esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Acquisition and Processing
### In-Situ Data Collection
#### 2.3.2 Crop Development  
Within each plot, five 2 m \(\times\) 2 m quadrats were used for bi-weekly ground-based monitoring of plant development and growth, synchronous with UAV image acquisitions. There are therefore, 25 quadrats (5 quadrats for each of the 5 plots) per field for cotton, maize, and peanut; and 30 quadrats (6 plots \(\times\) 5 quadrats) for millet and sorghum. The 4 corners of each plot were semi-permanently picketed and their position recorded with a differential GPS with centimeter accuracy in order to measure the exact same location at each field visit. At each bi-weekly field visit, non-destructive measurements of crop status, BBCH phenological development stage [24], weed prevalence rate, plant height, and ground cover (GC) fraction were assessed for each 2 m \(\times\) 2 m quadrat following standardized protocols [19] and recorded in dedicated data entry forms on smartphones.  
The ground coverage (GC fraction) of the soil background by green plant material was estimated using vertical photographs. These were taken with a 35 mm lens camera mounted on a L-shaped, 3 m telescopic pole, from nadir view above the canopy. Images were processed using the CAN-EYE freeware [25] to determine GC fraction by an human interactive process classifying pixels into bare ground and green vegetation classes.  
Figure 2: Schematic layout of the experiment in a hypothetical field of 1 ha.
# An Agent-Based Simulation of How Promotion Biases Impact Corporate Gender Diversity
## 3 The Simulation
### Core Elements of Our Simulation
#### 3.1.3 Simulating the Separation Process  
In this simulation, we do not distinguish between employees who quit and those who are fired, referring simply to \"separation\" as the act of an employee leaving the company. While it would be possible to capture the nuances of the different types of separations, for the purposes of this study the key value of separations is to create turnover at a rate comparable to what we see in real companies, so that the company's employee pool is refreshed over time.  
We simulate separations by assuming a certain annual company turnover rate and apply that rate uniformly at each rank at each time step. This means that, in a given year, the absolute number of employees who separate is greatest at the entry level and decreases with increasing rank. This ensures that the ratios of the number of employees across ranks remains consistent (subject to small fluctuations due to the timing of terminations and promotions) as the company grows over time. Notice that this also means that the average \"tenure\" of employees is directly proportional to rank: if there are twice as many entry-level employees than VPs, then, on average, the tenure of a VP will be about twice as long as that of an entry-level employee. This kind of relationship between rank and average tenure aligns with existing data from corporations [49].  
To determine which employees separate at each rank, at each time step we determine the number of employees who are likely to separate, and then we randomly select the required number of employees from a \"separation pool,\" a portion of the employees with the lowest promotion scores at that rank; in other words, the decision to terminate is influenced both by seniority (or lack thereof) and--if biases are present--by gender.  
Applying the same gender bias for separations as well as for promotions reflects two assumptions: first, that the sources of bias that drive promotion decisions are also behind termination decisions. This seems reasonable, given that typically the same manager is in charge of both decisions. Second, having access to career advancement opportunities is a crucial part in shaping job satisfaction, and in turn, will influence an employee's intention to leave the organization [50; 51]. Therefore, if biases cause women to be held back, they are more likely to become frustrated, which may increase the probability that they will separate voluntarily (i.e., quit). In preliminary studies, we have tried other separation processes, such as purely random selection. Including gender bias in separation heightens and accelerates some of the results we report, but does not alter our overall findings.
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement
### Classifiers
#### 3.2.3 Cnn  
Every sensor node in a WSN ([PERSON] et al.) is the same as a neuron in our nervous system. Sensor nodes pass data to a base station as neurons do in our nervous system. The neural networks are inspired by our nervous system. In this system, the brain has the ability to collect and process various datasets of text files, images, and video files. Our nervous system has the following layers of neurons namely input layer sensing cells, output layer controlling by commands and hidden layers.  
In neural networks, every neuron receives many inputs, and consolidates all inputs. After the consolidation, the information is passed to the next neuron.  
The following steps are followed in the CNN mechanism to receive, process and transmit signals to the neighboring neurons:  
* In the first step, the data sensed by the sensor node are to be reduced to a level of acceptance or minimum required level. At this input level, many sensors sense the data at high rates. This high data rate of sensing is not needed to get meaningful full data. This data reduction makes the system faster with an efficient method of sensing.
* The next step in CNN is to process the sensed and received data. Data processing in sensor nodes involves a lot of power and resources. In a neural network, the sensed data are processed on a common point.
* The final step in CNN is to provide communication between all the sensor nodes in the entire network. This process is used to predict the type of fault and classify the sensor nodes in the network. After the classification, the faulty nodes are replaced or connected with other nodes in WSN.
# Research on Micro-Vibration Analysis of Segmented Telescope Based on Opto-Mechanical Integration
## 4 Opto-Mechanical Integration Analysis
### Micro-Vibration Source  
The size of the optical system is large, and in orbit it is susceptible to micro-vibration from the space environment and the internal micro-vibration sources of the system. Among them, solar radiation pressure noise and gravity field changes are small and negligible, and the thermal deformation period is the same as the orbital period. At the same time, the  
Figure 8: Flowchart of opto-mechanical integration analysis.  
infrared refrigerator on the satellite has been running for a long time to produce micro-vibrations. As shown in Figure 9 the optical-mechanical system is fixed with the infrared refrigerator and suspended in the suspension system. A low stiffness suspension system composed of lifting belts and springs was used to simulate the zero gravity environment in orbit.  
6 accelerometers were uniformly placed on the infrared refrigerator mounting frame to measure the micro-vibration signals. A data point was collected every 0.002 s for a continuous measurement of 4 s for a total of 2000 moments. The acceleration signals measured by six accelerometers were averaged to obtain micro-vibration signals. The curves in time domain and frequency domain were shown in Figure 10. The amplitude of acceleration in the Z-direction is approximately 10 times that in the X and Y directions. Through Fourier transform of the time-domain data, the frequency domain range of 0-250 Hz is obtained. Characteristic peaks are observed at 43.3 Hz, 46.0 Hz, 50.8 Hz, 60.3 Hz, 80.2 Hz, and 99.8 Hz in all three directions. Among these, the amplitude at 99.8 Hz is the largest.  
Figure 10: Micro-vibration source data (**a**) Acceleration time domain signal data; (**b**) Acceleration frequency domain signal data.  
Figure 9: Micro-vibration signal test platform.
## 4 Test on Beams
### Failure Tests on the Retrofitted Beams
#### 4.3.1 Beam Deflections  
Beam deflections (8) were measured at 0.25 L, 0.75 L and at two points near 0.5 L (Figure 5b) during the application of the vertical load (F). The vertical load-deflection curves are given for each measurement point in Figure 7a,b. Deflection at each point was calculated as the mean value of measurements taken on both sides of beam (SS4.1). The deflection d1 and d4 were measured at 0.25 L and 0.75 L whereas the deflections d2 and d3 were taken at the point on the left and the one on the right of the midspan (0.5 L) (Figure 7a). The maximum vertical load values at failure were 427.5 kN and 425.5 kN for the beams DB1-R and DB2-R and the corresponding midspan deflection values were 6.48 mm and 7.25 mm.  
Figure 6: Extracted beam DB1 and DB2: (**a**) load-deflection curves and (**b**) maximum beam deflections during the elastic tests (L is the beam length).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & EcIexp & EcIIII & EcI0 & EcIexp/EcI0 & EcIIII/EcI0 \\ \hline DB1 & 56,858 & 31,148 & 224,240 & 0.25 & 0.14 \\ DB2 & 42,968 & 37,302 & 217,160 & 0.20 & 0.17 \\ \hline \end{tabular}
\end{table}
Table 3: Extracted beams DB1 and DB2: experimental (EcIexp), fully cracked (EcIIII) and uncracked (EcI0) flexural rigidity [kNm2].
## 2 Materials and Methods
### Quantitation of Free Aldehydes via HS-SPME-GC-MS  
Headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) was carried out according to [PERSON] et al. with minor modifications [4]. A cooled beer sample (5 mL) was transferred together with 50 \(\mu\)L of an internal standard (2 mg/L of p-fluorobenzaldehyde in ethanol) to a 20 mL headspace vial and stored in a cooled autosampler tay (17 \({}^{\circ}\)C). Extraction was performed using a CAR-PDMS-DVB fiber. First, the fiber was loaded with o-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine for 10 min at 40 \({}^{\circ}\)C. Then, the headspace of the sample was extracted for 30 min at 40 \({}^{\circ}\)C. The fiber was injected with a 1/5 split at 270 \({}^{\circ}\)C into a gas chromatography (GC) instrument (GC-Ultra 1300; Thermo Fisher Scientific, Waltham, MA, USA) coupled to a single quad mass spectrometer (ISQ 7000; Thermo Fisher Scientific). The GC instrument was equipped with a DB-5 column (length, 60 m; inner diameter, 0.25 mm; film thickness, 0.25 \(\mu\)m; Thermo Fisher Scientific). The carrier gas was helium (flow rate 1.85 mL/min). The starting temperature was maintained at 60 \({}^{\circ}\)C for 4 min, followed by heating at 5 K/min up to a final temperature of 250 \({}^{\circ}\)C, which was maintained for 3 min. A full scan mode (\(m/z\) 35-350) with a dwell time of 0.02 s was applied for the analysis. Each sample was analyzed in triplicate. Peak detection was performed using Xcalibur 3.1.66.10 (Thermo Fisher Scientific). Quantification was achieved by external calibration. The lowest calibration point was defined as the limit of quantification (LOQ), which was in accordance with previous studies [30; 31]. All measurements were performed in biological duplicate and technical triplicate.
###### Abstract  
Although solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been successfully used as drug delivery systems for about 30 years, the usage of these nanoparticles as food-grade nanovhelices for nutraceuticalals or bioactive compounds has been, relatively speaking, scarcely investigated. With fast-increasing interest in the incorporation of a wide range of bioactives in food formulations, as well as health awareness of consumers, there has been a renewed urge for the development of food-compatible SLNs and/or NLCs as nanovhelices for improving water dispersibility, stability, bioavailability, and bioactivities of many lipophilic nutraceuticalals or poorly soluble bioactives. In this review, the development of food-grade SLNs and NLCs, as well as their utilization as nanosized delivery systems for lipophilic or hydrophobic nutraceuticalals, was comprehensively reviewed. First, the structural composition and preparation methods of food-grade SLNs and NLCs were simply summarized. Next, some key issues about the usage of such nanoparticles as oral nanovhelices, e.g., incorporation and release of bioactives, oxidative stability, lipid digestion and absorption, and intestinal transport, were critically discussed. Then, recent advances in the utilization of SLNs and NLCs as nanovhelices for encapsulation and delivery of different liposome or poorly soluble nutraceuticalals or bioactives were comprehensively reviewed. The performance of such nanoparticles as nanovhelices for improving stability, bioavailability, and bioactivities of curcuminoids (and curcumin in particular) was also highlighted. Lastly, some strategies to improve the oral bioavailability and delivery of loaded nutraceuticalals in such nanoparticles were presented. The review will be relevant, providing state-of-the-art knowledge about the development of food-grade lipid-based nanovhelices for improving the stability and bioavailability of many nutraceuticalals.  
solid lipid nanoparticles (SLNs); nanostructured lipid carriers (NLCs); nanovhelice; nutraceuticalals; stability; bioavailability +
Footnote †: journal: Applied Sciences
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 6 Limitations and Future Work  
### Duplicated Tracklet  
As reported in MOTRv2 [29], tracklets that come close to another may end up following the wrong object. This problem is suppressed by the richer feature representation and filtered out by track NMS. However, we still observe this problem before track NMS, indicating a failure of continuous tracking. The underlying cause is still unknown, and we leave it for future work to further explore this problem.
# Computed Tomography-Driven Design and Fused Filament Fabrication of Graded Density Bone Scaffolds
## 1 Introduction
### Aim  
This work addressed the design and additive manufacturing of graded porous scaffolds based on CT scans HU values, aiming to tune the relative density of lattice structures by controlling the filament extrusion amount during deposition in order to replicate the tissue's native porosity.  
CAD modeling operations were used for the macro-shape design via B-rep modeling, while CAM software supported the infill generation, enabling the creation of straightforward lattice structures. Additionally, CAD volumetric tools were employed in processing the CT scan 3D scalar field and editing the G-code. Compared to previous studies, this approach bypasses the need for the complex geometric modeling of graded lattice structures, avoiding both implicit or B-rep representations, which opens new possibilities for scaffold design and manufacturing. The main innovations of this work lie in the novel integration of medical imaging data with FFF to create graded porous scaffolds. While considerations regarding the mechanical properties and biological experimentation fell outside the scope of the present work, they are nonetheless discussed throughout the paper.  
This paper is organized as follows. Section 2 describes the proposed methodology and its implementation, while Section 3 presents a case study focused on a femur bone segment, followed by a discussion of the results and the identified limitations.
## 3 Results  
### Moisture Content  
The observations of the moisture content of the control and enriched breads are shown in Figure 1a. The values of moisture content of the breads prepared with different additives ranged from 36.89-37.84% for the breads enriched with fresh pistachio hulls (FPH), 38.05-38.53% for the breads enriched with freeze-dried pistachio hulls (PHP), 35.90-36.95% for the breads enriched with fresh grape seeds (FGS), and 37.46-38.45% for the breads enriched with freeze-dried grape seeds (GSP). The minimum moisture content of the enriched bread (35.90%) was found for the bread with the 0.5% addition of FGS and the maximum moisture content (38.53%) for the bread with the 2.25% addition of PHP. A significant increase in moisture content was observed for all enriched bread samples compared with the control (33.70%). It was concluded that the weight loss of the bread decreased with the addition of FPH, FGS, PHP, and GSP. In agreement with our results, the moisture contents of bread samples enriched with broad bean hulls [27], cumin and caraway powder seeds and by-products [28], parsley leaf powder [29], and Plantago ovata husk [30] were increased. This may be attributed to the fact that flours with added dietary fiber prevent water evaporation. According to the ANOVA (Figure 1a), there were significant differences in moisture content between the control bread and all the enriched breads (\(p<0.05\)). In addition, the statistical analysis showed that only the type of additive had a significant effect (\(p<0.05\)) on the moisture content, while the level of additive was not significant (\(p>0.05\)). Figure 1a also shows that the moisture contents of the breads fortified with FHP and FGS increased slightly with the increasing addition levels, while the moisture contents of breads fortified with PHP and GSP decreased slightly with the increasing addition levels. In addition, breads fortified with powdered additives had higher moisture contents than the breads fortified with fresh additives. This was due to the higher water-holding capacity of powder products. Higher moisture content is both economical and necessary to extend the shelf life of bread [31].
## 3 Experimental Program
### Test Results and Discussion  
#### 3.5.1 Basic Behavior  
The test results of all specimens are presented in Table 2 and Figure 10, which depicts the effect of the wall thickness (\(T\)) of the pipeline, the diameter (\(D\)) of the pipeline, the buried depth (\(d\)) of the pipeline, and the impact height (\(H\)) of the drop hammer on the peak strain (\(\varepsilon_{\
## 3 Results and Discussions
### Ground-Based Validation
#### 3.2.2 Dahra Station  
In Dahra, single-channel LSTs reached the LSA SAF target accuracy for TCWV up to 30 mm (RMSD \(<\) 2 K, bias \(<\) 0.5 K; Figure 3c). For higher TCWVs, the PMW, SMW and GSW models had a high negative Meteosat LST minus _in situ_ LST bias (-4.3 K, -3.0 K and -2.2 K, respectively; Table 4, Figure 5). RMSD are significantly higher for PMW and SMW compared to GSW (\(\Delta\) 2.9 and 1.9 K, respectively; Table 4) and the theoretically-expected error (\(\Delta\) 3-4 K; Table 4 and Figure 2).  
Cloud contaminations and/or uncertainties in NWP (ECMWF), together with the limitations of the mono-channel methods under analysis, might explain the difference between the observed RMSD and the theoretical uncertainties.  
Errors due to cloud contamination are not accounted for in the theoretical uncertainty analysis. Clouds are usually significantly colder than the land surface, and cloud contamination should hence result in negative LST biases [8]. This hypothesis is supported by the high temporal scatter of the TOA-brightness temperature (example: Figure 5, 24 September 2010). Clear sky TOA brightness temperature is mainly driven by solar heating and follows a continuous diurnal cycle [10]. This is clearly not the case on 24 September 2010.  
In addition, uncertainties in TCWV fields might be higher in Dahra than the NWP uncertainties accounted for in the sensitivity study (Section 2.5). This second hypothesis is supported by the observed 1-2 K lower GSW bias compared to PMW and SMW (Table 4). As detailed in Section 3.1, GSWs perform atmospheric correction rather independently from the NWP model input, while PMW and SMW are quite sensitive to errors in NWP models. For a given (measured) TOA brightness temperature and a very dry atmospheric profile, which is wrongly assumed to be more moist by the NWP model, this can  
Figure 5: **(a)** LSA SAF’s operational GSW; **(b)** PMW.  
lead to a considerable underestimation of LST retrieved by single-channel models: As the true atmosphere becomes dryer, it also becomes more transparent, and the retrieved LST decreases [8]. We have characterized the NWP error in the sensitivity study by replacing the profiles at hour h by the corresponding ones at hour h + 6. This approach might not be valid for tropical conditions. Moreover, differences in viewing geometry between the ground and satellite radiometer can introduce uncertainties related to inaccurate emissivity in Dahra, where the surface emissivities varies strongly over the seasons [20].
## 3 Results and Discussion
### General  
3.1.1. Should Food Intake Be Assessed?  
**Recommendation 1**.: _In patients with advanced COPD (GOLD 3 and 4: E), GARIN suggests routinely evaluating daily food intake at all levels of care, integrating dietary advice early that includes increasing the number of meals per day, with a focus on energy-rich foods and proteins, to help improve nutritional status and quality of life._  
Grade of recommendation GPP, (96%) Strong consensus.  
Comment:  
Decreased food intake is considered one of the main causes of malnutrition in COPD patients [16; 30; 31]. Malnourished patients have lower intakes and eat fewer meals per day compared with well-nourished patients [12; 15; 16]. Insufficient intake has been observed during periods of hospitalization [12], especially during exacerbations [32]. Food intake decreases in more advanced stages of COPD [30].  
An energy and protein intake below 75% of requirements was considered a predictor of adverse events, and conversely, those patients with higher intakes had a trend of lower mortality risk [12].  
Intake has been related to fat-free mass index (FFMI); in the Norden study [33], patients with low FFMI levels, regardless of severity, had a higher symptom burden affecting intake.
# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 4 CPS Application Domains
### Transportation-Based CPS
#### 4.1.2 Threat Detection  
Threat detection involves ensuring the safety and reliability of transportation CPS by training models with the standard system behavior data to detect deviations from the said standard as anomalies. Some of these anomalous data may be targeted at cyber-attacks, trigger equipment failures, or cause environmental disturbances. For example, [PERSON] et al. (2016) proposed a framework combining trajectory data with environmental perception to detect outliers in driving behavior. The framework is comprised of trajectory processing, classification, and a mix of spatial-temporal-cost environments. [PERSON] et al. (2016) aimed to improve traffic safety by predicting accidents early on using video data recorded by dashboard cameras to study a dynamic spatial-temporal attention (DSTA) network model. The presented model combines both the dynamic temporal and spatial attention modules to focus on the most informative segments of a video and the spatial regions of frames. The gated recurrent unit module predicts the probability of a future accident.  
Likewise, [PERSON] et al. (2016) aimed to prevent traffic accidents by proposing CRFAST-GCN, a multi-branch spatial-temporal attention graph convolution network that extracts long- and short-term dependencies, semantic similarity, and periodicity. Furthermore, [PERSON] and [PERSON] (2017) considered improving the safety, performance, and development of intelligent transportation systems for autonomous vehicle in smart cities using digital twins and Al-based technologies. An architecture was proposed to use the 5G network so that resource load balancing scheduling could be provided to secure the transmission of autonomous vehicle data. A spatial-temporal graph convolution network technique was designed to forecast traffic flow in road networks, as well as real-space analysis of the compound traffic condition in the area of the road network using the concept of digital twin. These efforts can be mapped to the cube \(<\)X\({}_{1}\), \(Y_{2}\), \(Z_{3}\)\(>\) areas in Figure 3 and Table 1. It means that in these efforts, GNNs (\(Z_{3}\)) are utilized to detect the threats (\(Y_{2}\)) of transportation CPS (\(X_{1}\)).