esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Each of these communication techniques has its unique frequency of operation, available data rate, and particular application. Broadband-based PLC uses high-speed data rates, and it consists of various applications related to the internet, for example, voice over the internet. Home-based PLC networks are also an example of broadband PLC running at 200 MHz, which is much higher than the 1.8 MHz frequency range. The NB-PLC line uses lower data rates, thus supporting lower data rate applications for example sensing utility applications, remote control, and AMR. The frequency of operations for NB-PLC is below 500 kHz. Smart meters use the NB-PLC to transmit the data of consumption of users' energy in real-time. In Europe, the NB-PLC uses CENELEC bands that range between 3 kHz and 148.5 kHz. In the United States, the approved band of NB-PLC is in the range of 10 to 490 kHz, whereas the Japanese Association of Radio Industries and Businesses recommends a 10 to 450 kHz band, whereas in China, NB-PLC use a frequency band of 3 to 500 kHz [2; 5]. This paper utilizes NB-PLC for the control of various electrical appliances connected with the power grid to provide DSM services. Among such appliances, air conditioners consume a large amount of power in the summer months, which is a point of concern. This research work incorporates NB-PLC control on typical and variable frequency air conditioners and converts them from conventional air conditioners to SACs, which consume power in an optimized way. Moreover, the rapid growth of urbanization has also given rise to the demand of power consumption. This paper proposes a DSM control technique for SACs installed at residential, commercial, and industrial consumer ends to shave the peak power consumption profile. The NB-PLC control command is implemented from a master control room (MCR) towards SACs, and vice versa, by using smart energy profile 1.0 (SEP 1.0) communication protocol [6]. Control commands have the ability to change the temperature of thermostat between 1 \({}^{\circ}\)C and 2 \({}^{\circ}\)C in a way that does not cause discomfort but can play an effective role in shaving the peaks.
## 5 Conclusions
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year (Ref.)**} & **Biomedical** & **Best Overall** & **Sample Size** & **ACC (\%)** & **SN (\%)** & **SP (\%)** \\  & **Application** & **Classifier** & **Sample Size** & **ACC (\%)** & **SN (\%)** & **SP (\%)** \\ \hline
2012 [64] & Rheumatoid arthritis & Decision Trees & 52 (26 healthy, 26 ill) & & 96 & 92 \\ \hline
2013 [36] & Breast cancer & AdaBoost &
\begin{tabular}{c} 32 (9 malignant, \\ 12 benign, 11 normal) \\ \end{tabular} & 95 & & & \\ \hline
2013 [30] & Breast cancer & SVM &
\begin{tabular}{c} 63 (29 healthy, \\ 34 malignant) \\ \end{tabular} & 100 & & \\ \hline
2013 [47] & Breast cancer & Ensemble classifier &
\begin{tabular}{c} 146 (29 malignant, \\ 117 benign) \\ \end{tabular} & 88.7 & 81.3 & 95.5 \\ \hline
2013 [48] & Breast cancer & Ensemble classifier &
\begin{tabular}{c} 146 (29 malignant, \\ 117 benign) \\ \end{tabular} & 87.4 & 82.1 & 88.7 \\ \hline
2013 [50] & Breast cancer & Ensemble classifier &
\begin{tabular}{c} 146 (29 malignant, \\ 117 benign) \\ \end{tabular} & 90.0 & 82.5 & 91.8 \\ \hline
2013 [37] & Breast cancer & Naive-Bayes &
\begin{tabular}{c} 98 (77 malignant, \\ 21 healthy) \\ \end{tabular} & 71.8 & 82 & 37 \\ \hline
2014 [42] & Breast cancer & Euclidean Distance &
\begin{tabular}{c} 50 (14 malignant, \\ 19 benign, 17 cysts) \\ \end{tabular} & 84 & 85.7 & 86.5 \\ \hline
2014 [27] & Breast cancer & SVM &
\begin{tabular}{c} 22 (11 normal, \\ 11 abnormal) \\ \end{tabular} & 90.9 & 81.8 & 100 \\ \hline
2014 [28] & Breast cancer & SVM &
\begin{tabular}{c} 36 (24 normal, \\ 12 malignant) \\ \end{tabular} & 83.3 & & \\ \hline
2014 [49] & Breast cancer & Ensemble classifier &
\begin{tabular}{c} 146 (29 malignant, \\ 117 benign) \\ \end{tabular} & 89.0 & 81.9 & 90.8 \\ \hline
2014 [43] & Breast cancer & k-NN &
\begin{tabular}{c} 40 (26 normal, \\ 14 abnormal) \\ \end{tabular} & 92.0 & 78.6 & 100 \\ \hline
2014 [62] & Dry-eye disease & k-NN &
\begin{tabular}{c} 81 (40 responded and \\ 41 not responded) \\ \end{tabular} & 99.8 & 99.7 & 100 \\ \hline
2015 [66] & Back Pain & SVM &
\begin{tabular}{c} 1000 (300 healthy, \\ 200 faulty posture \\ and 500 lateral \\ spinal curvature) \\ \end{tabular} & 90 & 88 \\ \hline
2015 [31] & Breast cancer & SVM &
\begin{tabular}{c} 63 (29 healthy, \\ 34 malignant) \\ \end{tabular} & 100 & & \\ \hline
2015 [52] & Diabetic Foot & ANN &
\begin{tabular}{c} 60 (30 non-diabetic, \\ 30 diabetes) \\ \end{tabular} & 94.3 & 97.3 & 91.3 \\ \hline
2015 [38] & Breast cancer & k-NN &
\begin{tabular}{c} 18 (9 normal, \\ 9 abnormal) \\ \end{tabular} & 99.4 & & \\ \hline
2015 [41] & Breast cancer & SVM &
\begin{tabular}{c} 50 (37 normal, \\ 13 abnormal) \\ \end{tabular} & 88 & 76.9 & 91.9 \\ \hline
2015 [25] & Breast cancer & ANN &
\begin{tabular}{c} 306 thermograms of \\ 102 individuals \\ (123 unhealthy, \\ 183 healthy) \\ \end{tabular} & 90.4 & 87.6 & 89.7 \\ \hline \hline \end{tabular}
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 6 Conclusions  
This study proposes a method of estimating critical latency that causes two vessels to collide to avoid collisions in remote maneuvering. The basic concept of critical latency was established by applying the turning circles formed by the ship's turning, and based on this, critical latency was formulated using the radius of the turning circle and turning time ratio. Turning circle data were obtained through ship maneuvering simulations, and the estimated performance of critical latency was evaluated in the four designed scenarios. The study findings can be summarized as follows:  
* In the case of the cargo vessel (171.8 m long, 23.17 m wide) applied to the experiment, the collision occurred with a critical latency having an average range of 0.2-8.53 min depending on the rudder angle and with a critical latency having an average range of 0.2-12.74 min depending on the vessel speed.
* Evaluation of the estimated performance of critical latency demonstrated that the collisions caused by the designed route and critical latency can be identified in terms of location and time. As a result, the proposed method is expected to contribute to providing a clue to avoid ship collisions in remote maneuvering that may be caused by latency. In addition, the proposed method did not consider the scale of the vessel. In this regard, future studies about the method of considering the ship domain and collision avoidance assessment in estimating critical latency are required. It is expected to be conducted by introducing the ship domain concept [39] in which the two vessels can be safely separated in various encounter situations.  
**Author Contributions:** Conceptualization, J.-B.Y. and D.-J.P.; methodology, J.-B.Y.; software, J.-B.Y.; validation, J.-B.Y. and D.-J.P.; data curation, D.-J.P.; writing--original draft preparation, J.-B.Y.; writing--review and editing, D.-J.P.; supervision and funding acquisition, J.-B.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Development of Autonomous Ship Technology [grant number 20200615], funded by the Ministry of Oceans and Fisheries (MOF, Korea).  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Numerical Results  
In this section, we present the numerical assessment of both the link- and system-level of a massive/ultra-massive MU-MIMO downlink scheme operating in the mmWave/THz band integrated into a 5G NR system, where the APs are based on the low complexity ADMM-based hybrid precoding designs. Link-level results are presented in terms of bit error rate (BER) and measure the performance of the signal across the entire communication chain, from transmitter to receivers. Both the link- and system-level diagrams can be found in references [15; 27].
# Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery
## 2 Blister Defect Detection for PLB Based on CNN
### Dataset and Training  
Some images of both sides of PLB sheets were captured in the automatic line. The size of the PLB image obtained from the camera was 2448 \(\times\) 2048. The size of the input image in the CNN method was 219 \(\times\) 219. We cut the polar area in each image of the PLB into 160 patch images with  
Figure 6: Flow chart of FPA implementation.  
the size of 219 \(\times\) 219. Because each PLB has two sides, 320 patch images of each PLB were taken. Professional engineers selected 600 qualified PLBs and 600 blister PLBs to obtain images. After cutting out patches, a typical patch was selected as sample image. Finally, a blister sample image dataset was created including 11,600 qualified images and 10,460 blister images. Then, 1800 qualified images and 1400 blister images were selected randomly for test in the CNN training, and the other images were employed for training.  
It was obvious that the dataset was not large enough. To overcome the overfitting problem caused by small dataset training in CNN, transfer learning was employed. A set of pre-trained weights was transferred from ImageNet to the network proposed in this paper. After transferring network weights, the proposed network could be trained employing the blister image dataset. To enhance CNN image classification performance, batch normalization, dropout strategy and early stop scheme were used as in other image classification task.
## 4 Simulations and Experiments
### Simulations
#### 4.1.3 Simulation Results of Motion Control  
Figure 8 shows the simulation results of motion control which applied feedback signals processed by corresponding methods. Calculate the RMSE between position response and position command according to Equation (27). Compared with the response with gyro feedback, the RMSE of the response with feedback signal via DFBGA decreased 25.51% and the RMSE of the response with feedback signal via proposed method decreased 38.07%. It can be seen that applying the method proposed in this paper to the feedback loop of motion control has more accurate command response and will not cause system instability.
## 7 Discussion
### Comparative Analysis of Surveyed Works
#### 7.1.2 Domain-Specific Applications  
A significant portion of LLM4 Rec research is clustered in domains with dense interaction data, such as e-commerce and healthcare. However, educational and social applications are now increasingly represented due to demand for transparency, user control, and contextualization, See Table 13.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Base Model** & **Limitations** & **Scalability** & **Latency Suitabilitiy** & **Multimodal Cost/Eval** \\ \hline ClinicalBERT [85] & BERT & Limited to unstructured clinical notes & Moderate & Moderate & No & Moderate/ AUC = 0.768 \\ \hline CMS [154] & BERT + Sensor Fusion & Needs high-quality wearables & Low & Low & Yes & High/ F1 = 0.792 \\ \hline PALR [79] & Cross-Attention + BERT & Overfits on small datasets & Moderate & Moderate & No & Moderate/ HR@10 = 0.658 \\ \hline BeCoLLM [155] & GPT-style LLM + Behavior Science & Needs behavioral context history & High & High & Yes & High/ AUC = 0.812 \\ \hline ICL [153] & GPT-3 & Inconsistent for frequent changes & Moderate & High & No & Moderate/ F1 = 0.749 \\ \hline LoRec [156] & Transformer & Expensive adversarial training & Low & Low & No & High/ AUC = 0.784 \\ \hline XLNet4 Rec [34] & XLNet & Requires long exercise history & Moderate & High & No & High/ NDCG@10 = 0.662 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Healthcare-focused LLM4 Rec models. Source: Author’s own design.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model Type** & **Percentage** & **Examples/Characteristics** \\ \hline Transformer-Based Encoders & 38\% & BERT4 Rec, RoBERTa, UniSRec are optimized for sequential and ranking tasks. \\ \hline Generative Models & 26\% & GPT4 Rec, RecMind support conversational, narrative, and open-ended recommendations. \\ \hline Multimodal Models & 18\% & CLIP, RLMRec integrate visual, behavioral, and textual signals for richer context modeling. \\ \hline Scalable Models & 8\% & Megatron-LM, Switch Transformer leverages tensor and pipeline parallelism for deployment. \\ \hline Prompt/Instruction-Tuned & 10\% & FLAN, DeBERTa, and CoLLM enable rapid adaptation with few-shot or zero-shot prompts. \\ \hline \hline \end{tabular}
\end{table}
Table 12: Distribution of LLM architectures in surveyed works. Source: Author’s own design.
###### Abstract  
This study aimed to evaluate the subsurface drainage of a permeable friction course (PFC) via two-dimensional finite element analysis. To achieve the scope, PFCs with equivalent water flow paths of length values of 10, 15, 20, and 30 m and slope values of 0.5%, 2%, 4%, 6%, and 8% were modelled based on FEniCS and implemented entirely in Python programing language to extract the time for surface ponding according to a range of rainfall intensities. The results show that when the rainfall intensity and the length of equivalent water flow path of the PFC rose, the time for surface ponding decreased. For instance, with a rainfall intensity of 10 mm/h and a slope of 0.5%, when the length of equivalent water flow path increased by 20 m, the time for surface ponding dropped by 21 min. Moreover, when the slope of the equivalent water flow path and the thickness of the PFC increased, the time for surface ponding increased. For instance, with a rainfall intensity of 10 mm/h, and a PFC with an equivalent length of 10 m, when the slope increased by 16 times, the time for surface ponding increased more than two times. The current study highlights that the thickness of the PFC has the most influence on subsurface drainage. The findings of this study indicate that at high rainfall intensities, the subsurface drainage of a PFC is not sensitive to its geometric design. Further experimental investigations are needed to evaluate and validate the subsurface drainage of a PFC considering permeability, rutting, and environmental factors.  
subsurface drainage; time for surface ponding; permeable friction course; geometric design; Python; FEnics +
Footnote †: journal: Applied
## 6 Therapeutic Actions of Madecassoside and Other Major Compounds in _C. asiatica_
### Cardiovascular Diseases
Asiatic acid was also shown to reduce blood pressure and improve vascular function in L-NAME-induced hypertensive rats by enhancing NO bioavailability [174], possibly via upregulation of eNOS protein expression and suppression of inflammation and oxidative stress [175]. In a rat model of the metabolic syndrome (MS) induced by a high-carbohydrate-high-fat (HCHF) diet, asiatic acid supplementation improved the metabolic and cardiovascular complications [176], as well as improved vascular function and reduced activation of the renin-angiotensin system [177]. Asiatic acid could also protect the heart in experimental diabetes in the mouse by attenuating glycative injury and coagulatory disorders [178]. Atherosclerosis, chiefly in coronary artery disease (CAD) or carotid stenosis, is the leading cause of myocardial infarction and stroke, which together are responsible for more than 50% deaths worldwide [179]. Asiaticoside was shown to impede endothelial hyperpermeability [180], thus possibly disrupting development of early atherosclerotic events [181]. Similar observations were found in studies testing the efficacy of asiatic acid in protecting human aortic endothelial cells [182; 183].
## 6 Conclusions  
This work embedded the aero-engine core compressor preliminary design strategy into a preliminary optimal design method for the preliminary optimization of large-scale air separation main compressors. Six design parameters widely used to define the aero-engine compressor configuration, i.e., aspect ratio, reaction, solidity, rotation speed, outlet axial Mach number, and inlet radius ratio, were decomposed into design variables. Optimized compressor configurations were then refined with the throughflow theory-based detailed design and finally verified with CFD simulations.  
The method was illustrated by the optimization of 4-stage, 5-stage, 6-stage, and 7-stage axial-flow industrial process compressors with the same mass flow rate and total pressure ratio. The stage average total pressure ratio of the four compressors varies from 1.22 to 1.41. The CFD prediction indicated that the optimized design adiabatic efficiencies increased 0.80%, 2.44%, 0.56% and 1.34% compared to the initial design adiabatic efficiency for the 4-stage, 5-stage, 6-stage, and 7-stage axial-flow industrial process compressors, respectively.  
From the analysis of the optimal results, some important preliminary design guidelines can be obtained for the best design efficiency potential of multi-stage axial-flow compressors in a large-scale air separation device, as follows:  
1. As the number of stages increases, the optimal average aspect ratio increases from 1.44 to 1.58.
2. The optimal average solidity varies within a small range from 1.28 to 1.33.  
Figure 14: Rotor 1 outlet total pressure: (**a**) 4-stage; (**b**) 5-stage; (**c**) 6-stage; (**d**) 7-stage.  
3. The best efficiency is attainable at the average reaction range from 0.44 to 0.49 as the stage number increases.
4. As the stage number increases, optimal rotation speed decreases from 6125 rpm to 4053 rpm.
5. The optimal outlet axial Mach number is approximately 0.34.
6. The best efficiency is attainable when the inlet radius ratio is about 0.3.  
The developed method is quite generic, releasing the designers to be free from experience dependence. The extension to two-dimensional throughflow-based optimization and three-dimensional blade optimization of compressor is ongoing.  
Conceptualization, F.L. and C.Z.; methodology, F.L.; validation, F.L.; formal analysis, F.L.; investigation, F.L.; resources, F.L.; writing--original draft preparation, F.L.; writing--review and editing, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## Materials and Methods
### Vegetation Indices  
Eight LAI-sensitive narrowband VIs (Table 2) were calculated from the spectral reflectance data collected with the airborne sensor and the simulated dataset. The indices were calculated using AISA bands and model-simulated AISA bands that were closest to the original wavelengths.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Index** & **Equation** &
\begin{tabular}{c} **Central Wavelength** \\ **Used in This Study** \\ \end{tabular} & **Reference** \\ \hline BNDVI & \(\left(\text{R}_{800}-\text{R}_{450}\right)/\left(\text{R}_{800}+\text{R}_{450}\right)\) & \(\text{R}_{805}\), \(\text{R}_{452}\) & [21] \\ GARI & \(\text{R}_{800}/\text{R}_{530}-1\) & \(\text{R}_{805}\), \(\text{R}_{533}\) & [20] \\ GNDVI & \(\left(\text{R}_{800}-\text{R}_{530}\right)/\left(\text{R}_{800}+\text{R}_{530}\right)\) & \(\text{R}_{805}\), \(\text{R}_{533}\) & [22] \\ MSAVI & \(0.5\left[2\text{R}_{800}+1-\sqrt{\left(2\text{R}_{800}+1\right)^{2}-8\left( \text{R}_{800}-\text{R}_{680}\right)}\right]\) & \(\text{R}_{805}\), \(\text{R}_{682}\) & [18] \\ MSR\({}_{705}\) & \(\left(\text{R}_{750}/\text{R}_{705}-1\right)/\sqrt{\text{R}_{750}/\text{R}_{705 }+1}\) & \(\text{R}_{748}\), \(\text{R}_{701}\) & [3] \\ NDVI & \(\left(\text{R}_{800}-\text{R}_{680}\right)/\left(\text{R}_{800}+\text{R}_{680} \right)\) & \(\text{R}_{805}\), \(\text{R}_{682}\) & [16] \\ SAVI & \(\left(\text{R}_{800}-\text{R}_{680}\right)\left(1+0.5\right)/\left(\text{R}_{800 }+\text{R}_{680}+0.5\right)\) & \(\text{R}_{805}\), \(\text{R}_{682}\) & [17] \\ SR & \(\text{R}_{800}/\text{R}_{680}\) & \(\text{R}_{805}\), \(\text{R}_{682}\) & [19] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Narrow-band vegetation indices used in the study.
## 3 Improved Modulation Strategy Analysis of Six-Switch Converter Based on Time Average Modulation
So, the average current \(i_{4}{}^{\prime}(t)\) can be written as:  
\[i_{4}^{\prime}(t)=\frac{d_{1}+d_{3}+d_{4}-d_{6}}{2}i_{s}(t)-(1-d_{6})i_{o}(t) \tag{20}\]  
\[i_{c}^{\prime}(t)=\frac{d_{1}-d_{3}-d_{4}+d_{6}}{2}(i_{s}(t)-i_{o}(t)) \tag{21}\]  
Substituting \(d_{1}\), \(d_{3}\), \(d_{4}\) and \(d_{6}\) into Equation (20), it yields:  
\[ \begin{array}{l}i_{c}^{\prime}(t)=\frac{1}{4}\{[m_{r}I_{s}\cos\theta_{1}-m_ {r}I_{o}\cos(\theta_{o}-\varphi)\\ +m_{i}I_{s}\cos(\varphi-\theta_{1})-m_{i}I_{o}\cos\theta_{o}]\\ +[-m_{r}I_{s}\cos(2\omega t+\theta_{1})+m_{r}I_{o}\cos(2\omega t+2\theta_{1}- \theta_{o}+\varphi)\\ -m_{i}I_{s}\cos(2\omega t+\theta_{1}+\varphi)+m_{i}I_{o}\cos(2\omega t+2\theta _{1}-\theta_{o}+2\varphi)]\}\end{array}  \tag{22}\]  
The constant term in Equation (22) is identical to zero based on the assumptions that no switching or capacitor loss. \[ \begin{array}{l}i_{c}^{\prime}(t)=\frac{1}{4}[-m_{r}I_{s}\cos(2\omega t+ \theta_{1})\\ +m_{r}I_{o}\cos(2\omega t+2\theta_{1}-\theta_{o}+\varphi)\\ -m_{i}I_{s}\cos(2\omega t+\theta_{1}+\varphi)+m_{i}I_{o}\cos(2\omega t+2\theta _{1}-\theta_{o}+2\varphi)]\end{array}  \tag{23}\]  
Amplitude of current \(i_{c}\) can be got as below through expansion of (23). \[I_{c}^{\prime}=\frac{1}{4}m\sqrt{2(1+\cos\varphi)(I_{o}^{2}+I_{s}^{2}-2I_{o}I _{s}\cos(\varphi+\theta_{1}-\theta_{o}))} \tag{24}\]  
\(I_{c}^{{}^{\prime}}\) can reach its minimum when \(\varphi=\theta_{o}-\theta_{1}\):  
\[I_{\rm{cmin}}^{\prime}=\frac{1}{4}m|I_{o}-I_{s}|\sqrt{2(1+\cos(\theta_{o}- \theta_{1}))} \tag{25}\]  
The above has been obtained \(I_{cmin}=0.5m\,|\,I_{o}-I_{s}\,|\,\). Subtracting Equation (18) from (25) to compare the amplitude, it yields:  
\[ \begin{array}{l}I_{\rm{cmin}}-I_{\rm{cmin}}^{\prime}\\ =\frac{1}{4}m|I_{o}-I_{s}|\Big{(}2-\sqrt{2(1+\cos(\theta_{o}-\theta_{1}))} \Big{)}>0\end{array}  \tag{26}\]  
It can be known \(I_{cmin}>I_{c}^{{}^{\prime}}\,{}_{min}\) from (26). The calculation result shows that improved modulation strategy reduces the amplitude of \(I_{c}\) and the DC side voltage fluctuation. Therefore, a smaller capacitor \(C_{d}\) can be used to maintain the relative stability of the DC voltage \(v_{d}\), thereby achieving the purpose of reducing the capacitance. The derivations of \(I_{cmin}\) and \(I_{c}^{\prime}\,{}_{min}\) have already reflected the difference of modulation strategy. Therefore, whether the improved modulation strategy is effective can be judged by observing the fluctuation of DC side capacitor voltage under the line frequency cycle.