esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Development of an Advanced SR-JOINT (SR-II)
### Optimal SR-Joint Shape Design
#### 2.2.4 Results
Similar to the training result, the results of the SVM and statistical regression are shown in blue and red, respectively, and they were depicted in a lighter color as the data size decreased. The verification results were compared with the mean square error (_MSE_), as calculated using Equation (7). The _MSE_ is the mean square error between the predicted and test values; it is generally used as a measure of predictive accuracy. The verification result shows that the normalization technique yielded the lowest _MSE_ when the maximum normalization was applied, and the value predicted by the SVM was lower than that by the statistical regression. This shows that the SVM had higher prediction accuracy than the statistical regression and that it did not significantly affect the  
Figure 13: Normalized Load-max vs. training datasets for different regression method. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Coefficient of Determination**} \\ \hline \multicolumn{3}{c}{**Data Set [\%]**} & **Statistical Regression** & **SVM** \\ \hline
100 & 0.9603 & 0.9815 \\
75 & 0.9625 & 0.9757 \\
50 & 0.9777 & 0.9947 \\
25 & 0.9949 & 0.9982 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prediction accuracy of Load-max for SVM and statistical regression data with the number of data. amount of data. The case where maximum normalization was applied by subdividing each design variable (see Figure 15) is depicted in blue for SVM and in red for the statistical regression. The SVM was more accurate than the statistical regression, similar with the training results. The optimal design was implemented within the limiting conditions of the design variables, and the minimum load was indicated when the curling length was 5.7 mm, the distance was 6 mm, and the jig radius was 5 mm. \[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(y_{r}-y_{p}\right)^{2} \tag{7}\]  
As a result of this optimal design, molding analysis was performed when the curling length was 5.7 mm, the distance was 6 mm, and the jig radius was 5 mm, and the load was discovered to be 162.2 kN. It was discovered that the molding analysis result and the predicted value were extremely similar. The load was reduced to 47.59% when comparing this value with that of the conventional SR-joint. It is believed that the water tightness and pressure jig shape, which were disadvantageous to SR-I, can be improved based on this result. In the finite element analysis result, the contact area with the mold was measured to  
Figure 14: Comparison of mean square error for test data with SVM and statistical regression data. Figure 15: Normalized Load-max vs. test datasets for different regression methods. be 165.74 cm\({}^{2}\). The pressure was 9.79 MPa when this was substituted into Equation (2), i.e., a decrease by 44.40% compared with the conventional SR-joint pressure of 22.05 MPa.
## 3 Day-Ahead Active Power Market  
In the day-ahead active power market, an Initial Active Power Dispatch (IAPD) will be obtained by the MO for the forecasted load demand. This issue represents a bi-objective optimization problem in order to minimize the electrical energy costs and the gas emissions related to DERs and Disco. The generation units send their hourly selling bids, which consist of coupled quantity and price, to the MO. The total electrical energy costs generated by generation units are defined as:  
\[\mathit{COST}_{Energy}=\sum_{h=1}^{Nh}P_{Disco}^{h}\cdot\pi_{Disco}^{h}+\sum_{h= 1}^{Nh}\sum_{i=1}^{NDG}P_{DG,i}^{h}\cdot\pi_{DG,i}^{h}+\sum_{h=1}^{Nh}\sum_{i= 1}^{NWT}P_{WT,i}^{h}\cdot\pi_{WT,i}^{h} \tag{27}\]  
One of the most important emissions from the electricity sector is CO\({}_{2}\), which is represented by released pollution in terms of tons per MW. In order to economically illustrate the harmful effects of the emissions from the electricity sector activities on the environment, different techniques, such as penalties through carbon taxes or cap-and-trade technique, have been adopted [23]. In this study, the penalty cost function of the CO\({}_{2}\) emissions related to Disco, DGs, and WTs is calculated as follows:  
Figure 2: Reactive power capability of a WT considering the hourly wind power fluctuations.  
\[\begin{split} COST_{Disco}^{Emission}=\left\{\sum_{h=1}^{Nh}P_{Disco}^{h} \cdot\left(CO_{2,Disco}-CO_{2,cap}\right)\cdot EPC_{CO2}&if\ CO_{2,Disco}>CO_{2,cap}\\ 0&if\ CO_{2,Disco}\leq CO_{2,cap}\\ COST_{DG,l}^{Emission}=\left\{\sum_{h=1}^{Nh}P_{DG,l}^{h} \cdot\left(CO_{2,D,G,l}-CO_{2,cap}\right)\cdot EPC_{CO2}&if\ CO_{2,D,G,l}>CO_{2,cap}\\ 0&if\ CO_{2,D,G,i}\leq CO_{2,cap}\\ COST_{WT,i}^{Emission}=\left\{\sum_{h=1}^{Nh}P_{WT,i}^{h} \cdot\left(CO_{2,WT,i}-CO_{2,cap}\right)\cdot EPC_{CO2}&if\ CO_{2,WT,i}>CO_{2,cap}\\ 0&if\ CO_{2,WT,i}\leq CO_{2,cap}\\ \end{split}\right.\end{split} \tag{28}\]  
Hence, the total penalty cost of CO\({}_{2}\) emissions produced by generation units is expressed as:  
\[COST_{Emissions}=COST_{Disco}^{Emissions}+COST_{DG}^{Emissions}+COST_{WT}^{ Emissions} \tag{29}\]  
The MO runs the IAPD problem, which can be modeled by Equations (30)-(34), as a uniform price auction and determines the accepted selling bids and active power market schedule for the next day. The hourly market clearing price (MCP) is determined as the maximum selling bid price accepted for each hour.  
_Objective function:_  
\[min\ \ Z=COST_{Energy}+COST_{Emissions} \tag{30}\]  
_Constraints:_  
\[0\leq P_{Disco}^{h} \leq PBid_{Disco}^{h} \tag{31}\] \[0\leq P_{DG,i}^{h} \leq PBid_{DG,i}^{h}\] (32) \[0\leq P_{WT,i}^{h} \leq PBid_{WT,i}^{h}\] (33) \[P_{Disco}^{h}+\sum_{i=1}^{NDG}P_{DG,i}^{h}+\sum_{i=1}^{NWT}P_{WT,i }^{h}=\sum_{i=1}^{Nbus}P_{D,i}^{h} \tag{34}\]  
Equations (31)-(34) represent the limits on generation and Equation (34) represents the constraint of demand/supply balance. The result of the initial schedule is submitted to the DSO to examine it from a technical viewpoint.
# A Method for Underwater Biological Detection Based on Improved YOLOXs
###### Abstract  
This article proposes a lightweight underwater biological target detection network based on the improvement of YOLOXs, addressing the challenges of complex and dynamic underwater environments, limited memory in underwater devices, and constrained computational capabilities. Firstly, in the backbone network, GhostConv and GhostBottleneck are introduced to replace standard convolutions and the Bottleneck1 structure in CSPBottleneck_1, significantly reducing the model's parameter count and computational load, facilitating the construction of a lightweight network. Next, in the feature fusion network, a Contextual Transformer block replaces the 3 \(\times\) 3 convolution in CSPBottleneck_2. This enhances self-attention learning by leveraging the rich context between input keys, improving the model's representational capacity. Finally, the positioning loss function Focal_EloU Loss is employed to replace IoU Loss, enhancing the model's robustness and generalization ability, leading to faster and more accurate convergence during training. Our experimental results demonstrate that compared to the YOLOXs model, the proposed YOLOXs-GCE achieves a 1.1% improvement in mAP value, while reducing parameters by 24.47%, the computational load by 26.39%, and the model size by 23.87%. This effectively enhances the detection performance of the model, making it suitable for complex and dynamic underwater environments, as well as underwater devices with limited memory. The model meets the requirements of underwater target detection tasks.  
underwater target detection; YOLOXs; GhostNet; Contextual Transformer; Focal_EloU Loss +
Footnote †: journal: Applied Physics Letters  
1
Footnote †: journal: Applied Physics Letters
## 1 Introduction
Note that, when SAPF performs its intended mitigation function, it will at the same time draw a small amount of current (commonly referred as dc-link charging current \(i_{dc}\)) from the power system to regulate its switching losses. From another point of view, the charging current is drawn to maintain a constant dc-link voltage so that a steady output voltage can be produced at the ac side of the SAPF. As for the load configuration, in a three-phase four-wire system, the connected load can be a balanced three-phase load and balanced/unbalanced group of three single-phase loads. Ideally, a proper function SAPF should be able to remove harmonic and excessive neutral currents from the power system, and as a result, the source current \(i_{S}\) which is initially non-sinusoidal will recover its sinusoidal appearance, operate with fundamental frequency and in-phase with the source voltage \(v_{S}\), while the neutral current \(i_{N}\) will be ideally zero. Nevertheless, controlling a SAPF to perform mitigation function in a three-phase four-wire system is no easy task. In this work, its controller (as illustrated in Figure 1) needs to perform few inter-related functions which include extraction of fundamental component of load current \(i_{L}\), tracking instantaneous phase (sine function) of source voltage \(v_{S}\), estimation of \(I_{dc}\) (magnitude of \(i_{dc}\)), estimation of \(I_{balance}\) (magnitude of balancing current), derivation of reference current \(i_{S,\ ref}\), and lastly generation of gate switching pulses. It is worth noting that a typical SAPF will operate according to the characteristics of reference current signal \(i_{S,\ ref}\). A good functioning SAPF is the one with the ability to produce the desired injection current \(i_{inj}\) and concurrently maintain its dc-link capacitor voltage at constant level and voltage balance across each capacitor voltage (if more than one capacitor is employed). Hence, as an overall, the reference current signal \(i_{S,\ ref}\) will need to contain complete details on the power system's harmonics for harmonic cancellation, the power system's operating phases for synchronized operation, amount of dc-link charging current for regulation of dc-link voltage, and amount of balancing current for voltage balancing of split capacitors. This paper will solely discuss the process to generate reference current signal in which the ADALINE concept is applied. Particulars of the control process are provided in Section 3. Nevertheless, for a SAPF to work, other algorithms are also required to be implemented in its controller as illustrated in Figure 1. In this regard, to maintain voltage balance of split capacitor and constant overall dc-link voltage, a simple proportional-integral (PI) technique [23; 27] is adopted. Meanwhile, to convert the reference current signal into gate switching pulses, a standard hysteresis band current control (HBC) technique is adopted [23; 28]. Both PI and HBC are adopted because they are the most straightforward control techniques available to perform the aforementioned functions [3].
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 8 Future Directions in 6G Technology Development
### Extreme Global Network Coverage  
Optimized 3D localization, along with network planning, is essential during the installation of flying relays or BSs. Unmanned aerial vehicles (UAVs) or drones are attributed with resource and capacity limitations and involve heterogeneous processing, thus making it essential to conduct such research. Other important concerns regarding the worldwide network coverage that must be investigated and considered in future research include the use of technology for ensuring smart 3D roaming mechanisms for heterogeneous networks and also ensuring that various operators support secure and safe interoperability. Future research must evaluate the FL (fuzzy logic)-based optimization, which involves local training of ML models by different networks. The networks in FL-based optimization involve update sharing on models instead of information sharing, thus leading to a better 6G ecosystem across the world and ensuring universal network coverage at the same time. Moreover, channel estimation and dynamic link optimization have also been identified as popular subjects for future research leading to extensive global coverage. More effective and reasonable global coverage solutions can be developed by implementing complex procedures.
## 3 Methods for the Characterisation and Evaluation of the Skid Resistance of Asphalt Pavements
### Characterisation Methods for Asphalt Pavement Macro- and Microtextures
Surface roughness can also be divided according to the size of the wave spacing--wave spacing of more than 10 mm is considered part of a macrotexture; a wave distance of less than 1 mm is considered part of a microtexture; and a wave distance between the above two or with periodic changes is considered part of a wave degree. The above two types of road surface texture are only characterised differently, there are no fundamental differences. By analysing the way in which multiple pavement textures are characterised, the following conclusions can be drawn: microtexture predominantly affects skid resistance under low-speed and dry pavement conditions, whereas macrotexture is the primary contributor to frictional performance at high speeds on wet pavements [57]. The presence of macrotexture enables effective surface water drainage through the formation of flow channels, thereby minimising the likelihood of hydroplaning. Conversely, microtexture improves type-pavement adhesion by increasing the actual contact area between the tyre and the pavement surface. This effect becomes particularly significant under wet conditions where a thin water film exists, thereby enhancing skid resistance in such scenarios. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Texture Type** & **Wavelength (mm)** & **Amplitude (mm)** \\ \hline Unevenness & \(>\)500 & 1\(\sim\)200 \\ Large texture & 50\(-\)500 & 1\(\sim\)50 \\ Macrotexture & 0.5\(\sim\)50 & 0.2\(\sim\)10 \\ Microtexture & \(<\)0.5 & 0\(\sim\)0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Texture structure classification and formation reasons. Figure 4: Geometric scale of pavement textures.
# Large-Scale Grid Integration of Renewable Energy Resources with a Double Synchronous Controller
## 7 Results and Discussion
### Impacts of Virtual Mechanical Power Error (VMPE) on Operation of the DSC  
Selecting different values of VMPE in the DSC can affect the power grid stability. Figure 13 shows the grid frequency and the grid voltage magnitude of the DSC under three low, high, and very high values of VMPE. As can be seen in Figure 13, for the low values of the VMPE, accurate results are achieved for both state variables. According to Figure 13a,b, the unstable behaviors of the grid frequency and grid voltage magnitude reactions for these very high values of VMPE are obvious. Simulation results demonstrated that the VMPE and inertia effectively impact on the operation of the DSC. Also, for high values of VMPE, both steady-state and dynamic responses of all state variables can follow their desired values with a different margin that generally is superior for the DSC compared with the case due to various values of virtual inertia.
# Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review
## 4 DNNs and Irreversibly Compressed Images
Table 3 summarizes the results presented above. Based on the research discussed, training DL models with irreversibly compressed images has emerged as a significant concern in the design of these models for medical image analysis. The literature on the impact of irreversible compression on DL model performance presents varied findings. Some studies indicate that DL models are adversely affected only at extremely high compression ratios, while others suggest improved performance on compressed images due to learning from compression artifacts. Most authors agree that DL \begin{table}
\begin{tabular}{c c} \hline \hline
**Study** & **Findings** \\ \hline \multirow{4}{*}{[PERSON] et al. (2016) [67]} & DNNs were sensitive to noise/blur but robust to \\  & JPEG/ JPEG2000 compression; training on degraded images \\  & improved similar cases but reduced uncompressed accuracy. \\  & Performance dropped for quality factor \textless{} 10 for JPEG and \\  & PSNR \textless{} 30 for JPEG2000. \\ \hline \multirow{2}{*}{[PERSON] et al. (2017) [68]} & Higher JPEG compression enhanced CNN classification via \\  & compression artifacts. \\ \hline \multirow{4}{*}{[PERSON] et al. (2020) [72]} & Pathology images retained 95\% DL performance at 85\% \\  & compression; tolerable compression aligned with \\  & pathologists’ limits. \\ \hline \multirow{4}{*}{[PERSON] et al. (2020) [69]} & Partial decompression lowered DNN complexity with \\  & minimal accuracy loss; JPEG quality factor of 80 cut \\  & computation by 45\%, with 13\% accuracy reduction. \\ \hline \multirow{4}{*}{[PERSON] et al. (2021) [71]} & Mammogram classification declined only at extreme \\  & JPEG2000 compression (>5000:1). Mixed compression ratios \\ \cline{1-1}  & improved generalization; single-ratio training/testing \\ \cline{1-1}  & preserved accuracy. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of literature findings on the impact of irreversible compression artifacts on the training and testing of DNN models in image analysis. models exhibit invariance to irreversibly compressed images at lower compression ratios, while higher ratios do impact training and testing. However, a threshold compression ratio at which models perform better due to learning artifacts has not been established for general modalities or anatomical regions. The existing studies depend on specific datasets, restricting the broader applicability of their conclusions. Consequently, the current evidence is insufficient to form a unified consensus on the effects of irreversible compression on DL models for medical images. These findings provide valuable insights into various methodologies for evaluating DL models with uncompressed, compressed, and mixed image data, establishing a foundation for future studies.
# Technology Behavior Model--Impact of Extended Reality on Patient Surgery
## 3 Materials and Framework
### Technology Behavior Model  
The theory of planned behavior (TPB) focuses on behavior change in the subjective consciousness of an individual, and the technology acceptance model (TAM) focuses on the change of human behavior by external factors of new technologies and information [6]. These can be understood as one in which people are subjected to internal psychological changes in behavior and one in which people are subjected to external factors that change behavior. In contrast, when studying human behavior change, the TAM is only able to explain 30-40% of causality, ignoring the actual needs of individuals [48], while the TPB lacks research on poor behavior. Therefore, it includes both subjective and perceived consciousness, and a single use of one model cannot accurately identify the main behavioral intention used [43]. The current study is in the medical field, firstly for extended reality smart glasses, a new product, and the new technology is in line with the technology acceptance model. Secondly, it is the behavioral awareness of the individual through which the data is collected, the perceptual awareness. This in turn is consistent with the theory of planned behavior. Therefore, a new theoretical model, the technology behavior model (TBM), is proposed, using a combination of the existing TAM and TPB. This model enables a more comprehensive study of human behavior change as being influenced by both intrinsic and extrinsic factors. Additionally, this model is combined with extended reality technology.
## 5 Simulation and Analysis
### Case 2  
Theoretically, if the circuit parameters of two branches are identical, the switch of the PWM converter will be synchronized, so the zero-sequence duty ratio difference of the two converters will be zero. According to Equation (9), there will be no ZSCC in the system. In this case, the filter inductance of the AC side was equal, and the proposed control strategy was utilized, where \(L_{1}=L_{2}=3\)mH and other parameters are same as those listed in Table 1. The parameters of the control strategy proposed in this paper are proportional coefficient \(k_{\mathrm{pz}}=0.55\), integral coefficient \(k_{\mathrm{z}}=80\), resonance coefficient of the 3 rd harmonic \(k_{\mathrm{rz3}}=600\), resonance coefficient of the 9 th harmonic \(k_{\mathrm{rz9}}=400\), resonance coefficient of the 15 th harmonic \(k_{\mathrm{rz15}}=200\), and cut-off angular frequency \(\omega_{\mathrm{c}}=1\) rad/s. The ZSCC waveforms are shown in Figure 10.  
Figure 10: ZSCC of parallel system in equal inductance condition: (**a**) without any ZSCC suppression strategy; (**b**) with the proposed ZSCC suppression strategy.  
Figure 10a demonstrates that there was still a ZSCC in this case. However, the amplitude was small, and the peak-to-peak value between 0.3 and 0.6 s was 2.75 A. Therefore, it was impossible to make the switching time of the two converters exactly the same. As shown in Figure 10b, when the control strategy was added to the parallel system, the effect of reducing the ZSCC amplitude was obvious. The peak-to-peak value of ZSCC was 0.6 A. Compared with no control strategy, the ZSCC amplitude was reduced by 78%, which means that the control strategy proposed in this paper could effectively suppress ZSCC in this case.