esa-sceva/satcom-chunk-collection huggingface dataset:
# Automating the Analysis of Negative Test Verdicts:
## 1 Introduction
The KNN algorithm exhibits lower accuracy in predicting anomalies and requires more time for both training and prediction. Despite its advantages, supervised learning has limitations that can impact the effectiveness and usability of models trained using this approach. It requires labeled training data, which can be costly, time-consuming, or impractical to obtain. Additionally, the quality and accuracy of labels can affect model performance. Manual labeling can introduce human errors that are challenging to diagnose and correct. Supervised learning models are trained on a specific training dataset, limiting their ability to generalize. The dependence on the representativeness of training data is another consideration when deploying supervised learning models. If the training set does not reflect the full range of possible scenarios in the real environment, the model may be inadequately prepared to handle diverse situations. Overfitting sensitivity is also a drawback of supervised learning. If a model overfits the training data, it may not perform well on new data. In the analysis and forecasting of time series data, deep learning methods are preferred for their ability to autonomously identify relevant features during training. Applying machine learning to analyze data from numerous sensors is a challenge; aiming to detect faults as early as possible before machine replacement or repair and system downtime is necessary. Extracting essential features from a large amount of sensor data is a critical challenge. [PERSON] proposes a model based on the AE-GRU architecture, where the autoencoder (AE) extracts significant features from raw data, and the Gated Recurrent Unit (GRU) selects information from the sequence for forecasting [16]. Nevertheless, it is important to highlight that there are several additional challenges associated with embedded software testing, where AI can assist and optimize the entire process. In this study, we aim to investigate the applicability of openly available artificial intelligence algorithms designed to detect outliers in the context of embedded software testing. The objective is to assess their effectiveness in identifying anomalies within the test environment that have the potential to interfere with the results of embedded software testing. The results obtained will be utilized in further work to automate the step of verifying negative test results with artificial intelligence algorithms so that it is cost effective and efficient to implement this in a variety of real industrial automotive projects.
## 4 Landscape of Immunotherapy for HCC
### Anti-PD1/PD-L1 in Association with Intravenous Anti-VEGF Agents  
The IMbrave150 trial is a phase III clinical trial in which advanced HCC patients previously untreated were randomized to receive the combination of the anti-PD-L1 ategzolizumab plus the anti-VEGF bevacizumab (atezo+beva) or sorafenibib in a 2:1 ratio [7]. The combination of immunotherapy and antiangiogenic therapy is scientifically appealing considering that, through VEGF pathway inhibition, the immunosuppressive TME is convertedin an immunostimulatory milieu consequently to the normalization of tumor vasculature and an immunomododulator effect [44; 45]. The results of the IMbrave150 trial constitutes a milestone in HCC therapy, since atezo+beva was demonstrated to be significantly superior to sorafenib as a frontline systemic therapy, thus ending its long-lasting era [7]. Notably, the median OS in the atezo+beva group was 19.2 months (95% CI 17.0-23.7), the longest ever achieved with systemic therapy in HCC patients [8]. In addition, atezo+beva increased both progression-free survival (PFS) (6.9 months vs. 4.3 months, \(p<0.0001\)) and objective response rate (ORR) (30% vs. 11%, \(p<0.0001\)) compared to sorafenib [8]. Overall, the toxicity of the combination was manageable, with hypertension as the most commonly reported adverse event (AE) in 12% of cases [8]. Even though the proportion of both serious AEs (23% vs. 16%) and of patients that discontinued the treatment due to toxicity (22% vs. 12%) were slightly higher in the atezo+beva than in the sorafenib arm, the combination therapy was associated with a better quality of life [8].  
The strategy of combining ICIs with anti-VEGF antibodies proved to be fruitful also in other studies, as the trial assessing the treatment based on anti-PD-1 antibody still-imab combined with the anti-VEGF IBI305 (a bevacizumab biosimilar) demonstrated a statistically significant benefit compared to sorafenib in Chinese patients with HBV-related advanced HCC [46].
## 6 Results
### Line Topology  
Line topology is a typical solution used for monitoring pipeline applications. In this case, the information is relayed hop-by-hop until the gateway. Figure 17 illustrates an example of a line topology for a WSN. If a device along the line fails, the monitoring application will also fail.  
One of the targets of the proposed methodology is to identify dependability bottlenecks in the network. These impairments can be identified through the use of [PERSON]'s measure and criticality importance. A component importance analysis for this scenario is illustrated in Figures 18 and 19. Based on the [PERSON]'s measure (Figure 18), the field device \(Fd_{1}\) is the device more susceptible to cause a network failure. This behavior is confirmed by the criticality importance, as illustrated in Figure 19. In other words, if the field device \(Fd_{1}\) fails, there is a high probability that there is no path to the gateway due to a failure in an intermediate device.  
Consider a communication scenario where the application requires an improved reliability level and there is just one available spare device. The problem that must be addressed is to select which device should be equipped with the available spare device, so that the reliability level of the system is maximized. A sensitivity analysis for this problem is shown in Table 1.  
Figure 17: Typical line topology for a WSN.  
Figure 18: Analysis of the component importance based on Birnbaum measure for the line topology.  
presented, if the spare device is configured to be at the gateway or access point, the MTTF of network will be increased by around 2%. Thus, there is no real advantage to configure two spare devices in the gateway or in the access points. The best result, as expected, is attained when the spare device is configured to be at device \(Fd_{1}\). In this configuration, the network MTTF is increased by 19.23%. This configuration presents even better performance than the configuration with two spare devices in \(Fd_{3}\) or \(Fd_{4}\). Note that if the spare device have been configured in \(Fd_{4}\), the network MTTF would have been decreased by 1.95%.
# Numerical and Experimental Research on the Laser-Water Jet Scribing of Silicon
## 2 Numerical Simulation and Experimental Setup
### Theoretical Background
\(\varepsilon_{xy}\), \(\varepsilon_{yz}\), and \(\varepsilon_{zx}\) are the theoretical elastic shear strain. \(\tau_{xy}\), \(\tau_{yz}\), and \(\tau_{zx}\) are the shear stress. 2. Dynamic displacement equations describing the thermoelastic force [18]: \[ \begin{array}{l}(\lambda+G)\frac{\partial\varepsilon}{\partial x}+G\
abla^{ 2}u-\beta\frac{\partial t}{\partial x}=0\\ (\lambda+G)\frac{\partial\varepsilon}{\partial y}+G\
abla^{2}v-\beta\frac{ \partial t}{\partial y}=0\\ (\lambda+G)\frac{\partial\varepsilon}{\partial z}+G\
abla^{2}w-\beta\frac{ \partial t}{\partial z}=0\\ \end{array} \] (5) where \(\
abla^{2}\) is Laplace operator, \(\
abla^{2}=\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{\partial y ^{2}}+\frac{\partial^{2}}{\partial z^{2}}\). \(u\), \(v\), and \(w\) are the displacement along X, Y, and Z axes, respectively. 3. Deformation compatibility equation describing the thermoelastic force [18]: \[ \begin{array}{l}\
abla^{2}\sigma_{x}+\frac{1}{1+\mu}\frac{\partial^{2} \Theta}{\partial x^{2}}=-\varepsilon E\big{(}\frac{1}{1-\mu}\
abla^{2}t+ \frac{1}{1+\mu}\frac{\partial^{2}t}{\partial x^{2}}\big{)}\\ \
abla^{2}\sigma_{y}+\frac{1}{1+\mu}\frac{\partial^{2}\Theta}{\partial x^{2}} =-\varepsilon E\big{(}\frac{1}{1-\mu}\
abla^{2}t+\frac{1}{1+\mu}\frac{\partial ^{2}t}{\partial x^{2}}\big{)}\\ \
abla^{2}\sigma_{z}+\frac{1}{1+\mu}\frac{\partial^{2}\Theta}{\partial x^{2}} =-\varepsilon E\big{(}\frac{1}{1-\mu}\
abla^{2}t+\frac{1}{1+\mu}\frac{ \partial^{2}t}{\partial x^{2}}\big{)}\\ \
abla^{2}\tau_{xy}+\frac{1}{1+\mu}\frac{\partial^{2}\Theta}{\partial x^{2}} =-\frac{\varepsilon E}{1+\mu}\frac{\partial^{2}t}{\partial x^{2}}\\ \
abla^{2}\tau_{zx}+\frac{1}{1+\mu}\frac{\partial^{2}\Theta}{\partial x^{2}} =-\frac{\varepsilon E}{1+\mu}\frac{\partial^{2}t}{\partial x^{2}}\\ \end{array} \] (6) where \(\Theta\) is the volumetric stress, \(\Theta=\frac{E}{1-2\mu}(e-3\alpha_{\rm s}t)\). By solving Equations (1)-(6), the LWJ-induced temperature and strain distribution at the machining region could be characterized.
# Critical Review of Techniques for Food Emulsion Characterization
## 3 Techniques for Emulsion Characterization
### In Vitro Digestion  
One of the pivotal approaches to the characterization of emulsions involves analyzing their behavior in gastrointestinal digestion _in vitro_ models [210]. In recent years, growing attention has been given to comprehending and regulating the digestion of emulsified lipids, particularly within the food and pharmaceutical sectors. Emulsions are being actively developed as delivery systems for non-polar lipids, vitamins, nutraceuticalals, and pharmaceutical compounds. The primary objective is to encapsulate these components to ensure their effective release at specific target sites within the gastrointestinal tract.  
To date, a variety of _in vitro_ digestion models have been developed [211]. Recently, the INFOGEST model has been successfully validated when compared to human and animal models. This _in vitro_ model has proven useful for the characterization of the food matrices within a simulated digestion environment [212] and contributed to advancing our understanding of emulsion behavior during digestion [213].  
_In vitro_ digestion models can be categorized into single- and multiple-step models (Figure 15). The single-step models simulate a specific region of the gastrointestinal (GI) tract, such as the mouth, stomach, small intestine, or colon [120]. An example of this kind of model is the pH-stat method, which exclusively emulates the digestive processes occurring in the small intestine [214]. The multiple-step models aim to simulate two or more regions of the GI tract [215,216], or the entirety of the human GI tract. Irrespective of the chosen approach, the procedure typically involves the preparation of a food sample. Subsequently, this sample is subjected to one or more treatments intended to mimic specific regions within the human digestive tract. These treatments often entail the blending of the food sample with simulated digestive fluids characterized by specific compositions, including pH, mineral constituents, enzyme activity, and other relevant factors, all carried out under controlled mixing and temperature conditions [217, 218, 219].  
Figure 14: Mechanism of lipid oxidation in Pickering oil-in-water emulsion (R—unsaturated fatty acid, ROOH—fatty acid peroxide, RO®—alkoxy radical).
## 4 Results
### Burn-Type Differentiation
#### 4.1.2 Wildfire Detection  
The wildfire algorithm needs two parameters: a spatial clustering distance and a temporal clustering distance for aggregating FINN fires. We tuned these two parameters separately and analyzed the spatial and temporal relationships in FINN data (Figure S1). For spatial clustering, we clustered the fires observed on the same day. The clustering started at around 250 m, which means FINN did not have any fires within less than 250 m of each other. The number of clusters was increasing, and the rate of increase slowed down after 1200 m. This result showed that most FINN records clustered in the 250 m to 1200 m range. For temporal clustering, we clustered the fires which occurred closer than a specific distance within a number of consecutive days, the duration of which was determined by the algorithm itself. The \"elbow\" (the maximum curvature, also known as the knee, for the temporal clustering curve in Figure S1) was around 800 m, which indicates that the number of clusters increased intensively when the distance threshold was less than 800 m. For wildfire detection, we clustered FINN fire records in space and time simultaneously by using our wildfire detection algorithm. Clustering parameters were tuned at the same time, and the elbow method [39] suggested 1000 m as the spatial clustering distance and 800 m as the temporal clustering distance (Figure S2). For wildfire detection algorithm evaluation, we used the number of matched wildfires between algorithm-detected wildfires and wildland fire location full history (WFIGS [42]) records in the United States from 2014 to 2020. As a matching method, we relaxed the distance (1500 m) and date (3 days) (Algorithm S3) by considering the uncertainty of the wildfire's reported location and discovery date. A total of 3430 WFIGS records, whose burned area was higher than the minimum burned area of algorithm-detected wildfires, were selected. The algorithm detected 22,140 wildfires based on clustering FINN fires temporally and spatially, and 665 wildfires were identified by both WFIGS and our algorithm.
## 6 Conclusions  
The process uses hydrogen metallurgy to produce DRI and reduce fossil fuel consumption in the steel production process, which is an essential technical route to promote the green and sustainable development of the ISI. The study presented in this paper can support the commercialization of HDRSFs and offer some theoretical and practical references for their growth. The main conclusions are as follows:  
1. According to the law of conservation of mass and the first and second laws of thermodynamics, a calculation model of the overall material-energy-exergy analysis of HDRSFs has been established. The model comprehensively considers the input and output of the shaft furnace reduction and cooling sections and realizes a more comprehensive analysis and calculation of the energy utilization of HDRSFs.
2. An improved HDRSF process with waste heat recovery is proposed. In the improved process flow, the waste heat recovery of top gas and high-temperature cooling gas is realized by preheating reducing gas and iron ore. In addition, the process of purifying and recovering furnace top gas is rationally designed. The improved HDRSF process has the conditions for practical industrial application.
3. The calculation results show that the waste heat recovery process proposed in this paper can reduce the reducing gas demand for the per-ton DRI produced by the shaft furnace. When the temperature of reducing gas is 900 \({}^{\circ}\)C and H\({}_{2}\):CO = 1:1, the reducing gas demand per ton of DRI produced by the shaft furnace decreases from 2083 m\({}^{3}\) to 1557 m\({}^{3}\), which decreases by 25.3%.
4. In terms of the energy analysis and exergy analysis of the HDRSF process, when the recovery rate of waste heat reaches 40%, the energy consumption intensity per ton of DRI decreases from 2.75 \(\times\) 10\({}^{7}\) kJ to 1.70 \(\times\) 10\({}^{7}\) kJ, showing a remarkable energy-saving effect. The exergy intensity reduces from 1.08 \(\times\) 10\({}^{7}\) kJ to 1.05 \(\times\) 10\({}^{7}\) kJ, while the exergy input of the shaft furnace reduces from 2.48 \(\times\) 10\({}^{7}\) kJ to 1.86 \(\times\) 10\({}^{7}\) kJ. In general, the process proposed in this paper can realize the goal of energy conservation and consumption reduction in HDRSF production.  
Conceptualization, Y.J., Z.C. and W.Z.; methodology, Y.J. and Z.C.; software, Y.J.; validation, Y.J., Z.C. and T.J.; formal analysis, Y.J., Z.C. and T.J.; investigation, Y.J. and Z.C.; resources, Y.J. and Z.C.; data curation, Y.J., T.J. and X.L.; writing--original draft preparation, Y.J.; writing--review and editing, Y.J., Z.C., T.J. and X.L.; visualization, Y.J.; supervision, W.Z.; project administration, W.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Key Research and Development Program of China (Grant No. 2017 YFA0700300).  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 1 Introduction  
Many coastal and delta plains worldwide are affected by land subsidence phenomena [1; 2], which often involve inhabited areas causing conspicuous economic costs.  
Examples come from different geodynamic, geological, climatic and social contexts. In Italy, subsidence due to a combination of natural causes and anthropogenic activities is observed in the Venice coastaland the Po delta plain [3; 4; 5; 6], while in the United States in correspondence of the coastal areas of Southern Louisiana and Mississippi [7; 8; 9; 10; 11]. Other examples are observed in the Yellow River and Yangtze China Deltas [12; 13], along the Taiwan coastland [14; 15; 16], in the Indonesian Semerang city [17], in Thessaloniki coastal plain and municipality region (Northern Greece) [18; 19].  
Over the last decades, the land subsidence monitoring has been significantly improved thanks to the earth observation techniques based on Interferometric Synthetic Aperture Radar (InSAR). Furthermore, the recent advances in radar satellite capabilities and techniques based on the interferometric analysis of large datasets [20; 21; 22; 23; 24; 25; 26; 27; 28; 29] have allowed even better spatial and temporal resolutions.  
We applied multitemporal differential SAR Interferometry technique and specifically the Small Baselines Subset (SBAS) approach [20] to investigate the ground deformations of the Sibari Plain (SP), located in the northeastern sector of the Calabria Region (Southern Italy) covering an area of -500 km\({}^{2}\). This Plain involves an important economic weight due to the agricultural production and a cultural appeal related to the presence of the ancient Sybaris, a powerful Greek colony in Magna Grecia founded in 720 BC. The area is also susceptible to flooding risk, reduced by means of a drainage channels network.  
The used SAR datasets cover the temporal interval spanning from May 2003 and September 2013 for both ascending and descending orbits acquired by Envisat and COSMO-SkyMed satellites (for details see Table 1). Envisat is a European Space Agency (ESA) satellite operating in the C-Band (5.6 cm of wavelength) launched in the 2002 and operating up to 2012, with a revisiting period of 35 days, covering an area of about 100 \(\times\) 100 km. The COSMO-SkyMed mission consists of a constellation from the Agenzia Spaziale Italiana (ASI) in the X-Band frequency (3.2 cm of wavelength). The first satellite of the constellation has been launched in June 2007 and the constellation (four satellites) has been fully operative by the end of 2010. The used acquisitions (Himage mode) are characterized by a swath of about 40 \(\times\) 40 km\({}^{2}\) with a revisiting time period of 16 days.  
Moreover, the availability of ascending and descending datasets allowed us to discriminate the vertical and east-west displacement components.  
Finally, the retrieved InSAR results were validated by means of GPS measurements then analyzed and interpreted considering the available geological and hydrogeological information as well as new data collected during our field surveys.
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
## 3 Materials and Methods
### Effectiveness Verification  
The duration of the marking and verification process was also analysed. The study aimed to determine whether the algorithm's efficiency depends on the given parameters. The analysis consisted of taking 50 measurements of the marking and verification process duration for each scenario, then calculating the average duration. A single measurement consisted of recording a timestamp just before marking began (after the model was loaded) and another timestamp after marking was completed (before the model was saved). Duration was calculated as the difference between the two time points. The computer on which the measurements were taken was equipped with an I7-4770 3.40 GHz processor and DDR3 2 \(\times\) 8 GB 1600 MHz CL9 RAM.
###### Abstract  
This study focuses on the development and characterization of solid dispersions (SDs) of Gefitinib (GEF) to improve its aqueous solubility and therapeutic activity against lung cancer. SDs were prepared by the co-precipitation method with tocopheryl-polyethylene-glycol succinate-1000 (TPGS) (F1), sodium lauryl sulfate (SLS) (F2) and complexation of F1 with hydroxypropyl \(\beta\)-cyclodextrinxtrin (HP-\(\beta\)-CD) (F3). Optimal formulations (F1 and F3) were used against A549 cells to determine the apoptosis, expressions of p53 and caspases. F3 has shown the highest solubility (1271.21 \(\mathrm{\SIUnitSymbolMicro m}\)/mL), followed by F1 (1003.69 \(\mathrm{\SIUnitSymbolMicro m}\)/mL), F2 (707.81 \(\mathrm{\SIUnitSymbolMicro m}\)/mL) and GEF pure (303.85 \(\mathrm{\SIUnitSymbolMicro m}\)/mL) in 0.1N HCl. Dissolution at 1.2 pH significantly enhanced the release from F3 (99.19%), followed by F1 (94.76%), F2 (85.70%) and GEF pure (37.26%) during 120 min. Compaculation of GEF-TPGS with HP-\(\beta\)-CD significantly improved drug release with high dissolution efficiency (78.57%) in 24.9 min of mean dissolution time. Differential scanning calorimetry revealed crystalline to amorphous conversion of GEF in SDs, which was confirmed by scanning electron microscopy. Fourier transform infrared and proton nuclear magnetic resonance spectral analysis revealed no interaction between GEF and excipients. The IC\({}_{50}\) values were 2.239, 3.135 and 4.471 \(\mathrm{\SIUnitSymbolMicro m}\) for F3, F1 and GEF pure, respectively, against A549 cells. Increased expressions of p53 (5.9-, 4.6- and 3.04-fold), caspase-3 (5.38-, 3.78- and 3.01-fold) and caspase-9 (5.35-, 3.76- and 2.47-fold) in the case of F3, F1 and GEF pure, respectively, as compared to the untreated A549 cells indicated improved apoptotic potential of the SDs. TPGS SDs and their complexation with HP-\(\beta\)-CD improved the solubility, dissolution and efficacy of GEF against A549 cells. So, they can be a suitable alternative to the conventional GEF formulations against non-small-cell lung cancers.  
Gefitinib; TPGS; solid dispersions; HP \(\beta\)-CD; lung cancer; A549 cells +
Footnote †: journal: Applied Sciences
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 3 Deep-Coupled Navigation System Design
### Design of the Integrated Navigation Filter  
#### 3.4.1 State Model  
The deep fusion of the combined GNSS and SINS is accomplished by an integrated navigation filter. The state vector matrix \(\mathbf{X}_{\text{nav}}\) of the navigation filter is  
\[\mathbf{X}_{\text{nav}}=\left[\phi_{E}^{G},\phi_{N}^{G},\phi_{U}^{G},\delta V_{E}^ {G},\delta V_{N}^{G},\delta V_{U}^{G},\delta x,\delta y,\delta z,e_{bx}^{b}, \varepsilon_{by}^{b},\varepsilon_{bz}^{b},\
abla_{bx}^{b},\
abla_{by}^{b}, \
abla_{by}^{b},\delta t_{u},\delta t_{f}\right]^{T} \tag{21}\]  
where \(\left(\phi_{E}^{G},\phi_{N}^{G},\phi_{U}^{G}\right)\) are the grid attitude error angles; \(\left(\delta V_{E}^{G},\delta V_{N}^{G},\delta V_{U}^{G}\right)\) are the grid velocity errors; \(\left(\delta x,\delta y,\delta z\right)\) are the position errors along the \(x,y,z\) axes in \(e\) frame; \(\left(\varepsilon_{bx}^{b},\varepsilon_{by}^{b},\varepsilon_{bz}^{b}\right)\) are the gyro constant drifts along the \(x,y,z\) axes in \(b\) frame; \(\left(\
abla_{bx}^{b},\
abla_{by}^{b},\
abla_{bz}^{b}\right)\) are the accelerometer constant biases along the \(x,y,z\) axes in \(b\) frame; and \(\delta t_{u}\) and \(\delta t_{f}\) are the clock bias and clock drift of the GPS receiver, respectively.