esa-sceva/satcom-chunk-collection huggingface dataset:
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 4 Centralized Protocols
### Mhct  
In mmWave 5G networks, owing to the overwhelming connections and traffic flow, and because of the signal attenuation over distance, multi-hop mechanisms are adopted to improve the flow throughput. A multi-hop concurrent transmission (MHCT) was introduced by [PERSON] et al. in [49] to address these issues. The authors presented a novel hop selection measure to select data relays and forward the data using the selected relays. Selecting shorter links ensures high data transmission data rates. Consequently, as the hop count increases, heavy traffic also becomes visible, and the need for an appropriate hop selection is critical for this scenario. Upon receiving transmission requests, a PNC assigns appropriate relay hops by accumulating the global network information, separation distance among neighboring nodes, antenna directions, and traffic. In addition, the PNC creates weighted graphs between nodes to facilitate relay hop assignment. The final concurrent transmission method is then implemented based on this assignment.  
In a mmWave network topology, the PNC maintains track of all updates and changes. When there are traffic demands, the PNC estimates the mean connection length and the traffic load, depending upon the topology updates. Eventually, the PNC enumerates the lowest cumulative weights using the Dijkstra algorithm and finally assigns hop selection.  
* Advantages: The MHCT protocol has enabled multi-gigabits-per-second transmission at the indoor mmWave WPANs. To maximize the flow throughput, multi-hop transmissions can be used to overcome the link outage problem and counter the extreme propagation loss at the mmWave band.
* Disadvantages: The number of hops used for each traffic flow is highly influenced by the network topology. The summation of the link length to power for each traffic flow decreases as the number of short hops for each traffic flow rises, whereas the summation of the node loads increases. Hence, congestion increases at the nodes as the number of hops increases.
## 2 Machine Learning Prediction Methods
### Support Vector Machine  
Support vector machine, a supervised learning method for classification, regression, and outlier detection, constructs the hyperplane that acts as a boundary between distinct data points and thus the output can be deduced hereafter [11]. Two distinctive versions of SVM are shown in Figure 1. For classification problem in Figure 1a, data points that lie at the edge of an area closest to the hyperplanes are considered as support vectors. The space between these two regions is the margin between the classes. Hyperplanes will determine the number of classes incurred in the dataset and the output of unseen data will be predicted according to which class holds the most similarity with the new data. As for regression problem in Figure 1b, an approximation of such hyperplane to a non-linear function is constructed at the maximal margin with linear regression. Hence, the additional parameter, known as the \(\varepsilon\)-insensitive loss is introduced to tolerate some deviations that lie inside the \(\varepsilon\) region tube [12].  
Figure 1: Overview of SVM algorithm: (**a**) SVM for classification; (**b**) SVM for regression.  
The boundary lines (dashed lines) across the hyperplane (solid line) in SVR (stands for support vector regression) are defined with regards to parameter \(\varepsilon\), in which the resulting lines are the shifted function in the amount of \(-\varepsilon\) and \(+\varepsilon\) from the hyperplane (assume it is a straight line with an equation of \(<\!\!w\), \(x_{i}>+b\)). The SVR uses a penalty concept introduced by parameter \(C\) (cost factor) for output variables outside the boundaries either above \((\xi_{i})\) or below \((\xi_{i}^{*})\). Nevertheless, data points inside the boundaries will be exempted. Since support vectors represent the data points located near these boundary lines (see Figure 1b), if the \(\varepsilon\) moves further from the hyperplane, the number of support vectors decreases; otherwise, the number of support vectors increases as the \(\varepsilon\) approximates towards the hyperplane. Finally, since most realistic problems aren't linear, the kernel trick is commonly performed by mapping training data onto the high-dimensional feature space. Kernel functions, e.g., linear, polynomial, radial basis function (RBF), sigmoid, hyperbolic tangent, etc., are used to convert the once inseparable input data into the separable ones.  
The parameter \(\varepsilon\) has brought a couple of advantages, yet is sometimes difficult to tune. Hence, scholars from Australian National University proposed the substitution of parameter \(\varepsilon\) into parameter \(\
u\) (hereinafter referred to as \(\
u\)-SVM) to avoid such a tedious parameter tuning process for regression [13]. Moreover, parameter \(\
u\) is also applicable for classification, where it becomes the replacement for cost factor \(C\)[14]. Values of parameter \(\
u\) with the upper bound of training margin errors and lower bound for the support vectors are recommended from 0 to 1 so that the \(\
u\)-SVM can offer a more meaningful parameter interpretation [15].
## 2 Literature Review  
[PERSON] et al. [11] explored and analyzed two distinct datasets at two distinct phases of course delivery (20% and 50%) utilizing several graphical, statistical, and quantitative approaches. The feature analysis offers understanding as to the nature of distinct features regarded and utilizes in the selection of ML techniques and their parameters. Moreover, this work presents a systematic model dependent upon the Gini index and \(p\)-value for selecting an appropriate ensemble learner in a group of six potential ML techniques. [PERSON] et al. [12]progressed an accurate prediction pedagogical method, considering the pronounced nature and novelty of presented approach in Educational Data Mining. The base classifications containing RT, j48, kNN, and naive Bayes (NB) were estimated on a 10-fold cross-validation model. In addition, the filter procedure as over-sampling (SMOTE) and under-sampling (Spread subsampling) were exploited to examine some important alterations in outcomes amongst meta and base classifications.  
[PERSON] et al. [13] forecasted student academic performance in a technical institution in India. The data pre-processed and factor analysis were executed on the attained dataset for removing the anomaly from the data, decreasing the dimensionality of the data, and attaining the most correlated feature. [PERSON] et al. [14] generated two datasets concentrating on two distinct angles. In the primary dataset classification and forecast, the type of students (bad, medium, and good) on a particular course was dependent upon its prerequisite course efficiency. This can be executed during the artificial intelligence (AI) course. The secondary dataset also classified and forecasted the last grade (A, B, C) of an arbitrary subject; our data can be established in such a way that the data are only concentrated on the efficiency of the midterm exam.  
Despite all the studies performed on FS process, to the best of our knowledge, only few works have carried out an FS-based classification model for EDM. Earlier works have used ML models for EDM without contributing much significance to the FS process. At the same time, the parameters involved in the ML models (i.e., ELM) considerably affect the overall classification performance. Since the trial-and-error method for parameter tuning is a tedious and erroneous process, metaheuristic algorithms can be applied. Therefore, metaheuristic optimization algorithms can be designed to optimally tune the parameters related to the ML models to improve the overall classification performance.
## 3 A Routing Protocol Based on Energy and Link Quality (REL)  
This section outlines a routing protocol based on energy and link quality (REL) (The implementation of REL is available for downloading at [[http://www.gercom.ufpa.br/downloads/wsn/](http://www.gercom.ufpa.br/downloads/wsn/)]([http://www.gercom.ufpa.br/downloads/wsn/](http://www.gercom.ufpa.br/downloads/wsn/))) for WSNs/IoT applications, such as comfortable homes and offices, healthcare, environmental monitoring and smart cities. REL uses the link quality of wireless links and residual energy during the routing selection process to increase the system's reliability and assure QoS support for IoT applications. Additionally, it includes an event-driven mechanism to provide load balancing and avoid the premature death of nodes/networks.
# Dynamic Carrier-Sense Threshold Selection for Improving Spatial Reuse in Dense Wireless LANs
## 3 Proposed Protocol
### Measurement-Based Dynamic CST Control
While the model-based scheme selects CST conservatively by assuming that an interferer exists in the worst position, the measurement-based scheme could choose a CST which makes the nodes transmit more aggressively. Because of that, a larger \(P_{M}\) may be needed for measurement-based scheme to account for multiple interferers, compared to the model-based scheme. In the evaluations, we study the performance of protocols while varying the margin value.
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
###### Abstract  
Island ecosystems are particularly susceptible to climate change and human activities. The change of land use and land cover (LULC) has considerable impacts on island ecosystems, and there is a critical need for a free and open-source tool for detecting land cover fluctuations and spatial distribution. This study used Google Earth Engine (GEE) to explore land cover classification and the spatial pattern of major land cover change from 1990 to 2019 on Haitan Island, China. The land cover classification was performed using multiple spectral bands (RGB, NIR, SWIR), vegetation indices (NDVI, NDBI, MNDWI), and tasseled cap transformation of Landsat images based on the random forest supervised algorithm. The major land cover conversion processes (transfer to and from) between 1990 and 2019 were analyzed in detail for the years of 1990, 2000, 2007, and 2019, and the overall accuracies ranged from 88.43% to 91.08%, while the Kappa coefficients varied from 0.86 to 0.90. During 1990-2019, other land, cultivated land, sandy land, and water area decreased by 30.70%, 13.63%, 3.76%, and 0.95%, respectively, while forest and built-up land increased by 30.94% and 16.20% of the study area, respectively. The predominant land cover was other land (34.49%) and cultivated land (26.80%) in 1990, which transitioned to forest land (53.57%) and built-up land (23.07%) in 2019. Reforestation, cultivated land reduction, and built-up land expansion were the major land cover change processes on Haitan Island. The spatial pattern of forest, cultivated land, and built-up land change is mainly explained by the implementation of a 'Grain for Green Project' and 'Comprehensive Pilot Zone' policy on Haitan Island. Policy and human activities are the major drivers for land use change, including reforestation, population growth, and economic development. This study is unique because it demonstrates the use of GEE for continuous monitoring of the impact of reforestation efforts and urbanization in an island environment.  
**Keywords:** anthropogenic activities; forest change; GEE; LULC; urbanization
# Challenges of PBFT-Inspired Consensus for Blockchain and Enhancements over Neo dBFT
## 2 Background
### Ensuring Message Delivery on Peer-To-Peer: Challenges  
Purely asynchronous consensus depends on guaranteed message delivery for the underlying network [12], which is not a trivial property to assume for a decentralized network. To relay messages and avoid duplication, techniques such as Bloom Filters and probabilistic tables are usually employed [10]. Although this strongly favors efficiency and reduces overall memory usage, there is a risk of completely denying genuine messages, due to conflicts of previously generated message hashes. The probability of this event may be very small, yet not negligible in practice if the consensus mechanism depends on assuming all messages are eventually delivered.  
One way to reduce the probability of such failures, is to have separated filters for Priority (consensus) and Non-priority messages. Since most messages is of non-priority (transactions, blocks, etc.), they may overload the probabilistic tables with information that may cause non-deterministic conflicts on priority messages. With less info on priority tables (and perhaps a larger size), the conflict probability that yields false positives on message relay is reduced.  
Dealing with adversaries that may intentionally drop messages, thus not relaying them, is even harder. To guarantee that priority messages arrive, one must enforce redistribution for _every new node_ that connects to it, keeping a history of messages that may help current consensus to evolve,and dropping past messages that are not necessary anymore. Nodes should also disconnect and reconnect to others periodically, as stable bonds could lead to situations where a strong adversary can control important nodes and routes inside peer-to-peer.
## 2 Instruments and Technological Equipment  
### Instruments for Measuring Vibrations in Metalworking  
This article is based on experimental material obtained from studies of AE signals accompanying technological processes on machine tools of various groups. The method of AE registration was almost the same in all experiments. Figure 1 shows a typical scheme for recording AE signals on processing equipment.  
The position of the accelerometer was chosen so that there were no movable joints (bearings and guides) between it and the cutting area. The accelerometer was usually attached with a magnet or a threaded connection. Table 1 shows the characteristics of the accelerometers used.  
Table 1 indicates the frequency range where the voltage sensitivity of accelerometers has a constant value. However, for comparative assessments of changes in vibroacoustic activity, a more comprehensive range was used, including the resonant frequency and higher. In practice, data were obtained for the frequency range up to 40 kHz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Accelerometer AP2037-100**} \\ \hline \(K_{\mathrm{N}}\) & Voltage sensitivity & 10 & mV/ms\({}^{-2}\) \\ \hline \(f\) & Linear frequency range (+/\(-2\) dB) & 0.5–20,000 & Hz \\ \hline \(f_{\mathrm{R}}\) & Installation resonance frequency in the axial direction (\(>\)) & 50 & kHz \\ \hline \(\Delta\) & Transverse direction factor (\(\leq\)) & 5 & \% \\ \hline \multicolumn{4}{c}{**Accelerometer KD-35**} \\ \hline \(K_{\mathrm{N}}\) & Voltage sensitivity & 5 & mV/m/s\({}^{2}\) \\ \hline \(f\) & Linear frequency range (+/\(-2\) dB) & 0.4–12,000 & Hz \\ \hline \(f_{\mathrm{R}}\) & Installation resonance frequency in the axial direction (\(>\)) & 25 & kHz \\ \hline \(\Delta\) & Transverse direction factor (\(\leq\)) & 5 & \% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of accelerometers.  
Figure 1: Diagram of equipment for recording the VA signals generated by technological processes: 1—elastic system of technological equipment, 2—magnet for mounting the accelerometer, 3—accelerometers, 4—preamplifiers, 5—amplifiers model VshV003 (OOO Izmeritel, Taganrog, Russia), 6—an ADC E440 (L-card, St. Petersburg, Russia), 7—signal-recording device.  
The digitally saved records (Figure 1) were processed using time and frequency analysis methods. Spectral analysis was used to select the frequency ranges where the amplitudes of AE signals experienced more noticeable changes when the cutting tool condition or process conditions changed. In these ranges, narrow-band frequency filtering and smoothing of recordings were carried out by constructing RMS values of signal amplitudes with different averaging periods. This also helped to reduce the influence of noise on the AE signals, the presence of which is associated with the discreteness of physical processes of materials processing. The results of such processing of AE signals are presented in Section 3.
# Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks
## 4 Discussions and Comparative Study of the Testing Results  
The APs and mAPs of 12 cases for each of the six detectors (three regular and three deformable detectors) trained for 50 epochs are demonstrated in Tables 1-3, and in Figure 10. In general, the testing results prove that adding deformable modules can improve the detection accuracy. The optimal combination of parameters for each detector is found through a trial-and-error process.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Case** & **Anchor** & **RPN** & **Learning** & \multicolumn{2}{c}{**Without Deformable Module—[EMAIL_ADDRESS]**} & \multicolumn{2}{c}{**With Deformable Module—[EMAIL_ADDRESS]**} \\ **Number** & **Scale** & **Batchsize** & **Rate** & **Alligator** & **Longitudinal** & **Transverse** & **mAP** & **Alligator** & **Longitudinal** & **Transverse** & **mAP** \\ \hline
1 & & & & 0.0001 & 100 & 80.18 & 80.37 & 86.85 & 100 & 90.08 & 78.41 & 89.50 \\
2 & & & 0.0005 & 100 & 82.36 & 78.24 & 86.87 & 100 & 88.37 & 75.84 & 88.07 \\
3 & & & 0.001 & 99.71 & 85.69 & 78.01 & 87.91 & 80.87 & 89.59 \\
4 & & & 0.0001 & 100 & 84.77 & 78.25 & 87.67 & 100 & 89.51 & 78.02 & 89.18 \\
5 & & & 0.0005 & 100 & 87.06 & 77.56 & 88.21 & 100 & 88.01 & 79.16 & 89.06 \\
6 & & & 0.001 & 100 & 86.04 & 76.77 & 87.60 & 100 & 88.03 & 86.71 & 91.58 \\ \hline
7 & & & 0.0001 & 99.86 & 79.48 & 78.20 & 85.85 & 100 & 82.40 & 79.16 & 87.19 \\
8 & & & 0.0005 & 100 & 80.13 & 79.88 & 86.67 & 100 & 86.79 & 78.79 & 88.53 \\
9 & & & 0.001 & 100 & 78.34 & 78.09 & 85.48 & 100 & 79.91 & 79.60 & 86.50 \\
10 & & & 0.001 & 100 & 79.48 & 78.20 & 85.89 & 100 & 82.57 & 78.55 & 87.04 \\
11 & & & 0.001 & 78.83 & 78.69 & 85.84 & 100 & 82.12 & 78.82 & 86.98 \\
12 & & & 0.001 & 100 & 78.34 & 78.09 & 85.48 & 99.27 & 85.79 & 79.43 & 88.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Testing Results of Faster R-CNN.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Case** & **Anchor** & **RPN** & **Learning** & \multicolumn{2}{c}{**Without Deformable Module—[EMAIL_ADDRESS]**} & \multicolumn{2}{c}{**With Deformable Module—[EMAIL_ADDRESS]**} \\ **Number** & **Scale** & **Batchsize** & **Rate** & **Alligator** & **Longitudinal** & **Transverse** & **mAP** & **Alligator** & **Longitudinal** & **Transverse** & **mAP** \\ \hline
1 & & & & 0.0001 & 99.57 & 89.60 & 76.56 & 88.58 & 100 & 90.91 & 78.55 & 89.58 \\
2 & & & 0.0005 & 98.53 & 88.55 & 79.31 & 88.80 & 100 & 89.81 & 73.64 & 87.82 \\
3 & & & 0.001 & 97.89 & 79.06
## 5 RMNCE-TC Architecture Overview  
Figure 3 depicts the proposed architecture and illustrates how the proposed RMNCE approach forms the core component linking the INS, the adaptive UKF, and the satellite selection processes.  
As shown in Figure 3, the modified tightly-coupled system consists of 6 parts. Part 1 and Part 2 are traditional INS and GNSS procedures. In Part 3, the RMNCE is carried out based on the pseudo ranges and pseudo range rates inputted by INS and GNSS according to Equations (4)\(\sim\)(6), which is the key adaptive part of this architecture. Satellite selection is accomplished in Part 4 based on Equations (19)-(21) and the result is also transferred to Part 6 to expand the **R** when necessary. Part 5 provides additional height observation to enhance state observabilities. The adaptive UKF is carried out in Part 6 based on RMNCE and expanded **R**.
# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 3 Materials and Methods
### Experiment Design Options and Metric
Thus, we obtained nine classes in the original training set, identified this subset _Or9_, and compared it to all other individual classes. Based on the structural similarity index measure (SSIM) [57], it was possible to realise which of the remaining classes are more similar to _Or9_. Thus, in the _More_ scenario, the original test set was changed so that now it has the nine most similar classes to _Or9_. Naturally, some classes had to be moved from the original test set into the updated training or validation datasets. Conversely, in the _Less_ scenario, the opposite happened: the nine less similar classes to _Or9_ were identified, and now there is another different test dataset. Moreover, all FSL approaches with \(10,50\), and \(100\) epochs using the RESISC-45 dataset were executed again, considering \(30\) few-shot validation and \(200\) few-shot test tasks in this case. We used the implementation of the approaches as shown in the repository Easy Few-Shot Learning [58] with some adaptations. The backbone for all cases was ResNet-12 [59], and the optimiser was stochastic gradient descent (SGD) with learning rate = 0.1, momentum = 0.9, and weight decay = \(5\times 10^{-4}\). MultiStepLR was used to decay the learning rate of each parameter group by gamma = 0.1 once the number of epochs reaches one of the milestones: 150 and 180 epochs. Runnings were performed using the SDumont supercomputer [60]. Finally, the average accuracy across all few-shot test tasks was the metric used to determine the performance of the FSL solutions.