esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 3 Privacy-Protecting Attributes Classification for the Storage of Cloud Data
### Key Management
#### 3.2.3 Key Governance  
To ensure the security of cryptographic keys within the cloud service provider (CSP), key governance can be offered as a service. Further, owners of the data may have concerns about maintaining the key's privacy [55]. The utilization of hardware privacy modules has proven to be beneficial in terms of managing and safeguarding cryptographic keys [56]. Various key governance methods have been proposed, including the use of a group key governance system to handle encoded keys [14]. Additionally, the implementation of a master and session key governance system has been suggested [45]. These measures aim to enhance the security and privacy of cryptographic keys within the cloud environment.
## 3 Design and Development of the Measuring System
### Software Design and Data Algorithm
#### 3.3.2 Self-Inspection and Calibration Module  
The serial port control is configured using VISA (Virtual Instrument Software Architecture) in LabVIEW to read the address information of each sensor, so that its proximitor can successfully establish a connection with the computer, and the upper computer can read and analyze the measured data. After beginning, display the self-inspection progress bar via a control. If there is a sensor connection failure, display its number and report. Then, fix it until the self-inspection can complete normally and successfully. The purpose of the calibration module is to obtain the distance between each group of sensor probes. When the sensor array device is placed in the calibration platform, the measured displacement from the sensor probe to the calibrating metal plate will be transmitted to the program for analysis. Following the measuring principle, the known distance \(H^{{}^{\prime}}\) between the calibration plates is read, and the measured values of each sensor could be gained. Set the distance from the sensor probe 1 to the calibration metal panel 1 as \(h_{1}^{{}^{\prime}}\), and the distance from the other sensor probe to the metal panel 2 as \(h_{2}^{{}^{\prime}}\). Then, the displacement \(h\) for a group of probes would be obtained, namely \(h=H^{1}-h_{1}^{{}^{\prime}}-h_{2}^{{}^{\prime}}\). After calibration, all the displacements at each group will be stored and saved.  
Figure 10: The measurement processing workflow.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 3 Thermoplastic Resin Toughening Method
### Reaction-Induced Phase Separation Theory  
The comprehensive performance of epoxy resin/thermoplastic resin blend systems is closely related to the phase structure of the blend systems [52]. Homogeneous systems cannot significantly improve the fracture toughness of materials, while the construction of multi-phase morphology through dynamic phase separation during the curing process is the key to achieving toughening and strengthening [53].  
Figure 4: (**A**) Schematic representation of the morphologies of epoxy modified with phenoxy and CSR during curing. (**B**) Critical strain energy release rate (GIC) and corresponding critical stress intensity factor (KIC) of neat EP and toughened with 10 wt% phenoxy, phenoxy/CSR-MB (9/1, 7.5/2.5, 5/5, 2.5/7.5 wt%), and 10 wt% CSR-MB. Reproduced from Ref. [46] with permission of MDPI.  
In the late 1980s, [PERSON] et al. [54] introduced the concept of reaction-induced phase separation. Before or early in the curing reaction, when thermoplastic resins and epoxy resins are mixed, a homogeneous system is formed. At this stage, due to the small size of the chemical structures and molecules, the compatibility between the two is relatively good. As the curing reaction progresses, the molecular weight of the thermosetting resin gradually increases, reducing compatibility with the thermoplastic resin. This results in a thermodynamically incompatible system where phase separation begins, leading to the formation of a biphasic structure.  
[PERSON] et al. [55; 56] also investigated phase separation in thermoplastic resin/epoxy systems. They found that the development of phase structures is crucial for toughening epoxy resins, with double-continuous and phase-inverted structures providing more pronounced toughening effects. [PERSON] et al. [57; 58] further studied the phase separation process in epoxy resins, proposing that, as the content of thermoplastic resins increases, the phase structure of the blend transitions from an island structure to a double-continuous structure and ultimately to a phase-inverted structure. The double-continuous phase structure provides the best toughness.
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 1 Introduction and Definitions
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Year** & **Brief Description** & **Methods** & **Wireless Technologies** & **Metrics** \\ \hline \multirow{6}{*}{[26]} & \multirow{6}{*}{2021} & & Three design methodologies: communication-centric, & \multirow{6}{*}{802.11 ad} & \multirow{6}{*}{802.11 ad} \\  & & signal-processing techniques and signal and channel & & & \\  & & mathematical models for & performance metrics; & & \\  & & ISAC/JCR; a comparison of & & multi-beam optimization; & & \\  & & radar and communication goals & time–frequency domain-based optimization; etc. & & \\ \hline \multirow{6}{*}{[41]} & \multirow{6}{*}{2022} & & Both geometric and ML algorithms are addressed and compared, using time, angle, or power measurements; a & \multirow{6}{*}{5G/6G} & Location accuracy, LOS and obstacle detection probability \\  & & & & variety of analytical tools are overviewed & \\ \hline \multirow{6}{*}{[42]} & \multirow{6}{*}{2023} & & Use of OFDM with other methods such as LFM and phase coding and of OTFS for the waveform design; frequency-based and super-resolution methods for the signal-processing part; & \multirow{6}{*}{5G/6G} & Target resolution, AF, Doppler sensitivity and tolerance, PAPR, MI, DIR, complexity, accuracy, CRLB,... \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{6}{*}{[43]} & \multirow{6}{*}{2023} & & Detailed discussions on various precoding and waveform design mechanisms and various optimization mechanisms & \multirow{6}{*}{5G/6G} & User rate, capacity, detection probability, estimation resolution,... \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The top surveys in the current literature addressing joint radio-based positioning, sensing, and communication notions (e.g., ISAC, SLAM, JPSAC, etc. ).
## The NB-IoT Channels and Coverage Enhancements
### Coverage Enhancements and Repetitions  
An NB-IoT channel coverage extension (CE) is implemented by means of repetitions at the expense of higher data rates. The technology design increases the coverage gain of the NB-IoT by 20 dB compared with GPRS carriers, which corresponds to an MCL reaching 164 dB. A repetition-based mechanism allows the NB-IoT devices to be deployable in either remote areas or the challenging, and usually inaccessible, indoor places, such as apartment basements or underground pipelines. The number of repetitions in downlink for NPDCCH and NPDSCH may be configured from predefined values ranging from 1 to 2048, and the number of repetitions configurable for both uplink channels(NPRACH and NPUSCH) ranges from 1 to 128. The concept of repetitions differs from retransmissions, which is applied in cases where message delivery is unsuccessful. The number of repetitions of each channel is determined by evolved node-B (eNB) configuration. However, the shared channels may additionally employ dynamic link adaptation. Operators may define up to 3 CE levels in a single NB-IoT cell simultaneously. The CE levels are defined by, for example, dedicated settings for channel repetitions applicable to each level. The CE level is selected by the NB-IoT UE, accordingly to the network-defined thresholds, based on received signal conditions in downlink (e.g., RSRP). In other words, NB-IoT UE determines predefined NPRACH settings dedicated to every CE level, and the network schedules further transmissions with regard to the NPRACH configuration selected by the UE during the random access procedure. All scheduling information is signaled to the UE through an NPDCCH downlink control information (DCI) object [25]. DCI contains the desired configuration of shared channels, including the number of repetitions, resource assignment, or the MCS.
# A Review of Air Pollution Monitoring Technology for Ports
## 2 Detection Methods for Air Pollutants in Ports  
For ports, the main air pollutants differ in different periods. According to a survey by [PERSON] [21], the air pollutants in ports at different periods are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Pollutant Type** & **Health Effects** \\ \hline \multirow{3}{*}{SO\({}_{2}\)} & It can increase the incidence of respiratory diseases, and the condition of patients with chronic diseases will deteriorate rapidly \\ \hline \multirow{3}{*}{NO\({}_{2}\)} & Damage the airway and cause delayed pulmonary edema, adult respiratory distress syndrome, etc. \\ \cline{2-3}  & \multirow{3}{*}{CO} & Long-term exposure to low concentrations of CO will have varying degrees of impact on the cardiovascular system and nervous system and may cause hypoxemia \\ \hline \multirow{3}{*}{PM2.5 and PM10} & Affects the ventilatory function of the lungs, which can lead to premature death in people with cardiopulmonary disease \\ \cline{1-1}  & \multirow{3}{*}{PM2.5 and PM10} & Affects the ventilatory function of the lungs, which can lead to premature death in people with cardiopulmonary disease \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \end{tabular}
\end{table}
Table 2: Common air pollutants and their impact on health.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Port Development** & **Project Type** & **Air Pollution Sources** & **Main Pollutants** \\ \hline \multirow{3}{*}{Construction Period} & \multirow{3}{*}{Construction work} & Sand and gravel stockpiling, loading and unloading and mixing \\  & & Dust from vehicle loading and unloading & Dust \\ \cline{1-1}  & & Road and site dust & \\ \cline{1-1}  & & Cement unpacking and other dust & \\ \hline \end{tabular}
\end{table}
Table 3: Main air pollutants and pollution sources in the port (Adapted from [21]).  
Based on the main air pollutant components of the port summarized in the table, this paper will mainly discuss the detection methods and monitoring means for these air pollutants.
# Using Science and Technology to Unveil The Hidden Delicacy Terfezia arenaria, a Desert Truffle
## 2 Materials and Methods
### First Steps in Developing a Non-Destructive and Rapid Identification Method for T. arenaria
#### 2.4.1 Phase 1: E-Nose Training  
For the training process (Figure 1), _T. arenaria_'s samples were subjected to one of two pre-analysis incubation temperatures: (**a**) 40 \({}^{\circ}\)C treatment with samples incubated for 1 h at 40 \({}^{\circ}\)C; and (**b**) RT treatment with samples incubated for 1 h at room temperature (i.e., 24 \({}^{\circ}\)C). The samples used for analysing _T. arenaria_'s VOCs profile with the e-nose were clean as previously described, and were kept at 4 \({}^{\circ}\)C until analysis in the first 48 h post-harvest. Three fresh _T. arenaria_ truffles were analysed separately. Two replicates with 4 g of _T. arenaria_ were weighed and introduced in a 10 mL vial for each sporocarp and training method. The _T. arenaria_ truffles were identified as: Terf1, Terf2 and Terf3. The Cyanrose-320 was mounted on a tripod, which could be adjusted for inserting the e-nose needle into the vials for headspace reading. Ten readings per samples were performed. The e-nose was coupled to the computer and PCnose software was used to set the list of parameter settings of the Cyanrose-320 (see Table S1), data acquisition, and analysis. To finish the training phase, an internal data cross-validation was used to assess the accuracy of sample classification in relation to their respective class labels, serving as a measure of effectiveness for the e-nose system [67].  
Figure 1: Schematic representation of the two phases of the non-destructive and rapid identification method for _T. arenaria_ identification using the E-nose Cyanrose-320. First, the e-nose was trained with _T. arenaria_ samples (**A**), and then mushroom and truffle samples belonging to five species were tested for an accurate identification of _T. arenaria_ (**B**). Two pre-analysis incubation temperatures were tested in both phases: samples were kept at room temperature (RT) or heated at 40 \({}^{\circ}\)C.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
The positions of the transmitting point, relay node, and receiving point are defined, the number of reflections is determined, and the mirror points and reflection points at each level are calculated. The validity of the mirror points and the boundaries of the reflective surfaces are assessed. Finally, the reflection paths and the number of effective reflection paths at each reflection point are analyzed. In Section 4, parameters such as the reflection coefficient and incident angle are calculated to determine the reflected field strength at the receiving point. In Section 5, Python simulation software is used to analyze the impact of the positions of the transmitting and receiving points on the number of effective reflection paths. Additionally, the effects of various factors, such as the roadway width and transmission frequency, on the reflected field strength at the receiving point are examined. In Section 6, HFSS simulation software is employed to establish a U-shaped roadway model, using a half-wave dipole antenna as the radiation source. This section analyzes the influence of different factors on the field strength at the receiving point to verify the accuracy of the findings from Section 5.
# Adaptive Emergency Call Service for Disaster Management
## 5 Simulation Results
### Test Environment
#### 5.1.3 Rosenbrock Parameters  
The values of \(a,b\) parameters in the Rosenbrock function are empirically fixed for the three ranges of \(n_{c}\) as shown in Table 4. Figure 7 shows the impact of the \(n_{c}\) and \(n_{k}\) factors on the value of \(T_{\max}\).
## 3 Analytical Model and Experimental Setup  
### Analytical Model  
This section defines the coordinate system and aerodynamic parameters employed in this study. The coordinate system used is the airflow coordinate system (wind axis system) O-xa-ya-za, as depicted in Figure 12. The origin of the coordinates is located at the center of mass of the aircraft. In this system, the O-xa axis is parallel to the direction of the oncoming flow, pointing towards the nose of the aircraft; the O-za axis is perpendicular to the O-xa axis, pointing downwards; and the O-ya axis is perpendicular to the O-xa-za plane, following the right-hand rule, pointing towards the right side of the aircraft. The lift (\(L\)) and drag (\(D\)) of the aircraft are defined within this wind axis system. Here, the lift, \(L\), is considered to be positive in the negative direction of the O-za axis, the drag, \(D\), is positive in the negative direction of the O-xa axis, and the pitch moment, \(M\), is considered to be positive when the nose is raised, rotating about the O-ya axis.  
The force model for a BWB aircraft equipped with distributed electric BLI propulsors is as follows:  
Gravity: For a BWB aircraft with distributed electric BLI propulsors, gravity can be represented by a constant value.  
BLI fan thrust: The thrust of the BLI fan is related to its geometric characteristics and the rotation speed, which is represented as a function of the slipstream velocity at the duct exit [52].  
\[T=\dot{m}(V_{e}+V)-\dot{m}V \tag{1}\]  
where \(\dot{m}\) denotes the mass flow rate through the duct, \(V\) is the incoming flow velocity relative to the front, and \(V_{e}\) represents the slipstream velocity at the ducted fan exit.  
The definitions for the dimensionless aerodynamic forces and moment parameters are as follows [52]:  
\[C_{L}=\frac{L}{0.5\rho_{0}V_{0}^{2}S_{\text{ref}}} \tag{2}\]  
\[C_{D}=\frac{D}{0.5\rho_{0}V_{0}^{2}S_{\text{ref}}} \tag{3}\]  
\[C_{M}=\frac{M}{0.5\rho_{0}V_{0}^{2}S_{\text{ref}}c} \tag{4}\]  
\[K=\frac{C_{L}}{C_{D}} \tag{5}\]  
where \(C_{L}\), \(C_{D}\), and \(C_{M}\) represent the coefficients of the lift, drag, and pitch moment, respectively. The lift-to-drag ratio is denoted as \(K\), while \(\rho_{0}\) and \(V_{0}\) denote the density and velocity of the free-stream airflow well ahead of the aircraft, respectively. The terms \(L\), \(D\), and \(M\) signify the aircraft's lift, the total drag of the aircraft, and the pitch moment, respectively. \(S_{\text{ref}}\) refers to the total reference area of the aircraft, and \(c\) denotes the average aerodynamic chord length.  
Figure 12: Force model of BWB aircraft.
## 5 Results and Discussion
### End of Life (EOL) Prediction
#### 5.1.4 EOL Prediction Error  
In order to evaluate the evolution of the EOL prediction error with time, another test was performed. This was achieved by propagating the state for every cycle until the EOL. The relative error is evaluated using Equation (29) and an example is shown in Figure 12:  
\[Relative\ error=\frac{|EOL_{Predicted}-EOL_{Real}|}{EOL_{Real}}. \tag{29}\]  
In order to compute the EOL prediction, another expression was used instead of using the probability density function. This expression is:  
\[EOL_{Predicted}=\sum_{i=1}^{N_{t}}\omega_{i}^{i}\cdot EOL_{Predicted,k}^{i}. \tag{30}\]  
Figure 12 shows the EOL prediction error test for cell B0007. It is shown that the accuracy is at least 20%, which is fewer than 10 cycles once the algorithm has converged.  
Figure 11: EOL prediction in cell B0007.  
Figure 12: EOL prediction error in cell B0007.
###### Abstract  
In this study, Brillouin optical time domain analysis (BOTDA) sensing technology was utilized for monitoring settlement in a similarity model of a highway subgrade. As contact winding cannot be used for an optical fiber that is buried directly in the soil, uncoupling between the fiber and the soil can occur. Thus, an optical-fiber-embedded beam (OFEB) was developed, and a method for measuring and calculating the beam's deformation was proposed. A calibration test and a test on a similarity model of a subgrade were carried out to investigate the applicability and monitoring accuracy of the OFEB. It was concluded that the OFEB can accurately measure beam deflection, and the maximum relative error between measurements by the optical fiber and a displacement transducer was approximately 5%. The OFEB was embedded directly into a similarity model of a subgrade to monitor settlement. The deflection deformation of the OFEB was found to be close to the subgrade settlement over a certain settlement range, with a relative error below 8.1%. Thus, the OFEB can be used to measure large-range distributed settlement in a subgrade. A numerical simulation was performed to identify appropriate beam dimensions and material design parameters, thereby extending the measurement range before decoupling of the OFEB and the soil occurs. The enhancement of the measurement range and the accuracy of the OFEB based on the preliminary experiments carried out in this study enables further investigation of settlement monitoring.  
Keywords:road engineering; optical-fiber-embedded beam; BOTDA technology; subgrade settlement; similarity model +
Footnote †: journal: Applied Sciences
###### Abstract  
In the petroleum industry, multiphase transport pumps are a crucial technology for petroleum extraction. Therefore, the pressure fluctuation and internal-flow characteristics of multiphase transport pumps with different inlet-gas-volume fractions (IGVFs) have become an important research topic. Studying the pressure fluctuation and its effects on the performance of mixed-transport pumps under different IGVFs is significant for improving the running stability of pumps. In this work, steady and transient flow with different IGVFs was solved using the Navier-Stokes equation applied to a structured grid and the Shear Stress Transport (SST) turbulence model. The effects of IGVFs on the pressure pulsation and the performance of a three-stage, mixed-transport pump were studied. Results indicated that the numerical calculation results agreed well with the experimental data. The numerical method could predict the gas and liquid two-phase flow in the mixed-transport pump accurately. The pressure increase of this pump decreased with the increased flow quantity and the IGVFs. The efficiency improvement of the pump was limited by the increasing the flow rate. Under the rated-flow condition, a quantitative relationship was established between the relative discharge of the IGVF and the decrease in the pump head; when the IGVF exceeded 15%, the pressurization capacity decreased by more than 30%. Along the blade centerline direction, the pressure fluctuation amplitude near the suction surface of the impeller blade head gradually increased. Numerical simulation results showed that the dominant frequency of the pressure fluctuation of the impeller and diffuser was ten and seven times that of the rotation frequency, respectively. Thus, the IGVFs greatly influenced the dominant frequency of the pressure fluctuation. The air in the impeller primarily piled up at the suction surface of the blade head near the front cover. Under a centrifugal force, water was pushed to the back cover plate, making the gas-volume fraction near the front cover plate higher. Consequently, the distribution of gas content in the impeller became uneven. On the blade suction surface near the front cover plate, a low-velocity area caused by flow separation was generated, which further affected the pressure pulsation in the impeller. There were obvious vortices in the diffuser, and the vortex position had a tendency to move toward the inlet of the diffuser with an increased gas content. The flow pattern in the impeller was consistent, which indicated the great transport performance of this pump. In conclusion, through numerical simulation and experimental research, this study revealed the effects of the IGVFs on the performance and pressure pulsation of a mixed-transport pump under a gas-liquid two-phase flow condition. Our findings may serve as a guide for the optimization of a multiphase pump.  
deep-sea multiphase pump; energy characteristics; gas-liquid two-phase flow; pressure fluctuation; gas-volume fraction 2020; 10; 582; doi:10.3390/app10020582