esa-sceva/satcom-chunk-collection huggingface dataset:
# MHBase: A Distributed Real-Time Query Scheme for Meteorological Data Based on HBase
## 3 Proposed Design
### Index Model
#### 3.2.2.1 Indexed Store Algorithm (ISA)  
The idea of the storage algorithm is to use an indexed table to store the values of all indexed columns. The indexes will be updated at the time of data insertion. For one row, a server-side coprocessor will match the input with predefined indexed columns. If found, it generates a new rowkey corresponding to the old one by prefixing the region's address and index name and then puts it into the indexed table. In order to improve the performance, the put requests will be cached and then handled as a batch-put, even if there is only one \(Put\). These puts will eventually be inserted into user table and index table, respectively. The indexed store algorithm is shown as follows:  
**Algorithm 1. ** The Indexed Store Algorithm (ISA)  
**Input:**  
\(D\): the actual input key-value entry  
**Output:**  
\(F\): the set of all indexed entries of \(D\)  
**Procedure:**  
(1): get all columns in \(D\) as _colList_  
(2): get total predefined indexes in index table as _indexList_  
(3): get indexes columns list: _idxcol = colList \(\cap\) indexList_  
(4): **for** all idxcol\({}_{i}\)idxcol **do**  
(5): generate a new rowkey for each index entry and put this rowkey into \(F\)  
(6): **end for**  
(7): put actual input key-value into \(F\)  
(8): **return \(F\)** \begin{table}
\begin{tabular}{c c} \hline \hline
**Functions** & **Description** \\ \hline postOpen() & Called before region opens to read meta data and build indexes. \\ pre/postSplit() & Called before/after region splits to synchronize indexes. \\ postScannerOpen() & Used expanding scanner to query by indexes \\ prePut() & Called before put entry to update indexes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main functions in IndexRegionObserver. 3.2.2.2. Indexed Retrieve Algorithm (IRA)  
The retrieve algorithm is roughly divided into two steps. In the first step, we set a flag depending on whether selected conditions \(D\) contain unindexed columns. In the second step, we take a random seek via indexed table for indexed columns and use filters to filter rows for unindexed columns to get result sets and then fetch the result sets according to the flag and logical relationship \(OP\)_('AND' / 'OR')_. Finally, the coprocessors scan the sets and return the actual results to the client. Note that it will take a full table scan if selected predicate \(OP\) is '\(OR\)' and query conditions have unindexed columns. This is beyond the scope of this paper because it does not take advantage of indexes; however, the solution under this circumstance is given in our algorithm. The indexed retrieve algorithm is shown as follows. **Algorithm 2.
## 6 Conclusions
###### Acknowledgements.  
The authors are grateful to the Government of the Basque Country and the University of the Basque Country UPV/EHU through the SAIOTEK (S-PE11 UN112) and EHU12/26 research programs, respectively.  
**Author Contributions:** [PERSON] made the CFD study and [PERSON] developed and programmed the BEM based computations. They also wrote the manuscript. [PERSON], [PERSON] and [PERSON] made the thrust and bending calculations and provided constructive instructions in the process of preparing the paper.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **CLEAN** & **st8** & **\(\Delta\)1** & **st9** & **\(\Delta\)2** & **st8 st9** & **\(\Delta\)3** \\  & & **(Nm)** & **(Nm)** & **(\%)** & **(Nm)** & **(\%)** & **(Nm)** & **(\%)** \\ \hline \multirow{3}{*}{**NTM5**} & Seed 1 & 8,109,216 & 8,126,789 & 0.217 & 8,126,853 & 0.218 & 8,126,886 & 0.218 \\  & Seed 2 & 6,924,009 & 6,939,014 & 0.217 & 6,939,069 & 0.218 & 6,939,076 & 0.218 \\  & Seed 3 & 6,705,529 & 6,720,060 & 0.217 & 6,720,113 & 0.218 & 6,720,113 & 0.218 \\ \hline \multirow{3}{*}{**NTM7.5**} & Seed 1 & 17,207,278 & 17,244,535 & 0.217 & 17,244,704 & 0.218 & 17,244,704 & 0.218 \\  & Seed 2 & 15,237,018 & 15,270,036 & 0.217 & 15,270,173 & 0.218 & 15,270,173 & 0.218 \\  & Seed 3 & 14,801,073 & 14,833,130 & 0.217 & 14,833,251 & 0.217 & 14,833,310 & 0.218 \\ \hline \multirow{3}{*}{**NTM10**} & Seed 1 & 24,598,692 & 24,644,696 & 0.187 & 24,644,937 & 0.188 & 24,644,937 & 0.188 \\  & Seed 2 & 23,036,435 & 23,079,692 & 0.188 & 23,079,743 & 0.188 & 23,079,651 & 0.188 \\ \cline{1-1}  & Seed 3 & 22,072,422 & 22,113,547 & 0.186 & 22,113,587 & 0.187 & 22,113,697 & 0.187 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mean values of the bending moment at the root of the blade.
# A Review on SIW and Its Applications to Microwave Components
## 8 Evolutionary Structures of the SIW  
The conventional structure of the SIW has some limitations that can be linked to its evolution from the rectangular waveguide. Some of these limitations are associated with the bandwidth and compactness of the physical structure of the SIW, as explained in [39]. Several researchers have worked tirelessly to help mitigate said limitations by proposing and investigating alternative and more compact SIW structures, as reported in [14; 39; 40].  
One of the modified SIW structures reported in [41; 42; 43; 44; 45; 46; 47; 48] is the substrate-integrated folded waveguide (SIFW) as indicated in Figure 6a. This version of the SIW allows for a 50% decrease in the physical size of a device due to the folded structure. The downside of this version of the SIW is that it exhibits a slightly higher loss mechanisms when compared to the standard/conventional version. The folding of the structure also means the requirement for a dual-layer fabrication process which is slightly more complex when compared to a single-layer fabrication process.  
Another modified version of the SIW is reported in [49; 50; 51; 52; 53; 54; 55]. This version is known to as the half-mode SIW (HMSIW) as shown in Figure 6b. This version also allows for a 50% decrease in the physical dimension of a device such as the SIFW. However, unlike the SIFW, this version uses a symmetry plane to replace half of the standard SIW structure. The symmetry plane acts as a virtual magnetic wall used to replace one half of the physical structure. This means that the HMSIW, like the standard SIW, is also manufactured using the single-layer manufacturing process. This is an added advantage over the SIFW.  
The substrate-integrated ridge waveguide (SIRW) is another evolution of the standard SIW that has been investigated and reported in [56; 57; 58; 59; 60]. This version has the physical structure given in Figure 6c. The SIRW is a version of the SIW where a ridge is realised using a row of thin, partial-hight metallic vias positioned in the centre of the extended side of the SIW. This topology of the SIRW ensures that bandwidth improvement and compactness are achieved in an SIW device.  
One other modified version of the SIW that has been investigated and reported in [61; 62; 63; 64; 65] is the substrate-integrated slab waveguide (SISW) as given in Figure 6d. In this evolution of the SIW, the dielectric material is regularly perforated with air-filled vias which are in the horizontal section of the SIW. A further significant reduction in the physical size of a SIW device has been reported in [66], where the folded half-mode substrate-integrated  
Figure 6: Cross section of the different versions of the compact SIW structures: (**a**) substrate-integrated folded waveguide; (**b**) half-mode substrate-integrated waveguide; (**c**) substrate-integrated ridge waveguide; (**d**) substrate-integrated slab waveguide [14].  
waveguide (FHMSIW) was used to implement a 3 dB coupler, hence further reducing the physical device size to almost half of the equivalent HMSIW coupler device.
# Application of Thermoelectric Generators for Low-Temperature-Gradient Energy Harvesting
## 4 Efficiency Determination of the Best Combination of TEG + DC/DC Converter
### Cold Starting  
Conventional circuits are based on active circuits that require some level of energy to operate. In the cold start scenario, there is not enough stored energy to supply the circuit components, so the system cannot harvest energy and is stuck in a shutdown state. Circuits with cold starting capability allow scavenging energy in these critical situations, when the storage element is completely depleted. Cold start situations can occur after manufacturing, when the circuit has never been powered on, or when the storage element is completely depleted after prolonged periods of power shortage.  
This paper also analyzes the cold start capability of the proposed energy harvesting solution by charging a battery pack with two NiMH cells in series (BM2000C1450 AA2S1 PATP nickel battery, GlobTek) that were discharged during a previous experiment. The initial voltage between the terminals of the two cells was 1.85 V, or 0.925 V per cell, which corresponds to a discharged condition, since NiMH batteries are considered discharged below 1 V per cell [43]. Figure 9 shows the output voltage generated by TEG #1 and the battery voltage during a cold start situation, where it can be seen that the system is capable of charging the batteries.
## 1 Introduction
In the present investigation, to accredit practical, low-cost, efficient solutions, we successfully modified NiO with carbon quantum dots (CQDs) to augment the NiO response with a carbon material which can uniformly spread over surfaces and pores and create a homogeneous network. Among all possible solutions, CQDs appeared to be a very convenient option as they can be easily obtained by pyrolysis of any carboxylic acid [46] alone or in combination with other pyrolysis-sensitive components such as Tris-HCl [47]. Although primarily and extensively used as fluorescent probes in metal detection and cell labeling [48], CQDs proved to be a remarkable modifier of NiO, achieving high CH4 detection performance. The novelty of this paper is the improvement of the response toward CH4 sensing both by playing on the NiO morphology and by employing CQDs. This creates edges and grain boundaries where the methane molecules react with oxygen and, via CQD sheathing, aiding the electronic processes in chemoresistive detection [49]. The electronic properties of CQDs were explored by [PERSON] et al. [50] who synthesized poly(4-styrene sulfonate)-stabilized carbon quantum dots via citric acid pyrolysis and deposited them on an ITO (indium tin oxide) surface. In general, CQDs are effective for their electrocatalytic properties [51], with conductive polymers sometimes playing a role [52]. In the present paper, CQDs@NiO were achieved via separate synthesis of the NiO substrate and CQDs, followed by mixing via phase sonication and stirring. A sensing platform was constructed to allow sensitive detection and simultaneous monitoring of the ambient parameters (humidity and temperature), employing low-cost commercial components and open-access software. One of the most overlooked aspects when testing sensor performance is the moisture resistance. On the other hand, the CQDs@NiO could be applied to reduce the influence of environmental humidity. Upon optimization of the operational parameters, it turns out that the synthesized composite material has very good performance in CH4 sensing, in humid environments, with a high selectivity, where the best performance was achieved by the 1%CQDs@NiO, i.e., the sensor built using NiO with 1% weight load of CQDs. As compared to SnO2-based sensors [41; 42; 43; 28; 44; 45], the optimum temperature of the 1%CQDs@NiO sensor is equal or lower (150 \({}^{\circ}\)C), with detection of a lower concentration of CH4. The response/recovery time is on average lower (with the exception of the Pd-SnO2-based sensor [42]). Altogether, the combination of response parameters is improved with respect to previous sensors, and the gas response expressed as Rg/Ra (gas resistance vs. air resistance) is higher. Furthermore,this type of sensor has the advantage of a facile synthesis using low-cost materials. Conversely, since the performance is largely dependent on the amount of CQDs, it may require some effort to tune the synthesis toward the best working composition.
## 2 System Model and Problem Statement
### Problem Statement
\tag{11}\]  
For \(n<0\), \(s_{\text{P}}(n)\) can be considered as 0. Vectors \(\mathbf{y}_{\text{P}}\), \(\mathbf{h}\), and \(\mathbf{w}\) are defined as  
\[\mathbf{y}_{\text{P}}=(y_{\text{P}}(0),y_{\text{P}}(1),\cdots,y_{\text{P}}(N-1))^ {T}, \tag{12}\]  
\[h=(h(0),h(1),\cdots,h(L-1))^{T}, \tag{13}\]  
\[\mathbf{w}=(w(0),w(1),\cdots,w(N-1))^{T}. \tag{14}\]  
Since Toeplitz matrix \(\mathbf{\Lambda}\) satisfies the restricted isometry property (RIP) [31] and the UWA channel is typically sparse, the CS-based methods can be used to estimate the channel. However, existing CS-based channel estimation algorithms usually do not consider the estimation of sparsity and set the sparsity as a fixed value. Thus, these algorithms are not practical. In this work, taking the excellent autocorrelation characteristic of the designed preamble signal into consideration, we should resolve the issue of how to estimate the sparsity of a UWA channel, and make the OMP algorithm more practical.
## 4 Experimental Setup and Results  
In our experimental study for an intelligent jamming system, we used several tools to show the efficiency of the denial-of-service attack, including the following:  
1. Nestor software by Rohde & Schwarz and TSMA 6 [13].
2. Portable receiver PR 100 [17].
3. A smartphone using QualiPock software and registered with Digi Mobil Romania.
4. Laptop with OS Kali Linux.
5. Hack-RF SDR with the following specifications [18]:  
Frequency range = 1-6000 MHz.  
Half duplex.  
ADC resolution = 8 bit.  
Bandwidth = 20 MHz.  
Directional antenna adapted to mobile communication frequency ranges.  
Python code.
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 5 Conclusions  
This study provides a comprehensive investigation into the BLE signal mapping within indoor environments, focusing on the impact of BLE beacon positions, gateway configurations, and the influence of body effects on signal strength and localization accuracy. The results indicate that BLE signal strength is significantly affected by the placement of gateways, with ceiling-mounted gateways generally offering more stable and stronger signals compared to wall-mounted ones. The body positions of the BLE beacons also play a crucial role, with positions such as the shoulder demonstrating more reliable signal reception and localization accuracy, while the front and back pocket positions showed higher variability and localization errors.  
The Radial Basis Function methodology used for indoor localization effectively estimated positions, with lower localization errors observed with an increased number of gateways. The study emphasizes the importance of gateway placement and body position in optimizing BLE signal performance for indoor localization applications. These findings provide valuable insights for the development of more accurate and reliable BLE-based indoor positioning systems.  
Conceptualization, B.B. and K.Y.; methodology, B.B.; software, B.B. and F.H.; validation, M.A.; formal analysis, B.B.; investigation, B.B.; resources, M.A.; data curation, B.B.; writing--original draft preparation, B.B.; writing--review and editing, K.Y.; visualization, B.B.; supervision, B.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data will be available to download from [[https://github.com/Brahim-Benaissa/RSSI](https://github.com/Brahim-Benaissa/RSSI)]([https://github.com/Brahim-Benaissa/RSSI](https://github.com/Brahim-Benaissa/RSSI)) (accessed on 11 January 2025).  
Conflicts of Interest: Author [PERSON] was employed by the company AIT Austrian Institute of Technology GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 5 Enabling Technologies to Support Smart Mobility
### Overview of Enabling Technologies
The sources that now feed data into the datasets that make up Big Data are immense and include smartphones, data from social networks, vehicle sensors, GPS and location applications, traffic light sensors and other road locations, public agencies, connected cars, and camera feeds. The General Data Protection Regulation may constitute an obstacle to the use and adoption of Big Data in smart mobility services. Some examples include:  
* Real-time information about the location collected from smartphones that can be used for various services on people's mobility can be a major barrier in terms of privacy;  
Figure 10: Contribution of some of the enabling technologies to smart mobility. * The collection of images of people obtained from services for mapping and recognition purposes;
* The on-board units of the vehicles might be associated with plate numbers which are considered indirect identifiers;
* The lack of verification of the accuracy and relevance of the information collected. Big Data, combined with AI techniques, can allow significant improvements in several aspects and dimensions of smart mobility [124]:
* Port scales can be optimized if data are shared in advance in order to improve planning and resource allocation. In Hamburg, for example, electric vehicles and real-time navigation are used to ensure the smooth flow of traffic and thus reduce congestion;
* Traffic lights can be controlled based on traffic flow if the necessary data are collected. In this way, traffic in cities can be optimized and congestion can be reduced. This is an example currently in practice in the city of Hong Kong;
* IoT sensors and CCTV cameras can also be used for traffic management, thus reducing congestion;
* Open data about mobility can be of added value to citizens that can be aware of real time traffic data and plan their day with more information so it can be more efficient;
* Parking spaces can be monitored, and drivers can reduce parking time and also CO2 emissions;
* On-demand and more adaptive modes of capacity planning and operations can be achieved using data from sensors, cameras and vehicles so that information to commuters can be given so they can plan their routes more efficiently;
* Fleet efficiency can be obtained by combining real-time traffic data to produce route optimization using AI techniques, reducing wait times and optimizing energy consumption;
* Increase citizens' security by preventing anomaly detection and anticipating incidents.
# The Hydroological Balance in Micro-Watersheds Is Affected by Climate Change and Land Use Changes
## 2 Materials and Methods
### Data and Sources of Information
#### 2.3.3 Land Use Change  
Through a supervised classification, the estimation of the change in land use that the study area has suffered in the last 26 years was made. The supervised classification was developed in the QGIS software using the plugin proposed by [PERSON] [50] called Semi-Automatic Classification Plugin (SCP, Lansing, MI, USA) that allows downloading satellite images from the United States Geological Survey (USGS), preprocessing and postprocessing them. For this research, the chosen satellites were Landsat 5 and 8. The images correspond to the years: 1995, 2008, and 2021, with a pixel size of 30 m and with a percentage of cloud cover of less than 20%. First, the images were preprocessed using an ensemble of false-colored bands, which were used as input for classification.  
According to [PERSON] [27], ten land cover classes were defined for the study area: (1) temperate forest (TF); (2) reforestation (R); (3) secondary vegetation (SV); (4) grassland (G); (5) mine (M); (6) rainfed agriculture (RA); (7) irrigated agriculture (IA); (8) protected agriculture (PA); (9) urban (U); and (10) water bodies (W). In the supervised classification, the SCP tool called Random Forest Classification [50] was used, where at least ten reconnaissance polygons were drawn per land-use class. Subsequently, a pixel-level modeling reliability report was evaluated to determine the performance of the tool, and some errors were identified and corrected by creating new reconnaissance polygons to change the value of the pixels identified as incorrect.  
Through the application of the SCP tool called Land cover change, the results of the classifications of the periods 1995-2008 and 2008-2021 were compared to evaluate changes in land cover and obtain confusion matrices that quantify said changes as well as permanence of unchanged areas. Cartography was also obtained with the specific spatial location of the surfaces that have undergone some type of change in the determined time periods (Figure 3).
## 2 Materials and Methods
### Satellite-Based Datasets
#### 2.1.4 Air Temperature Datasets  
Air temperature products can be used to provide physiological information for vegetation ([PERSON] et al., 2018). In this study, we used GLDAS/Noah L4 3-hourly 0.25-degree products (download address: [[http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings](http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings)]([http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings](http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings))). At the basin scale (14,700 km\({}^{2}\)), this highlyaccurate temperature product is suitable for water and energy-cycling studies [(40; 41)]. The GLDAS data, acquired at 3-h intervals, was produced on February 24, 2000; a detailed description can be seen in [PERSON] et al. [(42)].