esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Deep Learning for Electronic Health Records
## 3 EHR Datasets
### Medical Information Mart for Intensive Care (MIMIC)  
The MIMIC-III [16] data set was issued by the Massachusetts Institute of Technology (MIT) in 2016. It has almost 1400 citations and 60,000 ICU hospitalization records. It is the best real-world dataset of in-hospital treatment and monitoring currently available free to researchers worldwide. The structure depicted in Figure 2 is the basic structure of the database.  
The MIMIC-III dataset has a large amount of medical information which mainly includes the waveform dataset and the clinical dataset, which can be divided into 21 data tables and 5 dictionary information auxiliary tables according to the record content. The data tables include admissions, callout, caregivers, chartevents, cptevents, datetimeevents, diagnoses_icd, drgcodes, icustays, inputevents_cv, inputevents_mv, labevents, micro-biologyevents, noteevents, outputevents, patients, prescriptions, procedureevents_mv,  
Figure 1: Number of papers published on AI algorithms in the EHR field over the past decade. This area is receiving more attention from computer researchers every year.  
Figure 2: MIMIC-III dataset collection and dataset construction schematic.  
procedures_icd. Dictionary information auxiliary tables include d_cpt, d_icd_diagnoses, d_icd_procedures, d_items, and d_labitems.  
Therefore, users can use both traditional statistical methods to study the relationship between treatments and prognoses, as well as data mining and machine learning algorithms to research corresponding topics. Furthermore, after years of development, MIT has released the MIMIC-IV [17] data set recently, which modularizes the MIMIC-III data table and allows the division structure to represent data independence and differences between modules.
# Can Gestural Filler Reduce User-Perceived Latency in Conversation with Digital Humans?
## 2 Related Work
### Non-Verbal Cues in the Conversation  
Non-verbal cues, such as eye gaze, facial expressions, gestures, and postures, significantly influence real communication [42]. Not only verbal cues, but also non-verbal cues, including leaning over, nodding gestures, and smiling expressions, could effectively build rapport during communication [43]. Furthermore, gestures of understanding could build a sense of a bond and rapport between clinicians and patients [44].  
In some cases, facial expressions and gestures are more effective at delivering information compared to verbal cues. For example, in a study performed by [PERSON] [51], participants watched videos in which speakers described objects or concepts and were asked to rate their comprehension of the videos. He compared three video representation conditions: audiovisual with lip and facial cues, audiovisual without lip and facial cues, and audio alone. The result demonstrated that visual cues could significantly improve the participants' comprehension and that, with more noise, there was more dependency on visual cues.  
Likewise, non-verbal cues play important roles in human-agent and human-robot interaction. Researchers have found that the embodied conversational agent's nodding and glancing influenced the avatar's lifelikeness and fluidity of interaction [30]. Furthermore, users felt more emotional connection and reliability with a virtual human who mimicked the facial expression and intonation of the users [31].  
In human-human interaction, speech and the corresponding behaviors occur simultaneously. However, the mismatch between speech and gestures of the speaker can affect the listener's perception. [PERSON] et al. [45] performed a user study comparing speech-gesture mismatch and match conditions and found that participants considered the inaccuracy of the conversation higher in the mismatch condition. The omission of information was also higher in the mismatch condition. They also reported that listeners identified the fundamental meanings of the utterances through the gestures of the speaker [46].
## 4 Implementation
### Test Setup
#### 4.3.2 Test 2  
In this test, as in the previous one, the robot had to navigate to a place near the table such that the arm could plan a trajectory up to the object in the table. In this case, a wall was placed near the object to see if the algorithm could discard poses that were behind the wall. The robot's initial pose was variable (Equation (19)) and the object's initial pose was constant (Equation (20)). As in the first test, the robot's initial \(y_{r}^{w}\) coordinate was constant so that the robot always began at the same distance from the table. The state codification defined in Equation (21) is composed of the following 8 variables:  
Robot's position in object's coordinate system: \(x_{r}^{obj},x_{r}^{obj}\)  
Robot's rotation on \(z\) axis in object's coordinate system: \(yaw_{r}^{obj}\)  
Robot's linear velocities on \(x\) and \(y\) axes: \(v_{x},v_{y}\)  
Robot's angular velocity in \(z\) axis: \(\omega_{z}\)  
Distance between the robot and object's coordinate system origin: \(d(p_{r},\mathbb{O})\)  
Remaining time steps to end the episode: \(t\)  
\[x_{r}^{w}=[0.0,3.0] y_{r}^{w}=-1.5 yaw_{r}^{w}=[-\pi,\pi]\] (19) \[x_{obj}^{w}=1.7 y_{obj}^{w}=0.75 z_{obj}^{w}=1.005\] (20) \[s=[x_{r}^{obj} y_{r
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 4 Experimental Results
### WSN Simulation
#### 4.2.1 Topology Deployment Efficiency  
The proposed star/circle topology employs structured circular arrangements to prioritize coverage in high-risk wildfire zones while reducing redundant node deployment.  
Figure 8: A portion of a 2D Mesh topology designed using CupCarbon.  
Figure 7: **(a)** Correlation between temperature, humidity, and fire occurrence, and **(b)** fire risk output values for input conditions: temperature = 32 \({}^{\circ}\)C, humidity = 65.  
Figure 9 presents a comparison of node distribution strategies in WSN-based wildfire detection, contrasting the conventional 2D Mesh topology with the proposed star/circle approach. The node count for the 2D Mesh topology is calculated using Equation (13) [63].  
\[N=\frac{2A\pi}{r^{2}\sqrt{27}} \tag{13}\]  
The results show that as the monitored area increases, the star/circle topology becomes significantly more efficient than 2D Mesh.  
While both topologies require the same number of nodes at small scales (e.g., a 100 m radius), the star/circle approach reduces node count by 53% at a 1500 m radius and by 55% at 3000 m. This highlights its advantage in large-scale wildfire detection by improving energy efficiency and lowering deployment costs.
###### Abstract  
This paper studied the joint probability distribution of wind speed, wind direction, and wind height. The measured wind field data of a coastal plain in Zhongshan city, Guangdong Province, China, were taken as the research object. A three-dimensional joint distribution modeling method, based on the copula function and the AL (angular-linear) model, is proposed. Firstly, the wind speed is modeled by the common distribution model, and the Weibull distribution is selected. Secondly, the mvM (mixed von Mises distribution) was used to fit the wind direction probability density, and the joint distribution of wind speed and wind direction was established based on the AL model. Finally, a three-dimensional joint distribution model of wind speed, wind direction, and height was established by considering the effect of height through the copula function. The results showed that Weibull distribution can better describe the wind speed distribution in this region. The north-south wind prevailed in this region, and the probability of the main wind direction decreased with the increase in height. The joint distribution of wind speed and direction, based on the AL model, fitted well with the measured data, and the final three-dimensional distribution model had a good fitting effect.  
copula function; AL model; wind speed; joint distribution modeling +
Footnote †: journal: Applied Sciences
# First Evidences of Ionospheric Plasma Depletions Observations Using GNSS-R Data from CYGNSS
## 2 Materials and Methods
### CYGNSS Dataset Description  
The open-access CYGNSS database is provided by the Physical Oceanography Distributed Active Archive Center (PO.DAAC) [14]. The data is recorded continuously since 17 March 2017, for each of the 8 satellites in orbit. The data includes the DDM (Delay-Doppler Map), that is the cross-correlation of the reflected signal with a replica of the transmitted signal for different delay lags, and Doppler frequencies [15]. CYGNSS provides DDMs from up to the 4 channels tracking the signals received from 4 different GPS satellites, with a sample rate of 1 Hz. For each sample, auxiliary data such as the timestamp, the position of both CYGNSS satellite and the emitting GPS spacecraft are provided, together with the post-computed position of the specular point for the reflected signal on the Earth's ellipsoid.  
In any case, the data from reflections is only available from around 40\({}^{\circ}\)N to 40\({}^{\circ}\)S around the equator, as the satellite orbit inclination is around 35\({}^{\circ}\). The orbital period is \(\sim\)95 min but, as the constellation is formed by 8 satellites distributed along the orbit, the overall revisit time is much less. Figure 1 shows the average number of samples measured during one day by the whole constellation as a function of the latitude per different cell sizes. CYGNSS has an almost circular Low Earth Orbit (LEO) at an altitude of \(\sim\)520 km, at an average speed of 7.6 km/s, much larger than the GPS satellites linear velocity (3.89 km/s), which complete an orbit every 12 h. Table 1 summarizes the mission parameters.  
Figure 1: Average number of samples per day and per cell, for different cell sizes (from 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) to 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\)) for the 8 Cyclone Global Navigation Satellite System (CYGNSS) satellites tracking up to 4 GPS satellites simultaneously from 90 min period and 35\({}^{\circ}\) of inclination orbit.
## 2 Materials and Methods
### Vehicles and Size Constraints  
To demonstrate the effectiveness of the proposed parking optimization, three different vehicle types were considered:  
* Citroen Ami [37]--A small electric vehicle selected for its compact dimensions, which provide an advantage in narrow or irregular parking spaces.
* Volkswagen Crafter [38]--A large van chosen to illustrate the challenges associated with parking oversized vehicles.
* Standard Vehicle--A reference vehicle with typical width and length dimensions, used for comparison purposes.  
In the optimization process, the most efficient parking arrangement was sought for each scenario, accommodating one Ami, one Crafter, and as many standard vehicles as possible.  
In this study, the reference vehicle width was set at 2.05 m [39]. To ensure safe parking and prevent potential collisions, additional safety buffers were incorporated into each vehicle's dimensions to account for real-world maneuvering clearances. A buffer of 0.30 m was added to each side of the vehicles (resulting in a total of 0.60 m when two vehicles are adjacent), and a 0.20 m buffer was added to the front (totaling 0.40 m when two vehicles are parked facing each other). In Turkiye, the minimum parking space width is generally specified as between 2.40 m and 2.50 m according to various regulations [40]. This range is considered suitable for studies where the objective is to accommodate as many vehicles as possible within a given area. These safety buffers were integrated into the optimization model, effectively expanding each vehicle's occupied space and ensuring that the generated solutions are both feasible and safe for real-world implementation. The actual vehicle dimensions, along with the adjusted dimensions including the safety buffers, are presented in Table 1.
## 3 Results and Discussion
### Optimization of Cycle Times
#### 3.2.3 Heat Recovery Time  
For heat recovery time optimization, the adsorption/desorption and switching times were kept constant at 300 and 50 s, respectively; the cooling capacity and COP predicted by  
Figure 4: Effect of cycle time on chiller average cooling capacity.  
Figure 5: Effect of switching time on chiller average cooling capacity and COP with heat.  
the model are presented in Figure 6. It is very clear that the optimum heat recovery time considering cooling capacity and COP is between 20 and 30 s while the variation in average capacity is very small.  
Since the increase in heat recovery time reduced the average cooling capacity and increased the COP, a compromise of 30 s for heat recovery time was chosen.
# Synchronous Rectification Method for CLLC Resonant Converters Based on State-Trajectory Models
## 2 The SR Process for CLLC Resonant Converter  
The topology of the CLLC resonant converter is shown in Figure 1. It consists of two H-bridge circuits on the primary and secondary sides; the primary-side switching devices are denoted as \(S_{1}\sim S_{4}\), and the secondary-side switching devices are denoted as \(S_{5}\sim S_{8}\). The resonant tank is symmetrically distributed, and the resonant tank comprises resonant capacitors \(C_{r1}\), \(C_{r2}\), resonant inductors \(L_{r1}\), \(L_{r2}\), magnetizing inductors \(L_{m}\), and a transformer. The parameters of the resonant tank are designed symmetrically, following the specific relationship equations \(L_{r1}=n^{2}L_{r2}\), \(C_{r1}=\frac{C_{r2}}{n^{2}}\). For simplicity, parasitic parameters are neglected in the subsequent modeling process.  
In the driving of SR, several main issues arise: turning on too early or too late, and turning off too early or too late. As shown in Figure 2a,b, the late turn-on or early turn-off of the rectifier's signal will cause the current on the secondary side to pass through  
Figure 1: Topology of the CLLC converter.  
the body diode of the MOSFET. While this does not affect the operation of the resonant converter, it results in additional conduction losses and reduced efficiency. As shown in Figure 2c,d, if the rectifier signal turns on early or turns off late, the load will be discharged, causing the output current to flow back to the rectifier in the reverse direction. This leads to energy transfer from the secondary side to the primary side, resulting in a large circulating current. As the circulating current continues to increase, the primary-side circuit may be damaged.
## 6 Simulations and Result Analysis
### Simulation Results and Analysis
#### 6.2.2 Influence of the Number of Cores  
To study the influence of the number of cores on algorithm performance, Figures 14-16 exhibit the PFs obtained by two algorithms with different numbers and cores when requests equal 400, 500, and 600, respectively. We discuss the simulation results and give relevant explanations of PFs obtained by the two algorithms in terms of three aspects: the whole change trend, solution dominance, and solution diversity.  
1. Change Trend of PFs As the number of cores increases, the PFs obtained by the two algorithms are getting better, i.e., PFs evolve towards the origin of coordinates. This is because MCF-EONs with more cores can provide more spectrum resources for connection requests, and both metrics will be smaller.
2. Solutions Dominance It can be seen from the three figures that the proposed algorithm can obtain a majority of solutions that dominate those obtained by the baseline algorithm (called dominated solutions) except in the case of \(\mid\!R\!\mid\!=\!500\) and \(\mid\!C\!\mid\!=\!3\), and it dominated almost all solutions in Figure 14 for \(\mid\!C\!\mid\!=\!5\). This is because core switching can alleviate the
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 4 Experimental Results and Analysis
### Experimental Setup
#### 4.1.2 Implementation Details  
All experiments were conducted on a workstation equipped with an Intel i9-10900K CPU and an NVIDIA RTX 4070 GPU. The model was trained using the following hyperparameters:  
* Learning rate: Initial value of \(2\times 10^{-4}\), with cosine annealing scheduling.
* Batch size: 16.
* Training epochs: 30.
* Optimizer: AdamW (weight decay of \(1\times 10^{-4}\)).  
During the inference phase, the detection confidence threshold was set to 0.5, the IoU (Intersection over Union) threshold to 0.7, and the maximum track retention time to 30 frames.
## 4 Test Scenario: AC Signal Analysis of the Sum of 4 Sines with Arbitrary Frequencies
### AC Signal Analysis Precision  
This paragraph examines the results of the analysis of the test scenario. As shown in Tables 10 and 11, the spectral leakage has a significant impact on the accuracy of the results. DFT-based methods are the most affected by spectral leakage. The influence of the more significant spectral dispersion is still strong despite the corrections. This can be mainly seen in the results of the third and fourth sine signals, as shown in Figure 7. DTFT and cross-correlation gave very similar results, but it clearly shows that in AC signal analysis with cross-correlation of the third sine, which has the lowest amplitude, is worse than the other sines. On the other hand, sine-fitting methods deliver flawless amplitude results. Overall, sine-fitting and DFT with correction are among the better solutions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**A** & **0.0500** & **0.0250** & **0.0060** & **0.0180** \\ \hline DFT & 0.0498 & 0.0225 & 0.0049 & 0.0169 \\ DFT (w. correction) & 0.0500 & 0.0250 & 0.0061 & 0.0179 \\ DTFT & 0.0499 & 0.0252 & 0.0060 & 0.0175 \\ X-corr & 0.0499 & 0.0251 & 0.0065 & 0.0178 \\ Sine-fit & 0.0500 & 0.0250 & 0.0060 & 0.0180 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Amplitude results returned by AC signal analysis algorithms for the considered test scenario.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Req. Cases**} & \multicolumn{2}{c}{**Actual Memory Use in kB**} \\ \cline{3-4}  & & **Native Embed-** & **STM32 Using** \\  & & **ded/Double** & **CMSIS-DSP/Float** \\ \hline FFT & 2062–4146 & 32.09 kB (4110 cases) & 8.14 kB (2086 cases) \\ \hline Goertzel & 28 & 224 bytes & 112 bytes \\ \hline X-corr (without LUT) & 8204 & 64.05 kB & - \\ \hline X-corr (with LUT) & 26,633 & - & 74.01 kB \\ \hline Sine-fitting (linear) & 49,936 & 390.12 kB & 195.06 kB \\ \hline Sine-fitting (non-linear, float) & \(\approx\)55,450 & 208.81 kB (55,457 c.) & 216.56 kB (55,441 c.) \\ \hline Sine-fitting (non-linear, double) & \(\approx\)55,450 & 417.14 kB (55,457 c.) & - \\ \hline DTFT (without LUT) & 18 & 132 bytes & - \\ \hline DTFT (with LUT) & 16,402 & 128.13 kB & 64.06 kB \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of the memory consumption of the AC signal analysis methods in the native C and vectorized hardware-accelerated implementation.  
The phase analysis, as shown in Table 11, shows similar deviation results as the amplitude. The DFT is strongly influenced by spectral leakage, and despite the correction, the deviations of all signals are still to be classified as high. DTFT shows better results, in this case, thanks to the lower influence of the other signals. The phases calculated by cross-correlation show a significant deviation from the expected ones, with all different phases being wrong except for the first and fourth signals. The sine fitting gave perfect results. It can be concluded that sine fitting methods are the best solution, with DTFT and FFT (with correction) being the runners-up.
## 5 Application to the Satellite Module Layout Problem
### Results and Analysis  
The previously detailed algorithm is applied to the described multi-container SMLP and the results are compared to those published in the following:  
* Ref. [20]: a dual-system cooperative co-evolutionary algorithm (called Oboe-CCEA) is developed for the multi-container SMLP based on both [PERSON]'s coevolutionary framework [89] and the variable-grain model [135]. However, the assignment is an input of the algorithm, i.e., it is not optimized but taken from [108] and based upon human experience. It must be noted that this study does not consider the functional constraints \(\mathbf{g}_{H}\) and \(\mathbf{g}_{E}\). The rest of the mathematical formulation remains identical.
* Ref. [19]: a two-stage algorithm called Dynamic FS is developed to solve both assignment and layout tasks of the multi-container SMLP. Heuristic rules are used to assign the components. They are mathematically translated into a multi-objective optimization problem and an NSGA-II algorithm is employed to solve it. The assignment list is then determined using a fuzzy decision-making method. The layout task is performed using the NDCCDE/DPSO algorithm [112], which corresponds to a dual-system cooperative co-evolutionary algorithm.
## 3 Addressing the Dynamic Traveling Salesman Problem
### Ant Colony Optimization Metaheuristic
#### 3.1.1 Constructing Solutions  
With a probability \((1-q_{0})\), where \(0\leq q_{0}\leq 1\) is a parameter of the decision rule, the \(k\)-th chooses the next city \(j\) from city \(i\), from a probability distribution that is defined as follows:  
\[p_{ij}^{k}=\begin{cases}\frac{\left[\eta_{ij}\right]^{\alpha}\left[\eta_{ij} \right]^{\beta}}{\sum_{i\in\mathcal{N}_{i}^{k}\left[\tau_{ij}\right]^{\alpha} \left[\eta_{ij}\right]^{\beta}}},&\text{if }j\in\mathcal{N}_{i}^{k},\\ 0,&\text{otherwise}\end{cases} \tag{3}\]  
where \(\tau_{ij}\) and \(\eta_{ij}\) are the existing pheromone trail and heuristic information values, respectively, \(\mathcal{N}_{i}^{k}\) is the set of cities that ant \(k\) has not visited yet, and \(\alpha\) and \(\beta\) are the two parameters that determine the relative influence of the pheromone trail and heuristic information, respectively. With probability \(q_{0}\), ant \(k\) chooses the next city, i.e., \(j\), with the maximum probability as follows [27]:  
\[j=\underset{j\in\mathcal{N}_{i}^{k}}{\operatorname{argmax}}\ \left[\tau_{ij}\right]^{\alpha} \left[\eta_{ij}\right]^{\beta}. \tag{4}\]  
This selection process will continue until each ant has visited all cities once and only once, as shown in Algorithm 1. The constructed solutions will be evaluated based on Equation (3).
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### User Association (UA)
#### 4.2.3 Open Issues and Proposed Solutions  
In 6G networks, continuous connectivity is a key to network automation and intelligence. However, UA plays a prominent role by ensuring that the load is distributed evenly between SBSs and MBSs. The user and BS association challenge will require additional consideration and attention in the future. The UA verification for each BS is the agent's operation, and the benefit is the network load balance. Because of urban traffic, the space-time dimension includes several patterns. The association patterns obtained during the training phase of reinforcement learning allow for simultaneous load balancing of BSs, considering both the dynamic changes in the environment and the space-time regularities. The reinforcement learning generates a UA matrix for each BS based on the similarities between the current situation and historical patterns. Although information such as channel quality, SINR, and backhaul capacity are essential characteristics of a UA, QoE of the user is of equal or higher priority. Users in the 6G system are assigned to specific cells according to their particular needs. Thus, the above method is best for 6G distributed UA schemes. Researchers are using dependent and independent due to network changes and more devices with different needs.