esa-sceva/satcom-chunk-collection huggingface dataset:
# On Edge Pruning of Communication Networks under an Age-of-Information Framework
## 8 Simulation
### Experimental Settings  
We use a machine with a processor of Intel(R) Core(TM) i5-7200U CPU at 2.50 GHz with an installed memory (RAM) of 16.0 GB. We employ a Python 3.7 environment in order to make the simulation of the network.  
For the deterministic case, we randomly generate a large number of different series-parallel networks with certain parameters to determine the specifications of the graphs. First, the size of the network is determined depending on the number of nodes \(n\) and the expected number of average parallel edges \(L\), we will call this factor the degree of parallelism. This factor introduces the redundancy in the parallel links of the network. Furthermore, the deterministic time gap \(\tau\) of each one of the links is assigned randomly to be an integer from \([1,m]\). Lastly, to construct each one of the random series-parallel graphs, we have another parameter \(p\) that may introduce a bias towards having a parallel combination over a series combination, where \(p\) is the probability for each node to be inserted as a parallel combination. In addition, we insert additional random shortcuts to the graphs after their generation. We define a shortcut as a link which the graph remains a series-parallel graph with exactly the designated two terminals (nodes \(s\) and \(d\)) after removing it. The number of additional random shortcuts is represented as a percentage of the total number of original links \(s\). We will examine the case of removing the shortcuts originally generated from the graphs too.  
To examine the probabilistic case, we construct two simple representative series-parallel networks, one with high clustering distribution of nodes, and another one with low clustering of nodes. The wait time associated with the links of the networks are distributed  
Figure 5: Flowchart showing the process of determining the weakest link in the probabilistic case.  
independently and exponentially. The total number of nodes is 50 nodes, and the total number of links is 60 links. The distribution of the \(\lambda\) values of the links that characterize the average wait time of each link is normally distributed over the links of the network where \(\lambda\)\(\sim\)\(N(\lambda_{\text{mean}},\sigma^{2})\). The topologies are illustrated in Figure 6. The messages are generated in a way that follows the Poisson process where the average generation rate is \(\mu\). The wait time on the nodes is negligible while the order of the messages is important. We use two values of \(\mu\), we consider \(\mu=2\) and \(\mu=4\). Furthermore, we study the model under \(\lambda_{\text{mean}}=1,2,3\). In addition, we study the model under two different values of the standard deviation for the distribution of wait times, we consider \(\sigma=1,2\). For the deterministic case, the topologies are randomly generated SP topologies.
## 3 Other Applications
### Environmental Monitoring Other Than Rainfall Estimation
#### 3.1.3 Precipitation Type Identification  
By analyzing the different attenuation patterns of snow and rainfall on microwaves, [PERSON] et al. [117; 118] suggested that dual-frequency CMLs could be used to distinguish between rainfall, snow and wet snow. Subsequently, [PERSON] et al. employed the attenuation dynamics from CMLs as a discriminant feature, and successively proposed Fisher-based linear [63] and kernel discriminant [56] methods to distinguish between rainfall and sleet. With the emergence of deep learning methods, [PERSON] et al. [119] reported the success of utilizing the extreme learning machine algorithm to construct the hydrometeor identification model. The results show that the model performance improves as the input features and the overall frequency or frequency difference (in the case of dual- and tri frequency) increase. The algorithm for classifying rainfall types can be found in [120], where the authors implemented the distinction between convective and stratiform rainfall by constructing deep learning models based on the differential attenuation rates of multi-frequency dual-polarization CMLs. Moreover, [PERSON] et al. [121] derived total mixed cumulative precipitation containing rain, snow and sleet using at least three CMLs on the same path, and the results were in fair agreement with the rain gauge.
## 4 Methodology
### Damage Assessment  
Damage assessment based on social networks can be obtained by the activity-based approach [4]. A standard activity measure is the ratio of messages posted for each scale (spatial unit), which is substantially associated with per capital official damage [21]. In this research, the number of damage-related tweets per population of each spatial unit was applied for the estimation of earthquake damage in three spatial units, which included county, city, and 10 \(\times\) 10 km grids.
###### Abstract  
This work communicates the discovery of a sandy buried 10.5 km diameter near-circular structure in the eastern part of the Great Sahara in North Africa. Rimaal, meaning \"sand\" in Arabic, is given as the name for this structure since it is largely concealed beneath the Sahara Aeolian sand. Remote sensing image fusion and transformation of multispectral data (from Landsat-8) and synthetic aperture radar (from Sentinel-1 and ALOS PALSAR), of dual wavelengths (C and L-bands) and multi-polarization (HV, VV, HH, and HV), were adopted in this work. The optical and microwave hybrid imagery enabled the combining of surface spectral properties and subsurface roughness information for better understanding of the Rimaal structure. The long wavelength of the radar, in particular, enabled the penetration of desert sands and the revealing of the proposed structure. The structure exhibits a clear outer rim with traces of concentric faults, an annular flat basin and an inner ring surrounding remnants of a highly eroded central peak. Radar imagery clearly shows the interior wall of the structure is incised with radial pattern gullies that originate at or near the crater periphery, implying a much steeper rim wall in the past. In addition, data reveals a circumferential of a paleoriver course that flows along a curved path parallel to the crater's western margin indicating the plausible presence of a concentric ring graben related to the inferred structure. The defined crater boundary is coincident with a shallow semi-circular-like basin in the SRTM elevation data. The structure portrays considerable modifications by extensive long-term Aeolian and fluvial erosion. Residing in the Cretaceous Nubian Sandstone formation suggests an old age of \(\leq\)65 Ma for the structure. If proven to be of an impact origin, the Rimaal structure could help in understanding the early evolution of the landscape of the Eastern Sahara and holds promise for hosting economically valuable ore deposits and hydrocarbon resources in the region.  
ALOS PALSAR; Sentinel-1; SRTM; data fusion; minimum noise fraction 2018 10.3390/rs10060880 5 June 2018
## 7 Results and Discussion
### Energy Consumed in Driving Cycles  
To validate the developed model, we first analyze the energy consumed in each cycle with a final ratio of 4.3. Figure 14 illustrates this comparison, which shows a minimum discrepancy between model and experiment of \(-2.39\%\) for the WLTC-2 cycle. By converse, a maximum relative difference of \(+6.66\%\) is found for the NEDC profile.  
When analyzing the lower ratio of 3.54, the lowest discrepancy is \(+8.29\%\) for the NEDC case, whereas the largest variation is found with WLTC-3: \(+11.77\%\).  
When comparing the experimental results between both final ratios, the lower final ratio of 3.54 always involves an adavantage in terms of consumed energy; with savings ranging from 10.76 Wh (NEDC) to 72.5 Wh (WLTC-3). These figures represent an energy increase on the original ratio configuration of \(+2.81\%\) and \(+8.28\%\), respectively.  
Figure 14: Energy consumed in driving cycles with final ratios 4.3 and 3.54. Model and test results are compared for both ratios.  
Figure 13: Energy consumed at a constant speed.
# Non-Interactive Decision Trees and Applications with Multi-Bit TFHE
## 3 Preliminaries
### Combinational Digital Circuits
#### 3.2.1 Comparator  
A comparator decides whether two numbers are greater or less than the other or equal. It receives two \(n\)-bit binary numbers, \(X\) and \(Y\), and outputs the comparison outcome as a bit. There are two common types of comparators. An equality comparator indicates whether \(X\) equals \(Y\), and a magnitude comparator indicates whether the value of \(X\) is greater or lesser than the \(Y\) value.  
The equality comparator is the more straightforward circuit compared to the magnitude comparator. It uses the XNOR gate to check that bits in \(X\) and \(Y\) corresponding to the same column are equal. If it is valid for all the columns, the numbers are equal.  
The magnitude comparator also compares each bit of two numbers, such as \(X=X_{3}X_{2}X_{1}\) and \(Y=Y_{3}Y_{2}Y_{1}\). It starts from the most significant bits, checking if the pair of bits is equal and assigning the result to \(E_{3}\).  
\[E_{3}=\bar{X}_{3}\bar{Y}_{3}+X_{3}Y_{3} \tag{8}\]  
It then continues checking for the next most significant pair till it reaches the least significant pair of bits. Hence, \(E_{2}=\bar{X}_{2}\bar{Y}_{2}+X_{2}Y_{2}\) and \(E_{1}=\bar{X}_{1}\bar{Y}_{1}+X_{1}Y_{1}\).  
If \(X>Y\), then the following equation evaluates to 1.  
\[X>Y=X_{3}\bar{Y}_{3}+E_{3}X_{2}\bar{Y}_{2}+E_{3}E_{2}X_{1}\bar{Y}_{1} \tag{9}\]  
Suppose the most significant bits are \(X_{3}>Y_{3}\), where \(X>Y\), irrespective of the bit values in other places of the number. This logic corresponds to the first term \(X_{3}\bar{Y}_{3}\) in the equation. On the other hand, if the most significant bits are equal, \(X>Y\) if the next significant bit \(X_{2}\) is greater than \(Y_{2}\) as evaluated by the second term \(E_{3}X_{2}\bar{Y}_{2}\) in the equation. Finally, the third term \(E_{3}E_{2}X_{1}\bar{Y}_{1}\) evaluates to one if the least significant bit \(X_{1}\) is greater than \(Y_{1}\) and all other bits are the same.  
The IC 7485 is a TTL family 16-pin 4-bit magnitude comparator with outputs \(X=Y,X>Y\), and \(X<Y\). Words of larger length are compared by cascading these comparator chips one after another, as shown in Figure 2.  
The rightmost cascading inputs are connected to fixed logic values, and the leftmost cascading outputs provide important final outputs. Instead of the 4-bit comparator, a single-bit comparator can be used at each step. It tracks whether the numbers corresponding to the compared bit pairs are greater or lesser.  
Figure 2: A 32-bit comparator circuit by cascading 4-bit IC 7485 comparator.
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 2 Geometry Model and Operating Parameters  
### Geometry Model  
Manganese nodules are generally found in the seabed with a depth of 3000 to 6000 m. As shown schematically in Figure 1, the hydraulic mineral lifting system is mainly composed of the mineral collection vehicle, the flexible pipe, the buffer station, the lift pump, the vertical pipe, and the production support vessel. Manganese nodules are collected using the mineral collection vehicle and then pumped via the flexible pipe to the buffer station. The vertical pipe connected to the lift pump is suspended beneath the production support vessel through which the mixture of manganese nodules and seawater is pumped to the sea level. Essentially, the pipe, or pipe segments, covering such a long vertical distance, plays an important role in the lift of manganese nodules. The solid-liquid two-phase flow in the pipe directly affect the stability of the whole system and is influenced by multiple operating parameters such as particle size and solid volume fraction. An unreasonable combination of the operating parameters will incur particle retention and then the increase of local solid concentration. Furthermore, the whole system will be paralyzed due to the blockage of the pipe. Therefore, it is of significance to plumb the flow characteristics in the pipe.
# Analysis of Fluid Velocity inside an Agricultural Sprayer Using Generalized Linear Mixed Models
## 3 Results and Discussion  
### Distribution Analysis of the Targeted Variable: Fluid Velocity  
The analysis of the fluid velocity (cm/s) variable as a whole (n = 7200) revealed an average value of \(11.25\pm 6.08\) cm/s, within a range of values from 0.69 cm/s to 61.73 cm/s, highlighting a mismatch with the normal distribution (K\(-\)S = 0.08; \(p<0.001\)). The representation of the data based on the operational parameters of interest: LT, NN, and P, showed various extremes and outliers in the right tail of the distribution, with Skewness = 1.24, and Kurtosis = 2.68 (Figure 3). It is known that, to develop models that describe what is observed in reality, the variable should not be transformed, nor should extremes and outliers be treated and/or eliminated [32].
# Multi-Source Satellite Imagery and Machine Learning
## 7 Discussion
This imbalance may bias the models toward dominant classes at the expense of underrepresented ones. Additionally, while the data sources (optical, radar, morphometric) provide valuable insights, they remain static snapshots and may not fully capture the spatiotemporal variability of geological formations. The complex geological structures of the Western Highlands of Cameroon further complicate classification, with intertwined tectonic and volcanic features adding layers of complexity. Several avenues could improve model performance and refine results. Integrating hyperspectral or LiDAR data could enhance mineralogical and topographical insights, improving model accuracy. Also, exploring convolutional neural networks (CNNs) or graph neural networks (GNNs) could better capture spatial relationships and complex data structures. Applying oversampling techniques for minority classes or cost-sensitive learning approaches could also mitigate class imbalance effects. The study's findings have significant implications for natural resource management and geological risk prevention. The methods developed here can be adapted to other regions with similar geological contexts, contributing to more effective land management and a deeper understanding of geological systems.
# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 5 Discussion
### Robustness of Informative Features  
To evaluate the robustness of our approach, we conducted additional testing on a separate, much larger dataset using the informative psycholinguistic and textual features identified in this study. The new dataset, consisting of 187,943 Reddit posts, initially included 300 TF-IDF and 108 LIWC features [19]. We applied 24 TF-IDF and 14 LIWC features identified in Experiment 2 to this larger dataset and compared the results. The same set of four algorithms was trained: logistic regression, naive Bayes, random forest, and support vector machine.  
Our findings show that the performance of the models trained with our selected features was comparable to that of models using the original feature set. Specifically, the models trained with the original features achieved an average F1 score of 97.08% compared to 95.29% for the models trained with our selected features. This represents a 1.79% decrease in F1 scores. A similar decrease of 1.84% was observed in the average accuracy scores: models trained with all features achieved 96.95%, while those trained with the selected features reached 95.11%.  
However, while the models trained with selected features achieved slightly lower performance, they utilized only 9.31% of the original features: 38 Boruta-selected features compared to 408 original features. This reduction in features resulted in an approximately 18-fold decrease in the average training time. In particular, the models using our informative features completed training in 2.22 s, whereas the models trained with the original features took an average of 39.29 s to train. The most significant reduction in training time--from 128 s to 0.52 s--was observed in the SVM model.  
These results support the robustness and efficiency of our approach. They demonstrate that the features we identified in this study are not only effective on expert-labeled datasets but also generalize well to a much larger Reddit dataset.
## 4 Performances Evaluation
### Verification Experiments  
The recognition modality called \"Verification\" determines whether an individual turns out to be who he claims to be. Verification systems perform one-to-one comparisons between a test template, extracted from a sample released by a user, and a stored reference template, corresponding to the claimed identity. Performances of the 2D and 3D methods are evaluated by comparing each template with all the others. In this way, a much higher number of scores (2016) than those achievable with the standard reference-query method is obtained [29]. As is known, a genuine score is the result of a comparison between templates belonging to the same user, while an impostor score is from different users.  
There are two types of errors in biometric systems: false acceptance rate (FAR) and false reject rate (FRR), i.e., the frequency of mistakenly accepted non-legitimate users and rejected legitimate users, respectively. Different feature extraction techniques are compared through the receiver operating characteristic (ROC) curves, which plot 1-FRR vs FAR [46; 47] (see Figure 12). A method is more accurate the closer its ROC curve is to the upper left corner of the graph.  
In this work, two parameters that are often used to synthetically evaluate recognition performances of the various extraction techniques are examined: the area under the ROC curve (AUC) [48] and the equal error rate (EER), which is the error when FRR = FAR. Table 1 reports achieved values of EER and AUC for all the experimental methods. As can be seen, the results are satisfactory for every case. In particular, the use of the proposed SRAD filter always improves recognition results while the two binarization methods are almost equivalent, with a slight preference for [PERSON]'s method. Furthermore, results demonstrate that the 3D template in any case provides better recognition perfo  
Figure 11: Example of acquisition discarded due to the presence of too thin of regions to contain a complete vascular pattern.  
The best results were achieved by using the SRAD filter, Ridler adaptive threshold, and 3D template: EER = 2% and AUC = 99.92%.
## 1 Introduction  
With the continuous development of integrated circuit technology, in the field of cutting-edge processing, the traditional macro scale has been constantly moving towards the micro scale. A large number of micro/nano structure devices with unique optical and electrical properties have emerged. At present, micro/nano structure devices have been widely used in many fields such as industrial production, aerospace and national defense research. Typical applications include super surfaces [1], terahertz sensors [2], MEMS devices [3; 4; 5], micro optical devices [6; 7; 8] and micro fluids [9; 10; 11]. As mentioned in the literature [12], a layer of silicon is coated on the surface of aluminum film etched with metal circle holes, and the optical performance of the visible light band is adjusted by tuning the thickness of the silicon film to show different colors. At the same time, the author also proposed [13] to etch a hoof-shaped structure on the surface of metal film, and adjust the shift of transmission peak in the near-infrared band by adjusting the polarization direction of incident light, so as to realize the controlling of on/off state of transmission light. Currently, in the design of various micro/nano structures and optoelectronic devices, simulation software is mainly used to simulate light propagation. In the process of simulation, the whole structure needs to be meshed. When the substrate thickness of the structure is giant-sized large (compared with the response band) in practical application, the number of mesh divisions will increase exponentially. Therefore, the size of the structure directly affects the working time and accuracy of the simulation. Herein, the equivalent-circuit model used in the theoretical analysis is used for reference, in which the determination of equivalent circuit parameters makes it possible to determine the transmission coefficient. Furthermore, the determination of the transmission coefficient provides a theoretical basis for slicing the thick substrate [14].  
In this paper, the transparent substrate and the metal film are sliced evenly to obtain thin samples. Then the optical \(S\) parameters of mid infrared band are obtained through full wave simulation. Furthermore, the ABCD matrix parameters are calculated by the cascade theory of two port transmission lines. Finally, according to the thickness requirements, the overall optical response parameters are obtained through multiple cascade calculations. At the same time, through full wave simulation and calculation, the results are extended to the whole visible and near infrared bands. At last, a surface plasmon filter is constructed to verify it.
## 4 Results Analysis
To perform the simulations, MatLab R2024a software was used on an ASUS TUF GAMING A15 laptop with the following specifications: an AMD Ryzen 7 6800H processor with Radeon Graphics at 3.2 GHz, 8 GB of RAM, and an NVIDIA GeForce RTX 3050 Ti Laptop GPU. The computational metrics evaluated are  
Time to generate the trajectory with the RRT planner: 41.26 s.  
Time taken by Robot 1 to move the object: With the SMC: 36.67 s and with the NLMPC: 135.78 s.  
Memory usage and CPU and GPU loads: Memory usage was kept below 50% of the available 8 GB, and the CPU and GPU loads remained at optimal levels, without overloading any components. This demonstrates that it is not necessary to have a supercomputer to implement this system, as it can be effectively carried out with a computer with normal specifications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Robot 1**} & \multicolumn{2}{c}{**Robot 2**} \\ \cline{2-5}  & **NLMPC** & **SMC** & **NLMPC** & **SMC** \\ \hline \(\text{e}_{\text{ss}}\) & 0.0017 & 0.0131 & \(1.0697\times 10^{-6}\) & 0.0023 \\ ISE & 0.9576 & 0.3609 & 0.0199 & 0.0619 \\ IAE & 0.9598 & 0.9220 & 0.1652 & 0.6896 \\ ITAE & 1.4984 & 9.1086 & 8.2306 & 32.432 \\ IA & 1.0033 & 1.0611 & 0.1652 & 0.8578 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance indices in the robot’s joints. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Robot 1**} & \multicolumn{2}{c}{**Robot 2**} \\ \cline{2-5}  & **NLMPC** & **SMC** & **NLMPC** & **SMC** \\ \hline \(\text{e}_{\text{ss}}\) & \(-0.0013\) & \(-0.0079\) & \(4.2821\times 10^{-7}\) & 0.0156 \\ ISE & \(1.5217\times 10^{-5}\) & 0.0148 & 9.0684 \(\times 10^{-4}\) & 0.0615 \\ IAE & 0.0104 & 0.4811 & 0.0332 & 0.7363 \\ ITAE & 0.1594 & 7.1649 & 1.6643 & 34.544 \\ IA & 0.0378 & 0.4750 & 0.033197 & 0.63847 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance indices in the trajectory.