esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Encoder-Decoder and Attention  
The encoder-decoder architecture developed in the domain of machine translation is constructed of two separated mostly recurrent networks called the encoder and the decoder. Figure 5 shows an example of such architecture using LSTM networks. The goal of the encoder network is to provide a compressed representation of the input sequence which is then passed to the decoder as the initial state. The decoder creates the output sequence subsequently one by one using the output from the preceding time instance as the input at the following one. Because of this, the encoder-decoder architecture is very well suited for time series problems.  
The encoder-decoder model is very powerful but has however one significant limitation. While encoding the input sequence, often the information relevant for the creation of a correctly decoded output becomes lost. This is solved using the attention mechanism. At its core, the attention concept evaluates the similarity score between each encoder output  
Figure 4: A Long Short-Term Memory cell.  
Figure 5: An example of the encoder-decoder architecture with Long Short-Term Memory cells.  
and the currently produced decoder output. The goal is to draw attention to those encoder sequence parts which are most significant for the current decoder output. In [21] first the similarity score between each encoder state stored in \(h_{j}\) and the previous decoder output \(s_{t-1}\) is calculated (10). The softmax function is applied to the similarity coefficients (11). From that, the context vector is calculated (12) which is then concatenated with the decoder hidden state of the time instance \(t-1\).  
\[e_{ij}=v_{a}^{T}tanh(W_{a}s_{t-1}+U_{a}h_{j}) \tag{10}\]  
\[\alpha_{ij}=\frac{exp(e_{ij})}{\sum_{k=1}^{T_{x}}exp(e_{ik})} \tag{11}\]  
\[c_{i}=\sum_{j=1}^{T_{x}}\alpha_{ij}h_{j} \tag{12}\]  
Using these equations, the attention is drawn to those parts of the encoder which are most significant while decoding the \(i\)th element of the sequence.
## 2 Description of Deep Learning Model
### RNN-Based Models
#### 2.1.2. Gated Recurrent Unit  
Though proven effective, the LSTM networks have a high computational cost. As a simplified version of LSTM, GRU retains cell units analogous to the forget gate mechanism, albeit omitting the output gate. Consequently, this design choice leads to a reduction in the overall parameters [34], as visually depicted in Figure 3.  
The GRU network is like the LSTM network but has a simplified structure with two gates: an update gate and a reset gate. The role of the update gate determines the amount of historical information to be carried forward to the current time step. The update gate incorporates the previous hidden state (h\({}_{t-1}\)) and the current input (x\({}_{t}\)) as input parameters, generating an output that lies within the interval of 0 and 1, denoted as \(G_{u}\).  
\[G_{u}=\sigma(W_{\mathrm{xu}}x_{t}+W_{\mathrm{hu}}h_{t-1}+b_{u}) \tag{7}\]  
The reset gate controls how much of the historical information should be forgotten. The reset gate takes the previous hidden state (h\({}_{t-1}\)) and the current input (x\({}_{t}\)), generating an output confined within the range of 0 and 1, symbolized as \(G_{r}\).  
\[G_{r}=\sigma(W_{\mathrm{xr}}x_{t}+W_{\mathrm{hr}}h_{t-1}+b_{r}) \tag{8}\]  
The candidate activation process calculates a fresh candidate activation value, which is a fusion of the previous hidden state and the current input. This operation involves  
Figure 3: Structure of a GRU cell with reset gate (G\({}_{r}\)) and update gate (G\({}_{u}\)).  
taking the previous hidden state, the present input, and the reset gate as input parameters, yielding the candidate activation denoted by \(\tilde{h}_{t}\).  
\[\tilde{h}_{t}=\tau(W_{xh}x_{t}+W_{hh}(G_{t}h_{t-1}+b_{h})) \tag{9}\]  
The hidden state combines the previous hidden state and the candidate activation to produce the current hidden state, denoted by \(h_{t}\).  
\[h_{t}=(1-G_{u})h_{t-1}+G_{u}\tilde{h}_{t} \tag{10}\]
## 1 Introduction
### Satellite Mapping in Estuarine Environments  
Satellite-based habitat mapping in estuarine environments has a long history [7] and recent advances show great promise for improvements in mapping wetlands more frequently and at ever finer scales to accurately capture changes in these dynamic systems. The preponderance of studies highlight supervised, object-based, classification as the most promising method for mapping habitat types in a wetland environment with very high resolution imagery [8; 9; 10]. Complex species-specific wetland classifications have found some success: mapping oysters using hyperspectral data with accuracy ranging from 62% to 78% in muddy and rocky substrates respectively [11], distinguishing mangrove types using WorldView-2 imagery with 89% accuracy [12], and using image texture to supplement multispectral imagery to differentiate nearly a dozen similar cover types with up to 78% accuracy [13]. Many studies have also relied on synthetic aperture radar (SAR) for wetland classification [14; 15], as well as the fusion of SAR and multispectral imagery for land cover and habitat mapping [16]. For example, estuarine vegetation mapping studies fusing WorldView-2 and LiDAR data have reported up to 95% accuracy with extensive fieldwork [17]. Data-fusion of ultra-high spatial resolution multispectral UAS imagery with digital surface models derived from UAS imagery appears poised to provide another method with high accuracy for complex classifications [18; 19].  
The above success stories come with some caveats for wider application: (1) the highest resolution satellites can be costly, and often do not provide consistent revisits without government or commercial tasking, (2) hyperspectral imagery, though promising and increasing in availability, has very limited coverage, and (3) many automated satellite classification methods require extensive field work for training and validation. The present study provides an analysis workflow that can mitigate high field work burdens through the use of UAS-based imagery, tests image processing techniques for increasing accuracy, and compares products produced using higher spatial/spectral resolution platforms (e.g., WorldView) to more accessible higher revisit rate sensors (e.g., RapidEye).
## 4 Results and Discussion
### Coverage Prediction via Ray-Tracing
#### 4.3.4 Human Body Shadowing  
A human body obstructing the LOS path causes significant attenuation, with additional losses up to 20 dB. The simulated PL decreases by 10 dB at the shadow border when  
Figure 9: Received power prediction using ray-launching algorithm, for an access point location identical to the TX antenna location used in the NLOS1 path loss measurements. **(a)** Omnidirectional antenna with 3 dBi gain. Full lines represent the best reflected paths, and dashed lines represent paths with ceiling reflections. **(b)** Directional antenna with 23 dBi gain. **(c)** Legend.  
taking into account diffraction loss using a double KED model, which corresponds to the measurements using the TG platform. However, if we take into account reflection in the PL simulations, the path with minimum PL is mostly a reflected path. We conclude that the simulation time decreases with a limited impact on simulation accuracy by not taking diffraction into account. For simulation of a fixed link, i.e., when estimating the performance of a system without adaptive beamsteering, the diffraction model is needed.
# Utilizing Different Machine Learning Techniques to Examine Speeding Violations
## 2 Materials and Methods
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{2}\]  
Accuracy can also be calculated in terms of positives and negatives, as seen below. Where:  
* TP: True-positive classified instances. * TN: True-negative classified instances. * FP: False-positive classified instances. * FN: False-negative classified instances. TP, FP, TN, and FN form what is called a confusion matrix. It is a matrix used for classifier performance evaluation. It contains the actual and predicted values. Figure 5 shows the layout of the confusion matrix. * True positive is the number of times the predicted value is positive, and it is truly positive [43]. * False positive is the number of times the predicted value is positive, but it is truly negative [43]. * True negative is the number of times the predicted value is negative, and it is truly negative [43]. * False negative is the number of times the predicted value is negative, but it is truly positive [43]. RMSE measures the difference between the predicted and observed values. \[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{N}(\hat{y}_{i}-y_{i})^{2}}{N}} \tag{3}\]  
where:  
* RMSE: Root Mean Squared Error. * \(\hat{y}_{i}\): _i_th observation predicted value. * \(y_{i}\): _i_th observation actual value. * \(N\): Observations number. Figure 5: Confusion Matrix [43]. The F-measure, also called the F1-score, is used to measure model accuracy on a dataset. It is used to evaluate classification systems that classify examples as 'positive' or 'negative'. The F-score integrates the precision and recall of the model. Equation (4) below for the standard F1-score is the harmonic mean for the precision and recall of the model [44]. \[\text{F}-\text{measure}=2\;*\;\frac{precision*recall}{precision+recall} \tag{4}\]  
Precision and recall are two ways to measure how well a classifier works in binary and multiclass classification problems. Precision is calculated by dividing the number of true positives by the sum of true positives and false positives. Recall, in comparison, is the ratio of instances that were correctly classified (True Positives) to the total number of instances that should have been classified as Positive (True Positives + False Negatives) [42]. Where: Precision = TP/(TP + FP), and Recall = TP/(TP + FN). Kappa statistic is a measure of how close the classified instances are to the true ones. \[\text{Gini index}=1-\sum_{k=1}^{n-1}[p(k|t)^{2}] \tag{1}\]0  
where:  
* \(p_{\text{o}}\): Observed instances. * \(p_{\text{e}}\): Expected instances. ROC curve is a binary classification performance measure. It is a probability curve that plots the True Positive Rate (TPR) against the False Positive Rate (FPR) at various threshold values. The area under the ROC curve indicates a test's overall classification ability. The AUC measures a classifier's ability to distinguish between classes and is used to summarize the Receiver Operating Characteristic (ROC) curve. The greater the AUC, the more accurate the model, discriminating between positive and negative classes.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Requirements on Tools in MLOps
#### 5.1.2 Model Training Phase (TP)  
Training of ML models is a resource-intensive, complex, and iterative process. ML is capable of offering multiple correct solutions for a single problem, unlike traditional technologies that offer only one perfect solution [58]. In this section, we define requirements based on challenges faced during the training of an ML model.  
TP-1: Model Type Selection  
In providing a user with the choice of defining a model type, as the selection between forest tree or naive Bayes, the training is made configurable. When offering the basis for the solution of multiple problem types, such supportive tools for model training may also be applied for further operations within an organization.  
TP-2: Hyperparameter Tuning  
Once an algorithm for solving a well-defined problem is chosen, its configuration (e.g., the model's hyperparameters) must be selected. Concerning automating this technically sophisticated task, various AutoML approaches can lighten the data scientists' workload by applying CASH techniques.  
TP-3: Tracking  
ML is intrinsically approximate, and the development process is iterative. As the number of iterations increases, the information necessary for reproducing that experiment also grows. Tracking in ML is a process through which all modeling-related information, i.e., the algorithm, its hyperparameters, and the observed performance based on some metrics, could be saved automatically in a database. This collection of meta-information helps compare algorithms, runs, and performance metrics.  
Tracking the training runs, configured hyperparameters, and the corresponding model quality metrics are the overall foundation of the model's quality. Especially when implementing multiple different ML models as a solution to a problem in parallel, comparing results of measurements may help in selecting feasible model candidates. ML experiment tracking tools are an essential part of a data scientist tool kit.  
TP-4: Integration Friendly  
As there is a multitude of different ML frameworks (e.g., Pytorch, Scikit-Learn, or Tensorflow), each with a focus on a specific ML technique, concept, or implementation details, the integration of arbitrary frameworks in the MLOps workflow enables diverse technical approaches for problem-solving.
## 1 Introduction
### Objectives and Research Questions  
The present study aims to evaluate the capacity of a generative AI (Microsoft Copilot) compared to an instructional video to generate learning and positive emotional effects when university students practice web programming topics in PHP.  
The research questions to be considered in this study are:  
1. RQ1: What differences in learning exist between students who practice web programming using Microsoft Copilot versus an instructional video?
2. RQ2: What differences exist between students who practice web programming using Microsoft Copilot and those using an instructional video regarding cognitive absorption effects: enjoyment, control, focused immersion, temporal dissociation, and curiosity?
3. RQ3: What differences in effects exist between students who practice using Microsoft Copilot and those using an instructional video in the dimensions of technology acceptance: ease of use, perceived usefulness, and intention to use?
## Data  
### Habitat Survey  
This study used data from the Second National Survey on Natural Environment in South Korea, which included the identification of habitats of endangered species of wild animals and plants. From 1997 to 2003, the National Institute for Environmental Studies conducted a survey of species appearances and spatial distributions of wild animals by experts from various research institutes. Surveys were performed from February to October every year for mammals to consider changes in seasonal patterns. The survey methods were based on field observations, including direct observations, community surveys, tracking, feces, and footprints, to investigate the species occurrence and spatial distribution of wild animals. The locations observed or detected by the traces were geocoded via Global Positioning System and were composed of GIS data.  
Figure 1: Study area: (**a**) South Korea in East Asia; and (**b**) South Korea.  
The marten has the second largest sphere of action among mammals in the Korean Peninsula after the Asiatic black bear. Martens prefer broadleaf forests and mainly inhabit mature forests, such as fourth-grade forests that have \(\geq\)50% occupancy rates of 31-40-year-old trees with diameters of \(\geq\)30 cm. Martens inhabiting the Jirisan area occur at a density of 1-1.6 per 10 km\({}^{2}\), and are diurnal animals with a wide range of behaviors, moving an average of 11.2 \(\pm\) 5.4 km per day. Martens mainly hunt Eurasian red squirrels, rats, and other rodents as food, but they also hunt hares, young roe deer, and wild boars. They also consume tree fruit, such as those from the lotus persimmon tree. In winter, they often hunt for food in shrubs on the edge of forests and frequently cross two-lane roads in their radius of action [34].  
Leopard cats have activity areas of 3.69 \(\pm\) 1.34 km\({}^{2}\), and the core space of the species is estimated to be 0.64 \(\pm\) 0.47 km\({}^{2}\)[5]. Leopard cats prefer forests, as well as adjacent grassland and agricultural land, as habitats. They have a high preference for inland wetlands, such as wild grassland along riverbanks. Owing to their behavioral radius and noturnality of movement in a variety of regions, they suffer a high frequency of road kills on roads adjacent to forests and rivers. Rodents, birds, and small mammals are their main sources of food, and forest ridges and valleys are used as main transport routes by leopard cats.  
In the Second National Survey on Natural Environment, martens were found at 156 points, and leopard cats were found at 630 points in the study area (Figure 2). In this study, the observed data were divided randomly in half for model training and validation. This confirmed that the distribution of martens is mainly limited to large forest areas, including the Mt. Baekdu range. Meanwhile, leopard cats inhabit a wide range of emergence sites, from major forests to rivers and agricultural areas.
# Crushing Analysis and Optimization of Adjacent Variable Thickness Hexagonal Tubes
## 2 Materials and Methods
### Finite Element Modelling  
The explicit nonlinear finite element analysis code LS-DYNA [24] was used to investigate the crashworthiness of the AVTHTs. In Figure 2, the finite element (FE) model consists of three components: a fixed plate, a moving plate, and an AVTHT, whose geometric dimension is consistent with that of the specimen tubes. The two plates were defined as rigid bodies using hexahedral solid elements with the material Mat_Rigid (Mat_20) from the material library in LS-DYNA. The tube was modeled using Bellytschko-Tsay shell elements with five integration points through the thickness and one integration point in the element plane. Mat_24 was employed to determine the elastic-plastic characteristics of this material. In Figure 3, a comparison of the true and the engineering stress-strain curves is shown, and the true stress-stain curve is applied for Mat_24. The hourglass control and reduced integration techniques were employed to avoid spurious zero energy deformation modes and volumetric locking, respectively. Moreover, the tube plates of AVTHT were assigned to two independently variable thicknesses. Therefore, two different components with different section property cards in LS-DYNA were established for tube plates with thicknesses of \(t1\) and \(t2\). To balance the computing time and accuracy, we reduced the element size of the AVTHT from 2 mm to 0.6 mm with an interval of 0.2 mm to perform the mesh convergence analysis. The results indicated that when the mesh size was 1 mm, the value of energy absorption became stable; thus, a mesh size of 1 mm was selected for the AVTHT model. Moreover, the mesh size of the two rigid plates was set at 2 mm, which can satisfy the requirements of efficiency and accuracy.  
Figure 3: Stress-plastic strain curves.  
For contact algorithms in simulations, the \"AUTOMATIC_SURFACE_TO_SURFACE\" contact type was used for the contact between the two plates and the test tube. The \"AUTOMATIC_SINGLE_SURFACE\" was used for self-contact with the AVTHT. The static and dynamic frictional coefficients in these contacts were all set at 0.15 [22].  
Due to the quasi-static test condition, the strain-rate effect of the aluminum alloy AA6061O was ignored for the numerical simulation in this study. To improve computational efficiency, we sped up the loading velocity [14], and in [26], it was proven effective when TW structures were compressed with quasi-static loading in simulations. Herein, the moving plate moved upward with a velocity of 1000 mm/s in the normal direction [16; 27; 28], and the fixed plate was fixed throughout the simulation.
# 2D Triangulation of Signals Source by Pole-Polar Geometric Models
## 1 Introduction
### Standard Methods and Considerations
#### 1.2.2 Time-Based Approaches (ToA and TDoA)  
Reference [15] use sound waves to locate a receiver device using signal data based on TDoA measurements. The location computation is formulated as an optimization problem applying a set of constraint previously determined. The basic idea is to shrink the radius of all circles at a constant step length until the intersection area reaches a small threshold. This operation needs to perform \(N\) acquisitions of data (called phase). For each set of acquired data is applied an optimization algorithm. These acquisitions of data end when the optimization algorithm finds a solution considered satisfactory. The computational complexity of the proposed method is about \(O\big{(}10\big{(}nN^{2}+n^{2}N\big{)}\big{)}\), where \(n\) is the number of nodes (emitters).  
Time-based approaches are based on electromagnetic (or sound) wave propagation that moves at a speed \(v\) close to light speed, \(c\approx 3\times 10^{8}\) m/s (in the vacuum). In the Earth's atmosphere \(v=c/n\), \(n\) is the refraction coefficient of the atmosphere (\(n=1.000292\) to dry air at conditions \(0\ ^{\circ}\)C and \(1\ atm\). The accurate value is \(v=299,704,944\ \ \text{m/s}\)). The physical model (7) responsible for computing the radial distance \(d\) between emitter and receiver depends only on the instant \(t_{E}\) when the emitter sends the signal and the instant \(t_{R}\) when the receiver get it.  
\[d=v(t_{R}-t_{E}) \tag{7}\]  
The model (7) can be applied only in case that emitter and receiver are time-synchronized. Ensuring this synchronization is not always a simple task. It must be considered that \(v\) is an astronomical quantity and small deviations of precision in \((t_{R}-t_{E})\) results in large errors (for each 1 ms error in the time registration there can be a location error higher than 300 km).
# Quality Assessment of Dry Soil Mixing Columns in Soft Soil Areas of Eastern China
## 6 Conclusions  
This paper proposed a comprehensive quality assessment method, and its engineering application was discussed. Based on the statistical analysis of test results and a quality assessment of the DSM columns in five soft soil areas, the following conclusions were reached:  
1. The test results of the SPT blow count, UCS, and SCQD of the DSM columns were scattered broadly, particularly in shallow ground, which indicates the quality of DSM columns varied greatly. The blow counts of SPT were mainly within the 10-30 range, the UCS was mainly distributed in the 0-2.2 MPa range, and the SCQD was mainly distributed in the 40-90% range.
2. The mean values of the SPT blow count, UCS, and SCQD of the DSM columns decreased with depth: the greater the depth, the worse the quality of DSM columns. The mean blow counts of SPT in all soft soil areas were below 25 blows. The mean strength of DSM columns in the littoral soft soil area was greater than those in other areas and less than 1.0 MPa. The DSM columns in the alluvial soft soil (Ancient Yellow River) area had the lowest mean strength.
3. For all soft soil areas, the proportions of great quality, good quality, general quality, and unqualified soil decreased in that order. The quality assessment of 8627 DSM columns showed the proportions of great quality and unqualified were 64.84% and 1.4%, respectively. The greater the depth of the DSM column, the more likely it was to be unqualified.  
Data curation, H.X., H.W. and D.Q.; funding acquisition, G.D.; project administration, G.D.; writing--original draft, H.P.; writing--review & editing, H.P. and G.D. All authors have read and agreed to the published version of the manuscript.  
The research was financed by the National Science Foundation of China (Grant No. 41977241).  
Not applicable.  
The authors also thanks to Jiangsu Provincial Department of Transport for the help in statistical data collection.  
The authors declare no conflict of interest.  
Figure 8: The unqualified DSM columns at different depths.