esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Different PLS Calibration Models
These results demonstrate that spectral preprocessing and appropriate variable selection strategies can significantly enhance the quantity performance of the model. Machine learning methods could be faster and more effective for quantitative analyses of biofuels in adulterated diesel/b biodiesel blends. There are many different approaches in the literature to study the partial or total replacement of biodiesel with different vegetable oils, proving that this issue is very important and should be approached from different perspectives. In the literature, in the batch \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Calibration** \\ **Models** \\ \end{tabular} } & \multirow{2}{*}{**LVs**} & \multirow{2}{*}{
\begin{tabular}{c} **Variable** \\ **Number** \\ \end{tabular} } & \multicolumn{3}{c}{**LOO-CV**} & \multicolumn{3}{c}{**Prediction Set**} \\ \cline{3-8}  & & & **R2 cv** & **RMSECV\% (\(v/v\))** & **R2P** & **RMSEP\% (\(v/v\))** & **MREP\%** & **RPD\({}_{\text{P}}\)** \\ \hline PLS & 7 & 1044 & 0.9738 & 0.1912 & 0.9868 & 0.1654 & 9.51 & 8.70 \\ D1 st & 6 & 1044 & 0.9741 & 0.1661 & 0.9842 & 0.1931 & 11.07 & 7.96 \\ D1 st -MSC & 5 & 1044 & 0.9812 & 0.1558 & 0.9932 & 0.1413 & 7.10 & 12.13 \\ D1 st-MSC-siPLS & 5 & 378 & 0.9842 & 0.1329 & 0.9899 & 0.1367 & 6.31 & 9.95 \\ D1 st-MSC-iPLS & 6 & 87 & 0.9798 & 0.1692 & 0.9853 & 0.2263 & 13.41 & 8.25 \\ D1 st-MSC-biPLS & 7 & 261 & 0.9780 & 0.1811 & 0.9837 & 0.1879 & 9.59 & 7.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of predictive performances of PLS calibration models. Figure 5: The prediction performance of the model proposed in this work for biodiesel adulterants. determination of the identity of biodiesel-diesel blends (concentration and feedstock) by using UFGC, GC has been successfully applied [48]. [PERSON] et al. [2] found that the prediction of biodiesel concentration in blended fuels combined partial least squares (PLS) calibration with the C-H eigenzones of Raman spectroscopy. The quantitative and qualitative analyses of biodiesel using NMR spectroscopic methods were carried out by [PERSON] [24]. Although there have been many studies that have made significant contributions to the field, there is still a need to develop a rapid detection method to address the quantification of biodiesel in adulterated fuels. Our work is a pioneering study that demonstrates the feasibility of Raman spectroscopy as a quantitative detection technique for biodiesel containing adulterants. In addition, Raman spectroscopy has the potential to be an alternative technique for use by government agencies or quality control laboratories due to its unique capabilities.
# Solid-State Foaming Process Optimization for the Production of Shape Memory Polymer Composite Foam
## 1 Introduction
### Shape Memory Polymer Composites  
Shape memory composites have higher levels of strength and stiffness. [PERSON] et al. [7] inserted Fe\({}_{3}\)O\({}_{4}\) nanoparticles into a poly(d,l-lactide) (PDLLA) matrix and found that the more Fe\({}_{3}\)O\({}_{4}\) added to the structure, the higher the tensile strength obtained. According to the authors, this is attributed to the interfacial interaction between the nanoparticle (NP) phase and the polymer phase in the composite matrix. [PERSON] and [PERSON] [8] reported an increment in the tensile strength of epoxy resin after the addition of different volume percentages of graphene oxide.  
Similarly, [PERSON] et al. [9] synthesized an SMP composite using ultrasound technology composed of poly(methyl methacrylate) and carbon nanotubes. The composite was triggered by heat generated from local application of electric voltage. The ease of control of the shape memory process via local application of electricity on a synthesized composite makes it a good contribution in medical applications such as intravascular medical devices.  
[PERSON] and [PERSON] [10] studied the effect of adding nanoclay to epoxy foam and also reported an increment in compressive strength and toughness as a greater weight percentage of nanoclay was added to the matrix, due to the interaction between nanoclay and the epoxy foam structure, which leads to a change in the intrinsic behavior of the foam under loading. This offers further advantages over SMPs. The reviewed literature of the  
Figure 1: Thermo-mechanical cycle steps performed on shape memory materials.  
solid-state foaming process has not been fully explored; process performance with respect to various polymers needs to be studied, and the selection of optimal process parameters for a specific polymer need to be identified. Therefore, this research was concerned with studying the foaming process of polyester-based thermoset polymers, where the foam ratio and density were used as process performance indicators. The optimum foaming parameters need to be identified based on the highest foaming ratio and the lowest foaming density for the prepared samples.
###### Abstract  
To quantify the rotational demands of elite female field hockey with respect to position. Twenty-eight (\(n\) = 28) elite international field hockey players were recruited during the 2018-2020 seasons. Players were monitored with GPS technology and heart-rate monitors. Methods: Activity was categorised into total distance (m), relative total distance (m-min\({}^{-1}\)), high-speed distance (m; 16 km\(\cdot\)h\({}^{-1}\)), relative high-speed distance (m-min\({}^{-1}\)), max velocity (km\(\cdot\)h\({}^{-1}\)), and percentage maximal velocity (%Vmax). Physiological demands were assessed via heart rate measures (ppm and % HR\({}_{\text{max}}\)) and time \(>\) 80% heart rate maximum. Results: A single rotation equated to 7 \(\pm\) 0.8 min. Players covered a mean total distance of was 868 \(\pm\) 132 m (125.7 \(\pm\) 5.9 m\(\cdot\)min\({}^{-1}\)) with 140 \(\pm\) 39 m at high-speed (21.7 \(\pm\) 3.6 m-min\({}^{-1}\)). A significant difference was reported for relative total (\(p\)\(\leq\) 0.001), and high-speed (\(p\)\(\leq\) 0.001), distance across positional. Forward's were reported to cover the most relative total and high-speed distance (\(d\) = 1.0) when compared to defenders and midfielders. Conclusion: The study provides normative data for rotational demands of elite female field hockey. Coaches should consider these demands when developing training drills to better optimise the positional physical and physiological demands of competitive match-play.  
team sport; GPS; intermittent exercise; running performance; high intensity work-rate +
Footnote †: journal: Applied Sciences
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 1 Introduction
### Deep Learning-Based Methods
The feature maps extracted through the CSBlock were used to generate high-resolution images using subpixel convolution and residual blocks. Subpixel convolution is used for upsampling low-resolution input images to high-resolution output images, and the residual block helps generate natural high-resolution images by calculating the residual between the high-resolution and low-resolution images. CS-SISR is trained by minimizing the L1 loss using 48 \(\times\) 48-sized low- and high-resolution patches. This method emphasizes the high-frequency features and utilizes them to generate natural high-resolution images. CS-SISR is a novel deep learning-based method that can generate more accurate and natural high-resolution images than existing methods by emphasizing high-frequency features and utilizing diverse feature information. This can enhance the diagnostic accuracy and time efficiency of medical images, as shown in Figure 1, which presents a melanoma image (ISIC 2020 Challenge dataset). \begin{table}
\begin{tabular}{c c c} \hline
**Component** & **Description** & **Primary Role** \\ \hline Channel–Spatial (CS) Block & \begin{tabular}{c} Applies channel and spatial attention \\ mechanisms to enhance high-frequency \\ information. \\ \end{tabular} & \begin{tabular}{c} Enhances high-frequency details for \\ better feature extraction. \\ \end{tabular} \\ Residual Block & \begin{tabular}{c} Calculates the residual between \\ high-resolution and low-resolution \\ images to improve reconstruction. \\ \end{tabular} & \begin{tabular}{c} Generates natural and detailed \\ high-resolution images. \\ \end{tabular} \\ Sub-Pixel Convolution & \begin{tabular}{c} Upsamples low-resolution input images \\ to high-resolution output images. \\ \end{tabular} & \begin{tabular}{c} Increases image resolution while \\ preserving details. \\ \end{tabular} \\ L1 Loss Function & \
\begin{tabular}{c} Minimizes the difference between low- \\ and high-resolution image patches \\ during training. \\ \end{tabular} &
\begin{tabular}{c} Improves the quality of \\ high-resolution image reconstruction. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Key components of CS-SISR.
# Pathological Gait Classification Using Early and Late Fusion of Foot Pressure and Skeleton Data
## 6 Results and Discussions  
In this section, a comprehensive evaluation and discussion of our approach are presented. First, the performance of our single-modal models, namely MViTv2_base and MViTv2_small, is assessed and discussed using the foot pressure dataset. Second, the ST-GCN architecture, focusing on the skeleton dataset, is evaluated and discussed. Third, the outcomes of our proposed multi-modal approach are scrutinized and discussed, employing early fusion. Lastly, the results of our proposed multi-modal technique, which employed late fusion, considering various weight configurations, are examined and discussed.  
To assess the performance of our proposed single-modal and multi-modal classification models, a leave-one-subject-out cross-validation approach was employed. This entailed using data from one subject as the validation set, whereas the remaining data served as the training set. Consequently, the training procedure was performed 12 times, and the resultant average validation accuracy was calculated. The training process for both the foot-pressure and ST-GCN models was conducted independently for 200 epochs. For the foot-pressure model, the AdamW (Adam with Weight decay) optimizer was employed, whereas the ST-GCN model was optimized using the Adam optimizer. Additionally, in the case of the transformer-based model, the learning rate was set to 0.00006 with a weight decay of 0.01, and the batch size was set to eight. For ST-GCN, the learning rate was set at 0.1, with a weight decay of 0.0001, and a batch size of 16. The cross-entropy loss function was used.  
The system specifications utilized for the evaluation in our study consist of an Intel (Santa Clara, CA, USA) CPU with 32 GB of RAM and an NVIDIA (Santa Clara, CA, USA) GeForce RTX 3060 GPU. The implementation of the models in this study was executed using PyTorch 2.1. For the 118 frames of the skeleton dataset, the system takes 11.31 s to evaluate, which processes ten frames per second, so it is applicable in the real world.
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 4 Classification of ML Models Applied in the Optimization of Wastewater Treatment Processes
### ML Models Applied in the Optimization of Wastewater Treatment Processes
#### 4.2.4 Autoencoders and Deep Belief Networks (DBNs)  
Autoencoders and DBNs are important architectures belonging to the DL domain, each with specific characteristics and applications. So, the autoencoders (which contain an encoder that compresses the input and a decoder that reconstructs the input) are an ANN type, used to learn efficient codifications from input data in order to reduce the dimensionality and to improve feature learning [39; 58]. DBNs are a class of generative ANN model whose architecture is composed of multiple layers of stochastic variables. This type of network is used because of its ability to model complex distributions, having various applications in the environmental domain (the prediction of complex relationships through data patterns identification) [5].  
While DBNs are more oriented to understanding data distribution, autoencoders are focusing on reconstructing the model inputs, making them suitable (because of their versatility) for various types of environmental applications, such as anomaly detection, data sensors compression and key features extraction, prediction of water quality variations or pollutant concentrations, accurate real-time monitoring and predictions for water quality and treatment efficiency, environmental monitoring, complex prediction tasks in WWTPs, in wastewater treatment, etc., and they continue to evolve and to improve [84].
## 4 Analysis of Settlement Data and Discussion  
### GNSS Settlement Analysis Compared with Total Station Measurement  
With careful analysis of GNSS monitoring results, it becomes apparent that there was a notable and substantial increase in settlement during the initial days leading up to March 7 th. This increase can be attributed to the unfinished settlement trend and initial setting and curing age of cement grout used for repairing the settling zone which was experienced by our MDD/GNSS system during that specific period.  
For comparison and validation of GNSS results, we compared the GNSS monitoring data with the total station (Figure 11) monitoring results obtained from a location near the MDD/GNSS box controller on the side of the slab track. As shown in Figure 12, there is a notable difference between GNSS and total station monitoring results. This disparity is mainly due to differences in the locations where the total station monitoring points were taken as shown in Figure 11. We confirmed that the track foundation soil exactly  
Figure 10: GNSS original monitoring results and estimated displacement using the resampling method.  
Figure 9: GNSS original monitoring results and estimated displacement using the LOESS trend model.  
underneath the GNSS rover position was repaired by using the cement grout to prevent further settlement before installing the MDD/GNSS system. The results in Figure 10 reveal that there was still a trend of generating ongoing settlement during the initial days in this zone. However, the total station was used to monitor the settlement along the sideline of the track slab which was located some distance away from the grouted zone of the track. The side of the track slab was displaced vertically lower than that of the GNSS rover position.  
Figure 11: Total station measurement along the track side.  
Figure 12: Comparison of GNSS and total station monitoring results.  
Even though the comparison shows some similarity in the trends of settlement measurements between GNSS and total station monitoring, it reveals that better predictable results of measurements for this kind of repaired zone using cement grouting could be obtained through the GNSS system adapted in this study.
# Fracture and Deflection of Orthodontic Miniscrews--A Systematic Review
###### Abstract  
Orthodontic miniscrews (MSs) are used for enhancing orthodontic anchorage either by supporting the teeth of the reactive unit or by obviating the need for the reactive unit altogether. Despite MSs' popularity, their clinical application is not lacking in complications. The limited space of the insertion site (inter-radicular space), temporary use (limiting osseointegration) and the necessity to minimize the biological cost of insertion (bone incision) required the size of this auxiliary to be reduced, making it susceptible to mechanical failure. This review aimed to investigate factors influencing MS plastic deformation and fracture. The search applied five engines: PubMed, PMC, Web of Science, Scopus, Embase, and EBSo. Quality assessment was performed according to the QUIN tool. After a thorough search process, 22 articles were included in this review. The most important factor influencing miniscrews' plastic deformation and fracture was the screw diameter. The MS length and metal alloy did not influence its plastic deformation or fracture. The cylindrical design of the screw is preferable. If the cortical bone thickness in the insertion site exceeds 3 mm, pre-drilling upon insertion is recommended. Orthodontic MSs should not be reused. There is a need for high-quality clinical studies on the subject of MS deformation and fracture. The PROSPERO number is CRD42024509895.  
orthodontic mini-implants; orthodontic screw; TAD; miniscrew; skeletal anchorage; fracture; deflection; plastic deformation; distortion
## 3 Methods
### Data Processing Flow
#### 3.4.4 Data Resampling and Scaling  
Data resampling was performed to address the imbalance issue in the training datasets, as shown in Figure 5. The famous resampling approaches include oversampling, undersampling, and both combinations. Incorrect selection of the resampling method may lead to invalid classification results. In this study, the actual number and value of the minority classes were essential, as they correspond to the actual impairment level of the patient. Several studies have shown the promising performance of undersampling approaches to maintain the number of minority classes and produce good classification results [40; 41; 42]. This study utilized a random oversampling approach, employing Imblearn library in Python with a sampling strategy to resample all classes except the minority class.  
Data scaling was performed for the second mechanism in the following step, as shown in Figure 5. Data scaling is a standard measure involving the treatment of data before inputting them to machine learning algorithms. This technique is instrumental in managing diverse data input scales and improving machine learning models' performance. One such data-scaling techniques is z-score scaling, in which z-score normalization or a standardization term is employed. The z score scales the data instances by removing the unit mean and dividing it by the unit variance. Several studies have proven the performance of the z-score method in enhancing classification accuracy and efficiency. [PERSON] et al. showed that z-score scaling decreased the number of epochs required for the learning network [43]. [PERSON] et al. achieved improved accuracy after z-score scaling [44]. [PERSON] et al. reported that classification performance was significantly improved after z-score scaling [45].
# Balancing the Leakage Currents in Nanometer CMOS Logic--A Challenging Goal
## 6 Conclusions  
In this work we have discussed several shortcomings of the Balanced Static Power Logic (BSPL) approach introduced in [27]. Most notably, we have found that balancing drain-source currents is not sufficient anymore in nanometer process technologies due to the significant impact of the gate leakage on the data dependency of the leakage currents. In fact, it is also insufficient to consider only the switched off transistors when analyzing the leakage currents of a gate. In our experiments 85-92% of the remaining data dependency of BSPL gates in 40 nm and 65 nm technologies originated from leakages through the gate insulator of switched on transistors. We argue that generic cell structures which balance both drain-source currents _and_ gate leakages, would be highly cost-inefficient. Therefore, we propose an alternative strategy based on systematically adding always-off transistors to determined signal lines of classic CMOS logic gates in a step-by-step process to balance out any input dependency. We showed that our designed _improved Balanced Static Power Logic (iBSPL)_ cells require less area and energy, while at the same time being faster and more secure in both of our analyzed commercial process technologies 40 nm and 65 nm.  
Figure 12: Average of 500 MCDPA attacks using 5000 simulations of noisy SBox leakage currents for random inputs modeling different levels of intra-die process variations (65 nm).  
Figure 13: MCDPA attack on 50,000 simulated dynamic power traces for random input transitions.  
As future work on this topic we plan to construct full cell layouts including drive strength tuning, and perform characterization of the iBSPL logic gates to create a usable and manufacturable standard cell library. Afterwards we want to tape-out a test chip using our iBSPL library to perform an experimental validation of its merits on a fabricated silicon chip.  
Methodology, B.F.; investigation, A.M.; writing--original draft preparation, B.F.; writing--review and editing, T.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Deutsche Forschungsgemeinschaft (DFG) through the Germany's Excellence Strategy under Grant EXC 2092 CASA-390781972, and in part by the Project \"Aged but Fit: Long Lasting Security for Trusted Platforms\" under Grant 418658052.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Methodology for C/V of Radar Altimeters
### Ground Estimate of the Water Height  
Ground estimate of the water height was derived by the means of a geodetic grade GNSS system, composed of a dual-frequency receiver and micro-centered antenna, embarked on the ship. To enable projecting the height of the antenna o that of the water surface, the distance between the antenna and the water surface was measured, continuously, by means of an RLS radar unit, sharing a common support. The offset between the phase centers of the two instruments was measured at the beginning of the survey and it was re-checked at the end of the survey (Figure 3). We used the IGS tables to take the internal offset between the phase centers of the GPS antenna and the Antenna Reference Point (ARP), into account.  
We used the PPP strategy to process the GNSS data collected by the unit onboard. The result in the antenna position and the ellipsoidal height, was processed every 1s. As much as possible, the boat kept moving at a constant velocity of 11-12 km/h. This constrain enabled the distance between successive points to remain constant and the boat trim to remain unchanged.  
The GNSS data were processed using the GINS geodetic software developed by CNES [21]. GINS was run in PPP mode. We selected the PPP mode instead of the traditional Double difference mode for two main reasons. First, several studies have shown that when the baseline between the base station and the moving receiver exceeded 10 km (which was the case, in the middle of the lake), PPP mode performed better than the DD mode, mostly because of changes in the atmospheric delays. Second, in the DD mode, the total error was a combination of the error on the baseline and that on the position of the base station [19].  
The PPP mode required precise satellite orbit and clock products. We used the products delivered by the CNES-CLS IGS Analysis Center [19].  
As the GINS software provided GPS coordinates that were conventionally tide-free, they could be directly compared to the altimetry observations. In addition to the boat coordinates, a GPS receiver  
Figure 4: Impact of boat velocity on the GPS antenna height. For a velocity of 12 km/h the bow of the ship was lower by 8 cm in average, with respect to the height, when the boat was not moving.  
clock offset every epoch, a zenithal wet tropospheric delay every hour (piece-wise linear), and GPS phase ambiguities was estimated. In this configuration, the expected positioning precision was around 2 to 3 cm, in the vertical direction, with no significant bias for the few-hour sessions [22; 23]. When comparing the GPS profile along the track of the satellite (Sentinel-3A or Jason-3), the GPS processing errors were averaged.