esa-sceva/satcom-chunk-collection huggingface dataset:
# From Google Earth Studio to Hologram: A Pipeline for Architectural Visualization
## 2 Materials and Methods
### Defining Recording Object Orientation and Navigation in Google Earth  
As Earth Studio lacked built-in tools for measuring distances and angles, Google Earth was used for these measurements. Google Earth was configured using the same dataset and navigation settings as Google Earth Studio. Using Google Earth, the recording angle width (Wr), optimal camera key point for viewing the building model in front view, and the distance from the camera to the target point on the building (r) were determined (Figure 2).  
The recording angle width (Wr) refers to the actual width of the area captured within a frame. Based on the frame dimensions and diagonal field of view (FOV) used by the camera on Google Earth, which is approximately 40\({}^{\circ}\), the distance from the camera to the building (r) can be determined using Equation (1). This integrated calculation allows for the precise control of camera positioning in relation to the target area, ensuring consistent and accurate spatial data acquisition for 3D visualization and holographic projection in virtual urban environments. In this calculation, (a) represents the aspect ratio of the frame, which  
Figure 2: A total of 721 images was captured to create multiple-perspective views spanning a total arc of 120\({}^{\circ}\) around the objects on the Google Earth navigation circle.  
is calculated as the ratio of the width to the height of the captured frame. The equation is derived from trigonometric relationships involving the diagonal FOV and geometric conversions between the width and diagonal length, incorporating the aspect ratio and the known real-world frame width.  
\[r=\frac{Wr}{2\times\left(\tan\frac{FOV_{d}}{2}\times\frac{a}{\sqrt{1+a^{2}}}\right)} \tag{1}\]  
The heading value of the front viewpoint, measured in degrees along a 0\({}^{\circ}\) to 360\({}^{\circ}\) circular scale according to the navigation systems of Google Earth and Google Earth Studio, must be determined first. This heading is a critical reference parameter because it is used to calculate the heading values of the other viewpoints positioned along a 120\({}^{\circ}\) sector around the front viewpoint with the building as the center point of the arc. For example, when the front viewpoint is defined as 0\({}^{\circ}\) on the 360\({}^{\circ}\) navigation circle used in Google Earth and Google Earth Studio, the 120\({}^{\circ}\) sector for image data collection spans 300-60\({}^{\circ}\) and is centered around the target building. Similarly, if the front viewpoint is positioned at 290\({}^{\circ}\), the corresponding sector extends from 230\({}^{\circ}\) to 350\({}^{\circ}\). When the front viewpoint is set to 40\({}^{\circ}\), the image acquisition sector ranges from 340\({}^{\circ}\) to 100\({}^{\circ}\). These examples illustrate how the angular sectors are symmetrically defined around a central viewpoint in a circular coordinate system.
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 4 Experimental Validation
### Experimental Results and Analysis
#### 4.4.3 Ablation Experiments  
In order to verify the role of each module in the ODGNet, ablation experiments are conducted. In the ablation experiments, the ODConv is replaced by ordinary convolution, the SA module is replaced by a shorting module, and the primary feature fusion module is directly removed. The ablation results are shown in Table 3. The results show that the ODConv and SA module contribute the most to the network, and the primary feature fusion module utilizes multi-scale features to further improve the detection accuracy.
## 4 Discussion
First, the use of the Optogait(r) optical system provided highly reliable and objective measurements of spatiotemporal gait parameters, validated in prior research for older adults [12]. Second, the inclusion of a well-characterized sample of 242 community-dwelling older Koreans aged 75 years and above, assessed with both DXA-based muscle mass measurements and the short physical performance battery (SPPB), allowed for the robust classification of sarcopenia according to the AWGS 2019 criteria. Third, the development of a novel predictive model combining gait speed and double-limb stance represents an innovative advancement, demonstrating improved sensitivity and specificity over individual parameters. These strengths underscore the study's potential to inform clinical practice by providing a practical, non-invasive approach to sarcopenia screening. Despite the strengths of this study, several limitations should be acknowledged. First, its cross-sectional design limits the ability to determine causal relationships between gait parameters and sarcopenia-related impairments. Longitudinal studies are needed to assess whether gait changes predict sarcopenia progression. Second, the study sample consists only of community-dwelling older adults in Korea, which may limit the generalizability of the findings to institutionalized or more diverse populations. Additionally, the study does not explore the neuromuscular mechanisms underlying gait alterations in sarcopenia, which could be further investigated using electromyographic (EMG) or biomechanical assessments. Lastly, while gait parameters showed predictive value, further research is needed to assess their clinical applicability and establish threshold values for sarcopenia screening in routine assessments. Addressing these limitations will help improve the clinical relevance and broader applicability of gait analysis in sarcopenia detection.
###### Abstract  
Chloride ion penetration frequently leads to steel corrosion and reduces the durability of reinforced concrete. Although previous studies have investigated the chloride ion permeability of some fiber concrete, the chloride ion permeability of the basalt fiber reinforced concrete (BFRC) has not been widely investigated. Considering that BFRC may be subjected to various exposure environments, this paper focused on exploring the chloride ion permeability of BFRC under the coupling effect of elevated temperatures and compression. Results demonstrated that the chloride ion content in concrete increased linearly with temperature. After exposure to different elevated temperatures, the chloride ion content in BFRC varied greatly with increasing stress. The compressive stress ratio threshold for the chloride ion penetration was measured. A calculation model of BFRC chloride ion diffusion coefficient under the coupling effect of elevated temperatures and mechanical damage (loading test) was proposed.  
basalt fiber reinforced concrete (BFRC); elevated temperature; compression experiment; chloride ion penetration +
Footnote †: journal: Applied Sciences
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 4 New Approach: Parallel Vector Processing Based Algorithm
### Word To Bit  
The final operation of the algorithm is unpadding the output vector of the ordering phase from the redundant zeros that were added to the original data in the \"bit to word\" phase.  
This concludes the vector-wise permutation stage implementation. Given \(N\) bit couplets and matching parameters, following these phases, the result vector will contain \(N\) bit couplets after permutation. The described are compatible with the DVB-RCS2 standard and are as generic as possible, having little data adjustments to best fit CEVA's DSP and being able to be reconstructed to fit any VP with very few and minor changes to the overall algorithm. The asymptotic tight bound computational complexity of the vector-wise algorithm can be written as  
\[O(N+\lceil\frac{N}{P}\rceil\cdot P)\simeq O(N)\]
## 3 Results
In the final phase of experiments, optimizing and classification processes were repeated by choosing the best five features to measure the effect of feature selection algorithms on the classification algorithm. In this regard, weight, FCVC, CALC, FAVC, and FAF features were selected using chi-square; FAF, CALC, TUE, FAVC, and FCVC features were selected using F-Classify; and weight, FAF, smoke, FAVC, and CALC features were selected using mutual information classification. The same lowest and highest values were used for parameter optimization, and the exact steps were applied using the same datasets. The optimum hyperparameters for each feature selection technique are shown in Table 6.  
After parameter optimization, a separate model was trained ten times for each method using optimum hyperparameters, similarly to the model that used all of the features in the training and testing datasets. Table 7 shows the mean accuracy, mean weighted average F1-score (mean F1-score), SD between accuracy computed in each trial (SD accuracy), SD between weighted average F1-score computed in each trial (SD F1-score), sensitivity, specificity, and Brier score for multi-class classification. The result of the model, which was trained used all of the features, was also added to this table to easily make comparisons between models. The results showed that the model obtained using the features selected with F-Classify had a better performance. Furthermore, when Brier scores were examined, scores of 0.094, 0.147, 0.122, and 0.194 were obtained for the NN which was created after feature selection with the NN model, where all variables from the chi-square, F-Classify, and mutual information classification variable selection algorithms were used. The lowest Brier score was obtained for the NN model using all variables (Brier score = 0.094) and this was the model showing the best calibration. When variable selection methods were compared in terms of calibration, the NN model created after the F-Classify algorithm showed the best calibration (Brier score = 0.122). Therefore, F-Classify was the approach that showed the best performance, provided the best calibration, and selected the most relevant features for the model among the feature selection methods. From this point of view, physical activity, alcohol consumption, the duration of use of technological devices, frequent consumption of high-calorie meals, and the frequency of vegetable consumption are the most important features affecting obesity levels.
## 5 Numerical Results
### Connection Probability Verification  
Figure 5 shows the relationship between connection probability and the increasing power. The results show that the connection probability increases gradually as the power increases. Additionally, under the same conditions, we can see that the connection probability under the \(\beta\)-GPP model is superior to the PPP model. The more uniform the distribution of the nodes, the higher the connection probability of the network, which indicates that the regular distribution of the nodes reduces the interference between nodes. At the exact \(\beta\), the connection probability can be increased by increasing the number of hops of relay nodes.  
Figure 6a shows the influence of beam width on connection probability under different power. The spatial self-interference is reduced due to the directional antenna, which improves the stability of the connection between the nodes. The connection probability increases as the beam width decreases. It also shows that the connection probability of nodes with a particular regular distribution is better than that of the nodes that are randomly distributed. When the power increases to a certain level, the advantage of antenna gain can be ignored but, at this point, a large amount of energy consumption will increase. Figure 6b shows the relationship between the connection probability and the beam width for the  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Definition** & **Parameters** & **Values** \\ \hline End-to-end distance & \(d\) & \(\sum_{i=1}^{N}d_{i}\) \\ Interference power coefficient & \(k\) & \(P_{I}/P\) \\ Transmission power coefficient & \(P_{i}\) & \(p\) \\ Effective signal coefficient & \(\Theta_{b}\), \(\Theta_{g}\) & 1 \\ Threshold value & \(Q\) & 1 \\ Background noise & \(T_{0}\) & 1 \\ Length coefficient & \(L\) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 5: Connection probability versus P with different \(N\) and node deployment, where \(\text{sita}=\text{pi}/3\).  
same power. As the beam width increases, the connection probability decreases. However, when the beam width exceeds \(\pi\), the connection probability decreases significantly, which is due to a sudden increase in signal interference between nodes. Again, it shows that the number of hops of the relay nodes improves the connection probability. Additionally, all the results demonstrate that the distribution rules of the nodes are beneficial for reducing signal interference and improving the connection probability.  
Figure 7 illustrates the effect of the degree of beam alignment on the connection probability. The connection probability is highest when the beams are perfectly aligned. The perfect alignment of the beam reduces the signal loss and the information capacity carried can be completely absorbed by the target node, thus improving the information transmission efficiency; otherwise, the energy carried by the beam will be wasted with the directional offset.
## 3 Results
### Concentration of VOCs and Aroma Compounds/Data of Toxic Guidelines
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**Chemical Abstracts**} & \multicolumn{2}{c}{**Vapor Samples**} & \multirow{2}{*}{**RD\({}_{\text{gg}}\)1**} & \multicolumn{2}{c}{**Sensory**} \\  & & **Service (CAS)** & & **(n = 4)** & & **Initiation2** ** & **DNE3**  \\  & & **Number** & **Mean Conc. (ppm)** & **(ppm)** & **(ppm)** & **(ppm)** \\ \hline
1 & (+)-\(\alpha\)-Pinee & 7785-70-8 & 0.73 \(\pm\) 0.19 & 1053 & 31.6 & 0.69 \\
2 & (-)-\(\beta\)-Pinee & 18172-67-3 & 0.90 \(\pm\) 0.19 & 4663 & 140 & 1.04 \\
3 & \(\beta\)-Myrence & 123-35-3 & 0.74 \(\pm\) 0.14 & n.d. 4 & - & n.d. \\
4 & (+)-Limonene & 138-86-3 & 3.39 \(\pm\) 0.47 & 1076 & 32.3 & 12.14 \\
5 & 3-Octanol & 589-98-0 & 1.28 \(\pm\) 0.14 & 256 & 7.68 & n.d. \\
6 & (+)-Isomenthone & 491-07-6 & 7.09 \(\pm\) 0.84 & n.d. & - & 6.35 \\
7 & (–)-Isomenthone & 1196-31-2 & 3.84 \(\pm\) 0.31 & n.d. & - & 6.35 \\
8 & Methyl acetate & 89-48-5 & 0.74 \(\pm\) 0.04 & n.d. & - & 4.20 \\
9 & Neomenth & 491-01-0 & 1.32 \(\pm\) 0.08 & n.d. & - & 10.49 \\
10 & (–)-MENDhold & 1490-04-6 & 5.49 \(\pm\) 0.31 & 45 & 1.35 & 20.95 \\
11 & Perberine & 89-81-6 & 0.26 \(\pm\) 0.01 & n.d. & - & n.d. \\
12 & m-Eugenol & 501-19-9 & 0.25 \(\pm\) 0.04 & n.d. & - & 3.20 \\ \hline \hline \end{tabular}  
* \({}^{1}\) RD\({}_{\text{50}}\): concentration that depresses the respiratory rate by 50% due to sensory irritation [23]; 2 Sensory irritation = 0.03 \(\times\) RD\({}_{\text{50}}\)[23]; 3 DNEL: derived no-effect level for substances assumed to have a threshold exposure level [24]; 4 n.d.: no data.  
\end{table} 
Table 1: Concentrations of aroma compounds quantified in vapor samples taken from a nicotine-free inhalator and toxic guidelines. Figure 2: Chemical structure of volatile aroma compounds in vapor samples from a nicotine-free inhalator. acid (0.0016 ppm). The other 12 VOCs were quantified as present at a level lower than 1 ppb. Concentrations of all VOCs were lower than the exposure limit (sensory irritation, DNEL, and OSHA). The concentration of acrolein was above the sensory irritation level but lower than the DNEL and OSHA criteria.
## 2 Description of Proposed Algorithms
### Algorithm Based on Flower Pollination and Local Search Methods
#### 2.1.2 Step-by-Step Algorithm  
We have implemented two variants of the FP: the original one, which uses only the objective function value (v.1, \(N_{LOC}=0\)), and the modified one, in which the minimization trajectory is constructed established on the modified trust region method (v.2, \(N_{LOC}>0\)), beginning from each point obtained by using the \"flower pollination\" procedure. In both variants, a measure of biodiversity is calculated at the sixth step. See Algorithm 1.  
```
1. Set the algorithmic parameters. Choose starting point \(x^{0}\in X\). Suppose \(x^{REC}=x^{0}\), where \(x^{REC}\) is the record point. Compute \(f^{REC}=f(x^{REC})\). Suppose \(K=0\), where \(K\) is the algorithm iteration number.
2. Create a random initial set of agents (population) \(P^{0}=\big{\{}p_{1}=x^{0},p_{2},\ldots,p_{m}\big{\}}\), \(p_{j}\in X,\ j=\overline{1,m}\).
3. Carry out a selection of the initial population: perform \(m\) local descents, each of \(N_{LOC}\) iterations of the local search method, each starts with its agent (individual) of the initial population, get \(\{s_{1},s_{2},\ldots,s_{m}\}\).
4. Improve the record point, take \(x^{REC}=\text{argmin}\{f(s_{l}),l=\overline{1,m}\big{\}}\).
5. Substitute in the population \(P^{0}\) of the individuals \(\{p_{1},p_{2},\ldots,p_{m}\}\) by the agents \(\{s_{1},s_{2},\ldots,s_{m}\}\). On the \(K\)-th iteration:
6. Calculate a measure of biodiversity \(M_{bio}=1/\left(m-1\right)\sum\limits_{l=1}^{m-1}\big{\|}p_{l+1}-p_{l}\big{\|}\).
7. If the stoppage criterion \(M_{bio}<\varepsilon_{bio}\) is satisfied, then output \(x^{REC}\), the algorithm is terminated.
8. Build a tentative population. In a cycle for each \(l=\overline{1,m}\): 8.1. Create a random numeric \(r_{l}\in[0,1]\subset R\). 8.2. If \(r_{l}\leq P_{s}\), then a vector \(L\) of size \(m\) is formed, which obeys the Levy distribution (see, for example, [13,14]); a \"global pollination\" is performed: \(p_{new}=p_{l}+L\big{(}p_{l}-x^{REC}\big{)}\). 8.3. If \(r_{l}>P_{s}\), then generate two random indexes \(j_{1},j_{2}:\ j_{1}\
eq j_{2}\) (one of the two integer indexes \(j_{1},j_{2}\) can be equal to the index \(l\)), generate a random number \(\varepsilon\in[0,1]\subset R\), perform a \"local pollination\": \(p_{new}=p_{l}+\varepsilon\big{(}p_{j_{1}}-p_{j_{2}}\big{)}\).
8.4. The old agent is replaced with a new one (in case of improvement): if \(f(p_{new})\leq f(p_{l})\), then \(p_{l}=p_{new}\).
```  
**Algorithm 1** FP```
**Algorithm 1**_Cont._
## 2 The Proposed Pulse-OTFS Signal  
This section introduces the mathematical model of the OTFS modulation and the proposed signal, derives the expressions of ACF and PSD, and discusses the signal properties with different parameters.
# Model-Free Method for Damage Localization of Grid Structure
###### Abstract  
A model-free damage identification method for grid structures based on displacement difference is proposed. The inherent relationship between the displacement difference and the position of structural damage was deduced in detail by the Sherman-Morrison-Woodbury formula, and the basic principle of damage localization of the grid structure was obtained. That is, except for the tensile and compressive deformations of the damaged elements, the deformations of other elements were small, and only rigid body displacements occurred before and after the structural damage. According to this rule, a method for identifying the position of the damage was proposed for the space grid structure by using the rate of change of length for each element. Taking a space grid structure with a large number of elements as an example, the elastic modulus reduction method was used to simulate the damage to the elements, and the static and dynamic test parameters were simulated respectively to obtain the difference in displacement before and after the structural damage. The rate of change of length of each element was calculated based on the obtained displacement difference, and data noise was added to the simulation. The results indicated that the element with the larger length change rate in the structure was the most likely to be damaged, and the damaged element can be accurately evaluated even in the presence of noise in data.  
g pacs: +
Footnote †: journal: Applied
## 4 Control of the Double-T Converter
### Energy Balance of Stacks  
Using expressions (1)-(4) and (11) and knowing that the independent currents of the topology are four, the control system is designed. Taking into account the energy balance approach, the energy of the _series_ stack is controlled by \(I_{se}\) (see (12) and (15)) via a proportional-integral (_PI_) controller. For _derivation_ stacks, the control action is performed by the ac current \(i_{u1}\) whereas for _output_ stacks, the energy control is carried out by the ac current \(i_{u2}\). It should be noted in Figure 5 that \(i_{u1}\) and \(i_{u2}\) circulate through both T-structures. Therefore, for the energy control of the converter, there are four _PI_ controllers.  
To impose the dc currents \(I_{se_{1}}\) and \(I_{se_{2}}\) from energy _series\({}_{1,2}\)_ controllers, another two _PI_ controllers are used. As the energy control loops and the current control loops are nested, in this work they are decoupled using natural frequencies \(\omega_{n}\) of the controllers in a ratio of 1/70. The natural frequency of the _series\({}_{1,2}\)_ energy controllers is \(\omega_{n_{e}}\) and the natural frequency of the current controllers is \(\omega_{n_{e}}\), then \(\omega_{n_{e}}/\omega_{n_{e}}=70\). Figure 6 shows the control scheme for the energy balance of the _series_ stacks.  
To impose alternating currents \(i_{u1}\) and \(i_{u2}\) from energy controllers in _derivation_ stacks, proportional-resonant (_PR_) controllers are used. In this work, the frequency used for \(i_{u1}\) and \(i_{u2}\) is \(f_{c}=500\)\([\mathrm{Hz}]\). This frequency should consider the \"trade-off\" between switching losses in the H-bridges and the dynamic response in the _derivation_ energy balance controllers. Additionally, in this work, for the proposed operating point, the magnitude of \(v_{u}\) is constant as \(V_{u}=0.7V_{o}\). The value of \(V_{u}\) is fundamental for the energy control of the _derivation_ and _output_ stacks. It is desired for \(V_{u}\) to be high so that the dynamics of the control are fast, but it must not exceed the maximum output voltage to avoid overmodulation and the consequent voltage distortion. For the simulation study \(V_{u}=9.1\) kV. Since the output direct voltage is \(V_{o}=13 kV_{DC}\), then \(V_{arm}=13\) kV + 9.1 kV = 22.1 kV is within the value that the converter arm can deliver. The maximum value to be delivered by the arm is \(V_{arm_{max}}=3\) kV\(\cdot 8=24\) kV. Figure 7 shows the energy balance control scheme of the converter stacks.  
Figure 6: _Series_ stacks energy and current control scheme.