esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft
## 4 Simulation Results and Discussion
### Controller Implementation Results  
As discussed previously, tuning an inherently unstable rotorcraft is a tedious task. The prevailing methodology for designing PID-controlled rotorcraft is by linearising the model of interest and then using the available wealth of analytical tools to find the gains that meet performance specifications [36]. The other method is empirical in nature and relies on the ability to excite the system and measure the output and use the Ziegler-Nichols method to find the gains. The following, however, presents the results of PID controller gains achieved through the optimisation techniques based on computational intelligence as presented in the previous section. These are compared to the results found by manual tuning.
## 2 External Load Monitoring
### Power Meters and Linear Encoder  
The measurements of power meters, including power output, acceleration, speed, cadence, average power, and peak power, among others, can be used for monitoring and quantifying external load [2; 31]. These parameters are particularly important for sports like cycling and provide coaches with information on adaptation and performance related to the training program [31]; power meters can inform about exercise power at different exercise intensities due to the high correlation with power increment during incremental tests in cycling (r = 0.992 to 0.997) [32]. Power parameters can be recorded during tests, training, or competition by specific measurement instruments and analyzed later usingsoftware [2]. While power meter measurements are valid, the expected power output may vary depending on the conditions or location of the meter [31; 32].  
The linear encoder is a sensor device that measures linear position and velocity during exercises and can be attached to barbells or body segments [33]. When combined with an external constant load or synchronized with load measuring devices (such as strain gauges), mechanical power during the movement can also be assessed, allowing the monitoring of external load during strength and/or power training [34]. Indeed, linear encoder sensors have provided reliable measurements of power and velocity variables during strength training and are recommended to simplify the testing and monitoring of training routines for athletes and non-athletes [35].
# Evaluation of GNSS-TEC Data-Driven IRI-2016 Model for Electron Density
## 2 Data and Methodology
### Experimental Data
#### 2.1.3 COSMIC Data  
The FORMOSAT-3/COSMIC mission is a GNSS radio occultation remote sensing system jointly developed by United States and Taiwan, China. The global daily electron density profiles that can be provided in the pre-operational period of FORMOSAT-3/COSMIC can reach up to 2500 or so per day. FORMOSAT-7/COSMIC-2, launched in 2019, can provide up to 5000 electron density profiles per day, but its coverage is only within a latitude of 35\({}^{\circ}\) north and south. COSMIC data can be downloaded from CDAAC's official website ([[https://cdaac-www.cosmic.ucar.edu](https://cdaac-www.cosmic.ucar.edu)]([https://cdaac-www.cosmic.ucar.edu](https://cdaac-www.cosmic.ucar.edu)), accessed on 3 July 2024). In this paper, we use the data of the COSMIC occultation inversion's level 2 product (ionPrf), which contains information on the time of occurrence of the occultation event, its position, electron density profile, NmF2, and hmrF2 necessary for the validation analysis. Despite the quality checking method adopted by CDAAC in inverting the electron density profiles, the final released electron density profile products still have some quality problems [28]. Therefore, we completed the necessary quality control with reference to the topside electron density profile slope and the mean deviation (MD) of the electron density fluctuations proposed by [PERSON] et al. [29]. Electron density profiles with latitude and longitude offsets less than 10\({}^{\circ}\), slopes less than \(-\)0.2 and greater than \(-\)9 in the 420 km-490 km range, MDs not exceeding 3, and hmrF2 between 180 km and 420 km have been selected in this paper.
# R-Curve Behavior of Polyhedral Oligomeric Silsesquioxane (POSS)-Epoxy Nanocomposites
## 2 Materials and Methods
### Characterization of Properties  
#### 2.1.1 Fracture Toughness: Linear Elastic Fracture Mechanics  
Fracture toughness was determined by performing a single-edge notch bend test as per ASTM D-5045 [12]. Samples were machined from the cast plate with nominal dimensions of 54.0 \(\times\) 12.7 \(\times\) 6.3 mm. A 4.5-mm-deep notch was cut by using a diamond precision saw (MK370, MK Diamond Products Inc., Torrance, CA, USA). Then, the tip of the notch was tapped lightly with a fresh razor blade by using a hammer to initiate a natural precrack. The nominal crack length was kept between 5.7 and 6.9 mm to maintain an \(a/W\) ratio of 0.45-0.55 as per the ASTM standard. The precracked single-edge notch specimens were loaded under three-point bending by using the universal testing machine (Instron 5567, Norwood, MA, USA). Tests were performed in a displacement-controlled mode at a fixed crosshead speed of 0.5 mm/min till the point of specimen failure. The load and crosshead displacement were recorded during testing. The fracture toughness of nanocomposites was measured in terms of the critical stress intensity factor (\(K_{IC}\)) calculated from the peak load (\(P\)), as per ASTM standard. As discussed later, this approach was found to be valid for POSS-epoxy nanocomposites that contained less than 1.0 wt.% of POSS. We have  
\[K_{lc}=\frac{P}{B\sqrt{W}}f(\frac{a}{W}), \tag{1}\]  
where \(P\) is the maximum applied force, \(B\) is the thickness of specimen, \(W\) is width of the specimen and \(f\) is the geometry factor [12]. We have  
\[f(\frac{a}{W})=\frac{3\frac{S}{W}\sqrt{\frac{a}{W}}}{2(1+2\frac{a}{W})(1-\frac {a}{W})^{3/2}}\times[1.99-(\frac{a}{W})(1-\frac{a}{W})(2.15-3.93\frac{a}{W}+2 7(\frac{a}{W})^{2})]. \tag{2}\]  
Figure 2: Figure shows molecular network without (**a**) and with (**b**) POSS molecule. Inclusion of POSS in the epoxy network increases the free volume that contributed in increase of ductility parameter.
## 2 Piecewise-Linear Frequency Shifting Algorithm  
In traditional digital hearing aid algorithms, the audio signal is usually enhanced by a sound compensation scheme at certain frequencies according to pure tone hearing thresholds. However, such a scheme does not consider the loss of frequency discrimination. In order to deal with the deteriorated frequency resolution of hearing-impaired patients, a piecewise-linear frequency shifting algorithm for digital hearing aids is proposed. This algorithm is designed with the aim of improving the frequency resolution of the patient in a certain frequency range and at the same time enhancing speech intelligibility of speech sounds buried in noise. The relationship between frequencies before and after piecewise-linear frequency shifting is shown in Figure 1. Here, for the enhanced center frequency \(f_{0}=4\) kHz in frequency region \([B,C]\) where the ear is less sensitive, the original frequency components are stretched, from \([f_{l},f_{h}]\) to \([f_{l}^{\prime},f_{h}^{\prime}]\), as shown in Figure 1. This step attempts to move the components out of the insensitive region. The scale of stretching is defined as Equation (1) and can be adjusted according to different application situations.  
To avoid overlapping frequency components, the adjacent frequency ranges are compressed with parameters that are adjustable. As shown in Figure 1, segment AB defines a frequency region from \(f_{a}\) to \(f_{l}\) and is compressed by a compressing ratio \(\beta_{1}\) in accordance with Equation (2). Similarly, segment CD defines a frequency region from \(f_{h}\) to \(f_{d}\) which is compressed by a compressing ratio \(\beta_{2}\), as shown in Equation (3) below.  
\[\gamma=\frac{f_{h}^{\prime}-f_{l}^{\prime}}{f_{h}-f_{l}} \tag{1}\]  
\[\beta_{1}=\frac{f_{l}^{\prime}-f_{a}^{\prime}}{f_{l}-f_{a}} \tag{2}\]  
\[\beta_{2}=\frac{f_{d}^{\prime}-f_{h}^{\prime}}{f_{d}-f_{h}} \tag{3}\]  
The range of frequency stretching \([B,C]\) is decided according to the results of the frequency discrimination test. In order to control the distortion of the output signal, the value of the stretching ratio \(\gamma\) is justified. The bigger \(\gamma\) is, the worse the distortion is. The values of \(\beta_{1}\) and \(\beta_{2}\) are set according to the compression fields and \(\gamma\). In speech recognition, speech sounds are recognized according to  
Figure 1: The relationship between frequencies before and after piecewise-linear frequency shifting.  
the relative positions of frequency components. Because the frequency distance among the frequency components is not changed by the frequency shifting algorithm, the components identification is not influenced. Although the waveform could eventually be distorted to some extent, the distortion level on speech recognition is considered acceptable.
## 3 Simulation Results
### Trade-Off Analysis  
In the integration of communication and radar sensing systems, understanding the trade-offs between the different operational modes is essential for optimizing the overall performance. This section conducts a comprehensive analysis, comparing the key system metrics between the traditional downlink communication and sensing (DCAS) and SSF D-JCAS.
## 2 Materials and Methods
### Data Extraction  
The data extraction process involved collecting key information across the following dimensions:  
* Literature Metadata: Includes the title, author, publication year, and journal.
* Study Area Details: Geographic location or coordinates, administrative division, and area size (where unspecified, inferred from administrative data or statistical yearbooks).
* Soil pH: Predominantly acidic soils with pH values below 6.5 (average ~5.5).
* Soil/Crop Type: Includes paddy soils, brick-red soils, red soils, and volcanic ash soils. The main crops are rice and vegetables, with some fruit cultivation.
* Measurement Methods of Cd Concentration: Includes inductively coupled plasma mass spectrometry (ICP-MS, ICP-AES, ICP-OES), flame atomic absorption spectroscopy, and graphite furnace atomic absorption spectroscopy (GF-AAS).
* Detection Limits and Accuracy: Cd detection limits reported in the literature ranged up to 0.003 mg/kg. Where unspecified, a default limit of 0.01 mg/kg was assumed based on significant figures.
* Sampling Details: Includes the number of sampling points and sampling time, inferred as the publication year minus two years when not explicitly stated.
* Cd Content in Soil: Cd data extracted from text, tables, or figures, including minimum, maximum, standard deviation, and coefficient of variation. When these were unavailable, an estimated standard deviation was calculated as one-tenth of the Cd concentration [36].  
After full-text screening and validation (Figure 2), the final dataset comprised 165 records of Cd concentrations from 4110 sampling points across Hainan Island, sourced from 38 publications spanning 2000-2024 (Table S1).
## 5 Discussion
### Balancing Theoretical Optimality and Practical Constraints in Network Optimization  
The existence of an optimal solution for traffic network optimization has long been debated. While some studies suggest that complex networks can be iteratively refined to approach a theoretical optimal state, others argue that engineering practice must prioritize user needs, with solutions varying accordingly. This paper supports the view that an optimal solution does exist, but also emphasizes that engineering practice must take into account real-world constraints. From Figure 9, it can be seen that the theoretical optimal construction sequence can be found through the enumeration algorithm, and the practical engineering should also conform to the optimal sequence as much as possible. However, even when an optimal construction sequence is determined through enumeration algorithms, factors such as investment scale, workforce distribution, and institutional inertia may prevent strict adherence to the optimal path. For example, although constructing high-speed railways offers the highest efficiency gain in theory, limitations in funding, a shortage of skilled labor, and the need to maintain balanced regional employment render a mono-prioritized construction strategy unrealistic. Therefore, rather than aiming to fully replicate the theoretical optimum, planners should use it as a guiding benchmark, adjusting implementation plans to suit engineering feasibility while still striving toward performance improvement.
## 5 WST Applications
### Viticulture  
Plant monitoring, also called phytomonitoring, is easier using WST. For example, with the help of WST the owner of vineyard can manage the vineyard works more efficiently and automatically. [PERSON] _et al_. described a variety of sensor network configurations and applications that can address different priorities in the vineyard [73].  
[PERSON] _et al_. implemented a WSN in a vineyard consisted of 65 motes of 916 MHz. Temperatures measurements were collected during one month. The information was used for addressing two important parameters in wine production: heat summation and potential frost damage [74].  
[PERSON] _et al_. shown the feasibility of a ZigBee based remote sensing network, intended for precision viticulture. The network nodes were powered by batteries that are recharged with energy harvested from the environment [75].
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 4 Numerical Results
### The Required BS Density  
We conclude our numerical exposure with the ultimate metric of interest--the required BS density for the relaying scheme with the proposed last hop selection. To this aim, Figure 11 shows the gains in the required BS density as compared to the baseline BS-only deployment as a function of the vehicles density, \(\lambda\), for different technology penetration rate \(P_{E}\), the required bridge availability, \(P_{A}\), and the windshield antenna location. Note that the required BS-only density is 1.55 units/km. As one may observe, for all the considered values of \(P_{E}\) and \(P_{A}\), the best gains are observed for rather small values of \(\lambda\). The rationale  
Figure 10: Histogram of the types of reflections used on different segments of the path.  
Figure 9: Data rate as a function of vehicles density and last hop selection strategies.  
is that, under these conditions, the LoS path is efficiently utilized to reach higher distances. Once the density increases, there is gradual reduction in the considered metric. In addition, logically, higher gains are observed for smaller values of the bridge availability and higher technology penetration rates. Speaking in absolute numbers, the maximal gains are in the range of 20-30% for the best identified parameters--loose bridge availability, \(P_{A}=0.8\), and high technology penetration rate, \(P_{E}=0.9\).
## 6 Results and Analysis
### Storage Overhead  
In the proposed protocol, we outlined the storage overhead and memory requirement, including the public/private key of the TS and the communication parties, IDs, random number, timestamp, and resultant length of the session key, as shown in Figure 4. In the view of the used parameters between the patient (A), TS, and Doctor (B), we have analyzed and compared the storage cost of TPKE [15], the smart card keeps \(\{ID,\ B,p\}\) and thus the storage overhead is \((160+1022\times 2)=2208\) bits. A-TIMS [16] stores \((1600+1024\times 3)=3232\) bits and an efficient authentication and key agreement EAKA protocol [40] requires \((160\times 5)=800\) bits in the smart card for basics parameters. The AAS [41] scheme improves the protocol for TIMS and consumes 960-bits storage to process authentication and authorization which contains \(\{P,u,B,R,\ p,\ q\}\).  
In our SAPS protocol, the end parties need to keep \(\{\text{P}_{\text{W}},\text{ID},\ X_{\text{A}},\text{N}_{\text{i}},\text{T}_{ \text{i}},\ E_{K_{A-TS}}\}\) for identification, authentication, and session key establishment. From an evaluation point of view, it has been supposed  
Figure 4: Storage cost for Secure Authentication and Prescription Safety (SAPS).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Network Field & 1300 \(\times\) 1300 m \\ Initial Energy at Smart Device & 1000 Joules \\ Tx Power at Smart Device & 0.819 Joules \\ Receiving Power & 0.049 Joules \\ Queue Type & Queue/DropTail/PriQue \\ Max Packet in Queue & 55 Packets \\ Agent Trace & ON \\ Router Trace & ON \\ Number of Nodes & 500 Nodes \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation Parameters.  
that secret identity, one-way hash operation, and timestamps are 160-bits in size, whereas the ECC recommended size by NIST for a key is 160-bits and 64-bit for the secret key for encrypting passwords generated using the TS's shared credentials. So, the total storage overheads of proposed scheme are \((160+160\times 2)\ =480\) bits, which is stored at both ends and the TS has more computation and storage capabilities. In our scenario, more parameters such as ID masking, public/private key and pseudorandom generation processes are accomplished at the TS.
# Personalization of Robot Behavior Using Approach Based on Model Predictive Control
## 4 Evaluation
### Discussion  
In the test data (Table 2) and in the evaluation (Figure 5), the best performance was achieved by the LGBM algorithm. The second one was Random Forest. However, in the robot domain, the time complexity of algorithms should also be taken into account. LGBM is also faster than Random Forest. This can be explained by comparing the sizes of the models. The LGBM model is more than 10\(\times\) smaller than the Random Forest model, mainly due to the number of trees (500) in the Random Forest model, which was selected as the optimal configuration for performance. The Decision Tree algorithm takes third place according to the F1 score. It is very fast and has a low execution time variance and a small number of outliers. Therefore, it could be used in time-demanding settings. It is especially important in MPC, because the model is applied many times. The reason is the simplicity of the Decision Tree model. The performance of the KNN model, which is also very simple, is unstable, with a high number of longer execution times despite maintaining a low median.  
In addition, performance differences and hardware issues can arise between simulations and real-world experiments. To minimize these discrepancies, we use the same software framework (as shown in Figure 1) and the same servers used in real-world experiments with the Pepper robot.  
Finally, to resolve uncertainties about the validity of simulations, the most reliable approach is to conduct numerous experiments with real users.  
So far, we have run very few experiments on the real robot using the framework designed and user model based on LGBM trained offline. The proposed adaptation of robot behavior based on the MPC approach worked well.  
Figure 6: Box plots presenting adaptive decision time with the use of the following models: (random)—Random model, (ONER)—One rule model, (AdaBoost)—AdaBoost model, (LGBM)—LGBM Model, (RF)—Random Forest model.
# Conversion of Stearic Acid into Bio-Gasoline over Pd/ZSM-5 Catalysts with Enhanced Accessibility
## 3 Results and Discussion
### Catalysts Properties
The acid properties of the Pd-containing catalysts were fairly similar to those of the original supports, which can be found elsewhere [36], with only slight variations in the overall acidity being observed. Figure 3 illustrates TEM micrographs of the Pd-containing catalysts. The Pd/n-ZSM-5 (32) was constituted by agglomerates of nanounits with sizes in the range 40-100 nm. The materials based on hierarchical zeolitic supports presented a sponge-like aspect, being formed by aggregates (200-400 nm) of very small zeolitic nanounits (5-10 nm). In all cases, the presence of Pd nanoparticles deposited over the external surface of the zeolite crystals or located within the aggregates was clearly observed. As inferred from Table 1, the average size of the Pd nanoparticles, determined from the TEM images, was lower for the catalysts prepared from the hierarchical support compared to that of the nanocrystalline ZSM-5, denoting that the mesoporous present in the former led to a better dispersion of the metal, as it has been earlier reported [36]. Likewise, increasing the Si/Al ratio of the hierarchical zeolitic supports led to progressively larger Pd nanoparticles. Therefore, it can be concluded that the presence of Al in the hierarchical supports afforded a better Pd dispersion. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Pd/n-ZSM-5 (32)** & **Pd/h-ZSM-5 (33)** & **Pd/h-ZSM-5 (51)** & **Pd/h-ZSM-5 (122)** \\ \hline Si/Al Molar Ratio & 32 & 33 & 51 & 122 \\ Pd Content (wt \%) & 0.84 & 0.96 & 0.91 & 0.94 \\ Pd Mean Size (nm) & 23 & 13 & 14 & 17 \\ BET Surface Area (m\({}^{2}\) g\({}^{-1}\)) & 377 & 477 & 479 & 486 \\ Total Pores Volume (cm\({}^{3}\) g\({}^{-1}\)) & 0.434 & 0.497 & 0.471 & 0.557 \\ Microporces Volume (cm\({}^{2}\) g\({}^{-1}\)) & 0.171 & 0.130 & 0.157 & 0.165 \\ Microporse Surface Area (m\({}^{2}\)/g) & 274 & 209 & 252 & 265 \\ External/Mesopores Surface Area (m\({}^{2}\)/g) & 103 & 268 & 227 & 221 \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \end{tabular}
\end{table} 
Table 1: Physicochemical properties of the catalysts.
## 3 Results
### Effect of F2 on _mRNA_ Expression of Adipogenic Transcription Factors on White Adipose Tissue  
The epididymal WAT from mice of the HFD control group and F2-treated group were isolated and immediately stored at \(-80\)\({}^{\circ}\)C before performing RT-PCR. RT-PCR was performed to determine the gene expression levels of major adipogenic markers such as PPARy, C/EBP\(\alpha\), SREBP-1c, aP2, lipoprotein lipase (LPL), adiponectin, leptin, and an inflammatory marker, interleukin-6 (IL-6), on WAT. As shown in Figure 5, the mRNA  
Figure 4: Effect of F2 on the mice serum lipid profile. The (**A**) total cholesterol and (**B**) triglyceride levels were measured in Figure 4. A one-way ANOVA was applied to calculate statistical significance. * \(p<0.05\); and ** \(p<0.01\) vs. HFD control. Normal = standard diet; HFD = high-fat diet control; F2-46 = F2 (46 mg/kg); GC = _G. cambogia_ (200 mg/kg).  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Groups**} & **Serum Insulin** & **Blood Glucose** & \multirow{2}{*}{**HOMA-IR**} \\  & **(μ/mL)** & **(mmol/L)** & \\ \hline Normal & 4.12 \(\pm\) 0.89 ** & 7.78 \(\pm\) 0.86 & 1.44 \(\pm\) 0.42 ** \\ HFD & 20.87 \(\pm\) 12.66 & 8.57 \(\pm\) 1.17 & 8.09 \(\pm\) 5.18 \\ F2-46 & 10.18 \(\pm\) 2.01 * & 8.28 \(\pm\) 1.05 & 3.68 \(\pm\) 0.45 * \\ GC & 4.69 \(\pm\) 2.10 ** & 8.63 \(\pm\) 1.43 & 1.86 \(\pm\) 1.12 ** \\ \hline \end{tabular}
\end{table}
Table 5: Effect of F2 on fasting blood glucose, serum insulin, and HOMA-IR.  
expression level of some of the key adipogenic transcription factors such as PPAR, SREBPP-1c, aP2, and the inflammatory marker, IL-6, were found to be significantly down-regulated in the WAT of mice treated with F2.