esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Achievable Regions
### Minimization of Energy Consumed  
To achieve an optimized result to calculate the energy consumed, the linear-based functions and constraints are the best way starting from the objective function and followed by linear constraints. The objective function of the minimization of energy consumed is defined as:  
\[\sum\
olimits_{n=0}^{max}min\ \left(\mathrm{Energy}_{\mathrm{con}}\ (n)\right)\ \forall n\ \in\ n_{\max} \tag{7}\]  
The linear function of energy consumed minimum is given in Equations (8)-(10).  
\[\mathrm{F}_{1}:\ E_{st},\ E_{end}\leq E_{I} \tag{8}\]  
\[\mathrm{F}_{2}:\ N_{L}\ \leq\ N_{l}^{min} \tag{9}\]  
\[\mathrm{F}_{3}:\ C_{r}\ \leq\ C_{max} \tag{10}\]  
Equation (8) deals with the required energy to start the transmission and consumption should be less than the initial amount of energy of the node. Equation (9) discusses the next layer node that has the minimum energy consumption. Equation (10) is about the transmission range with its maximum transmission range of the node. The proposed scheme of calculation of the energy consumed per node is in Equation (11) as follows where energy consumed includes both starting and ending value of energy per node i.e.,:  
\[Energy_{consumed}\ (n)=\sum\
olimits_{n=0}^{max}min\ \left(Energy_{con}(n)\right)\ \forall n\ \in\ n_{\max} \tag{11}\]  
\[Energy_{consumed}\ (n)=E_{st}+E_{end}\ \forall n\ \in\ n_{\max} \tag{12}\]  
where,  
\[E_{st}=P_{tx}\frac{Data\ size\ (n)}{Data\ rate(n)} \tag{13}\]\({}^{\prime}\)_E\({}_{st^{\prime}}\)_ is the amount of energy when the transmission is started and, 'P\({}_{br^{\prime}}\)' is the transmission power.  
\[E_{end}=P_{rcv}\frac{Data\;size\;(n)}{Data\;rate(n)} \tag{14}\]  
'_E\({}_{end}\)_' is the amount of energy when the transmission is ended, while '_P\({}_{rcv}\)'_ is the transmission power.  
**Graphical expression:** Graphical analysis is present in order to clear the picture of the feasible empty regions. For example, data size = 100 bytes, data rate = 60 kbps, _P\({}_{tx}\)_ = {0.5,1, 1.5, 2}W and _P\({}_{rcv}\)_ = {0.015,0.025, 0.05,......,0.1}W, then feasible region for energy minimization is computed as per the aforementioned constraints in Equations (8)-(10):  
\[1\;\leq\;E_{st}\;\leq\;4 \tag{15}\]  
\[0.1\;\leq\;E_{rcv}\;\leq\;0.3 \tag{16}\]  
\[1.1\;\leq\;E_{st}\;+\;E_{rcv}\;\leq\;0.43 \tag{17}\]  
The boundary of the feasible region can be plotted in Figure 10 from the above equations:  
\[P1\;(1,0.1)=1.1\;\text{J}\]  
\[P2\;(1,0.3)=1.3\;\text{J}\]  
\[P3\;(4,0.3)=4.3\;\text{J}\]  
\[P4\;(4,0.1)=4.1\;\text{J}\]  
Hence by selecting any values from these plotting points obtain the available minimization of energy consumption.
###### Abstract  
Surgical gesture recognition is an important research direction in the field of computer-assisted intervention. Currently, research on surgical gesture recognition primarily focuses on robotic surgery, with a lack of studies in traditional surgery, particularly open surgery. Therefore, this study established a dataset simulating open surgery for research on surgical gesture recognition in the field of open surgery. With the assistance of professional surgeons, we defined a vocabulary of 10 surgical gestures based on suturing tasks in open procedures. In addition, this paper proposes a surgical gesture recognition method that integrates the R3D network with a multi-head attention mechanism (R3D-MHA). This method uses the R3D network to extract spatiotemporal features and combines it with the multi-head attention mechanism for relational learning of these features. The effectiveness of the R3D-MHA method in the field of open surgery gesture recognition was validated through two experiments: offline recognition and online recognition. The accuracy at the gesture instance level for offline recognition was 92.3%, and the frame accuracy for online recognition was 73.4%. Finally, its performance was further validated on the publicly available JIGSAWS dataset. Compared to other online recognition methods, the accuracy improved without using additional data. This work lays the foundation for research on surgical gesture recognition in open surgery and has significant applications in process monitoring, surgeon skill assessment and educational training for open surgeries.  
surgical gesture recognition; open surgery; R3D; multi-head attention mechanism; artificial intelligence; deep learning Article Research on Surgical Gesture Recognition in Open Surgery Based on Fusion of R3D and Multi-Head Attention Mechanism  
[PERSON]  
1 Tanjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China 12 National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China 2
## 2 Materials and Methods  
### Bacterial Cultures  
Nine bacterial strains isolated from the surfaces of healthy honey bees in a previous study [27] were selected based on their abundance in the microbiota of bees for testing their capability to produce 2-heptanone in a liquid culture. The selected isolates from the CREA-AA bacterial collection were the following: two strains of _Acetobacteraceae bacterium_ (BO_L(L)1 and IM_G(L)3); three strains of _Bacillus thuringiensis_ (BL_G1, PD_L1 and RN_G(L)2); one of _Bifidobacterium asteroides_ (LE_V(L)2) and three _Apilactobacillus kunkeei_ (BO_G1, LE_L(L)2 and LG_V1). They were stored in FGYP broth [29] with 20% (\(v/v\)) glycerol at \(-80\)\({}^{\circ}\)C, and prior to use, bacterial colonies were regenerated in the same medium containing 10 g of D-Trutose, 10 g of D-glucose, 10 g of yeast extract, 5 g of polypeptide, 2 g of sodium acetate, 0.5 g of Tween 80, 0.2 g of MgSO\({}_{4}\)*7H\({}_{2}\)O, 0.01 g of MnSO\({}_{4}\)*4H\({}_{2}\)O, 0.01 g of FeSO\({}_{4}\)*7H\({}_{2}\)O and 0.01 g of NaCl per liter, pH 6.8. This medium was the same selective medium used for isolating bacterial strains from honey bees; the use of fructose and glucose as growth substrates supported the optimal growth, considering that these isolates originated from a sugar-rich environment such as an apiary [27]. Liquid cultures for the analysis of 2-heptanone were obtained by growing bacterial isolates in FGYP broth (50 mL) at 30 \({}^{\circ}\)C under aerobic conditions and shaking at 125 rpm for three days to permit the fermentation of carbohydrates and accumulation of byproducts. After incubation, the concentration of the cells in the stationary phase was around 10\({}^{7}\) cell/mL (OD 0.8). The cell-free supernatants (CFS) were recovered from the fermentation broth by centrifuging (14,000\(\times\) g, 10 min), filter-sterilizing (using cellulose acetate syringe filters, 0.22-um pore size, GVS Life Sciences, Bologna, Italy) and keeping them at \(-\)80 \({}^{\circ}\)C until analysis.
## 3 Results and Discussion
### Experimental Validation of Free Beam  
To validate the effectiveness of the method proposed in this paper, an actual beam structure of free boundary is used for the experiment as shown in Figure 16. The Bruel & Kjaer RT Pro Photon 7.0 data acquisition system, PCB 208C01 force sensor, and PCB 352B10 piezoelectric accelerometer are used for measuring response signal. The simulated nonstationary excitation is imported into the Modal Shop K20070E01 vibration exciter through Teledyne LeCroy T3 ARG40 signal generator and the vibration exciter excites the free beam structure. Finally, the excitation signal and response signal are obtained by force sensor and accelerometer, as shown in Figures 17 and 18. Currently, a roving accelerometer testing based on output-only modal estimation is implemented to extract the practical modal properties of the beam structure; nine measurement positions on the  
Figure 15: Stabilization diagram of 20-DOF chain model roughly showing the first 12 natural frequencies of the identified structural modes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{3}{c}{**Natural Frequency (rad/s)**} & \multicolumn{3}{c}{**Damping Ratio (\%)**} & \multirow{2}{*}{**MAC**} \\ \cline{2-2} \cline{5-8}  & **Exact** & & & & & & & \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 5: Modal parameter identification result of a 20-DOF system subjected to nonstationary white noise input.  
The length, width, and height of the beam structure used for this paper are 300 mm, 20 mm, and 15 mm, respectively, the mass is 245.7 g, and the material is 6061-T6 aluminum alloy. According to the theory of engineering vibration, the natural frequencies of the first three groups of modes are about 865.42 Hz, 2385.61 Hz, and 4676.75 Hz, as listed in Table 6. The data are used as a reference for experimental modal analysis (EMA). The natural frequencies of modes and mode shapes match the analytic solution, meaning the experimental results are reliable. For the EMA procedure, the Bruel & Kjaer RT Pro Photon 7.0 data acquisition system is employed to respectively extract the excitation data induced from a 086C01 PCB impulse hammer impacting on the structure as well as the response data obtained from a 352B10 PCB piezoelectric accelerometer, and the frequency response function of each measurement degree of freedom can be obtained through roving accelerometer testing to perform modal estimation.  
This paper uses the modal parameter obtained by EMA as a reference frame and uses the ambient response to compare the identification results of ERA/DC, as shown in Table 6 and Figure 19. It is observed that the frequency errors are less than 4%, and the maximum error of damping ratio is 48.52%. Figure 19 shows the identified mode shapes which are approximately coincident with theoretical mode shapes, and the modal assurance criteria (MAC) are larger than 0.77
# Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
## 3 Results and Discussion
### Comparative Study
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **PSNR** & **SSIM** & **FPS** \\ \hline Original & 16.15 (+0.00) & 0.7407 (+0.0000) & – \\ UDCP [2] & 10.66 (\(-\)5.49) & 0.4827 (\(-\)0.2580) & 0.1 \\ WaterNet [5] & 16.11 (\(-\)0.04) & 0.7872 (\(+\)0.0465) & 8.3 \\ Ucolor [9] & 21.04 (+4.89) & 0.8865 (\(+\)0.1458) & 1.7 \\ UGAN [11] & 21.03 (+4.88) & 0.8637 (\(+\)0.1230) & 190 \\ FUnIE-GAN [6] & 16.75 (+0.60) & 0.8160 (+0.0753) & 307 \\ \hline
**MTUR-Net** & **22.60** (+6.45) & **0.9008** (+0.1601) & **30.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison the State-of-the-Art Methods Using the PSNR and SSIM on the Test-R90 Dataset [5]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **UCIQE** & **UIQM** \\ \hline Original & 0.5149 (+0.0000) & 1.898 (+0.000) \\ UDCP [2] & 0.5306 (+0.0157) & 1.156 (\(-\)0.742) \\ WaterNet [5] & 0.5489 (+0.0340) & 2.405 (+0.507) \\ Ucolor [9] & 0.5468 (+0.0319) & 2.450 (+0.552) \\ UGAN [11] & 0.5989 (+0.0840) & 3.114 (+1.216) \\ FUnIE-GAN [6] & 0.5622 (+0.0473) & 2.704 (+0.806) \\ \hline
**MTUR-Net** & **0.5868** (+0.0719) & **2.752** (+0.854) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: UCIQE [27] Scores and UIQM [28] Scores of Different Methods on Test-C60. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **UCIQE** & **UIQM** \\ \hline Original & 0.5247 (+0.0000) & 2.740 (+0.000) \\ UDCP [2] & 0.5808 (+0.0561) & 1.875 (\(-\)0.865) \\ WaterNet [5] & 0.5150 (\(-\)0.0097) & 2.651 (\(-\)0.089) \\ Ucolor [9] & 0.5790 (+0.0543) & 3.272 (+0.532) \\ UGAN [11] & 0.6083 (+0.0836) & 3.251 (+0.511) \\ FUnIE-GAN [6] & 0.5905 (+0.0658) & 3.111 (+0.371) \\ \hline
**MTUR-Net** & **0.6071** (+0.0824) & **3.166** (+0.426) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: UCIQE [27] Scores and UIQM [28] Scores of Different Methods on SAUD. In order to further verify the effect of the MTUR method and avoid the influence of our subjective judgment of the visualization results, we conducted further research. We prepared 420 pictures extended from the Test-C60 test set, each image corresponding to seven different types (raw, MTUR, FUnIE-GAN, UGAN, Ucolor, UDCP, and WaterNet), and then we invited 20 volunteers to evaluate the quality of the images in terms of chromatic aberration, visibility, clarity, etc., and they selected the best performance without knowing the corresponding experimental method of each image. After that, we summarized the results, as shown in Figure 6. As shown in the graph, the score obtained was the total number of times each of the six methods received the best rating for its own output image. Here, we see that the MTUR had the highest number of best ratings.
# Intrusion Detection Method Based on Preprocessing of Highly Correlated and Imbalanced Data
## 1 Introduction  
### Motivation  
The rapid development of information technologies has made computer systems an integral part of modern life. Their efficiency and stability are critical for security, quality of service, and even public safety.  
According to Check Point Software's 2023 Cyber Security Report [1], the number of registered criminal offenses related to computer usage continues to increase each year. This suggests the presence of a problem in ensuring the stable and uninterrupted operation of computer systems and networks, highlighting that modern approaches to assessing their state are insufficiently effective.
## 3 Methods and Materials
### Models Development and Training
#### 3.5.1 Machine Learning (ML) Algorithms  
* Support vector machine (SVM): SVM finds an optimal decision boundary (hyperplane) in a high-dimensional space to separate different classes. It maximizes the margin between classes and can handle  
Figure 8: The flowchart of the GWO algorithm.  
non-linear classification using kernel methods. SVM is suitable for handling high-dimensional data such as sMRI, but training time can be long [5].
* Naive Bayes (NB): NB is based on [PERSON] theorem and assumes that features are independent and contribute independently to the final prediction. Gaussian NB, which follows the normal distribution, is used in this work. NB is known for its simplicity and efficiency, and it is particularly useful when dealing with large datasets [52; 53].
* Decision Tree (DT): DT is a flowchart-like tree structure that represents a series of decisions and their outcomes. Starting from a root node, it uses a top-down greedy search to produce a DT with decision and leaf nodes without backtracking over the space of possible branches. The DT branches describe the dataset's features. The final classification is made at the leaf nodes of the tree. DTs are intuitive and easy to interpret, but they can be prone to overfitting [54].
* Random Forest (RF): RF is an ensemble learning method that combines multiple weak learners (e.g., DTs) to create a robust model. Each tree is trained on a random subset of the original dataset using a bagging technique. The final prediction is determined by majority voting. RF improves performance and reduces overfitting compared to a single DT [17].
* Extreme Gradient Boosting (XGB): XGB is an optimized version of the GBC algorithm. It is designed for speed and performance, utilizing three techniques: implementation of sparse-aware that handles missing data values automatically; utilization of a block structure to facilitate the parallelization of tree construction; and continuous training to further enhance the model's performance that has been fitted to new data [14; 55].
* Category Boosting (CatBoost): CatBoost is a gradient boosting approach that specifically works well with categorical features. It creates a set of DTs with identical splitting criteria throughout the entire level. Each successive tree is produced with a lower loss than the previous one. CatBoost is well-balanced and less sensitive to overfitting [55].
* Multilayer perceptron (MLP): MLP is an artificial neural network with input, output, and hidden layers. The input layer receives the data represented by a digital vector to be processed. Classification is handled by the output layer. A number of hidden layers make up the MLP's true computational engine. MLP processes input data through linear and nonlinear transformations, transmitting the data forward. The network learns from feedback on prediction errors through the backpropagation learning process, adjusting weights for improved predictions [56].
# Effects of Plyometric and Short Sprint with Change-of-Direction Training in Male U17 Soccer Players
## 4 Discussion
### Effect of Training on Jump Performance  
Jump ability is an important performance characteristic in team sports, particularly in soccer [32]. Significant differences in vertical jump heights exist among soccer players of different competition levels [32]. There are few studies evaluating jump performance after a combined PSSCoD training program. Our data are in accordance with the existing literature, which has reported improved jump performance after several programs, including combined PSSCoD training, pylometric training with and without external load, change-of-direction training, and resistance training [19; 20; 22; 23; 24; 25; 31; 33].  
Recently, [PERSON] et al. [20] studied the effects of the combination of 6-week pylometric training and change-of-direction exercises in prepubertal male soccer players, reporting improved jump performance (squat jump and long jump) but no gain in counter-termovement jump performance and multiple 5 bound. Additionally, [PERSON] et al. [21] studied the effects of 6 weeks of pylometric and change-of-direction training in elite youth soccer players (U18), and found that both types of training improve the horizontal jump performance. Indeed, gains in vertical and horizontal jumping as a result of combined pylometric and sprint with change-of-direction training in male U15 handball players have been observed [6]. Improved jump performance as a result of pylometric training may be partially attributable to improved motor recruitment, the elastic benefits to the SSC, and/or a muscle typology shifts [9; 34].
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview  
Antennas deployed in a UAV tracking system must conform to either as a steering beam (SB) or an array antenna (AA) format. The SB configuration involves just only one antenna unit, such as parabolic dish. However, the AA entails the fusion of many micro antenna elements to functions as an antenna structure. In this section, we present a brief overview on the different antenna and antenna arrays suitable for deployment as a UAV antenna tracking system. Some of the examples of the notable antennas are classified, as shown in Figure 3 and discussed hereafter.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.13 Li-Fi  
Li-Fi is a bidirectional short-range wireless technology that uses a visible light communication (VLC) system for data transmission to transfer and receive data. Li-Fi uses overhead LED lighting commonly found in homes as a means of transport and a photo-diode for decoding data. It has a maximum speed of 224 Gbps, which allows a high-definition video to be downloaded in seconds. Because Li-Fi is reliable in light use, it is limited in range since light cannot pass through objects, which makes Li-Fi effective only in closed spaces [47]. Even though its range limitation could be seen as a problem, this limitation provides an additional layer of security by prevents data from leaking into public spaces, thus preventing malicious actors from accessing your network [48].
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 5 Simulation Environment and Results
\[Efficiency(\%)=\frac{\textit{RequiredDataPackets}}{\textit{TotalTransmittedDataPackets}} \times 100 \tag{10}\]  
In Figure 9, we observe that the number of critical data packets increases linearly with time. However, the number of vehicles is distributed randomly by applying the Poisson point distribution with a mean value of twenty (\(\lambda=20\)). The proposed scheme broadcasts a much lower number of critical data packets. On the other hand, the naive scheme broadcasts a massive number of critical data packets. The main reason is that only the CHs in the proposed scheme are responsible for broadcasting the critical data packets to every member vehicle within a cluster. However, in a naive method, every vehicle broadcasts the critical data packets and causes the broadcast storm during push-based data dissemination. Therefore, the proposed scheme mitigates the broadcast storm effect during push-based data forwarding within a VNDN environment. Figure 10 demonstrates the total number of data packets broadcast with respect to the vehicles' density. As the number of vehicles increases, the number of total data packets broadcast increases as well. The ratio between the proposed and naive schemes rises as the number of vehicles increases. In the naive method, the broadcast of critical data packets is directly proportional to the number of vehicles. In contrast, in the proposed scheme, the number of critical data packets is proportional to the number of clusters and CHs. As vehicles' density increases, the number of clusters and CHs increases. As a result, the effect of the broadcast storm is mitigated because the CHs are responsible for broadcasting the critical data packets. Figure 8: Efficiency in percentage.
# Integrated Information in Relational Quantum Dynamics (RQD)
## Appendix C Proof of Proposition 1 (Canonical Optimal Witness)
### Construction and Properties of \(W_{\rho}\)
Equivalently, since adding \(\gamma I\) to \(W_{\rho}\) changes all expectations by \(\gamma\), one can choose \(\gamma=-\mathrm{Tr}[W_{\rho},\sigma^{*}]\) to achieve \(\mathrm{Tr}[(W_{\rho}+\gamma I)\sigma^{*}]=0\) and still \(\mathrm{Tr}[(W_{\rho}+\gamma I)\pi]\geq 0\) for all \(\pi\); we absorb any such trivial shift into \(W_{\rho}\) itself. Thus we have \(\mathrm{Tr}[W_{\rho},\sigma^{*}]=0\), and so \(\mathrm{Tr}[W_{\rho},\pi]\geq 0\) for all product states \(\pi\). In particular, taking \(\pi=\sigma^{*}\) yields \(\mathrm{Tr}[W_{\rho},\sigma^{*}]=0\) consistently. Combining (i) and (ii), we conclude that for every product state \(\pi\) on the bipartition \(A|B\),  
\[\mathrm{Tr}[W_{\rho},\pi]\ \geq\ 0\,,\qquad\mathrm{while}\qquad\mathrm{Tr}[W_{ \rho},\rho]\ =\ -\Phi(\rho)\ <\ 0\,. \tag{10}\]  
Equations (10) and (10) meet the textbook definition of an entanglement witness for the partition \(A|B\): \(W_{\rho}\) defines a hyperplane separating \(\rho\) (which lies \"below\" the plane) from the entire set of unentangled states (which lie on or above the plane). In words, no separable state on this cut has an expectation value under \(W_{\rho}\) as low as that of \(\rho\). The magnitude of the negative expectation \(|\mathrm{Tr}[W_{\rho},\rho]|=\Phi(\rho)\) quantifies the extent to which \(\rho\) fails the separability test imposed by \(W_{\rho}\).