esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Temperature Field and Velocity Field  
#### 3.1.1 Temperature Field (T)  
In the numerical simulation, the temperature gradient is used for initialization calculation. Figure 7 shows the temperature distribution simulated by different turbulence models in a quasi-steady state. When the hydrothermal plume receives vertical momentum, hot fluids mix violently with cold seawater. As hydrothermal plume gradually moves away from the vent orifice and entrails the cooler fluid, its buoyancy effect diminishes gradually and its temperature drops. After the neutrally buoyant plume height, the temperature contour line flattens out, and the temperature of hydrothermal plume gradually approaches the ambient temperature. There are some differences in the temperature distribution between the two models. At the seafloor, the variation interval of the temperature contour of the rke model is smaller than that of the sst model. At the maximum plume rising height, there are some fluctuations in the temperature contour of the rke model, while the temperature contour of the sst model is too idealized.  
The relationship between temperature, depth and time during detection is shown in Figure A1 in Appendix B. The relationship between temperature and depth is obtained. The plume temperature calculated by the rke model and the sst model is analyzed based on this temperature. When z/Z\({}_{\text{max}}\) is less than 0.1, the temperature of the hydrothermal plume decreases rapidly from 362 \({}^{\circ}\)C to 4 \({}^{\circ}\)C (Figure 8). The variation trend of the temperature curve shows that the simulated temperature variation of the plume is consistent with the law of plume movement. According to the simulation results of environmental conditions (case2), the RMS of temperature of the rke model is 0.0408 and that of the sst model is 0.0406. The root mean square error of temperature and the variation trend of the curve  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Simulation** & **T\({}_{\text{exit}}\) (\({}^{\circ}\)C)** & **T\({}_{\text{top}}\) (\({}^{\circ}\)C)** & **w\({}_{\text{exit}}\)** & **B\({}_{\text{exit}}\)** & **N** \\
**Run (rke and sst)** & & & **(m s\({}^{-1}\))** & **(m\({}^{4}\) s\({}^{-3}\))** & **(s\({}^{-1}\))** \\ \hline case 1 & 362 & 1.957 & 0.1 & 0.008183 & 0.000401 \\ case 2 * & 362 & 1.957 & 0.2 & 0.016365 & 0.000401 \\  
show that the numerical simulation of hydrothermal plume in this paper is in agreement with reality.  
In the same working conditions, compared with the reference value, the calculation temperature of the rke model is higher than that of the sst model. But there is relatively little difference between the two models. In conclusion, the prediction effects of the rke model and the sst model are similar in terms of hydrothermal plume temperature.
# On Parameter Estimation for Bandlimited Optical Intensity Channels
## 4 Estimator Algorithms
### Flat Receiver Filter  
It is to be recalled that the estimators in Section 4.1 utilize a receiver MF specified by \(q(t)=h(t)\), which means that the Nyquist criterion will be violated at the MF output. In consequence, a more complicated detection process would be necessary to circumvent a degradation of the performance by inter-symbol interference (ISI) effects. Since a real-valued root-Nyquist function does not exist for bandlimited intensity links [6], we will explore a solution based on a flat receiver filter preserving the required orthogonality.  
In particular, it is suggested that the receiver MF is substituted by a filter exhibiting a rectangular shape in the frequency domain occupying the same bandwidth as the user signal in (3), which is determined by \(2(1+\alpha)/T\). Therefore, the corresponding impulse response develops as  
\[q(t) = \frac{2(1+\alpha)}{\sqrt{T}}\sin c[2(1+\alpha)t/T], \tag{26}\]  
which means that \(g(t)=h(t)\otimes q(t)=\sqrt{T}h(t)\), i.e., the Nyquist property is not violated.  
With respect to the timing estimator developed before, this requires only the correlation filter in (19) to be modified insofar as \(h(t)\) is replaced by (26). On the other hand, the algorithm for estimating the amplitude in (25) simplifies significantly, because \(g(0)=1\) and \(g[(n-k)T]=0\) for all \(n\
eq k\), which yields  
\[\hat{A}=\frac{\Lambda_{\hat{\mu}}}{\sum\limits_{k=0}^{L-1}\omega_{k}^{2}}. \tag{27}\]  
Nevertheless, the price to be paid for this approach is that the noise component at the output of the flat receiver filter increases compared to MF solutions. As a result, the jitter variance degrades somewhat, but it exhibits no error floor.
# Sequential Recommendations on GitHub Repository
## 3 Dataset and Models
### Baseline Recommendation Models  
In our experiment, we assume a set of given users as \(\mathcal{U}=\{u_{1},u_{2},...,u_{|\mathcal{U}|}\}\) and a set of repositories as \(\mathcal{R}=\{r_{1},r_{2},...,r_{|\mathcal{R}|}\}\). \(S^{u}=\{r_{1}^{u},...,r_{t}^{u},...,r_{|\mathcal{S}^{u}|}^{u}\}\) represents the interaction sequence in chronological and sequential order of each user, where user \(u\) interacts at time step \(t\) under \(r_{t}^{u}\in\mathcal{R}\). However, unlike other tasks, \(t\) stands for the sequential order of interactions, not the absolute timestamp as in temporal-based recommendation systems [5; 41; 42], similar to the prominent DL-based recommendation models, Caser, BERT4 Rec [12], and sequential-based methods [6; 43].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & & **GitHub Dataset** \\ \hline
**Feature** & **Type** & **Description** \\ \hline train & Interactions dataset & Note to refer to a custom spotlight file to explore each sequence of train dataset \\ \hline valid & Interactions dataset & Note to refer to a custom spotlight file to explore each sequence of valid dataset \\ \hline test & Interactions dataset & Note to refer to a custom spotlight file to explore each sequence of test dataset \\ \hline repo\_index2 id & list & Even though it is in a list format it is used to retrieve unique ids within a custom spotlight file \\ \hline user\_index2 id & list & Even though it is in a list format it is used to retrieve unique ids within a custom spotlight file \\ \hline languages & list & A unique set of computer program languages list in all repositories \\ \hline lang2 id & dict & A python dictionary format with each language and its id \\ \hline id2 lang & dict & A python dictionary format with each repository id and its language id \\ \hline descriptions & dict & A python dictionary format with each repository id and its text description \\ \hline descriptions\_parsed & dict & A python dictionary format with each repository id and list of its parse description ids \\ \hline voca & list & A list of parsed vocabulary from repository descriptions \\ \hline voca2 id & dict & A python dictionary formation with parsed vocabulary and its unique id \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of GitHub dataset features.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **\#Users** & **\#Items** & **\#Interactions** & **Avg.inter./User** & **Avg.inter./Item** & **Sparsity** \\ \hline ML 1M & 6,022 & 3,043 & 999,154 & 165.25 & 327.03 & 94.57\% \\ ML 20M & 138,493 & 18,345 & 19,984,024 & 144.30 & 1089.34 & 99.21\% \\ Beauty & 22,363 & 12,101 & 198,502 & 8.88 & 16.40 & 99.93\% \\ Video Games & 108,063 & 31,390 & 21,105,584 & 9.54 & 21.72 & 99.38\% \\ BGG [37] & 388,413 & 87,208 & 47,351,708 & 121.91 & 542.97 & 99.86\% \\ GitHub & 4,135,902 & 5,264,307 & 59,765,590 & 14.45 & 11.35 & 99.99\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of datasets.
## 2 Planar Deformation Measurement Methods in Masonry Specimens
### Proposed Optical Approach
#### 2.2.3 Digital Image Processing  
The image digital processing was supported in a computational routine developed in MATLAB(r) (MathWorks, Natick, Massachusetts, USA), aiming at the determination of image coordinates of the geometrical center of both the reference and monitoring targets. In the case of the reference targets, the adopted target identification convention was a numerical sequence between one and eight, starting in the target located in the image upper left corner and following a clockwise direction for the following targets located in the measurement referential (see Figure 7a). For the case of the monitoring targets (located in the inner region of the measurement referential, as shown in Figure 7a), the mentioned numerical sequence was extended to these targets, continuing with the monitoring target located in the upper left corner of the referential inner region and following the clockwise direction for the remaining targets.  
Figure 6: Experimental setup: (**a**) global view; (**b**) classical and optical measurement systems.  
Figure 7: Target digital image processing: (**a**) original image; (**b**) binary image for one of the targets; (**c**) image with centroids.  
The developed algorithm (schematically shown in Appendix A) is composed by the following sequential tasks:  
1. Initialization (execution display configuration, memory, and command window cleaning, opened images closure and variable numerical format definition);
2. Assignment of numerical values to the input quantities: 1. Number of images; 2. Threshold (in gray level); 3. Window dimension (in pixels) around the targets, based on the lateral dimension of the target support and expected displacement; 4. Approximate image coordinates of the targets centroids (in pixels);
3. File opening for the record of target image coordinates;
5. For each acquired image, execution of the following tasks: 1. Variable definition related to the original image designation; 2. Original image opening; 3. Matrix initialization with the targets individual sub-images; 4. Counter initialization related to the coordinates; 5. For each target visible on the image, execution of the following operations: 1. Definition of the interest area in the original image; 2. Matrix initialization related to the target binary sub-image; 3. Generation of the binary image by thresholding; 4. Target identification; 5. Determination of the target centroid in the binary sub-image; 6. Conversion of the target centroid image coordinates for the original image coordinate system; 7. Record the target image coordinates; 8. Record the targets image coordinates in the output file; 9. Graphical representation of the centroids in the image (optional);
6. Closure of the targets image coordinates record file.  
Figure 7 shows an example regarding three sub-images of a digitally processed target.
# Health State Assessment of Road Tunnel Based on Improved Extension Cloud Model
## 4 Case Application
### Process of Tunnel Health Evaluation
Comparative analysis of the evaluation results with the standard results shows that the evaluation results are basically consistent, thereby verifying the applicability and feasibility of this method. In addition, the downstream channel of the Beilongmen Tunnel was rated as Class 2 by the standard in 2017-2018, while the evaluation level determined by this method was a Level III health alert state. The reason is that the dynamic indicators such as the crack quantity change rate, leakage water quantity change rate, and pavement condition index (PCI) change rate of the tunnel showed a significant increase in 2017-2018 compared to 2016. It indicates an accelerated development of various diseases, and rapid deterioration of health. Therefore, a higher evaluation level using the gray system model. However, the difference between the evaluation results and the standard results cannot be explained in some studies that focus only on static indicators [16]. This reflects the influence of differences in the health change trend of the road tunnel on health assessment and demonstrates the advantage of dynamic indicators. In addition, the uncertainty is overcome effectively by the extension cloud model in the evaluation process. More information can be provided under the same health level status by the level of characteristic values and confidence factors. For example, the level characteristic values \(E_{x,u}\) for upstream and downstream tunnels in 2016 were 2.33 and 2.98, respectively, indicating that the downstream tunnel was in a more dangerous state. The randomness of sample data and the fuzziness of assessment are reflected by entropy \(E_{n,u}\) in tunnel health assessment. The confidence factors in the results are all less than 0.05, indicating that the evaluation results are relatively reliable. It can be seen that not only is the difference in health level between the same health level reflected by the extension cloud evaluation method but also randomness or fuzziness is effectively dealt with in the evaluation process.
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 1: Multi-Source Input
#### 2.1.1 Multiple Sources of Satellite Imagery
The CORINE 2006 dataset selected in this study is based on SPOT-4/-5 and IRS P6 satellite imagery. The forest fire information was extracted from the Burned Surfaces Product, which was derived from the European Forest Fire Information System using MODIS images. It revealed all forest fires burning 40 ha of surface area. Instantly accessible harmonised datasets (e.g., CORINE land cover product and burned surfaces product) based on multi-source satellite imagery have enabled researchers to evaluate attributes of urban forests with high efficiency. In general, the application of the multi-source approach could be limited by two technical barriers. First, weather conditions (e.g., haze and cloud coverage) and different image dates; both pose major challenges and there is no guarantee that all images obtained within a certain time period are of high quality for classification purposes. For example, in their 20-year temporal analysis, [PERSON] et al. (2016) discarded poor-quality images affected by cloudiness or haze. The feasibility of using multiple sources of satellite imagery should be a primary consideration, especially for time-sensitive research. Second, satellite images are rarely available at both HSR and spectral resolution. Hyperion EO-1 imagery is one of the few hyperspectral satellite images with 242 bands, yet it only has a spatial resolution of 30 m. Till now, few tree-level studies (e.g., identifying individual tree species and tree crowns) have relied solely on multiple source applications of satellite images.
# Antenna Arrays for Line-of-Sight Massive MIMO: Half Wavelength Is Not Enough
## 3 User Correlation Analysis
As an example, let us imagine a communication system with just two user terminals: when user Terminal A is in the angular position of a grating lobe relative to user Terminal B, it could be very difficult to distinguish them. Figure 3: Two terminal correlation analysis for the \(2\lambda\) equispaced array. (**Left**) Image map with the correlation level for variable \(r_{1}\) and \(r_{2}\); (**Right**) plot of the correlation coefficient for some fixed values of \(r_{2}\). Figure 2: Two terminal correlation analysis for the \(\lambda/2\) equispaced array. (**Left**) Image map with the correlation level for variable \(r_{1}\) and \(r_{2}\); (**Right**) plot of the correlation coefficient for some fixed values of \(r_{2}\). We would like to specify that in an array not working in the far field region, we could not rigorously talk of grating lobes, but for the sake of simplicity, we will keep on using this definition for the rest of the manuscript. According to the correlation analysis, it seems that the possible advantage of distinguish aligned user terminals could be negated by the effect of grating lobes. These results suggest that a non-equispaced array, not affected by grating lobes, could get the advantages of a larger size of the array, without the drawbacks of the grating lobes, but to confirm this hypothesis, we are first required to analyze what happens with multiple terminals.
# Flaw Size Quantification for Cable Flaw Inspection System with Inductive Search Coil Sensor
## 2 Methodology
### Data Processing Procedure  
In order to prepare and analyze the acquired response signals obtained from the measured coil inductance in the experiment, several sequential steps are followed, as shown in Figure 4. Firstly, the inductance of the coil used in the inspection method is measured, providing a baseline reference. Subsequently, to enhance signal quality, filtering and smoothing techniques are applied to mitigate noise and the signal baseline are removed. Subsequently, differential analysis is conducted to accentuate variations and changes in the signal characteristics. Finally, relevant features encompassing attributes such as amplitude, frequency and shape patterns are extracted from the processed signals. These methodical steps ensure effective processing and analysis of the experimental data, thus enabling accurate detection and assessment of flaws present in the bridge cables.
# A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
## 3 Results
### Real-Time River Monitoring Station and Early Warning System  
The real-time river monitoring station continuously and accurately measures the river's water level using ultrasonic sensors. These data are transmitted to a server via the internet using microcontroller and IoT (Internet of Things) technology.  
Connections established using the PHP programming language process the data collected from the sensors and store them in a database. The data stored on the server are analyzed by computation files and displayed on a web page. This web interface allows users to easily access the data and provides real-time information.  
On the web page, measurements are displayed in real time with date and time information. The discharge value is calculated in cubic meters (m\({}^{3}\)/s), and the river depth is provided in meters. Additionally, the maximum and minimum discharge and river depth values for the last 1 day, 7 days, and 1 month can be viewed in real time. The data monitoring system is also available on a separate website, where the data sent to the server can be viewed and downloaded in various formats such as Excel and PDF.  
The primary goal of the real-time river monitoring station is to detect flood risks early and promptly warm relevant parties. When the water level exceeds a certain threshold, the system automatically sends alerts. The values read by the ultrasonic sensor are processed, and emergency alerts are sent to predefined email addresses based on the level of risk. The web interface can be accessed at [[https://nehirgozlem.com/](https://nehirgozlem.com/)]([https://nehirgozlem.com/](https://nehirgozlem.com/)).
## 3 Experimental Design of Fragment-Penetrating Composite Honeycomb Sandwich Structure
### Analysis of Energy Absorption Characteristics of Composite Honeycomb Sandwich Structure  
In addition to the direct observation of the damage to the structure, the energy absorption characteristics of the honeycomb sandwich structure are also the main indicators used to measure the damage performance of the structure. The total energy absorption and structural energy absorption rates are commonly used mathematical research methods.  
In the state of high-speed penetration, the total energy absorption of the structure is obtained by using the law of conservation of energy according to the different velocities after penetrating the target. Assuming that the energy dissipation in the process of fragment penetration is absorbed by the sandwich structure, then  
\[W_{TE}=W_{R}+W_{E} \tag{3}\]  
\(W_{TE}\) is the total energy, that is, the initial kinetic energy of the fragment; \(W_{R}\) is the residual kinetic energy of the fragment; and \(W_{E}\) is the total energy absorbed by the sandwich structure.  
\[W_{TE}=0.5 mv_{r}^{2} \tag{4}\]  
\[W_{R}=0.5 mv_{i}^{2} \tag{5}\]  
where \(m\) is the mass of the fragment, \(v_{r}\) is the residual velocity of the fragment, and \(v_{i}\) is the initial velocity of the high fragment. Then, the total energy absorption of the sandwich structure is:  
\[W_{E}=W_{TE}-W_{R}=0.5m\Big{(}v_{i}^{2}-v_{r}^{2}\Big{)} \tag{6}\]  
Figure 6: Damage to six structures at different velocities. (**a**) is NOMEX-AL-1; (**b**) is NOMEX-C-1; (**c**) is ST-6-6-1; (**d**) is ST-3-6-1.  
It can be seen from Table 7 that, in addition to the influence of field factors on the deflection of fragments, with the increase in fragment impact velocity, the total energy absorption per unit volume of six kinds of sandwich structures is also increasing gradually. Comparing the two skin materials, AL and C, it can be seen that the overall dynamic response of the metal material to the honeycomb sandwich structure is more obvious, so that the energy absorption value of the structure is larger.  
The range of the three structures of ST-6-6, ST-6-3, and ST-3-6 reaching the peak is mainly concentrated in the impact velocity of more than 200 m/s. With the increase in fragment impact velocity, the overall energy absorption of each honeycomb structure basically shows an upward trend, and the overall energy absorption of ST-3-3 is the largest. The average cell density of ST-6-3 and ST-3-6 is the same, and the total energy absorption under different impact velocities is not much different from the energy absorption per unit volume. However, with the increase in impact velocity, the energy absorption trend of the two structures is not as obvious as that of ST-6-6.
## 2 Materials and Methods  
### Instrumentation  
The field-portable laser-induced breakdown spectrometer used in this study was design and assembled in our lab. It mainly includes the following: (1) an 8-channel optical fiber spectrometer: Avantes-Mini2048 CL spectrometer (200-1100 nm) with the spectral resolution better than 0.1 nm; (2) compact Nd: YAG pulse laser (1064 nm, pulse width \(\leq\)8 ns, maximum pulse energy 80 mJ); (3) power supply system: 24 V 20 Ah lithium battery and 220 V AC switchable to meet the needs of field testing; (4) optical coupling device; (5) the sample holder with a 32 mm circular groove is used to carry a 30 mm pulverized coal tablet, and the sample table can be rotated along the designated diameter according to a preset speed to achieve multiple tests of the same sample. The whole system weighs less than 10 kg and is easy to carry for field tests.
# Reliable User Datagram Protocol as a Solution to Latencies in Network Games
## 3 RUDP Implementation in the Transport Layer to Handle Latency
### Node and Process Model for RUDP Using OPNET Simulation  
Figures 5 and 6 represents the RUDP node model and the RUDP process model, respectively. The former was implemented by using app UDP module on Tpal. Layer 2 of this model was developed to use Medium-Access Control (MAC) and to carry out the TCP/IP transmission.  
The additional important attributes in the RUDP node model are the port numbers, the number of retransmissions, time-out periods and the transmission protocol. The RUDP process model was implemented to use all TCP, UDP and RUDP transmission protocols. For the RUDP model, a methodology that guarantees packet reliability by transmitting the packets in accordance with the 'Sequential (order) Numbers', and receiving the Ack packets after the message deliveries has been completed.  
This is called the 'Selective Repeat Protocol'. If an Ack packet does not arrive within the designated time-out period, the same packet will be retransmitted until a fixed maximum number of retransmissions has been achieved. This model is expected to discard the retransmitting packet if it is unable to receive a relevant Ack packet in the end. The number of retransmissions and time-out periods can be pre-defined using the 'Property Window' such that the user can run the simulations by altering them each time.