esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
An equation-derived body fat estimator, namely the Clinica Universidad de Navarra Body Adiposity Estimator (CUN-BAE), was established to assess the body fat percentage in adults. However, its efficiency compared to that of the bioelectrical impedance analysis (BIA) approach remains under-researched. This study aimed to assess the agreement between the body fat percentages measured using a BIA and estimated using the CUN-BAE in a sample of Lithuanian professional athletes. A single cross-sectional study was conducted using the BIA technique to measure and the CUN-BAE equation to calculate the body fat percentages of 323 study participants. The Bland-Altman plot system was applied to comparing both the body fat percentages estimated using the CUN-BAE equation and those obtained via the BIA approach. The average values of the body fat percentages found in the total sample of elite athletes and estimated using the BIA and CUN-BAE equaled 18.4 \(\pm\) 5.3% and 18.7 \(\pm\) 6.6%, respectively (ICC: 0.91; 95% confidence interval (CI): 0.88; 0.93). This study found that the CUN-BAE method overestimated the BIA's calculation of the body fat percentages by 2.7% on average. Meanwhile, the comparison of adiposity in the athletes using the CUN-BAE equation and the BIA methods demonstrated a similar, although not identical, accuracy. The BIA method cannot be replaced by the CUN-BAE equation in routine sports medicine practice due to moderately sized limits of agreement (95% CI: -6.5; 7.1), even when the access to body fat measurement devices is limited. From a public health perspective, the outcomes derived from the CUN-BAE equation can possibly be extrapolated to females and to individuals competing in strength-power sports, as well as to populations of adults.  
adiposity; athletes; bioelectrical impedance analysis; body composition; body fat percentage; body adiposity estimator; physical activity; public health 2025
# Total Ionizing Dose Effects on a Delay-Based Physical Unclonable Function Implemented in FPGAs
## 4 Experimental Results and Discussion
### RO-PUF Quality Factors
#### 4.3.1 Uniformity  
Figure 9 depicts the uniformity of the PUF response during the experiment. For both DUTs, the response bits are fairly evenly distributed among '0' and '1', showing almost an ideal distribution throughout the entire experiment. The average number of 1's in the PUF response for FPGA1 and FPGA2 are 51.43% and 49.61%, respectively.
# Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders
## 6 Experimental Results
### Dataset Description  
Three dance sequences have been recorded using the VICON motion capturing platform [32]. These dance sequences refer to three different performances (dances), each executed simultaneously by two dancers (one male and one female). The recording process took place at the School of Physical Education and Sport Science of the University of Thessaly in Trikala Greece in January 2019. All sequences are Greek traditional folkloric dances, the selection of which was made by dance experts of traditional dances of the Schools of Sport Science of the Universities of Thessaloniki and Thessaly in Greece. The selection fulfils (i) different types of complexities in the dance main patterns, (ii) circular performances of the dance, (iii) different styles and (iv) different rhythmical tempos. All dancers are professional actors and each dance was executed twice per actor so as to record different paths of the same choreography. Figure 6 presents a photo of the environment used for the acquisition of the dance sequences using the VICON motion interface.  
In Table 1, we also present a brief description of the dances along with their main steps. These steps have been defined by the dance experts who have designed the whole choreography and refer to the main variations of the dance as acquired through the VICON capturing module. Thus, the main steps of the dance in Table 1 do not refer to the steps of the choreography as being taught to a dancer trainee but to the main \"activities\" of the dance as being captured by the digitization unit. For instance, Sirtos (3-beat), consists, in its digital space, of six main choreographic units; (1) Initial Posture (IP)--the dancer faces a forward position; (2) Cross Leg (CL)--the dancer crosses the legs as she/he is moving, the left leg is in front of the right; (3) Initial Posture (IP)--again the dancer faces a forward position; (4) Left Leg Up (LLU)--the dancer rises her/his left leg up; (5) Initial Posture (IP)--after lowering her/his leg, the dancer is again in an in front position; (6) Right Leg Up (RLU)--the dancer rises her/his right leg up (see, Figure 7). Then, the main patterns of the dance stop and the choreography starts from scratch.  
Figure 6: The motion capturing process takes into consideration the appropriate topology of the passive markers according to the VICON manual. 40 passive markers have been attached to the body of each dancer.
# Shear Wave Velocity Estimation Based on Deep-Q Network
## 5 Numerical Experiments
### Case 1
* The DQN framework attains the lowest misfit and the shortest running time over others. Furthermore, the DQN framework has the lowest relative errors of all estimated geoacoustic parameters with a few exceptions of \(h_{2}\) and \(Vs_{2}\). Two geoacoustic parameters are picked for further statistical analysis since the DQN framework performs the best inversion on \(Vs_{3}\) and does not perform the best on \(h_{2}\). The \begin{table}
\begin{tabular}{c c c c c} \hline  & **GA** & **ASSA** & **DE** & **DQN Framework** \\ \hline \(h_{1}\) & 3.27\% & 2.21\% & 2.17\% & **0.39\%** \\ \(h_{2}\) & 7.26\% & **0.56\%** & 3.28\% & 2.05\% \\ \(h_{3}\) & 10.31\% & 0.68\% & 2.72\% & **0.16\%** \\ \(h_{4}\) & 11.38\% & 14.30\% & 20.36\% & **4.83\%** \\ \(Vs_{1}\) & 2.03\% & 1.03\% & 1.21\% & **0.61\%** \\ \(Vs_{2}\) & 0.26\% & 0.14\% & **0.08\%** & 0.87\% \\ \(Vs_{3}\) & 1.98\% & 0.31\% & 0.52\% & **0.04\%** \\ \(Vs_{4}\) & 1.00\% & 0.40\% & 0.59\% & **0.06\%** \\ \(Vs_{5}\) & 1.06\% & 1.84\% & 2.49\% & **0.61\%** \\ Misfit (m/s) & 17.60 & 12.46 & 15.72 & **6.03** \\ Running time (s) & 181.03 & 876.29 & 371.01 & **112.70** \\ \hline \end{tabular}
\end{table} 
Table 6: Performance analysis for Case 1. Figure 4: The estimated shear wave velocity profiles with the ground truth in Case 1.
# Application of Single-Electron Transistor to Biomolecule and Ion Sensors
## 5 Experimental Demonstrations of Biomolecule Sensing Based on a Single-Electron Transistor
### Ion (pH) Detection  
For the pH detection [20], the \(I_{d}\)-\(V_{g}\) characteristics were measured for three different buffer solutions: 50 mM phthalate, pH 4; 50 mM phosphate, pH 7; and 10 mM tetraborate, pH 9 (Figure 8). The reference electrode in the buffer solutions was used as a gate. With the increasing pH, the \(I_{d}\)-\(V_{g}\) curve shifted toward the high \(V_{g}\) side. Both in the increase and decrease in pH, the positions of \(I_{d}\)-\(V_{g}\) curves for the same pH coincide well. Therefore, a reproducible pH response was observed using an SET biosensor for the first time.  
Figure 7: Contour plot of drain current \(I_{d}\) as a function of drain voltage (\(V_{d}\)) and back-gate voltage (\(V_{bg}\)) at room temperature. Reproduced with permission from [20], Copyright 2011, AIP Publishing LLC.
## 4 Simulation Experiment Design
### Data Transmission Mechanism of Roadside Units (RSUs) and On-Board Units (OBUs)  
The simulation scenario involves two key components: RSUs and OBUs on vehicles. RSUs follow a simple data transmission pattern. At 950 s, an RSU broadcasts a data packet, entering a dormant state afterward, refraining from sending or receiving additional data packets. This configuration ensures that the scenario's simulated traffic flow aligns with the traffic flow demands. The processing mechanism of OBUs on vehicles after receiving a data packet is depicted in Figure 6.  
The first decision in Figure 6 is to avoid the invalid behavior of repeatedly receiving the same data packet, and the second decision is to limit the packet lifecycle. The design of the random interval time for the broadcasting of data packets aims to circumvent a scenario wherein a high volume of vehicles simultaneously transmits data packets in a brief time span. Such a situation could lead to severe degradation in the SNR, thereby reducing the number of vehicles that can successfully receive the data packets.
## 3 Results  
Analysis of the overall trend in ISQ changes revealed a continued increase in ISQ from baseline T0 to the 90 th day, while for the control group, the same analysis showed an initial decrease in ISQ and a subsequent increase starting from the 14 th day (Figure 2). On the 14 th day, the ISQ resulted statistically higher in G2 (64.40 \(\pm\) 3.85) than in G1 (58.40 \(\pm\) 3.58) (effect size: 1.61, \(p\) = 0.010). This significant trend continued until the 21 st day (G1: 61.20 \(\pm\) 3.63, G2: 67.60 \(\pm\) 2.97, effect size: 1.93, \(p\) = 0.006) and after 50 days from the implant placement (G1: 65.20 \(\pm\) 3.35, G2: 71.20 \(\pm\) 1.79, effect size, 2.23, \(p\) = 0.010). In G2, a series of statistically significant differences emerged in the comparisons between T0 and the subsequent follow-up; specifically, ISQ resulted significantly higher than baseline (62.60 \(\pm\) 4.34) on the 14 th day (64.40 \(\pm\) 3.85, \(p\) = 0.049), on the 21 st day (67.60 \(\pm\) 2.97, \(p\) < 0.001), on the 50 th day (71.20 \(\pm\) 1.79, \(p\) < 0.001), and on the 90 th day (72.40 \(\pm\) 1.52, \(p\) < 0.001).  
During the follow-up visits, the soft tissues around the implants of both groups (G1 and G2) were healthy. None of the patients experienced inflammation or discomfort.
## 2 System and Channel Models
### FSO Link Statistical Distributions  
Since the FSO link experiences Gamma-Gamma fading distribution, the probability density function (PDF) of the instantaneous SNR \(\gamma_{1}\) under different detection techniques can be defined as [5]:  
\[f_{\gamma_{1}}(\gamma_{1})=\omega\gamma^{-1}G_{1,3}^{3,0}\left(M\left(\frac{ \gamma_{1}}{\mu_{r}}\right)^{1/r}\left|\begin{array}{c}\xi^{2}+1\\ \xi^{2},\alpha,\beta\end{array}\right.\right), \tag{2}\]  
where \(\omega=\frac{\xi^{2}}{r(\Gamma(\alpha)\Gamma(\alpha))}\), \(M=\frac{\xi^{2}a\beta}{(1+\xi^{2})}\), \(\Gamma(.)\) signifies the Gamma function, \(\xi\) signifies the pointing error, \(\alpha\) and \(\beta\) are the scintillation parameters which are specified in references [34; 35], the \(r\) parameter shows the type of detection at the HAP relay node (i.e., \(r=1\) for heterodyne detection, and \(r=2\) for IM/DD), \(\mu_{r}\) indicates the average electrical SNR of the link. Specifically, for heterodyne detection, \(\mu_{1}=\overline{\gamma}_{1}\) with \(\overline{\gamma}_{1}\) represents the average instantaneous SNR of the link, and, for IM/DD, \(\mu_{2}=\overline{\gamma}_{1}\frac{a\beta^{2}(\xi^{2}+1)}{(\xi^{2}+1)(a+1)( \beta+1)}\).  
By integrating (2) through the integral identity detailed in Reference [36] (Equation (26)), the cumulative density function (CDF) of the instantaneous SNR \(\gamma_{1}\) can be expressed as:  
\[F_{\gamma_{1}}(\gamma_{1})=\frac{\xi^{2}r^{\alpha+\beta-2}}{2\pi^{r-1}(\Gamma( \alpha)\Gamma(\alpha))}G_{r+1,3,r+1}^{3r,1}\left(\frac{(a\beta)^{r}}{r^{2}\mu _{r}}\gamma_{1}\left|\begin{array}{c}1,\ \psi_{1}\\ \psi_{2},0\end{array}\right.\right), \tag{3}\]  
where \(\psi_{1}=\Delta\big{(}r,\ \xi^{2}+1\big{)}\) and \(\psi_{2}=\Delta\big{(}r,\ \xi^{2}\big{)},\ \Delta(r,\ \alpha),\ \Delta(r,\beta)\) with \(\Delta(\psi,a)=\frac{a}{\psi^{2}},\frac{a+1}{\psi},\ldots,\ \frac{a+\psi-1}{\psi}\).
## 2 Related Work  
Deep learning models namely--DNN (deep neural networks), CNN (convolutional neural networks), LSTM (long short-term memory) and hybrid CNN-LSTM are employed to solve human activity recognition (HAR) in [20]. The authors study the effect of sliding windows for preprocessing time-series data using four models and show improvement in accuracy, latency, and processing costs. Furthermore, the authors in [21] provide an extensive characterization of windowing technique. They show the impact of diverse window sizes for HAR task. Other interesting techniques like adaptive sliding windows are studied in [22] for assisted living application. The authors of [23], explore pose pattern recognition for sensors and extend the study to evaluate the impact on sliding windows. Their study is in alignment with the prior research that shows the introduction of overlapping windows increases the accuracy of pattern recognition.  
Overall, through a literature study, we garner results on the significance of the choice of the optimal window size and sliding windows. To the best of authors' knowledge, there is scarce research on sliding window variation for anomaly detection task using SNNs. Through this empirical study, we attempt to understand the impact of sliding windows on the performance for anomaly detection task incorporated into bio-inspired networks like SNNs. We evaluate the relation between spiking neurons and sliding windows in terms of accuracy and onset latency. We further expand and elaborate the results on mel channel variation to investigate the intricate parameterization on the feature extraction front that impacts the performance of the siren sound detection task.  
In parallel with preprocessing the data, SNNs inherit other properties which are engraved in neuroscientific studies. Prior research works have shown that leak channels exist in various synaptic transmissions in the visual cortex [24] and in sodium ion leak channels [25]. On neuromorphic datasets NMNIST and SHD, for different spiking neurons, leakages are studied for spatio-temporal pattern recognition in [26]. Authors in [26] explore the impact of synaptic and membrane time constants for three different spiking neuron models on pattern recognition and conclude the significance of neuronal leakage for both temporal features and the explicit presence of recurrent connections. Authors in [27] showed the importance of leak for LIF neurons in terms of robustness to noise by acting as high frequency filter. In parallel, authors also comment on the statistical relationship of sparsity introduced through leaky models and hardware efficiency through synaptic operations. In this paper, our aim is to understand if there is a relation between sliding windows and neuronal decays. This work attempts to partially answer this question by conducting an empirical evaluation of neuronal time constants in recurrent SNNs for acoustic anomaly detection.
## 4 Results and Discussion
### FTIR Results  
Figure 5a shows the FTIR transmittance of BA and TGPFs of different compositions cured at the same temperature (200 \({}^{\circ}\)C), and Figure 5b presents the FTIR analysis of BA and TGPFs with the same BA content (40%) but cured at different temperatures. Different peaks can be seen in Figure 5, which represent specific bond vibrations. Drop lines have been superimposed on the FTIR curves with reference to the bottom ash curve to show any shift of the same peaks in the FTIR curves of TGPFs. Between 3719-2925 cm\({}^{-1}\), OH\({}^{-}\) stretching bonds can be observed. At 1636 cm\({}^{-1}\), OH\({}^{-}\) bending vibrations can be observed. These peaks have shifted towards higher wavenumbers in the case of TGPFs as compared to bottom ash. The appearance of OH\({}^{-}\) bonds shows the presence of moisture which is absorbed from the atmosphere by TGPFs during handling. This confirms the hygroscopic nature of TGPFs. The CO\({}_{3}\)\({}^{-2}\) stretching and bending bonds appear at 1423 cm\({}^{-1}\) and 865 cm\({}^{-1}\), respectively. The appearance of these bonds in the FTIR curves confirms the presence of sodium carbonate in TGPFs, which is formed by the reaction of the SS used as the foaming agent and the CO\({}_{2}\) absorbed from the atmosphere. It can also be seen from Figure 5 that the carbonate stretching peaks shifted towards higher wavenumbers in the curves of TGPFs. All these results of the FTIR analysis are in accordance with results of [PERSON] et al. [10].  
Peaks of Si-O-T asymmetric and symmetric stretching of BA appear at 1089 cm\({}^{-1}\) and 761 cm\({}^{-1}\), respectively. The asymmetric Si-O-T peaks of the TGPFs are more prominent as compared to BA. The Si-O-T asymmetric stretching peaks of TGPFs are deeper than BA, and these curves also shifted towards the lower wavenumbers, which indicates that geopolymerization occurred.  
Figure 6 is a plot of calculated peak areas of the Si-O-T asymmetric stretching bonds appearing in the FTIR curves of TGPFs in Figure 5a versus BA content. The peak area of the asymmetric stretching bond is a measure of the degree of geopolymerization [10; 18]. It can be seen from Figure 6 that as the BA content increases, the peak area of the asymmetric stretching bonds increases, and hence the degree of geopolymerization increases with the increasing BA content at a constant CT.  
Figure 5: FTIR analysis of BA and TGPFs of (**a**) different compositions cured at 200 \({}^{\circ}\)C and (**b**) 40% BA composition cured at different temperatures.  
It can be seen from Figure 5b that the peaks of Si-O-T asymmetric stretching bonds of BA40-200 and BA40-600 have been shifted more towards lower wavenumbers and are also deeper, as compared to the peaks of BA40-400 and BA40-500. This shows that the degree of geopolymerization is higher in BA40-200 and BA40-600 than in BA40-400 and BA40-500.
# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 4 Result and Discussion
### Impact of Maximum Booking Time  
We investigated the impact of maximum booking time on the utilization ratio. The maximum booking time is how many hours the carsharing system allows customers to make reservations before its starting time. Increasing the booking time gives more flexibility to the customer, since they can plan their trips in advance. For round-trips, the operator only checks the time constraint. However, if the operator allows round-trips and one-way trips, they should consider time and station constraints, since when the one-way service car is returned to another station, it prevents a new reservation from the same departure station to use the car. The simulation was run with a 50:50 proportion of reservations between round-trips and one-way trips.  
The previous results had a fixed maximum booking time of three hours, i.e., customers could make reservations a maximum of three hours before the starting time. This simulation used different maximum booking times and investigated the effect on the utilization ratio, as shown in Figure 14. Four simulation scenarios were considered: combinations between the average number of reservations per day and the total number of cars. Over all the scenarios considered, increasing the maximum booking slightly reduced the utilization ratio. This is because as the booking time increases, the chance of a free car reduces as more cars are booked by customers. If the number of reservations is high, then the utilization is high, and vice versa. The simulation with 2000 reservations showed high utilization compared to the case of 1000 reservations. The maximum booking time had more impact for high numbers of reservation and low numbers of cars, as shown in Figure 14.  
In this study, when the car is booked by the customer, the car is reserved by the system. The next reservation is allowed when the car is returned to the destination station. Thus, if an operator allows one-way service, they should decide the maximum booking time with respect to their current total number of reservations and operational cars. A long booking time will reduce utilization (i.e., reduce the number of accepted requests), particularly if the operator has a high number of reservations and a low number of operational cars. However, a previous study showed that a long-term reservation system can produce better results, i.e., it can serve more demands from customers [18]. In a previous study, a different operational model was applied as the reservations are performed at least one day before (all demand is known in advance), thus the operator can perform relocation to serve these reservations. In our study, we only focus on last-minute reservation, i.e., the customer has information about the state of the system and reserves a car for a trip starting in a short period of time. Furthermore, in our study, for accepted reservations the operators keep the selected vehicle reserved until it is picked up by the customer who reserved the car.  
Figure 14: Impact of the booking time on the utilization ratio.
# AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops
## 4 Case Study: Diagnosing the Insect Pest Affecting a Crop  
### Methodology  
This section describes the case study performed to validate the contribution of the AgriEnt platform to the control and management of crop insect pests. This case study focused on diagnosing the insect pest affecting a crop based on a set of symptoms gathered by the farmer. To this end, this methodology was followed:  
1. People involved in selection. Twenty people from the Costa Region of Ecuador with experience in the managing of crops were asked to collect all symptoms they visually perceived when an insect pest is affecting their crops. The distribution of age of people involved ranged from 32 to 45 years old. These people needed to have been in charge of sugarcane, cocoa, corn, rice, banana, or soya field crops during two or more years. This requirement was necessary, so people were able to detect most symptoms. Since this case study involves humans, it was necessary that farmers being aware they were being just collaborating to evaluate the AgriEnt platform and the evaluation results would not affect their work.
2. Providing symptoms through the Web application. The AgriEnt Web application was introduced to the people involved in this case study. Then, they were asked to provide symptoms through this application. For instance, a farmer in charge of rice crops provided the system the following symptoms: laceration of the tender leaves, yellow streaks on the leaves of young seedlings, nursery and main field damaged, and terminal rolling and drying of leaves from tip to base. This task was performed over a period of four months (February-May 2019) during which 149 sets of symptoms were collected. A description of the sets of symptoms collected is provided in Table 5.
3. Crop insect pest diagnosis. Each time users provided a set of symptoms through the Web application, the AgriEnt platform provided a diagnosis about the insect pest. For instance, considering the set of symptoms described in the previous point, the platform diagnosed the _Stenchuetothrips biformis_ insect pest. As has been mentioned, to perform this diagnosis, the platform considers the knowledge modeled by the ontology as well as the rules defined. Similar  
Figure 3: Main user interface of the AgriEnt Web platform.  
processes were performed for all set of symptoms provided by the farmers which refer to all crops considered by the ontology.
4. Measuring the accuracy of AgriEnt regarding insect pest diagnosis. Once all insect pest diagnostics were performed by the platform, these ones were compared with the diagnosis provided by the group of professionals involved in this case study. Then, the accuracy of AgriEnt regarding crop insect pest diagnosis was calculated by using Equation (3). \[\text{Accuracy}=\text{C/A}\ ^{*}\ 100\] (3) where C refers to the test cases where AgriEnt produced a correct insect pest diagnosis. Meanwhile, A refers to all test cases that were provided as input to AgriEnt for diagnosis purposes. The next section presents and discusses the results obtained by AgriEnt.