esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Potential plastic hinges are located at both the top and bottom of the columns, presenting issues such as internal force redistribution [30]. In practical applications, frame-style two-column bents serve as a prevalent design choice for bridge piers. With the increasing demand for broader bridge widths to accommodate more traffic lanes, the application of the two-column pier system is likely to become more widespread [31]. Consequently, it is imperative to conduct systematic research on the seismic performance of precast segmental two-column bents. [PERSON] et al. [32] proposed a precast bridge bent structure where the column was connected to the footing by a socket at the bottom and connected to the cap beam through grouted corrugated ducts at the top. The results of the quasi-static cyclic test showed that the precast bridge bent was suitable for accelerated construction in high-seismic regions. [PERSON] et al. [33] designed three sets of precast concrete frame piers with hybrid systems. The quasi-static test results showed that the overall seismic performance of frame piers with tie beams was less acceptable than those with cap beams. [PERSON] et al. [34] conducted experiments on the cap-column joints of precast concrete frames, and the results showed that the seismic performance of the cap-column joint is affected mainly by the connection type of the joint. Beyond the findings above, extensive experimental results demonstrate that the seismic performance of precast segmental two-column bents is significantly influenced by different connection types [33; 35]. Therefore, it is crucial to explore these connection methods. In this study, quasi-static cyclic tests were conducted on precast two-column bents connected with both grouted splice sleeves and grouted central tenon, namely the proposedGSS-GCT connection. Three large-scale two-column bent specimens were fabricated and cyclically loaded to fail to verify the \"GSS-GCT\" connection type. The seismic behaviors of the bents were compared and evaluated in terms of the lateral load-carrying capacity, displacement ductility, energy dissipation capacity, residual displacement, and pinching effect. Five levels of the damage state for the three bent specimens were then qualitatively and quantitatively divided based on experimental results. Additionally, the capacity spectrum method was adopted to evaluate the seismic performance of the three bents under the low, medium, and high seismic activities.
## 5 Discussion
### Accident Prevention Suggestions  
Based on accident investigation report data, this study quantified the degree of risk coupling in both ACs through methods such as risk factor extraction, risk type identification, and coupling analysis. Then, the differences in the risk coupling mechanisms of PA and SA were analyzed, and the key risk coupling scenarios and potential interactions between risk types in both ACs were identified. The formation of PA is inevitable and easily intervened, while the formation of SA is random and more difficult to intervene in. It is necessary to provide accident prevention recommendations based on the different risk coupling mechanisms leading to PA and SA, to reduce the occurrence of PA and prevent their evolution into SA. This constitutes the primary practical application of our study. Furthermore, accident investigation recommendations are provided to help investigators determine the scope of the investigation for both ACs and focus on key risk factors, as listed in Table 7.  
After PA, focus on investigating the driver, the operating management unit of the accident vehicle, and the supervising unit.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**AC**} & **Key Risk Types** & \multirow{2}{*}{**Risk Factors**} & **Prevention and Control Measures** & **Investigation Suggestions** \\ \hline \multirow{6}{*}{PA Management} & Negligence in safety management, lack of supervision, insufficient education and training, insufficient equipment, ineffective dynamic monitoring, insufficient hazard identification, etc. & Strengthen dynamic monitoring of key vehicles, regularly check safety training and education records, conduct regular safety hazard inspections and rectifications, and strengthen traffic safety publicity through various channels such as online platforms and offline activities. Focus on providing safety training for driver's operational vehicles. & After PA, collect and analyze information such as rainfall, snowfall, and visibility for the accident location, with particular focus on agglomerate fog in mountainous areas. \\ \cline{1-1}  & Rainy, snowy, foggy, low visibility. & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accident prevention and investigation suggestions.  
Figure 15: Risk coupling values for the four-factor coupling type.
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 3 Proposed XOR Binary Gravitational Search Algorithm  
To alleviate the problems mentioned in Section 2, the BGSA is modified to have an XOR operator in its update rule for the acceleration term as follows:  
\[A_{i}(t)=\sum_{j\in\{1,...,k_{b}\}}r_{j}G(t)\frac{M_{rj}(t)(X_{rj}(t)\oplus X_ {i}(t))}{\|X_{i}(t)-X_{j}(t)\|^{r_{p}}+\varepsilon} \tag{13}\]  
where \(X_{rj}(t)\) is the \(j\)th solution in the repository and \(\oplus\) represents the XOR operator acting bit-wise on \(X_{rj}(t)\) and \(X_{i}(t)\) with its truth table being presented in Table 1. Furthermore, the equation used for \(G(t)\) is as follows.  
\[G\left(t\right)= \alpha_{0}\left(1-\frac{t}{t_{max}}\right)+\alpha_{1} \tag{14}\]  
where \(\alpha_{0}\) and \(\alpha_{1}\) are selected as 0.8 and 0.2, respectively. The probability function \(P(V_{i}^{j})\) is modified as follows:  
\[P\left(V_{i}^{j}(t)\right)=0.5+0.5\text{tan}h\left(0.5V_{i}^{j}(t)\right),\ i=1,...,N,j=1,...,d \tag{15}\]  
where \(tanh(.)\) is defined as in (11). The update equation for the velocity vector as well as position vector remains the same as (8) and (9). Moreover, the mass parameter in the proposed algorithm associated with particles in repository is updated as follows:  
\[M_{ri}(t)= \frac{m_{ri}(t)}{\sum_{k=1}^{N}m_{rk}(t)} \tag{16}\]  
and \(m_{ri}(t)\) is non-normalized mass value corresponding to \(i\)th particle at iteration number \(t\) which represents the quality of solution and is defined using the fitness of particles in repository as follows:  
\[m_{ri}(t)= \frac{f(X_{i})-f_{r,worst}(t)}{f_{r,best}(t)-f_{r,worst}(t)} \tag{17}\]  
where \(f(X_{ri})\) is the fitness function associated with particle \(X_{ri}\) in the repository, \(f_{r,worst}(t)\) and \(f_{r,best}(t)\) represent the worst and the best fitness function of particles in repository.  
While the original version of BGSA is memory-less, in the proposed algorithm a repository is added to the algorithm. In each iteration, a tournaments selection procedure is performed between the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**A** & **B** & **A@B** \\ \hline
0 & 0 & \(-1\) \\ \hline
1 & 0 & 1 \\ \hline
1 & 1 & \(-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Truth table for an exclusive or (XOR) operator.  
current generation and old repository and the new repository is updated as a result of this selection procedure. Tournaments selection is widely known to be an algorithm to preserve the diversity in the solutions. The flowchart of the proposed XOR BGSA with repository is depicted in Figure 1, where \(V_{max}\) refers to the maximum absolute value of velocity in each dimension.
## 4 The Proposed Watermarking Scheme
### The Insertion Process  
The data embedding is carried out as follows.  
* The audio signal is segmented into \(N\)-sample blocks and intDCT transformed.
* Each transformed audio block is splitted into eight sub-bands. This number of sub-bands was experimentally determined, to achieve a trade-off between auditive transparency and payload, as each sub-band is a potential carrier of one bit.
* Transformed sub-bands are divided by \(2^{8}\); the CD-quality audio dynamic range is \([2^{-15},2^{15}-1]\), which for a small set of samples, entropy computation will lead the same entropy value at any time, \(\log\frac{N}{8}\), as the sub-band distributions will be uniform. This situation applies for both audio blocks and transformed blocks, as intDCT is a linear transform.
* For each sub-band in a transformed block, entropy is computed; then, the average entropy of all sub-bands is computed and set as the embedding threshold for that block in a similar manner as in [21].
* Data is embedded into sub-bands showing a higher entropy than the threshold; embedding is carried out using one of the strategies described in Section 3.
* Finally, the watermarked audio block is returned to the time domain by the inverse intDCT, and thus the watermarked audio is obtained.  
Figure 3 shows the insertion process.
# Long-Term Forecasting Using MAMTF: A Matrix Attention Model Based on the Time and Frequency Domains
## 2 Related Work
[25] used the Fourier transform to design a frequency-domain-based encoder structure, which integrates multi-scale information to discover the frequency features and integrates the frequency domain features with the time domain features so that the extracted features are more comprehensive. [PERSON] et al. [26] used a discrete wavelet transform to obtain the frequency domain data, extracted the features of the time domain and frequency domain data using a convolutional neural network, and finally used a long and short-term memory network to make predictions. [PERSON] et al. [27] divided the time series into period components and trend components, extracted the period features in the frequency domain, extracted the trend features in the time domain, and finally fused the two features to obtain the final prediction results. [PERSON] et al. [28] mapped the data to the frequency domain using a wavelet transform and used the NSNP system to make a single-step prediction. Although the above researchers have achieved good results by performing feature extraction on the time-frequency dual domain for prediction, the work carried out was mainly for short-term prediction, with the help of existing models, and our model brings this time-frequency dual-domain feature extraction approach to long-term prediction.
# Semi-Empirical Satellite-to-Ground Quantum Key Distribution Model for Realistic Receivers
## Appendix C 300 mm and 600 mm Aperture Ground Stations  
The experimental setups with 300 mm (see Figure 1) [38] and 600 mm apertures (see Figure 2) [35] are suggested for satellite-to-ground QKD. The ground stations are designed to function with optical radiation at a wavelength of 850 nm (quantum channel), 532 nm for the synchronisation procedure (SCM) and guiding system (GT), and 671 nm for the pointing by the laser (3W beacon LD).  
The system works as follows: once reaching the receiving telescope, the quantum signal enters the processing and analysis unit, followed by the polarisation analyzer, which functions as a quantum state decoder. After that, the signal is transmitted via an optical fibre and detected by single-photon detectors.  
The ground station with the 300 mm aperture has a smaller design than the fixed station (Figure 10), because of the compact optical parts of the optical signal analysis and processing module and quantum state decoder. The optical elements for the portable ground station were picked up with a higher transmittance in comparison to the fixed one. In particular, a hard-coated bandpass filter (>90% transmission; Figure 10--IF1) was used instead of the laser line filter (60% transmission; Figure 10--IF1). As a result, the ground station's optical efficiency was improved.
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 3 Proposed Sensors  
In this work, various celestial-based sensor systems, as well as inter-satellite links, are proposed and models presented. Derivations are not incorporated in this work, as the paper focuses more on implementation and performance. Celestial sensors include star trackers, horizon, point source triangulation, and lunar craters.
# Selected Applications of Terahertz Pulses in Medicine and Industry
## 2 Terahertz Imaging and Spectroscopy in Medicine and Biology
Initially, it was thought that the enhanced absorption by cancer was due to the enhanced blood content and water absorption; however, as described earlier, more recent work has suggested that methylation of DNA, specifically methylated cytidine, which occurs in the early stages of cancer development, produces a molecular resonance fingerprint in the terahertz region. At present, medical/biological applications are at an early stage and considerable progress is expected in analysis and signal processing. Markers and contrast agents can be developed, which enhance the detection and reduce false positive occurrence in the analysis. For example, Fe\({}_{3}\)O\({}_{4}\) and Gd\({}_{2}\)O\({}_{3}\) nanoparticles have been used in the detection of early-stage gastric cancer. Given the high level of interest in terahertz imaging technology, it is also possible that the best course will be to combine this with another imaging modality, such as near-infrared or optical computed tomography (OCT). Terahertz pulsed imaging still has some challenges, namely the depth of penetration of skin, which may be limited by water absorption and may not be sufficient. This may be overcome with the use of nanoparticles and markers. The technique requires relativity flat surfaces, and this can be overcome using surgical robots, which can follow the surface of the skin. However, for investigating interfaces, it can resolve features that cannot be observed by X-rays, such as ductal carcinoma in situ, which is a manifestation of breast cancer.
# Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log
## 3 Result and Discussion
### Illegal Logging Application  
After testing the proposed system in the local access layer, the next step was to apply the testing scenario at the remote access layer. As seen in Figure 15, the display of the web application shows that the Tx1 has been moved to location -6.89022 latitude, 107.745549 longitude as far as 202 m from the gateway. This testing is carried out to measure the delivery of tracking information for the user in the form of monitoring devices and telegrams.  
On the displayed web, the sensor node's position is shown as default, but the capture position in the display is the current position when the sensor node is moved away from the gateway. The map location of Illegal Logging Tracking (ILT) will display the new position of the moving log. The monitor display information has also revealed information about the value of sensor sound, sensor gyro, GPS parameter, and the time when the system tracks the new position.  
Meanwhile, the display on the telegram and tracking google map application can be seen in Figure 16. The map location of ILT also displayed the new position of the moving log. The telegram was received by the user at about 7 km from the location or after driving for about 51 min to the location. To access the My Tracks, click the new site and the tracking map displayed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gateway** & **Sensor** & **GPS New Location** & **Direction** & **Processing** & **Range (Meter)** \\
**Position** & **Node** & **GPS New Location** & **Degree** & **Time (s)** & **Range (Meter)** \\ \hline \multirow{4}{*}{Latitude} & TX1 & Lat \(-\)6.893650 & \multirow{2}{*}{240\({}^{\circ}\)} & \multirow{2}{*}{25} & \multirow{2}{*}{217.87} \\  & & Long 107.744788 & & & \\ \cline{1-1} \cline{5-5} \cline
# Adsorption of Lead and Nickel on to Expanded Graphite Decorated with Manganese Oxide Nanoparticles
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Thermodynamic Analysis  
The thermodynamic parameters of the adsorption process, such as the Gibbs free energy change (\(\Delta G^{0}\)), enthalpy change (\(\Delta H^{0}\)), and entropy change (\(\Delta S^{0}\)) were analyzed through variations of the thermodynamic equilibrium constant (\(K_{0}\)) at the investigated temperatures (298, 308, and 323 K). In this work, \(K_{0}\) values of the adsorption of metal ions onto the prepared MONPs-EG were calculated based on the method proposed by [PERSON] (1973) [PERSON] (1973), which can be defined as in the following Equation:  
\[K_{0}=\frac{a_{s}}{a_{e}}=\frac{\gamma_{s}}{\gamma_{e}}\frac{C_{s}}{C_{e}} \tag{9}\]  
where \(a_{s}\) and \(\gamma_{s}\) are respectively the activity and activity coefficient of adsorbed metal ions on the MONPs-EG; \(a_{e}\) and \(\gamma_{e}\) are respectively the activity and activity coefficient of metal ions in the solution at equilibrium; \(C_{s}\) (mM) is the concentration of metal ions that adsorbed on the prepared adsorbent; and \(C_{e}\) (mM) is the concentration of metal ions in the solution at equilibrium.  
When the concentration of metal ions in the solution approaches zero, resulting in \(C_{s}\to 0\) and \(C_{e}\to 0\), and the activity coefficients approach unity Biggar and [PERSON] (1973). Equation (9) can be rewritten as:  
\[\lim_{C_{s}\to 0}\frac{C_{s}}{C_{e}}=\frac{a_{s}}{a_{e}}=K_{0} \tag{10}\]  
The thermodynamic parameters were calculated as follows:  
Gibbs free energy change of the adsorption process was directly estimated by Equation (11):  
\[\Delta G^{0}=-RT\ln(K_{0}) \tag{11}\]  
where \(R\) is the universal gas constant (8.314 J/mol/K); and \(T\) (K) is the absolute solution temperature.  
The Gibbs free energy change is a function of the change in enthalpy and entropy, which is expressed by Equation (12).  
\[\Delta G^{0}=\Delta H^{0}-T\Delta S^{0} \tag{12}\]  
Combining Equations (11) and (12) resulted in Equation (13).  
\[\ln(K_{0})=-\frac{\Delta H^{0}}{R}\frac{1}{T}+\frac{\Delta S^{0}}{R} \tag{13}\]  
\(\Delta H^{0}\) and \(\Delta S^{0}\) of the adsorption process will be, respectively, obtained from the slope (\(-\Delta H^{0}\)/R) and the intercept (\(\Delta S^{0}\)/\(R\)) of the plots of \(\ln(K_{0})\) as a function of \(1\)/\(T\).
## 4 Experimental Results
### Dataset Description  
Data from the CERTH/ITI Smart Home were retrieved to test the proposed methodology, as detailed in Section 3. Data were collected using a multisensor (i.e., an Aetolec MultiSensor 6 (Aeon Labs, Hamburg, Germany) [32]). The specifications of the sensor utilized are described in Table 3. Lux values from two rooms throughout the year were utilized in the test (Figure 8).  
Figure 7: Cutting-edge technologies in the CERTH/ITI Smart Home.  
Table 4 presents the necessary statistics for the luminance of the two rooms throughout the four seasons. As can be observed, the luminance values during the fall were influenced by the declining daylight hours and by the lower sun angles, resulting in slightly lower luminance levels compared to the summer. Winter was characterized by shorter daylight periods and lower solar altitudes. During spring, the luminance values were influenced by the changing weather patterns. As the winter receded, the increase in daylight hours and the emergence of new foliage resulted in a rise in luminance levels. Last but not least, summer was distinguished by longer daylight hours and higher solar altitudes.  
Figure 8: Illuminance sensors in CERTH’s Smart Home.  
\begin{table}
\begin{tabular}{l} \hline \multicolumn{2}{l}{**Specifications of Aeotec MultiSensor 6**} \\ \hline Motion sensor: 16 feet, 120° field of view. \\ \hline Humidity sensor: 20\% to 95\%, accuracy +/ – 5\% at 77 °F. \\ \hline Temperature sensor: 14 °F (\(-\)10 °C)\(\sim\)140 °F (60 °C), \\ accuracy +/ – 32.9 °F (0.5 °C). \\ \hline Light sensor: 0 lux to 1000 lux; vibration \\ Sensor: acts as a tamper switch, ON or OFF. \\ \hline Operation temperature (top): \(-\)4 °F (\(-\)20 °C)\(\sim\)140 °F (40 °C). \\ \hline Storing temperature: \(-\)40°F (\(-\)40 °C)\(\sim\)176 °F (80 °C). \\ \hline Working humidity: 8\(\sim\)80\%; operational \\ site: indoor and outdoor. \\ \hline Power: DC 5V 500 mA via USB cable or 2 \(\times\) CR123A \\ batteries (not included); weight: 2.6 oz. \\ \hline \multicolumn{2}{l}{**General description**} \\ \hline Six-in-one Z-Wave Plus MultiSensor: motion, humidity, \\ temperature, light lux, UV, vibration sensor. \\ \hline Up to 2-year battery life or power via USB; \\ batteries not included. \\ \hline Super-small at 1.65 inches; can be installed in \\ corners, in-wall, on shelves, or recessed in ceilings \\ with recessor accessory. \\ \hline Works with certified Z-Wave gateways and \\ tested with Fibaro, HomeSeer, Indigo 7, \\ openHAB, SmartThings, and Vera; \\ Z-Wave Plus certified—certification number: ZC10-15070011. \\ \hline Illuminance reporting value range of [0 to 1024], reported \\ every time the value changes. \\ \hline \end{tabular}
\end{table}
Table 3: Aeotec MultiSensor 6 specifications.  
These elements contributed to higher luminance values during this season than during other seasons.
## Appendix C Results of the Snow Imagery Pre-Processing
Furthermore, considering the importance of the dynamic range to the feature matching process, the TIFF format (accepted by Photoscan) was theoretically the best-suited format with a higher bit range. Images could also have been exported in TIFF format after pre-processing; however, this led to larger images and therefore longer computation times for the SfM reconstruction. On a test set of seven image pairs, using the TIFF format only increased the number of matched points across image pairs by 6-7% compared to the JPEG format. Thus JPEG was preferred over the RAW format to speed up DEM generation, with some minor details lost in the compression process.