esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Our Image Captioning Method
#### 2.3.3 Extracting Tags with YOLO9000  
We extracted the tags feature using YOLO9000 model [20]. The YOLO9000 model is based on the previous YOLO model [6]. [PERSON] et al. [20] proposed various improvements to the YOLO model and then jointly trained it on both classification dataset and detection dataset to create YOLO9000 model. Due to the jointly training, the model is able to predict detections for objects that do not have labeled data in the detection dataset. The YOLO9000 model also shows better performance in both speed and accuracy compared to some other methods such as Faster R-CNN [21] and SSD [22] (Table 1). In our previous work [17], we used a Mask R-CNN model [5] trained on MSCOCO dataset [23] as our tags detector. The Mask R-CNN [5] can only detect up to 80 different tags of the MSCOCO dataset while the YOLO9000 can detect over 9000 different tags. This motivates us to replace the Mask R-CNN model with the YOLO9000 model. The increased number of different tags can help the model deal with concepts not in 80 different concepts of MSCOCO dataset and also enhance the information for the captions generating module.  
From a given image as input, we first extract a list of tags using YOLO9000. We only keep top 20 tags with highest probabilities. We then break each tag into words if the tag contains more than one word. Redundant words are eliminated so that the list will contain only unique words. After this process, an image will produce at most 23 words. Therefore, we choose the maximum size of our list to be 23. If the list has fewer than 23 words, we pad special <NULL> tokens into the list to keep all the lists at the same size.  
Each word \(i\), including <NULL> token, is represented by a one-hot vector \(V_{i}\) of N dimensions (N is the size of the vocabulary set). We then embed each word into d-dimension space using word embedding method [24]. Concretely, we use an embedding matrix \(E\) and compute the dot product \(v_{i}\) between the one-hot representation \(V_{i}\) of a word and the embedding matrix \(E\) as in Equation (1).  
\[v_{i}=V_{i}\cdot E \tag{1}\]  
The embedding matrix is initially assigned with random value and then trained along with the whole model. The purpose of using embedding matrix is to transfer representation of a word into a more meaningful space that can represent the semantic relationship between words. After the embedding process, a list of words from the image will be represented by a list of d-dimension  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Detection Frameworks** & **mAP** & **FPS** \\ \hline Faster R-CNN VGG-16 [21] & 73.2 & 7 \\ Faster R-CNN ResNet [21] & 76.4 & 5 \\ \hline SSD300 [22] & 74.3 & 46 \\ SSD500 [22] & 76.8 & 19 \\ \hline YOLO9000 [20] 480 \(\times\) 480 & 77.8 & 59 \\ YOLO9000 [20] 544 \(\times\) 544 & 78.6 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between YOLO9000 and other methods. The higher is the mAP score, the better (source from [20]).  
embedded vectors. This tags features will bed fed into the tag attention module to produce the feature vector for generating captions.
## 5 Discussion
### Time Efficiency of the Method  
All the experiments are implemented on an NVIDIA Geforce RTX 3060 12-GB GPU. The approach in this paper is divided into two phases in Figure 13, where building extraction and damage detection do not include training time. The first stage is the pre-earthquake stage, which takes 195 min. The second stage is the post-earthquake stage, which based on the acquired images; the building damage detection and center point vectorization take 20 min, and the buried distribution of people takes 30 min.  
The overall time spent is about 245 min, which meets the requirement of 72 h of rescue. The pre-earthquake phase takes a long time, but it can simulate the results in advance. In the pre-earthquake, relevant information can be extracted in advance to improve the efficiency of the personnel buried pressure model. The post-earthquake phase requires remote sensing images of earthquakes that have already occurred and cannot be predicted in advance. So, the length of the post-earthquake phase cannot be shortened.
# NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters
## 4 Experimental Results
### Experimental Results Analysis
Figure 3: Throughput improvement of NUDIF over EdgeShard-multi for each device count. In contrast, as the number of devices in the cluster increases, NUDIF maintains a high average resource utilization rate without significant fluctuations. This is attributed to its non-uniform deployment strategy, which allows multiple instances of each sub-model to be deployed across multiple devices. By dynamically allocating sub-model instances based on the computational power and memory capacity of each device, NUDIF ensures efficient operation within device capabilities and avoids idleness. Consequently, compared to EdgeShard-single, NUDIF's average resource utilization rate is 42.27% higher, and compared to EdgeShard-multi, it is 8.07% higher. Additionally, we performed a comparative analysis of inference latency, as shown in Figure 5. EdgeShard-single exhibits shorter inference latency, as it only needs to complete a single set of inference instances. The inference latencies of EdgeShard-multi and NUDIF are nearly identical. Calculations show that NUDIF increases the average inference latency by 2.03 s compared to EdgeShard-single, and by 0.086 s compared to EdgeShard-multi, with both differences falling within an acceptable range. Although NUDIF introduces a slight increase in inference latency, it significantly enhances throughput and device resource utilization, making it a more advantageous choice. Figure 4: Resource utilization comparison among three methods. Figure 5: Inference latency comparison among three methods.
## 5 Experimental Results and Discussion
### Performance Analyses  
For the Qualitative performance analysis, we use three databases: CASIA-Iris-Thousand, UBIRIS.v2 and LG2200. For three datasets analysis are performed and tested on different layers. Results in terms of recognition accuracy peaks are shown in Figure 10. In some middle layers for all CNNs, for CASIA-Iris-Thousand dataset: VGG on layer 9, Inception on layer 10, ResNet on layer 11 and DenseNet on layer 5. For the UBIRIS.v2 dataset: VGG on layer 9, Inception on layer 10, ResNet on layer 13 and DenseNet on layer 4. For the LG2200 dataset: VGG on layer 9, Inception on layer 10, ResNet on layer 11 and DenseNet on layer 6. All this three databases uses rather low Daugman matcher rate of 91%. As inception layers is done on layer 10, it converges to the peak more quickly than other layers. ResNet allow the gradient to flow throughout the network due to its property of skip connections. Due to gradient flow condition, ResNet make the network performance much better in terms of iris recognition accuracy. DenseNet allows neurons to interact easily due to its rich dense connections. It can observed that peak results do not occur toward the later layers of the CNNs (Figure 10). The main benefit of normalization is that the original iris images are more complex compared to the normalized iris image. Hence, encoding the normalized iris image do not need large number of layers for the process and the accuracy can be achieved in the middle layers. Among all five CNNs, DenseNet achieves the best recognition accuracy of 99.2% at layer 6 on the CASIA-Iris-Thousand database and 99.5% at layer 4 on the UBIRIS.v2 database and 98.9% at layer 6 on the LG2200 database.
# Sound Localization Framework for Construction Site Monitoring
## 6 System Validation on a Real Site  
### Interior Environment Tests  
Interior environment tests were performed at Sungkyunkwan University in a warehouse made of brick walls and a steel roof with dimensions of 30 \(\times\) 12 \(\times\) 6 m (l \(\times\) w \(\times\) h). The collision sounds and noises of construction material were recorded and played on a Bluetooth speaker to create an environment similar to an actual site (Figure 9). We aimed to determine the location of the impact sounds that registered over a certain decibel value instead of simply classifying and assessing the characteristics of the sound sources. Therefore, it was possible to capture the noise forms of the construction sites in waveforms by situating a Bluetooth speaker in an enclosed space, and there was no difficulty in verifying the effectiveness of the proposed algorithm. There was an actual pre-test case where the experiment involved a microphone error of 0.5 m. This may depend on the location, but such an error results in a relatively higher error range and even system errors in some cases. In the sound signal, if the parameter, such as actual microphone location, slightly changes after measuring the distances between the microphones repeatedly, the measured value may turn out to be relatively higher. Thus, the experiment was conducted repeatedly without changing the parameters to verify the framework in the given experiment environment for the actual algorithm's accuracy and system's safety.  
The first interior environment test focused on the borderline points that were relatively vulnerable compared to the sound-source estimations within the array space. The angles formed by the microphones are arranged on the floor level. The origin point was 120\({}^{\circ}\), and the angles between the pairs of microphones were divided by 20\({}^{\circ}\) to create 15 test points  
Figure 9: Construction site test environment.  
(yellow color points in Figure 10). The test point coordinates are shown in Figure 10. A total of 1141 tests were performed (over 75 tests at each point).  
The second interior environment test verified the maximum monitoring range of the proposed system. Considering that construction sites have a large range and are complex, it is necessary for the system to be accurate, monitor a large area, and immediately report its findings to make on-site installation easy. Therefore, in the second test, the test points were moved outside the monitoring range within the array to test the maximum detection range of the proposed system (Figure 11).
## 3 Proposed System
### Bike Data Sharing Agents  
Bike Data Sharing Agents are responsible for obtaining data from the BSS, which usually offers two kinds of data:  
* Station information: It indicates the total number of available docks and bicycles at each station at a given time, that is, the current situation at the station. This data is usually available and provided to the end users in order to keep them informed about the stations with available bicycles. If this data is periodically polled, pickups and returns at each station can be calculated regularly. This kind of information only provides us with knowledge about variations at each station without specifying the flow of bikes from one station to another or data regarding the users involved in these changes. It is usually provided through an API in real time by a great amount of BSS, so much so that there is a project called PyBikes [27; 40] which unifies the access to this information through a common API for a large number of current BSS. The multi agent system presented in this paper includes a station data agent that periodically collects this information for the target BSS.
* Trip information: This sort of information is not commonly published but it is usually available in csv files offered every month as open data [41]. It is related to the trips recorded by the BSS: a user picks a bike at a specific station at a given time and later returns it at another station. This kind of data discloses more details as it provides insights on the flow of bikes between stations and the users who perform the trips recorded in the system. This information will be especially useful for the visualization tool and demand prediction in BSS with registered users (occasional users are not included). There is a trip data agent in the system that asynchronously gets data from a public or private data source.  
This group of agents sends the collected information to the Data Persistence Agent, which is in charge of storing the raw data that will later be processed by the Processor Agent.
# An Energy-Efficient Strategy for Microcontrollers
## 4 Experiments and Results
### Processor Clock Frequencies and Energy Consumption
By combining the results in Figures 6 and 8, we listed the energy consumed by the whole system, the microcontroller itself, and the peripheral components in Table 1. The STM32F407 processor will use the 16 MHz HSE as the processor clock if the clock configuration does not present; thus, the 16 MHz is regarded as the baseline frequency for the table data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**HCLK** **(MHz)**} & \multirow{2}{*}{**Execution**} & \multicolumn{3}{c}{**Energy Consumption (mJ)**} & \multirow{2}{*}{**Energy** **Saved (\%)**} \\ \cline{3-3} \cline{5-6}  & & & **Total** & & **Processor** & **Peripherals** \\ \hline
4 & 37.680 & 1.672 & 0.168 & 1.504 & \(-\)197.0 \\
8 & 18.693 & 0.929 & 0.169 & 0.760 & \(-\)65.0 \\
16 & 9.350 & 0.563 & 0.223 & 0.340 & 0 \\
32 & 4.660 & 0.414 & 0.217 & 0.197 & 26.5 \\
48 & 3.106 & 0.330 & 0.198 & 0.132 & 41.4 \\
64 & 2.326 & 0.282 & 0.188 & 0.094 & 49.9 \\
96 & 1.553 & 0.244 & 0.181 & 0.063 & 56.7 \\
128 & 1.165 & 0.230 & 0.183 & 0.047 & 59.1 \\
168 & 0.887 & 0.222 & 0.186 & 0.036 & 60.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Energy consumption of the whole system and peripherals at different processor clock frequencies. Figure 8: The relation between the energy consumption and the processor frequency of the entire core board. According to these results, it is apparent that, in embedded systems with peripheral components, the energy consumption of computationally intensive tasks is monotonically decreasing as the processor clock frequency increases. With a higher processor clock frequency, energy is saved because the power-on duration of the peripheral components is shortened.
# Numerical Investigation of the Hydrodynamic Characteristics of 3-Fin Surfboard Configurations
## 2 Materials and Methods
### Numerical Methods  
The ANSYS R19.1 Fluent simulation software was used for all CFD simulations. These simulations were solved at an angle of attack (AOA) of 0, 5, 10, 15, 20, 30, and 45\({}^{\circ}\) to simulate the various stages of a critical turn on a surfboard. For all simulations, the steady RANS equations have been solved using the two-equation k-\(\omega\) SST turbulence model. The COUPLED algorithm was used for pressure-velocity coupling with second-order discretisation schemes, and all residuals were set at \(1\times 10^{-3}\).  
The lift and drag forces on the solid fin surfaces were computed from the simulation. The lift forces were taken perpendicular to fluid flow and the fin surface, whilst the drag forces were calculated parallel to flow direction (Figure 3b). These forces were then converted to dimensionless coefficients, \(C_{L}\) and \(C_{D}\), respectively,  
\[C_{L}=\frac{L}{\frac{1}{2}\!\cdot\!\rho\!\cdot\!v^{2}\!\cdot\!A} \tag{1}\]  
\[C_{D}=\frac{D}{\frac{1}{2}\!\cdot\!\rho\!\cdot\!v^{2}\!\cdot\!A} \tag{2}\]  
where \(L\) and \(D\) are the lift and drag, respectively, \(\rho\) is the density, \(\
u\) is the free stream velocity, and \(A\) is the reference surface area (total plan area of the 3 fins; see Table 1), as seen in airfoil theory [11]. To create a profile of the fin's performance through a turn, the coefficients were graphed against the AOA.  
Figure 2: Optimised fin set for Winkipop, Victoria, Australia.
## 3 RRSS Protocol
### Network Structure  
As in Figure 2, in this paper sensor nodes are deployed in a 3D space. Sink nodes are distributed evenly on the water surface. Each sink node is equipped with both a radio modem and acoustic modem. Underwater sensor nodes are deployed underwater randomly and equipped with an acoustic modem. Acoustic modems are used to transmit acoustic signals between underwater nodes. Radio modems are used to transmit RF signal between sink nodes and the data center. Each sensor node is equipped with a timer and an instrument for measuring RSS. All underwater sensor nodes can generate and relay data packets. Underwater sensor nodes are mobile in the horizontal direction with water current; the vertical movement can be negligible. In RRSS, we assume the energy of sink nodes is unlimited. This assumption is reasonable because sink nodes can be charged by solar energy. For underwater sensor nodes, once the energy runs out, the node will die. Meanwhile, we assume sink nodes can transmit at enough power to cover the whole area of interest. The transmitting power of underwater sensor nodes is either the same or smaller than sink nodes. We consider the delivery is successful when data packets are received by any one of the sink nodes.
## 3 Simulation Results and Discussion
### Performance Analysis
#### 3.2.2 GDOP Test
Figure 7: Geometry dilution of precision for 24 h in geostationary orbit with longitude. The results of GDOP simulation for 24 h at a geostationary orbit of 128\({}^{\circ}\)E longitude are shown in Figure 9 as an example. The mean of the GDOP for multi-GNSS using only the main lobe signal and with the side lobe signal together was 134.04 and 12.84, respectively. Most of the single system using only the main-lobe signal failed to obtain a suitable GDOP because the number of visible satellite or GDOP value did not satisfy the minimum conditions. In this scenario, GDOP over the threshold of 1000 is excluded. However, the GDOP can be relatively lower at the opposite side due to BDS satellites observed additionally, i.e., GDOP benefits greatly from non-MEO BDS satellites in this case. In particular, the GDOP of BDS has a value that is fairly small enough to approach multi-GNSSs when using the side-lobe signal together. The mean of the GDOP using the side lobe signal together was 8.27. These are summarized in Table 3.  
Figure 8: Sky-plot for 24 h when looking at the nadir in GEO. Figure 9: GDOP for 24 h in geostationary orbit above Korea (128\({}^{\circ}\)E).
# An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features
###### Abstract  
With the rapid increase in the number of drivers, traffic accidents due to driver distraction is a major threat around the world. In this paper, we present a novel long-term recurrent convolutional network (LRCN) model for real-time driver activity recognition during both day- and nighttime conditions. Unlike existing works that use static input images and rely on major pre-processing measures, we employ a TimeDistributed convolutional neural network (TimeDis-CNN) layer to process a sequential input to learn the spatial and temporal information of the driver activity without requiring any major pre-processing effort. A pre-trained (CNN) layer is applied for robust initialization and extraction of the primary spatial features of the sequential image inputs. Then, a long short-term memory (LSTM) network is employed to recognize and synthesize the dynamical long-range temporal information of the driver's activity. The proposed system is capable of detecting nine types of driver activities: driving, drinking, testing, smoking, talking, controlling, looking outside, head nodding, and fainting. For evaluation, we utilized a real vehicle environment and collected data from 35 participants, where 14 of the drivers were in real driving scenarios and the remaining in non-driving conditions. The proposed model achieved accuracies of 88.7% and 92.4% for the daytime and nighttime datasets, respectively. Moreover, the binary classifier's accuracy in determining whether the driver is non-distracted or in a distracted state was 93.9% and 99.2% for the daytime and nighttime datasets, respectively. In addition, we deployed the proposed model on a Jetson Xavier embedded board and verified its effectiveness by conducting real-time predictions.  
driver activity; driver distraction; convolutional neural network; long-term recurrent convolutional network; nighttime recognition; spatio-temporal features +
Footnote †: journal: Applied sciences
# Laser Processing of Intraocular Lenses
## 2 Materials and Methods
### Femtosecond Laser Micromachining Setup  
An ultrashort fiber-based laser system was employed (HE-1060-1 uJ-fs, Fianium, Southampton, UK), which emits laser pulses with a duration of 370 fs at a wavelength of 1064 nm. The laser has maximum pulse energy of 1 uJ, and a variable repetition rate up to 1 MHz. The power values were monitored using a thermal power sensor (S302C,Thorlabs, Newton, NJ, USA). The laser beam was focused onto the IOL surface using a \(\times 20\) achromatic standard objective lens with a numerical aperture (\(NA\)) of 0.4 (Edmund Optics, Barrington, IL, USA). The beam spot size was \(\sim\)3.6 \(\upmu\)m as calculated by the equation  
\[d=4M^{2}\lambda f/\pi D_{0}, \tag{1}\]  
where \(M^{2}\) is the beam quality (1.2), \(\lambda\) is the laser wavelength, \(f\) is the focal length of the lens (8.3 mm), and \(D_{0}\) is the diameter of the laser beam before the lens (3.8 mm). The IOL samples were placed on \(X\)-\(Y\) motorized linear stages with a nominal resolution of 1 nm (Smaract, CLS-92152, Oldenburg, Germany), allowing for precise movement and positioning (Figure 1). This setup was designed to facilitate controlled and precise laser-induced modifications on the IOLs' surface. A CMOS monochrome camera (Teledyne Dalsa, Genie Nano M640, Waterloo, ON, Canada) was used for imaging and determining the laser focus position on the IOLs.
## 3 Application to Cultural Heritage Science
### Elemental Analysis of Bronze Artefacts
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**XRF**} & \multicolumn{3}{c}{\(\mu\)**XES—40 MeV/\(c\)**} & \multicolumn{3}{c}{\(\mu\)**XES—18 MeV/\(c\)**} \\ \hline \multirow{2}{*}{**Coin**} & **Mean Au** & **Mean Ag** & **Mean Cu** & **Au** & **Ag** & **Cu** & **Au** & **Ag** & **Cu** \\  & **[wt.\%]** & **[wt.\%]** & **[wt.\%]** & **[wt.\%]** & **[wt.\%]** & **[wt.\%]** & **[wt.\%]** & **[wt.\%]** & **[wt.\%]** \\ \hline
**Tiberius** & 99.73 & 0.27 & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{\(>\)99\%} & \multirow{2}{*}{\(<\)1\%} & \multirow{2}{*}{-} & \multirow{2}{*}{\(>\)99\%} & \multirow{2}{*}{\(<\)1\%} & \multirow{2}{*}{-} \\
**[AD 14–37]** & (0.1)\% & (0.01)\% & & & & & & & \\ \hline
**Hadrian** & 99.55 & 0.45 & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{\(>\)99\%} & \multirow{2}{*}{\(<\)1\%} & \multirow{2}{*}{-} & \multirow{2}{*}{\(>\)99\%} & \multirow{2}{*}{\(<\)1\%} & \multirow{2}{*}{-} \\
**[AD 134–138]** & (0.1)\% & (0.01)\% & & & & & & & \\ \hline
**Julian II** & 95.58 & 4.18 & 0.24 & \multirow{2}{*}{96 (1)\%} & \multirow{2}{*}{4 (1)\%} & \multirow{2}{*}{-} & \multirow{2}{*}{96 (1)\%} & \multirow{2}{*}{4 (1)\%} & \multirow{2}{*}{-} \\
**[AD 361–363]** & (0.3)\% & (0.03)\% & (0.02)\% & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the XRF and Muon analysis. The results of the muon analysis are reported in the right columns. It is evident that the information coming from the surface (at 18 MeV/c) and the core (40 MeV/c) have no relevant difference: in this case, the surface is representative of the bulk composition. The data was taken from and adapted with permission from Ref. [37] © 2021, Elsevier Ltd.  
Figure 10: Drawing of the two samples and measured areas (red). (**a**) Model n.6779; (**b**) model n.6780.