esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application to Experimental Data
### Individual Measurements and Processing
To this end, we set \(w_{d}=\left(y-E\left(X\right)\right)/Std\left(X\right)\). That is, the image intensities \(y\) are standardized with regard to the null distribution of background signal intensities \(X\) for each sensor. After registration to a common coordinate system based on manually defined location correspondences in the data, the final set of hits from all sensors is plotted in Figure 7. Obviously, the false alarms considerably outnumber the actual groove hits. This is due to our sensitive detection rules, intending that no actual defect is missed during individual processing. Figure 7: Hit locations per sensor in a common coordinate system similar to that in Figure 4. (**a**) ET; (**b**) ML; (**c**) TT. Darker colors correspond to higher SNR. Note that the color scale is clipped to a maximum of 10 to prevent non-groove hits from dominating. Axes \(x\) and \(y\) are not to scale. The tips of the triangular markers indicate the groove positions. The two arrows point to prominent crack-like indications (false positives) in the ET and TT images. Of course, in a single-sensor inspection task, a much more stringent detection criterion is appropriate to limit the number of false alarms. However, this possibly leads to worse sensitivity to small flaws. In contrast, our data fusion approach is supposed to discard most false hits while maintaining high sensitivity to small defects. We will now briefly compare the individual sensor results. In contrast to ET and TT, the MFL hits cluster spatially. This is because the background variations in this data set are not homogeneous, possibly due to inhomogeneities in the internal magnetic field. MFL data are missing in the strip between the two groove rows. The shallowest groove nr. 15 features low SNR in the ET and TT data due to its shallowness. MFL in contrast is more sensitive. Moreover, grooves nr. 8 and 9 stand out in the TT data, because their confidences are even weaker than the shallower grooves nr. 10-14. Interestingly, in Figure 7, spatial defect-like patterns are formed, although the specimen is not expected to contain any flaws other than the known grooves. For example, see the vertical lines from TT, or the diagonally oriented lines from ET, as highlighted by the arrows. As previously discussed, using individual inspection, it is not easy to classify these obvious indications as structural indications or flaws. In spite of their regular structure, we label these patterns as non-defect indications during the following evaluation, if our multi-sensor data set is not able to give a reliable confirmation. On the other hand, there are a few off-groove locations where different sensors behave consistently. These regions could in fact represent unknown but real defects, and will therefore be excluded from the following evaluation. Note that hits within disregarded areas are not shown in this figure. Moreover, the confidence associated with each hit is not shown, because all hits exceed the chosen threshold of 99% as explained before.
# DNA Barcoding as a Plant Identification Method
## 4 Chloroplast DNA Barcodes
### _rbcL_  
_rbcL_ (ribulose bisphosphate carboxylase/oxygenase large subunit) is a candidate locus for comparing at the levels of family and genus; however, it is not suitable for species identification as it has modest discriminatory power. This marker has been one of the most studied among the plastid genome, with wide representation from all major groups and many available sequences in GenBank [20; 21; 30; 39]. It was the first gene sequenced from the plant chloroplast genome and encodes the large subunit of rululose-1,5-bisphosphatecarboxylase/oxygenase (RUBISCO), a critical photosynthetic enzyme [48]. _rbcL_ is easy amplify and sequence but has a slow evolutionary rate [7; 39]. Its length is approximately 1430 bp and thus at least two sets of primers are needed to sequence the entire coding sequence [21; 35]. _rbcL_ meets most of the desired criteria and can be used in conjunction with other markers [30; 37]. It is also widely used for alga, peptidoglycan, and angiosperm identification [6; 32].
# Performance Analysis of Existing ITS Technologies: Evaluation and Coexistence
## 6 Conclusions and Future Work  
The high demands of C-ITS services for data exchange require efficient resource management and a procedure to ensure the appropriate performance. This paper provides an analysis model of LTE-V2X and IEEE 802.11p/ITS-G5, which allows evaluating and comparing their boundaries in environment change situations, such as network density, packet size and channel capacities. In addition, based on obtained results, we propose a coexistence solution that relies on the density sharing between ITS technologies exploiting their benefits to maximise system performances. Furthermore, we identify the optimal configurations for each ITS technology to show that resilient systems via an adaptive arrangement are required to adapt to environment-changing situations. All the above findings show different trade offs and limitations between the considered ITS technologies. These conclusions are essential to better understand their behaviour in a realistic environment.  
The recommendation on the adaptive reconfiguration concept works effectively as an excellent metric to limit such trade offs. However, many research efforts are needed to improve this concept, which can be further combined with density-sharing distribution to define road profiles. In addition, the new 5G generation has introduced more flexibility in managing resource allocation (e.g., numerologies and MAC layer aspects) [36]. However, these enhancements will affect the performance of V2X applications. Therefore, evaluating this technology under the same conditions is needed for advancing C-ITS use cases.  
Conceptualization, S.M., H.A., L.M. and M.B.; methodology, S.M., H.A., L.M. and M.B.; writing--original draft preparation, S.M.; writing--review and editing, S.M., H.A., L.M. and M.B. All authors have read and agreed to the published version of the manuscript.  
Figure 18: Sequence diagram for reconfiguration diffusion from a vehicle and infrastructure.
## 2 Constellation Design
### Constellation Technical Characteristics
#### 2.2.1 A Low-Latitude Satellite Constellation  
Currently, most remote sensing satellites primarily focus on the medium and high latitudes, where the economy is the most active. Large-inclination and polar-orbiting satellites are often used in these regions, which results in a significant inadequacy of observation coverage in low-latitude regions. HSC addresses this issue by adopting a 30\({}^{\circ}\) inclination orbit. Its observation range is mostly limited between the Tropic of Cancer and the Tropic of Capricorm, which ensures the high coverage of low-latitude regions. In addition, the composition of 10 satellites can significantly improve the revisiting frequency and enhance the spatial coverage of the regions of interest.
## 3 Conclusions  
We investigated two approaches of preparing TiO\({}_{2}\) films, using pastes made from the same solvothermally-synthesised anatase nanoparticles, which were in turn incorporated into solar cells. Both methods resulted in high-crystallinity anatase TiO\({}_{2}\) nanoparticle films. The approach which eliminated the drying step led to highly transparent photoanodes (SANP-T) along with slightly higher surface area and uniform pore size distribution, resulting in increased dye loading compared to that obtained with the more conventional drying step (SANP-S). The differences in PCE between SANP-T\(\,\)I\(\,\)D149 (6.4%) and SANP-S\(\,\)I\(\,\)D149 (6.9%) were modest, with slightly lower \(J_{sc}\) and FF attributed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Devices** & \(R_{\text{S}}\) (\(\Omega\)**-cm\({}^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{ \text{ \text{ }}}}}}}}}}}}}}}}\)) & \(R_{ct2}\) (\(\Omega\)**-cm\({}^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{{ \text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{{  \texttexttexttexttexttexttexttexttexttext {{      }}}}}}}}}}}}}}}}}\)\) & \\ \hline SANP-S\(\,\)I\(\,\)D149 & 4.6 \(\pm\) 0.1 & 2.3 \(\pm\) 0.2 & 21 \(\pm\) 0.3 & 1550 \(\pm\) 100 \\ SANP-T\(\,\)I\(\,\)D149 & 4.8 \(\pm\) 0.1 & 2.4 \(\pm\) 0.2 & 24 \(\pm\) 0.4 & 1615 \(\pm\) 100 \\ SANP-S\(\,\)I\(\,\)N719 & 5.8 \(\pm\) 0.1 & 2.0 \(\pm\) 0.1 & 24 \(\pm\) 0.3 & 800 \(\pm\) 100 \\ SANP-T\(\,\)I\(\,\)/N719 & 5.2 \(\pm\) 0.1 & 2.0 \(\pm\) 0.2 & 25 \(\pm\) 0.5 & 900 \(\pm\) 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: EIS parameters of the devices based on SANP-S and SANP-T sensitised with D149 or N719, where \(R_{\text{s}}\) is the series resistance, \(R_{ct1}\) and \(R_{ct2}\) are the charge transfer resistance of the mediator at the counter and in the working electrode respectively, and \(CPE_{1}\) and \(CPE_{2}\) are the constant phase elements associated with the counter and working electrodes, respectively [32; 36].  
Figure 7: Electrochemical impedance spectroscopy (EIS) measurements. (**a**) Nyquist and (**b**) Bode plots of devices based on SANP-S and SANP-T sensitised with either D149 or N719.  
to a lower LHE and \(\Phi_{cc}\), respectively. On the other hand, the differences in N719 devices resulted solely from lower \(J_{sc}\), which was due to lower light scattering and a low extinction co-efficient. This indicates that D149, or other dyes with high extinction coefficients, is more promising for BIPV applications than N719. Unfortunately, from the perspective of BIPV applications, much of the development of DSCs has been strongly focused on increasing the performance of devices with N719, meaning that highly scattering structures were desirable (as can be seen from the differences in performance between the two N719-based architectures). This work suggests that many of the reported TiO\({}_{2}\) nanoparticles materials may be suitable for such an application if they were to be incorporated into a paste without a drying step.
## 4 SCMA Receiver  
At the receiver, the detection and decoding of the SCMA codewords are directly dependent on the design of the codebooks and channel noise. In [4; 5; 6; 7; 8; 9; 10], MPA and minimum Euclidean distance are used for detection and decoding. In [11] and [21], supervised learning using neural networks is used for uplink detection and decoding with four codewords (two bits). In this work a neural network was used as shown in Figure 2.  
Figure 1: Minimum distance between points of the mother constellation.  
Figure 2: Subcarrier 1 IQ constellation.  
The resource allocation shown in Equation (2), defines which subcarrier each user uses to send their data. From Figure 3, it is observed that User 1 will use SC 1 and SC 2 (solid lines).  
The codebook resulting from Equation (10) is given by Equation (11), where it is also observed that only the first two rows are occupied with the data of each codeword.  
\[\left[\begin{array}{  
Figure 4 shows the neural network learning precision for the SVD and phase rotation methods. The neural network with SVD-SCMA reached an accuracy of 93.96%, while SCMA by phase rotation had 94.71%.
## 4 Experiment and Analysis
### Experimental Results
#### 4.3.5 Ablation Experiments  
To verify the effectiveness of the module proposed in this paper, we designed ablation experiments, as shown in Table 6. From Table 6, we can see that the SPP module improved the _precision_ and \(mAP\), but the I decreased slightly, while the SE attention mechanism improved the \(F1\) score and \(mAP\), but the precision and recall remained unchanged. Improving  
Figure 8: PR curves of the YOLOv4-tiny algorithm and the improved algorithm.  
Figure 9: Comparison of PR curves from the same category between the YOLOv4-tiny algorithm and the improved algorithm.  
the FPN structure had the most obvious effect, improving the four performance indexes (_precision_, _recall_, \(F\)1, and _mAP_). In summary, the improved method proposed in this paper achieved good results and can effectively improve the detection algorithm's performance.  
Considering that the learning rate has some influence on the training results, we conducted a relevant experimental validation. The experimental results are shown in Table 7. We adjusted the learning rate within the interval from 0.002 to 0.003, based on the default learning rate. As can be seen from the table, precision and recall were almost unchanged with different learning rates, and the _mAP_ and \(F\)1 scores did not change much. _mAP_ was the highest at a learning rate of 0.0025, and the \(F\)1 score was the highest at the default parameter of 0.00261. We chose 0.0025 as the learning rate.
# Immersive Two-Channel Recordings Based on Personalized BRIRs and Their Applications in Industry
## 3 Personalized BRIRs
Unlike traditional HRIRs, which offer a generic approach to spatial audio by averaging measurements from multiple individuals or just a dummy head, personalized BRIRs are tailored to the unique anatomical and environmental characteristics of each listener [34]. A broader comparison is presented in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Sound Localization** & **Personalization** & **Ease of Use** \\ \hline Personalized BRIRs & High precision & Highly personalized & Require \\ \hline Database HRTFs & Moderate precision & Generic & Easy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of personalized BRIRs and database HRTFs. At the core of personalized BRIRs is their ability to capture how sound interacts with the listener's specific environment and anatomy. This involves detailed measurements of an individual's head, ears, and torso, as well as the acoustic properties of the environment where the audio is being experienced. The benefits of personalized BRIRs are manifold. In VR and AR applications, the enhanced spatial cues provided by personalized BRIRs significantly improve the sense of presence and immersion, making virtual environments more believable and engaging. Studies have shown that when BRIRs are customized to an individual's unique anatomy, the accuracy of sound localization improves, leading to a more natural and satisfying user experience [35]. Assuming that the collected set of BRIRs is to be used to convert multichannel recordings from speaker systems to two-channel audio for headphones, then having additional information about room characteristics could be very helpful, especially in the aspect of working in a closed environment and wanting to recreate natural acoustics, as well as achieving the best the immersion experience [36]. Taking into account the overall experience, what stands out the most is how using just two-channel devices such as headphones, not only the room acoustics but also the specific sound source localization can be replicated. Externalization, i.e., the impression of sound coming from all around the person instead of its centralization in the listener's head, is the key to making any binaural exposure more plausible and engaging.
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 3 Uncertainty Estimation in Neural Networks -- Mathematics and Algorithm
### Uncertainty Estimation for Embedding Vectors
#### 3.3.2 Embedding Heads  
Like the Bayesian module, the mean and variance heads \(\varphi^{(p)},\varphi^{(\Sigma)}\) are also bottleneck-blocks but without any weight-sampling (see Figure 1(b),c). They are both applied to each of the \(K\) versions of the resulting feature tensor \({}^{k}F\) separately which then consequently resultsin \(K\) versions of the raw mean features \({}^{k}\widehat{M}\in\mathbb{R}^{d\times 16\times 8}\) (Equation (5)) and the variance features \({}^{k}S\in\mathbb{R}^{d\times 16\times 8}\) (Equation (6)).  
\[{}^{k}\widehat{M} =\varphi^{(\mu)}\Big{(}{}^{k}F\Big{)} \tag{5}\] \[{}^{k}S =\varphi^{(\Sigma)}\Big{(}{}^{k}F\Big{)} \tag{6}\]  
The raw mean features \({}^{k}\widehat{M}\) and the variance features \({}^{k}S\) are then utilized according to Equation (7) by applying element-wise division (\(\oslash\)) to obtain the mean features \({}^{k}M\in\mathbb{R}^{d\times 16\times 8}\). This represents the first manner in which our approach employs the uncertainty values to enhance the embedding. In contrast to most approaches, this application of the data uncertainty estimate is also employed during inference.  
\[{}^{k}M={}^{k}\widehat{M}\oslash^{k}S \tag{7}\]  
The subsequent Global Average Pooling (GAP) is applied to each of the \(K\) versions of the raw mean features \({}^{k}\widehat{M}\), the mean features \({}^{k}M\), and the variance features \({}^{k}S\) separately, collapsing the width and height dimensions. This results in \(K\) versions of each of the raw mean vector \({}^{k}\widehat{\mu}\in\mathbb{R}^{d}\) (Equation (8)), the mean vector \({}^{k}\mu\in\mathbb{R}^{d}\) (Equation (9)), and the variance vector \({}^{k}\Sigma\in\mathbb{R}^{d}\) (Equation (10)).  
\[{}^{k}\widehat{\mu} =\text{GAP}\Big{(}{}^{k}\widehat{M}\Big{)} \tag{8}\] \[{}^{k}\mu =\text{GAP}\Big{(}{}^{k}M\Big{)}\] (9) \[{}^{k}\Sigma =\text{GAP}\Big{(}{}^{k}S\Big{)} \tag{10}\]  
In order to obtain the final mean vector \(\mu\in\mathbb{R}^{d}\), the element-wise mean is computed across the \(K\) versions of the mean vector \({}^{k}\mu\) (Equation (11)). Here, \(\mathbb{E}\) refers to the expected value of the indicated values.  
\[\mu_{i}=\mathbb{E}\Big{\{}{}^{k}\mu_{i}\Big{\}}_{k=1}^{K} \tag{11}\]  
By utilizing the Bayesian module and the embedding heads, the three types of uncertainty can be estimated, which will be explained in detail below.
## 6 Experimental Results
### Implementation Details  
Our network was performed with the popular DL framework TensorFlow version 2.12.0 and Windows 10 operating system, the programming language used was Python 3.9, and we used the Adam optimizer to train the models. All experiments were implemented on an 11 th Gen Intel(R) Core(TM) i5-11500 @ 2.70 GHz 2.71 GHz CPU with a batch size set to 200. For each dataset, we selected 70% as the training set and 30% as the test set.  
Figure 11: Sky plot of different locations with the satellite visibility labeled from ground truth. (**a**) D1; (**b**) D2; (**c**) D3; (**d**) D4. (Green is the LOS satellite, and pink is the NLOS satellite).  
Figure 12: Data sliding window of D1, D2, D3, and D4.
## 1 Introduction
### Cholesterol Transport  
The primary cholesterol transporter from the liver to other organs (kidneys, muscles, and adrenal cortex) is low-density lipoprotein (LDL), containing most plasma cholesterol. LDL particles, formed from very low-density lipoproteins (VLDL), retain apoB-100, but lose other apolipoproteins to HDL and contain fewer triglycerides (TAG) than VLDL, but more cholesterol esters (CE) and cholesterol. LDL particles function by depositing free cholesterol on the surface of cell membranes or by binding to a membrane receptor that recognizes apolipoprotein B-100 (apoB-100) [8].  
Cholesterol is transported in the plasma mostly as cholesteryl esters associated with lipoproteins. Cholesterol from the diet is transported within chylomicrons (CM) from the small intestine to the liver. Cholesterol synthesized de novo and any dietary cholesterol in the liver exceeding the hepatic demand is transported in the serum within LDL particles. The liver synthesizes VLDL, which is then converted to LDL by endothelial lipoprotein lipase. Cholesterol in plasma membranes can be extracted by HDL and esterified by the HDL-linked LCAT enzyme. Cholesterol extracted from peripheral tissues by HDL can then be transferred to VLDL and LDL by LCAT--the HDL-associated protein that transfers cholesterol esters. Reverse cholesterol transport allows peripheral cholesterol to return to the liver in LDL. Finally, cholesterol is excreted in the bile as free cholesterol or bile salts after being converted to bile acids in the liver.  
Figure 1: Cholesterol biosynthesis pathway presented in Systems Biology Graphical Notation (SBGN) [7]. See Section 1.3 for more details. The explanation of abbreviations used in the diagram: HMG-CoA — 3-hydroxy-3-methyl-glutaryl coenzyme A; ATP — adenosine triphosphate; IPP — isopentyl pyrophosphate; DPP — 3,3-dimethylallyl pyrophosphate; GPP — geranyl pyrophosphate; FPP — famesyl pyrophosphate; PP — pyrophosphate; NADPH — nicotinamide adenine dinucleotide phosphate.  
Three mechanisms counteract the accumulation of cholesterol in the cell:  
1. Cholesterol derived from the remnants of CM, HDL, and LDL particles inhibits the synthesis of endogenous cholesterol through the concentration and activity of HMG-CoA;
2. Excess cholesterol exceeding the current metabolic needs of the cell causes it to be esterified by acyl-CoA: cholesterol acyltransferase (ACAT). The activity of this enzyme increases with increasing cholesterol content inside the cell. The ACAT transfers a fatty acid residue from the acyl-CoA to the -OH group of cholesterol, forming a cholesterol ester that can be stored in the cell.
3. High cholesterol content in the cell causes a decrease in the synthesis of the LDL receptor (LDL-R) by reducing the transcription of the corresponding gene. This helps to reduce the uptake of LDL-C into the cell.
# Effect of Built Environment Factors on Pedestrian Safety in Portuguese Urban Areas
## 2 Factors Influencing Pedestrian Safety
### Factors Associated with the Built Environment  
Several studies have analyzed the effect of land use on the frequency of pedestrian crashes. Residential land use was associated with a significantly lower probability of pedestrian crash [10], while on the other hand, a higher fraction of industrial, commercial, and open land use types was associated with a higher probability of crashes [9; 10; 16]. In other studies, mixed land use was associated with higher frequency and risk of pedestrian crashes [12; 17; 19].  
Other factors in the group of built environments that some authors have identified as contributing to an increase in crash frequency involving pedestrians include the existence of bus stops and the presence of parking spaces along the street. One of the causes may be due to the constant presence of the buses or vehicles parked, which can block the view of pedestrians and drivers. Additionally, zones with transport services and on-street parking are expected to have more human activities, associated with more conflicts between pedestrians and vehicles [9; 16; 17]. However, some authors found that higher bus stop density appears to contribute to less-injurious crash rates [19].