esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Evaluation of Dye Removal  
For pH optimization, 50 mg/L of the dye solutions were reacted with 200 mg/L K\({}_{2}\)FeO\({}_{4}\) in a jar tester at a pH range of 3-12. The dye removal efficiency was examined as a function of the K\({}_{2}\)FeO\({}_{4}\) dose (25, 50, and 100 mg/L as Fe). The dye concentration was varied to 25, 50, and 100 mg/L.  
The removal efficiency of K\({}_{2}\)FeO\({}_{4}\) was then compared with that of NaOCl (oxidant) and FeCl\({}_{3}\cdot\)6H\({}_{2}\)O (coagulant). The concentration of NaOCl and FeCl\({}_{3}\cdot\)6H\({}_{2}\)O increased from 7.05 to 84.6 mg/L. In this test, the dye concentrations were 25 and 50 mg/L.  
The removal efficiency was evaluated using ultraviolet-visible (UV-Vis) spectrometry, and the concentrations of OD and LGB were determined at wavelengths of 483 nm and 633 nm, respectively, using a UV-Vis spectrophotometer (HACH, DR 6000). The concentrations of the two types of dyes were derived from the calibration curves obtained through experiments using standard solutions. The dye removal efficiency (R(%)) was calculated using the following equation:  
\[R(\%)=\frac{C_{0}-C}{C_{0}}\ \times 100\]  
where \(C_{0}\) is the initial dye concentration (mg/L) and \(C\) is the final dye concentration (mg/L).  
All experiments were conducted in triplicate for reproducibility and reliability. In each plot, the data are presented with standard deviation (error bars). The standard deviation of the results was less than 3%, thus, error bars are not clearly shown.
## 3 Experimental Results
### Displacement Results under Thermal Load
#### 3.4.2 Relative Displacement between the Track System and the Concrete Box Bridge  
Figure 19 shows the mid-span vertical relative displacement of the ballastless track-bridge. Figure 19a shows the influence of the thermal loading cycle on the variation of the track slab top surface temperature with the concrete box beam mid-span vertical relative displacement. Under the action of the thermal loading, the displacement of the track structure has a linear relationship with the track slab  
Figure 17: Camber of the beam along the longitudinal direction.  
Figure 18: Mid-span displacement of the concrete box beam.  
top surface temperature. As the number of thermal loading cycles increases, the temperature-relative displacement plot gradually shifts toward the direction of the displacement increases, and the slope of the plot gradually decreases. In the initiation stage and stabilization stage of the interlayer gap, the spacing between adjacent plots is not significant, while in the expansion stage of the gap, the distance between adjacent plots is larger.  
Figure 19b shows the variation of the track-bridge vertical relative displacement according to the thermal loading cycles. The relative displacement induced by the same temperature load follows the increase of the number of thermal loading cycles and gradually increases. During the three stages of development of the gap, the displacement increase rate shows a slow-fast-slow change. When the track slab top surface temperature is increased to 30 \({}^{\circ}\)C, the relative displacement of the track structure after the first thermal loading cycle is 1.08 mm, while that at the end of the 18 th thermal loading cycle is 1.27 mm, showing an increase of 17.59%.
# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 4 Results and Discussions
### Lithological Classification Using Selected Features
In future studies, other surface parameters related to the physical or chemical properties of rocks should be considered for lithological identification, such as permittivity, thermal characteristics, albedo, and indices related to rock chemical components. In addition, the geographical environment, especially land surface temperature and atmospheric moisture content, may influence the surface morphology of rocks [48], such as changed surface structural characteristics, which may result from sedimentation occurring within weathered fractures. The geographic environment in a specific study area may limit the robustness of this strategy, which would also limit the accuracy of image classification. Hence, for other regions or other rock types, more analysis and validation is necessary.
# Accuracy of Code GNSS Receivers under Various Conditions
## 4 Results
### Surface
As a result of the analysis of Table 2, it was noted that during the measurement of the point, the smallest average error of the N component with a value of 0.6 m was obtained by the X_2 phone, the E component with a value of 0.9 m by the G_2 receiver, and the standard deviation of the NE components was obtained by the X_2 phone (0.0 m). The recorded data by receiver G_1 had the largest mean error and standard deviation of the E component. A significant difference was observed between the accuracies of handheld receivers operating in two modes (GPS+GLONASS and GPS). Instrument G_2 (GPS+GLONASS) recorded data with a lower mean error of NEU components and better accuracy in comparison to receiver G_1 (GPS). Smartphone X_1 positioned the user with an error of 1 to 3 m (NE) and a bias of 2.5 m. Phone X_2 determined horizontal coordinates with an error of approximately 1 m. Phone X_1 lost signal during the linear and surface moving measurements, so its errors \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Object**} & \multirow{2}{*}{**Coordinate/Error [m]**} & \multicolumn{5}{c}{**Receiver**} \\ \cline{3-7}  & & **G\_1** & **G\_2** & **X\_1** & **X\_2** & **X\_3** \\ \hline \multirow{4}{*}{**Point**} & N/\(\sigma_{\text{N}}\) & 3.0/2.0 & 0.8/0.7 & 2.8/2.4 & 0.6/0.3 & 2.0/0.0 \\  & E/\(\sigma_{\text{E}}\) & 4.8/3.0 & 0.9/0.7 & 1.2/2.5 & 1.1/1.2 & 1.4/0.0 \\  & U/\(\sigma_{\text{U}}\) & 9.1/1.0 & 8.7/0.4 & - & - & - \\  & N/\(\sigma_{\text{N}}\) & 3.0/2.5 & 3.4/2.5 & 43.0/28.5 & 8.1/3.7 & 10.5/4.0 \\
**Line** & E/\(\sigma_{\text{E}}\) & 1.4/1.1 & 2.0/1.5 & 4.1/2.7 & 1.8/1.6 & 2.0/1.2 \\  & U/\(\sigma_{\text{U}}\) & 3.0/3.2 & 42.1/6.6 & - & - & - \\  & N/\(\sigma_{\text{N}}\) & 2.6/1.9 & 1.9/1.6 & 20.4/11.6 & 5.6/3.2 & 5.5/3.1 \\
**Circle** & E/\(\sigma_{\text{E}}\) & 2.8/1.8 & 1.9/1.4 & 30.3/25.6 & 5.1/3.2 & 4.4/2.8 \\  & U/\(\sigma_{\text{U}}\) & 2.6/2.6 & 40.1/7.1 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Biases of the results obtained (in meters). Figure 13: Plot of U-component deviations of surface measurements with code receivers. ranged from 20 to 43 m for horizontal coordinates. When analyzing the average errors and bias of the linear measurement, the best results were observed for receiver G_1. For the N component, the error value was 3.0 m and the standard deviation 2.5 m, while the error was 1.4 m and the standard deviation was 1.1 m for the E component. The G_2 receiver obtained better results when measuring a point and a circle (1.9 m error for NE components) compared to the G_1 receiver (2.6-2.8 error for NE components), but during height determination on the move, the Garmin receiver using only GPS provided less error. During measurements on the move, mobiles X_2, X_3 determined horizontal coordinates with a similar error and standard deviation.
## 4 The Preliminary Experiment of Atmospheric Density Inversion for SWARM-A
### Inversion of SWARM-A Atmospheric Density by Semi-Long-Axis Decay Method
#### 4.2.1 SWARM-A Precision Orbital Data Processing  
The Keplerian orbital roots, including e, n, and f, as defined in Equation (5), can be calculated from the position and velocity of the satellite in the precision orbital data of the SWARM-A star. The mass of the satellite, \(M\), can be obtained directly from the accelerometer data. The product of the atmospheric damping coefficient and the cross-section, \(C_{D}*A\), can be calculated by the model, as mentioned in Section 4.1. Consequently, the atmospheric density can be determined by calculating the extent of change observed in the half-length axis. The primary objective in inverting the density using the half-length axis decay method is to calculate the decay rate of the half-length axis due to atmospheric drag. The algorithm will be subjected to a comprehensive analysis in the following section.
# Sport-Specific Abdominal Wall Muscle Differences:
## 4 Discussion
### Limitations and Future Lines  
This study has several limitations that should be acknowledged. The study design was cross-sectional, which precludes the ability to draw causal inferences about the relationship between sport-specific training and abdominal muscle adaptations. Another limitation of this study is the absence of data on participants' hydration status, dietary intake, and recent physical activity levels. Variations in these factors can influence muscle morphology and ultrasound measurements. Hydration levels, in particular, can affect muscle thickness and echogenicity, while diet and recent physical activity can alter muscle tone and size. The present study only compared the left and right sides of the abdominal musculature independently, without averaging the measurements (R+L/2) for each player. Consequently, we did not evaluate whether basketball players still have a thicker average RA muscle mass when considering the average of both sides, which should be considered as a limitation. Future research should aim to address these limitations by including larger and more diverse samples, including both amateur and elite athletes from various sports disciplines. Longitudinal studies are needed to investigate the causal effects of sport-specific training on abdominal muscle morphology and to monitor changes over time. Further research should also consider dynamic assessments of muscle function during sport-specific tasks to provide a more comprehensive understanding of muscle performance. Additionally, exploring the impact of different training programs, including core stability and strength training, on abdominal muscle adaptations across different sports could provide valuable insights for developing targeted training and rehabilitation protocols. Integrating advanced imaging techniques such as MRI could also enhance the precision and detail of muscle morphology assessments.
## 2 Materials and Methods
### Apparent Position Variation Using Kinematic Precise Point Positioning  
The RINEX files of 2337 GNSS stations were processed using the Canadian Spatial Reference System (CSRS-PPP) online service ([[https://webapp.geod.nrcan.gc.ca/geod/tools-outils/PPP.php](https://webapp.geod.nrcan.gc.ca/geod/tools-outils/PPP.php)]([https://webapp.geod.nrcan.gc.ca/geod/tools-outils/PPP.php](https://webapp.geod.nrcan.gc.ca/geod/tools-outils/PPP.php)), last accessed on 6 January 2022) [41] with ambiguity resolution (PPP-AR) mode. The CSRS-PPP provides centimeter-level estimations with converged float solutions [41; 42; 43; 44].  
Usually, the CSRS-PPP report can provide a different reference start value for different days. To facilitate the evaluation of the apparent position variation time series, we process the data to have an equal reference for all the used data. At each of the 2337 stations, we applied the common noise filter to correct the time series of the North, East, and Up components, using the equation [29]:  
\[CAPday_{t}=APday_{t}-RP_{t} \tag{5}\]  
where \(t\) is the epoch, \(CAPday_{t}\) is the corrected apparent position, \(APday_{t}\) is the apparent uncorrected position, and \(RP_{t}\) is the reference position. We use the average of AP, \(\overline{AP}\), from the same reference days mentioned in Equation (1) to calculate \(RP_{t}\).  
At each of the 2337 stations, the maximum error was obtained within the selected five days. Subsequently, the error of each station per component was classified by inter vals, counting the percentage of the total number of stations that fell into each interval. In addition, a 3D position error was calculated as:  
\[3D_{t}=\sqrt{East_{t}^{2}+North_{t}^{2}+Up_{t}^{2}} \tag{6}\]  
We use two threshold values. First, we selected threshold values for maximum 3D positioning error greater than or equal to 10 cm (3D \(\geqslant\) 10 cm) since according to data on quiet days, over 90% of the GNSS stations had 3D errors of less than 10 cm, while during ionospheric disturbances, only \(\sim\)40% kept this level of accuracy [29]. Second, we applied the Equation (6) to the horizontal and the vertical components presented in [29,30], obtaining the threshold of the 3D positioning error root mean square (3D-RMS) greater than or equal to 3 cm (3D-RMS \(\geqslant\) 3 cm).
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 1 Introduction
In practice, the third party may blame the situation of returning erroneous or incomplete query result on the communication process. For example, when the user finds out the erroneous or incomplete query result by verifying the corresponding verification object, the third party may claim that it has returned the correct and complete query result and the reason for missing data in other aspects such as the network communication problem. To address the above problems, we designed a Security Enhanced Query Result Verification Scheme (SEQRVS) for the outsourced data in LBS. Specifically, the contribution of our paper can be summarized as follows. 1. Based on the outsourcing service of LBS, a secure keyword search and query result verification mode over encrypted outsourced data were constructed. In this mode, we improved the construction process of the verification object on the basis of analyzing the deficiency of the scheme [9]. Therefore, while retaining fine-grained query result verification, our scheme can effectively prevent the third party (i.e., the fog side) from obtaining additional knowledge from the structure of the verification object, and further enhance the security of the outsourced data. 2. To prevent the third party (i.e., the fog side) from deducing the meaning of the outsourced data stored in itself by the way of statistics, we designed the one-to-n lookup table as the storage structure of the outsourced data. By implementing this storage structure, the fog side not only knows nothing about what data are requested by the user and which query object is returned to the user, but also cannot determine the correspondence between the query index and the corresponding query object, which further enhances the ability of privacy preservation for the outsourced data. 3. To prevent the third party (i.e., the fog side) from attributing dishonest behaviors (e.g., storage errors) to the unreliability of network communication, we introduced the blockchain to guarantee the non-repudiation of the query result from the third party. A comprehensive security analysis is provided to show the security of the mode and the storage structure adopted in our scheme, and extensive simulation results also demonstrate the security and practicality of the proposed scheme. The rest of this paper is organized as follows. We review the related work in Section 2. The background, including the system model, threat model, and preliminary techniques to be used in the paper are described in Section 3. We propose the process of data outsourcing, data retrieval, and data verification of the scheme in Section 4. Then, we analyze the security and evaluate the performance of our proposed scheme in Sections 5 and 6. In Section 7, we conclude the paper.
# Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir
## 2 Data, Methods, and Techniques
### Model
These would be horizontal lines of constant values \(C_{chl}\) = {1, 5, 10, 20, 50} mg/m\({}^{3}\) for all depths h. In order to check the quality of the model (14), we write it for a single wavelength:  
\[ \begin{array}{l}C_{chl}=\exp\big{[}a_{0}+a_{3}\ln\big{(}R-\alpha_{b}^{*} \exp(-k^{*}\cdot C\cdot 2h)\big{)}+\\ a_{11}\big{(}\ln\big{(}R-\alpha_{b}^{*}\exp(-k^{*}\cdot C\cdot 2h)\big{)}\big{)}^{2} \big{]}\end{array}  \tag{15}\]The parameters of model (15) were determined on the basis of changes in reflectance R as a function of h, determined from Equations (7)-(9). The following parameters were obtained: \(a_{0}=-0.994654\), \(a_{3}=-3.91129\), \(\alpha_{b}^{*}=0.293404\), \(k^{*}=0.04999945\) (NTU\(\cdot\)m)\({}^{-1}\), \(a_{11}=-0.767181\). Parameters: \(a_{b}^{*}\) and \(k^{*}\) are almost equal to \(\alpha_{b}=0.3\), \(k=0.05\) (NTU\(\cdot\)m)\({}^{-1}\) used in Equations (7)-(9). Formally, \(\alpha_{b}^{*}\) should be equal to \(\alpha_{s}+\alpha_{b}(1-\alpha_{s})=0.002+0.3(1-0.002)=0.3014\), as in Equation (12). The compliance of the parameters \(\alpha_{b}^{*}\) and \(k^{*}\) with the parameters \(\alpha_{b}\) and \(k\) confirms that a correction (13) for the bottom reflectance in the models (14) and (15) was needed. Model (15) is less accurate for shallow depths and higher concentrations of chlorophyll a C\({}_{\rm chl}\) (Figure 13), along with model (14). It would be difficult to use a combination of Equations (7)-(9) to calculate chlorophyll a concentrations on the basis of the reflectance recorded by a satellite; such reflectance is a combination of both a chlorophyll a reflectance and the reflectance of other substances present in the water. Such calculations were performed for the wavelengths of 665 nm and 705 nm. However, accurate parameters of models (7)-(9) for  
Figure 12: Changes in reflectance R as a function of depth h for the sensor above the water surface at different concentrations of chlorophyll a \(C_{chl}=\{1,5,10,20,50\}\) mg/m\({}^{3}\). Figure 13: Chlorophyll a C\({}_{\rm chl}\) concentrations as a function of depth h for Equations (7)–(9) and model (15) (approximation). turbidity could not be found. Therefore, model (14) has been proposed as the one that that includes the reflectance for different wavelengths.
# Effects of Nozzle Exit Angle on the Pressure Characteristics of SRWJs Used for Deep-Hole Drilling
## 1 Introduction
Moreover, [PERSON] et al. [18] numerically studied the characteristics of the flow field inside organ-pipe nozzles of different geometries, and experimentally investigated performance. Furthermore, [PERSON] and [PERSON] [19] mathematically studied the behavior of the collapse of a cavitating vortex ring within a SRWJ and claimed that the bubble ring collapse should be more erosive than that of an equivalent spherical bubble. Most recently, we studied the preferred Strouhal number used in SRWJs, and found that the optimum values are 0.315 and 0.278 respectively, corresponding to inlet pressures of 10 MPa and 20 MPa [20]. Furthermore, a series of experimental investigations on SRWJs have also been conducted, ranging from the effects of nozzle inner surface roughness to the influence of feeding pipe diameter [21; 22; 23; 24; 25; 26; 27; 28; 29]. These investigations on the mechanisms and principles of SRWJs have contributed to the understanding of the jet and improvement of the jet's performance. Another method for improving the performance of SRWJs commonly used in the field of waterjets is by optimizing the structure of the nozzle. For instance, [PERSON] and [PERSON] [30] conducted orthogonal experiments using a variety of geometric sizes of self-excited oscillation nozzles and found that the diameter of the suction nozzle was the most important factor and even determined the performance of the jet. Moreover, [PERSON] and [PERSON] [31] performed a study using a series of nozzle thicknesses, to tryi to clarify the effect on the pattern of jet oscillation. They claimed that within the fluid-resonant regime, the acoustic tones are controlled by the impingement distance. In addition, [PERSON] et al. [32] found that the frequency of SRWJs decreases with the increase of the length to diameter ratio of the chamber. Most recently, our research group experimentally studied the effects of the downstream contraction ratio of an organ-pipe nozzle on SRWJs and found that the development trends of the pressure oscillations against standoff distance were greatly affected [33]. Therefore, it can be concluded that the structural parameters play a significant and important role in improving the performance of SRWJs. To the best of our knowledge, there is currently rather little literature focusing on the effects of the exit angle of the organ-pipe nozzle. Therefore, the present study intended to fill this gap by experimentally investigating the pressure characteristics of SRWJs issuing from organ-pipe nozzles of different exit angles, because the interaction between the shear layer of the jet and the nozzle lip is a predominant source of the pressure oscillations [12]. The axial pressure oscillation peaks and amplitudes were studied, and a non-dimensional analysis was performed, so as to figure out the effects of the exit angle. The research aimed to improve the performance of the SRWJ by offering better geometries for an organ-pipe nozzle.
## 1 Introduction
The parameter extraction and analysis of CRLH structure using a dispersion diagram are provided along with the simulated and measured results in Section 2. The SIW Butler matrix is used as the feeding structure of the CRLH antenna for beam forming, and the results are discussed in Section 3.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 1 Introduction
### Contributions  
* This work reviews and conducts a security analysis of [PERSON]'s protocol and demonstrates that session key disclosure and impersonation attacks are possible only by eavesdropping on public-channel messages. Furthermore, because [PERSON]'s protocol incurs high computational and communication costs due to using ECC, this work proposes a more secure and lightweight authentication protocol that re solves these problems to provide secure and useful services to legitimate users in the IoD environment.
* The proposed protocol can resist potential security threats, such as impersonation attacks, ephemeral secret leakage attacks, and physical drone capture attacks, ensuring mutual authentication and anonymity.
* This study performs a formal security analysis using the \"Automated Verification of Internet Security Protocols and Applications (AVISPA)\" [14], evaluating security against \"replay\" and \"man-in-the-middle (MITM)\" attacks. Moreover, this work performs a formal security analysis using the \"real-or-random (RoR) model\" [15] and \"Burrows-Abadi-Needham (BAN) logic\" [16] to prove and evaluate the session key security and mutual authentication ability of the proposed protocol.
* This work presents a performance analysis by comparing the computational cost, communication cost, and security features of the proposed protocol with those of other related protocols, including [PERSON]'s protocol.