esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Task** & **Models and Accuracy** & **References** \\ \hline Recognize fruits and vegetables & KNN, 97.5\% & [39] \\ Classify six apple varieties & SVM, 96\% & [40] \\ Apple grading & ANN, 96\% & [41] \\ Automatic apple sorting & Decision Tree, 73–96\% & [42] \\ Classify six apple varieties & SVM, 95.27\% & [15] \\ Classify seven fruit types & Naive Bayes, 95\% & [21] \\ Classify 15 fruit types & MLP, 97\% & [22] \\ Classify fruit types & NN, 99–100\% & [23] \\ Classify ten fruit types & SVM, 95.3\% & [24] \\ Classify six fruit types & SVM, 91.67\% & [19] \\ Identify fruit types & KNN, 96\% & [20] \\ Detect rotten fruits & SVM, 98\% & [20] \\ Classify ten apple varieties & MLP, 98.62\% & Proposed Method \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of performance of the proposed method with previously reported works. as an additional feature, which has been shown to enhance the classification accuracy of apple varieties with increased classes. According to the performance metrics (Table 2) of all models, MLP performed best, with the highest values for accuracy (98.62%), precision (98.59%), recall (98.41%), and F1-score (98.48%). MLP also has the highest AUC-ROC (99.99%) and AUC-PR (99.99%) values among the four models, indicating excellent discrimination power. [PERSON]'s kappa and MCC values are also high, indicating strong agreement between predicted and actual classes. SVM has also performed well with accuracy (98.17%), precision (98.2%), recall (97.97%), and F1-score (98.07%). It also has a high AUC-ROC (99.85%) and AUC-PR (98.89%), indicating good discrimination power and performing well across different classification thresholds. Similarly, [PERSON]'s kappa and MCC values are also high, as in MLP, signifying strong agreement between predicted and actual classes. Relatively, RFC and KNN have lower accuracy, precision, recall, and F1-score values compared to SVM and MLP, especially an accuracy of 91.28% obtained by KNN is significantly different from all models. However, they still have good discrimination power, as indicated by their high AUC-ROC and AUC-PR values. [PERSON]'s kappa and MCC values are also above 90%, indicating strong agreement between predicted and actual classes. Based on the provided performance metrics, MLP and SVM appear to be the best-performing models for this dataset, showing that HOG and color moment can be successfully applied to train ML models and classify apple varieties.
# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 2 Related Work
In this study, a field survey was conducted in Xi'an, a tourist city in China, collecting travel mode data (airplane, high-speed rail (HSR), conventional trains, and express buses) to obtain a comprehensive range of passenger characteristics, including relatively under-researched factors such as passengers' online ticketing methods. Research on individual decision-making processes is fundamental to discovering macroscopic behavioral rules in travel mode selection. [PERSON] et al. [30] designed a behavioral experiment with a process-tracing method to obtain data on repeated travel mode choices under different contexts. A stochastic and dynamic model based on Decision Space Theory has been proven to be reliable and is used to reproduce and analyze the repeated decision-making processes. Passengers can use this system to facilitate their choice of travel mode selection. Traffic policies can increase the use of Park and Ride by making passengers re-evaluate, weigh, and compare relevant information. Based on the list of studies covered in this section, one can see the active area of study regarding transportation mode selection. Most studies attempt to explain this using utility theories, which state that the mode of transportation with maximum utility in a given situation will be chosen based on travel costs and duration, among other variables. There are many alternative transportation methods for urban transportation. These transportation modes change according to the priorities of the passengers. Travel demand may change for many reasons such as heavy or light traffic in cities and the use of different roads in traffic [30; 31]. [PERSON] and [PERSON] conducted a study on which is the most accurate travel mode choice in a transportation network. A solution was attempted using Deep Reinforcement Learning (DRL) by formulating a Markov Decision Process (MDP). It provides a suggestion with DRL algorithms by clustering the people who will travel. SUMO was used for verification purposes and the studies were confirmed in this simulation environment. Finding the ideal transportation mode was attempted by using various transportation vehicles such as cars, subways, buses, and time windows. [PERSON] and [PERSON] [32] aimed to find the best means of transportation dynamically using RL. Based on the interaction of passengers with the environment, continuous recommendations were provided by using reward functions. It was used for a large-scale system and the best means of transportation between the destination and the starting points were selected. It also showed effective performance in different traffic conditions.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Alternative Low-Power Wireless Technologies
#### 2.2.7. Insteon  
Insteon [76] is a proprietary mesh networking technology specifically designed for home and personal electronics applications. Insteon makes use of both Radio Frequency (RF) signals and home'sexisting electrical wiring infrastructure (PLC) to transmit data from one device to another. Insteon is able to utilize RF-only devices, power-line-only devices or can simultaneously support both types of communication systems. Therefore, it is considered as one of the most reliable home automation technology. Insteon devices are called peers because all Insteon devices are able to transmit, receive and relay other messages completely independent of a controller. Insteon communication range can be extended by means of a multi-hop approach. In this method, an Insteon network uses two or more hops to deliver information from a source to a destination. Similar to Z-Wave, Insteon also limited the maximum number of hops allowed for each message to four. In addition, in PLC applications, Insteon operates at 131.65 kHz and uses Binary Phase-Shift Keying (BPSK) modulation technique; in RF applications it operates in the ISM (902-924 MHz) band and uses Frequency-Shift Keying (FSK) modulation scheme. Insteon utilizes Automatic Repeat request (ARQ) scheme to achieve reliable data transmission over unreliable or noisy communication channels. Insteon supports instantaneous data rates of 13.165 kb/s. It also supports a number of encryption methods such as rolling-code, managed-key and public-key [77].
## 4 Influence of Dynamic on Waveform
### Delay Change Rate  
In [21; 22], the dependence of the DDCR on the moving direction of LEO and GNSS satellites, the height and the incidence angle of LEO satellite are simulated, however, the analytical expression as the function of the navigation parameters, such as Doppler frequency or pseudo range rate, is not presented. Here, a relationship linking DDCR with the Doppler frequency difference between reflected and direct signals is given as (the detail derivation could be found in the Appendix A).  
\[DDCR(t)\approx\lambda\Delta f(t) \tag{10}\]  
where \(\lambda\) is the wavelength of the signal, which is 0.19 m for GPS L1; \(\Delta f(t)\) is the Doppler frequency difference of reflected and direct signals at the time \(t\). Figure 6 gives the tendency of the simulated and computed DCR with the changing of the time, in which the simulated DDCR has ignored difference with the computed one using Equation (10). Furthermore, it is noticeable that the DDCR shows time-varying behavior which illustrates that the delay difference between direct and reflected signals is nonlinear function as the time as presented in Figure 4 and mentioned above.
## 4 Conclusion and Further Work  
This study proposes a method in which an additional shaping tendon is inserted along an arbitrary path, complementing the main driving tendons responsible for constant curvature bending. The shaping tendon is demonstrated to affect the bending shape of the overtube. This study also includes a mathematical analysis of how this additional tendon impacts the bending behavior. Moreover, it was found that the design parameters, particularly the path of tendon insertion, play a critical role in determining the final bending shape of the overtube. However, as the energy-based modeling equations proposed in this study only minimally account for friction, errors may arise in systems where friction is the dominant factor.  
In future work, research will focus on the possible clinical applicability and respective validation. As mentioned in the Comprehensive Discussion Section, for the proposed method to be applied in real clinical environments, the geometric characteristics of commercial endoscopes must be incorporated. Therefore, research will focus on deriving the design parameters of joints that achieve two degrees of bending freedom, retroflexion bending, and radius of bending curvature using tendons inserted in parallel paths. This will also include developing an overall design guideline, encompassing methods to determine the tendon path of shaping tendons capable of achieving user-desired bending shapes. Additionally, the clinical effectiveness of the proposed method will be validated through ex vivo tests in constrained spaces, such as the gastrointestinal tract.  
This work was supported by 2024 Research Fund of Myongji University.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The author declares no conflict of interest.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 5 Results and Evaluation
### Limitations and Extensions of Experimental Parameters  
Some experimental parameters in this study are subject to limitations but also offer extensibility. In the simulation settings described in Section 4.1, the height of the sound source was fixed at 1.5 m and the horizontal distance at 2.5 m in a 3D anechoic chamber. The element spacing was fixed at 0.02 m, which can be varied for comparative experiments. The 5 s, 2 kHz sine wave used as the audio source can be replaced with other signals, provided that the sampling rate \(f_{\text{s}}\) and sampling time \(T\) are adjusted accordingly.
# A Low-Cost Electronic System for Human-Body Communication
## 4 Evaluation Methodology
### Results
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Hardware**} & \multirow{2}{*}{**Modulation**} & \multicolumn{2}{c}{**Bit Rate**} & \multicolumn{2}{c}{**Frequency**} & \multirow{2}{*}{**BER**} & \multicolumn{2}{c}{**Channel**} & \multicolumn{1}{c}{**Peak**} \\  & & & **(kops)** & & **Range** & **Length** & **Current** & **Current** \\  & & & **(kHz)** & & **(cm)** & **(mA)** & **(mW)** \\ \hline Proposed & Eletronic & OOK & \(11.11\) & \(10\) & \(10^{-4}\) & 20 & \(1\) & 561.47 \\
[10] & Eletronic & DBPSK & \(4.8\) & \(100^{-250}\) & \(10^{-4}\) & 35 & \(1\) & 400 \\
[14] & Transformer & FSK & \(2.4\) & \(20^{-40}\) & \(10^{-6}\) & 10 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main results compared to the most significant related works. Figure 6: Data recorded by the system connected to the human forearm. Distance between driver and sensing electrodes was 12 cm. However, the proposed circuit has different bottlenecks. The communication is limited by a 10 kHz filter at the sensing amplifier and by the I/O and processing constraints of the Arduino and analog to digital converter (ADC). The low-pass filter cutoff frequency must be increased, and a faster processor and ADC should be employed to raise the data rate of the actual circuit. This goal is a challenge, but is out of this work's scope since we have focused first on designing a low-cost off-the-shelf HBC system.
## 4 Results and Discussion
### Model Validation from Experimental Data
#### 4.1.2 Velocity  
The comparison of simulated and measured air velocity of points in the kitchen (A1-A3, B1-B5) and the exhaust time system (C1-C3, F1-F3 and G) is shown in Figure 8. As shown, the values of simulated air velocity of points in the kitchen were closer to the measured values, while the simulated air velocity of points in the exhaust system had slightly higher values than the measured values. The deviation in all cases was within 10%.  
Figure 8: Validation of velocity field of CCKEF system. (**a**) Measurement points in kitchen; (**b**) Measurement points in exhaust time system.  
Figure 7: Validation of temperature field of CCKEF system. (**a**) Measurement points in kitchen (**b**) Measurement points in exhaust time system.
# Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach
## 3 Results
### Tuna Weight Estimation  
The ICCAT proposes the use of the expression proposed in [10] to estimate tuna weight (W) from tuna length (SFL), which demonstrated good fitting accuracy during the purse seine fishing season in the Mediterranean:  
\[\text{W}=2.8684\cdot 10^{-5}.\text{SFL}^{2.907}, \tag{1}\]  
However, in [11; 12], relationships between weight and linear dimensions of BFT were analysed using data from Grup Balfego harvests. One of the conclusions drawn was that weight could be more accurately estimated by considering various dimensions apart from length. The following equation was proposed for use when both length and width (A) are available:  
\[\text{M3}=7.2179\cdot 10^{-5}.\text{SFL}^{2.07092}.\text{A} \tag{2}\]  
Figure 9: Evolution of the condition factor (in blue) with different environmental variables (water temperature and dissolved oxygen concentration) and amounts of feed (in orange).  
Considering that the automatic system can provide both length and width measurements, the weight of each sample can be estimated using Equation (2). The accuracy of each equation was previously examined in [11; 12], yielding a mean relative error of 25.68% for Equation (1) and 4.88% for Equation (2).  
Between March and May 2021, 645 out of the 724 BFT (89%) were harvested, and their SFL and weight were recorded at Grup Balfego. In Figure 10, the weights from harvests and the estimations using the SFL-W relationship from [10] are presented, along with the weights estimated using the M3 equation and the automatic system measurements in the last recordings before harvesting (21-23 May). As depicted in Figure 10, the weights estimated from SFL measured at harvesting differ from the weights at harvesting after the fish has remained in the fattening cages for some months. Nevertheless, the weights estimated with the M3 equation and the automatic system measurements align well with the measured weights at harvesting. Moreover, as illustrated in Figure 11, the weights in May 2021 are higher than in July 2020 for the same lengths, confirming the greater fish growth in weight under farming conditions compared to that in the wild.
# Learning Kernel Stein Discrepancy for Training Energy-Based Models
## 3 Maximum Mean Discrepancy and Kernel Stein Discrepancy
### The Connection between MMD and KSD  
In this section, we will discuss and demonstrate the connection between MMD and KSD below.  
**Lemma 1**.: _If \(k\Big{(}y_{j},\ y^{\prime}_{j}\Big{)}\) is a strictly positive definite kernel, the kernel \(k^{\prime}\Big{(}y_{j},\ y^{\prime}_{j}\Big{)}\) is still a strictly positive definition._  
\[k^{\prime}\Big{(}y_{j},y^{\prime}_{j}\Big{)}=\big{(}s_{q}\big{(}y_{j}\big{)}-s _{p}\big{(}y_{j}\big{)}\big{)}^{T}k\Big{(}y_{j},y^{\prime}_{j}\Big{)}\Big{(}s_ {q}\Big{(}y^{\prime}_{j}\Big{)}-s_{p}\Big{(}y^{\prime}_{j}\Big{)}\Big{)} \tag{11}\]  
**Proof**. It is easy to demonstrate this using the definition of strictly positive definite kernel and eigenvalue decomposition. \(\square\)  
Assume \(k^{\prime}\Big{(}y_{j},\ y^{\prime}_{j}\Big{)}\) is a manually assigned and specific positive definite kernel. Replace the kernel function \(k\) in Formula (4) with \(k^{\prime}\Big{(}y_{j},\ y^{\prime}_{j}\Big{)}\); the square of MMD can be written as below:  
\[\begin{split} MMD_{K}^{2}&=E_{q}\Big{[}s_{q}\big{(}s _{q}\big{(}x_{i}\big{)}-s_{p}\big{(}x_{i}\big{)}\big{)}^{T}k\big{(}x_{i},x^{ \prime}_{i}\big{)}\big{(}s_{q}\big{(}x^{\prime}_{i}\big{)}-s_{p}\big{(}x^{ \prime}_{i}\big{)}\big{)}\Big{]}\\ &\quad-2E_{p,q}\Big{[}\big{(}s_{q}\big{(}x_{i}\big{)}-s_{p}\big{(} x_{i}\big{)}\big{)}^{T}k\big{(}x_{i},y_{j}\big{)}\big{(}s_{q}\big{(}y_{j}\big{)}-s_{p} \big{(}y_{j}\big{)}\big{)}\Big{]}\\ &\quad+E_{p}\Big{[}\big{(}s_{q}\big{(}y_{j}\big{)}-s_{p}\big{(}y_{ j}\big{)}\big{)}^{T}k\Big{(}y_{j},y^{\prime}_{j}\Big{)}\Big{(}s_{q}\big{(}y^{ \prime}_{j}\big{)}-s_{p}\Big{(}y^{\prime}_{j}\big{)}\Big{)}\Big{]}\end{split} \tag{12}\]  
**Lemma 2**.: _If \(k\left(y_{j},\ y_{j}^{\prime}\right)\) is both in the Stein class of \(p\) and \(q\), then:_  
\[MMD_{k^{\prime}}^{2}=E_{p}\Big{[}\big{(}s_{q}\big{(}y_{j}\big{)}-s_{p}\big{(}y_{ j}\big{)}\big{)}^{T}k\Big{(}y_{j},y_{j}^{\prime}\Big{)}\left(s_{q}\big{(}y_{j}^{ \prime}\big{)}-s_{p}\big{(}y_{j}^{\prime}\big{)}\right)\Big{]} \tag{13}\]  
Lemma 2 is proved in Appendix A. We can see the right-hand sides of Formula (8) and Formula (13) make no odds. So, the Maximum Mean Discrepancy is equivalent to the Kernelized Stein Discrepancy when the kernel satisfies the conditions in Lemma 2.
## 3 Modified Hybrid Integration Processing
### DFRC OFDM Radar Signal in Modulation Symbol Frequency  
Since \(\psi_{l}\), \(\beta_{R,l}\), \(\widetilde{\beta}_{v,l}(p)\) and \(\beta_{v,l}\) are constant values within one pulse, we can ignore noise \(\mathbf{N}\), \(\psi_{l}\), \(\beta_{R,l}\), \(\widetilde{\beta}_{v,l}(p)\) and \(\beta_{v,l}\) from (15), and \(\mathbf{Z}\) is decomposed into \(\widetilde{\mathbf{R}}\) and \(\widetilde{\mathbf{V}}\):  
\[\widetilde{\mathbf{R}}=\left[0,\exp\left(-j2\pi\Delta f\frac{2R}{c}\right), \cdots,\exp\left(-j2\pi(N_{s}-1)\Delta f\frac{2R}{c}\right)\right], \tag{16}\]  
and  
\[\mathbf{\tilde{V}}=[0,\exp(-j2\pi T_{OFDM}M_{d,l}),\cdots,\exp(-j2\pi(S-1)T_{ OFDM}f_{d,l})], \tag{17}\]  
where \(f_{d,l}=2v_{l}/\lambda\), \(\lambda=c/f_{c}\) and \(T_{OFDM}\) represents the time duration of one symbol.  
Then, \(\mathbf{Y}\) can be rewritten as follows:  
\[\mathbf{Z}_{p}=\mathbf{\tilde{V}}\otimes\mathbf{\tilde{R}}. \tag{18}\]  
The frequency modulation symbol frame of \(\mathbf{Z}\) is considered as a matrix:  
\[\mathbf{Z}_{p}=\begin{pmatrix}g(0,0)&g(1,0)&\cdots&g(N_{s}-1,0)\\ g(0,1)&g(1,1)&\cdots&g(N_{s}-1,1)\\ \vdots&\vdots&\ddots&\vdots\\ g(0,S-1)&g(1,S-1)&\cdots&g(N_{s}-1,(S-1))\end{pmatrix} \tag{19}\]  
where each column represents one OFDM symbol and each row represents a subcarrier.  
If the bandwidth of the OFDM signal is significantly narrower than the carrier frequency, it can be seen that, for a fixed OFDM symbol index and a single modulation symbol, the Doppler frequency induces an identical phase shift across all subcarriers. The Doppler effect causes a linear phase shift between successive modulation symbols at a fixed subcarrier index \(n\), corresponding to a phase change of \(2\pi f_{d,l}\) over the entire transmitted OFDM symbol duration. Additionally, the phase shift between successive modulation symbols on a single subcarrier is not affected by the distance to the reflecting object. The key observation is that the modulation symbols are influenced orthogonally by the range and Doppler effects generated by reflecting objects, respectively. Doppler introduces a linear phase change only along the time axis, whereas the range introduces a linear phase shift along the frequency axis. This orthogonality is feasible when the observation time is short enough for the reflecting object to remain within a range-resolution cell. Therefore, effective signal processing techniques must enable the separate recovery of range and Doppler information.
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 7 Open Challenges and Future Directions
### Multi-Drones  
Our scheme currently investigates the use of a single drone and could be simply extended to the use of several drones by sharing between them the areas or anchor nodes to cover. However, due to the dynamics of the anchors at the upmost layers, such a static splitting might not be optimal and a dynamic area responsibility could be set as in [29]. Another interesting technique to be investigated is K-means [34] to jointly derive anchor points and assigning them to each drone.
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 1 Introduction
While in [23], a downscaling algorithm namely geographically weighted regression (GWR) was introduced, which considered a regional regression model. Recently, a spatial data mining algorithm which introduced a series of land surface variables (NDVI, land surface temperature (LST) for both day (LST-d) and night (LST-n), DEM, etc.) was proposed [24; 25]. While the resolution of IMERG was still too coarse (0.1\({}^{\circ}\)), and it was important to downscale IMERG data into a finer resolution (\(\sim\)1 km) for applications at the regional or basin scales. Many studies had evaluated the quality and accuracy of IMERG data, but few focused on the downscaling performance of IMERG. Random forest (RF) is a nonparametric statistical regression algorithm developed from classification and regression tree (CART). In this study, the IMERG and TMPA data were explored to obtain downscaled results at the 1 km scale based on the nonparametric regression models constructed using the RF model. Moreover, both the original satellite-based precipitation estimates (TMPA and IMERG) and the downscaled results were furtherly analyzed and compared to reveal their differences at different spatial scales. The objectives of this study were as follows: (1) to compare the quality of TMPA and IMERG over the TP at both annual and monthly temporal scales against ground observations; (2) to obtain downscaled results (\(\sim\)1 km) based on TMPA and IMERG data, respectively; and (3) to generate the monthly downscaled results based on those at annual scale.