esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper focuses on the design of a new optical cone and pendulum scanning imaging mode for micro-nanosatellites. This kind of satellite uses a high-resolution camera with a small imaging plane to achieve high-resolution and ultra-wide coverage imaging through the three-dimensional motion of the camera's wobble, satellite spin, and satellite orbital motion. First, this paper designs a single-camera constant speed OCPSI (optical cone and pendulum scanning imaging) mode. On the premise of ensuring coverage, the motion parameters and imaging parameters are derived. Then, in order to improve the performance and imaging quality of the system, a dual-camera variable speed OCPSI mode is designed. In this method, in order to reduce the overlap ratio, the camera is oscillated at a variable speed. Turn on the cameras in turn at the same time to minimize the overlap. This paper details these working modes. The simulation experiment is carried out using the satellite orbit of 500 km, the focal length of 360 mm, the pixel size of 2.5 \(\upmu\)m, the resolution of [5120 \(\times\) 5120], the number of imaging frames in the pendulum scanning hoop of 10, and the initial camera inclination angle of 30\({}^{\circ}\). The single-camera constant speed OCPSI mode has an effective swath of 1060 km at a ground sampling distance of 5.3 m. The dual-camera variable speed OCPSI mode has an effective width of 966 km under the same conditions. Finally, the ground experiment prototype of OCPSI imaging theory is designed. We choose a camera with a pixel size of 3.45 \(\upmu\)m, a resolution of [1440 \(\times\) 1080], and a focal length of 25 mm. The ground experiment was carried out at the initial camera inclination angle of 10\({}^{\circ}\), the number of imaging frames in the pendulum scanning hoop of 3, and the orbit height of 11 m. The experimental result is that the effective width of OCPSI imaging mode reaches 10.8 m. Compared with the traditional push-broom mode using the same camera, the effective width of 1.64 m is increased by seven times, and the effective width of 3.83 m is increased by three times compared to the traditional whisk-broom imaging mode. This study innovatively integrates three-dimensional motion imaging into aerospace remote sensing and provides a reference for the research on the realization of high-resolution and ultra-wide coverage of micro-nano remote sensing satellites.  
remote sensing; high resolution; wide-swath imaging; optical imaging; cone and pendulum scanning; micro-nanosatellite 2022
## 3 Methods
### Preprocessing  
Data preprocessing was conducted to ensure the best and most relevant data for training the models, employing multiple techniques.  
The three extra EOG channels measure eye movements and are easily influenced by non-study-induced factors such as attention levels and blinks. Therefore, these channels were excluded from training to avoid introducing noise and confounding variables.  
During motor imagery, there is usually a change in neural oscillations indicating Event-Related Desynchronisation (ERD) and Event-Related Synchronisation (ERS), which are mostly observed in the alpha (8-13 Hz) and beta frequency bands (18-24 Hz) [28]. These provide good features for model training. Signal filtration was employed to increase the signal-to-noise ratio. The sharp transition band between the pass and stop bands, as well as the low ripple in the pass band, made the Chebyshev type 2 bandpass filter (Figure 3) the filter of choice. The effectiveness of this filter is primarily determined by the order of the filter \(N\) and, to a lesser extent, the stop-band attenuation requirement \(R_{s}\). The amplitude response is given as  
\[|H(f)|=\frac{1}{\sqrt{1+\varepsilon^{2}T_{n}^{2}(\frac{f_{s}}{f})}} \tag{1}\]  
where  
\(\varepsilon\) is parameter related to the ripples in the stop-band;  
\(T_{n}\) is the \(n\)th-order Chebyshev polynomial;  
\(f_{s}\) is the stop-band edge frequency; and  
\(f\) is the frequency variable.  
The ripple factor is related to the stop-band attenuation by  
\[\varepsilon=\sqrt{10^{0.1R_{s}}-1}. \tag{2}\]  
These two hyperparameters, \(N\) and \(R_{s}\), were included in the hyperparameter optimisation process.  
Figure 3: Transfer function of the Chebyshev filter (type 2) [29].  
In addition, z-score normalisation is applied to the signals to ensure that all channels are on the same scale, increasing comparability and handling variability in the dataset. The z-score normalisation is defined as  
\[z=\frac{x-\mu}{\sigma} \tag{3}\]  
where  
\begin{tabular}{l l} \(z\) & is the standardised value; \\ \(x\) & is the original value; \\ \(\mu\) & is the mean of the distribution; and \\ \(\sigma\) & is the standard deviation of the distribution \\ \end{tabular}  
Furthermore, due to the limited size of the training dataset, data augmentation was applied by segmenting and recombining signals in the time domain [30]. As illustrated in Figure 4, each training trial is divided into multiple non-overlapping segments. A large amount of new data are generated by randomly concatenating segments from different trials of the same class. This technique was specifically applied to the Conformer model. Further research is needed to analyse the impact of data augmentation across a broader range of models.
## 4 Analysis Results and Discussion
### Relative Slips of Friction and Fastener Springs
When the friction coefficient was 0.3 or less, the secondary slip occurred from the train operation, which may eventually accumulate if trains are operated repeatedly, thereby making it difficult to control the longitudinal displacement. The QHT on the railway bridge of this study was designed such that the post-installed anchors could resist the effects of train and temperature loads when the friction coefficient between track and bridge was 0.3 or higher. The shear resistance of the anchors was 240 kN according to the structural calculations. In a previous study [23], an analysis was performed on the track-bridge interaction of QHTs on which the post-installed anchors are installed. As intended in the design, the effect of the load dis not exceed the anchor resistance until the friction coefficient reached 0.3. Table 4 shows the maximum applying loads at the anchor block and the load to design resistance ratio depending on the frictional coefficient between the track and the bridge when anchors are installed. As examined in the analysis, the friction coefficient between the track slab and bridge deck should be maintained at at least 0.5 before anchors are installed for QHTs on a railway bridge. When the friction coefficient is expected to be less than 0.5, trains should be advised to operate slowly to minimize the traction and braking loads. It is not easy to maintain the friction coefficient of tracks installed at \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Frictional Coeff**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Temperature**} & \multicolumn{2}{c}{**Total**} \\ \cline{2-7}  & **Load [kN]** & **Load/Resistance** & **Load [kN]** & **Load/Resistance** & **Load [kN]** & **Load/Resistance** \\  & & **Ratio** & & **Ratio** & & **Ratio** \\ \hline
0.7 & 5.60 & 0.023 & 26.63 & 0.111 & 32.23 & 0.134 & O.K. \\
0.5 & 7.69 & 0.032 & 114.80 & 0.478 & 122.49 & 0.510 & O.K. \\
0.3 & 12.35 & 0.051 & 151.40 & 0.631 & 163.75 & 0.682 & O.K. \\
0.1 & 84.98 & 0.354 & 186.99 & 0.779 & 271.97 & 1.133 & N.G. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum applied load at anchors with respect to friction coefficient. Figure 14: Maximum relative longitudinal displacement between a QHT slab and bridge deck. sites; therefore, anchors should be installed immediately when quick-hardening concrete develops sufficient strength after constructing QHTs. If the bridge span arrangement, material properties, and loading conditions are different from those of the bridge structure considered in this research, the results should be analyzed accordingly using the method proposed in this paper. The resulting critical frictional coefficient required to avoid the slippage of track segments varies with these conditions.
# Arabidopsis Disulfide Reductase, Trx-h2, Functions as an RNA Chaperone under Cold Stress
## 3 Results
### Trx-h2 Overexpression Complements the Cold-Sensitive Phenotype of the E. coli BX04 Mutant  
Based on the results of the cold-shock treatment (Figure 3), we verified the ability of Trx-h2 to impart cold tolerance in the cold-sensitive _E. coli_ BX04 mutant cells that lack four _Csp_ genes (_CspA_, _CspB_, _CspE_, and _CspC_); proteins encoded by these genes are essential for bacterial growth at low temperature [28]. In this experiment, _E. coli_ BX04 cells were transformed with the _pINIII_ empty vector (control) or with _pINIII_ vector carrying _Trx-h2_ or _Trx-h3_, and incubated at 37 \({}^{\circ}\)C or 19 \({}^{\circ}\)C; Trx-h3 served as a control because it contains no extra N-terminal amino acids, unlike Trx-h2. No difference was observed in the growth of BX04 cells expressing _Trx-h2_, _Trx-h3_, or _pINIII_ empty vector at optimal growing conditions (37 \({}^{\circ}\)C) (Figure 4a). However, at low temperature (19 \({}^{\circ}\)C), the survival rate of _E. coli_ expressing Trx-h2 was approximately 100-fold higher than that of _E. coli_ mutants expressing the _pINIII_ empty vector or _Trx-h3_ (Figure 4b). In addition, the survival of cells expressing the _CspA_ gene used as a positive control was almost similar with Trx-h2 expressing cells at 19 \({}^{\circ}\)C. These results explicitly demonstrate that Trx-h2 complements the cold-sensitive phenotype of _E. coli_ BX04 mutant cells at low temperatures.  
Figure 4: Trx-h2 restores the cold-sensitive phenotype of _E. coli_ BX04 mutant cells lacking four _cold-shock protein_ (_Csp_) genes. (**a**,**b**) Images showing the growth of _E. coli_ BX04 cells expressing _Trx-h2_ on LB-agar media at 37 \({}^{\circ}\)C for 1 day (**a**) and at 19 \({}^{\circ}\)C for 5 days (**b**).
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods  
### Anomaly Definition  
This section presents is a critical step in the dataset's preparation to define anomalies. The process determines the scope of anomalies investigated in the study. The definition of anomalous and normal data is based on the analysis results of the operational satellite team. The analysis generally depends on the satellite engineering model, on-orbit condition, and several previous tests before launch. Moreover, the definition is a reference for the subsequent process of dataset labeling. Labeling is a manual process of classifying the data points as normal (label: 0) or anomalous (label: 1). According to the telemetry data analysis, two types of anomalies are happening in the solar panel system of BIRDS satellites: solar panel failure (more than one cell) and solar cell failure, which are described in detail in the following subsections.  
The anomalous points are marked in red, and the regular points are in black. The data point is marked as an anomaly if it relates to the conditions of a solar panel or solar cell failure.
## 2 Methods and Available Data
### Lattice Boltzmann Simulations  
The LB simulation technique for obtaining the material characteristics of fiber-based porous GDL material has been demonstrated previously [9; 10; 34]. A series of BW images defines the micro-structure of a porous paper-type GDL. The geometry was described by a stochastic model, and its representations were analyzed by [PERSON] et al. [9; 34]. In this study, this method was used to determine the permeability of the Toray 090 GDL represented by a series of BW images, through-plane and in-plane. The permeability was determined by [9] using LB simulations with the D3Q19 scheme, applying fixed velocity conditions. They considered a small buffer region upstream and downstream of the GDL in order to avoid physically meaningless local conditions. Because of the stochastic nature of the fiber directions, the in-plane characteristics only needed to be calculated for one direction. Based on the LB simulations, the dependency of the permeability on the binder model, as well as the compression, was identified.  
The 3D structure of one of the data points introduced in Section 2.3 is shown in Figure 4. The two top fiber layers are colored in yellow and red to illustrate the irregular geometry. The through-plane simulation (the flow direction is from the bottom to the top) is shown by path lines calculated using the LB method [9]. For better visibility of the flow lines, the micro-structure was faded out from the rightmost half of the domain. The color of the flow lines represent the velocity. The through-plane permeability calculated from the 3D flow field via Darcy's Law forms the basis of the training data for the proposed ML model. The simulation on compressed material was applied in the same manner after the geometry was modified. For the uncompressed material, each fiber layer was represented by five images, as shown in Figure 3. A compression of 10% was emulated by merging adjacent images of two fiber layers to one, leaving nine images to represent two fiber layers. The compression creates BW images with no gray level ones and was introduced in previous studies [9]. The coarse method of geometry modification led to consistent simulation results for compression of up to 30%. Especially for higher compression, the resulting geometry conflicted with the physics-oriented boundary conditions, and some simulations did not converge. For this reason, only 541 labeled geometries were available for training the ML model instead of 600.  
All of the transport simulations noted herein were performed previously [9]. At that time, they were run on a predecessor of the HPC cluster, JURECA [40].
## 3 Current Measurement Techniques
### Other Techniques Applicable to Both Soil Moisture and Snow Property Determination
#### 3.3.3 Satellite Products
Snowpacks in alpine regions typically exceed these values, causing increased errors as the emissions from the snowpack exceed those from the soil surface. Particularly in the case of passive microwave measurements, grid sizes of 25 km in alpine terrain will include a large variation in land use types. SWE measurement can be reduced by up to 50% by the presence of vegetation [81]. The largest errors in corrections for this are present when the cell is only partially covered by vegetation (in the case of alpine regions, this is often the case for a cell size of 25 km). For this reason, many products now combine MODIS measurements in order to identify the proportion of vegetative cover. Reference [83] discusses the calculated SWE and HS products available. As with active microwave measurements, the authors are not aware of any validated passive microwave products for the Alps. Indeed, the highest quality product (Globsnow) [83] actively masks mountainous areas.
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 2 Materials and Methods
### Fusion Embedding Layer
#### 2.3.1 Adaptive Spatial Embedding  
Generally, the traffic condition of a region is influenced by its surrounding areas. To adaptively mine the spatial feature of all nodes without employing predefined distance matrices or adjacency matrices, we first introduce an adaptive spatial embedding, \(E_{sp}\in\mathbb{R}^{N\times d_{sp}}\), where \(d_{sp}\) represents the dimensions of the embedding. \(E_{sp}\) is randomly initialized, with each row uniquely representing the spatial embedding of each node shared across all time slices. Then, the spatial correlations among all nodes can be expressed by multiplying \(E_{sp}\) with its transpose, \(E_{sp}^{T}\). Consequently, we can define the adaptive similarity matrix as follows:  
\[M_{sp}=softmax\left(PReLI\left(E_{sp}E_{sp}^{T}\right)\right) \tag{2}\]  
The PReLU function can adaptively adjust the slope for negative inputs, unlike the fixed slope in LeakyReLU, and, compared to ReLU, it allows a slight activation of negative inputs to prevent neuron death, thus offering greater flexibility. The softmax function is employed to standardize the adaptive similarity matrix. Each row represents the similarity between the current node and all other nodes. This matrix helps in identifying and emphasizing the relationships between nodes that exhibit similar traffic patterns, improving the model's ability to predict under varying conditions. Subsequently, the matrix will be further integrated with spatial self-attention as a mask matrix, enabling the model to dynamically explore spatial dependencies.
# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 3 Edge Computing and Deep Learning-Based Malware Detection Method
### Convolutional Neural Network (CNN) for Malware Detection  
There are various types of deep learning architectures [95]. A new method of analyzing malware is to convert the binary of the malware file into image pixels. A specific pattern (feature extraction/signature) of each malware can be clearly visualized by converting a one-dimensional array of binary malware into a two-dimensional image array [96]. As the color image has sharper features than the grayscale image, better accuracy in classifying malware can be achieved by converting the images to color images [97]. When the malware file is converted into an image, feature extraction is performed automatically, without manual feature extraction or prior domain knowledge, making it a very powerful and efficient deep learning network. Therefore, this study proposes a CNN-based deep learning model using image visualization technology for malware detection and classification in smart factories.  
The CNN is a deep learning model that accepts an image as input and classifies learnable parameters by assigning importance to various objects in the image using weights. CNNs are commonly used for pattern and image classification and may have many hidden layers consisting of convolutional layers, pooling layers, fully connected layers, and regularization layers [98].  
The CNN model proposed in this study consists of nine convolutional layers (CLs); each CL consists of a grid-shaped neuron grid, and each CL has a filter size between 8 and 128. As the kernel function, an output vector is generated based on the product of image weights and a rectified linear unit (ReLU) activation function. A pooling layer can be added after each CL, with the resulting image generated from the CL merged and supplied as an input to the max pooling layer. Further, a dropout of 0.45 and batch normalization are used to reduce overfitting and increase training speed [99]. The dropout layer is used to delete neurons at a specific rate in the neural network to prevent overfitting of the training data. The fully connected layer has a filter size of 128 and performs advanced detection of neural networks. This fully connected layer executes the merge operation and binds all neurons in the previous layer to each and every neuron in the subsequent layers of the architecture [100]. In the final output layer, the probability of input images belonging to each malicious code class is calculated through the Softmax activation function to classify multiple malware classes. The malware class with the highest probability score is finally assigned to the image.  
Figure 7 shows the process of classifying the malicious code family using the malicious code image generation and CNN model. Table 7 provides the description of the processing contents for each classification stage.
# Evaluation of Radio Communication Links of 4G Systems
## 2 Background
### Study and Simulation of the Radio Links of a 4G System
#### 2.2.3 Discussion of the Results  
According to the obtained results, the types of the medium on which the electromagnetic waves propagate directly influence the weakening of the signal. At the same time, the propagation is better when the obstacles are reduced. In conclusion of this part, we can increasingly classify the quality of propagation as poor quality as follow:  
1. Urban environment.
2. Dense urban environment.
3. Suburban environment.
4. Rural environment.  
The distance directly affects the received signal quality. As a result, for 4G LTE networks, the distance between the eNB and the mobile terminal must be considered to guarantee an acceptable link quality.  
For the height of the eNB and mobile terminal and according to the obtained results, it can conclude that the link is better when the height increases either at the side of the terminal or the eNB or both. This is explained by the decrease in the number of obstacles with the height until the link can be considered in free space.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Ice (SI)
#### 2.2.5 Scatterometer  
Although scatterometers are mainly designed for OSW measurements, they have also proved to be useful for SI studies [96; 97]. Scatterometers directly measure the Normalized Radar Cross Section (NRCS), from which the wind parameters can be extracted. The measured NRCS data can also be utilized for other applications, including SI studies [96]. Despite their coarser spatial resolution, scatterometers can provide daily data from polar regions to study SI, which makes them very useful for global SI monitoring [85]. Among various microwave scatterometers, the Ku-band NASA scatterometer (NSCAT) onboard the Advanced Earth Observing Satellite (ADEOS) platform, the Ku-band SeaWinds scatterometer instrument of the QuikSCAT, OSCAT onboard the OceanSat-2, and C-band ASCAT carried by MetOp-A are the most commonly used spaceborne scatterometers for SI studies.
## 4 Implementing L2S-M in a Cloud-Native Platform for Intra-Site Connectivity: K8s Case
### Virtual Network Management Flow
#### 4.2.1 Virtual Network Creation  
First of all, when a user wants to create a virtual network, as seen in Figure 4, the user will instruct K8s through its command-line interface (kubectl) to create the resource inside the cluster (i.e., a NAD with the definition of the virtual network). Once this creation is performed, the L2S-M operator picks up the K8s event and checks that this NAD definition corresponds to a virtual network. The way the L2S-M knows is that the NAD includes a virtual interface (i.e., an interface that is not physically defined in the host) called \"I2 sm-vNet\", informing L2S-M that the annotation is a virtual network. Once the event is picked up, the operator registers the creation inside its database, completing the creation of the virtual network.
## 4 Discussion
### Limitations and Future Work  
The evaluation of the sensor was performed on 15 healthy male subjects only. Firstly, scale-up validation was needed. Secondly, to eliminate the error caused by movement artefact, the IJP sensor was firmly fixed on the xiphoid process. In different body positions, the sensitivity to posture change might be different, which needs further investigation. Thirdly, the evaluation of wider age groups and genders could be performed to investigate if the IJP sensor is suitable for people with different physiological conditions. Furthermore, the study was performed on healthy subjects. The performance of the sensor on subjects with pathological conditions needs further investigation. The IJP sensor is a low-cost and wearable sensor that provides the possibility of daily RR monitoring. However, motion artefact is a major concern regarding the accuracy of RR monitoring during daily activities [11]. For the further application especially in daily healthcare monitoring, full validation on different levels of motion artefact is needed. Finally, more comfortable mounting mechanism such as the use of biocompatible and skin-friendly adhesive should be investigated.