esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The TEMS Algorithm
#### Algorithm Complexity Evaluation  
The algorithm complexity analysis considers the four steps in Algorithm 1. The task of information collection and system setup occur a single time in the system setup. This step identifies \"\(n\)\" mobile devices added to the network, and it has \"\(n\)\" processor cores. There are a total of \"\(n\)\" local MEC servers with the \"\(m\)\" core processors and a number of \"\(n\)\" wireless network channels to \"\(n\)\" tasks with a tuple of four variables each. The algorithm must choose among \"\(n\)\" possible options with three variables each and nine coefficients to the cost equation. Thus, for step 1, the complexity is defined in Equation (24).  
\[nm+mn+n+4n+3n+9= \tag{24}\] \[2 nm+8n+9=O(nm)\]However, the smartphone currently has a limit of eight cores. Additionally, simple IoT devices, for instance, Arduino Mega 2560, have a single core. MEC servers can be composed of up to five Raspberry Pi IV with four cores. Thus, the processor cores number is less than the amount of then IoT devices, i.e., \(m<<n\), and if \(m\) is a menusurable and a finite number, then it is reasonable to think that \(m\approx k\) and in this scenario \(O(nm)=O(kn)\approx O(n)\). Therefore, in step 1, the complexity is \(O(n)\).  
In step 2, the task allocation has a sort function with \(O(nlog(n)\) complexity in the worst case and \(O(n)\) in the best case. A seek is executed two times among \(n\) tasks into \(n\) local devices and MEC servers to achieve the lower execution time and energy consumption. This task has \(O(n^{2})\) complexity. The cloud allocation tasks have \(O(1)\) complexity. The TEMS algorithm was developed with Python programming, and the Python sorting executes three times. Thus, the complexity for step 2 is described in Equation (25).  
\[\begin{split} 3\:O(nlog(n))+2\:[O(n^{2})+O(1)]=\\ 2\:O(n^{2})+3\:O(nlog(n))+2\:O(1)=O(n^{2})\end{split} \tag{25}\]  
Thus, the step 2 has a complexity \(O(n^{2})\).  
Step 3 has \(n\) interactions of simple tasks, so \(O(n)\), and step 4 seeks the battery level in \(n\) IoT devices, i.e., \(O(n)\).  
Hence, considering all TEMS algorithm steps and exchanging these steps by respective individual complexity as in Equation (26),  
\[O(TEMS)=O(n)+O(n^{2})+O(n)+O(n)=O(n^{2}) \tag{26}\]  
Therefore, the algorithm complexity is \(O(n^{2})\).
## 2 Health Feature Parameters  
### Battery Degradation Datasets  
This paper used two datasets of lithium-ion batteries under various operating conditions to assess the prediction performance of the proposed method. The first dataset was from NASA lithium-ion battery dataset [33], and B0005 and B0006 batteries were selected as research objects. Both batteries had a rated capacity of 2.0 Ah, and both underwent 168 charge-discharge cycles, setting the battery failure threshold at 70% (1.4 Ah) of its rated capacity. The experimental data was generated through charge-discharge experiments at 24 \({}^{\circ}\)C, and the battery was initially charged at 1.5 A in constant current mode until the voltage reached 4.2 V. Then, the mode was changed to constant voltage charging until the charging current decreased to 20 mA. The discharge process employed a constant current of 2 A until the B0005 and B0006 batteries reached voltages of 2.7 V and 2.5 V.  
The second dataset was obtained from Oxford University's lithium-ion battery aging dataset [34], where Cell1 and Cell2 batteries were designated as research objects. The rated capacity of both batteries was 0.74 Ah and underwent 78 and 73 charge-discharge cycles, respectively. The failure threshold for lithium-ion batteries was set at 80% (0.59 Ah) of their rated capacity. The aging experiments were conducted at 40 \({}^{\circ}\)C. The batteries were charged initially at 1.48 A in constant current mode and then discharged to simulate Artemis urban driving conditions. After every 100 charge-discharge cycles, a capacity calibration experiment was conducted until the end of battery life, and the aging test was completed.  
These two datasets provide capacity degradation data and related information for the entire battery life cycle, which is suitable for state estimation experiments. The above specific battery parameters are detailed in Table 1, and the battery capacity variation with charge-discharge cycles is illustrated in Figure 1.
## 2 Materials and Methods
### Data Analysis  
#### 2.3.1 Preprocessing  
The large size of Raman hypercubes generated for each sample leads to computational complexity and hinders efficient data processing. Therefore, the data size was reduced in the spatial direction (by selecting the spatial region of interest) and in the spectral direction (by examining the spectral features of adulterants and keeping only the informative spectral range). The fluorescence signals are usually generated during laser-sample interaction in the Raman measurement, particularly for food materials, which may surpass the informative information. Therefore, the reformatted Raman hypercube was first unfolded to a 2D dataset and the fluorescence background was removed from the spectrum of each pixel using an adaptive, iteratively reweighted, penalized least square (airPLS) method [19]. This method fit the spectrum and subtracts the fitted baseline from the original spectrum to acquire a fluorescence-free spectrum. Moreover, a median filter with a 3 \(\times\) 3 moving window was used to remove high-frequency noise. The corrected 2D dataset was further refolded to a 3D one to generate univariate and bivariate images.  
Figure 1: Schematic diagram of the line-scan Raman imaging system.
## 2 Modeling and Controller Design  
### System Overview  
Figure 1 shows the complete system of solar and battery integration into MMC-based HVDC transmission network. The system consists of solar farms with a battery-based energy storage system. MMC2 terminal establishes the standalone AC grids for solar energy integration, whereas MMC1 controls the HVDC link voltage. The capacity is increased by scaling the output (Iabc) from the detailed model-based one unit of PV-Battery system through a controlled current source and multiplier (M).
## 4 Experimental Results and Analysis
### Ablation Experiments  
To further verify the effectiveness of SAWRM and SSMMM, the classification results obtained via the experiments on the IP and PU datasets with different algorithm models (M1\(\sim\)M4) are shown in Table 5. M1 indicates that only the spectral multi-manifold feature extraction method is used to complete the image dimensionality reduction process. M2 indicates that both the SAWRM and spectral multi-manifold feature extraction methods are used to accomplish the image dimensionality reduction. M3 indicates that both the fixed near-neighbor pixel representation and SSMMM methods are used to accomplish  
Figure 8: Effect of parameters \(\alpha\) and \(\beta\) using dimensionality reduction results: (**a**) IP dataset; (**b**) PU dataset.  
Figure 9: Effect of dimensionality d using two data sets: (**a**) IP dataset; (**b**) PU dataset.  
image dimensionality reduction. M4 indicates that both SAWRM and SSMMM are used to accomplish the image dimensionality reduction. The number of training sets on the IP and PU datasets is 18 and 32, respectively. Other parameters are chosen as the optimal values of the parameters discussed in Section 4.4. As can be seen in Table 5, the SAWRM and SSMMM methods can improve the dimensionality reduction performance of the whole algorithm, where the simultaneous addition of both modules makes the classification accuracy of the model reach the highest value.
# Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows
## 3 Proposed Dynamic Window Approach
### The Evaluation Function for DF_DWA
#### 3.3.2 Distance-to-the-Goal Term  
This term prizes the trajectory that makes the agent move towards the goal. The distance from the starting point to the goal is defined as the maximum distance \(d_{\max}\). Also, the reward function is normalized between 0 and 1. Therefore,  
\[distgoal(v_{t},\omega_{t})=1-\frac{d_{goal}(t)}{d_{\max}} \tag{20}\]  
where \(d_{goal}\) is the distance from the position of the agent to the goal, and \(d_{\max}\) is the distance from the starting point to the goal.
## 3 Results and Discussions
### Impact of Mountain Conductivity on LIVs  
The altitudinal variation in mountainous terrains results in distinct soil water content compared to flat ground, consequently changing the soil's dielectric properties. In this section, we compare the calculated LIVs for different mountain conductivities \(\sigma_{m}\) ranging from 0.001 to 0.1 S/m considering different lightning strike locations. The mountain height and width are fixed to 250 m and 400 m here, and the ground soil conductivities \(\sigma_{g}\) are set to 0.001 S/m. Figures 11 and 12 present the LIVs calculated along the distribution line, which is located 300 m horizontally from the mountain center, as shown in Figure 1.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Strike Location** & **Width** & **Close End (kV)** & **Center (kV)** & **Remote End (kV)** \\ \hline SL2 & 200 & 15.68 & 36.39 & 15.68 \\  & 300 & 15.58 & 36.15 & 15.58 \\  & 500 & 14.07 & 34.3800 & 14.07 \\  & 700 & 11.71 & 38.62 & 11.71 \\ SL3 & 200 & 2.95 & 7.96 & 2.95 \\  & 300 & 2.96 & 8.03 & 2.96 \\  & 500 & 2.99 & 8.22 & 2.99 \\  & 700 & 2.44 & 5.52 & 2.44 \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 11: Comparison among the lightning-induced voltages for Configuration A over Gaussian-shaped mountain, considering different \(\sigma_{m}\) ranging from 0.001 to 0.1 S/m. LIVs of line close end, center, and remote end when lightning strikes at **(a–c)** SL1, **(d–f)** SL2, **(g–d)** SL3, respectively.  
Figure 11a-c shows the variations in LIVs along the distribution line when lightning strikes at SL1 in Configuration A. It can be found that a clear inverse correlation was observed between soil conductivity and LIVs. For example, at the close end of the line, the LIVs under 0.001 S/m soil conductivity exceeded those calculated at 0.01 S/m and 0.1 S/m by 32% and 33%, respectively. The observed nonlinear reduction in LIVs can be explained by enhanced attenuation of lightning-induced EM fields during propagation. Figure 11d-f show the variations in LIVs for lightning strikes at SL2, contrary to the results of SL1 where the LIVs increase with higher mountain conductivity. For SL3, as shown in Figure 11g-i, the effect of mountain conductivity is less pronounced. Figure 12 illustrates the LIVs along the distribution line in Configuration B. From Figure 12a-c, it can be observed that LIVs increase with lower mountain conductivity when lightning strikes at SL1. This effect is more significant on LIVs at the center of the distribution line, the LIV under 0.001 S/m mountain conductivity is increased by about 23% and 220%, compared to those with mountain conductivity of 0.01 S/m and 0.1 S/m. For lightning strikes at SL2, as demonstrated in Figure 12d-f, LIVs are more sensitive to variations in mountain conductivity at the center of the distribution line. As the results show in Figure 12e, the LIV under lower mountain conductivity of 0.  
shown in Figure 12g-i. However, LIVs exhibit a notable change at the center of the line only when mountain conductivity reaches up to 0.1 S/m.
## 5 Analysis of Open Source Data Availability
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & **Belfast** & **Budapest** & **Granolles** & **Istanbul** & **Lisbon** & **Milan** & **Muenster** & **Rotterdam** & **Thessaloniki** & **Zagreb** \\ \hline PTN & – & – & – & – & 2 & 1 & 8 & 1 & – & – \\ OAM & – & 10 & – & – & – & 2 & 6 & 2 & 5 & 10 \\ LCE & – & – & – & – & – & – & – & – & – & – & – \\ MDS & – & – & – & – & – & – & – & – & – & – & – \\ TM & – & – & – & – & – & – & – & – & – & – & – \\ BEE & – & – & – & – & – & – & – & – & – & – & – \\ MWR & 3 & 13 & – & – & 12 & 3 & 16 & 6 & – & 5 \\ MWRe & – & – & – & – & – & – & – & – & – & – \\ UER & – & 7 & 8 & 1 & 2 & 5 & 18 & 16 & 3 & 12 \\ GA & – & – & – & – & – & – & – & – & 1 & – \\ PI & – & – & – & – & – & – & – & – & – & – \\ AQ & – & – & – & – & – & – & – & – & – & – \\ GHGe & – & 12 & – & – & – & – & 14 & 25 & 5 & 1 \\ REC & – & – & – & – & – & – & – & – & – & – \\ NE & – & – & – & – & – & – & – & – & – & – \\ GINI & – & – & – & – & – & – & – & – & – & – \\ VCR & – & – & – & – & – & – & – & – & – & – \\ CP & – & – & – & – & – & – & – & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of KPI-recorded years for each city. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & **London** & **Budapest** & **Madrid** & **Ankara** & **Lisbon** & **Roma** & **Berlin** & **Amsterdam** & **Athina** & **Zagreb** \\ \hline PTN & 1 & – & 3 & – & 2 & 2 & 9 & 1 & – & – \\ OAM & – & 10 & 3 & – & – & 9 & 18 & 2 & 5 & 10 \\ LCE & – & – & – & – & – & – & – & – & – & – \\ MWR & 4 & 13 & 3 & – & 12 & 13 & 16 & – & 5 \\ MWRe & – & – & – & – & – & – & – & – & – & – \\ UER & 7 & 7 & 24 & 1 & 2 & 7 & 21 & 16 & – & 12 \\ GHGe & 25 & 12 & 22 & – & – & 16 & 25 & 25 & – & 1 \\ REC & – & – & – & – & – & – & – & – & – \\ NE & – & – & – & – & – & – & – & – & – \\ GINI & – & – & – & – & – & – & – & – & – \\ VCR & – & – & – & – & – & – & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of KPI-recorded years for each capital city of the countries in Figure 3.  
data at a local level. By comparing the recorded KPIs of a city, its capital, and its country, differences in data collection practices and sustainability progress can be identified, offering insights into how local and national efforts align or differ across these dimensions. For example, Figure 4 illustrates a comparative analysis between Milan, Rome, and Italy, using KPIs categorized within the dimensions of transport, waste, employment, environment, social, and organization. The radar plot shows that Milan and Rome exhibit similar values in areas such as transport and employment, while Italy, as a country, has higher values in categories like organization and environment. This analysis highlights the discrepancies in data collection and reporting between the city and national levels, especially regarding sustainability progress across the four dimensions.
## 3 Proposed Method
### Blind Detection Process  
We consider two forms of detection process in the frame processing: front-side and back-side. Figure 3c,d show the detection process of the security code in the front-side and back-side forms, respectively.  
The security code is detected from the watermarked signal \(y_{k}(n)\) in the (\(k\))th frame. The watermarked signal \(y_{k-1}(n)\) in the (\(k-1\))th frame is the same as the host signal \(x_{k-1}(n)\) in the (\(k-1\))th frame in the detection process of the front-side form. The LP residue \(r_{k-1}(n)\) in the (\(k-1\))th frame is calculated from the watermarked signal \(y_{k-1}(n)\) in the (\(k-1\))th frame by using LP analysis. Then, the security code \(m(n)\) can be obtained accurately by multiplying LP residue \(r_{k-1}(n)\) in the (\(k-1\))th frame by \(y_{k}(n)\) in the (\(k\))th frame using the Equation (7).  
Similarly, the detection process can be done backward (back-side form) by switching the even/odd-numbered frames for the LP residue calculation and detection, as shown in Figure 3d.
# Estimation of Fatigue Crack AE Emissivity Based on the Palmer-Heald Model
## 4 Discussion
If, during the testing, the discrimination threshold is set to below 40 dB, the number of impulses recorded during AE testing will be higher. In addition, the proposed calculation model assumes that before loading, in accordance with [13], a complete load shedding was performed, otherwise, due to the Kaiser effect, the number of AE impulses will be slightly lower than expected values. It could be expected that the obtained phenomenological results concerning the random character of parameters \(D\) and \(D_{a}\) and the method of emissivity estimation, will be summarized on the class of structure steels, but the quantitative results are correct for steels of a pearlitic structural class and for thickness values of the same order that were considered above. Comparing the present study with the studies included in the literature review, some difference in aims and approaches should be noted. At this stage, the authors did not set themselves the fundamental task of identifying the processes that occur during the propagation of a fatigue crack. The emissivity of the material is evaluated integrally using a resonant sensor recommended for industrial applications. It is assumed that the estimate of parameter \(N_{\Sigma}\) obtained using the Palmer-Heald model can be improved for the case when the sensor is placed far from the defect on a distance characteristic of industrial testing.
# Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision
## 3 Model Construction
### Global Semantic Submodule-ViT-B/16  
After the convolutional branch extracts local texture, to complement the long-range dependencies and global structural information in the barcode image, this paper introduces ViT-B/16 as the second feature pathway, as shown in Figure 6. The network first divides the input ROI (I \(\in\mathbb{R}^{3\times 224\times 224}\)) into 16 \(\times\) 16 non-overlapping image patches and applies linear embedding E \(\in\mathbb{R}^{768\times 768}\) to each patch \(X_{p}\in\mathbb{R}^{3\times 16\times 16}\), resulting in a patch token sequence \(\left\{e_{p}\right\}_{p=1}^{196}\). Then, a learnable classification token \(t_{cls}\) is concatenated at the beginning of the sequence, and positional encodings P are added to preserve the relative positional information between patches, forming the input matrix:  
\[Z_{0}=\left[t_{cls};e_{1};\dots;e_{196}\right]\ +\ P,Z_{0}\in\mathbb{R}^{197 \times 768} \tag{9}\]  
Figure 5: ResNet50 Model Architecture. Residual network extracting features from 224 \(\times\) 224 barcode ROIs. Outputs 1024D vectors via GAP and linear projection for cross-modal fusion.  
This sequence then passes through L = 12 Transformer Encoder layers. For the l-th layer, multi-head scaled dot-product attention is first computed:  
\[Attention(Q,K,V)\ =\ Softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{10}\]  
where \(\text{Q}=\text{Z}_{l-1}\text{W}_{\text{Q}},\text{K}=\text{Z}_{l-1}\text{W}_{ \text{K}},\text{V}=\text{Z}_{l-1}\). The result is then updated through a residual connection and a feed-forward network to complete a feature update \(\text{Z}_{l}\). The final output \(\text{Z}_{l}\) has its first row (the CLS-token) denoted as \(f_{vit}\in\mathbb{R}^{768}\), which contains the global context semantics of the entire barcode image. To align with the convolutional branch in terms of feature dimension, a linear mapping is applied to \(f_{vit}\), resulting in a unified 1024-dimensional vector \(Z_{vit}\):  
\[Z_{vit}\ =\ W_{vit}f_{vit}\ +\ b_{vit},W_{vit}\in\mathbb{R}^{1024\times 768} \tag{11}\]  
By using global self-attention, ViT-B/16 models cross-patch dependencies for the barcode ROI. The contextual semantic vector generated by ViT-B/16 not only complements the convolutional branch's shortcomings in capturing long-range relationships but also provides a globally consistent and discriminative high-dimensional feature representation for the hierarchical attention fusion module, thereby improving the overall model's defect classification robustness and generalization ability.
# Energy Bus-Based Matrix Modeling and Optimal Scheduling for Integrated Energy Systems
## 3 Model Parameters  
### Steady-State Model Parameters  
Typically, the conversion efficiency \(\eta\) can use a constant in the optimal scheduling, as shown in Table 1[5]. However, in the IES with RESs, the fluctuations in the RESs often cause the devices to run in off-design conditions. The conversion efficiency of the boiler is almost linear, while the electric chiller can work periodically. Hence, \(\eta_{\text{g}}\) and \(COP_{\text{ec}}\) can use constant coefficients.  
In this study, \(\eta_{\text{pgu,e}}\) is fitted with the operating data of the real generator, and \(COP_{\text{ac}}\) is fitted with the simulation data in [33]. The polynomial fitting results with 95% confidence bounds are as follows:  
\[\eta_{\text{pgu,e}}=\begin{cases}0&PIR_{\text{pgu}}=0\\ -2.799 PIR_{\text{pgu}}^{4}+9.276 PIR_{\text{pgu}}^{3}-11.44 PIR_{\text{pgu}}^{2}+6. 454 PIR_{\text{pgu}}-1.181&PIR_{\text{pgu}}\geq 0.3288\end{cases} \tag{25}\]  
\[COP_{\text{ac}}=1.386 PIR_{\text{ac}}^{4}-1.943 PIR_{\text{ac}}^{3}-0.2784 PIR_{ \text{ac}}^{2}+1.214 PIR_{\text{ac}}+0.4216 \tag{26}\]  
where \(PIR\) is the part-input ratio and is defined as  
\[PIR=\frac{v_{\text{in}}}{v_{\text{in,rc}}} \tag{27}\]  
where \(v_{\text{in}}\) is the input value of the energy conversion device, and \(v_{\text{in,rc}}\) is the input value in the rated condition. Considering that the conversion is inefficient if the PGU runs under light-load conditions, we set \(PIR_{\text{pgu}}\geq 0.4\) in the experiment. \(\eta_{\text{pgu,e}}\) can be calculated via [33]  
\[\eta_{\text{pgu,th}}=0.8(1-\eta_{\text{pgu,e}}) \tag{28}\]  
The efficiency and fitting curves are plotted in Figures 3 and 4. The R-squared values of \(\eta_{\text{pgu,e}}\) and \(COP_{\text{ac}}\) are 0.9998 and 0.9771, respectively. The nonlinear parameter provides an exact description of each device under the off-design condition.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Converter** & **Efficiency** \\ \hline PGU & \(\eta_{\text{pgu,e}}=0.3\) \\ \(\eta_{\text{pgu,th}}=0.4\) \\ Gas boiler & \(\eta_{\text{g}}=0.8\) \\ Absorption chiller & \(COP_{\text{ac}}=0.7\) \\ Electric chiller & \(COP_{\text{ee}}=3\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The constant efficiency of the energy converters.  
Figure 3: The efficiency curves of the PGU.
## 1 Introduction
Many studies have analyzed kinematics and neuromuscular activations on ergometer rowing, as the indoor setting facilitates the instrumentation of the rowers and ergometer [14; 15; 16; 17; 18; 19]. On an ergometer, early isometric activation of the trunk extensors allows the transfer of the power generated by the legs. Then, a concentric contraction allows for the extension of the trunk with power production. After this, an eccentric contraction of the trunk flexors slows down the trunk until the maximum trunk extension is reached near the finish event [6; 17; 18; 19; 20]. At any stroke rate, trunk flexors and extensors show little to no co-contraction [4]. Similarly, arm extensors are initially isometrically activated to transfer legs and core power during the first part of the drive and then produce their own power by contracting concentrically [21]. While there have been studies that described the neuromuscular activity [22; 23], the body's kinematics [14; 24; 25; 26; 27], and studies investigating the effects of technical determinants on performance [2; 28], to the authors' knowledge, there is no study that investigates the modifying effects of neuromuscular activity and body kinematics on performance during scull rowing. During scull rowing, the intrinsic instability of the boat, blade management, and asymmetry introduced at the level of the arms could modify the temporal pattern activation of the core and arms with respect to ergometer rowing. Knowing how muscle activity influences rowing performance could provide valuable information to trainers. Therefore, the purpose of the current study was (1) to describe the core and upper-body kinematics and neuromuscular activity during rowing on water and (2) to investigate the modifying effects of technical determinants, kinematics, and muscle activations on the rowers' general performance defined by power transferred to the oars, average boat speed, and catch and finish slip angles.