esa-sceva/satcom-chunk-collection huggingface dataset:
# Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing
## 4 Data Provisioning Reference Architectures
### Classification of Data Sources  
Three major types of data have been identified in DIGITbrain:  
* Structured data;  
Figure 2: Example of a Software Architecture Fragment (SAF): Data exploration using JupyterLab/Jupyter Notebook.  
* Unstructured data;
* Streaming data.  
In the first case, data or pieces of data are organized according to a specific schema, potentially, with type and relation information between them (e.g., in the form of tables, rows, and columns, using primary key-foreign key relationships). Such data can be fetched or updated through some query language, such as SQL. Storage of these kinds are called databases, which can be SQL and NoSQL, relational, document, key-value-based, or graph databases. Widely used implementations include MySQL Server [25], MongoDB [26], PostgreSQL [27], Redis [28], InfluxDB [29], TimescaleDB [30], and Apache Cassandra [31].  
In case of unstructured data, the information is encapsulated in units called binary large objects (BLOBs) or simply files, which are organized in a flat or some hierarchical structure. Although data can potentially be read in chunks or parts (or from a specific position, respectively), the internal logical structure is typically not explicit. Examples of such storage resources are: SFTP servers [32], Amazon Simple Storage Service (S3), OpenStack Swift, and Apache HDFS [33].  
The third type of data addresses high-frequency data provisioning and (near) real-time processing. The primary goal is to forward a series of data packets (messages, events, and logs) of moderate size as fast as possible for a stream processing pipeline. Permanent storage for the streamed data is typically either not required or even infeasible because of the significant volume. A few examples of implementations are: Mosquito MQTT [34], Apache Kafka [16], and RabbitMQ [35].  
We note that the categorization above is not rigid, for instance, object stores do not prohibit the storing of files that are structured internally (e.g., XML and JSON); databases can contain unstructured contents inside (e.g., NoSQL); time-series databases can be a good choice for handling streamed data that can also be structured or unstructured, respectively. Our primary motivation was merely to ease the identification and selection of the appropriate solutions for building blocks in more complex Reference Architectures and assist in answering how to realize the speed layer, the batch layer, or stream processing in Lambda and Kappa architectures presented in Section 2.
## 3 Results  
### Chemical Analysis of the Polyurea  
The results of the FTIR test for both polyurea presented in Figure 2 show the bonds that constitute the two separate microphases segments of the polyurea. The two polyureas have very similar FTIR spectrums since no significant differences exist between the chemical structure of both polyureas.  
The soft segment of the polyureas consists of long aliphatic polyether chains and forms amorphous domains, while the hard portion is composed urea bonds [19; 20; 21; 22].  
The hard segment domains of both polyureas present at 3284-3287 cm\({}^{-1}\) correspond to N-H stretching vibrations. The band between 1600-1700 cm\({}^{-1}\) represents C=O stretching vibrations, is only present in polyurea B. The blast mitigation ability of the polyurea is controlled primarily by their hard domains. The irreversible densification and order-which increases upon the application of shock wave loads-of the hard realms of the polyurea lead to the shock wave attenuation and dispersion mechanisms that characterize this type of material. The extent of this blast mitigation effect is proportional to the hard domain volume fraction contained in the polyurea [13].  
The absorption bands that correspond to the C-N stretching vibrations are present at around 1540 cm\({}^{-1}\)[23]. The strong absorption peak at approximately 1080 cm\({}^{-1}\) corresponds to the isocyanate CN stretch alkyl amine group attached. The FTIR spectrum also shows the stretching vibrations for CH stretch alkane at proximately 2970 cm\({}^{-1}\), respectively.  
Finally, the contributing structures CO stretch carboxylic, alcohol, and anhydride are represented in the absorption peak at 1300 cm\({}^{-1}\), 1230 cm\({}^{-1}\), and 925 cm\({}^{-1}\), respectively [24].
## 1 Introduction
Therefore, in this study, a feature-wise fusion strategy of 2D optical images and point cloud data was proposed to enhance the road segmentation capability of deep learning models in areas where the use of optical images is inadequate due to object blockage and shadow problems. The high-resolution optical satellite images obtained from the Google Maps platform were combined with the contextual feature images derived from the point cloud obtained using airborne LiDAR data. The combination of these two types of data was carried out feature-wise in a deep residual U-Net-based deep learning model. The features generated by different ResNet backbones using only optical satellite images were fused with the geometric features calculated from the LiDAR point cloud before the final convolution layer of the model. This study provides insight into how to combine optical images and point-cloud data to improve the segmentation of roads. The proposed strategy can be implemented in any deep learning model architecture with proper modifications. The motivation of this study is to improve the road segmentation capability of the deep learning models by combining 2D and 3D information properly. The major objectives of the study were as follows: (1) the use of irregular point cloud data together with the 2D optical images in an end-to-end deep learning model was introduced as a feature-wise fusion strategy; (2) the improvement brought about by the LiDAR data was outlined together with the statistical results of the models and the prediction performance of the proposed fusion strategy was evaluated in areas where the road segmentation is challenging; (3) the consistency of the strategy was evaluated with different ResNet backbones in deep residual U-Net architecture. Finally, the relative importance of optical and LiDAR-derived features was calculated using the best-performing combined model to outline which features contribute to the improvement of road segmentation the most. The intent of this study is not to propose a deep learning architecture, but to provide a combination strategy for optical images and point-cloud data to improve the segmentation of roads. However, the proposed fusion strategy can be implemented in any model architecture with proper modifications. The motivation behind this study was to improve the road segmentation capability of any deep learning model, which has been proven to be successful in this regard, by combining 2D and 3D information properly.
## 5 Methodology
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ethereum** & **Hyperledger Fabric** & **ConsensSy Codefi** & **EOS** \\ \hline \multicolumn{4}{c}{**Programming Language Support**} \\ \hline
1. Solidity 2. Vyper & \begin{tabular}{c} 1. JavaScript \\ 2. Java 3. Go \\ 4. Node \\ \end{tabular} & \
\begin{tabular}{c} 1. Solidity \\ 2. Javascript 3. Node \\ \end{tabular} &
\begin{tabular}{c} 1. C++ \\ 1. C++ \\ \end{tabular} \\ \hline \multicolumn{4}{c}{**Blockchain Type**} \\ \hline Public & Private & Public & Public \\ \hline \multicolumn{4}{c}{**Consensus Algorithm**} \\ \hline
1. PoW 2. PoS & \
\begin{tabular}{c} 1. Raft 2. PBFT \\ 3. POET \\ 4. Apache Kafka \\ \end{tabular} & 1. DeFi &
\begin{tabular}{c} 1. DPoS 2. BFT \\ 3. PoW 4. PoS \\ \end{tabular} \\ \hline \multicolumn{4}{c}{**Performance**} \\ \hline
30 transactions & 300 transactions & 30 transactions & 4000 transactions \\ per second & per second & per second & per second \\ \hline \multicolumn{4}{c}{**IDEs**} \\ \hline
1. Web:Remix, EthFiddle, & & 1. Remix & 1. Visual Studio Code \\ ChainIDE, Replit & 1. Chaincode & 2. Visual Studio Code & 2. EOS Studio \\
2. Desktop: & 2. Visual Studio Code, & 3. Vegrant & 4. Web3 Stack & 3. EOSIO \\ Atom, JetBrains IDEs, & 4. Docker containers & 5. Truffle & 4. Swift SDK \\ Hard hat, Brownie, & & 6. Infura & 5. Java SDK \\ Dapp tools and Truffle & & & \\ \hline \multicolumn{4}{c}{**Transaction Flow**} \\ \hline
1. Construct the raw & \begin{tabular}{c} 1. Client initiates \\ the transaction. \\ 2. Endorsing peers \\ verify \& execute the \\ transaction. \\ 4. Broadcast transaction \\ 5. Miner node accepts the \\ transaction, finds \\ block, and broadcasts \\ 7. Local node receives/ \\ syncs the new block \\ \end{tabular} & \
\begin{tabular}{c} 1. Client initiates \\ the transaction. \\ 2. Endorsing peers \\ verify \& execute the \\ transaction. \\ 3. Proposal response \\ is inspected. \\ 4. Client assembles \\ endorsements \\ into a transaction. \\ 5. Validate transaction \\ 6. Ledger updated \\ \end{tabular} &
\begin{tabular}{c} 1. Transaction crafting \\ 2. Gas management \\ 3. Nonce attribution \\ 4. Key signature \\ 5. Transaction sending \\ 6. Transaction listening \\ and event streaming \\ 7. Transaction decoding \\ 8. Transaction response \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison of blockchain operating systems and their related features.
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 4 Results  
### Detection of Surface Water  
We find strong agreement (r\({}^{2}\) > 0.99) between lake boundaries manually delineated in high-resolution WorldView-2 and WorldView-3 images and water fraction classifications derived from Planet RapidEye and PlanetScope imagery (Figure 4). The normalized root mean square errors (NRMSE) vary from 5.7% to 18.7% for RapidEye, with \(>\)75% area showing the best agreement (NRMSE = 5.7%) with WorldView images (Figure 4). The NRMSEs vary less for PlanetScope, ranging from 10.9% to 12.6%. While the WorldView and RapidEye images are contemporaneous (taken 2 min apart), the three days between WorldView and PlanetScope image acquisitions may be responsible for the higher RMSEs for PlanetScope. The mean Euclidean distance between Planet and WorldView lake centroids is 9.8 \(\pm\) 4.1 m for RapidEye and 10.5 \(\pm\) 3.2 m for PlanetScope, comparable with the \(<\)10 m ground location accuracy Planet claims for its imagery [33]. This analysis was only performed for lakes where the classifications yielded similar lake geometry and thus the difference between the centroids primarily represents a geolocation offset rather than classification error. However, for all lakes, some of the offset may derive from classification differences, meaning our reported geolocation errors are conservative estimates of the actual offset. The direction of the offsets varies between different images but is generally consistent within a single image (Figure 5). For the 2 September PlanetScope image, the offset primarily trends northeast, with azimuths for individual lakes ranging from due north to due east. For RapidEye the offsets are more variable between lakes but generally trend within 45\({}^{\circ}\) of due north. In sum, combined, the \(\sim\)10% NRMSEs and 10 m ground offsets suggest water area can be reliably extracted from Planet imagery within reasonable error.
# Hypergraph-Clustering Method Based on an Improved Apriori Algorithm
## 2 Related Theoretical Foundations  
### The Concept of Hypergraph  
The hypergraph \(H\) can be defined as a binary group \(H=(V,E)\), where \(V\) is a set of vertices \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) and \(E\) is a set of hyperedges \(E=\{e_{1},e_{2},\ldots,e_{m}\}\) are finite sets, and \(e_{i}\
eq\phi(i=1,2,\ldots,m)\), \(\cup_{i=1}^{m}e_{i}=V\). The number of vertices contained in the hyperedge is called the rank. For \(r(H)=max|e_{i}|\), it is called the upper rank of the hypergraph, and for \(s(H)=min|e_{i}|\), it is called the lower rank of the hypergraph. If \(r(H)=s(H)\), then \(H\) is a uniform hypergraph. In hypergraph \(H\), the number of hyperedges containing node \(v_{i}\) is called the hyperdegree, denoted as \(d_{H}(v_{i})\).  
The incidence matrix \(I(H)\) of hypergraph \(H\) is a matrix where the rows correspond to the vertices of \(H\) and the columns correspond to the hyperedges. The adjacency matrix \(A(H)\) of hypergraph \(H\) is a square symmetric matrix, where the element \(a_{ij}\) represents the number of hyperedges containing nodes \(v_{i}\) and \(v_{j}\); the diagonal entries of \(A(H)\) are zero. For a set \(A\subseteq V\), \(H_{A}=(A,\{e_{i}\cap A|e_{i}\in E\wedge e_{i}\cap A\
eq\varnothing\})\) is called the sub-hypergraph of the hypergraph \(H=(V,E)\), or rather, the hypergraph on the vertices subset of \(H\) is called the sub-hypergraph of \(H\), such that for each hyperedge in the sub-hypergraph, there exists only one hyperedge in \(H\) that contains it.  
Figure 1 shows an example of a hypergraph. In this hypergraph, vertices with a degree of 1 are \(v_{1},v_{3},v_{4},v_{7}\) and \(v_{8}\), vertices with a degree of 2 are \(v_{2}\) and \(v_{5}\), the vertex with a degree of 3 is \(v_{6}\), and the four hyperedges in Figure 1 do not all contain the same number of vertices, making it a non-uniform hypergraph.
## 3 Control Strategy Design
### The Switching Mode
#### 3.3.2 Switching Mode for Retraction  
In the retraction process, the PR and LSRR modes are used for the NIMV system. While the LSRR mode uses the energy potential gravitation from the load to retract the cylinder, the PR mode uses energy from the pump to move the cylinder. If energy saving is the primary concern, the LSRR mode is chosen to operate the cylinder. However, the LSRR mode has its limitation, while the gravity of the load retracts the cylinder, the viscous friction and pressure drop from the cylinder to the \(K_{ab}\) valve to obstruct the movement of the cylinder. Thus, LSRR mode has a minimum load capability. For this reason, the switching mode of the PR and LSRR modes needs to be studied.  
The maximum velocity capability of the LSRR and PR mode is represented as:  
\[\dot{x}_{LSRR}=\dot{x}_{PR}=\frac{Q_{max}}{A_{a}} \tag{20}\]  
In both modes, the load capacity is considered based on the maximum pressure at the bore chamber, which is generated by closing the meter-out valve while the system is  
Figure 8: The load-velocity curve for switching mode on extension mode of NIMV system.  
holding the load. This pressure must not be over the maximum pressure setting on the system. Therefore, the load capability can be provided as follows:  
\[m_{LSRR}^{max}=m_{PR}=\frac{p_{max}A_{a}}{g}=\frac{p_{s}A_{a}}{g} \tag{21}\]  
where \(p_{max}\) is the maximum pressure at the bore chamber, in this case \(p_{max}=p_{s}\). Moreover, the minimum load capability of the LSRR mode can be calculated by Equation (23):  
\[m_{LSRR}^{\min}=\frac{\Delta p_{ab}A_{a}-B_{v}\dot{x}_{LSRR}}{g+\ddot{x}_{LSRR}} \tag{22}\]  
where \(\Delta p_{ab}\) is the pressure drop from the cylinder to the \(K_{ab}\) valve.  
Figure 9 describes the cylinder's load-velocity curve for retraction modes of the NIMV system at the maximum flow rate of 12.45 L/min and pressure of 100 bar. Although both modes have the same velocity range and the maximum load capability that is shown at the overlap region, the limit of the minimum load capability of the LSRR mode is the point that distinguishes between the two modes. Based on the condition of the system, the load capability of LSRR mode is from 97 to 1280 kg at the velocity of 0.165 m/s and the PR mode is from 0 to 1280 kg. Moreover, improving energy saving is one of the main priorities of this research.  
From the above results, the control strategy of the NIMV system is proposed for the boom excavator (shown in Figure 10). The target displacement and velocity signal are estimated by the joystick and sent to the controller along with the load signal, which is measured by the load cell. Based on the condition of the switching mode, the metering mode is chosen to operate the system. Through the VLFPB method for the valve and the VFPB method for the pump, the control signal is sent to the hydraulic valves and electric motor in the NIMV circuit, leading to the cylinder being operated.  
Figure 9: The load-velocity curve for switching mode on retraction mode of NIMV system.
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 2 System Model
### Interference Cancellation  
An effective approach to enhance the system capacity in the presence of signal interference is successive interference cancellation (SIC). Broadly speaking, SIC is an iterative reception scheme where signals are decoded one at a time, starting from the strongest, i.e., the one with the largest SINR. After the signal is decoded, its waveform is regenerated and subtracted from the aggregate received signal; then, the next strongest signal is decoded, regenerated, and subtracted; and so on [29].  
Given error-free decoding, good regeneration of reconstruction requires accurate synchronization and a high quality estimation of the channel impulse response, which implies when these operations are imperfect, the signal cancellation leaves some residual power that increases the noise level experienced at the successive decoding stages. Furthermore, the finite precision of the analog to digital converter (ADC) at the receiver also reduces the effectiveness of each cancellation cycle. Following [30], we model all these idiosyncrasies of the interference cancellation process by assuming that the cancellation of a signal with received power \(P\) leaves a residual interference power of \(z*P\), where \(0<z<1\) is called the residual power factor. This model is clearly approximate, since the residual interference in practical SIC systems depends strongly on the SINR value of the canceled signal [26].  
Considering that the decoded signal \(j\) is canceled from the overall received signal, leaving a fraction \(z\) of its power as residual interference, the SINR of next strongest signal \(k\) can be expressed as  
\[\gamma_{k}=\frac{P_{r}(d_{k})}{\sum_{h\
eq j,k}^{L}P_{r}(d_{h})+z*P_{r}(d_{j})+ N_{0}}. \tag{4}\]
## 2 Materials and Methods
### Gene Expression Levels of Osteoblast and Osteocyte Markers  
Total RNA was extracted using the RNeasy Mini Kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. The RNA yield was quantified using a spectrophotometer at 260 nm (A260). The RNA concentration ranged from 250 ng/\(\upmu\)L to 300 ng/\(\upmu\)L across samples. Purity was assessed by calculating the A260/A280 ratio, which ranged from 1.9 to 2.0, confirming the absence of significant protein contamination. The yield and purity of the extracted RNA were confirmed using a spectrophotometer. Total RNA was kept at \(-80\)\({}^{\circ}\)C after extraction. Reverse transcription was performed using the Quantitect Reverse Transcription Kit (Qiagen, Hilden, Germany) as per the manufacturer's guidelines. This included an initial step to remove genomic DNA contamination using gDNA Wipeout Buffer, and the RNA was directly used for reverse transcription with a master mix containing Quantiscript Reverse Transcriptase, RT Buffer, and RT Primer Mix to synthesize complementary DNA (cDNA). The reaction occurred at 42 \({}^{\circ}\)C, efficiently transcribing RNA with complex secondary structures while maintaining its integrity, and was inactivated at 95 \({}^{\circ}\)C. The total reaction volume and RNA input were standardized across all samples to ensure consistency in cDNA synthesis.  
Real-time PCR was conducted using the Luna(r) Universal qPCR Master Mix (New England Biolabs, Ipswich, MA, USA) with primers designed for target genes such as alkaline phosphatase (ALP), type 1 collagen (Col 1), osteoclast (OCN), run-related transcription factor 2 (Runx2), osteepontin (OPN), bone sialoprotein (BSP), and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) using Primer3 software, validated against the GeneBank database (Table 1). Primer specificity was confirmed through in silico analysis and melt curve analysis, which showed single peaks for each target gene. The qPCR reaction was set up with a final volume of 20 \(\upmu\)L, containing 10 \(\upmu\)L Luna Universal qPCR Master Mix (IX), 0.5 \(\upmu\)L forward primer (0.25 \(\upmu\)M), 0.5 \(\upmu\)L reverse primer (0.25 \(\upmu\)M), variabletemplate DNA (<100 ng), and nuclease-free water to adjust the volume. All components were thawed, mixed thoroughly, and centrifuged briefly to collect the liquid. Reactions were performed on a Bio-Rad iCycler (BioRad, Hercules, CA, USA) with a thermal profile of initial denaturation at 95 \({}^{\circ}\)C for 1 min, followed by 40 amplification cycles consisting of denaturation at 95 \({}^{\circ}\)C for 15 s and annealing extension at 60 \({}^{\circ}\)C for 30 s. Negative controls without template (NTC) were included to monitor contamination. This series of cycles was followed by a melt curve analysis to check the reaction specificity. The expression level of each targeted gene was normalized to GAPDH, the housekeeping gene. Each experiment was performed in triplicate, and data are presented as mean \(\pm\) SEM.
# Mechanical and Acoustic Emission (AE) Characteristics of Rocks under Biaxial Confinements
## 1 Introduction
In engineering fields, AE technique is regarded as a promising and flexible tool, which has been widely used in monitoring and even quantifying dynamic processes within loaded structural elements [44; 45]. The aforementioned efforts had provided constructive reference values and information for a better understanding of the relationship between rock fracture processes and AE characteristics. However, most of the previous studies mentioned above concentrated on the AE characteristics of single lithology rock under a certain test condition, and the ranges of intermediate principal stress were limited in most biaxial compression tests. Thus, more investigations of the influences of rock lithologies and the full range of intermediate principal stress on AE characteristics are needed. Therefore, in this study, three kinds of cubic rock specimens, i.e., granite, marble, and sandstone, were prepared, and the value of \(\sigma_{2}\) was preset from 0 (uniaxial compression) to \(\sigma_{1}=\sigma_{2}\) to analyze the detailed mechanical characteristics and AE properties of rocks under biaxial confinements. The results of this study will improve the theoretical system of damage mechanisms and AE characteristics of rocks in underground engineering, and provide a reference for engineering damage prediction and monitoring. In this paper, the mechanical characteristics of rocks under biaxial compression, such as stress-strain curves (lateral strain and axial strain) and the variation of biaxial compression strength with \(\sigma_{2}\), were first analyzed, and then the AE characteristics monitored by the two AE sensors installed on the free-surface of rock specimen, such as AE hit rate, AE count rate, b-value, RA-AF, and peak frequency, were studied. In addition, the failure modes of rocks were investigated, and finally, the D-P strength criterion was also verified.