esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of a Novel Fluidic Oscillator under Several Dimensional Modifications
## 6 Results
### Outlet Width Modification
In fact, the pressure momentum term slightly grows with the outlet width while the mass flow term remains rather constant. Their relation is about 345% for the maximum outlet width (outlet 6). The main conclusion from Figure 14 is that the net forces acting on the jet as it enters the mixing chamber are due to the pressure, but the mass flow forces have the same order of magnitude. Instantaneous velocity and pressure fields for the minimum outlet width (outlet 1), standard outlet width, and maximum outlet width (outlet 6) are presented in Figure 15. The maximum velocity and the maximum pressure are observed to decrease with the outlet width increase. Particularly for the minimum outlet width (outlet 1), the entire FO is pressurized. Regardless of the outlet width, we can clearly see the high stagnation pressure area on the mixing chamber lower inclined wall, which in fact is what triggers the self-sustained oscillations. Figure 15: Fluidic oscillator velocity magnitude (**a**,**c**,**e**) and pressure fields (**b**,**d**,**f**), for the minimum (outlet 1) (**a**,**b**), standard (**c**,**d**), and maximum (outlet6) (**e**,**f**) outlet widths evaluated. Reynolds number is kept constant at Re = 54,595. To more properly see the dynamics of the velocity and pressure fields, six videos which correspond to the three cases presented in Figure 15 are introduced in the appendix as Supplementary Materials. From the videos, we can clearly observe the reverse feedback channel mass flow appearing in all the cases studied. As this FO is a novel design that has never been studied at these Reynolds numbers, it clearly requires future work which could address the required dimensional modifications to reduce or even suppress the reverse flow in the feedback channels. In fact, the design presented here was mostly created to be able to evaluate the origin of the forces generating the self-sustained oscillations.
## 4 Experimental Evaluation
### A live animal test  
Finally, we made a simple proof-of-concept study of the network in live animals. We have already discussed that we were not attempting to carry out an animal location study, but to provide the tools for a future use. However, before providing these tools to researchers in charge of studying animal behaviors, we run some simple tests in animals to validate our system. The main idea for the animal test was to check \(i)\) if animal movements were able to swing the generator to produce enough energy for the ID transmission and \(ii)\) if the node size and dimensions are feasible at least for some mammals.  
We tested our system both in a dog and a reindeer. The best place for the secondary node location, turned out to be the animal neck (see Figure 10). When the animals move their neck (for example, because of a movement produced when looking for food on the ground), the generator is swung. The neck was selected because, to best take advantage of the energy, the generator needs to be swung from end to end. We also tested to adjust the secondary node to the legs and body of the animal. In this case, faster and more frequent movements were achieved. However, the magnet does not move along the whole generator. Typically, it did not cross the two coils, hence it did not produce enough energy for a frame transmission.  
Figure 10: A secondary node (a) in a hip bag around a dog neck in an Spanish courtyard and (b) mounted on a reflective collar around a reindeer neck in Northern Sweden.  
During the trial, two generators were used to power the secondary nodes, although it was noticed that the transmission was also achievable with just one generator. Communication ranges of over 100 m have been also observed in an open area when the kinetic converter was activated by the animal movement. Average data transmissions rates of one frame every three minutes were estimated from the first results depending on the animal's activity. The modules were operationally tested at temperatures as low as -12 \({}^{\circ}\)C in Northern Sweden, although they were also tested in a cold experimental chamber at temperatures of -25 \({}^{\circ}\)C. Moreover, all of the node's components are rated at -40C, for its correct operation in different test environments.
## 4 Results
Regional \(Q(\kappa_{R})\) slope variation within \(\pm 2\) standard deviations (Figure 6) are shown based on \(Q_{c}(f)\) increase with frequency for a different lapse time \(t_{L}\)[34]. Comparing high-frequency coda attenuation values with frequency independent, \(Q_{c}(f)\) and \(Q(\kappa_{R})\) for both stations it can be seen that they are comparable (difference is 32-67%). However, different azimuthal regional effects are neglected within average \(Q\) values under the assumption that they are affected by the same regional characteristics. \(Q(\kappa_{R})\) slope variation shown in Figure 6 could indicate influence of regional azimuthal data subsets attenuation effects. But also differences in the average shear wave velocity of the crustal structure at the contact of the Dinarides and the Pannonian Basin affect \(Q(\kappa_{R})\) values. Large differences among different \(Q\) approaches can be explained by inherent errors of \(Q\) and \(\kappa\) measurement (often in the order of \(\pm 50\%\)) and different attenuation methods in \(Q\) and \(\kappa\) calculation (different data, band-pass filters, time window length). This shows that frequency-dependent attenuation cannot be ruled out for \(\kappa\)[1; 33]. As expected, site kappa values for PTJ and ZAG are different as these stations are situated in different local site characteristics (Figure 1, Table 2). In terms of local site conditions, estimated \(\kappa_{0}\) values for both stations compared to \(V_{S30}\) values ([39; 40]; for PTJ: site type A (800 m/s) by EC8 [41] and for ZAG: site type B (360-800 m/s) by EC8) are within the range of existing \(\kappa_{0}\)-\(V_{S30}\) correlations [11; 32]. It confirms that lower values of \(\kappa_{0}\) are observed for sites on harder rocks (lower attenuation) and higher \(\kappa_{0}\) values for sites on softer rocks (higher attenuation). Evaluation of the site-specific \(\kappa_{0}\) is very sensitive since the AH84 method requires high-magnitude events, something that is difficult to acquire when the local site is situated in a low-to-moderate seismicity area. Correlations between \(\kappa_{0}\)-\(V_{S30}\)[11; 32] could be helpful to estimate \(\kappa_{0}\) for a certain target local site. However, both variables \(\kappa_{0}\) and \(V_{S30}\) have some uncertainties. Especially, the errors in \(V_{S30}\) may even be significantly higher than the error in \(\kappa_{0}\) (percentage-wise) due to numerous reasons (different geophysical methods, correlations, analysis, regions, etc. ), as observed in [36]. The preferred approach should be estimating the site kappa value from earthquake recordings, which is occasionally difficult in low seismicity areas. The scarcity in short distances affects the estimation of site kappa and constraining to fixed value within errors could be helpful. In contrast, greater distances recordings control the slope of kappa regression where potential uncertainties in location could exists (error-in-variable regression is preferred) as well as stronger influences of regional attenuation effects [36].
## 4 Problem and Algorithm Description
### Algorithm Description  
#### 4.2.1 Pigeon Swarm Optimization Algorithm  
The pigeon swarm optimization algorithm (PSOA) is proposed by [PERSON] and [PERSON] [30,31] in 2014. The algorithm is derived from the behavior of homing pigeons. PSOA consists of two models, namely, the map and compass operator and the landmark operator. PSOA has better optimization performance and faster convergence speed, comparing with other artificial intelligence algorithms [32,33].  
(1) Map and Compass Operator  
When all the pigeons are not familiar with the destination or landmark during the initialization of the algorithm, they determine the flying position and direction according to the magnetic field and the position of the sun. In this model, each pigeon updates its position according to the recent global optimal solution in the current iteration. On the assumption that the position and speed of the \(j\)-th pigeon are \(X_{j}\) and \(V_{j}\), respectively, \(X_{j}\) and \(V_{j}\) are updated according to Equations (8) and (9) in the \(t\)-th iteration.  
\[V_{j}(t)=V_{j}(t-1)\cdot e^{-Gt}+rand\cdot\left(X_{g}-X_{j}(t-1)\right) \tag{8}\]  
\[X_{j}(t)=X_{j}(t-1)+V_{j}(t) \tag{9}\]where \(G\) is the map and compass factor, _rand_ is a random number, and \(X_{g}\) is the global optimal position in the \(t\)-th iteration.  
(2) Landmark Operator  
After PSOA has run for some time, some pigeons may have found the destination or familiar landmarks. Thus, these pigeons can move to the destination quickly, and the others move behind them. On the assumption that \(X_{c}(t)\) is the center of the position of the pigeon whose fitness is the top \(N_{p}/2\), the position of each pigeon in the \(t\)-th iteration is  
\[N_{p}(t)=\frac{N_{p}(t-1)}{2} \tag{10}\]  
\[X_{c}(t)=\frac{\sum X_{j}(t)\cdot fitness\big{(}X_{j}(t)\big{)}}{\sum fitness \big{(}X_{j}(t)\big{)}} \tag{11}\]  
\[X_{j}(t)=X_{j}(t-1)+rand\cdot\big{(}X_{c}(t)-X_{j}(t-1)\big{)} \tag{12}\]  
where \(N_{p}(t)\) is the number of pigeons that meets the restriction of the condition in the \(t\)-th iteration and \(fitness(X_{j}(t))\) is the proportion of the fitness of the \(j\)-th pigeon to that of all the pigeons. In the minimum optimization problem, the fitness is formulated as follows:  
\[fitness\big{(}X_{j}(t)\big{)}=\frac{1}{f\big{(}X_{j}(t)\big{)}+\epsilon} \tag{13}\]  
where, \(f\) is the fitness function andis a small value.
## 4 Case Study
### Numerical Simulation Results
#### 4.1.2 Macro-Level Results  
The Korea Maritime Environment Corporation systematically manages the information on sunken ships and maintains the status data to assess the potential for marine pollution accidents. These data include the name of the sunken ship, date of sinking, ship type, accident type, depth, hazardous material mass, latitude, and longitude [38]. The data provided on 28 August 2023, were used for the macro-level case study. These included the data of 2287 sunken ships, recorded from 1983 to 2023, which included fishing boats, cargo ships, oil tankers, tugboats, and buoys, with fishing boats accounting for approximately 82% of the total.  
The pollution risk index was calculated by inputting the data into a pollution risk assessment model. However, because the comprehensive risk cannot be inferred using only the pollution risk index, a random index was added based on the collision risk level to calculate the comprehensive risk. Figure 16 shows the distribution of the comprehensive risk at each location for various levels of the collision risk index. The results confirmed that the comprehensive risk level tended to increase with increasing collision risk level.
## 3 Description of Adsorption Isotherms and Modeling Investigation  
The experimental adsorption isotherms of the dexamethasone and hydrocortisone on the MNIP and MMIP adsorbents are presented in Figure 1. As can be noted, the isotherms of adsorption are monotonic regarding the equilibrium concentration. At elevated dexamethasone or hydrocortisone equilibrium concentrations, the adsorption amounts for the MNIP and MMIP adsorbents tended to plateau, coinciding with the saturation of the adsorbents. The saturation plateau is correlated to the complete filling of the accessible adsorption sites of the dual-template MNIPs/MMIPs by the dexamethasone and hydrocortisone solutions, leading to a number of dexamethasone or hydrocortisone adsorption layers. In addition, the various adsorption amounts are helpful for designating the adsorbent affinity. Particularity, the MMIP adsorbent indicated greater selectivity towards the dexamethasone solution.  
In addition to experimental proof, which does not describe the adsorption process, an efficient modeling interpretation was necessary to further our understanding, and this will be depicted in the upcoming section. To this aim, three physical models were established based on statistical physics and tested on the experimental data. The analysis of the adsorption phenomena was carried out by utilizing the grand canonical ensemble to take account of the particle number variation by inserting a variable chemical potential in the adsorption phenomena [22; 23; 24].
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 5 Results and Discussion
### Results on 47-Class Dataset  
For the 47-class dataset (Table 3), the model without optical flow significantly outperforms the model with optical flow in terms of accuracy (92% vs. 54%). This suggests that the additional motion information introduces excessive noise or complicates model training,  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Inference Speed (ms/frame)** \\ \hline BiLSTM (No OF) & 78.95 & 88.89 & 72.73 & 80.00 & 45.85 \\ \hline Bi-LSTMOF-10 & 89.47 & 90.91 & 90.91 & 90.91 & 41.15 \\ Bi-LSTMOF-20 & 73.68 & 87.50 & 63.64 & 73.68 & 40.68 \\ Bi-LSTMOF-30 & 57.89 & 80.00 & 36.36 & 50.00 & 49.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison on the two-class dataset. Bold and yellow colors represent outstanding results among all experiments.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline BiLSTM (No OF) & 77.00 \\ Bi-LSTMOF-10 & 68.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison on the 13-class dataset.  
damaging classification accuracy. The high complexity of a 47-class dataset may require more refined motion processing techniques to prevent the loss of essential gesture features.
## 3 Design
### Parallel File Encryption in Distributed Architecture
CipherQueue is created to buffer encrypted subsets between the reader and sender components **(Line 3)**, and a separate reader thread is spawned to handle encryption **(Line 4)**. The AESReaderThread function opens the target file in binary mode **(Line 6)** and sequentially reads the assigned region in fixed-size subset units **(Lines 7-8)**. Each subset is encrypted using the AES key reconstructed from the PQC ciphertext and QKD-masked secret key **(Line 10)**, and the resulting ciphertext is inserted into the CipherQueue along with its subset index **(Line 11)**. Once the entire file range [offset_start, offset_end] is processed, a StopToken is pushed into the queue to signal completion **(Line 14)**. While the reader thread generates encrypted subsets, the main thread concurrently dequeues items from CipherQueue **(Line 17)** and appends them to a BatchBuffer **(Line 22)**. When the batch reaches a predefined size, a non-blocking send is triggered using MPI_Isend() **(Line 24)**, and the request handle is added to the PendingRequestslist **(Line 25)**. When the number of outstanding requests exceeds a threshold (e.g., 32), MPI_Waitall() is invoked to ensure network buffer safety and avoid congestion **(Line 27)**. After all encrypted subsets are processed and the StopToken is detected **(Lines 19-20)**, any remaining batch is transmitted **(Lines 31-32)**, and a final MPI_Waitall() ensures completion of all asynchronous transmissions **(Line 34)**. Our proposed scheme leverages an overlapped pipeline design, allowing parallel encryption and transmission to proceed concurrently with minimal blocking. This enables efficient and scalable distributed encryption even under high I/O and communication loads.
## 4 Overview: Signal Processing Methods
### Dimensionality Reduction Methods
#### 4.6.1 Linear Discriminant Analysis (LDA)  
Linear Discriminant Analysis (LDA) is a statistical method used for dimensionality reduction and classification tasks. It aims to find a linear combination of features that effectively distinguishes between multiple classes in a dataset by reducing within-class variability and maximizing between-class variability. LDA is widely applied in machine learning, pattern recognition, and data analysis, especially in classification challenges involving multiple classes. Figure 20 provides a simple illustration of how LDA works to classify the classes.
## 3 Test Results
### Test Series C20 and C21
The asymmetric test series C20 gave more favorable values than the symmetric push-out test series C21. In the test series C20, an additional load cell was used to measure possible horizontal support pressure. However, only negligible small loads were measured. This leads to the conclusion that no additional friction due to lateral pressure should have influenced the results. Further information and discussions on test series C20 and C21 will be given in [20]. The mean values (MV) and the resulting coefficients of variation (CoV) of the load-carrying capacities, \(F_{\text{max}}\), per notch are in the same range as observed in similar symmetrical push-out tests in several series performed in [13] or [20]; friction break-off was never observed in tests performed by [13; 24] or other test series in [20]. For notches with a depth of 20 mm, stiffness values typically range from 1000 kN/mm/m to 1500 kN/mm/m. This unit is commonly used to define the stiffness of notch connections and is obtained by dividing the slip modulus of a connection by the width of the notch. It may be useful to water the area of the notch before pouring the concrete. To better understand the influence of the condition of the timber surface and the watering process on friction break-off, further pure shear tests without TCC connections between timber and concrete are recommended. Both test setups appear suitable for determining shear behavior. However, it is important to only compare test results within one group of similarly tested specimens, as the test setup may result in varying levels of maximum forces (see Table 5). Figure 21: Ductile timber compression failure in front of the loaded edge of the notch for a specimen of (**a**) test series C20; (**b**) test series C21 [20].
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
## 2 Interference Management
### Future Perspectives
#### 2.3.1 Ultra-Fast Sampling with Pico-Second Laser Pulse  
The Photonic and digital hybrid system reduces the workload of digital computing with under-sampled signals. Instead of digitizing and processing all of the mixed signals, only statistical information, such as second- and fourth order-moment is digitized to find \(A^{-1}\). Such a reduction in workload is extremely important when the interference has a large bandwidth, and the edge computation chip on mobile devices has limited ADC and computation power. Figure 4 shows the under-sampled signal has the same statistical information as the original signal. Both received signals \(x_{1}\) and \(x_{2}\) are mixtures of a 16 QAM signal and Gaussian noise (Figure 4a). The symbol rate of the 16 QAM signal is 1 Gbd. The Gaussian noise has the same bandwidth as the 16 QAM signal. Figure 4b,c show the distributions of the sampled signal \(x_{1}\), with a sampling rate of 1 GHz and 1 MHz, respectively. By using a sampling rate three orders of magnitudes smaller than the symbol rate, the distribution of the under-sampled signal is the same as the distribution of the original signal, which means the second and fourth order of moments of the original signal can be measured correctly with the under-sampled signal.  
When the sampling rate is orders of magnitude smaller than the Nyquist frequency, the duty cycle \(\tau/T_{s}\) needs to be small enough, so the under-sampled signal can represent the statistical distribution of the original signal. This could also be explained by \(E(x_{1}^{2})\
eq E{(x_{1})}^{2}\), the time average of the square does not equal the square of the time average. In the hybrid system, the sampling frequency (\(1/T_{s}\)) can be higher than the frequency of the sine function does not need to be synchronized with the sine function, while the duration of each sample must be small enough. If the duration of each sample is comparable to \(T_{s}\), each sample represented an average of the changing signal, and the sampled signal cannot represent the distribution of the original signals.  
An ADC circuit with a small duty cycle can be used when the interference bandwidth is relatively small. For example, the 1 GHz mixed signal in Figure 4 is sampled at the 1 MHz sample rate (\(T_{s}=1\) us) and 1/2000 duty cycle (\(\tau=0.5\) ns). When interference bandwidth is larger than 1 GHz, is smaller than 0.5 ns, which is difficult to implement with ADC chips on mobile devices. To solve this problem, the hybrid system changes the laser source in Figure 2 from a continuous wave laser to a mode-locked pulse laser.  
The mode-locked laser generates a pulse sequence with both pulse widths and intervals tunable. The output of the optical amplitude modulator is a product of the laser pulse and the RF signal, and the laser pulse width controls the duration of each sample. Even ADC with a long sampling time \(\tau_{2}\) is used afterward, the sampled signal still represents information from the short sample duration \(\tau_{1}\).
## 2 Background
### You Only Look Once (YOLO)  
One of the most widely utilized object detection models currently is YOLO, due to its simple architecture, low complexity, easy implementation, and speed [17]. The authors of YOLO transformed the object detection problem into a regression problem instead of classification. A convolutional neural network predicts the bounding boxes as well as their position. Since this algorithm identifies objects and their positions with the help of bounding boxes by looking at the image only once, it is named You Only Look Once [18].  
The YOLO algorithm has undergone numerous versions since its release in 2016, demonstrating consistent improvements to the algorithm's performance. YOLOv8 was released in 2023 and presents various pre-trained model versions [19; 20]. For the development of this study, YOLOv8n architecture is employed due to its compact size and high performance [17]. One of the key reasons for choosing this YOLO version in our framework is the need to obtain a powerful, lightweight, and state-of-the-art model that can run efficiently on a CPU. We aim to implement the proposed framework in an industrial setting where hardware resources may be limited. Therefore, YOLOv8n was selected for its high accuracy and ability to run in real time on CPU-based systems.