esa-sceva/satcom-chunk-collection huggingface dataset:
# A PEKS-Based NDN Strategy for Name Privacy
## 4 Details of Implementation
### Choosing Parameters for PEKS  
Public key encryption with keyword search is based on pairing and bilinear mapping. So, we need to choose parameters that meet certain security levels. Table 1 shows that there are multiple options to achieve a certain security level. The bit sizes in the first column are those matching the security levels of the SKIPJACK, Triple-DES, AES-Small, AES-Medium, and AES-Large symmetric key encryption schemes. We consider choosing 80 bits security level to achieve better performance. To achieve that security level, it is necessary to choose \(r\), which is a subgroup size, 160 bits. Choosing the size of \(q^{k}\), which is a prime number, to the subgroup \(r\) is dependent not only on the extension field size \(k\) but also on an embedding degree \(\rho\) according to Equation (4) [30].  
\[\frac{\log q^{k}}{\log r}=\rho\cdot k \tag{4}\]  
In our case, we choose a curve with embedding degree 4 and \(\rho=2\) such that the size of \(q^{k}\)should lie between 960 bits and 1280 bits. With the value 512 bits for \(q\) size, \(q^{k}\) size becomes 1024. This value exists between the range as shown in Table 1. After choosing parameters, a pair of public and private keys are generated by a producer. The producer keeps private key for itself, and announces public key together with parameters.
## 6 Conclusions and Future Research  
The _traversal time and hop count analysis_ (TTHCA) algorithm is a MANET wormhole detection technique, introduced as an extension to the _ad hoc distance vector_ (AODV) routing protocol. A latentsecurity threat to TTHCA is that as each intermediate node and the destination node measures the packet traversal time, a _participation mode_ (PM) wormhole node can potentially provide false measurement values and avoid detection. This paper has analysed the conditions for a time tampering attack and proposed a security mechanism for TTHCA called the _\(\Delta T\) Vector_ extension for detecting false time values in PM _in-band_ (I-B) wormholes. This requires the destination node and each intermediate node to add their individual processing times of the _route request_ (RREQ) and _route reply_ (RREP) packages (\(\Delta T_{i}\)) to a vector parameter in the RREP instead of using a single total packet processing time parameter (\(\Delta T_{TOT}\)) as in the original TTHCA algorithm. This makes each individual \(\Delta T_{i}\) measurement available for a node requesting a route and suspicious \(\Delta T_{i}\) values caused by PM I-B wormhole nodes can thus be identified by an outlier detection method. The \(\Delta T\) Vector extension offers a notable security enhancement to the original TTHCA wormhole detection algorithm by providing an effective time tampering detection mechanism for PM wormholes, while retaining many of the smart features of TTHCA, particularly being a low-cost algorithm in terms of both computational complexity and network overheads.  
In terms of future research, minimisation of _false positive_ (FP) detections incurred by the \(\Delta T\) Vector extension is an important objective. The FP rate can potentially be decreased by not including nodes suspected of time tampering to the _graylist_, since a high \(\Delta T_{i}\) caused by time tampering is permanent compared with a temporarily high \(\Delta T_{i}\) due to queuing delays. An alternative strategy is to choose a higher confidence level for the outlier detection, though this will proportionally reduce the corresponding time tampering detection performance of the \(\Delta T\) Vector mechanism.
# Blind Interleaver Parameters Estimation Using Kolmogorov-Smirnov Test
## 3 Proposed Algorithm  
In this section, we explain the proposed blind interleaver parameters estimation algorithm. The proposed algorithm adopts the K-S test to measure the difference between the two distributions and multinomial distribution is used for controlling the false alarm rates. After introducing the K-S test briefly, the proposed algorithm is described in detail.  
The K-S test measures the fitness whether data are extracted from the same distribution [13]. In this scenario, we use this test as a measure to find the most different rank distribution. The degree of difference between the two distributions as calculated by this test is given by  
\[D=sup_{x}|P(x)-Q(x)| \tag{4}\]  
where \(P\) and \(Q\) are CDFs and \(sup\) is the supremum that denotes to the least upper bound of a set. This metric measures the maximum difference between the two cumulative distributions.
## 2 Materials and Methods
### Test Area  
The test site for this study is located near Krakow, Poland, encompassing approximately 50 \(\times\) 100 km. This area was selected for its highly heterogeneous landscape, which includes agricultural fields, grasslands, woodlands, and urban zones. These features reflect the diverse land use and land cover (LULC) patterns characteristic of Central Europe.  
The LUCAS (Land Use/Cover Area frame Survey) dataset provides detailed field-collected information on land use and land cover across Europe, making it valuable for remote sensing and land classification studies. Key attributes in LUCAS include land cover type, land use type, and additional descriptors like soil, vegetation, and crop type specifics. The survey categorizes land cover into broad classes such as cropland, woodland, grassland, shrubland, and artificial surfaces, with detailed subclasses for more precise analysis. Updated periodically, LUCAS serves as ground-truth data for validating satellite-based classifications and tracking land cover changes across Europe.  
LUCAS 2018 and LUCAS 2022 data are available and can be downloaded from the internet ([[https://figshare.com/articles/dataset/LUCAS_2018_Copernicus/12382667](https://figshare.com/articles/dataset/LUCAS_2018_Copernicus/12382667)]([https://figshare.com/articles/dataset/LUCAS_2018_Copernicus/12382667](https://figshare.com/articles/dataset/LUCAS_2018_Copernicus/12382667)), accessed on 25 December 2024) as vector files. The 2018 versions were chosen for the study due to the greater diversity of land cover/use forms (Table 1).  
The LUCAS 2018 database was used as reference data, which was initially prepared by selecting the following classes: Artificial land, Arable land, Woodland and Shrubland, Grassland, and Water (Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Title 1** & **PL** & **ROI** \\ \hline LUCAS 2018 & 23,087 & 438 more balanced classes \\ LUCAS 2022 & 10,576 & 304 mainly crops in detail \\ \hline \hline \end{tabular}
\end{table}
Table 1: LUCAS data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Level-1** & **Classes** & **LUCAS Code** & \
\begin{tabular}{c} **Code in Our** \\ **Research** \\ \end{tabular} &
\begin{tabular}{c} **Color on the** \\ **Maps** \\ \end{tabular} \\ \hline
100 & Artificial land & A & 3 & red \\
200 & Arable land & B & 4 & yellow \\
300 & Woodland and & C & 1 & dark green \\
500 & Grassland & E & 6 & light green \\
700 & Water & G & **8** & blue \\ \hline \hline \end{tabular}
\end{table}
Table 2: LUCAS classes selected for analysis.  
Figure 1 displays the location of the test site, situated in southern Poland near the major urban area of Krakow (Region of Interest, ROI). The entire area of Poland is densely covered, with 23,097 in situ measurement points marked in pink on the map. Within the ROI, there are 438 measurement points, highlighted in various colors. Figure 2 provides a zoomed-in view of the ROI.
## 3 Results
### Expression of pha Genes in the PA23 PHZ and/or PRN Mutants  
Six _pha_ genes, _phaC1_, _phaZ_, _phaC2_, _phaD_, _phaF_, and _phal_ were studied in _P. chlororaphis_ PA23, PA23-63 (_phz_'), PA23-8 (_prn_'), and PA23-63-1 (_phz_' _prn_'). In PA23-63 (_phz_') grown in RMM glucose, no change in expression levels of _phaC1_, _phaZ_, _phaC2_, and _phaD_ were observed (Figure 6). However, two _pha_ genes, _phaF_ and _phal_, were up-regulated significantly. In PA23-8 (_prn_'), no change in the _pha_ genes was observed, except for the _phaC1_ gene, which was down-regulated significantly. In PA23-63-1 (_phz_' _prn_'), expression levels of _phaZ_, _phaD_, _phaF_, and _phal_ genes were significantly up-regulated. In RMM octanoic acid, expression levels of the _phaC2_ gene were significantly increased in PA23-8 (_prn_') and the double mutant PA23-63-1 (_phz_' _prn_').  
Figure 5: Expression of _phzA_ in _P. chlororaphis_ PA23 mutants defective in the PHZ and/or PRN structural genes or in genes that regulate PHZ and PRN synthesis in cultures containing Ramsay’s Minimal Medium (RMM) plus (**A**) glucose or (**B**) octanoic acid as the carbon source. The housekeeping gene _rpoB_ was chosen to normalize expression values of target genes. Expression levels in the Wt were normalized to 1. Data was normalized and expression relative to wild type PA23 was calculated. Differentially expressed genes are indicated with an asterisk (* \(p\) < 0.01).  
Three regulatory mutants PA23 gacS, PA23 rpoS, and SR (_PA23 relA/spoT_) affecting PHZ and PRN production were studied for expression of _pha_ genes in glucose and octanoic medium (Figure 7). When grown in RMM glucose, the _gacS_ and _rpoS_ mutants both exhibited reduced expression of _phaC1_, _phaC2_, _phaF_, and _phal_ genes, while _phaZ_ and _phaD_ remained unaffected. In the _P. chloroform_ SR (PA23 relA/spoT) mutant, there was no significant change in the expression of any of the _pha_ genes. By way of comparison, expression of _phaC1_, _phaD_, _phaF_, _phaI_, and _phaZ_ was down-regulated in _P. chloroform_s PA23 gacS, _PA23 rpoS and the SR (PA23 relA/spoT) mutants grown in octanoic acid medium. Surprisingly, _phaC2_ was significantly upregulated in the SR mutant.  
Figure 6: Expression of _pha_ genes in _P. chlororaphis_ PA23 mutants defective in the PHZ and/or PRN structural genes or in genes that regulate PHZ and PRN synthesis in cultures containing Ramsay’s Minimal Medium (RMM) plus (**A**) glucose or (**B**) octanoic acid as the carbon source. The housekeeping gene _rpoB_ was chosen to normalize expression values of target genes. Expression levels in the WT were normalized to 1. Data was normalized and expression relative to wild type PA23 was calculated. Differentially expressed genes are indicated with an asterisk (* \(p\) < 0.01).
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 2 Signal Collection and Preprocessing
### Sliding Window Segmentation of the Signal  
This paper segments the signal using a sliding window approach, aiming to quickly output the shape and precise location of the fetal QRS complexes as the window slides during application. Hence, two types of data were used to train the neural network: signals containing fetal QRS complexes and signals without fetal QRS complexes. The dataset containing fetal QRS complexes requires that a window includes a complete set of fetal ECG QRS complexes in a manageable amount of time, ensuring the timeliness of the output. A fetal QRS complex is approximately 20 to 50 points long. Based on the characteristics of fetal ECG signals and application requirements, the chosen window length for this study is 60 points.  
As illustrated in Figure 3, the first image represents the raw data. The second image shows the signal after the amplification of the fetal ECG signals. For the signal in the second image, a sample entropy quality detection algorithm is applied to filter out maternal ECG signals that meet the quality criteria. The third image demonstrates (a) a signal of length 60 that includes fetal ECG components and (b) a signal that does not contain fetal ECG components. Through the signal segmentation operation, 46,620 segments containing fetal ECG signals and 46,821 segments without fetal ECG signals were identified across all channels of the maternal ECG.
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 3 Experiment and Results
### Study Area and Data  
The Miyun Reservoir and its drainage basin in the northeastern of the Miyun District, Beijing, were selected as the study area, as shown in Figure 4a. As the largest reservoir in Beijing, the main functions of the Miyun Reservoir include flood control, water supply, and water exchange for urban lakes. The upstream area of the reservoir has a continental monsoon climate with an average annual temperature of 6 \({}^{\circ}\)C to 10 \({}^{\circ}\)C and average annual precipitation of 500 mm to 700 mm. The annual precipitation distribution is very uneven, with precipitation in July and August accounting for 70% of the annual total, mostly in the form of heavy rainfall [44]. Providing 80% of Beijing's urban water supply, the water quality of the Miyun Reservoir is particularly important. The drainage basin of the reservoir is a remote and mountainous area with severe soil erosion. Therefore, monitoring the vegetation coverage information and water body coverage information with the GF-1 data could inform the development of effective measures to improve the ecological environment of the reservoir basin in the future [40]. Moreover, as the only surface drinking water source in Beijing, the water demand and water quality of the Miyun reservoir have direct effects on people's living and the sustainable development of the economy [45].  
The GF-1 satellite data processed in this paper were received for 10 years since April 2013. GF-1 WFV sensor data include four multispectral bands (red, green, blue, and NIR), with a spatial resolution of 16 m and a time revisit period of 4 days.  
Figure 4: **(a)** Massive GF-1 WFV imageries from April 2013 to April 2022 covering Beijing province; **(b)** the selected study area, the Miyun reservoir, located in Beijing; **(c)** the GF-1 WFV imageries covering the Miyun reservoir (Date: 22 October 2021).  
multispectral detection data from the GF-1 16 m medium-resolution camera can be used to monitor NDVI, NDWI, and other indices. The Miyun Reservoir as the study area of this paper is shown in Figure 4a, and the GF-1 data covering the Miyun Reservoir are presented in Figure 4b, which are organized and stored as scenes. On the basis of the proposed framework and GF-1 imagery data covering the study area from June to October in each year from 2014 to 2021, the normalized difference vegetation index (NDVI) [46] and normalized difference water index (NDWI) [47,48] were calculated to investigate the spatial and temporal dynamics of vegetation and water bodies in and around the Miyun Reservoir.
## 4 Discussion
### PRI and R690/R630 Tracked the Photosynthetic Recovery from Progressively Enhanced Drought Stresses  
The CO\({}_{2}\) assimilation rates and \(\rm\Phi\)PSII of \(Q\). _iex_ rapidly recovered to normal values that were similar with control (0-day drought level treatment) after five weeks of re-watering (Figure 3). The values of PRI and R690/R630 were increased to similar values after re-watering, indicating that both PRI and R690/R630 were also sensitive to the recovery of the plant. The significant correlations of differential PRI (\(\rm\Delta PRI_{recovery}\)) and R690/R630 (\(\rm\Delta R690/R630_{recovery}\)) with \(\rm\Delta A_{recovery}\), \(\rm\Delta F_{V}/F_{Mrecovery}\) and \(\rm\Delta Yield_{recovery}\) (Figure 5) illustrated their potential to monitor photosynthetic recovery response to drought stress.  
PRI was originally defined to detect the variability of zeaxanthin changes at 531 nm [21; 22]. However, it should be noted that 531 nm was the band associated with carotenoid pigment changes, including zeaxanthin, and also other carotenoid pigments such as lutein and neoxanthin [66]. These carotenoids both play key roles in energy dissipation of photosynthetic down- and up-regulation [67; 68]. However, studies have demonstrated that constitutive long-term changes of PRI were greatly attributable to carotenoid pigment pool sizes [27; 29; 58; 59]. Thus, the mechanism of PRI detecting photosynthetic recovery was probably due to the carotenoid pigments acting on the photochemical process. Also, during water stress and recovery, PRI was significantly correlated with carotenoid pigments in olive saplings [32].  
In contrast with PRI, changes in R690/R630 were probably related with ChlF emissions but not with chlorophyll pigments for evergreen broadleaves in this study. ChlF is the energy of absorbed photosynthetically active radiation (APAR) that is lost during the first steps of photosynthesis which involves the emission of red to far-red light (ca. 660 to 800 nm) [39]. ChlF is therefore associated with the fraction of APAR and also with light use efficiency (LUE), giving the possibility of using R690/R630 to monitor photosynthetic recovery after experiencing different drought stresses. Additionally, ChlF is mainly emitted from PSII, so the recovery of photochemistry could increase the ChlF emission, particularly in red wavelengths at 690 nm, which is close to the absorption peak of the pigments of PSII [39; 45]. Consequently, R690/R630 is a good indicator of photosynthetic recovery in response to progressive drought stress for evergreen leaves.
# Production Plant and Warehouse Automation with IoT and Industry 5.0
## 5 Discussion
Modern automation is a specialized framework wherein process administrators, specialized cycles, and PCs and IoT gadgets are completely interconnected. Modern robotization area vaults require a space-explicit improvement climate with restricted time and assets, just as a few groups in different positions. The area archive's construction likewise incorporates a domain repository motor, a fundamental layer with parts, and network layers [32]. Exploitation and monetary development in stockrooms with IoT and Industry 5.0 is a difficult issue in the current world when innovation is enhancing a standard premise. Ventures are experiencing issues sending these creating advancements because of the absence of a reasonable development of the executive's structure. An \"Outright Innovation Framework\" (AIM) is proposed to make developments a customary part of modern schedules. By applying novel ideas, the AIM proposes to tackle the issue of bombed modern cycles [44]. Organizations will be prepared to convey IoT and industry 5.0, assuming they consolidate configuration thinking, inventiveness, and mechanical drives. Assembling and creation utilizing IoT, supportability, and energy productivity in processes are needed for an enormous scope. Narrowband IoT (NBIoT) is a kind of IoT that provides energy production and long-haul processes. NBIoT utilizes incredibly little power and transfer speed. It is likewise utilized in cell networks; thus, in independent assembling plants and capacity frameworks, NBIoT is the most ideal choice for emerging countries [48]. The reliance and correspondence between IoT gadgets are uncertain, bringing about security and protection issues. A protected remote technique is introduced that utilizes blockchain innovation to keep up with the security and straightforwardness of every sensor movement, keeping programmers from obtaining the information. To contrast the security of IoT gadgets with the old strategy, the proposed approach is created and reproduced against various measurements, such as the shot at simple discovery, distortion assault, and the likelihood of an effective attack [17]. The stylish subjects of Industry 4.0, blockchain, business analytics, and the IoT drew countless specialists and scholastics. The fourth modern insurgency (Industry 4.0) means to make a brilliant assembling framework that utilizes innovation to make digital actual frameworks. The IoT (IoT) is an organization of numerous gadgets that utilize sensors, systems administration, and data innovations to convey the possible answers for changing the working and activities of many distribution centers and assembling plant frameworks [36]. Blockchain is a dispersed, decentralized innovation that endeavors to make frameworks with security, transparency, trustworthiness, namelessness, and genuineness for some reasons.
# HBIM: Background, Current Trends, and Future Prospects
## 4 Data Collection and Modeling
### Data Collection Techniques  
The unique characteristics of cultural heritage buildings make it difficult to standardize the collection of geometrical data required for the creation of an HBIM model. Hence, prior historical analysis and research should be conducted to establish a strategy for data collection, identify the structural and architectural elements, and determine the appropriate graphical scales to be collected [53]. When designing data collection processes for an HBIM model, other factors must also be considered, such as the scale and accessibility of the element or building to be modeled, access to equipment, cost, the time required, and expertise [54]. Additionally, for complex CH models, it is necessary to combine large-scale spatial information with specific detailed information. Two of the most commonly used techniques for data collection, laser scanning and photogrammetry, are described below. Several authors also combine both techniques to achieve high-accuracy 3D models [55; 56; 57]. These techniques, particularly 3D laser scanning, were initially rooted in traditional topographical technologies, such as theodolites and total stations, which are now used as auxiliary tools to complement data collection from both photogrammetry and 3D laser scanning sources [58; 59].