esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix B OHS RAG Questionnaire Original Socratic Dialogue  
Human: I have a questionnaire from [PERSON] about resilience abilities. Could you help adapt it for Occupational Health and Safety (OHS) in the manufacturing industry?  
LLM: Sure! We can contextualize the questions across four resilience abilities--Respond, Monitor, Learn, and Anticipate--specific to OHS in manufacturing. This will make it more applicable to that environment.  
Human: Great! Let's start with the ability to respond. Could you adapt the questions for this ability and suggest Likert-scale responses?  
LLM: Here's the adapted set of questions for \"Ability to Respond\" in the manufacturing context, with a 5-point Likert scale. Each question assesses an organization's readiness in OHS event response, from event lists to stop criteria.  
Human: Perfect! Let's proceed to the ability to monitor and create the Likert scales for that as well.  
LLM: Here's the contextualized set of questions and Likert responses for \"Ability to Monitor,\" assessing how effectively an organization monitors OHS indicators, measurement frequency, and analysis.  
Human: Excellent. Now, let's move on to the ability to learn with corresponding Likert scales.  
LLM: Here's the tailored list for \"Ability to Learn,\" with questions on data collection, incident classification, formalization, and implementation of lessons learned.  
Human: This looks good. Now let's complete the questionnaire with the ability to anticipate questions and the Likert scale options.  
LLM: Here's the adapted set of questions and responses for \"Ability to Anticipate,\" focusing on expertise, risk modeling, communication, and risk culture.  
Human: This is exactly what I needed! Now, I'd like to generate some synthetic data based on a Resilience Analysis Grid (RAG) assessment using the Likert scale. Could you provide an example?  
LLM: Certainly. Here's synthetic data based on the RAG scoring system. Each question has a corresponding Likert score to simulate an organization's OHS assessment.  
Human: Perfect. Now let's restructure the steps we've followed as a general LLM-based process.  
LLM: Here's the structured process:  
Understanding the Task--Analyze and contextualize the initial framework.  
Contextualization of Framework--Adapt questions for OHS in manufacturing.  
Creation of Measurement Tools--Develop Likert scales for assessment.  
Synthetic Data Generation--Generate data using RAG scoring.  
Feedback Iteration and Refinement--Incorporate user feedback to improve the process.
# Turbo Decoder Design based on an LUT-Normalized Log-MAP Algorithm
## 2 Turbo Decoding Principles and Log-MAP Algorithm
### Log-MAP Algorithm  
A priori information is computed by Equation (1), where the initial value of priori information is set to zero for the first iteration [8].  
\[\lambda_{a}(u_{k})=\log\frac{P(u_{k}=1)}{P(u_{k}=0)} \tag{1}\]  
In SISO, branch metric \(\gamma_{k}\) and extrinsic branch metric \(\gamma_{k}^{\epsilon}\) are computed by Equations (2) and (3), where \(n\) is the number of bits at the output of the component convolutional encoder of the turbo code, and \(i\) denotes the \(i\)-th bit of codeword [8].  
\[\gamma_{k}=u_{k}\cdot(\lambda_{c}(u_{k})+\lambda_{a}(u_{k}))+\sum_{i=1}^{n}p_{ k}^{(i)}\cdot\lambda_{c}(p_{k}^{(i)}) \tag{2}\]  
\[\gamma_{k}^{\epsilon}=\sum_{i=1}^{n}p_{k}^{(i)}\cdot\lambda_{c}(p_{k}^{(i)}) \tag{3}\]  
Then, we use Equations (4) and (5) to compute the forward and backward metrics recursively, \(s_{k}\) and \(s_{k-1}\) being the two states on opposite sides of the branch metric.  
\[\alpha_{k}(s_{k})=\max_{s_{k-1}}\bigl{[}\alpha_{k-1}(s_{k-1})+\gamma_{k}(s_{k- 1},s_{k})\bigr{]} \tag{4}\]  
\[\beta_{k}(s_{k})=\max_{s_{k+1}}\bigl{[}\beta_{k+1}(s_{k+1})+\gamma_{k}(s_{k},s _{k+1})\bigr{]} \tag{5}\]  
Finally, external information and posteriori information are computed by Equations (6) and (7), and generated posteriori information is taken as the prior information for another SISO decoder in the next iteration.  
\[\lambda_{e}(u_{k})=\max_{u_{k}=1}^{\star}\bigl{(}\alpha_{k-1}(s_{k-1})+\gamma_ {k}(s_{k-1},s_{k})+\beta_{k}(s_{k})\bigr{)}-\max_{u_{k}=0}^{\star}\bigl{(} \alpha_{k-1}(s_{k-1})+\gamma_{k}(s_{k-1},s_{k})+\beta_{k}(s_{k})\bigr{)} \tag{6}\]  
\[\lambda_{o}(u_{k})=\lambda_{e}(u_{k})-\lambda_{\mathrm{a}}(u_{k})-\lambda_{c} (u_{k}) \tag{7}\]  
The function \(\max^{\star}\) in Equations (4)-(6) is defined as  
\[\max^{\star}(\mathrm{a},\mathrm{b})=\max(\mathrm{a},\mathrm{b})+\log(1+e^{-| \mathrm{a}-\mathrm{b}|}) \tag{8}\]  
The variable _Lach_ is defined as Equation (9).  
\[\textit{Lach}=\lambda_{\mathrm{a}}(u_{k})+\lambda_{c}(u_{k}) \tag{9}\]
## 2 Methodologies
### Convolution Neural Network  
The architectures for the convolution neural network come in many different flavors [15]. However, the fundamental architecture must contain convolution layers with activation functions, pooling layers, and fully connected layer(s) for combining features before classification, as shown in Figure 5. The convolution layers extracted the feature maps, then the pooling layers concentrate the feature maps using various functions, such as maximizing or averaging values within a given window in order to reduce the processing complexity for the following layers. These layers are followed by a fully connected layer(s) before the classification. The proof of the effectiveness of CNN in feature extraction has been discussed in the literatures [16], so it will not be repeated in this paper. During the training phase, each of the labeled images is provided as input, and the CNN is expected to output the correct label. The entire training phase can take a long time, depending on the value set for number of epochs or the convergence threshold.  
Each convolution layer consists of a weight matrix \(w\) and \(a\) bias \(b\): the values of \(w\) and \(b\) would be initialized and updated during the training of the layer. The output of each convolution layer would be [17]:  
\[\mathrm{x}_{i}^{l}=\sigma(\sum_{i\in FM_{j}}w_{i}x_{j}^{l-1}\times k_{ij}^{l}+b _{j}^{l}) \tag{1}\]  
Figure 4: The system flowchart.  
Figure 5: The layers of a convolution neural network.  
where \(l\) is the layer, \(FM\) is the feature map, \(k\) is the convolution kernel, \(b\) is the bias, and \(\sigma(.)\) is the activation function for each connection from \(i\) to \(j\). The activation function is, generally, the ReLU function [17], where \(ReLU(x)=\max\left(0,x\right)\), and  
\[ReLU(x)=\max\left(0,\ x\right)\text{, and} \tag{2}\] \[\tfrac{d}{dx}ReLU(x)=\left\{\begin{array}{l}1,\ if\ x>0\\ 0,\ otherwise\end{array}\right.\]  
After experimentations probing for a good design, the following CNN network design is proposed for this investigation. In this design, the input layer accepts \(256\times 256\) images, and the middle layers contains five convolution layers with ReLU activation functions. Four of the convolution layers are followed by max pooling layers with \(2\times 2\) windows, and the last convolution layer with ReLU activation function is followed by one average pooling layer with a \(2\times 2\) window. The averaging pooling layer, which would then be the last pooling layer before the fully connected layer, is followed by a softmax function [17] used to map the output for classification. This proposed design for the CNN network is chosen based on a good tradeoff between the amount of training time and accuracy for the \(256\times 256\) input images.
## 2 Related Work
### Lstm  
LSTM, an enhanced variant of RNN, effectively mitigates the vanishing gradient problem by adaptively regulating the input and output values of its gates, thereby controlling the retention or forgetting of information within the cell state [27]. This capability enables LSTM to more effectively capture and leverage temporal dependencies in time series data. The LSTM architecture consists of three primary components: the forget gate, the input gate, and the output gate, with the information flow between these components governed by the following equations:  
Figure 1: A simple Autoencoder architecture.  
\[f_{t}=\sigma(W_{fx}x_{t}+W_{fh}h_{t-1}+b_{f}) \tag{4}\]  
\[\tilde{c}_{t}=\tanh(W_{cx}x_{t}+W_{ch}h_{t-1}+b_{c}) \tag{5}\]  
\[i_{t}=\sigma(W_{ix}x_{t}+W_{ih}h_{t-1}+b_{i}) \tag{6}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\tilde{c}_{t} \tag{7}\]  
\[o_{t}=\sigma(W_{ox}x_{t}+W_{oh}h_{t-1}+b_{o}) \tag{8}\]  
\[h_{t}=o_{t}\odot\tanh(c_{t}) \tag{9}\]  
where \(f_{t}\), \(i_{t}\), and \(o_{t}\) represent the outputs of the forget gate, input gate, and output gate, respectively. These outputs are \(n\)-dimensional vectors, where \(n\) corresponds to the number of hidden units in the LSTM's hidden state. The sigmoid activation function constrains each element of the output vector to the range [0,1], thereby facilitating the regulation of information flow. The hyperbolic tangent activation function maps each element of the output vector to the range [\(-\)1,1], enabling the representation of both positive and negative values. This characteristic facilitates the modeling of more complex relationships and ensures a balanced gradient flow during training. \(W_{fx}\), \(W_{cx}\), \(W_{ix}\), and \(W_{ox}\) represent the weight matrices between the input at the current time step \(x_{t}\) and the forget gate, input gate, and output gate, respectively. Each of these weight matrices has dimensions \(n\times m\), where \(m\) is the dimensionality of the input vector. \(W_{fh}\), \(W_{dh}\), \(W_{ih}\), and \(W_{oh}\) represent the weight matrices between the hidden state at the previous time step \(h_{t-1}\) and the forget gate, cell gate, input gate, and output gate, respectively. Each of these weight matrices has dimensions \(n\times n\). \(b_{f}\), \(b_{c}\), \(b_{i}\), and \(b_{o}\) are the \(n\)-dimensional bias vectors for the forget gate, input gate, and output gate, respectively.
## 3 Results and Discussions
### Quantified Results of Relative Optimal Model  
Based on the above discussion results, the AP of the optimal model M-R101-FPN was 92.1%, and the pavement surface defect was quantified by this model. Figure 15 shows four groups of testing images; Figure 15a shows the testing image results. Through modifying the testing code so that the model only outputs pixel segmented images, the pixel images were clustered and processed binarily to obtain Figure 15b; and then the pixel area of the defects in Figure 15b was extracted to compare with that label, and the results are shown in Figure 15c.  
Figure 13: Testing result of M-R50-C4: (**a**) original image, (**b**) LR = 0.01, (**c**) LR = 0.001, (**d**) LR = 0.0001.  
From the detection results, it can be seen that in the N_crack detection of Group 1, the predicted pixel area of the defect was 11,636, while the actual labeled area was 12,377, and the error rate was 5.9%; the error rate of the pixel area predicted by the three P_grooves in Group 4 was 10.6%, 5.2%, and 4%, respectively. Group 2 showed two P_cracks, and Group 3 showed two H_cracks; their error rate ranges from 10% to 16%, but this error rate was relatively low compared with that of the pixel area of the defect to the total image area. The actual labeled area of the P_cracks in Group 2 was 0.513% of the total image area, while the prediction result accounted for 0.586% of the total area, with an error rate of 0.073%. Overall, the relative quantification results of the model were excellent.  
Figure 14: Testing result of M-R101-FPN: (**a**) original image, (**b**) LR = 0.01, (**c**) LR = 0.001, (**d**) LR = 0.0001.  
Figure 15: Testing result: (**a**) M-R101-FPN, (**b**) clustering and mask banalizations, (**c**) quantitative results. Notes: [PERSON], [PERSON], [PERSON], Prop: Proportion of defect pixel area in the pixel area of the whole image.
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
###### Abstract  
Ultraviolet and infrared sensors at high quantum efficiency on-board a small satellite (UVSQ-SAT) is a CubeSat dedicated to the observation of the Earth and the Sun. This satellite has been in orbit since January 2021. It measures the Earth's outgoing shortwave and longwave radiations. The satellite does not have an active pointing system. To improve the accuracy of the Earth's radiative measurements and to resolve spatio-temporal fluctuations as much as possible, it is necessary to have a good knowledge of the attitude of the UVSQ-SAT CubeSat. The attitude determination of small satellites remains a challenge, and UVSQ-SAT represents a real and unique example to date for testing and validating different methods to improve the in-orbit attitude determination of a CubeSat. This paper presents the flight results of the UVSQ-SAT's attitude determination. The Tri-Axial Attitude Determination (TRIAD) method was used, which represents one of the simplest solutions to the spacecraft attitude determination problem. Another method based on the Multiplicative Extended Kalman Filter (MEKF) was used to improve the results obtained with the TRIAD method. In sunlight, the CubeSat attitude is determined at an accuracy better than \(3^{\circ}\) (at one \(\sigma\)) for both methods. During eclipses, the accuracy of the TRIAD method is \(14^{\circ}\), while it reaches \(10^{\circ}\) (at one \(\sigma\)) for the recursive MEKF method. Many future satellites could benefit from these studies in order to validate methods and configurations before launch.  
CubeSat attitude determination; TRIAD; Kalman filter; climate +
Footnote †: journal: Sensors  
0000-0002-0001-7361-0000][PERSON]  
0000-0002-0002-7361-0000][PERSON]  
0000-0002-0002-7361-0000][PERSON]  
0000-0002-0002-7361-0000][PERSON]  
0000-0002-0002-7361-0000][PERSON]  
1 Universite de Versailles Saint-Quentin-en-Viclines, Universite Paris-Saclay, Sorbonne Universite (SU), CNRS, LATMOS, 11 Boulevard d'Alembert, 78280 Guyancourt, France; [PERSON]] (A.F.); [PERSON]C.D.); [PERSON](T.B.); [PERSON](A.S.); [PERSON](P.K.)  
2 ACRI-ST CERGA, 10 Avenue Nicolas Copernica, 06130 Grasse, France; [PERSON]]  
3
# Practical Submodule Capacitor Sizing for Modular Multilevel Converter Considering Grid Faults
## 4 Design Consideration of SM Capacitors
### SM Ripple Voltage Requirement  
To ensure the stability and efficiency of the MMC, the SM voltage ripple must be kept in a safe range during steady-state operation. Defining the allowable peak-to-peak voltage ripple as \(V_{rip}\), the following condition should be satisfied:  
\[V_{smax}-V_{smin}\leq V_{rip} \tag{34}\]Solving Equation (34) yields the minimum capacitance requirement as  
\[C_{sm\_ripj}\geq\frac{V_{dc}I_{j}(\mathbf{F_{max}-F_{min}})}{8N\omega_{1}V_{rip}^ {2}} \tag{35}\]
## 3 Results and Discussion
### SEM and EDS
Therefore, we must assume that the rapid deposition of amorphous nuclei from the solution inhibits the growth of a brucite corrosion layer, while hydroxyl groups are embedded in disordered hydroxcarbonate hydrated phases that are immersed in the carbonated solution and gradually transform into calcium carbonate by dehydroxylation and dehydration reactions. Longer immersion times result in the observation by XRD of aragonite, another allotropic form of calcium carbonate. The study by SEM of samples immersed for one week revealed the formation of distinct morphologies other than the ordered calcite layer. Figure 7c,d shows SEM micrographs of the semi-spherical aragonite colonies observed in a sample immersed for 1 week. These colonies have sizes in the tens to fifty microns (Figure 7c) and are composed of distinctive sharp-tipped needle-like crystals (Figure 7d) described previously as typical of aragonite [41]. The non-faceted, non-plate morphology shows no preferred orientation, forming non-compact depositions protruding from the compact calcite layer.
## 3 Results and Discussions
### Integrated Use of Optical and SAR Images for Land Cover Classification  
The multispectral remote-sensing images, Gaofen-1/2 and Sentinel-2A/2B, for instance, and SAR images (e.g., GF-3, Sentinel-1A/B, etc.) are widely applied remote-sensing data sources in the classification of land cover and land use [36], especially for residence area identification [37]. The spectral indexes from optical remote-sensing images such as NDVI, NDWI and EVI can be served to discriminate between vegetated land and bare soil. The radar features of SAR remote-sensing images, on the other hand, are suitable to apply in the detection of buildings or residential regions because of the double-bounce effect in SAR images (see the bright spots in Figure 8). In addition, the optical images can be used in the classification of different types of rooftops on the basis of the difference of reflectance between the metal and the cement rooftop, according to the NDMRI index, which is proposed in this investigation. This is helpful when automatic workflow is applied to distinguish the inhabitance in rural districts from the commercial regional center. This scheme is extremely useful in the assessment of the sustainable development level among the regional cities in Western China. It can be concluded that the integrated use of indexes from optical and SAR images would enhance the robustness of classification for land cover and land use when a high-resolution remote-sensing application is concerned.
## 4 Experimental Results
### Considerations on Prediction Patterns as Maps  
What resembles a map, such as the display of a prediction pattern, is not necessarily a map but more a representation of information extraction through normalizations, conversions, assumptions, and integrations. At present, with what we know about a study area, we have to be satisfied by the higher part of the prediction-rate curve and by the corresponding combination pattern, perhaps fine-tuning the process. Otherwise, additional information must be used to choose a satisfactory part of the prediction pattern. Regarding the prediction patterns, would we want to discover instances of unmapped landslide trigger zones in the higher 10% ranked areas away from the known ones? Is the 50% combination pattern helpful in mapping more of what we know or that we do not know? Should we wait for the next seven landslides to see where they will appear? Where should we concentrate on more detailed mapping? How much of the study area should we consider unfit for particular land uses or developments? Would we opt for using cost/benefit or using safety criteria? Providing answers for decision making and subsequent risk analysis is the logical function of the prediction patterns of landslide susceptibility, as we have discussed.
## 2 Materials and Methods
### Forest Fires in South Korea and the Study Area  
Based on data from 2010, approximately 63% of the land area in South Korea was forest [42]. 59% of forest fires occurred in spring (March-May), and the area damaged by forest fires during spring accounted for approximately 83% of the total area burned that year. Gangwon Province in South Korea often has a dry spring because the air crossing a mountain often changes into dry conditions according to the Fohn phenomenon. Thus, many forest fires have occurred in this area. On 7-15 April 2000, the worst forest fire in South Korean history occurred in this area (the East Coast fire). The forest fire occurred in seven different places and the damaged area covered approximately 23,448 ha. Strong wind speeds (maximum 26.8 m/s) and low relative humidity (minimum 7%) accelerated the spread of the forest fire during this period [15]. After the East Coast fire, the Korea Forest Service (KFS) conducted forest management activities, such as tree planting and artificial regeneration, to aid forest recovery until 2005 [43].  
To monitor the post-fire state of the forest, the three severely burned areas (12,697 ha; 4054 ha; and 2244 ha) were investigated. The biggest forest fire happened near the coastline, and the smallest forest fire was in the upper area of our study (Figure 2). The burned areas consisted mainly of forests, but croplands and cities on the coastline were also included. The study area was set from 36.99\({}^{\circ}\)N to 37.6\({}^{\circ}\)N, and 128.75\({}^{\circ}\)E to 129.50\({}^{\circ}\)E (Figure 1a,b). Meteorological information in this study is as follows. The average annual air temperature is 12.6 \({}^{\circ}\)C, and annual cumulative precipitation is 1278.9 mm, based on 2016 data collected by a meteorological station located in Donghae city. The maximum air temperature is 37.1 \({}^{\circ}\)C and the minimum air temperature is \(-\)14.0 \({}^{\circ}\)C, since May 1992. It rains heavily from July to September.  
Before the forest fire, the forest was dominated by pine (_Pinus densiflora_) [43; 44], which covered approximately 69.5% of the study area. Pine-hardwood and hardwood covered approximately 27.6% and 3% of the study area, respectively [1]. Further, before the forest fire, 20-30 and 30-40 year old trees were spread across 54.44% and 22.62% of the study area, respectively [1].  
Figure 2: Forest fire areas detected using dNBR of Terra/MODIS. Yellow-Orange-Red pixels indicate forest fire severity divided into three groups using MODIS-based dNBR. Green pixels indicate reference area. Non-forest areas, indicated by grey pixels, were eliminated.
# Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network
## 4 Case Studies  
The DSWNN model proposed in this paper is a novel complex neural network with small-world characteristics, which was trained using an unsupervised learning technique. In order to test the performance of the proposed DSWNN model, the following case analyses related to fault identification, prediction and classification are given in this section. The experimental data were one year's SCADA data, which were collected from the SCADA system of thirty 2-MW wind turbines in a wind farm. Additionally, to increase the contrast, the classical deep belief network (DBN) model and deep neural network (DNN) model were used as the comparison methods.  
Pitch failures are mainly categorized by the pitch sensor and actuator. The pitch sensor fault occurs from dust on the encoder disc, mis-adjustment of the blade pitch bearing, temperature beyond the acceptable range and humidity or improper calibration. These causes can result in the unbalanced rotation of the rotor from the sensor bias and fixed outputs from the last measurements. These faults for the blade pitch sensor and actuator frequently appear and result in structural loading of the turbine due to rotor imbalance and affect the stability of the floating platform. These failures are mainly reactions to the signals of pitch angle, pitch torque, pitch motor, and so on. The specific monitoring SCADA parameters will be used to train the DSWNN, DBN and DNN models, and the specific parameter information is listed in Table 1. In the following case analyses, the DSWNN, DBN, and DNN were all chosen to have the same network structure and the training data.