esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Model, Algorithms, and Security Model
### Algorithms
#### 4.2.3 User Key Generation  
CAKeyGen(GPP,gid) \(\rightarrow\) (CAPK\({}_{\text{gid}}\), CASK\({}_{\text{gid}}\)): The user submits their gid as input to this algorithm. Taking GPP as another input, it produces the gid-related identity-key CASK\({}_{\text{gid}}\), which is held by the user. The public key CAPK\({}_{\text{gid}}\) is given to the AAs for generating attribute-related keys.  
AAKeyGen(S\({}_{\text{gid,k}}\))GPP, MPK, CAPK\({}_{\text{gid}}\), ASK\({}_{\text{k}}\)) \(\rightarrow\) (ASK\({}_{\text{gid,k}}\)): When a user requests kth authority for generating keys for an attribute set S\({}_{\text{gid,k}}\), AA\({}_{\text{k}}\) runs this algorithm with  
Figure 2: System model for SP-MAACS scheme.  
inputs as \(\mathrm{S_{gid,k}}\), GPP, MPK, CAPK\(\mathrm{gid}\), and ASK\(\mathrm{{}_{k}}\). If CAPK\(\mathrm{gid}\) is invalid, then it returns \(\bot\), else it returns corresponding attribute-related keys ASK\(\mathrm{{}_{gid,k}}\) for attribute set \(\mathrm{S_{gid,k}}\).
## 5 Experiments and Results
### Auditive Transparency
#### 5.2.1 Unsecured Data-Hiding  
The whole audio dataset [27] was fed to the watermark insertion process without any key-based security strategy. Figure 5 shows the PSNR results and PSNR distribution in this experiment.  
The PSNR mean and median were calculated to be 44.37 and 43.29, respectively; both values are very good for practical applications.  
ODG evaluation was carried out in similar way for the audio dataset. Figure 6 shows the ODG results and the ODG distribution in this experiment.  
As with PSNR, ODG mean and median were calculated as \(-0.9362\) and \(-0.6318\), respectively; again, both values are very good for practical applications.  
Figure 5: Peak signal-to-noise ratio (PSNR) results for watermarking without key-based security. (**a**) PSNR results. (**b**) PSNR distribution.
## 2 Materials and Methods
### Semantic Pairs  
The process of selecting affective words largely involved four steps. To establish a pool of affective words in the study, first, affective words were collected from the reviews on previous researches (17 domestic researches and 15 international researches) about affective evaluation, especially SD-based sensory tests for vehicle interiors [31; 46; 47; 48; 49; 50]. As a result, the first level of affective structure was established with 626 words by collecting affective words that had used in visual and tactile sensory evaluations, textile evaluations, and vehicle interior design evaluations. In addition to the literature review, a total of 180 online expert reviews (by general consumers with a good knowledge of vehicle interiors) from vehicle driving tests were collected, and only 49 reviews about vehicle instrumental panels or vehicle interior design were considered. For the text analysis, 2062 words were extracted, and 23-word groups for affective evaluations were classified. As a result, the most frequent affective words in real world were confirmed (warming, soft, and glossy), and they were included in the pool of affective words with 649 words. Second, duplicated words were removed, and similar ones were combined from the 649 words based on literature review and text analysis, as a result, 180 words were selected in the second stage. Third, the organized affective words were evaluated on suitability for leathers and instrument panels, and 108 words were selected including the most important affective words (bright, moist, sticky, and rugged). Fourth, antonyms and synonyms of each word were selected. Ultimately, a total of seven semantic pairs (14 words) were derived as sensory words through several group discussions. Moreover, an expert consultation was conducted to elicit the more appropriate words for the experiment. The process of selecting affective words was presented in Figure 1.  
Figure 1: Process of selecting affective words.  
The seven semantic pairs for the affective evaluation in this study were: \"Dark-Bright,\" \"Matt-Glossy,\" \"Cooling-Warming,\" \"Dry-Moist,\" \"Slippery-Sticky,\" \"Flat-Rugged,\" and \"Hard-Soft.\" These semantic pairs were classified as either visual pairs or tactile pairs. Two visual pairs, \"Dark-Bright\" for brightness and \"Matt-Glossy\" for glossiness could be perceived by mere observation. The brightness was influenced by contrast of color, and the gloss was influenced by illumination. The five tactile pairs, \"Slippery-Sticky,\" \"Flat-Rugged,\" and \"Hard-Soft\" for surface, \"Cooling-Warming\" for temperature, and \"Dry-Moist\" for humidity could be perceived by movement in contact. In particular, roughness, warmness, and humidity were perceived statically by mere touch whereas slipperiness and softness were perceived dynamically by rubbing, pressing, and tapping [17; 51]. The definitions of each semantic pair are presented in Table 1.
## 2 Materials and Methods
### Method  
#### 2.2.1 Electrophoretic Mobility  
We measured the electrophoretic mobilities (EPMs) of CNHoxs at different KCl and LSZ concentrations. The CNHox suspension used was dispersed with an ultrasonic disperser (US-2R, AS ONE Corporation, Osaka, Japan) to maintain an evenly dispersed suspension. All the samples were prepared with a CNHox concentration of 2 mg/L and a volume of 10 mL by mixing prescribed volumes of KCl solution, HCl or KOH solution, CNHox suspension, and de-ionized water. A capillary cell (DTS1070, Malvern Panalytical Ltd., Malvern, UK) was prewashed three times by using the sample after the preparation for each measurement. Then, each of the samples was injected into the capillary cell. The cell was inserted into a Zetasizer Nano-ZS (Malvern Panalytical Ltd, Malvern, UK) and the EPM was subsequently measured by using electrophoretic light scattering. The EPM was measured as soon as evenly mixed samples were prepared. The EPM measurements were carried out three times and completed within 5 min. The condition that \(\kappa a>1\) for irreversible CNHox aggregates was satisfied [38], where \(\kappa^{-1}\) is the thickness of the electric double layer \(\sim\)30 nm at 0.1 mM and \(a\) is the radius of the CNHox aggregates \(\sim\)100 nm. The flow in the pore of the CNHox was regarded as negligible. Therefore, the zeta potential \(\zeta\) can be reasonably obtained by using [PERSON]'s equation as follows:  
\[\mu=\frac{\epsilon_{0}\epsilon_{r}\zeta}{\eta}\, \tag{1}\]  
\(\mu\) is the EPM, \(\epsilon_{0}\) is permittivity of the vacuum, \(\epsilon_{r}\) is the relative dielectric constant of the water, and \(\eta\) is viscosity of the solution.
## 3 Mechanical Properties
### Building Parameters  
It is well-known that the DED building parameters, such as laser power, scan speed, and powder feeding rate are key factors that determine the quality of components. The identification of the most suitable combination of these process parameters can assure not only the achievement of dense samples but also can result in the formation of the desired microstructure and mechanical properties [31; 87]. As a result, many authors have investigated the effect of combinations of process parameters on mechanical properties of components. In the first rows of Table 4, data related to samples built with different parameters are reported according to the paper of [PERSON] et al. Considering that other DED parameters and machine configurations, such as laser spot size and standoff distance, can also affect the mechanical properties, data recorded in different research studies are not compared with each other In this way it is possible to underline the influence of process parameters on mechanical behavior of DED parts without neglecting some factors specific to the system employed in each study.  
As demonstrated by [PERSON] et al., there is a clear correlation between the tensile properties of DED AISI 316L steel samples and the delivered energy density. The lowest YS, US, and \(\varepsilon\) (300 MPa, 560 MPa, and 31%, respectively) values were in fact obtained with the highest power and the lowest scan speed (Table 4). The authors explained this effect through the correlation that exists between YS, PCAS, and the width and length of the columnar grains. As previously described, these microstructural features are strictly connected to the cooling rate and thermal gradient, generally controlled trough the building parameters [49].  
Similar results were obtained by [PERSON] et al., who studied the effect of the laser power and scan speed on the hardness values and tensile properties of DED AISI 316L samples [83]. It was demonstrated that in all the samples, the higher the laser power, the lower are YS and US, while the opposite is true for the scanning speed (Table 4). This effect was also explained by the different cooling rate of the materials under different building conditions. High power and low scan speed lead, in fact, to the formation of larger melt pools and lower cooling rate. Low power and high scan speed, on the contrary, lead to the formation of smaller melt pools that solidify with an extremely high cooling rate. The authors did not report, however, any clear correlation between energy input and elongation.
# Effect of PAS-LuxR Family Regulators on the Secondary Metabolism of _Streptomyces_
## 4 Application of PAS-LuxR Family Regulators
For example, the heterologous expression of antimycin biosynthetic gene clusters in _S. coelicolor_ is required to simultaneously express FscRI and thus activate the antimycin biosynthetic gene clusters. The heterologous expression of AmphRIV, NysRIV, or PteF can restore the production of pimaricin in \(pimM\) deletion strains. The expression of PimM can increase the yield of amphoteric and filipin in _S. nodosus_ and _S. avemitilis_, respectively. The expression of \(pimM\) in \(pteF\) deletion strain also restored the production of filipin [30]. These findings broaden the potential applications of PAS-LuxR family regulators, such as the activation ofsilenced gene clusters to obtain new products and the regulation of gene expression from different sources in combinatorial biosynthesis.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 3 Materials and Methods
### Network Architecture and Connectivity  
The network architecture is designed to take advantage of the heterogeneity of wireless communication technologies, prioritizing sensor communication across different protocols. The CC1352P sensors use LoRa to transmit basic environmental data, such as temperature and humidity, to the Laird Sentrius Gateway over long distances. Meanwhile, the LPSTK-CC1352R sensors rely on BLE for short-range communication, allowing real-time detection of nearby devices and monitoring of movement within the campus. Since the Laird Sentrius Gateway supports both LoRaWAN and BLE, it functions as a bridge between these two technologies, facilitating data exchange and forwarding the collected information to a local server for analysis. The experiment takes place in a university campus environment, including laboratories, offices, classrooms, and green areas, ensuring that data are collected across diverse real-world conditions with varying levels of interference and obstacles.  
The proposed IoT network consists of four CC1352P sensors, four LPSTK-CC1352R sensors, and a Laird Sentrius Gateway. The CC1352P sensors are strategically deployed across a given area to monitor environmental and industrial parameters. These sensors operate in two communication modes: LoRa for long-range data transmission to the gateway and BLE for short-range interactions with other BLE-enabled devices. The LPSTK-CC1352R sensors are distributed within closer proximity to the gateway, primarily utilizing BLE for direct communication while also having LoRa capabilities as a backup in case of BLE signal degradation.
# GNSS Applications to Assess Performance in Olympic Sailors: Laser Class
## 2 Materials and Methods
### Regatta  
The analyzed regatta was the Hempel World Cup Series: Genoa (Italy, 2019). This regatta was a qualifying competition for the World Cup and also for participation in the Olympic Games, although only the results obtained in the ranking of this regatta were used. The VMG (knots), distance (km), maneuvers (number of maneuvers), and time (hours) variables were obtained through the SAP-Sailing(r) application [25]. This application uses a TracTrac(r) GNSS, which is placed on the sailor. The GNSS device weighed 60 g, and it included a mobile connection and a battery. The data transmission frequency was 5 Hz.  
A total of 12 races were analyzed: 6 in the group of males and 6 in females. The wind speed ranges in the regatta were 3.5-8.3 and 4.1-8 knots in the female and male groups, respectively. The race course consisted of 6 legs (2 windward and 4 leeward): 2 upwind, 2 downwind, and 2 broad reach. The regatta race courses depend on the weather conditions and two types of courses were carried out (Figure 1): \"In\" and \"Out\". Both types of routes were carried out by males and females, and the regatta judges determined the realization of one or the other depending on the number of groups of sailors participating in the regatta and in each of the races.
## 2 Experimental Process/Program
### Testing of UHPC Beams
#### 2.3.2 Details of the Testing System  
The structural responses of UHPC beams (Table 6) to four-point loading were studied in the current research. The pertinent test details are presented in Figure 6, and the test was conducted using a hydraulic universal testing machine (AMSLER, 2000 kN capacity with a 0.4 mm/min loading rate). The test instruments included five different sets. First, three 60 mm concrete-strain gauges (C-1, C-2, and C-3) were attached at various locations (Figure 6) to evaluate the concrete strains. Secondly, two 5 mm electrical-sensitive strain gauges were fixed on the B to F beams' steel rebars (before casting) to obtain their strain responses. Thirdly, two vertically mounted LVDTs were attached beneath the beam's midspan to achieve vertical displacement. Additionally, two horizontally located LVDTs were attached near the beam's centerline to acquire the crack width response. Lastly, the applied loads were measured by load-cells bonded to the machine's actuator. A data acquisition device (Tokyo Sokki, model TDS-630 with a speed of 1000 channels in 0.1 s) was employed to synchronize the data of the above-mentioned instruments. After testing each beam, high-quality photos were captured to assess the cracking patterns and modes of failure.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Set**} & \multirow{2}{*}{**UHPC Batch**} & \multirow{2}{*}{**Beam**} & \multicolumn{6}{c}{**Dimensions**} & \multirow{2}{*}{\(\boldsymbol{\rho}\) **(\%)**} & \multirow{2}{*}{\(\boldsymbol{L/d}\)} \\ \cline{4-9}  & & & \(\boldsymbol{b}\) & \(\boldsymbol{h}\) & \(\boldsymbol{a}\) & \(\boldsymbol{d}\) & \\ \hline \multirow{2}{*}{A} & \multirow{2}{*}{B-1} & A1 & 150 & 150 & 225 & - & - & - \\  & & A2 & 150 & 150 & 225 & - & - & - \\ \hline \multirow{2}{*}{B} & \multirow{2}{*}{B-2} & B1 & 150 & 150 & 225 & 125 & 1.206 & 4.00 \\  & & B2 & 150 & 150 & 225 & 125 & 1.206 & 4.00 \\ \hline \multirow{2}{*}{C} & \multirow{2}{*}{B-3} & C1 & 150 & 150 & 207 & 115 & 1.311 & 4.35 \\  & & C2 & 150 & 150 & 207 & 115 & 1.311 & 4.35 \\ \hline \multirow{2}{*}{D} & \multirow{2}{*}{B-4} & D1 & 150 & 150 & 171 & 95 & 1.587 & 5.26 \\  & & D2 & 150 & 150 & 171 & 95 & 1.587 & 5.26 \\ \hline \multirow{2}{*}{E} & \multirow{2}{*}{B-5} & E1 & 150 & 150 & 153 & 85 & 1.774 & 5.88 \\  & & E2 & 150 & 150 & 153 & 85 & 1.774 & 5.88 \\ \hline \multirow{2}{*}{F} & \multirow{2}{*}{B-6} & F1 & 150 & 150 & 135 & 75 & 2.011 & 6.67 \\  & & F2 & 150 & 150 & 135 & 75 & 2.011 & 6.67 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reinforcement details for beam specimens.  
Figure 5: (**a**) Set A and (**b**) set B-F (dimensions are in mm).
## 4 Experiments
### Ablation Study
#### 4.5.1 Selection of the Maximum Convolution Kernel in the Convolution Group of the Decoder  
In SSD, the default maximum convolution kernel (MaxConvKernel) in the convolution group is set to half the side length of the feature map. We conducted comparative experiments using one-quarter, three-quarters, and the full side length of the feature map as the maximum convolution kernel in the convolution group to explore the optimal parameters for the maximum convolution kernel. As shown in Table 4, \(H\) represents the side length of the feature map. When using \(\frac{H}{4}\) as the maximum convolution kernel size, smaller convolution kernels occupy a larger proportion in the convolution group, allowing SSD to capture more information about small organs. Consequently, with \(\frac{H}{4}\), MINTFormer achieves higher Dice scores for small organs such as the aorta, gallbladder, and pancreas. When using \(H\) as the maximum convolution kernel in the convolution group, larger convolution kernels occupy a larger proportion, resulting in MINTFormer achieving better Dice scores for large organs such as the liver. However, this also leads to a loss of information about small organs. Overall, \(\frac{H}{2}\) is a more balanced choice.
# Subcritical Water Extraction of Ursolic Acid from _Hedyotis diffusa_
## 3. Results and Discussion
### Optimization of Subcritical Water Extraction  
The main objective of this work is to study SWE of \(H\). _diffusa_ maximizing UA yield in extracts. The \(F\)-value and \(p\)-value are used to check the significance of each coefficient. The larger \(F\)-value and smaller \(p\)-value indicates greater significance of the coefficient. According to Table 2, it can be seen that the UA yield was significantly influenced by three linear (\(X_{1}\), \(X_{2}\) and \(X_{3}\)) and two quadratic (\(X_{1}X_{2}\)) parameters, and two interaction (\(X_{1}\)\({}^{2}\), \(X_{2}\)\({}^{2}\)) parameters (\(p<0.01\)). The effects of variables and their interactions on the UA yield were illustrated by the two-dimensional contour plots and the three-dimensional response surface plots. Figure 3 describes regression Equation (3) by means of a graphic approach, which provides a method to visualize the relationships between UA yield and any two independent variables, while the third is fixed at its zero level. The maximum predicted response was located on the peak of the 3D response surface, the corresponding point for which is defined by the smallest ellipse in the contour diagram of the 2D contour plot [35]. Figure 3A is the response surface and contour plot showing the effect of particle size and temperature on the UA yield at the fixed value of solvent/solid ratio. It can be seen that the UA yield increased rapidly with the increase of particle size and temperature and reached a maximum value, followed by a decline, with their further increase. From Figure 3B,C, when particle size and temperature are fixed at level 0, the highest extraction yield was observed with an increase of liquid to material ratio. The prediction optimization values were calculated using Equation (3). The optimal extraction conditions given by the model were as follows: Particle size of 76 mesh, temperature of 157 \({}^{\circ}\)C and solvent/solid of 30 mL/g. Under these conditions, the model gave predicted values of extraction yield being 6.43 mg/g. The validation experiment was performed under the above optimized extraction conditions with modification particle size of 80 mesh, where UA yield reached 6.45 mg/g, slightly higher than the value given by the model. The good correlation between these results confirmed that the response model was suitable for reflecting the expected optimization. To further understand the nature of fitting, a normal probability plot can be applied to the residuals, which can show the difference between the predicted and experimental values [36]. The normal percentage probability and studentized residual plot are shown in Figure 4A. The data points indicate that neither response transformation was required nor was there any apparent problem with normality [37]. Thus, the analytical hypothesis is affirmed. To ascertain the most satisfactory fit of the developed models, residuals versus experimental runs for analyses of experimental data were made [38], as shown in Figure 4B. It is particularly helpful to determine whether the order of
## 6 Conclusions  
This study focused on the large section bifurcated small clear distance tunnel of the Xiamen Haicang Evacuate Channel project. The on-site monitoring scheme was introduced, and the monitoring results were analyzed to propose an automatic monitoring and control method for the middle rock pillar in the bifurcated small clear distance tunnel. The main conclusions drawn from the study are as follows:  
1. The necessary monitoring items of the tunnel section included advanced geological prediction, geological and support status observation, arch vault settlement, and peripheral convergence monitoring and measurement. This comprehensive monitoring approach effectively monitored the status of the construction stage.
2. The deformation process of the bifurcated small clear distance tunnel was verified by analyzing the accumulated deformation, deformation rate, control value of accumulated deformation, and control value of the deformation rate of the field monitoring data.
3. The proposed automatic monitoring and control method of the middle rock pillar can calculate the displacement of the measuring point, integrate the displacement criterion method of the middle rock pillar stability, and automatically judge whether the middle rock pillar is stable.
4. The proposed automatic monitoring and control method for the middle rock pillar of the bifurcated small clear distance tunnel adopted a microwave radar, spherical camera, automatic laser rangefinder, computer, and other equipment. This method effectively solves the problem of large accidental errors caused by many factors in traditional monitoring methods and realizes real-time automatic monitoring.  
Conceptualization, J.W.; methodology, J.W., T.C. and Z.W.; software, T.C. and Z.W.; validation, L.L., Z.L., J.W., A.C., H.L. and Y.L.; formal analysis, J.W. and T.C.; investigation, J.W., T.C., Z.L., A.C., H.L. and Y.L.; resources, J.W.; data curation, Z.W.; writing--original draft preparation, J.W., T.C, Z.W. and A.C.; writing--review and editing, J.W. and A.C.; project administration, L.L. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanghai Municipal Science and Technology Project (18 DZ1201301; 19 DZ1200900); Xiamen Road and Bridge Group (XM2017-TZ0151; XM2017-TZ0117); the project of Key Laboratory of Impact and Safety Engineering (Ningpo University), Ministry of Education (CJ202101); Shanghai Municipal Science and Technology Major Project (2021 SFLZDZX0100) and the Fundamental Research Funds for the Central Universities; Key Laboratory of Land Subscience Monitoring and Prevention, Ministry of Natural Resources of the People's Republic of China (No. KLLSMP202101; KLSMP202201); Suzhou Rail Transit Line 1 Co., Ltd. (SURT01 YJ1S10002); and China Railway 15 Bureau Group Co., Ltd. (CR15 CG-XLDYH7-2019-GC01).  
The study did not require ethical approval.  
The study did not involve humans.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.