esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Not only can the trajectory of UAV-assisted emergency communications affect the quality of the communication link, but also, too much ineffective movement can increase energy consumption; therefore, the effect of different trajectories of the Rx and the Tx on the channel characteristics needed to be studied. Based on the above status, we propose a multi-mobility multi-trajectory channel model, which can make the AG channel model more consistent with actual scenarios. The main contributions and novelties of this article are as follows:  
1. For this study, our proposed model took two approaches to realizing multi-trajectories. A Markov chain was introduced in the aerial part, by changing the azimuth and elevation angle of the UAV flight, to obtain a random trajectory. It could simulate the dive, climb, and level flight of the UAV in arbitrary 3D space. As most vehicles on the ground-Rx end do not make sharp turns, we used a smooth-turn (ST) mobility model to simulate the movement of the Rx end in two dimensions. Moreover, the trajectories of moving clusters were also considered, to mimic the movement of vehicles around the Rx. 2. Based on the proposed channel model, typical AG channel characteristics of UAV communications during different trajectories of the Tx and the Rx were studied, including PDP, temporal ACF, spatial CCF, stationary interval, and RMS DS. By analyzing and studying the effect of multi-mobility multi-trajectories on statistical properties, the non-stationary characteristics of the UAV AG channel were analyzed and compared. 3. Channel measurement of relevant scenarios was carried out. Some statistical properties of the proposed channel model were verified by actual measurement data, which demonstrated the accuracy of the proposed model. The remainder of this paper is structured as follows. In Section 2, a UAV-assisted communication network, with multi-mobility multi-trajectory cases, is introduced. UAV AG communication channel modeling and characterization are studied in Section 3. Numerical results and analysis are provided in Section 4. Conclusions are drawn in Section 5.
## 3 Results
### Summary Statistics of Excess Fluid in MR Lung Density Image: CF vs. Control
#### 3.1.4 Spatial Distribution of Excess Fluid Collections  
Spatial distribution is defined as the percentage of excess fluid in an ROI. Here, we define this as \(SD_{ROI}\) which gives the percent of the ROI that is covered by excess fluid (Definition 1).  
**Definition 1**.: _The spatial distribution of a region of interest of the lung is defined as the area of excess lung fluid in the region of interest (\(A_{ROI}^{F}\)) divided by the area of the region of interest (\(A_{ROI}\)). That is:_  
\[SD_{ROI}=\frac{A_{ROI}^{F}}{A_{ROI}}\.\]  
We found that \(SD_{ROI}\) distinguished CF from healthy control subjects overwhelmingly. Figure 8 shows the spatial distribution of fluid values for all ROIs in both the right and left lungs across all groups. There was a statistically significant increase in spatial distribution for CF subjects when compared to controls in 13 out of the 18 regions (\(p<0.05\), red asterisks in Figure 8).  
Figure 6: Maximum connected component pixel area per ROI in control and CF subjects. ROIs 1–9 are from the right lung and ROIs 10–18 are from the left lung. Red asterisks denote a statistically significant increase in CF subjects when compared to controls (\(p<0.05\)). Maximum connected component pixel area is plotted in log scale.  
Figure 7: The number of connected components per ROI in control and CF subjects. ROIs 1–9 are from the right lung and ROIs 10–18 are from the left lung. Red asterisks denote a statistically significant increase in CF subjects when compared to controls (\(p<0.05\)).  
3.1.5. Inference of Summary Statistic Importance  
After an analysis of the mean connected component area, the maximum connected component area, the number of connected components and the spatial distribution of excess fluid in each ROI for both CF subjects and healthy controls, we conclude that spatial distribution distinguishes CF lungs from healthy control lungs in the most comprehensive manner. We note that since lung size varies across subjects, the spatial distribution allows for the comparison of the proportion of the lung covered by excess mucus across subjects. Therefore, spatial distribution incorporates not only the amount (area) of excess lung fluid in an ROI but also the lung space occupied by the ROI, as this is not uniform across subjects. In the following, we group the spatial presentation of excess fluid in CF and control lungs by using information from the spatial distribution of abnormal fluid.
## 2 Basis of the QuEChERS Method
### Modifications in the Extraction Solvent
In this work, three solvent mixtures with different pH values were tested, including ACN:aqueous solution of ammonium carbonate (200 mg/L) (84:16, \(v/v\)) with pH 8.5, methanol:phosphoric acid (0.25%) (40:60, \(v/v\)) with pH 2.2, and ACN:ammonium acetate (10 mM) (10:20, \(v/v\)) with pH 6.5. The worst results were achieved using the mixture of methanol and phosphoric acid at pH 2.2, as recovery values achieved were lower than 60% in all the targeted analytes [35]. The recoveries improved with the mixture of ACN:ammonium acetate at pH 6.5, but the best results were provided with the mixture ACN:ammonium carbonate at pH 8.5, achieving recovery values in the range of 72-96% for all the analytes [35]. Regarding the substitution of ACN as an extraction solvent, [PERSON] et al. (2020) performed the QuECHERS extraction of 33 PAs and PANOs from sorghum, oregano, and mixed herbal tea samples using a combination of methanol:water:formic acid (60:39.6:0.4, \(v/v\)), achieving good analytical performance for all the compounds analyzed [33].
# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
## 3 Discussion
### DSA Data Analysis and Proposed Framework for Optimal Model Selection
#### 3.2.1 Location  
The location is the most applied factor in the traditional pricing scheme for DSA, such as a fixed price strategy and auction-based pricing mechanism [1]. The geographical environment and facilities surrounding the digital signage are the most crucial factors, as they strongly affect the accessibility and visibility of the digital signage, determining the audience attention level. For example, a higher rating of surrounding businesses can increase the crowd of a particular location, which indirectly increases the attention level toward the advertisement. Thus, the more prosperous a location is, the higher the attention level that can be obtained to improve the effectiveness of the advertisement. The different days and times also have a direct impact on the crowd size of the location. For example, the crowd on weekends will commonly be higher than the working days. Hence, it increases the demand for a DSA spot and results in higher prices. These indexes are indeed imperative in affecting the pricing decision. Considering its relationship with the attention level that can be obtained, the higher the value of these indexes, the higher the price.  
Figure 3 illustrated the comparison of the popularity indices of the signage with ID 1000 for 24 h on weekdays and during the weekend. The popularity indices show different peak hours of the weekdays and weekend. Notably, the popularity index is higher on weekends compared to weekdays. Thus, this exemplifies the assumption that the signage will be attracting higher attention levels during the weekend than on weekdays, resulting in higher prices for DSA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & **Example** & **Attention Level** & **Price** \\ \hline \multirow{2}{*}{**Location**} & High popularity, High rating of & \multirow{2}{*}{Increase \(\uparrow\)} & \multirow{2}{*}{Increase \(\uparrow\)} \\  & surrounding public facilities, business & & \\ \hline \multirow{2}{*}{**Weather and temperature**} & Raining, extremely high or low & \multirow{2}{*}{Decrease \(\downarrow\)} & \multirow{2}{*}{Decrease \(\downarrow\)} \\  & temperature and air humidity & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Environmental factors and its impact on the attention level and pricing of DSA.  
There is clearly a seasonal trend in the monthly and whole-dataset plots of the DSA data, as shown in Figures 4 and 5. This represents a non-stationary data characteristic. Generally, both figures show the same seasonal trend, with the popularity index rising and falling cyclically, affecting the amount of audience attention that can be grabbed. Thus, a dynamic pricing scheme can be applied for DSA to maximize profit.
## 1 Introduction
In light of these limitations, there is a pressing need to enhance our understanding of SGS by developing more comprehensive computational models that incorporate accurate vocal fold geometries alongside realistic vocal tract and ambient domains. Our current study addresses this gap by introducing a sophisticated in-house three-dimensional fluid-structure-acoustic interaction model that accurately represents vocal folds, the vocal tract, and the surrounding ambient domain. This improved anatomical precision aims to facilitate a more authentic representation of the intricate fluid and tissue interactions within the laryngeal-tracheal system. By systematically simulating the effects of varying SGS severity on the glottal flow rate, vocal fold vibrations, and acoustic outputs, we aim to elucidate the specific contributing factors and severity thresholds that lead to disrupted voice production associated with SGS. The paper is structured as follows: Section 2 introduces the governing equations for this in-house model along with details on the vocal fold geometries, material properties, vocal tract, and ambient domain setup. In Section 3, we first present the simulation results without SGS as a baseline, followed by systematic introduction of graded stenosis severities and analysis of the ensuing effects on aerodynamics, vocal fold vibration, and acoustics. Finally, Section 4 summarizes the key conclusions from our study regarding the potential mechanisms underlying the voice changes seen with subglottic stenosis.
## 2 Preliminary Experiments
### Gas Infiltration through Catalyst Bed
Figure 2: (**a**) Photograph of the gas infiltration experiment assembly. (**b**) Schematic of the test apparatus with a photograph of pellet cage partitions. To prepare an each test run, the enclosure was filled with water while the cage was rotating at maximum speed until the radius of the dry zone near the rotating axis reached the specified value. Then, the volume flow rate of the air supply was gradually increased until the dry zone radius reached 50 mm. The dry zone was surrounded by an outer ring of a water and air mixture. The maximum airflow rate (V\({}_{\text{air}}\)) and pressure difference (\(\Delta\)P) were measured. If the air flow rate was further increased above V\({}_{\text{air}}\), flushing of the liquid phase was expected. The values of V\({}_{\text{air}}\) are represented in Figure 4 for different rotation speeds (\(\omega\)) and initial dry radii (R\({}_{\text{d}}\)). For the partially filled cage cases, R\({}_{\text{d}}\) varied from 80 to 140 mm. As \(\omega\) increased, V\({}_{\text{air}}\) increased almost linearly, as depicted in Figure 4a. This was because the radial buoyancy force exerted on the gas phase was strengthened with increased centrifugal force. V\({}_{\text{air}}\) was larger for a larger R\({}_{\text{d}}\) because a greater viscous force was required to push the liquid phase toward the center due to the longer radial distance. When \(\omega\) was 200 rpm (round per minute) and R\({}_{\text{d}}\) was 100 mm, V\({}_{\text{air}}\) of the fully filled cage was 95 LPM, which was 15.8% larger than the 80 LPM for the partially filled cage. When \(\omega\) was 200 rpm and R\({}_{\text{d}}\) was 80 mm, V\({}_{\text{air}}\) of the fully filled cage was 42 LPM, which was 16.7% larger than 35 LPM for the partially filled cage. A smaller amount of water was preferred for the gas infiltration. Therefore, the most significant influence was made by the changes in R\({}_{\text{d}}\). For both partially and fully filled cages, V\({}_{\text{air}}\) was more than two times larger if R\({}_{\text{d}}\) was increased from 80 to 100 mm. The value of \(\Delta\)P, which corresponds to the pumping head at zero liquid flow rate, was measured at maximum air infiltration. The most significant influence was made by the rotation speed (Figure 5). \(\Delta\)P increased with \(\omega\) in a near square relationship. \(\Delta\)P also increased as the amount of water in the enclosure was increased. The target gas infiltration rate of 2.0 m\({}^{3}\)H\({}_{2}\)/h could be achieved at any rotation speed greater than 100 rpm. When the hydrogen production rate decreased during dehydrogenation, the amount of liquid within the reactor automatically increased, resulting in the readjustment of the hydrogen production rate. Figure 4: Maximum air infiltration rate through reactors with (**a**) partially filled pellet cage, and (**b**) fully filled pellet cage (LPM, liter per minute).
# Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure
## 4 Discussion
Systems based in communications provided by TSPs (GPRS [6], satellite [44], NBioT [45], SigFox [7]) do not require deployment of any additional communication infrastructure, but they will not work where there is no coverage. Furthermore, cellular links use much more energy for communication than LoRa; 2 W [46] against 25 mW. This has a remarkable impact in battery life but also in reduced radiofrequency radiation to the animals, which means less heating due to SAR (specific absorption rate). Such power is completely safe as shown in [47; 48; 49; 50; 51]. Other LPWAN technologies such as SigFox use similar amount of energy but the duty policies allow just 144 messages per day [52] and it is not enough to send movement data. Satellite communications main drawback is the associated cost, which is quite high if we are sending movement data. Finally, systems based in medium-range sensor networks such as Zigbee [8; 9; 10] require higher density of routers making the required infrastructure much larger. * Infrastructure: Our system can work in three different scenarios: using a LoRaWAN telecom infrastructure (e.g., Everynet [53]), deploying ad hoc LoRaWAN gateways connected to Internet or, as we also propose to find cattle, carrying (on a person or a drone [54]) a mobile offline gateway. We have not found any other approach that gives such flexibility. * Data: Derived from the challenging communications and long battery life requirements, most systems having wide coverage only send so much data; mainly GPS. Fewer systems have onboard non-volatile memory that provides larger data recording. Those systems using wireless sensor networks and even WiFi [55] are not limited in terms of data throughput and can monitor large number of parameters (but they require a dense infrastructure). Our system does not require such bulky infrastructure, but still thanks to choosing the right coding technique, it can send movement data in real time.
# Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
## 5 Challenges in Evolutionary Reinforcement Learning
### Issues Encountered by Reinforcement Learning
#### 5.1.6 Computational Efficiency  
Traditional reinforcement learning methods often grapple with the high cost and inefficiency of calculating the derivative of the optimal target, leading to poor stability and robustness in complex tasks [43]. This challenge is compounded when employing complex neural networks (NNs) as control strategies in most current approaches. These deep NNs, despite their potential for enhanced performance, complicate parameter tuning and computation. In response, ref. [43] introduced FiDi-RL, a novel method that integrates DRL with finite-difference (FiDi) policy search. By combining DDPG and Augmented Random Search (ARS), FiDi-RL enhances ARS's data efficiency. Empirical results have validated that FiDi-RL not only boosts ARS's performance and stability but also stands competitively among existing DRL methods.  
Complementing this, the CERM-ACER algorithm [97] addresses computational efficiency in reinforcement learning through an EA perspective, blending the CEM with the actor-critic with experiential replay (ACER). This synergistic approach enables policy parameters to make substantial jumps in the parameter space, allowing for more assertive updates per iteration. Consequently, CERM-ACER not only stabilizes the algorithm but also diminishes the necessity for extensive sample collection in complex environments, thus boosting computational efficiency.  
Similarly, the CGP algorithm [98] enhances the computational efficiency of Q-learning in continuous action domains. By fusing the CEM with deterministic neural network strategies, CGP employs heuristic sampling for Q function training while simultaneously training a policy network to emulate the CEM. This is mathematically represented by the L2 regression objective:  
\[J(\phi)=\mathbb{E}_{s\sim\varphi^{n\text{CEM}}}(\
abla_{\pi_{\phi}}\|\pi_{ \phi}(s_{t})-\pi_{CEM}(s_{t})\|^{2}), \tag{17}\]where \(J(\phi)\) denotes the objective function for training the policy network \(\pi_{\phi}\), \(s_{t}\) represents a state in the state space, \(\pi_{CEM}\) is the policy generated by the CEM, and \(\rho^{\pi_{CEM}}\) is the distribution over states as determined by the CEM policy. This strategy eliminates the need for costly sample iterations during inference, significantly accelerating the inference speed and reducing computational demands. CGP's efficacy in execution efficiency makes it particularly suited for real-time, compute-sensitive tasks.  
In summary, these approaches demonstrate how EAs can revolutionize reinforcement learning's computational efficiency. Starting from policy generation, these methods adeptly navigate the complexities of reinforcement learning, offering more efficient, stable, and robust solutions.
# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 5 Process Flow
### Environmental Assessment Algorithm  
The task of the environmental assessment algorithm is to expand the representation of sectors from point-like (30 by 30 m) to zones, while also cleaning up measurements that deviate from their surroundings in an island-like manner. The operational principle of the algorithm is as follows. First, it identifies all sectors that do not have cluster values. In Figure 3, these are denoted by the \"x\" symbols.  
The algorithm examines the 300 m radius environment of the given sector, indicated by the colored area. In this case, the blue areas do not have cluster numbers, meaning that there were no measurements in the respective sector. The other colors represent the behavioral properties associated with the cluster ID. The algorithm then assesses all such sectors found in the environment of the given point, calculates their distance from the point, and adds the reciprocal of the distance to the respective cluster group. Each cluster will have a value, and the cluster with the highest value will be assigned to the examined point. In Figure 3, it can be seen that in the environment of the examined point, the highest number of sectors belongs to cluster 2. However, due to its proximity, sector 3 has the strongest influence on the examined point, so the examined \"x\" point will belong to cluster 3.  
Next, the algorithm examines the island-like characteristics of individual points. It traverses through each point and, similar to the description above, examines the surroundings of each point. The difference is that it now examines areas with cluster IDs instead of points without clusters. The result of the environmental assessment function is illustrated in Figure 4. The expansion of individual point-like clusters occurs during the module's operation, resulting in contiguous areas with different behavioral properties.
## 4 Results
### Discussion and Future Work  
The experiment shows that NFD provides a satisfying result in detecting selected near-front objects and estimating their distance from the user. It provides a relatively affordable solution for visually impaired people with the concept of 'grab, wear, and go'. NFD utilized YOLOv4-tiny for object detection. It provides competitive performance in terms of accuracy among the other solutions for object detection. The training and inference speed outperform other solutions. However, the accuracy of distance estimation of NFD is not better than those with depth sensors, such as ToF cameras and LiDAR. However, inference output can directly locate detected objects in front at comparatively fewer resources (without going through the point cloud). On the other hand, using public objects, NFD can generally work in the entire city, whereas most of the existing solutions could only be utilized for indoor environments or particular areas. However, it can only recognize two types of outdoor public objects currently, implying that it can only work outdoors. Also, moving objects, such as humans and cars, are not detectable yet. To extend it, enhancing the dataset of the trained model for the improvement of the solution is needed in the future. The more high-quality data included in the dataset, the more accurate the prediction it can make using deep learning.  
One of the improvements suggested to NFD is the dataset, which includes the image depth information from ToF or RGBD camera. Training images in the dataset can improve the accuracy of prediction, as bias from the lens model and camera posture can be ignored. Additionally, to enhance the robustness of NFD, other common public objects such as fire hydrants, street name signposts, traffic signs, streetlights, and especially humans and cars should be included in the object detection. Such an improved solution will be helpful to the vision-impaired people to fully understand the environment, locate themselves in the street, and avoid static and moving obstacles.