esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
### Datasets  
Three different public datasets are included in this study: the Liver Tumor Segmentation (LiTS) dataset, the 3D Image Reconstruction for Comparison of Algorithms Database (3D-IRCADb), and the Segmentation of the Liver Competition 2007 (Sliver07) dataset. The LiTS dataset is the most widely used dataset for liver and tumor segmentation, including a training set of 131 CT scans and a test set of 70 CT scans, ranging from 42 to 1026 CT slices in each scan with a resolution of 512 \(\times\) 512 pixels for each CT slice and a slice spacing ranging from 0.45 mm to 6.0 mm. Four radiologists from six clinical centers manually labeled the training set. The labels for the test set are not available. The 3D-IRCADb dataset is a more challenging dataset, including more complex data on the liver and tumors with low contrast and an overlap between the liver and tumor regions for some cases. The 3D-IRCADb-01 dataset consists of CT scans with the ground truth of 10 male and 10 female patients. The 3D-IRCADb-02 dataset consists of two CT scans with the ground truth of the chest and abdomen. The resolution of each CT slice is 512 \(\times\) 512 pixels with a slice thickness from 1 mm to 4 mm. The range of the number of slices for each scan is from 74 to 260 slices with a pixel spacing varied from 0.57 mm to 0.87 mm. The Sliver07 dataset, which is the most challenging dataset, contains 20 CT scans with ground truth. Most of the scans contain diseased livers with cysts and tumors. The resolution of each CT slice is 512 \(\times\) 512 pixels with a slice thickness from 1 mm to 3 mm. The range of the number of slices for each scan is from 64 to 394 slices, with a pixel spacing varied from 0.58 mm to 0.82 mm. We derived 131, 20, and 20 CT scans with ground truth from the LiTS dataset, 3D-IRCADb dataset, and Sliver07 dataset, respectively. Figure 4 shows two example CT images from each of the three datasets, respectively.
## 2 Materials and Methods
### Data Collection  
Near-infrared (NIR) analysis was performed using a portable PoliSPEC-NIR spectrophotometer (courtesy of IT-Photonics S.r.l., Fara Vicentino, Italy). The device operates in the spectral range of 900-1700 nm, with a spectral resolution of 3.2 nm. It is equipped with a halogen lamp as light source and an InGaAs (Indium Gallium Arsenide) detector with a 256-pixel array. The instrument also features a front-mounted heat sink and a thermal control system to minimize signal distortion caused by temperature fluctuations during field use.  
Spectral data were collected at three distinct time points: 10 days before harvest (pre-harvest, PH), at harvest (day 0, D0), and after 60 days of refrigerated storage (day 60, D60). A total of 200 samples were analyzed. Measurements were performed in duplicate on opposite equatorial sides of each fruit, and the two spectra were averaged to obtain a representative spectrum for each sample.
# Heterologous Production of Flavour and Aroma Compounds in _Saccharomyces cerevisiae_
###### Abstract  
Over the last two decades, rapid progress in the field of synthetic biology has opened several avenues for the heterologous de novo production of complex biological compounds, such as biofuels, pharmaceuticals, and food additives in microbial hosts. This minireview addresses the usage of the yeast _Saccharomyces cerevisiae_ as a microbial cell factory for the production of flavour and aroma compounds, thereby providing a path towards a sustainable and efficient means of producing what are normally rare, and often expensive plant-derived chemicals.  
synthetic biology; _Saccharomyces cerevisiae_; genetic engineering; heterologous production of flavour and aroma compounds
## 5 Results and Discussion
### Deformation Measurement of the Liner
#### 5.2.4 FBG, DIC, and FEA Result Comparisons  
The results of DIC were compared with the values of deformation recorded using Bragg gratings. To achieve this goal, virtual extensometers of which the locations and directions were the same as the physical locations of the FBG sensors were used. Calculated strain from FEM was used for validation.  
Due to the limitations of the DIC method, the results were compared for only three sensors. One of them was placed on the dome in the longitudinal direction (LD1) and two located on the cylindrical part (CC1--circumferential and LC1--longitudinal). The sensor's location is marked in Figure 12.  
A comparison of the results of the deformation measurements recorded using all methods is presented in Figure 16. Correlation is visible regardless of the direction of the measurement. The biggest relative error is visible for the longitudinal direction on the cylindrical part of the liner (LC1). For the circumferential direction of the cylinder part (CC1), the maximum relative error does not exceed 5%.  
Figure 16: Chart presenting deformations recorded using FBG sensors, DIC method, and FEA model.  
Figure 15: Strain field variations caused by the air-bubble chain located close to the boss: circumferential strain field distribution in the dome region model without defects (**a**) and with an internal defect in the form of the elliptical void (**b**), and location and geometry of the defect (**c**).
## 1 Introduction  
Electric vehicles have become a focus of global research owing to their energy savings and environmental friendliness [1]. However, power batteries restrict the development of electric vehicles (_i.e._, the battery can cost as much as 30% of the total cost of the vehicle) [2]. Lithium-ion batteries are the ideal choice for electric vehicles due to their better performance, small volume, light weight, and low pollution [3]. However, the safety and reliability of lithium batteries are concerns for electric vehicle developers. When electric vehicles are used outdoors, poor pavement conditions, temperature, and load changes can cause performance degradation in lithium-ion batteries. Battery degradation may lead to leakage, insulation damage, and partial short-circuit. If the degradation is not detected timely, using the battery further will cause serious situations, such as a spontaneous combustion and explosions, especially if the current state of health has not been assessed in a timely fashion or the future state of health has not been estimated.  
Three examples of serious degradation-related incidents are as follows. (1) A Zotye pure electric car made in China spontaneously combusted on 11 April 2011 [4]. (2) The U.S. National Highway Traffic Safety Administration (NHTSA) subjected a GM Volt to a side-impact crash test on 12 May 2011, during which the batteries suffered a great impact and degraded [5]. Three weeks later, the temperature of the Volt's lithium-ion battery pack increased sharply, causing spontaneous combustion. (3) A Tesla Model S made in Norway suddenly caught fire in 2014 when it was charging in the fast charging station [6].  
To avoid such catastrophic incidents caused by the degradation of lithium-ion batteries and to predictively maintain the safety of vehicles, it is of great significance to carry out research on the RUL prognostics of lithium-ion batteries. With the rapid proliferation of lithium-ion battery applications, research based on data-driven methods (such as degradation model establishment, RUL prediction, and health states assessment) is summarized in this paper.  
The remainder of this paper is organized as follows. Section 2 analyzes three methods based on data-driven RUL prediction, which are based on the artificial intelligence, filtering techniques and the stochastic degradation process, respectively. Section 3 describes the widespread challenges that lithium-ion batteries face in operation, such as the influence of time-varying environments, random variable currents, self-recharge characteristics, and different system configurations. Section 4 concludes this paper and provides future research directions.
# Rotor Position Alignment of FSTPI Based PMSM Drive Using Low Frequency Signal Injection
###### Abstract  
A PMSM drive with an incremental encoder or using sensorless control requires alignment to a predetermined rotor position (initial position) or initial rotor position detection at start-up. It is desired to lock the rotor to a known state (usually zero angle) at start-up if the initial rotor position detection is not available or difficult to obtain. In this work, a simple and proper zero angle initial rotor position alignment of four-switch three-phase (FSTP) inverter-based PMSM drive is proposed. Low-frequency voltage signal is applied to the \(d\)-axis voltage reference of the open-loop FSTPI based PMSM drive scheme without requiring complex trigonometric calculations, PI current regulators and current sensing. Therefore, fluctuated capacitor voltages at the DC-link are obtained allowing current flow through phase \(a\) locking the rotor with zero angle, properly. The proposed method has been implemented using a low-cost FSTP voltage source inverter (VSI) for PMSM drive with a floating-point TMS320F28335 DSP. The effectiveness and the feasibility of the proposed zero angle initial rotor position alignment method for PMSM driven by FSTP inverter have been demonstrated through experimental results.  
permanent magnet synchronous motor (PMSM); four-switch three-phase (FSTP) inverter; reduced switch inverter; initial position alignment; signal injection +
Footnote †: journal: Applied Physics Letters
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
###### Abstract  
Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network partition(s), exhibiting adverse effects on network lifetime. Extending network lifetime while preserving energy and security thus becomes an interesting challenge. In this article, we address the aforesaid shortcomings with the proposed adaptive en-route filtering (AEF) scheme. In energy-aware routing, the fitness function, which is used to select forwarding nodes, considers residual energy and other factors as opposed to distance only. In pre-deterministic key distribution, keys are distributed based on the consideration of having paths with a different number of verification nodes. This, consequently, permits us to have multiple paths with different security levels that can be exploited to counter different attack intensities. Taken together, the integration of the special fitness function with the new key distribution approach enables the AEF to adapt the underlying dynamic network conditions. The simulation experiments under different settings show significant improvements in network lifetime.  
Keywords:filtering capacity; adaptive filtering; energy-aware routing; network lifetime; energy efficiency; wireless sensor networks +
Footnote †: journal: Sensors
## 4 Eigenvalue Fusion and other Fusion Methods for Detection OFDMA Signals  
The proposed eigenvalue fusion scheme can be applied to any wideband signal. From the previous section we saw that it can be directly applied to the level of subcarriers in OFDM signals, for example, or to the level of predefined sub-bands in general wideband signals. Moreover, it can be adapted to sense an OFDMA signal to a subchannel level. This adaptation is described in this section. For the sake of completeness, the sample fusion and the decision fusion are also described in the context of OFDMA signals.
## 2 Method
### Participant Eligibility Criteria  
We conducted this study from 9 January through 10 April 2019, in South Korea. Participant recruitment was in collaboration with a professional internet survey agency in Korea. Five hundred users of smartphones over the age of 19 were randomly selected as the study population. All participants gave obtained, Informed consent online before participation. Those who did not meet the following inclusion criteria were excluded: (1) those who refused to provide consent, (2) users of Android Lollipop 5.0.1, and (3) those who used smartphones for less than 3 h a day. Further, during the experiment, those who did not take the survey at weeks 0, 8, and 12, those who did not maintain at least 85% of data collection time per day (24 h), and those who did not switch on the smartphone for more than 3 days per week were excluded. All participants were given incentives as follows: (1) those who filled out the app survey at week 0 received 1000 won (about $1); (2) those who filled out the app survey at week 8 received 1000 won (about $1); (3) those who filled out the app survey at week 12 received 4000 won (about $4); (4) those who maintained the app installation for 13 weeks and filled out all the app surveys at weeks 0, 8, and 12 received 10,000 won (about $10). Excluded participants were informed via online message or email and given incentives depending on the duration of their participation. Underage participants were excluded from the study, as parental consent was legally required. Additionally, the approval process of the Korean IRB is strict for underage participants. This study proceeded in accordance with the Declaration of Helsinki and the Institutional Review Board (IRB) of the Catholic University of South Korea, St. Mary's Hospital (MC18 FNSI0020).  
Figure 1: Study design.
# Measurement and Analysis of Downhole Drill String Vibration Signal
## 1 Introduction
It provided an effective basis for identifying harmful vibrations in time and proposing damping measures during igneous rock drilling.
# Gradient-Based Aerodynamic Optimization of an Airfoil with Morphing Leading and Trailing Edges
## 2 Methodologies
### Feasible Morphing Airfoil Geometry Parameterization
#### 2.2.1 Generalized CST Equations
The first term \(A_{0}\) is relevant to the radius of curvature of the leading edge, whilst the last, \(A_{n}\), can be formulated from the trailing edge boat-tail angle \(\beta\), and both the vertical location of leading and trailing edge. Those properties enable the intuitiveness of the CST parameterization method and are beneficial to establish the morphing leading and trailing edge functionality that will be shown in the following sections. \[\frac{R_{\text{LE}}}{c} =\lim_{\psi\to 0^{+}}\frac{\left(1+\zeta^{\prime}(\psi)^{2} \right)^{3/2}}{|\zeta^{\prime\prime}(\psi)|}=\frac{A_{0}^{2}}{2} \tag{6}\] \[\tan(\beta) =\lim_{\psi\to 1^{-}}-\zeta^{\prime}(\psi)=A_{n}+\zeta_{\text{LE}}- \zeta_{\text{TE}}\]  
where \(R_{\text{LE}}\) is the dimensional radius of leading edge curvature; \(\zeta^{\prime}(\psi)\) and \(\zeta^{\prime\prime}(\psi)\) are first and second derivatives of CST formulation with respect to non-dimensional coordinate \(\psi\), respectively.
## 1 Introduction
### Diabetes Prediction in Real-Time Environment  
Intensive care of blood glucose levels helps in preventing and treating diabetic problems [4]. Innovative biosensors that may enable real-time monitoring of a patient's health, as well as recent advancements in information and communication technology (ICT), provide a new viewpoint on diabetes treatment. Diabetic patients can monitor their blood glucose levels by using self-monitoring blood glucose (SMBG) portable devices [5] or continuous glucose monitoring (CGM) sensors [6] to track glucose variations, because of which they will be able to respond quickly and with the necessary actions. The findings suggest that monitoring patients' glucose levels can help them control their disease and enhance their diabetes management performance [7]. The greatest option for improving diabetes care is glucose monitoring in a real-time system that includes sensors, a gateway (smartphone), and a cloud system [8]. It uses a smartphone as a gateway to acquire sensor data from a sensor node connected to the body [9]. Wireless technology is required for communication between the sensor node and the smartphone, as well as low-power operation for the sensor node, and the best choice for this is Bluetooth low energy (BLE) [10].
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 4 Experimental Results and Performance Evaluations
### Performance Improvement from Spatial Correlation Compression  
BESDC spatial correlation compression can help the SN device to reduce the sensor data transmission to the sink after collecting compressed sensor data (data correlation and temporal  
Figure 6: Data compression ratio in ED via BESDC.  
Figure 7: Energy consumption ratio in ED via BESDC.  
correlation compression) from its neighboring ED to the sink, comparing their similarity and compressing (spatial correlation). In the experiments for the compression and energy consumption performance in spatial correlation compression in BESDC, we use three different environmental datasets of PM 2.5, temperature and CO on two neighboring EDs (both in Taichung City, Taiwan) and two faraway EDs (in Northern and Southern Taiwan) to demonstrate the BESDC performance in spatial correlation compression and energy consumption on SN. Besides, we add the experimental results simulated for other energy-efficient IoT works [32] (i.e., DF-TS temperature as shown in Figures 8-11) from our previous work [37]) to further compare the performance improvement from the proposed BESDC.  
As shown in Figures 8 and 9, the performance results of data compression and energy consumption for SN with two neighboring EDs using the BESDC spatial correction compression scheme show that the compression ratio and energy consumption ratio can be improved to extend the SN lifetime for IEI applications.  
While considering spatial correlation compression for the SN with two faraway EDs, their data compression and energy consumption ratios on SN are shown in Figures 10 and 11, respectively. Compared with the performance results in Figures 8 and 9 (i.e., SN with two neighboring EDs), they have worse data compression and energy consumption ratios since two EDs are far away and have low spatial correlations in the experimental datasets.  
Figure 11: Energy consumption ratio in SN with “faraway” EDs by spatial correlation compression.  
Figure 10: Data compression ratio in SN with “faraway” EDs by spatial correlation compression.  
However, in the experimental results of SN with two neighboring EDs, the compression and energy consumption ratios of the temperature dataset in bounded error of 0.75% are almost the same as the bounded error of 0.5% (i.e., BESDC temperature in Figures 8 and 9). This is the same reason (as mentioned in the previous subsection) that the experimental dataset of temperature preserves much the same value ranges as the bounded errors given in 0.5% and 0.75%. Then, compared to the quantitative results from DF-TS temperature in Figures 8-11, the compression and energy-consumption ratios from BESDC temperature show better improvement than DF-TS temperature. Therefore, according to the overall performance results, BESDC can comprehensively improve the performance of data compression and energy consumption for the SN to extend the IoT lifetime of IEI applications.
# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
## 4 Experimental Results
### Recall Analysis  
In the intricate landscape of Sentiment Recognition, recall emerges as a critical metric, reflecting the model's capacity to accurately identify a wide range of emotions. This study delves into a comparative analysis of recall within the IEMOCAP and CMU-MOSI datasets, each known for their unique strengths and focuses. IEMOCAP excels in capturing a broad spectrum of realistic emotional expressions in audiovisual data, whereas CMU-MOSI distinguishes itself with an emphasis on multilingual Sentiment Recognition. The application of a tri-modal emotion identification approach in this study is pivotal. It facilitates a more comprehensive and context-aware categorization of emotions, adaptable to a variety of situations. This methodology enhances the depth and breadth of Sentiment Recognition, moving beyond conventional single-modal approaches.  
Table 9 and Figure 11 present a side-by-side comparison of the recall capabilities of models trained on these datasets. The results indicate a slightly higher recall for the  
Figure 10: Graphical representation of Precision.  
\begin{table}
\begin{tabular}{l r} \hline \hline
**Methods** & **Precision (\%)** \\ \hline SVM [47] & 76 \\ GBM [47] & 72 \\ VC(LR-SGD) [47] & 78 \\ CA-WGNN [Proposed] & 92 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Numerical Outcomes of Precision.  
CMU-MOSI dataset (0.94%) compared to IEMOCAP (0.93%), underscoring the nuanced differences in the datasets' abilities to accurately recognize a diverse range of emotions.  
This comparison not only highlights the specific strengths of each dataset but also emphasizes the importance of employing multimodal Sentiment Recognition frameworks for more accurate and versatile ER systems. The findings bolster the case for the ongoing refinement and enhancement of these technologies in the field of ER research.  
The evaluation of precision across various methods is critical in understanding their effectiveness in emotion recognition. Table 10 and Figure 12 present the precision metrics for both the proposed and existing methods. The CA-WGNN method demonstrates superior performance by achieving a 93% precision rate, in comparison with the existing methods where SVM attained 80%, GBM attained 79%, and VC(LR-SGD) attained 84%. This comparison clearly illustrates that the suggested CA-WGNN process outperforms the existing methods in terms of precision. The findings underscore the CA-WGNN's advanced capability in accurately identifying emotional nuances, setting a new benchmark for future research in the domain of emotion recognition.
# A Review of Image Inpainting Methods Based on Deep Learning
## 5 Applications
### Facial Inpainting  
Facial inpainting is a crucial application of image inpainting with a wide range of uses. For example, in criminal investigations, if a suspect's face is obstructed or facial features are damaged, accurately removing the obstruction is essential for improving facial recognition techniques.  
Facial features are essential components of the face, and facial images contain the topological structures between these features, making them highly structured and semantically rich. Facial images also vary based on gender, race, and expression. [PERSON] et al. [6] introduced a facial landmark prediction network. [PERSON] et al. [7] incorporated facial semantic labels in the first stage of dual-stage inpainting. [PERSON] et al. [8] embedded facial information into a latent space using variational autoencoders, guiding facial completion. [PERSON] et al. [9] added a noise prior, introducing a structural prior, which helps the model retain facial pose information, producing more realistic image content.  
Still, facial inpainting has many challenges. For example, the restored facial features and expressions are often not lifelike. Most datasets used in the literature are from European and American faces, so the results for Asian faces are not ideal. Hence, there is a need to create a dataset tailored to Asian facial features, aligning the algorithm more closely with Asian facial attributes.