esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Method
### Motor Modeling Based on the Altair Program  
#### 2.2.1 Analytic Model Features  
Altair Flux Motor software (2020) was used to perform numerical analysis to determine the performance and optimization conditions of the target object. Altair Flux Motor is a commercial software program designed for motor analysis, capable of predicting performance based on specific target variables [17]. To conduct numerical analysis of the motor, the final model was developed by entering key dimensions, including the type of external diameter, and stator and rotor materials. Further, detailed properties such as the characteristics of the permanent magnets, slot openings, slot widths, and windings were specified.  
The shape and size of permanent magnets, as well as the configuration of coils, are essential factors in generating the electromagnetic fields that interact within the motor. Therefore, increasing the size of these variables generally enhances the maximum torque  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Unit** & **Value** \\ \hline Target wheel torque & Nm & 75 \\ Voltage & V & 36 \\ Current & A & 14 \\ Target motor torque & Nm & 20 \\ Slot & ea & 72 \\ Pole & ea & 42 \\ Winding diameter & mm & 1.2 \\ Winding number & ea & \(21,23,25\) \\ Rotor out & mm & 403 \\ Rotor in & mm & 374 \\ Stator out & mm & 372 \\ Stator in & mm & 280 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic specifications.  
Figure 4: Shape and structure of an in-wheel electric motor. (**a**) Size of outer rotor; (**b**) model of outer rotor; (**c**) detailed structure of outer rotor; (**d**) frame connection.  
and output of the motor, including those of hubless motors. However, the electromagnetic forces produced by permanent magnets and coils can exacerbate negative outcomes such as cogging torque and torque ripple depending on variables such as the number and arrangement of slots and magnets as well as slot openings. Therefore, the influence of the number of turns per coil, slot opening, and the length and width of permanent magnets on the performance of the motor was investigated by setting them as variables.  
Subsequently, other parameters, such as input power, rotor speed, load, and time, were kept fixed for the numerical analysis. The shape of the motor developed by applying the values in Table 1, which is an outer-rotor type with permanent magnets located on the outside as the rotor and slots arranged on the inside as the stator, is shown in Figure 5.
## 2 Methodology
### INS/MNS Navigation model based on adaptive CKF
Predict the measurement: \[\hat{y}_{k}=\frac{1}{2n}\sum_{i=1}^{2n}y_{k}^{i}.\] (9) 3. Calculate the observation noise matrix function \(R(k)\): \[R(k)=\alpha R_{k-1}+(1-\alpha)\Big{(}\epsilon_{k}\xi_{k}^{T}+HP_{k}^{-}H^{T} \Big{)}\] (10) where \(\epsilon_{k}\) is the residual, that is, the difference between the true measurement and the state estimate. 1. Calculate the observed predicted covariance and cross-covariance:  
\[ \begin{split} P_{k}^{y}&=\frac{1}{2n}\sum\limits_{i=1}^{2 n}\big{(}y_{k}^{i}\big{)}\big{(}y_{k}^{i}\big{)}^{T}-(\hat{y}_{k})(\hat{y}_{k})^{T}+R(k)\\ P_{k}^{xy}&=\frac{1}{2n}\sum\limits_{i=1}^{2n}X_{k}^{i} \big{(}y_{k}^{i}\big{)}^{T}-\Big{(}\hat{x}_{k/k-1}^{i}\big{)}(\hat{y}_{k})^{T}. \end{split}  \tag{11}\]
2. Calculate the Kalman gain, state update, and corresponding error covariance matrix:  
\[K_{k}=P_{k}^{xy}\Big{(}P_{k}^{y}\Big{)}^{-1} \tag{12}\]  
\[\hat{x}_{k}=\hat{x}_{k/k-1}^{i}+K_{k}(Z_{k}-\hat{y}_{k}) \tag{13}\]  
\[P_{k}=P_{k}^{-}-K_{k}P_{k}^{y}K_{k}^{T}. \tag{14}\]  
The proposed combined navigation algorithm, adaptive CKF, involves iterating through temporal update and measurement update procedures, as shown in Figure 2. In this paper, the initial estimates for \(x_{0}\) and \(P_{0}\) are provided so that the state estimates \(\hat{x}_{k}\) can be computed recursively.
## 7 Conclusions  
In this paper, we focus on M2M networks for data-collection applications where hundreds of end-devices periodically transmit data bursts to an M2M gateway upon request. The frame slotted ALOHA (FSA) protocol has been identified in the past as a good approach for data collection, due to its simplicity and good performance when every end-device has exactly one data packet to transmit. In this work, we propose the use of reservation FSA (RFSA) for M2M networks, where the end-devices generate fragmented data messages that consist of \(L\) packets of fixed length and \(L\) is an exponentially-distributed random variable. We have proposed a theoretical model to calculate the average delay and energy consumption for the RFSA protocol. Analytical and simulated results show that there exists an optimum frame length \(m\) that minimizes the value of the average delay and energy consumed by the gateway. This optimum value depends on the number \(n\) of end-devices, being \(m\simeq n/5\) for RFSA and \(m\simeq n/2\) for FSA. Results also show that the energy consumed per end-device tends to a minimum value when \(m\geq n/2\) and \(m\geq n\) using RFSA and FSA, respectively. Existing algorithms for the estimation of the number of end-devices could be executed by the coordinator in order to adjust the frame length periodically to its optimal value periodically. The average delay and the average energy consumption  
Figure 7: Average energy consumed (left vertical axis) per end-device in a data collection round using \(m=n\) and \(m=n/2\) slots per frame. The energy savings provided by RFSA with respect to FSA is shown in the right vertical axis.  
in RFSA and FSA increase linearly with the total number of end-devices. RFSA outperforms FSA in terms of delay and energy consumption. In particular, the results show that RFSA can reduce the average delay and the coordinator's energy consumption by more than 25% with respect to FSA and the energy consumed per end-device by more than 50%. Therefore, the use of RFSA can considerably improve the energy efficiency of M2M networks. Future work aims at the design of an innovative reservation frame slotted-ALOHA protocol with dynamic adaptation in which the number of slots per frame can be dynamically adjusted to have a variable number of contention slots and reserved slots.  
This work was supported by the Research Projects ADVANTAGE (FP7-MCA-607774), P2P-SmarTest (H2020-646469), GEOCOM (TEC2011-27723-C02-01) and by the Generalitat de Catalunya under Grant 2014 SGR 1551.  
[PERSON] conducted the analysis, wrote the main manuscript text and prepared the figures. [PERSON] and [PERSON] conceived the theoretical model. All authors reviewed the manuscript.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**CT Procedure**} & \multirow{2}{*}{**Age (Years)**} & \multirow{2}{*}{**25 th**} & \multirow{2}{*}{**50 th**} & \multirow{2}{*}{**75 th**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**25 th**} & \multirow{2}{*}{**50 th**} & \multirow{2}{*}{**75 th**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} & \multirow{2}{*}{**SD**} \\ \cline{3-11}  & & & & & & & & & & & & \\ \hline \multirow{4}{*}{Head} & \(<\)1 & 25 & 25 & 25.8 & 51 & 25 & 6.55 & 379.3 & 414 & 440 & 920 & 337.6 & 131 \\ \cline{2-11}  & \(1\leq\) to \(<\) 5 & 25 & 25 & 25.8 & 51.6 & 25 & 7.8 & 479 & 505 & 564 & 984.6 & 427 & 153 \\ \cline{2-11}  & \(5\leq\) to \(<\) 10 & 30 & 30 & 30.2 & 60 & 19.7 & 11.6 & 510.7 & 604 & 660 & 1125 & 104 & 240 \\ \cline{2-11}  & \(10\leq\) to \(\leq\) 15 & 30.2 & 53 & 53.4 & 106.8 & 26 & 22.5 & 619.4 & 1031 & 1099 & 2305 & 281 & 462 \\ \hline \multirow{4}{*}{Chest} & \(<\)1 & 1.45 & 2.6 & 2.6 & 3.5 & 0.1 & 0.83 & 32.65 & 44.9 & 62.7 & 115 & 16.5 & 26 \\ \cline{2-11}  & \(1\leq\) to \(<\) 5 & 1.5 & 2.6 & 2.7 & 6.2 & 1.2 & 1.06 & 33.95 & 57.45 & 61.8 & 170 & 23 & 35.7 \\ \cline{2-11}  & \(5\leq\) to \(<\) 10 & 2.825 & 3.15 & 4.1 & 5.8 & 2.4 & 1.02 & 72.03 & 95.55 & 140 & 180 & 48.5 & 42.2 \\ \cline{2-11}  & \(10\leq\) to \(\leq\) 15 & 4.225 & 4.65 & 5.18 & 10.4 & 2.9 & 1.43 & 130.5 & 160 & 178 & 368 & 61.3 & 59 \\ \hline \multirow{4}{*}{Abdomen} & \(<\)1 & 3.7 & 4.2 & 5.2 & 18 & 1.72 & 3.81 & 83 & 94 & 170 & 364 & 34 & 71.7 \\ \cline{2-11}  & \(1\leq\) to \(<\) 5 & 3.7 & 4 & 4.2 & 8 & 3.3 & 1.35 & 117.7 & 127 & 159 & 280 & 79 & 54 \\ \cline{2-11}  & \(5\leq\) to \(<\) 10 & 4 & 4.2 & 7.05 & 8.3 & 2.7 & 1.81 & 151.5 & 175.5 & 241 & 305 & 104 & 58.8 \\ \cline{2-11}  & \(10\leq\) to \(\leq\) 15 & 3.25 & 3.65 & 4.25 & 7.3 & 2.9 & 1.45 & 146.5 & 154.5 & 179 & 332 & 103 & 69.9 \\ \hline \multirow{4}{*}{AP} & \(<\)1 & 3.7 & 3.7 & 3.7 & 4 & 1.1 & 0.49 & 75 & 76.65 & 93 & 120 & 22.4 & 18.3 \\ \cline{2-11}  & \(1\leq\) to \(<\) 5 & 3.7 & 4 & 4 & 13 & 2.8 & 2.1 & 116.5 & 130.2 & 147 & 422 & 89.4 & 68.8 \\ \cline{2-11}  & \(5\leq\) to \(<\) 10 & 4 & 4.3 & 7.25 & 8.1 & 3.7 & 1.7 & 150.5 & 163.5 & 230 & 362 & 68 & 69.7 \\ \cline{2-11}  & \(10\leq\) to \(\leq\) 15 & 3.775 & 4.5 & 5.13 & 7.9 & 3.1 & 1.15 & 165 & 210.2 & 260 & 330 & 123 & 54.6 \\ \hline \multirow{4}{*}{CAP} & \(<\)1 & 2.525 & 3.5 & 3.7 & 4 & 2.1 & 0.66 & 46.2 & 83 & 89.6 & 147 & 37.2 & 30.3 \\ \cline{2-11}  & \(1\leq\) to \(<\) 5 & 3.5 & 3.8 & 4.08 & 4.4 & 1.23 & 0.79 & 155.8 & 176.5 & 184 & 222 & 40 & 39 \\ \cline{2-11}  & \(5\leq\) to \(<\) 10 & 4.3 & 5.9 & 7.2 & 9.5 & 3.7 & 1.87 & 171.7 & 190.1 & 235 & 335 & 141.6 & 56.8 \\ \cline{2-11}  & \(10\leq\) to \(\leq\) 15 & 4.2 & 5.45 & 8.03 & 11.2 & 3.2 & 2.56 & 200.1 & 265 & 375 & 424 & 187 & 81 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The 25 th, 50 th, and 75 th percentiles for the pediatric CT procedures included in this study.
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 5 Evaluation  
In this section, we evaluate our combined SCS-HK and SI cancellation solution. Due to the large combination of possible setups and number of tests for each, we proceed as follows. We start by evaluating all SI cancellation algorithms in the fairest setup (Setup I) so as to identify the most appropriate cancellation algorithm in a balanced/fair scenario. After settling on a given algorithm, we then evaluate the three setups described so as to understand the capability of the selected SI cancellation algorithm in varying SDR locations/setups. Finally, choosing the SI cancellation algorithm with best performance and the fairest scenario, the effect of employing the SCS-HK scheme to further enhance the effect of SI cancellation is assessed.
# A Continuous Music Recommendation Method Considering Emotional Change
## 6 Experiments and Evaluation  
### Experimental Method  
To prove the effectiveness of the proposed continuous music recommendation method that reflects emotional changes, we conducted emotional analysis of music, user emotion prediction considering emotional changes, and evaluation of music recommendation results according to user emotions. The data analysis programming language R was used to evaluate the experiments.  
The results of the emotional analysis of the music were evaluated based on the accuracy of the calculation of the emotional value of the music. The user's emotion prediction results considering emotional changes were evaluated based on the accuracy of the predicted emotions by analyzing the emotional history of the user. The recommendation results based on the user's emotions were assessed based on the accuracy of recommending music that matched the user's emotional state.  
Evaluation metrics used to determine the accuracy of data analysis and prediction results include the mean squared error (MSE), root-mean-squared error (RMSE), mean absolute error (MAE), and normalized root-mean-squared error (NRMSE). These metrics measure the difference between actual and predicted values, with lower values indicating better model performance.  
Precision, recall, F1-score, balanced accuracy, and similarity were measured to evaluate the recommendation results based on user emotions. A value closer to 1 signifies higher performance, and the results are often expressed as percentages, with values closer to 100 indicating better performance. To evaluate the recommendation results, we selected the top n items from the list and conducted an experiment. Accuracy measures the proportion of correct predictions where the actual and predicted values align across all data. Balanced accuracy is a method used to mitigate exaggerated performance estimates in imbalanced datasets. In this study, balanced accuracy was calculated to account for the experimental data characteristics. Similarity assesses how closely recommended music matches the user's actual emotions. It quantifies the resemblance between the user's actual emotion vector and that of the recommended music.
# Examining Gender Bias of Convolutional Neural Networks via Facial Recognition
## 4 Discussion
### Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**CNN Model** & **Balanced** & **Male Dominant** &
\begin{tabular}{l} **Female** \\ **Dominant** \\ \end{tabular} & **Disparity \textgreater{} 10\%?** \\ \hline AlexNet & 69\% & 73\% & 62\% & YES \\ Xception & 63\% & 67\% & 55\% & YES \\ Inception v2 & 74\% & 78\% & 66\% & YES \\ Inception v3 & 76\% & 79\% & 65\% & YES \\ ResNet50 & 79\% & 80\% & 73\% & No \\ ResNet101 & 78\% & 80\% & 72\% & No \\ VGG16 & 84\% & 85\% & 81\% & No \\ VGG19 & 82\% & 82\% & 71\% & YES \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of tested CNNs by gender, WMCA Dataset, 80/20 split. As can be seen in Table 7, the VGG-16 and VGG-19 models were the clear winners in this dataset study, attaining over 90 percent accuracy for the male dominant image subset. They were also able to achieve a respectable 84 and 85 percent accuracy for the female dominant subset, meaning that the disparity for our gender imbalanced subsets was 7 and 5 percent for each of those CNN models, respectively. Although they had reduced accuracy when compared to the VGG models, both ResNet models as well as Inception version 2 were also able to keep the disparity for the gender imbalanced subsets below 10%, meaning that they handled potential gender bias reasonably well. Finally, for the combined datasets, we use all information gathered from our three datasets to create an overall picture of the accuracy of our CNN models, as well as how each handles potential gender bias. Comparing Tables 6 and 7, the accuracy of the CNN models with this combined dataset is generally lower than with the SiW Database. This could be due to a plateauing of accuracy levels from increasing the overall number of images that are trained and tested. However, the accuracy of the combined dataset is higher than both the WMCA Dataset and the CASIA-FASD Dataset. As can be seen in the last column in Table 8, the disparity between the accuracy results for the male dominant and female dominant image subsets is greater than 10% for 6 out of 8 of our models, meaning that it is very likely that those models are affected by gender bias. While the gender bias levels for both VGG16 and Resnet50 does not reach the 10 percent disparity threshold, differences of 5 percent and 8 percent, respectively, show that both models are not immune to potential biases. Due to these combined results, VGG16 and ResNet50 look to be very strong candidates for the creation of a hybrid CNN model that has both a high accuracy level, as well as resistance to gender bias.
## 2 Materials and Methods
### Analysis in Urban and Suburban Areas  
Based on the spatial analysis results, we selected two representative sites (Table 1). Site 18R is a cul-de-sac for vehicles situated in Weinan Street, in the east of the city centre. This urban street space is connected to an alley with less vehicular movement but a higher pedestrian flow. This site includes a large housing area that was built in 2008 with a Ground/ floor area ratio of 2.01. Street vendors occupy space at one end of the street, thereby altering it to function as a small market area. Site 25R is a suburban street called Hanxin Street on the east of the urban fringe. This new development originated around 2010 and includes a large nature park (2397 ha) and high-rise residential buildings. New residents have moved into the residential neighbourhood in recent years, but it still lacks essential amenities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Site 18R a cul-de-sac for Cars**} & \multicolumn{2}{c}{**Site 25R a Peri-Urban Street**} \\  & \multicolumn{2}{c}{**(N = 32)**} & \multicolumn{2}{c}{**(N = 21)**} \\ \cline{2-5}  & **Pedestrians** & **Street Vendors** & **Pedestrians** & **Street Vendors** \\  & **(N = 20)** & **(N = 12)** & **(N = 11)** & **(N = 10)** \\ \hline Age & & & & \\ Mean (SD) & 31.35 (19.60) & 41.92 (14.08) & 51.27 (12.44) & 37.70 (11.48) \\ \hline Female & 50\% & 33\% & 55\% & 40\% \\ \hline  & \multicolumn{2}{c}{Eastern part of the city centre} & \multicolumn{2}{c}{Eastern suburban} \\ Spatial & \multicolumn{2}{c}{Semi-enclosed space} & \multicolumn{2}{c}{Open space} \\ characteristics & \multicolumn{2}{c}{Developed space} & \multicolumn{2}{c}{Underdeveloped space} \\  & \multicolumn{2}{c}{High population density} & \multicolumn{2}{c}{Low population density} \\  & \multicolumn{2}{c}{Adequate amenities} & \multicolumn{2}{c}{Lack of amenities} \\ \hline Photos & & & & \\ \hline \hline \end{tabular} Note: participant No. 35 and 49 were two people together.  
\end{table}
Table 1: Characteristics of the semi-structured interviews (N = 53).  
On-site observations and semi-structured interviews with the people present at the two sites were conducted in July 2017, to gather findings and record the patterns of local, everyday lives. In order to record as comprehensively as possible 'the full experience of being in a place' [43], during this fieldwork phase, the lead author, who is proficient in local dialects, bought food from street vendors, allowing him to observe the sites being recorded without attracting attention. In the interests of efficiency, the maximum interview duration was limited to 15 min. The three main questions were: 'why do you come here?', 'what are your reasons for coming here?', and 'what do you like or dislike about this place?'. The space syntax results and the on-site observations were analysed alongside the semi-structured interviews that were subjected to thematic analysis [44]. The protocol of semi-structured interviews and on-site observations was approved by the joint authors and Ethics Committee at the Univerity of Edinburgh (06032017).
## 5 Open Problem and Research Challenges
### Open Challenges
#### 5.1.6 Time Synchronization  
As discussed previously, energy constraints become a significant problem along with the size of the network. A time synchronization strategy is needed to balance the energy utility of this massive structure and work together. Synchronization [93] plays a vital part in the transmission speed of messages. So, the researcher needs to work on this matter.
## 3 Parametric Study
### The Effect of Corner-Truncated Size (\(t\))  
Figure 13 illustrates the simulated RLBW and ARBW under variable corner-truncated size (\(t\)): 17, 27, and 37 mm. The variation in \(t\) had minimal effect on the RLBW, while the ARBW increased with the increase in \(t\). However, with \(t\) = 37 mm, AR was mostly greater than 3 dB (>3 dB), resulting in non-circular polarization. As a result, the optimal \(t\) was 27 mm.
## 1 Introduction
The review works on these topics and the applications of fibers carried out by [PERSON] and [PERSON] (2017) [30] and, most recently, by [PERSON] et al. (2021) are worth mentioning [31]. Ultrasonic homogenization is an innovative technique widely used in food, pharmaceutical, cosmetic, and biotechnology processes, especially on small- and medium-scale operations. The reason for this is due to high efficiency and safety operation [30]. An ultrasound operates by using high-frequency sound waves that generate pressure waves in a liquid, causing cavitation (the formation and implosion of microscopic bubbles) in the liquid medium. These cavitation phenomena create intense shear and turbulence forces, which cause larger particles or droplets to break up into smaller (even nanometer scale) and more uniform sizes. In dietary fibers, mechanical disruption may alter the surface's hydrophilicity and weaken the fiber matrix, thereby enhancing the solubility of previously insoluble fibers. Additionally, this effect is also employed to increase fiber extraction yields or to improve the efficiency of subsequent biochemical treatments, such as enzymatic reactions. The extent to which fibers are modified depends on factors such as their molecular weight, the intensity of the applied ultrasound, or the duration of the treatment. In the literature,there are numerous studies using techniques based on high-pressure processes or even microfluidization. Some published works are about palm fruit bunch fibers [32], corn bran [33], wheat bran fibers [34; 35; 36], rye bran [37], arabinzlan from wheat bran [38], soybean dietary fiber [39], papaya peel fiber [40], orange fiber [41], citrus fiber [42; 43], or flaxseed fiber [44], as some examples. However, there are fewer studies on the impact of an ultrasound on the rheological properties of aqueous fiber dispersions (see, e.g., [PERSON] and [PERSON] (2015) [45]) and, to our knowledge, none exist on the effect of ultrasonic homogenization processing parameters on the rheological properties and physical stability of aqueous dispersions of flaxseed fiber. For this reason, the aim of this study was to relate the amplitude and time of ultrasonic homogenization to macroscopic properties such as the viscoelasticity, viscosity, and physical stability of the aqueous dispersions of flaxseed fiber. The quality of the resulting samples is monitored by combining techniques such as rheology and multiple light scattering. This study aims to highlight the importance of suitable processing for each fiber in order to guarantee their techno-functional properties and at the same time, to extend the knowledge of the use of fibers and ultrasonic homogenization.
## 5 Open Problems and Future Opportunities for UAV Wireless Networks
### Channel Characteristics
The work in [73] provides a comprehensive review on the air-to-ground channel model and a brief description for air-to-ground channel measurement plans and the modeling campaign. Another study in [74] provides a comprehensive survey on air-to-ground propagation along with large-scale and small-scale fading models. In [75], both measurement-based models for path loss, delay spread and correlations among signals, and statistical models for the air-to-ground channel are provided. It reveals that the small-scale fading in air-to-ground link can be characterized by the Rician fading model, where the Rician \(K\)-factor depends on the elevation angle and the UAV altitude. There are some measurement-based models [76; 77; 78] that can determine some key factors for air-to-ground channel characteristics, but still cannot capture all variations under different environments. To overcome this issue, the study in [79] applies the unsupervised learning clustering technology to air-to-ground channel modeling. It derives the 3D temporary channel model based on received signal strength (RSS) measurements, and adapts an online learning approach. An online learning approach allows dynamic changes on network topology to be considered on the 3D temporary channel model, thereby significantly increasing the accuracy of the channel model. ML framework on air-to-ground channel model can enhance the accuracy; however, more research to realize practical air-to-ground communication link is necessary.