esa-sceva/satcom-chunk-collection huggingface dataset:
# Cloud Storage Data Verification Using Signcryption Scheme
## 3 Proposed Scheme
### Data Verification Use Case  
We present a use case to implement our improved ZSS signcryption scheme for verification of stored data in CSP by a Trusted Third Party Auditor (TPA) that can fulfill three desirable properties, public verifiability, privacy-preserving, and blockless verification, as shown in Figure 2. There are three entities involved: user, CSP, and TPA. The user has stored data in the CSP storage. Then, he wants to check his data integrity by appointing a TPA. The TPA generates a challenge variable and sends it to the corresponding CSP. After receiving the challenge from the TPA, the CSP generates proof and sends it to the TPA. Next, the TPA will verify whether the proof given by the CSP is correct through the validity of an equation. The details are described as follows.  
User divided data \(F\) into \(n\) shards of \(m\). \(F=\{m_{1},m_{2},m_{3},...,m_{n}\}\).  
User does a signcryption process for each data shard and generates \(\sigma_{i}\), where \(i\) is the index of each data shard.  
Therefore, the Signcryption \(\sigma_{i}=(V_{i},X_{i},Y_{i})\). Set of signcrypted data is \(Sign=\{\sigma_{1},\sigma_{2},\)\(\sigma_{3},...,\sigma_{n}\}\).  
User stores set of signcryption \(Sign\) to the CSP.  
User stores set of Signature \(Sn=\{V_{1},V_{2},V_{3},...,V_{n}\}\) to their local storage.  
CSP verify data from user by parsing each \(\sigma_{i}\) to \((V_{i},X_{i},Y_{i})\). Then CSP does the unsigncryption process. If successful, store the user's data; otherwise, reject it.  
When the user wants to verify their stored data in CSP, they generate a set of random numbers \(\{i\}_{i\in I}\) and also the set of the signature of the challenged data shards \(Sn=\{V_{i}\}_{i\in I}\), where \(i\) is the index of stored challenged data shards. Then, they send those two variables to the TPA.  
Upon receiving the request from the user, the TPA randomly chooses \(f_{i}\) from \(Z_{p}\) and generates a challenge \(\mathit{chal}=\{i,f_{i}\}_{i\in I}\). Then, it sends \(\mathit{chal}\) to the corresponding CSP.  
After receiving \(\mathit{chal}\) from the TPA, the CSP computes \(r_{i}=X_{i}\oplus Hash_{2}(V_{i},PK_{R},V_{i}SK_{R})\). Then, they can compute \(k_{i}=Hash_{3}(r_{i})\) to decrypt \(Y_{i}\). Therefore, \(m_{i}=Dec_{k_{i}}(Y_{i})\). After that, the CSP generates proof \(\delta\) and sends it to the TPA.  
\[\delta=\sum_{i\in I}f_{i}Hash_{1}(m_{i})P \tag{3}\]  
The TPA generates proof \(\omega\).  
\[\omega=\sum_{i\in I}f_{i}V_{i} \tag{4}\]  
Figure 2: Data verification scheme.  
Subsequently, the TPA checks whether Equation (5) holds.  
\[e(\delta+PK_{S},\omega)=e(P,P) \tag{5}\]  
Finally, the TPA reports the verification results to the user.
## 3 Vision, Key Features and Challenges of 6G
### Vision and Key Features of 6G
#### 3.1.2 Decentralized Network  
Due to the emergence of multi-access edge computing (MEC) in the 5G network, there are several limitations in the centralized network, e.g., privacy, security, trust, incompatibility of the existing protocol to the dynamic connectivity and distributed and ubiquitous computing [166]. Thus, it is necessary to prepare a blueprint of decentralized architecture to support such a dynamic and autonomous network. In this regard, blockchain is a promising technology for the future 6G network and is capable of dealing with these challenges. Blockchain technology can provide a decentralized network management framework that can be used for resource management, data sharing/storage, spectrum sharing and other challenges [172; 173; 174; 175; 189].
## 3 The Basic of GNSS
### Terminology of Positioning Systems  
The following defines the relevant technical positioning terms that are used to compare different satellite location systems in railway applications:  
* Positioning--determining the specific location of a vehicle in relation to the rail network;
* Location system--configuration of technical components that provide location information;
* Spatial resolution--resolution represents the smallest increment of location information;
* Temporal resolution--represents the time lapse between two identical satellite raids over the same point.
# Preparation of Steel Slag Ceramics with Different MgO/Al\({}_{2}\)O\({}_{3}\) [PERSON]
## 3 Results and Discussion  
### Calculation of Viscosity by FactSage Software  
Production of steel slag ceramics includes the processes of proportioning of raw materials, ball milling and mixing, forming, drying, and firing. The processes of drying and firing can be grouped as heat treatment, and the firing process consists of sintering and vitrification. The firing process can make the the ceramic body transform from porous and non-compact to densification. The particles become tight through the solid diffusion. The glass phase is produced during the process and serves as a reaction medium to allow diffusion at lower temperatures [25]. The viscosity of the glass phase directly affects crystallization/crystal growth. These values were predicted by the Viscosity module of FactSage software (FactSage5.5, Thermfact/CRCT, Montreal, Canada, 2016) [26; 27], and the results are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **M1 (g)** & **M2 (g)** & **M3 (g)** & **M4 (g)** & **M5 (g)** & **M6 (g)** \\ \hline SiO\({}_{2}\) & 49.79 & 48.82 & 50.64 & 48.23 & 47.41 & 47.60 \\ CaO & 18.92 & 18.55 & 19.96 & 19.00 & 17.71 & 16.88 \\ Al\({}_{2}\)O\({}_{3}\) & 7.57 & 9.35 & 8.41 & 12.68 & 15.13 & 17.63 \\ Fe\({}_{2}\)O\({}_{3}\) & 9.75 & 9.55 & 10.26 & 9.77 & 9.57 & 8.94 \\ MgO & 9.05 & 8.88 & 5.12 & 4.88 & 4.74 & 3.54 \\ K\({}_{2}\)O & 1.77 & 1.74 & 2.22 & 2.12 & 2.21 & 2.31 \\ Na\({}_{2}\)O & 0.31 & 0.30 & 0.42 & 0.40 & 0.39 & 0.42 \\ TiO\({}_{2}\) & 0.60 & 0.59 & 0.64 & 0.60 & 0.62 & 0.60 \\ MnO\({}_{2}\) & 0.92 & 0.91 & 0.98 & 0.94 & 0.88 & 0.79 \\ MgO/Al\({}_{2}\)O\({}_{3}\) & 1.2 & 0.9 & 0.6 & 0.4 & 0.3 & 0.2 \\ \hline \end{tabular}
\end{table}
Table 2: Main chemical compositions and characteristics of the experimental batches.  
The decrease in the MgO/Al\({}_{2}\)O\({}_{3}\) ratio has a significant effect on the viscosity of the samples. The optimum sintering temperature of each sample can be determined by testing its properties in sintering experiments at different temperatures. The optimum sintering temperature is 1120 \({}^{\circ}\)C for samples M1 and M2, 1130 \({}^{\circ}\)C for samples M3 and M4, and 1140 \({}^{\circ}\)C for samples M5 and M6. The viscosity showed an increasing trend with a decreasing MgO/Al\({}_{2}\)O\({}_{3}\) ratio. At the optimal sintering temperature, the viscosity of M1 was 12.4 Pa-s, while that of M6 was 70.1 Pa-s. Generally, when the viscosity is too low, phase separation occurs easily in the process of crystal formation [28] (as is the case for M1). Too high a viscosity is not beneficial for phase polymerization during the crystallization process (as in the case of M6). The better viscosity should be 20.5 Pa-s, as observed for sample M3, as the phase separation may be blocked, and the crystal polymerization will be promoted. These factors are favorable for the improvement of mechanical properties.
## 2 Materials and Methods
### Experiment 1
#### 2.1.4 EEG Measurement and Analysis  
A 64-channel Ag-AgCl electrode cap, an EGI NetAmps 200 amplifier, and Netstation acquisition software (Electric Geodesics, Inc., Eugene, OR, USA) were used for the EEG measurement. The average of all channels was used as the reference, and sampling was done at 250 Hz, with a band-pass filter ranging from 0.3 to 100 Hz. FFT was applied to EEG segments (4.09 s interval, 1024 points, Hanning window), which were overlapped by 50% (EMSE Data Editor 5.3, Source Signal Imaging, Inc., La Mesa, CA, USA). Data from the first 10 s were excluded from the study for reason of transients in attention. The average power in 10-12 Hz was adopted as the mu rhythm power. Each power value obtained was converted into a logarithm to ensure the normality of the data. EMSE Suite Data Editor 5.3 Release Candidate 3 (Source Signal Imaging, Inc.) was used for data analysis. Mu rhythm suppression in the observer watching the actor's action was adopted as the index of MNS.  
Mu rhythm suppression in each condition was calculated using data from the resting state as reference. Because the sensorimotor area showed mu rhythm suppression, EEG was analyzed in two ROIs: left central (LC) (4 electrodes around C3) and right central (RC) (4 electrodes around C4).  
The main effects of conditions (the TUI/OBJECT condition, the TUI/CAN condition, and the GUI condition) and brain areas (LC and RC), and their interactions were tested by two-way ANOVA. For multiple comparisons, we used modified sequentially rejective Bonferroni procedure. Before significance testing, outliers in each subject were identified by Smirnov-Grubbs test (\(p<0.01\)). All the outlier channels were excluded from the subsequent statistical analyses.
# Bearing Severity Fault Evaluation Using Contour Maps--Case Study
## 2 Method
### Contour Map  
The concept of a \"Contour Map\" is simple and well-known. Contour maps are graphs that, although being two-dimensional visually, they actually represent three dimensions, by including contour lines (or isolines) [39]. In this way, a contour map compactly represents a function of space using a small set of isolines. [PERSON] [51] defined isolines as lines of intersection between the points of a given surface and a family of parallel planes. Thus, each isoline classifies and separates the points of the underlying space, joining the points with a constant and equally spaced contour value [43,52], as shown in Figure 2. In this figure, the contour map is shown in two dimensions, where it is indicated that each isoline is associated with a height (frequency amplitude, in this case). These heights, represented by different colours on the contour map, corresponding to the real heights of the surfaces formed for each fault level. The curve corresponding to a specific failure level is shown in purple, as an example.  
As previously indicated, this research is based on contour maps, which are generated to describe the evolution of the failure of a bearing component, with respect to its fundamental frequency. Following the recommendations for the construction of contour maps, the spacing value (contour interval) between isolines will remain constant and, in this case, correspond to values of amplitude in the frequency of the vibration. The range of values is selected according to a series of criteria, which are detailed in the following sections.  
As an example, in Figure 3, the evolution of the amplitude of a fundamental frequency in the presence of a fault is observed, where the set of values that characterise the analysis  
Figure 2: Contour map of the envelope spectrum of the fundamental frequency of a bearing failure.  
amplitudes are in a range from 0.005 to 0.025, equally spaced to a contour interval equal to 0.005. The contour map is, therefore, equivalent to having the evolution curves of a fault frequency in a compact form.  
It should be noted that the signal associated with the normal operating state has an amplitude with a value other than zero. However, its magnitude is negligible, compared to the amplitude of a severe fault. For this reason, Figure 3 shows the closeness between the normal state signal and the zero-value axis. Table 2 presents the value of the amplitudes of the evolution of a fault frequency shown in Figure 3.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 4 Analysis
### Line of Sight Scenario
#### 4.1.2 RX at 700 m
2 FSK-50 can transmit only 138 short packets in the same period. At this distance from TX, a few long packets can be transmitted per hour. The reference 2 FSK-50 can transmit roughly 6 packets per hour while OFDM2-100 and OQPSK-50 just 10. We highlight those two PHYs. Therefore, if the user is interested in maximizing the throughput, 4 FSK-FEC-200, 2 FSK-100 and OFDM2-100 are the most performing PHYs to do so with short packet. With long packets, OFDM2-100 and OQPSK-50. Figure 8e shows the average electric charge consumption per packet of 127 B transmitted. For short packets, the reference consumes 2178 \(\upmu\)C whereas the less power-hungry in this case, OFDM2-100, consumes 964 \(\upmu\)C.  
For long packets, average power consumption is depicted in Figure 8f. 2 FSK-50 consumes in average 47.5 mC. The less electric charge-consuming PHYs in this scenario are OFDM2-100 and OQPSK-50, with 26.8 mC and 29.1 mC respectively. Should the potential user be focused on reducing electric charge consumption, dotted-pattern highlighted bars in Figure 8e,f depict the less electric charge-hungry PHYs in this case.
# Development of User-Participatory Crowdsensing System for Improved Privacy Preservation
## 4 Implementation of Proposed System
### Data Processing Node  
The data processing node includes an application provided by the _SP_. Figure 10 shows the structure of such an application. It includes a participant management module, a data processing module, the Kaa SDK, a participant database, and a sensing data management database.  
As shown in Figure 10, the session key management submodule in the participant management module stores and manages the ID and the session key used by a participant to provide sensing data,  
Figure 10: Application structure of data processing node.  
Figure 9: Profile management interface of Kaa SDK.  
(i.e., data provider). The participant database for storing session keys is implemented in PostgreSQL. The schema of the participant database is shown in Table 6. When the data provider sends a join request to a data processing node, the node generates a session key and stores it in the participant database along with the data provider ID. (1) The session key management submodule securely transfers the session key encrypted with this public to the data provider. (2) When providing the sensing data, the data provider encrypts the data with a session key.  
The data processing module consists of a schema-based data validation submodule and a data aggregation submodule. The former receives the sensing data from the data provider using the CoAP protocol. The encrypted sensing data is decrypted using the session key stored in the participant database. The data validation submodule checks whether the sensing data satisfy the schema specified by the data requester. If it is determined that the sensing data are valid, they are transferred to the data aggregation submodule, which collects and appropriately processes them in the sensing data management database. The databased is constructed using MongoDB, considering the characteristics of the crowdsensing system, which can handle various types of sensing data. (3) The data aggregation submodule transmits directly the encrypted sensing data with the ID of the data requester (\(u_{dr}\)) (i.e., public key) to the data requester. (4) When the requested sensing data are received, the data aggregation submodule sends the ID of the data provider and the reward payment amount to the \(SP\). Finally, the data aggregation submodule deletes all data related to the data provider including the session key, and the reward data are from the sensing data management database. If the data validation submodule determines that the sensing data are not valid, they are discarded.  
Through this series of processes, the personal information included in the sensing data and the ID of the data provider are not linked, and, at the time of providing the reward, the \(SP\) cannot know any information other than the reward payment amount.
## 2 Materials and Methods
### Chemical Component Characterization  
A quantity of 5 g of HR1901-W was dissolved in 10 mL of 50% methanol and filtered using a 0.45 uM polyvinylidene fluoride (PVDF) syringe filter (Shatman, Maidstone, UK). The filtrate was concentrated under reduced pressure. To the concentrated sample, 10 mL of methanol was added; then, the sample was sonicated for 10 min and centrifuged (3000 rpm/5 min). The supernatant was filtered using a 0.45 uM PVDF syringe filter and used as a microfraction and NMR test solution. For the characterization of compounds, preparative HPLC and NMR spectra were obtained. Identification of the main chemical components was performed by modifying [PERSON] et al. [37]. We isolated HR1901-W using a preparative HPLC instrument combined with a Waters e2695 separation module HPLC system, a Waters 2998 photodiode array detector (Waters Co., Milford, MA, USA), and a phenomenex luna C18 column (10 mm \(\times\) 250 mm \(\times\) 5.0 um; Phenomenex, Torrance, CA). The mobile phase comprised solvent A, 0.1% trifluoroacetic acid in distilled water (\(v/v\)), and solvent B, acetonitrile. The parameters for the HPLC analysis were as follows: 0-2 min: 90% A, 10% B; 2-15 min: 70% A, 30% B; 15-16 min: 30% A, 70% B; 16-18 min: 30% A, 70% B; 18-19 min: 30% A, 70% B; 19-22 min: 10% A, 90% B; 22-23 min: 10% A, 90% B; 23-30 min: 90% A, 10% B gradient system. The analysis time was 30 min, the detection wavelength was 200-400 nm, the sample injection volume was 90 uL, the flow rate was 2.5 mL/min, and the column temperature was maintained at 30 \({}^{\circ}\)C. \({}^{1}\)H NMR and \({}^{13}\)C NMR spectra were recorded in CDDI\({}_{3}\) using the JEOL JNM-ECX400 (\({}^{1}\)H-400 MHz, \({}^{13}\)C-100 MHz) FT NMR systems (Tokyo, Japan) with Me\({}_{4}\)Si as the internal standard.  
2-Furoic acid -\({}^{1}\)H-NMR (400 MHz, CDCl\({}_{3}\), Me\({}_{4}\)Si) \(\delta\): 6.58 (7-H), 7.20 (8-H), 7.72 (6-H) \({}^{13}\)C-NMR (100 MHz, CDCl\({}_{3}\), Me\({}_{4}\)Si) \(\delta\): 113.05 (2-C), 119.18 (3-C), 146.56 (1-C), 148.21 (4-C), 159.45 (5-C).
## 5 Results
### Clustering Effect
For the results of the K-means algorithm in all scenarios, because the K-means algorithm is very sensitive to the location of the initial clustering centers, once the choice is unreasonable, a bad effect is had on clustering results. Once the nodes are evenly distributed among clusters, their energy consumption within clusters is more uniform, and the network lifetime is significantly extended. Meanwhile, DCK-LEACH can solve this problem effectively (see Figure 3), while it uses the Canopy algorithm to calculate and optimize the initial K value of the K-means  
Figure 3: The first round of clustering results in scenario 1, 2, and 3.  
algorithm, and reduce the possibility of the clustering getting stuck in local optima, the clustering effect is better.
## 4 Production System Case Study
### Design of the Generic Case Study
#### 4.1.2 Key Performance Indicators  
The objectives could be qualitative or quantitative. The research goal was to study the behavior of the different models in different demand scenarios and configurations of a production system. The results were calculated for all model simulations to evaluate the response according to the following key performance indicators:  
* Cumulated market demand (# thousand products);
* Cumulated real demand (# thousand products);
* Cumulated production (# thousand products): the cumulative sum of all units produced over the 500 simulated-production weeks;
* \(\mathcal{O}\) Availability of the production plant (%);
* \(\mathcal{O}\) Performance at the final production step (# thousand products/week);
* \(\mathcal{O}\) Quality at the final production step with one-way and no loops (%);
* Cumulated capacity utilization of the production plant (%);
* \(\mathcal{O}\) Labor productivity (products/employee \(\times\) week);
* Cumulated stocks (# million products);
* \(\mathcal{O}\) Production lead time (# weeks);
* Cumulated service level (%);  
Figure 3: Structure of the simulation case study.  
* Profits ($ million);
* Cumulated operational costs ($ million);
* Cumulated investment ($ million);
* Return on investment (ROI) (%).
## 3 New Pulse Duration Limits of Modelocked Thin-Disk Lasers
### Challenges
In addition, typical gain cross-sections of broadband materials are moderate, which further contributes to a higher QML threshold. During this regime, peak powers can become significantly larger than in cw modelocked regime. * At short pulse durations (\(<\)200 fs), the SESAM rollover in reflectivity can already occur at moderate pulse fluences due to TPA. As we discussed in 2.3.1, this can lead to multi-pulsing instabilities at moderate pulse energy and can be one of the main limiting factors. * SESAMs with short recovery times are beneficial to push the pulse duration limits of such sources [28, 29]. A common way of tailoring the recovery time of the absorber is to grow the QW absorbers at low-temperature (LT). LT-growth introduces additional defects in the lattice that result in fast nonradiative decay times [73, 74, 75]. However, this results in extra nonsaturable losses, which can lead to increased thermal effects [52, 74]. Although these requirements represent additional challenges, we believe that current state-of-the-art SESAMs, based on the guidelines presented in the paragraph 2.3.6 are already suitable for the next power-scaling step with sub-100 fs pulse duration.
## 4 Results and Discussion
### Pressure Measurements
#### 4.1.1 Case 0--No Reduction in the Blood Vessel Lumen  
The fluid pressure plots in the ventricle and aorta were similar to one another during the inflation of the ventricular balloon, i.e., the pumping of fluid into the hydraulic system during the cardiac contraction cycle (systole). Also, the left ventricular pressure was slightly higher for this period compared to aortic pressure values (Figure 4a). Figure 4c shows the maximum fluid pressure values measured in the aorta (after the heart model), the left ventricle of the heart model, obtained during testing of the hydraulic system with the control valve open, meaning that there was no reduction in the blood vessel lumen. The maximum fluid pressures \(p_{max}\) in the aorta, depending on the frequency \(f\) of operation of the device between 60 and 160 cyc/min, ranged from 113 mm Hg for 60 cyc/min to 157 mm Hg for 140 cyc/min (Figure 4c). The differences in the maximum fluid pressures in the hydraulic system in the left heart ventricle and aorta were in the range of approximately 27 to 34 mm Hg, depending on the frequency of the device.  
The pressure in the pneumatic system, as measured with a pressure sensor located near the balloon set for the external gas supply system operating frequencies of 60, 100, 120, 140, and 160 cyc/min, ranging from about \(-\)170 mm Hg to about 732 mm Hg (Figure 4b). The maximum gas (helium) pressures upstream of the balloon set, when the control valve in the hydraulic system was open, varied from 443 mm Hg for 60 cyc/min to about 732 mm Hg for 160 cyc/min (Figure 4c).  
Figure 4: Pressures \(p\) in the hydraulic-pneumatic system for different operating frequencies \(f\) of the device for case 0—no reduction in the blood vessel lumen. (**a**) Fluid pressures in the hydraulic system as measured by the sensors. (**b**) Pressure \(p\) in the pneumatic system measured by a pressure sensor placed near the balloon assembly. (**c**) Maximum pressure values \(p_{max}\).