esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
By now, ctDNA testing for the determination of the driver and resistance mutations have entered the daily practice of clinical laboratories. Here we tried to focus on the main advantages as well as potential drawbacks of the OLcfA application on a routine NSCLC clinical setting. We also highlighted the fundamental bioinformatics elements to conduct an analysis consistent with the parameters predefined by the TSS local analysis software. From analytical evaluation of the OLcfA, we were able to demonstrate an accurate detection of low-frequency variants in cfDNA from plasma samples of post-TKI NSCLC patients. The assay performed well also when sub-optimal amounts of cfDNA input were available for NGS library preparation, without affecting the variant detection rate. The assay also revealed to be effective for patients with a known _EGFR_ primary mutation receiving TKI therapy to detect upcoming _EGFR_ Thr790 Met mutation, even present below 0.1% MAF in the blood, with no lowering of the specificity. These data, together with those presented in our previous study, support clinical implementation of the OLcfA panel in medicine precision laboratories for NGS liquid biopsy of advanced NSCLC patients.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/8/2895/s1](http://www.mdpi.com/2076-3417/10/8/2895/s1)]([http://www.mdpi.com/2076-3417/10/8/2895/s1](http://www.mdpi.com/2076-3417/10/8/2895/s1)).  
**Table S1:**_EGFR_ mutations found in all 61 mutated plasma samples.  
**Author Contributions:** Conceptualization, G.D.L. and M.D.; methodology, G.D.L., S.L., R.C. and M.D.; validation, G.D.L. and M.D.; formal analysis, G.D.L.; investigation, G.D.L. and M.D.; resources, M.M., C.G., G.R., M.T. and M.G.D.B.; data curation, G.D.L.; writing--original draft preparation, G.D.L.; writing--review and editing, M.D. and S.C.; visualization, G.D.L.; supervision, S.Z.; project administration, S.Z. and M.D.; funding acquisition, S.C. and S.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Italian Ministry of Health (GR2011-12; 02350922) and partially by the Compagnia San Paolo (2017-0529) to [PERSON], by the Italian Ministry of Health \(5\times 1000\) funds 2013, 2014 and 2015 to [PERSON].  
**Acknowledgments:** [PERSON] and [PERSON] for bioinformatics support (ThermoFisher Scientific).
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
###### Abstract  
The paper deals with performance enhancement of low-cost, consumer-grade inertial sensors realized by means of Micro Electro-Mechanical Systems (MEMS) technology. Focusing their attention on the reduction of bias instability and random walk-driven drift of cost-effective MEMS accelerometers and gyroscopes, the authors hereinafter propose a suitable method, based on a redundant configuration and complemented with a proper measurement procedure, to improve the performance of low-cost, consumer-grade MEMS sensors. The performance of the method is assessed by means of an adequate prototype and compared with that assured by a commercial, expensive, tactical-grade MEMS inertial measurement unit, taken as reference. Obtained results highlight the promising reliability and efficacy of the method in estimating position, velocity, and attitude of vehicles; in particular, bias instability and random walk reduction greater than 25% is, in fact, experienced. Moreover, differences as low as 0.025 rad and 0.89 m are obtained when comparing position and attitude estimates provided by the prototype and those granted by the tactical-grade MEMS IMU.  
accelerometer; Allan variance; GNSS/INS; gyroscope; inertial measurement unit; inertial navigation systems; integrated navigation; MEMS; sensor redundancy; unmanned aircraft systems +
Footnote †: journal: Sensors
## 2 Theoretical Analysis of the near Field of an Ultra-Large-Scale Metasurface  
Theoretical analysis of the near-field calculation model is presented in Figure 1, validating the feasibility of generating vortex beams using ultra-large-scale metasurfaces. This study conducted numerical calculations for near-field metasurfaces under both noise-free and noisy conditions and compared the differences in the generated orbital angular momentum (OAM) beams at observation distances of 0.9 m and 1.2 m.
## 2 Materials
### Sentinel-2 Data and Preprocessing  
The remote sensing data used in this study were Sentinel-2 imagery. Sentinel-2 is a constellation of satellites, Sentinel-2A and Sentinel-2B, which were launched by the European Space Agency (ESA) on June 2015 and March 2017, respectively. Each satellite carries a MultiSpectral Instrument (MSI) that provides a variety of spectral bands covering the visible, near infrared and shortwave infrared bands. The MSI contains four bands at 10 m, six bands at 20 m and three bands at 60 m [36]. It is of great importance that the MSI incorporates three bands in the red-edge region, centered at 705, 740 and 783 nm, and two Shortwave Infrared (SWIR) bands centered at 1610 and 2190 nm at 20 m (S2-20 m). Many studies have revealed that the red-edge bands and SWIR bands have the potential to improve the accuracy of LAI retrievals [37; 38; 39], which open great opportunities for crop LAI retrievals considering Sentinel-2's high revisit frequency. The Sentinel-2 spectral band characteristics are shown by Table 2.  
The acquired Sentinel-2 imagery products are Level-1C products, which are top of atmosphere (TOA) reflectances [40]. Atmospheric correction was conducted using the Sen2 Cor (2.5.5, ESA, Frascati, Italy, 2018) and Sentinel Application Platform (SNAP) toolbox (6.0.1, ESA, Frascati, Italy, 2018) provided by the ESA to produce Level-2A bottom of atmosphere (BOA) products [41; 42]. A flowchart of Sentinel-2 preprocessing is presented in Figure 3.  
In this study, aiming at making full use of the spectral bands of Sentinel-2 (especially the red-edge and SWIR bands), we focus on S2-20 m (the same size as our quadr  
Figure 3: Flowchart of the preprocessing of Sentinel-2 imagery.  
Figure 2: Field campaign cotton LAI descriptive statistics on three observation dates, shown with boxplots. The box extends from the lower to upper quantile values of the field observation data, with a line at the median, the whiskers extend from the box to show the range of the data. Flier points are those past the end of the whiskers, named as outliers (**red dots**).  
considering the red-edge and SWIR spectral bands of Sentinel-2 imagery. A resampling process was performed using the Nearest Neighbor method with 4 spectral bands (B2, B3, B4 and B8) from 10 m to 20 m in the SNAP toolbox. Three of the atmospheric spectral bands at 60 m were not used in this study because these bands contributed to atmospheric applications, such as aerosol correction (B1), water vapor correction (B9) and cirrus detection (B10) [42]. The reflectance data collected on the Sentinel-2 images were using Extract by Points on the ArcToolbox of ArcGIS Desktop software (10.5, Environmental Systems Research Institute, Redlands, CA, USA, 2016), the points used to extract data on the Sentinel-2 images are the center GPS coordinate of the three sample points that represent one quadrat.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 8 Results
### German Credit Dataset Results  
Table 8 and Figure 4 summarizes the performance of various fair machine learning algorithms on the German Credit dataset. The results reveal a complex interplay between accuracy and fairness, with  
In terms of predictive accuracy, the top-performing models were Fairlearn-LR (\(0.7493\pm 0.0214\)), FairGBM (\(0.7463\pm 0.0182\)), Prejudice Remover (\(0.7440\pm 0.0176\)), and Fair Random Forest (\(0.7423\pm 0.0196\)). Despite their strong predictive performance, these models demonstrated notable fairness disparities. In particular, Fair Random Forest and Prejudice Remover exhibited substantial gaps in key fairness metrics, including accuracy differences of up to \(0.1519\) and false positive rate (FPR) differences as high as \(0.1456\), indicating considerable imbalance in treatment across demographic groups.  
AdaFair, by contrast, achieved perfect fairness across all evaluated metrics, with all disparity values equal to zero. However, this came at the cost of reduced accuracy (\(0.7000\)), exemplifying the classical fairness-accuracy trade-off. A similar trend, albeit less pronounced, was observed with Fair Decision Trees, which maintained relatively low fairness disparities (e.g., statistical parity difference of \(0.0271\) and FPR difference of \(0.0369\)), but with a lower predictive accuracy of \(0.6677\).  
Fairboost emerged as a strong compromise model, offering competitive accuracy (\(0.7293\pm 0.0173\)) while maintaining moderate and consistent fairness metrics. For instance, it reported a statistical parity difference of \(0.0537\) and a TPR difference of \(0.0438\), indicating a relatively balanced performance without disproportionately sacrificing either accuracy or fairness.  
The NNB-based models exhibited divergent and generally less favorable behavior. NNB-DF achieved a reasonable accuracy (\(0.7190\)), but also showed the highest accuracy disparity (\(0.1993\)) and FPR difference (\(0.2285\)), reflecting substantial bias in classification outcomes. NNB-Parity performed slightly worse in terms of accuracy and still struggled with fairness, as evidenced by a TPR difference of \(0.1470\).  
In summary, no model achieved a perfect balance between fairness and predictive performance on the German Credit dataset. While AdaFair delivered ideal fairness metrics, it did so at the cost of utility. On the other hand, Fairboost and Fairlearn-DT emerged as more pragmatic options, offering relatively fair outcomes while preserving useful levels of predictive accuracy.
## 2 Materials and Methods
The amount of Mn and Ce impregnated on MnCe-GAC was measured by extracting MnCe-GAC in a HNO\({}_{3}\) (10%) solution for 3 h at 180 \({}^{\circ}\)C. Total Mn or Ce in the extracted solution was measured by inductively coupled plasma (ICP-AES, Kontron, S-35). The amount of Mn and Ce on the spent MnCe-GAC (after treatment) was measured as well. The loss of Mn and Ce on MnCe-GAC after the treatment processes was determined by comparing the amount of Mn and Ce between newly prepared MnCe-GAC and spent MnCe-GAC. All labware were acid washed and thoroughly rinsed with deionized water. Chemicals used in this study were reagent grade, unless specified. Hydrogen peroxide (35%) was purchased from Sigma-Aldrich Chemical Co. H\({}_{2}\)O\({}_{2}\) was added into the reactor by a syringe pump at a constant feed rate. Ozone was produced by the ozone generator OW-K2/O \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Plant (\({}^{\text{(1)}}\)** & **Product** & **pH** & **Cond. (\({}^{\text{(2)}}\)** & **SS** & **COD** & **BOD** & **Nitrate** & **Sulfate** & **NH\({}_{3}\)-N** & **TMAH** & **Discharge** \\ \hline  & & & **\(\mu\)S/cm** & **mg/L** & **mg/L** & **mg/L** & **mg/L** & **mg/L** & **mg/L** & **mg/L** & **mg/L** & **CMD** \\ \hline Plant 1 & LCD-GS & 7.1 & 1351 & 32.7 & 82.2 & 38.44 & 6.84 & 130.46 & 2.12 & 35.03 & 5447 \\ Plant 2 & PV, TFT-LCD & 6.8 & 2255 & 40.4 & 80 & 36.65 & 30.42 & 156.59 & 3.24 & 127.86 & 9892 \\ Plant 3 & TFT-LCD & 6.9 & 3588 & 79.4 & 29.66 & 16.93 & 42.18 & 160.32 & 9.78 & 100.21 & 12,381 \\ Plant 4 & TFT-LCD & 6.7 & 3254 & 82.0 & 205.1 & 70.31 & 15.25 & 309.75 & 1.73 & 64.97 & 8738 \\ Plant 5 & FM, DRAM & 6.9 & 1546 & 16.1 & 28.8 & 14.78 & 8.01 & 281.31 & 6.87 & 39.76 & 5691 \\ Plant 6 & IC & 6.5 & 7390 & 6.4 & 150.1 & 35.70 & 188.16 & 187.62 & 12.54 & 91.19 & 3565 \\ Plant 7 & IC & 6.5 & 7394 & 4.8 & 148.7 & 31.03 & 274.28 & 1850.13 & 9.75 & 84.41 & 3503 \\ Plant 8 & IC & 6.4 & 10,941 & 6.5 & 137.3 & 37.87 & 96.99 & 3167.68 & 13.96 & 109.43 & 4062 \\ Plant 9 & IC & 6.5 & 10,643 & 25.0 & 335.7 & 139.33 & 166.73 & 1212.35 & ND (\({}^{\text{(1)}}\)) & 84.38 & 17,778 \\ Plant 10 & IC-P\(\alpha\)T & 7.2 & 1568 & 33.3 & 40.3 & 13.65 & 78.31 & 337.31 & ND & 27.33 & 3586 \\ \hline \hline \end{tabular} \({}^{\text{(1)}}\) LCD-GS = LCD glass substrate; PV = photovoltaic cells; FM = flash memory; IC-P\(\alpha\)T = IC packaging and testing;  
\({}^{\text{(2)}}\) Cond. = conductivity; \({}^{\text{(3)}}\) ND = under detection limit.  
\end{table} 
Table 1: Characteristics of wastewaters discharged from high-tech industry plants (\(n=3\)). Figure 1: Experimental set-up of MnCe-GAC processes (1—oxygen cylinder, 2—ozone generator, 3—flow meter, 4—hydrogen peroxide storage tank, 5—mixer, 6—reactor, 7—peristaltic pump, 8—activated carbon, 9—pH meter). (Taiwan) and was introduced into the reactor via a fine bubble diffusor. A standard iodometric titration method was used to determine the concentration of ozone [21].
# GNSS Real-Time Precise Point Positioning in Arctic Northeast Passage
## 1 Introduction
Iridium, which pioneered the low-Earth-orbit (LEO) communication satellite, has considerable coverage in the polar regions thanks to its special orbital space structure, offering effective and reliable real-time ship communications in the Arctic regions ([[https://www.iridium.com/network/](https://www.iridium.com/network/)]([https://www.iridium.com/network/](https://www.iridium.com/network/)), accessed on 1 February 2022). This has opened an opportunity for the RT-PPP method to be used for polar-region ship navigation and oil/gas exploitation. Some work has been performed to evaluate the performance of the PPP algorithm in polar regions. However, all tests are based on data collected from only a few polar IGS stations, which cannot truly reflect the positioning accuracy of the sailing ships on sea routes. To address this limitation, we have carried out more comprehensive experiments. We installed a GNSS device on the M/V TIANHUI, which continuously collected data in the NEP during the period between 10 September and 20 September 2019. We assessed the performance of the RT-PPP in the NEP in terms of GNSS signal and real-time product quality, and single- and dual-frequency GNSS positioning accuracy. Our paper is organized as follows: Section 2 describes the related background knowledge, including GNSS signal quality and the RT-PPP model, and then describes our NEP RT-PPP experiments. Section 3 analyzes the signal quality of the GNSS in the NEP and shows the RT-PPP performance, including the accuracy of CLK93 products, and SF and DF RT-PPP positioning results, comparisons, and validations between single-GPS and GPS/Galileo/BDS combinations. In Section 4, we discuss the effectiveness of real-time ionosphere products on SF RT-PPP positioning, which is followed by our summaries and conclusions in Section 5.
###### Abstract  
The establishment of a structural health monitoring (SHM) system for the damage and defects of composite structures is of great theoretical and engineering value to ensure their production and operational safety. Advanced machine learning technologies, such as deep learning, have become one of the main driving forces for state monitoring and predictive analysis of these structures. However, it is difficult to obtain sufficient data to train the deep learning model, which may fail to build an accurate and efficient SHM model. To overcome this problem, a new method based on Lamb waves and the diffusion model (DM) is proposed to realize the identification and classification of different defects for carbon-fiber-reinforced polymer (CFRP) structures. In this study, DM is used as the generation model of data enhancement, and the optimized and improved DDPM model is constructed in this experiment. The deep residual neural network (DenseNet) is used to identify and classify the defect features from the Lamb wave signals. Experimental and test results show that the deep learning framework designed in this study based on DenseNet classification and DDPM data enhancement can accurately detect and classify damage signals of common defects in CFRP composite plates.  
structural health monitoring; diffusion model; DenseNet; Lamb wave
## 3 Results and Discussion  
### IoT bioreactor Performance  
The IoT bioreactor operated successfully for 40 consecutive days, with no issues related to substrate supply or the supporting devices' functioning essential for its operation. On the 41 st day, a malfunction occurred in the oleic acid peristaltic pump, which led to increased added volume to the bioreactor. This resulted in an OLR of 2.30 \(\pm\) 0.03 g VS L\({}^{-1}\) d\({}^{-1}\). While this issue impacted biogas production, it did not interfere with the monitoring capabilities of the IoT system.  
Data from the sensors were collected and stored in the database without interruption, and there were no connectivity problems with the ESP32 during data transmission. Overall, the IoT system's performance was highly satisfactory and intervention or corrective action was not required in terms of data collection, transmission, or recording. Similar performance has been observed in other studies [30; 31], highlighting the robustness and reliability of the system in maintaining continuous, effective monitoring.
# Causality Analysis with Different Probabilistic Distributions Using Transfer Entropy
## 2 Mathematical Modeling
### Cauchy Probabilistic Density Function  
The Cauchy PDF is an example of a fat-tailed distribution belonging to the family of stable distributions. This distribution is symmetrical with the following density function:  
\[F_{\delta,\gamma}^{Cauchy}(x)=\frac{1}{\pi\gamma}\Biggl{(}\frac{\gamma^{2}}{ \left(x-\delta\right)^{2}+\gamma^{2}}\Biggr{)}, \tag{10}\]where: \(\delta\in\mathbb{R}\) is the distribution's _location_ (mean) parameter, and \(\gamma>0\) is the distribution's _scale_ factor.  
It is interesting to mention separately here, because control signals often exhibit Cauchy properties [34].
# Unfair and Risky? Profit Allocation in Closed-Loop Supply Chains by Cooperative Game Approaches
## 1 Introduction  
Under the increasingly stressful environment, more and more enterprises remanufacture and recycle preowned goods, forming a closed-loop supply chain. Circular economy has been recognized as more efficient and competitive than linear economy [1]. Circular closed-loop supply chains (CLSCs) management provides a new perspective to the sustainability [2, 3]. There remain challenges in managing circular supply chains effectively and various strategies are proposed by researchers [4, 5, 6].  
One of the primary objectives of CLSCs is to maximize channel profits. Decision makers in CLSCs are usually not absolutely rational [7, 8]. Behavioral factors such as risk aversion and fairness concern have impacts on operational decisions and profit distribution [9]. Under the uncertainties of market demand or recycling profit, CLSC decision makers tend to avoid potential risks. CLSC members are often concerned with the proportion of their profits in the total CLSC profit. Price strategy is a primary methodology to coordinate CLSC management.  
This research addresses two themes: (1) derivation of equilibrated production quantities, prices and profits for a CLSC with both risk aversion and fairness concern under the five models; (2) evaluation of risk aversion and fairness concern's effect on profit allocations in cooperative game settings. Supply chains often have a single manufacturer and multiple retailers [10]. In this research, we incorporate retailers' risk aversion and fairness concern into a two-echelon CLSC consisting of a manufacturer (M), a risk-averse retailer (L) without fairness concern, and a risk-neutral retailer (F) having fairness concern. By cooperative game approaches, we examine five scenarios: (1) a centralized model where one central decision maker plans for all agents (CC); (2) a decentralized model where each agent makesdecisions independently (DC); (3) L and F compose an alliance (LF); (4) M and L compose an alliance (ML) and (5) M and F compose an alliance (MF).
# PV-Powered Electric Vehicle Charging Stations: Preliminary Requirements and Feasibility Conditions
## 6 Conclusions  
This study focuses on the preliminary requirements, feasibility conditions, and business model of PV-powered EV charging stations in an urban area. The simulation results show that the EV charging demand are not constrained; the EV user can charge from slow to fast mode, and no restrictions for EV battery capacity (\(10\%<soc_{EV_{v}}<100\%\)). However, the PV benefits have increased when the average daily urban/peri-urban trip of 20-40 km is considered, with an EV consumption of 10-15 kWh/km, which gives the daily charge needed of 2-6 kWh. In addition, the maximum PV benefits are proved for the daily EV charging instead of weekly and when the park time is known.  
For the requirement and feasibility conditions, two charging modes are possible; slow mode up to 7 kW based mainly on PV energy and storage and EV filling capacity up to 6 kWh, fast mode from 7 kW and up to 22 kW based mainly on public grid energy. Proper sizing of the stationary storage system is required and social acceptance relative to longer charging duration for slow mode and higher charging price for fast mode, so a business model is important.  
The two main concerns, highlighted in the case studies, are the control-command of the system, i.e., PV-powered EV charging station based on stationary storage and public grid, and the business model that is able to influence consumer behavior through price charging.  
Future studies will concentrate on defining the proper size of the stationary storage, adjust its physical limits based on the charging station dimension, and propose a business model with appropriate price charging for EV users. Then, validate the simulation with real-time experimental tests. In addition, a human-system interface has to be built according to the PV-powered charging station definition and requirements. Furthermore, defining the barriers, solutions for PV-powered EV charging stations, and a survey on social acceptance must be conducted, and proposing new services vehicle-to-everything (V2X) associated with PV-powered EV charging stations.  
Conceptualization, S.C.-M., M.S. and F.L.; methodology, S.C.-M., M.S. and F.L.; software, S.C.-M., M.S. and F.L.; validation, S.C.-M., M.S. and F.L.; formal analysis, S.C.-M., M.S. and F.L.; investigation, S.C.-M., M.S., F.L. and Y.K.; resources, S.C.-M., M.S., F.L. and Y.K.; data curation, S.C.-M., M.S., F.L. and Y.K.; writing--original draft preparation, S.C.-M.; writing--review and editing, M.S. and F.L.; visualization, S.C.-M., M.S., F.L. and Y.K.; supervision, M.S.; project administration, M.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ADEME France, project PV2E_Mobility, grant number #1905C0043.  
Not applicable.  
The authors declare no conflict of interest.
# E-SAWM: A Semantic Analysis-Based ODF Watermarking Algorithm for Edge Cloud Scenarios
## 3 Model and Algorithm
### Dynamic Watermarking Algorithm for OFD  
To address the aforementioned scenario, we propose E-SAWM, a watermarking algorithm based on semantic analysis. At the file level, we incorporate a watermark into the key structural file, Content.xml of OFD. This integration renders the entire OFD page corrupted if the attacker deletes the content.xml file. At the content level, we leverage semantic analysis of the structural statements within Content.xml to generate highly realistic pseudo structural statements. These pseudo structural statements, carrying the watermark, are distributed and embedded within each Content.xml file. This distributed embedding approach ensures that the watermark remains concealed, making it challenging for attackers to identify its existence, location, and content. Furthermore, E-SAWM exhibits robustness against attempts to destroy or tamper with the watermark fields.