esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Method of Assembly Tolerance Types Based on Degree of Freedom
## 5 A DOF-Based Method for Screening and Optimizing Tolerance Items
### Algorithm for Acquiring CPDF  
The Boolean operation is widely used in set operations, and the DOF vector of the tolerance zone can be regarded as a form of set. Therefore, by analyzing the data in Table 3, the flow of algorithm to find the CPDF vector can be obtained, as shown in Figure 4.  
* Step 1: Analyze the geometric functional requirements of the assembly body, specify the reference datum feature features and the measured feature features of the geometric functional tolerance of the assembly body, and determine the global coordinate system.
* Step 2: Determine the DOF vector \(V_{t}\) of the tolerance zone of the measured feature element and the DOF vector \(V_{f}\) of the reference datum feature element in the assembly.
* Step 3: Calculate the common DOF \(V_{c}\); \(V_{c}\) is defined as the result of the Boolean operation between the vector \(V_{t}\) and \(V_{f}\), and the Boolean operation formula is shown in Equation (13): \[V_{c}=V_{t}\cap V_{f}\] (13)  
Calculation rule of Formula (8): The elements of the two vectors, if the value of the element is \"1\", then the Boolean operation value will be \"1\"; otherwise, the value will be \"0\". From the Boolean rules, it can be seen that the DOF in a certain direction in vector \(V_{c}\) is \"1\", which means that the DOF in a certain direction in the vector \(V_{c}\) affects the geometrical functional requirements of the assembly, and needs to be taken into account when choosing the type of tolerance for the assembly feature elements.  
* Step 4: Determine the CPDF vector \(V_{0}\). When the geometric functional tolerance of the assembly is the orientation tolerance, set an auxiliary calculation vector \(V_{p}=\begin{pmatrix}0&0&0&1&1&1\end{pmatrix}\), solving for CPDF \(V_{0}\), the expression is shown in Formula (14): \[V_{0}=V_{c}\cap V_{p}\] (14)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & \multicolumn{4}{c}{**Measured Feature Elements**} & \multicolumn{2}{c}{**Plane**} \\  & & **Line** & & & & & \\  & **Cléments** & & **DOFs Vit** & **CPDF V0** & & **DOFs Vit** & **CPDF V0** \\  & **Vf(Tx,Ty,Tx)** & & **(Tx,Ty,Tx,** & & **(Tx,Ty,Tx,** & & **(Tx,Ty,Tx,** \\  & **Rx,Ry,Rz)** & & **Rx,Ry,Rz)** & & **Rx,Ry,Rz)** \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._When the geometric functional requirement of the assembly is the position tolerance, the expression for the CPDF vector \(V_{0}\) is given in Equation (15):  
\[V_{0}=V_{c} \tag{15}\]
## 3 Methods
### VNIR-Based Prediction Methods  
According to the VNIR-based SOM predicting demands, the PLS-MARS method was proposed to effectively represent the nonlinear relationship between the data with local difference and numerous variables (see Section 3.3.1). To verify the effectiveness of the proposed PLS-MARS method, typical prediction methods, such as MLR, SVM, PLS, and GWR, were used to construct VNIR-based SOM prediction models. To realize a more comparative analysis with the proposed PLS-MARS method, the GWR method was also combined with PLS (named PLS-GWR) to construct a prediction model. Eventually, a series of evaluation indices was used to evaluate the simulation and prediction performance of the prediction models, which is described in detail in Section 3.3.2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Prediction Model**} & \multirow{2}{*}{**Selection Method**} & \multicolumn{2}{c}{**Original Data**} & \multicolumn{2}{c}{**Predicted Data**} & \multicolumn{2}{c}{**Residuals**} \\ \cline{3-8}  & & **I** & **Z-Score** & **I** & **Z-Score** & **I** & **Z-Score** \\ \hline \multirow{8}{*}{Partial least squares–based geographic weighted regression (PLS-GWR)} & Concentration gradient (C) method & 1.15 & 6.33 & 1.06 & 5.83 & 0.01 & 0.20 \\  & Kennard-Stone (KS) method & 1.28 & 5.87 & 1.15 & 5.23 & \(-\)0.07 & \(-\)1.27 \\  & Rank–Kennard-Stone & 1.33 & 6.30 & 1.28 & 6.03 & \(-\)0.10 & \(-\)1.35 \\  & Rank–KS method & & & & & & \\  & Regional multi-variable & & & & & & \\  & associate rule mining and & & & & & & \\  & Rank–Kennard-Stone & 1.39 & 6.17 & 1.24 & 5.49 & \(-\)0.07 & \(-\)0.96 \\  & (MVARC-R-KS) method & & & & & & \\ \hline \multirow{2}{*}{Partial least squares–based multivariate adaptive regression (PLS-MARS)} & C & 1.15 & 6.33 & 1.03 & 5.69 & \(-\)0.01 & 0.06 \\  & KS & 1.28 & 5.87 & 1.12 & 5.15 & 0.07 & 1.42 \\  & Rank–KS & 1.33 & 6.30 & 1.12 & 5.30 & \(-\)0.01 & 0.01 \\  & MVARC-R-KS & 1.39 & 6.17 & 1.17 & 5.23 & 0.08 & 1.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: [PERSON]’s I index and z-score of data related with prediction models calibrated by different calibration set selection methods.
## 3 Intelligent Control Strategies for Departure from the Parking Lot
### Timing Control Strategy  
Timing control strategy takes the control frequency of the gate lever, known as the \"mediation rate\" in the following, as an optimization goal. As shown in Figure 2, the control strategy takes the road capacity of the main road as a reference, considers the departure demand of vehicles in the parking lot, and regularly adjusts the number of vehicles driving onto the ground-level main road. As a result, the sum of vehicles on the ground-level main roads and the vehicles in the exit lane of parking lots is less than that of the control targets.  
On the one hand, the mediation rate of the intelligent lever lift system must meet the normal departure demand of vehicles in the parking lot to avoid a long queue in the parking lot. On the other hand, an excessive mediation rate of the intelligent system leads to a situation in which a large number of vehicles rapidly pour into the road from the parking lot, which exceeds the traffic capacity of downstream intersections and affects the efficiency of regional traffic. In Equations (1)-(4), the formulations for the mediation rate \(r\) and the lift period \(T\) of the intelligent lever lift system at any exit of the parking lot in the timing control strategy are shown.  
First, for any exit \(m\) of the parking lot in an intelligent lever lift system, the sum of mediation rate \(r\) and the upstream road traffic demand \(q\) should not exceed the downstream intersection traffic capacity \(C\) with the turning \(n\):  
\[C_{n}\geq\sum_{l}^{L}\sum_{m}^{M}r_{lmn}+q_{n},\forall n \tag{1}\]  
where \(r_{lmn}\) represents the meditation rate of any exit \(m\) of the parking lot in the regional plot \(l\) with turning \(n\); \(C_{n}\) represents the traffic capacity of entrances of intersection \(n\); and \(q_{n}\) represents the upstream traffic flow of the corresponding intersection.  
Secondly, the mediation rate of all turns of the regional plot \(l\) parking lot exit is not lower than the vehicle departure demand of the regional plot \(D_{l}\):  
\[\sum_{n}^{N}r_{lmn}\geq D_{l},\forall l \tag{2}\]  
Finally, we calculate the fixed lever lift period of the intelligent lever lift system for the exit \(m\) of the parking lot in regional plot \(l\):  
\[T_{lm}=\max\left(\frac{3600}{\frac{N}{n}r_{lmn}},T_{\min}\right),\forall l,m \tag{3}\]  
Figure 2: Parameters of a timing control strategy.  
\[T_{\rm min}=\frac{3600}{D_{l}} \tag{4}\]  
where \(T_{lm}\) represents the cycle length of the intelligent lever lift system at the exit \(m\) of the parking lot of the regional plot \(l\) and \(T_{\rm min}\) represents the minimum cycle length. Therefore, according to the above restrictions, the cycle length of each gate can be solved, and the timing control scheme of the gate levers at the entrance and exit of each parking lot can be obtained.
## 5 Discussion  
### Estimation of ECe of HH Mode and VV Mode Using Linear Regression Analysis  
The ECe data of HH and VV modes, as the primary input for soil salinity prediction, were estimated based on the derived equation from linear regression analysis. The derived equations of HH and VV modes provided the R values, with a value of 0.873 and 0.861, the R\({}^{2}\), 0.762 and 0.742, and the \(p\)-value, with a value of 0.000 and 0.000, respectively. As a result, ECa has a very strong positive relationship with ECe, and both R\({}^{2}\) can explain the variance in observations, ECe, about 76% and 74% [62]. The derived \(p\)-values of the two modes, with a value of less than 0.01, show very strong evidence of a difference between means in ECa and ECe sample comparison [47; 48].  
This study agrees with many similar studies [35; 36; 71]. [PERSON] and [PERSON] [36] examined the statistical relationship between ECa and ECe for salinity predicting and mapping. They found that ECa values were highly correlated with ECe values, with  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Soil Salinity**} & \multicolumn{4}{c}{**Potential Groundwater Availability**} \\ \cline{2-5}  & **TDS \textgreater{} 1500 mg/L, Expected Well Yield \textless{} \textless{} 2 m\textgreater{}/h** & TDS \textgreater{} 1500 mg/L, Expected Well Yield 2-10 m\textgreater{}/h** \\ \cline{2-5}  & **Area in sq. km.** & **Percent** & **Area in sq. km.** & **Percent** \\ \hline \multicolumn{5}{c}{**HH measuring mode**} \\ \hline Non-saline & 0.00 & 0.00 & 9.53 & 1.74 \\ Slightly saline & 2.21 & 0.40 & 10.35 & 1.89 \\ Moderately saline & 24.72 & 4.53 & 52.80 & 9.67 \\ Strongly saline & 60.20 & 11.03 & 123.72 & 22.66 \\ Very strongly saline & 115.32 & 21.12 & 147.15 & 26.95 \\ Total & 202.44 & 37.08 & 343.54 & 62.92 \\ \hline \multicolumn{5}{c}{**VV measuring mode**} \\ \hline Non-saline & 0.00 & 0.00 & 0.00 & 0.00 \\ Slightly saline & 2.21 & 0.40 & 0.43 & 0.08 \\ Moderately saline & 3.88 & 0.71 & 49.72 & 9.11 \\ Strongly saline & 52.68 & 9.65 & 92.86 & 17.01 \\ Very strongly saline & 143.67 & 26.31 & 200.54 & 36.73 \\ Total & 202.44 & 37.08 & 343.54 & 62.92 \\ \hline \end{tabular}
\end{table}
Table 22: Area and percentage of potential groundwater availability and soil salinity assertion classification of two measuring modes.  
R\({}^{2}\) > 0.70. Likewise, [PERSON] et al. [71] used an EM38 sensor to measure ECa in four land-use types: natural desert, natural vegetation, apple orchard, and winter wheat farmland, and to identify a linear relationship with 60 soil ECe samples. They found a strong linear relationship between observed ECa and measured ECe, with an R\({}^{2}\) value of 0.87. They used the derived equation to convert all observed ECa data into ECe data for spatial interpolation with the OK method to classify soil salinity severity.
# Assessing the Efficacy of Seawater Batteries Using NASICON Solid Electrolyte
## 1 Introduction
Despite their significant contributions to the development of materials and platforms suitable for operating in seawater, the unit cell performance only reached 2.6 V and 2 W, resulting in limited applications. SWB modules with both series and parallel connections necessitate research for applications with high operating voltage and high-power driving conditions [15]. The storage of electrochemical energy can be achieved by using conventional seawater batteries that combine a sodiation/desodiation anode and an electrolysis cathode. The design of a seawater battery consists of an anode in an organic electrolyte and a seawater cathode with a current collector, which can be utilized for energy storage and water desalination. The optimal anode compartment for a high-performance seawater battery must include electrolyte and electrode material. The positive electrode was made up of an organic electrolyte and an electrode material used for the anode of the cell. Anode materials and their associated anode compartments must meet multiple criteria in addition to their reversible ion absorption ability. Elemental sodium is often used as an electrode material due to its abundant supply and theoretical capacity of 1166 mAh per kilogram. In the end, lightweight metals or alloy materials such as magnesium or aluminum offer the possibility of a high theoretical specific capacity (Mg: 2200 mAh g\({}^{-1}\), Al: 2980 mAh g\({}^{-1}\)) and can even serve as electrodes. Various carbon materials are being utilized to explore the cathode side. Carbon felt has demonstrated mechanical stability, high flexibility, and conductivity. Carbon sponge electrolytic is another electrolyte in the cathode that is openly surrounded by a highly interconnected and macroporous framework. Low discharge voltage gaps, high voltage efficiency, high power density, and long-term cycling stability were achieved thanks to the bifunctional electrocatalytic OER and ORR activities. In another study, the performance of an activated carbon cloth as a current collector on the cathode side was assessed against that of a reduced carbon felt surface. The carbon cloth electrode's electrical formation of double layers is aided by the electrolytic OER/ORR activity [16]. This study aims to produce and examine ceramic membranes that contain Na\({}_{3,06}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\) and Na\({}_{3,18}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\). Furthermore, we will examine their resilience in solid electrolytes used in seawater batteries. Our aim is to conduct electrochemical testing on NASICON ceramic membrane batteries in an electrochemical cell with seawater as the aqueous electrolyte.
# 5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation
## 3 Proposed Method
### Algorithm for Image Encryption
#### 3.2.2 Permutation  
* Random numbers are selected to achieve pixel permutation, P, Q, and R;
* With the help of these numbers, five sequences or indexes A, B, C, D, and E are generated;
* A pixel is shuffled in a row with sequence A and in a column with sequence B to create confusion;
* Here two times shuffling is done with row and column, the first time with A, B sequence and the second time with C and D sequence;
* The XOR process is the final stage in this encryption procedure. When using the XOR technique, the pixel intensities are changed to a new one, which cannot be inverted unless having the chaos key.  
The flow diagram in Figure 6 expresses the above-stated encryption algorithm, which is detailed in the following steps:  
Figure 5: Individual plot for \(x\), \(y\), \(z\), \(w\), and \(s\) dimensions.  
Step 1: Read plaintext image \(I\) (the image size is set to 256 \(\times\) 256 pixels);
* Step 2: The chaotic sequences are constructed iteratively using the Gauss chaotic Equations (2)-(6) and the initial values of \(x\), \(y\), \(z\), \(w\), and \(s\);
* Step 3: Using random numbers and chaotic sequences, generate index sequences A, B, C, D, and E;
* Step 4: To produce a confusion matrix, bring the index sequence A from Step 1 and shuffle the pixels in rows and columns with sequence B;
* Step 5: Pixels are shuffled in the row with sequence C and in the column with sequence D once more, resulting in scrambled image \(IS\);
* Step 6: The index sequence E is then XORed with the scrambled image \(IS\), resulting in the encrypted image.
# Can We Improve Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data?
## 6 Conclusions
This might be attributed (at least partially) to the fact that the temporal resolution of the available track data (six hours) is not yet sufficient to allow parametric models to capture the short-term variations of track, translation speed, or wind asymmetry. This highlights the need for even higher temporal sampling of data (location of the cyclone center, maximum wind speed, wind radii, etc. ), and for greater efforts to improve the efficiency of numerical atmospheric models. [PERSON] conceived the idea and wrote most of the paper; [PERSON], [PERSON] and [PERSON] contributed to the development of the analysis tools; [PERSON], [PERSON] and [PERSON] arranged the figures and corrected a number of errors in the first version of the manuscript. [PERSON] wrote the description part on remote sensing data. [PERSON], [PERSON] and [PERSON] prepared the project C3 AF which funded about 66% of the work presented here. [PERSON], [PERSON] and [PERSON] played a major role for obtaining the IRD grant (about one third of total fundings). The manuscript also greatly benefited from their proofreading and suggestions. This research was funded by the ERDF/C3 AF project (grant number: CR/16-115) and the ANR/TIREX (ANR-18-OURA-0002-05). The main author also benefited from a grant by the IRD (Institut de Recherche pour le Developpement). Our warm thanks to the teams working on CYGNSS and ASCAT for their efforts, and to make the data available. ASCAT and CYGNSS data were provided here by the KNMI and PODAAC data center respectively. Data at Fort-de-France and Sainte-Lucie were made available by the CEREMA (CANDHIS database).The significant wave heights at station 42060 were provided by NDBC. The authors declare no conflict of interest.
# A Novel DV-HOP and APIT Localization Algorithm with BAT-SA Algorithm
###### Abstract  
Localization technology is essential for making wireless sensor networks(WSN)'s information processing and information collecting applications actually feasible. The beacon information is made available to the unknown nodes using the route exchange protocol. These data are more useful for determining the coordinates of neighboring nodes. Consequently, it was discovered that the algorithm for localizing nodes always has a flaw. Consequently, a brand-new metaheuristic termed Bat with simulated annealing is proposed to fix the flaw in the WSN standard node localization technique. The overall effectiveness of identifying the nodes is enhanced as a result of the large reduction in localization errors. The most popular localization estimation methods are the distance vector hop (DV-Hop) technique and approximate point-in-triangulation (APIT), which have high node localization accuracy and simple deployment in real-time environments. The primary benefits and their disadvantages, which give it a slight disadvantage in preference, are presented in this work. Both strategies are compared for their conventional performance and efficiency when combined with the Bat-SA algorithm.  
meta heuristic; Bat; location algorithm; DV-Hop; simulated annealing 2023
## 5 Multipath Mitigation Results
### Scenario 2: Three-Path Channel  
In the simulations above, we consider a single-path situation. However, this is not the case for some applications, especially for applications in urban and indoor environments where the number of paths could be much larger. So in the following test, we added extra two paths to the received signal in _Scenario 1_, and thus we had a LOS contaminated by three multipath signals. The first multipath component is identical to the multipath signal in _Scenario 1_; the second and third multipath components have 4 dB and 5 dB attenuation with 150 m and 225 m delay relative to the LOS component, respectively. The delays are approximately equivalent to half and 0.75 code chip for BOC(1,1) signal. Figure 12 shows the correlation function in this situation. We can see that when there are three multipath components, the final correlation function of the incoming signal is not only seriously distorted, but also shift to the right, which will result in significant ranging bias to the receiver.  
Figure 13 plots the RMSE cures of different methods in the presence of three multipath signals. Compared with the single-path test, all four RMSE curves of code phase estimation increase dramatically. CCRW and MEDLINE both keep very large RMSE values, and this condition has not been obviously improved with the increase of C/N\({}_{0}\). Whereas for the two aFFT methods, the RMSE curves go down significantly as C/N\({}_{0}\) gets larger. When the C/N\({}_{0}\) comes to 40 dB-Hz, the RMSE curves of two aFFT method are both lower than 10 meters. For \(L=64\), the RMSE is almost 50 m smaller than MEDLINE and 60 m smaller than CCRW, so the aFFT method is robust enough to the number of the multipath signals, but for MEDLINE and CCRW, once there are many secondary rays in the environment, the performance of code delay estimation will deteriorate dramatically.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
### Measuring Waves  
As with sea-level measurements, waves can be measured by fixed sensors, remote sensors and surface-following devices. Acoustic Doppler Current Profilers with pressure sensors can be deployed either on the sea bed, and/or within the intertidal zone to measure waves by combining information on water velocity and pressure data to derive wave information, but they are expensive (~USD 20,000-30,000). Radar reflection [11] and terrestrial LiDAR [12] can also be used to derive wave data, although they need to be mounted on suitable vertical structures at the shoreline or to existing infrastructure, e.g., piers.  
There are two main types of wave buoy: systems using inertia-based sensors, and those using GNSS receivers. Since the early 1960s, wave buoys have been built using multiple inertia-based sensors. These are robust, reliable, and therefore commonly used in national wave buoy networks. Examples include the Datawell Directional Waverider Mk III [13] and the TRIAXYS Direction Wave Buoy [14]; both incorporate accelerometers, gyros, and flux gate compass sensors to derive wave data. These are expensive, require calibration [15], and in the larger sizes are difficult to deploy with small boats.  
Over the past twenty years GNSS receivers have been installed on buoys and developed especially for measuring waves using data from a single GNSS receiver. Examples include the Datawell Mini Direction Waverider GPS [16] and the Sofar Spotter Wave Buoy [17], which can be easily deployed from a small boat. Wave statistics from signal receiver set-ups can be calculated using three velocity components derived from the Doppler shift of the satellite signal frequency. The advantages of using GNSS buoys as wave buoys are that the resulting wave data already have a reference frame, are lower in cost than inertial-based wave buoys and are smaller, (e.g., the Sofar Spotter has a USD 5000 list price in July 2021, weighs 5.4 kg and has size of 0.42 m \(\times\) 0.31 m). However, the main disadvantage is that GNSS receivers can suffer from satellite drop outs during extreme conditions such as spilling or breaking waves covering the antenna.  
Wave buoys are not routinely deployed within the inertial zone; coastal deployments are often in deeper offshore water. However, a low-cost wave buoy using an electronic inertial measurement unit (IMU) sensor, deployed in the surf zone, was capable of detecting breaking waves [18]. A small buoy was deployed for short periods at different water depthswithin the surf zone from 1.0 to 9.0 m; the mooring was secured to the sea-bed with a drag anchor.