esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Design of the RF Cavity for an 11 MeV Superconducting Cyclotron
## 4 Design and Simulation of the RF Cavity  
### Design of the RF Cavity  
In a particle accelerator, the RF cavity can generate a sinusoidal electric field with a desired resonant frequency. There are two types of RF cavities in cyclotrons: the half-wavelength resonator (HWR) and quarter-wavelength resonator (QWR). The resonance frequencies of HWR and QWR depend mainly on the length of the resonator, and QWR is more compact than HWR for the same resonance frequency. The design of the RF cavity depends on various parameters such as the resonant frequency, Dee number, extraction radius, and number of accelerated harmonics. The RF cavity of a classical cyclotron uses a quarter-wavelength resonant cavity [22; 23].  
Table 1 outlines the technical parameters of the 11 MeV cyclotron's RF cavity. A classical cyclotron is used, with a nonhomogeneous magnetic pole gap height. The design of high-frequency cavities is challenging due to the small overall size of the superconducting cyclotron used to accommodate the cavity. The design of the RF cavity is mainly based on the classical 180\({}^{\circ}\) Dee, and the inner conductor part of the cavity mainly consists of the Dee, Stem, and Dummy Dee. A quarter-wavelength coaxial line resonant cavity was utilized. The main part of the cavity consists of an external rectangular box and an internal resonator.
# Ion Exchange MIEX(r) GOLD Resin as a Promising Sorbent for the Removal of PFAS Compounds
## 3 Results and Discussion
### Regeneration of MIEX(r) GOLD Resin  
Previous research on resin recovery from PFAS adsorption showed that combined solutions of alcohols (e.g., methanol, ethanol) and inorganic salts (e.g., NH4 Cl, NaCl) were much more effective than using inorganic salts only, and the types and concentrations of alcohols and salts varied for different resins [7; 17; 32]. Based on these studies, three different regeneration conditions were selected to find out the most efficient regenerant for PFOA and PFBA saturated MIEX(r) GOLD resin. These regenerants were, namely, 60% MeOH with saturated NaCl, 60% MeOH with saturated NH4 Cl, and 60% EtOH with saturated NaCl.  
The results indicated that 60% MeOH with saturated NaCl proved to be the best regenerant for the desorption of both PFBA and PFOA. An alkaline condition (60% MeOH with 0.001% NaOH) was also studied to evaluate the regenerating efficiency and was found completely ineffective. A similar ineffective observation with a 2% NaOH solution for regenerating strong anion resins (A600E, A520E, and A532E resins) was reported by [PERSON] et al. [7]. Based on all results, MeOH with saturated NaCl could be considered the best regenerating solvent. We believe the mechanism of resin recovery is based on the dual effect of organic solvent MeOH and inorganic salt NaCl: while Cl\({}^{-}\) is effective in desorbing the anionic hydrophilic head of the PFAS molecule from the ion exchange site of resin, MeOH assists in desorbing the hydrophobic carbon-fluorine tail from the resin backbone [38].  
Further regeneration studies using 70% methanol with saturated NaCl to maximize PFAS desorption and regeneration efficiency were undertaken. The study was extended to include two sulfonic acid-containing PFAS (PFOS and PFHxS) to understand any effect from the PFAS chemical structure. Desorption of PFHxS and PFOS was found to be 98.3 \(\pm\) 0.5% and 100%, respectively, whereas desorption of PFOA was only 43.3% \(\pm\) 1.9. Based on these results, it is clear that the resin regeneration efficiency also depends on the type of PFAS compounds (i.e., functional group). The regeneration of sulfonic acid-containing PFAS (PFOS and PFHxS)-adsorbed resin is much higher than carboxylic acid-containing PFAS compounds (e.g., PFOA). As discussed earlier, the inorganic salt detaches the hydrophilic head of the PFAS molecule and MeOH desorbs the hydrophobic carbon-fluorine chain from the resin backbone. It seems that the chloride has a greater influence on the electrostatic interaction of the sulfonic acid head group compared to the carboxylic acid group of PFASs with the cationic quaternary ammonium group of resin, either through displacement or attraction of the PFAS. Moreover, MeOH plays a key role in desorbing the hydrophobic moiety, with the higher hydrophobicity of sulfonated PFASs compared to carboxylic acid PFASs being another reason, resulting in higher regeneration efficiency of sulfonic acid-containing PFAS (PFOS and PFHxS)-adsorbed resin compared to carboxylic acid-containing PFAS compounds.
## 4 Emerging Biogas Upgrading Technologies
### Separation via Phase Addition
#### 4.1.1 Inorganic Solvent Scrubbing
\[\text{Na}_{2}\text{CO}_{3}+\text{Ca(OH)}_{2}\rightleftharpoons 2\text{NaOH}+\text{ CaCO}_{3} \tag{5}\]  
\[\text{CaCO}_{3}\rightleftharpoons\text{CaO}+\text{CO}_{2} \tag{6}\]  
\[\text{CaO}+\text{H}_{2}\text{O}\rightleftharpoons\text{Ca(OH)}_{2} \tag{7}\]  
The regeneration of alkali compounds using waste material has been studied by [PERSON] and co-authors [93; 94; 95]. They have implemented a process through which NaOH and KOH are used for biogas upgrading, and regeneration is achieved with treated Air Pollution Control (APC) residues from Waste-to-Energy plants. These residues originate from cleaning flue gas before it is released to the atmosphere [96], and apart from calcium and potassium hydroxide, they also contain VOCs, dioxins, sulfates, and other contaminants; for this reason, they are considered a hazardous waste [97]. For CO\({}_{2}\) absorption, the reactions that take place are described in Equations (1) and (2). APC residues are rich in Ca(OH)\({}_{2}\), which reacts with Na\({}_{2}\)CO\({}_{3}\) and K\({}_{2}\)CO\({}_{3}\), as stated in Equation (5). After that, the alkali compound is recirculated to the absorption column, and CaCO\({}_{3}\) is removed as a solid. The main advantages of this regeneration process are that the captured CO\({}_{2}\) is permanently stored and that it is accomplished using waste material. The mentioned researchers have called this method Alkali Absorption with Regeneration (AwR). [PERSON] and co-authors have studied an innovative solution to regenerating ammonia for the capture of CO\({}_{2}\) in flue gases from combustion processes [82]. The method consists of using a weakly basic ion-exchange resin that contains amine-functional groups. This resin can regenerate, at ambient temperature, the ammonia from the ammonia carbonate, which is the compound that is formed during CO\({}_{2}\) absorption (Equation (4)). When the resin is saturated, it must be regenerated. This can be done via heating with water at a temperature of 50 \({}^{\circ}\)C or higher (higher efficiency is achieved at higher temperatures). Chemical formulas for ammonia and resin regeneration can be found in Equations (8) and (9), where \(Ra\) is the ion-exchange resin. \[\text{(NH${}_{4}$)}\text{HCO}_{3}+Ra\rightleftharpoons Ra\cdot\text{H}_{2} \text{CO}_{3}+\text{NH${}_{3}$} \tag{8}\]  
\[Ra\cdot\text{H}_{2}\text{CO}_{3}\rightleftharpoons Ra+\text{CO}_{2}+\text{H}_{2} \text{O} \tag{9}\]
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 5 LiDAR in Space Missions  
The first spaceborne laser altimetry system, deployed during Apollo missions, marked the beginning of LiDAR development [110; 111]. This section provides an overview of key historical milestones and currently operational spaceborne LiDAR instruments, highlighting their contributions to the advancement of LiDAR technology.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 3 System Model and Problem Statement  
### Network Model  
As shown in Figure 1, there are \(N\) sensor nodes and \(M\) sink nodes. These nodes constitute a tree topology in which the sink node is located at the top level of the tree. The tree structure is autonomously configured via a tree routing protocol (see Section 4). The sink node acts as a controller to aggregate the data sampled from the sensor nodes and forms a tree structure. We assume that both the sensor node and the sink node have fixed positions and the link quality between nodes depends on geographical positions. Each sensor node then selects a parent node among its neighbors and passes the data to the parent node until it reaches a sink node.
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 3 Case Study
### Experimental Design
#### 3.4.3 Model Performance Evaluation Metrics  
After the model training is completed, the data in the test set is predicted and the future results are denormalized. Four performance indicators are employed to evaluate the model performance: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and the determination coefficient (R\({}^{2}\)). The formulas are presented in Equations (10)-(13).  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|\left(y_{i}-\hat{y}_{i}\right)\right| \tag{10}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{11}\]  
\[\text{MAPE}=\frac{1}{n}\times\sum_{1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i} }\right|\times 100\% \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Hyperparameter**} & \multirow{2}{*}{**Value or Space**} & \multicolumn{1}{c}{\multirow{2}{*}{**Predicted Sequence**}} \\  & & & **Length (mm)** \\ \hline \multirow{5}{*}{LSPP} & Number of heads & [3; 4; 5; 6; 7; 8; 9] & \\  & Batch size & [32; 64; 96; 128] & \\  & ProbSparse attn factor & [2; 3; 4; 5; 6, 7; 8; 9; 10; 11] & \\  & Learning rate & [0.0001, 0.001, 0.01, 0.1] & \\  & Activation function & [PERSON] & \\  & optimizer & [PERSON] & \\ \hline \multirow{5}{*}{LSTM} & LSTM units & [32; 64; 128] & \\  & Hidden layer units & [20; 50; 100] & \\  & Learning rate & [0.0001, 0.001, 0.01, 0.1] & \\  & Activation function & Relu & \\  & optimizer & [PERSON] & \\ \hline \multirow{5}{*}{GRU} & GRU units & [32; 64; 128] & \\  & Hidden layer units & [20; 50; 100] & \\  & Learning rate: & [0.0001, 0.001, 0.01, 0.1] & \\  & Activation function & [PERSON] & \\  & optimizer & [PERSON] & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experiment on prediction ability of different models.  
\[\text{R}^{2}=1-\frac{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})^{2}}}{\sum\limits_{ i=1}^{n}{(y_{i}-\overline{y})}^{2}} \tag{13}\]  
where \(\hat{y}_{i}\) is used to represent the predicted value of the \(i\)-th sample point, \(y_{i}\) is the true value of the \(i\)-th sample, and \(\overline{y}\) is the mean of the true value of the sample.
## 5 Experiment  
### Experiment Deployment  
As shown in Figure 12, it is a coal-mine roadway simulation experiment environment to verify the feasibility of the anchor-rod identification and positioning method. Based on the multivision sensor to collect the roadway environment, as shown in Table 2, the main parameters of the visual sensor are constructed, and the experimental detection platform shown in Figure 12 is constructed. The simulation of the bolt row spacing in the roadway is 0.8 m, the spacing of the single row of anchor rods is 0.8 m, and the multivision sensor is 1.5 m away from the side plate. The experimental principle and process are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Ideal range & 0.6–6 m \\ Length \(\times\) Depth \(\times\) Height & 124 mm \(\times\) 26 mm \(\times\) 29 mm \\ Field of view & \(86^{\circ}\pm 3^{\circ}\times 57^{\circ}\pm 3^{\circ}\) \\ Depth output resolution & 1280 \(\times\) 720 \\ RGB frame resolution & 1280 \(\times\) 720 \\ Frame rate & 30 fps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main parameters of the sensor.  
Figure 12: Coal-mine roadway simulation experiment environment and platform.  
Before the experiment, the internal and external parameters of the sensor were determined by calibration, and the RGB camera optical center was used as the coordinate origin. The direction of the roadway parallel to the side panel is the \(x\)-axis, the direction perpendicular to the roof is the \(y\)-axis, and the direction perpendicular to the side panel is the \(z\)-axis. The detection model proposed in this paper is used to identify and detect the anchor rod, and the model's performance is verified by ablation experiments. The pixel coordinates of the target point are obtained through the model, the corresponding depth values are obtained in the depth map, and the positioning results are converted into coordinates in the RGB camera coordinate system. The coordinate value is corrected by linear fitting to obtain the position information of the target point. Because there will be invalid areas in the depth map when the distance is too far, only eight anchors in three rows are located in the experiment. By comparing the measured value with the actual value and the fitting value, the accurate positioning of the anchor rod is finally realized.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 3 Preliminary Knowledge and Analysis
### Secure AODV  
The latest release of the SAODV (Secure AODV) RFC protocol was proposed by [PERSON] in September 2006 [29]. SAODV is a security extension of the AODV protocol, based on the public key cryptography of RSA. SAODV adds security fields to the extended AODV routing packets, and all of the end nodes and intermediate nodes need to authenticate the grouping, so that the SAODV can guarantee the security in the routing discovery process and the route maintaining process. SAODV uses a digital signature and hash chain scheme to protect the routing messages: the digital signature is used to ensure the integrity of non-mutable fields in the routing message, and the hash chain is used to keep the mutable field (such as the hop count) from being misrepresented.
## 3 Our Multiparty Private Set Intersection Protocol
### Protocol Correctness  
In the protocol, the server obtains the final intersection result, from which we start to illustrate the correctness of our protocol.  
For \(j\in\{1,2,\ldots,n_{t}\}\):  
\[Dec(c_{j}) =Dec(c_{1,j}\times\cdots\times c_{t-1,j}\times w_{j})\] \[=Dec((c_{1,1}^{\dagger}\times\cdots\times c_{j,1}^{k})\times \cdots\times(c_{j,t-1}^{1}\times\cdots\times c_{j,t-1}^{k})\] \[\quad\times Enc(r_{j})\times Enc(S_{t}[j]))\] \[=Dec((ERBF_{1}(h_{1}(S_{t}[j]))\times\cdots\times ERBF_{1}(h_{k}( S_{t}[j])))\times\cdots\] \[\quad\times(ERBF_{t-1}(h_{1}(S_{t}[j]))\times\cdots\times ERBF_{t-1 }(h_{k}(S_{t}[j])))\] \[\quad\times Enc(r_{j})\times Enc(S_{t}[j]))\] \[=(RBF_{1}(h_{1}(S_{t}[j]))\times RBF_{1}(h_{k}(S_{t}[j])))\times\cdots\] \[\quad\times(RBF_{t-1}(h_{1}(S_{t}[j]))\times RBF_{t-1}(h_{k}(S_{t }[j])))\times r_{j}\times S_{t}[j]\]  
If there is \(Dec(c_{j})=r_{j}\times S_{t}[j]\ mod\ p\), then  
\[\forall i\in\{1,\ldots,t-1\},BF_{i}(h_{1}(S_{t}[j]))\times\cdots\times BF_{i} (h_{k}(S_{t}[j]))=1 \tag{19}\]  
In this case, by the nature of Bloom filters, we know that \(S_{t}[j]\) is in the sets of all \(\{S_{1},\cdots,S_{t-1}\}\), i.e., \(S_{t}[j]\) is an intersection element of the participants \(\{P_{1},\cdots,P_{t}\}\).  
If there is \(Dec(c_{j})\
eq r_{j}\times S_{t}[j]\ mod\ p\), then  
\[\exists i\in\{1,\ldots,t-1\},RBF_{i}(h_{1}(S_{t}[j]))\times\cdots\times RBF_{ i}(h_{k}(S_{t}[j]))\
eq 1 \tag{20}\]  
In this case, there exists \(i\in\{1,...,t-1\}\) such that the values in the randomized Bloom filter \(RBF_{i}\) that \(S_{t}[j]\) maps to are not all \(1\), i.e., \(S_{t}[j]\) is not an intersection element of the participants \(\{P_{1},\cdots,P_{t}\}\).  
In summary, we can determine whether an element belongs to the intersection by using the nature of the Bloom filter. Therefore, the protocol for secure computation of the  
Figure 1: Our Multiparty Private Set Intersection protocol.  
intersection of non-equilibrium multiparty sets based on the threshold ElGamal encryption scheme is correct.
# Landslide Displacement Prediction Method Based on GA-Elman Model
###### Abstract
(2012), who used the Elman neural network model to verify the feasibility of monitoring and forecasting landslides [25]. According to the nonlinear characteristics of landslide displacement monitoring data, [PERSON] et al. (2013) proposed an improved Elman recurrent neural network and proved the accuracy of Elman neural network in short-term prediction [26]. [PERSON] et al. (2018) realized the dynamic prediction of loess landslide monitoring data through the Elman neural network and got a better prediction effect [27]. [PERSON] et al. (2015) constructed the rolling prediction model of open-pit slope by combining local mean decomposition with Elman neural network [28]. However, Elman neural network also has some common shortcomings of neural network. It mainly includes the disadvantages of self-existence weight updating method, which are that it easily falls into local minimum solution, hidden neurons need to be determined by trial and error repeatedly and slow convergence rate. To solve these problems, a landslide displacement prediction method based on adaptive genetic algorithm and Elman neural network model is proposed in this study. The displacement monitoring data of a slow-varying landslide in Guizhou karst mountainous area are selected to predict and verify the landslide displacement. This study analyzes the internal development process and external environmental factors of the landslide. The weight, threshold and network structure of Elman neural network are optimized by using adaptive genetic algorithm (GA), which can effectively solve the problem that it easily falls into local minimum solution. This model can improve the prediction performance of the whole neural network and effectively improve the accuracy of landslide displacement prediction.
###### Abstract  
The Single-frequency Single-epoch double-differenced baseline resolution technique of Global Positioning System (GPS) provides a good opportunity for monitoring the displacement or deflection behavior of bridges under different loading conditions in real-time. However, for single GPS, a high success rate baseline solution is difficult to achieve due to the lack of sufficient visible satellites and the low accuracy of float solutions. Galileo Satellite Navigation System (Galileo) has 14 medium earth orbit satellites (as of May 2018) that can be used to supplement GPS. The frequency bands of Galileo overlap with that of GPS on E1/L1 and E5a/L5, which is conducive to the combination of observations in integration positioning. Accordingly, Galileo augmenting GPS is an effective and necessary approach to improve the positioning availability and reliability. Moreover, using the baseline length constraint can improve the accuracy of float solutions, narrow the search space, and finally increase the success rate of ambiguity resolution and positioning. The single-frequency single-epoch double-differenced GPS/Galileo mathematical model with baseline length constraint is deduced in this paper. Two sets of GNSS real bridge data were used for further analysis on the improvement of GPS/Galileo with baseline length constraint when compared to single GPS. Finally, a Fast Fourier Transformation (FFT) algorithm was adopted for precisely detecting the local dominant frequencies of XB, YB, and ZB direction of the two stations.  
bridge dynamic monitoring; Galileo; GPS; baseline length constraint 1.1 438; doi:10.3390/rs11040438
## 4 Analysis of Factors Influencing Hydraulic Fracture Propagation Behavior
### Influence of Segment Length on Multi-Fracture Propagation  
The segment length in hydraulic fracturing of coal seam roofs is a key factor influencing roof-weakening. Generally, a smaller segment length leads to more densely and uniform cracks, which facilitate stress release in the roof. However, this comes with higher economic costs and longer construction timelines. Therefore, this study examines segment length of 12, 20, 30, and 40 m to investigate its impact on the propagation of multiple cracks.  
Figures 11 and 12 show that, as segment length increases, fractures propagate more uniformly. At smaller segment lengths, fractures deflect due to stress interference, exacerbating asymmetry and forming complex branches. At 40 m, fractures extend symmetrically with narrow, straight damage zones, following the maximum principal stress. Figure 13 shows that the left fracture extends slightly before shortening, and the fracture length stabilized at approximately 35 m, possibly due to rock heterogeneity. The central fracture continuously shortens, and the crack length decreased from 52.03 m to 45.84 m, indicating a 12% reduction, likely because the wider segment length disperses fluid energy, while interference from the left fracture further inhibits its growth. The length of the right fracture initially decreases and then increases.  
Figure 11: Fracture propagation characteristics under different segment length conditions. (a) 12 m; (b) 20 m; (c) 30 m; (d) 40 m.
# _Laasonia inermis_ as an Active Corrosion Inhibitor for Mild Steel in Hydrochloric Acid
## 3 Results and Discussion
### GC-MS Analysis Results for Lawsonia internis  
The GC-MS analysis of the extract is presented in Figure 7. It is very clear from the graph that _Lawsonia internis_ (LI) has eight prominent organic compounds, including n-hexadecanoic acid, hexadecanoic acid ethyl ester, oleic acid, octadecanoic acid, E-13 docosenoic acid, hexadecanoic acid-2,3-dihydroxy ester, hexadecanal, and squalene. These compounds collectively contribute to the corrosion inhibition of the mild steel by forming a stable, adherent, and hydrophobic protective film on the metal's surface, thus significantly reducing the rate of the corrosion [27]. n-Hexadecanoic acid (palmitic acid) is a saturated fatty acid that forms a hydrophobic film on the steel's surface, preventing direct contact with the corrosive medium. This hydrophobicity is crucial in minimising water penetration, which is necessary for the corrosion process [28]. Hexadecanoic acid ethyl ester (ethyl palmitate), an ester of palmitic acid, enhances the film's formation by increasing its stability and effectiveness. The ester linkage provides an additional interaction with the metal's surface, thereby reinforcing the barrier [28]. E-13 docosenoic acid is metabolised to oleic acid, an unsaturated fatty acid, creating a compact and adherent layer on the steel, which effectively blocks active corrosion sites and reduces metal dissolution. The presence of the  
Figure 6: (**a**) SEM picture of polished mild steel surface; (**b**,**c**) SEM photographs of mild steel surfaces dipped in 0.1 M and 1 M HCl; (**d**) SEM picture of mild steel dipped in 0.1 M HCl containing LI; (**e**) SEM picture of mild steel dipped in 1 M HCl containing LI.  
double bond in the oleic acid introduces kinks to the molecular structure, allowing for it to pack tightly and form a robust barrier [29]. Octadecanoic acid (stearic acid), another saturated fatty acid, forms a protective film similar to that of palmitic acid, acting as a barrier against corrosive agents. The long alkyl chain in stearic acid contributes to a hydrophobic layer that further prevents moisture penetration [30]. Hexadecanoic acid-2,3-dihydroxy ester, an ester with polar groups, strongly adsorbs on the metal's surface because of the presence of hydroxyl groups. These polar groups increase the binding affinity to the metal, creating a robust protective layer that reduces corrosion rates [31]. Hexadecanal, an aldehyde, forms complexes with the metal's surface, contributing to the formation of a protective film that prevents oxidation and corrosion. The aldehyde group can interact with the metal, forming a stable complex that acts as a barrier [30,31]. The combined effect of these mechanisms results in a stable and effective protective layer that minimises the direct exposure of the steel to the corrosive hydrochloric acid environment, significantly reducing the rate of the corrosion [27].