esa-sceva/satcom-chunk-collection huggingface dataset:
# Physicochemical Properties and Volatile Profile of Chito: A Traditional Dry-Cured Goat Meat Product
## 3 Results and Discussion
### Lipid Oxidation  
Figure 1 shows the lipid oxidation (TBARS) degree of chito. In 2021 and 2023, P treatment had higher TBARS values (\(P<0.05\)) than NP, whereas, in 2022, there were no differences (\(P>0.05\)) among the treatments. These results could be attributed to sodium chloride and fat content, as well as drying and pressing. [PERSON] et al. [28] reported that the increase in malondialdehydedehyde (MDA) could be associated with the prooxidant effect of metallic ions present as impurities in the salt used in meat products such as dry-cured Celta ham.  
Moreover, drying is performed in the open air, which accelerates the lipid oxidation process. [PERSON] et al. [29] argued that, during the drying process, the evaporation of moisture results in fat exudates to the surface, which can enhance the contact between fat and oxygen, thereby promoting lipid oxidation. Thus, the TBARS values rise with an increase in fat content in the food [4]. On the other hand, comparing between years (considering the same treatment), in 2021 and 2022, NP and P treatments had higher (\(P<0.05\)) lipid oxidation levels compared with 2023. For 2022, it is notable that the P treatment showed TBARS values of 6.32 mg MDA/Kg meat. This finding indicates that lipid oxidation occurred at the highest level in P for this year (2022), corresponding well with the highest fat and sodium chloride content found in this treatment, as well as the packaging, which consisted of jute sacks, which expose meat to atmospheric oxygen (the primary oxidizing agent). Thus, this finding differs from data previously reported by [PERSON] et al. [21] and [PERSON] et al. [30], who observed that cured goat legs and salted-ripened goat meat (mantas) presented lower TBARS values (4.96 and 1.87 mg of MDA/Kg, respectively). These results are also in contrast with what has been described in the literature for similar dry-cured meat products, such as alpaca charqui, where the mean values for TBARS were between 2.5 and 4 mg of MDA/Kg [20].  
Figure 1: Mean values of lipid oxidation (TBARS, mg of malondialdehydedehyde/kg of meat) in chito. NP: non-pressed (immediate consumption); P: pressed (for sale). 1; 2; Means within the same year with different letters are significantly different (\(P<0.05\)). 1; 2; Means within the same treatment with different letters are significantly different (\(P<0.05\)).  
In this sense, it is known that the acceptable limit of the oxidation degree is <2 mg MDA/Kg [31], a value that is associated with the threshold of consumer detection of rancid flavor. However, this limit could not be considered as real due to some authors having argued different lipid oxidation detection or acceptability limits depending on the type of meat product [11; 32]. Thus, some authors, such as [PERSON] et al. [33], indicated that TBARS values remain higher in dehydrated meat products compared to fresh and smoked meat products.
# Direct Temperature Measurements of Cardiac Stent during MRI Examinations
## 2 Materials and Methods  
### Temperature Measurements  
A commercial platinum-chromium metal cardiac stent, the SynergyTM XD Monorail (Boston Scientific, Marlborough, MA, USA) with a length of 24 mm and a diameter of 2.5 mm, was used during the experiments. The measurements were performed using a phantom consisting of an empty box (18.0 cm \(\times\) 28.5 cm \(\times\) 18.0 cm) and made in Polymentilenciralate (PMMA) material. The PMMA box was filled with nongelled saline solution for 4 cm in height. The stent was placed on a polystyrene cube (1 cm side). The temperature measurements were conducted using the M3300 fluoride thermometer (Luxtron Corporation, Santa Clara, CA, USA). The device uses 4 channels coupled with fiber optic probes. The measurement temperature range of the probes is 0 \({}^{\circ}\)C-120 \({}^{\circ}\)C with an accuracy of \(\pm\)0.2 \({}^{\circ}\)C. The M3300 probes are entirely nonmetallic in construction and are only 0.5 mm in diameter. The M3300 device was connected to a PC by a RS-232 serial port in order to store the acquired data. The four probes, named CH1, CH2, CH3 and CH4, were positioned in the box: CH1 far from the stent, to monitor the temperature of the environment surrounding the stent; on the tips of the stent (CH2 and CH4); and in the center of the stent (CH3), respectively, as illustrated in Figure 1.  
Three different measurements were performed, in three different days to test the ability of the method to generate similar results for multiple experiments. This step was essential to understand the reliability of probes to detect the temporal and spatial patterns of temperature measurements. Before starting them, the box filled with saline solution and the stent were positioned inside the magnetic room for two hours to allow the system to reach thermal equilibrium.
_Appl. Sci._**2012**, \(2\), 641-653; doi:10.3390/app2030641  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
**Continuous Process for Biodiesel Production in Packed Bed Reactor from Waste Frying Oil Using Potassium Hydroxide Supported on _Jatropha curcas_ Fruit Shell as Solid Catalyst  
**[PERSON] 1,2,*, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1, [PERSON] 1 and [PERSON] 1**  
Department of Materials Science and Engineering, Faculty of Engineering and Industrial Technology, Silpakorn University, Nakhon Pathom 73000, Thailand; E-Mails: nchaiyut(@hotmail.com (N.C.); [EMAIL_ADDRESS] (V.L.); [EMAIL_ADDRESS] (C.R.); [EMAIL_ADDRESS] (T.C.); [EMAIL_ADDRESS] (N.K.)  
National Center of Excellence for Petroleum, Petrochemicals and Advanced Materials, Chulalongkorn University, Bangkok 10330, Thailand  
Author to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] or achanai(@su.ac.th; Tel./Fax: +66-34-219-363.  
_Received: 31 July 2012; in revised form: 24 August 2012 / Accepted: 24 August 2012 / Published: 29 August 2012_  
**Abstract:** The transesterification of waste frying oil (WFO) with methanol in the presence of potassium hydroxide catalyst supported on _Jatropha curcas_ fruit shell activated carbon (KOH/JS) was studied. The catalyst systems were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and the Brunauer-Emmett-Teller (BET) method. The effects of reaction variables such as residence time, reaction temperature, methanol/oil molar ratio and catalyst bed height in packed bed reactor (PBR) on the yield of biodiesel were investigated. SEM images showed that KOH was well distributed on the catalyst support. The optimum conditions for achieving the conversion yield of 86.7% consisted of a residence time of 2 h, reaction temperature of 60 \({}^{\circ}\)C, methanol/oil molar ratio of 16 and catalyst bed height of 250 mm. KOH/JS could be used repeatedly five times without any activation treatment, and no significant activity loss was observed. The results confirmed that KOH/JS catalyst had a great potential to be used for industrial application in the transesterification of WFO. The fuel properties of biodiesel were also determined.  
**Keywords:** biodiesel; packed bed reactor; waste frying oil; _Jatropha curcas_ fruit shell
# Predicting Team Well-Being through Face Video Analysis with AI
## 3 Methods
### Multi-Face Tracking  
Each video is analyzed to determine the respective trajectory of each face present in the recording, using a **multi-face tracking** approach. All faces present in a single video frame are detected and embedded using the _RetinaFace_ model [34] and the _ArcFace_ model [35], respectively. The _RetinaFace_ model detects a set of faces \(F=\{F_{1},F_{2},\ldots,F_{m}\}\) in a given frame. Each \(F_{m}\in F\) is transformed to a lower dimension face embedding \(E=\{\mathbf{f}_{1},\mathbf{f}_{2},\ldots,\mathbf{f}_{m}\}\) using _ArcFace_ for greater computational efficiency. Finally, an ID database is generated by clustering a sample of frames from the video based on the number of individuals per team. It is then used to identify and track each individual in the video through face identification. The challenge of re-identification--the process of correctly identifying person identities across video frames--is tackled by calculating the cosine distances between preprocessed face templates \(I=\{\mathbf{i}_{1},\mathbf{i}_{2},\ldots,\mathbf{i}_{n}\}\) and the detected face embeddings \(E\). Then the Hungarian algorithm [36] is used to solve the assignment problem. This approach allows an efficient tracking of multiple faces in a video stream. No tracking algorithm in the traditional sense is implemented, while the focus is on facial attributes.
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
## 3 Results
### Minimum Detectable Activity Concentration (MDAC)  
The minimum detectable activity concentration (MDAC) is an essential parameter of the detector system, which represents the lowest activity concentration of a radionuclide that can be determined to be present in a sample at a certain confidence level at a specific measurement time. At a confidence level of 95%, MDAC can be expressed as:  
\[\text{MDAC}=\frac{L_{D}}{tI_{E}\varepsilon_{sp}V}=\frac{2.71+4.65\sqrt{N_{b}}}{ tI_{E}\varepsilon_{sp}V} \tag{2}\]  
where \(N_{b}\) refers to the background count, \(t\) refers to the live detection time, \(I_{E}\) refers to the emissivity of the corresponding \(\gamma\)-ray, \(\varepsilon_{sp}\) refers to the detection efficiency of the detector for that \(\gamma\)-ray, and \(V\) refers to the detection volume of the detector. For the background count \(N_{b}\), \(C-3\sigma\) is used as the left marker of the region of interest (ROI), and \(C+3\sigma\) is used as the righte marker of the ROI. Here, \(C\) is the channel location for the energy of the characteristic peak, \(\sigma\) is the standard deviation of the peak that fits a normal distribution. At the measurement time of 120 h and 1 h, the MDACs of the device developed in the study for targeting radionuclides are listed in Table 4. The MDAC for \({}^{137}\)Cs in 1 h is 1.10 Bq/L, which is higher than that of NaI detector (approximately 0.2 Bq/L [6; 17; 18]). However, considering that the radioactivity in the seawater adjacent to the NPP will be fairly high in the event of an accident [19; 20; 21], the MDACs of the device developed in this study will be low enough to quantify the activity of radionuclides released from the accident.
## 3 Materials and Methods
### Model Evaluation and Comparison  
To validate and compare the developed model, four quantitative statistics were used to assess the performance of the evaluation methods: coefficient of determination (\(R^{2}\)), the ratio of the root-mean-square error (\(RSR\)) to the standard deviation of measured data, Nash-Sutcliffe coefficient (\(NSE\)), and mean bias error (\(MBE\)). The following equations are used to express these indices:  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum \limits_{i=1}^{n}\left(y_{i}-\hat{y}\right)^{2}} \tag{7}\]\[RSR=\frac{\sqrt{\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y}_{i})}^{2}}}}}{\sqrt{\sum \limits_{i=1}^{n}{{(y_{i}-\overline{y})}^{2}}}} \tag{8}\]  
\[NSE=1-\frac{\sum\limits_{i=1}^{n}{{(y_{i}-\hat{y}_{i})}^{2}}}{\sum\limits_{i=1} ^{n}{{(y_{i}-\overline{y})}^{2}}} \tag{9}\]  
\[MBE=\frac{1}{n}{\sum\
olimits_{i=1}^{n}{{(\hat{y}_{i}-y_{i})}}} \tag{10}\]  
In the equations, \(n\) is the number of data, \(y_{i}\) and \(\hat{y}_{i}\) are the actual and predicted output of \(i\)th sample of the data, respectively; \(\overline{y}\) is the averaged actual output of the data. The \(R^{2}\) coefficient goes from 0 to 1, with a higher \(R^{2}\) value indicating a more efficient model. When \(R^{2}\) is larger than 0.8 and nearer to 1, the model is considered effective [44]. The \(NSE\) scale runs from \(-\infty\) to 1, with 1 being a perfect match. An \(NSE\) value of more than 0.65 indicates a strong correlation [45; 46]. The root-mean-square error (\(RMSE\)) observations' standard deviation ratio is calculated as the ratio of the \(RMSE\) and standard deviation of measured data. The \(RSR\) varies from an optimal value of 0 to a significant positive value. A lower \(RSR\) signifies a lower \(RMSE\), indicating the higher predictive efficiency of the model. \(RSR\) classification ranges were described as very good, good, acceptable and unacceptable with ranges of \(0.00\leq RSR\leq 0.50\), \(0.50\leq RSR\leq 0.60\), \(0.60\leq RSR\leq 0.70\) and \(RSR>0.70\), respectively [47]. It is self-evident that the lower the \(RSR\) criteria is, the better is the model.
# Vertical-Type Ni/GaN UV Photodetectors Fabricated on Free-Standing GaN Substrates
## 2 Experimental Section  
The GaN layers used for the photodetectors were grown on free-standing GaN substrates by metalorganic chemical vapor deposition (MOCVD). The free-standing GaN substrates were in c-axis orientation. The donor concentration and resistivity of the GaN substrate was \(1\times 10^{18}\) cm\({}^{-3}\) and 0.01 \(\Omega\)-cm, respectively, measured by the Hall effect. Trimethylgallium (TMG) and ammonia (NH\({}_{3}\)) were used as precursors, while the mixed nitrogen and hydrogen were served as the carrier gases. The homo-epitaxial GaN layers were deposited at a relatively lower growth rate of 2.61 \(\upmu\)m/h to reduce the incorporation of unintentionally doped impurities and improve the surface morphology [11]. For Schottky-type photodetectors, a semi-transparent Schottky contact with 5-nm-thick Ni, was deposited by E-beam evaporation patterned by laser lithography and a standard lift-off process. The multiple layers of Ti/Al/Au (20/100/100 nm) were deposited on the backside of the substrates as the Ohmic contacts by sputtering deposition. The schematic structure of the Schottky-type photodetectors is shown in the inset of Figure 1a. The current-voltage (\(I-V\)) measurements were performed using an Advantest picoammeter R8340A and a DC voltage source R6144. The photoresponse was measured in the range from 630 to 300 nm by using a 500 W Xenon lamp. The incident light power was calibrated by a commercial Si photodiode.
## 3 Results
### Phase Transformation  
The phase transformations of slurries without glass powder and with 10% and 20% glass powder as a binder component were analysed over 7, 28, and 56 days of hydration using X-ray diffractometry. The results of the analyses are shown in Figure 9a,b.  
Figure 8: Fracture images of mortar samples after flexural strength testing: (**a**) 0S-10 PET, (**b**) 20S-SPET. The circles show where the PET grains were torn out.  
Figure 7: Compressive strength of mortars after (**a**) 28 days of curing and (**b**) 56 days of curing.  
The phase composition of cement slurries with glass powder does not diverge from that of reference slurries. However, differences were observed in the intensity of the reflections, signalling the presence of individual phases (Figure 9a). As hydration processes unfold, the intensity of the reflections from alite and belite diminishes, while portlandite  
Figure 9: Analysis of phase transformations of slurries with 0%, 10%, and 20% glass powder content; (a) summary of diffractograms of cement slurries after 7, 28, and 56 days of hydration. Explanation of abbreviations: A—alite; A + B—alite and belite reflections; E—ettringite; P—portlandite; C’—C-S-H; (b) changes in portlandite reflection intensity (angle 34\({}^{\circ}\) 2 Theta) as a function of time.  
and the C-S-H phase materialise as hydration products. In slurries where part of the cement has been supplanted by glass powder, the intensity of the alite phase is inferior to that of the reference slurries, attributable to the diminished content of alite in the slurries where the binder is an amalgamation of cement and glass powder. Contrary to anticipation, elevated reflections from the portlandite were identified in the slurries with 10% glass powder compared to those where cement constituted 100% of the binder, with the intensity of these reflections decreasing over time (Figure 9b). In slurries with a 20% glass content, the portlandite reflection intensities were analogous to those documented in the reference sample. The noted decline in portlandite intensity over a period of 28-56 days denotes that the glass interacts with the portlandite, substantiating its pozzolanic properties. However, the nominal decreases in Ca(OH)\({}_{2}\) intensity at approximately 34 degrees 2 Theta suggest exceedingly poor pozzolanic properties of the glass under investigation.
## 4 Results
### Japanese Experience on Development of Community Disaster Resilience
#### 4.2.2 Initiatives in the Takahama District, Ehime Prefecture  
In this section, a case study was initiated in the Takahama district of the Ehime prefecture, in which the sediment-disaster hazard warning area was established in 2015 to describe the effect of legal zoning [48]. The case study was conducted by semi-structured interviews.  
As the Takahama district is facing the sea, the Tsunami inundation area (of 1.2 m to 2.5 m in depth) is expected in 70% of the district. The topography of the area is densely populated with houses on the flat land between the sea, the main road, and the hillside, some of which are old wooden houses. In 2018, the population was about 8000, with an ageing rate of about 35%. The ageing population was another issue that was faced by the district.  
In the past, the district had suffered tremendous damage from high tides. In the forecast of the damage caused by the Nankai Trough earthquake, which is feared to occur in the future, it is assumed that seismic motion may not only lead to earthquakes but also tsunami, liquefaction, and sediment disasters during heavy rainfall. In addition, in dense wooden residential areas, there is also a rising concern on fires during earthquakes.  
Due to the geographical characteristics of the area, the voluntary disaster prevention organizations in the district took the lead in planning of the operation of evacuation centers and developing the basic rules for evacuation. Such an evacuation was expected to include the participation of the whole community, including primary and secondary schools, police, firefighters, and companies [50]. See Figure 8 for cooperation system based on voluntary disaster prevention organizations.  
Figure 8: Cooperation system based on voluntary disaster prevention organizations [51].  
Figure 7: Designation of warning areas for different types of disaster. Areas in red are the sediment disaster special hazard areas; areas in yellow are the sediment disaster hazard areas; and areas in green are the safe areas [48].  
One of the important features of the research project was that the local disaster drills were held on the same day as the Sunday school visit day of the elementary and junior high schools, with the cooperation of local residents, children, students, and their parents. The disaster drills held in 2015 were attended by about 1000 local residents, who were informed about the contents of the district disaster prevention plan and the rules for evacuation shelters. See Figure 9 for disaster drills that included the participation of people with various professions from various generation. In addition, the junior high school students took the initiative to participate in the management of the evacuation shelters, encouraging them to change their mindset from being the ones to be helped to being the ones to help.
# Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages
###### Abstract  
Patient-reported health data, especially patient-reported outcomes measures, are vital for improving clinical care but are often limited by memory bias, cognitive load, and inflexible questionnaires. Patients prefer conversational symptom reporting, highlighting the need for robust methods in symptom extraction and conversational intelligence. This study presents a weakly-supervised pipeline for training and evaluating medical Named Entity Recognition (NER) models across eight languages, with a focus on low-resource settings. A merged English medical corpus, annotated using the Stanza i2b2 model, was translated into German, Greek, Spanish, Italian, Portuguese, Polish, and Slovenian, preserving the entity annotations medical problems, diagnostic tests, and treatments. Data augmentation addressed the class imbalance, and the fine-tuned BERT-based models outperformed baselines consistently. The English model achieved the highest F1 score (80.07%), followed by German (78.70%), Spanish (77.61%), Portuguese (77.21%), Slovenian (75.72%), Italian (75.60%), Polish (75.56%), and Greek (69.10%). Compared to the existing baselines, our models demonstrated notable performance gains, particularly in English, Spanish, and Italian. This research underscores the feasibility and effectiveness of weakly-supervised multilingual approaches for medical entity extraction, contributing to improved information access in clinical narratives--especially in under-resourced languages.  
Keywords:low-resource languages; machine translation; medical entity extraction; NER; NLP; patient-reported outcomes; weakly-supervised learning +
Footnote †: journal: Applied sciences
# Pointwise Nonparametric Estimation of Odds Ratio Curves with R: Introducing the flexOR Package
## 4 Examples of Application  
This section demonstrates the application of the flexOR package integrated with the R statistical program [15]. Here, we illustrate the functionality of the package through analyses conducted on two real datasets. The first dataset involves a reanalysis of data from 811 patients admitted with acute coronary syndrome (ACS) to the Santiago University Teaching Hospital between September 2003 and March 2007. A primary objective of this study is to evaluate the predictive power of fasting blood glucose levels alongside other variables within this dataset.  
The second dataset considered is collected by the US National Institute of Diabetes and Digestive and Kidney Diseases, focusing on a cohort of women aged 21 and above, of Pima Indian heritage, residing near Phoenix, Arizona. This dataset, available in the mlbench package of R, is commonly used to predict the likelihood of diabetes based on specific diagnostic measurements. It comprises 768 observations and includes variables such as age, plasma blood glucose, diastolic blood pressure, and body mass index, among others. Researchers utilize this dataset extensively to explore and develop predictive models for diabetes, leveraging the rich array of variables it offers.