esa-sceva/satcom-chunk-collection huggingface dataset:
# Accumulative Roll Bonding--A Review
## 3 Parameters
### Surface Parameters  
Mechanical strength is significantly influenced by surface characteristics in both aspects of cleanliness and roughness [34]. Metal sheets, in two stages of production and maintenance, are affected by surface contaminations disturbing nearly all surface-dependent operations. Those thermal treated sheets and the materials produced by hot forming processes possess surface contaminants, e.g., oxides. Furthermore, lubricants used in sheet metal forming processes are the other sources of surface contamination among which, grease, moisture and chemical compounds usually remain on the surface after the production, acting as bonding barriers, and hence could seriously affect the bonding quality of ARBed materials and, therefore, must be removed. It would be wrong to think that post forming cleaning processes will readily eliminate such contaminants and if they remain on the surface, it would weaken the bonding strength especially when rolling at ambient temperatures [35; 36]. As a result, very careful surface preparation is required before the application of any solid state bonding operation [37]. The removal of contaminant layers from material surfaces can be performed by chemical and/or mechanical treatments.  
[PERSON] and [PERSON] [38] investigated the effects of surface preparation parameters such as roughness, scratch-brushing on bonding quality. Scratch-brushing parameters encompass peripheral speed, brushing load and stiffness governing surface roughness. Shown in Figure 3a, increasing surface  
Figure 2: Trend in material selection for ARB experiments based on Google Scholar.  
roughness leads to enhanced peeling strength, and increased brittleness of surface layers that can consequently be broken easily providing more virgin layers for extruding and ensuing bonding. On the other hand, scratch brushing both cleases and roughens the surface layers. It grants more asperities promoting localized shear deformation and breaks unavoidable oxide films during the roll bonding operation, therefore, contributing to better bonding of the two metal sheets [38; 39; 40].  
There exist different surface preparation methods among which degreasing (for example in acetone) followed by scratch-brushing gives a far better bond strength. It has also been concluded that bond strength enhances, with decreasing the delay time between surface preparation operation and the consequent rolling. Figure 3b indicates that if the rolling process is carried out within the first 10 min, the bond quality is not affected, however, longer delay reduces the bond strength. [PERSON] and [PERSON] observed that the bond strength significantly decreases after 15 min of surface preparation. Also, they identified two other important parameters in scratch-brushing, namely wire diameter and length.
# Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks
## 6 Conclusions  
In this paper, we developed a method for sign language recognition using a RGB-D camera. We detect the hands, body, and facial features, convert them to 3D and use them as input features for classifiers based on recurrent neural networks. We compare three different architectures: recurrent neural networks (RNN), long short-term memories (LSTM), and gated recurrent units (GRU). LSTM performed the best with noisy inputs, and GRU performed best without noisy inputs and less trainable parameters.  
We collected a dataset of 30 dynamic signs from the Mexican Sign Language with 100 samples of each sign and used it for training, validation, and testing. Our best model obtained an accuracy above 97% on the test set.  
We want to extend the number of signs to recognize and integrate this method in a prototype in future work.  
Conceptualization, D.-M.C.-E. and J.T.; methodology, K.M.-P. and D.-M.C.-E.; software, K.M.-P.; validation, K.M.-P.; formal analysis, D.-M.C.-E., A.-M.H.-N.; investigation, D.-M.C.-E., K.M.-P., J.T.; resources, T.G.-R., A.-M.H.-N., D.-M.C.-E., A.R.-P.; writing--original draft preparation, K.M.-P., D.-M.C.-E., J.T.; writing--review and editing, A.-M.H.-N., D.-M.C.-E., T.G.-R., A.R.-P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data collected for this work can be downloaded from [[https://github.com/ICKMejia/Mexican-Sign-Language-Recognition](https://github.com/ICKMejia/Mexican-Sign-Language-Recognition)]([https://github.com/ICKMejia/Mexican-Sign-Language-Recognition](https://github.com/ICKMejia/Mexican-Sign-Language-Recognition)).  
The authors wish to acknowledge the support for this work by the scholarship granted by Consejo Nacional de Ciencia y Tecnologia (CONACyT). We also want to thank Universidad Autonoma de Queretaro (UAQ) through project FOPER-2021-FIFO2482.  
The authors declare no conflict of interest in the publication of this paper.
## 2 Preliminaries
### Cascading Failure Mechanism  
In industrial field scenarios, wireless sensor networks are usually required to transmit complex data, including sound, images, film, and video, to meet each task scenario's needs, so wireless sensor nodes suffer from more significant data overload impact [23]. When damage to the node leads to a change in the network topology, it redistributes data traffic transmission. Subject to the hardware cost, there is no guarantee that the node processing capacity is always more significant than the load, thus causing a new round of traffic redistribution, which will eventually cause a large-scale cascading failure of the network.  
Due to the hierarchical topology approach, common nodes only need to collect environmental data, the cluster head nodes integrate the data and transmit them to the sink nodes through multi-hop transmission, the sink nodes provide data to the terminal devices. The multi-hop transmission method between cluster head nodes makes cascading failures occur only between cluster head node layers [41]. As shown in Figure 2, cluster head node 1 is not working properly due to data shocks or external environmental damage, so the data originally forwarded and collected by node 1 needs to be transmitted to the adjacent nodes 2, 3, and 5. The adjacent nodes receive the load from node 1 according to a certain distribution ratio. Unfortunately, the current network load of nodes 3 and 5 exceeds their maximum processing range, causing node 3 and node 5 to fail. This action will eventually cause a large network crash.
## 3 Datasets
### Deimos-2 Stereo Pairs  
Deimos-2 is a VHR optical satellite fully owned and operated by Deimos Imaging, an UrtheCast company. It was launched in 2009 and operates from a Sun-synchronous orbit at a mean altitude of 620 km, which allows an average revisit time of two days worldwide (one day at mid-latitudes). It can provide 1 m PAN and 4 m multispectral (MS) images (R, G, B, NIR) within a swath of 12 km at nadir. The images are distributed in two different processing levels. The level 1B (L1B) product involves calibration and radiometric correction, but it does not include resampling to a map grid. This basic product includes the RPCs (sensor camera model) and the metadata with gain and bias values for each band. The level 1C (L1C) product turns out to be a more elaborated product that has been calibrated, radiometrically corrected, manually orthorectified and resampled to a map grid.  
In this study, two Deimos-2 PAN stereo images L1B taken on 30 July 2019 and 5 August 2019 were acquired at the study site. Both stereo pairs had a fairly similar acquisition geometry (Figure 2). The metadata of the four individual PAN Deimos-2 images from the two stereo pairs, including viewing geometry, sun position and other acquisition parameters, are shown in Table 1.
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 4 Simulation Setup and Result Analysis
### Mobility Model  
It is a very vital step to choose an appropriate mobility model for VANETs simulation. The mobility model must exhibit the real vehicular characteristics. The mobility model also affects the performance of  
Figure 3: Limitation of speed based improved greedy forwarding strategy used in GyTAR, and E-GyTAR.  
protocols [2,3]. The mobility models for vehicular environment usually have two categories. These are macroscopic and microscopic mobility models. The macroscopic mobility is actually a reflection of mobility constraints such as roads, number of road lanes, the city streets, speed limits, traffic lights, traffic density, and traffic flows. The microscopic mobility exhibits the characteristics such as vehicles conduct with each other and with the infrastructure [35,43]. Both micro and macro mobility categories are made available by the VanetMobiSim. It is an extended version of CANU (Communication in Ad-hoc Networks for Ubiquitous Computing) mobility simulation model [35,42,43].
###### Abstract  
Emergency observations are missions executed by Earth observation satellites to support urgent ground operations. Emergency observations become more important for meeting the requirements of highly dynamic and highly time-sensitive observation missions, such as disaster monitoring and early warning. Considering the complex scheduling problem of Earth observation satellites under emergency conditions, a multi-satellite dynamic mission scheduling model based on mission priority is proposed in this paper. A calculation model of mission priority is designed for emergency missions based on seven impact factors. In the satellite mission scheduling, the resource constraints of scheduling are analyzed in detail, and the optimization objective function is built to maximize the observation mission priority and mission revenues, and minimize the waiting time for missions that require urgency for execution time. Then, the hybrid genetic tabu search algorithm is used to obtain the initial satellite scheduling plan. In case of the dynamic arrival of new emergency missions before scheduling plan releases, a dynamic scheduling algorithm based on mission priority is proposed to solve the scheduling problem caused by newly arrived missions and to obtain the scheduling plan of newly arrived missions. A simulation experiment was conducted for different numbers of initial missions and newly arrived missions, and the scheduling results were evaluated with a model performance evaluation function. The results show that the execution probability of high-priority missions increased because the mission priority was taken into account in the model. In the case of more satellite resources, when new missions dynamically arrived, the satellite resources can be reasonably allocated to these missions based on the mission priority. Overall, this approach reduces the complexity of the dynamic adjustment and maintains the stability of the initial scheduling plan.  
mission priority; Earth observation satellite; satellite mission scheduling; emergency response; multi-constraint optimization problem; hybrid genetic tabu search algorithm 2019 19 1430 10.3390/s19061430
## 2 Methods
### Transformer Encoder Module  
The Transformer encoder in this paper shown in the right half of Figure 2a is inspired by PVT [25] and designed in a stepped format. This design prevents the impact of resolution changes typical in columnar structures while ensuring compatibility with the CNN encoder  
Figure 2: Illustration of the encoder module structure: (**a**) The left shows the CEM, and the right shows the TEM. (**b**) The attention mechanism of the TEM.  
architecture. Each stage of the TEM includes spatial embedding, positional embedding, and attention operations.  
Spatial embedding transforms images into lower-dimensional vector representations through basic \(3\times 3\) convolution operations, effectively abstracting high-level features. Low-level features in the images aid in defining object boundaries, while high-level features represent complex semantic concepts such as shapes and inter-object relationships. This enables the model to interpret image content in a manner similar to human perception. To prevent boundary information loss during patch segmentation, overlapping patches are employed, differing from standard image partitioning methods. This approach preserves more contextual information, reduces blocking artifacts, and enhances local continuity.  
As previously noted, [PERSON] et al. [34] demonstrated that CNNs can implicitly learn and encode absolute positional information without explicit encoding. This design eliminates the inefficiency and rigidity associated with fixed-resolution positional embeddings in practical applications. Positional embedding adjusts feature dimensions via \(1\times 1\) convolution, while a \(3\times 3\) convolution implicitly encodes positional information to more effectively model spatial relationships. Additionally, \(GELU\) is employed instead of \(ReLU\) for activation, as it preserves more information through Gaussian smoothing rather than discarding small values. Its smooth gradient transitions further enhance stability during optimization.  
\[Attention=softmax(\frac{QK^{T}}{\sqrt{d}})V \tag{1}\]  
The attention operation, as shown in Figure 2b and Equation (1), generates \(Q\) (query), \(K\) (key), and \(V\) (value) matrices from the input features. It performs a dot product between \(Q\) and \(K\) to compute attention scores, which can be interpreted as cosine similarity for each pixel pair. Pixels with higher similarity receive increased attention scores, indicating they should allocate more \"attention\" to each other, thereby facilitating mutual feature learning. The dot product of attention scores with \(V\) aggregates global contextual information. To ensure numerical stability and prevent gradient vanishing, the final result is scaled by the square root of the feature dimension \(\sqrt{d}\). This scaling also helps to prevent excessive output dimensions and reduces the numerical range, ensuring that the model remains stable during training. After computing global attention, a linear layer further adjusts the feature dimensions and transforms the attention output, enhancing the model's representational power.
# Experimental Validation of a GNSS Receiver Antenna Absolute Field Calibration System
## 2 Materials and Methods
### LMMT Robotic Calibration System
The calibration rotation sequence is defined by an azimuthal increment of 5\({}^{\circ}\) within the 0\({}^{\circ}\) to 360\({}^{\circ}\) range and a zenithal increment of 5\({}^{\circ}\) within the \(-70^{\circ}\) to 70\({}^{\circ}\) range. Consequently, the full calibration procedure lasts approximately 2 h. On both stations simultaneously  
Figure 4: Visualization of the personal computer’s internal clock error within the time synchronization test. The test has been divided into three phases based on the working status of the developed Time Synchronization module (TiSy): TiSy OFF, TiSy ON, and TiSy OFF. (AUT and REF), under identical receiver settings and an observation interval of 10 Hz, raw carrier-phase observations are registered. Generally, the LMMT calibration system and PCC modeling are based on the triple-difference (TD) approach. Simultaneously registered raw carrier-phase observations are differentiated both spatially and temporally to generate the well-known time-differences of carrier-phase double-differences. By forming TDs over a short-baseline between epochs \(t_{k}\) and \(t_{k+1}\), corresponding to two time-adjacent AUT orientations (attitudes) \(k\) and (\(k+1\)) and satellites \(i\) and \(j\), the high temporal and spatial correlation of GNSS signals is leveraged. This approach effectively cancels out most of the GNSS error budget, leaving the final TDs to contain primarily the AUT PCCs and the differential carrier-phase noise \(\partial\epsilon_{\text{T,R}}^{ij}\)[17; 32; 33; 34; 35]:  
\[TD_{\text{T,R}}^{ij}(t_{k},t_{k+1})=-PCC_{\text{T}}^{j}(t_{k+1})+PCC_{\text{T} }^{i}(t_{k+1})+PCC_{\text{T}}^{j}(t_{k})-PCC_{\text{T}}^{i}(t_{k})+\partial \epsilon_{\text{T,R}}^{ij}. \tag{3}\]  
In line with the standard approach, the parametrization of the AUT's PCC model using the precalculated TD values is most effectively achieved through spherical harmonics (SH) expansion. The basic equation for SH expansion is given by [17; 32; 33; 34; 35]:  
\[PCC\Big{(}\alpha^{i},z^{i}\Big{)}=\sum_{m=0}^{m_{\text{max}}}\sum_{n=0}^{m} \widetilde{P}_{nm}\Big{[}\cos\Big{(}z^{i}\Big{)}\Big{]}\cdot\Big{[}a_{mn}\cos \Big{(}n\alpha^{i}\Big{)}+b_{mn}\sin\Big{(}n\alpha^{i}\Big{)}\Big{]}, \tag{4}\]  
where \(\widetilde{P}_{nm}\) is the fully normalized Legendre function [39], \(\alpha^{i}\) and \(z^{i}\) are the azimuth and zenith angles in the AF, \(a_{mn}\) and \(b_{mn}\) are the SH coefficients. At LMMT, the SH resolution is defined as \(m=n=8\), resulting in a total of 91 unknown coefficients, which are determined through a conditional least squares (CLSQ) adjustment. Following the determination of SH coefficients, the PCCs for the entire AUT hemisphere are calculated, converted into PCO/PCV, and exported in accordance with the ANTEX file format. A comprehensive description of the GNSS receiver antenna calibration facility at LMMT in Croatia, along with the associated calibration methodology, is presented in [PERSON] et al. [17] and [PERSON] [32].
# Malicious URL Detection Model Based on Bidirectional Gated Recurrent Unit and Attention Mechanism
###### Abstract
However, RNNs can easily experience gradient disappearance or gradient explosion, which will affect the accuracy of malicious URL detection and classification. [PERSON] et al. [16] proposed a hybrid deep learning method called URLdeepDetect for click-time URL analysis and classification to detect malicious URLs. [PERSON] et al. [17] used a character-level embedded coding format to represent URLs and designed CNN and LSTM models to detect malicious URLs. However, the accuracy of the model was only 93.59%, which could not detect malicious URLs well. [PERSON] et al. [18] combined the HTTP parameters generated by user requests with the corresponding URLs and compared six vectorization methods in the data preprocessing stage. Deep neural networks imitate the human brain mechanism to interpret data; the brain does not simply process incoming information in a moment, but instead it promotes the perception of the scene through attention, expectation, and prior knowledge, extracting interested characteristics to understand information. Therefore, researchers presented the attention mechanism [19], which achieves the purpose of obtaining more effective information by giving higher weights to the concerned parts in deep learning and has achieved good results in machine translation, false news discrimination [20], and other tasks. Attention mechanisms were inspired by the fact that deep neural networks [15] do not distinguish information in the URLs when identifying malicious URLs, and the key information is not fully utilized as a result. The attention mechanism can effectively allocate the information's characteristics and improve the deep learning efficiency so as to obtain better recognition performance of malicious URLs. Based on the above analysis, this paper proposes a malicious URL detection method based on a bidirectional GRU and an attention mechanism. This method applies Word2 Vec to train the word vector of URLs, uses BiGRU to extract all the sequence information of URLs and learn the relationships between sequences, and introduces an attention mechanism to strengthen the model to learn useful information. In addition, to prevent overfitting during model training, a dropout mechanism is added to the input layer to improve the accuracy of malicious URL detection and classification.
## 2 Materials and Methods
### The Proposed Method
```
Input: Original data matrix \(\mathbf{X}\in\mathbb{R}^{d_{x}\times n}\) with \(n\) samples, table matrix \(\mathbf{Y}_{l}\) Output: The low-dimensional indicator matrix \(\mathbf{F}\in\mathbb{R}^{d_{l}\times n}\)
1 foreachsample\(\mathbf{X}_{i}\)do
2 Reshape vectorized \(\mathbf{X}_{i}\) into the pixel matrix \(\mathbf{Pix}_{i}\) based on the height and width; Transform \(\mathbf{Pix}_{i}\) into the frequency domain for feature extraction and interference reduction, calculate \(\mathbf{HF}_{i}\) by (4);
3 Assemble the frequency information \(\mathbf{HF}_{i}\) into a new data matrix \(\mathbf{HF}\) by (5);
4 end foreach\(i\leq n\)do
5 foreach\(j\leq n\)do
6 if\(\mathbf{HF}_{j}\in\mathbf{HF}_{i}\)'s neighborhoodthen
7 Calculate the similarity \(\mathbf{S}_{ij}\) in the original high-dimensional manifold space by high-frequency kernel function (9);
8
9 end foreach
10 else
11 Set the similarity as zero \(\mathbf{S}_{ij}=0\);
12
13 end foreach
14
15 end foreach
16 Construct the graph structure by calculating the Laplacian matrix \(\mathbf{L}\) by \(\mathbf{L}=\mathbf{D}-\mathbf{S}\);
17 Assign the labeled values to matrix \(\mathbf{L}\) according to \(\mathbf{Y}_{l}\) by (19);
18 Obtain the low-dimensional mapping and solve the semi-supervised classification simultaneously by (20). ```  
**Algorithm 1**FMDR for semi-supervised classification  
One of the prominent advantages of FMDR is its minimal use of computing resources. The high-frequency texture components are extracted by 2D DFT and filtering, with a computational complexity of \(O(n)\). The similarity is calculated by the HFK, which adopts matrix multiplication instead of a loop algorithm, only with the computational complexity \(O(1)\). The complexities of dimensionality reduction and classification are \(O(n)\) and \(O(3n)\)respectively. Finally, the total computational complexity of FMDR is \(O(n+1+n+3n)\), which is simplified to \(O(n)\) with the assumption that \(n\) will be large.