esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Boston Brace as a Research Subject  
The Boston brace system's purpose is to enable non-surgical scoliosis treatment by attempting to prevent the progression of scoliosis in growing patients [18]. Generally, the system is effective when applied to arcs with an apex among T-6 and L-3. A Boston brace is usually ineffective for curves with apexes outside of this range [16]. In this regard, a Boston brace cannot be rigid in its top section and must only rest on the patient's amrpits to protect the orthoses from shifting.  
For this study, a Boston brace for the treatment of the left lumbar curve was chosen. A 4 mm thick sheet of polypropylene was thermally moulded into an orthosis. Polyurethane foam was used to line the inside of the orthoses. An example of a Boston brace and a diagram of the scoliosis described above are presented in Figure 1. In comparison to other types of orthoses, such as the Cheneau brace, which has a spatial system of several corrective forces, the Boston brace has a three-point system of corrective forces [19]. Forces \(F_{1}\) and \(F_{3}\) act on the spine by exerting pressure on the ribs, while force \(F_{2}\) acts on the pelvis. Vector \(F_{3}\) is the corrective force, and vectors \(F_{1}\) and \(F_{2}\) make up the counteracting force. The application of a relatively simple system of forces makes it possible to simplify the model and thus create a more plausible numerical model.  
Figure 1: Boston brace—diagram of the left lumbar curve with a simplified correction scheme (view from front). Forces exerted by the brace: \(F_{1}\) and \(F_{3}\)—forces acting on the thoracic cage, \(F_{2}\)—force applied to the pelvis.  
In the conducted research, it was proposed to modify the brace structure in such a way that it does not change the distribution of the corrective forces of the orthosis after optimizing its structure. Thus, the assumed pressure distribution on the trunk should remain unchanged. Point loads were assumed in the model. In a real case, the corrective forces of the orthosis are distributed over a certain body surface while keeping the pressure values within the allowable range. The issue of modelling the brace pressures on the torso is complex and has been devoted a lot of space in the literature, therefore it was not the subject of experimental research in this work.
The implementation of a flower-shaped fractal, incorporating rectangular and circular slots inside the ground plane, has been proposed as a means to mitigate isolation [23]. The utilization of a fractal geometry of modified Sierpinski, incorporating a squared stub in the ground plane, has been shown to offer improved bandwidth and isolation, as seen in reference [24]. Metamaterials have been employed to achieve improved isolation, as indicated by previous studies [25; 26]. Shorting pins are also used for isolation enhancement [27]. A 5G-shaped strip, including a modified G shape on the patch and a partial ground structure enhance antenna bandwidth, ECC, and isolation [28]. The utilization of Sierpinski triangle fractal geometry, coupled with a composite right/left back structure, offers improved isolation and enhanced bandwidth [29]. The objective of this research work is to design a novel fractal geometry-based compact MIMO antenna that could be able to support multiple wireless communication systemsincluding mm-wave 5G with superior diversity performance, improved isolation, and broader impedance bandwidths at multiple bands. The suggested antenna is a two-port wideband multiple input multiple output antenna that is constructed with fractal geometry and has a defective ground construction. Therefore, the novel aspects of our work include its multiband characteristics as well as its wide bandwidth while maintaining good mutual coupling. The suggested antenna has resonant frequencies of 9.5 GHz, 11.1 GHz, 13.4 GHz, 15.8 GHz, 21.1 GHz, and 26.6 GHz, an impedance bandwidth of \(-\)10 dB that covers 1000, 1000, 600, 800, and 10,300 MHz, a maximum isolation of \(-\)48.37 dB, an ECC of 0.004, a size of 28.22 mm \(\times\) 44 mm, and an excellent diversity gain across the entire band. New fractal antenna geometry, miniaturized size, multiband operation, enhanced operating bandwidth, desirable radiation patterns, high isolation, and superior diversity characteristics are the findings obtained by the prescribed two-port MIMO radiator. The work proposal outlined in this document is presented in the following sections: The technique for designing the reference antenna is addressed in Section 2, subsequent to which the outcomes and analysis are presented in Section 3. A comparison with previous research is provided in Section 4, and the paper concludes with Section 5.
## 3 Results
### Iris-Exosome Protects nHEKs from the Loss of Cell Viability Caused by Oxidative Stress  
Before analyzing the effects of _Iris_-exosomes on H\({}_{2}\)O\({}_{2}\)-induced cellular stress in nHEKs, we assessed the potential cytotoxicity of the nHEKs treated with _Iris_-exosomes for 24 h. As shown in Figure 2a, nHEKs treated with a concentration of less than 10\({}^{7}\) (particles/mL) showed no cytotoxicity. In order to evaluate the effect of H\({}_{2}\)O\({}_{2}\) on nHEKs, cell viability was determined via 24 h treatment with various concentrations of H\({}_{2}\)O\({}_{2}\). Consequently, cell viability was reduced by 80% at 200 \(\upmu\)M (Figure 2b). Therefore, 10\({}^{5}\), 10\({}^{6}\), and 10\({}^{7}\)_Iris_-exosomes were used in the following experiments along with 200 \(\upmu\)M H\({}_{2}\)O\({}_{2}\). The MTT assay was performed on the nHEKs to determine whether the exosome treatment could recover the reduction in cell viability caused by H\({}_{2}\)O\({}_{2}\). The viability of nHEKs pretreated with _Iris_-exosomes increased in a concentration-dependent manner compared with that of cells treated with H\({}_{2}\)O\({}_{2}\) alone (Figure 2c). These results indicate that _Iris_-exosomes have protective effects against the H\({}_{2}\)O\({}_{2}\)-induced loss of cell viability in nHEKs.  
Furthermore, we conducted the same experiment on nHDFs to determine whether the protective effects observed in nHEKs are also found in other types of primary skin cells. As shown in Supplementary Figure S1a, nHDFs showed no cytotoxicity and increased cell viability at concentrations below 10\({}^{9}\) (particles/mL). Furthermore, the viability of the nHDFs was reduced by 80% at 300 \(\upmu\)M H\({}_{2}\)O\({}_{2}\) (Supplementary Figure S1b). Therefore, we treated 10\({}^{6}\), 10\({}^{7}\), and 10\({}^{8}\)_Iris_-exosomes with 300 \(\upmu\)M H\({}_{2}\)O\({}_{2}\). nHDFs were treated with exosomes to determine whether exosome treatment could restore the viability of the cells post-H\({}_{2}\)O\({}_{2}\) treatment. Compared to the cells treated with H\({}_{2}\)O\({}_{2}\) alone, pretreatment with _Iris_-exosomes increased the viability of the nHDFs in a concentration-dependent manner  
Figure 2: Protective effect of _Iris_-exosome on cell viability against H\({}_{2}\)O\({}_{2}\)-induced cytotoxicity in human keratinocytes. (**a**) Cell viabilities pretreated with _Iris_-exosomes for 24 h and examined using WST-1 assay. (**b**) Cell viability of nHEKs with H\({}_{2}\)O\({}_{2}\) for 24 h. (**c**) nHEKs were treated with 200 \(\upmu\)M H\({}_{2}\)O\({}_{2}\) for 24 h after _Iris_-exosomes for 4 h and cell viability was measured via MTT assay. Values are expressed as mean \(\pm\) SD of three independent experiments. * \(p\) \(<\) 0.05 and *** \(p\) \(<\) 0.001 compared with untreated cells; *** \(p\) \(<\) 0.001 compared with H\({}_{2}\)O\({}_{2}\)-treated cells.  
(Supplementary Figure S1c). Considering these results, it is evident that _Iris_-exosomes are protective against the reduction in cell viability caused by H2O2 in not only nHEKs but also nHDFs.
## 4 Security and Performance Analyses  
This section presents the authentication proof using BAN logic [34], analyzes the security of the proposed scheme and provides performance and functionality comparisons between the related schemes and the proposed scheme.
## 5 Results and Discussion
### Review Rating Prediction
#### 5.2.3 Results Based on Yelp 2014 Dataset  
Table 7 represents a comparative analysis of the Review Rating Prediction model for several performance metrics through altering training data. The analysis of TD-Spider TaylorChoA-GRU for the precision, recall and F-measure is listed below. In 90% of training data, the precision of the developed TD-Spider TaylorChoA-GRU is 0.925. The recall of TD-Spider TaylorChoA-GRU is 0.941 for 90% of training data and, considering 90% of training data, the F-measure of the developed model is 0.933.  
Figure 10 represents the accuracy and loss for the Review Rating Prediction model. When the iteration is 0, the accuracy is 0.629. When the iteration is 0, the loss is 0.370. Here, both the testing accuracy and training accuracy of the proposed method is high in the 300 th iteration. Hence, the proposed method yields a better performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & **Data (\%)** & **DNN** & **CNN+LSTM** & **Bi-GRU** & **CNN** & **[3]** & **[34]** & **[17]** & **Proposed** \\ \hline \multirow{4}{*}{Precision} & 60\% & 0.641 & 0.695 & 0.741 & 0.785 & 0.808 & 0.841 & 0.848 & 0.854 \\  & 70\% & 0.685 & 0.714 & 0.765 & 0.802 & 0.837 & 0.865 & 0.869 & 0.875 \\  & 80\% & 0.701 & 0.775 & 0.825 & 0.865 & 0.876 & 0.895 & 0.907 & 0.914 \\  & 90\% & 0.725 & 0.799 & 0.841 & 0.895 & 0.908 & 0.914 & 0.919 & 0.925 \\ \hline \multirow{4}{*}{Recall} & 60\% & 0.665 & 0.714 & 0.754 & 0.801 & 0.836 & 0.854 & 0.859 & 0.866 \\  & 70\% & 0.701 & 0.732 & 0.775 & 0.814 & 0.859 & 0.875 & 0.880 & 0.885 \\  & 80\% & 0.714 & 0.795 & 0.833 & 0.875 & 0.888 & 0.901 & 0.908 & 0.914 \\  & 90\% & 0.733 & 0.814 & 0.854 & 0.912 & 0.927 & 0.933 & 0.939 & 0.941 \\ \hline \multirow{4}{*}{F-measure} & 60\% & 0.653 & 0.704 & 0.748 & 0.793 & 0.821 & 0.848 & 0.853 & 0.860 \\  & 70\% & 0.693 & 0.723 & 0.770 & 0.808 & 0.848 & 0.870 & 0.874 & 0.880 \\ \cline{1-1}  & 80\% & 0.708 & 0.785 & 0.829 & 0.870 & 0.882 & 0.898 & 0.907 & 0.914 \\ \cline{1-1}  & 90\% & 0.729 & 0.806 & 0.848 & 0.904 & 0.917 & 0.923 & 0.928 & 0.933 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative analysis of Review Rating Prediction for Yelp 2014 dataset in terms of precision, recall and F-measure.  
Figure 9: Performance analysis based on (**a**) accuracy and (**b**) loss for Review Rating Prediction using Yelp 2013 dataset.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 3 Case Studies and Issues for Conventional CVR
### Case Studies: Non-Optimal Results
#### 3.1.4 Clay Electric Cooperative (CEC)  
CEC employed voltage reduction as a peak demand reduction tool on ten substations for over 10 years. Using this approach, CEC reduced its substation voltages from 126 V to under 124 V (1.7%) to avoid monthly peak demand charges, reporting a 1% load reduction per 1% voltage reduction, or a CVR factor of 1.0 [24].
## 4 Experiments
### Ablation and Sensitivity Analysis
#### 5.3.1 Ablation Study  
The influence of the pre-trained encoder was examined through the implementation of experiments on the CIFAR-10 dataset, with an imbalance ratio of 50. The results, shown in Table 6, indicated that removing the pre-trained encoder significantly lowered classification performance across all datasets. Additionally, CL-SR demonstrated superior performance when comparing our framework to standalone oversampling methods. Further exploration into the sequence of implementing the pre-trained encoder and oversampling method revealed that altering the original sequence, initially transforming the 3D image array into a feature vector followed by SMOTE-RSB, and then utilizing the pre-trained encoder and a linear classifier, resulted in a 20% performance drop. This highlights the critical role of the synergistic integration of contrastive learning and oversampling in our proposed method.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**CIFAR-10-STEP**} & \multicolumn{4}{c}{**CIFAR-10-LT**} \\ \cline{2-7}  & **ACSA** & **FM** & **GM** & **ACSA** & **FM** & **GM** \\ \hline Benchmark & 0.2702 & 0.2887 & 0.4816 & 0.2693 & 0.2415 & 0.4134 \\ Benchmark+SMOTE-RSB & 0.3002 & 0.3158 & 0.5088 & 0.2893 & 0.3110 & 0.4781 \\ CL-SR & 0.5180 & 0.5641 & 0.7422 & 0.7043 & 0.7243 & 0.8253 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation studies on CIFAR-10 (IR = 50).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** & **Error Rate (\%)** \\ \hline Benchmark & 44.4 & 55.6 \\ Focal Loss & 43.7 & 56.3 \\ CB Re-sampling & 45.1 & 54.9 \\ NCM & 47.3 & 52.7 \\ cRT & 49.5 & 50.5 \\ LWS & 49.9 & 50.1 \\ LDAM & 51.1 & 48.9 \\ PaCo & 58.2 & 41.8 \\ CL-SR & 68.67 & 31.33 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Top-1 accuracy and error rate evaluated on ImageNet-LT.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 4 Discussion and Perspectives
### Perspectives  
This long experiment of GNSS monitoring of a bridge suggests three lines of improvement that we briefly describe:  
* We strive to shorten the time unit of adjustments by going to 15-30 min in such a way as to improve latency while still remaining with RMS under 1 cm.
* Parallel experiments show that it is possible to combine GNSS observations with other sources of data, primarily inclinometers, to improve the monitoring capability of the system. Further, an external source of information, such as SAR, can be integrated into a unique solution.
* We expect that future data from monitoring sites will be endowed with specific expert systems that also combine completely different information, such as temperatures (of the air and the structure), the level of rain by pluviometers placed near the structure,and other ancillary variables such as the level of water of a river the bridge is crossing, or the level of water of an artificial lake barring the monitored dam.  
Thus, the monitoring practice seems to be subject of quite interesting technological and scientific developments in the near future.  
Conceptualization, all authors; methodology, F.S., S.C. and S.B.; software, S.C.; validation, S.C. and S.B.; data curation, L.P.; writing--original draft preparation, F.S. and S.C.; writing--review and editing, L.P., S.C. and S.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors acknowledge Autostrade per l'Italia SpA, algoWatt SpA and GReD SrI for the effective and enthusiastic cooperation in providing data and supporting the described experience.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
###### Abstract  
The efficiency and profitability of photovoltaic (PV) plants are highly controlled by their operation and maintenance (O&M) procedures. Today, the effective diagnosis of any possible fault of PV plants remains a technical and economic challenge, especially when dealing with large-scale PV plants. Currently, PV plant monitoring is carried out by either electrical performance measurements or image processing. The first approach presents limited fault detection ability, it is costly and time-consuming, and it is incapable of fast identification of the physical location of the fault. In the second approach, Infrared Thermography (IRT) imaging has been used for the characterization of PV module failures, but their setup and processing are rather complex and an experienced technician is required. The use of Unmanned Aerial Vehicles (UAVs) for IRT imaging of PV plants for health status monitoring of PV modules has been identified as a cost-effective approach that offers 10--15 fold lower inspection times than conventional techniques. However, previous works have not performed a comprehensive approach in the context of automated UAV inspection using IRT. This work provides a fully automated approach for the: (a) detection, (b) classification, and (c) geopositioning of the thermal defects in the PV modules. The system has been tested on a real PV plant in Spain. The obtained results indicate that an autonomous solution can be implemented for a full characterization of the thermal defects.  
photovoltaic thermographic inspection; infrared imaging analysis; unmanned aerial vehicle; infrared thermography; operation and maintenance +
Footnote †: journal: Applied Sciences
# Adaptive Relay Selection Scheme by Using Compound Channel
## 6 Conclusions  
As one of the relay selection techniques, the proposed scheme uses a new algorithm for enhancing the performance of single link. A new algorithm consists of two stages. The first selection stage guarantees the reliability by considering the outage occurrence. The three cases are defined according to \(Q\) in the first selection stage. In the second selection stage, antenna selection is performed based on the compound channel for each case. Through two stages, the proposed scheme obtains diversity gain from various combinations of relays and antennas. The proposed scheme achieves improved BER performance than other schemes. As the channel variation of relays increases, the proposed scheme shows the improved BER performance. Furthermore, the diversity gain of proposed scheme increases with the number of available relays and antennas.  
Conceptualization, Y.-J.N.; methodology, Y.-J.N. and W.-S.L.; software, Y.-J.N.; validation, Y.-J.N., W.-S.L., H.-K.S., D.H. and Y.-H.Y.; formal analysis, Y.-J.N.; investigation, W.-S.L., M.-J.P.; writing--original draft preparation, Y.-J.N.; writing--review and editing, Y.-J.N.; supervision, H.-K.S., D.H. and Y.-H.Y.; project administration, H.-K.S., D.H. and Y.-H.Y. All authors have read and agreed to the published version of the manuscript. This work was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2018-0-01423) supervised by the IITP(Institute for Information & communications Technology Promotion) and was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2020R1A6A1A03038540).  
The authors declare no conflict of interest.  
Figure 4: BER performance with 8 antennas
## 3 Results and Discussions
### MaxJitter
In highway scenarios, however, GPSR benefits from a higher MaxJitter, as it allows the protocol to space out packets more effectively in a setting with minimal risk of collisions. The absence of frequent route discovery procedures means that GPSR's delay performance improves with a higher MaxJitter on highways, where packet timing flexibility enhances the network's ability to handle non-colliding transmissions. In conclusion, the delay performance of AODV and GPSR across city and highway environments reveals how network topology and protocol design influence the optimal MaxJitter setting. In city environments, AODV performs better with higher MaxJitter due to the reduction in packet collisions and retransmissions, while GPSR suffers from increased delays. In highway scenarios, GPSR benefits from higher MaxJitter, while AODV incurs additional delays due to its route discovery process. These findings highlight the need to carefully adjust MaxJitter based on both the routing protocol and the specific network environment to minimize delays. Figure 5: Delay comparison between VANET scenarios and MaxJitter.
# Digital Planning Technique for Surgical Guides for Prosthetic Implants before Orthodontic Treatment
## 3 Case Reports  
### Case No. 1  
A 20-year-old Caucasian female patient searched for orthodontic treatment, complaining about her smile aesthetics. The anamnesis revealed that the patient had experienced trauma to teeth 11 and 21 at an early age. A clinical examination showed that there was crowding in the upper and lower arches (Figures 2 and 3). The patient presented with Angle Class I on the right side and Angle Class II on the left side. Tooth 35 was missing. The panoramic radiograph revealed that teeth 11 and 21 were treated endodontically (Figure 4). From anamnesis, it was reported that an endodontic treatment was performed due to experienced trauma. Furthermore, tooth 11 was affected by caries. After an examination under a microscope by an endodontist, it was decided that tooth 21 was fractured and should be extracted. Figure 1: Flow and responsibilities of guided implantation prior to orthodontic treatment. Figure 2: Initial pictures of patient in case number 1: (**a**) extraoral pictures, and (**b**) intraoral pictures. After therapeutic treatment, the patient was referred to both an orthodontist and a surgeon for consultation. Four possible treatment plans were presented:  
1. The extraction of tooth 21, followed by orthodontic treatment with aligners and guided implant surgery of tooth 21 at the beginning of the orthodontic treatment. 2. The extraction of tooth 21, followed by orthodontic treatment with braces with upper arch distalization and implant surgery of tooth 21 after the orthodontic treatment. 3. The extraction of tooth 21, followed by orthodontic treatment with the extraction of the first or second premolars. This treatment plan was offered but not recommended by the orthodontist. 4. The extraction of tooth 21 with no orthodontic treatment, and implant surgery without a surgical guide. The advantages and disadvantages of the suggested treatment plans are presented below in Table 1.  
Figure 4: Initial panoramic radiograph. Figure 3: Virtual dental impressions. After consideration, the patient chose to proceed with plan number I, with the extraction of tooth 21 and implant surgery followed by orthodontic treatment with clear aligners. The workflow of the digital planning for orthodontic treatment supported by implants was as written in Section 2 (Figure 5). It should be stressed that the implant of tooth 21 was placed distally in relation to the center of the alveolus, and this is due to the final position of tooth 21, which must be more distal compared to the initial position of the extracted tooth 21.
## 2 Materials and Methods
### Guided Grad-CAM  
Grad-CAM visualizations are class-discriminative and localize important regions in input images, but unlike gradient visualization methods they do not provide highly detailed pixel-space maps. Using pointwise multiplication combines the advantages of both guided back-propagation and Grad-CAM visualizations. The resulting guided Grad-CAM visualization is class-distinctive and of high resolution. The adaptation to semantic segmentation is shown in Figure 4. The process is similar to the original method [11], which aims to explain the decisions of classification networks. Based on guided Grad-CAM's class-distinctness and high-resolution maps, it seems to be a superior visualization method. However, this assumption is only true if only interpretability, but not faithfulness is taken into account as well. Faithfulness is the ability to accurately explain the internal process of the model. Intuitively, there is a tradeoff between the interpretability and faithfulness: a more faithful visualization is typically less interpretable and vice versa [11]. The problem of measuring faithfulness and interpretability is that there is no actual ground truth for comparison. It is stated by [31] that Guided Grad-CAM is not faithful to a trained network and the results would be visually similar if the guided back-propagation map were replaced by an edge detection algorithm in the guided Grad-CAM method. It is a widely used visualization method nonetheless and can contribute to the understanding of a model.