esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The thermal radiation spectrum of the suggested structure has been evaluated under different circumstances at various frequency ranges in order to control the thermal radiation spectrum and evaluate its tunability. The use of an MQP arrangement in tuning thermal radiation, which provides a wider degree of freedom in structural design compared to periodic and quasi-periodic arrangements, is the fundamental distinction between this work and other investigations. An adjustable THz filter for both s and p polarization was produced as a result of the suggested structure's ability to allow THz waves to propagate. The tunability of THz filters the proposed structures and offers practical advantages in tailoring the transmission characteristics of THz waves which exhibit unique and adjustable properties, allowing for precise control of the filter's response in the THz frequency range. Tunability is achieved by manipulating the different parameters like thickness of layers, number of layers, the utilized materials, and the angle of incident. When the angle of incidence changes, both the maximum thermal radiation spectrum and the bandwidth of the PBGs can be adjusted. In terms of the highest amount of thermal radiation, it was discovered that the structure produces a sufficient omnidirectional spectrum. The manipulation of the period number also affects the control of band-gaps and thermal radiation spectra. As the periods increase, well-behaved peaks and PBGs form in the transmission and thermal radiation spectrum. To name a few applications in the THz region, the proposed structure can be used to improve THz filters, sensors, thermal antennas, and selective thermal emitters.  
Conceptualization, A.M., A.B. and M.P.; Methodology, A.M.; Software, A.M.; Validation, A.K. and M.P.; Formal analysis, A.M. and A.B.; Investigation, A.M. and A.K.; Resources, A.K., A.B. and M.P.; Writing--original draft, A.M.; Writing--review & editing, A.M. and A.K.; Visualization, A.M. and A.B.; Supervision, A.K.; Project administration, A.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external fundings.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.  
Figure 5: The thermal radiation power spectrum of 1D MQP PC (with FB and ML sequences) as a function of frequency at T = 300 K, under several numbers of periods M = 1, 3, and 5, for TE mode. Here, thicknesses of the layers are considered as \(d_{A}=28\) μm, \(d_{B}=12\) μm, \(d_{C}=3\) μm, and \(d_{D}=12\) μm.
## 2 Materials and Methods
### Data Analysis  
Negative SIF values were excluded from the analysis, as well as the SIF values greater than 1.5 W\(\cdot\)m\({}^{-2}\cdot\)nm\({}^{-1}\cdot\)sr\({}^{-1}\). Measurements outside the range \(\mu\pm 3\sigma\) (where \(\mu\) and \(\sigma\) are the mean and standard deviation, respectively) were also not considered. The filtered data  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site Name** & **Latitude** & **Longitude** & **PFT** & \begin{tabular}{c} **Maximum** \\ **Temperature** \\ \end{tabular} & \
\begin{tabular}{c} **Minimum** \\ **Temperature** \\ \end{tabular} &
\begin{tabular}{c} **Retrieval** \\ **Method** \\ \end{tabular} \\ \hline XTS & 40.18\({}^{\circ}\)N & 116.44\({}^{\circ}\)E & C3 CRO & 29.52 \({}^{\circ}\)C & \(-11.21\)\({}^{\circ}\)C & DOAS \\ Niwot Ridge & 40.03\({}^{\circ}\)N & 105.55\({}^{\circ}\)W & BoENF & 19.34 \({}^{\circ}\)C & \(-15.88\)\({}^{\circ}\)C & DOAS \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the observation sites. The maximum and minimum temperatures of XTS and Niwot Ridge sites were obtained over the respective observation period.  
Figure 3: Locations of the selected homogeneous samples for group A (**a**) and group B (**b**) of each PFT in the Northern Hemisphere. Niwot Ridge (green star) and XTS (yellow star) were the two tower-based observation sites considered.  
were classified into 14 PFTs, and each PFT was grouped into subset A or subset B, based on the selection criteria described in Section 2.4. Linear regression analysis was conducted to explore the relationship between SIF and GPP in response to temperature. In addition, in order to remove the potential influence of other environmental variables (e.g., PAR and VPD), we conducted partial correlation analysis to study the influence of temperature conditions on the GPP/SIF ratio.
# Simulation of Test Arch Based on Concrete Damage Plasticity Model and Damage Evolution Analysis
## 5 Analysis of Results
### Analysis of Structural Damage Evolution
#### 5.2.1 Damage Conditions at the Foot of the Arch  
Figures 16 and 17 depict the stiffness alterations in elements following damage to the top and bottom slabs of the arch foot, respectively. From the results in Figure 16 (Results I and II), it was observed that the damage to the arch foot top slab first started from the corners and then gradually extended to the surface of the web. The degradation on the top surface commenced at the edges, advancing toward the center, culminating in a substantial region of stiffness reduction in the elements. This failure was confined to the surface of the damage zone in the arch rib and showed no effect on the reinforcing steel framework. Results III, IV, and V illustrated that while the concrete protective layer on the top slab sustained damage, its impact on the bottom slab remained insignificant until complete failure ensued. As damage continued to evolve, the deterioration of the bottom  
Figure 13: Damage conditions at the top plate of the arch foot: (**a**) X-direction displacement at position 2\(\mathbf{\mu}\); (**b**) Y-direction displacement at position 2\(\mathbf{\mu}\); (**c**) X-direction displacement at position 11\(\mathbf{\mu}\); (**d**) Y-direction displacement at position 11\(\mathbf{\mu}\).  
slab extended from the web surface upwards to the top slab, with pronounced damage to its protective layer manifesting only after comprehensive failure of the web surface.  
By comparing Figures 16 and 17, it was found that the progression of element stiffness failure in the bottom slab at the arch foot was similar to that at the top slab. The initiation of damage emerged from the corners and methodically progressed across the web, culminating in a comprehensive cross-sectional failure. Moreover, after the elemental failures on the web surface, the damage in the bottom slab region started to evolve, steadily spreading deeper into the section. This suggested that varying initial damage sites might influence the preliminary local damage patterns at the arch foot, but not significantly alter the ensuing progression trajectory.
## 6 Architecture Measurements
### Bandwidth, Jitter, Delay, Lost Packets and Number of Packets with Errors  
We took as a starting point the topology shown in Figure 16, but this time it was performed in a hard indoor environment with many WiFi networks working in parallel. In order to take these measurements, a Wireshark network protocol analyzer was used [34]. First, we sent variable bitrate streams during four minutes from node 5 to node 13. The path followed was: node 5 - node 6 - node 10 - node 13. The bandwidth consumed is shown in Figure 29. It varied from 0 to 1551.26 Kbps. The mean value was 750.81 Kbps.  
The measured delay is shown in Figure 30. We obtained a mean value of 19.16 milliseconds (which is a real time values because it is lower than 50 milliseconds). The delay varied from 0 to 1,826.41 milliseconds.  
Figure 28: New topology when node 6 fails.  
Figure 29: Bandwidth consumed during the test.  
Then, we measured the jitter of the packets during the test. The result is shown in Figure 31. We obtained a mean value of 31.11 milliseconds. The maximum value was 171.14 milliseconds and the minimum value was 0 milliseconds.  
During this test, 15,537 packets were sent, 1,475 of them were lost (9.49% of dropped packets), and 183 of them had sequence errors. Those values are not so bad if we take into account that eight IEEE 802.11b/g networks were detected in the place where we performed the test. Packet loss can be caused by any of the following: signal degradation over the air, oversaturated network links, corrupted packets rejected in-transit, faulty networking hardware, faulty network drivers or normal routing routines.
## 4 Case Study
### Simulation Analysis
#### 4.2.2 User's Carbon Responsibility Analysis  
According to the carbon emission flow theory, the node location of users is the main reason affecting their carbon responsibility. Therefore, in order to study the relationship between the result of carbon responsibility allocation and the network topology, three load nodes with different topological locations were set to simulate the users in different geographical locations in the real power system and the carbon potential of their nodes was calculated and analyzed.  
Figure 7 shows the carbon intensity of each user node in the pool market calculated according to the carbon emission flow theory after the market is cleared. User 1 represents node 9, user 2 represents node 7, and user 3 represents node 5.  
From 16:00 to 23:00, the carbon intensities of users fluctuates greatly. In combination with Figure 6, it can be seen that this is because the proportion of new energy's bid-winning power in the pool market fluctuates significantly during this period. The above verifies the carbon intensity and can more accurately track the carbon emission of the system.  
At the same time, through the comparison between users, it can be seen that the carbon intensity of user 1 is much more affected by the clearing result than that of users 2 and 3. According to the carbon emission flow theory, the allocation of carbon responsibility in the pool market is constrained by the network topology. In view of the market clearing results, the power flow calculation shows that the demand of users 2 and 3 is mainly provided by thermal power units and the slight adjustment of the output structure of generators has little impact on the carbon intensities of their nodes. Moreover, the results show that the different locations of users and generators in the power grid lead to large differences in the carbon responsibility of users in the pool market. However, the carbon responsibility of users in the bilateral market is directly determined by the type of power supply. Therefore, through the hybrid power market mechanism, users with higher carbon responsibility can meet part of their electricity demand through the bilateral market, decrease the proportion of thermal power purchased in the pool market, and reduce their carbon emission costs.  
Figure 7: User carbon intensity in the pool market.
## 4 Model Prediction Performance Discussion
### Metrics of the Model Validation and Evaluation  
In order to quantitatively evaluate the performances of the training and testing models, this study selected root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R\({}^{2}\)) values as the evaluation metrics to describe the correspondence between the predicted and measured values. Smaller RMSE and MAE values reflected the higher prediction accuracy of the model, while an R\({}^{2}\) value closer to one indicated a better fit between the predictive model and the actual results. The evaluation indices were calculated as shown in Equations (7)-(9).  
\[RMAE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(y_{true}^{(i)}-y_{predict}^{(i)}) }^{2}}}, \tag{7}\]  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}{\left|{y_{true}^{(i)}-y_{predict}^{(i)}} \right|}},\text{ and } \tag{8}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{(y_{true}^{(i)}-y_{predict}^{(i)})}^{2}}{ \sum\limits_{i=1}^{n}{(y_{true}^{(i)}-\overline{y}_{true}^{(i)})}^{2}}, \tag{9}\]  
where \(y_{true}^{(i)}\) is the measured tunnel uplift maximum displacement of sample \(i\) in the database, \(y_{predict}^{(i)}\) is the predicted maximum tunnel uplift displacement of sample \(i\) in the database, \(\overline{y}_{true}\) is the measured average value of the maximum tunnel uplift displacement, and \(n\) is the total number of samples in the database.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**XGBoost Hyperparameter** & **Search Scope** & **Default Value** & **Optimal Value** \\ \hline n\_estimators & 1–50 & 100 & 43 \\ max\_depth & 1–20 & 6 & 4 \\ learning\_rate & 0.00001–1 & 0.3 & 0.6633 \\ subsample & 0.1–1 & 1 & 0.9131 \\ gamma & 0–20 & 0 & 14.6795 \\ reg\_alpha & 0–20 & 0 & 1.5322 \\ reg\_lambda & 0–20 & 1 & 16.5579 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Search scopes and optimal hyperparameters for each of the XGBoost parameters in the BO tuning process.
# Case Study of Power Plants in the Slovak Republic and Construction of Microgrid and Smart Grid
## 6 Analysis of Results and Design of a Protected System
### Design of a Protection System  
Conventional protection systems can be used for thermal and hydropower plants together with diesel generators, but for photovoltaic station and battery systems is needed for the development of a new protection system due to their shortcomings for the microgrid systems described in Section 5.1. The current connection conditions and the photovoltaic stations protection system did not provide sufficient protection for the electricity system and in some cases may react incorrectly. Another of the main problems with photovoltaic systems and battery systems was simulated, where the current measuring devices measure the sinusoidal course or the evaluated data according to this course did not correspond to the actual values in the grid. Therefore, there is a need to develop measurement and data processing systems that reliably locate the fault location and disconnect it. Differ  
Figure 28: FFT analysis of output power of hydropower plant model.  
ential protections, impedance protections, and directional overcurrent protections with mutual communication have the most suitable perspective for independent protection of individual sources.
## 4 Numerical Results and Discussion  
The previous section introduces the proposed optimal beamwidth maximizing uplink coverage probability. In this section, we compare the proposed scheme with the following:  
1. 3 GPP setting: the equivalent antenna aperture is 2 m, the max receive antenna gain is 30 dBi, and the half-power beamwidth is 4.4127\({}^{\circ}\)[18].
2. 3 GPP setting with three-color frequency reuse (FRF3): The frequency band is divided into three sub-bands, and the adjacent beams are allocated to different sub-bands. Other assumptions follow the 3 GPP setting. The three-color frequency reuse is also known as frequency reuse factor three.
3. Exhaustive antenna aperture search: exhaustively search the equivalent antenna aperture with the highest uplink coverage probability in a predefined target SINR for each simulated time. The exhaustive antenna aperture search denotes the upper bound of the uplink coverage probability for non-frequency reuse.  
We calculate the uplink coverage probability using the Monte Carlo method. The evaluated scenarios are the NTN rural and NTN urban [16]. To fully address the interference from other satellites, we simulate a 19-beam satellite scenario with the wrap-around mechanism. Other simulation assumptions are summarized in Table 2. We evaluate the performance with target SINR \(-5.6\) dB and \(-8.61\) dB. The target SINR \(-5.6\) dB and \(-8.61\) dB denote the required SINR to satisfy \(0.02\) block error rate for VoNR with 10 and 20 times PUSCH repetition (transport block size is 184 bits and modulation order is quadrature phase shift keying).
# Low-Cost Rapid Fabrication of Conformal Liquid-Metal Patterns
## 3 Results
### Subtractive LM Patterning by Physical Scraping  
Subtractive patterning of LM can increase the resolution of gaps patterned in the sprayed LM film. In this process, a homogenous LM film was sprayed onto a PDMS substrate. Then, a 30-gauge sharp-edged needle was mounted to the print-head of an Inkredible 3D printer (Figure 11). A single-layer pattern was uploaded to the printer. The printer moves the needle along the pattern, scraping the sprayed LM film. Figure 12a shows microscopic images for the gaps in the LM created by the scraping. This subtractive method created a minimum gap size of 34 \(\upmu\)m. Figure 12b shows the variation of the needle \(z\) position versus the scraped gap width. The \(z=0\) position corresponds to the needle position that created the minimum gap size (34 \(\upmu\)m) in the LM using the 34-gauge needle. The height between the substrate and the tip of the needle was adjusted to scrape the sprayed LM layer without visibly damaging the PDMS substrate. As the \(z\) position increases, the needle is pushed further into the substrate, creating a wider gap. As the gap increases, the measured resistance across the LM gap also increases (Figure 12c). The measured resistance of 1 cm\({}^{2}\) of sprayed LM was 240 m\(\Omega\) before scraping. The measured resistance of the smallest scraped gap, 34 \(\upmu\)m, was 14 M\(\Omega\). The minimum resistance of the scraped gaps (Figure 12) is six orders of magnitude higher than the resistance of LM lines. This provides enough contrast between conductive and insulating patterns for circuits operating from DC to approximately 2 GHz. For higher-frequency applications, gaps of less than 100 \(\upmu\)m can have significant capacitive coupling. Supplementary Figure S1 shows the simulated insertion loss of a transmission line with capacitive coupling.  
Figure 10: Measurement of gap width between sprayed LM. (**a**) Images of the polyimide mask and the sprayed LM. (**b**) Images of the polyimide mask, with the separator running through the center of the photos. (**c**) Images of the gaps between sprayed LM. (**d**) Measured gap width between sprayed LM lines vs. the polyimide separator width.
# A Hybrid SWIM Data Naming Scheme Based on TLC Structure
## 4 Experiment and Result Analysis
### Analysis of Fault Tolerance of Route Resolution Based on TLC Aggregation  
Suppose a SWIM node has a collection of content objects, their OID contains the same first \(j\) TLC, and an OID contains \(J\) TLC, TLC1, TLC2, TLC3 and so on. TLC \(J\) In the SWIM node, the \((J-j)\) suffixes in each \(\Omega\) OID are aggregated based on Bloom filter to generate an sOID, TLC1, TLC2,..., TLC\(j\),..., Digest TLC \((j+1)\),..., Digest TLC\(i\), \(i\ =\ j+1\),..., \(J\). When another node requests content to resolve the route, a false match may occur. Due to suffix aggregation, even if the first \(j\) TLCs in the published sOID match the requested content sOID, or each digest TLC in the sOID can show that the corresponding TLC in the subscription OID exists, but the publishing node may not the requested content. When the false positive rate during TLC aggregation occurs, this route resolution error occurs at least once.  
Use \(\Omega_{i}\ (i\ =\ j+1,\ldots,J)\) to represent the collection of the first TLC in the content OID held by the publishing node:  
\[\Omega_{i}\ =\ \{TLC_{i}\ of\ OID:\ OID\in\Omega\} \tag{1}\]  
It should be noted that some OIDs may have the same TLC. To have a false positive rate, k \(\geq\) 1 filters are required, and the routing error resolution probability as follows.  
\[\prod_{h\ =\ 1}^{k}\left(\left(1-\frac{n_{i\bar{h}}}{u_{i\bar{h}}}\right)p_{ f},i_{h}\right) \tag{2}\]  
Among them, \(n_{i}\ =\ |\Omega|\) represents the number of different TLCs in the set, and \(u_{i}\) represents the number of possible values obtained by the TLC \(i\).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Layer** & **Name Length** & **Percentage** & **Number of Names** \\ \hline
1 & 4.11 & 0.01 & 224 \\
2 & 14.82 & 27.30 & 2,129,835 \\
3 & 20.63 & 15.38 & 391,377 \\
4 & 25.76 & 0.85 & 21,664 \\
5–9 & 31.86 & 0.06 & 1694 \\ \hline \end{tabular}
\end{table}
Table 3: Three-meter prefix table.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data Set** & **Test 1** & **Test 2** & **Test 3** & **Test 4** & **Test 5** \\ \hline Data size & 1 m & 2 m & 3 m & 4 m & 5 m \\ Data set & Test 6 & Test 7 & Test 8 & Test 9 & Test 10 \\ Data size & 6 m & 7 m & 8 m & 9 m & 10 m \\ \hline \end{tabular}
\end{table}
Table 5: Experimental collection data set.  
the collection increases, because Bloom filters rarely exhibit false positives. However, when r starts to increase, the trend is just the opposite. When r is large, the reliability of the Bloom filter decreases. The Bloom filter aggregation scheme is almost the same as the prefix aggregation scheme. However, when the number of TLCs in the prefix starts to decrease, the performance of the prefix aggregation scheme is even worse.
## 3 Methods
### Ridge Regression  
Ridge regression is an improved bias estimation method based on least square estimation [36]. The method optimizes the model by introducing the term L2 regularization to obtain more realistic and reliable regression coefficients, effectively solving the problems of multicollinearity among variables and improving the overall fitting effect of the model. The ridge regression formula is defined as follows:  
\[\hat{\beta}_{ridge}=argmin\left[\sum_{i=1}^{n}\left(y_{i}-\beta_{0}-\sum_{j=1}^ {p}\beta_{j}x_{ij}\right)^{2}+\lambda\sum_{j=1}^{p}\beta_{j}^{2}\right] \tag{2}\]  
where \(\hat{\beta}_{ridge}\) is the vector of coefficients obtained from ridge regression, \(\beta_{j}\) is the coefficients of the \(j\)th feature, \(y_{i}\) is the target value of the \(i\)th sample, \(x_{ij}\) is the \(j\)th feature value of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Health Features** & **B0005** & **B0006** & **Cell1** & **Cell2** \\ \hline EVDCT & 0.9915 & 0.9941 & 0.8666 & 0.9930 \\ IC Peak & 0.9953 & 0.9802 & 0.8829 & 0.9016 \\ PD Peak & 0.8287 & 0.9085 & 0.9247 & 0.9212 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pearson correlation coefficient of health features.  
\(i\)th sample, \(n\) is the number of samples, and \(p\) is the number of features. Where \(\lambda\) is the coefficient of the penalty term, \(\lambda\geq 0\). The greater the coefficient value, the more drastic the contraction of the coefficient vector. When \(\lambda=0\), the above formula represents the least squares estimation (LSE), so LSE can be considered a special case of ridge regression.  
The matrix form of ridge regression is:  
\[\hat{\beta}_{ridge}=\left(X^{T}X+\lambda I\right)^{-1}X^{T}Y \tag{3}\]  
where \(I\) is the identity matrix, when the eigenvector has a collinearity problem, the matrix \(X^{T}X\) is irreversible and cannot be solved for \(\hat{\beta}_{ridge}\). The penalty term added in ridge regression makes the matrix \(X^{T}X+\lambda I\) full rank and invertible, which makes \(\hat{\beta}_{ridge}\) solvable, and it can be concluded that ridge regression functions to reduce the singularity of the eigenmatrix, and at the same time compresses the size of the coefficient vectors, thus mitigating the risk of overfitting.