esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Temperature and Salinity Measurement Using Frequency Comb
## 3 Materials and Methods
### Geometry Distance of the Water  
Based on the theoretical analysis carried out above, the measurement of water geometric distance is indispensable. In order to obtain this value, two steps are required.  
To the beginning, half part of the water container is full of water, and the other is empty. Thus, a distance value _OPD_ can be acquired. This distance value introduces the optical path difference between MA1 and MA2.  
Afterwards, both sides of the container should be empty, and the optical distance of MA1 will be changed to \(L_{w2}\). Therefore, a new distance value _OPD2_ will be obtained, which stands for the optical distance between new MA1 and MA2. Please note that when one side of the water tank is full of water, \(L_{w}\) is slightly longer than \(L_{u}\), which has been mentioned above. Hence, when both sides of the water tank are empty, \(L_{u}\) will be longer than \(L_{w2}\). Consequently, the water geometry distance can be calculated by the following Equations:  
\[L_{w2}=(L_{0}+L_{2}+D+L_{3})n_{a}+2 dn_{g} \tag{14}\]  
\[OPD2=L_{a}-L_{w2}=(L_{1}-L_{3}+L_{4})n_{a} \tag{15}\]  
\[L_{w}-L_{w2}=OPD+OPD2=D(n_{w}-n_{a}) \tag{16}\]  
\[D=\frac{L_{w}-L_{w2}}{n_{w}-n_{a}}=\frac{OPD+OPD2}{n_{w}-n_{a}} \tag{17}\]  
The group refractive index \(n_{w}\) and \(n_{a}\) can be calculated via the empirical formulas. After the values _OPD_ and _OPD2_ are acquired, \(D\) can be calculated.
# Assessment of Quad-Frequency Long-Baseline Positioning with BeiDou-3 and Galileo Observations
## 1 Introduction
In general, it is easier to solve the ambiguities of EWL/WL based on the observation information of multi-frequency and multi-system, so it is equivalent to adding higher-precision 'pseudo-range' observations than dual-frequency observations. Based on these advantages, it is conducive to construct a multi-scale positioning model that can meet different scenarios to better meet the diversified and differentiated needs of modern surveying and emerging location service industries for high-precision positioning [20]. In this study, we try to make full use of all BDS-3 and Galileo quad-frequency data to improve the performance of high-precision and rapid long-baseline positioning. Then, we try to find out the frequency point that can be eliminated in the quad-frequency signals, which means that we select three frequencies to obtain the theoretically equivalent positioning precision with quad-frequency signals. In this way, the dimension of matrix is reduced and the calculation efficiency is also improved to make preparations for adding new satellite navigation systems in the future. The structure of this paper is as follows. In Section 2, the multi-frequency single-epoch DD mathematical model of BDS-3 and Galileo is derived. In Section 3, satellite availability and RDOP values are analyzed and positioning performance and EWL/Second EWL/WL ambiguity fractions in the four combination schemes are analyzed. In Section 4, the success rate of theoretical EWL AR with different total errors is discussed. Some conclusions are given in Section 5.
## 5 Results of the TMC Uncertainties Assessments
### Uncertainty Assessment of TBR for Nuclear Data from TENDL Library  
As the TENDL-2017 library provides almost for all nuclides' 300 random files, the global assessment of the TBR uncertainty for the nuclear data involved in the MCNP calculations was performed for this library. These files generated during the evaluation procedure include nuclear data variations, not only of all cross sections but also for all particles spectra (differential and double differential), providing consistent variated nuclear data for the TMC calculations. The random files for n+\({}^{6}\)Li and n+\({}^{9}\)Be were also generated utilizing TENDL-2017 data. The total number of the nuclides used in the material definitions in the MCNP HCPB or WCLL geometry models is approximately 120, which results in \(\sim\)36,000 random files available for the TMC calculations. With such calculations, it is feasible to assess the sensitivity of the TBR integral response to the uncertainty of the nuclear data involved in the MCNP particle transport calculations. To ensure the convergence of the results and the reliable determination of the mean value (the most probable) and the standard deviation (assuming the well-resolved symmetry of the distribution), 10,000 TMC cycles were applied to obtain the TBR statistical distributions.  
Figure 9 presents the TBR probability distributions for the HCPB and WCLL DEMOs. These results were fitted with Gaussian distributions and shown as lines in Figure 9. The mean TBR values are 1.164 and 1.124 for the HCPB and WCLL DEMOs, respectively. The reference TBRs for the consolidated blankets designs obtained within the EUROfusion program are 1.181 [27] and 1.150 [28] for the HCPB and WCLL DEMOs. The mean TBR for both cases are shifted to the smaller numbers. As the evaluated data for the n+\({}^{6}\)Li and n+\({}^{9}\)Be are the same as in the TENDL-2017 and JEFF-3.2 libraries, the differences in the mean TBR value come from different evaluated data for the chemical elements constituting Eurofer steel. The standard deviations were calculated to be \(\sigma\) = 0.034 (\(\sim\)2.9%) and \(\sigma\) = 0.040 (\(\sim\)3.6%) for the HCPB and WCLL results, respectively. The statistical uncertainty of the MCNP particle transport calculations is \(<\)0.1%, and it does not affect the TMC results.
## 2 Instrument Design
### Critical Components
#### 2.2.2 Input switch  
Central to the radiometer operation is the electro-mechanical \"Single Pole 6 Throw\" input switch (SW). This precision RF switch (Agilent 87106B) is controlled _via_ a TTL level signal to toggle between the different noise sources fed to the receiver path (Figure 2). As the switch is part of the MA front-end, it has to meet high demands in terms of its insertion loss \(L_{\rm SW}\), repeatability, isolation and life-time. It is important for \(L_{\rm SW}\) to be low and repeatable to minimize and control the noise added to the different inputs. High isolation is essential to prevent unwanted signals from interfering. For L-band frequencies, the maximum insertion loss is rated at \(L_{\rm SW}=0.15\) dB for \(10^{7}\) operations. The specified repeatability of the switch of 0.03 dB would imply that the residual noise \(T_{\rm RM\,0}\) can vary considerably (\(\approx 1.8\) K). However, the repeatability measured was \(<\)0.005 dB (see Section 3.1.1) and therefore affects \(T_{\rm RM\,0}\) less than 0.3 K at \(T_{0}=313\) K. Hence, the switch performance is sufficient to function within the radiometer's lifetime (at least 5 years), assuming that a full measurement cycle is performed every minute.
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## 5 Alternative Key Management Taxonomies in DTNs
### Centralised, Decentralised and Distributed  
Key management solutions in DTN can be divided into three major categories, namely (a) centralised, (b) decentralised, and (c) distributed architecture. The schemes in the first category mandate the use of a TTP, and so far are the most commonly used and studied in the respective literature. Decentralised schemes on the other hand use more than one group to manage key distribution and are used with the purpose of sharing the overhead between the parties. The latter category of schemes pertains to group key management protocols, and therefore it imposes multiple cryptographic operations. This typically results to large communication and computational overheads [53]. Unfortunately, the various solutions proposed for the existing wired/wireless networks cannot apply to DTNs, because of the communication and computational overhead [53]. Also, in the DTN literature, the difference between decentralised and distributed models is unclear and sometimes is considered the same. Overall, due to its nature, the distributed model is more tolerant to infrastructure failures. This is in contrary to the centralised model which consists a single point of failure. Moreover, in the centralised model, join and leave operations for members are straightforward, but all communications require interaction with the TTP. The decentralised/distributed model not only makes privacy a hard issue to deal with, but also the more nodes in the network the more storage is needed, which is impractical in DTNs with low storage capabilities. Generally, works following the PGP philosophy are decentralised and distributed, while the rest of them which require a TTP, such as CA or PKG, are centralised. Lastly, it is to be noted that the three-fold taxonomy included in this subsection is most commonly used in group communications [67]. Tables 1-5 summarise the architecture used by each scheme.
## 3 Results
### Statistical Comparison of Method Performance
#### 3.3.1 Classification Performance Analysis  
We compared the classification performance metrics (accuracy, sensitivity, specificity, and ROC-AUC) between traditional ML methods (\(n=11\)) and deep learning methods (\(n=7\)), with hybrid approaches (\(n=3\)) analyzed separately due to the limited sample size. Table 2 presents the mean values and statistical comparison results.  
The statistical analysis confirms that deep learning methods achieved significantly higher performance across all classification metrics compared to traditional ML methods. The accuracy of DL methods was 8.6 percentage points higher on average (\(p=0.032\)), with similar advantages in sensitivity (9.2 percentage points, \(p=0.041\)), specificity (7.4 percentage points, \(p=0.046\)), and ROC-AUC (0.05 points, \(p=0.027\)).  
Hybrid approaches demonstrated the highest performance metrics across all categories, though the small sample size (\(n=3\)) limited the statistical power for formal comparison. The notably high specificity of hybrid approaches (97.3%) suggests they may be particularly valuable for ruling in NPH when positive.  
We also conducted subgroup analysis based on the classification task. For NPH vs. the healthy control classification, the performance advantage of DL methods was most pronounced (accuracy difference: 11.2 percentage points, \(p=0.018\)), while for NPH vs. other neurodegenerative conditions, the difference was smaller but still significant (accuracy difference: 6.4 percentage points, \(p=0.043\)).
# Salt-Fog Corrosion Behavior of GCr15 Steels Treated by Ultrasonic Strengthening Grinding Process
## 2 Materials and Methods
### Salt Spray Process of the Samples  
Each processed sample was cut into three plates with a diameter of 10 mm to calculate the mean values of experimental data. The salt frog test was performed using HT-YW-60 salt spray test machine and based on the standard ASTM B117. The working temperature was 35 (\(\pm\)2) \({}^{\circ}\)C, the precipitation rate of the salt spray was 1.5 mL/(cm\({}^{2}\)-h) with an air pressure of 96 kpa. PH value was set in the range of 6.5-7.2, and the total spray time was 96 h.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Chemical Element** & **C** & **Si** & **Mn** & **Cr** & **P** & **S** & **Ni** & **Cu** \\ \hline Content (wt\%) & 0.95–1.05 & 0.15–0.35 & 0.20–0.40 & 1.30–1.60 & 0.027 & 0.02 & 0.30 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical element composition of GCr15 steels.  
Figure 2: (**a**) Schematic diagram of ultrasonic processing equipment; (**b**) ultrasonic shoot peening treatment; (**c**) dry ultrasonic strengthening grinding process treatment; (**d**) wet ultrasonic strengthening grinding process treatment.
## 4 Feature Extraction Method for Sparse Trajectories
### Feature Extraction by GMM
#### 4.1.2 Aggregation of User Trajectories into RPs  
As mentioned at the beginning of this section, using trajectory \(T_{k}\) of ID \(k\) imputed based on Section 3.3 as feature vectors is not realistic. Accordingly, we define the feature vector to aggregate trajectory \(T_{k}\) of data length \(\mathcal{T}\) into RPs obtained via the GMM. In particular, the nearest RP for each coordinate in trajectory \(T_{k}\) is determined, and the obtained RP is assigned as the representative for that coordinate. The number of times that a coordinate is assigned to a certain RP is stored in the RP and is regarded as the stay time of ID \(k\) at that RP. This operation generates feature vectors \(f_{k}^{\text{GMM}},\forall k\in K\) with \(n\) features based on trajectory \(T_{k},\forall k\in K\).  
The specific procedures are indicated below. Let the coordinates of ID \(k\) obtained at time \(t\) be \(\mathbf{x}_{k,t}=(x_{k,t}^{\text{lat}},x_{k,t}^{\text{lon}})\). The cluster variable is denoted by \(z_{i}\), which equals 1 if sample \(\mathbf{x}_{k,t}\) belongs to the Gaussian distribution \(i\in I\); otherwise, it equals 0. In this context, the probability that \(z_{i}=1\), given \(\mathbf{x}_{k,t}\), is \(p(z_{i}=1\mid\mathbf{x}_{k,t})\), and from [PERSON] theorem,  
\[p(z_{i}=1\mid\mathbf{x}_{k,t}) =\frac{p(z_{i}=1)p(\mathbf{x}_{k,t}\mid z_{i}=1)}{\sum_{i\in I}p(z_{i }=1)p(\mathbf{x}_{k,t}\mid z_{i}=1)}\] \[=\frac{\pi_{i}p(\mathbf{x}_{k,t})}{\sum_{i\in I}\pi_{i}p(\mathbf{x}_{k,t} \mid z_{i}=1)}\]  
is obtained. Here, because \(p(\mathbf{x}_{k,t}\mid z_{i}=1)\) is the probability of sample \(\mathbf{x}_{k,t}\) on the \(i\)-th Gaussian discussion of the GMD,  
\[p(z_{i}=1\mid\mathbf{x}_{k,t})=\frac{\pi_{i}N(\mathbf{x}_{k,t}\mid\mathbf{\mu}_{i},\mathbf{ \Sigma}_{i})}{\sum_{i\in I}\pi_{i}N(\mathbf{x}_{k,t}\mid\mathbf{\mu}_{i},\mathbf{\Sigma}_{ i})} \tag{2}\]  
is obtained based on  
\[p(\mathbf{x}_{k,t}\mid z_{i}=1)=N(\mathbf{x}_{k,t}\mid\mathbf{\mu}_{i},\mathbf{\Sigma}_{i}).\]  
Equation (2) is the conditional probability that \(z_{i}=1\) given sample \(\mathbf{x}_{k,t}\); thus, the Gaussian distribution \(i\) that maximizes Equation (2), i.e.,  
\[\operatorname*{argmax}_{i\in I}p(z_{i}=1\mid\mathbf{x}_{k,t}),\]  
is the distribution to which sample \(\mathbf{x}_{k,t}\) belongs. By applying this operation to \(\mathbf{x}_{k,t},\forall t\in\mathbb{N}_{\leq\mathcal{T}}\), we can transform \(T_{k}\) of ID \(k\) to an \(n\)-dimensional feature vector \(f_{k}^{\text{GMM}}\) and generate a training dataset for the ML model. Note that the components of vector \(f_{k}^{\text{GMM}}\) are assumed to be \(f_{i},i\in I\), \(f_{i}\in\{x\in\mathbb{R}_{\geq 0}\mid x\leq\mathcal{T}\}\) and \(\sum_{i\in I}f_{i}=\mathcal{T}\). Because this \(n\)-dimensional feature vector is obtained by aggregating the target area into \(n\) RPs, the size of \(n\) is regarded as the expressiveness for the source area and may affect the classification accuracy.  
A diagram of this procedure is presented in Figure 3.
# Deep-Learning-Powered GRU Model for Flight Ticket Fare Forecasting
## 3 Methodology
### Data Preprocessing  
To improve the quality of the data, data preprocessing techniques were applied to the raw data collected from the sources. Figure 3 shows the overall data processing stages. Some errors in the raw data were first corrected in the data cleaning stage, and feature engineering was then conducted. Feature engineering involves creating new features from existing data fields to enable the model to better capture underlying patterns within data. Based on the data analysis in the previous section, a few new features, including Season, Travel Day, Travel Month, IsWeekend (Yes/No), IsHoliday (Yes/No), and NumOfStops, etc., were created, as listed in Table 4. These features exhibit specific characteristics of the flight data that influence ticket fares and are meaningful for business decisions. For instance, Season and Travel Month might indicate long-term seasonal demand for flights, while IsWeekend could help to identify the price difference between weekends and weekdays. Similarly, IsHoliday can be used to analyze whether or not prices change during holidays. The feature NumOfStops may be helpful for differentiating prices between direct and connecting flights. Overall, those features provide a comprehensive view of flight data and help build a more reliable prediction model since they are correlated to flight ticket fares.  
To prepare the data for the machine learning algorithms, we further applied two data transformation techniques: data encoding and data normalization. In data encoding, the categorical features were converted into numerical representations by one-hot encoding in the form of binary vectors such that the features were acceptable for most ML algorithms. Next, numerical data such as ticket fare were normalized into the region between 0 and 1 according to their dynamic range based on the following formula:  
\[\centering\ x^{\prime}=\frac{x-X_{min}}{X_{max}-X_{min}}\@add@centering \tag{1}\]  
where \(X_{max}\) and \(X_{min}\) denote the maximum and minimum values of the feature, respectively. Data normalization ensures that the numerical features of various flights are on compatible scales so as to have similar impacts on the prediction model. Finally, the preprocessed data were split into training, validation, and testing datasets with 44 decision features. The training dataset was used to train the model, the validation dataset was used to evaluate the performance during training, and the unseen testing dataset was used to assess the generalization capability of the model.
# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 2 From Vehicular Ad Hoc Networks to Internet of Vehicles
### Internet of Vehicle (IoV)
#### 2.2.2 Key Technologies of IoV
* _Communication and its application technology_--IoV mainly depends on two communication technologies: short-distance wireless communication and long-distance mobile communication technologies. In the former, RFID sensors and WIFI-like communication technologies are predominant, while in the latter, GPRS, LTE, 5G and other mobile communication technologies are the main focus. These communication technologies are more concerned with applications, such as automatic toll collection, data packet transmission, video surveillance and so on. * _Internet technology_--IoV can integrate the existing technologies and applications of the Internet and mobile Internet, but it is necessary to develop the characteristic Internet applications of IoV. Only in this way can more commercial benefits be brought to IoV. On the whole, differences between VANET and IoV are shown in Table 1. IoV takes vehicles as nodes and information sources, and it connects the acquired information to the platform network through wireless communication and other technical means for analysis and management. Its core is information acquisition and feedback control, so as to realize the interconnection in V2X. With the increasing urban traffic congestion and the continuous progress of ITS technology, IoV has good development prospects. Just as PCs enter the Internet and mobile phones enter the mobile Internet, vehicles will surely enter the IoV and will go far.
## 3 Plasma Sources
### Dielectric Barrier Discharge (DBD) Plasma Source  
Dielectric barrier discharge (DBD) plasma systems involve electrical discharge between two electrodes separated by an insulating dielectric barrier. It uses high-voltage alternating current, ranging from lower RF to microwave frequencies. The plasma is created through high-voltage pulses and the lead frames are passed through the plasma, which operates continuously. The plasma is created by capacitive coupling, as shown in Figure 3, which presents a schematic of a DBD plasma [13]. The dielectric limits the current flow into the plasma, thus preventing arcing. The discharge is sustained due to the fact that, at atmospheric pressure, frequent collisions may occur due to the high density of gas molecules [55].  
DBD sources have performance that compares well to that of pulsed DC systems. These systems also allow for operation under atmospheric conditions and can be used for inline plasma systems. However, not all dielectrics can operate with DBD sources [6]. DBD plasmas are used for cleaning applications and surface preparations; however, systems based on DBD mostly operate at atmospheric pressure, which means that they would not require vacuum chambers to operate. Table 7 shows the grading of parameters for DBD plasma sources.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **References** & **Parametric Score** \\ \hline Electron Temperature &
\begin{tabular}{c} ECR systems can reach very high electron temperatures \\ ranging between 10 and 20 eV [36]. Some high-power \\ systems can even exceed 30 eV up to 50 eV; however, these \\ are not lead frame cleaning systems [14; 54].
# Blockchain-Based Secured Access Control in an IoT System
## 5 Proposed Solution
### MAC Policy  
MAC stands for Mandatory Access Control and offers tighter security because only Blockchain Managers (BCMs) can access or modify the access control policy, which can, in turn, reduce security errors. MAC is based on the security clearance of a subject (user), such as secret, top secret or confidential and an object's resource classification, such as secret, top secret or confidential. Clearance and classification data are stored in the security labels.  
The determination of granting access to a request is based upon the clearance levels of subjects and the classification levels of objects. MAC policy is enforced by comparing the clearance of the subject to that of the object according to the Bell-LaPadula model, which is used for enforcing access control based on multilevel security policies [29]. Moreover, it focuses on maintaining data confidentiality and guarantees that users do not acquire access to resources above their security clearance, as shown in Figure 3.
## 3 Earth Surface Effects on GNSS-R Relevant to Land Applications  
The potential of spaceborne GNSS-R for land applications is evaluated, first at a global scale and then at a regional scale. The results are displayed in a 0.1\({}^{\circ}\) latitude/longitude grid with a 1\({}^{\circ}\) pixel size used in the spatial averaging, performed with steps of 0.1\({}^{\circ}\). The total number of observations over land surfaces is \(\sim\)35,000 per month (\(\sim\)70,000 considering direct and reflected signals), and the maps are constructed using a mean value of \(\sim\)25 \(\pm\) 4 measurements per pixel. There are more data availability over target areas at larger latitude than over the equator because of the SMAP's orbit parameters (polar orbit). On the other hand, the area of the selected pixel size gradually reduces from the equator to the poles. The lat/lon grid was selected to homogenize the number of measurements per pixel. In the close up over Africa, the selected spatial averaging size was 2\({}^{\circ}\), also performed with steps of 0.1\({}^{\circ}\). The pixel size was selected to be larger than that of global averaged maps because of the of lower data availability. The waveforms' parameters (SNR, PR, and trailing & leading edges) are geo-located over the specular points on the Earth's surface. Four different relatively \"homogeneous\" areas are considered to study statistically the waveforms' properties associated with different scattering mechanisms. In particular, the coordinates of these regions are: (a) tropical forests: latitude = \((-3,3)^{\circ}\) by longitude = \((15,25)^{\circ}\); (b) boreal forests: latitude = \((50,70)^{\circ}\) by longitude = \((90,140)^{\circ}\); (c) Greenland: latitude = \((75,83)^{\circ}\) by longitude = \((-53,-31)^{\circ}\); and (d) Sahara Desert: latitude = \((19,28)^{\circ}\) by longitude = \((-7,27)^{\circ}\). Four different three-months seasons (January-March, April-June, July-September, and October-December) have been selected to analyze the effect of seasonal variations at a regional scale on soil moisture and vegetation water content.