esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Hydrological Models
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Description** & **Range** & **Calibrated Value** \\ \hline _WM_ & Areal tension water capacity (mm) & Humid regions: 100–160 & 134.16 \\ \hline _WLM_ & Fraction of upper water in WM & 0–0.6 & 0.26 \\ \hline _WLM_ & Fraction of lower water in WM & 0.4–1 & 0.65 \\ \hline \(K\) & Evaporation coefficient & 0–0.6 & 0.55 \\ \hline \(C\) & Coefficient of evapotranspiration in the lower soil layer & 0.2–1 & 0.62 \\ \hline _IM_ & Impermeable coefficient & 0–0.2 & 0.07 \\ \hline \(B\) & Tension water distribution index & 0–0.02 & 0.01 \\ \hline _EX_ & Free water distribution index & 1–1.5 & 1.39 \\ \hline _SM_ & Areal free water capacity (mm) & 0–60 & 59.98 \\ \hline _KI_ & Fraction of free water to interflow & 0–1 & 0.50 \\ \hline _KG_ & Fraction of free water to groundwater & 0–1, KI + KG \(<\) 1 & 0.50 \\ \hline _CI_ & Interflow recession coefficient & 0.8–1 & 0.80 \\ \hline _CG_ & Groundwater recession coefficient & 0.8–1 & 0.90 \\ \hline \(N\) & Number of reservoirs of the Nash model & 0–5 & 0.27 \\ \hline \(K\) & Storage constant of the Nash model & 0–5 & 3.04 \\ \hline \multirow{6}{*}{GR4J} & \multirow{2}{*}{X1} & Production reservoir: storage of rainfall on the soil surface (mm) & 1–750 & 187.07 \\ \cline{1-1}  & & Groundwater exchange coefficient: a function of groundwater exchange & \multirow{2}{*}{\(-\)10–10} & \multirow{2}{*}{2.91} \\  & & that influences the routing reservoir & & \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{X3} & Routing storage: amount of water that can be stored in soil porous (mm) & 1–400 & 68.84 \\ \cline{1-1} \cline{2-5}  & & Time peak: the time when the ordinate peak of flood hydrograph is created & 0.5–10 & 1.33 \\ \hline \end{tabular}
\end{table} 
Table 2: Calibrated parameters in both XAJ and GR4J models. The basic inputs for both models include catchment-average precipitation, temperature, and potential evapotranspiration, with outputs being the runoff at the catchment outlet. Catchment-average precipitation is calculated using the Thiessen Polygon method. The model calibration period spans from 2014 to 2017, with a validation period that spans from 2018 to 2020. During calibration, the Nash-Sutcliffe Efficiency coefficient (\(NSE\)) and the volumetric error (\(RE\)) are used as objective functions, with optimization performed using the SCE-UA algorithm. The formulas for \(NSE\) and \(RE\) are as follows:  
\[NSE=1-\frac{\sum\limits_{i=1}^{n}{(Q_{i}^{obs}-Q_{i}^{sim})}}{\sum\limits_{i= 1}^{n}{(Q_{i}^{obs}-\overline{Q_{i}^{obs}})}} \tag{3}\]  
\[RE(\%)=\frac{\sum\limits_{i=1}^{n}{(Q_{i}^{obs}-Q_{i}^{sim})}}{\sum\limits_{i =1}^{n}{(Q_{i}^{obs})}}\times 100 \tag{4}\]  
where \(Q_{i}^{obs}\) represents the observed daily runoff, \(Q_{i}^{sim}\) represents the simulated daily runoff, and \(\overline{Q}^{obs}\) denotes the mean of the observed runoff over multiple days. The \(NSE\) value ranges from \(-\infty\) to 1, with 1 being the optimal value. The \(RE\) value ranges from 0 to \(\infty\), with 0 being the optimal value.
# Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale
## 3 Methodology
### Effective Area  
In this section, we present formulas for calculating the inductance and the effective field-sensitive area of the three washers, Figure 2 shows the uncoupled SQUID washer. The geometry and size of the washer, along with the distribution of the magnetic field it captures, collectively determine the effective field-sensitive area of the washer coil within SQUID devices. Due to flux compression caused by the superconducting structures, \(A_{\text{eff}}\) is usually quite different from the geometric dimensions of the SQUID hole [16], and the effective area determines the amount of magnetic flux that the SQUID loop can capture. Furthermore, the magnitude of the effective area directly influences the total flux traversing the SQUID loop and affects both the device noise and the quality factor. The relevant figure of merit for a magnetometer is the rms magnetic field noise:  
\[S_{B}^{1/2}(f)=\frac{S_{\Phi}^{1/2}(f)}{A_{\text{eff}}} \tag{13}\]  
Figure 2: Uncoupled SQUID diagram. It features two Josephson junctions located at the slit terminals within the SQUID loop—square washer characterized by a central hole and an additional slit. The magnetic field noise can be optimized by enhancing these parameters to achieve better alignment for an improved sensor quality factor. The washer interrupts the inductive loop via the slit below to establish connections with the two Josephson junctions, which serve as sensitive structures. In accordance with the empirical formula for slit inductance,  
\[L_{sl}\approx\frac{0.3 pH}{\mu m} \tag{14}\]  
This value is significantly smaller than the total washer inductance. For simplicity, we initially disregard the contribution of the slit to the washer inductance and effective area. Consequently, our optimization efforts are directed towards the design of other washer parameters to attain high field sensitivity and minimize device noise. Due to flux compression caused by superconducting structures, the effective area often significantly deviates from the geometrical dimensions of the SQUID hole, \(A_{\text{eff}}\) is not proportional to the geometrical area of the structure, as the size and shape of the effective area directly influence the noise characteristics and sensitivity of SQUIDs. Under the assumption that, in idealized superconducting structures, the film thickness and the London penetration depth are markedly less than the linewidth and spacing, analytical equations for the effective area of regular polygons with N edges have been derived through numerical analysis. The formula for the perimeter of a regular polygon is \(c=N\text{tan}\big{(}\frac{\pi}{N}\big{)}d\).
# Link Pruning for Community Detection in Social Networks
## 3 Our Proposed Framework
### Calculation of Link Attributes
#### 3.1.2 Number of Common Triangles  
The Common Neighbor (CN) index or the number of common neighbors between two nodes is a popular metric for link prediction in graphs [26]. The number of common triangles including two nodes is equivalent to the CN index when the two nodes are adjacent. To calculate the number of common triangles, the number of triangles to which both nodes \(u\) and \(v\) belong is counted for an attribute of \(\{u,v\}\in E\). A triangle is a structure in which three nodes are interconnected in a graph, which is regarded as the basic structure for a community. It has an integer value of zero or higher, and its value increases as the number of common neighbors increases. The number of common triangles for two interconnected nodes \(u\) and \(v\) is calculated as follows.  
\[\text{CommonTriangles}(u,v)=|N(u)\cap N(v)|-2\]
# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
## 3 The Proposed Method  
### Enhancing the Quality of Reference PRNU Noise  
The quality of the estimated reference PRNU is crucial for the SCI algorithm. Limited to the filtering method in extracting the PRNU noise process, we cannot extract an ideal PRNU noise from a set of images. In fact, every reference PRNU noise inevitably exists unfavourable components for SCI. Although the SEA scheme can successfully remove periodic artefacts in the discrete Fourier transform (DFT) domain, the spectrum after SEA processing is not very flat and most of the energy is concentrated on the low-frequency components.  
In our proposed scheme, the reference PRNU noise with SEA processing is represented as follows  
\[R=S+V, \tag{5}\]  
where \(R\) is the reference PRNU noise after SEA processing, \(S\) is the reference PRNU noise in the ideal case, which should be a WGN, and \(V\) is the interference noise in the reference PRNU noise. The image content information is represented by a set of neighbouring pixels with different intensity levels. We know that an ideal reference PRNU noise seldom contains edge structure, and any area of an image should have a unique and uncorrelated PRNU noise. However, the interference noise, leaking from the image information into the reference PRNU noise, retains edge structures and neighbouring pixels are correlated. We can judge whether interference noise exists or not in the reference PRNU noise according to the correlation between neighbouring pixel blocks. In brief, if a local area contains interference noise, it means that it is highly similar to its adjacent pixel blocks.  
In this work, we apply PCA to estimate the interference noise for the purpose of improving the quality of the reference PRNU noise. The flowchart of the proposed scheme is shown in Figure 1. First, the interference noise is extracted from the reference PRNU noise. Before using the PCA technique to extract the interference noise, a PCA training sample set needs to be constructed. Using the method of local pixel matching, the most appropriate samples are selected to construct the PCA training dataset. In the PCA domain, the interference noise is estimated and then transformed to the spatial domain. Finally, the output reference PRNU noise is the residual between the reference PRNU noise \(R\) and the interference noise \(V\) obtained by PCA processing.
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 2 Fundamentals of VOCC System Architecture  
### System Architecture  
The VOCC system architecture is described in Figure 1. Firstly, the transmitted bits are applied to error coding and modulation. Then, the bits are conveyed by the statuses On and Off of LEDs in the LED panel. Afterwards, the image of the LED panel is captured using a camera in other vehicles. To obtain the transmitted bits, the captured images are processed using LED panel detection and LED bit detection algorithms. After applying error decoding, the transmitted bits are received.  
In LED panel detection, the positions of the LED panels in the image are located. In LED bit detection, the detected LED panels are closely examined to determine the On or Off status of each LED chip in the panels. These statuses are then translated to the logical bits 1 and 0.
# Drug-Food Interactions with a Focus on Mediterranean Diet
## 3 Mediterranean Food Products and Potential DFIs
### Drug-Med Diet Interactions
#### 3.2.3 Fish and Sea Food  
Fish and sea food (clams, cockles, crabs, groupers, lobsters, mackerel, mussels, octopuses, oysters, salmons, sardines, sea basses, shrimps, squids, sea breams, tunas, etc.) are the main sources of protein and fat within the Med-D diet. They contain high amounts of essential amino acids along with n-3 fatty acids (FA) (i.e., eicosapentaenoic acid and docosahexaenoic acid) especially the pelagic fishes (sardines, anchovies, mackerels etc.) [161]. Apart of the nutritional value in Med-D, marine n-3 FAs are known to have positive effects on human health such as a protecting role in cardiovascular diseases, inflammation, diabetes, neurocognitive disorders etc. [162]. Regarding DFIs, omega-3 FAs seem to reduce coagulation factors (i.e., fibrinogen and prothrombin), thus in theory can potentiate the effects of anticoagulants [163]. As of today there have been some case reports of interactions between warfarin co-administration with fish-oil supplements, but the results were not repeated in a retrospective study of a larger cohort of patients with atrial fibrillation and deep vein thrombosis [164, 165, 166]. Considering also that DS usually have a higher content of n-3 FAs than the consumed food, the potential DFI is of minor importance and negligible for patients that remain adherent in their treatment plan.
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 2 Materials and Methods
### Acoustic Feature Extraction
#### 2.2.1 Root Mean Square Power  
The moving RMS power is calculated based on Equation (1),  
Figure 2: Time series, from top to bottom, of audio intensity, audio RMS, internal/external temperature, and internal/external humidity readings in a hive with 3 boxes and 30 full frames of bees (date: 12 August 2021).  
\[y_{RMS}(i)=\sqrt{\frac{1}{N}\sum_{k=i}^{i+N}x(k)^{2}},\qquad i=0,1,2,\ldots,L-N-1 \tag{1}\]  
where \(L\) is the signal length and \(N\) the windowed segment length. In the speech-processing community, such a windowed segment is referred to as a \"frame\". We use the same notation here, not to be confused with the frames within the beehive. As such, the term signal frame will be used henceforth. In this experiment, we set the signal frame length to 1 s and used 50% overlapping shifts.  
Having access to the acoustics of an empty hive allows us to identify certain environmental factors that would be present in both the empty and the occupied hives. As shown in Figure 3a, the empty hive has a fairly constant value with noise-related fluctuations around this value, whereas the occupied hive has an increase in audio power during the day, which stabilizes around the afternoon and later drops in the evening. A close comparison between the two curves, however, shows events that are common in both boxes, thus signaling environmental factors that could affect the monitoring system. For example, after close investigation, it was found that the abrupt peaks seen in both curves were a train rail squealing from a passenger train that ran behind the building (1 km distance) in which the apiary was located or car horns from nearby traffic. The slower peaks, in turn, were related to beekeepers' voices being recorded by the microphone when they spoke near the hive. Another environmental factor that appeared throughout the recordings was the noises of raindrops hitting the hive boxes on rainy days. A segment of data showing this factor is shown in Figure 3b. Given these three prominent environment noises, this paper investigated the impact that they have on three other popular audio features and an automated monitoring system based on them.  
Figure 3: RMS curves of a typical colony with three full boxes of bees (blue curve) and an empty box (orange) as measured on (**a**) 12 August 2021, and (**b**) 24 September 2021.
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 3 Proposed Security Solution for Spatial Files
### Strong Encryption for Spatial Files
The first two approaches compare file encryption and decryption performance within ECMSDK using the Oracle-provided DBMS_CRYPTO_TOOLKIT against the performance provided by the _OpenSSL_ tool (a Windows executable that is called externally from our encryption scripts). The third approach compares the encryption of spatial files within ECMSDK versus those stored directly on the OS (_Windows NTFS_). For the first approach, the time taken to encrypt an ECMSDK file was retrieved using encrypt and decrypt PL/SQL procedures using the SQLPlus tool. For the second and third approaches, times were retrieved using Windows batch scripts with OpenSSL encryption and decryption commands. Both these scripts and their usages are attached in Supplementary Materials online ([[https://github.com/sharmapn/DBFSFileCrypto](https://github.com/sharmapn/DBFSFileCrypto)]([https://github.com/sharmapn/DBFSFileCrypto](https://github.com/sharmapn/DBFSFileCrypto)), accessed on 18 April 2024). Note this GitHub repository also contains most other Supplementary Materials referred to in this article. All experiments were performed on a Dell Inspiron machine with an Intel 1.6 GHz i5-8265u processor and 8 GB RAM manufactured by Dell Technologies, sourced from Xiamen, China. The software utilised consisted of Oracle 11g Database Release 2 running on the Windows 10 OS. The ESRI shapefiles used in the investigation are sourced from publicly available datasets ([[https://mapcruzin.com/download-free-arcgis-shapefiles.htm](https://mapcruzin.com/download-free-arcgis-shapefiles.htm)]([https://mapcruzin.com/download-free-arcgis-shapefiles.htm](https://mapcruzin.com/download-free-arcgis-shapefiles.htm)) (accessed on 25 April 2024)). The encryption times for spatial files using the AES algorithm using all three approaches have a linear correlation to file size. The version of the algorithm implemented in DBMS_CRYPTO_TOOLKIT is approximately 6.6 times slower than when using the OpenSSL tool for encrypting spatial files in ECMSDK, which encrypts and decrypts 1 Mb of file size in approximately 0.15 s (or an average of 8.74 Mb per second) using a 32-character key. Using the OpenSSL tool (version 3.0.1) on ECMSDK with different key lengths results in a small time difference: for example, the average difference in time between 16- and 24-byte keys is about 8%, but the time difference between 24- and 32-byte keys is about 1%.
## 3 Results and Discussion
Consequently, analyzing downstream aroma compounds, such as alcohols and lactones, provides insights into the loss pathways of hexanal and (_Z_)-3-hexenal in HHP and HTST sterilization-treated red raspberry juices. As demonstrated in Figure 3a,b, the concentrations of hexanol and (_Z_)-3-hexenol significantly increased in juices following HHP and HTST treatments. Given that the residual activities of ADH and AAT exceeded 25%, it is speculated that the observed loss of (_Z_)-3-hexenal and hexanal during processing can be attributed to their enzymatic conversion by ADH and AAT within the LOX pathway [34]. Additionally, Figure 3c,d showed significant increases in \(\delta\)-decalactone and dihydroactinidiolide contents following HHP and HTST treatments, indicating that conversions related to the \(\beta\)-oxidation pathway occur during sterilization. Considering both the LOX oxidation pathway and the \(\beta\)-oxidation pathway utilize fatty acid peroxides as substrates [35], it is speculated that the loss of C6 and C9 aldehydes may also be related to the competitive inhibition of \(\beta\)-oxidation to LOX oxidation pathway.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### Improvement and Refinement of Flood Maps
#### 3.4.1 DEM Support: Water Depth Model and Shaded Relief  
We used 5 m and 25 m resolution DEMs produced using LiDAR data and photogrammetry techniques, freely available for downloading from the geoportal service of Regione Piemonte [82], and the National Geographic Institute of Spain (IGN is the Spanish acronym) [83]. We used the methodology described in [57; 84] With this method, water height information based on ground truth data flooded area polygons based on the intersection of remote sensing data overlapped with DEM are used to compute a raster of water level (WL). Then, the difference between the WL and the DEM allows obtaining of the simulated water depth model (WD) map. The water depth is also an essential parameter for damage assessment. We also used the shade relief model derived from DEMs and aerial photos from the WMS services of Regione Piemonte or IGN to detect geomorphological features such as terraces or embankments that could constrain inundation areas.
# Enhanced Dual Reversible Data Hiding Using Combined Approaches
## 2 Preliminaries
### Underflow and Overflow Management  
In the embedding phase of our steganographic method, the careful handling of pixel values at the boundaries of the allowable range (0-255) is essential to prevent underflow and overflow. These safeguards ensure the accuracy of data extraction and maintain the integrity of pixel values throughout the process.  
Underflow occurs when a pixel value drops below 0, while overflow results from a pixel value exceeding 255. Such conditions can lead to errors during data extraction, as pixel values must remain within the defined range to ensure proper interpretation. To address this, pixel pairs at the extremes--(0, 0) and (255, 255)--are excluded from both data embedding and extraction. By avoiding these edge cases, the system minimizes the risk of misinterpretation and ensures that the hidden data and pixel values are preserved accurately.  
Initially, the complete set of pixel pairs available for data hiding is defined as (0, 0), (1, 1),..., (255, 255), where each pair contains identical pixel values. However, to prevent potential issues arising from boundary cases, such as calculated pixel values exceeding the valid range, the pairs (0, 0) and (255, 255) are omitted. The revised set for data embedding is therefore limited to (1, 1), (2, 2),..., (254, 254). This adjustment reduces ambiguity and errors, ensuring a smoother and more reliable embedding process.  
For example, when using a pixel pair (1, 1) with a data range of 0 to 4, the possible resulting pixel pairs include (1, 2), (1, 0), (0, 1), (1, 1), and (2, 1). By carefully selecting and managing these pairs, decoding challenges are effectively mitigated, resulting in a more secure and robust steganographic procedure. Through the exclusion of extreme pixel valuesand the precise adjustment of pixel pairs, this method significantly enhances the reliability, efficiency, and security of the steganography system.
# On the Design and Implementation of a Blockchain-Based Data Management System for ETO Manufacturing
## 3 Architecture of Blockchain-based Process Data Management for ETO Manufacturing
### Blockchain-Based Production Process Management for ETO Manufacturing  
Due to the diverse and discrete process steps in ETO-type production, the traditional method of tracing data stored in the centralized database of the enterprise will result in low timeliness of data verification and the risk of data tampering and loss. The blockchain uses distributed ledgers instead of a central database to store enterprise production data. This approach can record product information positively in real-time, enhance the validity and reliability of the data, and improve the traceability of the production process.  
A blockchain-based product data management system (BPDMS) is constructed for ETO-type production, as shown in Figure 2. The BPDMS can be divided into three levels from top to bottom: business logic, data acquisition, and the blockchain network. The three layers are interrelated.
## 2 Materials and Methods
### Evaluating Differences between VPP Estimates and MODIS-LSP Products
#### 2.4.1 Comparison with MODIS-Derived LSP  
The MODIS land cover dynamics product (MCD12Q2) is a commonly used global LSP product, which includes data from 2001 to present at a spatial resolution of 500 m [6; 47]. The MODIS-LSP from MCD12Q2 Collection 6 data was derived from the two-band EVI (EVI2) time series calculated from the MODIS nadir bidirectional reflectance distribution function-adjusted surface reflectance product (MCD43A4). The algorithm for detecting the MODIS-LSP Collection 6 metrics differs substantially from the previous MODIS-LSP Collection 5 product. The EVI2 time series was first smoothed using a QA/quality check-weighted penalized cubic smoothing spline approach [34]. Then, the MODIS-SOS was extracted as the first date when the smoothed EVI2 time series crossed 15% of the smoothed EVI2 amplitude. Similarly, the MODIS-EOS was extracted as the last date when the smoothed EVI2 time series crossed 15% of the smoothed EVI2 amplitude. More information about the MODIS-LSP Collection 6 product can be found in [34].  
Based on [47], seven MODIS tiles (h23v02, h12v03, h12v04, h18v04, h27v05, h20v07, and h20v08) including diverse ecoregions, climatic zones, and vegetation types were selected as the study area (Figure S3). The corresponding area to each code is shown in the global sinusoidal MODIS tile map ([[https://modis-land.gsfc.nasa.gov/MODLAND_grid.html](https://modis-land.gsfc.nasa.gov/MODLAND_grid.html)]([https://modis-land.gsfc.nasa.gov/MODLAND_grid.html](https://modis-land.gsfc.nasa.gov/MODLAND_grid.html)) accessed on 11 June 2021). The MODIS-LSP Collection 6 data for the seven tiles from 2001 to 2017 were downloaded from [[https://edftl01.cr.usgs.gov/MOTA/MCD12Q2.006/accessed](https://edftl01.cr.usgs.gov/MOTA/MCD12Q2.006/accessed)]([https://edftl01.cr.usgs.gov/MOTA/MCD12Q2.006/accessed](https://edftl01.cr.usgs.gov/MOTA/MCD12Q2.006/accessed)) on 11 June 2021).