esa-sceva/satcom-chunk-collection huggingface dataset:
# Internal Backpressure for Onboard Crospoint-Buffered Switches with Port Multiplexing
###### Abstract  
Onboard switching, as a core technology in satellite networks, offers robust support for the advancement of the Space-Air-Ground Integrated Network (SAGIN). The onboard switching fabric is tasked with achieving high-speed and large-capacity data exchange and ensuring service quality within the constraints of limited resources. However, the current onboard crossbar switching fabric is confronted with internal traffic congestion issues. To address this, this paper introduces an internal backpressure scheme for the crosspoint buffered switching. This scheme entails the incorporation of a buffer queue at the output port. Once an output queue reaches the maximum queue capacity due to traffic congestion, a backpressure (BP) signal is transmitted to the input end, thereby preventing the input port from sending data to the output port, alleviating crosspoint buffer overload. A validation and performance evaluation of various backpressure algorithms are conducted. The results demonstrate that the implementation of a backpressure mechanism in switching fabric can significantly reduce packet loss rate under network congestion conditions.  
satellite network; onboard switching; buffered crossbar; backpressure; traffic control Article
## 3 Method
### Repeated Pattern Grid Extraction  
The output from the proposed method is a grid showing the location and size of the repeated patterns presented on the input images. The refined pattern sizes \(d^{**}\) obtained from the previous optimization step are used to decide the final grid.  
To avoid \(d^{i,j}\) being very different from \(d^{**}\), the given \(d^{i,j}\) is further screened by  
\[D_{f_{n}}^{*}=\left\{d_{f_{n}}^{i,j}\in D_{f_{n}}:||d_{f_{n}}^{i,j}-d^{**}||<3 \sigma\right\} \tag{9}\]  
where parameter \(\sigma\) describes the radius of the neighborhood, showing the consistency of the displacement vectors for each filter/layer \(f_{n}\).  
Figure 4: (**a**) An illustration of how the optimization algorithm works in the \(x\) direction. The correlation value changes when the template patch moves along the \(x\) direction and meets the minimum pixel distance. (**b**) Repeated pattern grid before optimization; (**c**) repeated pattern grid after optimization.  
Next, a centroid \(c^{*}=(c_{x},c_{y})\) is extracted for the pattern by gathering and projecting the selected displacement vector votes \(d_{f_{n}}^{i,j}\in D_{f_{n}}^{*}\) into a modular space (Figure 5a), and the centroid of the rectangle of these activations is calculated as follows:  
\[c^{*}=\arg\min_{\begin{subarray}{c}\mathcal{M}\\ f_{n}^{i,j}\in D_{f_{n}}^{*}\end{subarray}}W_{i_{i},\,j,f_{n}}\cdot\left| \left|\mathcal{M}\left(d_{f_{n}}^{i,j}-c\right)-d^{**}/2\right|\right| \tag{10}\]  
where \(\mathcal{M}(\cdot)\) converts the vector into modular space \(\mathcal{M}:R^{2}\to R\). In Equation (10), the votes are weighted by \(W_{i_{1},\,j,f_{n}^{*}}\), the strength of the activations, which is calculated as follows:  
\[W_{i_{1},\,j,f_{n}^{*}}=\frac{1}{\left|D_{f_{n}}^{*}\right|\,+\,\lambda} \cdot exp\left(-\frac{\left|\left|d_{f_{n}}^{i,j}-d^{**}\right|\right|^{2}}{2 \sigma^{2}}\right) \tag{11}\]  
where the parameter \(\lambda\) is a prior on the number of expected repetitions and is set to 0.8, following the same setting of [2,12]; to avoid biasing a certain layer over the others, \(\sigma\) is set to 1 to treat all layers equally.  
Finally, the centroid is projected back into image space and an elastic repeated pattern grid is computed by a random sample consensus (RANSAC) algorithm [41] based on the identified centroids and optimized repeated size \(d^{**}\); a few example results are shown in Figure 5b.
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 6 Discussion of Findings and Consideration for Processing Approaches  
### Preface  
The preceding analysis of a common batched range-Doppler processing indicates that the target movement can severely affect its capability to detect and to track relatively fast moving targets. The velocity spans in which linear movement effects need to be specially regarded are these in which the limits of classical 'narrowband processing' like the stationary assumption (Section 2.5/Section 5.2) or the first unambiguous Doppler span determined by the processing scheme (Section 4) would be exceeded. Thereby, a common range-Doppler processing scheme is limited to a certain fixed time-bandwidth product for a maximal measured effective velocity (Section 2.5) or, respectively, it is constrained to an acceptable implication at a certain speed (Section 4). This is particularly problematic if a wide velocity span has to be covered. If the introduced implications are not considered, a large coherent integration gain will not be maintained by range-Doppler processing against relatively fast moving target returns. The further analysis indicated that in particular the instantaneous bandwidth-dependent impact (Section 5) and each key parameter related to the movement like the effective velocity gives a strong and quadratic implication for any further increase in case of an already exceeded boundary (Section 5.1). Even counterintuitive relations can then be present so that more loss might be generated compared to the case of no increase (Section 5.4). This is especially important if large desired integration gains were the motivation for a high time-bandwidth product in the first place. In the case that the spread target return was wrongly treated as a confined point scatterer, this could lead in consequence to probably even two close-by targets detected (Figure 12) or several detached track heads as it was visible in Figures 5 and 14. Another necessary condition for coherent processing are actual hardware demands that were not further discussed in this article but are important to be considered as well. Long covered timespans and high instantaneous bandwidths lead to increased demands regarding stability, linearity and intermodulation requirements that have to be properly addressed. Furthermore, the synchronisation to the received signal stream [32] will influence the accuracy and capability of target ranging.
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 4 Protocol Design
### Window Size Estimation  
#### 4.2.1 Block Size Estimation  
We propose a practical algorithm to estimate the optimized \(K_{i}^{\prime}\) based on a digital second order loop filter. The advantage of loop-filter is that we can achieve real-time tracking control while reducing the effect of instantaneous channel varying. We can obtain the input calibration factor \(\Delta n_{i}^{\prime}\left(k\right)\) of the digital filter from Equation (10):  
\[\Delta n_{i}^{\prime}\left(k\right)=\left\{\begin{array}{ll}\alpha K-N_{r,i+1 }\left(k\right),&if\;\;ACK\;\;or\;\;NACK\\ 0,&if\;\;none\end{array}\right. \tag{10}\]  
where \(K\) is the number of original data packets in a block, \(\alpha\) is the redundant factor and \(\alpha K\) represents the number of encoded packets that a receiver needs for successful decoding on average. For the fountain code we used here, we can set \(\alpha=1.2\) empirically as explained in Section 5.4. \(N_{r,i+1}\left(k\right)\) is the number of encoded packets actually received at node \(i+1\) in the network for the \(k\)th block, and \(N_{r,i+1}\left(k\right)\) is sent together with ACK and NACK packets for window size estimation. \(\Delta n_{i}^{\prime}\left(k\right)\) will be a negative number mostly when an ACK packet is received, which indicates that we could reduce the sending of redundant encoded packets and vice versa. Thus, we can update the sending block size according to Equation (11):  
\[\begin{split}& K_{i}^{\prime}\left(k+1\right)=K_{i}^{\prime} \left(k\right)+Ki\times A_{c}(k)+Kp\times\Delta n_{i}^{\prime}\left(k\right) \\ & A_{c}(k)=\sum\limits_{j=1}^{k}\Delta n_{i}^{\prime}\left(j\right) \end{split} \tag{11}\]  
where \(\Delta n_{i}^{\prime}\left(k\right)\) is the input variable obtained from Equation (10). \(A_{c}(k)\) is the cumulative factor and represents the impact of slowly varying environment. \(Ki\) and \(Kp\) are the impact factors that represent the influence of statistic and transient change of the underwater channel, respectively. Then, \(K_{i}^{\prime}\left(k+1\right)\) is the sending window size estimation at node \(i\) for sending the \(\left(k+1\right)\)th block. With the initial value \(K_{i}^{\prime}(1)=\alpha K\) and \(A_{c}(1)=0\) for the first block, we can obtain the asymptotic optimal estimate of the sending block window size \(K_{i}^{\prime}\) for node \(i\).
###### Abstract  
Global Navigation Satellite Systems (GNSS) allow the determination of the 3D position of a point on the Earth's surface by measuring the distance from the receiver antenna to the orbital position of at least four satellites. Selecting and buying a GNSS receiver, depending on farm needs, is the first step for implementing precision agriculture. The aim of this work is to compare the positioning accuracy of four GNSS receivers, different for technical features and working modes: L1/L2 frequency survey-grade Real-Time Kinematic (RTK)-capable Stonex S7-G (S7); L1 frequency RTK-capable Stonex S5 (S5); L1 frequency Thales MobileMapper Pro (TMMP); low-cost L1 frequency Quanum GPS Logger V2 (QLV2). In order to evaluate the positioning accuracy of these receivers, i.e., the distance of the determined points from a reference trajectory, different tests, distinguished by the use or not of Real-Time Kinematic (RTK) differential correction data and/or an external antenna, were carried out. The results show that all satellite receivers tested carried out with the external antenna had an improvement in positioning accuracy. The Thales MobileMapper Pro satellite receiver showed the worst positioning accuracy. The low-cost Quanum GPS Logger V2 receiver surprisingly showed an average positioning error of only 0.550 m. The positioning accuracy of the above-mentioned receiver was slightly worse than that obtained using Stonex S7-G without the external antenna and differential correction (maximum positioning error 0.749 m). However, this accuracy was even better than that recorded using Stonex S5 without differential correction, both with and without the external antenna (average positioning error of 0.962 m and 1.368 m).  
antenna; differential correction; GIS; GLONASS; GPS; precision agriculture; RTK
## 3 Numerical Results
### Vortex Solitons
As for the vortex solitons with \(d=0.5,m=2\), the evolution of their profiles is similar to that of the vortex solitons with \(d=0.5,m=3\). To investigate the influence of the nonlocality, we consider the vortex solitons under strong nonlocal nonlinearity (\(d=2\)) with \(m=2\), which are shown in Figure 13. For the vortex solitons near the edge of bandgap, additional humps are generated, as shown in Figure 13b,c. Their profiles turn into diamond shapes. When the vortex soliton is far from the edge of the bandgap, the profiles are complete (six poles arranged symmetrically around a circle) and then become incomplete, as shown in Figure 13a,d. The evolution of these vortex solitons can be explained as follows. First, the strong nonlocality enhances the internal interaction of vortex solitons. The vortex solitons under stronger nonlocal nonlinearity have more potential to break the complete profile. Second, there are always linear modes inside the optical energy bands. Thus, solitons are harder to be localized as they are close to the edge of the bandgap. In the PT-symmetric triangular lattices, vortex solitons near the edge of the bandgap are likely to have more humps. Third, the main difference between the vortex solitons with \(m=2\) and \(m=3\) is the symmetry of their phase structures. From Figure 10, it is clear that the phase structure with \(m=3\) has higher symmetry than the phase structure with \(m=2\), which indicates the vortex solitons with \(m=3\) are more likely to maintain their profiles. The above analyses demonstrate that the propagation constant, the degree of nonlocality, and the topological charge codetermine the profiles of vortex solitons in nonlocal nonlinear triangular lattices.
### SEVIRI Errors of Omission and Commission
According to Equation (1), \(\phi_{\text{FRP}}\) id\({}_{\text{FRP}}\)ndicates the ratio of the seasonal sum of FRP measured by SEVIRI to that concurrently measured by MODIS. \begin{table}
\begin{tabular}{l c c c c} \hline  & **ROI 1** & **ROI 2** & **ROI 3** & **ROI 4** \\ \hline  & \multicolumn{2}{c}{\(\phi_{FRP}=0.75\)} & \multicolumn{2}{c}{\(\phi_{FRP}=0.62\)} & \multicolumn{2}{c}{\(\phi_{FRP}=0.50\)} & \multicolumn{2}{c}{\(\phi_{FRP}=0.37\)} \\ \hline SEVIRI error of omission & 25\% & 45\% & 68\% & 74\% \\ \hline Surrounded by SEVIRI non-potential fire pixels & 4\% (16\%) & 13\% (28\%) & 26\% (39\%) & 30\% (40\%) \\ Adjacent to SEVIRI cloud or cloud edge & 12\% (49\%) & 15\% (33\%) & 20\% (30\%) & 23\% (31\%) \\ Adjacent to SEVIRI potential fire pixel that did & 5\% (22\%) & 13\% (29\%) & 17\% (25\%) & 16\% (21\%) \\ not breach the contextual thresholds & 2\% (10\%) & 2\% (4\%) & 2\% (3\%) & 1\% (1\%) \\ \hline Adjacent to SEVIRI suplimit pixel or pixel & 1\% (4\%) & 2\% (4\%) & 2\% (3\%) & 4\% (5\%) \\ where the test for sunglint failed & 24\% & 18\% & 32\% & 25\% \\ \hline \end{tabular}
\end{table} 
Table 2: Seasonal SEVIRI errors of omission and commission (shown in gray) for each region of interest (ROI) depicted in Figure 1a between 1 October 2008 and 30 April 2009. Errors of omission are decomposed into five potential reasons for the failed SEVIRI detection. Open values indicate the absolute proportion of MODIS active fire pixels missed by SEVIRI as a result of a particular artefact, and values in parentheses indicate the relative proportion of SEVIRI’s omission rate in an ROI attributed to a particular artefact.
## 3 Concept and Working Principle of Competing Miners in a Blockchain  
### Proof-of-Work  
The blockchain has a few different processes of how to verify the integrity and validity of the data in the chain [27]. The most common process is the PoW. This process allows the transactions inside a blockchain to be verified without the use of a third party.  
The PoW is the most commonly used process amongst the blockchains. It was reported in 2016 that the PoW was being used in more than 90% of the total existing digital cryptocurrencies [27]. The PoW is based on cryptography, which is an advanced form of mathematics. However, since advanced mathematical equations are used, only powerful computers are able to solve the PoW if the difficulty is set too high. This results in not only a significant amount of electricity used but also a very limited number of transactions that can be processed at the same time. In Table 1 we can see the transaction speed of different existing cryptocurrencies, each with different settings on their verification process.  
The Definition of Difficulty  
In the PoW, difficulty is a measurement of how difficult it is to mine a block in a blockchain. Setting the difficulty level to high means that it would take additional computing power to be able to verify the transactions entered on a blockchain. This process is also called mining. In cryptocurrency, difficulty is used as a parameter to keep the average time between blocks steady as the network's hash power changes.  
Having a high difficulty level can be of significant importance because of the security it provides. When a hash is created, it starts with the difficulty of 0. This is because the hash is deterministic, meaning that it will remain the same as long as the data on which the hash is created are the same. In other words, there is no added complexity to the solving of the hash. To add difficulty to a hash, the PoW uses something called a nonce, which is an abbreviation of \"number only used once\". Nonces are added at the start of the hash as random strings, and the level of difficulty determines how many nonces are added. Since the nonce is a random unpredictable string, the complexity multiplies every time a new nonce is added. Figure 2 shows what a PoW hash with a difficulty level of 5 will typically look like.
## 2 Literature Review
### LoRa
#### 2.2.2 LoRa Network Architecture
Usage of the redundancy helps the LoRa signal tolerate short interferences. The value of CR has to be modified based on the settings of the channel utilized for data transmission. If the channel has too many interferences in, then it is suggested to surge the CR value. However, by increasing the CR value, the transmission time will be increased;  
(iv) Data Rate: The data rate, also called the bit rate, is used to represent the transmission speed. Usually, it is measured in bits per second and is known as the total number of bits that can be transferred from the transmitter to receiver during a transmission. \[\mathrm{R_{b}}=\mathrm{SF}*\frac{\left[\frac{4}{4+\mathrm{CR}}\right]}{\frac {2^{\mathrm{SW}}}{\mathrm{BW}}} \tag{1}\]  
where,  
\(\mathrm{R_{b}}=\mathrm{Data}\) rate in bits per second  
\(\mathrm{SF}=\mathrm{Spreading}\) factor (6,7,8,9,10,11,12)  
\(\mathrm{BW}=\mathrm{Bandwidth}\) (10.4, 15.6 20.8, 31.25, 41.7, 62.5, 125, 250, 500)  
\(\mathrm{CR}=\mathrm{Code}\) rate (1,2,3,4);  
(v) Receiver Sensitivity: The least signal intensity that a receiver can sense is measured by the receiver sensitivity. It informs us of the weakest signal that a receiver can detect and analyze. The sensitivity of the receiver is measured in decibels (dBm). The typical range for the receiver sensitivity of LoRa is up to \(-130\) dBm. The various parameters that affect the receiver sensitivity are the receiver noise figure (NF), BW, and signal to noise ratio (SNR). Usually, the noise figure is fixed based on the implemented hardware. SNR is required by the modulation scheme. SNR and BW are important variables to be considered while designing the LoRa. The formula for calculating the receiver sensitivity of LoRa is mentioned below. \[\mathrm{S}=-174+10\log_{10}^{\mathrm{BW}}+\mathrm{NF}+\mathrm{SNR} \tag{2}\]  
(vi) Link Budget: The link budget is calculated using transmitted power and receiver sensitivity. It is also known as received power. The formula for the link budget of LoRa is mentioned below Equation (3). \[Link\;Budget_{(dBm)}=\left(Transmitted\;Power_{(dBm)}+Receiver\;Sensitivity_{( dBm)}\right) \tag{3}\]
# Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture
## 4 Conclusions and Discussions  
### Conclusions  
This study investigated the DNN neural network to obtain 1D S-wave velocities of the near surface using the frequency-dependent Rayleigh wave dispersion curves. Based on the results, the following conclusions can be drawn:  
1. Based on the ergodicity and orderliness of strata evolution and the constrained MC theory, we can construct innumerable rational velocity models that can effectively describe the more complex near-surface conditions. This approach could offer several benefits in terms of generating a wide range of training datasets that are necessary for effectively training DNN;
2. The effectiveness of the proposed DNN was first tested using a synthetic dataset. The training and validation loss curves and the accuracy of the validation dataset show that the performance of DNN tends to be stable after 110 epochs, and the accuracy of inversion results reaches ~90%;
3. Calculation of the relative errors for different network layers shows that the errors do not decrease all the way and will reach a minimum at a certain number of samples. The relative errors of different learning rates also have no simple linear relationship; the model performed best when the learning rate was 0.01 in our study, and the best number of network layers was six in our DNN architecture. This may be attributed to the highly nonlinear properties of the Rayleigh wave dispersion problem;
4. To apply DNN to real dispersion curves extracted from ambient noise in the Yellow River Delta, we rebuilt the 8000 pairs training dataset, and the training process took ~15 s. The results showed good consistency with that of the VFSA-DHSM inversion and previous statistics of the S-wave velocities, which may help to provide an alternative for deriving S-wave velocity models for complex near-surface structures.
# Event Detection Using a Self-Constructed Dependency and Graph Convolution Network
## 4 Experiments
### Ablation Experiments  
#### 4.4.1 Analysis of Impact of Different Graph Structures on the Model  
The above-described experiments have shown that the proposed model can effectively improve event detection performance compared with existing methods. To further verify the impact of different graph structures on the model, ablation was conducted for several edge types introduced in the graph. Table 3 shows the event detection performance of using a dependency parsing tree, a pruning dependency parsing tree, entity feature edge, and combining entity feature and pruning dependency parsing tree to build a graph. Among them, the result of introducing the edge of entity features only was the worst. A possible reason is that this method helps the model recognize the entity features in the event mention but also increases the complexity of the edge in the graph, leading to the decline in experimental results. Our proposed method of dependency parsing tree pruning combined with entity feature edge construction produced the best result because it can not only reduce unnecessary parameters in the model, but also help the model to identify important entity feature information in the event detection task.
## 2 Design, Fabrication and Characterization  
### Microstrip Design  
A microstrip is a strip transmission line separated from the ground plane by a substrate. Some RF components such as filter, power divider and antenna can be designed based on a microstrip. PDMS has the advantages of a high dielectric constant, good mechanical property and relative low dielectric loss [20]. Herein, a flexible microstrip with PDMS substrate and a strip conductor made of AgMP/PDMS composite was designed. The conductivity, \(\sigma\), of the strip conductor was 3.33 \(\times\) 10\({}^{5}\) S/m, which was calculated from \(\sigma=\frac{L}{RWTS}\), where \(R\), \(L\), \(W\) and \(T\)s are the resistance, length, width and thickness of the strip conductor, respectively. The resistance, R, was measured by a digital multimeter (UT71B, Uni-Trend Technology Inc.China). The structure is shown in Figure 1a. In order to match 50 \(\Omega\) system impedance, the geometric parameters of the microstrip were calculated and are listed in Table 1.  
Figure 1: (**a**) The structure of the microstrip line, and (**b**) e photograph of a microstrip sample with sub-miniature-A (SMA) connectors, and the ground plane is on the other side of the transparent polydimethylsiloxane (PDMS) film.