esa-sceva/satcom-chunk-collection huggingface dataset:
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 5 LiDAR in Space Missions
### Spaceborne LiDARs for Terrestrial Applications
#### 5.1.2 GLAS--The Geoscience Laser Altimeter System  
The Geoscience Laser Altimeter System (GLAS), onboard NASA's Ice, Cloud, and Land Elevation Satellite (ICESat), was launched on 13 January 2003, and operated for approximately 6.5 years in a 600 km polar orbit with a 94\({}^{\circ}\) inclination. Its primary objective was to measure ice-sheet mass balance and assess the effects of atmospheric and climate changes on polar ice masses and global sea level trends. GLAS also acquired valuable data on cloud and aerosol distribution, land topography, sea ice conditions, and vegetation cover [144; 21].
## 4 Experimental Results
### Accuracy Performance  
In this section, we discuss the accuracy performance on ORL, Extended YaleB, and CMU-PIE datasets. The accuracy is evaluated by GAR (Genuine Accept Rate) and EER (Equal Error Rate) at a given 1% FAR (False Acceptance Rate). The results on these three datasets are listed in Table 1. It can be seen that all GARs are larger than 97% at a fixed 1% FAR, and EERs are less than 2% where the key length of bio-key is varied from 128, 256, 1024, and 2048. Generally, since the generated binary code contains more noise when the key length is longer, it may reduce the authentication accuracy of the bio-key. However, as the key length increases, our proposed scheme also achieves better accuracy. For example, we obtain a better GAR of 99.62% and EER of 0.51% at fixed 1% FAR for a length size of 1024 bits on the ORL dataset. This is because that our method based on the DNN model can effectively learn the mapping relationship between binary code and biometric image. In general, accuracy performance can be well preserved under the different key lengths and the intra-user variations.  
To further illustrate the accuracy performance of our methods, we compare our approach with other state-of-art methods [15; 64; 65; 66] on Extended YaleB and CMU-PIE dataset. Tables 2 and 3 show the comparison results on the Extended YaleB and CMU-PIE datasets, respectively. On the one hand, our method can earn a GAR of 99.40% for 128 bits binary code, which outperforms the Genetic-ECOC method [64] at GAR at a  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Length**} & \multicolumn{2}{c}{**ORL**} & \multicolumn{2}{c}{**Extended YaleB**} & \multicolumn{2}{c}{**CMU-PIE**} \\ \cline{2-7}  & **GAR** &  
fixed 1% FAR on the Extended YaleB dataset. On the other hand, references [64, 66] may perform extremely well; they are close to our performance. However, their lengths of the codeword are both less than 90, and as such they offer lower security to brute force attacks. In Table 1, we can find that the GAR and EER of our method are 99.97% and 1.49%, respectively, for a length size of 128 on the CMU-PIE dataset. Thus, our algorithm outperforms reference [65] even if our length of the codeword is less than [65]. In summary, our method can achieve a higher GAR and a lower EER than Hybrid [65], BDA [66], MEB coding [15], and Genetic-ECOC [64] on the CMU-PUE dataset. The reason is that our proposed scheme adopts the DNN model based on feature extraction network and binary code mapping network to generate robust binary code, which can enhance the compactness of intra-class and discrepancy of inter-class. Our proposed method can effectively perform better accuracy than the state-of-the-art methods under the intra-user variations such as illumination, pose, and expressions. Therefore, our approach can enhance stability under the multi-shot enrolment.
## 3 Background Theory
### BAN Logic  
Burrows-Eternal-Needham (BAN) Logic is a formal testing method used to test protocols so that users can determine the security and trustworthiness of the information exchanged. This makes BAN logic often referred to as the logic of belief. BAN logic testing is carried out with the assumption that every exchange is carried out on an insecure medium and is susceptible to eavesdropping [39; 40].
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 2 Materials and Methods
### Spatial-Temporal Attention Layer
#### 2.4.2 Temporal Self-Attention  
In the temporal dimension, dependencies between traffic conditions across various time slices are evident, such as periodicity and trends that fluctuate in different scenarios. To adaptively capture dynamic temporal correlations, a temporal self-attention (TSA) module is utilized. Specifically, we initially calculate the query, key, and value (QKV) matrices for node n as follows:  
\[Q_{n}^{T}=X_{n:r}W_{Q}^{T},K_{n}^{T}=X_{n:r}W_{K}^{T},V_{n}^{T}=X_{n:r}W_{V}^{T} \tag{14}\]  
where \(W_{Q}^{T}\), \(W_{K}^{T}\), and \(W_{V}^{T}\in\mathbb{R}^{d\times d^{\prime}}\) represent learnable parameter matrices and \(d^{\prime}\) denotes the dimension of these learnable matrices. Then, the temporal correlations (attention scores) between all time slices for node n can be obtained via the self-attention operation as follows:  
\[A_{n}^{T}=\frac{Q_{n}^{T}\times\left(K_{n}^{T}\right)^{T}}{\sqrt{d^{\prime}}} \tag{15}\]  
It is evident that the temporal self-attention module is capable of effectively exploring the temporal dynamic dependencies for diverse nodes. Furthermore, the temporal self-attention module possesses an expansive global receptive field feature, enabling it to capture long-range temporal dependencies. Then, the output of the temporal self-attention module can be obtained as follows:  
\[TSA\left(Q_{n}^{T},K_{n}^{T},V_{n}^{T}\right)=softmax(A_{n}^{T})V_{n}^{T} \tag{16}\]  
Finally, we expand TSA into multi-head temporal self-attention. The output can be obtained as follows:  
\[Z_{TSA}=TSA_{1}\parallel TSA_{2}\parallel\dots\parallel TSA_{h_{tsa}} \tag{17}\]  
where \(h_{tsa}\) is the number of attention heads of the temporal self-attention module.
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 4 ArPico VBL
### ArPico Object Properties  
Each ArPico pictorial object captured in a camera view will be compared with all markers in a library to see if it matches with one of them. Due to the light and visibility conditions, sometimes a captured picture may not be very clear. It is important to have clearly distinguishable markers in an ArPico library to reduce erroneous identifications. In this section, we present some desirable properties for ArPico markers. Any picture that cannot meet these conditions should not be used for ArPico VBL.  
Before a pictorial object is added into ArPico library, its distinctiveness and uniqueness are required to be verified using the binary marker code generated in Section 4.1. Then we check the self-transition \(T\) and rotation difference \(D(m_{i})\) of the binary code to ensure the marker is unique in four 90-degree rotations. Meanwhile, it also need to compare with other existing codes in the dictionary to minimize the possibility of detection errors.  
Figure 4: ArPico Marker Conversion Example.  
The self-transition \(T\) represents how many pairs of neighbour binary codes in a pictorial object have different values. A good code should have many transitions from 0 to 1 or from 1 to 0 that give it many significant features. Assuming the binary block is a \(n\times m\) matrix, the self-transition \(T\) can be represented by:  
\[T=\sum_{x=1}^{n}\sum_{y=1}^{m-1}P(x,y)\otimes P(x,y+1)+\sum_{x=1}^{n-1}\sum_{y =1}^{m}P(x,y)\otimes P(x+1,y) \tag{1}\]  
where P(x,y) is the binary code value at block (x,y). \(T\) is a threshold to inspect if the code is unique enough.  
The rotation difference \(D(m_{i})\) is used to check the differences of the picture when it rotates 90, 180 and 270 degrees.The rotation difference \(D(m_{i})\) can be defined as:  
\[D(m_{i})=\min_{m_{k}\in A(m_{i})}\{H(m_{i},m_{k})\} \tag{2}\]  
where \(H(m_{i},m_{j})\) is the hamming distance of two markers and \(A(m_{i})\) is a set of rotation markers of \(m_{i}\). The rotation difference \(D(m_{i})\) need to be large enough so that the marker can be detected correctly in different rotations.  
The last step is to check the global uniqueness of the picture. In order to avoid detection mistakes, the humming distance between the new candidate and the other existing pictures in the library needs to be larger than a threshold. After the marker can pass this test, the pictorial object will be included in the object library; otherwise it will not be used for ArPico VBL.
# Improvement of Concrete Crack Segmentation Performance Using Stacking Ensemble Learning
## 4 Results and Discussion
### Predicted Images of Five Segmentation Models  
Figure 11 shows the crack-prediction results and target images of the four models comprising five architectures. The target image was an image of cracks on a heavily discolored concrete surface. This is a problem with segmentation owing to the discolored area, and a large difference in prediction results can be observed depending on the architecture.  
DeepLabV3+ showed the most similar results to the Target. The combination of upsampling and skip connections predicts the degree of cracking at an appropriate level without being excessively detailed or rough. In addition, according to the four models, the prediction results are the most similar and are judged to be the most suitable architecture for this dataset.  
In the case of DANet, it was inferred that the part identified as a crack in the image was highlighted more thickly, and cracks with small widths were often ignored and omitted. The inference results are consistent for each model.  
DeepLabV3 often incorrectly infers discolored concrete areas as cracks; in particular, it is challenging to detect detailed cracks. The location and degree of noise differed owing to the significant differences among the models.  
Figure 10: IoU score versus Epochs: (**a**) FCN, (**b**)UNet, (**c**) DeepLabV3, (**d**) DeepLabV3+, and (**e**) DANet.  
The FCN does not capture the crack information. Much information is decoded with a small number of parameters; therefore, it cannot show a correctly interpreted result. In particular, the FCN is not able to capture the crack information.  
Figure 11: Typical images predicted by segmentation models.  
some cases, none of the crack pixels could be found for each model, and even if cracks were found, they were in the wrong location or were roughly predicted. Because it is challenging to present detailed results of FCN to predict cracks, FCN was excluded from the subsequent stacking ensemble learning.  
UNet identifies cracks with the thinnest points, but the results are at a level where only wide cracks can be identified. This is because learning still needs to be completed. Depending on the additional data augmentation and epochs, it is possible to infer cracks at a more detailed level.
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 2 Methods
### Decoder
#### 2.2.1 Focused Linear Attention
Therefore, \(f_{c}\) can divide the features into several groups according to the axis closest to them, improving the feature similarity within the same groups and reducing the feature redundancy between different groups. On the other hand, feature diversity is also the reason for the poor performance of LNA, and one of the deeper reasons behind it is the attention matrix's rank [35; 36]. In fact, the rank of the attention matrix in LNA is limited by the number of tokens \(N\) and the channel number \(d\):  
\[r\Big{(}\delta(Q)\delta(K)^{\top}\Big{)}\leq\min\{r(\delta(Q)),\ r(\delta(K)) \}\leq\min\{N,d\}, \tag{7}\]  
where the value of \(d\) is much smaller than \(N\) in the present Vision Transformer, and \(r\) denotes the rank. As a result, the rank of the attention matrix tends to change on a smaller scale, which may lead to the multi-line attention maps becoming homogeneous. In addition, the homogeneity of the attention weights has an adverse impact on the final aggregated features. The FLA addresses this problem via a simple strategy. Specifically, a depth-wise separable convolution (_DWC_) module is used to compensate for the deficiency of the self-attention matrix:  
\[O=\delta_{c}(Q)\Big{(}\delta_{c}(K)^{\top}V\Big{)}+DWC(V). \tag{8}\]  
We can better understand the role of the _DWC_ by thinking of it as a type of attention. In other words, each query focuses on a few close features instead of all the features, \(V\). This locality ensures that when the LNA encounters a situation where different queries produce the same value, it can still obtain diverse outputs from the local features, thus maintaining the feature representation effectually. From the perspective of rank, the  
Figure 2: The distribution of different vectors for the Softmax attention mechanism and LNA: (**a**) Softmax attention mechanism, (**b**) LNA, and (**c**) numerical visualization. rank of the attention matrix in LNA may restore to the full rank state when the _DWC_ is added, ensuring the feature diversity, as with the Softmax attention mechanism. As a result, the linear complexity ensures that the model can adapt to a larger receptive field while maintaining the same computational complexity and modeling the fine-grained global information.
# TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles
## 3 TAKEN-Traffic Knowledge-Based Navigation for CAVs
### Traffic Knowledge Generation-Analysis Module
#### 3.1.3 Low-Level Decision Making  
The task of motion planning involves finding the best path from the source to the destination (high-level planning) and taking care of events such as lane changing, obeying traffic rules, identification and tracking of static and dynamic obstacles, collision detection, etc. (low-level planning). The work focuses on low-level planning by designing a prioritised behavioural model which would enable safe overtaking of vehicles, object collision avoidance, roadblocks and abidance by the traffic rules. Behaviour planning involves making high-level decisions to ensure safe driving. The behavioural models are triggered purely based on the visual perception stack's inferences. The TAKEN system defines them as a state machine to handle pedestrians in the scene (Figure 6A), vehicles approaching the traffic light (Figure 6B) and perform overtaking or handling the situation of roadblocks (Figure 6C). Integrating these concepts helps us in designing a level three autonomous vehicle and enables manoeuvring in medium traffic. They are represented in Figures 4 and 5. The case of overtaking a vehicle--Figure 6C--is highlighted in Figure 4A and Figure 4C. The self-driving vehicle uses cameras to estimate the depth of various objects in the scene and estimates their time of collision with that object. The red triangles in the aforementioned figures show the collision event boundaries for a given velocity. On one hand, the process of low-level decision making enables the agent to take alternate immediate routes in the case of obstructions but on the other hand, the Stanley controller [31] provides the corrected steering angle, enforcing the vehicle the adhere to its goal track and these put together enables safe overtaking. Figure 4C, explains one of the extreme cases of overtaking in which a self-driving agent initially decides to overtake because of the estimated feasibility but the vehicle which is being overtaken and the approaching vehicle apparently increases their velocities. In such a case the self-driving agent comes to a halt until there is enough safe space available for its manoeuvring. The same concept is extended when the obstructions are pedestrians and it is represented in Figure 4B.
## 3 Mathematical Formulation and Methodology
### Filtering  
The first step after collecting the measured signal power level data is to separate small-scale fading from large-scale attenuation. For this, we used the moving average filter given by [36].  
\[y[i]=\frac{1}{N+M+1}\sum_{K=-N}^{M}x[i+k], \tag{1}\]  
where \(x[i+k]\) are the signal samples, \(i\) is the filter sample index, \(y[i]\) is the \(i\)-th filter output sample, and \(N\) and \(M\) are the number of samples of the input signal before and after the \(i\)-th sample, respectively. A symmetric window was considered, i.e., \(N=M\).  
The size of the moving average window is obtained empirically, i.e., we choose the smallest possible window whose output samples pass on the Kolmogorov--Smirnov (KS) test [37] for any of the fading distributions (Rayleigh, Rice, Nakagami, or Weibull) with a 95% confidence index. In this way, each measurement campaign has its specific filtering window.
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 1 Introduction
The experimental results showed that the proposed method could achieve high accuracy in structural damage detection and also complete minor damage identification with an accuracy of over 96.9%. This paper proposes a frame structure damage detection method based on a one-dimensional convolutional neural network. First, a three-layer framework model was designed. Second, the displacement data of each node was collected under the environmental excitation. Additionally, the data was transformed into the interlayer displacement, which formed a damage dataset. Third, the damage datasets were classified by the convolutional neural network. This research's content is described as follows: Section 2 introduces the overall framework of the method and the architecture of the proposed CNN; Section 3 implements the single damage location detection; Section 4 implements the multiple damage location detection; Section 5 implements damage degree detection; Section 6 visualizes the convolutional neural network; Section 7 concludes this article.
# ODIN112-AI-Assisted Emergency Services in Romania
## 2 Related Work
### Automatic Speech Recognition
#### 2.1.4 Hybrid Architectures  
The common perception is that the two solutions (i.e., Hidden Markov Model-based and Deep Neural Network-based architectures) are competitors in the field of Automatic Speech Recognition. Still, experiments indicate that the two technologies can be used together to benefit from the advantages of both types of models. In the past, they have shown remarkable accuracy and flexibility. Deep Neural Networks can be used for providing probability estimates for the state of the HMM, or smaller Hidden Markov Models can be used for aligning training data for the DNN.  
One well-known open-source toolkit for speech recognition that employs a hybrid architecture is Kaldi [14]. The best-performing recipe provided by Kaldi obtained a WER of 8.76% when trained on the LibriSpeech dataset. The best result for a hybrid architecture, combining HMMs and DNNs, and the same dataset is a 5.0% WER.  
Figure 3: Architecture of DeepSpeech Automatic Speech Recognition models.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
###### Abstract
Unfortunately, data carriers alone cannot track the delivery process of tobacco products. The information in the system can be easily maliciously or deliberately falsified by anyone. Blockchain technology can solve the problem of information reliability. [PERSON] et al. [10] proposed a blockchain-based method for agriculture and food supply chains, where the research showed that blockchain can make the supply chain more transparent and reliable. Therefore, blockchain is a new technology that can realize the traceability and authentication of the logistics record. In this study, we proposed a traceable and verifiable tobacco products logistics system that involved blockchain, GPS and RFID technologies. All the tobacco packages produced by the manufacturer must have an RFID tag with ID. These IDs are issued by an official organization. After the tobacco products are produced, all the logistics processes must be sent and chained in the blockchain center. The proposed scheme achieves the goal of data decentralization, is hard to tamper with, has traceability, and is authenticated. It is also convenient for the consumer to verify, for manufacturers to manage, and for the auditor to audit.
# A Cell Counting Framework Based on Random Forest and Density Map
###### Abstract  
Cell counting is a fundamental part of biomedical and pathological research. Predicting a density map is the mainstream method to count cells. As an easy-trained and well-generalized model, the random forest is often used to learn the cell images and predict the density maps. However, it cannot predict the data that are beyond the training data, which may result in underestimation. To overcome this problem, we propose a cell counting framework to predict the density map by detecting cells. The cell counting framework contains two parts: the training data preparation and the detection framework. The former makes sure that the cells can be detected even when overlapping, and the latter makes sure the count result accurate and robust. The proposed method uses multiple random forests to predict various probability maps where the cells can be detected by Hessian matrix. Take all the detection results into consideration to get the density map and achieve better performance. We conducted experiments on three public cell datasets. Experimental results showed that the proposed model performs better than the traditional random forest (RF) in terms of accuracy and robustness, and even superior to some state-of-the-art deep learning models. Especially when the training data are small, which is the usual case in cell counting, the count errors on VGG cells, and MBM cells were decreased from 3.4 to 2.9, from 11.3 to 9.3, respectively. The proposed model can obtain the lowest count error and achieves state-of-the-art.  
Keywords:cell count; random forest; detection; density map +
Footnote †: journal: Applied Sciences