esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cloud Microservices Architecture for Data Integrity Verifiability Based on Blockchain
## 4 The Verifiability Blockchain Service Interface (VBSI)
### Users
#### 4.4.1 Administration  
It provides the four CRUD operations: creation, read, update, and delete. When a request is received to create a user, function \(userCreation(D,Hx)\) is processed. Where:  
\[P_{d}\in D;To\in D;U_{t}\in D;K_{p}^{+}\in D;Ev\in D;Gas\in D;D_{p}\in D \tag{8}\]  
Let \(P_{d}\) be credentials related with personal information of the user to be created, such as _username_, _email_, and a hashed _password_; \(U_{t}\) be the type of user to be created; \(K_{p}^{+}\) be the public key of the user; _Gas_ is the value required to execute the transaction in the blockchain; \(D_{p}\) be a set of permissions that the user will be granted expressed in \(JSON\) format (Section 4.4.3 explains more about the permissions); and \(Hx\) be the hashed of the previous messages. When a user is created, it also requires to create its smart contract, calling a _storing_ operation (see Section 4.2), receiving \(R\) and it is returned also.  
When update and delete are requested \((userUpdate(D,Hx,R)\) and \(userDelete(D,Hx,R)\) respectively), it is assumed that each user has its own smart contract, as it was previously created. These operations require generating new transactions over the existing smart contract, returning a new answer of the blockchain \(R^{\prime}\). These operations internally call for the _storing_ service.  
Our model follows the idea of [51], which is designed with three general types of users: _Root_, _Administrators_ and _TUsers_. The following conditions are followed to build a tree structure:  
* The system must have only one root, it follows the idea of [36].
* A public key must be used for only one type of user (validated via off-chain).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Service Name** & **Function Name** & **Input** & **Output** & **Storing** \\ \hline \multirow{4}{*}{Administration} & \(userCreation()\) & \(D,Hx\) & \(R\) & \(\surd\) \\ \cline{2-5}  & \(userRead()\) & \(To\) & \(J\) & \\ \cline{2-5}  & \(userUpdate()\) & \(D,Hx,R\) & \(R^{\prime}\) & \(\surd\) \\ \cline{2-5}  & \(userDelete()\) & \(D,Hx,R\) & \(R^{\prime}\) & \(\surd\) \\ \hline \multirow{4}{*}{Authentication} & \(authenticate()\) & \(P_{d},U_{t},Ev\) & \(To\) & \(\surd\) \\ \cline{2-5}  & \(tokenRenovation()\) & \(To\) & \(To\) & \(\surd\) \\ \cline{2-5}  & \(tokenIsValid()\) & \(To\) & \(bool\) & \\ \hline \multirow{2}{*}{Authorization} & \(permit()\) & \(To,Ev,R_{S}\) & \(bool\) & \\ \cline{2-5}  & \(permissions()\) & \(To\) & \(JSON\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: User’s microservice.  
* The root user can create administrators (validated via off-chain and blockchain).
* Administrators can create other administrators and TUsers (validated via off-chain and blockchain).
* When a TUser is created, also a _Resource_ smart contract is created (instruction sent via off-chain to the blockchain).
## 3 Applications of the Tactile Internet
### Self-Driving Vehicles
Figure 4 illustrates the platooning of self-driven vehicles by maintaining a fixed distance 'D' between them. All vehicles establish a connection to the nearest base station, thus reaching a fog network to execute the complex computation of the traffic.
## 4 Case Study
### The Impact of Track Quality on Vibration Levels  
Another topic of particular relevance is related to track quality. Due to the significant dispersion in the power spectral density functions discussed in Section 2.2, it is essential to evaluate the impact of this variability on the ground vibration response. To achieve this, extreme track conditions for each proposal will be considered. Figure 11 illustrates the ground vibration response at an observation point located on the surface of the ground, 30 m away from the center of the tunnel (observation point B, see Figure 5).  
Considering the previously highlighted discrepancies in the PSD functions, the findings are not surprising. However, this issue is far from a minor question, as the adoption of a specific proposal can result in considerable discrepancies in vibration predictions. Indeed, from an engineering standpoint, where real data on the uneveness profile (or its evolution) are lacking, the selection of a particular PSD function is standard practice. However, this decision holds paramount importance, as it has the potential to significantly influence all subsequent conclusions.  
Upon general analysis, it becomes apparent that the incorporation of [PERSON] and Hellenbroich's proposed PSD function introduces a notable contrast compared to the other cases. Specifically, the maximum vibration levels induced for the worst track condition are lower or close than the vibration levels observed for the best track condition in both the SNCF and FRA proposals, respectively. From another perspective, the PSD function for the worst track condition leads to similar results in the SNCF and Frederich proposals. However, the consideration of a favorable scenario significantly impacts the final results, with the Frederich proposal leading to vibration levels more than 20 dB lower.  
Additionally, from a track maintenance perspective, a crucial practical finding emerges for all the proposals: there is a significant increase in vibration levels when track quality deteriorates from high to low quality. Therefore, if ground-borne vibration is likely to be a problem, then it is vital to maintain a high-quality rail uneveness profile.  
Figure 10: Normalized CPU times for different numbers of sinusoidal functions.
## 2 Materials and Methods
### Data Analysis  
The data were analyzed using the statistical software package SPSS v26.0 for Windows (SPSS Inc., Chicago, IL, USA). The level of statistical significance was set at \(p<0.05\). The Kolmogorov-Smirnov test was used to examine the normality of the variables. Continuous variables were described using means and standard deviations, and categorical variables were described using percentages and frequencies. Pearson correlation was used to analyze potential relationships between the independent variables and dependent variables. Additionally, all statistical analyses were adjusted for confounding variables, including sex, age, BMI, and physical activity practice. Furthermore, a confidence level of 95% (\(p<0.05\)) was used to assess the association between the independent variables (lower limb strength, abdominal strength, vertical jump, and grip strength) and the dependent variables (Stroop correct responses, Stroop fastest correct response, Stroop average response time, Stroop average correct response time, and word recall). Finally, the effect size of the differences between sexes in the descriptive data of participants was interpreted using Cohen's d statistic. An effect size of \(<\)0.2 reflects a negligible difference; \(\geq\)0.2 but \(\leq\)0.5 a small difference; \(\geq\)0.5 but \(\leq\)0.8 a moderate difference; and \(\geq\)0.8 a large difference.
## 5 THG Efficiency in Dependence of SOD Coefficients and Phase Mismatching between Interacting Waves
### Crucial Influence of Phase Mismatching between TH and FW on Frequency Conversion Efficiency
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Normal SOD at FF**} & \multicolumn{6}{c}{**Anomalous SOD at FF**} \\ \hline \(\Delta_{31}k\) & \(\eta_{max}\) & \(z_{max}\) & \(\tau_{3}\) & \(\tau_{3}^{\omega}\) & TH pulse shape & \(\Delta_{31}k\) & \(\eta_{max}\) & \(z_{max}\) & \(\tau_{3}\) & \(\tau_{3}^{\omega}\) & TH pulse shape \\ \hline
0 & 0.51 & 13 & 0.804 & 3.15 & Smooth & 0 & 0.58 & 15 & 0.79 & 3.675 & Smooth \\ \hline
0.01 & 0.49 & 13 & 0.802 & 3.104 & Smooth & 0.01 & 0.54 & 16 & 0.827 & 3.578 & Smooth \\ \hline \(-0.01\) & 0.54 & 14 & 0.787 & 3.105 & Smooth & \(-0.01\) & 0.58 & 17 & 0.796 & 3.661 & Smooth \\ \hline
0.1 & 0.48 & 11 & 0.849 & 3 & Smooth & 0.1 & 0.43 & 12 & 0.873 & 3.52 & Smooth \\ \hline \(-0.1\) & 0.45 & 14 & 0.83 & 3.798 & Smooth & \(-0.1\) & 0.45 & 15 & 0.612 & 4.219 & Smooth \\ \hline
0.2 & 0.31 & 9 & 0.888 & 3.072 & Smooth & 0.2 & 0.33 & 9 & 0.847 & 3.523 & Smooth \\ \hline \(-0.2\) & 0.27 & 10 & 0.732 & 4.112 & Smooth & \(-0.2\) & 0.26 & 9 & 0.626 & 4.067 & Smooth \\ \hline
0.4 & 0.18 & 6 & 0.842 & 3.354 & Smooth & 0.4 & 0.19 & 6.5 & 0.877 & 3.253 & Smooth \\ \hline \(-0.4\) & 0.14 & 5.5 & 0.67 & 4.24 & Smooth & \(-0.4\) & 0.13 & 5.4 & 0.672 & 4.084 & Smooth \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Influence of \(\Delta_{31}k\) on the frequency tripling process at \(\gamma=1,\Delta_{21}k=20\). Nevertheless, the phase mismatching \(\Delta_{31}k\) must not be higher because already at \(\Delta_{31}k=\pm 0.1\) the energy conversion efficiency becomes low: only about 10% of the incident pulse energy is converted to the TH. Thus, a very important conclusion for practice follows from Tables 4-6: one can achieve essential spectral broadening of the TH pulse by choosing the phase mismatching between TH and FW.
## 2 Materials Furthermore, Methods  
### Hardware Description  
The main hardware that we used is a Thorlabs PK4 FYC2 PEA that was produced with several piezoelectric chips wedged with epoxy and glass beads. A maximum displacement of 38.5 \(\upmu\)m is achieved with a driving voltage of 150 V. As the device is frequently handled for precise and micrometric displacement, the manufacturer included four strain gauges arrayed in a Wheatstone bridge so that the measurement could be obtained from resistance variation, which provides a better resolution [29].  
The PEA accessories include a driver cube KPZ101, whose input signal is 0-10 V and is transformed into a 0-150 V to manage the PEA. As the PEA delivers an output voltage that is in the order of milivolts, a pre-amplifier augments this value into a 0-2 V signal. The latter is transferred into a reader cube KSG101, which transforms the voltage into 0-10 V. Further technical details of the PEA and its peripherals are summarized in Table 1.  
Since the input and output signals of the PEA are in the range of 0-10 V, we, therefore, used a proper platform for acquisition and control, such as the dSpace DS1104 board. We linked this device to a Dell Precision Workstation T3500 through a peripheral component interconnect (PCI) for monitoring and control. Moreover, the DS1104 has an external connector panel CP1104 that we connected to the PEA driver and reader.  
In regards to the software, we developed the proposed strutures in Simulink by Mathworks and embedded them through the platform dSpace real-time interface (RTI). This allowed us to generate and manage a real-time control algorithm, while the RTI helped to reduce the compilation time. All the information was acquired with ControlDesk and processed with MATLAB by Mathworks. The explained relation between software and hardware is detailed in Figure 1. The chosen sampling time was 1 kHz as it is a suitable match with the acquisition and the hardware physical limits.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PEA PK4 FYC2** & **Values** & **Units** \\ \hline Maximum displacement & 38.5 & \(\upmu\)m \\ Blocking force & 1000 & N \\ Resonant frequency & 34 & kHz \\ Maximum error & 15 & \% \\ \hline
**Driver Cube KPZ101** & & \\ \hline Output driving voltage for PEA & 150 & V \\ Input driving voltage & 0–10 & V \\ Maximum output bandwidth & 1 & kHz \\ \hline
**Reader Cube KSG101** & & \\ \hline Output range & 0–10 & V \\ Resolution & 1 & nm \\ \hline
**Pre-Amplifier AMP002** & & \\ \hline Output range & 0–2 & V \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thorlabs hardware used.
# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 4 Case Analysis
### mRMR Key Factor Analysis  
Based on the results of multi-energy coupling scenario analysis, we obtained the corresponding load forecasting index system (as shown in Section 2.3 in this paper). First, we needed to screen the key influencing factors for load forecasting, so as to improve the efficiency of load forecasting. The incremental search method was adopted to select the feature influence factor according to Formula (6), and the selection results are shown in Tables 4 and 5. It can be seen that for the local multi-energy coupling self-load supply, the main impact is the wind  
The influence factors of the first three multi-energy coupling characteristics were input into the AFWA-LSSVM model for further calculation.
## 3 Results
### Grasping Experimental Results  
The measurement results are presented for the grasping motion during which the values of the current were measured.  
In Figure 22, the panels in the top row describe the current draw when actuating the thumb, index and middle finger, and ring and pinky finger, respectively. In the first column, the second and third panel display the drawn current from actuating the four fingers and five fingers. In the bottom right panel, a comparison can be seen between the five actuation modes. The solid lines represent the flexion movement while the dashed lines are for extension.  
In Figure 23, the grasping force is measured. In the top left panel, the grasping force of the index and middle finger group is quantified over a 5 s time window. In the bottom-left corner, the panel illustrates the grasping force of the ring and pinky finger. Finally, in the right-side panel the four-finger actuation force is determined, reaching a peak of 2.8 kgf.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Part Name** & **Build Time (min)** & **Material Weight (g)** & **Filament Length (m)** & **Cost [RON]** \\ \hline
1 & Battery cover & 40 & 6 & 2.12 & 2.00 \\
2 & Distal right & 39 & 5 & 1.61 & 1.86 \\
3 & Distal left & 28 & 4 & 1.26 & 1.40 \\
4 & Median right & 34 & 4 & 1.45 & 1.63 \\
5 & Median left & 35 & 4 & 1.35 & 1.67 \\
6 & Proximal right & 41 & 5 & 1.73 & 1.95 \\
7 & Proximal left & 38 & 5 & 1.65 & 1.86 \\
8 & Distal thumb right & 46 & 6 & 1.9 & 2.23 \\
9 & Distal thumb left & 32 & 4 & 1.41 & 1.53 \\
10 & Proximal thumb right & 46 & 6 & 1.85 & 2.23 \\
11 & Proximal thumb left & 37 & 5 & 1.65 & 1.81 \\
12 & Distal pretension & 8 & 1 & 0.36 & 0.42 \\
13 & Large spacer & 5 & 1 & 0.29 & 0.28 \\
14 & Palm structure & 17 h 10 & 82 & 27.62 & 45.66 \\
15 & Palm dorsal cover & 14 h 43 & 85 & 28.37 & 40.18 \\
16 & Palm spacer & 4 & 1 & 0.27 & 0.33 \\
17 & Lower gaamtlet & 19 h 54 & 122 & 40.83 & 54.78 \\
18 & Upper gaamtlet & 1 d 7 h 29 & 195 & 65.4 & 86.82 \\
19 & Motor housing cover & 31 & 5 & 1.63 & 1.58 \\
20 & Motor housing & 2 h 1 & 14 & 4.63 & 5.67 \\
21 & Motor spool & 28 & 3 & 1 & 1.30 \\
22 & Lower housing of & 43 & 6 & 1.93 & 2.09 \\  & locking system & & & & \\
23 & Upper housing of & 41 & 6 & 1.88 & 2.05 \\  & locking system & & & & \\
24 & Angular spacer of & 21 & 3 & 1 & 1.02 \\  & locking system & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Manufacturing costs of 3D-printed parts.  
Figure 23: Comparison of grasping force generated based on current drawn with respect to time during actuation.  
Figure 22: Comparison of current drawn with respect to time for grasp actuation.
## 4 Proposal for an Ontology Building Process
### Evaluation Stage
#### 4.3.3 Application-Based Evaluation  
In the application-based evaluation, the ontology is integrated into a real-world application through a knowledge graph, for example, and its performance is assessed based on how well it supports the application's tasks. The application-based evaluation of ontologies is a process that assesses the suitability, performance, and effectiveness of an ontology within the context of a specific application or use case. Unlike theoretical evaluations, which focus on an ontology's internal structure and logical consistency, application-based evaluations emphasize real-world utility and applicability.  
Consistency is also a focus in the evaluation process since the ontology must not contain conflicting definitions or rules. Reasoning engines are often used to check for logical inconsistencies within the ontology to ensure that the ontology can support automated reasoning without producing errors or contradictions. Another evaluation involves the seamless integration of the ontology with existing systems and data sources. This validation should be made in joint with domain specialists, and the results must demonstrate that the ontology can interact with different systems and exchange data without the loss of meaning or accuracy.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 4 I/O Detection Scheme
### Machine Learning Approaches
#### 4.2.4 K-Nearest Neighbors Classifier  
The KNNs technique is a nonparametric method used for both classification and regression [25], where the input consists of training examples in the feature space. The KNN technique is one of the simplest of all machine learning techniques, in which it only approximated locally, and all computations are calculated in the classification state. Here, an object is classified by the majority of its neighbors, where the object is assigned to the most common class among its nearest neighbors.  
Due to the simplicity of the technique, KNN is very easy to implement; however, it can still handle unstructured data and simple valuation with good estimation of the test set. With lazy learning, the KNN calculation is performed in the test phase; thus, with a large dataset, the test phase requires more time than other machine learning techniques. This characteristic makes offline learning impossible for KNN. Another drawback of KNN is its sensitivity to noise, which makes it unattractive for complex problems.
## 5 Test Results and Analyses
When the number in each cycle is too small, it is difficult to recover the output signal to the original waveform. The noise increases, and the signal amplitude decreases. However, there are enough sampling numbers to maintain the signal amplitude unchanged in the TIDAC circuit. So, in contrast, the SNR of the output signal of a single DAC circuit is much lower than that of the TIDAC circuit.
## 2 Site, Data, and Methods
### Methods  
The high spatial and temporal resolution characteristics of CDWL data can be utilized to estimate the turbulent kinetic energy dissipation rate (TKEDR) at different heights [(32; 33; 34)]. To calculate the TKEDR while taking into account the temporal resolution, the CDWL used the VAD scanning mode with the azimuth interval of 12\({}^{\circ}\). The TKEDR threshold method is one of the effective approaches to estimate the MLH [(35; 36)]. The threshold of TKEDR in this paper is set at 10\({}^{-4}\) m\({}^{2}\) s\({}^{-3}\). When there is a cloud, the HWCT (Haar wavelet covariance transform) method is first used to identify the cloud [(19)]; secondly, when the cloud coverage time exceeds 10 min and causes a drastic change in the MLH, the MLH data during this period are removed. The error analysis for calculating TKEDR and MLH was conducted by [PERSON] [(37; 32)]. The same type of CDWL instrument has been applied and verified for the estimation of TKEDR and MLH in various application scenarios [(38; 39; 19)].  
The typical TKEDR and MLH inversion results are presented in Figure 2. At night, the radiation cooling of the desert underlying surface facilitates the formation of the inversion layer [(40)]. Compared to the night, the MLH during the daytime (8:00\(-\)21:00 BJT) can better reflect the dust pollution. In summer, the MLH exceeded 4 km (Figure 2(31-33)). In the winter (Figure 2(46-54)), the MLH is generally lower than 1 km. To investigate the variation characteristics of the ML, all the MLH data were statistically analyzed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Wavelength & 1.5 \(\upmu\)m \\ Pulse energy & 130 \(\upmu\)J \\ Pulse duration & 200 ns \\ Pulse repletion frequency & 10 kHz \\ Diameter of telescope & 100 mm \\ Radial spatial resolution & 30 m \\ Azimuth scanning range & 0-360\({}^{\circ}\) \\ Elevation angle & 70\({}^{\circ}\) \\ Sample rate of ADC & 500 MS/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters of the CDWL.  
Figure 2: The typical atmospheric mixing layer height results calculated by using the TKEDR threshold method.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## Materials
### Remote Sensing Data  
In this study, a database of radar (SAR) and optical data was prepared over the study site described in Section 2.1. While the S1 satellite constellation provides SAR images, the S2 satellite provides optical images. Table 1 summarizes the characteristics of remote sensing data used in the study. More details on each data type are further provided in the following subsection.  
Figure 3: Distribution of the number of agricultural plots per class of crop type.
# DNA Nanodevices as Mechanical Probes of Protein Structure and Function
## 1 Introduction  
The ability to probe protein structure and function at the single-molecule level has been a key motivation of biology, chemistry and physics for decades. Innovations like super-resolution microscopy, optical tweezers, atomic force microscopy (AFM) probing, and Forster resonance energy transfer (FRET) studies have opened new windows into the biological world by enabling analysis of single protein molecules with unprecedented precision. Structural biology, ranging from X-ray crystallography and nuclear magnetic resonance (NMR) to the recent revolution in cryo-electron microscopy (cryo-EM), has also played an indispensable role in determining the three-dimensional conformation of proteins, and thereby elucidating their function at the molecular level. In the past 15 years or so, DNA mechanical nanodevices have emerged as powerful new alternative tools for probing proteins with single-molecule resolution. The DNA origami technique [1; 2], by which a long DNA scaffold strand is folded into a well-defined, addressable, and mechanically robust nanoobject, has in particular been a boon for designing structures on a similar length scale as proteins, which can immobilize them or apply forces to them in interesting ways to elucidate their function. In this perspective, we outline recent advances in the use of rigid DNA origami objects and lattices to probe the structure of proteins, or to interrogate their function at the single-molecule level. We focus on three key properties that make DNA nanoscafolds particularly well-suited to this task (Figure 1): (1) their rigidity, which in turn allows for protein attachment with a controlled orientation for structural biology studies; (2) their ability to space multiple proteins with nanoscale control, or to confine them within a defined volume; and (3) their ability to impart forces at the nanoscale, and thereby probe the biophysics of attached proteins. We then close with a brief discussion of two areas for future investigation for improving DNA nanomechanical devices: (1) rigid, multipoint attachment of proteins on a DNA scaffold and (2) computational modeling of hybrid protein-DNA nanomaterials. Our goal is to highlight some key recent advances in these areas and to focus on how the mechanical nature of DNA scaffolds can facilitate new insights into protein science, not to provide an exhaustive overview of the rich intersection of protein science and DNA nanotechnology. For a more complete treatment of hybridprotein-DNA nanomaterials beyond the scope of this perspective, we refer the interested reader to several key reviews [3; 4; 5; 6].