esa-sceva/satcom-chunk-collection huggingface dataset:
# A Wideband Non-Stationary 3D GBSM for HAP-MIMO Communication Systems at Millimeter-Wave Bands
## 3 GBSM Non-Stationary Channel Statistical Properties
### Spatial Ccf  
In wide-sense stationary uncorrelated scattering (WSSUS) channels, the spatial CCF depends on the relative SBS and TMS antenna element spacing \(\delta_{T}\) and \(\delta_{R}\), respectively. However, for non-stationary systems, spatial CCF is not only related to relative antenna spacing but also to time \(t\). Therefore, based on this dependency, the spatial CCF of the non-stationary channel model can be referred to as the local spatial CCF and can be expressed as [41]  
\[R_{qm,pn;\Delta q,\Delta p}(t,f)=R_{qm,pn;\Delta q,\Delta p}^{\text{LoS}}(t,f) +R_{qm,pn;\Delta q,\Delta p}^{\text{NLoS}}(t,f), \tag{29}\]  
\[R_{qm,pn;\Delta q,\Delta p}^{\text{LoS}}(t,f)=\frac{K}{K+1}E[h_{qm,pn}^{\text {LoS}}(t)h_{qm,pn;\Delta q,\Delta p}^{\text{LoS}}(t)e^{i2\pi\Omega_{f,1}}], \tag{30}\]  
\[R_{qm,pn;\Delta q,\Delta p}^{\text{NLoS}}(t,f)=\frac{K}{K+1}\sum_{i=1}^{n}E[h _{qm,pn}^{i^{*}}(t)h_{qm,pn;\Delta q,\Delta p}^{i}(t)e^{i2\pi\Omega_{f,2}}], \tag{31}\]  
\[\Omega_{f,1}=f(\tau_{qm,pn}^{\text{LoS}}(t)-\tau_{qm,pn}^{\text{LoS}}(t+ \Delta t)),\Omega_{f,2}=f(\tau_{qm,pn}^{i}(t)-\tau_{qm,pn}^{i}(t+\Delta t)), \tag{32}\]  
where \(n\) denotes the number of scatterers and \(\tau\) denotes the delay of each path.
# Application of DMLF in Pulse Ranging LiDAR System
## 2 Methods
### Model of Transmitted Pulse and Echo Pulse in LiDAR  
In this section, we will describe the LiDAR pulse model during transmitted and echo process. As is known, if the trailer of a short pulse can be ignored and its normalized time-domain envelope is approximately Gaussian-shaped, it is written as  
\[S_{t}(t)=e^{-\left(\frac{B(t-t_{d})}{\tau}\right)^{2}} \tag{2}\]  
where \(p=\sqrt{4\ln 2}\), \(t_{d}\) is the fixed delay of transmitted pulse and \(\tau\) is the pulse width.  
During the operation of pulsed LiDAR, the echo signal reflected from target surface will be disturbed by a lot of interference, such as atmospheric attenuation, optical transmis  
Figure 1: The schematic diagram of pulsed LiDAR system based on differential method based on linear fitting (DMLF).  
sion attenuation, and target reflectivity et al. Hence, the echo pulse model can be described as [3]  
\[S_{r}(t)=\frac{\sqrt{2}E_{t}\tau_{a}^{2}\tau_{o}A_{R}\rho_{t}\cos^{2}(\theta)}{ \pi R^{2}}\cdot\frac{p}{\tau\sqrt{2\pi}}\cdot e^{(-(\frac{p(t-t_{g}-2R/c}{\tau} )^{2})})+N_{n} \tag{3}\]  
where \(E_{t}\) is the transmitted energy of LIDAR. \(\tau_{a}\) is the two-way atmospheric transmission loss. \(\tau_{o}\) is the transmission efficiency of the optics. \(A_{R}\) is the receiver area of the detector. \(\rho_{t}\) is the target's reflectivity. \(\theta\) is the local incidence angle. \(R\) is the distance between the target and Lthe iDAR transmitted system. \(N_{n}\) is random noise, which includes background noise, speckle noise, and thermal noise.  
In order to calculate easily during the processing of echo signal, we use SNR to replace the \(\tau_{a}\), \(\tau_{o}\) and other difficult-to-measure parameters in Equation (3). The SNR is the ratio between the mean peak power received on the detector and the root mean square (RMS) of the photo-electronic chain without signal. TNR refers to the detection threshold value to the RMS of noise ratio, written as [22]  
\[TNR=\sqrt{\left[2\ln\left(\frac{2R_{\max}\Delta f}{\sqrt{3}cP_{af}}\right) \right]} \tag{4}\]  
where \(R_{\max}\) is the maximum detection distance, \(\Delta f\) is the matched bandwidth of receiver, and \(p_{af}\) is the false alarm rate within a single pulse, which determines the detecting threshold value of the effective signal \(\rm V_{T}\).  
We have normalized the peak value to 1 in the emission pulse model, so the noise power spectrum density of the LiDAR system becomes \(p_{n}=10^{-(TNR/10)}\), where the RMS of noise amplitude is \(S_{n}=\sqrt{p_{n}}\times randn\), the return pulse power spectrum density is \(p_{s}=p_{n}\times 10^{(SNR/10)}\). Therefore, the simpler echo pulse model can be expressed as  
\[S_{r}(t)=B\cdot e^{(-\left(\frac{p(t-t_{g}-2R/c)}{\tau}\right)^{2}+S_{n})} \tag{5}\]  
where the \(R\) refers to the distance of return signal and \(B=\sqrt{p_{s}}\) is for the attenuated factor.
## 3 Methodology
### Microwave Data Downscaling
#### 3.3.2 Residual Variability (Variogram) Analysis  
Variogram analysis is a crucial tool to assess spatial variability in geospatial datasets. For this study, we analyzed the residual variability before and after downscaling to quantify the effectiveness of the downscaling procedure.  
1. **Residual Calculation:** After applying the regression models for downscaling, the residuals are calculated by subtracting the predicted DLST values from the original LST values at a 25 km resolution. With residual correction enabled, the residuals are further refined and added back to the downscaled LST values to correct for any spatial discrepancies. \[r_{i}=LST_{i}-DLST_{i}.\] (7) where \(r_{i}\) represents the residuals, \(LST_{i}\) is the original LST value, and \(DLST_{i}\) is the predicted downscaled LST value.
2. **Variogram Construction:** A variogram is computed for the residuals both before and after the residual correction. The variogram quantifies the spatial autocorrelation of the residuals by measuring how the differences between values change as a function of the distance between them. A typical variogram is constructed using the following formula: \[\gamma(h)=\frac{1}{2}\cdot\frac{1}{N(h)}\sum_{i=1}^{N(h)}\big{[}Z(x_{i})-Z(x_ {j})\big{]}^{2}\] (8) where \(\gamma(h)\) is the semivariance at lag distance \(h\), \(Z(x_{i})\) and \(Z(x_{j})\) are the values at locations \(x_{i}\) and \(x_{j}\), and \(N(h)\) is the number of pairs of data points separated by the distance \(h\).
3. **Pre- and Post-Correction Variogram:** The variogram is computed for the residuals both before and after the residual correction step. This provides insight into how the spatial variability of the residuals is reduced after the correction, thus allowing for a more accurate representation of the spatial pattern in the downscaled LST data. The analysis of the variogram enables us to assess the spatial structure and effectiveness of the downscaling process in reducing residuals.  
By combining regression-based downscaling with residual correction and variogram analysis, this approach provides a robust method for enhancing the spatial resolution of MWRI LST data and ensuring that the downscaled data retains its spatial integrity and accuracy.
# BGPcoin: Blockchain-Based Internet Number Resource Authority and BGP Security Solution
## 4 Design of BGPcoin: Roles, Components and Functions
### BGPcoin Resource Revocation  
In BGPcoin, every ASN resource has its valid period. Once expired, ASN will be recalled back to its RIR, and the holdership in the \(ASNData\) becomes invalid. If the holder intends to renew the holdership, he/she pays an amount of fee offline and then requests RIR to send an \(ASNupdate\) transaction to renew the valid period.  
As for IP address space, BGPcoin has a consent-based revocation to help revoke IP address space bilaterally. Our method corrects the power imbalance in RPKI, which actually allows an authority to revoke an IP address space that has been issued to its descendant unilaterally. Every IP address resource has a consent list to indicate each layer's possessor's consent to recall the resource to higher possessors, i.e., an IP address resource is modeled as:  
\[<\pi,state,RIR,NIR,owner,lease,consent\_vector>\]  
Figure 3: IP division cost for IPallocate/IPassign.  
To indicate its consent to have its resource taken away, a possessor sets its consent flag in the resource. If one of the possessor wants to revoke the IP address resource, it needs all the consents from these resource's lower possessors. If one consent is refused, this indicates that there is an argument between the revoker and the consent refuser, and BGPcoin raises an alarm and reminds the related possessors to inspect the situation out-of-band.
## 5 IETF ROLL Working Group
### Implementation and Evaluation
#### 5.2.7 Link Estimation  
The estimation of the link quality with neighboring nodes is done by datapath validation via _Expected Transmission Count_ (ETX) link cost estimation. The ETX equals the average number of transmissions for a packet to be successfully delivered to its next hop. The current selection mechanism prefers parents with the lowest rank. When there is more than one parent with the lowest rank, the first node is chosen as preferred parent, this has the advantage that no energy is consumed for evaluating the link quality to other neighboring nodes. As a downside, it only evaluates the currently used link; no alternative paths via newly discovered links are investigated. After some time this can lead to a sub-optimal routing topology. In [66] the solution of passive probing is introduced. Hereby the quality of a newly discovered link is initialized with the best value. This will force nodes to investigate all neighboring nodes as possible parent. At startup this solution will require more energy, but this energy is used to investigate the most optimal path, which can lead to a better overall energy efficiency.  
However, in dense networks, the limited node memory results in constant re-evaluation of neighboring nodes, because neighbors in the neighbor table are continuously replaced by more recently used or heard neighbors. The authors of [66] suggest to use cache management to solve rediscovery and re-evaluation.  
End-to-end link quality for a point-to-point route is currently not monitored in the RPL framework. A point-to-point link and its quality are currently determined by the individual links between the intermediate nodes. The nodes decide when to switch to a different parent based on the objective function. In _A Mechanism to Measure the Routing Metrics along a Point-to-point Route in a LowPower and Lossy Network_ (draft-ietf-roll-p2p-measurement-07) [67] a mechanism is described to analyze the quality of the current route and to allow the router to initiate the discovery of a better route.  
Similar to the previous draft, an on demand discovery mechanism for routes with specified metric constraints is presented in _Reactive Discovery of Point-to-Point Routes in Low Power and Lossy Networks_ (draft-ietf-roll-p2p-rpl-15) [68].
# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 3 State of the Art Methods for Laser Line Segmentation
### FFT Filtering Based Method  
In Reference [20], fast Fourier transform [25] was used to remove the uneven arc light caused background and then the image was segmented by threshold selection. The selected welding parameters are as follows. The welding speed is 0 mm/s, the peak current is 160 A and the base current is 80 A. With these welding parameters, the change of the weld pool's geometry is relatively slow compared to that in Reference [19]. As a result, the captured images are relatively easier for automatic image processing. Hence, the authors could segment the captured images by specifying the threshold manually after FFT filtering.  
Figure 1: The configuration of the weld pool monitoring system.
# Assessing Ecosystem and Urban Services for Landscape Suitability Mapping
## 3 Results and Discussion
### Sensitivity Analysis
#### 3.2.3 Model's Behaviour under Changing Service Weights  
Results reported in Figure 8 show the suitability values in the economic activities' scenarios under changing service weights in the different landscape classes.  
These results show clearly that the effect of the weights' increase with inverse proportionality to the weight increment both in supply-based and demand-based suitability index. This evidence means that weights have a slight influence on suitability, while the services' ranking influences the results strongly, as the diversity of trends shown by the different cases shows. Moreover, since the graphs show an equal shape of the curves in Figure 7 (scenarios calculated with a relative weight of 9/indifference case), it is possible to conclude that the relation between the weights' increase and the suitability increase is monotonic in each scenario analysed.  
In general, the sensitivity analysis showed that the services' ranking strongly influences results. On the one hand, it means that the model is very sensitive to stakeholders' preferences, and it can be used to measure suitability, differentiating results based on such preferences. On the other hand, these results indicate that the model is more sensitive to the relative importance of criteria than their relative weight. This evidence suggests that when PCMs are filled, and two questions are asked for each pair of services compared (\"which service is more important between these two?\" and \"how much this service is more important than the other one?\"), the answer to the first question is much more important than that to the second one. Consequently, more accuracy is required for the definition of the service ranking than for the service weight. In this view, since PCMs' completion is a really demanding technique to calculate service ranking, as the number of pairwise comparisons can become overwhelming [62], different techniques, such as ranking or scale rating [63], could also be used for defining service order to simplify the definition of relative importance at the expenses of the service weight accuracy. In this direction, further investigation may be necessary.
# Performance of Ship-Based QKD Under the Influence of Sea-Surface Atmospheric Turbulence
## 3 The Influence of Sea-Surface Turbulence on the Performances of a Ship-Based QKD
### The Ship-to-Ship QKD System  
In a ship-to-ship QKD system, the transmitter is fixed on a ship, and the receiver is fixed on another ship. Figure 8 shows a diagram of secure keys versus transmission distance under different intensity turbulence conditions. The transmission distances of the system are 32 km, 16 km, and 3 km under a weak turbulence, middle turbulence, and strong turbulence atmospheres, respectively. It can be seen that the performance of QKD is seriously affected by the atmospheric turbulence when it is transmitted horizontally at the sea surface, which leads to a sharp decrease in the QKD transmittable distance and secure keys with an increase in turbulence intensity.  
Based on the above results, the QKD system's coding rate with a horizontal transmission distance of 10 km was investigated. As shown in Figure 9, the number of received photons decreases sharply when the light is transmitted over a certain distance under sea-surface turbulence. Therefore, the QKD system can only transmit over a few kilometers under strong turbulence, and when the turbulence is strong enough, the signal cannot be received or encoded.  
Figure 8: Secure keys versus transmission distance.  
Figure 7: Photon counts (**a**) and QBER (**b**) versus satellite orbital altitude at a 15\({}^{\circ}\) zenith angle.
# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 4 Design Implementation
### Traffic Control Protocol  
Figure 6 shows the retransmission mechanism of IVSP. The traffic control is different for both I and P frame packet transmission. If parent node receives any packet, it sends an acknowledgement (ACK) to tell its child node and transmission of that particular packet is completed. If parent node does not receive packet, it returns sequence number of lost packet to the child node. The child node retransmits the lost packet again. The child node must guarantee to receive these return packets. Each packet needs to be confirmed by both the child node and the parent node in order to protect the loss of packet in an efficient manner. If child node is not able to receive any confirmed packet, it will send a timeout message to its parent. The parent node will send the return packet again. If the parent node successfully receives the packet, the packet is immediately removed from the child node queue. If the parent still cannot receive the requested packets after calling for retransmission for several times, it thinks that the packets are lost at its son and will not recall these packets anymore. The lost packets will be transferred to lost I frame queue or lost P frame queue, and these packets will be retransmitted during normal state.  
The differences between the lost I frame queue and the lost P frame queue are retransmission time and packet priority. The retransmission of the lost I-frame packet has low priority, and the retransmission of the lost P-frame packet has the lowest priority. The retransmission of these packets should be arranged when link is not busy to avoid congestion. When sink node gets out of order packets, it adjusts the packet order of packets according to packet sequence number. If parent node has lost few packets, the parent node sends their sequence number. If these packets are found, these  
Figure 6: retransmission mechanism.  
packets will be retransmitted when congestion is alleviated. If parent node cannot receive the lost packets after few attempts, it will not recall the lost packets.  
Retransmission probability of I frame and P frame packets is an important parameter in IVSP. If retransmission probability is very small, there is not enough of a chance to retransmit the lost packets. Therefore, packet loss rate will be much higher. If I frame packet is lost in transmission, all P frame packets after it cannot be decoded correctly and will be useless. Therefore, we give more retransmission probability to lost I frame packets and less retransmission probability to P frame packets. However, too many retransmissions can increase delay, which is not desirable in wireless multimedia sensor networks. Therefore, the appropriate retransmission probability should be carefully chosen to meet the retransmission requirements so that the impact of retransmissions on normal network traffic can be minimized.
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 3 Method
### Probabilistic Modeling for Latent Space  
In order to constrain the latent space, a distribution of the latent space of normal images needs to be estimated. Deep autoregressive models allow for stable, parallel, and easy-to-optimize density estimation of sequence data. Another advantage is the ability to provide data likelihood, which makes our anomaly detection possible. PixelCNN [29] was one of the first autoregressive models implemented using convolutional neural networks applied to image generation, and many improved models have been developed based on it. Here, we use PixelSNAIL [14], an enhanced version of PixelCNN that adds the Self-Attention mechanism [42] to model long-term dependencies.  
To estimate the probability distribution of the image, PixelSNAIL transforms the joint distribution of all pixels into a product of chain probabilities as shown in Equation (4). Each pixel is modeled as a conditional distribution, indicating the current pixel value is determined by all pixels preceding it:  
\[p(\mathbf{x})=p\left(x_{1},\ldots,x_{n}\right)=\prod_{i=1}^{n}p\left(x_{i}\mid x _{1},\ldots,x_{i-1}\right) \tag{4}\]  
The conditional distribution \(p\left(x_{i}\mid x_{<i}\right)\) is parameterized by a convolutional neural network and finally connected to a softmax layer to estimate the probability of 256 values. Mask convolution is used to guarantee the conditional relationship preventing the model from reading pixels below. The whole training process can be parallelized using the GPU.  
When the VQ-VAE training is completed, as shown in step 2 in Figure 1, we extract the index table set \(\left\{z^{(1)},z^{(2)},\ldots,z^{(N)}\right\}\) by encoding all \(N\) normal images. For any \(z^{(i)}\) in the index table set, since it records only the index of the embedded vector in the codebook, one can treat it as a single-channel image. Each \"pixel\" in the index table has \(K\) possible values, depending on the size of the codebook.  
The process of modeling the probability distribution of the index table by PixelSNAIL is analogous to the process for a low-resolution image as described above. The network is trained using the cross-entropy loss function with the expectation that the inference of the network will be identical to the true index. After training, the network can capture the pattern of normal images in latent space, predicting the most likely current index number based on the preceding index order.
## 4 Performance Analysis and Discussion  
This section first introduces the simulation network model. The simulation metrics for measuring the performance of the proposed Reliable Routing Mechanism (RRM) are them presented. The performance comparisons among the proposed RRM, AODV [4] and DSR [5] mechanisms are then presented.
## 4 Experimental Setup
The input data for these models will consist of RSSI values collected during three different movement scenarios in the testing phase. Figure 8 illustrates the sidewinding trajectory of the robot through the experimental area. Each point on the map corresponds to four RSSI readings from each receiver, along with the horizontal and vertical coordinates, X and Y, respectively. To test the trained ML models, we propose three distinct scenarios, each featuring a different movement trajectory for the TurtleBot3 Burger mobile robot. The robot's movement paths in these test scenarios are illustrated in Figure 9. Similar to the process of creating the radio map for training data, the robot transmitted signals at a frequency of 2 Hz. Its speed varied between 2 cm/s and 22 cm/s, depending on the chosen route. The three selected paths are illustrated in Figure 9 below: \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Frequency & 868 MHz \\ \hline Spreading factor (SF) & 7 \\ \hline Transmission power (TP) & 10 dBm \\ \hline Bandwidth (BW) & 125 kHz \\ \hline Coding rate (CR) & 4/5 \\ \hline Sensitivity & \(-\)130 dBm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters of the LA66 LoRaWAN module. Figure 8: The movement trajectory for collecting training data. The first route involves a stepped diagonal movement across the experimental area. This trajectory can be considered easy, as it contains fewer turns, and most of the path is straight, which allows the robot to move at maximum speed. The second route follows a sinusoidal trajectory, which presents more complexity than the first due to its smooth curves, necessitating that the robot reduce its speed. In the final route, both the amplitude and frequency of the sinusoidal trajectory from the second route are increased, making it the most challenging. During the more difficult turns, the robot moved at a minimum speed of 2 cm/s.