esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Summary of Design Specifications for Beam Bracing System with Torsional Bracing  
### LTB Strength of Beam Bracing Systems  
In the AISC design standard [1], the torsional brace requirements are provided based on the buckling strength of a beam with a continuous torsional brace along its length, as presented in Taylor and Ojalvo [2] and modified for cross-section distortion in Yura [5], and shown in Equation (1).  
\[M_{r}\leq M_{cr}=\sqrt{\left(C_{bu}M_{o}\right)^{2}+\frac{C_{b}^{2}EI_{eff} \overline{\beta_{T}}}{2C_{tt}}}. \tag{1}\]  
where \(M_{r}\) denotes the flexural strength of girder bracing system, \(M_{cr}\) denotes the buckling strength of a girder with a continuous torsional brace along its length, \(C_{bu}\) denotes the moment gradient factor for the entire girder, \(C_{b}\) denotes the moment gradient factor for the beam segment between torsional bracing, \(M_{o}\) denotes the LTB strength of beams without the torsional brace, \(E\) denotes the modulus of elasticity, and \(I_{eff}\) denotes the effective moment of inertia of the beam section; \(C_{tt}\), which is the load height factor of the beam, is 1.2 for top flange loading and 1.0 for centroidal loading, and \(\overline{\beta_{T}}\) denotes the continuous torsional brace stiffness per unit length, which is equal to \(n\beta_{T}/L\).  
The moment gradient factors can be computed by the following expression, which was proposed by [PERSON] et al. [21].  
\[C_{b}=\left(\frac{12.5M_{max}}{2.5M_{max}+3M_{A}+4M_{B}+3M_{C}}\right)R_{m} \leq 3.0. \tag{2}\]  
where \(M_{max}\) is the maximum bending moment, \(M_{A}\) is the magnitude of the moment at the quarter location of the target girders, \(M_{B}\) is the magnitude of the moment at the mid-location of the target girders, \(M_{C}\) is the magnitude of the moment at the three-quarter location of the target girders, and \(R_{m}\) is a factor that considers the reverse curvature bending moment.  
Neglecting the unbraced beam buckling term provides a conservative estimate of the torsional brace stiffness requirements. Thus, the required brace stiffness \(\beta_{T}\) to provide the maximum LTB strength of the braced beams is expressed in Equation (3) as follows:  
\[\beta_{T}=\frac{2.4 LM_{T}^{2}}{nEI_{eff}C_{b}^{2}}. \tag{3}\]  
[PERSON] [5] developed the strength requirement for beam torsional bracing based upon an assumed initial twist imperfection of \(\theta_{o}=0.002L_{b}/h_{o}\), where \(h_{o}\) is equal to the depth of the beam. The strength requirement of the beam bracing is simply determined by the required bracing stiffness multiplied by the assumed initial twist imperfection in Equation (4). In Equation (4), the effective bracing stiffness is equal to two times the ideal torsional bracing stiffness, which provides two times the conservative bracing moment.  
\[M_{br}=\beta_{T}\theta_{o}=\frac{0.024M_{r}L}{nC_{b}L_{br}}. \tag{4}\]
# Dynamic Modeling and Characteristic Analysis of Articulated Steering Vehicles
## 2 Dynamic Model of ASVs
### Dynamic Model of Frames
#### 2.1.3 Kinematic Constraints of Frames  
According to Figure 2, the two frames are connected to each other at the central articulated point. The motions of the frames are related to each other in the longitudinal and lateral directions. The detailed constraints of the two frames in different directions can be given by  
\[\left\{\begin{array}{l}v_{x2}=v_{x1}\cos\varphi-\left(v_{y1}-L_{f2}\omega_{z1 }\right)\sin\varphi\\ v_{y2}=v_{x1}\sin\varphi+\left(v_{y1}-L_{f2}\omega_{z1}\right)\cos\varphi-L_{r2 }\omega_{z2}\end{array}\right. \tag{9}\]  
Additionally, the heading angles of the frames are constrained by the articulated angle, which is expressed as  
\[\varphi=\psi_{1}-\psi_{2} \tag{10}\]  
where \(\psi_{1}\) and \(\psi_{2}\) are the heading angles of the front and rear frames.  
The kinematic constraints in Equation (9) can be processed with differentiation, and the acceleration constraints can be obtained:  
\[\left\{\begin{array}{l}\dot{v}_{x2}=\dot{v}_{x1}\cos\varphi-\left(\dot{v}_{y 1}-L_{f2}\dot{\omega}_{z1}\right)\sin\varphi-\\ \left(v_{x1}\sin\varphi+v_{y1}\cos\varphi-L_{f2}\omega_{z1}\cos\varphi\right) \left(\omega_{z1}-\omega_{z2}\right)\\ \dot{v}_{y2}=\dot{v}_{x1}\sin\varphi+\left(\dot{v}_{y1}-L_{f2}\dot{\omega}_{z1 }\right)\cos\varphi-\\ L_{r2}\dot{\omega}_{z2}+\left(v_{x1}\cos\varphi-v_{y1}\sin\varphi+L_{f2} \omega_{z1}\sin\varphi\right)\left(\omega_{z1}-\omega_{z2}\right)\end{array}\right. \tag{11}\]
# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 3 Background
### Applications of RST in Modern Machine Learning  
RST-based feature selection has become increasingly relevant in modern machine learning (ML), offering solutions to challenges like high dimensionality, uncertainty, and the need for interpretability [29]. Its ability to derive minimal feature subsets while preserving data structure makes it a powerful tool across diverse ML applications. In healthcare, RST enhances predictive models by identifying critical features for tasks like disease classification [30], balancing accuracy with transparency essential for clinical decision making. In natural language processing (NLP), RST aids in processing noisy text data, enabling robust sentiment analysis and topic modeling by focusing on key linguistic attributes. Computer vision benefits from RST through efficient feature selection for image classification and segmentation, where it reduces computational overhead while maintaining performance [31].  
Beyond traditional domains, RST supports ML in resource-constrained environments, such as IoT systems, by optimizing feature sets for real-time predictive tasks like anomaly detection or equipment monitoring. In speech recognition, RST improves model robustness by selecting essential acoustic features from noisy audio, enhancing accuracy in diverse conditions. Similarly, in predictive maintenance [32], RST identifies key indicators from industrial time-series data, enabling efficient failure prediction under uncertainty. Its integration with deep learning further exemplifies its versatility, where hybrid approaches combine RST's interpretability with neural networks' predictive power, as seen in domains requiring explainable outcomes, such as autonomous systems. These applications underscore RST's potential to address scalability and interpretability challenges in ML, paving the way for its continued evolution in data-driven innovation.
# Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm
## 3 Proposed Fingerprint Algorithm
### LoRa Fingerprint Map Generation  
LoRa signals from an end-device cannot be measured for every location in the service area. Therefore, the fingerprint map should be interpolated using data from sample training points. Figure 7 shows the LoRa RSSI data acquisition and fingerprint map interpolation processes. Fingerprint maps can vary with the surrounding environment, and therefore, they must be updated periodically.  
A LoRa end-device transmits signals to several gateways simultaneously. Each signal includes an ID, timestamp, and GPS coordinates. GPS coordinates can be measured using the end-device or using external devices. Each gateway measures the intensity of the signals from end-devices. The measured RSSI values are then sent to the positioning server and stored with their corresponding GPS coordinates. When sample points are visualized with their RSSI values for each gateway, the results are represented on a map, as shown in the lower right part of Figure 7. The fingerprint maps (i.e., interpolated results) are represented as heat maps, as shown in the lower left part of Figure 7.  
Figure 6: Positioning using LoRa fingerprint.  
We needed to interpolate fingerprint maps from sample point data because complete RSSI measurements could not be obtained and were unnecessary for the entire service area. It is rather difficult to choose optimal two-dimensional interpolation methods to solve practical real-world problems. We used the radial basis function (RBF) interpolation method for interpolating fingerprint maps in this study. RBF interpolation is a traditional method that has been used for radio map generation [18]. RBF Interpolation can approximate an unknown value at a point by adding a linear combination of the RBFs [18; 19]. Figure 8 shows the RBF interpolation with 2D scattered data. The advantage of RBF is that it can generate accurate and visually attractive surfaces, making it suitable for interpolated radio map generation [20]. Furthermore, it has low time complexity. We compared the interpolated results of LoRa signals with linear, cubic, quantile, Gaussian, and thin plate RBF functions included in the Python SciPy library.  
We divided the data into training and test datasets. We generated interpolated fingerprint maps from the training dataset. We calculated residuals between the me  
Figure 8: Interpolation of f(x,y) using Gaussian RBF with 25 sample points: (**a**) Random data points. (**b**) the RBF centered at the collocation points (**c**) shows the interpolated surface [21].  
Figure 7: LoRa RSSI data acquisition and fingerprint map generation.  
interpolated value for each test data point. We compared the average and the standard deviation of residuals as shown in Table 1. The linear RBF interpolation method showed the lowest residuals. We also compared the results on the fingerprint maps as shown in Figure 9, and the linear RBF interpolation method showed the good distribution in extrapolation areas.
# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 4 Results
Although this SCAN site is located an elevation of approximately 600 m (which is equal to CYGNSS's 600 m threshold for year 2017 as mentioned previously), we included it in the analysis for both years. Nevertheless, it appears that even an elevation that is equal to the altitude threshold of the CYGNSS SP calculation algorithm for 2017 would be erroneous. Figures 9 and 10 show per-site comparisons for selected sites between the CYGNSS-based SM estimations and daily ISMN measurements for years 2017 and 2018, respectively. These figures show both time-series to visualize the annual trends and scatter plots to illustrate the correlation between the reference and predicted SM values. First of all, the ability of the algorithm to generate sub-daily SM predictions for multiple CYGNSS observations on a day can be observed in these figures. For instance, the figure grid-line that corresponds to October on the top-first plot (Uvalde) in Figure 9 shows well that there were two CYGNSS observations that fell into the ISMN site representativeness window on that particular day-of-year, and the algorithm was able to generate two different SM predictions that are so close to the reference SM value. Figures 9 and 10 are prepared in a way that the top four ISMN sites are selected from among those where the SM retrieval algorithm performed the best, the next one is picked from those with decent results, and the bottom-most ISMN site is chosen from a set of stations where the algorithm has the poorest performance, with respect to both the correlation and retrieval errors. For example, Uapb-Point Remove was selected for the bottom-most plot of Figure 10 while Yanco-Y8 is the station with the lowest Pearson's R value (0.2529). This is because, even though Uapb-Point Remove has a close Pearson'R to Yanco-Y8, it has a much higher ubRMSE (0.0558) for SM estimations. Similarly, Vance site has a high ubRMSE of 0.0744 for 2018, but it has a high Pearson's R of 0.8489 at the same time. Hence, it was also chosen for the bottom-most. Figure 9: SM retrievals for 2017. In situ SM data are color-coded such that magenta and blue circles correspond to days with freeze temperatures and invalid data, respectively. Scatter plots are also given. Figure 10: SM retrievals for year 2018. In situ SM data are color-coded such that magenta and blue circles correspond to days with freeze temperatures and invalid data, respectively. Scatter plots are also given.
# Cost-Effective Temperature Sensor for Monitoring the Setting Time of Concrete
## 1 Introduction
The use of technologies such as Bluetooth and Wi-Fi have enabled efficient and secure communication between devices, which, in turn, improves efficiency and reduces operating costs. There are several technologies that can be used which are being studied and even marketed for temperature monitoring in concrete structures, such as passive wireless sensors, negative temperature coefficient (NTC) thermistors, resistance temperature detectors (RTD), thermocouples, thermometers, and sensors based on semiconductors, among others [21; 22]. However, many of these devices are only suitable for monitoring temperature on the surface or immediately around the concrete structure, excluding temperatures within a concrete matrix. Other trending options are fiber optics and Bragg Gratings in Fiber Optics (FBG) [23; 24; 25; 26; 27; 28], which have advantages such as multiplexing capability, small dimensions, and easy installation. Although the cost of the FBG sensor is acceptable due to advanced manufacturing technology, an interrogator is required for its use, which is relatively expensive compared to other electrical data acquisition systems. In addition, the FBG interrogator is usually fragile and has strict requirements for operating temperatures and relative humidity, and another computer is needed for data storage [29]. Sensors are commonly placed on the reinforcement bars before concrete is poured; the device works by measuring temperature and humidity, focusing on the assessment of the temperature increase inside the concrete structure from the time of pouring to the completion of the project. The alkaline environment of concrete, the interference of the concrete material with the data transmission, the stability of the signal transmission, database size, scale issues, and limited lifetime are some of the main issues identified during the study of the use of sensors in concrete [30; 31; 32; 33]. In this research, a system for concrete temperature measurements has been developed with low-cost commercial sensors, a microprocessor, and a platform for data collection and real-time visualization, in order to provide the industry with a continuous measurement system that ensures optimal curing conditions for concrete structures. With the implementation of this system, we aim to facilitate the use of this type of non-destructive tests that allow real-time monitoring of the curing state of our concrete and detect possible deficiencies during curing due to its low cost, easy installation, and intuitive operation.
## 4 Hardware Design
### Protection Measure  
The design of an automatic electricity monitoring system needs to consider lightning and fire protection as well as waterproofness. Therefore, transient voltage suppressors are added to protect devices from damage due to lightning strikes by intercepting such strikes and safely passing their extremely high voltage. Thermistors are also included in our design. A thermistor is a type of resistor whose resistance varies with temperature. Thermistors are widely used as inrush current limiters, temperature sensors, overcurrent protectors, and self-regulating heating elements. In addition, waterproof sealing is also needed for collectors and concentrators.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Chip**} & **Tx current** & **Rx** & **Sleep** \\  & **at 10 dbm** & **current** & \\ \hline ATmega128L & 8 mA & 8 mA & 30 uA \\ nRF905 & 29 mA & 13 mA & 0 \\ SP485E & 1 mA & 1 mA & 0 \\ AT45 DB041D & 25 uA & 27 uA & 25 uA \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average current of the data collector
## 2 Results
### Comparison of Pullulan Production between the IC and OC  
To evaluate the pullulan produced by strain ZH27, cultivations were scaled up to 7 L under the initial condition (IC) and optimized condition (OC). In order to enhance the pullulan titer, the use of YPS medium (using sucrose to replace glucose in YPD) was established as the optimal seed medium for incubating strain ZH27 for 36 h with a suitable inoculum size of 5% for fermentation (Figure S1). Figure 4 illustrates that pullulan titers under the OC experienced a rapid accumulation after 36 h, reaching a maximum of 115.4 \(\pm\) 1.82 g/L with a yield of 0.77 g/g and a productivity of 0.87 g/L/h at 132 h. This pullulan titer was more than twice compared with that under the IC. Total sugars under the OC were utilized more rapidly and more thoroughly than those under the IC, leaving the total sugar content of 9.6% at 132 h (Figure 4). The consumption rate of total sugars under the OC within 132 h was 1.03 g/L/h, which was higher than the rate of 0.67 g/L/h under the IC (Figure 4). Interestingly, the reducing sugars maintained relatively high levels under the OC within the first 60 h but were subsequently consumed more rapidly and more completely compared with those under the IC (Figure 4). There were slight increases in DCWs and growth rates under the OC than those under the IC after 60 h (Figure 4). The DCW (21.4 \(\pm\) 0.3 g/L) and growth rate (0.16 g/L/h) of strain ZH27 under the OC at 132 h were slightly higher than those under the IC (DCW, 20.4 \(\pm\) 0.2 g/L; growth rate, 0.15 g/L/h). These results indicate that the cells of strain ZH27 exhibited robust growth and high pullulan production under high osmotic stress. Notably, the final supernatant viscosity at 132 h significantly decreased to 306 mPa-s under the OC compared with that (10,753 mPa-s) under the IC (Table S3). This highlights that the optimization of conditions for pullulan production by strain ZH27 can remarkably enhance its production level.
# Efficient Reversible Data Hiding Scheme for AMBTC-Compressed Images
## 2 Related Work
### [PERSON] et al.'s Scheme  
In 2013, [PERSON] et al. [19] proposed a reversible data hiding scheme for BTC-compressed images based on the joint neighbor coding technique, which was inspired by [PERSON] et al. [22]. In this scheme, the \(P\)\(\times\)\(Q\) image is first encoded by the BTC algorithm with an \(n\)\(\times\)\(n\) image block to generate a high mean table, \(H=H_{1}\), \(H_{2}\),..., \(H_{(P\times Q)/(n\times n)}\), low mean table, \(L=L_{1}\), \(L_{2}\),..., \(L_{(P\times Q)/(n\times n)}\), and bitmap table, \(B=B_{1}\), \(B_{2}\),..., \(B_{(P\times Q)/(n\times n)}\). Then, the same embedding process is used to embed a secret message into the two mean table tables, \(H\) and \(L\). Therefore, the high mean table \(H\) is used to demonstrate this embedding process as follows. Let \(M\) be a binary pseudo-random sequence, and the length of \(M\) is similar to that of the secret message. Figure 2 shows there are four neighboring high mean values, and  
Figure 1: Example of the AMBTC algorithm.  
they are selected to encode the current high mean value \(H_{\text{cur}}\). The set of four neighboring high mean values is denoted as \(H_{\text{ref}}\), which is marked in the light blue area, and their corresponding indicators are (00), (01), (10), and (11), respectively.  
The embedding process of [PERSON] et al.'s scheme is described in detail as follows.  
Step 1: Read two bits \(b_{M}\) from \(M\) and two bits \(b_{\text{S}}\) from the secret message \(S\).  
Step 2: Determine the reference indicator \(b_{\text{ref}}=b_{\text{M}}\) XOR \(b_{\text{S}}\).  
Step 3: According to the value of \(b_{\text{ref}}\), select the corresponding \(H_{\text{ref}}\) and calculate the difference value \(D=H_{\text{cur}}-H_{\text{ref}}\).  
Step 4: Classify \(D\) into four cases and encode \(D\) to \(D^{\prime}\) using Equation (3)  
\[D^{\prime}=\left\{\begin{array}{lr}11||(D^{m})_{2}&0\leq D\leq 2^{m}-1\\ 10||(-D^{m})_{2}&-(2^{m}-1)\leq D\leq 2^{m}-1\\ 01||(D^{8})_{2}&D>2^{m}-1\\ 00||(D^{8})_{2}&D\leq-(2^{m}-1)\end{array}\right. \tag{3}\]  
where \(m\) is a predefined parameter and ranges in [1,8], \(|~{}|~{}\) is the concatenation function, and \((D^{m})_{2}\) and \((D^{8})_{2}\) represent the \(m\) bits and eight bits of binary information of the difference value \(D\).  
Step 5: Concatenate the reference indicator \(b_{\text{ref}}\) and the value \(D^{\prime}\) to generate the final codestream of the current high mean value as \(b_{\text{ref}}\)\(|~{}|D^{\prime}\).  
Step 6: Repeat Steps 1 to 5 until the entire high mean values are encoded completely.  
After the above six steps are finished, the codestream CS\({}_{\text{H}}\) of the high mean table is obtained. Obviously, [PERSON] et al. used the joint neighbor coding technique to embed a secret message into the two mean tables, \(H\) and \(L\). This scheme could guarantee that the successful restoration of the original BTC-compressed image is achieved after the secret message was extracted. However, their scheme offered a low embedding capacity.
# Coupled Model of Heat and Power Flow in Unventilated PV/PCM Wall-Validation in a Component Scale
## 3 Results and Discussions
### Model Validation  
In order to validate the proposed model of the PV/PCM component, two types of results were compared: measured and calculated values of temperature on the backside of the PV panel and values of the power generated by the PV. All the results presented in this section were obtained using the CHC method for latent heat accumulation.  
First, good agreement was obtained in terms of temperature fluctuations during the two selected days (Figure 8). The largest observed differences between the temperatures obtained from calculations and measurement were: 2.7 \({}^{\circ}\)C and 4.6 \({}^{\circ}\)C for the first and second days of measurement, respectively. However, these values occurred mainly at the beginning and at the end of the analyzed periods of time, and the MBE does not exceed 1.3 \({}^{\circ}\)C and 2.0 \({}^{\circ}\)C for the first and second days of the measurements, respectively. The discrepancy in the results in the morning hours could be caused by an unprecise definition of solar irradiance before measurement started. The measurements of solar irradiance on the PV panel surface were started along with the start of the temperature measurements. Thermal calculations for the earlier hours were done for the values of diffuse and global solar radiation. Furthermore, at night, a sudden drop in solar irradiance can be observed at about 4 p.m. (Figure 3a) which resulted in a fast decrease in measured temperature. Due to hourlyaveraged values used in ESP-r, this sudden decrease in solar irradiance value was not reflected in the simulation results. However, the correlation coefficient for the measured and calculated data was at the level of 0.99 for both days.  
Based on the results presented in Figure 9, it can be observed that the proposed model also allows for a good prediction of the power generation. Although the calculated temperatures were overestimated compared to the measured values, this phenomenon cannot be observed in terms of energy production (Table 3). Calculated values of total energy production by PV are almost equal to the measured ones, but for the first day the simulated value is slightly lower while for the second day, it is slightly higher. Such phenomena can be the result of the occurrence of instantaneous peaks of solar irradiance during the measurements and hourly averaged values used in calculations.  
Figure 7: PV panel backside temperature values obtained using two methods of PCM definition.