esa-sceva/satcom-chunk-collection huggingface dataset:
# Twitter Data Mining to Map Pedestrian Experience of Open Spaces
## 2 Materials and Methods
### Data Pre-Processing  
The phase of _data pre-processing_ includes several steps that eliminate parts of tweets that contain elements that may unnecessarily affect the sentiment score. The goal is to reduce the \"noise\" that comes with social networks data, such as links, slang, stop words, etc., as much as possible so that data is in a text format that can be parsed and classified. For these steps, Python's String methods and the library Natural Language Toolkit (NLTK) were used [24]. NLTK is a suite of libraries and programs that helps with work in natural language processing (NLP) for English.  
Data pre-processing steps include the following:  
* Capitalization/case-folding--converting upper case to lower case. When case-sensitive analysis is used, two same words can be treated as different.
* Punctuation--Removing punctuation, digits and special characters that don't convey any sentiment.
* Remove stop words--removing stop words which do not affect the meaning of the sentence, removing short words that contain 3 or less characters (_his, all, are, at_, etc.).
* Spelling correction--correcting spelling conducted because it could affect the sentiment.
* Stemming--normalizing words from the inflected form to be able to analyse words in their root dictionary form. For example, _loves, loving, lovable_ are often used in the same context, and in stemming affixes are eliminated to the root word \"love.\"
* Lemmatization--normalizing words using vocabulary and morphological analysis of words. As in stemming, the word is converted to its root dictionary form; however, unlike in stemming, lemmatization considers context and normalizes words to its meaningful root word (i.e., changing \"worse\" to \"bad\").
* Data cleaning--removing URL and username, as this data will not provide any additional information needed for this stage of research. Examples of tweets before and after data pre-processing are given in Table 1.
## 2 Antenna Design
### Single Communication Mode
#### 3.3.2 Communication Mode #2  
Communication Mode #2 is conducted by NB Antenna #2. In Mode #2, NB Antenna #2 is used to communicate in the mid-frequency band (5-8 GHz). Figure 8 shows the S-parameter characteristics of NB Antenna #2. The \(-\)10 dB reflection coefficient bandwidth of NB Antenna #2 ranges from 5.31 GHz to 8.62 GHz (fractional bandwidth: 47.5%) with three resonant frequencies at 5.5 GHz, 6.52 GHz, and 7.65 GHz. The isolation between the UWB antenna and NB Antenna #2 is greater than 19.1 dB, with a peak value of 34 dB at 5.95 GHz. NB Antenna #2 is designed with the same form as NB Antenna #1. Due to the large separation (greater than \(\lambda_{g}\)/2) between the two input ports of the UWB antenna and NB Antenna #2, high isolation performance is maintained in the desired mid-frequency band.  
To verify the operating principle of NB Antenna #2, current distributions of NB Antenna #2 at resonant frequencies of 5.5 GHz, 6.52 GHz, and 7.65 GHz are shown in Figure 9. As shown in Figure 9,b, the loop type current excites the ground modes. In Figure 9, the vertical current mode is excited on the ground at 5.5 GHz. In Figure 9, the high order horizontal current mode is excited on the ground at 6.52 GHz. Therefore, high isolation can be obtained at 6.52 GHz. As shown in Figure 9, the inverted C-shaped current on NB Antenna #2 excites the vertical ground mode.  
Figure 8: Simulated S-parameters of the proposed antennas for communication mode #2.  
Figure 9: _Cont._Since the UWB antenna is orthogonal to the horizontal ground mode of NB Antenna #2 at 6.52 GHz, the isolation performance at 6.52 GHz is highest among the three resonant frequencies. However, the isolation performance over the entire mid-frequency band remains high (greater than 19 dB) due to the relatively large separation between the UWB antenna and NB Antenna #2.
# Effective Privacy-Preserving Collection of Health Data from a User's Wearable Device
## 3 Problem Definition, Background and Naive Solution
### Naive Solution  
In this subsection, we introduce a straightforward privacy-preserving solution based on the use of LDP which consists of wearable device processing (i.e., data owner) and server processing of the collected data (i.e., data aggregator).  
Wearable device processing: For explanatory purposes, we focus on the \(i\)-th user, \(U_{i}\in U\). By the sequential composition property of LDP, the privacy budget, \(\epsilon\), is first partitioned into \(n\) privacy budgets. Then, given original health data \(S_{i}=\left((t_{1},x_{1}^{i}),(t_{2},x_{2}^{i}),\cdots,(t_{n},x_{n}^{i})\right)\) represented as a sequence of length \(n\), each smaller privacy budget is used to generate a perturbed sequence \(PS_{i}=\left((t_{1},px_{1}^{i}),(t_{2},px_{2}^{i}),\cdots,(t_{n},px_{n}^{i})\right)\). Here, \(px_{d}^{i}\) is obtained using the LDP mechanism as follows:\[px_{d}^{i}=x_{d}^{i}+Lap(\frac{\Delta s}{\epsilon/n}),\]  
where \(\Delta s\) corresponds to the local sensitivity defined as \(\Delta s=x_{max}-x_{min}\) and \(Lap(\frac{\Delta s}{\epsilon/n})\) denotes a random noise sampled from a Laplace distribution with mean \(\mu=0\) and scale \(sf=\frac{\Delta s}{\epsilon/n}\). Note that this satisfies the \(\epsilon\)-differential privacy because of \(\epsilon=\sum_{h=1}^{n}\epsilon/n=n\times(\epsilon/n)\). Then, the user \(U_{i}\) reports the perturbed sequence \(PS_{i}\) instead of the original data \(S_{i}\), which guarantees that the original data of the data owner is not exposed to the (untrusted) data aggregator.  
Server processing of the collected data: Once it has been received the perturbed sequences from all the data owners \(PS=\{PS_{1},PS_{2},\cdots PS_{w}\}\), the data aggregator computes the aggregate statistics \(AS=\left((t_{1},ax_{1}),(t_{2},ax_{2}),\cdots,(t_{n},ax_{n})\right)\). Here, the value of \(ax_{d}\) at timestamp, \(t_{d}\), is estimated by using the perturbed values as follows:  
\[ax_{d}=\frac{1}{w}\times\sum_{h=1}^{w}px_{d}^{h}.\]  
The expected error incurred by this estimation is known to be linearly proportional to the sequence length \(n\)[6]. Thus, this scheme is not suitable when the sequence length, \(n\), is large.
## 5 Simulation and Analysis  
### LDACS System Parameters  
The simulation platform is designed and built according to the LDACS technical specifications, and Table 2 shows the specified simulation technical parameters.  
In all simulation experiments, the LDACS system OFDM signal incoming direction is set to \(10^{\circ}\). Two DME signals are considered, each with a signal-to-interference ratio (SIR) of \(-5\) dB, and their directions are set at \(-30^{\circ}\) and \(40^{\circ}\), respectively. The noise is modeled as Gaussian white noise, and the simulation parameters used in the algorithm are \(\eta=0.001\), \(P=4\), \(Q=10\).  
In the beamforming experiments, this paper simulates five other INCM reconstruction-based algorithms mentioned in Section 4.4 to validate the performance of the proposed algorithm.
# Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model
## 2 Materials and Methods  
### Study Area and Data Information  
The study area is located in Yancheng, China, within the Huaihe River Basin, at a latitude of 32.75\({}^{\circ}\) and a longitude of 120.25\({}^{\circ}\), with an elevation of 3 m. Yancheng is home to the Dafeng Farm, one of China's significant agricultural regions. The dataset includes temperature, humidity, wind speed, sunshine duration, and precipitation. It was obtained from the China Meteorological Data Service Center and has undergone strict manual review and rigorous quality control, ensuring high reliability with no prolonged missing periods. No extreme outliers were detected upon inspection, and the missing data rate was 0.26%. Missing values were imputed using linear interpolation. The dataset consists of daily meteorological data from 1980 to 2023, spanning 44 years. Among these, data from 1980 to 2018 (39 years) were used for training the model, while data from 2019 to 2023 (5 years) were used for testing the model.
## 4 Potential Field Autonomous Navigation System
### Potential Field Navigation Algorithm  
The potential field algorithm uses the two virtual forces (repulsive and attractive forces) to lead the robot to the destination. The repulsive force interacts between the robot and obstacles and pushes the robot away from the obstacles. The attractive force interacts between the robot and the goal position to pull the robot toward the destination. Both forces are calculated using the distance data from the 36 points on the searching radius surrounding the robot, to the obstacles and goal positions, respectively.  
The attractive force is a positive value, which is proportional to the distance from the goal position, and it becomes \"0\" at the goal position. The attractive force from \(p_{n}\)(nth point on the search radius) in Figure 8 is expressed as follows:  
\[U_{n_{attractive}}(p_{n})=C_{a}\ \bigg{[}(f_{x}-p_{n_{y}})^{2}+\Big{(}f_{y}-p_ {n_{y}}\Big{)}^{2}\bigg{]}, \tag{3}\]  
Figure 8: Sensor system of the potential field navigation system (search radius).  
where \(C_{a}\) is the coefficient used for balancing the effect of the attractive force, \((f_{x},f_{y})\) are the coordinates of the goal position, and \((p_{n_{x}},p_{n_{y}})\) is the x and y coordinates of point \(p_{n}\).  
The repulsive force is inversely proportional to the distance from an obstacle, and it approaches \"0\" as the robot moves farther from the obstacle. The repulsive force from \(p_{n}\) on the search radius corresponding to the \(i^{\text{th}}\) obstacle is described in Figure 8 and expressed as follows:  
\[U_{n_{\text{ \emph{repulsive}}}}(p_{n})=\frac{C_{r}}{\left(f_{x}-O_{i_{x_{n}}} \right)^{2}+\left(f_{y}-O_{i_{y_{n}}}\right)^{2}}, \tag{4}\]  
where \(C_{r}\) is the coefficient used for balancing the effect of the repulsive force.  
Therefore, the total potential (\(U_{n_{\text{ \emph{total}}}}\)) from \(d_{n}\)(n\({}^{\text{th}}\) distance data) in the search radius described in Figure 8 is expressed as the summation of the attractive force (\(U_{n_{\text{ \emph{attractive}}}}\)) and repulsive force (\(U_{n_{\text{ \emph{repulsive}}}}\)), as given below:  
\[U_{n_{\text{ \emph{total}}}}=U_{n_{\text{ \emph{attractive}}}}+U_{n_{\text{ \emph{repulsive}}}}. \tag{5}\]  
After calculating \(n\) potential data, the steering angle of the mobile robot in the next step (\(\theta(k+1)\)) is determined as \(\Delta\theta_{p}n_{min}\), where \(n_{min}=\left\{n\left|\min\left(U_{n_{\text{ \emph{total}}}}\right)\right.\right\}\) to move the robot to the position where the value of the total potential (\(U_{n_{\text{ \emph{total}}}}\)) is minimized.
# Effect of Soil Damping on the Soil-Pile-Structure Interaction Analyses in Cohesionless Soils
## 1 Introduction  
Designing a pile foundation is critical under lateral loading, especially in high seismicity zones. Two commonly used methods are available for the analyses: Three-dimensional finite element (or finite difference) [1; 2; 3; 4] or beam on Winkler Foundation (BNWF) [5; 6; 7] approaches. Both methods require the definition of nonlinear soil behavior under dynamic loads. Historically, the assumption of linear behavior for the pile and the superstructure is valid in most cases, and the linear soil-pile-structure interaction has been well studied by various researchers [8; 9; 10]. However, soils exhibit highly nonlinear behavior even under low strains, and the main uncertainty in the analyses arises from the modeling approach taking this nonlinearity into account [7; 11; 12; 13].  
The nonlinear behavior of soils has been studied by many researchers experimentally [14; 15; 16; 17; 18]. The equivalent-linear analysis method was developed initially [19], and the modulus degradation curves have expressed the soil nonlinearity in these analyses. The equivalent-linear analysis is a theoretically linear method, but the soil nonlinearity and damping are integrated into the analyses by an iterative approach. The fully nonlinear techniques have been developed to represent the nonlinear soil behavior accurately. However, a major difficulty in the fully nonlinear methods is the definition of unloading/reloading behavior which governs the damping ratio. The effect of several soil constitutive models on the soil response has been studied by 1-dimensional site response analyses [20; 21; 22; 23].  
Several other 3D constitutive models are available to simulate soil behavior [24; 25; 26]. However, a wide variety of damping performances exist under dynamic loading. Studies have been conducted on the soil-pile-structure interaction [2; 27; 28; 29]. However, the effect of soil damping on the structure and pile responses has not been studied thoroughly.  
The main purpose of this study is to show the effect of the soil hysteretic damping on the pile-soil-structure system response using relatively simple constitutive models. Two different centrifuge test setups of Gohl [30] and [PERSON] [31] were simulated by three-dimensional models in FLAC\({}^{\text{3D}}\)[32] for verification purposes. Fully nonlinear analyses were performed for the single pile-soil-structure systems. Since the earthquake records used in the centrifuge tests were limited, a new numerical model was created, having a single pile-structure model embedded in a single cohesionless soil layer and analyzed using six different earthquake records. The methods followed in the study are given in detail in Section 2. The results of the analyses are presented in Section 3.
## 3 Results
### Magnetic Properties
#### 3.3.1 Magnetization Measurements
The magnetization curves vs. T measured after cooling the sample without an applied magnetic field (ZFC) of bare and ex situ citrate-coated citrate-coated NPs do not show a peak to allow the determination of the blocking temperature (T\({}_{\text{B}}\)). Indeed, the ZFC curve of sample A shows a broad peak around 200 K which might correspond to T\({}_{\text{B}}\). Such broad ZFC curves suggest a polydispersion of the primary NPs and/or their aggregates and the existence of magnetic interactions among the NPs [40]. The ZFC curve of sample C shows a peak at 85 K which corresponds to the T\({}_{\text{b}}\) of the NPs. T\({}_{\text{b}}\) is much higher than the value expected for non-interacting magnetite NPs with a diameter of 5 nm. The observed higher T\({}_{\text{b}}\) can be attributed to the magnetic dipole interactions among the NPs. These interactions and the size distribution of the NPs are responsible for the broadness of the peak. Hence, the ZFC-FC curves can be explained taking into account the smaller size of the in situ citrate-coated magnetite NPs compared to bare and ex situ citrate-coated NPs. Large aggregates of magnetite NPs (size 100-200 nm) should be ferrimagnetic since their size is well above the limiting value (10-30 nm) for single domain, superparamagnetic magnetic particles. On the contrary magnetic measurements do show that all the three samples are superparamagnetic. [PERSON] et al. reported that aggregates of magnetite NPs with a size around 100 nm exhibit a superparamagnetic behavior [41]. It is remarkable that the magnetic properties of the aggregates of magnetite NPs, bare as well as coated with citrate, are dominated by those of the primary NPs. On the other hand for aggregates (40 nm in diameter) of magnetite single NPs (ca. 10 nm in diameter), prepared by co-precipitation in the presence of polyarginine, it was found that the magnetic properties are determined essentially by the size of the aggregates rather than by that of the constituents NPs. This behaviour was attributed to the same crystallographic orientation of the individual NPs [42].
## 3 Methodology  
### The Scope of Research Studies  
The following research tasks were performed:  
* Indication of determinants affecting the assembly time (number of tool changes, the number of changes in assembly direction and its stability);
* Measurement of assembly time on an example mechanical part;
* A set of prepared input and output data has been implemented in the neural network;
* Determination of constant parameters of the neural network model:1. 3 input neurons (number of tool changes, number of changes in assembly directions, and stability of the assembly unit) and 1 output neuron (assembly time);
2. Percentage of teaching (80%), testing (10%), and verification (10%) examples;
3. Regression model (determination of the quantitative and floating-point numerical values).
* Development of the most effective model of neural network: 1. Changing network learning algorithms (steepest gradient, scaled conjugate gradient, Broyden-Fletcher-Goldfarb-Shanno, and RBFT radial basis function teaching);
2. Network topography (multilayer perceptron and network with radial basis functions);
3. Activation functions (linear, sigmoidal, exponential, hyperbolic, and sine);
4. Number of hidden neurons (1-12).
* Selection of the most effective network model, taking into account the error of the sum of squared differences generated by the network;
* Introduction of previously untested data to the network, allowing verification of the effectiveness of prediction of assembly time.  
This methodology first defined general research tasks and then performed network testing based on a specific example of a mechanical part. The graphic concept of the proposed method is presented in Figure 3.  
In the future, this research may be extended to the verification of other parts of machines and devices based on the developed model of the neural network. The selected criteria determining the assembly time are universal and it is assumed that they are also adequate to other solutions.
## 6 Experimental Evaluation
### Experimental Results: Performance of RTF Estimators
The performance of the different algorithms degrades with the reverberation level, as the variations of the RTF are harder to track, although our proposal is more robust against reverberant environments than sub-space approaches.
## 3 Performance of IWP with Steel Anchor Box
### FE Analysis Results and Performance Comparison
#### 3.1.2 Utilization Rate of Steel Anchor Box  
Here, the horizontal cable force refers to the sum of the horizontal forces of two cables on a single end of the segment, and the utilization rate is defined as the proportion of horizontal cable force borne by the steel anchor box. Table 5 presents the utilization rates of the internal, exposed, and IWP structures for each segment. To eliminate the influence of the bottom boundary of the models, the top 1-5 segments of the steel anchor boxes were selected for comparative analysis. As shown in Table 1, the proportion of horizontal cable force taken by the exposed steel anchor boxes is approximately 50-70%. The IWP and internal steel anchor boxes have similar capacities for bearing horizontal cable forces, with utilization rates exceeding 83% for all but the top two segments. This indicates that the addition of fixed ends to the IWP detail only enhances the local stiffness of the pylon wall, with minimal impact on the overall longitudinal stiffness of the pylon wall. As a result, the IWP type achieves a high utilization rate similar to that of the internal type.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Concrete** & **Steel** & **Prestress Tendon** & **Stud** & **PBL Connector** \\ \hline Modulus of elasticity & 34.5 GPa & 210.0 GPa & 195.0 GPa & Diameter & 22 mm & 70 mm \\ Poisson ratio & 0.2 & 0.3 & 0.3 & Shear Stiffness & 302 kN/mm & 543 kN/mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material properties and connector properties in FEM analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Section Number**} & \multirow{2}{*}{**Horizontal**} & \multirow{2}{*}{**Cable Force/kN**} & \multicolumn{4}{c}{**Horizontal Load/kN**} & \multicolumn{4}{c}{**Proportion/\%**} \\  & & & **Internal** & **Exposed** & **IWP** & **Internal** & **Exposed** & **IWP** \\ \hline
1 & 14,678 & 7045 & 7339 & 7045 & 48 & 50 & 48 \\
2 & 14,678 & 10,715 & 9247 & 10,568 & 73 & 63 & 72 \\
3 & 13,272 & 11,148 & 9158 & 11,016 & 84 & 69 & 83 \\
4 & 13,272 & 11,016 & 9025 & 11,016 & 83 & 68 & 83 \\
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 2 Materials and Methods  
### Study Site  
Haitan Island is the main island of Pingtan County, Fujian, China (Figure 1). It covers approximately 267 km\({}^{2}\) land area and is comprised of seven towns and four villages. The region is characterized by marine accumulation plains, with the highest elevation in Junshan mountain (434.40 m above the mean sea level). The plains are mainly located in the middle of the island, while mountains and hills are in the north and south. The soil here is poor and acidic (laterite soil: 46%, sandy soil: 23.35%) [27]. The area is dominated by marine monsoon climate, with a mean annual temperature of 19.6 \({}^{\circ}\)C and mean annual precipitation of 1000-1200 mm.  
Figure 1: Location of the study site, Haitan Island, within Pingtan County, Fujian, China.  
It has an extensive sandy coastline, including many beaches, such as Changjiang, Tannan, Longfengtou, and Haitan. Due to its geographic location, the residents' living environment is impacted by frequent strong winds, especially the prevailing north-northeast wind in the winter season [6, 23, 28]. Many reforestation measures have been implemented for the purpose of environmental protection [29]. The major planted forest types are beach sheoak (_Casuarina equisetifolia_), black pine (_Pinus thunbergii_), acacia (_Acacia confusa_), and slash pine (_Pinus elliotti_) [27].
## 2 Basic SSA and Its Improvement
### Improved Sparrow Algorithm
#### 2.2.3 Gaussian Perturbation  
The sparrow algorithm, like the majority of swarm intelligence optimization algorithms, suffer from the problem of falling into a local optimum, which results in poor searching accuracy. To address this issue, it is possible to use the fitness value of the population optimal individual to determine whether the algorithm is trapped in a local optimum [25]. If the global optimal individual's fitness value is less than a threshold in two consecutive iterations, the algorithm is said to be trapped in a local optimum. At this time, a Gaussian random wandering strategy can be introduced to apply perturbation to the optimal position to help the algorithm to jump out of the local optimum. The improved way of updating the position of the investigators is shown in Equations (6)-(8).  
\[\hat{X}_{t}=Gaussian(G_{t},\sigma) \tag{6}\]  
\[\sigma=\cos\left(\pi/2\cdot(t/T)^{2}\right)\cdot(G_{t}-X_{t}^{*}) \tag{7}\]  
\[G_{t}=\left\{\begin{array}{l}\hat{X}_{t},f(\overline{X}_{t})<f(G_{t})\\ G_{t},\text{ }otherwise\end{array}\right. \tag{8}\]  
where \(X_{t}\) is the position of the optimal individual after applying Gaussian perturbation at the \(t\)-th iteration, and \(X_{t}^{*}\) is a random individual of the population.