esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermoplastic Materials for the Metal Replacement of Non-Structural Components in Marine Engines
## Appendix A Computational Details
Expansion of the first row of Equation (10) yields:  
\[\sigma_{11}=\text{C}_{11}\epsilon_{11} \tag{11}\]  
By deforming the cell along directions 2 or 3, identical expressions may be generated. Thus, three distinct values of the elastic constant \(\text{C}_{11}\) are obtained for each tension or compression simulation. \(\text{C}_{11}\) may be further averaged as the strain is delivered evenly in each direction (\(\epsilon_{11}=\epsilon_{22}=\epsilon_{33}\)). The elastic bulk modulus K under hydrostatic tension and compression is given by Equation (11):  
\[\text{K}=\frac{1}{3}\bigg{(}\frac{\sigma_{11}+\sigma_{22}+\sigma_{33}}{\epsilon _{11}+\epsilon_{22}+\epsilon_{33}}\bigg{)} \tag{12}\]  
Consequently, by noting that \(\epsilon_{\text{ii}}\) is equal to 0.05 along all directions, K can be simply calculated using Equation (12) by inputting the normal stress components generated from the appropriate simulations. Lastly, the essential correlations between the elastic constant \(\text{C}_{11}\), the bulk modulus K, the elastic (Young) modulus E, and the [PERSON]'s ratio \(\
u\) that is:  
\[\text{C}_{11}=\frac{(1-\
u)\text{E}}{(1+\
u)(1-2\
u)} \tag{13}\]  
\[\text{K}=\frac{\text{E}}{3(1-2\
u)} \tag{14}\]  
allow the estimation of the values for \(\
u\) and E after K and \(\text{C}_{11}\) are obtained as a consequence of the simulation processes described before. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System** & **E (GPa)** & **E\({}_{\text{f}}\) (-)** & **System** & **E (GPa)** & **E\({}_{\text{f}}\) (-)** \\ \hline PA6 & 1.1 \(\pm\) 0.1 & & & & \\
10\% wt GFs/PA6 & 2.2 \(\pm\) 0.2 & 2.0 & 10\% wt CF/PA6 & 3.2 \(\pm\) 0.2 & 2.9 \\
20\% wt GFs/PA6 & 3.4 \(\pm\) 0.3 & 3.1 & 20\% wt CF/PA6 & 6.4 \(\pm\) 0.3 & 5.8 \\
30\% wt GFs/PA6 & 4.6 \(\pm\) 0.3 & 4.2 & 30\% wt CF/PA6 & 9.1 \(\pm\) 0.2 & 8.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values of the Young modulus E and the corresponding enhancement factor \(\text{E}_{\text{f}}\) (=E of the (nano)composite/E of the pristine polymeric matrix) for the system based on Nylon 6 (PA6) and different loadings of glass fibres (GFs) or carbon fibres (CFs) (system 1).
## 2 Methods
### Experimental Setup and Analysis  
A total of 1000 images of surge arrester pointer-type instruments were captured in the field (resolution 600 \(\times\) 800), and then randomly divided into 800 images for the training set and 200 images for the validation set. These images cover meter readings from different angles, backgrounds, and lighting conditions to ensure that the trained model has strong generalization ability. These were then fed into localization models with different attention mechanisms for training and detection. In this study, Table 1 enumerates the configuration parameters for both software and hardware utilized in the experiment.  
This study utilizes evaluation metrics such as Precision (\(P\)), Recall (\(R\)), Average Precision (\(AP\)), and mean Average Precision (\(mAP\)) to evaluate the model's performance. The formulas for calculating these parameters are as follows.  
Figure 8: The Process of Recognizing the Scale of a Pointer-type Surge Arrester Instrument. (**a**) The coordinate system; (**b**) Scale recognition results. (Note: The Chinese characters in the image are as follows: Alarm, Model: JQC-2/800, Surge Arrester Operation Detector, Date: March 2023, Number: 3831, Shanghai Leichuan Surge Arrester Co., Ltd.)  
Figure 7: Color Extraction of Scale Bars.  
Precision (_P_) is defined as the proportion of positive samples that the model correctly identifies out of the total positive samples that it identifies. The formula for calculation is  
\[P=\frac{TP}{(TP+FP)} \tag{6}\]  
where, _TP_ represents the number of True Positives, and _FP_ represents the number of False Positives.  
Recall (_R_) is the measure of the model's correctly identified positive samples in relation to the total number of actual positive samples. The formula for calculation is  
\[R=\frac{TP}{TP+FN} \tag{7}\]  
where _FN_ is the number of false negatives, which are the correct samples that were missed.  
Average Precision (_AP_) denotes the mean of precision scores at various recall thresholds, typically derived by constructing a precision-recall curve and computing the area beneath this curve.  
\[AP=\int_{0}^{1}P(R)dR \tag{8}\]  
where _P_(_R_) is the P-R curve on the range [0,1].  
Mean Average Precision (_mAP_) refers to the average of _AP_ across all classes or queries, which is a common metric in the context of multiple classes or multiple queries. In some cases, _mAP_ might also be the average of _AP_ across all levels of detection difficulty or at different recall cut-off points:  
\[mAP=\frac{\sum_{i=1}^{n}AP}{n} \tag{9}\]  
where \(n\) is the number of categories.  
These indicators offer an all-encompassing view for assessing the model's efficacy, taking into account the accuracy of the model's detections as well as its capacity to encompass positive samples.
# A Review: Thermal Stability of Methylammonium Lead Halide Based Perovskite Solar Cells
## 4 Impact of Thermal Stress on Various Layers and Possible Solutions  
We now move the stability discussion to the other important layers of perovskite solar cells. In particular, we will discuss how the temperature affects the electron transport layer (ETL), the hole transport layer (HTL), and the metal electrode.
## 3 Results
### Calibration and Validation of the Eco-Hydrological Model
#### 3.1.2 Calibration and Validation of Evapotranspiration and GPP Component  
To validate the evapotranspiration (ET) simulation results, this study collected daily ET data from 2008-2012 from the Eddy flux data (49 19\({}^{\circ}\)-49 21\({}^{\prime}\) N, 119 55\({}^{\circ}\)-119 58\({}^{\prime}\) E, located at the Xiertala Ranch in Hailar City) for model applicability evaluation against SWAT-simulated data. Daily ET values simulated by the RHESys model from 2009-2012 were extracted at the patch scale corresponding to the ecological station's geographic location and compared to observed values. The results indicated that the simulation met the performance criteria of NSE > 0.4 and PBIAS > 25% (Figure 3), though the model exhibited some underestimation, particularly at the beginning of the growing season from 2009 to 2011, when observed ET values at the ecological station were significantly higher than those simulated by the model. This discrepancy may be due to the additional water input from thawing permafrost, which occurs as a result of significant summer temperature increases in the cold region of the Hailar River basin, a process not accounted for in RHESys.  
Furthermore, a comparative analysis of monthly ET simulations from RHESys and SWAT over the period 1990-2012 revealed a correlation coefficient above 0.85. However, at the monthly scale, RHESys also exhibited some underestimation compared to SWAT. Based on the spatial distribution map of average ET over the years 2000-2012, a notable underestimation was observed in the forest-grassland ecotone when compared to MODIS_ET (8-day, 500 m) data (Figure 4). This may be attributed to the lack of specific consideration for the forest-grassland ecotone during vegetation type classification in RHESys, which is an issue common to most vegetation models. Future research could focus on addressing this gap to enhance the accuracy of ET simulations. Nevertheless, RHESys remains suitable for simulating ET in the middle and upper reaches of the Hailar River basin.  
Figure 3: Evaluation of ET: (**a**) comparison between observed and simulated ET by RHESys model; (**b**) comparison between simulated ET by RHESys model and by SWAT model.  
This study focuses only on interannual variation characteristics of GPP (Gross Primary Production). Therefore, MODIS GPP values (8-day, 500 m resolution) from 2003 to 2012 were used to validate the model-simulated GPP values (Figure 5). The comparison results show that the RHESSys model achieved an annual mean GPP correlation coefficient (R\({}^{2}\)) of 0.64, with \(p<0.01\). In terms of interannual variation, aside from a lower simulated value in 2007, the model and MODIS GPP values showed minimal differences and a relatively consistent trend, indicating that RHESSys can effectively capture changes in GPP characteristics for vegetation in the middle and upper reaches of the Hailar River basin.
## 4 Experimental Section
### Bacterial Strains, Plasmids, and Growth Conditions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strains** & **Genotype** & **Source** \\ \hline BW25113 & _rmB3_\(\Delta\)_lacZ4787 hsdR514_\(\Delta\)_(araBAD)567_\(\Delta\)_(rhoBAD)568 rph-1_ & [59] \\ \hline BW25113 \(\Delta\)_orrT_ & BW25113 \(\Delta\)_orrT_\(\Omega\) Km\({}^{\rm R}\) & [59] \\ \hline BW25113 \(\Delta\)_ghoT_\(\Delta\)_kan_ & BW25113 \(\Delta\)_ghoT_ & [42] \\ \hline BW25113 \(\Delta\)_orrT_\(\Delta\)_kan_ & BW25113 \(\Delta\)_orrT_ & This study \\ \hline BW25113 \(\Delta\)_magsR_\(\Delta\)_magsA_\(\Delta\)_kan_ & BW25113 \(\Delta\)_magsR_\(\Delta\)_magsA_ & [43] \\ \hline
**Plasmids** & **Genotype** & **Source** \\ \hline pCA24N & Cm\({}^{\rm R}\); _lacR_\({}^{\prime}\), _lacR_\({}^{\prime}\), [60] \\ \hline pCA24N-_ghoT_ & Cm\({}^{\rm R}\); _lacR_, P\({}_{\rm T5\to::::}\):_ghoT\({}^{\prime}\)_ & [60] \\ \hline pCA24N-_orrT_ & Cm\({}^{\rm R}\); _lacR_, P\({}_{\rm T5\to::}\):_orrT\({}^{\prime}\)_ & [60] \\ \hline pCA24N-_relA_ & Cm\({}^{\rm R}\); _lacR_, P\({}_{\rm T5\to::}\):_erLA\({}^{\prime}\)_ & [60] \\ \hline pCA24N-_orrT_ACG & Cm\({}^{\rm R}\); _lacR_, P\({}_{\rm T5\to::}\):_orrT\({}^{\prime}\)_ with start codon AZG replaced with ACG & This study \\ \hline pCA24N-_orrT_F38R & Cm\({}^{\rm R}\); _lacR_, P\({}_{\rm T5\to::}\):_orrT\({}^{\prime}\)_ with TT changed to CG at nucleotide position & This study \\ \hline pCA24N-_ghoT_F38R & Cm\({}^{\rm R}\); _lacF_, pCA24N P\({}_{\rm T5\to::}\):_ghoT\({}^{\prime}\)_ with TT changed to CG at nucleotide position 160-161 relative to the 6 xHis-GhoT start codon & [42] \\ \hline pCA24N-_ghoT_F38R & Cm\({}^{\rm R}\); _lacF_, pCA24N P\({}_{\rm T5\to::}\):_orrT\({}^{\prime}\)-ydcY\({}^{\prime}\)_ & This study \\ \hline pCA24N-_orrT-ghoS_ & Cm\({}^{\rm R}\); _lacR_, pCA24N P\({}_{\rm T5\to::}\):_orrT\({}^{\prime}\)-ghoS\({}^{\ast}\)_ & This study \\ \hline pCA24N-_orrT_-B & Cm\({}^{\rm R}\); _lacR_, pCA24N P\({}_{\rm T5\to::}\):_orrT\({}^{\ast}\)_ with the _BamHI_ site in the _N_-terminus linker deleted. & This study \\ \hline pCP20 & Ap\({}^{\rm R}\); Cm\({}^{\rm R}\), _FLP\({}^{\ast}\)_, \(\lambda\)_c1857\({}^{\ast}\), \(\lambda\)_p_\({}^{\rm R}\)Rep\({}^{\rm us}\) & [61] \\ \hline pBS(Kan) & Km\({}^{\rm R}\); cloning vector & [62] \\ \hline pBS(Kan)-_magsR_ & Km\({}^{\rm R}\); pBS(Kan) P\({}_{\rm inc}\):_magsR\({}^{\ast}\)_ & [43] \\ \hline pBS(Kan)-_magsR-magsA_ & Km\({}^{\rm R}\); pBS(Kan) P\({}_{\rm inc}\)::_magsR\({}^{\ast}\)_-magsA\({}^{\ast}\)_ & [43] \\ \hline \end{tabular}
\end{table} 
Table 2: Bacterial strains and plasmids used in this study. Double colon (::) indicates fusion of a promoter and gene. \begin{table}
\begin{tabular}{c c} \hline \hline
**Purpose/Name** & **Sequence (5’ to 3’)** \\ \hline
**Plasmid construction** & \\ _ydcY-Sal_-F & TTTTTTGCACATTA\(\Delta\)**AA**AG**AG**AGA**ATA**ACT**AT**GTCC**ACT**TG**G**G**AT**GA**AG**GT**CA** \\ _ydcY-Hind_III-R & TTTTTTGCACAAGCTTAGACGTCATTTTAATCAGAGGATGGTG \\ _ghoS-Sal_-F & TTTTTTGCACATTA\(\Delta\)**AA**AG**AG**AGA**ATA**ACT**GTGA**AG**AG**TGA**AAA**ACA**AG**GT**CA**AT** \\ _ghoS-Hind_III-R & TTTTTTGCACAAGCTTCTTATCCCTG
## 5 Use Case Description with the OGC SensorThings Standard
### Tested Sensors
#### 5.2.2 Libellium Waspmote  
The second type of payload analysed is that produced by Waspmote devices from the company Libellium [99]. These devices are user-programmable and can produce payloads in two formats: the first format presents the payload as a string made up of ASCII characters, while the second format presents the string as a series of bytes [101].  
* **ASCII String**: This format prioritizes ease of understanding the data over packet size. ASCII characters allow the sensor to send data transparently without converting them to specific measurement types. The connector can directly extract values by reading the payload, without needing to know the data types. However, each character takes up one byte, making compression difficult and resulting in larger frame sizes.
* **Binary String**: This format is designed for more compressed frames, sacrificing readability. Each byte represents a specific field's value, aiming to minimize payload size and maximize information transmission. The sensor converts each measurement to its corresponding data type based on the manufacturer's specifications. The connector must know these specifications to extract and \"translate\" the values before sending them to the central server.  
**Payload Syntax**: Both ASCII and binary frames share a similar structure, consisting of two parts: a fixed header with device and communication information, and variable fields with sensor measurements in a key-value format. The key difference is that, in ASCII frames, the character \"#\" delimits each field, while in binary frames, \"#\" marks the end of a variable-length field, and other fields have fixed lengths. Measurements are expressed as key-value pairs, and the manufacturer provides a table detailing the sensors' possible measurements, including properties like ID, size, unit, and precision. This information is crucial for the connector to extract and convert data for the server.  
**Additional Requirements**: In addition to those listed in the general model, the Libellium connector requires further specifications:  
* The THING entity in the OGC SensorThings system must be associated with the unique Waspmote serial ID.
* The device transmits only simple frames, meaning that all information is contained in a single frame and is not divided into multiple frames.
# Design and Analysis of the High-Speed Underwater Glider with a Bladder-Type Buoyancy Engine
## 5 Conclusions  
In this study, a UG capable of cruising at a maximum speed of 2 knots or more was designed and manufactured according to the characteristics of the Korean peninsula. The maximum movement speed of the UG was studied by comparing the mathematically calculated results with the actual sea area experimental results.  
The steady-state motion in water was estimated by considering the lift force, and the buoyancy control capacity was calculated to obtain the target maximum moving speed of 2 knots. The submerged angle was calculated as 35\({}^{\circ}\)-40\({}^{\circ}\) from the circumnavigation trajectory to create the optimal motion state. In addition, the hull outline was designed using empirical formulas, and the reliability was secured by obtaining a resistance coefficient equivalent to that of the existing commercial underwater vehicle through CFD. Reliability was verified by simulating the behavior of the UG through dynamic modeling by constructing a six-degree-of-freedom equation of an underwater body.  
The actual sea area experiment verified the UG's movement speed. As a result of the experiment, it was confirmed that the maximum moving speed, 2.11 knots, was reached during submersion, and the average moving speed was over 1 knot, except for the section where the motion was changed. According to the experimental results, similar to the results analyzed in the design, it was confirmed that the maximum moving speed of the UG was 2 knots, and high-speed circumnavigation was possible.  
In this study, the speed measurement sensor was not installed as a spatial constraint of the underwater glider. Therefore, a numerical calculation had to be performed to calculate the movement speed. In future studies, we will compare the results by preparing the speed measurement sensor.  
Conceptualization, D.-H.J. and H.-S.C.; methodology, S.-K.J.; software, D.-H.J. and S.-H.K.; validation, D.-H.J. and [PERSON]-W.H.; resources, J.-H.L. ([PERSON]); data curation, D.-H.J.; writing--original draft preparation, D.-H.J.; writing--review and editing, S.-K.J. and H.-S.C.; supervision, S.-K.J.; project administration, J.-H.L. ([PERSON]); funding acquisition, J.-H.L. ([PERSON]); All authors have read and agreed to the published version of the manuscript.  
This research was supported by Unmanned Vehicles Core Technology Research and Development Program through the National Research Foundation of Korea (NRF) and Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science and ICT, the Republic of Korea (NRF-2020M3C1C1A02086324).  
Not applicable.  
The authors declare no conflict of interest.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
###### Abstract  
In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic approach is introduced in this article. A unified approach to privacy-preserving interpretable and transferable learning is considered for studying and optimizing the trade-offs between the privacy, interpretability, and transferability aspects of trustworthy AI. A variational membership-mapping Bayesian model is used for the analytical approximation of the defined information theoretic measures for privacy leakage, interpretability, and transferability. The approach consists of approximating the information theoretic measures by maximizing a lower-bound using variational optimization. The approach is demonstrated through numerous experiments on benchmark datasets and a real-world biomedical application concerned with the detection of mental stress in individuals using heart rate variability analysis.  
privacy; interpretability; transferability; information theory; membership mappings; variational optimization; machine and deep learning +
Footnote †: journal:  
Declaration of Responsible AI\" [8], \"UK AI Code\" [9], \"AI4 People\" [4], \"Ethics Guidelines for Trustworthy AI\" [1], \"OECD Principles on AI\" [10], \"Goverance Principles for the New Generation Artificial Intelligence\" [11], and \"Guidance for Regulation of Artificial Intelligence Applications\" [12]. However, it was argued in [13] that AI ethics lack a reinforcement mechanism, and economic incentives could easily override commitment to ethical principles and values.  
The five principles of ethical AI [4] (i.e., _benefence, non-maleficence, autonomy, justice_, and _explicability_) have been adopted for TAI [6]. Benefence refers to promoting the well-being of humans, preserving dignity, and sustaining the planet. Non-maleficence refers to avoiding bringing harm to people and is especially concerned with the protection of people's privacy and security. Autonomy refers to the promotion of human autonomy, agency, and oversight including the restriction of AI systems' autonomy, where necessary. Justice refers to using AI for correcting past wrongs, ensuring shared benefits through AI, and preventing the creation of new harms and inequities by AI. Explicability comprises an epistemological sense and an ethical sense. Explicability refers in the epistemological sense to the explainable AI developed by creating interpretable AI models with high levels of performance and accuracy. In the ethical sense, explicability refers to accountable AI.
# Third-Generation Sequencing in Clinical Practice: The New Era of Precision Medicine?
## 4 Cancer Diseases  
The last clinical context where ONT performances have been tested in the last decade is oncologic disorders. In fact, the study of cancer genetics often concerns different genomic and epigenomic aberrations [i.e., single nucleotide variants (SNVs), insertions/deletions, CNVs, structural variants (SVs), methylation patterns] all detectable by NS.  
Recently, it was demonstrated how NS combined with Cas9-based target enrichment enabled the analysis of ten clinically-relevant cancer-associated loci: _BRAF_, _KRAS_, and _TP53_ SNVs, chromosomes 5 and 7 deletions, and the methylation status of _GPX1_, _GSTP1_, _KRT19_, _SLC12A4_, _TPM2_[13]. To date, no other single technology can address the same issues simultaneously.  
Among oncologic disorders, NS has been widely tested in the study of blood cancers, malignancies often associated with genomic aberrations, for which the technology offers a wide range of chances [14; 15]. Once again, long-read production and rapidity are advantages of ONT. The ability to produce long-reads simplifies the SVs characterization and, in particular, detects fusion genes (i.e., _BCR-ABL1_, _PML-RARA_, _AMLI-ETO_, _CBFB-MYH11_) frequently observed in hematological malignancies [16; 17; 18]. On the other hand, NS could significantly speed up the diagnostic procedure of acute leukemias, whose timeliness is essential and could impact the patient outcome. The possibility to study (in 24 h) the most frequently mutated genes (_NPMI_, _FLT3_, _CEBPA_, _TP53_, _IDH1_, and _IDH2_) in adult acute myeloid leukemia (AML) is an example of the ONT capability to upgrade the AML diagnostics, improving the patient's management [19]. In the near future, simple, cheap, and fast sequencing strategies could deepen the knowledge of cancer genetics, increasing the power of precision medicine and the patients' chances of survival.
# Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves
## 4 Discussion
However, because the HSL color space and HSV color space can theoretically aggregate illuminance information into L [13] or V, the HSL color system and the HSV color system are considered useful in environments with continuously changing lighting conditions. In contrast, in halogen lighting conditions, the color becomes closer to orange, and thus, it may be difficult to separate color and illumination information in the HSL color space or HSV color space, resulting in lower accuracy of the SPAD value prediction. In the HAL+LED model (Figure 5a), which was constructed from data under halogen and LED light sources, the G values were important, whereas the B values were important in the LED model (Figure 5b). Previous studies have reported that the blue and green bands contribute significantly to SPAD value prediction [19]. The importance of these variables in this study differed depending on the light source, implying that the important variables for SPAD value prediction vary depending on the light environment. Therefore, it is important to incorporate light environment information, such as the color temperature and illuminance of the light source, for image-based SPAD prediction. Although this study was conducted in a closed indoor environment, there exists a wide range of fields where image-based SPAD value prediction can be conducted, ranging from fields [28] to greenhouses [30]. The results of this study can be used in environmentswhere the leaf color can be obtained under a stable light source, as in previous studies [18]. However, when images are acquired under unstable light environments, such as in the fields or in greenhouses, the accuracy of the SPAD value prediction becomes an issue. In addition, the maximum illuminance in this study was 5000 lx, whereas the outdoor environment was highly illuminant, ranging from 10,000 to 100,000 lx. Further study is therefore needed to collect observation data for building data-driven models specifically in outdoor environments for the proposed method to be applied to a variety of lighting conditions. Furthermore, although this study predicted the SPAD value using Random Forests, XGBoost, and LightGBM, the accuracy of SPAD value prediction may be improved by applying other algorithms, such as Deep Neural Networks. Furthermore, the accuracy changes depending on the compatibility between the input data and the algorithm. In this study, unlike the results of previous studies [19], the Random Forests algorithm achieved the highest accuracy. Further studies on SPAD value prediction using novel algorithms on other datasets may illustrate how different algorithms perform well on datasets with different features and descriptive statistics.