esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 4 Simulation and Numerical Results
### Time Delay Estimation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SNR (dB)** & **Parameter K** & **Multipath Gain** & **Delay (s)** \\ \hline \multirow{3}{*}{\(-5\)} & 5 & 0.1 & \(-0.966\times 10^{-3}\) \\  & 5 & 0.5 & \(-0.949\times 10^{-3}\) \\  & 5 & 0.8 & \(-0.965\times 10^{-3}\) \\ \hline \multirow{3}{*}{\(-4\)} & 5 & 0.1 & \(-0.992\times 10^{-3}\) \\  & 5 & 0.5 & \(-0.956\times 10^{-3}\) \\  & 5 & 0.8 & \(-1.034\times 10^{-3}\) \\ \hline \multirow{3}{*}{\(-3\)} & 5 & 0.1 & \(-0.983\times 10^{-3}\) \\  & 5 & 0.5 & \(-0.937\times 10^{-3}\) \\  & 5 & 0.8 & \(-0.964\times 10^{-3}\) \\ \hline \multirow{3}{*}{\(-2\)} & 5 & 0.1 & \(-0.987\times 10^{-3}\) \\  & 5 & 0.5 & \(-0.993\times 10^{-3}\) \\  & 5 & 0.8 & \(-0.955\times 10^{-3}\) \\ \hline \multirow{3}{*}{\(-1\)} & 5 & 0.1 & \(-1.026\times 10^{-3}\) \\  & 5 & 0.5 & \(-0.961\times 10^{-3}\) \\  & 5 & 0.8 & \(-0.969\times 10^{-3}\) \\ \hline \multirow{3}{*}{0} & 5 & 0.1 & \(-0.958\times 10^{-3}\) \\  & 5 & 0.5 & \(-0.944\times 10^{-3}\) \\  & 5 & 0.8 & \(-0.937\times 10^{-3}\) \\ \hline \multirow{3}{*}{1} & 5 & 0.1 & \(-0.956\times 10^{-3}\) \\  & 5 & 0.5 & \(-0.926\times 10^{-3}\) \\  & 5 & 0.8 & \(-1.024\times 10^{-3}\) \\ \hline \multirow{3}{*}{2} & 5 & 0.1 & \(-0.966\times 10^{-3}\) \\  & 5 & 0.5 & \(-1.016\times 10^{-3}\) \\  & 5 & 0.8 & \(-1.023\times 10^{-3}\) \\ \hline \multirow{3}{*}{3} & 5 & 0.1 & \(-0.989\times 10^{-3}\) \\  & 5 & 0.5 & \(-1.017\times 10^{-3}\) \\ \cline{1-1}  & 5 & 0.8 & \(-0.980\times 10^{-3}\) \\ \hline \end{tabular}
\end{table} 
Table 1: LMSTDE Delay Estimation with Multipath Gain Change. Table 2 shows that since m-sequence has good autocorrelation characteristics, the time delay of MPPSK signal after the spread spectrum can be estimated by autocorrelation method, so that multipath separation can be achieved in the receiver. To measure the bandwidth of the two multipath separation
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
###### Abstract  
The standard Bluetooth Low-Energy mesh networks assume the use of flooding for multihop communications. The flooding approach causes network overheads and delays due to continuous message broadcasting in the absence of a routing mechanism. Among the routing protocols, AODV is one of the most popular and robust routing protocol for wireless ad hoc networks. In this paper, we optimized the AODV protocol for Bluetooth Low-Energy communication to make it more efficient in comparison to the mesh protocol. With the proposed protocol (Optimized AODV (O-AODV)), we were able to achieve lower overheads, end-to-end delay, and average per-hop one-way delay in comparison to the BLE mesh (flooding) protocol and AODV protocol for all three scenarios (linear topology with ten nodes, multipath topology with six and ten nodes). In addition, the proposed protocol exhibited practically constant route requests and route reply setup times. Furthermore, the proposed protocol demonstrated a better Packet Delivery Ratio (PDR) for O-AODV (84%) in comparison to AODV (71%), but lower than the PDR of the mesh (flooding) protocol with 93%.  
Bluetooth Low-Energy; BLE mesh networks; AODV +
Footnote †: journal: Electronics  
0021  
Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks  
[PERSON]  
1 School of Computer Sciences, Universitat Sains Malaysia, Penang 11800, Malaysia; [EMAIL_ADDRESS] (M.R.G.); [EMAIL_ADDRESS] (G.C.S.)  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 General Analysis of the Binomial Adaptive Compression Method
### Analysis of the Proposed Compression Method
Many compression problems do not obey the binomial probability distribution and have forbidden sequences and other restrictions. The inability to take them into account in the method under consideration reduces the degree of compression and the versatility of its application. Additionally, converting binomial numbers to binary numbers and vice versa is a time-consuming procedure that requires a reduction in conversion time.
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 3 Materials and Methods
### GNN-Based Spatial Representation Module
#### 3.6.1 Graph Construction  
We construct a graph \(G=(V,E)\), where each node \(v\in V\) represents a spatial region in the CNN feature map. Each node is initialized with a feature vector extracted from the CNN at its corresponding location. Given a CNN-generated feature map of size \(H\times W\times D\), the graph consists of \(N=H\times W\) nodes, where each node is associated with a \(D\)-dimensional feature vector. The edge set \(E\) defines the connectivity between nodes according to spatial adjacency, and each node \((i,j)\) is connected to its immediate neighbors in a four-connected grid structure, linking to adjacent nodes in the left, right, top, and bottom directions. This was implemented using relative coordinate offset neighbors = [(\(-\)1, 0), (1, 0), (0, \(-\)1), (0, 1)] that represent the top, bottom, left, and right neighbors, respectively. That is, only neighbors that fall within the valid spatial boundaries of the feature map are included in the graph. The grid-based graph structure preserves the spatial relationships in the CNN feature map, facilitating information propagation through the GCN. The 2D grid connectivity is computationally efficient and is well-suited for structured feature extraction from medical images. In addition, self-loops are included, which allow each node \((i,j)\) to retain its feature information during the Graph Convolutional Network (GCN) aggregation process. This helps stabilize the model by preserving local feature representations. That is, the self-loop is an edge that connects a node to itself.
# Smart Roads for Autonomous Accident Detection and Warnings
## 4 Hardware and Experimental Setup  
Developing and testing the entire system on hardware is an interesting part of the proposed work. The experimental setup, microcontroller boards, and sensors are selected, considering the cost and availability. We selected an Arduino Uno R3 board and a PIC 4550 microcontroller [55; 56; 57] with different sensors and actuators. The Arduino website [57] is an open-source helping website that provides a basic code for different sensor modules that work with Arduino boards. The proposed AALS system is built to minimize computing power needed to detect accidents, and does not need artificial intelligence or a machine learning technique. With the AALS system, accidents are detected by monitoring input from different sensors and comparing them to threshold values. After the successful detection of an accident, the alerts are generated to protect oncoming vehicles from MVCs. The help request message is generated and sent wirelessly to the immediately downstream node. All nodes are interconnected wirelessly in such a way that each node listens to the next node and sends a message downstream to the node. Messages are sent only in case of an accident or after a RESET event, where the RESET button was pressed. To build the whole system, the following sensor modules and components are used:  
1. Arduino Uno R3;
2. An IR sensor module
3. A microphone sensor module;
4. A smoke detection module;
5. A GPS 8M module;
6. A HC-12 wireless communication module;
7. Breadboards and jumper wires;
8. A relay module;
9. A golden yellow light and siren;
10. 16X2 LCD with an I2C module.
## 5 Data and Results
### Test Sequences  
We have worked in this scenario with five different sequences in order to validate the proposal. In sequence \(-2^{\circ}\)_Fog_, we find a single human being performing different activities in the environment. The human is walking almost all of the time, but also runs, crouches and sits in the central concrete platform. The sequence was recorded when fog partially covered the scene. In this sequence, it is difficult to distinguish the human in the visible light spectrum. However, the pedestrian can be recognized with relative ease in the visible-light spectrum. However, it is not simple to detect the human when he/she is close to the building. The \(8^{\circ}\)_Night_ sequence was recorded in order to evaluate the performance of our proposal at night (in the darkness). Visibility is almost zero in the color spectrum, and the infrared spectrum also presents problems. We see in this sequence that buildings remain hot, due to the heat that they have accumulated during daylight hours. Therefore, buildings are sometimes confused with humans walking around them. In this second sequence, there are two people walking in the scene. Both pedestrians occasionally cross each other's path.  
In sequence \(3^{\circ}\)_Sunny_, initially, we have a human being who is walking in the environment. Sometimes, he performs different actions, like crouching. Later, a second man appears, walking on different paths. Finally, the two humans cross their paths and meet in the concrete deck. The following sequence, \(15^{\circ}\)_Cloudy_, presents a number of more complex actions. These actions are performed by a single human being. Let us highlight the action of sitting on the concrete deck core. The increase in temperature causes the appearance of human shadows on the platform. There is no doubt that in this way, complexity increases for human segmentation in the infrared spectrum. The last sequence is called \(28^{\circ}\)_Sunny_ and is the most complex for detecting people in the thermal infrared spectrum. In this sequence, we have three pedestrians walking in the scene and carrying out actions, such as sitting, crossing their paths and meeting. The high temperature makes it very difficult to distinguish humans in the infrared spectrum, especially in the concrete deck.
# The Effect of Chemical Activating Agent on the Properties of Activated Carbon from Sago Waste
## 3 Results  
### Porous Properties  
Figure 1 illustrates the effect of the chemical activating agents on the adsorption-desorption isotherms of activated carbon from sago waste. The activated carbon prepared with acid activating agents (ZnCl\({}_{2}\) and H\({}_{3}\)PO\({}_{4}\)) indicated a Type I isotherm according to the IUPAC classification [47]. This is evidenced by a sharp increase in nitrogen adsorption at low pressures, followed by a long plateau region at higher pressures, indicating a micro-porosity behavior. However, a narrow hysteresis can be seen due to the existence of mesopores [45]. Conversely, the activated carbon prepared with base treatment (KOH and KMnO\({}_{4}\)) indicated a Type IV or Type II isotherm with a clear hysteresis at high pressures. Consequently, this indicates a progressive development of mesopores, as well as a distribution of pore sizes.  
Table 1 depicts the porous properties of activated carbon from sago waste prepared using various chemical activating agents. It revealed that the mesopore volume (V\({}_{\text{Mes.}}\)) of activated carbon prepared with base treatment (45.7% and 37.7%) was higher than those treated with acid (33.3% and 25.2%). These results prove that the carbon with acid treatment contracts microporovosity, while those with base treatment show mesoporosity behavior.  
Figure 1: Effect on chemical activating agent on the adsorption-desorption isotherms of activated carbon from sago waste.  
However, the carbon with acid treatment has a higher total pore volume and nitrogen adsorbed compared to those with base treatment, resulting in a larger pore development due to an increase in char burn-off, confirmed by a lower yield value [3].
## 3 Modeling and Results
### Results and Accuracy Verification
This blue dotted line represents a random guess line, representing the performance level of the model in random prediction situations. The accuracy of the predictive model validation set is evaluated by constructing a confusion matrix visualization (Figure 9). The Random Forest algorithm model constructed by miceforest's imputed dataset correctly retrieved 220 of the 228 investable projects. The \"recall rate\" reached 96% (220/228), and among the 223 investable mining projects, three mining projects were misjudged, and the accuracy rate was 98.6% (220/223). That is to say, 1.4% of all investable mining projects will be misjudged as positive samples, and theoretically, according to this model, to study whether to put it into production, the failure rate is 1.4%.
# Experimental Study of Tool Wear in Electrochemical Discharge Machining
## 4 Discussion
### Film Formation Time  
Figure 7 shows the comparison of the gas film formation time before and after machining with the increase in voltage. The data collected at the beginning of the experiment are represented by Start, and End is the data collected after 200 s, which can reflect the change in the gas film formation time before and after the tool wears to a certain extent. It can be seen from Figure 7 that when the voltage is low, the gas film formation time is long; as the voltage increases, the gas film formation time will be significantly shortened. This is because the electrochemical reaction intensifies with the increase in voltage, and the bubble generation rate increases significantly, which significantly shortens the gas film formation time. With the further increase in voltage, the shortening of gas film formation time becomes slower and eventually stabilizes. The trend in the gas film formation time before and after machining is consistent. From another perspective, according to [PERSON] et al. [27], the gas film formation time is related to the time required by the local heating of electrolyte, which also depends on the resistance between the electrodes (local temperature of the electrolyte). At high voltages, the spark discharges become more intense, and the conductivity of the electrolyte is enhanced with the increase in the local temperature of the electrolyte, leading to the decrease in resistance between the electrodes. As a result, the gas film formation time is shortened. This is also consistent with the experimental results.  
Figure 6: Partial view of the tool wear under different voltages: (**a**) the base tool; (**b**) 28 V, 500 Hz, 50%, 15 wt%, 2000 rpm; (**c**) 28 V, 500 Hz, 50%, 15 wt%, 2000 rpm; (**d**) 52 V, 500 Hz, 50%, 15 wt%, 2000 rpm; (**e**) 52 V, 500 Hz, 50%, 15 wt%, 2000 rpm; and (**f**) 58 V, 500 Hz, 50%, 15 wt%, 2000 rpm.  
Figure 7: Relationship between the voltage and film formation time before and after machining.  
When the cathode has loss after 200 s of machining, it can be seen from Figure 7 that the gas film formation time generally shows a decreasing trend, but the reduction is not significant. [PERSON] et al. [26], in their conclusion, stated that the critical voltage also increases with the increase in the diameter of the tool electrode. According to this conclusion, the critical voltage of the cathode after loss is reduced, and the energy required to generate spark discharges has also decreased. At the same critical voltage, the voltage is inversely proportional to the gas film formation time. Lowering its critical voltage will shorten the gas film formation time. Although the gas film formation time is shortened and the spark discharge time is increased in the same cycle according to the results, its processing efficiency is improved, but due to the small reduction, the improvement of processing efficiency is limited. Moreover, the loss of the cathode affects the machining accuracy.
## 5 Discussion
### Analysis of Solution Times  
Figure 4 displays the runtime performance of PCC\({}^{2}\)LS on classical common UDG benchmark instances. Each instance undergoes 10 runs, and the average time to achieve the best solution is calculated. It is evident that across 41 instances, the average time to reach the best solution does not exceed 70 s. Particularly for the initial 25 relatively smaller instances, PCC\({}^{2}\)LS efficiently identifies the best solution promptly.
## 2 Related Works
### Kernel Density Estimation  
From a probabilistic point of view, data is a specification of one of the various possibilities of a particular random variable. As one piece of data is only a part of a random variable, a large number of data is needed to understand the overall appearance of the random variable. In general, the method of estimating the distribution characteristics of the original random variable from the distribution of observed data is called density estimation. Estimating the characteristics of a random variable \(X\) is equivalent to estimating the probability density function of that variable. For example, when there is a probability density function \(f(X)\) of a random variable \(X\), \(f(X=a)\) refers to a relative likelihood that \(X\) has the value of \(a\).  
Density estimation methods can be divided into parametric and non-parametric methods. The simplest method of non-parametric density estimation is the histogram representation. This is a way of expressing the frequency of a given data divided by a specific bin. However, histograms have a discontinuity at the boundaries of the histogram bin, and this is a disadvantage in high dimensional data where the bins are different according to the size and the starting position.  
Parametric density estimation uses the pre-defined model of the probability density function and estimates the parameters of the model from the data. The kernel density estimation is one of the parametric density estimation methods that solves the problem of the histogram method by using the kernel function. There are two general conditions for the kernel function. One is that it should be symmetric about the origin and non-negative, and the other is that the summation of the integral should be 1. For example, Gaussian, Epanechnik, and Uniform are typical kernel functions.  
\[f(x)=\frac{1}{n\hbar}\sum_{i=1}^{n}K\bigg{(}\frac{x_{i}-x}{h}\bigg{)} \tag{8}\]  
In Equation (8), the \(h\) which controls the size of the neighborhood around \(x\) is the smoothing parameter. The function \(K\) is called the kernel which controls the weight given to the observations \(x_{i}\) at each point \(x\) based on their proximity. Therefore, it can control the shape of the probability distribution. The kernel function \(f(\cdot)\) for the observed data \(x\) can make the probability density function by summing all these kernel functions and dividing them by the total number of the data. The estimated probability density function reflects the characteristics of the observed data. By using this function, it is possible to sample new data from a distribution of observed data.
## 5 New Trends and Future Research Directions in Enterprise Networking
### Sd-Wan  
SD-WAN has rapidly taken over as the standard WAN technology due to its increased flexibility, enhanced performance, and robust security [82; 84; 85]. Compared to traditional WANs, SD-WAN provides a layered architecture that is far simpler to run. It also offers a network design that transfers the management and control layers to the cloud via a centralized controller [86]. It is anticipated that there will be further adoptions and migrations from the conventional enterprise WAN technology to SD-WAN. Since SD-WAN does not rely on public lines, there is considerable reduction in cost due to utilization of public internet. Utilizing SD-WAN optimizes the current IT infrastructure, increasing network capacity, decreasing latency, and improving network scalability. Organizations that have already embraced the technology will also concentrate on leveraging its extensive potentials. That is, by rationalizing their applications through the use of universal customer premises equipment (UCE) and virtual network functions (VNFs) [85; 87]. The network functions are separated into data, control, and application planes by SD-WAN (see Figure 7), in the same way as SDN. The data plane transports application and user data, while the control plane performs system configuration, monitoring, and making routing decisions [82]. However, the application plane, commonly known as orchestration plane, leverages on the services provided  
Figure 7: SD-WAN architecture [82]. by the control plane. Despite these benefits, SD-WAN technologies can be affected by limitations that impact network security and performance, especially in latency-sensitive applications. Some of them include configuration errors, software vulnerabilities, and interoperability and scalability problems. To address these challenges, ref. [88] proposed SD-WAN-based multi-objective networking improvement measures, such as new transport protocols, NFV, and ML for networking.
# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 1 Introduction
Certain approaches also exploit the catadioptric reference for localization tasks [39]; however, our proposal loosens the unified projection model to a panoramic conversion, without estimating any specific parametrized landmarks, but the entire omnidirectional image instead, since it inherently encodes visual landmarks inside. To that aim, several contributions are devised so as to produce robust feedback and to reinforce the matching, which ultimately impacts the localization of the robot. Moreover, straightforward visual SLAM results are also presented as a further final application. The basis is on a dynamic adaption of the current uncertainty of the system, with an associated propagation to the system state. Fusing this information with a Bayesian regression technique [40] permits obtaining a sensor data distribution [41], in order to extract the probability of matching occurrence, which becomes potentially useful for weighting robust features and avoiding false positives. In this context, EKF priors represent a fundamental tool for the developments. Similarly, some works focus on prior inputs in the EKF for a wide scope of modeling, such as deformable environment modeling with FEM (Finite Element Method) [42; 43], motion as a visual odometry [44], with systematic parameters [45] or through robot kinematics [46]. In general terms, they all succeed in relaxing the dimensionality of the EKF estimation. However, here, we take most of the filter-based prior stage in the opposite direction, as in [47], where these data are taken as a posterior output to be propagated for inference purposes in the matching. That is, no parametrization for the map of the environment, for the robot, neither for the camera motion is input into the EKF. The structure of this paper is as follows: Section 2 presents the general characteristics of the monocular omnidirectional system and the developments to adapt the epipolar constraint to the omnidirectional geometry. Section 3 comprises the implementation of the robust visual localization, where the dynamic uncertainty management and the Bayesian regression inference are also devised. Section 4 provides a brief explanation of the omnidirectional SLAM approach, as the target application with which the robust localization is aimed to be fused. Section 5 presents the real experimental setup so as to assess the suitability and the benefits of the approach with comparison results under publicly-available real datasets. Section 6 establishes a discussion on the previous results. Section 7 states the main conclusions extracted from this research work.