esa-sceva/satcom-chunk-collection huggingface dataset:
# CarFree: Hassle-Free Object Detection Dataset Generation Using Carla Autonomous Driving Simulator
## 4 Overall Architecture  
Figure 2 shows the overall design of CarFree that comprises the following parts: (i) the Carla server in the left, (ii) the data extraction part in the middle, and (iii) the GT generation part in the right.  
Regarding the server, we use the unmodified stock version from the Carla repository. Thus, if Carla is already installed in the system, it can be used without any modification. The data extraction part is implemented as three Carla clients as listed in the middle of Figure 2. They control the simulated world in terms of its weather and lighting conditions (weather.py) and the surrounding vehicles and pedestrians (spawn.py). Then it extracts the data listed in Section 3.2 (extract.py). Since they are implemented as Carla clients, the data extraction stage works in real time, by the time progress in the Carla server. In contrast, the GT generation stage runs as a separate process that works offline. It calculates the 2D bounding boxes of vehicles and pedestrians from the extracted data as in the following steps:  
* First, the eight points of each 3D bounding box are projected to the 2D image coordinate of the forward-facing camera (Section 5.1).
* Second, for the projected eight points, their minimum area bounding box that barely encloses them is derived (Section 5.2).
* Third, occluded objects are removed because, for example, objects behind a wall are still present at this stage (Section 5.3).
* Fourth, each bounding box is tightly fitted to the object by removing the gap between the initial bounding box and the actual object boundary (Section 5.4).  
The resulting bounding boxes should be recorded in an appropriate file format. For that, there is no standard format, but we have several candidates for that purpose. For example, the Darknet object detection framework [16] employs a plain text file format with object class numbers and 2D bounding boxes represented by its center point \((x,y)\) along with its height (\(h\)) and width (\(w\)). Other frameworks such as Detectron2 use a JSON (JavaScript Object Notation)-based file format with slightly different representation methods. This study follows the Darknet's convention. However, because the conversion between different formats is straightforward, we can easily apply our method to other frameworks.
## 3 Methods
### Baseline Model: HiSup
#### 3.2.2 Boundary Enhancement  
Due to the widespread irregularity and fuzzy boundaries of building shapes in the semantic mask results, HiSup employs the boundary enhancement module to utilize the line attraction field results \(M_{A}\) to constrain the semantic mask results \(M_{S}\), thereby making the building shapes in the semantic mask more regular and the boundaries clearer. Specifically, firstly, \(M_{A}\) is concatenated with the feature map \(F\) extracted by the backbone network to obtain the fused feature map \(M_{AF}\), incorporating the line constraint information into \(F\). Then, \(M_{AF}\) is fed into the semantic mask classification head to predict the semantic mask image \(M_{AS}\). Subsequently, the semantic mask loss function is employed to calculate the discrepancy between \(M_{AS}\) and the semantic mask labels. Finally, through backpropagation, \(M_{A}\) receives additional supervision from the semantic mask labels, enabling it to have a better representation. After further gradient backpropagation, the backbone network enhances the expression of building shapes in F to facilitate the generation of \(M_{A}\). Consequently, the backbone network learns the correct building shapes guided by \(M_{A}\).  
To further reduce the merging of buildings and confusion with the background, we improved the loss function of HiSup. Dice Loss [46] was added to the binary cross-entropy-based segmentation loss \(L_{seg}\) to allow the model to focus more on the boundaries of building targets and reduce the adhesion between building polygons. The segmentation loss \(L_{seg}\) of the model can be represented as follows:  
\[L_{seg}(\theta)=BCE\Big{(}M_{AS},M_{S}^{GT}\Big{)}+BCE\Big{(}M_{S},M_{S}^{GT} \Big{)}+\theta L_{dice} \tag{3}\]  
where BCE represents the binary cross-entropy loss, \(M_{AS}\) represents the semantic mask result guided by the line features, \(M_{S}^{GT}\) represents the semantic mask label for the building, \(L_{dice}\) represents the Dice Loss, \(\theta\) is the coefficient. Dice Loss can be represented as follows:  
\[L_{dice}=1-\frac{2 TP}{2 TP+FP+FN} \tag{4}\]  
where \(TP\) represents the number of true positive pixels, \(FP\) represents the number of false positive pixels, and \(FN\) represents the number of false negative pixels.
# Plant Extracts for the Control of _Listeria monocytogenes_ in Meat Products
## 3 Results
### Growth Control of L. monocytogenes by Plant Extracts in Artificially Contaminated Cooked Ham
Cooked ham is commonly considered a safe product, but recontamination by psychotrophic pathogens such as _L. monocytogenes_ can occur during post-processing. The use of plant extracts can determine the elimination of the pathogens; however, the effectiveness of this inhibition is strongly correlated to the quantity of the pathogen and of the plant extracts used.
## 3 Controller Design
### Linear State-Feedback Control  
For designing this particular controller, the SOFC system in Equation (2) is approximated again by a third-order lag model ( see Section 3.3), which is transformed into its controllable canonical form  
\[\dot{\mathbf{x}}=\begin{bmatrix}0&1&0\\ 0&0&1\\ -a_{31}&-a_{32}&-a_{33}\end{bmatrix}\cdot\mathbf{x}+\begin{bmatrix}0\\ 0\\ b\end{bmatrix}\cdot u_{\text{SF}}\enspace. \tag{27}\]  
The output of this approximate system representation is given by  
\[y=\begin{bmatrix}1&0&0\end{bmatrix}\cdot\mathbf{x} \tag{28}\]  
with the same states \(\mathbf{x}\) as in Equation (12) and the input variable \(u_{\text{SF}}=\dot{m}_{\text{H2,SF}}\). The controller gain \(\mathbf{k}^{\text{T}}\) included in the control law  
\[u_{\text{SF}}=-\mathbf{k}^{\text{T}}\cdot\mathbf{x} \tag{29}\]  
was determined with the help of eigenvalue placement of the closed-loop poles  
\[\det(s\cdot\mathbf{I}-(\mathbf{A}-\mathbf{b}\cdot\mathbf{k}^{\text{T}}))\overset {!}{=}\prod_{i=1}^{n=3}(s-s_{i,d}) \tag{30}\]  
in the \(s\)-plane satisfying Hurwitz stability, i.e., all real parts of the eigenvalues are strictly negative according to \(\Re\{s_{i,d}\}<0\) and the eigenvalues are faster than the desired eigenvalues \(s_{i,d}\) of the open-loop system in Equation (27) [17].  
To guarantee steady-state accuracy, a dynamic feedforward control  
\[u_{\text{ff}}=\mathbf{k}_{\text{v}}^{\text{T}}\cdot\begin{bmatrix}P_{\text{el, d}}\\ \dot{P}_{\text{el,d}}\\ \dot{P}_{\text{el,d}}\\ \dot{P}_{\text{el,d}}\end{bmatrix} \tag{31}\]  
was added, where \(\mathbf{k}_{\text{v}}^{\text{T}}\) is determined with the numerator coefficients of the inverse transfer function of the controlled system in Equations (27)-(29).  
Similar to the sliding-mode control in Section 3.3, a possible gain error resulting from model uncertainty was corrected with a static gain compensation  
\[K_{d}=\frac{P_{\text{el,d}}}{P_{\text{el,m}}}\enspace, \tag{32}\]  
shown in Figure 7.
## 5 Results and Discussion
### Packet Delivery  
Figure 5(a)-d show how many data packets the BS has received, giving information on network throughput. In Figure 5(a), with m = 0 and \(\alpha=0\), the EPOA-CHS algorithm demonstrates improved network throughput compared to SEP, DEEC, Z-SEP, and PSO-ECSM. The network throughput of EPOA-CHS specifically improved by 102.3%, 94.8%, 69.8%, and 8.67% in comparison to SEP, DEEC, Z-SEP, and PSO-ECSM, respectively. In Figure 5(b), where m = 0.1 and \(\alpha=2\), the EPOA-CHS algorithm continues to exhibit superior network throughput, with an increase of 87.9%, 79.4%, 36.6%, and 6.86% compared to SEP, DEEC, Z-SEP, and PSO-ECSM, respectively. Figure 5(c) represents the results for m = 0.2 and \(\alpha=3\), showing that the network throughput of EPOA-CHS improved by 56.8%, 53.2%, 25.4%, and 5.61% relative to SEP, DEEC, Z-SEP, and PSO-ECSM. Finally, Figure 5(d) demonstrates the performance for m = 0.3 and \(\alpha=1.5\), with the EPOA-CHS algorithm achieving network throughput increases of 87.6%, 78.5%, 33.7%, and 4.76% compared to SEP, DEEC, Z-SEP, and PSO-ECSM, respectively. These results clearly highlight the enhanced network throughput achieved by the EPOA-CHS algorithm across different scenarios and its superiority over the evaluated protocols.
## 2 Materials and Methods
### Train Tracking  
The train tracking algorithm works on the inputs from the vibration detection and works on a one-second basis. It takes the classification vector \(v_{i}\) and outputs the current positions of the trains present in the signal denoted by \(P_{j}\), where the index \(j\) runs through the active trains at the given second \(i\). The train tracking algorithm itself consists of three steps, which we will discuss individually subsequently:  
1. Extraction of edges from the data.
2. Assigning the edges to objects and creating new objects.
3. Applying a Kalman filter to smooth the trajectories of all the objects.  
**Edge Detection.** The edge detection is based on a K-means clustering of wavelet responses of the vibration detection vector \(v_{i}\). We use Ricker wavelets of two different widths, namely 128 and 256, cable segments to compute the convolution with the detection vector \(v_{i}\). From both of these convolutions, we extract 512 consecutive values as feature values for the K-means clustering algorithm. A prototypical dataset of vibration detections is used to train the algorithm with four centers which correspond to background, leading edges, trailing edges, and train signal. Note that at this point leading edges are edges that mark the start of a train viewed from the interrogator device, while trailing edges are edges that mark the end of a train. If the train moves towards the interrogator, this leading edge will also be the leading edge of the train; if the train moves away from the interrogator, the leading edge viewed from the interrogator will be the trailing edge of the train. In the following, we will call all these edges _detected edges_.  
**Edge Assignment and object creation.** The tracking algorithm works internally with two types of objects: tracked edges and tracked trains, which we will denote by \(E_{i}\), \(i\in I\) and \(P_{j}\), \(j\in J\), respectively. Note that tracked edges are different objects from the detected edges identified in the previous step. In the following, we will describe how the detected edges are assigned to the tracked trains and the tracked edges. This is repeated every second with the corresponding detected edges. Therefore, all the  
results in the leading and the trailing edge of the train. The resulting constant velocity model leads to the following transition and observation model  
\[F =\begin{pmatrix}1&0&1&0\\ 0&1&1&0\\ 0&0&1&0\\ -1&1&0&0\end{pmatrix} \tag{5}\] \[H =\begin{pmatrix}1&0&0&0\\ 0&1&0&0\end{pmatrix}.\]
## 3 Development of ANN Model
### Autoencoder  
Dimension reduction, or feature extraction in machine learning terminology, particularly in image analysis applications, is also possible using the method called autoencoding, which is an unsupervised learning technique using an ANN. Autoencoder uses the (normalized) raw data as input and output at the same time. To benefit from the dimension reduction, the classical autoencoder sets the number of nodes in the hidden layer intentionally less than the input layer dimension [46]. A typical neural network architecture of an autoencoder with fewer hidden layer nodes than in the input is shown in Figure 2. Autoencoder takes the raw data \(X^{b}\) from the input layer and predicts the output \(\hat{X}^{b}\) such that X and \(\hat{X}^{b}\) are close. Because the data goes through a hidden layer, which has fewer nodes than the number of nodes in the input layer, the autoencoder tries to squeeze the information in the input to fit into the hidden layer. This process in called encoding. After the compression, the autoencoder decodes the data to reconstruct the input. When the autoencoder finds a way to successfully reconstruct the data \(\hat{X}^{b}\), which is close enough to the input \(X^{b}\), we can conclude that the squeezed information in the hidden layer has sufficient information to closely describe the original data \(X^{b}\) but with a reduced dimension. We refer to the compressed data as code. Because the code is considered to have sufficient information to describe the raw data, using the code for training the learning algorithm would be (almost) equivalent to training using the original data, and the dimension of the data is reduced, as in PCA.  
A similar benefit, however, can be achieved by setting the number of nodes in the hidden layer to be greater than in the input with sparsity constraints [47; 48; 49]; this allows a greater number of hidden layer nodes than in the input layer, of which only a few are activated, while most of the nodes remain inactive. Such an autoencoder is called the sparse autoencoder and is known to have the advantage that, by using a dimension higher than the original data, the likelihood that each experimental datapoint is easily distinguishable is increased [50]. In this article, we used this sparse autoencoder with one hidden layer consisting of 20 units (whereas the original data had 10 variables). The coded data was then fed into the learning algorithm with one hidden layer with 24 nodes for training.  
Figure 2: Simplified typical neural network architecture to implement an autoencoder.
## 1 Introduction
If the capacity limits are violated, some loads will be removed from the island. In this study, we focus on solving the PSIP, where the coherent generator groups are given. Although the PSIP has been investigated in the literature, few researchers have proposed meta-heuristics to solve it. In this study, we propose a tailored tabu search algorithm [13], which is one of the most successful meta-heuristics, to solve this problem. When handling the PSIP, we have to face two challenges. The first challenge is the large scale of the power system. The number of splitting strategies (_i.e._, feasible solutions) exponentially increases with the scale of the power system. The second one is that the splitting strategy must be generated in a very short time (less than one second in practical applications), as we need to react to the disturbance as quickly as possible. If the reaction speed is too slow, a catastrophic consequence may have occurred and the splitting strategy generated becomes meaningless. Our tabu search algorithm employs a novel two-stage approach to construct a good initial solution, which makes the improvement stage very effective. We introduce the concept of the boundary node and propose a neighborhood for the improvement stage based on the movement of boundary nodes. The numerical experiments show that our tabu search algorithm is capable of achieving high-quality solutions for the power systems with up to 3120 nodes within 0.7 s.  
The remainder of this paper is structured as follows. We first provide an overview of related research in Section 2. In Section 3, we then formally describe the problem. Our proposed tabu search algorithm is detailed in Section 4. Section 5 demonstrates the effectiveness of our algorithm by numerical experiments, comparisons with some existing methods and simulations. Section 6 concludes this study with some closing remarks.
# An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring
## 3 The ECG Circuit Design
### The Schematic Diagram of the ECG Circuit  
The Schematic Diagram of the ECG circuit is shown in Figure 2. All the factors that might affect the accuracy of the ECG signal measurement have been considered in our circuit design and programming code. An instrumentation amplifier (IA) is used to take the difference between LA and RA signals, and different stages of filters are used to reduce noise. The right leg drive is implemented to eliminate common mode interference with human body, and we have used extra stages of amplifiers to amplify signal. R-peak detection is done through the peak detector to measure the heart rate. The design and function of each stage is explained in details in the following subsections.
# Low Complexity DSP for High Speed Optical Access Networking +
## 2 Experimental Setup
The past noise samples can be estimated via \(\gamma[k\text{-}h]\text{-}\tilde{y}^{\prime}[k\text{-}h]\) thanks  
Figure 1: (**a**) Experimental setup of a 40 Gb/s lane rate next generation passive optical network (NG-PON) downstream link using pulse amplitude modulation (PAM-4) and the diagram of the receiver digital signal processing (DSP). (**b**) Detailed architecture of the nonlinear equalizer. to the decision feedback. The nonlinear DSP has excellent reconfigurability by switching ON or OFF the DFE and NS filter and part of the taps with lower weights can be disabled, depending on the channel conditions and transceiver power requirement. A low complexity 10G-Ethernet PON (EPON) FEC Reed-Solomon [RS(255,223)] with 14.35% overhead, 6.56 dB net coding gain and a threshold BER of \(10^{-3}\) is assumed here [26]. Considering the superior performance of the proposed NLE, a 5.84% overhead KP-4 RS(544, 514) FEC with a threshold BER of 2.2 \(\times\)\(10^{-4}\), widely adopted in high speed Ethernet links, can also be used [27].
## 2 Related Literature
Here, SCHs nodes will take care of data transmission to mobile BS, while regular CH nodes will collect the data from cluster member nodes. For selecting SCHs, a Mamdani-based fuzzy inference engine is used, and fuzzy rules are formulated based on the parameters such as battery power, node centrality, etc. Similarly, [PERSON] and [PERSON] [26] have proposed a cluster head selection scheme using fuzzy logic. These authors have used parameters such as remaining energy, neighbors alive, and distance to BS to devise the fuzzy rules for selecting the CH nodes. Though several articles are focusing on clustering strategies for WSNs, there are no ideal hierarchical routing protocols. Hence, there are a lot of opportunities in developing energy-efficient routing protocols for WSNs. EOICHD is another cluster-based routing protocol proposed in [27]. EOICHD is presented as an improvement over LEACH and LEACH-Centralized (LEACH-C) protocol, and it uses the concept of separating selected CH nodes by an optimal distance. The separation of CHs by a certain distance leads to a uniform distribution of CHs across the network. The hypothesis of the optimal distance parameter mentioned in [27] needs further investigation. The motivation of the current research article is to understand the behavior of the EOICHD protocol. In this article, we are extending the work presented in [27] by proposing variants of the EOICHD protocol and checking the EOICHD protocol's behavior. EOICHD protocol is further described briefly in Section 4.
# Trajectory Classification and Recognition of Planar Mechanisms Based on ResNet18 Network
## 4 Training Model
### Classification of Trajectories in Regular and Inverted Configurations  
Each type of mechanism is trained with 10,000 samples for both regular and inverted trajectory images, and the test set includes 500 images for both regular and inverted trajectories. The training error and test results using the ResNet18 network are shown in Figures 8 and 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Predicted Results** & **AlexNet** & **VGG16** & **ResNet18** \\ \hline \multirow{2}{*}{Regular configurations} & Regular & 487 & 490 & 497 \\ \cline{2-5}  & Inverted & 13 & 10 & 3 \\ \hline \multirow{2}{*}{Inverted configurations} & Regular & 33 & 17 & 22 \\ \cline{2-5}  & Inverted & 467 & 479 & 478 \\ \hline \multirow{2}{*}{Training time (s)} & 12,012 & 41,642 & 11,423 \\ \cline{2-5}  & Memory (MB) & 222.7 & 524.5 & 43.7 \\ \hline \multicolumn{5}{c}{Accuracy rating} & 95.4\% & 96.3\% & 97.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three model test results table.  
Figure 7: Comparison of three kinds of network training errors.  
In Figure 8, only correctly classified results are shown. However, since the number of test samples is fixed, the problem is essentially a binary classification problem (regular and inverted configurations). Therefore, incorrectly classified images would belong to the other class. For example, trajectory images of Class A planar five-bar mechanisms in regular configuration would only be incorrectly classified as Class A planar five-bar mechanisms in inverted configuration. From Figure 8, it can be observed that during the training process, the error consistently decreases for each type of mechanism. Initially, the error decreases rapidly and then slows down gradually, eventually stabilizing. This indicates that the model has good generalization and robustness. Figure 9 shows that the method achieves high accuracy in classifying mechanisms in the test set, ranging from 96.40% to 98.80%.
# Dispersive FSO Performance Estimation with Gaussian Pulses and Laplace Modeled Time Jitter
## 2 System Model
### Total Power Losses Estimation  
The total losses, \(L\), can be elegantly described as a negatively exponential function of the distance propagated, \(z\), [15; 33; 34; 16]:  
\[L(z)=\exp(-lz), \tag{2}\]  
where \(l\) denotes the loss coefficient expressed in power losses per distance unit. The values of the \(l\) coefficient are related to the current weather conditions.