esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Implementation and Evaluation
### Experimental Evaluation  
To measure the reliability we used the same star topology to communicate with a group of five members that were either 1-, 2-, or 3-hops away from the gateway. We varied the percentage of packet loss in the network in 5% steps and measured the reliability of getting responses to the respective requests. We repeated the same experiment for our group communication solution and for multicasts. We run each experiment 50 times. Figure 18 shows the effect of packet loss on the communication reliability in our 1-, 2-, and 3-hop star network. Multicasts are not transported reliably and thus reliability of the network decreases as soon as there is packet loss in the network. When using our unicast group communication solution, CoAP confirmable messages are used. In our 1-hop test topology reliability was higher than 99% even when the packet loss of the network reached 25%. At 30% packet loss the reliability is reduced to 97% (compared to 49% in the case of multicasts). Figure 18 also shows that the packet loss increases with an increasing hop count, both for unicast and multicast communication. This is due to the fact that every message (both request and reply) between a client and a server has an additional chance of getting dropped at each hop on the way to its destination. Nevertheless, in our 2-hop network 100% reliability was maintained for unicast communication until a packet loss ratio of 10%. In the 3-hop network the unicast reliability started dropping below 100% already by 5% packet loss. The dashed and the dotted lines in Figure 18 are the theoretically expected values up to networks with 4-hops, while the points are the actual obtained results from the experiments (up to networks with 3-hops). It is clear that there is a good match between both.  
Figure 19 shows the effect of packet loss on the reliability of the complete group in our 1-, 2-, and 3-hop star network. Certainly the reliability of a complete group is less than the reliability of its individual members, since the loss of a message to or from a single member, renders the complete group request unsuccessful. In these cases the use of multicasts does not provide good results. Already  
Figure 18: The reliability of individual group members is a lot better when using entity based group communication.  
at 5% packet loss the reliability of a 1-hop network drops to 80% and below 20% for a 3-hop network. In contrast, our unicast based group communication maintains 100% reliability in the 1-hop network and only drops to 92% in the case of 3-hop network. Figure 19 shows good match between the theoretically expected values (the lines) and the actual obtained results from the experiments (the points).
# Beyond 5G Fronthaul Based on FSO Using Spread Spectrum Codes and Graphene Modulators
## 2 Novel Network Architecture Design
data encoding, the resulting signals are recombined in optical fiber and fed into an FSO transmitter (see Figure 1). The FSO transmitter via the FSO splitter sends over the free space the signals to the individual RU. However, signals are transmitted over a single channel that gives rise to a cross-talk known as multi-access interference (MAI). MAI affects the receiver of the RU and is known to limit the number of simultaneous signals and the achievable BER. It is noteworthy that the OCDMA encoding procedure does not modify the signal's phase, but only the signal intensity, since it is based on an incoherent coding approach. Lastly, at the receiver side, the FSO receiver collects the transmitted RF signal. The OCDMA decoder, as illustrated in Figure 2, decodes the received signal by removing the temporal translation introduced by the encoder, and thus realigns the signal into a single pulse called an OCDMA correlation peak. The decoder's output is directly detected by an avalanche photodetector (APD), where its electrical output signal is then integrated over the chip period, and finally compared to a threshold level by a threshold decision device, and the transmitted data bit is finally retrieved. The APD and thermal noises as well as the MAI are all considered during the system's BER performance assessment. The focus is also on the cross-talk among the transmitted signals, generally considered to be a dominant noise source upon OCDMA coding techniques [49; 57]. It is worth mentioning, without any loss of generality, that due to the expected signal levels, the non-linear impairments are neglected. Lastly, it is worth mentioning that the choice for APD receivers is due to the proposed network scenario, which may trigger random signal fluctuations at the receiver due to the orientation/positioning deviation of the antennas at the RU premises [112; 113; 114]. Hence, the APD receiver is a better choice over traditional PIN receivers, as it grants more flexibility to the system [112; 113; 114; 115].
## 3 Method
### Pupil Center Location Algorithm  
The research of pupil center location has gone through different stages, such as ellipse fitting, Hough transform and other methods. Since the shape of pupil is similar to ellipse, the ellipse fitting algorithm based on least square method [47; 48] is widely used. The method used in this paper is ellipse fitting algorithm based on least square method. Firstly, preprocess eye image area features, it includes steps such as grayscale, mean filter, image binarization and contour extraction, then the pupil center is located by ellipse fitting based on the binary image obtained above. It is demonstrated that the pupil center coordinate fitted by this algorithm is accurate.  
The general expression of ellipse is:  
\[Ax^{2}+Bxy+Cy^{2}+Dx+Ey+F=0 \tag{4}\]  
The least square ellipse fitting method can minimize the sum of squares of measurement errors. The main purpose of this technique is to find a set of parameters to minimize the distance between data points and ellipses. According to the principle of least square method, the problem of curve fitting is transformed into the sum of squares of algebraic distance, so \(G\) in Equation (5) is the objective  
Figure 3: The principle diagram of mapping equation based on homography transformation. The gaze point of the screen is \((U,V)\), the coordinate of the pupil center mapped to the eye diagram of the camera system is \((x,y)\), the coordinates of pupil center is \((x_{p},y_{p},z_{p})\).  
Figure 2: Structure diagram of the head entrust stent.  
function. In order to avoid the occurrence of zero solution, constraints are required. In addition, if the fitting result must be ellipse rather than other conic curves, \(4 AC-B^{2}>0\) needs to be ensured. In this paper, use the constraint method \(4 AC-B^{2}=1\)[49].  
\[G(A,B,C,D,E,F)=\sum_{i=1}^{n}(Ax_{i}^{2}+Bx_{i}y_{i}+Cy_{i}^{2}+Dx_{i}+Ey_{i}+F) \tag{5}\]  
The coefficient of the objective function is determined by the minimum value. According to the extreme value principle, the partial derivative of each coefficient is obtained respectively. When the partial derivative is 0, the minimum value of the function can be obtained.  
\[\frac{\partial G}{\partial A}=\frac{\partial G}{\partial B}=\frac{\partial G} {\partial C}=\frac{\partial G}{\partial D}=\frac{\partial G}{\partial E}=0 \tag{6}\]  
Here, the center position of the ellipse is represented by (\(X_{0}\), \(Y_{0}\)). After the transformation of the expression, the central coordinates of the pupil can be represented by Equations (5) and (6).  
\[\left\{\begin{aligned} X_{0}&=\frac{BE-2 CD}{4 AC-B^{2} }\\ Y_{0}&=\frac{BD-2 AE}{4 AC-B^{2}}\end{aligned}\right. \tag{7}\]  
Combined with other constraints, the values of other different coefficients in the elliptic equation can be calculated, and then the center of the ellipse can be obtained.
## 3 Method
### Graph Convolutional Neural Network
Doing the Fourier inverse transform on both ends of equation, we can obtain:  
\[f*g=F^{-1}(F(f)*F(g)) \tag{7}\]  
where \(F^{-1}\)\((f)\) denotes the Fourier inverse transform of the signal \(f\). Using the convolution theorem, we can multiply the signal in the spectral space and then use the Fourier inverse transform to convert the signal to the original space to realize the graph convolution, thus avoiding the difficult problem of defining the convolution caused by the graph data not satisfying the translation invariance. The Fourier transform on the graph depends on the Laplace matrix on the graph, and in the following, we will give the definition of the Fourier transform on the graph. The definition of the Fourier transform on the graph depends on the eigenvectors of the Laplace matrix. Using the eigenvectors as a set of bases under the spectral space, the Fourier transform of the signal \(x\) on the graph is: \(x=U\xi\). Using the Fourier transform and the inverse transform on the graph, we can implement the graph convolution operator based on the convolution theorem:  
\[x_{G}^{*}y=U((U^{T}x))\odot(U^{T}y) \tag{8}\]  
where \(G^{*}\) denotes the graph convolution operator, x, y denotes the signal of the node domain on the graph, and \(\odot\) refers to the Hadamard multiplication, which denotes the multiplication of the corresponding elements of two vectors. We replace the vector \(U^{T}y\) by a diagonal array \(g_{\theta}\), then Hadamard multiplication can be transformed into matrix multiplication. By acting the convolution kernel \(g\theta\) on the signal, the graph convolution can be expressed in the following form: \(Ug_{\theta},\ U^{T}x\). The convolution theorem provides a way to define convolution on a graph by Fourier transform. Based on this definition of the convolution operator, a number of graph convolution neural networks have emerged at home and abroad.
# Hierarchical Reinforcement Learning for Crude Oil Supply Chain Scheduling
## 4 Hierarchical RL Scheduling Scheme
### Lower-Level Agent  
The goal of the lower-level agent is to search for a feasible execution strategy (including a processing volume \(q_{r}^{t}(\forall v_{r}\in V_{r})\) and a transportation plan \(w_{e_{fr}}^{t}(\forall e_{fr}\in E_{fr})\)) in the current time step \(t\), which minimizes the overall transportation cost \(\mathcal{H}^{t}\) and satisfies the one-step constraints. In other words, the lower-level agent solves the one-step problem of Equation (7a). Constraints are composed of two parts; one is obtained from the upper agent, including \(a_{t}\) representing the end-stage inventory volume of each facility to substitute Equation (7b) and \(\tilde{U}_{qr}\) representing the remained processing volume until time step \(t\) to substitute Equation (7e), and the other part is obtained according to the problem itself, including processing constraints in refineries as Equation (7d) and the transportation volume constraints on the roads as Equation (7c).  
Since the constraints and the objective function are linear concerning the processing volume \(q_{r}^{t}\) and transportation volume \(w_{e_{fr}}^{t}\), this problem can be formalized as a large LP problem and solved with traditional solvers. However, because \(a_{t}\) might be unreasonable at the beginning of training, without dealing with the constraints, this LP problem is likely to be unsolvable. In this case, as a lower-level agent, the emergence of no solution not only makes the decision-making of this step fail but also terminates this training episode. In consequence, the upper-level agent is held back on the convergence speed.  
To solve this problem, we relax the constraints. The main idea is to relax all equality constraints, design corresponding barrier functions according to each original equality condition, and add them to the objective function. In this way, in most cases, the linear programming problem has a solution, and the objective function value \(J\) corresponding to the optimal solution reflects the quality of \(a_{t}\), which can be sent back to the upper agent as a suitable incentive signal.
## 2 Methods
### Evaluation of the Volume Variation  
The OARs delineated on the reference s-CT were considered as reference structures and were isotropically expanded to obtain the so-called expanded volume (EV) using 6 margins: 3 mm, 5 mm, 8 mm, 10 mm, 15 mm, and 20 mm [16].  
For each patient, a union volume (UV) was created for each OAR by considering the Boolean union between the reference structure on the s-CT and the same structure on all the available CBCTs. Then, the intersections between the UV and each of the 6 EVs were computed, obtaining 6 intersection volumes (IV) for each OAR.  
The structure of the s-CT was included in order to consider the entire displacement of the OAR from the s-CT, which represents the delivered plan, to the daily treatment session (CBCT).  
Afterward, the probability of enveloping the structure's displacement during treatment was expressed by the percentage volume (\(V\%\)) of each IV with respect to the UV, which was calculated as:  
\[V\%=\frac{IV}{UV}\times 100\]  
The margin corresponding to \(V\%>95\%\) was considered a conservative estimate of the margin necessary to take the organ motion into account [16] during the entire treatment. For each structure, the \(V\%>95\%\) and the corresponding margin were calculated. For those patients whose \(V\%\) was not able to achieve the 95% threshold, the margin corresponding to the highest \(V\%\) was considered. The optimal margin required for each organ to take its motion into account was defined by evaluating the median margin corresponding to \(V\%>95\%\) (or the greatest) for all the patients. Since each structure presented with a population lower than 20 samples, with an asymmetrical data distribution, the median value was considered a good estimation parameter for the optimal margin definition. A schematic representation of the workflow and the involved volumes can be seen in Figures 1 and 2, respectively.  
With regard to the bladder and rectum, in order to include not only the volume variation due to the organ motion but also the deformation and variability due to the filling and emptying status, the differential variations in the daily volume (V_day), with respect to the reference volume (V_ref), on the s-CTs were evaluated [21]. For every structure, the mean volume on the available CBCTs was calculated for each patient, and differences between the populations of data and the corresponding volumes on the s-CTs were assessed using Wilcoxon signed-rank tests. In addition, the median volume on the s-CTs and CBCTs were calculated for all the patients. As in the case of the DSC and Mean_DA, a box plot analysis was performed for each structure's volume.  
Figure 1: A schematic workflow of the methods.
## 3 Energy Efficiency Problem Model for SONs
### Energy-Efficient Problem Model
This formulation is subject to several critical constraints: the total data flow on any link must not surpass its designated capacity (bandwidth limitation); the volume of traffic entering any satellite must equal the volume exiting, ensuring flow conservation; and the remaining battery capacity, \(C(t)\), of any satellite must consistently stay within operational bounds (\(0\leq C(t)\leq C_{\max}\)). The desired outputs of this problem are twofold: firstly, an energy-efficient network topology (\(G_{Spk}(V_{Spk},E_{Spk})\)) for each snapshot, where \(V_{Spk}\) represents the active network nodes and \(E_{Spk}\) denotes the active links during snapshot \(k\), and secondly, a corresponding set of optimized service paths (\(Path(G_{Spk})\)) for routing traffic within this energy-efficient topology. The overarching solution objective is to optimize key performance metrics across all snapshots. Specifically, for each snapshot period, the aim is to minimize the network's overall life consumption (\(L_{i}\)), packet loss rate (\(B_{i}\)), and average service delay (\(D_{i}\)), as collectively represented by the objective function in Equation (20), which seeks to minimize the sum of these metrics over all \(N\) snapshots. \[Min\left(\sum_{i=1}^{N}L_{i},\sum_{i=1}^{N}B_{i},\sum_{i=1}^{N}D_{i}\right) \tag{20}\]
# A Lightweight YOLOv5 Optimization of Coordinate Attention
## 3 Proposal Model
### Reduce the Number of Network Channels  
Firstly, the YOLOv5-RC model is focused to reduce the network width of YOLOv5 by lowering the width factor from 0.5 to 0.2 and 0.3, from Equations (1) and (2), when the overall number of channels is reduced by 40% or 60%, and both by 40% or 60%, which should result in a 64% and 84% reduction in both the number of convolution parameters and FLOPs in the backbone.  
From Table 1, it can be found that depending on the setting of the width factor, the number of most convolution parameters is reduced by approximately 64% and 84%. The first convolution is used to replace the previous Focus layer with a specific requirement for the number of channels, which made the number of parameters in the first three layers drop less. At the same time, reduction of channels is an intuitive method that inevitably loses some features, which also brings some accuracy loss. We would therefore add a detection head and insert attention in the backbone. Table 1 also reflects the additional layers of the new detection head in the backbone and the new coordinate attention.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**YOLOv5s**} & \multicolumn{3}{c}{**YOLOv5-RC-0.3**} & \multicolumn{3}{c}{**YOLOv5-RC-0.2**} \\ \hline
**Block** & **Parameters** & \(C_{\textbf{in}}\) & \(C_{\textbf{out}}\) & **Parameters** & \(C_{\textbf{in}}\) & \(C_{\textbf{out}}\) & **Parameters** & \(C_{\textbf{in}}\) & \(C_{\textbf{out}}\) \\ \hline Conv & 3520 & 3 & 32 & 2640 & 3 & 24 & 1760 & 3 & 16 \\ Conv & 18,560 & 32 & 64 & 8720 & 24 & 40 & 4672 & 16 & 32 \\ C3 & 18,816 & 64 & 64 & 7440 & 40 & 40 & 4800 & 32 & 32 \\ Conv & 73,984 & 64 & 128 & 28,960 & 40 & 80 & 16,240 & 32 & 56 \\ C3 & 115,712 & 128 & 128 & 45,440 & 80 & 80 & 22,400 & 56 & 56 \\ Conv & 295,424 & 128 & 256 & 115,520 & 80 & 160 & 52,624 & 64 & 104 \\ C3 & 625,152 & 256 & 256 & 244,800 & 160 & 160 & 103,792 & 104 & 104 \\ Conv & / & / & / & 334,544 & 160 & 232 & 150,080 & 104 & 160 \\ C3 & / & / & / & 243,600 & 232 & 232 & 116,160 & 160 & 160 \\ Conv & 1,180,672 & 256 & 512 & 652,000 & 232 & 312 & 299,936 & 160 & 208 \\ C3 & 1,182,720 & 512 & 512 & 439,920 & 312 & 312 & 195,936 & 208 & 208 \\ CA & / & / & / & 9075 & 312 & 312 & 5432 & 208 & 208 \\ SPPF & 656,896 & 512 & 512 & 244,296 & 312 & 312 & 108,784 & 208 & 208 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the parameters of the modules in backbone after reducing the number of channels of YOLOv5s by 40% and 60% respectively.
## 4 Results
### Ground Measured Results  
From all recorded measurements of rice height and biomass, the mean value of each plot was provided. Figure 4a presents the temporal variance of rice height versus the acquisition dates of Sentinel-1 data for all plots.  
During the vegetative period (from May to July), rice height increases rapidly, and then small variation is observed during the reproductive phase (from July to September), while it stays constant in the ripening period. Rice height measurements show a high variability due to a quick growth of rice during the first three months (Figure 4a). The same observation is found for rice biomass, before the ripening period, it increases rapidly and during this stage, the biomass is stabilized (Figure 4b). The same trend was observed in [14; 24; 47].
## 4 [PERSON]'s Finite Element Simulation Analysis
### Analysis of Simulation Results
#### 4.2.4 Vibration Suppression Evaluation and Control Strategy  
Due to the differences in the response of a vibration system under excitation in the resonance band and the non-resonance band, the damping characteristics in these two frequency regions will be discussed and evaluated separately. This will serve as a control basis for semi-active control currents.  
As shown in Figure 13, two interfaces are set as follows: the resonance suppression interface at z = 5 and the non-resonance suppression interface at z = 2. By using these two evaluation interfaces, the response region is divided into three frequency bands: above the resonance suppression interface, it is in the resonance band; below the non-resonance suppression interface, it is in the non-resonance band; when between the two interfaces, it is in the near-resonance frequency.  
Taking into account energy savings and effective vibration suppression, once the vibration within a specific frequency band is detected, the critical magnetic potential value corresponding to the damping evaluation (i.e., the intersection between the evaluation interface in Figure 6 and each response surface) is used to provide damping. These critical magnetic potential values can be extracted, as shown in Figures 22-26.  
Figure 23: Magnetic potential value at 70 kg.  
Figure 22: Magnetic potential value at 60 kg.  
For vibrations occurring within the resonance band, maximum magnetic force is applied to achieve maximum damping, aiming to minimize discomfort for humans and ensure the longevity of the structure.  
Based on the critical magnetic potential data, a model reference adaptive control (MRAC) framework can be introduced to better optimize vibration suppression control [32].  
A dynamic model of magnetic potential damping force for eddy current damping structure can be established as follows:  
\[\tau(s)=K\cdot F_{c}(s)+d(s) \tag{30}\]  
Figure 26: Magnetic potential value at 100 kg.  
Figure 24: Magnetic potential value at 80 kg.  
Figure 25: Magnetic potential value at 90 kg.  
where \(\mathbf{F_{c}}\) is the actual magnetic potential, \(\mathbf{K}\) is the damping gain related to the magnetic potential, and (\(\mathbf{ds}\)) is the disturbance.  
The adaptive law can be designed as follows:  
\[e=F_{c}-F_{c}^{r} \tag{31}\]  
\[\dot{\theta}=-\Gamma\cdot e\cdot\varphi \tag{32}\]  
where \(\mathbf{e}\) is the minimum error, \(\mathbf{F_{c}}^{r}\) is the reference magnetic potential, \(\mathbf{\theta}\) is the parameter to be adjusted, \(-\mathbf{\Gamma}\) is the adaptive augmented matrix, and \(\mathbf{\phi}\) is the regression vector.  
The designed eddy current damping structure has great potential for designing adaptive control architectures. By using a model reference adaptive regulator, the electromagnetic current is adjusted in real-time to control the damping coefficient and achieve vibration suppression. The design needs to combine parameter estimation, stability analysis, and experimental verification to ensure system robustness.