esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Body-to-Body Communication and Connectivity  
Due to the growing number of connected devices (smart-phones, computers, game consoles, sensors, and wireless gadgets) to Internet, every human being is considered as a part of a BBN network that could be deployed at any time, anywhere in a context of Internet of humans (IoH) or Internet of things (IoT). Therefore, diverse deployment strategies are possible and various drawbacks are likely to be faced by the BBN network connectivity in a real deployment. Connectivity depends on the following factors: (1) dissemination strategy based on which data is transmitted among the wireless network; (2) communication range: which depends on the used wireless standard (i.e., BAN/MBAN,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Referred Works** & [31] & [29; 30] & [33; 34] & [32] & [35] \\ \hline \multirow{3}{*}{**Standard(s)**} & IEEE802.15.6 & IEEE802.15.6 NB; & IEEE802.15.6 NB; & IEEE802.15.6 NB; & IEEE802.15.6 & IEEE802.15.6 \\  & NB; Zigbee & BT-LE; Zigbee & HBC & HBC \\ \hline \multirow{2}{*}{**Frequency(ies)**} & 0.36–0.51 GHz; & 2.36 GHz; 2.4 GHz; & \multirow{2}{*}{2.36 GHz; 2.4 GHz} & \multirow{2}{*}{21 MHz} & \multirow{2}{*}{21 MHz} \\  & 2.36–2.5 GHz & 780/868/915/950 MHz & & & \\ \hline \multirow{2}{*}{**Data-rates**} & \multirow{2}{*}{N/A} & \multirow{2}{*}{1000 kbps} & 250/971 kbps; & 164; 328; & \multirow{2}{*}{763 kbps} \\  & & & 2 Mb/s & 1.3125 Mb/s & \\ \hline \multirow{2}{*}{**Power**} & \multirow{2}{*}{13.2–18 mW} & \multirow{2}{*}{1.7–12.3 mW} & RX: 3.8 mW + & \multirow{2}{*}{5.5 mW (peak)} & \multirow{2}{*}{4.5 nl/bit} \\  & & & 200 \(\upmu\)W; TX: & 4.6–4.4 mW + 80 \(\upmu\)W & \\ \hline \multirow{2}{*}{**Front-end**} & \multirow{2}{*}{0.18 \(\upmu\) CMOS} & \multirow{2}{*}{0.13 \(\upmu\) CMOS} & \multirow{2}{*}{90 nm CMOS} & Discrete \\  & & & & components \\ \hline \multirow{2}{*}{**Digital baseband**} & \multirow{2}{*}{N/A} & FPGA (not documented) & \multirow{2}{*}{40 nm LP-CMOS} & \multirow{2}{*}{N/A} & Xilinx Virtex 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Essential properties of IEEE 802.15.6 publicly documented implementations.  
BLE, ZigBee, etc.); (3) routing protocol which must be appropriate for the application context; and (4) experiment area: indoor/outdoor with natural/artificial obstacles. So, let us focus first on the various dissemination and routing techniques with regard to their impact on connectivity while meeting the applications requirements.
# Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
## 1 Introduction
In [11], a tree-structured DFTFB is presented for detecting the edges of channels. Another study in [12] proposes a modulated FB combined polyphase and DFT for less complexity. In recent years, obtaining wideband spectrum states based on estimation or prediction has caught the attention of many researchers, which could reduce the detection time by estimating or predicting the channel state instead of directly detecting each channel. In this regard, two-dimensional frequent pattern mining is commonly used [13], but to identify frequent patterns requires a large amount of calculations, which also vary in different environments. Another action to take is the wideband spectrum prediction based on channel correlation clustering and estimation [14; 15], which would deal with wider bandwidth than 1 GHz, but the prediction accuracy may not meet the needs of SUs, for it is based on statistics. Mostly, the wideband spectrum-sensing methods have either been constrained at the theoretical level due to processing speed in hardware and inherent complexity in implementation, or have low detection accuracy. Method based on the FB is achievable, which is advanced that makes a detour around the technical bottleneck of directly processing wideband signals, making it less demanding for hardware processing speed, yet rarely able to adjust the resolution dynamically. The RFB is available for adjusting the spectrum-sensing resolution by changing the number of divided sub-bands, which means the varying number of sub-bands in FB. Based on the fast filter bank (FFB), the variable bandwidth FB [16] is configurable with a cascade structure, where each stage of the prototype low-pass filter needs to be designed separately. As the number of divided sub-bands increases, the computation gets even more complexity. The coefficient decimation filter bank (CDFB) [17; 18] serves to decimate, configure and store the coefficients of the calculated prototype filter to change the sub-band bandwidth, which is yet held back by more storage space and high complexity. Because computational complexity and detection accuracy both depend on the number of sub-bands in the FB, it is necessary to develop RFB to balance the above two contradictions. For simplicity, an easy-to-go improved polyphase DFT filter bank (P-DFTFB) [19] for real signals is adopted to design reconfigurable FB in this paper. Coefficient decimation is performed to obtain a variable bandwidth in the FB, and the last spectrum-sensing result provides a feedback on how to select the number of FB sub-bands for the next detection time, in attempt to achieve a wideband spectrum sensing with adjustable resolution and reduce the complexity.
## 3 Measurement Setup
### Metrics
#### 3.2.1 Core Latency: Duration of the First HTTP Request  
Core latency involves the time needed for the first HTTP request and the return of corresponding data (often, a basic HTML document only, without pictures). This is an indicator for a first data response that can indicate progress in the browser window. In order to measure the impact of Tor on surfing to important websites around the world, a Top 500 list of domains was selected. The list was provided by SEOmoz [40] and represents the 500 most linked-to websites on the Internet. During any single run of the experiment the Perl script triggers an HTTP request for each website (from the Top 500 list), measuring the performance once with Tor and once without Tor. All Tor requests were directed over the Socks proxy _Privacy_. Privacy then forwarded the request to the Tor network. The fact that current Tor client installations based on the _Tor Browser Bundle_ do not need a proxy server increases ease of installation and use, but cannot significantly influence connection speed. Therefore, our results are transferrable to this case. Using the Privacy filter option could slow down the browsing experience because every page  
Figure 3: Network Packets of a Direct Web Access.  
is first completely downloaded by Privoxy and than forwarded. However, we did not activate Privoxy filters during our experiments, therefore pages were directly and continuously transferred.  
Tor clients were running as daemons on our nodes over the whole time of the experiment. There might be some influence on certain measurements (temporarily increased latency) in the moment when Tor builds new circuits. Nevertheless, this reflects best the browsing experience since also in normal web browsing the user will be confronted with Tor building new circuits. Static data like images, videos or Javascript files were excluded from the experiment. For each request, date, time, request duration and received bytes were logged. This script was built in order to run until it has made a request to all 500 web sites.
# DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments
## 3 System Introduction
### Dynamic Filtering Strategy  
Our dynamic filtering strategy consists of three key components: dynamic feature point detection, object detection thread, and the multi-view geometry method. The first component detects dynamic features using epipolar constraints. Frame-to-frame correspondences are exploited to compute the fundamental matrix, and feature points violating epipolar line constraints are discarded as dynamic outliers. The second component is built on Yolov5s, which can extract semantic information from images in real time and generate a dynamic mask, denoted as \(mask_{object}\), for subsequent dynamic feature removal in the image. The third component involves multi-view geometry, which, based on the association data between the current frame and the reference keyframe, generates another dynamic mask, \(mask_{geometry}\). Finally, the two dynamic masks are then used together to identify dynamic features accurately.
## 3 Random Variables
### Uncertainties of Soil Parameters  
The required inputs for conducting probabilistic analyses, including key parameters like the mean and standard deviation of stochastic variables, inherently possess uncertainties. These uncertainties are commonly estimated through statistical analysis of relevant data where available, and by the professional judgment of experts, which may be subject to refinement over time. For instance, COVs ranging from 1% to 10% have been reported in the literature for soil bulk density [32; 33; 34]. The COVs for drag coefficient and bearing resistance in the Poncelet model are lacking in the literature to the best of the authors' knowledge. However, penetration data for laboratory-prepared soils have recently become available [19; 35; 36; 37; 38]. These data were employed to develop, for the first time, statistical distributions for the empirical parameters affecting the phenomenological penetration model deployed.  
The dependence between soil parameters can be considered in sophisticated reliability studies. For example, [PERSON] et al. [39] investigated the dependence structures of various soil parameters using data from a construction site in Shenzhen, China. Employing copula theory, the study examined correlations between soil parameters and selected appropriate copulas to model their dependence structures, emphasizing the significance of such structures in reliability analysis. However, the lack of information regarding the dependency between the Poncelet parameters and soil density thwarted the use of similar sophisticated approaches in this work. Thus, all soil random variables were treated as independent random variables.
## 3 Models
### Variable Selection, Parameter Estimation  
#### 3.4.1 Variable Selection  
In the variable selection, we involve the selection of function variables that determines the target variables when limiting the number of variables from the system. The variable selection framework plays an important role in terms of avoiding over-fitting, promoting  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Models** & **Strengths** & **Weaknesses** \\ \hline M1 (GA) & 1. The concept is simple to explain. & 1. Implementation remains an art. \\  & 2. Works well with a combination of & 2. It takes a long time to compute. \\  & discrete/continuous problems. & 3. It requires less knowledge about \\  & 3. It’s ideal for a noisy environment. & the problem, but it can be difficult \\  & 4. Write robustly to local & to design an objective function \\  & minima/maxima. & and get the representation and \\  & & operators correct. \\ \hline M2 (RNN) & 1. It can process inputs of any length. & 1. It can be challenging to train it. \\  & 2. The model size does not increase & 2. The computation is sluggish \\  & as the input increases. & because of its recurrent existence. \\  & 3. Weights can be shared between & 3. Problems like exploding and \\  & time steps. & gradient vanishing are common. \\  & 4. It is designed to remember each piece & 4. When using relu or tahn as \\  & of information over time, which is & activation functions, processing \\  & extremely useful in any time series & long sequences becomes \\  & predictor. & extremely difficult. \\ \hline M3 (KNN) & 1. There is no need for a training period. & 1. It is ineffective when dealing \\  & 2. It is simple to implement. & with large dataset. \\  & 3. Inference is based on the & 2. With high dimensions, it does \\  & approximation of a large number of & not work well. \\  & samples. & 3. Sensitive to noisy data, missing \\  & 4. New data can be easily added. & values, and outliers. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model comparisons.  
analysis of the patterns, and computational time reduction. We have various variable selection methods, but in this paper, we use least absolute shrinkage and selection operator (Asso) [42].
## 3 Results and Discussion  
### Compost Characteristics  
#### 3.1.1 Physicochemical Parameters  
Among the physicochemical parameters (Table 3), _moisture content_ is a significant factor, as water provides a medium for transporting dissolved nutrients required for microbial activities in the compost [25]. The moisture content of the substrates at the end of the composting process was within the optimal moisture range (45-60%) [57; 58], while in the stored compost (two weeks later) it was much lower--21.8% (C1) and 17.9% (C2)--with coefficients of variation determining the indicator as slightly variable (Cv = 7.66-8.10%) (Table 3). The moisture of fresh GW composts was in line with the results of [5] (48.6-56.9%); at the same time, the values were lower than those for compost from plant residue and green biomass (68.6-71.4%) reported by [59] and slightly variable than the compost from mixtures of kitchen and green wastes (mainly raked leaves and grass clippings) (45-70%) [25]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{
\begin{tabular}{c} **Heavy Metal,** \\ **mg kg\({}^{-1}\) dm** \\ \end{tabular} } & \multicolumn{6}{c}{**Score Value, S\({}_{j}\)**} & \multicolumn{3}{c}{**Weighing Factor, W\({}_{j}\)**} \\ \cline{2-9}  & & **5** & **4** & **3** & **2** & **1** & **0** & **Factor, W\({}_{j}\)** \\ \hline
1 & Zn & \textless{}150 & 151–300 & 301–500 & 501–700 & 701–900 & \textgreater{}900 & 1 \\
2 & Cu & \textless{}51 & 51–100 & 101–200 & 201–400 & 401–600 & \textgreater{}600 & 2 \\
3 & Cd & \textless{}0.3 & 0.3–0.6 & 0.7–1.0 & 1.1–2.0 & 2.0–4.0 & \textgreater{}4.0 & 5 \\
4 & Pb & \textless{}51 & 51–100 & 101–150 & 151–250 & 251–400 & \textgreater{}400 & 3 \\
5 & Ni & \textless{}21 & 21–40 & 41–80 & 81–120 & 121–160 & \textgreater{}160 & 1 \\
6 & Cr & \textless{}51 & 51–100 & 101–150 & 151–250 & 251–350 & \textgreater{}350 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Criteria for clean index (CI) scoring (Source: [42]). Adapted with permission from [[PERSON], [PERSON], [PERSON]], 2022, Elsevier. \(pH\) is a good indicator for assessing the effectiveness of the different composite stages as well as the quality of the end-product. Normally the fresh compost has a pH value close to neutral [60]. In our study, pH values and the coefficients of variation were almost equal for both composets (C1 and C2) and indicated a slightly alkaline reaction of the resulting
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 3 Methodology
### Soil Resistivity Analysis  
Soil resistivity plays a significant role in the overall performance of grounding systems. Variations in soil resistivity due to seasonal changes or soil composition can significantly affect the effectiveness of a grounding system.
## 4 Results and Discussion  
### Measurements of Activity  
Spectroscopy measurements were carried out under secular equilibrium conditions, considering the lines of the short-living decay products. Using the coaxial HPGe detector, gamma spectrometry measurements were made for each of the 13 undergarment products (Table 1). For \({}^{238}\)U, results were estimated from the average of gamma emissions from \({}^{214}\)Pb (295 keV and 351.8 keV) and \({}^{214}\)Bi (609 keV and 1764 keV), while for \({}^{232}\)Th these were estimated from the average of the gamma emissions of \({}^{212}\)Pb (238 keV), \({}^{228}\)Ac (338 keV and 911 keV), \({}^{212}\)Bi (727 keV), and \({}^{208}\)Tl (583 keV and 2614 keV).
## 4 Methodology
### User Spacecraft
#### 4.3.3 LANDSAT-8  
The LANDSAT-8 mission is one of the satellites in the LANDSAT series that precedes LANDSAT-9. As with LANDSAT-9, it was developed and operated in collaboration with NASA and USGS, with comparable objectives and instrumentation on board [13].  
On 11 February 2013, the LANDSAT-8 spacecraft was launched from the Kennedy Space Center in Florida, USA. It was positioned in a polar low-Earth orbit at an altitude of approximately 705 km, inclined at 98.2 degrees with an eccentricity of 0.0007.  
Analogous to the support provided for LANDSAT-9, a semi-annual proficiency DOWD OD analysis was also carried out by the FDF for LANDSAT-8. Using an arc length equivalent to 11 LANDSAT-8 revolutions, the LANDSAT-8 orbit was reconstructed through three DOWD measurement passes received on 27 and 28 October 2022. During these passes, the TDRS-6, -7, -10, and -11 satellites were used to acquire DOWD measurements.
## 2 Antenna Arrays  
Antenna arrays are the fundamental element in the field of communication engineering. The antenna arrays mainly increase the output power, gain, and directivity. In this research, some regular structured linearly arranged antenna arrays are considered for their similar geometric structure.
# Volatile Profile of Nuts, Key Odorants and Analytical Methods for Quantification
## 3 Analysis of VOCs Present in Nuts
Based on this, to identify the most relevant compounds present in the sensorial profile of nuts, sometimes the odor activity value (OAV) is referenced. This value is calculated as the ratio between the concentration of a volatile compound present in a sample and the perception threshold concentration of the same compound. Volatiles with OAV values higher than 1 are detectable by consumers and, for this reason, are considered key odorants. [PERSON] et al. investigated the volatile profile of peanuts and 26 compounds were quantified, but only 11 of them presented OAVs higher than 1 [40]. The compounds with a higher impact on the overall flavor of raw peanuts were 3-isopropyl-2-methoxypyrazine, acetic acid, 3-methylthiopropanal, 2,3-pentanedione and hexanal [40]. Meanwhile, 5-methyl-4-heptanone and 2-methoxy-3,5-dimethylpyrazine were the volatiles with higher OAVs present in raw hazelnuts [41]. On the other hand, 2,3-pentanedione, methional, 2-acetyl-1-pyrroline and 2-Acetyl-3,4,5,6(or 1,4,5,6)-tetrahydropyridine were detected in rawamonds. However, OAVs for rawamonds were significantly lower than the ones obtained for the major representative volatiles in peanuts and hazelnuts [42]. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{3}{c}{**Equilibration**} & \multicolumn{3}{c}{**Extraction Conditions**} & & \\  & & & & & **Conditions** & & & & & & & & \\ \hline  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{6}{c}{**Equilibration**} & \multicolumn{6}{c}{**Extraction Conditions**} \\ \hline  & & & & \multicolumn{6}{c}{**Comditions**} & \multicolumn{6}{c}{**Extraction Conditions**} \\ \hline
Nevertheless, when thermal treatment is applied to nuts, the OAV profile changes, and a higher number of compounds with values greater than 1 appears. The peanut's key odorants, responsible for the typical roasted notes, are methanethiol followed by 2,3-pentanedione. Furthermore, some products of the initial steps of the Maillard reaction appeared with high OAV values such as 3-methylhiopropanal, 2-acetyl-1-pyrroline, 3-methylbutanal, and 2-methylbutanal [40]. In the case of pan-roasted hazelnuts, the volatile compounds which presented the highest OAV values resulted to be 3-methylbutanal, followed by distance by 4-hydroxy-2-5-dimethyl-3(2H)-furanone, (E,E)-2,4-decadienal, nonenal, hexenal and octanal [41]. Additionally, in the case of dry- or oil-roasted almonds, the principal biomarkers were 2,3-pentanedione, methional, followed by 2-acetyl-1-pyrroline and 4-hydroxy-2,5-dimethyl-3(2H)-furanone [42], even though other compounds in lower quantities could also be used as biomarkers.
# Diatomaceous Soils and Advances in Geotechnical Engineering--Part I
## 2 Evolution of Concepts Surrounding Diatomaceous Soils  
It has been recognized that there is the terminology used when mentioning soils with the presence of diatom fossils is imprecise and arbitrary. In several cases, they are called \"diatomaceous soil\" when the deposits have been formed from the frustules and not from the algae themselves (diatoms). Likewise, the use of terms such as \"diatomites\" or \"diatomaceous oozes\" is evidenced, without specifying the technical particularities that lead to such denominations. Therefore, it is necessary to delve into the methodological proposals that some authors have projected for the correct categorization of this type of fossiliferous soil.  
Most diatomaceous soils (DSs) and diatomites originated from sedimentation processes [48]. During the process of stratified accumulation, the frustules can be subject to deterioration phenomena, such as total or partial dissolution [49]; fracturing by friction due to transport (mostly in aqueous media); crushing by excessive loads transmitted by upper strata; and proximity to geological fault zones, among others.  
Diatoms are microscopic, unicellular, eukaryotic, protist, photosynthetic algae, with a siliceous skeleton called a frustule [38,50], and are found in almost all fresh and saline aquatic environments [29,48] and even on soils and wet surfaces, either planktonic (floating) or benthic (adhering to substrates) [51], and in all climatic conditions [52]. They are extremely sensitive to physical and chemical changes in such environments, particularly in turbidity, temperature, light, pH, nutrients, and salinity [53]. Typical sizes vary between 20 and 200 \(\upmu\)m in diameter or length, depending on the species. Some cases reach 2 mm. These can be recognized as solitary or in colonies and are joined by filaments or chains [54]. They are primarily found in lacustrine and low-energy marine environments [15].  
The cell wall of diatoms (frustules) is of special interest, being composed chiefly of silica [55], and gives rise to nanometer structures [56]; see Figure 1. Species evolution creates distinctive patterns in the perforations of the frustules, which are used for visual classification [57]. The physical characteristics of the frustules depend on the biotic and environmental conditions of the depositional periods, especially concerning the nature of the algae, the depth of the aquatic sedimentation media, and the intensity of the light regulating the photosynthetic processes [48].  
Frustules are impregnations of polymerized silicic acid, which is the same material as quartz. There are two types of diatoms: pennate, with bilateral symmetry and predominantly found in freshwater, and central, with radial symmetry and generally found in marine areas [8]; see Figures 2 and 3. As shown in Figure 4, for a specific type of diatom, two overlapping pieces are identified. There is an external theca or epiteca and an internal theca or hypotheca [57], which are made up of 95% silica.
## 2 Materials and Methods
### Qualitative Analysis of Metabolites Using Q-TOF/MS  
_E. globulus_ ethanol extracts (1 g/10 mL) were filtered using a 0.2 um syringe filter and injected into a Poroshell 120 EC-C18 column (2.1 \(\times\) 100 mm, 2.7 um, Agilent, Santa Clara, CA, USA). The LC-Q-TOF/MS was a Shimadzu NEXERA LC (Kyoto, Japan) connected to a SCIEX X500R QTOF (Framingham, MA, USA). As the mobile phase, water containing 0.1% acetic acid (A) and acetonitrile (B) was used at a flow rate of 1 mL/min. For the solvent conditions, a gradient solvent system that constantly increased the proportion of mobile phase B from 0% to 100% over 60 min was used. The MS conditions were set at a capillary voltage of 5.5 kV and temperature of 450 \({}^{\circ}\)C in positive ionization mode. The collision energy was 10 V and the desolvation gas flow was 800 L/h. The turbo spray ionization was conducted under the following conditions: ion spray voltage, 5500 V; source temperature, 550 \({}^{\circ}\)C; curtain gas (N\({}_{2}\)) flow, 30 psi. The MS/MS data was set in a range from 100 to 10,000 Da in the mode of IDA-dependent. The identification of each peak on the BPI gram was performed using SCIEX OS V1.5 software.