esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Integration of Sorption Systems On-board
### Cascade System with Pre-Conditioning  
In several applications, there is a contemporary need to freeze the fish and store it at a temperature level between \(-5\)\({}^{\circ}\)C and \(0\)\({}^{\circ}\)C. In addition, as will be discussed in the next section, the heat available on-board is often in excess with respect to the needs of a sorption unit. It is then possible to exploit this situation using a cascade system with pre-conditioning, as shown in the schematics of Figure 11. This system is similar to the former one, since it is based on a cascade cycle with the sorption unit as top cycle and the compression unit as bottom cycle, but it includes an extra component, i.e., a pre-conditioning unit. This consists of a heat exchanger that exploits a fraction of the cooling production of the sorption machine, to cool down the fish from seawater temperature to \(-5\)\({}^{\circ}\)C/0 \({}^{\circ}\)C. The fish can then be sent to the storage room or introduced into the cold room for refrigeration, thus increasing the overall efficiency of the system, in order to reduce the load that the compression chiller has to cover, also reducing the temperature drop to which the fish is subjected. Similar to the previous case, the system can work with variable temperature range according to the working pair and technology applied. However, with respect to the cascade system, the sorption unit should be oversized, in order to be able to cover part of the cooling load directly, and an extra HEX is needed (pre-conditioning unit, 6 in Figure 11).  
Figure 10: Thermodynamic cycle for R744. Dashed line: reference stand-alone compression cycle; solid line: cascade cycle. saturated liquid curve (in blue) and saturation vapor curve (in red).
# A High Power Factor LED Driver with Intrinsic Current Balancing Capability
## 3 Mathematical Equations for Parameters Design  
### Flyback-Typed Power-Factor Correction Circuit  
Based on analysis of the operation modes, \(i_{L1}\) rises linearly from zero at the beginning of Mode I and reaches a peak value at the end of Mode II. From (3), the peak value of \(i_{L1}\) is equal to  
\[i_{L1,\ peak}(t)=\frac{DT_{s}V_{m}\sin(2\pi f_{L}t)}{L_{1}}=\frac{DV_{m}\sin(2 \pi f_{L}t)}{L_{1}f_{s}}, \tag{17}\]  
where \(D\) represents the duty ratio of \(S_{2}\). Figure 4 shows the conceptual waveform of \(T_{1}\) primary current and the input current. The cutoff frequency of \(L_{f}\) and \(C_{f}\) will be designed to be less than one-eighth of \(f_{s}\) so that most of the high-frequency components of \(i_{L1}\) can be removed and the line input voltage only provides the average of \(i_{L1}\) over one high frequency cycle.  
\[i_{in}(t)=\overline{i_{L1}(t)}=\frac{1}{T_{S}}{\int_{0}^{T_{s}}i_{L1}(t)\cdot dt }=\frac{D^{2}V_{m}}{2L_{1}f_{s}}\sin{(2\pi f_{L}t)}. \tag{18}\]  
It can be seen from Equation (18) that the line  
The \(LED\) power can be expressed as  
\[P_{LED}=\frac{\eta D^{2}V_{m}^{2}}{4L_{1}f_{s}}, \tag{20}\]  
where \(\eta\) represents the energy-conversion efficiency. Equation (20) indicates that the output power can be controlled by regulating either the switching frequency or the duty ratio.  
To ensure DCM operation, the drop time of the \(T_{1}\) secondary current from peak to zero must be less than \((1-D)T_{s}\).  
\[T_{off}(t)=\frac{V_{m}|\sin(2\pi f_{L}t)|}{nV_{dc}}DT_{s}<(1-D)T_{s}. \tag{21}\]  
From Equation (21), when \(V_{dc}\) is high enough, it is guaranteed that the flyback converter can operate at DCM during the entire period of the input voltage.  
\[V_{dc}>\frac{DV_{m}}{n(1-D)}, \tag{22}\]
# Automating Visual Blockage Classification of Culverts with Deep Learning
## 7 Conclusions and Future Directions  
The idea of using visual analytic for the culvert blockage analysis has been successfully pitched by implementing existing CNN models for culvert blockage classification. Dataset from three different sources (i.e., ICOB, VHD, SIC) has been developed with a diversity of clear and blocked culvert instances for training the convolutional neural network (CNN) models. From the analysis, it has been observed that the NASNet model performed best among all in terms of classification performance; however, it was the slowest in relative comparison of processing times. Based on the classification performance and processing times, MobileNet was recommended model to be deployed for real-world applications. Deep learning models were benchmarked against conventional machine learning algorithms and reported significantly improved performance. From the false positive (FP) and false negative (FN) instances, background noise and oversimplified labeling criteria were found potential factors for degraded performance. A detection-classification pipeline was proposed with higher blockage classification accuracy (i.e., 94%) as a potential solution for real-world implementation. Furthermore, a partial automation framework based on the model class prediction probability was introduced to facilitate the manual visual inspections of culverts. A visual attention based approach and problem-specific CNN design are potential future directions of this research. Furthermore, study the impact of high-resolution images on the accuracy and developing a hybrid model taking into account information from multiple sensors are the potential concepts that can be investigated in future.  
Conceptualization and methodology, U.I., J.B., W.L. and P.P.; investigation, U.I; writing--original draft preparation, U.I.; writing--review and editing, U.I. and J.B.; supervision, J.B., W.L. and P.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Wollongong City Council (WCC) in partnership with Shellharbour, Kiana and Shoalhaven Councils, Landlesae and the University of Wollongong's SMART Infrastructure Facility. This program also received funding from the Australian Government under the Smart Cities and Suburbs Program (SCS69244). We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan RTX GPU used for this research.  
I would like to thank WCC for providing resources and support to carry out this study. Furthermore, I would like to thank the University of Wollongong (UOW) and the Higher Education Commission (HEC) of Pakistan for funding my Ph.D. studies.  
The authors declare no conflicts of interest.
# Sulfuric Acid Resistance of CNT-Cementitious Composites
## 3 Test Results and Discussion
### Electric Resistance  
Figure 11 shows the electrical resistance of cement mortar incorporating CNTs before and after deterioration caused by sulfuric acid. The electrical properties of the specimen before damage revealed that as the CNTs were incorporated, its resistance greatly decreased by up to 90%, and it was further decreased in SWCNT cementitious composites compared to MWCNT composites. This is because CNTs, which are conductive nanomaterials, were incorporated into the cementitious composites to give conductivity through which  
Figure 9: Scanning electron microscopy (SEM) images before and after damage by sulfuric acid attack.  
electric current can flow, and the electrical resistance value was then reduced despite the bundle phenomenon due to the van der Waals force. It denotes that the CNT cementitious composites have self-sensing ability. In addition, it was found that the electrical resistance value increased after deterioration due to sulfuric acid attack. The electrical resistance of plain cement mortar significantly increased when immersed in a 10% sulfuric acid solution because an increase in pore area was induced by the hydrate relaxation, resulting in poor electrical conductivity. It was observed that there was a slight increase in the electrical resistance of the CNT cementitious mixtures because the disconnection between CNTs occurred in the composites as the deterioration progressed. However, it was ascertained that since there was no significant difference in conductivity, the self-sensing performance could be demonstrated.
## 2 Materials and Methods  
### Data Collection  
Before the present investigation, we obtained approval from the Ethics Committees of our institution (Approved number: 4-04). Written informed consent was obtained from each patient after the oral explanations of the details of the present study were given. None of the eligible patients declined enrolment.  
Four hundred and twenty hands of patients diagnosed with CTS at our hospital from March 2013 to September 2022 were included. The diagnosis of carpal tunnel syndrome was made by a hand surgeon based on numbness in the median nerve area, carpal tunnel Tinel's signs, and electrophysiology results. The severity of carpal tunnel syndrome was classified following previous reports [7; 11]. The patients underwent open carpal tunnel release (CTR) with small skin incisions and underwent NCS and CTSI-JSSH scoring before and one year after surgery. The average age of the patients was 68.3 \(\pm\) 12.1 years (26-97 years), with 102 male and 318 female hands. Cases with diabetic peripheral neuropathy or cervical spine disorders were excluded. For NCS, the distance between the stimulus and derivation electrodes was set to 7 cm, and the DML of the abductor pollicis brevis was recorded with maximum electrical stimulation at the wrist joint. SCV was measured by the retrograde method with maximum electrical stimulation at the wrist joint and derivation at the index finger. The distance between the stimulus and derivation electrodes was 14 cm. The preoperative and one-year postoperative values were classified into Stages 1-5 using the electrophysiological severity classification with the cutoff values as DML \(\geq\) 4.5 ms and SCV \(<\) 40.0 m/s [7,11]. All tests were performed by the same examiner using MEB2200 ([PERSON], Tokyo, Japan).  
CTSI-JSSH is composed of the severity score of symptoms (CTSI-SS) and the scale of functional status (CTSI-FS). CTSI-SS is calculated as the average of the contents of 11 items evaluated from 1 to 5 points, with mild being 1 point and most severe being 5 points. CTSI-FS is used for finger function evaluation, and the average of 8 questions is calculated, with mild being 1 point and most severe being 5 points. The CTSI-JSSH questionnaire was completed by the patients themselves outside the examination room.
## 3 Results and Discussion
### Effect of the Roller's Rotational Speed on the Unthreshed Seed Percentage at Different Feed Rates  
The unthreshed seed percentage decreased with the increase of roller rotational speed for all feeding rates. It decreased from 3.67% to 2.09%, 3.11% to 1.65%, 2.55% to 1.21% and 2.87% to 1.47% with increasing roller rotational speeds of 150, 200, 250 and 300 rpm at feed rates of 600 kg/h, 700 kg/h, 800 kg/h and 900 kg/h, respectively, as shown in Figure 5). These results were in agreement with previous studies [8] in the same operational conditions, while the obtained data of that study showed that the unthreshed seed decreased with the increase of the feed rate from 600 kg/h to 800 kg/h and then increased with the increase of the feed rate from 800 kg/h to 900 kg/h. The increase in the roller's rotational speed may lead to increased sunflower head friction with the threshing bars and the threshing box wall, increasing the sunflower heads's contact with each other and leading to a reduction of the unthreshed seed percentage. On the other hand, increasing the feed rate also led to an increase in the friction action between the sunflower heads up to a limited point.
## 3 Results  
### Patient Characteristics Analysis  
The analyzed patients' demographics are shown in Table 1. The mean age of the patients was 34 years, and 21.85% of the patients were younger than 18 years. Patients with Spetzler-Martin grade 3 or higher accounted for 55.46% of the total patient group. The mean total dose was 25.74 Gy for radiosurgery, and the meanidus size was 2.75 cm.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Unit or**} & **Instance (\%) or** \\  & & **Sub-Category** & **Mean \(\pm\) SD** \\ \hline Age & years & 34 \(\pm\) 13 \\ \hline Age \(\leq\) 18 & years & 26 (21.85\%) \\ \hline \multirow{3}{*}{Sex} & Male & 82(68.91\%) \\ \cline{2-3}  & Female & 37(31.09\%) \\ \hline \multirow{3}{*}{Previous rupture} & Yes & 64 (53.78\%) \\ \cline{2-3}  & No & 55 (46.22\%) \\ \hline \multirow{3}{*}{Prior embolization} & Yes & 6 (5.04\%) \\ \cline{2-3}  & No & 113 (94.96\%) \\ \hline \multirow{3}{*}{AVM Location} & Lobar & 60 (50.42\%) \\ \cline{2-3}  & Deep & 59 (49.58\%) \\ \hline \multirow{3}{*}{SM grade} & 1 & 14 (11.76\%) \\ \cline{2-3}  & 2 & 39 (32.77\%) \\ \cline{2-3}  & 3 & 43 (36.13\%) \\ \cline{2-3}  & 4 & 15 (12.61\%) \\ \cline{2-3}  & 5 & 8 (6.72\%) \\ \cline{2-3}  & Median score & 3 \\ \hline \multirow{3}{*}{AVM Volume} & mm\({}^{3}\) & Mean 7163 (IQR Q1: 1190, Q3: 7140, min: 64, max: 15,600) \\ \cline{2-3}  & 0 & 4 (3.36\%) \\ \cline{2-3}  & 1 & 24 (20.17\%) \\ \cline{2-3}  & 2 & 46 (38.66\%) \\ \cline{2-3}  & 3 & 40 (33.61\%) \\ \cline{2-3}  & 4 & 5 (4.20\%) \\ \hline Modified AVM score & Median score & 2.05 \\ \hline \multirow{3}{*}{Deep vein drainage} & Yes & 51 (42.86\%) \\ \cline{2-3}  & No & 68 (57.14\%) \\ \hline FD & Gy & 20.28 \(\pm\) 6.49) \\ \hline \multirow{3}{*}{Fx} & Sz & 102 (85.71\%) \\ \cline{2-3}  & Fx & 17 (14.29\%) \\ \hline TD & Gy & 25.74 \(\pm\) 8.58 \\ \hline Dmax & Gy & 30.02 \(\pm\) 9.83 \\ \hline Nidus size & cm & 2.75 \(\pm\) 1.78 \\ \hline \hline \end{tabular} Note: SD = standard deviation; AVM = arteriovenous malformation; SM = Spetzler-Martin grade; FD = fractional dose; Fx = fraction; Sz = single fraction; TD = total dose; Dmax = maximum dose; Gy = the unit of ionizing radiation dose (J/kg).  
\end{table}
Table 1: Patients’ characteristics (n = 119).
###### Abstract  
Aroma profiles, key aroma compound quantification, and cluster analysis of 15 brands of extra-virgin olive oils (EVOOs) from three countries (Spain, Italy, and Greece) were investigated in the current study. Aroma compounds were isolated from the oil by using solvent-assisted flavor evaporation (SAFE) and solid-phase micro-extraction (SPME) and analyzed by gas chromatography-olfactometry mass spectrometry (GC-MS/O). A total of 89 compounds were screened by SPME/SAFE-GC-MS/O with chromatographic columns in 15 brands of samples. Eighty and 54 compounds were respectively identified by SPME- and SAFE-GC-MS/O. Of those, 44 compounds were detected by both methods. Undecanol, (Z)-4-decenal, (E)-2-dodecenal, and 2-nonanone extracted by SAFE were not found in EVOOs before. Eight classes of aroma compounds were identified, including 17 alcohols, 22 aldehydes, 9 ketones, 4 acids, 14 esters, 5 aromatics, 12 alkene, and 6 others. Eleven compounds were identified as the key aroma compounds in alternative brands of EVOOs by SAFE-aroma extract dilution analysis (AEDA). Hexanal, (E)-2-hexanal, (E)-3-hexenol, acetic acid, and (E)-2-heptenal were the common key aroma compounds by AEDA and odor activity values (OAVs). From the cluster analysis of the heatmap, the aroma compounds of all the Spain EVOOs were similar, and there were some differences from the samples of Italy and Greece. It suggested that both the amount and concentration of aroma compounds determine the similarity of aroma in EVOOs.  
extra-virgin olive oils (EVOOs); headspace solid-phase micro-extraction (SPME); solvent-assisted flavor evaporation (SAFE); cluster analysis; quantification +
Footnote †: journal: _articles
## 4 Conclusions  
This study investigated the influence of long-range transport as one of the PM\({}_{2.5}\) factors in agricultural regions and estimated an indicator of long-range transport. There was an influence of long-range transport during the observation period. Concentrations of gaseous materials and PM compositions were inspected to find proof of long-range transport. The obtain results imply long-range transport based on changes in gas concentrations and PM compositions. In addition, the AOD, ERA wind field, back-trajectory analysis for CO/CO\({}_{2}\), and satellite observation results were used to identify additional proof for long-range transport. The specific characteristic of long-range transport in the study area during this study was then determined. Long-range transport can be analyzed along with concentrations of CO, NH\({}_{3}\), OC, and water-soluble ions in the future. Furthermore, we confirmed that the ratio of 12 Dice/NaT in VOCs had a proportional relationship with an indicator of long-range transport. This suggests that VOCs can be used as markers for long-range transport. Atmospheric pollutants are influenced not only by local factors but also by wide-ranging factors such as intercountry transport. It is hoped that, based on the results presented in this study, a further understanding of the impact of atmospheric pollutants between regions and countries can be achieved, facilitating not only regional atmospheric pollutant management but also global atmospheric pollutant management.  
[PERSON] and [PERSON] equally contributed to this work by completing the experiment measurements, data analysis, and manuscript preparation. [PERSON]-H.O., [PERSON]-H.Y., [PERSON], [PERSON] and [PERSON]-H.K. contributed to research sample analyses. [PERSON]-S.B. contributed to the experimental planning and data analysis. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Cooperative Research Program for Agriculture Science & Technology Development (PJ0170302023) Rural Development Administration, Republic of Korea.  
Not applicable.  
The data will be available upon request.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**r\({}^{2}\)** & **Cf/NaT** & **12 Dice/NaT** & **TcCE/NaT** & **CB/NaT** \\ \hline PM\({}_{2.5}\) & 0.323 & 0.879 & 0.382 & 0.505 \\ CO & 0.321 & 0.907 & 0.436 & 0.525 \\ eBC & 0.084 & 0.792 & 0.630 & 0.663 \\ EC & 0.026 & 0.610 & 0.610 & 0.609 \\ OC & 0.104 & 0.783 & 0.629 & 0.641 \\ W5 OC & 0.101 & 0.647 & 0.442 & 0.509 \\ NH\({}_{4}\)\({}^{+}\) & 0.220 & 0.780 & 0.409 & 0.504 \\ NO\({}_{3}\)\({}^{-}\) & 0.180 & 0.834 & 0.519 & 0.585 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation coefficient of determinations between VOC ratios for long-range transport and measured compounds.
# The Influence of Fermentation Vessels on Yeast Microbiota and Main Parameters of Sauerkraut
## Results and Discussion
anomalous_ was also isolated from fermented Pakistani sourdough [13] and from fermented bread sourdough [14], and in both cases the growth of other fungi was inhibited during fermentation. A similar phenomenon was also found during our analyses, between the third and seventh day of fermentation (Figure 1), when less diversity of yeast cultures was found, even though, according to the results of the quantitative analysis (Figure 2), the highest levels of yeast occurred at that time in these settings. This could be related to the ability of _W. anomalous_ and/or _T. delbrueckii_ strains to produce killer toxins, and we had already detected these types of strains in our earlier analyses during sauerkraut fermentation [5]. There is a considerable amount of published information on the widely intergenic killing spectrum of _Wickerhamomyces_ toxins against different cultures of fungi and yeast [15]. Figure 1: Distribution of yeast strains during fermentation of different cabbage varieties in stonewearvessels (Dh—_Deharyomyces hansenii_, Pf—_Pichia fermentatus_, Rm—_Rhodotorula mucilaginousa_, Td—_Torulospora delbrueckii_, and Wa—_Wickerhamomyces anomalus_). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Strain**} & \multirow{2}{*}{**5.8S-ITS rRNA Gene (bp)**} & \multicolumn{3}{c}{**Restriction Fragments (bp)**} & \multicolumn{2}{c}{**Species Identification by 5.8S-ITS rRNA Gene Sequencing**} \\ \cline{2-7}  & & **C/pl** & _Hinyl_1 & _Hail_1 & **Closest Species** & **Homology \%** \\ \hline PP9 & 450 & 170 + 100 + 100 + 80 & 250 + 200 & 340 + 80 + 30 & _Pichia fermentatus_ & 100.0 & MT645416 \\ Wa1 & 650 & 575 & 310 + 310 & 600 + 50 & _Wikelenomyces anomalies_ & 99.0 & KY657575 \\ Wa7 & 650 & 575 & 310 + 310 & 600 + 50 & _Wikelenomyces anomalies_ & 99.6 & KY105860 \\ Wa22 & 650 & 575 & 310 + 310 & 600 + 50 & _Wikelenomyces anomalies_ & 98.4 & KY80795 \\ Rn8 & 640 & 320 + 240 + 80 & 340 + 225 + 75 & 425 + 215 & _Roktoral media media/_ & 100.0 & QQ992821 \\ Rm26 & 640 & 320 + 240 + 80 & 340 + 225 + 75 & 425 + 215 & _Roktoral media media/_ & 99.8 & JQ293997 \\ \cline{2-7} Td5 & 800 & 330 + 220 + 150 + 100 & 800 & 410 + 390 & _Toralysys address civil_ & 98.2 & KY105612 \\ \cline{2-7} Dh17 & 650 & 300 + 300 + 50 & 325 + 325 & 420 + 150 + 90 & _Dubranym  
The yeast _Torulaspora delbrueckii_ was present only in the samples from the third day of fermentation of Galaxy cabbage. _T. delbrueckii_ is mainly associated with wine [16] and beer fermentation [17], where it can also be used as a starter culture, enriching the produced wine with substances with a pleasant fruity and floral aroma, mainly esters. The presence of this microorganism was also found in products with high salinity, e.g., during the ripening of Feta-type cheese, where the salt concentration was approximately 8% [18].
# Heat and Flow Study of the Internally Finned Tubes with Different Fin Geometries
## 1 Introduction
[14] studied numerically the idea of using multiple twisted tape inserts, and they found the Nusselt number ratio to be up to 2.82 and the PEC to be in the range of 1.64 to 2.46. The idea of using an internally finned spiral tube has been examined by [PERSON] et al. [15], and they found the PEC to be up to 3.35. [PERSON] et al. [16] examined the use of 4, 6, and 8 internal fins in a tube. They found that the greater number of fins leads to greater Nusselt number enhancements, and generally, they found improvements of up to 189% with the Reynolds number at 1000 and up to 256% with the Reynolds number at 1500. It is obvious that there are many studies about the investigation of turbulators for enhancing the thermal performance of engineering devices. However, the literature does not include studies that compare different kinds of internal fins in order to determine the impact of the fin geometry on the thermal enhancement percentage. Therefore, the goal of this analysis regards the comparison of different fin geometries for enhancing the heat transfer rates in a tube. The reference case of the empty tube is compared with the circular fins, rectangular fins, and triangular fins. The comparative study is conducted with water as the working fluids and by simulating different mass flow rates and inlet temperature levels. The simulation is carried out via developed models in SolidWorks Flow Simulation, while validation evidence is provided. In the last step of this work, approximation equations for the calculation of the friction factor and the calculation of the Nusselt number are proposed for the four examined cases based on the calculated results.
## 7 Discussion
### Time Analysis  
The study of forecasting models, initiated in 1971, has experienced remarkable growth, with a surge in electrical load forecasting models after 1995. The implementation of the Kyoto Protocol in 2005 further stimulated model development. In particular, 87% of the models were developed in the last 19 years. While STLF models slightly declined after 2020, hybrid STLF models showed consistent growth. Machine learning gained prominence in the 1990s, with a notable increase in both statistical and machine learning techniques after 2007. The use of metaheuristic techniques in forecasting increased after 2009. Despite the increased accuracy of intelligent computing techniques, hybrid models have seen a more significant increase in the last eight years.