esa-sceva/satcom-chunk-collection huggingface dataset:
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 4 Analysis and Findings from Experiments
### Building the Dataset  
A comprehensive printed image dataset was developed for Bangla character detection, including a total of 60 unique classes: 11 vowels (Table 3), 39 consonants (Table 4), 10 numeric digits (Table 5), and 24 compound characters. Each class consists of 500 annotated samples, resulting in a total of 35,265 labeled images, which are divided into training (70%), validation (20%), and testing (10%) subsets.  
The dataset includes variations in font style and visual complexity, as illustrated in Figure 2. Several characters are visually similar, which poses a challenge for detection models; examples of such cases are listed in Table 6.  
Manual annotation was performed using the Labellmg tool, with each character assigned a unique class ID to ensure precise localization and classification. To enhance generalization, various data augmentation techniques were applied, including brightness adjustment, horizontal flipping, scaling, and rotation. These augmentations expose the model to a wider range of visual variations, thereby improving robustness and accuracy.  
Training was conducted using the default YOLO parameters, such as input resolution and anchor box dimensions. This ensured consistent performance evaluation while maintaining a balance between training time and detection accuracy, ultimately supporting reliable real-world deployment.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Specific Configuration Information** \\ \hline \multirow{4}{*}{Hardware} & System: Windows 10 \\  & CPU: Intel(R) Core (TM) [EMAIL_ADDRESS] GHz \\  & RAM: 16 GB \\  & Graphics Card: NVIDIA GeForce RTX 3090 \\ \hline Software Environment & OpenCV 4.10, CUDA 12.4, PyTorch 2.5.1. \\ \hline \multirow{2}{*}{Training Parameters} & Epoch: 150, Batch: 16, Learning Rate: 0.001, \\  & Optimizer: [PERSON]. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuring the software and hardware environment table.
## 5 Experiment and Analysis of Results
### Analysis of Experimental Data
#### 5.3.2 Comparative analysis of Different Algorithm Experiments  
The XML file is parsed in the image folder of the dataset and the actual number of vehicles is counted in the dataset. The optimized YOLOv3-DL model is used to detect the number of vehicles in the statistical data set, which is called the number of statistics. The Precision, Recall, and Accuracy of vehicle statistics was calculated and compared with the YOLOv3 model. The experimental results are shown in Tables 1 and 2.  
From the results in Tables 1 and 2, under the presupposition, we found that the actual vehicle base of the data set is large. Compared with the results of the YOLOv3 model, the statistical accuracy of the YOLOv3-DL model is increased by 3.86% and the recall rate is increased by 3.65%. In the data set labeling, we do not fully label the vehicles that are far away, the targets are small, and the images are blurred. The actual number in the table represents the total number of vehicles labeled with the Labellmg tool when our previous data set is prepared. In Tables 1 and 2, the number of statistical vehicles in the model data set is higher than the actual number of vehicles. It can be explained that the above two models can be used to detect vehicles with large targets and vehicles with small targets, so the above table appears. The number of statistics in China is higher than the actual number, which indirectly shows that the above model has better generalization in vehicles' detection.
# RF Energy Harvesting
## 3 Commercially Available RF Energy Harvesters  
In recent years, much research has been done to allow charging devices to use energy harvested from electromagnetic waves. The simplest harvester circuits can be built with basic electronic parts [9]. However, high-end chips that are capable of providing RF energy harvesting are available. Some of them have become available on the market as commercial products. The following such solutions are presented.
# Real-Time Path Planning for Strategic Missions
## 5 Results and Discussion
### Experiment 1: Goal Seeking  
To perform the experiments, Figure 7 illustrates the scenario to be described by a navigation grid. The mobile robot Pioneer 3 DX simulates the military agent or rescuer. The environment has an Optitrack motion capture system, which emulates a GPS in real outdoor environments. It is worth noting that obstructions, obstacles and obstructions of routes in general are also detected by this tracking system in order to provide the necessary information for the route planning algorithm.  
Figure 6: Simulation to check the computational cost for the code in (**a**) a \(5\times 5\) grid and (**b**) a \(50\times 50\) grid.  
Figure 7: The scenario to be described by a navigation grid.  
For the resulting figures, the solid black line indicates the path the robot followed, the solid green line indicates the route planned by the algorithm and the dashed red line indicates the route the robot used as reference.  
In the sequence, the first experiment shown in Figure 8 is detailed step-by-step, highlighting the events that occur from a snapshot to another. First, there are no obstacles in the environment, as illustrated in Figure 8a. So the LPA* algorithm finds the optimal path to the goal. Next, tree obstacles are inserted at the spots 4, 10 and 13, as stressed by the X-signal on the map of Figure 8b. Consequently, the algorithm looks for another feasible solution. It is worth mentioning that the route only changes if necessary. Note that obstacles 4 and 10 require a change of route, unlike obstacle 13. In other words, LPA* algorithm replans the route whenever an obstruction corrupts the previous computed route, which does not happen when obstacle 13 is placed in the environment. Once again, note that obstacle 13 does not obstruct the route, even the one created after placing obstacles 4 and 10. Following, another event occurs blocking region 14, so the robot can no longer follow its path. As described before, the algorithm is executed and finds another path. However, the robot is not on the new route and it is necessary to reach it. Thus, a \"shortcut\" is created connecting the robot's current position to its nearest point on the path. It is worth stressing that this shortcut is not always direct. Instead, the Dijkstra algorithm guarantees a collision-free path that fulfills such a constraint. Figure 8c illustrates this block, replan and shortcut situation. Finally, regions 17 and 24 receive the occupied tag, as shown in Figure 8d,e, respectively. Notice the LPA* runs over again and establishes the path to be followed until the robot reaches its goal, as shown Figure 8f.  
A video for the experiment is available at: [[https://youtu.be/YU7](https://youtu.be/YU7) JyGgXclA]([https://youtu.be/YU7](https://youtu.be/YU7) JyGgXclA). Through video and Figure 8, it can be concluded that the tracking system accurately captured all obstacles and the algorithm, when perceiving an obstacle, quickly redesigned the route. Thus, the robot is able to achieve the goal.
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 2 Methodology
### Derivation of Water Surface Elevation  
An ATC sensor was also installed in the buoy to measure the acceleration, tilt, and azimuth of the moving buoy. The measurements were conducted hourly, each with a duration of 10 min and a sampling rate of 1 Hz. One of the three measurement axes of the accelerometer was perpendicular to the deck of the buoy hull. In this work, the water surface elevations obtained from the GNSS buoy were compared with those obtained from the ATC sensor. Here, we adopted [PERSON]'s method [22] for transforming the ATC accelerations into water surface elevations. The procedures for determining the water surface elevations from the measured accelerations are provided in Appendix A.
# Investigating the Reliability of Nonlinear Static Procedures
###### Abstract  
This paper presents, firstly, an overview of the nonlinear static procedures (NSPs) given in different codes and research studies available in the literature, followed by the results achieved by the authors to evaluate the reliability of the safety level that they guarantee. The latter is estimated by adopting the fragility curve concept. In particular, 125 models of a masonry building case study are generated through a Monte Carlo process to obtain numerical fragility curves by applying various NSPs. More specifically, among the NSPs, the N2 method (based on the use of inelastic response spectra) with different alternatives and the capacity spectrum method (CSM)--based on the use of overdamped response spectra--are investigated. As a reference solution to estimate the reliability of the nonlinear static approach, nonlinear dynamic analyses (NLDAs) are carried out using the cloud method and a set of 125 accelerograms; the results are post-processed to derive fragility curves under the assumption of a lognormal distribution. The focus of this investigation is to quantify the influence that the NSP method's choices imply, such as the criteria adopted to calculate the displacement demand of a structure or those for the bilinearization of the pushover curve. The results show that the N2 methods are all non-conservative. The only method that provides a good approximation of the capacity of the analyzed URM structures as derived from NLDAs is the CSM. In particular, bilinearization is proven to have a relevant impact on the results when using the N2 method to calculate displacement capacities, whereas the CSM method is not affected at all by such an assumption. The results obtained may have a significant impact on engineering practice and in outlining future directions regarding the methods to be recommended in codes.  
nonlinear static procedures; seismic assessment; N2 method; capacity spectrum method; unreinforced masonry buildings; in-plane global response Article
# Improved Functions of Fermented Coffee by Lactic Acid Bacteria
## 5 Conclusions  
In the following study, we examined fermented coffee extracts using three lactic acid bacteria strains, _P. pentosacueus_ KNUT 0384, _L. paracasei_ CJNU 1840, and _L. plantarum_ CJNU 0441. We discovered improved characteristics in the fermented coffee extract. First, there was an increase in antibacterial activity against _Listeria monocytogenes_ and _Streptococcus mutans_, which are known pathogens. Furthermore, the fermented coffee extract's antioxidative activity increased, implying the production of phytochemicals with free radical-scavenging characteristics. In addition, (CGA) chlorogenic acid, a bioactive molecule linked to a variety of health advantages, such as antimicrobial, anti-inflammatory, and anti-oxidative effects, was found to be high in the fermented coffee extract. Thus, coffee with high CGA content may be used to make'specialty coffee' to reduce inflammation and alleviate the damage caused by free radicals in consumers' bodies. Finally, the caffeine concentration was found to be decreased in the extract, which may be of interest to some people who prefer to consume less caffeine or people sensitive to high caffeine concentrations with certain medical conditions such as IBS (irritable bowel syndrome). Overall, these results highlight the positive impact of the fermentation process with lactic acid bacteria on the chemical composition and functional properties of coffee extract, providing valuable insights for potential applications in the food industry, specifically in the specialty coffee industry to produce a specialty coffee with a strong sensory profile along with medicinal features.  
As fermentation affects all phenolic compounds in coffee, the profile and bioactivity of compounds other than CGA, like caffeic acid, p-coumaric acid, ferulic acid, catechins, and flavonoids, could be analyzed. However, future research could focus on the mechanism of action of fermented coffee to better understand its functionality in consumers' health as well as sensory evaluation.  
Conceptualization, G.-S.M.; methodology, S.-G.K. and G.-S.M.; software, S.-G.K. and A.A.; validation, S.-G.K. and G.-S.M.; formal analysis, S.-G.K. and A.A.; investigation, S.-G.K.; resources, G.-S.M.; data curation, S.-G.K., A.A. and G.-S.M.; writing original draft preparation, S.-G.K., A.A. and G.-S.M.; writing review and editing, S.-G.K., A.A. and G.-S.M.; visualization, S.-G.K. and A.A.; supervision, G.-S.M.; project administration, G.-S.M.; funding acquisition, G.-S.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2021R1A6A1A03046418).  
Not applicable.  
The data used to support the conclusion are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
# Cerebrovascular Reactivity Assessment during Carbon Dioxide Inhalation Using SPECT
## 2 Materials and Methods  
### Subjects and SPECT Acquisition and Reconstruction  
Ten healthy right-handed subjects (male:female = 7:3; age (mean \(\pm\) standard deviation (SD)) = 23.8 \(\pm\) 2.10 years) voluntarily participated in this study (see Table 1) after it was approved by the institutional review board (IRB) in Gil Medical Center, Gachon University College of Medicine (IRB number: GDIRB2018-308), and written informed consent was signed. None of the participants had any history of cerebrovascular, neurological, neuropsychiatric, or neuropsychological diseases that involved cognitive decline. The procedure was carried out in accordance with the approved guidelines. A one-day protocol of two consecutive SPECT (1 st and 2 nd) images with the administration of \({}^{99\text{m}}\)Tc-HMPAO was conducted (Figure 1). Ten minutes after the first 20 mCi \({}^{99\text{m}}\)Tc-HMPAO injection, a 1 st SPECT was performed for an imaging duration of 15 min. When the 1 st SPECT was completed, a gas mixture containing about 5% CO\({}_{2}\) was administered for 15 min through the subject's nose at a flow rate of 13-15 L per minute, which was controlled by a gas regulator. The 2 nd 40 mCi \({}^{99\text{m}}\)Tc-HMPAO for the 2 nd SPECT was injected five minutes after the 1 st SPECT was completed. Ten minutes after the 2 nd \({}^{99\text{m}}\)Tc-HMPAO injection, the 2 nd SPECT was performed for the same imaging duration as the 1 st SPECT. SPECT imaging was performed using a dual-headed rotating gamma camera system (Symbia T16, Siemens Medical Solutions, Erlangen, Germany) equipped with a low-energy, high-resolution (LEHR) parallel-hole collimator. The projection data were collected with 32 views per camera head in a 128 \(\times\) 128 matrix at 25 s per angle with a zoom factor of 1.45 in the step-and-shoot mode. This one-day protocol study took a total of about 55 min. These SPECT images were reconstructed on a 128 \(\times\) 128 matrix using Flash (Siemens) three-dimensional iterative reconstruction (eight iterations and four subsets) after Gaussian-filtering (full width at half maximum (FWHM) = 6.6 mm). After reconstruction of the trans-axial tomographic images parallel to the orbitomeatal line, attenuation correction using [PERSON]'s method [19] was performed for each slice with a uniform attenuation coefficient of 0.11 cm, as shown in Figure 2.  
Figure 1: The acquisition protocol for the same day (one-day) consecutive first (20 mCi) and second (40 mCi) technetium 99m-labeled hexamethyl-propyleneamine-oxime (\({}^{99m}\)Tc-HMPAO) single-photon emission computed tomography (SPECT) imaging. A gas mixture including about 5% CO\({}_{2}\) gas was administered through each subject’s nose. The flow rate, controlled by a gas regulator, was 13–15 L per minute during approximately 15 min. Each scan time was 15 min (25 s \(\times\) 32 steps, step-and-shoot mode) for each first and second SPECT. Figure 2: Diagram representing SPECT cerebrovascular reactivity (CVR) processing using the first and second SPECT data.
## 3 Numerical Validation of the Damping Estimation Methodology
### Sensitivity Analysis in Relation to the Free Vibration Period
#### 3.2.2 Influence Line of the Midspan Displacements  
As mentioned before, the objective of evaluating the influence line at the deck's midspan is to estimate the distance from the bridge's extremity for which the load starts to lose influence in the bridge's response. To achieve this goal, the influence line was obtained through a static analysis in which a 147 kN load (equivalent to a typical train axle load) was applied in successive positions throughout the bridge deck and in the track extension. For each position of the load, the midspan displacement of the bridge was obtained and plotted to form the influence lines presented in Figure 3 for each bridge. In this Figure, it is possible to observe the position where the load enters and leaves the bridge, as well as the longitudinal position for which the load does not affect the bridge's responses anymore. As can be observed, when the load is acting already outside the bridge in the track extension, the midspan displacements are not null, meaning that the continuity provided by the track still influences the bridge response. Depending on the deck size, the distance for which the load has a negligible effect in the response is different, but it is always between 2.4 and 2.8 m. Therefore, it can be concluded that the free vibration of the bridge starts only when the last load is already 2.4-2.8 m away from the bridge's extremity. These results will be compared with those obtained in the dynamic receptance analysis presented in the next subsection.
## 3 Database-I: BWOS and BWS
### ANN Models for DB-I  
Based on the correlation values, six different ANN models are proposed for RC BWOS and eight for BWS, as described in Table 2. The selection of these ANN models is based on the conventional mechanics' assumption for estimating the RC member response at the ULS.  
Figure 7a,b describe the outcome of the different ANN models for the DB-I for the BWOS and BWS with their counterpart experimental values. Figure 8a,b describe the performance of these ANN models with respect to the R, MSE and MAE (as discussed earlier). Therefore, from Figures 7 and 8, it is observed the BWOS-4 (i.e., \(d,M_{f}/bd^{2},f_{c},a_{v}/d\)) and BWS-7 (i.e., \(d,b/d,Mf_{c}bd^{2},f_{c},\rho w,f_{gw},a_{v}/d\)) achieved a reasonably good value of \(R\) while showing the minimum values of \(MSE\) and \(MAE\) as compared to the other models.  
\begin{table}
\begin{tabular}{c c} \hline
**Vu** & \\ \hline \hline  & \multicolumn{2}{c}{_BWOS_} \\ \hline EXP & \(b,d,\rho_{l},f_{d},f_{c},a_{v}/d\), \\ BWOS-1 & \(b,d,\rho_{l},f_{d},f_{c},a_{v}/d\) \\ BWOS-2 & \(b,d,M_{f},f_{c},a_{v}/d\) \\ BWOS-3 & \(b/d,M_{f}/d\bar{f}_{c}bd^{2},a_{v}/d\) \\ BWOS-4 & \(d,M_{f}/bd^{2},f_{c},a_{v}/d\) \\ BWOS-5 & \(d,b/d,M_{f}/bd^{2},a_{v}/d\) \\ BWOS-6 & \(d,b/d,M_{f}/d\bar{f}_{c}bd^{2},f_{c},a_{v}/d\) \\ \hline  & \multicolumn{2}{c}{_BWS_} \\ \hline EXP & \(b,d,\rho_{l},f_{d},f_{c},\rho_{w},f_{gw},a_{v}/d\) \\ BWS-1 & \(b,d,\rho_{l},f_{d},f_{c},\rho_{w},f_{gw},a_{v}/d\) \\ BWS-2 & \(b,d,M_{f},f_{c},\rho_{w},f_{gw},  
Figure 8: _Cont_.  
Figure 7: ANNs Models for (**a**) BWOS and (**b**) BWS.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 4 Protocol Description
### Handshake Phase  
During the handshake phase, two types of packets are employed: HANDSHAKE-S and HANDSHAKE-R. HANDSHAKE-S, sent by the initiating node, follows the format (TypeID, NodeID, PacketID, HC, Q-packet). TypeID designates the packet type, NodeID embodies the node ID of the HANDSHAKE-S sender, PacketID stands for HANDSHAKE-S ID (distinct for each request-initiating packet), HC indicates the hop count value of the HANDSHAKE-S sender, and Q-packet constitutes a data packet queue. This queue includes pending forwarding data represented by (pktsrc, pktid) pairs, where pktsrc represents the source node ID and pktid is the data packet ID.  
HANDSHAKE-R serves as the response packet sent by a node upon receiving HANDSHAKE-S, having the format (TypeID, NodeID, PacketID, HC, Packet-Bit, Energy, Link-Quality). TypeID denotes the packet type, NodeID designates the node ID of HANDSHAKE-R sender, PacketID signifies the received HANDSHAKE-S ID, HC indicates the hop count value of the HANDSHAKE-R sender, and Packet-Bit indicates whether node y holds pending forwarding data in its cache, with the corresponding queue bit set to 1 if data are present. Energy denotes the energy of the HANDSHAKE-R sender, and Link-Quality quantifies the link quality between two nodes engaged in the handshake, computed as detailed below.  
Prior to the initiation of data transmission between nodes, a handshake phase is initiated. When node x possesses one or more packets intended for forwarding, it proceeds to broadcast HANDSHAKE-S packets. In the event that a HANDSHAKE-R response is not received from neighboring nodes within the specified time window, the hop count of the node is subsequently adjusted to its maximal value. Subsequently, an additional temporalinterval is observed. Should a response fail to be received even during this extended period, the node proceeds to effectuate the discarding of the data packet.  
Upon receiving the HANDSHAKE-S packet from node x, node y conducts a hop count comparison between the two nodes. If HC(x) is greater than the node y's hop count, node y checks its own cache queue for the presence of data packets included in the Q-packet. If such data packets are found, the corresponding bit is set to 1, following which node y generates a HANDSHAKE-R packet and sends it back to node x. However, if HC(x) is less than the node y's, node y simply discards the received HANDSHAKE-S packet. The pseudocode for the procedure is given in Algorithm 2.  
```
1:After node y receives the HANDSHAKE-S packet from node x
2:Compare the hops of nodes x and y
3:ifHC(x) < HC(y) then
4:drop HANDSHAKE-S
5:else
6:node y checks its own cache queue for data packets included in the Q-packet
7:if node y has already received packet in Q-packet then
8:Set packet position in Packet-Bit to 1
9:Calculate Link-Quality
10:Send HANDSHAKE-R = (TypeID, NodeID, PacketID, HC, Packet-Bit, Energy, Link-Quality)
11:endif
12:endif
```  
**Algorithm 2********Upon receiving HANDSHAKE-S from node x
# Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks
## 4 Proposed Algorithm
### Target SINR Updating Algorithm  
In our algorithm, the state of Q-learning is determined by the target SINR. To set the target SINR to reflect the interference state, the target SINR at time interval \(T\), denoted as \(\gamma_{min}^{T}\), is adjusted as follows:  
\[\gamma_{min}^{T+1}=\begin{cases}\mathbb{C}*\gamma^{+}+\left(1-\mathbb{C}\right)* \gamma_{min}^{T},&\tau^{T}\geq\tau_{max}\\ \mathbb{C}*\left(\frac{\gamma_{median}+\gamma_{0}}{2}\right)+\left(1-\mathbb{C }\right)*\gamma_{min,}^{T},&\tau^{T}\leq\tau_{min}\end{cases}, \tag{17}\]  
where \(\mathbb{C}\) is the weight factor, \(\gamma^{+}\) is the largest SINR value of the devices, \(\tau^{T}\) is the average outage probability for the time interval \(T\), and \(\tau_{max}\) is the maximum outage probability. \(\gamma_{median}\) is the median SINR value of the devices; \(\gamma_{0}\) is the SINR constraint, and \(\tau_{min}\) is the minimum outage probability.  
The changed target SINR affects the learning, and therefore, the agent can make an optimal decision using the changed target SINR. However, as the optimal actions in the changed target SINR may not be optimal in the original target SINR, those actions may not be selected. To solve this problem, we must adjust the Q-table. At this time, only the Q-table of the BBU is adjusted as follows:  
\[Q^{T+1}(s,a)=log_{2}\left(Q^{T}(s,a)-1\right)\forall[s,a]. \tag{18}\]