esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Sample Characteristics and Food Neoplobia Levels  
In total, 210 sample units were collected over two months, which were randomly divided into two groups: 105 \"non-informed\" and 105 \"informed\" subjects. Their sociodemographic characteristics are summarized in Table 2.  
With reference to gender, the sample showed a prevalence of female (>60%) in both subgroups compared to male (>30%). With regard to the age of the interviewees, in both groups, the prevalence is between 18-30 years and 40-60 years. The least represented age group, in both groups, is the over 60 age group. The education level is high in both groups. Most of the respondents are in possession of a degree (45.3% of the \"informed\" and 61.9% of the \"uninformed\"). However, several authors report that an imbalance towards females as well as young and educated participants in online survey methodologies' samples are common [20; 50].  
As regards the average monthly household expenditure on food, most respondents spend between 200 and 400 euro per month in both groups. Only a small proportion of respondents spend more than 600 euro/month on food supply. Regarding eating habits, in both groups, we found a prevalence of omnivores, with more than 90% of participants, and a low component of vegetarians and other forms of eating habits. Moreover, most respondents were unfamiliar with the term \"entomophagy\". Finally, in both groups, almost all respondents stated that they had never tasted or consumed insects as such or their derivatives.  
Table 2 shows the results of the food neophobia level in non-informed and informed conditions. The calculated FNS scores of quartiles were different for non-informed and informed conditions. In non-informed condition, the group with low FN had an FNS score (18.6) within the lowest quartile (FNS score \(\leq 22\)), the group with medium FN had an FNS score (28.4) within the second and the third quartile (22 \(<\) FNS score \(<36\)), and the high neophobic group had an FNS score (42.9) within the highest quartile (FNS score \(\geq 36\)). In the informed condition, the FNS scores of quartiles were the following: the lowest quartile had an FNS score of \(\leq\)26 and the nephilic group an FNS mean score of 20.0; the medium FN group with an FNS score of 31.8 was within the second and third quartiles (26 \(<\) FNS score \(<39\)); the high neophobic group with a FNS score of 44.4 was within the highest quartile (FNS score \(\geq 39\)). Data determined in the non-informed condition were similar to those reported by [PERSON] et al. [49], with some minor differences. [PERSON] et al. [49] report the lowest FNS scores for the first three quartiles (low \(\leq 18<\) medium \(<36\geq\) high) and the lowest FNS mean score for low and medium FN levels (14.2, 26.1, respectively).  
In the informed group, the information about edible insects brings about a slight increase in FNS mean score in each FN level, a decrease in low neophobic subjects (24.8%) and an increase in respondents displaying high FN levels (30.5%).
# Design and Calibration of a Sensory System of an Adaptive Gripper
## 2 Materials and Methods
### Sensory System Design
#### 2.2.1. Slip Sensor Design
The designed sensor has the following important advantages:  
* Precise detection of slip: the combination of translational and rotational movements, amplified by the rack-and-pinion mechanism, increases the sensitivity of the sensor;
* High resolution: the 1024-pulse incremental optical encoder ensures precise detection of even the smallest movements;
* Optimal integration: the compact design and kinematic compatibility allow for easy integration into the gripping module.
## 3 Results
### CS Estimation for Artificial Light Sources  
As discussed in Section 2.2.3, \(RGB\) sensor responses must be mapped to corresponding \(XYZ\) tristimulus values first to calculate the \(z\) coordinate required in [PERSON] et al. 's CS approximation formula given by Equation (1). Under the assumption of sensor linearity, an additional linear mapping can be established between the sensor's green channel output and the likewise-required vertical illuminance \(E_{\text{v}}\). The training database of measured light sources used for constructing these mappings is shown in Figure 9a for a single illuminance level of 750 lx. It comprises different technologies of artificial white light production, including halogen, xenon, compact, and fluorescent lamps, as well as phosphor-converted LEDs (pc-LEDs) and multi-channel LED luminaires. All spectra were measured using a calibrated CSS-45 spectroradiometer (Gigahertz Optik GmbH, Turkenfeld, Germany) with an optical bandwidth of 10 nm and measurement uncertainties in the \(x\) and \(y\) coordinates of \(\pm\)0.002, in a CCT of \(\pm\)4% (between 1700 and 17,000 K), and in illuminance \(E\) of \(\pm\)4%. Based on the training data, the virtual sensor responses \(R_{i}G_{i}B_{i}\) were then calculated for different illuminance levels, and the matrix optimization for mapping these sensor responses to \(X_{\text{SS},i}Y_{\text{SS},i}Z_{\text{SS},i}\) tristimulus values was initiated as discussed in Section 2.2.3. Table 2 summarizes the optimization results as calculated on the training data for different sizes of the applied transformation matrix. As can be seen, the overall best performance (\(\hat{=}\) smallest average prediction error \(\Delta u^{\prime}v^{\prime}\)) was obtained for a simple linear \(3\times 3\) transform. Comparably good results can also be observed for matrices of sizes \(3\times 19\), \(3\times 20\), and \(3\times 22\). respectively. However, due to its simplicity, in particular with regard to a potential hardware implementation, the \(3\times 3\) approach appears to be most convenient to be applied in the present case. The corresponding optimized matrix elements, as well as the derived relationship between the illuminance \(E_{\text{v}}\) and the sensor's green channel output \(G_{i}\) are depicted in Table 3. In addition, Table 3 tabulates the color sensor's CS predictions for the light sources from the training dataset at an illuminance of 750 lx and compares them to the CS values obtained when applying [PERSON] et al. 's original model directly to the measured data instead, which was assumed to represent the ground truth. As can be seen, maximal deviations in CS values of less than 0.028 were observed for this selection of test light sources, which is less than 10 % of the CS threshold of 0.3 for which positive effects on sleep quality, mood, and behavior are expected from the literature [43,109]. A sufficiently high measurement accuracy can thus be concluded.
## 7 Results and Discussion
### Results
#### 7.2.3 Residual Energy  
Our proposed scheme avoids the random selection of cluster heads (CHs) and uneven distribution in the network. Randomly selecting and distributing cluster heads (CHs) in a distributed and centralized network can lead to long-distance transmission and more energy consumption. Therefore, our schemes decrease energy consumption compared to FBCFP, TEEN and LEACH, as shown in Figure 10a,b with different mobility speeds of the nodes. In our proposed schemes, the cluster head (CH) was based on game theory by considering various factors (i.e., minimum distance, mobility and energy of nodes). Due to these, energy dissipation is reduced during communication, and nodes in the network consume less energy. The FBCFP, TEEN and LEACH spent more of their energy on cluster heads (CHs) selection at each round, data transmission and monitoring its cluster members. The average performances of the proposed schemes, CRP-GR and CRP-G, with respect to residual energy were 69% and 52%. The existing schemes, such as FBCFP, TEEN and LEACH had average performances of 37%, 24% and 19%, accordingly.  
Figure 10: Residual energy vs number of rounds.  
Figure 9: Packet delivery ratio vs number of rounds.
## 2 Materials and Methods
### FTIR Spectroscopy  
The primary purpose of using FTIR spectroscopy was to investigate any specific interaction between collagen and PRGF components, or any chemical modifications that may have occurred as a result of this interaction. FTIR spectroscopy was carried out using Spectrum BXII spectrophotometer (PerkinElmer, Waltham, MA, USA), equipped with MIRacle ATR accessory (ZnSe crystal), operating in the range 400-4000 cm\({}^{-1}\), with a scanning speed of 32 cm\({}^{-1}\) and spectral width 2.0 cm\({}^{-1}\). A comparison was made between the spectra recorded from the original membranes (as provided from the supplier, no treatment) and the samples modified with PRGF.
## 3 Problem Formulation
### Model Formulation  
In this subsection, we present a bi-objective MIP model based on the problem setting described above. The model captures the integrated planning decisions required for route selection, ship assignment, and demand acceptance in a container liner shipping network. It focuses on strategic decisions for which routes to operate, the type and quantity of ships assigned to each operated route, the amount of weekly transport demand for each OD pair to accept, the allocation of accepted demand across the operated routes, and the number of ships to charter in and charter out. The objective is to maximize the weekly total profit and the total weekly transport volume across all OD pairs while adhering to practical constraints such as ship assignment limitations, fleet availability, demand-related transport volume constraints, and transport capacity requirements. Table 1 summarizes the notations used in the model. \begin{table}
\begin{tabular}{l l} \hline \hline  & **Route and OD Pair-Related Parameters** \\ \hline \(R\) & The set of all candidate routes, \(r\in R\) \\ \(L_{r}\) & The set of all legs between consecutive ports of call on route \(r\), \(l_{r}\in L_{r}\) \\ \(W\) & The set of OD pairs, \((o,d)\in W\) \\ \(R_{od}\) & The subset of feasible routes connecting \((o,d)\), \(R_{od}\subseteq R\) \\ \(W_{l_{r}}\) & The set of OD pairs that are routed via leg \(l_{r}\), \(W_{l_{r}}\subseteq W\) \\ \(N_{r}\) & The total number of ships required to operate route \(r\) \\ \(D_{od}\) & The weekly transport demand for \((o,d)\) \\ \(p_{od}\) & The revenue earned per TEU transported for \((o,d)\) \\ \hline \multicolumn{2}{c}{**Ship-Related Parameters**} \\ \hline \(S\) & The set of all ship types, \(s\in S\) \\ \(\hat{S}\) & The set of China-built ship types, \(\hat{S}\subseteq S\) \\ \(Q_{s}\) & The transport capacity of a ship of type \(s\) \\ \(A_{s}\) & The number of ships of type \(s\) owned by the shipping company \\ \(c_{s}^{\text{in}}\) & The weekly cost of chartering in a ship of type \(s\) \\ \(c_{s}^{\text{out}}\) & The weekly revenue from chartering out a ship of type \(s\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations used in the model formulation. The MIP model, formulated as Model (1), is developed to optimize route selection, ship assignment, weekly transport allocation, and chartering decisions, and can be written as follows:  
\[\max \sum\limits_{(o,d)\in W}\sum\limits_{r\in R_{\mathit{wd}}}p_{od} \cdot z_{odr}-\sum\limits_{r\in R}\sum\limits_{s\in S}\frac{n_{sr}}{N_{r}}(F_{ sr}+B_{sr})-\sum\limits_{r\in R}\sum\limits_{s\in S}\frac{n_{sr}}{N_{r}}\mathrm{ Bextra}-\sum\limits_{s\in S}\mathrm{c_{s}^{in}}\cdot n_{s}^{in}+\sum \limits_{s\in S}\mathrm{c_{s}^{out}}\cdot n_{s}^{out}\] (1-1) \[\max \sum\limits_{(o,d)\in W}\sum\limits_{r\in R_{\mathit{wd}}}z_{odr}\] s.t.
## Performance Analysis and Improvement of WPAN MAC for Home Networks  
**[PERSON] * and [PERSON]**  
UWB Wireless Communication Research Center, Inha University, Nam-Gu, Incheon 402-751, Korea;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-10-5775-7536; Fax: +82-32-876-7349  
_Received: 18 January 2010; in revised form: 3 February 2010 / Accepted: 18 March 2010 / Published: 29 March 2010_  
**Abstract:** The wireless personal area network (WPAN) is an emerging wireless technology for future short range indoor and outdoor communication applications. The IEEE 802.15.3 medium access control (MAC) is proposed to coordinate the access to the wireless medium among the competing devices, especially for short range and high data rate applications in home networks. In this paper we use analytical modeling to study the performance analysis of WPAN (IEEE 802.15.3) MAC in terms of throughput, efficient bandwidth utilization, and delay with various ACK policies under error channel condition. This allows us to introduce a K-Dly-ACK-AGG policy, payload size adjustment mechanism, and Improved Backoff algorithm to improve the performance of the WPAN MAC. Performance evaluation results demonstrate the impact of our improvements on network capacity. Moreover, these results can be very useful to WPAN application designers and protocol architects to easily and correctly implement WPAN for home networking.  
**Keywords:** MAC protocol; IEEE 802.15.3; backoff algorithm; performance analysis; home networking
## 2 Materials and Methods
### Characterizations  
A high-resolution transmission electron microscope (HRTEM) (Titan G2 Chemi STEM CS Probe, FEI, Hillsboro, Oregon, USA; 200 kV accelerating voltages) was employed to measure the nanoparticle particle diameters. For the measurement, a minute amount of the nanoparticle suspension sample diluted with triple-distilled water was dropped onto an amorphous carbon film coated-copper grid (PELCO No.160, TED PELLA, Inc., Redding, CA, USA). The sample was dried in air at room temperature prior to the measurements. The hydrodynamic diameters of the nanoparticles were measured using a dynamic light-scattering (DLS) particle size analyzer (Zetasizer Nano ZS, Malvern, Malvern, UK) and a nanoparticle suspension sample diluted with triple-distilled water (0.1 mM Mn). The zeta potentials (Zetasizer Nano ZS, Malvern, Malvern, UK) were measured using a nanoparticle suspension sample diluted with triple-distilled water (0.1 mM Mn). The structural characterization of the nanoparticles was performed using an X-ray powder diffractometer (XRD) (XTERT PRO MRD, Philips, The Netherlands) with unfiltered CuK\(\alpha\) radiation (\(\lambda\) = 1.54184 A) and powder samples. The Mn concentration in the aqueous nanoparticle suspension sample was determined via inductively coupled plasma-atomic emission spectroscopy (ICP-AES) (IRIS/ AP, Thermo Jarrell Ash Co., Waltham, MA, USA). Fourier transform infrared (FT-IR) absorption spectroscopy (Galaxy 7020A, Mattson Instrument, Inc., Madison, WI, USA) was used to prove the nanoparticle surface coating. KBr pellets of the powder samples were used to record the FT-IR absorption spectra. Thermogravimetric analysis (TGA) (SDT-Q600, TA Instrument, New Castle, DE, USA) was used to estimate the surface-coating amount on the nanoparticles. The TGA curve of the powder sample was recorded between room temperature and 700 C under an N2 flow. A vibrating sample magnetometer (VSM) (7407-S, Lake Shore Cryotronics Inc., Westerville, OH, USA) was employed to investigate the magnetic properties of the nanoparticles using the powder sample (approximately 15-20 mg). The magnetization (M) versus the applied field (H) (i.e., M\(-\)H) curves (-2.0 T \(\leq\) H \(\leq\) 2.0 T) at T = 100 and 300 K were recorded. The net mass of the MnO nanoparticles extracted from the TGA curve was used to estimate the net M value of the sample (only MnO nanoparticles without FA-PAA).
# A Multimode Fusion-Based Aviation Communication System
## Abbreviations  
\begin{tabular}{l l}
**Abbreviation** & **Meaning** \\ Ad hoc network & infrastructureless network \\
5G & the fifth generation of mobile network technology. \\ RTK & real-time kinematic \\ ADS-B & automatic dependent surveillance-broadcast \\ JSC & joint sensing-communication \\ CSUN & cooperative sensing unmanned aerial vehicle network \\ D-S & Dempster-Shafer \\ NOMA & non-orthogonal multiple access \\
6G & the sixth generation of mobile network technology. \\ UAV & unmanned aerial vehicle \\ GBSs & ground base stations \\ LTE & long term evolution \\ OFDM & orthogonal frequency division multiplex \\ SoC & system on chip \\ RF & radio frequency \\
5 GNR & 5G new radio \\ SA & standalone \\ NSA & non-standalone \\ FDD & frequency-division duplex \\ TDD & time-division duplex \\ DC & dual carrier \\ HSDPA & high speed downlink packet access \\ HSPA+ & evolved high speed packet access \\ \end{tabular}
## 3 Results
### Experimental Group Results  
The results before and after the intervention in the EG indicated that there were statistically significant differences in all the variables studied: aerobic endurance [F (1, 66) = 60.095, \(p\) < 0.001, \(\eta^{2}\) = 0.477, 95% CI \(-\)63.214, \(-\)37.321], lower body strength [F (1, 66) = 20.785, \(p\) < 0.001, \(\eta^{2}\) = 0.240, 95% CI \(-\)1.695, \(-\)0.662], upper body strength [F (1, 66) = 31.574, \(p\) < 0.001, \(\eta^{2}\) = 0.324, 95% CI \(-\)4.235, \(-\)2.015], upper body flexibility [F (1, 66) = 5.942, \(p\) = 0.017, \(\eta^{2}\) = 0.083, 95% CI \(-\)4.288, \(-\)0.426], lower body flexibility [F (1, 66) = 21.639, \(p\) < 0.001, \(\eta^{2}\) = 0.247, 95% CI \(-\)4.5543, \(-\)1.814], dynamic balance [F (1, 66) = 10.678, \(p\) = 0.002, \(\eta^{2}\) = 0.139, 95% CI 0.195, 0.805], hand pressure strength right [F (1, 66) = 20.520, \(p\) < 0.001, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & & **CG Pre (n = 35)** & **EG Pre (n = 35)** & _p-Value_ \\ \hline \multirow{2}{*}{**Aerobic endurance (m)**} & women & 395.78 \(\pm\) 84.97 & 435.46 \(\pm\) 64.01 & 0.045 * \\  & men & 468.50 \(\pm\) 65.76 & 455.57 \(\pm\) 70.51 & 0.831 \\ \hline \multirow{2}{*}{**Lower body strength (Number of repetitions)**} & women & 11.87 \(\pm\) 3.27 & 13.35 \(\pm\) 2.43 & 0.046 * \\  & men & 13.50 \(\pm\) 0.70 & 14.28 \(\pm\) 2.05 & 0.731 \\ \hline \multirow{2}{*}{**Upper body strength (Number of repetitions)**} & women & 15.06 \(\pm\) 4.04 & 15.57 \(\pm\) 5.30 & 0.688 \\  & men & 17.50 \(\pm\) 4.94 & 15.85 \(\pm\) 7.03 & 0.679 \\ \hline \multirow{2}{*}{**Lower body flexibility (cm)**} & women & \(-\)4.03 \(\pm\) 10.20 & 0.39 \(\pm\) 5.82 & 0.046 \\  & men & \(-\)11.00 \(\pm\) 7.07 & \(-\)5.28 \(\pm\) 8.45 & 0.403 \\ \hline \multirow{2}{*}{**Upper body flexibility (cm)**} & women & \(-\)9.78 \(\pm\) 9.18 & \(-\)11.28 \(\pm\) 10.65 & 0.547 \\  & men & \(-\)21.50 \(\pm\) 2.12 & \(-\)19.42 \(\pm\) 7.54 & 0.789 \\ \hline \multirow{2}{*}{**Dynamic balance (seconds)**} & women & 7.06 \(\pm\) 2.04 & 7.00 \(\pm\) 1.49 & 0.896 \\  & men & 6.50 \(\pm\) 2.12 & 6.14 \(\pm\) 1.46 & 0.804 \\ \hline \multirow{2}{*}{**Hand pressure strength right (kg)**} & women & 53.45 \(\pm\) 11.46 & 45.82 \(\pm\) 10.50 & 0.046 \\  & men & 92.00 \(\pm\) 1.41 & 77.28 \(\pm\) 18.91 & 0.128 \\ \hline \multirow{2}{*}{**Hand pressure strength left (kg)**} & women & 51.30 \(\pm\) 11.99 & 45.60 \(\pm\) 8.63 & 0.067 \\  & men & 77.50 \(\pm\) 3.53 & 74.14 \(\pm\) 21.33 & 0.726 \\ \hline \hline \end{tabular} Note: * \(p\) < 0.05.  
\end{table} 
Table 3: Pre-intervention comparison on the Senior Fitness Test battery variables of the CG and the EG according to sex.
## 5 Numerical Examples
### Analysis of Results and Discussion  
#### 5.4.1 Analysis of Results  
1. Different methods used in the first stage of the two-stage procedure can lead to different planning results.
2. Among the FCFS, SFLA, and ISFLA, the experimental results showed the ISFLA outperformed the two others, but at the cost of longer computational times.
3. The FCFS rule is simple and fast in finding a solution. However, with an expected lower waiting time (cost), the FCFS rule lacks the capacity to improve solution quality continuously.
4. The increase in iterative runs enables ISFLA to explore a wider solution space. This helps improve solution quality, but at the cost of longer computational times.
5. When the ISFLA repositions a target ship towards the +Y/-Y direction, the handling time for the target ship will increase; when repositioning a target ships towards the +X direction, the waiting time for the target ship will increase. Since the movement towards the +Y/-Y direction results in a smaller increase in handling cost for a target ship, these two directions will have a higher priority, and this can result in better utilization of quay space. However, due to the limited quay space, movement towards the +X direction is sometimes necessary for a target ship. Such repositioning of ships leads to the finding of a near or the optimal solution.
6. As the average computational times required for the ISFLA to solve a problem size of 60 calling ships is about 1.4 h, the ISFLA is thus concluded to be applicable in practice.
7. For the ISFLA, increasing the total number of iterative runs usually leads to the finding of a higher-quality solution, due to the wider exploration of the solution space.
8. Both cost factors c1 and c2 are found able to affect the selection of repositioning direction for a target ship. They can affect the choices of moving direction toward +Y, -Y, or +X for a target ship. In this research, the two cost rates are set to USD 1000/h.
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
## 2 Problem Formulation
### MPC Algorithm  
Given the system model, Equation (2); the objective function, Equation (3); and the constraint equation on Equation (16), it is possible to develop the MPC algorithm, as outlined by [PERSON] [21]. First, the following MPC gains are formed:  
\[F=\left[(CA)^{\top},\ \ (CA^{2})^{\top},\ \ \ldots\ \ \ \ldots\ \ \ (CA^{N_{P}})^{\top}\right]^{\top},\]  
\[\Phi=\begin{bmatrix}\begin{array}{cccc}(CA)&0&\ldots&0\\ (CAB)&(CB)&&0\\ \vdots&&\ddots&\vdots\\ (CA^{N_{P}-1}B&(CA^{N_{P}-2}B&\ldots&(CA^{N_{P}-N_{C}}B\end{array}\end{array} \end{bmatrix},\]  
and  
\[E=\phi^{\top}\phi,\ H=(Fx(k)-R(k))^{\top}\phi.\]  
Using these definitions, the predicted output vector becomes \(Y(k)=Fx(k)+\phi U(k)\). This allows the quadratic form of the objective in Equation (5) to be rewritten as  
\[J(k)=(Fx(k)-R(k))^{\top}(Fx(k)-R(k))+2(Fx(k)-R(k))^{\top}\phi U(k)+U(k)^{\top} \phi^{\top}\phi U(k). \tag{21}\]  
The minimization of the objective function can now be expressed using terms that depend only on \(U(k)\),  
\[\min J(k)=\min[2(Fx(k)-R(k))^{\top}\phi U(k)+U(k)^{\top}\phi^{\top}\phi U(k)]. \tag{22}\]  
Using Matlab(r) quadratic programming optimization routine at Equation (21) is solved under the constraints in Equation (16). A receding horizon control is implemented by keeping \(u(k)\), the first 10 elements \(U(k)\). All system states, inputs, and outputs are then updated using \(u(k)\) and state-space, Equation (2), for the next time \(k=k+1\), and the process is then repeated for the next time step.
# Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
## 3 Deep-CNN Shape Sensing  
In the field of soft robotics, accurately predicting the shape of the robot without affecting its inherent properties or performance is crucial. In this study, we utilized a CNN deep learning model to estimate the curvature parameters of a soft robot based on data from the 16 sensing points of the e-textile soft sensor, which covers a single section of the continuum robot, as shown in Figure 4. Each sensing point functions as a sensor pad, created by sandwiching the soft textile material between two crossed copper connections. The selection of a CNN is due to its exceptional ability to process spatially correlated data, capturing the deformation patterns of the soft robot. CNNs have proven to be highly effective in various applications where spatial relationships are important, including image and video recognition, medical image analysis, and natural language processing.  
Figure 4: A single-section continuum robot is equipped with a soft e-textile sensor featuring 16 sensing points arranged in a 4 \(\times\) 4 matrix.  
Figure 3: Diagram of the Soft E-Textile Sensor Interface: This illustration highlights the e-textile sensor at the center, flanked by copper electrodes, and encased within fabric insulation layers.