esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Triterpenoids
### 8,9-ene-triterpenoids
\begin{tabular}{c c c c c} \hline \hline No. & Trivial Names & Bioactivities (IC\({}_{\text{eq}}\)/MIC or ED\({}_{\text{sq}}\)) & Sources & References \\ \hline
206. &
\begin{tabular}{c} (24\(\ell\))-36,7a,15a,26-Tetra- \\ hydroxyanosta-8,24-diene \\ \end{tabular} & - & G. _casaurinica_ & [234] \\ \hline
207. &
\begin{tabular}{c} 7\(\ell\),15a,20-Tribydroxy-3,11,23- \\ trioxo-5a-lanoska-8-em-26-oic \\ acid \\ \end{tabular} & - & G. _Incidum_ & [235] \\ \hline  
begin{tabular}{c} 12\(\ell\)-Activacy-36,7-6-dihydroxy- \\ 11,15,23-trioxo-5a-lanoska-8-20- \\ dien-26-oic \\ acid \\ \end{tabular}  & - & \begin{tabular}{c} G. _Incidum_ \\ (frul bodies) \\ \end{tabular}  & [65] \\ \hline
232. & Methyll ganolucidate C & - & \begin{tabular}{c} G. _Incidum_ \\ (frul bodies) \\ \end{tabular}  & [65] \\ \hline
233. & \ \begin{tabular}{c} 12\(\ell\)-Activacy-36,7-6-dihydroxy- \\ 11,15,23-trioxo-5a-lanoska-8-20- \\ dien-26-oic \\ acid \\ \end{tabular}  & - & \begin{tabular}{c} G. _Incidum_ \\ (frul bodies) \\ \end{tabular}  & [65] \\ \hline
8,9-dihydro-triterpenoids  
From a lipophilic extract of the fruiting body of _G. lucidum_, two dihydrogenaoderic acids, 8\(\beta\),9\(\alpha\)-dihydrogenaoderic acid J (**246**) and methyl 8\(\beta\),9\(\alpha\)-dihydrogenaoderate J (**247**), (Figure 5) were first isolated [224]. Ma et al. [224] used Nuclear Overhauser Effect Spectroscopy (NOESY) experiments to confirm the absolute configuration of H-8 and H-9 as \(\beta\) and \(\alpha\). 8\(\beta\),9\(\alpha\)-Dihydrogenaoderic acid C (**248**) (Figure 5) was also isolated from this fungus [176]. Then, Peng et al. [193] studied the chemical constituents of _G. resinaceum_, and gandoresins A and B (**249**, **250**) (Figure 5) were identified to be 8\(\beta\),9\(\alpha\)-dihydrogenaoderate derivatives. Meanwhile, in an in vitro model, compound **250** showed inhibitory effects against the increase in alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in HepG2 cells induced using H\({}_{2}\)O\({}_{2}\) compared to a control group in the range of its maximum non-toxic concentration. Fornicatin C (**251**) (Figure 5) from _G. fornicatum_ also possessed an 8\(\beta\),9\(\alpha\)-dihydrolanostane skeleton. However, 18-COOH was shifted to C-12 and a double bond was present between C-13 and C-17 [61]. Liu et al. [74] isolated gandoresins C (**252**) (Figure 5) from _G. theaeccolumn_ with the 8\(\beta\),9\(\alpha\)-dihydrolanostane skeleton and exhibited hepatotoxicity activity at a concentration of 10 \(\upmu\)M.  
8,9-epoxy-triterpenoids  
Lanostanol was formed through a MVA pathway as an 8,9-ene skeleton; nevertheless, the double bond was transformed to an epoxy group due to the presence of oxygease's [135]. 8\(\alpha\),9\(\alpha\)-Epoxy-3,7,11,15,23-pentaoxo-5\(\alpha\)-lanosta-26-oic acid (**253**) (Figure 6) was
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## 4 The Proposed Protocol
### Data Combining Strategy  
Once the corresponding relay node is selected, the received data are transmitted to the coordinator through the implanted sensor and relay node. The collected data combining scheme at the coordinator can be expressed as  
\[y_{D}=\alpha y_{SD}+\beta y_{RD} \tag{13}\]  
where \(y_{D}\) represents the combined output signal at the coordinator; and \(\alpha\) and \(\beta\) are the weights of the two communication links \(SD\) and \(RD\), respectively. The ratio \(\alpha/\beta\) reflects the communication link quality and captures the influence of the channel shadowing effect on the transmission channel. The ratio can be formulated as a function of distance when the relay node is selected:  
\[\frac{\alpha}{\beta}=\frac{d_{SR_{j}}+d_{R_{j}D}}{d_{R_{j}D}} \tag{14}\]  
where \(\alpha/\beta\) is related to the transmission distances \(d_{SR_{j}}\) and \(d_{R_{j}D}\).
# Physicochemical Properties and Volatile Profile of Chito: A Traditional Dry-Cured Goat Meat Product
## 3 Results and Discussion
### Odor Activity Value (OAV)
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{OAV} \\ \hline
**Volatile Compound** & **Odor Threshold (\(\mu\)g/kg)** & **Ref.** & **NP 2021** & **P 2021** & **NP 2022** & **P 2022** & **NP 2023** & **P 2023** \\ \hline
2-Undecanal & 0.78 & [38] & nd & nd & 1259.29 & 1396.62 & 621.97 & 1124.91 \\ Tridecanal & 10 & [38] & nd & nd & 21.62 & 23.62 & 9.55 & 18.86 \\ Tetecanal & 110 & [38] & nd & nd & 2.54 & 1.95 & 1.06 & 5.27 \\
**Ketones** & & & & & & & & \\ 2-Hepntanone & 140 & [38, 52] & nd & nd & 1.84 & 0.97 & 0.40 & 0.65 \\
2-Nonanone & 41–82 & [38] & nd & nd & 4.31 & 5.35 & 2.16 & 6.88 \\
2-Decanone & 8–41 & [38] & nd & nd & 28.27 & 23.12 & 14.32 & 27.51 \\
2-Undecanone & 5.5 & [38] & nd & nd & 44.91 & 70.85 & 23.61 & 41.61 \\ Geranylacetone & 0.06 & [43] & nd & nd & 2.43 & 1.85 & 0.98 & 1.97 \\
**Aleohols** & & & & & & & & \\ 1-Hexanol & 5.6 & [39] & nd & nd & 7.71 & 16.99 & 9.49 & 12.59 \\
1-Heptanol & 5.4 & [39] & nd & 0.48 & 29.35 & 41.83 & 31.64 & 42.81 \\
1-Octan-3 ol & 1.5 & [38, 39] & 1.12 & 1.33 & 222.67 & 252.79 & 175.14 & 209.99 \\
1-Octanol & 120 & [38] & nd & nd & 2.12 & 2.82 & 2.11 & 2.24 \\
1-Dodecanol & 16 & [38] & nd & nd & 3.39 & 5.51 & 8.53 & 3.42 \\
**Tepepnes** & & & & & & & & \\ \(p\)-Cymene & 5.01 & [38] & nd & nd & 8.59 & 10.22 & 4.31 & 4.95 \\ D-Limonene & 10 & [53] & 0.00 & 0.00 & 1.34 & 3.69 & 1.27 & 0.91 \\ Caryophyllene & 0.064 & [43] & 0.15 & 0.05 & 3.39 & 5.34 & 4.86 & 1.92 \\ \hline \hline \end{tabular} Ref.: References. NP: non-pressed (immediate consumption); P: pressed (for sale). nd: the compounds not detected in the sample.  
\end{table} 
Table 6: _Cont._  
Figure 2: VIP scores of the volatile compounds in the chito (NP and P). NP: non-pressed (immediate consumption); P: pressed (for sale).
## 2 Networking Architectures  
In FANET, UAVs maintain two basic functions to perform various tasks i.e., relaying UAV-to-UAV (U2U) communications and gathering network information. Besides, UAVs also perform two additional tasks: they work as remote access units to extend the coverage of infrastructures, and they can develop a dynamic network with heterogeneous resources. According to the roles performed by UAVs in a FANET architecture, two networking modes must be enabled: UAV-to-UAV (U2U) and UAV-to-Infrastructure (U2I). For this purpose, one of the UAVs is referred to as the \"backbone UAV,\" as it plays the role of a gateway in the FANET architecture. The backbone UAV collects data from the member UAVs (through U2U) and then relays the aggregated data to the ground station (GS) using U2I communication.  
[PERSON] et al. (2018) presented two types of architectures for connecting multiple UAVs, i.e., centralized and decentralized. Centralized architecture involves the ground station as a central node, and all the UAVs are directly linked to it, as illustrated in Figure 1(a). Thus, data transmission between any two UAVs needs to be sent via the ground station (GS). Contrarily, UAVs can communicate directly or indirectly in the decentralized architecture without relying on a GS. The authors further introduced three types of decentralized architectures: UAV ad-hoc network, multi-group UAV ad-hoc network, and multi-layer UAV ad-hoc network. Similarly, in ([PERSON] et al., 2018), the authors explored such architectures for the deployment of FANET. The three architectures are discussed in the following paragraphs.
# Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review
## 4 Radiomics in Osteoporosis Diagnosis
### Radiomics Based on Radiographs in Osteoporosis Diagnosis
Similarly, the osteoporosis classification model developed by integrating texture features and structural information from lumbar radiographs with a deep learning classification network achieved an average accuracy, sensitivity, and specificity that were 9.83% higher than those of direct diagnoses made by clinicians without assistance [68]. These findings suggested that a fusion method combining multidimensional data could leverage the advantages of both radiomics and deep learning to improve the accuracy of osteoporosis diagnosis, which supported clinicians in improving diagnostic capabilities and providing more precise and reliable medical services for patients. Early and severe osteoporosis is associated with endocrinopathies or metastatic involvement, necessitating accurate assessment of bone status [69; 70]. Although traditional methods based on radiographs continued to play a crucial role in osteoporosis diagnosis, their limitations highlighted the need for more efficient and feasible tools. Combining the cost-effectiveness of radiographs with innovative diagnostic technologies, such as radiomics and AI, will offer new opportunities for screening and personalized treatment of osteoporosis.
## 5 Conclusions  
We have described a new non-invasive method based on the spectral analysis of audible scattered sounds to conclude the concentration of liquid mixtures according to their chemical composition with low cost. The spectral information was analyzed by a metaheuristic algorithm. ELM was integrated to implement the fitness function of the optimization algorithm GGA and extract a reduced set of frequencies as a classifier. The acoustical response spectrum of the samples to MLS sounds was used after a previous comparison with other sounds, like chirps, square pulses, and white noise. It was sufficient to examine the spectrum response at a few frequencies instead of analyzing the whole range of audible frequencies.  
The experiments were carried out with 364 measurements from 28 samples of distilled water and fructose mixtures (150 mL) with a fructose concentration varying between 0 and 9 g/L. The 28 concentrations were grouped into three sets with increasing difficulty: ten between concentrations 0 g/L and 9 g/L with \(\pm\)1 g/L increments, nine concentrations between 2.1 g/L and 2.9 g/L with \(\pm\)0.1 g/L increments, and nine between 2.01 g/L and 2.09 g/L with \(\pm\)0.01 g/L increments.  
This work has allowed us to reduce the problem to a set of only nine frequencies on (3-15) kHz able to classify samples with concentrations of any of the three sets described. In the most complex case, the proposed classifier was able to discriminate fructose concentrations with variations of \(\pm\)0.01 g/L with an average accuracy of 98.65%. The higher the concentration difference, the better the classification accuracy. For samples with increments of \(\pm\)0.1 g/L the average accuracy is 98.98%, and when the concentration increments are \(\pm\)1 g/L, the average accuracy rises to 99.82%.  
The optimization algorithm returned different solutions with similar performance. The solution to the problem was not unique. It is important to note that changes in the number of types are likely to change the set of frequencies selected in the solutions. Each frequency had a different weight in the classification process. Future research will be focused on analyzing other chemicals, more complex mixtures and improving the accuracy of this sensing method.  
Conceptualization, J.A.M.R.; methodology, J.A.M.R., P.G.D., M.U.M. and J.A.H.; software, P.G.D. and M.U.M.; validation, J.A.M.R. and J.A.H.; formal analysis, J.A.M.R., P.G.D. and J.A.H.; investigation, J.A.M.R., P.G.D., M.U.M. and J.A.H.; resources, M.U.M. and J.A.M.R.; data curation, P.G.D. and M.U.M.; writing--original draft preparation, J.A.M.R., P.G.D., M.U.M. and J.A.H.; writing--review and editing, J.A.M.R., P.G.D., M.U.M. and J.A.H.; visualization, P.G.D. and M.U.M.; supervision, J.A.M.R. All authors have read and agreed to the published version of the manuscript.  
No external funding sources were used for this research.  
The study does not include research on humans or animals.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Deep Learning LSTM Model
#### 2.2.2 Input and Output Parameters  
The LSTM model utilizes 11 relevant factors which were illustrated in Section 2.1.2 as input parameters: local time (LT), annual accumulated days (DOY), solar zenith angle (CH), geographic latitude (LAT), geographic longitude (LON), integration in the first 33 h of \(ap\) (\(ap(\tau)\)), sunspot number (SSN), F10.7, magnetic declination (\(D\)), magnetic inclination angle (\(I\)), and the observed value of foF2 at the previous moment. The output parameter of the model is the foF2 value at the current moment.
# Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System
## 4 Airspace Clustering Steps Based on Multi-A \({}^{*}\) Algorithm
### Airspace Projection Image Preprocessing
#### 4.2.2 Projection Image Processing  
In order to highlight the target area of the airspace projection, increase the visual contrast and transform the airspace projection map into a digital coordinate matrix that can represent the airspace location information. In this paper, the color projection two-dimensional plan is converted into gray image. The conversion method is mainly used to calculate the gray value \(I_{gray}\) of each pixel in the \(RGB\) image through \(Gamma\) correction. The calculation formula is as follows:  
\[I_{gray}(x,y)=\sqrt{\frac{I_{R}(x,y)^{2.2}+\left[1.5\cdot I_{G}(x,y)\right]^{ 2.2}+\left[0.6\cdot I_{B}(x,y)\right]^{2.2}}{1+1.5^{2.2}+0.6^{2.2}}} \tag{3}\]  
By giving each pixel of the original image a new \(RGB\) value, a two-dimensional grayscale image of airspace projection is generated (as shown in the Figure 5a). The gray level equalization can also be used to further process the gray image, which can make the gray image contrast more intense, and the proportion of each gray level tends to be the same. The processed image is shown in the Figure 5b.  
In order to further process and analyze the two-dimensional airspace projection plan and obtain effective airspace location information, its gray image needs to be binarized. In this way, the collection property of the obtained image is only related to the position of the point whose pixel value is 0 or 255, and it does not involve the multi-level value of the pixel, making the processing simple and the amount of data processing and compression small. The basic method of binarization processing is to determine all pixels whose gray level is greater than or equal to the threshold as belonging to a specific object, and the gray value is 255, otherwise these pixels are excluded from the object area, and the gray value is 0. In this paper, the adaptive threshold is calculated by subtracting the average value of the neighborhood pixel values within the radius around any pixel to realize the binary processing of gray image. The calculation formula of adaptive threshold is as follows:  
\[T(x,y)=\frac{\sum_{s=-r}^{r}\sum_{t=-r}^{r}f(x+s,y+t)}{\left(2r+1\right)^{2}}-C \tag{4}\]  
The airspace projection gray scale image after binary processing is shown in the Figure 5. At the same time, through the Matlab \"imread\" function to read the binary image, you can obtain the binary matrix of the airspace location information, and make the necessary data preparation for the subsequent clustering processing. Perform basic image processing on the task airspace projection in the example in the previous section to obtain the black and white binary projection image as shown in the following Figure 6.  
Figure 5: Task airspace projection effect picture (**a**) 2D grayscale image of airspace projection; (**b**) gray level equalization processing gray level image; (**c**) black and white binary projection.
## 3 Controller Design
### PLC-PID Controller  
In the experimental setup, the rpm value is achieved using the encoder. Based on this value, frequency control is performed. If the rpm value differs from the set value of 1500, the speed of the asynchronous motor is adjusted as a result. Since the SG is mechanically connected to the asynchronous motor, the speed (rpm) of the SG also changes. This adjustment is achieved by sending a proportional 0-10 V signal from the analog output of PLC to the analog input of the drive. When the SG reaches 1500 rpm, the drive frequency is set to 50 Hz, as illustrated in Figure 8.  
Figure 7: Fuzzy PID and PSO-Fuzzy PID control scheme.  
Frequency control of SG under varying load conditions is conducted using the PID_Compact function of the TIA Portal V17 interface. The PID controller works according to Equation (3).  
\[y=K_{p}\bigg{[}(b\cdot w-x)+\frac{1}{T_{I}\cdot s}(w-x)+\frac{T_{D}\cdot s}{a\cdot T _{D}\cdot s+1}(c\cdot w-x)\bigg{]} \tag{3}\]  
where \(y\) is the output value of the PID controller, \(K_{p}\) is the proportional gain, \(s\) is the Laplace operator, \(b\) is proportional action weighting, \(w\) is the setpoint, \(x\) is the process value, \(T_{I}\) is the integral action time, \(T_{D}\) is the derivative action time, \(a\) is derivative delay coefficient, and \(c\) is the derivative action weighting [43]. Figure 9 shows the PID block diagram for speed control using the PLC.  
Figure 8: Flow chart of frequency control.  
In the TIA Portal interface, the PID compact function was used for speed regulation. The settings were configured based on the working scenario. Within the PID compact configuration window, the basic settings were defined. The controller type was set to'speed'. In the Input/Output parameters section, 'Input' and 'Output_PER (analog)' were selected, as the speed is continuously measured by the encoder, and the control signal for regulating frequency is analog. The setpoint for speed regulation was set to 1500 rpm.  
After configuring the settings, the real/physical system was initialized. The setup window in PID compact was accessed, where initial and fine-tuning processes were performed.  
Figure 10: PID_Compact in TIA Portal.  
Figure 9: PID block diagram for speed control using PLC.  
During system operation, the PLC calculated the appropriate _Kp_, _Ki_, and _Kd_ values. The final values, after the initial setup and fine-tuning, are shown in Figure 11.
## 6 Discussion
### Discussion for Scenario 1: Pick and Place  
For the pick and place scenario, integrating precise hardware and efficient software is paramount. The following discussion addresses the necessary hardware and software tools for successful implementation.  
Hardware:  
In terms of hardware, the type of cobot is crucial, as is the type of vision system to be integrated with the cobot. A lightweight yet capable cobot, such as Universal Robots' UR10e or FANUC CRX-10 iA, ensures consistent pick-and-place performance. The chosen cobot should be equipped with cameras for 3D spatial recognition such as the Intel RealSense D455 or Ensenso N series.  
For pick and place, the end effector is also of very high importance, and also the force sensors. The end effector should be equipped with adaptive grippers (like the OnRobot RG2 or Robotiq 2F-85) which handle diverse object shapes and weights. Force-torque sensors (like the Robotiq FT300) should be used for accurate force application and collision detection.  
Software:  
Software components should ensure precision in repetitive tasks, enhancing productivity and worker-cobot collaboration. In particular, task coordination needs a good control platform (e.g., ROS (Robot Operating System)). A vocal interface ensures intuitive communication between cobots and workers (examples of such interfaces are Google Dialogflow or Amazon Lex). For trajectory control and optimization, the MoveIt application is a possible choice. Object recognition could be achieved by TensorFlow or OpenCV for precise object identification and pose estimation.
## 4 Simulation
### Active Suspension Roll Controller  
To verify the effect of the fuzzy genetic PI roll controller proposed in Section 3.1, we compared it with a passive suspension under a ramp steering maneuver. The speed is set to 80 km/h, and the tire-road friction coefficient is 0.9. The steering wheel angel input is shown in Figure 6a, and the roll stability performance is shown in Figure 6b. Anti-roll torque is evenly distributed between the front and rear axles. So, the suspension force in the front and rear axles is the same, which is shown in Figure 6c.  
As shown in Figure 6b, when the passive suspension is used, the body roll angle is large, which is 0.056 rad. After the active suspension is applied, the body roll angle finally remains at 0.006 rad, which is 88% less than the passive suspension. The active suspension can effectively suppress the vehicle body roll by adjusting the active forces of the left and right suspensions to generate an anti-roll torque. Compared with the passive suspension, the roll stability is greatly improved.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Value** & **Symbol** & **Value** \\ \hline \(Q_{1}\) & 0.02 & \(A_{1}\) & 5 \\ \(Q_{2}\) & 80 & \(A_{2}\) & 2 \\ \(Q_{3}\) & 0.00005 & \(A_{3}\) & 1 \\ \(Q_{4}\) & 0.00005 & \(\epsilon_{1}\) & 0.02 \\ \(Q_{5}\) & 0.00001 & \(k_{d1}\) & 50 \\ \(w\) & 0.56 & \(k\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modeling and control parameters in Section 3.  
Figure 6: Simulation of active suspension roll controller. (**a**) steering wheel angle; (**b**) roll angle; and (**c**) suspension force.
## 6 Conclusions  
The tilt angle and orientation play an important role in maximizing the solar radiation collected by a PV panel. In this paper, the ergodic method is applied to the mathematical model of extraterrestrial solar radiation to determine the monthly optimum tilt angles and azimuth angles in six Chinese cities with different climates. The results of the ergodic method serve as a reference group. Then, the calculation of the optimum angles based on HS theory is presented. By comparing the results of the HS and PSO methods with standard results, the following conclusions can be drawn:  
1. In most cases, the best orientation is due south (optimum azimuth angle, 180\({}^{\circ}\)) in the selected cities. Except when the azimuth angle equals 180\({}^{\circ}\), the extraterrestrial solar radiation decreases as the tilt angle increases.
2. The optimum tilt angle increases during the winter months and reaches a maximum in December for all of the stations. To enhance the energy collected by the panel, if possible, the tilt angle should be changed once a month.
3. According to MPE, MAPE, MABE, and RMSE, errors with the HS method are less than those with PSO. Moreover, the extraterrestrial solar radiation of HS is larger than that of PSO. The application of HS performs better in the search for \(\beta_{\text{opt}}\).
4. The proposed approach, the HS method, provides an accurate and simple alternative to the ergodic method. The experimental results of the HS method are very close to the standard values.  
The research is supported by National Natural Science Foundation of China (Program No. 51507052), the Fundamental Research Funds for the Central Universities (Program No. 2015B02714), Jiangsu Key Laboratory of Smart Grid Technology and Equipment, and Science and Technology Project of SGCC (Research and application of key technologies for operation and control of urban smart photovoltaic energy storage charging tower under \"source-grid-load\" interactive environment). The authors also thank the China Meteorological Administration.  
[PERSON] conceived and designed the experiments and wrote the paper; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools.  
The authors declare no conflict of interest.