esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 ML for SatCom RRM: Architecture and Techniques
### Supervised Learning for RRM  
SL is a technique for deducing a function from training data. The training data consist of pairs of objects: One component of the couple is the input data, and the other is the desired results. The function's output can be a numerical value (as in regression problems) or a class label (as in classification problems). Supervised Learning aims to create a function capable of predicting the value corresponding to any valid input object after having observed a set of examples: the training data. To perform this, one must generalize from the presented data to previously unknown situations [42].  
On the other hand, neural networks (NN) are a model inspired by the behavior of the human brain. It consists of a set of nodes known as artificial neurons that transmit signals to each other. These signals are transmitted from input to output. The NN consists of three different layers: (i) the input layer, where the input variables that can represent the characteristics of the system are located; (ii) the output layer, where the output variables are located and represent the information acquired from the input variables; (iii) and the hidden layers, where information is propagated and analyzed to obtain an expected output. NN training is performed through backpropagation, which consists of propagating the error between the desired and expected output from the last layer to the first, modifying the weights of the neural connections until the error is minimized [43].  
In that sense, NNs represent a viable option to optimize and automate the RRM in a multibeam SatComs system. We will focus specifically on approaching the problem using Deep Neural Networks (DNN) [30; 31] and Convolutional Neural Networks (CNN) [33].  
When the system is offline, training data are generated first, and labels are assigned to training data to minimize the RRM cost function. NN training is performed offline, and resource management responds to the traffic demand using supervised learning.  
In other words, NN represents only an intelligent switch for the payload that changes from one configuration to another whenever the system requirements change; the change is made instantaneously, i.e., the impact of a delay in updating resources on capacity is negligible. NN performance during training is crucial, and it is also important to avoid overfitting, as this will depend on resource management.
# Radar Error Correction Method Based on Improved Sparrow Search Algorithm
###### Abstract  
Aiming at the problem of the limited application range and low accuracy of existing radar calibration methods, this paper studies the radar calibration method based on cooperative targets, and establishes the integrated radar measurement error model. Then, the improved sparrow search algorithm (ISSA) is used to estimate the systematic error, so as to avoid the loss of partial accuracy caused by the process of approximating the nonlinear equation to the linear equation, thus improving the radar calibration effect. The sparrow search algorithm (SSA) is improved through integrating various strategies, and the convergence speed and stability of the algorithm are also improved. The simulation results show that the ISSA can solve radar systematic errors more accurately than the generalized least square method, Kalman filter, and SSA. It takes less time the than SSA and has a certain stability and real-time performance. The radar measurement error after correction is obviously smaller than that before correction, indicating that the proposed method is feasible and effective.  
radar calibration; systematic error estimation; sparrow search algorithm; multi-strategy fusion +
Footnote †: journal: Applied sciences  
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# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 2 Related Works  
### Pedestrian Trajectory Prediction  
Pedestrian trajectory prediction can be considered as a sequential prediction task that anticipates the future path of an agent by using historical trajectories. A family of methods based on RNNs have been proposed to model temporal dependencies, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU). Social-LSTM [6] modeled the temporal dependency and predicted the future locations with LSTM, which computed the social interaction between the specific pedestrian and neighbors within a certain distance. Study [31] combined LSTM and the soft-attention mechanism [17] to model social interactions among pedestrians. Since the current prediction is computed based on the results of the previous time steps, RNN/LSTM-based methods are often time-consuming for predictions. In addition, they inevitably accumulate errors and make the long-term predictions inaccurate. Graph is another popular choice for trajectory prediction combined with CNN or RNN/LSTM. Social-STGCNN [19] considered the pedestrians as graph nodes and the edges were weighted by the relative distance among the agents. Combined with graph attention network,. STGAT [9] constructed a spatial-temporal model to predict pedestrian future trajectories. However, the number of nodes in graph depends on the number of pedestrians in a scene. Hence, the size of graph will grow significantly in the crowded scenes. Moreover, there are some other works elaborated on in this task. SGAN [7] adopted Generative Adversarial Network (GAN) [32] for multi-modal trajectory predictions. DDTPP [33] evaluated the most likely destinations of the pedestrian using the destination classifier (DC) and predicted the future trajectory with the destination-specific trajectory model (DTM). Study [34] formulated the trajectory prediction task as a reverse process of motion indeterminacy diffusion. MemoNet [35] is an instance-based approach that predicted the movement intentions of agents by looking for similar scenarios in the training data. STAR [27] constructed spatial and temporal transformers [22] to model the spatial interactions and temporal dependencies, respectively. Inspired by [27], we adopted transformers to model the trajectory sequences. Different from existing works, we constructed a sparse temporal transformer and made predictions in a non-autoregressive way.
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 3 Broadcast Feedback on Packet Collisions
### Packet Collision Sensing
#### 3.2.1 Feedback Provider Qualification Based on Signal-to-Interference-and-Noise Ratio  
An obvious necessary condition for the received transmission to be a collision is that the receiver fails to decode it. It happens when the signal to interference plus noise ratio (SINR) on the given resource R, denoted \(\sigma\) (R), falls short of a certain threshold \(\theta_{\sigma}\) as given in inequality (1):  
\[\sigma(\text{R})<\theta_{\sigma}. \tag{1}\]  
By using this condition, the logic disqualifies those receivers in the high SINR, high RSSI region in Figure 2 as a feedback provider. The threshold \(\theta_{\sigma}\) used in the condition is a function of the modulation and channel coding (MCS) among others. The SINR threshold can be obtained as Equation (2):  
\[\theta_{\sigma}(m,B)=2^{\frac{b_{Hz}(m,B)}{1-\phi}} \tag{2}\]  
where \(m\) is the MCS level, \(B\) is the transport block size, \(\phi\) is the parametric loss in the Shannon equation, and \(b_{Hz}\) is the number of data bits per second per Hz, carried by the given MCS [17]. For instance, for MCS level 7 and the message size of 300 bytes used in the experiments, \(\theta_{\sigma}\) is 7.30 dB for \(\phi=0.6\)[17].  
Figure 2: Logic to determine whether to provide a feedback at the broadcast receiver.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 2 Related Works  
### Trust-Based Security Mechanisms  
Trust-based security mechanisms have gained popularity in Wireless Sensor Networks (WSN) and the Internet of Things (IoT) [28; 29; 30; 31; 32; 33]. These mechanisms focus on ensuring the privacy and security of network nodes. Several trust-based algorithms have been introduced in recent studies. [PERSON] et al. [34] introduced a weighted clustering trust model algorithm that demonstrated significant improvements in identifying malicious nodes. [PERSON] et al. [35] proposed a trust-based next-hop node selection algorithm, which improved the network performance in terms of the data packet transmission, delay, and error rates. [PERSON] et al. [36] presented a cloud-based trust evaluation approach that is sensitive to various attacks and capable of improving malicious node detection accuracy. [PERSON] et al. [37] introduced trust entropy and a standard structural entropy mechanism for detecting malicious behavior in sensor systems. [PERSON] et al. [38] utilized machine learning to include parameters such as friendship and community interest, shedding light on the evolution of trust in an entity over time. In another study, [PERSON] et al. [39] employed a heuristic approach based on machine learning to amalgamate trust-related attributes, successfully distinguishing between trustworthy and untrustworthy nodes within the network. [PERSON] et al. [40] introduced an incremental Support Vector Machine (iSVM) method for simulating various attack patterns, outperforming other methods. [PERSON] et al. [41] used the K-Nearest Neighbors (KNN) algorithm to detect selfish behavior in entities effectively. [PERSON] et al. [42] employed machine learning methods for trust parameter aggregation and a mixed propagation approach to classify users and detect attack types. However, many of these methods have high computational complexity and are unsuitable for real-time communication scenarios when combined with deep learning. They did not further investigate the application of security mechanisms in route planning. Currently, there is not a market solution that simultaneously considers low delay, high security, and computational efficiency for multi-route planning. Therefore, our work intends to fill this blank.
## 2 Materials and Methods
### Tracking Filter
The fused measurements \(e_{x}\) and \(e_{y}\) will simply become \(e_{hue,x}\) and \(e_{hue,y}\), respectively, while \(\zeta_{x}\) and \(\zeta_{y}\) will become \(\zeta_{hue,x}\) and \(\zeta_{hue,y}\). Likewise, if no detection data has been received from the hue processing node while a valid result has been given by the neural net, \(\zeta_{hue}\) will be a zero-vector and \(\zeta_{nn}\) will be calculated using (9), with the fused measurements \(e_{x}\) and \(e_{y}\) respectively becoming \(e_{nn,x}\) and \(e_{nn,y}\), and \(\zeta_{x}\) and \(\zeta_{y}\) becoming \(\zeta_{nn,x}\) and \(\zeta_{nn,y}\). Finally, if both nodes have sent valid object detection data, their respective variances and fused measurements are calculated as has been described above in (9)-(14). In addition to distance-based variance calculation, the filter node contains several heuristics aimed at improving data fusion and detector behavior:  
* _Kinect pan movement flagging_. The filter will not take into account measurements acquired while the Kinect sensor was actively rotating, and will only react once a valid docking mechanism-aligned position has been reached. * _Region of interest crop_. As the filter converges, any filter outputs outside of the designated realistic \"region of interest\" (\(-1\) to \(1\)) are discarded. * _Continuous detection requirement_. The filter will only output fused measurements after a preset number of consecutive frames have been determined to contain an aMussel, by any of the two detectors. This acts as a sort of outlier rejection for brief usually single-frame false-positive detections. * _Measurement discontinuation detection_. If more than \(3\) s have passed without fresh measurements from either detector, the filter will stop feeding measurements into the docking controller, and the filter will be flushed and reinitialized. This ensures no action is taken based on outdated information. If the aPad was approaching an aMussel and lost it from view this long, as per the previously described state machine the docking algorithm will return to the Search state.
# Variation of Acoustic Transmission Spectrum during the Muscle Fatigue Process
###### Abstract  
The timely assessment of muscle fatigue makes sense for reducing the risk of musculoskeletal injury during exercise. In general, muscle fatigue is accompanied by physiological changes. These changes affect the acoustic transmission properties of the skeletal muscles. This study investigated the variation of the acoustic transmission spectrum (ATS) of human upper arm muscles during sustained static contractions (SC). Based on the B-ultrasound image and radiofrequency (RF) ultrasonic echoes, we abstracted the RF echo signals from the subcutaneous fatty / fascia (SFF) and deep fascia/bone (DFB) interfaces. By dividing the echo spectrum of the DFB by the spectrum of the SFF, we obtained the ATS of the upper arm muscles. Then, by fitting the ATS with both the linear function (\(A(f)=af+b\)) and power-law function (\(A(f)=af^{\beta}\)), we analyzed the variations of \(a\), \(b\), \(\alpha\), and \(\beta\) along with the SC process of skeletal muscle. As muscle fatigue increases, the slope \(a\) decreases and the intercept \(b\) increases linearly; the \(\alpha\) increases exponentially and \(\beta\) decreases linearly. In addition, the variation magnitude of ATS relates to the maximum voluntary contraction (MVC) force and the strength of the SC motion. These results suggest that a comprehensive analysis of ATS is a potential metric for assessing muscle fatigue.  
muscle fatigue; acoustic transmission spectrum; RF ultrasonic echo; static contraction +
Footnote †: journal: Medical  
Sustained exercise can induce muscle fatigue as well as relevant changes in the physiological conditions of muscle [17; 18], such as myofibrillar disruptions [19], metabolite [3] and interstitial fluid concentration increases [20]. The spectrum of RF signals relates to the information about the size, concentration, and acoustic impedance of the scatterers [21; 22], which is effective in evaluating tissue changes [23; 24; 25]. Therefore, the acoustic transmission spectrum (ATS) can reflect physiological changes in skeletal muscle and is potentially an effective indicator for assessing muscle fatigue.  
In this study, we investigated variations of ATS in human upper arm muscle fatigue. We acquired the RF signals and B-ultrasound images during the static contraction (SC) of the upper arm muscles of nine healthy male volunteers. We abstracted the RF signals reflected from the subcutaneous fatty/fascia (SFF) and deep fascia/bone (DFB) interfaces, respectively. The ATS of the skeletal muscle was calculated by dividing the echo spectrum of the DFB by the SFF spectrum. Then, we fitted the ATS with linear function (\(A(f)=af+b\)) and power-law function (\(A(f)=af^{\beta}\)) and analyzed variations of \(a,b\), \(\alpha\), and \(\beta\) along with the SC of skeletal muscle.
## 3 Results and Discussion  
Ultrashort pulse laser processing of transparent Foturan glass with ps pulses was rarely addressed in the literatures. [PERSON] et al. [44] reported the possibility to modify Foturan glass by a ps Nd:YAG (neodymium-doped yttrium aluminum garnet (Nd:Y\({}_{3}\)Al\({}_{5}\)O\({}_{12}\))) laser source delivering pulses of 30-ps duration at 532-nm wavelength and a repetition rate of 10 Hz. The same group showed the fabrication of 3D channels in Foturan bulk by HF etching of crystallized regions developed by annealing using a CO\({}_{2}\) laser treatment [45].  
We recently proposed the PLAE technique as an alternative to FLAE [28] by demonstrating the successful fabrication of basic 3D embedded microchannels. Ps lasers could then provide advantage over fs lasers for large-area processing of some transparent materials both on surface and in volume. Higher average power and longer pulse width can modify more volume per pulse by depositing larger energy in the material. Specifically, lower peak intensity associated with the longer pulse width allowed us to irradiate the glass with higher energy pulse without generation of damage, which increases electron density and, thus, the precipitation of more Ag atoms. The larger amount of Ag atoms was then generated, which allowed us to more efficiently develop a larger glass crystalline area by the annealing treatment. Thus, the processing times can be shortened for large-area modification as compared with fs lasers. The developed area can be finally removed by HF chemical etching. In a comparison of fabricating a comparable structure, irradiation time was approximately 1.5 h for laser pulses of 360 fs at 522 nm (2 mm/s scanning speed and 250 KHz laser repetition rate), while approximately 10 min only were necessary for 10-ps laser pulses at 532 nm (a 0.9-mm/s scanning speed and 500-KHz laser repetition rate). The shortened irradiation time for the ps laser is mostly due to the reduced number of planes scanned using line-by-line laser irradiation required to cover the desired modified volume. In the ps case, the number of planes was significantly reduced as compared with fs processing due to the larger modified volume per pulse. A more efficient process was experimentally achieved with UV pulses, since the critical dose was one order of magnitude smaller than that of VIS pulses (laser power conversion efficiencies from the fundamental beam were 1:2 for VIS and 1:3 for UV) [28].
# Inverted F Type Antenna Design for the Cattle Activity and
## 3 Simulation and Measurement
The length of L2, the vertical length of the antenna, has been tuned with the received signal intensity (RSI) of communication between the main receiver board and the receiver board. The length of L2 is \(\sim 100\) mm, which is \(\sim 100\) mm, which is \(\sim 100\) mm, which is \(\sim 100\) mm, which is \(\sim 100\) mm, which is \(\sim 100\) mm. Figure 10: Fabricated and tested IFA antenna with RF board and RF module and optimum length of L2. Figure 9: Simulated antenna radiation patterns of the IFA antenna. and the sensor module in the case as in Figure 10b. The L2 is shortened from 30 to 0 mm every 1 mm and measured the RSI of the system. The length of L2 has been optimized when the received signal strength at the main receiver is at its maximum. The optimum length of L2 from the simulation was 5 mm and 5 mm also with the measurement with a PNA network analyzer. When the antenna was installed on the physical RF module and tested with RSI, the optimum length of L2 is 4 mm, as shown in Figure 10c. The optimized and tuned IFA antenna has been installed on the RF module, and the RSI has been measured at the same setup and distance and compared with the RSI of the RF board with the original chip antenna, as shown in Figure 2. The 14 dB patch antenna has been used for the receiver antenna, and the RSI has been measured for the two different cases. Table 1 compares the RSI of two different antennas on the RF module: the original chip antenna and the proposed IFA antenna. The 256 RSI has been sampled per second and averaged since the RSI is so jumpy, even though transceivers are not moving. The steady RSI has been used and compared. The RSI of the chip antenna was \(-\)50 dBm while the proposed IFA antenna was \(-\)30 dBm with the same receiver antenna. There is 20 dB enhancement with the proposed IFA antenna.
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 4 C-PBFT Consensus Algorithm
### Double-Layer Consensus Structure
#### 4.4.2 Global Consensus  
The primary node of Layer 1 makes global consensus according to the time order of messages in the transaction pool. The primary node of Layer 1 sends \(<<PRE-PREPARE^{1},v,h,d>_{,}e>_{\sigma i}\) messages to all regional primary nodes, where \(\sigma_{i}\) represents the signature of the primary node of Layer 1, \(i\). The preparation phase, preparation phase, and confirmation phase in the global consensus are similar to those in the local consensus and will not be repeated here.  
All regional master nodes will reply to the message \(\left\langle REPLY^{1},v,t,c,i,r\right\rangle_{\sigma i}\) directly to the requesting vehicle. If the requesting vehicle receives \(f+1\)\(REPLY^{1}\) messages, the consensus is successful. \(i\) indicates the number of the primary node in the area, \(REPLY^{1}\) is the flag description, \(v\) indicates the number of the current view, \(t\) stands for the timestamp, \(c\) indicates the requested vehicle number, and \(r\) indicates the result of the requested operation. After the above process is completed, the master nodes in all areas send the reply message \(REPLY^{1}\) directly to the client, that is, to request the vehicle. After the client receives \(f+1\)\(REPLY^{1}\), the consensus is successful. After the two\(-\)tier consensus of the target regional committee is completed, the remaining nodes of the blockchain network copy the data locally.  
The algorithm pseudocode is described in Algorithm 3.  
```
Input \(<REPLY^{2},v,t,c,i,r>_{ei}\) Output \(<REPLY^{1},v,t,c,i,r>_{ei}\)
1. while valid \(\left\langle REPLY^{2},v,t,c,i,r\right\rangle_{ei}\) received = True do
2. if number of valid \(<REPLY^{2},v,t,c,i,r>_{ei}>f+1\)then
3. broadcast \(<REQUIST^{1},o,t,c>_{cc}\) to superior \(r^{1}\) leader.
4. end if
5. end while
6. while valid \(\left\langle REQUEST^{1},o,t,c\right\rangle_{cc}\) received = True do
7. broadcast \(<<PRE-PREPARE^{1},v,n,d>_{ep},e_{1}>\) to \(r^{1}\) all nodes.
8. end while
9. while valid \(<<PRE-PREPARE^{1},v,n,d>_{ep},e_{1}>\) received = True do
10. broadcast \(<PREPARE^{1},v,n,d,i>_{ei}\) to \(r^{1}\) all nodes.
11. end while
12. while valid \(<PREPARE^{1},v,n,d,i>_{ei}\) received = True do
13. if number of valid \(<PREPARE^{1},v,n,d,i>_{ei}\) received = True do
14. if number of valid \(<PREPARE^{1},v,n,d,i>_{ei}>2f+1\)then
15. broadcast \(<COMMIT^{1},v,n,d,i>_{ei}\) to \(r^{1}_{ei}\) all nodes.
16. end if
17. while valid \(<COMMIT^{1},v,n,d,i>_{ei}\) received = True do
18. if number of valid \(<COMMIT^{1},v,n,d,i>_{ei}>2f+1\)then
19. reply \(<REPLY^{1},v,t,c,i,r>_{ei}\) to client.
20. end if
21. end while
```  
**Algorithm 3** Global Consensus Pseudocode