate modeling. According to Figure 1, when vehicles pass through an unsignalized intersection, conflicts exist in different directions. Starting from the north direction, let \(i\in\mathcal{I}=\{1,2,3,4\}\) represent the four different directions. Then, the set of conflicting directions can be represented as \(\mathcal{C}=\{(1,2),(1,4),(2,3),(3,4)\}\). To ensure the safe passage of vehicles through the intersection, we assume that vehicles from conflicting directions cannot simultaneously appear in the conflict zone at the same time. If the intersection adopts a Vehicle-to-Infrastructure (V2I) control mode, the length of the control zone is \(l\), and the free-flow speed of each road segment at the intersection is \(v_{i},i\in\mathcal{I}\). For a given optimization time window \(T\), let \(\mathcal{N}_{i}\) represent the set of vehicles entering the control zone from the i-th direction within the optimization time window, where \(j\in\mathcal{N}_{i}=\{1,2,\cdots,|\mathcal{N}_{i}|\},i\in\mathcal{I}\). Suppose \(t_{ij}^{-}\) and \(t_{ij}^{+}\) represent the times when the j-th vehicle from the i-th direction enters the control zone and the conflict zone, respectively. The problem to be solved in this paper is to optimize the time \(t_{ij}^{+}\) for vehicles entering the conflict zone of the intersection within the optimization time window, while ensuring vehicle safety, to reduce vehicle delays.
## 2 Related Works
A comparable ensemble learning model proposed in this work is given in Figure 2.  
In Figure 2, model 1 depicts long short-term memory (LSTM), gated recurrent units (GRU), BiLSTM, and BiGRU, and Model 2 is a convolutional neural network (CNN). After performing various preprocessing techniques, the experiments have been carried out with the combination of BiLSTM or BiGRU and CNN. It achieved an accuracy of 96%, and an ACU of 0.99 with BiGRU + CNN by selecting 60 features and tuning the hyperparameters. However, this method necessitates the selection of significant model parameters and more time to train the classification model. Figure 1: Methodology block diagram. Table 2 provides hyperparameter tuning performed in related works. From the related works, it is understood that selecting significant features in [31; 34; 37; 39] and an ensemble of classifiers as in [31; 32; 37; 39; 42; 44; 46] can substantially enhance the performance of machine learning algorithms in predicting the coronary heart disease risk at an early stage. However, the feature selection is a challenging task as it grows exponentially depending on the number of features in the dataset. The accuracy of the classifiers in predicting coronary heart disease risk varies between 81.19% [35] to 96.72% [41]. Researchers [40; 41; 43; 44] have not employed any feature selection techniques in their works. From Table 2, very few research studies have optimized hyperparameters since \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Model** & **Hyperparameters Tuned** \\ \hline
[31] & RF & Number of decision trees, E \\  & & depth of each tree, D \\ \hline
[36] & Deep learning & Dropout layer \\ \hline
[37] & KNN & Number of nearest neighbors, K \\ \hline  & SVM & Kernel \\ \hline  & LR & Regularization strength, C \\ \hline  & ANN & Number of hidden neurons \\ \hline
[38] & DNN & Number of neurons in the first hidden layer, N1 \\  & & Number of neurons in the second hidden layer, N2 \\ \hline
[41] & SVM & Complexity parameter, C and Kernel \\ \hline
[44] & AutoPrognosis & Weighted ML pipelines with XGBoost ensemble (200 estimators) \\ \hline
[46] & Deep learning & Number of epochs, dropout rate, \\  & optimizer, learning rate \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter tuning performed in related works. Figure 2: The ensemble learning model. tuning numerous ML models in order to identify the optimal hyperparameters is time-consuming. Nonetheless, effective dimensionality reduction and optimization of classifier hyperparameters can increase classifier performance in precisely predicting coronary heart disease risk.
## 4 Simulation Verification
### Fading Channel  
#### 4.2.1 Large-Scale Fading Caused by Shadowing Effect  
This simulation takes a polar code with a code length of (1024, 307) as an example, with \(E_{b}/N_{0}\) set to 2.5 dB, the maximum number of iterations set to 60, and the number of decoders \(L=5\). The log-normal shadowing effect with standard deviations ranging from 2 to 6 dB is considered, and time diversity is adopted as the diversity technique. The bit error rate (BER) is shown in the Figure 9 below:  
The simulation results show that the bit error rate (BER) of the path selection criterion proposed in this paper is reduced by 17.2-25.5% compared to the BP decoding of polar codes, and the improvement effect is better than that of the BPL decoding of polar codes based on traditional path selection criteria.
## 4 Circuit Design  
Synthesis of lumped chaotic oscillators based on knowledge of describing ordinary differential equations is a simple task with multiple solutions. The most straightforward approach is known as the concept of analog computers. Following this approach, only three basic building blocks are necessary: inverting integrators, differential amplifiers, and two ports with the prescribed nonlinear transfer curve. These circuits can be designed either in voltage mode [38; 39] or current mode [40]. The first case is much more common because of the commercial availability of active elements and easily measurable state variables. The corresponding circuit implementation of the analyzed dynamical system is provided via Figure 6a, and its behavior is uniquely determined by the following set of ordinary differential equations:  
Figure 5: Topographically scaled surface-contour plot of LLE as a two-dimensional function of cubic terms associated with nonlinear transconductances \(y_{12}(v_{2})\) and \(y_{21}(v_{1})\). Bifurcation diagram fitted to the trumpet attractors; cross section chosen as \(v_{2}=-1.5\) V, while state variable \(v_{1}\) is stored.  
\[C_{1}\frac{d}{dt}v_{1}=-\frac{v_{1}}{R_{green}}-\frac{v_{2}}{R_{black}}+\frac{K^{2} }{R_{md}}v_{2}{}^{3}\,\ \ C_{2}\frac{d}{dt}v_{2}=-\frac{v_{3}}{R_{brown}}-\frac{K^{2}}{R_{blue}}v_{1}{}^{3 }\,\ \ C_{3}\frac{d}{dt}v_{3}=\frac{v_{2}}{R_{white}}\, \tag{8}\]  
where \(C_{1}\), \(C_{2}\), and \(C_{3}\) are the left, middle, and right capacitor, respectively. Note that seven active devices are needed for network design. The time constant is a compromise between the speed of integration (should be high due to long transients) and parasitic properties of active elements (should be low such that parasitic properties can be neglected). In our case, it is chosen as \(\tau=10^{4}\times 10^{-8}=100\)\(\upmu\)s. In the first experiments, red and blue resistors were supposed to be of variable values, i.e., potentiometers. Much better results were achieved if system dissipation also became an adjustable quantity, i.e., the green resistor was substituted by a potentiometer. It is worth noting that OrCAD PSpice circuit simulator has been used to validate the upcoming experimental results before construction of the oscillator.  
The oscillator can be simplified by the following different idea. Assume that each differential equation represents the sum of the currents measured at independent nodes. One can easily obtain the electronic system visualized in Figure 6b and described by mathematical expression  
\[C_{1}\frac{d}{dt}v_{1}=-\frac{v_{1}}{R_{1}}-g_{1}\cdot v_{2}+\frac{K^{2}}{R_{ 3}}v_{2}{}^{3}\,\ \ C_{2}\frac{d}{dt}v_{2}=-i_{L}-\frac{K^{2}}{R_{blue}}v_{1}{}^{3}\,\ \ L\frac{d}{dt}i_{L}=v_{2}\, \tag{9}\]  
where \(K=0.1\) is the internally trimmed constant of a four-quadrant analog multiplier. Obviously, the number of active elements decreases to five. On the other hand, the third state variable is not easily measurable. Note that, without the introduction of state variable rescaling, transadmittance amplifier \(g_{1}\) should be able to handle high input voltages without output current distortion.
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 3 Parameters That Affect Thermal Conductivity
### Nanoparticles (NPs)
#### 3.1.2 Size  
The influence of the size of the NPs is still controversial among the scientific community. [PERSON] and [PERSON] [29] believed that the reduction of the size of NPs increases the effective surface area, thickens the interfacial layering, and enhances the Brownian motion. For these reasons, the thermal conductivity of the nanofluids increases with the reduction of the particle diameter. [PERSON] et al. [30] measured the thermal conductivity of water-based NF with Al\({}_{2}\)O\({}_{3}\)NPs of different sizes and registered a reduction of the thermal conductivity with increasing sizes. [PERSON] et al. [31] also compared NFs with alumina NPs of 45 and 150 nm of dimensions. The higher enhancement was reported for the smaller particles. Although most of the results presented in the available literature agree with that proposition, a few others suggest opposite conclusions. [PERSON] and [PERSON] [32] registered an increase of the thermal conductivity with increasing particle size for different types of NPs. [PERSON] et al. [33] compared water-based NFs with four different sizes of SiC NPs and obtained higher values of thermal conductivity with the bigger particles. It should also be noted that for nearly micrometer-sized particles, the Brownian motion is not present, and the thermal conductivity remains unchanged. For a small range of particle size, some authors reported a linear increase of conductivity with increasing particle size due to the reduced interfacial thermal resistance of the larger particles [5; 26].
## 2 Methodology
### Data Collection and Pre-Processing
#### 2.2.3 Color Changes Magnification  
Since our study focuses on improving accuracy of the RR estimation by the image resolution enhancement, we compare the proposed DL-based solution with other existing algorithms aimed at enhancing breathing signals. Precisely, the robustness of the SR CNN network is evaluated together with the Eulerian Video Magnification (EVM) algorithm [22] on both datasets collected by us. EVM allows for visualizing information which is invisible to a naked eye, e.g., the blood flow or chest movements. It is achieved by spatial decomposition of video frames and temporal filtering. As a result, we get a signal that represents these hidden changes. To reveal them, the signal is magnified and added back to the input video.  
In our work, the filtering frequency range (a parameter of EVM) was set to 0.16-0.33 Hz, assuming that a breathing rate of a healthy adult varies between 10-20 breaths per minute (bpm). The amplification factor of 20 was applied for signal magnification, as previously verified in [11].  
All thermal sequences used for testing (i.e., sequences from volunteers 16 th-31 st for the Lepton and 16 th-40 th for the SC3000 dataset) were enhanced with EVM. Examples of frames from consecutive 2-sec windows (every 25 th frame) with magnified color changes are presented in Figure 5. Frames were cropped to the nostril area, where the breathing signal is the most visible due to temperature differences between inhaled and exhaled air.
# Shiga Toxin Therapeutics: Beyond Neutralization
## 2 Shiga Toxin Structure and Activity  
Shiga toxins are AB5 toxins composed of a single A subunit and a pentameric B subunit [2; 25]. Shiga toxins bind to the cell membrane glycolipid globotriacyclceramide (Gb3) via three binding sites on the B subunit to initiate endocytosis and gain cellular entry [26; 27]. Retrograde trafficking machinery shuttle the toxin from the early endosome through the Golgi and endoplasmic reticulum (ER) [28]. During this process, furin-like proteases cleave a target site within the A subunit to create a catalytically active A subunit [29; 30]. The active toxin is then transported from the ER into the cytosol to reach its target, the 288 rRNA of ribosomes [31; 32]. Shiga toxins depuriunate the conserved adenine residue 2260 within the \(\mathrm{sarcin}\)-ricin loop of 28S eukaryotic rRNA via N-glycosadive activity to inhibit binding by the elongation factor EIF2a, thus terminating peptide elongation [31; 33]. Other ribosome-inactivating toxins act on the \(\mathrm{sarcin}\)-ricin loop of 28S eukaryotic rRNA, and the specific mechanism of toxicity via depurination is shared between Shiga toxins and the plant-derived toxin ricin [32]. Shiga toxins are highly toxic to Gb3-positive cells in culture, though their sensitivity is known to vary widely between cell lines and toxin subtypes [34; 35; 36]. Shiga toxicity in rodents, pigs, rabbits, and non-human primates differs in the distribution of tissues affected, presumably due to species differences in cellular Gb3 expression and localization. Rodents and rabbits develop gastrointestinal and renal tubular epithelial lesions when challenged with toxin, but fail to develop the glomerular endothelial damage and clinical HUS seen in intoxated human patients and non-human primates [37; 38; 39; 40]. Toxin-induced alterations in ribosomal structure and activity initiate cell stress pathways known as the unfolded protein response (UPR) and the ribonucle stress response (RSR), which activate a variety of pro-inflammatory and pro-apoptotic cellular effector proteins that contribute to cellular dysfunction and disease [41; 42; 43; 44; 45; 46]. The outcomes of these stress responses are inflammatory cytokine secretion, cellular apoptosis, and endothelial dysfunction, all of which are potential contributors to Shiga toxin-induced disease in vivo.
# Synchronous Rectification Method for CLLC Resonant Converters Based on State-Trajectory Models
## 4 Discussion
### Verification of Accuracy
As shown in Figure 19, the proposed SR method significantly enhances the overall operating efficiency of the CLLC resonant converter. Figure 19: Uncontrolled rectification efficiency curves and synchronous rectification curves. Figure 18: CLLC resonant converter over-resonant state switching load SR waveform, input voltage 200 V, output voltage 160 V, output current 3.5 A switching to 1 A. (**A–C**) are enlarged diagrams of the corresponding points. Figure 20 compares the efficiency of the proposed synchronous rectification scheme with the SR scheme reported in the literature [16] and the conventional model-based approach. It can be observed that the proposed method achieves higher efficiency compared to the technique described in [16]. This improvement in efficiency is attributed to the higher accuracy of the proposed method, resulting in a significantly smaller computed SR timing error.
## 3 Experiment Setup and Evaluation
### Hyperparameters  
Properly selecting parameters is a challenging task, especially for deep learning algorithms. This research aimed not to find the optimal parameters for each algorithm but to identify a potential candidate for use in challenging mining tasks. In the case of the Viola-Jones algorithm, the selection of the learning rate and the number of cascades was directly related to the training time. Decreasing the learning rate increased the training time without substantially improving the algorithm's accuracy. This effect was compounded when using the Haar-like feature in the detector's training. For the ACF algorithm, the main modifications were directly related to the ROI and the number of weak classifiers. The ROI size was obtained from the mean sizes of the training bounding boxes, and the number of weak classifiers was varied to assess its impact on the results.  
A straightforward guide must be used to select the appropriate parameters in the deep learning-based detectors. However, specifically for rock detection, the optimizer that yielded the best results was Adaptive Moment Estimation (ADAM), although it does not imply it will always be the case. This method offers very good results and has been used in several investigations [47; 48]. Batch size selection is related to the computational power available for loading images, depending on the machine's hardware used for training. The learning rate is the most crucial parameter in training, chosen primarily to avoid significantly altering neuron weights during the stages. This decision was made because pre-trained backbones from other datasets were used as the basis for training. Moreover,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Exp-1** & **Exp-2** & **Exp-3** & **Exp-4** \\ \hline Epochs & 10 & 10 & 10 & 10 \\ ROI & 23 \(\times\) 23 & 23 \(\times\) 23 & 52 \(\times\) 52 & 52 \(\times\) 52 \\ Weak classifiers & 2048 & Var & 2048 & Var \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiments with the ACF algorithm.  
similar configurations have been used in other research studies [14; 15; 16; 17]. The number of epochs was chosen to be the same across all algorithms. The hyperparameters that were used are listed in Table 2.
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
### SSA  
Let us briefly describe the SSA algorithm for signal extraction from a time series \(\mathsf{X}=(x_{1},\ldots,x_{N})\) of length \(N\), following [7]. We assume that  
\[\mathsf{X}=\mathsf{S}+\mathsf{N},\]  
where \(\mathsf{S}\) is a signal, and \(\mathsf{N}\) is random noise with zero expectation. The algorithm has two parameters, the window length \(L\), \(1<L<N\), and the number of components \(r\), \(r<\min(L,K)\), where \(K=N-L+1\). First, the time series is transformed into a trajectory matrix \(\mathbf{X}\) of size \(L\times K\) of the time series \(\mathsf{X}\):  
\[\mathbf{X}=\mathcal{T}_{\mathrm{L}}(\mathsf{X})=\begin{pmatrix}x_{1}&x_{2}& \ldots&x_{K}\\ x_{2}&x_{3}&\ldots&x_{K+1}\\ \vdots&\vdots&\vdots&\vdots\\ x_{L}&x_{L+1}&\ldots&x_{N}\end{pmatrix}, \tag{1}\]  
where the embedding operator \(\mathcal{T}_{L}\) denotes the bijection between \(\mathbb{R}^{N}\) and \(\mathcal{H}\), and \(\mathcal{H}\) is the set of Hankel matrices of size \(L\times K\) with equal values on the anti-diagonals \(i+j=\mathrm{const}\).  
The SSA estimator of the signal is defined as the composition  
\[\widetilde{\mathsf{S}}=\mathcal{T}_{L}^{-1}\circ\Pi_{\mathcal{H}}\circ\Pi_{ \mathcal{M}_{r}}\circ\mathcal{T}_{\mathrm{L}}(\mathsf{X}), \tag{2}\]  
where \(\Pi_{\mathcal{H}}\) is the orthogonal projector on the set of Hankel matrices \(\mathcal{H}\), and \(\Pi_{\mathcal{M}_{r}}\) is the projector on the set \(\mathcal{M}_{r}\) of \(L\times K\) matrices of rank at most \(r\). In both cases, we consider the projector by the Frobenius norm. The projection by \(\Pi_{\mathcal{H}}\) is constructed by averaging the values along the anti-diagonals [Section 6.2] in [3], and the result of \(\Pi_{\mathcal{M}_{r}}\) can be obtained via singular value decomposition as the sum of the leading \(r\) summands (Eckart-Young theorem [13; 14]).  
From the description of the algorithm, it follows that to adequately estimate a signal, its signal trajectory matrix must be of rank \(r\) or well approximated by a matrix of rank \(r\). This raises the following notion.
## 3 Classical Routing Protocols
### Leach  
Low Energy Adaptive Clustering Hierarchy (LEACH) [66; 67] is a TDMA-based medium access control (MAC) protocol that uses a clustering strategy to ensure uniform  
Figure 6: Taxonomy of clustering strategies.  
energy distribution among sensors on the network. The sensor nodes are organized in groups, transmitting important data to the BS through the CH. As indicated in Figure 7, the end-users can receive data through the core network via the Internet. Additionally, the data transmission modes in cluster-based networks can be categorized into three types [12]: intra-cluster, inter-cluster, and long-haul communication. When cluster members transmit data to their respective CHs according to TDMA scheduling, then it is called intra-cluster transmission. The data exchange between CHs is managed through inter-cluster transmission. Finally, the CH sends their combined data to the BS in the long-haul transmission method.  
The LEACH protocol operation consists of several rounds, where each round consists of two broad phases, i.e., the setup phase and steady-state phase, as shown in Figure 8.  
The initial setup phase consists of clustering and CH selection. Each cluster node is involved by a random value generated in the window [0, 1] during the CH selection process [68]. The node will be declared CH if the number generated is lower than the member node's predefined \(T(n)\) value. Equation (1) indicates the value of the \(T(n)\) threshold. The CH is also responsible for assigning TDMA schedules for the corresponding cluster members.  
\[T(n)=\left\{\begin{array}{l}\frac{P}{1-P\left({\it{mod}}\frac{1}{P}\right)}; \forall\;n\in G\\ 0;\;{\it{Otherwise}}\end{array}\right. \tag{1}\]  
where, \(P\) is the desired percentage of sensor nodes which may be CH, \(r\) refers to the current round, and \(G\) to a set of nodes that in the previous \(1/P\) rounds have not been involved in the CH selection. Since only these nodes participate in the CH selection process, which in  
Figure 8: Phases of LEACH protocol.  
Figure 7: System model of a clustered sensor network.  
the last \(1/P\) rounds was not CH, every sensor node of a cluster is therefore equally likely to become CH. The energy dissipation between the sensor nodes is therefore spread uniformly over the network.  
During the steady-state stage, cluster nodes will transmit sensed data based on the assigned TDMA schedule to their respective CH nodes. The nodes can transmit data only during a particular time slot; all other nodes go to sleep state by switching off their radios. In this way, the intra-cluster collision can be avoided. The main objective of the LEACH protocol is to increase energy efficiency by adopting a rotation-based CH selection procedure using a random number.