esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 2 Material and Methods
### Main Processing
#### 2.4.1 Generating an Initial Benchmark Forest Mask  
The first step of the forest disturbance monitoring workflow is the generation of an initial benchmark forest/non-forest (FNF) mask (see Figure 3), which accurately represents the forest area right at the beginning of the change detection window. The forest masks were derived with slightly different approaches for Gabon and Peru, due to differences in optical data availability. For the Peru test site, the FNF map is based on all optical data from 03/2015-03/2016, in total 13 images from both Landsat 8 and Sentinel-2 sensors. We started with deriving training data for the FNF classification from a low cloud covered Sentinel-2 scene from 10 March 2016 and from RapidEye and VHR data sets from 2015 and early 2016 (for details see Section 2.4.3). Subsequently, we used the Sentinel-2 scene from 10 March 2016 and the training data (forest and non-forest areas) to train a classifier and derive a classification model with the Orfeo Toolbox (www.orfeo-toolbox.org). We then tested different classification approaches (Maximum Likelihood, Random Forest, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Infrared Index (NDII7) thresholding) and different input data options, and found the Random Forest based machine learning classifier to deliver the best overall accuracies for the benchmark FNF map [76]. We then applied the Random Forest classification model derived from the 10 March 2016 Sentinel-2 scene to all other pre-processed optical images from 03/2015-03/2016, resulting in 13 individual FNF maps with class categories \"forest\", \"non-forest\", and \"no data\" (e.g., clouds). For the final FNF map, we apply a weighted majority approach, where FNF masks that lie near the reference date (10 March 2016) in the temporal domain are attributed the highest weights. This weighting approach increases the overall accuracy of the FNF map by 3% [76].  
For Gabon, we calculated the FNF mask for the benchmark year 2013. This reference year was a specification from the related project. In 2013, Sentinel-2 data was not yet available. Therefore, the FNF mask in Gabon is solely based on Landsat 8 data. For the FNF mask classification, we chose the Landsat 8 scene with lowest cloud cover in 2013 (02.08.2013). We collected the required training data (forest and non-forest areas) from VHR image chips that were distributed over the test site and then used the Random Forest classifier of OrfeoToolbox to train and classify the Landsat 8 scene while using all Landsat 8 spectral bands. The remaining cloud holes were filled with the classification results of additional scenes with low cloud cover (01.07.2013; 17.07.2013) to guarantee a complete coverage of the final FNF mask.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 2 Language Models and Attention Mechanism
### Attention Layer  
To be able to align the input and output words correctly, the attention layer helps the decoder understand which inputs are more important. This enables the decoder to focus on the right place or context during the prediction of each output token. The inputs and targets are first converted to embeddings or initial representations of the words. The attention mechanism then uses these encoded representations. Query vectors (\(Q\)) represent the decoder hidden states, and the key (K) and value (\(V\)) vector pairs come from the encoder hidden states. To compute the similarity score between queries and keys, the dot products between the query and key vectors are computed. If the key (\(K\)) and query (\(Q\)) are similarly aligned, then their dot product will yield a higher score. Therefore, higher scores of a particular key indicate that it is relatively more important to query than others with lower scores. To obtain the probabilities of the match between keys and queries, the scores are run through softmax to fit a distribution between 0 and 1. These probabilities act as an indexing mechanism regarding the value (\(V\)) vector. So, these probabilities are further multiplied with the value vectors (\(V\)), which results in alignment vectors. Equation (4) represents this computation from the attention layer.  
\[\begin{split} Z&=attention(Q,K,V)=W_{A}V\\ &=softmax\Bigg{(}\frac{QK^{T}}{\sqrt{(}d_{k})}\Bigg{)}V\end{split} \tag{4}\]  
To help speed up the pretraining process, the teacher forcing technique is used, which leads to faster convergence and higher accuracy. In teacher forcing, instead of the model's output from the previous timestep, the ground truth (correct answer) is fed as an input at each timestep. To enable this teacher forcing, the preattention decoder takes the target tokens and shifts them one place to the right.
## 1 Introduction
Existing resilience frameworks do not sufficiently account for the unique recovery mechanisms of prefabricated systems, such as rapid module replacement, modular restoration paths, and reduced on-site mobilization times [34; 35; 36]. Moreover, differences in structural configuration and joint detailing, such as the use of special connections like the MT connection versus conventional connections, are not captured in current downtime models, meaning their influence on recovery time has not been systematically evaluated. This study addresses the above gap by developing a generalized seismic resilience assessment framework that accommodates both cast-in situ and PCaPC buildings. The proposed methodology enhances the FEMA P-58 performance assessment procedure by incorporating a component-based function tree and a novel repair scheduling model informed by construction characteristics. In particular, the model introduces two key input parameters: the prefabrication ratio and the types of prefabricated components. These parameters govern the allocation of repair strategies and recovery sequences across stories. By integrating probabilistic damage simulation with a prefabrication-informed repair scheduling logic, the framework generates time-dependent functionality loss and recovery trajectories that reflect both structural and nonstructural repair processes. UnlikeFEMA P-58 and REDi, the proposed framework dynamically adjusts repair sequences based on building-specific construction configurations, enabling a more accurate downtime estimation for a broad range of structural systems. While the case study in this paper focuses on PCaPC frame structures to demonstrate the framework's application, the proposed model is not limited to this typology. Its scheduling logic and modularity considerations are generalizable to both conventional cast-in situ and hybrid construction systems. Table 1 provides a comparative summary of key features of FEMA P-58, REDi, and the proposed framework, with emphasis on the incorporation of prefabrication effects in repair planning. The remainder of this paper is organized as follows. Section 2 describes the proposed evaluation framework, including its hierarchical modeling of building functions and repair sequences. Section 3 applies the framework to three PC frame buildings with varying levels  
of prefabrication and presents recovery time results under different hazard levels. Section 4 discusses key findings, including the influence of prefabrication on recovery trajectories, and highlights future research needs.
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 4 Information Data of Indirect Online Measurement on Tool Wear
### Multi-Information Fusion  
Multi-information fusion is a new solution, which can deal with complex practical problems. In industrial applications, this information is accomplished by more than one sensor [102]. Using multiple sensors can compare the information obtained from different resources, and it is easier to determine the status of tools and workpieces [20]. [PERSON] et al. [26] studied the fusion of cutting force and acoustic emission feature. [PERSON] et al. [27] studied the fusion of cutting force, spindle current, spindle vibration and machining sound feature. [PERSON] et al. [103] studied the fusion of acoustic emission and cutting force features. [PERSON] et al. [104] studied the fusion of cutting force, vibration, acoustic emission and the  
Figure 4: An installation diagram for power sensor (drawn from the literature [100]).  
spindle power feature. [PERSON] et al. [105] studied the fusion of acoustic emission and sound features. [PERSON] et al. [106] studied the fusion of cutting force, vibration and acoustic emission features. [PERSON] et al. [107] studied the feature fusion of acoustic emission and cutting power. According to the results of these studies, these multi-information fusion technologies achieved good results in tool wear monitoring.  
The installation diagram of the multi-information fusion of sensors is shown in Figure 5.  
Scholars conducted a lot of research on the relevant information data of tool wear. Table 1 lists the references, sensors and application environments corresponding to each type of data information in this paper.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Data** & **References** & \
\begin{tabular}{c} **Main Sensors or** \\ **Tools Used** \\ \end{tabular} &
\begin{tabular}{c} **Main Application Environment** \\ **and Characteristics** \\ \end{tabular} \\ \hline \multirow{3}{*}{cutting force} & [38; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52] & three-phase dynamometer, dynamometer, strain force sensor, piezoelectric force sensor & applied to turning, milling, drilling, etc., real-time detection, easy to use, widely used, not suitable for processing large workpieces, interference control \\ \hline \multirow{3}{*}{ vibration} & [53; 54; 55; 56; 57; 58; 59; 60; 61; 62] & acceleration meter, laser vibration meter, vibration sensor & applied to turning, milling, drilling, etc., real-time detection, sensitive, limited use, easy to lose data \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Research literature and research situation of data information related to tool wear.  
Figure 5: An installation diagram of multi-information fusion of sensors (drawn from the literature [106]).
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 3 Proposed AVSR Method Based on DCM Attention
### Seq2 seq Transformer
#### 3.2.4 Bi-Modal Self-Attention Decoder  
We use an architecture similar to the seq2 seq transformer (TM-seq2 seq) model by [PERSON] et al. [20]. We use six decoder blocks. Each block has one multi-head self attention and two multi-head encoder-decoder attentions. Each encoder-decoder attention uses previous decoder outputs after the self attention as queries and DCM attention outputs as keys and values. Then, the two encoder-decoder attention outputs (\(\mathbf{AV}_{c}\), \(\mathbf{VA}_{c}\)), as shown in Figure 4, are concatenated channel-wise and fed to fusion layers for calculating attentional vector \(\tilde{s}_{i}\) at decoder time step \(i\), which is expressed as  
\[\tilde{s}_{i}=\text{LayerNorm}(\text{FusionLayer}([\mathbf{AV}_{c},\mathbf{ VA}_{c}])+[\mathbf{AV}_{c},\mathbf{VA}_{c}]), \tag{17}\]  
Figure 3: DCM attention architecture. Either audio or video encoder output as a query \(Q\) is fused with the other modality as a key \(K\) and a value \(V\) in a multi-head attention. AV and VA denote the audio query encoder and video query encoder outputs, respectively.  
where FusionLayer denotes modality fusion layers composed of two fully connected layers, rectified linear units, and dropout as shown in Figure 4. Finally, like (8), the predictive character distribution is obtained from calculating the attentional vector \(\tilde{s}_{i}\). Detailed hyper-parameters in the multi-head attentions and the feed-forward layers are identical to those in the encoder.
# Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network
## 3 Methodology
### Integrated BiLSTM Model  
We proposed a novel method to predict the hourly ST, which includes two sub-models: The daily average ST prediction model and the ST amplitude prediction model. The hourly ST estimation is the summation of the daily average ST prediction results and ST amplitude prediction results, because in the Section 2, we have defined that ST amplitude was obtained by subtracting the daily average ST from the hourly ST. A flow chart of the model is shown in Figure 5, and the input parameters of the two submodels are given in Table 2.  
Figure 4: Structures of bidirectional long short-term memory network (BiLSTM) (**a**) and LSTM (**b**).  
Figure 5: Flowchart of integrated BiLSTM ST prediction model.  
The topology of each sub-model is set to six layers, namely, an input layer with 12 features and 1 timestep; 4 hidden layers, in which the numbers of neurons of each layer are 14, 14, 14, and 6, respectively; and an output layer with 1 neuron, the loss function of which is the MSE. In addition, an adaptive moment estimation algorithm (Adam) [40] is selected as the optimizer.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 3 Modeling of UWA Channel Problems and Challenges
### Propagation Loss Modeling  
The factors contributing to the loss of propagation include absorption and attenuation, an irregularity of propagation, and geometric spread. Since there are so many vector factors affecting propagation, the first part is very difficult to model. Attenuation by absorption happens when acoustic energy is transformed into heat in seawater. The important elements of absorption attenuation include the frequency of transmission, ecological conditions, and wavelength. The fact that frequency is directly related to energy is confirmed by the consumption of more energy at higher frequencies. The attenuation for the range of transmission (dB) associated with a frequency \(f\) is calculated using the function given below. The attenuation \(A\) in (dB) occurring for a frequency \(f\), and over a range of transmission \(l\) is obtained using the mathematical equation in [30; 153]:  
\[10 logA(1,f)=k.10 logl=l\alpha \tag{10}\]  
Figure 14: Temperature and depth-dependent speed variation.  
where, \(\alpha\) is the coefficient of absorption and is measured in dB/km and \(k\) defines the factor for geometric spread. A sound wave undergoes spread loss while propagating away from the source. Spread loss is the signal weakening resulting from the geometrical spread. The two types of spread include spherical (for long-distance communication) and cylindrical (for short-distance communications). The following formula can be used to measure the spreading loss [154]:  
\[PL_{spread}=klogl, \tag{11}\]  
where, \(PL_{spread}\) is the propagation loss caused to spreading given in decibels (dB) and \(l\) defines the range of transmission measured in meters. For cylindrical spreading, the geometric spreading factor is set to one while for spherical spreading it is set to two. In practice, '1.5' is employed when the two forms of spreading are predictable and inevitable.
## 3 Results
### General Characteristics of Predictive Maintenance Studies  
A text-mining approach was employed to extract and analyze the most frequently occurring terms from the reviewed studies. The dataset included titles, abstracts, and keywords from the total research papers retrieved, using the Scopus export tool. To ensure consistency and enhance data quality, a systematic preprocessing procedure was applied using Python, including the removal of non-alphabetic characters, conversion of text to lowercase, and filtering of common words, incorporating both standard and domain-specific terms. Additionally, duplicates and redundant terms were removed. A word frequency analysis was then performed to identify dominant terms. Following a similar approach to [15], a visual representation of term frequency was generated using Python's word cloud library, where the size of each word was proportional to its relative frequency within the dataset.  
As shown in Figure 3, the most prominent terms highlight the strong focus on PdM and ML applications. Other key terms, such as monitoring, decision, and condition, emphasize data-driven maintenance strategies, while detection, classification, and anomaly suggest discussions on fault detection and predictive analytics. Additionally, terms like industrial, things, and internet reflect the integration of Internet of Things (IoT) technologies. Meanwhile, algorithms and forecasting point to model development considerations.
## 5 Power Optimization  
### Power Consumption Optimization Test Method  
The power consumption of a rotary tillage operation is influenced by many factors, such as motion parameters, the structure of the rotary tillage knife, cutting angle, cutting depth, soil properties, and so on. In order to achieve controllable simulation variables, this paper mainly explores the influence that roll motion parameters and rotary blade structure parameters have on the power consumption of two types of tools. In order to optimize the power consumption of the rotary blade/blade roller, there are three specific factors that influence the parameters. The forward speed (X\({}_{1}\)) and rotation speed (X\({}_{2}\)) are selected as the factors for the motion parameters, the bending angle is selected as the key structural design parameter affecting the cutting performance of the rotary blade [24], and the bending angle (X\({}_{3}\)) is selected as the structural parameter optimization factor.  
In this paper, the three-factor and three-level orthogonal test was used to conduct a simulation test with the cutting power consumption optimization index. The coding level of the test factors is shown in Table 3, and the simulation test results are shown in Table 4.  
Figure 10: Cutting resistance curve.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Factors**} \\ \cline{2-4}
**Coding** & \multicolumn{2}{c}{**Motion Parameters**} & \multicolumn{2}{c}{**Structural Parameters**} \\ \cline{2-4}  & \multicolumn{2}{c}{**X\({}_{1}\)**} & \multicolumn{2}{c}{**X\({}_{2}\)**} & \multicolumn{2}{c}{**X\({}_{3}\)**} \\  & \multicolumn{2}{c}{**Forward Speed m/h**} & \multicolumn{2}{c}{**Rotation Speed r/min**} & \multicolumn{2}{c}{**Bending Angle\({}^{c}\)**} \\ \hline \(1\) & \(1400\) & \(140\) & \(110\) \\ \(0\) & \(1100\) & \(120\) & \(120\) \\ \(-1\) & \(800\) & \(100\) & \(130\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation test factor levels.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 4 Software Design
The interface allows real-time visualization of the spectral features. The API provides access to the local long-term storage data and to modify the device configuration. The configuration allows flexibility per device to change the rates at which data are recorded locally and uploaded, the satellite constellations being monitored, the local ML model used for detection, and the spectral processing configuration such as the center frequency, the frequency resolution, and the sample rate. Figure 8 shows a screenshot of the server front end with real-time interference visualization. In addition to the main modules, system utilization is recorded for device health monitoring and automatic restarts in case of failures. An example system utilization with all processing enabled is shown in Figure 9. On average, the internal system health monitoring measured a CPU utilization of 31.8% and 645 MB of random access memory (RAM). It indicates that the Raspberry Pi is can handle the whole system without issue. The GNSS pHat is used for accurate timing of the system. It allows millisecond precision synchronization among multiple device recordings. Each sensor nodes synchronizes to the sensor network using the GNSS time from the NMEA messages. It is achieved with the with the 1-pulse per second (PPS) signal form the GNSS pHat over the GPIO of the SBC. Lastly, a significant improvement in the software is to employ the 64-bit instruction set, which was released in Q1 2022 [97]. A noticeable improvement in processing efficiency for the SDR processing in GNU Radio was observed. It led to using a finer 64-channel FFT for the spectral processing instead of the older 32-channel FFT. Figure 8: Screenshot of the web server front end, with interference visualizations of the spectral power and spectral kurtosis. The real-time measurements are done in a controlled laboratory.
## 3 Results and Discussion
### Analysis and Discussion  
#### 3.2.1 Anova  
According to the test results in Table 6, the best regression model was proposed, and variance analysis was performed. The optimal model was chosen according to R-squared, Adjusted R-squared (Adj: R-squared), Adeq. precision, [PERSON]'s test value (F-value) and the probability \"Prob \(>\) F-value\" (\(p\)-value) [21]. Analysis of variance (ANOVA) can be used to evaluate the relationship between design and response variables, and to evaluate whether the model is significant. Design-Expert 8.0 software showed that the secondary models are all significant for the response variables SP, FS and CS. The analysis results of the model and variance are showed in Tables 7 and 8, respectively.
## 3 Methodology
### GPU-Based PBD Cloth Framework (Reality Collision Version)
#### 3.2.4 RoomMesh-Based Collision Handling Algorithm  
To enable realistic physical interactions with real-world structures, the proposed system utilizes RoomMesh data captured by the Meta Quest platform. RoomMesh represents surrounding environments, such as desks, walls, and chairs, using a detailed triangulated mesh.  
However, directly performing collision detection against the entire RoomMesh, which typically contains thousands of vertices and triangles, is computationally expensive and impractical for real-time simulation. To address this issue, the system dynamically extracts a SubMesh based on user-defined regions of interest (ROIs).  
The user interaction flow is as follows:  
* Press the **A button** to visualize the complete RoomMesh.
* Use the **right-hand trigger button** to select six points, defining the region of interest.
* Construct an Axis-Aligned Bounding Box (AABB) from the selected points.
* Extract only the vertices and triangles of the RoomMesh that intersect the AABB to form the SubMesh.  
By restricting collision calculations to the SubMesh:  
* Memory usage is significantly reduced.
* Computational load for collision detection is minimized.
* Real-time responsiveness of cloth-environment interaction is enhanced.  
The process of SubMesh extraction and filtering is summarized in Algorithm 2.  
```
1:RoomMesh vertices \(V\), triangles \(T\), selected points \(\{P_{1},P_{2},\ldots,P_{6}\}\)
2:SubMesh vertices \(V^{\prime}\), triangles \(T^{\prime}\)
3:Compute \(min\_point\) and \(max\_point\) from the selected points
4:Construct an Axis-Aligned Bounding Box (AABB) using \(min\_point\) and \(max\_point\)
5:Initialize empty lists \(new\_vertices\) and \(new\_triangles\)
6:Initialize empty mapping \(vertex\_mapping\)
7:for each triangle \((i_{0},i_{1},i_{2})\in T\)do
8: Transform \(i_{0}\), \(i_{1},i_{2}\) to world coordinates
9:if any vertex \(wv_{0}\), \(wv_{1}\), or \(wv_{2}\) is inside AABB then
10:for each \(idx\in\{i_{0},i_{1},i_{2}\}\)do
11:if\(idx\) not in \(vertex\_mapping\)then
12: Add \(wv\_idx\) to \(new\_vertices\)
13: Map \(idx\) to new index
14:endif
15:endfor
16: Add new triangle using mapped indices
17:endif
18:endfor
19:Create SubMesh from \(new\_vertices\) and \(new\_triangles\)
20:Instantiate SubMesh as a GameObject with necessary components
```  
**Algorithm 2** SubMesh extraction based on user-defined selection box