esa-sceva/satcom-chunk-collection huggingface dataset:
# Refactoring for Java-Structured Concurrency
## 7 Evaluation
### Research Questions
#### 7.3.2 Results of RQ2  
It is indeed challenging to measure developers' efforts accurately. Ideally, we would observe developers during their refactoring processes and determine how much time was saved using _ReStruct_. However, the amount of time required for refactoring may vary depending on the familiarity of different developers with the unstructured concurrency and structured concurrency codes. We will answer this question by examining the changes in the number of lines of code before and after refactoring, as well as the amount of time taken for the refactoring process.  
Table 3 presents a summary of the number of code segments refactored in each project, along with the corresponding changes in the code lines (columns 2-3). SLOC1 represents the number of lines of code before refactoring, while SLOC2 represents the number of lines of code after refactoring. Column 4 records the changes in the number of code lines after refactoring. Overall, a total of 82 unstructured code segments were refactored across all the projects, with an average of 110 K lines of code per project and an average of 38 lines modified in each project. We carried out an introspective examination of the code. The examination revealed that these segments were scattered in multiple files in these projects. Searching for a small amount of target code and refactoring it in such large Java projects are time consuming and labor intensive. The last column in Table 3 shows the amount of refactoring time for each project, with a total time of 191 s to refactor the seven projects, averaging 27.3 s per project. Among them, the _SystemML_ project is the largest, with 400 K lines of code and a refactoring time of 63.8 s, the longest among all the projects. The projects _BigStitcher_, _ModernJava_, and _CodeJam_ are relatively small in scale, and their refactoring times are 14.5, 17.8, and 12.3 s, respectively. For projects _Light-4j_, _MetaOmGraph_, and _TrackMate_, which are medium-sized, with an average of 70 K lines of code, the refactoring times varied from 24.8 to 33.3 s. From these refactoring times, we found that the refactoring time for _ReStruct_ is primarily spent on program analysis, and for larger projects, more time is spent on refactoring analysis. Compared to manual refactoring, our tool (_ReStruct_) is fully automated, and even for projects with more than 400 K lines of code, _ReStruct_ can complete refactoring in one minute. These results indicate that _ReStruct_ can save developers significant effort.
# Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)
## 2 Method
### Development of ANN Model Architecture
#### 2.2.2 Determination of Epoch Number  
An epoch refers to one complete cycle of training an ANN model, during which the model's weights are adjusted through both forward and backward propagation. This cycle is repeated multiple times throughout the training process to iteratively improve the model's performance. The number of epochs plays a crucial role in training, as it directly affects how well the model learns and generalizes unseen data. As a hyperparameter, the number of epochs determines how many times the learning algorithm will process the entire training dataset. Too few epochs can result in an underfit model, while too many can lead to overfitting. In this study, additional epochs were run, and training was stopped once the error converged, optimizing both error minimization and computational efficiency.  
After running an additional 200 epochs and calculating the model error (model loss), the results showed that the error for both training and test data began to converge after approximately 40 epochs and remained stable up to 200 epochs. To ensure a conservative result, 50 epochs were selected as the minimum number required to achieve a robust result, as shown in Figure 7.  
Figure 6: The structure of the generalized ANN model built using the extracted dataset with real data (395 data points).
# Sparse Plane Wave Approximation of Acoustic Modes to Address Basis Mismatch
## 3 Proposed Combined Two-Stage Method
### Stage 2: Estimation of PWAs and Directions Using Parametric SBL
#### 3.2.2 Probabilistic Model for SBL  
From the SBL perspective, we attempt to find a function that maps arbitrary inputs \(\mathbf{r}\) (field point positions) to targets \(\psi\) (acoustic mode values) based on training data \(\{\psi(\mathbf{r}_{m})\}_{m=1}^{M}\) (observations at samplings) to solve Equation (8) [26]. This function depends on a set of parameters \(\mathbf{w}\) (PWAs).  
Within the sparse Bayesian framework, we first developed a probabilistic model. We assumed a complex, Gaussian-distributed additive noise \(\mathbf{\epsilon}\) with zero mean and precision \(\tau\)according to Equation (8). The likelihood function of the model \(\mathbf{\psi}=\mathbf{H}(\mathbf{\vartheta},\mathbf{\varphi})\mathbf{w}+\mathbf{\epsilon}\) then becomes  
\[P(\mathbf{\psi}|\mathbf{w},\tau)=\mathcal{CN}(\mathbf{\psi}|\mathbf{H}(\mathbf{\vartheta},\mathbf{ \varphi})\mathbf{w},\tau^{-1}\mathbf{I}_{M}), \tag{9}\]  
where \(\mathcal{CN}(\cdot)\) indicates the complex Gaussian distribution and \(\mathbf{I}_{M}\) is an identity matrix of size \(M\). A Gamma prior \(\mathrm{Ga}(\cdot)\) with parameters \(a\) and \(b\) is considered over \(\tau\) in this study for tractable inference of \(\tau\) described as follows:  
\[P(\tau)=\mathrm{Ga}(\tau|a,b). \tag{10}\]  
The components of PWAs \(\mathbf{w}\) are probabilistically independent of each other. Assuming that the \(r\)th entry follows a zero-mean, complex Gaussian prior with precision \(\alpha_{r}\); this leads to the equation below,  
\[P(\mathbf{w}|\mathbf{\alpha})=\prod_{r=1}^{R^{\prime}}\mathcal{CN}(w_{r}|0,\alpha_{r} ^{-1}). \tag{11}\]  
Furthermore, a Gamma prior with parameters \(c\) and \(d\) is imposed on each hyperparameter \(\alpha_{r}\), which yields  
\[P(\mathbf{\alpha})=\prod_{r=1}^{R^{\prime}}\mathrm{Ga}(\alpha_{r}|c,d). \tag{12}\]  
Figure 1 shows the described probabilistic model of the variables and their hierarchical relationships.
## Methods
### Data
#### 2.2.2 Satellite Data  
Two different datasets covering the period 2002-2014 from the MODIS sensor onboard the Terra and Aqua satellites were utilized. A MAIAC pre-processed MODIS time series dataset was provided by NASA's Goddard Space Flight Center with 1-2 daily observations from July 2002 to June 2014. The data consist of gridded surface reflectances in band 1-12 for a 3 \(\times\) 3 pixel window centered at the position of the flux tower at a spatial resolution of 1 km \(\times\) 1 km. The MAIAC algorithm utilizes time-series processing of MODIS measurements to take into account different view angles  
Figure 1: Hyytiala Forestry Field Centre in southern Finland. Sources: Esri, 2016.  
and simultaneously build a dynamic land-water-snow classification and cloud mask to aid in the correction [40; 41]. MAIAC data for most of the globe are available through NASA GSFC supercomputer and can be obtained from Dr. [PERSON] by request.  
In addition, MOD13Q1 250 m spatial resolution NDVI products were acquired for the 3 \(\times\) 3 pixel window centered on the site. The NDVI product is calculated as  
\[\mathrm{NDVI}_{\mathrm{prod}}=\ \frac{R_{841-876}-R_{620-670}}{R_{841-876}+R_{620-670}} \tag{1}\]  
where \(R\) is the reflectance at the given wavelengths in nm (near infrared and red reflectance).  
The product dataset is comprised of 16-day composite NDVI values (23 observations per year), which were built using the Constrained View Maximum Value Composite algorithm (CV-MVC). The algorithm accounts for negatively biased noise resulting from weather conditions by selecting the maximum NDVI value for each pixel over a set period and is designed to favor observations closest to nadir view [35].  
The size of the footprint of the flux tower varies depending on micrometeorological conditions, but approaches that of a pixel from the MODIS sensor (250 m-1000 m depending on wavebands). In accordance with previous studies [10; 44], the single pixel centered on the tower was assessed to be representative of the tower footprint.
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 3 Challenges Brought by Crowd Density
### Motion Blur and Postural Diversity  
The rapid change in individual movement in the crowd not only causes the target edge captured in the image to be blurred, affecting the accuracy of feature extraction based on clear contours and details; but, it may also cause misrecognition. The blur effect can confuse the understanding of target types and behaviors in visual algorithms [17]. Especially for those individuals who accelerate instantaneously or suddenly turn; their performance in the image is often trailing and residual images, which further reduces the stability and accuracy of the behavior recognition model. In addition, the joints of the human body are highly flexible, and the combination of different bending degrees and directions of each joint will show completely different postures. In the context of movement, this flexibility makes the human posture extremely susceptible to changes due to external forces and other external conditions [18]. A series of actions, whether it is head tilting, arm waving, switching between standing and walking, or adjusting the walking speed, will affect the judgment of pedestrian abnormal behaviors by the behavior recognition model.
# Survey of Smart Parking Systems
## 3 Types of SPS Reported in the Literature
### Parking Guidance and Information Systems  
PGIS are systems whose main objective is to guide the user to available parking slots, thereby reducing the time expended searching for free spaces. In this sense, they become a driver's assistant and help them to park their car. For this reason, the system must know the status of each parking slot. PGIS use different types of technologies such as Global Positioning System (GPS) or physical proximity sensors, among others, which allow the driver's current position to be identified. These are described in Section 4. While this technology increases the likelihood of finding free parking slots, it, in turn, changes the driver's behavior from searching for to competing for parking [26].  
As PGIS can be implemented for use in public parking, arranged in different areas of a city, usually on the streets, or in private parking that is found in other places such as buildings or fields, this allows these systems to be quite versatile in this respect.  
This type of SPS can be divided into four layers. The first layer consists of a parking monitoring mechanism that detects when a vehicle arrives at or leaves a parking slot. All of these techniques are described in Section 4. The second layer is an information disseminating mechanism. This is the function provided to all users, which provides information about the availability of parking slots. Various techniques can be used such as mobile applications [27; 28], web applications [29; 30], and even SMS [31]. The third layer consists of a telecommunications network. Here, the communication between all system layers is established. In the development of a PGIS, it is necessary to determine the network structure that will be used for communication. Deployments can use 3G/4G [32], Wi-Fi [33; 34], vehicular ad hoc network (VANET) [28], wireless sensor networks (WSN) [35], or ZigBee wireless network [36]. Finally, the fourth layer describes a control center, which processes the occupancy data from parking facilities and controls the display of availability information for system users.  
The advantage to using PGIS lies in the accurate information that is provided in a timely manner so that motorists can make more informed parking decisions. It reduces unnecessary movement and leads to better use of the parking on offer. The liveliest discussion concerning PGIS is in terms of which VDT to use, the details of which are developed in Section 4, while how they are related to PGIS is established.
# Removing False Targets for Cyclic Prefixed OFDM Sensing with Extended Ranging +
## 1 Introduction
More details of USF will be presented in Section 2. However, as illustrated in [12; 32], USF suffers from a false target issue. Specifically, obvious peaks (much stronger than background noises) are observed at locations different from that of the true target. In a previous work [33], we intuitively explained the reasons for false targets employing some special cases, and developed a simple expedient solution that can cause a decrease of use signal power and, hence, the detecting probability. In this work, we perform an in-depth investigation to more rigorously unveil the reasons for the false target issue in the USF. Moreover, we will develop more effective solutions to removing false targets without losing the power of useful signals. Our main contributions are summarised below. We introduce a concise matrix format of USF, which conveniently enables the investigation into the false target issue. We unveil that false targets are mainly caused by the periodic CP signals in the CP-OFDM waveform. We also derive the properties of false targets, including positions and strengths. Accordingly, we develop novel methods, removing the false targets without decreasing the detecting probability of true targets. Simulations are provided to confirm the validity of our analysis and the effectiveness of the proposed solution. The rest of the paper is arranged as follows. Section 2 establishes the signal model. Section 3 first reviews USF and another classical OFDM sensing method and then illustrates the false target problem. Section 4 investigates the reasons for and features of false targets. Section 5 develops two novel methods for handling false targets. Simulation results are presented in Section 6, and conclusions are given in Section 7.
# Active Control Loop of the BOROWIEC SLR Space Debris Tracking System
## 2 Satellite Laser Ranging Process
### Ephemeris Uncertainty  
The tracking process behind an orbital object is based on a predictive model. The theoretical trajectory is predicted on the basis of the ephemeris (forecast) containing archival information obtained during previous positional measurements of the object in the time domain. In connection with the above, the quality of the reflection of the follow-up movement of the telescope in relation to the theoretical trajectory of the orbital object depends on the accuracy of the ephemeris data. Ephemeris data are obtained based on:  
* Laser acquisition (distance to the object in the time domain, with centimeter accuracy);
* Passive optical acquisition (the angular position of the object in the time domain, with arcsec accuracy);
* Radar acquisition (distance and/or the angular position of the object in the time domain).  
It should be emphasized that measurements of the position of an orbital object are performed in two ways: point-based (tracking behind a given orbital object \(\Rightarrow\) high resolution, high measuring accuracy) and in survey mode (wide-angle measurement of a given part of the sky \(\Rightarrow\) low resolution, low measurement accuracy, possible number of simultaneously tracked objects \(\gg 1\)). In the case of laser measurements of cooperative satellites, ephemerides are created based on archival data obtained by the laser stations in the form of Consolidated Prediction Format files (CPF). This ephemeris contains information about the spatial position of the object \((X,Y,Z)\) in the time domain. Thus, the task of tracking an object comes down to interpolating the ephemeris vector of the positions of the object in the time domain and transforming the interpolated values to the telescope's reference frame \((Az/El)\). However, in most cases, ephemerides are created on the basis of radar measurements (survey mode) and are made available in the form of TLE ephemerides with significant positional uncertainty. The TLE ephemeris for any moment of time \(t_{n}\) is calculated on the basis of the given orbit parameters together with their initial epoch \(t_{0}\), therefore, unlike the ephemeris of the CPF type, software calculations are necessary to determine the values of \(X,Y,Z\). The uncertainty of the orbit predicted on the basis of the TLE ephemeris compared to the orbit interpolated from the ephemeris of the CPF type is shown in Figure 3.  
Here, attention should be paid to the change in the quality of ephemeris over time. In the case of objects from the LEO region, the orbit parameters change due to the influence of the atmosphere and gravity (Figure 4) [16]. Therefore, to properly track an orbital object, it is necessary to use the current ephemeris data.  
Figure 2: Block diagram of the SLR tracking control algorithm.
## 5 Results and Discussion
### Numerical Studies
#### 5.1.1 Recovery of the Absorption Coefficient of the Top Layer  
We first considered Problem-I mentioned in Section 4.4.2. The absorption and scattering coefficients of the bottom layer were fixed at 4.3 cm\({}^{-1}\) and 250 cm\({}^{-1}\). The scattering coefficient of the top layer was chosen to be 40 cm\({}^{-1}\), and the values of true and extracted absorption coefficients, obtained by solving Equation (4) are tabulated in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Phantom ID**} & **True Value** & **Extracted Value** & **Std. Deviation** \\  & \(\mu_{a}\) **(cm\({}^{-1}\))** & \(\mu_{a}\) **(cm\({}^{-1}\))** & **(cm\({}^{-1}\))** \\ \hline
1 & 2.0 & 1.9 & 0.1 \\
2 & 2.5 & 2.4 & 0.1 \\
3 & 4.0 & 4.0 & 0.2 \\
4 & 6.0 & 6.0 & 0.2 \\
5 & 8.0 & 8.0 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reconstruction of absorption coefficient of the top layer from simulated data with prior knowledge of scattering coefficient of top layer and both optical properties of bottom layer (Problem-I).
## 4 Conclusions  
This article presents the design and implementation of a buried probe for a Wireless Underground Sensor Network (WUSN). Initially, we conducted a comprehensive analysis of the soil's electrical properties, highlighting the challenges associated with underground communication as a primary consideration in the probe's design.  
We discussed the various constraints encountered during the design process, including the selection of capacitance, inductance, and the type of varactor used. These components are critical in optimizing the probe's performance and ensuring effective signal transmission through the soil medium.  
Additionally, we proposed an advanced underground probe specifically tuned to resonate at a frequency of 433 MHz. Our findings indicate a reasonable estimation of the dielectric permittivity, ranging from 2 to 15, which is essential for understanding the soil's electromagnetic behavior.  
While the results demonstrate the probe's efficacy in predicting dielectric permittivity, we acknowledge that further enhancements are required, particularly for low permittivity values. These improvements could enhance the probe's performance and broaden its applicability in various soil conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specifications** & **Value** \\ \hline Frequency & 433 MHz \\ \hline Capacitance type & Circular \\ \hline Radius of the capacitance & 10 cm \\ \hline Thickness of the capacitance & 5 cm \\ \hline Inductance & 0.9 nH \\ \hline Varactor model & SMV1801 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The probe specifications.  
**Author Contributions:** Conceptualization, M.S. and N.B.; methodology, S.G.; software, M.S.; validation, R.G. and J.F.; formal analysis, R.G.; investigation, S.G.; resources, N.B.; data curation, M.S.; writing--original draft preparation, M.S.; writing--review and editing, R.G.; visualization, J.F.; supervision, N.B.; project administration, N.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors would like to express their gratitude to the University of Tunis El Manar for its support throughout this research project.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} VCO & Voltage-Controlled Oscillator \\ MHz & Megahertz \\ WSNs & Wireless Sensor Networks \\ WUSN & Wireless Underground Sensor Network \\ IoT & Internet of Things \\ TDR & Time Domain Reflectometry \\ FDR & Frequency Domain Reflectometry \\ CW & Continuous Wave \\ ADC & Analog-to-Digital Converter \\ UHF & Ultra-High Frequency \\ LoRa & Long-Range \\ KHz & Kilohertz \\ CRC & Cyclic Redundancy Check \\ RSSI & Received Signal Strength Indicator \\ RF & Radio Frequency \\ ISM & Industrial, Scientific, and Medical frequency bands. \\ \end{tabular}
## 3 Results and Discussion
### Delithiation  
The study of the delithiation process was focused on the determination of the binding energy between the lithium ion and the components of the anode material composite. Therefore, we calculated the profiles of the potential barrier for the detachment of the lithium ion via high-precision DFT method. Since the supercell of the composite contained several hundred atoms, it was built several cells containing a single lithium ion and a fragment of the composite: graphene, nanotube (9,9), and a silicon cluster. During the interaction between a lithium ion and a composite fragment the equilibrium structure of such compound was revealed by optimization of the supercell atomic structure. For the graphene-Li cell, the Monkhorst-Pack partition was 25\(\times\)25\(\times\)1, for the SWCNT-cell--1\(\times\)1\(\times\)65 and for the Si-Li cell--10\(\times\)10\(\times\)10. Then the lithium ion was moved away from the composite fragment to the distance of 10 A with a step of 0.5 A. At each step the cell energy and the binding energy between lithium and each of the composite fragments were calculated. Figure 5 shows the graphs of binding energy. Thee limitation of the Li atom from the outer surface of the nanotube required the least amount of energy, about 0.544 eV, while the lithium ion interacted with three atoms of the tube. These results correspond to [53] where the binding energy between tube (9,9) and lithium ion was in the range of 0.5-0.6 eV. The binding energy of lithium and graphene was bigger and equaled to 1.09 eV, while the lithium ion interacted with six graphene atoms. This value is equal to twice the value of the binding energy between lithium and the SWCNT. The highest binding energy was found for the interaction of lithium with silicon and silicon clusters: for Li-Si and Li-Sil16 compounds it was equaled to 1.78 eV and 1.92 eV, respectively. The obtained results are in good agreement with the works [54; 55]. Thus, the most energy-consuming process is the elimination of lithium from silicon structures.