esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Relation to Some Existing Methods  
We will now consider elements of the proposed method which differentiate the EM from existing techniques of pattern processing. The parameter DStr is the function of isotropic and anisotropic edges/vertices. Thus, the pixel is not the basis for the construction of the EM. This differentiates our proposed method from image processing technology, which is based on Shannon entropy.  
The parameter DSize characterizes the deviation in layer thickness across a 2-D plane from a layer in which the thickness remains constant. Thus, the DSize can be distinguished from pattern characteristics like curvature, perimeter, convexity, circularity, compactness, and other geometrical entities that are used in the application of statistical methods to image quantification [52; 53; 54; 55; 56].  
There are various methods that have been developed for the quantification of shapes [57; 58], pattern complexity [59; 60], and disorder [61]. To the best of our knowledge, none of these methods and none of the mathematical tools used for image processing, such as fractal dimension [62], mutual information [63], Minkowski functional [64], signal analysis [65], fuzzy sets [66], and statistical methods [67] use an isotropic layered pattern as the reference image and isotropic and anisotropic layers for the morphological descriptions of the shapes and patterns under study.  
In the present work, the term \"arbitrary binary pattern structure\" is defined as a mutual arrangement of lines with breaks and confluences that comprise a pattern, as well as line irregularities. Line irregularities mean that lines could have any possible configurations. DStr = 0 for a straight line, and increases as irregularities become more complicated. This result is in agreement with [PERSON]'s findings [68].  
In his famous work, [PERSON] studied the distribution of visual information along smooth contours [41]. [PERSON] and [PERSON] described [PERSON]'s finding in the following way, \"In 1954, [PERSON] proposed that information along a visual contour is concentrated in regions of high magnitude of curvature, rather than distributed uniformly\" [69] (p. 243). Our results of experiments with river meanders, coastlines of islands, and a bathymetric map are in agreement with [PERSON]'s findings [68] and [PERSON] and [PERSON]'s statement \"... that information generally increases with curvature and is greater for concave as compared with convex turns... \" [69] (p. 247). Additionally, the DStr and DSize are applicable for the quantification of contours and patterns that are comprised of straight lines alone.
# Improved PR Control Without Load Current Sensors and Phase-Locked Loops for APFs
## 3 A PLL-Less Load Current Sensorless Improved PR Control  
In order to achieve high-precision compensation of harmonics and reactive components of parallel active power filters under complex working conditions, the previous section discussed the mathematical model and operation mechanism of APF from the perspective of system structure and instantaneous power theory and clarified the key role of reference current generation in filtering performance. However, traditional control strategies generally rely on voltage/current sensors (especially load-side current) and PLLs for coordinate transformation, which results in complex system structures and limited real-time and robustness. To overcome the above problems, this paper proposes an improved PR control strategy, the core feature of which is that no PLL or load-side current sensor is required, and only the grid-side voltage and current signals are used to achieve dynamic tracking of reference current and harmonic compensation.  
This section will carry out the design and analysis around the core modules of the strategy, mainly including the calculation method of reference current, the PR controller structure, and the parameter tuning process based on root locus and frequency domain analysis methods.
## 4 Grouting Design and Scheme
### Yujingshan Tunnel Side Wall Grouting Reinforcement Scheme  
Due to the limitation of construction conditions, the back step grouting is used for construct penetration grouting in the backfill region. The grouting parameters listed in Table 3 are adopted in grout engineering of Yujingshan tunnel side wall. In consideration of environment protection and the safety of drinking water for surrounding residents, the materials used in this project are mainly sulphate aluminium cement (SAC) and cement-sodium silicate (C-S).  
The arrangement of grouting holes shown in Figure 11 are based on the results of theory calculation and in-situ grouting test. The arrangement of grouting holes depends on the positional relationship between the karst cave and the tunnel infrastructure. 108 mm and 50 mm sleeve valve pipes are used for grouting in the left and right region of tunnel axis, respectively. The grouting holes around the reinforcement region are mainly grouted with cement-sodium silicate slurry to form the stop slurry curtain. In the central reinforcement region, the SAC slurry is used to improve the structural strength. In addition, the curtain for slurry stopping is adopted in D3K279 + 872 section right lane, D3K279 + 913 section and D3K279 + 955.5 section left lane. Due to the weakness mechanical properties of engineering spoil and crush of surrounding rock, grouting holes collapse frequently during grouting hole drilling, which seriously affects the construction progress. The casing drilling method is used to improve the construction efficiency of the grouting holes.  
Figure 11: The arrangement of grouting holes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Value** & **Index** & **Value** \\ \hline Stop pressure & 2–3 MPa & Backfill material ratio & Water: Cement: Soil = 1.6:1:1 \\ \hline Penetration distance & 1.5–2.1 m & Backfilled rubble & 5–10 mm \\ \hline Grouting flow & 77–165 L/min & Back step distance & 1 m \\ \hline Single slurry ratio (SAC) & W:C = (0.6–1):1 &
\begin{tabular}{c} Cement-sodium \\ silicate slurry (C-S) \\ \end{tabular} & W:C:S = (0.6–1):1:1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Penetration grouting parameters.  
Due to the irregular nature of the karst cave section, the grouting drilling design is various in different tunnel sections. Figure 12 shows the grouting drilling design in typical sections of Yujingshan tunnel.  
In the processing of penetration grouting construction, the pumping pressure and slurry flow rate are the criteria for stopping penetration grouting. If the pumping pressure reaches the designed stopping pressure and remains for 900 s, the grouting can be finished. However, if the flow rate of slurry is 1.5 times of the designed grouting flow rate, and the pumping pressure is still less than the designed pressure, the gelatinization time of slurry should shorten to bring the grouting pressure up to the designed pumping pressure.
## 2 Materials and Methods
### Data and Data Processing
#### 2.2.2 Meteorological Data  
The air pressure, wind speed and direction, air temperature and humidity, precipitation, and radiation components (upward short-wave radiation, downward short-wave radiation, upward long-wave radiation) from May to September in 2014 were collected from meteorological stations at the flux towers to study the source area of field GPP. Half-hourly data were derived from the observed 10-min data. Missing data were filled-in by using the linear interpolation.
# An Overview of Design Techniques for Two-Dimensional Leaky-Wave Antennas
###### Abstract  
Two-dimensional leaky-wave antennas offer effective, compact, single-feeder, easy-to-fabricate solutions to the longstanding problem of realizing a simultaneously directive and low-profile radiating device. These traveling-wave antennas have been thus proposed as wideband, reconfigurable, or frequency-scanning radiating structures in different application contexts, spacing from the microwave to terahertz frequency range. These diverse contexts call for a comprehensive guide to characterizing and designing two-dimensional leaky-wave antennas. In this work, a review of numerical techniques for the analysis of either quasi-uniform or radially periodic leaky-wave antennas is proposed in order to provide the reader with straightforward yet effective design guidelines. Theoretical results are corroborated through full-wave simulations of realistic three-dimensional models of the considered devices, thus demonstrating the effectiveness of the proposed methods.  
leaky-wave antennas; numerical methods; Fabry-Perot cavity antennas; radially periodic leaky-wave antennas; metasurfaces 2020
# Individual Environmental Risk Assessment and Management in Industry 4.0: An IoT-Based Model
## 2 Materials and Methods
### System Architecture: Server Side
#### 2.2.2 Applications Running on the Server and Data Privacy Concerns  
The server runs on a Virtual Private Server (VPS) provided by a web hosting company. The VPS runs Ubuntu 20.04 operating system and has 2 GB of memory, 2 CPUs, 2 GB disk, and 2 GB of bandwidth. Ubuntu operating system was chosen because it is a free operating system with very good documentation, and it is largely used both on desktops and servers. Our server is composed of the following entities:  
* Mosquitto [34]: an MQTT broker that receives the measures sent by the monitoring devices.
* Telegraf [35]: an MQTT client that subscribes to the topics that contain the exposure information of the employees and writes them in InfluxDB.
* InfluxDB [36]: a time-series database that stores the exposure information of the workers.
* Grafana dashboard [37]: Reads InfluxDB to generate graphics showing exposure information for each worker. Grafana, like Mosquitto, Telegraf, and InfluxDB, was chosen because it is used in a wide variety of projects and has good documentation.
* Alarm sender: Reads InfluxDB to check whether each of the measures reached or exceeded the limits established by applicable legislation and/or standards and publishes through MQTT the proper value--0 if all measures are inside the limits or 1 if at least one measure reached the limit. Monitoring devices subscribe to the alarm and turn on the green or red LED, respectively.
* MongoDB [38]: a database that stores employees' information.
* Webserver: This shows the graphics generated by Grafana with information about the workers' exposure and the alarms. The server provides an interface to allow managers and/or OSH technicians to change settings, add new people, update data, and inform when a worker retires or leaves the company. These data are stored in MongoDB.
* Machine learning module: This module is under development and aims to help employers to make long-term decisions in OSH by suggesting points that deserve attention (e.g., a worker, an entire department, or a subsidiary). This module must consider the health history of each worker, as informed on admission, the risks inherent to each job, and data on exposure to hazardous agents. For example, considering workers A and B, who have the same job and only perform slightly different activities, the system may suggest that worker A, who has a history of allergies, starts performing activities with exposure to dust only for a short time. Regarding worker B, who has a history of skin cancer and has been exposed to the sun often and for long periods, the system might suggest the company to allocate B mainly to indoor activities. Managers and the OSH team, based on this information, may choose to exchange activities between A and B. In this way, the module will provide recommendations for the OSH team, aiming to avoid overexposure to hazardous agents.
## 5 Results and Analysis
### Data Throughput  
Sleep mode strategies for energy efficiency significantly influence the average achievable data rate in the network. Figure 13 illustrates that in a network without sleep mode, both the average user rate and sum rate remain constant. This is due to the consistent number of active BS densities across the network. Furthermore, it establishes the upper limit on the maximum number of users served by BSs. However, with RSM, both the average sum rate and average user rate perform poorly due to its random selection of BSs to be turned off. In contrast, LAS and dynamically DOSS exhibit superior performance.  
Figure 12: Probability of success.
## 4 Experiment and Analysis  
### Modeling  
We input the processed input dataset (latitude and longitude information, rainfall, air temperature, land cover type, NDVI and four topographic factors (elevation, slope, slope direction, and shading); and the output dataset (ground-based GNSS-IR soil moisture data) into the GA-BP neural network for iterative training. We obtained the optimal GA-BP multi-data fusion soil moisture model after 300 training cycles. Figure 6. shows scatter plots of the Inversion values and GNSS-IR soil moisture actual values for the entire data set of the optimal model and the test set, along with Pearson correlation coefficient R values, RMSE, bias, and ubRMSE. In addition, Table 3 shows the accuracy statistics of the modeled measurement stations.  
It was considering that the Pearson correlation coefficient R takes the values of [0-0.4], [0.4-0.6], and [0.6-1] as weak, medium, and strong correlations, respectively. The above table shows that 33 stations with Pearson correlation coefficients greater than 0.6 are strongly correlated, accounting for 75% of all stations. 3 stations are moderately correlated, and the remaining 18.1% are weakly correlated. The overall correlation of all stations reached 0.8770, and despite the poor performance of some stations, a nonlinear regression of the entire data was very effective. Like the correlation, the SMAP task was produced  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy Index** & **Scope** & **Number of Measuring Stations** & **Average** \\ \hline \multirow{3}{*}{R} & 0–0.4 & 5 & \\  & 0.4–0.6 & 3 & 0.8967 \\  & 0.6–0.1 & 36 & \\ \hline \multirow{3}{*}{RMSE} & -0.04 & 29 & \\  & 0.04–0.06 & 10 & 0.0408 \\  & \textgreater{}0.06 & 5 & \\ \hline \multirow{3}{*}{bias} & \textless{}0.02 & 22 & \\  & 0.02–0.03 & 16 & 0.0002 \\  & \textgreater{}0.03 & 6 & \\ \hline \multirow{3}{*}{ubRMSE} & \textless{}0.04 & 33 & \\  & 0.04–0.06 & 9 & 0.0407 \\ \cline{1-1}  & \textgreater{}0.06 & 2 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy statistics of modeled measurement stations including R, RMSE, bias, and ubRMSE.  
Figure 6: (**a**) Scatterplot of random sampling retrieval for the whole dataset; (**b**) Scatterplot of the test set data (15 February 2014–1 March 2014) for 44 modeling stations, providing Pearson correlation coefficients R, RMSE, bias, and ubRMSE.  
with a precision of 0.04 due to its accuracy. Therefore, RMSE below 0.04 is considered high performance, [0.04-0.06] as medium performance, and greater than 0.06 as more unreasonable performance. In this case, there were 28 stations with RMSE less than 0.04, accounting for 63.6% of all stations. We are further combined, with the ubRMSE, it can be seen that there are 30 stations with ubRMSE less than 0.04 (high performance), accounting for 68.1% of all stations. Based on the four of the above table, it can be seen that the short delay estimation of our algorithm seems to be consistent with the average short delay level of most stations. The vast majority of stations achieved good modeling results, except for a few stations that did not meet expectations.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 3 New Requirement Space Development
### Sufficiency  
Sufficiency is the first emerging requirement such that users can access highly immersive environments from any device. It becomes outstanding with the growth of mobile devices and computational complexity. Partially/completely offloading computing tasks to remote sites for meeting the resource requirement is a common choice in mobile cloud computing [52; 55]. Based on computation offloading, two concerns can be derived: energy efficiency and quality of service (QoS)/quality of experience (QoE). Energy efficiency concerns the device energy consumption for local computing or communication to remote sites [67]. The authors of [68] provided an energy consumption model for smartphone task offloading in WLAN and mobile network (3G/4G). QoS/QoE concerns the cost and benefit of offloading [69]. The cost includes communication overhead and response latency. The benefit includes graphic (i.e., image or video) quality improvement, quantified by bitrate or frame rate. Moreover, both energy efficiency and QoS/QoE are sensitive to network conditions. If network availability is low, code offloading will increase both energy consumption and latency [54].
# Age Assessment through Third Molar Teeth Developmental Stage in Children
## 1 Introduction  
There are multiple reasons for the importance of age determination, such as forensic evaluation; criminal responsibility age; and other factors concerning legal and social benefits, employment and marriage [1; 2; 3; 4; 5]. Age assessment can be performed in different ways including morphological age (weight and height), sexual development, skeletal hand and wrist development, and dental development, with the latter two as the more common methods [6]. However, a study conducted in Brazil found that estimating age by examining teeth development is more accurate than estimating age by hand and wrist development [7].  
By adolescence, dental age estimation becomes more difficult, as most teeth are in the process of completing their apical formation. Therefore, the remaining third molars can be used in age evaluation in late adolescence [6].  
Most research using third molars for age evaluation are based on the [PERSON] evaluation key, which is divided into eight stages of tooth development. The assessment isbased on three main components during the development of permanent dentition: crown formation, root formation and root apex closure [8,9].  
The purpose of the current study was to correlate the dental developmental age of third molar teeth with the chronological age of adolescent patients and to estimate the third molar developmental stage at which it can be assumed with a high probability that the subject is over the age of criminal responsibility.
# In Vitro Evaluation of a Non-Invasive Photoplethysmography Based Intracranial Pressure Sensor
## 2 Material and Methods
### The nICP Sensor and Measuring System  
An optical custom-made reflectance sensor placed on top of the intracranial chamber acquired PPG signals from the brain phantom. The nICP sensor comprised a high-intensity light-emitting diode (OIS-330-810-X, Osaoptolight, Berlin, Germany) with a peak emission wavelength at 810 nm, whereas the detector was a silicone photodiode (VBPW34S, Vishay Intertechnology, Malvern, PA, USA) with a large active area, which was positioned at a distance of 35 mm from the light source. This inter-optodes distance was chosen as it guaranteed the light penetration necessary to interrogate the cerebral cortex, as commonly used in cerebral Near Infrared Spectroscopy measurements [18]. The latter was analysed by the authors in a previous publication [19]. In addition, the emission of near-infrared light at the isobestic point of 810 nm ensures that the measurements are unaffected by changes in blood oxygenation (i.e., changes in oxygenated and deoxygenated haemoglobin concentrations).  
An instrumentation unit was employed to drive the light source and process the signals detected by the photodiode. The platform (_ZenPPG_) has been described in detail by [PERSON] et al., and it was responsible for supplying the driving currents to the LEDs, transforming and amplifying light intensities into voltages, and pre-processing the acquired signals [20].
# Towards an Automatic Recognition of Artifacts and Features in Plethysmographic Traces
## 4 Detection of Artifacts
### Model Description  
This study considered a model based on Long Short-Term Memory [36] networks, a class of recurrent neural networks (RNNs) [37] designed to effectively capture temporal patterns in sequential data, such as plethysmographic traces. The LSTM architecture preserves long-term dependencies while filtering out irrelevant variations, making it particularly well suited for detecting transient anomalies within the signal. The network model was implemented using Keras [38], with TensorFlow [39] as the backend framework.  
In order to capture temporal dependencies, the network model incorporates two bidirectional LSTM layers, which are designed to process sequential data with a length of 50 timesteps, learning both past and future dependencies within the sequence. The first bidirectional LSTM layer consists of 64 units in each direction, producing an output of size 128 that encodes information from both the forward and backward temporal contexts. The second bidirectional LSTM layer processes this sequence further, reducing the dimensionality to 64 units and providing a compact temporal representation.  
The output of the second LSTM layer is fed into a series of dense layers, forming a Multi-Layer Perceptron [40]. An intermediate dense layer reshapes the sequence representation back to its original length of 50 timesteps, with one unit per timestep. Subsequently, three additional dense layers, each with one output unit, generate the predicted values. The entire architecture consists of 78,923 trainable parameters among the two LSTM layers and the dense layers. Table 1 summarizes the number and type of layers, the output size, and the number of trainable parameters for each layer in the model.  
For each sequence of 50 timesteps, the model provides three values derived from a standard quantile regression. These values represent the prediction for the next point in the sequence (corresponding to the 50 th quantile), as well as the 10 th and the 90 th quantiles of the distribution modeling the output.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Layer Type** & **Output Shape** & **Number of Parameters** \\ \hline Input & 50, 2 & - \\ Bidirectional (1) & 50, 128 & 34,304 \\ Bidirectional (2) & 64 & 41,216 \\ Dense & 50 & 3250 \\ Dense & 1 & 51 \\ Output & 1 & 51 \\ \hline Total parameters & & 78,923 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of the LSTM network architecture, showing the layer type, output shape, and number of parameters.  
The model was trained from a sample of seven patients selected for their minimal noise and absence of artifacts in the JVP waveform. The Adam optimizer [41] was used to minimize the loss function. Once trained, the model was then applied to other patients who shared the same body position (supine or sitting) as those in the training dataset. In the evaluation phase, the probability distribution function of the output was expected to exhibit a narrow peak centered close to the predicted value, while in presence of an artifact the function tended to be wider.