esa-sceva/satcom-chunk-collection huggingface dataset:
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
## 3 Proposed Reputation-Based Scheme
### Design and Modules
#### 3.2.2 The Monitor (C2C Based Detection Mechanism)
If no packet is overheard from \(F\), then \(M\) assumes that \(F\) dropped the packet received from \(S\) and updates the dropped record of the \(F\) node. Finally, \(M\) sends C2C information to the reputation module for reputation calculation. The details are given in Algorithm 3. ```
1.Receive packet
2.Ifpackettype\(==\)RREQthen
3.Ifcurrentnode\(==\)destinationthen
4.SendRREP
5.else
6.Additselfinthepathandbroadcastthepacket
7.Endif
8.Elsepackettype\(==\)data
9.Ifsource\(==\)newnodethen
10.additsdetailstoC2C table
11.Elsechecktintintheselfishnodeslist
12.Ifavailableintheselfishlistthen
13.Discardthepacket
14.Elseupdatetheconsumption
15.Endif
16.Endif
17.Endif
18.Calculate reputation-basedonC2C
19.Ifreputation<thresholdthen
20.Addsendertotheselfishlistand discardthepacket
21.Endif
```  
**Algorithm 2**Reputation calculation for sender node (\(S\)) by intermediate node (\(F\))  
Using this framework where the sender, forwarder and neighbor nodes take part in the reputation calculation, the false accusation problem can be avoided. In false accusation, nodes spread false reputation values about other nodes. Our scheme can efficiently handle this problem. For example, in Figure 1, when a sender node \(S\) transmits a packet to a forwarder node \(F\), both the sender \(S\) and the neighbor node \(M\) update the contribution of the \(F\) as the forwarder node \(F\) forwards the message. As a result, both the sender and the neighbor nodes have the same value of contribution for node \(F\). Similarly, for sender \(S\), the \(F\) and \(M\) have the same consumption value.
###### Abstract  
Video streaming is one of the challenging issues in vehicular ad-hoc networks (VANETs) due to their highly dynamic topology and frequent connectivity disruptions. Recent developments in the routing protocol methods used in VANETs have contributed to improvements in the quality of experience (QoE) of the received video. One of these methods is the selection of the next-hop relay vehicle. In this paper, a QoE-aware geographic protocol for video streaming over VANETs is proposed. The selection process of the next relay vehicle is based on a correlated formula of QoE and quality of service (QoS) factors to enhance the users' QoE. The simulation results show that the proposed GeoQoE-Vanet outperforms both GPSR and GPSR-2P protocols in providing the best end-user QoE of video streaming service.  
Keywords:vehicular ad-hoc networks (VANETs); geographic routing protocol; video streaming; quality of service (QoS); quality of experience (QoE); quality metrics +
Footnote †: journal: Computer Science
# HelpResponder--System for the Security of First Responder Interventions
## 3 Global Description of the Architecture
### The Monitoring System  
The approach proposed in this paper consists of a system capable of analyzing the conditions in which a hostile environment is found before performing the intervention by the first-responder team. These could be very useful for giving real-time feedback during the intervention process. Figure 1 shows the complete architecture consisting of the following modules: (1) a Beacon Network for ranging/sensing, which is deployed in smart buildings, (2) the Mobile Ground Autonomous Vehicle (MGAV), geo-located by ranging with the central node and including (3) an onboard fire detection infrared vision system, (4) the protective firefighter equipment with embedded IMU sensors for position tracking, (5) a real-time communication link, and (6) an Advanced Command Post Interface (ACPI).  
The Beacon Network (1) distributed over an interior space will be deployed in the indoor environment (Nordic Semiconductor nRF52840 PDK [33]). They are configured in a star topology, in which a device or central node collects data from the peripheral beacons or nodes. These peripheral beacons could collect environmental parameters such as temperature or CO concentration and detect flames' presence in their surroundings. This proposal will use this sensor network used to provide communication to send information and location. This network will be used by the MGAV, which includes the communication module to navigate in the indoor environment. The development of beacons is integrated on an ATEX box to include and protect the electronics from operating in the evaluated scenarios' conditions.  
The main contribution of the MGAV is the integration of a Fire Detection System to locate the fire focus emergency with real-time video capture and processing equipment indoors and without visibility by the fire smoke. The MGAV is based on [34] and uses sensor networks and three ultrasonic sensors for free space detection to navigate the environment. The ACPI interface is used to monitor the critical information collected from the environment showing the path followed by the MGAV to find the point close to fire focus emergency using a typical dashboard. We hypothesize that the beacon network is deployed in the environment and that there is an a priori known map of space. Our research is oriented to facilitate the system to detect points of interest and provide the most optimal route tracking to the emergency services. This type of service usually operates with maps of the areas to be intervened. Our objective in this work is to investigate a platform that responds to minimize risks in an intervention by validating our architecture and algorithms. In the future, our objective is the ability to deploy this platform in an infrastructure that is not known. The next section will explain this paper's main contribution: the Fire Detection System using the data collected by the MGAV.  
Figure 1: Diagram of the proposed monitoring system for indoor first-responder interventions.
## Materials and Methods
### Loss of Control Predictive Model Development
#### 2.4.4 Performance of the Model (ROC and AUC)  
As the true instances (loss of control) are uncommon, the classes in our sample were not balanced. Therefore, in addition to accuracy metric, we used receiver operating characteristic (ROC) curve analysis as a robust metric to assess the ability of the model to classify or predict the loss of control. The optimal cut-point (decision threshold) that determined if the rider had full control of the vehicle during the braking event was established with the highest Youden index, varying the compromise between the false negative and false positives probabilities, i.e., the threshold with the highest'sensitivity + specificity' value [20].  
The area under an ROC curve (AUC) measures the performance of a diagnostic test, using a combination of the values of sensitivity (proportion of actual positives or _loss of control_ cases that are correctly identified as such) and specificity (proportion of actual negatives or _full control_ cases that are correctly identified as such). AUC is a typical performance indicator in machine learning and statistics [21]. The possible values of AUC range from 0.5 (no diagnostic ability) to 1.0 (perfect diagnostic ability). Additionally, we used Hosmer-Lemeshow as inferential goodness-of-fit test.
# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 4 Empirical Evaluation
### Dataset  
The Lung Image Database Consortium image collection (LIDC-IDRI) [59] was developed through a collaboration between the Foundation for the National Institutes of Health (FNIH) and the Food and Drug Administration (FDA). This valuable dataset includes 1018 CT scans from 1010 individuals, each paired with an XML file. The XML file holds detailed annotations made by four experienced radiologists to meticulously identify and catalog every nodule visible in the scans. The annotation process is divided into two main phases to ensure thoroughness. In the first phase, known as the blinded-read phase, each radiologist independently reviews the scans to pinpoint lesions, marking those considered as \"nodule \(<\) 3 mm\" and \"non-nodule \(\geq\) 3 mm\". Following this, in the unblinded-read phase, they revisit their initial findings and consider their peers' anonymous annotations. Within this dataset, a total of 7371 lesions were marked as nodules by at least one reviewing radiologist. Of these, 2669 lesions were unanimously classified as \"nodule 3 mm\" by all four radiologists, with 928 lesions consistently identified by every radiologist involved. These 2669 lesions were then subjected to detailed assessments, including intellectual nodule evaluations and the provision of precise nodule outlines. It is important to note that the original LIDC-IDRI dataset does not include 3D images. To bridge this gap, a meticulous manual process was employed using Rhinoceros 3D software, enabling the creation of 3D representations for these images. Rhinoceros 3D, version 8.0, developed by [PERSON], is sourced from Seattle, Washington, United States of America [60].  
In addition to the details about the LIDC-IDRI dataset, it is important to outline the preprocessing steps to prepare the data for our analysis. Initially, DICOM images were converted into a more accessible format using the PyDicom library, a Python-based tool that facilitates seamless integration and manipulation within our analysis framework. Each CT scan underwent normalization to standardize pixel intensity values, enhancing data consistency for our machine-learning models.  
To enhance the quality and reliability of the data used in training, we applied data augmentation techniques, including rotation, zoom, and horizontal flipping. These methods increased our model's robustness against new, unseen data variations. Subsequently, each augmented image was resized to uniform dimensions, streamlining processing and ensuring consistency in the input to convolutional neural networks. Additionally, the annotations from the radiologists were precisely converted into mask images that accurately represent the locations of the nodules in the CT scans, providing a reliable ground truth for supervised learning. These crucial preparatory steps ensured that the dataset was optimally prepared for achieving high-performance results in our study.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Optimization Based Methods
#### 3.3.4 [PERSON]'s Method  
In Reference [134], a method to design the FIR filter based on genetic algorithm (GA) is proposed. We summarize this algorithm in Algorithm 1.  
Figure 7: **(a)** Frequency responses of the designed filters by the PSO algorithm and the RPSO algorithm. **(b)** Frequency responses of the designed filters by the PSO algorithm and the DEPSO algorithm.  
```
0: Initialize parameters: population size \(NP\), current generation \(g=1\), maximum generation \(G_{max}\), swarm \(S\);
0: The best resolution \(BS\);
1:while\(g\leq G_{max}\)do
2:for\(i=1\) to \(NP\)do
3: Evaluate fitness of \(S_{g}\);
4:endfor
5:for\(i=1\) to \(NP\)do
6: Select operation to \(S_{g}\);
7:endfor
8:for\(i=1\) to \(NP/2\)do
9: Crossover operation to \(S_{g}\);
10:endfor
11:for\(i=1\) to \(NP\)do
12: Mutation operation to \(S_{g}\);
13:endfor
14:for\(i=1\) to \(NP\)do
15:\(S_{g+1}=S_{g}\);
16:endfor
17:\(g=g+1\);
18:endwhile
19:return The best resolution \(BS\).
```  
**Algorithm 1** The GA Algorithm
## 2 Data and Segmentation Methods
### Methodology
#### 2.2.2 Supervised Denoise Pretraining  
Rather than to use random values for neural network weights initialization, we pretrained the network first as a denoising autoencoder. The motivation behind this is to help to propagate gradient during the training, help to not get stuck in local minima and increase overall network stability. First some additional noise was added to the input image and then the network was trained to reconstruct the original image. Preparation of the data for this process is described in more detail in Section 2.1.4. In order to make the experiment better comparable over all the examined architectures, 50 epochs limit was used for all the trainings. It resulted in 84.47 percent pixel accuracy in reconstructing the original image (see Figure 4--the network was able to extract the input image features quite successfully). Resulting weights were then used for initialization the fine-tuning phase where 3D Dense-U-Net network was trained using spine dataset. Not only the network started first epoch with validation accuracy over 80 percent (randomly initialized weights have under 30 percent accuracy during first epoch) but also helped the deeper layers to be trained correctly and prevent so called gradient vanishing, which would have significant impact on neural network training capabilities.
## 2 Materials and Methods
### Experimental Procedures  
* In the laboratory: The participants were briefed about the experimental procedures and equipment when they entered the laboratory. Subsequently, EMG electrodes were placed over the AD of the participants. Following a detailed demonstration and explanation by the researchers, the participants practiced the relevant movements until they became proficient. The participants were allowed to rest for five minutes between each step to avoid fatigue. The experiment involved the following steps: Step 1: Simulation of fruit thinning without the PULE: Each participant raised their hand and held it at 100\({}^{\circ}\) for 90 s. The state in which the hand of the participant was naturally drooping was defined as 0\({}^{\circ}\). This action was repeated three times with a five-minute break between each action to avoid fatigue. Step 2: Simulation of pesticide spraying without the PULE: The participant was asked to move his hand from 0\({}^{\circ}\) to 100\({}^{\circ}\), swinging back and forth for 90 s., guided by a metronome with a frequency of 2 s. This action was repeated three times with a five-minute break between each action to avoid fatigue. Step 3: Steps 1 and 2 were repeated with the PULE. Figure 1a shows the image of the PULE on a participant during the fruit thinning simulation. Step 4: The participants performed the maximum voluntary contractions (MVCs), and this action was repeated three times with a one-minute break between each action to avoid fatigue. To avoid fatigue of the AD before the experiment started, the MVC measurements were performed at the end.
* In the orchard: The pomelo farmers and jujube farmers were briefed about the experimental procedures and equipment when they entered the orchards. Subsequently, EMG electrodes were placed over the AD, LD, and PD of the farmers after a systematic demonstration and clarification by the researchers. The experiment in the orchard involved the following steps: Step 1: The pesticide spraying of the pomelo tree or the fruit thinning of the jujube tree without the exoskeleton had a duration of 30 min. Step 2: The pesticide spraying of the pomelo tree or fruit thinning of the jujube tree with the exoskeleton had a duration of 30 min. Figure 1b shows the image of the pesticide spraying activity of the pomelo tree without the PULE. Step 1 and Step 2 were separated by more than twelve hours to avoid fatigue. Because the pomelo farmers must wear protective clothing during the entire pesticide spraying process, it was necessary to ensure that the EMG electrodes did not fall off during the experiment.  
Step 3: Each farmer performed the MVCs, and this action was repeated three times with a one-minute break in between each action to avoid fatigue [23]. The MVC measurements were performed at the end to avoid fatigue of the AD, LD, and PD [17].
## 4 Motion Planning Application and Analysis
### NMPC Problem Formulation  
In order to deal with the constraints of obstacles conveniently, SDM is extended based on the curvilinear coordinate, \(s\)-\(e_{1}\)[32]:  
\[\dot{s} =\tfrac{1}{1-k_{1}}\big{(}v_{x}\cos(e_{2})-v_{y}\sin(e_{2})\big{)},\] \[\dot{e}_{1} =v_{x}\sin(e_{2})+v_{y}\cos(e_{2}), \tag{8}\] \[\dot{e}_{2} =\omega-k\dot{s}.\]  
As depicted in Figure 3, \(s\) denotes the longitudinal driving distance, \(k\) is the curvature of the reference lane, \(e_{1}\) and \(e_{2}\) are the lateral position and heading error with respect to the reference lane, respectively. With the definition of the state \(\mathbf{x}\), control input \(\mathbf{u}\) and output \(\mathbf{y}\) as \(\mathbf{x}=\big{[}v_{x}\,v_{y}\,\omega\,s\,e_{1}\,e_{2}\big{]}^{\mathsf{T}}\), \(\mathbf{u}=\big{[}F_{T}\,\delta_{f}\big{]}^{\mathsf{T}}\) and \(\mathbf{y}=[v_{x}\,e_{1}\,e_{2}]^{\mathsf{T}}\), the state function is obtained by combining Equations (7) and (8) together:  
\[\dot{\mathbf{x}}=f(\mathbf{x},\mathbf{u})=\left[\begin{array}{c}\dfrac{2\zeta_{yf}\big{(} v_{y}+l_{f}\omega-\delta_{y}\big{)}\sin(\delta_{f})}{mv_{x}}\cr\dfrac{2\zeta_{yf} \big{(}\delta_{f}v_{x}-\delta_{y}\big{)}\cos(\delta_{f})}{mv_{x}}\cr\dfrac{2 \zeta_{yf}l_{f}\big{(}\delta_{y}v_{x}-v_{y}l_{f}\omega\big{)}\cos(\delta_{f} )}{l_{z}v_{x}}\end{array}\right]+\left[\begin{array}{c}\dfrac{F_{T}}{m}+v_{ y}\omega\cr\dfrac{2\zeta_{yf}\big{(}v_{y}-l_{f}\omega\big{)}}{mv_{y}}-v_{x} \omega\cr\dfrac{2\zeta_{yf}(v_{y}-l_{f}\omega)}{l_{z}v_{x}}\cr\dfrac{v_{x} \cos(e_{2})-v_{y}\sin(e_{2})}{1-kv_{1}}\cr v_{x}\sin(e_{2})+v_{y}\cos(e_{2}) \cr\omega-k\dot{s}\end{array}\right] \tag{9}\]  
NMPC motion planning needs to solve a nonlinear program at each sampling instant. The finite-dimensional discrete nonlinear program for it is  
\[\min_{\mathbf{X},\mathbf{U}}\sum_{i=0}^{N-1}\dfrac{\|y_{i}-y_{des}\|_{\mathbf{Q}}^{2}+\| \mathbf{u}_{i}\|_{\mathbf{P}}^{2}+\|\Delta\mathbf{u}_{i}\|_{\mathbf{R}}^{2}+\rho_{e}\epsilon_{ i}{}^{2}}{2}, \tag{10}\]  
\[\text{s.t.}\,\mathbf{x}(0)=\mathbf{x}_{0}, \tag{11}\]  
\[\mathbf{x}_{i+1}=f(\mathbf{x}_{i},\mathbf{uare the lateral position and longitudinal distance of the \(j\)-th obstacle. The minor axis \(a_{j,i}\) and major axis \(b_{j,i}\) of the ellipse are determined by the size of the subject vehicle and the obstacles, as well as the relative speed. The larger the size and relative speed of the obstacles and subject vehicle are, the larger the ellipse becomes. Detailed information about the spatiotemporal elliptical constraint can be found in [32]. The slack variable \(\epsilon\) relaxes the constraint to prevent the infeasibility. In Equation (14), \(\overline{v}_{x,i}\) is the maximum speed. In Equation (15), \(\overline{\epsilon}_{1,i}\) and \(\underline{\epsilon}_{1,i}\) are the constraints of road. Equations (16) and (17) are constraints of the limitation of vehicle performance and actuators. In \(\mathbf{y}_{des}\), \(v_{xdes}\) is the desired speed, \(e_{1 des}\) is the lateral distance from the center of lane to the vehicle's center of gravity.
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 2 Preliminaries
### Background  
#### 2.1.1 ZigBee Physical Layer  
IEEE 802.15.4 standard [15] specifies how a ZigBee device operates to transmit and receive data. Even though ZigBee devices can work on both 2.4 GHz (worldwide), 900 MHz, and 868 MHz ISM bands, this paper focuses on 2.4 GHz, which is the most common ZigBee frequency band. On 2.4 GHz, there are sixteen ZigBee channels with a bandwidth of 2 MHz.  
The throughput of ZigBee is 250 kbps. The architectures of ZigBee transmitter and receiver are illustrated in Figure 1.  
At a ZigBee sender, every four bits are assembled into one symbol. For example, bits \"1001\" is assembled to be symbol '9'. Each symbol is spread into 32 chips according to Table 1. Later, each chip (0/1) is modulated by Offset Quadrature Phase-Shift Keying (O-QPSK). Specifically, in half-sine O-QPSK modulator, chips with even index are modulated into I-phase while chips with odd index are modulated into Q-phase, as illustrated in Figure 2. Each chip consists of 4 samples, which form a half-sine waveform. The duration of each chip is 1 us and the chip rate of ZigBee is 2 Mchips/s. Moreover, there is a half-sine time offset (0.5 us) between Q-phase and I-phase. Then, an digital-to-analog converter (DAC) converts I/Q samples into analog signal, which is further sent out by radio front-end. Finally, every ZigBee symbol is transmitted into air as a 16 us signal.  
At the ZigBee receiver, an analog-to digital converter (ADC) converts the baseband signal into I/Q samples at a sampling rate of 4 M/s. Hence, every ZigBee symbol has 64 samples. Quadrature demodulator outputs phase offset between two consecutive samples. The time recovery block would compensate sampling offset and decimates 64 phase offset values into 32 phase offset values, which are also called soft values. Chip  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Chips Sequence** \\ \hline
0 & 110110011100001101010010010110 \\
1 & 11101101100111000011010100100010 \\ \(\vdots\) & \(\vdots\) \\
15 & 1100100101010000011101110111000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: IEEE 802.15.4 Chips Sequence.  
Figure 2: Half-Sine O-QPSK Modulator.  
is determined according to the sign of soft value. If the soft value is above zero, the corresponding ZigBee chip is decided to be '1'; otherwise, it is '0'. The distance between the received 32 chips and ideal chip sequence is called Hamming distance. Symbol and bits are determined by mapping the received 32 chips into the IEEE 802.15.4 chips sequence with minimum Hamming distance.
# Quad-Beam 4 \(\times\) 2 Array Antenna for Millimeter-Wave 5G Applications
## 3 Array Antenna Design  
In an array antenna, increasing the aperture size leads to higher gain. However, one of the major challenges is designing an efficient feed network that can distribute power evenly across all elements. To address this, Ant1 is transformed into an array structure using a parallel feed network. This approach simplifies the design while ensuring that  
Figure 8: Effect on the reflection coefficient of the single-element antenna from the variations in the major (\(El1\)) and minor (\(Ev1\)) axes of an elliptical patch in (**a**) and (**b**), respectively. Likewise, the variations in the resonance frequency are due to variations in the major (\(El2\)) and minor (\(Ev2\)) axes of the elliptical slot in (**c**) and (**d**), respectively.  
equal power and phase are delivered to each element. Additionally, a slight improvement in bandwidth is observed with the array configuration.  
In the case of Ant1, the elliptical structure spans 0.74\(\lambda_{1}\) along the y-axis, which necessitates an inter-element spacing of greater than \(\lambda_{1}/2\) to minimize the mutual coupling effects. However, this spacing also contributes to the formation of grating lobes, which result in a distinctive quad-beam radiation pattern. The evolution of the radiation behavior has been systematically studied, progressing from a basic \(2\times 1\) array to the final \(4\times 2\) array configuration. This progression demonstrates how non-uniform spacing and array geometry play critical roles in achieving the desired multi-beam performance.