esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Comfort and Sustainability in University Classrooms: A Study in Mediterranean Climate Zones
## 3 Results
### Adaptive Thermal Comfort Model
Applying the adaptive equation of the ASHRAE 55 standard, the average comfort temperature obtained was 26.11 \(\pm\) 1.11 \({}^{\circ}\)C, with extreme values of 28.12 and 24.46 \({}^{\circ}\)C. In comparison, the model proposed in this study generated a  
Figure 9: Comparison of the adaptive thermal comfort model with standards: (**a**) NV mode and ASHRAE standard 55, (**b**) NV mode and EN 16798-1:2020 and (c) AC mode and CIBSE guide. \(T_{c}\) approximately 2 \({}^{\circ}\)C lower than that calculated according to the ASHRAE 55 standard, suggesting significant differences in the prediction of thermal comfort under specific NV conditions. In this regard, previous studies have pointed out that the ASHRAE 55 adaptive equations tend to underestimate comfort in hot climates [79; 80; 81]. Figure 9b shows the model proposed in this work against the adaptive model of EN 16798-1:2020 expressed with the equation \(T_{c}\) = 0.33\(\cdot\)\(T_{rm}\) + 18.8, also for the NV mode. When analysing the acceptability zones, it was observed that 76.67% of the sample was within the \(\pm\)4 \({}^{\circ}\)C thermal comfort zone (represented by the blue dashed line), 60.00% within the \(\pm\)3 \({}^{\circ}\)C zone (green dashed line), and 40.00% in the more restricted \(\pm\)2 \({}^{\circ}\)C zone (black dashed line). However, 23.33% of the sample fell outside any defined comfort range. For this adaptive model, the mean \(T_{c}\) was 26.77 \(\pm\) 0.71 \({}^{\circ}\)C, with maximum and minimum temperatures of 29.14 and 25.10 \({}^{\circ}\)C, respectively. Thus, the proposed model temperatures were about 2.50 \({}^{\circ}\)C lower. These results are in line with several works that have shown that European standards may not adequately reflect thermal comfort needs in hot regions, where user expectations and environmental conditions differentiate thermal preferences. This finding reinforces the importance of developing localised adaptive models that are more representative of real conditions [81; 82]. Finally, and in line with the previous cases, Figure 9c shows the \(T_{c}\) according to the model proposed for the AC tests and according to the CIBSE guide (\(T_{c}\) = 0.09\(\cdot\)\(T_{rm}\) + 22.7). In this case, 69.23% of the cases were within the only existing comfort zone, while the remaining 30.77% were outside it. According to this adaptive model, the mean \(T_{c}\) was 25.27 \(\pm\) 0.12 \({}^{\circ}\)C, with a maximum temperature of 25.53 \({}^{\circ}\)C and a minimum temperature of 25.17 \({}^{\circ}\)C. In this case, and contrary to the NV tests, the \(T_{c}\) calculated with the model of this work was slightly higher. In this context, several studies have pointed out that comfort predictions in controlled systems (such as AC) tend to be more accurate, as occupants are less dependent on physiological or behavioural adaptations to achieve comfort [47; 55; 62].
# Using Explainable Machine Learning to Explore the Impact of Synoptic Reporting on Prostate Cancer
## 2 Materials and Methods
### Pre-Processing
#### 2.2.1 Feature Engineering  
Firstly, we engineered a new feature: the European Association of Urology risk group (EAU), which classifies patients into risk groups for biochemical recurrence of localized and locally advanced prostate cancer [45]. It provides a prognostic profile of a patient's tumor by assigning it to one of four categories based on prostate-specific antigen (PSA), Gleason score, and clinical T and clinical N values (Table 1). For the latter, the 7 th edition of the Union for International Cancer Control (UICC) classification of malignant tumors was used for patients diagnosed between 2011 and 2017 [46], and the 8 th edition was used for patients diagnosed between 2017 and 2018 [47]. However, this did not have an influence on the EAU feature, since the definition of cT and cN did not change between versions [48].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Localized** & **Locally Advanced** \\ \hline
**Low Risk** & **Intermediate Risk** & **High Risk** & **High Risk** \\ \hline PSA \(<10\) ng/mL & PSA 10–20 ng/mL & PSA \(>20\) ng/mL & Any PSA \\ and GS \(<7\) & or GS \(=7\) & or GS \(>7\) & Any GS \\ and cT1-2a & or cT2b & or cT2c & cT3-4 or cN+ \\ \hline \hline \end{tabular} PSA: prostate-specific antigen; GS Gleason score; cT: clinical T; cN: clinical N.  
\end{table}
Table 1: Risk groups for biochemical recurrence of localized and locally advanced prostate cancer as defined by the European Association of Urology.  
Figure 1: Flowchart depicting inclusion/exclusion criteria. The preliminary dataset consisted of 17,587 patients and 41 columns.
## 2 Background Knowledge
### Confusion Matrix  
A confusion matrix, as shown in Table 1, also known as a contingency table or an error matrix, is the matrix created when each column represents the predicted outcomes of a predicted class, while each row represents the occurrence of instances in an actual class. It allows for easy visualization of errors and comparisons. It derives its name from the fact that it shows the errors made by a forecasting model when it is \"confused\", and is an appropriate choice as it will check for errors not just for the recommended courses but also for all instances of the test data.  
In this research, two classes are used: the positive class and the negative class. Such a matrix is called a table of confusion. It is a 2-by-2 matrix that contains the instances of true positives, false positives, true negatives, and false negatives. [PERSON] [25] uses the error matrix for the accuracy assessment of land-cover maps constructed from remotely sensed data. [PERSON] [26] employs the confusion matrix and its derivative metrics for his guide to using receiver operating characteristic (ROC) graphs for visualizing, organizing and selecting classifiers based on their performance. [PERSON] and [PERSON] [27] use a generalized confusion matrix for assessing the estimates of areas based on remotely sensed data.
## Results
### Jointly Fine-Tuning Robust Feature Sets
The softmax cost function evaluates the probabilities of all the labels, so that it increases the one-off accuracy of the model. These models take the advantage of using two different feature sets and also take the advantage of training the data set on two different DNNs. The JFN-A model trains and minimizes the error by using three different cross-entropy functions as cost functions. The effects of hidden nodes per layer and the number of hidden layers on the classification accuracy of the JFN-A are shown in Figure 7.  
Figure 7 presents the accuracies for different number of hidden nodes for part 3 of the JFN-A. The highest accuracy is observed when the number of nodes was 512. Moreover, Figure 7 shows how the accuracy significantly decreases when the number of hidden layers exceeds 2. Since we have two level of classification, the inputs of the Part 3 in the JFN-A network are high level features, which do not need deep architecture to enhance these features further, while the purpose is to jointly fine-tune the features together. The top row in Figure 8 shows a set of images that are incorrectly classified, and the bottom row shows a set of images that are classified correctly by the proposed networks. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline
**Features** & **0-2** & **4-6** & **8-13** & **15-20** & **25-32** & **38-43** & **48-53** & **60-** & **Accuracy** & **1-Off Acc** \\ \hline (1) Superpixels Features & & & & & & & & & & \\ concatenated with their & 87.56 & 56.82 & 43.57 & 31.05 & 78.31 & 24.81 & 29.70 & 60.96 & 58.31 & 86.17 \\ \hline (2) Facial Features & & & & & & & & & & \\ concatenated with their & 86.96 & 65.96 & 45.88 & 35.24 & 78.98 & 41.22 & 15.35 & 83.66 & 63.22 & 94.38 \\ robust features & & & & & & & & & & \\ \hline (1) + (2) as input & 88.5 & 63.44 & 49.84 & 37.97 & 82.55 & 43.29 & 27.92 & 81.61 & 65.55 & 94.86 \\ for JFN-SS & & 88.72 & 68.58 & 48.23 & 34.86 & 82.56 & 35.89 & 28.46 & 83.67 & 65.20 & 91.39 \\ \hline \end{tabular}
\end{table} 
Table 2: Overall classification accuracies for facial and superpixels concatenated with their robust features by JFN-A and JFN-SS on Adience database (%). Figure 7: The accuracy of the JFN-A with respect to (**a**) the number of hidden nodes and (**b**) the number of hidden layers.
## 4 Discussion
### Economic Feasibility and Technological Challenges  
This review draws special attention to the economic practicality of converting esters and fatty acids into H2 gas for SAF production, as evidenced by [55]. The notable discrepancy in the minimum fuel selling price (MFSP) between different scenarios in this study underscores the significant influence of process optimisation on cost-effectiveness. This finding emphasises the vital need for constant enhancement of production technologies to ensure economic viability.  
The impact of regional factors like solar energy potential and market conditions is markedly underlined by studies [52; 71]. These studies are indispensable, particularly when contemplating the amalgamation of renewable energy sources into e-fuel production processes and the consequential effects on elements like the levelised cost of methane (LCOM) and the overall project cost.  
The life cycle assessment (LCA) evaluates the environmental impacts of the entire production chain, from biomass collection and gasification to FT synthesis and the final use of sustainable aviation fuel (SAF). The study demonstrates that a 50% blending ratio of SAF can reduce emissions by about 60 Mt CO2e, which accounts for approximately 43% of the emissions from China's aviation sector in 2023. The analysis highlights the significant decarbonisation potential of SAF, especially from crop residues like corn straw, which can substantially reduce the carbon footprint of aviation fuel [73].  
The life cycle assessment (LCA) examines the environmental impacts of producing low-carbon hydrogen via electrolysis, capturing CO2 through direct air capture (DAC), and synthesising synthetic natural gas (SNG). The study reveals that SNG can reduce life cycle greenhouse gas (GHG) emissions by 52-88% compared to fossil natural gas, depending on the energy sources used for electrolysis and DAC. Utilising renewable energy sources such as wind, solar, nuclear, and hydro for producing low-carbon hydrogen and capturing CO2 significantly reduces the overall carbon footprint of SNG, aligning with sustainable energy goals in USA [74].  
The approach of incorporating life cycle assessments alongside techno-economic evaluations presents a comprehensive understanding of the sustainability of e-fuels [75]. This approach is crucial to guarantee that the long-term environmental outcomes are in harmony with the initial economic estimations [76]. Advancements in processes such as high-temperature electrolysis and the reverse water-gas shift (RWGS) reaction, as observed by [55], are crucial. These technologies exhibit high carbon conversion efficiency, but their economic viability remains heavily contingent on their scale of operation and future technological enhancements [77; 78; 79; 80]. These studies highlight the potential of innovative pathways for producing sustainable aviation fuels, emphasising their economic viability and substantial environmental benefits [81; 82]. The findings can inform policy decisions, guide industry practices, and promote further research in sustainable fuel production [83; 84; 85].
## 4 Discussion  
Radiation protection is extremely important for medical workers, many of whom are exposed to radiation daily [32; 33; 34]. Therefore, our group (and many others) have conducted extensive studies of radiation protection for healthcare workers and patients [35; 36; 37; 38; 39; 40; 41]. The eye lens is composed of tissue with high radiosensitivity; therefore, lens protection is an important issue in medical radiology [42; 43; 44; 45]. In the present study, the annual lens doses estimated by DOSIRIS for PET nurses, PET radiographers, and RI radiographers were considerably less than 20 mSv. The estimated numbers of procedures that would need to be performed for the annual lens exposure dose to exceed 20 mSv were 12,216 for PET nurses, 24,000 for PET radiographers, and 12,128 for RI radiographers. It is highly unlikely that a single staff member would perform this many examinations in one year; therefore, the possibility of exceeding the dose limit can be considered extremely low. However, doses per examination may be higher in other hospitals. Most of the radionuclides in the RI examinations conducted at our facility are derived from gamma sources (i.e., \({}^{99m}\)Tc); the lens doses of RI radiographers may be higher in facilities that use beta-radiation sources. Table 4 shows the annual lens doses estimated in previous studies. In the study by [PERSON] et al. [46], the annual H\({}_{p}\)(3) value of PET nurses was estimated using a Monte Carlo simulation based on measured H\({}_{p}\)(10) values. In the study by [PERSON] et al. [47], the H\({}_{p}\)(3) values of RTs involved in NM examinations, including PET scans, were measured directly to estimate the annual dose. [PERSON] et al. [48] estimated the H\({}_{p}\)(3) values of staff involved in the preparation and administration of radiopharmaceutical. [PERSON] et al. [49] estimated the annual lens doses of PET and RI nurses based on daily doses. Similarly, [PERSON] et al. [50] estimated the annual lens doses of medical staff involved in NM examinations. The lens-dose estimates in this study are comparable with--or lower than--the estimates in the previous investigations. In particular, the annual doses of PET nurses according to [PERSON] et al. were much higher than the annual doses in the present study. These differences may be explained by our use of automatic injectors; [PERSON] et al. [49] administered PET drugs manually, which increases the radionuclide exposure time. Multiple studies have estimated the eye-lens doses of nurses and RTs. According to [PERSON] et al. [21], the annual lens dose of nurses involved in cardiovascular IVR examinations was 3.26 \(\pm\) 1.95 mSv, whereas [PERSON] et al. [51] reported a mean annual lens dose of nurses involved in urologic IVR examinations of 7.3 \(\pm\) 3.4 mSv. [PERSON] et al. [52] estimated that the mean annual lens dose of nurses involved in endoscopic retrograde
## 3 Results
### Effect of Variables on the Response  
The response surface of Y as a function of curing agent dosage (m) and curing time (t) is presented in Figure 2. It can be learnt from Figure 2 that the adhesion strength reached the maximum with a curing agent dosage of 40 wt%. As stated above, the curing system used in the HJT silver paste consists of epoxy resin E51 and the curing agent MeTHPA. In actual fact, MeTHPA is a kind of reactive anhydride curing agent, which completes the curing process by reacting with epoxy resin in the curing system. Usually, the dosage of anhydride curing agent per 100 g epoxy resin is calculated by Equation (5):  
\[\text{W}=\text{M\text{-}E\text{-}K} \tag{5}\]  
where W is the dosage of curing agent per 100 g epoxy resin (g), M is the molar mass of anhydride curing agent, E is the epoxy value of epoxy resin, which means the molar amount of epoxy group contained in 100 g epoxy resin, and K is an experimental coefficient (0.5-1.1). The experimental coefficient K is a key parameter for the curing process. Higher or lower mass fraction of MeTHPA will lead to excessive unreacted curing agent or epoxy resin left in the curing system, which causes a negative effect on the curing process and thus reduces the adhesion strength. The molar mass of MeTHPA (W) is 166.18; the epoxy value of E51 (E) is 0.51. In this E51-MeTHPA curing system, K can be calculated as 0.79, based on the optimal curing agent dosage of 40 wt%. It can also be learnt from Figure 2 that the adhesion strength increased with the increase of curing time. After the curing time reached a certain level, the adhesion strength value tended to be constant. This is mainly because as the curing reaction proceeds, the crosslink density increases and the epoxy resin changes from a rubbery state to a glass state, hindering the molecule motion in the glass state. As a result, a further crosslinking reaction is difficult to reach [24]. The response surface of Y as a function of treatment temperature (T) and curing time (t) is presented in Figure 3. It demonstrates that there was a significant interaction of curing time and treatment temperature. The increase of curing time had a positive effect on adhesion strength when the treatment temperature was less than about 195 \({}^{\circ}\)C. For example, the adhesion strength increased by 26.4% (from 678 to 857 kPa) with an increasing curing time from 34 to 46 min at 180 \({}^{\circ}\)C. However, when the treatment temperature was under a higher level (>195 \({}^{\circ}\)C), the effect of the curing time was significantly weakened. The adhesion strength only increased by about 3% (from 1038 to 1071 kPa) with an increasing curing time from 34 to 46 min at 210 \({}^{\circ}\)C. Generally, in the E51-MeTHPA curing system, the relationship between the reaction rate and temperature conforms to the Arrhenius formula [25]. It should be noted that, at relatively high temperatures, the curing reaction is completed
## 5 Simulation Results
### Parametric Evaluation of the Proposed Method
#### 5.2.2 SOC Activation Zone Range  
The effect of SOC control activation zones is examined for \(SOC_{b,min+}\) equal to 21% (red/dash-dotted), 25% (blue/continuous) and 29% (green/dashed), while implementing the proposed control scheme. \(K_{b}\) is re-tuned in each case, according to the principles described in Section 2.1, so that \(P_{b,soc}^{*}=-1\) pu when \(SOC_{b}=(SOC_{b,min-}+SOC_{b,min+})/2\). Figure 12 shows that the SOC control activation zone has a negligible effect on system frequency, with narrower zones leading to slightly higher frequency variations, due to the steeper \(c_{b,\text{up}}\) slope. As with \(K_{b}\), lower \(SOC_{b,min+}\) values lead to deeper battery energy reserve depletion following the disturbance. BESS cycles are 0.38, 0.43 and 0.46 for \(SOC_{b,min+}\) equal to 29%, 25% and 21%, respectively. For \(SOC_{b,min+}\) = 29%, the storage unit provides its full power reserves for only \(\sim\)40 s, then reaches \(SOC_{b,min+}\). Overall, a narrower SOC activation zone seems preferable, as it would allow improved exploitation of available energy capacity, without any substantial impact on frequency quality. Its main drawback is a deeper discharge of the batteries.
# Detection of Sub-Aroma Threshold Concentrations of Wine Methoxyprazines by Multidimensional GCMS
## 3 Results and Discussion
### Wine Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wine** & **IPMP (ng/L)** & **SMBP (ng/L)** & **IBMP (ng/L)** \\ \hline Sauvignon blanc & 0.259 \(\pm\) 0.031 & 0.191 \(\pm\) 0.061 & 0.470 \(\pm\) 0.164 \\ Rose & Detected (0.248 \(\pm\) 0.026) & 0.192 \(\pm\) 0.058 & 5.80 \(\pm\) 0.42 \\ Pinot noir & Not Detected & 0.330 \(\pm\) 0.370 & 0.360 \(\pm\) 0.070 \\ \hline IBMP—2–Isobutyl-3-methoxypyrazine, d\({}_{3}\)-IBMP—deuterated 2-Isobutyl-3-methoxypyrazine, & & & & \\ IPMP—2–Isopropyl-3-methoxypyrazine, d\({}_{3}\)-IPMP—deuterated 2-Isopropyl-3-methoxypyrazine, & & & \\ SBMP—2-sec-Butyl-3-methoxypyrazine, Detected—above the limit of detection, but below the limit of quantitation. no SBMP was detected in the wines in their study. While other studies have examined MPs in wine, there are few studies that have been performed at these low concentrations [27; 28]. Therefore, when comparing to these studies the values obtained in the current study would fall below the previously reported level of quantification. Not all compounds contribute equally to the aroma profile of a wine. Some compounds directly impart definable aromas, as these compounds exceed the perception threshold. The compounds this method is intended to study are those below the perception threshold. These compounds contribute to an aromatic group, in this instance green aromas, and can act additively, as enhancers, and/or as inhibitors [29]. Consequently, being able to detect compounds at trace levels has become necessary to begin to understand the interactions that aromatic compounds have with each other and how they correlate to sensory profiles, thereby being able to define key odorants [30].
## 3 Results and Discussion
### FTIR-Spectra  
Figure 6 shows the FTIR spectra of the unheated and heated potato protein-maltodextrin fibers after four different heating times (6, 12, 24, 48 h) and the potato protein isolate, maltodextrin DE 21 and DE 2. The major band at 1000 cm\({}^{-1}\) was a combination of the vibration of C-O-C glycoside and C-C bonds of pyranoid ring of the glucose monomer, as well as stretching of the C-OH bond of the side group [43]. The peaks around 1636 and 1530 cm\({}^{-1}\), belonging to the amide-I and amide-II regions, respectively, showed the proteins. The amide I band was composed of the stretching vibrations of the C-N and C=O groups. The band of the amide II region came from the in-plane N-H bending and the stretching vibrations of the C-N group and the C-C group [44]. After heating the fibers, the amide I band of potato protein-maltodextrin blends shifted toward higher wavenumbers especially in blend A (0.05 g/mL potato protein), from 1636 to 1644 cm\({}^{-1}\). All other blends had wavenumbers between 1640 and 1643 cm\({}^{-1}\) and no clear shift was observed. The shift was previously attributed to glycation via the first stage of the Maillard reaction during which amino groups are consumed while Schiff bases are formed [44]. It had also been reported that a shift of the amide II region from 1596 to 1581 cm\({}^{-1}\) would occur after glycation. However, the shift in the amide II region was not clearly visible. Due to the relatively low protein content of the fibers, the peak of the amide II region was less pronounced at 1530 cm\({}^{-1}\) for the unheated fibers and 1540 cm\({}^{-1}\) for the heated mixed fibers, and it was not possible to detect a shift in wavenumber between the different heating times (1538-1540 cm\({}^{-1}\)). It was suggested that the potato protein was not completely homogeneously distributed in the fibers in accordance with a previous study [25].  
Figure 5: Photos of the fibers from blend (**A**) (80:10:5), (**B**) (80:10:10), (**C**) (80:10:15) and (**D**) (80:10:20) unheated and after 48 h of heating at 65 \({}^{\circ}\)C and 75% RH.