esa-sceva/satcom-chunk-collection huggingface dataset:
# A Parameter Self-Tuning Decoupling Controller Based on an Improved ADRC for Tension Systems
## 4 Simulation and Analysis of Decoupled Controller for Global Tension System
### Simulation and Analysis of Self-Tuning of ADRC Parameters Based on GA  
Set the simulation time to 10 s, optimize tuning \(\beta_{1}\), \(\beta_{2}\), \(K_{p}\) in each ADRC based on the steps in Figure 4, and the parameter change curves during the simulation are as follows:  
Figure 5 shows GA is used to tune initialization parameters at a specific time point. The results in Figure 5a-c demonstrate that all three parameters of the ADRCs are tuned at only 0.5 s, except for the three-parameter values of the ADRCs in Group 3, which fail to meet the requirements for adjusting parameters and, therefore, do not change. Figure 6 demonstrates the results of the simulation using initialization and optimized tuning parameters. Simulation results from (a), (b), (d), and (e) indicate that the tension of each segment has different degrees of oscillation and overshooting at the beginning and finally tends to be stable when the initialization parameters are used for simulation, while the tension of each segment is stable within the set range with no oscillations or overshooting when using optimization adjust parameters. Results (c) are the same because ADRC3's initialization parameters are optimized for tuning.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Value** & **Units** \\ \hline \(A\) & 2.7\(\times\) 10\({}^{-5}\) & m\({}^{2}\) \\ \(E\) & 4.89 \(\times\) 10\({}^{9}\) & Pa \\ \(E_{c1}\) & 0.463 \(\times\) 10\({}^{9}\) & Pa \\ \(R_{0}\) & 0.5 & m \\ \(R_{1},R_{4}\) & 0.0925 & m \\ \(R_{2}\) & 0.93 & m \\ \(R_{3}\) & 0.3 & m \\ \(R_{5}\) & 0.5 & m \\ \(r_{1},r_{2}\) & 0.152 & m \\ \(L_{1}^{*}\) & 4.628 & m \\ \(L_{2}\) & 2.65 & m \\ \(L_{3}\) & 20.2 & m \\ \(L_{4}^{*}\) & 2.3 & m \\ \(L_{5}^{*}\) & 1.391 & m \\ \(L_{d1},L_{d2}\) & 10 & m \\ \(L_{c1}\) & 3.5 & m \\ \(l_{1}\) & 0.154 & m \\ \(l_{2}\) & 0.35 & m \\ \hline \hline \end{tabular}
\end{table}
Table 7: Global tension system parameters.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Controller**} & \multicolumn{3}{c}{**Parameters**} \\ \cline{2-4}  & \(\mathbf{k_{p}}\) & \(\mathbf{k_{i}}\) & \(\mathbf{k_{d}}\) \\ \hline PID1 & 1 & 12 & 0 \\ PID2 & 1 & 12 & 0 \\ PID3 & 1 & 12 & 0 \\ PID4 & 1 & 12 & 0 \\ PID5 & 1 & 12 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The parameters list of PID controller Based on the comparison, it can be concluded that the tuning of ADRC parameters using GA is more efficient and effective compared to the traditional empirical method of parameter tuning. GA is a good solution for the problem of lengthy and complicated tuning in ADRC. Table 9 displays the results of optimized tuning of the parameters in Table 4 using GA.
# IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring
## 4 Experiment and Results
### Latency Analysis  
The most important advantage of fog computing is reducing the latency between the edge and the cloud. In our experiments, the latency from the edge to the cloud when the fog was not used was 1.7 s for the transmission of one 1.5 KB JSON file on the 3G network. This time is required when estimations are performed on the cloud and to warn the driver if a violation occurs. When the fog layer is used for the estimation, the warning message requires around 0.08 s, and transmission from the fog to the cloud for the violation double check requires 0.6 s. Thus, the fog layer reduces the time required for estimation and for violation warnings.
## 4 Methodology
### Digital Image Processing
#### 4.2.1 Optimal Index Analysis  
The optimal index method analyzes all RGB color combinations based on the total variance and correlation coefficients of multispectral remote-sensing data. Currently, the most widely used method for selecting the best bands is the optimal index factor (OIF) method, which comprehensively considers the information content of single-band images and the correlation between different bands [65; 66]. It is closer to the basic principle of band selection, and the calculation is simple and easy to implement [62; 67]. The formula for calculating OIF is OIF = \(\sum_{i=1}^{3}{Si/\sum_{j=1}^{3}{\left|{Rij}\right|}}\), where Si is the standard deviation of the \(i\)-th band, and Rij is the correlation coefficient between the \(i\)-th and \(j\)-th bands [68; 69]. OIF sorts all possible RGB band combinations generated from remote-sensing images, and the band with the highest OIF value usually has the most information.  
Figure 3: Flowchart showing the data used and methodology followed in the current study.
## 4 Results and Discussion
### Trace Element Composition
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Bead**} & \multicolumn{8}{c}{**Chemical Composition (wt.\%)**} & \multirow{2}{*}{**Total**} \\ \cline{3-10} \cline{6-10}  **Number** & & & & & & & & & & \\ \cline{3-10}  & & & & & & & & & \\ \hline \multirow{2}{*}{mr-13} & \multirow{2}{*}{Gold} & 64.43 & 0.22 & ND & 0.43 & ND & 11.32 & 13.46 & 10.14 & 100 \\  & & _0.98_ & _0.11_ & - & _0.14_ & - & _2.15_ & _1.52_ & _3.62_ & - \\ \hline \multirow{2}{*}{mr-14} & \multirow{2}{*}{Gold} & 62.10 & 0.32 & 1.28 & 1.09 & ND & 2.34 & 4.38 & 28.49 & 100 \\  & & _6.40_ & _0.26_ & _0.46_ & _0.11_ & - & _1.93_ & _2.08_ & _2.16_ & - \\ \hline \multirow{2}{*}{mr-15} & \multirow{2}{*}{Gold} & 67.90 & 0.30 & ND & 0.49 & ND & 5.79 & 22.30 & 3.22 & 100 \\  & & _8.50_ & _0.09_ & - & _0.06_ & - & _1.45_ & _9.02_ & _0.58_ & - \\ \hline \multirow{2}{*}{mr-16} & \multirow{2}{*}{Sliver} & 63.60 & 0.11 & ND & 0.37 & ND & 7.07 & 4.93 & 23.92 & 100 \\  & & _4.98_ & _0.13_ & - & _0.14_ & - & _0.26_ & _1.45_ & _3.91_ & - \\ \hline \multirow{2}{*}{mr-17} & \multirow{2}{*}{Sliver} & 73.54 & ND & ND & 0.36 & ND & 6.19 & 4.05 & 15.77 & 100 \\  & & _8.62_ & - & - & _0.10_ & - & _0.74_ & _1.61_ & _6.51_ & - \\ \hline \multirow{2}{*}{mr-18} & \multirow{2}{*}{Sliver} & 74.74 & 0.14 & 0.57 & 0.85 & 10.50 & ND & 1.85 & 11.33 & 100 \\  & & _7.37_ & _0.15_ & _1.02_ & _0.96_ & _0.63_ & - & _1.28_ & _6.65_ & - \\ \hline \hline \end{tabular}  
* ND: Not Detected.  
\end{table} 
Table 2: SEM-EDS analytical results of metal foil in foil glass beads excavated from the Tomb [PERSON] in Korea.
## 4 Artificial Intelligence for Vehicular Sensor Data Processing
### AI Algorithms Applicable to Virtual Sensors
#### 4.2.2 Classifier Trees  
An important alternative to an ANN is the linear regression tree--each branch (computing a weighted sum with an own set of coefficients) is like an \"ANN without activation\" decision; this decision is now represented by the switching to another specific branch.  
The regression of sub-domains is apparently profitable for training datasets like the ones produced at an engine test-bench with specific load and/or speed \"steps\". We explored this alternative of classifier trees using WEKA (Waikato Environment for Knowledge Analysis) [39]. This softwareenvironment was used, in the broader context of this paragraph, in several AI algorithm assessments as a comprehensive \"workbench for machine learning\".  
WEKA encloses many classifiers and regressors (Bayes classifiers, SVMs (support-vector machines) for supervised ML, etc.), as well as decision trees, e.g., ID3 (Iterative Dichotomiser type 3) or C4.5 (improved version of ID3 of the CART--classification and regression tree-- type). In other fields of data analytics, e.g., clustering, WEKA offers methods like k-means (for groups of k measurement means) and EM (expectation-maximization) (of the likelihood of a statistical model estimated parameters with the observed data).  
The evaluated classifier tree (applied to the engine test-bench dataset), belonging to a fifth category (in reference to the above-mentioned type 3 and 4 algorithms) builds a model (M) via a \"divide et impera\" procedure, presented in Figure 5. We chose the optimized M5 method (M5P, or the M5 method that has been \"pruned\" or \"trimmed\") that starts with a preliminary data sub-domain division and then applies the linear M5 model to each such sub-domain. The M5P alternatives offered by WEKA included RandomTree (random successive approximation of the tree), RandomForest (random combination of trees), and RepTree (repetitive combination of several trees).  
Nevertheless, precaution is needed in adopting tree models: even if training data may exhibit a discrete structure for one or more inputs, this may only be frequent on experimental benches (e.g., in the standard testing of engine duty cycles and/or step-wise dynamometric brake regime) but not in road vehicles use with continuous load and speed variation.  
Such a \"model contamination\" due to a pseudo-discrete input data structure is similar to the \"memory effect\" caused by scarce training sets; their number can be as low as the number of nodes available in the ANN models--an odd synthesis could even allocate an unwanted individual neuron activated only for the respective single set of inputs and delivering just the particular training output.
# The eIDAS Regulation: A Survey of Technological Trends for European Electronic Identity Schemes
## 9 Lessons Learned
### Security Considerations
In an attack, this origin would be different from the attacker's origin, where the QR code is displayed to the user; (ii) the authentication request can be made short-lived. An attacker would have to request a new authentication request (e.g., QR code) frequently and update it on its website as well;(iii) the eID means app can warn the user when logging in at a new application for the first time; (iv) the eID app can create user-specific QR codes that the attacker cannot generate; and (v) the eID means may demand the user to provide an additional authentication factor after the usage of QR code. Concerning the solutions that are using PWD + SMS, we would like to elaborate on some security considerations in their case. The OTP sent through SMS is susceptible to various kinds of attacks, such as SS7 vulnerabilities, malware attacks on smartphones, and SIM swapping [65; 66]. This is the main reason why both NIST and PCI-CSS [8; 67] deprecate the use of SMS. However, this solution is still the most often used implementation within MSs eID means (13 eID means). We identify the following two reasons for this choice. First, SMS is easy to integrate into user experiences based on the use of smartphones. Second, SMS can easily support scenarios where the user has a mobile phone that is not a smartphone with internet connectivity. As a final remark, we would like to highlight some security considerations specific to the implementation choices of solutions based on OIDC. Most of the solutions under analysis are secure against code interception/replay attacks [32; 52] due to the adoption of PKCE. Furthermore, most of the solutions are adopting the usage of request or request_uri OIDC parameter and the private_key_jwt client authentication methods. These parameters can play an essential role in the security improvement of the solutions. The former provides a way to encapsulate the authorization query parameters in a single object to avoid tampering and provide message integrity. The latter provides a higher level of protection in comparison with symmetric methods (e.g., client_secret) as reported in [33] due to the lack of demand for storing secret keys at the authorization servers.
## 5 Conclusions  
In this paper, solvent-free CaCO\({}_{3}\) NFs and modified asphalt binders were prepared based on CaCO\({}_{3}\) NPs. Through the tests of CaCO\({}_{3}\) NFs' microstructure and properties, as well as the modified asphalt binders' performance, the main conclusions can be summarized as follows.  
1. The results of microstructural characterization proved that the CaCO\({}_{3}\) NFs were successfully prepared. Besides, the TG-DSC test confirmed that they were good enough to be applied in the asphalt binder at conventional temperatures due to their excellent thermal stability.
2. After the CaCO\({}_{3}\) NFs addition, for modified asphalt binders, the penetration and softening point increased, and the ductility obviously increased, indicating that the high-temperature performance was negatively affected and the low-temperature performance was improved with the addition of CaCO\({}_{3}\) NFs.
3. The effects of CaCO\({}_{3}\) NFs addition on rheological properties were investigated by DSR tests. The addition had a slight negative effect on the high-temperature properties of asphalt binder. However, the positive influence at low temperatures was rather evident, especially at lower temperature (i.e., below -20 \({}^{\circ}\)C).
4. The BBR tests further confirmed that the addition of CaCO\({}_{3}\) NFs enhanced the low-temperature cracking performance of the asphalt binder, and the influence gradually became more obvious with the increase of the modifier percent at temperatures below -12 \({}^{\circ}\)C. However, further research is needed to determine the optimal amount.
5. SEM observation indicates that the compatibility with asphalt binder was improved after chemically grafting the surface of the CaCO\({}_{3}\) NPs into CaCO\({}_{3}\) NFs. Compared with the 5% CaCO\({}_{3}\) NPs addition, the same dosage of CaCO\({}_{3}\) NFs showed better compatibility with asphalt binder.
6. The CaCO\({}_{3}\) NFs addition provides new ideas for the better application of original nanomaterials in asphalt binder. However, based on the content of each component in the CaCO\({}_{3}\) NFs, the price required was about three times that of the original CaCO\({}_{3}\) NPs at the same weight. Moreover, the energy consumption during the complicated preparation process also increased costs, so inexpensive production technology is required for further development.  
Conceptualization, R.L. and J.P.; Formal analysis, T.L. and Y.L.; Methodology, C.S., R.L. and J.C.; Supervision, R.L. and J.Z.; Validation, C.S. and R.L.; Writing--original draft, C.S.; Writing--review & editing, C.S. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Low-temperature bending rheological parameters of short-term aged (STA) and long-term aged (LTA) asphalt binders: (**a**) creep stiffness modulus; (**b**) creep rate.  
**Funding:** This research was funded by [the Special Fund for Basic Scientific Research of Central College of Chang* an University] grant number [Nos. 300102219316 and 300102219308]. The authors gratefully acknowledge their financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Materials and Methods
### Statistical Analysis  
The IBM statistical package of the social sciences (SPSS) (IBM, Chicago, IL, USA) version 27.0 was used for data analysis. All the variables were tested for normality using the Shapiro-Wilks test, and all those that were not normally distributed were log-transformed. The Grubb test was used to detect outliers, while non-negative matrix factorization was used to deal with all the missing values. All the participant's characteristics are expressed as means and standard deviation. The difference between gender stratified by SSBs quartiles was determined using a one-way analysis of variance (ANOVA). The post hoc test and Bonferroni correction test were used for multiple comparisons of the observed mean. The cut-off points (19, 40, 56 and 104) and percentiles (20, 50, 65 and 90) were used to stratify SSBs data into quartiles Q\({}_{1}\), Q\({}_{2}\), Q\({}_{3}\), Q\({}_{4}\): (Q\({}_{20}\) = 19; Q\({}_{50}\) = 40; Q\({}_{65}\) = 56; Q\({}_{90}\) = 104). High SSBswas defined as \(\geq\)55 g/day intake and recommended limit as \(\leq\)55 g/day sugar by SA-FBDG, which is equivalent to 6-10% of total energy intake [30]. A Mantel-Haenszel extension test for trend was used to assess whether there was a linear trend in the SSBs quartiles. Binary logistic regression analysis was applied to determine the association between SSBs consumption and MetS components. The significance level was set at \(p\leq 0.05\).
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Channel Model  
This part presents the channel model of the VLC-based channel model for LEDs. A general VLC channel model comprises two primary components, namely the propagation paths and channel characteristics. The paths may include line-of-sight (LOS) and NLOS components, with both direct and reflected transmissions. Channel characteristics include path loss, delay spread, and channel gain, which are influenced by the environment and system design.
# Effect of Electrospun Nanofiber Additive on Selected Mechanical Properties of Hardened Cement Paste
## 2 Materials and Experimental Work
### Sample Preparation and Testing Methods  
As is known, research on the mechanical properties of hardened cement pastes is very difficult to conduct, which is due to the changes in their volume and the difficulty of controlling spontaneous cracking during the maturation process. For this reason, cement paste samples usually have very small dimensions. Figure 2 shows the briquette and cubic samples used for the tensile strength test and the compressive strength test in compliance with the specifications of ASTM C307-03 (2012) [29] and ASTM C109/109M-16a [30], respectively. A set of three briquettes and three cubic samples of each composite cementitious material as well as of the plain cement paste was prepared with the constant water/binder ratio of 0.5. The water/binder ratio was chosen for consistency with previous works as well as for comparison with the literature [21; 22]. All of the samples were matured for 28 days in water under laboratory conditions, namely, an ambient temperature of \(23\pm 2\)\({}^{\circ}\)C and relative humidity of 50%. The SEM samples were prepared by using a cross section obtained from the failure sample from the tensile strength test. SEM samples had a square section with a polish surface and dimensions of \(5\times 5\) mm. For thermogravimetric analyses, five pulverized samples were prepared.  
Figure 2: Samples for mechanical strength tests: (**a**) briquette samples; (**b**) cubic samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Applied Voltage (kV)** & **Syringe’s Volume (mL)** & **Taylor Cone** & **Needle-to-Collector** & **Pumping Speed** (\(\mu\)L/min) \\ \hline
12 & 12 & 20 & 60 & 30 \\ \hline \end{tabular}
\end{table}
Table 3: Input factors of the electrospinning process.  
In this study, the tensile strength tests were conducted by means of a mortar tensile strength test device with a capacity of 5 kN, in compliance with ASTM C307-03 (2012) [29]. The compressive strength tests were conducted by means of a hydraulic universal testing machine with a capacity of 1000 kN according to ASTM C109/109M-16a [30]. The SEM analyses were conducted using the Zeiss Merlin Compact system, with input parameters that were set up as follows: an accelerating voltage of 3-5 kV and a working distance of 7.1-7.9 mm. To obtain the highest resolution of SEM results, the samples were coated with a 5 A platinum layer. The TEM analyses were done using the FEI Tecnai F30 Twin system, where the input parameters were set up as follows: an acceleration voltage of 300 kV. In addition, the TGA analysis was conducted using a TA Instruments SDT-Q600, and the input parameters were set as follows: nitrogen atmosphere condition, a flow rate of 100 mL/min, heating velocity of 10 \({}^{\circ}\)C/min, and a temperature range from room temperature to 1000 \({}^{\circ}\)C.
## 6 Structural Performances  
### Seismic and Wind-Resistant Systems  
Due to the evaluation of geometric and material nonlinearities, time history and nonlinear inelastic analyses are commonly adopted to predict the precise structural performance of PFISBs [111]. According to [PERSON] and [PERSON] [11], 3D IMUs in PFISB braced systems has a reduced structural capacity because of torsion but a raised base-shear due to additional columns. The peak displacement, period ratio, and inter-story drift for the multi-story PFISB were found within code restrictions [112]. This protected the structural stability of the PFISB in seismic zones. Due to the discontinuous columns and beams, rigid diaphragms are challenging to achieve, resulting in a poor damage prediction. So, it is critical to measure the performance against fundamental design objectives. For the 11-story PFISB structures, [PERSON] et al. [113] found that increasing the joint stiffness reduces the drift ratio. The stiffness study on the IMC of the PFISBs' structure found that increasing the translational stiffness reduces the inter-story drift ratios better than increasing the rotational stiffness [114]. The 18-story PFISB with a concrete core met the design code's requirements in both fields and numerical simulation [115]. However, using dampers in the IMC was advised.
## 4 Proposed Framework
### Preliminaries
#### 4.1.3 Hyperledger Indy  
It is a permissioned public Hyperledger where anyone can read from the ledger, but only authorized principals can write on it. It is based on Sovrin [27] and aims to offer a self-sovereign identity based on a blockchain. For example, the government of British Columbia uses it in Canada to implement its eID solution [28].  
It could be seen as a ledger that contains certificates linking DIDs to public keys and signed by a Trust Anchor TA. A TA is an individual or an organization (government, insurance, hospital, clinic, etc.) that is already known and trusted by Hyperledger Indy. They act as certification authorities, adding a new Trust Anchor to the ledger. They have the right to write in the ledger. Whenever a TA delivers a document (an Identity) containing fresh DID, they add a link (certificate) between the DID and its public key to Indy, as shown in Table 3. This entry is added to the ledger to attest that the owner of the identity containing the DID \(D_{a}\) is the one who knows the private key associated with \(K_{a}\). The main information of this entry are \"\(D_{a},\ K_{a}^{0}\), DID-TA, \(\{H(D_{a},K_{a},\text{DID-TA})\}_{K_{a}^{-1}}\)\", where DID-TA is the DID of the Trust Anchor that has signed this record. Indy contains many transactions that have this kind of record. The same TA could sign many associations between DIDs and public keys, as shown in Table 3.  
Hyperledger Indy also contains information related to TAs. We find a certificate for each trusted authority containing its DID, its service endpoint (URI or IP address, transport protocol, and port), public key, the schema of its delivered documents (the list of attributes that could be found in the credential), etc. Therefore, having the DID of a document, we can easily obtain the producer's public key and join him through its service endpoint.  
Assume that an agent \(B\) wants to prove to \(A\) that he has an attribute included in a credential containing the DID \(D_{b}\); then, they need to proceed as follows:  
* The agent \(B\) sends the credential containing the DID \(D_{b}\) to \(A\).
* \(A\) reads from Hyperledger Indy, the public key \(k_{b}\) associated with the DID \(D_{b}\).
* \(A\) asks \(B\) to prove that he owns the private key associated with \(k_{b}\) using a challenge-response protocol such as the example of Table 4. At the first step, \(A\) sends to \(B\) a fresh random number \(N_{a}\) (called a nonce and used as a challenge). At the second step, \(B\) returns the nonce signed using the appropriate private key \(k_{b}^{-1}\).To verify if the challenge was answered correctly, \(A\) checks if the message was signed using the private key associated with \(k_{b}\).  
DIDs and public keys are randomly generated by the document's owner in which the DID appears. DIDs and public keys are not necessarily correlated to the name of their owners, allowing them to receive anonymous services.