esa-sceva/satcom-chunk-collection huggingface dataset:
# VPP Participation in the FCR Cooperation Considering Opportunity Costs
## 2 Methodology
* **Comparison of potential revenue and opportunity costs--**Put simply, if the potential revenue is lower than the opportunity costs, this means that the VPP would be losing money in the market FCR session; therefore, the logical decision is not to participate, in which case, the binary _decision_ variable \(Dec_{c,t}\) would be 0, as per Equation (13). If the VPP participates in the market session, the potential revenue becomes a revenue, as per Equation (14). * **UPw revenue share--**One assumption of this study was that the HE owner would be happy to earn at least as much in the FCR market as it would in the hydrogen market; therefore, according to Equation (15), the UPw reserve share \(UPRS_{c,t}\) equals the respective opportunity costs. Naturally, if the VPP does not participate in the market tender, \(Dec_{c,t}=0\), and the opportunity costs are null. * **DNw revenue share--**Likewise, the solar and wind plants receive their revenue share \(DNRS_{c,t}\), according to Equation (16). * **VPP revenue share--**The VPP owner, after paying both the upward and downward providers, reserves the remaining revenue \(VRS_{c,t}\) for itself, according to Equation (17). This value can be understood as a profit of the VPP. \[DH_{c,t}= \begin{cases}0,\text{if }G_{c,t}<=HMin_{c}\\ \\ PN_{c},\text{if }G_{c,t}>=HMax_{c}\\ \\ (G_{c,t}-HMin_{c})\times\frac{PN_{c}}{HMax_{c}-HMin_{c}},\\ \\ \text{otherwise}\end{cases}  \tag{1}\]  
\[DN_{c,t}=G_{c,t}\times VGS_{c}\times RGS_{c} \tag{2}\]  
\[UP_{c,t}=DH_{c,t}\times RDS_{c} \tag{3}\]  
\[B_{c,t}=min(DN_{c,t},UP_{c,t},D_{c,t}) \tag{4}\]  
\[VPR_{c,t}=CBMP_{t}\times B_{c,t} \tag{5}\]  
\[DN\_en_{c,t}=B_{c,t}\times\frac{DN\_en_{FR,t}}{D_{FR,t}} \tag{6}\]  
\[UP\_en_{c,t}=B_{c,t}\times\frac{UP\_en_{FR,t}}{D_{FR,t}} \tag{7}\]  
\[DN\_OPC_{c,t}=DN\_en_{c,t}\times EMP_{c,t} \tag{8}\]\[CAE_{c,t}=UP\_en_{c,t}\times PPAP \tag{9}\] \[NPH_{c,t}=UP\_en_{c,t}\times Ef\] (10) \[UP\_OPC_{c,t}=NPH_{c,t}\times PH-CAE_{c,t}\] (11) \[UP_{c,t}=DH_{c,t}\times RDS_{c} \tag{3}\]0 (12) \[Dec_{c,t}= \begin{cases}0,\text{if}\ OPC_{c,t}>VPR_{c,t}\\ \\ 1,\text{otherwise}\end{cases} \] (13) \[VR_{c,t}=Dec_{c,t}\times VPR_{c,t}\] (14) \[UPRS_{c,t}=UP\_OPC_{c,t}\times Dec_{c,t}\] (15) \[DNRS_{c,t}=DN\_OPC_{c,t}\times Dec_{c,t}\] (16) \[VRS_{c,t}=(VR_{c,t}-OPC_{c,t})\times Dec_{c,t} \tag{17}\]  
The inputs, user-defined variables, and outputs are given in Table 1. \"Inputs\" refer to the given data, whereas \"user-defined variables\" can be changed in the spreadsheet to achieve different \"outputs\". The results presented for the scenarios thus result from different values of \"user-defined variables\". All the variables from Table 1 were assumed to be static over the years.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Bio-Inspired Energy-Efficient Routing Protocols (Bio-Inspired E2 RPs)
#### 5.2.7 Comparison of Cluster-Based Energy-Efficient Routing Protocols (C-bE2 RPs)  
A thorough analysis of the E2 RPs, which are based on a cluster for UWSNs, is shown in Table 9. By reducing the number of data packet transmissions, the E2 RPs based on cluster are intended to conserve energy. Since energy efficiency is the major goal of all of the aforementioned routing protocols, the remaining energy of the sensor Rx nodes is an important factor for choosing the CH. However, the cluster center and the distance to the BS are key factors when choosing the CH. The E2 RPs that are based on cluster are employed to improve network performance and reduced energy usage. However, the primary drawback of these E2 RPs is their high network load and CH early termination, in extremely congested networks. There are several techniques that are suggested in [133; 134; 140; 142; 143; 144; 145] for UWSNs in regards to energy efficiency. All of these routing methods have the potential to significantly increase the load balancing of the network's communicating nodes, but they come at a higher computational cost. When deploying the communicating nodes, hierarchical or E2 RPs based on layers may be employed to provide better and more effective cluster formation and good network performance. The aforementioned discussion indicates that many E2 RPs are based on layer approaches that improve network performance. In inter cluster-based communication, multi-hop data packet forwarding becomes more frequent if all inter cluster-based communication is, carried out via a single hop. Due to the close proximity of the communicating nodes, multi-hop inter cluster communication requires less energy than single-hop inter cluster communication. Furthermore, localization operation may appear difficult for the sensor Rx nodes in a dynamic UWSN environment, because it utilizes more hardware and energy. Therefore, the performance of localization-free techniques was excellent in terms of cost and energy efficiency.
# Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection
## 1 Introduction
The forecasting of the next singular point position decides the interval of traffic flow prediction. * We use the Vasicek model to predict the traffic flow's trend from the current moment to the predicted next singular point. The matrix decomposition is imported to deal with the unequal size problem during an optimization solution. The rest of the paper is organized as follows: Section 2 discusses related work about short-term traffic flow predictions. The framework and main process of the traffic flow prediction are proposed in Section 3. Section 4 introduces the singular point detection and prediction methods based on the multiscale wavelet transform. The prediction of the next singular point position with the weighted function is also given. Section 5 proposes an algorithm of unequal interval traffic flow prediction based on the Vasicek model. Performance evaluations are presented in Section 6. Finally, the concluding remarks are drawn in Section 7.
## 3 Results
### AI and ML for Rehabilitation Systems
#### 3.3.1 AI Algorithms and Machine Learning for Personalized Rehabilitation and Recovery Prediction
A mathematical model using an Adaptive Boosting (AdaBoost) algorithm demonstrated the potential for individualized therapeutic interventions through robust personalization techniques [52], achieving an 84% accuracy as reflected in Table 4. A subsequent analysis aimed at predicting the effectiveness of rehabilitation programs for orthopedic and neurological patients demonstrated the utility of tree-based ensemble machine learning models, showcasing the potential of AI for predicting functional improvement. Similar efforts have applied ML techniques to predict rehabilitation success using clinical and patient-reported outcome measures (PROMs) and clinical measurements (CROMs). Regression and classification algorithms estimating rehabilitation success based on admission and discharge differences have produced F-scores exceeding 65%. Applications of ANNs have further advanced rehabilitation outcome evaluations, particularly for patients with chronic neurological conditions. A retrospective study predicting improvements in the Barthel Index (BI) using demographic, clinical, and ICF (International Classification of Functioning, Disability, and Health) codes achieved an accuracy of 86.4%, identifying mobility codes as strong predictors, as reported in Table 3[46; 54; 55]. Deep learning approaches have played a pivotal role in advancing home-based remote rehabilitation [56]. A deep learning system designed to assess rehabilitation exercises through a real-time motion capture analysis achieved high accuracies of 89% for the range of motion classification and 98% for the compensatory pattern recognition, indicating its potential to enhance access to quality rehabilitation care beyond clinical settings. Deep learning models such as the CNN, Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and CNN-LSTM demonstrated impressive performances on the KIMORE and UI-PRMD datasets, where the CNN achieved accuracy rates of 93.08% and 99.7%, respectively, as shown in Table 3[47]. These results demonstrate the effectiveness of deep learning techniques in rehabilitation exercise classification and disease identification. The development of a responsible AI model designed to support home-based rehabilitation through a hybrid machine learning approach reinforces the emphasis on individualized rehabilitation, combining ensemble learning and hybrid stacking to ensure personalization, clear interpretability, and the alignment with ART design principles, all while keeping computational demands minimal. This model achieved a 100% accuracy in tasks like the Five Time Sit To Stand (FTSTS) test and Timed Up and Go (TUG) test and improved performance by 5% and 15%, respectively, compared to previous systems relying on intrusive monitoring [57].
## Appendix A  
\begin{tabular}{l l l l} \hline \hline
**Item** & **Answer Options** & **P1** & **P2** & **P3** \\ \hline
**Background knowledge** & & & & \\ How much do you know about SNOMED CT? & Nothing-Very much & 5 & 5 & 4 \\ How much do you know about SNOMED CT Postcoordination? & Nothing-Very much & 5 & 4 & 4 \\ \hline
**Overall impression** & & & & \\ WASP has a consistent, clearly recognizable look and design. & Strongly disagree-Strongly agree & 4 & 5 & 4 \\ Functionality of buttons and controls is identifiable based on their label & Strongly disagree-Strongly agree & 5 & 4 & 4 \\ and design. & Strongly disagree-Strongly agree & 5 & 4 & 4 \\ The individual pages are organized clearly. & Strongly disagree-Strongly agree & 5 & 4 & 4 \\ Where tooltips or information fields are given, these provided useful & Strongly disagree-Strongly agree & 5 & 5 & 5 \\ The user is informed of incorrect entries. & Strongly disagree-Strongly agree & 4 & 5 & 2 \\ \hline
**PCE creation based on MRCM** & & & & \\ How effective was the selection of the individual attributes? & Very ineffective-Very effective & 5 & 5 & 4 \\ How easy was it to choose an attribute value of an attribute? & Very hard-Very easy & 3 & 4 & 4 \\ How easy was it overall to create a PCE based on the Concept Model? & Very hard-Very easy & 4 & 4 & 4 \\ Could you create the PCE like you wanted? & Not at all-Yes, completely & 5 & 5 & 5 \\ If not: Which problems have occurred? & – & & & \\ \hline
**PCE creation based on Template** & & & & \\ How easy was it to choose an appropriate Template? & Very hard-Very easy & 4 & 3 & 5 \\ How easy was it overall to create a PCE based on a Template? & Very hard-Very easy & 4 & 5 & 4 \\ Could you create the PCE like you wanted? & Not at all-Yes, completely & 5 & 5 & 5 \\ If not: Which problems have occurred? & – & & & \\ \hline
**Template generation** & & & & \\ How effective was the selection of the individual attributes? & Very ineffective-Very effective & 5 & 5 & 4 \\ How easy was it to choose or adjust the value range of an attribute? & Very hard-Very easy & 4 & 4 & 4 \\ How helpful were the default value ranges and default cardinalities? & Not helpful at all-Very helpful & 4 & 5 & – \\ How intuitive was the generation of a Term Template? & Very counterintuitive-Very intuitive & 4 & 4 & 4 \\ How easy was it overall to create a Template? & Very hard-Very easy & 4 & 5 & 3 \\ Could you create the Template like you wanted? & Not at all-Yes, completely & 5 & 5 & – \\ If not: Which problems have occurred? & – & & & \\ \hline \hline \end{tabular}
# Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns
## 3 Wave-Optics Numerical Simulations of Atmospheric Sensing Trials: SIM Datasets
For each frame stamp \(m\) and the \(C_{n}^{2}(m)\) value defined by Equation (1), the corresponding \(m\)th turbulence realization \(\left\{\varphi_{l}(\tau_{i,j},m)\right\}\) was computed. The intensity distribution at the optical receiver plane \(I(\tau_{i,j},m)\) was obtained by simulating propagation of the beacon beam through the \(m\)th turbulence realization using the split-step operator technique. Several scintillation images selected from the SIM-KO and ATM#1 datasets corresponding to approximately equal \(C_{n}^{2}\) values are compared in Figure 4. From a visual assessment, the spatial structures of the simulated and experimentally measured scintillation patterns are quite similar under weak-to-medium turbulence conditions (\(C_{n}^{2}\leq 1.0\cdot 10^{-14}\) m\({}^{-2/3}\)), while noticeably different for higher \(C_{n}^{2}\) values (right row images in Figure 4). As discussed in Section 6, this difference is related with the inhomogeneity of turbulence strength along the propagation path.
# A Staring Tracking Measurement Method of Resident Space Objects Based on the Star Tacker
## 3 Results
### Numerical Simulation of the Proposed STM Method
#### 3.2.2 Simulation of the Measurement Accuracy  
The following simulation was carried out to evaluate the accuracy of the RSO observation arc. The measurement accuracy of the proposed method was verified by the 5000 dynamic star images. In simulated images, the measured RSO is randomly selected from star points in the simulated star image to avoid accidental errors. The true position vector of the measured RSO in the ECI frame can be obtained by adding the position vector to the selected star and the star tracker position vector. The centroid coordinates of stars and the RSO are calculated by the centroid extraction algorithm described in [34] to determine their unit observation vectors in the camera frame. The position vector of the RSO  
Figure 10: The simulated observation arcs of the RSO in the STM method.  
can be determined according to the method proposed in Section 2.2. The errors of right ascensions and declinations \(\Delta\alpha\) and \(\Delta\delta\) are shown in Figure 11. The maximum absolute value of \(\Delta\alpha\) is 19.5648\({}^{\prime\prime}\), and the maximum absolute value of \(\Delta\delta\) is 19.6906\({}^{\prime\prime}\).  
The root-mean-square error (RMSE) of the results are calculated through Equation (24) to evaluate the effectiveness of the proposed STM method. The simulation results show that the RMSE of the ascension is 3.1556\({}^{\prime\prime}\), and that the RMSE of declination is 2.9295\({}^{\prime\prime}\).  
\[\left\{\begin{array}{l}\Delta_{\alpha RMSE}=\sqrt{\frac{\frac{n}{\sum \limits_{i=1}^{n}{(\delta_{0}-\delta_{0})}^{2}}}{n}},i=1,2,3,\ldots\ldots,n\\ \Delta_{\beta RMSE}=\sqrt{\frac{\frac{n}{\sum\limits_{i=1}^{n}{(\delta_{0}- \delta_{0})}^{2}}}{n}},i=1,2,3,\ldots\ldots,n\end{array}\right. \tag{24}\]
## 3 Results Analysis
### Experimental Results for Time Measurements
#### 3.1.4 Summary of Time Acquisition Experiment Results  
The results present above are collected in Table 1. It consists of main statistical parameters of experiment datasets: median value, MAD, minimal and maximal values of the obtained results. Statistical parameters are provided for the different conditions of the experiments: investigated function (_TSC_ or _System Call_), platform or experimental environment (Host OS, QEMU, ESXi, Xen, and  
Figure 8: Measurements of timer cost for the IO/network testbed on: (**a**) Host OS; (**b**) Xen; (**c**) Virtual Box; (**d**) QEMU; and (**e**) VMware ESXi. Figure 9: CCDF representation of measurements of time cost for the IO/network testbed. Vbox) and the load type under which the experiment was performed (idle System, CPU-bound, and IO/network-bound). To identify hidden factors and determine whether a given set contains any additional properties, an analysis of variance (ANOVA on ranks) was performed. This test was targeted to determine which factors significantly affect the four parameters of the dataset: median, MAD, max, and min. Considering function, load and platform columns of the table as the different factors, Kruskal-Wallis analysis on ranks and post hoc tests using [PERSON]'s test of timer cost datasets were used. The mentioned methods allow the investigation of the effects of multiple factors on the statistical parameters: the median value of the dataset, MAD, and its minimum and maximum values. Variance analysis of medians, MADs, minimum and maximum values shows that time acquisition function is a significant factor for all virtualization types. However, virtualization itself is a significant factor only on the Virtual Box as a virtualization platform. Load type has a significant effect only on the MAD parameter, however MAD changes insignificantly switching from Idle to CPU load. Interaction between factors function and platform affects significantly median, max, and min values, but not MAD. Interaction between factors platform and load affects only MAD. There is no significant difference for median, MAD, max, and min values when switching between Host OS and ESXi platforms, ignoring other factors. Instead, much more effect was evident in switching from _TSC_ to _System Call_. It significantly affects the median, max values on Xen and QEMU platforms as well as in the case of idle system, CPU-bound and IO/network-bound load. Analysis of interaction effects between one platform-load and other platform-load factor shows that a significant effect is present in the following cases: idle system switching from Xen to Host OS or
## 4 Characteristics of Different Time Distribution Systems
### White Rabbit
#### 4.4.3 Flexibility  
If the 5G network is set up for communication in a power grid, we can take advantage of this infrastructure to conduct time synchronization at the same time, which makes it a very flexible time synchronization method.  
With existing network infrastructure already in place for communication purposes, 5G timing can be a cost-effective solution, comparable to NTP and PTP.
## 4 Proposed Method
### Improved Attention Feature Fusion: CAF
Then, to effectively capture the relationship between channels, these two features are concatenated along the channel dimension and input into the \(1\times 1\) convolution \(F_{1}\), yielding the intermediate feature:  
\[f=\delta\Big{(}F_{1}\Big{(}[z^{w},z^{h}]\Big{)}\Big{)}, \tag{13}\]  
where \([_{\cdot}]\) denotes the concatenation along the channel dimension; and \(\delta\) is the nonlinear activation function. Next, the intermediate features are split into two 1D tensors of length  
Figure 7: AFF implementation process. The dashed arrow represents \((1-f_{att}(X_{1}\oplus X_{2}))\). \(W\) and \(H\), respectively, denoted as \(f^{h}\) and \(f^{w}\), and input into two new \(1\times 1\) convolutions \(F_{h}\) and \(F_{w}\), to obtain two attention weights:  
\[ \begin{split}& g^{h}=\sigma\Big{(}F_{h}\Big{(}f^{h}\Big{)}\Big{)}, \\ & g^{w}=\sigma(F_{w}(f^{w})).\end{split}  \tag{14}\]  
where \(\sigma\) is the sigmoid function. Based on this, we modify Equation (14) to obtain the output of CAF \(y_{CAF}\):  
\[y_{CAF}(i,j)=[x_{1}(i,j)+x_{2}(i,j)]\times[g^{w}(i)g^{h}(j)x_{1}(i,j)]+[1-g^{w }(i)g^{h}(j)]x_{2}(i,j)] \tag{15}\]  
where \(x_{1}(i,j)\) and \(x_{2}(i,j)\) are elements of the two types of high-level features.To improve the visual clarity and readability of the formula representation, all variables in Equation (14) are assumed to belong to the \(c\)-th channel by default. Figure 8 shows a flow chart of CAF. The features extracted by the TG2 lightweight parallel feature extraction module can be adaptively weighted and fused with channel and spatial information through CAF. This not only achieves a lightweight design but also effectively enhances the network's feature representation capability.
# Lateral-Torsional Buckling of Cantilever Steel Beams under 2 Types of Complex Loads
## 3 Verification and Analysis of Matrix Equation
### Verification of the Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{_I (m)_} & \multicolumn{3}{c}{**CL**} & \multicolumn{3}{c}{**CUDL**} \\  & \(M_{\text{FEM}}\) (kN\(\cdot\)m) & \(M_{\text{th}}\) (kN\(\cdot\)m) & \(M_{\text{th}}\)/M\({}_{\text{FEM}}\) & \(M_{\text{FEM}}\) (kN\(\cdot\)m) & \(M_{\text{th}}\) (kN\(\cdot\)m) & \(M_{\text{th}}\)/M\({}_{\text{FEM}}\) \\ \hline \multirow{3}{*}{1.5} & TF & 19.25 & 20.23 & 1.05 & 23.12 & 24.20 & 1.05 \\  & SC & 83.94 & 83.77 & 1.00 & 103.07 & 103.81 & 1.01 \\  & BF & 91.07 & 89.48 & 0.98 & 114.41 & 111.96 & 0.98 \\ \hline \multirow{3}{*}{2} & TF & 17.34 & 18.86 & 1.09 & 20.58 & 22.30 & 1.08 \\  & SC & 52.04 & 51.50 & 0.99 & 63.72 & 63.37 & 0.99 \\  & BF & 55.64 & 54.38 & 0.98 & 68.70 & 67.51 & 0.98 \\ \hline \multirow{3}{*}{3} & TF & 14.19 & 14.89 & 1.05 & 16.61 & 17.40 & 1.05 \\  & SC & 27.54 & 27.25 & 0.99 & 33.48 & 33.14 & 0.99 \\ \cline{1-1}  & BF & 28.89 & 28.33 & 0.98 & 35.24 & 34.72 & 0.99 \\ \hline \multirow{3}{*}{4} & TF & 11.64 & 11.95 & 1.03 & 13.44 & 13.89 & 1.03 \\ \cline{1-1}  & SC & 18.12 & 17.95 & 0.99 & 21.84 & 21.66 & 0.99 \\ \cline{1-1}  & BF & 18.72 & 18.50 & 0.99 & 22.80 & 22.45 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The critical moment predicted by finite element analysis (\(M_{\text{FEM}}\)) and theoretically predicted (\(M_{\text{th}}\)) critical moment for Section II—reinforced bottom flange. In addition, the impact of the number of generalized coordinates (\(n\)) on the accuracy of calculation results is studied. The specimens of section II in Table 1 are selected and their beam lengths are 4 m. The specific results are shown in Table 7 and Figure 6. According to Table 7 and Figure 6, the maximum error between \(M_{\text{th}}\) and \(M_{\text{FEM}}\) is only 3.61%, and calculation accuracy is high when \(n\) is increased to the convergence term. The maximum error between \(M_{\text{th}}\) and \(M_{\text{FEM}}\) is only 3.96% when \(n\) is 10, which is within the allowable error range, and is only 0.35% higher than that corresponding to the convergence term. When \(n\) is equal to 1 or 2, the error between \(M_{\text{th}}\) and \(M_{\text{FEM}}\) is greater than 17%. Through the comparative analysis of finite elements, it can be seen that the critical moment of cantilever beam under the load cases studied in this paper can be calculated within the allowable error range when \(n\) is greater than or equal to 10, and when \(n\) is equal to 1 or 2, the error is too large to calculate. Therefore, the program in Figure 3 needs to be modified. The value of \(n\) in this program can directly take any value greater than 10, such as 100, without the need for a loop statement. It can greatly improve the efficiency of the procedure in Figure 3. In addition, the procedure of this paper refences the principles of GBTUL software in reference [48]. The specific content of the code can be found in Appendix A. At the same time, a user graphical interface is developed for users.