esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 6 Evaluation
### Simulation and Results
#### 6.3.1 Simulation Setup  
We use Matlab R2018a to conduct our simulations, and run all algorithms on a local machine with an Intel Core-i5 2.5 GHz, 6 GB RAM, and Microsoft Windows 7 OS. Assume that there is a region A of size 10 km \(\times\) 10 km with 100 \(\times\) 100 locations. We construct a full-mesh network consisting of 100 \(\times\) 100 nodes to simulate the locations in region A. To evaluate the effectiveness of our SCTB algorithm, we introduce the concept of service category similarity, which is symbolized by \(L_{S}\) in this paper. Suppose that under normal circumstances, the kind of service category of user A is not related to the kind of service category of user B, then the service category similarity between user A and user B is \(L_{S}=0\). Assume that under certain space-time conditions, the probability of user A's kind of service category is half the same as user B's, then we denote the service category similarity between user A and user B is \(L_{S}=0.5\). We perform the simulation in the following four different scenarios and average performance results were obtained for running each scenario for 30 min.  
**Scenario-1**: There are 50 \(\times\) 100 users with transmission radius of \(tr\) = 50 m randomly distributed in region A. Assume that the total kinds of service category in the data-pool are 16. All users should join in the clustering process, if a user \(U_{a}\) receives a request aggregating massage when he wants a LBSquery, he will response for it and join in that clustering group. Otherwise, \(U_{a}\) will broadcast the request aggregating message as an initiator, and wait for other users to join. A certain number of users who send requests at a time interval. The ratio of the certain number of users to the total number of users is \(1/10\), and the time interval is \(0.1\)s. The rate of \(th_{l}\) to \(th_{k}\) is \(th_{l}=th_{k}/2\). We set the service category similarity \(L_{S}=0\), which means the probability of service category similarity among users is \(0\%\).  
**Scenario-2**: We set the service category similarity \(L_{S}=0.5\), which means the probability of service category similarity among users is \(50\%\). The rest of the conditions are the same as Scenario-1.  
**Scenario-3**: We set the service category similarity \(L_{S}=0.75\), which means the probability of service category similarity among users is \(75\%\). The rest of the conditions are the same as Scenario-1.  
**Scenario-4**: Users move independently with the same velocity \(v=1m/s\), and a representative \(U_{a}\) sends clustering requests continuously in the \(60\)s under different \(L_{S}\). The rest of the conditions are the same as Scenario-1.
# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 3 Methods
### Extraction and Analysis of Spatial Textural Features
#### 3.1.2 Optimal Feature Combinations of Spectra and Textural Vectors  
The texture images derived from seven spectral channels (VNIR-SWIR images) will yield hundreds of combinations of texture vectors for classification. Hence, feature selection has to be conducted before the image classification. Feature selection is a technique for selecting optimal combinations of vectors that consistently provide good performance in object classification [55]. In this paper, feature selection is conducted by three steps, including (i) selecting some criterion to evaluate the effectiveness of input vector in lithological classification; (ii) selecting vector combinations which have the best performance (or achieve to the defined standard); and (iii) setting the selected vectors as the optimal features, then feature selection is accomplished. Given that we were initially interested in whether the incorporation of textural features would aid spectral features to improve classification accuracy, different combinations of texture images, fused with all seven spectral bands, were examined to select the optimal input feature vectors.  
The effectiveness of input feature vectors in lithological classification can be measured by the separability between two classes. The J-M distance has been mostly used for evaluating the separability between two classes [56; 57; 58] and was applied here to select the optimal feature vectors. The larger the class separability of the input vectors, the greater the J-M distance. The J-M distance is the average distance between two class-density functions, which is calculated using Equations (2) and (3).  
\[J\text{-}M\left(i,j\right)=2\left(1-e^{-a}\right) \tag{2}\]  
\[a=\frac{1}{8}\left(m_{i}-m_{j}\right)^{T}\left(\frac{2}{\left(C_{j}-C_{i} \right)}\right)\left(m_{i}-m_{j}\right)+\frac{1}{2}\text{ln}\sqrt{\frac{\left| \left(C_{j}-C_{i}\right)/2\right|}{\left|C_{i}\right|\cdot\left|C_{j}\right|}} \tag{3}\]  
where \(i\) and \(j\) are the two classes being compared, \(m_{i}\) and \(m_{j}\) is the mean vector of class \(i\) and \(j\), respectively, and \(C_{i}\) and \(C_{j}\) is the covariance matrix of the feature response of class \(i\) and \(j\) respectively. The value of J-M ranges from 0.0 to 2.0, and measures the pairwise class separability. A large J-M distance value indicates higher class separability.
## 3 Method  
### Multiple Gaussian Model  
In general, normal AEs induce a positive TA while CEs induce a negative TA. However, there are different types of eddies that cause varying vertical anomalies. For example, cyclonic warm-core eddies (CWEs) and anticyclonic warm-core eddies (AWEs) induce positive and negative temperature anomalies at different depths [26]. Based on these research results, most eddies have warm/cold anomalies in the vertical structure, which consist of more than two peaks/extreme points (in this study, \(T_{E}^{\prime}\) denotes  
Figure 4: Examples of Argo temperature profiles and the corresponding climatic temperature profiles. (**a**) shows the Argo temperature profiles randomly selected on 8 October 2011. 1, 2 are within CEs with position 171.477\({}^{\circ}\)E, 43.889\({}^{\circ}\)N and 167.831\({}^{\circ}\)E, 18.34\({}^{\circ}\)N, while 3, 4 are within AEs with position 148.604\({}^{\circ}\)E, 34.895\({}^{\circ}\)N and 152.858\({}^{\circ}\)E, 11.12\({}^{\circ}\)N. The corresponding climatic profiles are shown in (**b**). Figure 5: Geographical distribution of vertical profiles of eddies from January 2002 to December 2021 per 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) grid in the NWPO region. (**a**) is the distribution of profiles for AEs and (**b**) is for CEs. the extreme values of temperature anomaly within the eddy profile). To better fit the TA profiles, we have used the well-established and computationally convenient Gaussian function for fitted models as in (2), which fits the bell-shaped curve very well. \[f(x_{n})=\sum_{i=1}^{M}a_{i}e^{\frac{-\left(x_{n}-x_{i}\right)^{2}}{2\sigma_{i} ^{2}}}(n\text{ is }0,1,2\ldots 198) \tag{2}\]  
where \(u_{i}\) represents the depth of the extreme value of the '\(i\)th' Gaussian function. \(a_{i}\) and \(\sigma_{i}\) are coefficients representing the amplitude and the standard deviation of the Gaussian curve, respectively. \(M\) represents the number of combinations of Gaussian functions, which could be 1, 2, or 3 in this research. Furthermore, for profiles fitted with a single Gaussian function, \(a_{i}\) is equal to \(T_{Ei}\)', which implies the extreme values of the profile within the vertically localised region. The steepness of the Gaussian function is determined by \(\sigma_{i}\). This value represents the vertical range of influence of the temperature anomaly. For instance, using the 3\(\sigma\) criterion (Lajda criterion), we consider the range of influence of the '\(i\)th' temperature anomaly fluctuation to be [\(u_{i}-3\sigma_{i}\), \(u_{i}+3\sigma_{i}\)]. These parameters are crucial for the thermal structure of mesoscale eddies, especially for the vertical distribution and regulation of heat. The MG model-fitting procedure developed in this study can automatically extract the best-fit combination solution of Gaussian functions for the TA profiles. The processing steps are summarised in Algorithm 1 and described in detail below.
## 4 Results
### Average Magnitude and Angular Error  
To evaluate the results of the new method, we compared the method to the statistical data processing \"angular limitation filter.\" Based on the rotation law of the tidal current ellipse, the angular limitation method considers the rotation direction and appropriate angle constraint, and then identifies and eliminates spurious vectors. Table 1 shows the AAE and AME values of the original 9-day data and the method after processing values during drifting buoy observations. The 9-day-average AAE value is 37.82\({}^{\circ}\), and the AME value is 0.57 in the original data. In [PERSON] [26] et al., the AAE and AME values are 51.76\({}^{\circ}\) and 0.50, respectively. Both methods have improved the results. The improvement of \"multi-correlation coefficient optimization\" for the AME value is better than that of the \"angle limitation filter,\" and the averaged AAE value decreased to 30.28\({}^{\circ}\), which means that the improvement is approximately 20%. The average AAE value obtained by the angular limitation filter has decreased to 34.56\({}^{\circ}\), which means the improvement is approximately 9%. Comparison of the two filtering methods shows that the multi-correlation coefficient optimization method is better than the angular limitation filter method. Using the T-test method, we take the AME value and AAE values in Table 1 as samples to test whether the results of the two methods are statistically different from the original data. It is found that there is no significant difference between the data processed by the two methods and the original data, which may be due to the small proportion of spurious vectors. However, compared with the \"angular limitation filter\", the difference between the data processed by the new method and the original data is slightly larger, indicating that the new method contributes a further improvement. The AAE and AME results of the multi-correlation coefficient optimization method show that the direction and speed of the GOCI data are  
Figure 9: **(a)**-I showing the observed SSC vectors from the drifting buoy (BUOY; black arrows) and estimated from the satellite (GOCI; blue arrows) in the East China Sea. The blue dashed arrows represent the spurious vectors, and the red dashed arrows represent the substitution vectors. (**a**)-II showing the corresponding OSU tidal current vectors. The rotation direction can be determined. (**b**) Observed SSC vectors from drifting buoy and estimated from GOCI in the southern Yellow Sea. (**c**) Observed SSC vectors from drifting buoy and estimated from GOCI in the northern Yellow Sea.  
similar to those of the measured buoy trajectory. Therefore, the MCC method is reliable for inverting tracer concentration to obtain the SSCs offshore of China.
# Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System
## 2 Kaya Bridge, High-Speed Trainsand Monitoring System Descriptions  
Kaya Bridge, shown in Figure 1a, is a composite steel box girder bridge with 50 m span supports two line high-speed railways. The cross-section of the bridge is shown in Figure 1b. Two longitudinal girders are used with a spacing of 6.5 m and reinforced concrete deck with 14.00 m wide, which is used to provide the large stiffness for such heavy live loads. The use of a steel box as the deck system is another feature in the design of this bridge, which is adopted to reduce uneven deflection and torsion of the deck. The bridge design criteria at the mid span are presented in Table 1.  
Figure 1: _Cont_.  
The next-generation high-speed developed train (HEMU-430X) is intended to travel with a maximum speed of 430 km/h (Figure 2).The high-speed train is controlled to pass over the bridge in the passage track with four different speeds, _i.e._, 290, 360, 400, and 406 km/h.  
To monitor the behavior of the bridge as per the passing of the high speed trains, a wireless SHM system was designed and installed on the Kaya Bridge shortly after it was opened to use the HEMU-430X train, as shown in Figure 3. This on-line concise SHM system was designed with a minimum of 20 sensors to monitor the key parameters. Moreover, a total of 20 accelerometer and strain gauge sensors (five sensors on each railway track) with sampling frequency of 100 Hz were installed on the main girders under each railway track in each direction with equal-spacing of 8.3 m. The sensors were installed at the bottom flange of the main girders in the vertical direction to detect and monitor the vertical vibration and fatigue of the bridge, as illustrated in Figure 3. The entire system consists of a set of sensors, data acquisition, data transmission, data management and a structural evaluation mechanism. The primary purpose of the system is to monitor in-service performance of the bridge structure under high-speed trains, and to provide early warning of abnormal changes in in-service performance of the bridge. Herein, the structural parameters to be monitored in the Kaya Bridge were determined using the structural sensitivity analysis with the finite element model under the designed train speeds action, as shown in [PERSON] _et al._[5]. Other important parameters including accelerations, strains, deformations and fatigue of the steel box girder bridge were monitored and studied under different speeds of the passing high-speed trains.  
\begin{table}
\begin{tabular}{c c c} \hline
**Review Factor** & **Criteria** & **Note** \\ \hline Vertical acceleration & 4.9 m/s\({}^{2}\) & For concrete track \\ Displacement as safety & 82 mm & Under 350 km/h \\ Displacement as comfort & 22 mm & Under 350 km/h \\ Track twist & 0.4 mm/m & By dynamic analysis \\ \hline \end{tabular}
\end{table}
Table 1: Railway Bridge design criteria [5].  
Figure 1: Kaya bridge (**a**) view and (**b**) cross section (_dimensions in mm_).  
Figure 2: Axial spacing and loading of the high-speed train.
# Single-Shot Full Characterization of the Spatial Wavefunction of Light Fields via Stokes Tomography
## 2 Principle and Methods
Remarkably, the complete information about the signal SCA is already hidden in the morphological feature of the observed spatially variant SoP, which manifests in the spatial polarization orientation functions, defined as follows:  
\[\phi(\mathbf{r})=\frac{1}{2}\text{atan2}(S_{2}(r),S_{1}(r)), \tag{4}\]  
where \(\text{atan2}(y,x)\in[0,2\pi)\) is the 2-argument arctangent. Since the function \(2\phi(\mathbf{r})\) describes the relative phase between the two polarization components for each position in the beam cross section, and since the ancillary wavefront is completely known, we can retrieve the wavefront of the signal from the relation \(2\phi(\mathbf{r})=v(\mathbf{r},z_{m})\). On this basis, combined with the signal amplitude \(u(\mathbf{r},z_{m})\) that has been obtained in the SoP tomography (projection result on \(\hat{\epsilon}_{-}\)), the complete information about the signal beam at the measuring plane, i.e., \(\psi_{\text{sig. }}(\mathbf{r},z_{m})\), is determined. Regarding the accuracy (or uncertainty) of the wavefront determination, in this technique, the wavefront information of the beam is first converted to the intramodal phase of the vector mode in Equation (3), reflected in the spatial polarization orientation in Equation (4), and then determined via spatial Stokes tomography. That is, the accuracy of the wavefront estimation relies on that of the polarization orientation estimation for each position (or pixel) in the beam cross section. From this perspective, the accuracy reaches its maximum as the local polarization reaches a linear SoP, i.e., as \(\sqrt{a}g(\mathbf{r}_{0})=\sqrt{1-au}(\mathbf{r}_{0})\) for the position \(\mathbf{r}_{0}\), which corresponds to a minimum value (zero) in \(S_{3}\), and gradually decreases as it approaches a circular SoP. Therefore, we can use the spatial concurrence of the observed spatially varying SoP, given by \(\text{C}(\mathbf{r})=\sqrt{1-S_{3}^{2}(\mathbf{r})}\in[0,\,1]\), to indicate the accuracy of the wavefront estimation [20; 25], as shown schematically in Figure 1a. For a region with a near-circular SoP, i.e., \(\left|\sqrt{a}g(\mathbf{r}_{0})-\sqrt{1-au}(\mathbf{r}_{0})\right|\approx 1\), one can adjust the power of ancillary light (i.e., \(a\)) to make the local concurrence \(\text{C}(\mathbf{r}_{0})\) approach 1 and remeasure the SoP distribution there. Figure 1: (**a**) Schematic of the principle. (**b**) Experimental setup, where the key components are the single-mode fiber (SFM), polarizing beam splitter (PBS), nonpolarizing beam splitter (NPBS), polarizing filter (PF), spatial light modulation (SLM), and lens (L1–L4).
## 3 Results and Discussion
### Evaluation of Qualitative Yield
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Log**} & **Mid Diameter** & **Length** & **Quantity** & **Bark** & **Volume [m\({}^{3}\)]** & **Type of Lumber** & **Qualitative** \\  & **[cm]** & **[m]** & **[ks]** & **[m\({}^{3}\)]** & **Volume [m\({}^{3}\)]** & **Radial [m\({}^{3}\)]** & **Tangential [m\({}^{3}\)]** & **yield [\%]** \\ \hline V\({}_{1}\) & 43.0 & 4.10 & 1 & 0.0345 & 0.56 & 0.109 & 0.263 & 19.43 \\ \hline V\({}_{2}\) & 45.0 & 3.95 & 1 & 0.0364 & 0.59 & 0.118 & 0.345 & 19.94 \\ \hline V\({}_{3}\) & 52.0 & 4.02 & 1 & 0.0476 & 0.80 & 0.079 & 0.508 & 9.80 \\ \hline V\({}_{4}\) & 60.5 & 4.08 & 1 & 0.0680 & 1.10 & 0.045 & 0.884 & 4.04 \\ \hline V\({}_{5}\) & 48.0 & 4.18 & 1 & 0.0439 & 0.71 & 0.447 & 0.000 & 62.69 \\ \hline V\({}_{6}\) & 50.0 & 4.01 & 1 & 0.0378 & 0.74 & 0.355 & 0.089 & 47.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of qualitative yield using selected log-sawing patterns. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**The Sawing Patterns**} & **Our Research (Beech Logs)** & **[PERSON] et al. 2021 [7]—Oak Logs** & **[PERSON] et al. 2021 [18]—Beech Logs** \\ \cline{2-7}  & **Average of** & **Average of** & **Average of** & **Average of** & **Average of** & **Average of** \\  & **Quantitative** & **Qualitative** & **Quantitative** & **Qualitative** & **Quantitative** & **Qualitative** \\  & **Yield [\%]** & **Yield [\%]** & **Yield [\%]** & **Yield [\%]** & **Yield [\%]** & **Yield [\%]** \\ \hline \hline \multirow{2}{*}{**Through and** **Through** **sawing**} & \multirow{2}{*}{_72.28_} & \multirow{2}{*}{_19.69_} & \multirow{2}{*}{_57.29_} & \multirow{2}{*}{_87.89_} & \multirow{2}{*}{_56.80_} & \multirow{2}{*}{-} \\  & & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & \\ \hline \hline \multirow{2}{*}{**Cant sawing**} & \multirow{2}{*}{_78.50_} & \multirow{2}{*}{_6.92_} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{_60.50_} & \multirow{2}{*}{-} \\ \cline{1-1} \cline{5-5}  & & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & \\ \hline \hline \multirow{2}{*}{**Quarter sawing**} & \multirow{2}{*}{_61.26_} & \multirow{2}{*}{_55.28_} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{_60.50_} & \multirow{2}{*}{-} \\ \cline{1-1} \cline{5-5}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of qualitative and quantitative yields with other researchers for three chosen sawing patterns. Figure 5: Effect of log-sawing pattern on qualitative yield evaluated by analysis of variance (ANOVA). Figure 6: Effect of log-sawing pattern on quantitative yield evaluated by analysis of variance. Figure 7: The effect of the log-sawing pattern on the resulting volume of tangential (**right** side) or radial (**left** side) lumber as evaluated by analysis of variance (ANOVA).
## 2 Materials and Methods
### Study Locations and Experimental Procedures
#### 2.1.2 Method of Data Collection  
Data collections were carried out during the dry and wet season months of June and November, respectively, in the two cities. The method deployed was the drive test protocol, whereby a field vehicle was used for mobility along the routes, with data taken at intervals of 1 km using the base station as the reference point. Two routes (A and B) were considered in Ikorodu, with a length of 10 km each (Figure 1a), while three were identified in Akure (1b). However, only two routes (A and B) with a maximum length of 16 km were used for analysis in this study. A digital signal level meter with a spectrum (Satlink WS-6936) was used to measure the received power in (dBm) of the transmitted signal, while a handheld GPS was used to capture the coordinates of the data points and the transmitter-to-receiver distance (Tx-Rx) of the data points from the base stations. Five datasets of the power received (dBm), Tx-Rx (km), latitude (\({}^{\circ}\)N), longitude (\({}^{\circ}\)E), and elevation (m) were measured and recorded for each data point. The accuracy of the Satlink WS-6936 and the GPS Garmin Map78s were validated in an initial test measurement. A few measured coordinates of the data locations were compared with Google online coordinates of the locations and found to be in agreement. Also, the received power measured around the base station using the Satlink was also validated at the base station(s) before the main measurement campaign.  
The data were sorted according to route and season, with the mean values of the routes (A and B) for both seasons used for computational analysis to enhance the reliability of the results.
# Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation
## 2 Literature Review  
### State Representation Modeling for a Deep-Reinforcement-Learning-Based  
_Interactive Recommendation_  
State representation modeling for deep RL-based IRS is crucial to model the user behavior during the recommendation process. Therefore, many previous studies have investigated this topic in the past decades. Among the state-of-the-art methods, sequential models are the most popular adopted. Models such as recurrent neural networks (RNN), and convolutional neural networks (CNN) are widely used in modeling the sequential dependencies over the user-item interactions in sequential recommender systems and IRS [26]. For instance, long short-term memory (LSTM) [13; 27; 28] and gated recurrent unit (GRU) [6; 16] have been applied in IRS to capture the long-term dependencies over user interactions in IRS. In [5], simple recurrent unit (SRU) [29] was used to improve the learning efficiency in IRS. In [30], CNN was also introduced to IRS to model user behaviors. Despite the effectiveness of the above methods, most of these methods used left-to-right sequential models to model the sequential dependencies. They encoded each user's current state based on the hidden representation learned by the sequential models. However, the real-world interaction data do not often follow the left-to-right rigid order, as indicated in a sequence [26; 31]. Therefore, the aforementioned methods may have limitations in dealing with real-world data containing long and noisy dependencies [32].  
The adoption of attention-based models for IRS also can be found in various studies, including [4; 22]. In [22], they used attentive recurrent neural networks to capture the order over the historical items. Moreover, in [33], a multi-head attention network with GRU models the state representations. Recently, some advanced models have been proposed for modeling the states of RL-based interactive recommendation. Embracing the knowledge graph (KG) with sequential models evolves knowledge-based state representation models [34]. Such works can be found in [14; 35; 36; 37; 38]. Unfortunately, knowledge-based state representation needs extra knowledge to interpret the states, which is not always available in some interactive recommendation scenarios.