esa-sceva/satcom-chunk-collection huggingface dataset:
# State of the Art of Tactile Micro Coordinate Metrology
## 4 System Calibration and Performance Verification
### Stage Calibration and Error Correction  
The way of calibrating the measurement stage depends much on its design (Section 3). Measurement stages for micro CMMs with incremental scales are calibrated and corrected similarly to conventional machines, _i.e._, the position accuracy is measured with a laser interferometer and the geometrical errors are usually determined with the help of artefacts. For the F25 machine some measurement results were published by PTB [37]. They carried out straightness measurements on a Zerodur gauge block assembly along the horizontal \(y\)-axis over 43 mm, resulting in a straightness deviation of about 30 nm with a reproducibility that would allow for a further correction down to a few nanometers residual error. A similar measurement along the vertical \(z\)-axis over a length of 1.75 mm (limited by the length of the stylus shaft) resulted in a straightness deviation of less than 10 nm.  
Figure 6: Sphericity map of a 1 mm ruby sphere with 33 nm form deviation. The 3D plot shows results obtained by probing the surface with a 10\({}^{\circ}\) resolution in longitude and a 5\({}^{\circ}\) resolution in latitude.  
The calibration of interferometer based stages includes the calibration of the laser wavelength (given by the optical frequency and the refractive index determined by the compensation unit), and--more critically--the flatness and squareness of the faces of the reference mirror cube. For the flatness calibration of the reference mirrors, high precision reference artefacts are used. IBS reported on flatness measurements using a Zerodur block with a metallic coating allowing for a capacitive sensor to be used instead of the tactile probe [29] und thus improving resolution and noise due to surface roughness and probe repeatability. The squareness calibration was made using the same artefact by applying a reversal technique.  
The calibration and correction of the Zerodur reference mirror of the METAS micro CMM has been made in a similar way as described above [35]. For this, a Zerodur block of 100 mm \(\times\) 60 mm \(\times\) 40 mm with form deviations <10 nm was used. The gold coated block was scanned by a capacitance sensor. Where possible, reversal techniques were applied to separate the errors. The resulting errors were then mapped in correction files. The calibration of the squareness in \(x/y\)-plane was made with the help of a ball plate. The squareness of the \(z\)-axis with respect to horizontal axes was calibrated with the help of a \(\mathcal{O}\) 44 mm tungsten carbide sphere, by scanning the sphere in two half-circles in the \(x/z\)- and the \(y/z\)-planes and adjusting the corresponding orthogonality angles as to obtain minimum residuals from a circle. Although somewhat less sensitive than checking the orthogonality by, e.g., measuring a ball bar in diagonal directions, the method using a sphere is more efficient and provides an independent performance check.
## 3 Methodology
### Two-Target DIP Learning  
In Section 3.1, we obtained multiple preliminary images \(\{p_{1},p_{2},\ldots,p_{n-1},p_{n}\}\) from the given noisy image to generate recovered intermediate images. In this section, we introduce our unsupervised training scheme that uses these preliminary images. Our method uses different random noise image \(z_{i}\) as the network input and uses both the given noisy image and preliminary image \(p_{i}\) as learning targets. As discussed in Section 2, using a cleaner preliminary image as the DIP learning target can restrict the DIP searching space and improve the denoising effect. However, much useful information is still contained in the given noisy image. To further improve the denoising effect, we propose a two-target DIP learning strategy that uses both the given noisy image and the preliminary image as learning targets to generate intermediate denoising images. Figure 6 shows the framework of two-target DIP learning.  
The unsupervised two-target denoising model can be described as  
\[\theta^{*}=\underset{\theta}{\text{argmin}}L(f_{\theta}(z_{i});y,p_{i})\quad subject \quad to\quad G_{out}=f_{\theta^{*}}(z_{i}) \tag{5}\]  
Figure 5: Example of the down-sampling images with the randomly selected down-sampled generator.  
where \(y\) denotes the given noisy image, \(p_{i}\) is the preliminary image generated by the cleaner intermediate image-generation approach, and \(i=1,2,\ldots,n-1,n\). The loss function is defined as follows:  
\[L_{\text{total}}=L_{\text{pre}}+L_{\text{noisy}} \tag{6}\]  
where the term \(L_{\text{noisy}}\) measures the MSE between the generated image \(d_{i}\) and given noisy image \(y\)  
\[L_{\text{noisy}}=||d_{i}-y||^{2} \tag{7}\]  
and the term \(L_{\text{pre}}\) measures the MSE between the generated image \(d_{i}\) and intermediate image \(p_{i}\)  
\[L_{\text{pre}}=||d_{i}-p_{i}||^{2} \tag{8}\]  
The loss function \(L_{\text{pre}}\) was used to constrain the range of DIP output. Using \(L_{\text{pre}}\) can effectively prevent overfitting and improve the reconstruction results. We conduct the ablation experiments to demonstrate the effectiveness of the two-target DIP learning strategy and describe the experimental results in Section 4.2.
## 3 Results and Validation
### Validation
Furthermore, most of the MHSR remote sensors are not equipped with the deep blue band and the DB method cannot be applied to these data; therefore, the proposed method is definitely more applicable. The retrieved AOD using the DB+DT method from both Aqua and Terra at Dalanzadgad site only is validated alone and it is presented in the right plot of Figure 7. It shows that the R2 value is quite small and the RMSE is a better indicator for evaluating the performance of the AOD-retrieving algorithms at sites with very low aerosol loading. This is a similar phenomenon to the validation of GF1/WFV. The RMSE from GF1/WFV's retrieval (0.0301) is much smaller than that from MODIS's retrieval (0.0762). Figure 7: **(a)** The validation of the MODIS deep blue + dark target (DB+DT) algorithm using ground measured AOD from 4 AERONET sites around the world and **(b)** the validation for the MODIS DB+DT algorithm at Dalanzadgad site only (right). Note MODIS has observations twice daily, therefore it has a large number of data samples (~5000 in total). On the other hand, the revisiting period of Sentinel-2/MSI is 10-day before July 2017 and 5-day after July 2017, respectively. The ratios of the numbers of observations between MSI and MODIS are 1/20 and 1/10, respectively.
## 2 Challenges and Routing in 6G Networks
### Challenges of Routing  
High efficient routing is an important issue for the 6G and satellite networks. The existing routing protocols were designed many years ago. In the era in which they were designed, it was, naturally, not foreseen that the technology, the internet, and the number of users would experience such an explosion. Although traditional routing protocols are far from meeting the needs of 6G networks, they are also insufficient to meet the commitments of 6G. Existing routing protocols fail to meet the needs for the following reasons, and these can also be seen in summary form in Figure 3.
# Review of Adaptive Shock Control Systems
## 2 Literature Review of Adaptive Concepts for Shock Control
### Plasma Heating Elements  
The possibility to influence the air flow and the boundary layer as well as reducing the drag (e.g., of a blunt body) by inducing a plasma has already been investigated widely [89; 90; 91; 92; 93; 94; 95]. With regards to transonic shock reduction, [PERSON] et al. [96; 97] (2012, 2019) describe the possibility to influence the air flow on an aircraft wing by creating a plasma. Such concepts consist basically of a plasma generator, a power source, a sensor and a controller. As shown in Figure 15, the plasma generator could, e.g., be conducted with two electrodes (a) or one single electrode (b), whereby these electrodes could also be recessed into the surface.  
The electric current in the plasma heats up the surrounding air flow which leads to changes in temperature and pressure. This can increase the local speed of sound limit which reduces the Mach number and consequently the strength of the shock. Under certain circumstances, the formation of the shock could be prevented entirely [97].  
According to [PERSON] et al. [98] (2012), drag reductions of about 13% have been simulated, using a thermal source term model for a RAE2822 airfoil at Mach 0.81. The simulated approach seems to be similar to the patents of [PERSON] et al. [96; 97], however, no information is given in [98] if these drag reductions refer to the total airfoil drag or only to wave drag. Furthermore, a necessary temperature of 3000 K is denoted in [98].
## 5 Element Technologies for Line Recognition
### Detecting Line Signs and Flow Arrows in a Diagram and Determining Line Types
#### 5.3.1 Deep Neural Network for Line Sign Recognition in a Diagram  
In this study, RetinaNet [23] was applied to detect line signs and flow arrows. RetinaNet has the feature pyramid net architecture, as shown in Figure 15. Unlike the existing detection algorithms in which cross-entropy loss is used for classifier loss calculation, RetinaNet uses a focal loss, which is a modification of the cross-entropy loss. Focal loss is a loss function developed to resolve the problem of class imbalance between the foreground where the target object is located and the background where the target object is not located. A case in which the number of data that each class has in a dataset is different is referred to as class imbalance. Since the dataset constructed in this study shows the imbalance in which the number of data for certain line signs such as double slash sign lines is very small compared to other line signs, RetinaNet using focal loss is an appropriate deep neural network for this case.  
Figure 14: Change of line types and addition of flow directions using a deep learning model.  
Figure 15: The architecture of RetinaNet [23].  
Anchor refers to a bounding box with a predetermined size and ratio that is used to detect various types of objects in an image. The anchor facilitates relatively fast convergence of the loss function during training and affects the accuracy of object detection. If the inappropriate size of an anchor is set, it leads to a difference from the bounding box of the object to be detected, resulting in poor learning performance. Therefore, to achieve enhanced learning performance, selecting appropriate anchor parameters is important. The basic anchor sizes used in [23] are322, 642, 1282, 2562, 5122, the basic anchor ratios are 0.5, 1, 2, and the basic anchor scales are 1, 1.269, 1.587. However, in the case of the horizontal O sign line, the size is 50 \(\times\) 25, and the vertical length is smaller than the minimum anchor size of 32. In the case of the horizontal dotted line symbol, the ratio is 3.33, showing the difference from the basic anchor ratio. In this study, we have set the optimal anchor parameters for the training dataset using the anchor optimizer. The selected optimal anchor parameters are 322, 642, 1282, 2562, 5122 in size, 0.289, 0.581, 1.0, 1.721, 3.457 in ratio, and 0.949, 1.182, and 1.543 in scale, respectively.
# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 3 Local Mapping on Vehicle  
### Semantic Segmentation and Detection  
To improve the accuracy and efficiency of image perception, semantic segmentation and object detection methods were used to extract semantic features from images [19; 20; 21; 22; 23; 24; 25]. To further advance this approach, a neural network model, called OneNet, was proposed. OneNet integrates the detection and segmentation of all semantic elements and is composed of an encoding-decoding main network, target detection head, semantic segmentation head, and instance segmentation head. Figure 2 shows the architecture of OneNet.  
In OneNet, ResNet18 is adopted as the backbone network to extract features from input images [26]. The target detection head is used to extract air elements, such as traffic lights, traffic signs, and other elements, and the semantic segmentation head is responsible for extracting ground elements, such as arrows, dotted line boxes, lane stop lines, etc. [27]. The instance segmentation head is used to extract lane line semantic elements [28]. The effectiveness of this approach is demonstrated in Figure 3, which shows the segmentation detection results [29].
## 3 Results and Discussion
### Amino Acid Composition  
With the spread of sustainable diets, the protein quality of plant foods and other alternative sources is becoming an increasingly important aspect of nutrition. The amino acid composition is a key component of protein quality as it determines the level of protein utilization. Common metrics evaluating protein quality are the PDCAAS (Protein Digestibility-Corrected Amino Acid Score) and DIAAS (Digestible Indispensable Amino Acid Score). PDCAAS calculates the proportion of dietary amino acids that are absorbed and provides a score based on how well the amino acid profile of the test protein aligns with human amino acid requirements. On the other hand, DIAAS offers a more accurate measure of amino acid bioavailability by assessing the concentrations that reach the terminal ileum, rather than focusing on what remains in feces. [40]. Table 3 shows the amino acid composition of our nutraceutical samples. In addition, the total amino acid content (TAA) as well as the ratios of essential (EAAs) and branched-chain amino acids (BCAAs) are also presented. The highest amount of total amino acids was observed in the spirulina powder (526.4 mg/g), while rosehip, aronia, and lucuma powders contained amino acids in small concentrations (below 40 mg/g). The total amino acid contents of the samples are comparable with the amounts of crude protein. In general, glutamic acid was the most abundant amino acid in the examined samples, followed by aspartic acid, leucine, alanine, and arginine. On the other hand, cysteine, methionine, and histidine were present in relatively low amounts. The ratio of essential amino acids to total amino acids ranged between 24.9 and 39.9%. The highest EAA ratios were observed in the cereal grass powders, followed by spirulina and spinach. On the other hand, goji berry, beetroot, and aronia powders showed relatively low EAA ratios.
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 2 Preliminaries
### Model of TTSA
#### 2.1.3 Fitness Function  
The objective function of the task optimization allocation of the above output results is the maximum value of the transmission effect evaluation, which can be described as:  
\[\textit{Max}:\ \ \textit{Value}(\textit{Result})=\frac{\sum_{i=1}^{n}\textit{ Va}_{i\ \textit{Tr}_{i}.\textit{TaFr}}}{n} \tag{10}\]  
According to the _Result_ of the task allocation in the sequence, the evaluation value of each equipment allocation task is calculated and averaged to obtain the evaluation value of the entire result sequence. In the formula, \(i\) is the equipment sequence number, \(\textit{Tr}_{i}.\textit{Tafr}\) is the operating frequency band of the \(i\)th equipment transmission task, and \(\textit{Va}_{i\ \textit{Tr}_{i}.\textit{TaFr}}\) represents the comprehensive evaluation value of the \(i\)th equipment operating in the frequency band \(\textit{Tr}_{i}.\textit{TaFr}\) in the comprehensive evaluation _ValueMatrix_ of the transmission effect.  
According to the above model framework and data model description, the data structure parameters are as outlined in Table 1.
## 3 Latin America and Colombia
### Monitoring and Inspection  
The global human population reached 8 billion in mid-November 2022 [27], and Latin America has one of the highest population growth rates after Africa and Asia. Its rise has also increased the energy power demand, requiring an increase in power system expansion and transmission line inspection [28]. Furthermore, vegetation detection has been implemented near high-voltage transmission lines using UAV and LiDAR technology [29].  
Some applications in the civil engineering area must also be mentioned. This particular area has some of the most dangerous assessments for people. Digital image processing and UAV-captured images have been used to evaluate facades [30], assist in the inspection of complicated access constructions, and create a safety construction plan for high-rise buildings [31]. Although the literature shows some advances in research and projects, the use of UAS technology in the electrical sector remains low for Colombia and the Latin American region.
# Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth
## 3 Antenna Prototype Fabrication and Measurement
### Measurement of \(S_{11}\)  
The fabricated antenna was placed into the seven-layer phantom for measurements. More specifically, the antenna was vertically inserted in the skull layer; hence, the top and bottom of the antenna were touching the fat and dura layer, respectively (see Figure 2). Figure 9 shows the test setup for \(S_{11}\) measurement. The phantom (with the antenna inside) was placed on a mount made with low permittivity and low loss Rohacell(r) foam. The coaxial cable sticking out from the phantom was connected to a vector network analyzer (Anritsu MS2038C). Figure 10 compares the measured and simulated \(S_{11}\) of the proposed and conventional Vivaldi antennas. The latter is an ordinary Vivaldi that does not have the vertical slot array, as depicted in the inset of Figure 10. The red and black lines correspond to \(S_{11}\) responses of the proposed and conventional Vivaldi, respectively. The measured \(S_{11}\) data indicated that the resonant frequency of the proposed Vivaldi was 2 GHz lower than the conventional Vivaldi (4.3 GHz versus 6.3 GHz), which confirms the antenna miniaturization effect caused by the vertical slot array. The simulation results of \(S_{11}\) are drawn with solid lines. They agree well with the measurements. The measured resonant frequencies were higher than those from simulations by approximately 300 MHz and 150 MHz for the proposed and conventional antennas, respectively, possibly because of slight discrepancies in the phantom's material properties. We note that the phantom's \(\varepsilon_{\mathrm{r}}\) and tan\(\delta\) are highly affected by the amount of water evaporation with time. It is hard to avoid the water evaporation in spite of precautions when handling the semi-solid phantoms. Solutions to prevent this difficulty can include wrapping each tissue phantom with a thin layer of low permittivity material or using 3D-printed biomaterials with a low moisture content.  
Figure 8: Fabricated antenna prototype: (**a**) antenna printed on a substrate, (**b**) antenna connected to a coaxial cable, and (**c**) antenna covered by a superstrate.  
Figure 9: Test setup for \(S_{11}\) measurement.
## 4 Results
### Environmental Cost of Concrete  
Figure 4 shows the environmental cost evaluation results of the evaluation targets. According to Figure 4, the environmental costs of the evaluation targets ranged from 5.88 USD/m\({}^{3}\) (C-R30) to 8.79 USD/m\({}^{3}\) (F-W30), and that of the standard mixture was calculated to be 6.78 USD/m\({}^{3}\). Environmental costs tended to increase with unit cement volume (F-B30 \(>\) F-W30 = Std. \(>\) F-S30 \(>\) C-B30 \(>\) F-R30 \(>\) C-R30) in the concrete mixture proportion, while F-W30 rather represented the highest environmental costs, even though less cement was used than F-B30. High environmental costs were observed from F-B30 and F-W30, which exhibited higher EP evaluation results than other mixtures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Target**} & **ADP** & **GWP** & **ODP** & **AP** & **POCP** & **EP** \\  & **(kg-Sb\({}_{\text{eq}}\)/m\({}^{3}\))** & **(kg-CO\({}_{\text{2 eq}}\)/m\({}^{3}\))** & **(kg-CFC1\({}_{\text{eq}}\)/m\({}^{3}\))** & **(kg-SO\({}_{\text{2 eq}}\)/m\({}^{3}\))** & **(kg-C\({}_{2}\)H\({}_{\text{eq}}\)/m\({}^{3}\))** & **(kg-PO\({}_{4}\)\({}^{3-\text{eq}}\)/m\({}^{3}\))** \\ \hline Std. & \(1.32\times 10^{0}\) & \(3.58\times 10^{2}\) & \(2.06\times 10^{-6}\) & \(3.86\times 10^{-1}\) & \(2.46\times 10^{-1}\) & \(4.15\times 10^{-2}\) \\ F-R30 & \(1.18\times 10^{0}\) & \(3.15\times 10^{2}\) & \(2.47\times 10^{-6}\) & \(3.58\times 10^{-1}\) & \(2.18\times 10^{-1}\) & \(4.00\times 10^{-2}\) \\ F-S30 & \(1.29\times 10^{0}\) & \(3.47\times 10^{2}\) & \(1.97\times 10^{-6}\) & \(3.74\times 10^{-1}\) & \(2.39\times 10^{-1}\) & \(4.00\times 10^{-2}\) \\ F-B30 & \(1.43\times 10^{0}\) & \(3.86\times 10^{2}\) & \(2.17\times 10^{-6}\) & \(4.16\times 10^{-1}\) & \(2.65\times 10^{-1}\) & \(4.45\times 10^{-2}\) \\ F-W30 & \(1.32\times 10^{0}\) & \(3.24\times 10^{2}\) & \(2.02\times 10^{-6}\) & \(5.30\times 10^{-1}\) & \(2.49\times 10^{-1}\) & \(9.15\times 10^{-2}\) \\ C-R30 & \(1.12\times 10^{0}\) & \(2.98\times 10^{2}\) & \(2.48\times 10^{-6}\) & \(3.41\times 10^{-1}\) & \(2.08\times 10^{-1}\) & \(3.83\times 10^{-2}\) \\ C-B30 & \(1.24\times 10^{0}\) & \(3.34\times 10^{2}\) & \(1.93\times 10^{-6}\) & \(3.60\times 10^{-1}\) & \(2.30\times 10^{-1}\) & \(3.85\times 10^{-2}\) \\ \hline \end{tabular}  
* ADP: abiotic depletion potential, GWP: global warming potential, ODP: ozone depletion potential, AP: acidification potential, POCP: photochemical ozone creation potential, EP: eutrophication potential.  
\end{table}
Table 7: Results of the Evaluation of the Six Environmental Impacts of the Evaluation Targets.
# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 2 Related Work
In order to adapt to the local brightness of the input image sequence, this paper proposes a new weighted average adaptive factor exposure evaluation scheme; unlike previous enhancement schemes, to enhance the details of brighter and darker regions a local detail enhancement mechanism is proposed, which only processes the brighter and darker regions determined by the average brightness while leaving the other regions unchanged.