esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Gaussian Process Model Results  
Simulations were conducted using the unit load segment finite element model for each of the 160 data points determined by the Latin Hypercube design. Results are presented in Table 2. This was the data input needed to compute the Gaussian process regression. Root Means Square Error (RMSE) and Mean Absolute Percent Error (MAPE) results are presented in Table 7. For each of the unit load representations, the model with the lowest MAPE and RMSE was selected. The data subset of k = 4 was selected as the one with the best goodness of fit for all four models. The overall error level of the GP model was considered low, with an average RMSE of 0.05 and a MAPE of 0.08. The highest error level was for the model with three columns, and it had an RMSE of 0.05 and a MAPE of 0.13. The highest prediction accuracy was presented in the six-column model, with a RMSE of 0.03 and a MAPE of 0.03.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source** & \(p\)**-Value** \\ \hline Pallet Friction & \textless{}0.0001 \\ Columns & \textless{}0.0001 \\ Height & \textless{}0.0001 \\ Box Friction & \textless{}0.0001 \\ Height \(\times\) Pallet Friction & \textless{}0.0001 \\ Columns \(\times\) Height & \textless{}0.0001 \\ Height \(\times\) Box Friction & \textless{}0.0001 \\ Columns \(\times\) Height \(\times\) Pallet Friction & \textless{}0.0002 \\ Columns \(\times\) Height \(\times\) Box Friction & \textless{}0.0013 \\ Columns \(\times\) Pallet Friction & \textless{}0.0025 \textasci{}^{*}\) \\ Height \(\times\) Pallet Friction \(\times\) Box Friction & \textless{}0.0032 \\ Columns \(\times\) Box Friction & \textless{}0.0749 \textasci{}^{*}\) \\ Columns \(\times\) Pallet Friction \(\times\) Box Friction & \textless{}0.2725 \\ Pallet Friction \(\times\) Box Friction & \textless{}0.2773 \textasci{}^{*}\) \\ Columns \(\times\) Height \(\times\) Pallet Friction \(\times\) Box Friction & \textless{}0.5086 \\ \hline \hline \end{tabular} \({}^{\text{\#}}\) denotes effects with containing effects above them.  
\end{table}
Table 6: Results of the 24-full factorial design ANOVA (\(p\)-values) for the significant effects of on the board deflection ratio.  
The Gaussian process model provides a reliable method to efficiently predict the ratio of change in the bending of a unit load segment, with a low error estimate. This is a significant aspect since finite element modeling places a high initial cost to conduct everyday analysis of unit load design.
# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
## 4 Experimental Results
### F1-Score Analysis  
The F1-score, a crucial statistic in Sentiment Recognition systems, serves as a harmonious balance between precision and recall, encapsulating the overall accuracy of the system. In the context of trimodal emotion detection, the F1-score acquires an elevated significance as it reflects the system's ability to effectively integrate and interpret data across text, audio, and video modalities.  
In this study, the robustness and adaptability of the trimodal emotion detection technique are scrutinized by comparing the F1-scores obtained from the IEMOCAP and CMU-MOSI datasets. Achieving high F1 scores is indicative of a model's precision in identifying emotions, thereby underscoring the system's efficiency across diverse datasets and modalities. This is crucial for gaining a comprehensive understanding of emotional expressions.  
Table 11 and Figure 13 present a comparative analysis of the F1-scores for the IEMOCAP and CMU-MOSI datasets, based on their performance in ER. The data reveals a marginally higher F1-score for the CMU-MOSI dataset (0.94%) as compared to that of IEMOCAP (0.93%), highlighting the nuanced efficacy of the models in processing emotional data.  
Figure 12: Graphical representation of Recall.  
Figure 13: The F1-scores comparison between the IEMOCAP and CMU-MOSI datasets.  
This comparative exploration of F1 scores not only sheds light on the performance metrics of the datasets but also provides invaluable insights for future advancements in TERsystems, aiming for models that are both accurate and versatile.  
The meticulous comparison of precision metrics underscores the advancements and effectiveness of emotion recognition technologies. In Table 12 and Figure 14, we detail the precision rates for both the proposed and existing methods. The CA-WGNN method stands out by achieving a remarkable 91% precision, in contrast to the existing methods where SVM and GBM each attained 76%, and VC(LR-SGD) reached 81%. This comparison vividly illustrates the superior performance of the proposed CA-WGNN method, affirming its capacity to achieve higher precision rates than the conventional approaches. The results highlight the efficiency of the CA-WGNN and pave the way for future innovations in the field of emotion recognition, emphasizing the potential for further advancements and the continuous need for research and development in this dynamic area.
## 3 Markers Matching
### First Frame  
Take the case of two cameras, for example, because of the error, the two lights from same marker cannot be intersected in the water. As shown in Figure 2, the yellow line indicates the light in the ideal case and the red line indicates the light in actual measurement, hence the intersection point cannot be used to judge whether markers are matched. Despite the two lights being in different planes, their vertical distance is short, especially compared to the shortest distance of the optical path of unmatched markers. Hence, the shortest distance of the light can be used as the basis to filter the data. If the shortest distance of light is greater than the threshold, the markers must be unmatched. Because the general marker will be captured by more than two cameras, we can get many measurement points near the actual position of the marker. If there is more than a pair of measurement points in the vicinity, they must be matched.  
Due to the flat structure of fish, the markers are set on both sides of fish body and the cameras which have captured same marker are adjacent. To simplify the calculation, the candidate points of 3D reconstruction can be obtained by consecutively matching two adjacent cameras.  
The markers matching process is shown in Figure 3. Assume that the intrinsic parameters and pose of each camera and 2D coordinates of each marker on the image are known. The flow chart demonstrates how the markers, originally in 2D coordinates, become the candidate points through the coordinate system conversion and data screening.  
Figure 3: Markers matching flow chart.  
Figure 2: Marker matching diagram considering the error.
## 3 TRM Technique
### Methods of Channel Estimation in VTRM  
#### 3.4.1 MP Algorithm  
The focusing effect of VTRM is related to the accuracy of UWA channel estimation. When the channel estimation is not precise, the poor focusing effect will deteriorate the performance of the system when using the VTRM technique. Consequently, improving the accuracy of channel estimation is the key to improve the performance of the VTRM communication system. When a signal is sparse or compressible, we can obtain the condensed representation of the compressed signal by a linear projection, and the data can reconstruct the original digital signal in an undistorted or low distortion way. It is known to us that CIR of UWA channels are sparse, that is, the energy of UWA channels is mainly concentrated in a few paths while most of the paths are zero or very small. Due to this characteristic, UWA channels can be modeled by compressed sensing theory.  
A detection signal should be first transmitted to pass through the UWA channel to estimate the CIR, and the received detection signal is expressed as  
\[y(n)=x(n)\otimes h(n)+v(n). \tag{20}\]  
Suppose the length of detected signals is \(N\), and the length of the UWA channel is \(L\). Then the above formula can be expressed as  
\[\left[\begin{array}{c}y(0)\\ y(1)\\ \vdots\\ y(N-2)\\ y(N+L-1)\\ \end{array}\right]=\left[\begin{array}{c}x(0)\\ x(1)\\ \vdots\\ 0\\ 0\\ \end{array}\right]h(0)+\left[\begin{array}{c}0\\ x(0)\\ \vdots\\ 0\\ 0\\ \end{array}\right]h(1)+\cdots+\left[\begin{array}{c}0\\ 0\\ \vdots\\ x(N-1)\\ x(N)\\ \end{array}\right]h(L-1)+\left[\begin{array}{c}v(0)\\ v(1)\\ \vdots\\ v(N+L-2)\\ v(N+L-1)\\ \end{array}\right]. \tag{21}\]  
It can also be written as  
\[y(n)=\sum_{l=0}^{L-1}xh(l)+v(n). \tag{22}\]  
It may be noted that the received signal is regarded as a linear combination of the detection signal [25]. If sparse decomposition of the received signal is carried out, the CIR can be obtained by the compressive sensing reconstruction algorithm.  
The MP algorithm is one of the most basic methods for sparse signal reconstruction. In this algorithm, the sparse coefficient of the CIR of the UWA channel is obtained by optimizing the objective function, and the CIR is decomposed into the combination of some atoms in the dictionary. For specific steps of implementation of this algorithm please refer to Reference [26]. First, the received signal is selected as residual. In each iteration process, the atom which matches the residual best is selected from the dictionary to estimate the component of CIR, meanwhile the residual is update. When the residual satisfies the threshold, the iteration process stops.
# Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks
## 2 Related Works  
### Relevant Routing Algorithm for WSN  
In WSNs, sink nodes are connected to multiple sensor nodes within a multi-hop network to acquire data from the sensor nodes. Because all sensor nodes are battery-powered and periodically generate a small amount of sensing data, their duration of continuous operation is limited. This problem can be addressed via the use of a data aggregation technique to enhance the energy efficiency of each sink node, thereby increasing the duration of continuous network operation [16; 17; 18]. In other words, the sensing information received from each node can be merged in the WSN relay node and delivered as a single dataset. For example, in the case of a WSN that measures the maximum/minimum temperature of a smart greenhouse, the relay node can be considered efficient when it exclusively transmits the said maximum/minimum temperature value from among several values received by multiple sensor nodes [19; 20]. In several scenarios involving WSNs, the use of a data merging technique can reduce the data throughput and increase the network survival time. The authors in [21] present a routing method to be used in a WSN environment, where all of the sensor-collected data can be gathered at the sink node. This method was designed by considering in-network computational techniques, such as data merging, and, therefore, it was named \"data-centric routing\" [21]. This method corresponds to the existing address-centric routing scheme that aims to minimize the routing cost by reducing the distance between the source and destination nodes without considering in-network calculations, such as data aggregation. Therefore, data-centric routing aims to improve transmission or energy efficiency, instead of ensuring fast transmission, by considering data merging at intermediate nodes in the sensor network.  
Data-centric routing in WSNs has been investigated to reduce the energy consumption of nodes or to increase the network survival time [22; 23; 24; 25]. However, merging a large amount of data at intermediate nodes to reduce transmission-energy consumption causes a resultant increase in the waiting time. This increases the overall sensor-data-acquisition time at the sink node.  
On the other hand, if an intermediate node transfers the collected data directly to the sink node without sufficient data merging, the resulting sensor-data-acquisition time can be reduced. However, this comes at the cost of an increase in the energy consumed by the node owing to an increase in the number of data transmissions. Therefore, a trade-off exists between the sensor-data-acquisition time and the energy consumed by the nodes.
## 3 Analysis of Boost Converter  
A DC-DC boost converter, as shown in Figure 3, works in two modes based on the characteristics of the inductor current. When the switch 'S' is turned ON, the inductor current builds up, indicating that the energy is stored in the inductor 'L'. When switch 'S' is tuned OFF, the inductor becomes connected to the load. Because the inductor behaves as a current source, it preserves its voltage polarity in this mode to maintain the current in the same direction. During this process, the inductor current drops, indicating that energy is transferred to the load. During CCM mode, the inductor current never falls to zero, and volt-second balance is maintained during both ON time '\(T_{on}\)' and OFF time '\(T_{off}\)' of the converter. As is well known, the critical or minimum value of an inductor for staying in CCM mode can be designed based on Equation (1):  
\[L_{min}=\frac{(1-D)^{2}\times D\times R}{2f_{s}} \tag{1}\]  
Here, \(L_{min}\) is used as a base value of inductance, \(R\) represents the load resistance, and \(f_{s}\) is the switching frequency. Similarly, the minimum capacitor value \(C_{min}\) is expressed by Equation (2):  
\[C_{min}=\frac{D}{R\times V_{rf}\times f_{s}} \tag{2}\]  
where \(V_{rf}\) is the ripple voltage, which is evaluated using Equation (3):  
\[V_{rf}=\frac{\Delta V_{o}}{V_{o}}. \tag{3}\]
## 3 Results
### Model Comparison
This indicates that RF has a certain prediction accuracy even without parameter optimization. The error is within a controllable range. Combined with Figures 16 and 17, it can be seen that GA-RF has the best prediction effect. According to the model performance evaluation indexes mentioned in Section 2.4, the predicted performance evaluation indexes of GA-RF, RF, GA-BPNN and BPNN models are calculated as shown in Table 4. The RMSE, MAPE, and MAE of the GA-RF model are about 0.2713, 1.09, and 0.1961, respectively. These values are all smaller than other prediction models. According to the meaning of each evaluation index, the smaller the value, the higher the prediction accuracy of the model. The RMSE of GA-RF, RF, GA-BPNN, and BPNN are 0.2713, 0.3628, 0.3941 and 0.4354, respectively. The RMSE of GA-RF is significantly smaller than that of other prediction models, indicating that GA-RF has the highest prediction accuracy. According to the site situation of 22104 working face in Shangwan Mine, the oxygen concentration at the return air corner is less than 18.0%, which  
Figure 12:0 GA-RF, RF, GA-BPNN, BPNN return air corner oxygen concentration prediction data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **GA-RF** & **RF** \\ \hline Number of trees & 398 & 500 \\  & & 1 \\  & & 2 \\ Number of leaves & 1 & 3 \\  & & 4 \\  & & 5 \\ \hline
**Index** & **GA-BPNN** & **BPNN** \\ \hline Number of hidden neurons & 9 & 13 \\ Initial weight and threshold & Optimization & Random \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Internal parameters of GA-RF, RF, GA-BPNN, and BPNN prediction model. can be determined as low oxygen, and the sensor accuracy reaches one decimal place. Therefore, when the difference of RMSE values of each model differences, it will have a significant impact on the prediction accuracy of low oxygen. The results show that the GA-RF model has higher universality and accuracy in the prediction of oxygen concentration at return air corner. Compared with the GA-RF model, the prediction accuracy of RF, GA-BPNN and BPNN is not enough.
# Optimizing Foreign Language Learning in Virtual Reality:
## 4 VR-CCL Framework for Optimized Language Learning
### Personalization and Adaptivity (Based on Input Hypothesis Theory)
#### 4.3.4 Personalized Assessment and Feedback  
Evaluative processes are the linchpin of efficacious learning, granting pedagogues and pupils a lucid window into advancement and domains necessitating refinement. However, conventional evaluative methodologies frequently espouse a homogenized blueprint, potentially sidelining distinct pupils' multifaceted requisites and prowess. Within the sphere of VR linguistic pedagogy lies a golden prospect to metamorphose this schema via individualized appraisals and responsive feedback channels.  
Evaluation can be effortlessly interwoven into the educational tapestry within the VR ambit. Visualize post a simulated tete-a-tete with a vernacular conversationalist, the system promptly dissecting a pupil's articulation, syntactical choices, and lexicon deployment. This expeditious critique, sculpted around the pupil's execution, can elucidate commendable facets and spotlight zones beckoning deeper exploration.  
VR's fluidic canvas beckons malleable appraisals. Hinging on a pupil's prowess in a delineated segment, ensuing examinations can modulate in intricacy, vouching that they perpetually encounter calibrated challenges. This fluidity does not merely sustain pupil immersion but also vouches that those evaluations genuinely mirror their acumen.  
Another boon tethered to VR-oriented bespoke critiques is its proclivity for immersive rectification. Rather than a mere quantitative grade or inscribed critique, pupils can be ushered into lifelike tableaux where errors can be addressed and amended. Envision a pupil grappling with orchestrating a meal order in Castilian nuances; they could find themselves ensocored in a virtual Iberian bistro, revisiting the dialogue until it is impeccably etched.  
Bespoke evaluations and critiques within VR linguistic pedagogy unfurl a more granular, dynamic, and experiential avenue for gauging prowess. By resonating with individual requisites and proffering instantaneous, milieu-rich critiques, VR harbors the potential to elevate the evaluative journey, rendering it profoundly poignant and impactful for the linguistic voyager.
## 3 Results  
### Testing the Accuracy of the Results Obtained Using the SA Algorithm  
The verification of the results obtained with the use of the simulated annealing algorithm is presented in this chapter. The results were compared with the results obtained with the use of other algorithms. Due to its popularity and ease of use, the [66] method of experimental analysis was adopted to verify the algorithm's effectiveness. The method of experimental analysis is based on a posteriori evaluation of the behavior of the studied algorithm (in this case the approximation error). The evaluation is based on the results of calculations on a limited but representative sample of specific examples. Such a sample is most often created by randomly generating subsets of examples of a given problem or using previously created \"difficult\" test examples used by other researchers. The quality of the obtained results is compared with one of the values: the optimal value, the lower limit, the suboptimal value obtained with another approximate algorithm, or the value obtained randomly. The method of experimental analysis used in the article has the following form. In the beginning, four groups of examples of construction projects were randomly generated with the following sizes: \(n=6\) units, \(n=10\) units, \(n=15\) units,and \(n=20\) units. In each group, five examples were randomly generated with sizes: \(m=3\) works (activities), \(m=5\) works, and \(m=7\) works. Hence the number of all examples was 60. For each example, the schedule with the lowest cost to complete the project was searched for. Each example was solved five times with a dedicated simulated annealing algorithm and comparison algorithms: random search (RS) [67] and climbing search (CR) [68]. For each comparative algorithm, the percentage relative difference (PRD) was calculated according to the following relationships:  
\[PRD(\text{SA}+\text{SA})\_\text{RS}=100\%\frac{K^{\text{RS}}-K^{\text{SA}+ \text{SA}}}{K^{\text{RS}}}, \tag{27}\]  
\[PRD(\text{SA}+\text{SA})\_\text{CR}=100\%\frac{K^{\text{CR}}-K^{\text{SA}+ \text{SA}}}{K^{\text{CR}}}, \tag{28}\]  
where:  
\(K^{\text{SA}+\text{SA}}\)--the value of the adopted objective function obtained by means of the SA + SA algorithm,  
\(K^{\text{RS}}\)--the value of the adopted objective function obtained by means of the RS algorithm,  
\(K^{\text{CR}}\)--the value of the adopted objective function obtained by means of the CR algorithm. The average \(PRD\) of the applied algorithms are presented in Table 1.  
The mean values of PRD for the tested simulated annealing algorithm show that this algorithm always obtains better results than those obtained with the RS and CR comparative algorithms. The average PRD error of the tested simulated annealing algorithm in relation to the RS algorithm for all sizes of optimization tasks is \(-12.90\%\) and usually increased with the increase in the size of the task (even up to approx. \(-20\%\)). The average PRD error of the tested simulated annealing algorithm in relation to the CR algorithm for all sizes of
###### Abstract  
Establishing global secure networks is a potential implementation of continuous-variable quantum key distribution (CVQKD) but it is also challenged with respect to long-distance transmission. The discrete modulation (DM) can make up for the shortage of transmission distance in that it has a unique advantage against all side-channel attacks; however, its further performance improvement requires source preparation in the presence of noise and loss. Here, we consider the effects of photon catalysis (PC) on the DM-involved source preparation for improving the transmission distance. We address a zero-photon-catalysis (ZPC)-based source preparation for enhancing the DM-CVQKD system. The statistical fluctuation is taken into account for the practical security analysis. Numerical simulations show that the ZPC-based source preparation can not only achieve the long-distance transmission, but also contributes to the reasonable increase of the secret key rate.  
continuous-variable; quantum key distribution; non-gaussian operation; quantum communications +
Footnote †: journal: Applied