esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The objective of the present analysis is the energy, exergy and financial investigation of a novel CO\({}_{2}\)-based transcritical Rankine cycle which incorporates parabolic trough collectors inside the cycle for direct heating of the supercritical CO\({}_{2}\). The system is examined in detail for a default scenario, as well as there is parametric analysis, optimization and daily investigation. The results of this work have been obtained by a validated mathematical model which has been programed in Engineering Equation Solver. Below, the most valuable outcomes are summarized:  
In the default scenario with 800 K turbine inlet temperature and 200 bar inlet turbine pressure, the collector efficiency is 73.62%, the cycle efficiency is 32.96%, the system efficiency is 24.27% and the electricity product is 44.14 kW.  
The increase of the pressure level enhances the collector, the cycle and the system efficiency. On the other hand, the high-temperature increase is beneficial for the cycle performance but not for the collector performance. Thus, for every high-pressure level, there is an optimal high-temperature level that leads to maximum system performance and consequently electricity production.  
The parametric analysis proved that the lower heat rejection temperature and the higher solar irradiation levels lead to increased performance of the examined system.  
The monthly performance of the system shows that the maximum monthly production is found in July and the maximum system efficiency in June. On the other hand, the minimum electricity product is found in January and the minimum system efficiency in December. The yearly system efficiency is 19.80% and the yearly electricity yield is 81.16 MWh.  
The financial analysis proved that the suggested investment is viable. More specifically, the NPV is 143.5 k\(\ell\), the IRR is 11.94%, the simple payback period is 7.88 years, and the payback period is 8.12 years. The yearly CO\({}_{2}\) emissions avoidance is calculated to be 48.7 tons and the total CO\({}_{2}\) emissions avoidance in the 25-year period is 1217.5 tons.  
The comparison of the found results with other literature studies, in energy and economic terms, shows that the present solar CO\({}_{2}\)-based transcritical Rankine cycle is better than other solar-driven conventional ORC, something that proves that the proposed system is a promising idea for future power systems.  
E.B.: investigation, software, writing and reviewing the original paper; C.T.: supervisions, writing and reviewing the original paper. All authors have read and agreed to the published version of the manuscript.  
This research is co-financed by Greece and the European Union (European Social Fund--ESF) through the Operational Programme <<Human Resources Development, Education and Lifelong Learning>> in the context of the project \"Reinforcement of Postdoctoral Researchers--2 nd Cycle\" (MIS-5033021), implemented by the State Scholarships Foundation (IKY).
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 4 Field Test
### Image Acquisition  
Due to the contour-type flight plans, which require continuous adjustments of the drone's attitude to follow the route, precise flight control is a primary consideration to ensure compliance with the planned route. Additionally, wind conditions are typically stronger in hillside areas, and stability needs to be considered regarding the drone's length. We used a self-made 4-rotor UAV platform of 75 cm in length, equipped with an autonomous flight controller DJI N3, expanded with A3 Upgrade Kit high-performance navigation module. The flight speed was set to 4 m/s. When the UAVs operate in a mountainous area, the battery endurance will decrease due to the reduced air pressure and temperature. Sufficient flight time should be considered to complete the planned tasks for each flight to ensure flight safety. Because the Liugui area has steep gradients, the battery endurance can only be maintained for 20 min at low and 15 min at high altitudes. Thus, the flight plans were divided into three sessions. The grid-type collected 80 images during  
Figure 8: Geographical location of the study areas. (**a**) Study area location, (**b**) Liugui study area shown as a three-dimensional model.  
a 45 min flight session, while the contour-type collected 166 images during a 55 min flight session. The locations of each image are shown in Figure 9.
## 4 Discussion
### Strengths and Weaknesses of this Study  
A major strength of this study is that it uses nationally representative data and that it is able to compare directly COVID-19 and health characteristics between sexual orientation groups without using data linkage. Linking population-based administrative information from two or more databases is very useful in providing detailed individual information from different sources for research purposes. In particular, it can answer questions requiring large sample sizes or detailed data on populations, such as sexual minority or gender minority status. However, there can be biases from linkage errors, or where records cannot be linked or are linked incorrectly, reducing the accuracy of the results [18]. Using a single database means no linkage errors, but is limited by the questions asked for that dataset. In this situation, the results are also limited by small sample sizes, so the ability to pick out small differences in COVID-19 symptoms and the probability of having a COVID-19 test will be unlikely. We were unable to analyse hospitalisation data due to the small sample sizes.  
Another limitation is that sexual orientation is determined by a single question and does not take into account other aspects of sexual preference, such as behaviour or ideation. Furthermore, while the assessment was conducted twice, changes in sexual orientation were not included in the analysis, and only the last recorded sexual orientation was used. Another limitation is that it was not possible to analyse by gender identity, as the UKLHS does not include that attribute in the dataset. We chose not to include the 'other' and 'prefer not to say' groups, as the sexual orientation identity of people responding thus is unclear.
# Characterization of Two Hydrogen Peroxide Resistant Peroxidases from _Rhodococcus apacus_ 1 Cp
## 2 Materials and Methods
### Enzyme Characterization  
Two assays were used to characterize DypA and DypB. First, peroxidase activity was determined spectrophotometrically using the 2,2\({}^{\prime}\)-azino-bis [3-ethylbenzthiaazoline-6-sulphonic acid] (ABTS)-assay. Standard ABTS assay contained 1 mM ABTS, 0.1 M KPi buffer at pH 5.0, 0.25 mM H\({}_{2}\)O\({}_{2}\) and the corresponding enzyme. After mixing, the increase in extinction at 420 nm was recorded for 1 min at room temperature. ABTS assay was further optimized to determine the best conditions for the DyPs from _Rhodococcus apacus_ 1 CP. The pH profile was determined with the standard ABTS assay using Britton-Robinson buffer (25 mM KH\({}_{2}\)PO\({}_{4}\), 25 mM H\({}_{3}\)BO\({}_{3}\), 25 mM trisodium citrate... 2 H\({}_{2}\)O, 25 mM HCl, pH 2-6 with NaOH). Moreover, the influence of the buffer composition on ABTS conversion was investigated by testing different buffers such as 25 mM citrate buffer, 25 mM citric acid phosphate buffer (25 mM citric acid, 50 mM Na\({}_{2}\)HPO\({}_{4}\)), 25 mM sodium acetate buffer (25 mM sodium acetate, 25 mM acetic acid), and 25 mM KPi.  
Meanwhile, dye-decolorizing peroxidase activity was determined spectrophotometrically using the Reactive Blue 5 (RB5)-assay [42]. Typically, 1 mL RB5-assay contained 500 \(\upmu\)L 288 \(\upmu\)M RB5 solution, (480-x) \(\upmu\)L 25 mM citrate buffer (pH 3.6), 20 \(\upmu\)L 0.03% H\({}_{2}\)O\({}_{2}\) and an appropriate amount of enzyme (2.5 nM DypA and 22.6 nM DypB). After mixing, the decrease in extinction at 600 nm was detected immediately and run for 1 min at 20 \({}^{\circ}\)C for DypA and 30 \({}^{\circ}\)C for DypB.  
The temperature optimum was determined for both the optimized ABTS and RB5 assay for each enzyme varying the temperature from 4-60 \({}^{\circ}\)C. The H\({}_{2}\)O\({}_{2}\) tolerance in the presence of ABTS was tested using the optimized ABTS assay. The H\({}_{2}\)O\({}_{2}\) concentration was varied from 0.02-200 mM.  
Control reactions were performed without enzyme, H\({}_{2}\)O\({}_{2}\) or both. Conversion of substrate was only observed when both enzyme and H\({}_{2}\)O\({}_{2}\) were present. For reference, 1 U is defined as the conversion of 1 \(\upmu\)mol ABTS or 1 \(\upmu\)mol RB5 within 1 min. Measurements were carried out at least in triplicates.
# Detection of Mineralization Zones Using Aeromagnetic Data
## 3 Methodology
### Analytical Signal (AS) Technique  
One of the most essential features of the AS technique for deriving magnetic parameters from the aeromagnetic data is that it is independent of rock magnetization. Mineral deposits with large concentrations of iron minerals are frequently linked with ophiolitic serpentinite rocks, ophiolitic basic and metavolcanic rocks, and gabbroic rocks, which have high AS amplitudes. This demonstrates that the AS amplitude is dependent on the causal body's magnetizing amplitude [26; 27], where the 3D AS, as shown in Equation (3), is the square root of the sum of the squares of the derivatives in the x, y, and z directions. The AS amplitude can be used to map the borders of rock units and delineate underlying structural features.  
\[\text{AS (x, y)}=\sqrt{\left(\frac{\partial\text{M}}{\partial\text{x}} \right)^{2}+\left(\frac{\partial\text{M}}{\partial\text{y}}\right)^{2}+\left( \frac{\partial\text{M}}{\partial\text{z}}\right)^{2}} \tag{3}\]
## 4 Natural Antioxidants and Their Role in Cosmetics
### Minerals  
#### 4.4.1 Selenium (Se)  
In the human body, selenium plays many complex roles, including in immune system responses, preventing cognitive disorders, dementia, and cardiovascular diseases, and in oxidative defense [120]. Nuts, cereals, seafood, and meat are the richest sources of selenium in the diet [121].  
The physiological effect of Se is mainly due to its incorporation in selenoproteins Selenoproteins have been implicated in many metabolic and functional pathways, such as aging. Thus, the Se-containing enzyme glutathione peroxidase can help lower free radical reactions to tolerable levels by reducing H\({}_{2}\)O\({}_{2}\) to H\({}_{2}\)O and organic hydroperoxides (ROOH) to alcohol (ROH). The Se-dependent glutathione peroxidoxides (GPX1-4 and GPX6) and thioredoxin reductases (TrxR1-3) suppress oxidative stress. GPX1 is a major metabolic form of body Se against severe oxidative stress [122]  
Insufficient Se levels in an organism are associated with such inflammatory skin diseases as psoriasis and atopic dermatitis [122]. The only selenium compound approved for use in cosmetic products is selenium sulfide. Usually, it occurs in anti-dandruff shampoos and preparations intended for people suffering from seborrheic dermatitis. The shampoos and rinses recommended in these cases contain 2.5 and 1% selenium sulphide, respectively. Selenium is omnipresent in cosmetics but most often its presence is not clearly marked. This element often appears as a component of various raw materials used in the cosmetic industry, such as vegetable extracts, oils, clays, milk, and honey. Thermal water from La Roche-Posay (the Poitou-Charentes region in France) contains a lot of selenium (approx. 53 \(\upmu\)g/L). Creams, face, and body mists produced on the basis of this water effectively protect against the effects of free radicals, delay the skin aging process, improve its elasticity, and have a softening and smoothing effect. Selenium is also present in some formulations used during mesotherapy [123].  
The health effects of selenium (Se) depend largely on the dose, as the difference between beneficial and harmful levels is marginal. At appropriate nutritional levels, selenium acts as a potent antioxidant, protecting cells from oxidative damage. However, when consumed in excess, it shifts to a prooxidant role, potentially causing oxidative stress and cellular damage [122].
## 4 Security
### Integrity of Information  
The selected industrial standards benefit from the security mechanisms included in the IEEE 802.15.4 standard that ensure data integrity at data link layer. An additional _Message Integrity Code_ (MIC) is inserted at the queue of the data to protect. The data are signed and the receiver is able to determine whether the data have been tampered with or not. Data integrity protection can be provided in complement with encryption by using the _enhanced combined encryption and authentication block cipher mode_ (CCM*). Depending on the desired security level, the length of the MIC can be set to 32, 64 or 128 bits. The longer the code is, the higher the integrity protection is, but also the greater the overhead is. The length should therefore be selected carefully.  
Message and Device Authenticity  
In addition to provide hop-to-hop data integrity, the MIC allows to authenticate the packets by using secret symmetric keys known by both sender and receiver. For example, the shared network key and the unique session key are used in WirelessHART to authenticate the messages at data link and network layers respectively [45]. The authenticated packets are thus recognized as originated by authorized members of the network. However, before message authentication can be performed, each device must be first authenticated during the join procedure. Even if the name of the keys may vary between the standards, their functions are similar. Devices willing to participate to the network exchange _join requests_ and _join responses_ with the network manager and use public key and asymmetric private key kept inside the joining devices. These keys are used for computing the data link and network MICs respectively and may be either preloaded in the devices at the factory or distributed by a unique trusted center, which maintains and updates the security keys. In WirelessHART, an additional join key is used to encrypt the _join request_. Once the device is recognized as authorized member, it can exchange authenticated data with the other members. As each standard is based on a central entity responsible for the network management and keeping tracks of the participating devices, the probability is very low that the attacks such as Sybil and node replication attacks may be performed successfully. For example, in WirelessHART, the network manager links each device with a unique identity [45]. The identification is completed by a list of unique IDs maintained at the gateways. The network manager identifier and the gateway ID are used conjointly with the session key to maintain sessions between the device and the network manager as well as the gateway respectively. Devices claiming the same identity as an existing one or sharing multiple identities would be immediately discovered, as these would already be listed.
## 1 Introduction
Validation of repeatability and filtering of outliers by performing 50 iterations of the AMCM to ensure numerical stability of the results within the selected tolerance. 3. Characterization of the measurand's \(PDF\), combining skewness and kurtosis analysis of the AMCM results and performing normality contrasts of a sample of the measurand's \(PDF\). These improvements are not only applicable in the case of custody transfer meters but can be useful in any of the AMCM applications. To validate the efficacy of the proposed methodology, this scheme is applied to the calibration results of a Positive Displacement Meter (PD) against a 5000 L standard tank in an ISO 17025 [21] accredited volume laboratory. In this calibration, the proposed methodology allows for a reduction in the uncertainty compared to GUM. Thus, it has been proven that using the GUM methodology does not assign the \(PDF\) of the result correctly because the proposed AMCM obtains a distribution that is neither Gaussian nor Student's t-type. The relative expanded uncertainty obtained is reduced by 7.1% with AMCM compared to GUM method. A Flatten-Gaussian distribution [22; 23] is proposed as the \(PDF\) of the final result. The use of this \(PDF\) guarantees the uncertainty requirement to be transferable to other users. In fact, only if the \(PDF\) is known, further Monte Carlo simulation can be performed.
## 5 Ethical and Privacy Considerations  
It is essential to acknowledge that, in IoT environments where devices handle vast amounts of sensitive data, addressing privacy and ethical concerns is not only important but also a necessity. IoT systems often collect personal or organizational data, making them potential targets for malicious activities, such as botnet attacks. Any models developed for such environments must, therefore, be designed to ensure compliance with well-established data protection regulations, such as the General Data Protection Regulation (GDPR) [63, 64] and the California Consumer Privacy Act (CCPA) [65, 66]. These regulations mandate strict standards on how data are collected, processed, and stored, ensuring that the rights of users are protected.  
To meet these requirements, privacy-preserving techniques, such as data anonymization and encryption, should be integrated into the model. Anonymization removes personally identifiable information (PII) from the dataset, reducing the risk of privacy violations during the data processing stages [67]. Encryption further enhances security by safeguarding data both at rest and in transit, ensuring that unauthorized individuals cannot access sensitive information even if the data are intercepted or breached [68].  
Additionally, the concept of federated learning [69] is an important avenue to explore for the model. This technique allows the model to be trained on multiple IoT devices without requiring raw data to be transferred to a central server. Instead, only the necessary model updates are shared, which helps to preserve user privacy while still enabling the model to benefit from large-scale, decentralized data. Federated learning can significantly reduce privacy risks, making it a promising solution for the resource-constrained and privacy-sensitive IoT landscape.  
Ethical considerations must also be accounted for to avoid the potential misuse of data or excessive monitoring, which can lead to privacy infringements. By embedding principles of transparency and accountability [70], the system can ensure that its operations are clear to users, and that any automated decisions or detections are explainable. This is particularly crucial to prevent unintended biases or false positives, which could result in inaccurate or unfair detection outcomes. Furthermore, the model should be designed in a way that respects user consent and autonomy, aligning with the broader ethical frameworks that govern IoT security practices.
## 3 Results and Discussions
### Chemical Characterization of Pyrolytic Oil  
3.3.1. TGA Analysis  
Figure 1 shows the TGA and DTG curves of the oils obtained without the catalyst (data from [PERSON] et al., [12]) and with three different studied catalysts.  
Similar behavior was shown independent of the catalyst used during the pyrolysis. The pyrolytic oil samples presented a large mass loss between approximately 370 K and 670 K, which corresponds to the volatilization of compounds. Similar results were found by [PERSON] et al. [38]. These authors showed the weight changes in the function of the temperature of pyrolysis oil obtained from the pyrolysis of plastic wastes. Additionally, a comparison of TG curves of oils from catalytic pyrolysis with those obtained without catalyst shows that the catalytic pyrolysis reduced the volatilization temperature due to the presence of lighter compounds. The catalyzed oils show an equal mass loss up to about 470 K (naptha cutoff). Additionally, the oils catalyzed by HY and HBEA have similar behavior up to 550 K (kerosene cutoff). However, although all catalyzed oils have approximately the same amount of diesel cut (13.29 to 13.71%), the lower mass loss of HUSY compared to the others may be associated with the interaction between the kerosene and diesel cuts due to the low content of kerosene in oils obtained in the catalytic pyrolysis of plastic with a HUSY catalyst (HUSY: 12.29%, HBEA: 15.98% and HY: 17.9%). The behavior is markedly different, dominated by the heavy fraction. It is evident that the highest amount of bottoms is in the oil obtained by the HUSY catalyst (HUSY: 65.12%, HBEA: 41.12% and HY: 50.97%). These results show that the liquid fraction obtained by HBEA zeolite, the zeolite with the largest external surface (218 m\({}^{2}\)/g), has the highest amount of volatile compounds (58.88%), followed by HY zeolite (174 m\({}^{2}\)/g), which had liquid fractions with the highest API degrees. In addition, the results are consistent with the data obtained by simulated distillation that was reported in Figure 2.  
Figure 1: Thermogravimetric curves of the pyrolytic oil samples were obtained by thermal (without catalyst) and catalytic cracking (HY, HUSY and HBEA).  
Figure 2 shows simulated distillation curves of the pyrolytic oils obtained by thermal and catalytic cracking. These curves show that the liquid fraction obtained by the HBEA catalyst produced the lightest compounds, followed by the HY and HUSY catalysts because of the external surface area (HBEA: 218 m\({}^{2}\)/g, HY: 174 m\({}^{2}\)/g and HUSY: 78 m\({}^{2}\)/g). The latter, although it is catalyzed oil, has more heavy compounds (65.12% of bottoms) than the oil obtained without catalyst (59.02% of bottoms), and therefore has the lowest API gravity.
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 5 Sizing Procedure Examples
### Estimating the Mean Power Price Given an Existing PV Installation and a Number of EVs  
Let us now suppose that the owner of an existing PV installation of 100 kWp in the same region wants to estimate the Mean Power price of the energy needed to feed 100 EVs with the smart charging strategy. The procedure for deriving this price, according to Figure 3, is the following:  
* From PVGIS: Annual PV Production \(E_{PV}=100[\mathrm{kWp}]\times 1.4\left[\frac{\mathrm{MWh}}{\mathrm{KWP}} \right]=140\) MWh;
* From Equation (8): Annual EV Charging Consumption \(E_{EV}=12\times 100\left[\frac{\mathrm{kWh}}{\mathrm{badge}}\right]\times 100[ \mathrm{badge}]=120\) MWh;
* Production-to-Consumption ratio \(PTC=E_{PV}/E_{EV}=1.16\);
* Self-Production Ratio (for Smart Charging) according to Figure 12: \(SPR=0.6\);
* \(Pr_{CPO}\) can then be estimated from Equation (6);
* \(Pr_{CPO}=Pr_{ESEV-CPO}-SPR(Pr_{ESEV-CPO}-Pr_{LP-CPO})\approx 287\)\(\upcelsius\)/MWh;