esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Optimum Excavation/Filling
At the beginning of each iteration, \(S_{2}\) is the sum of n-i+1 components of \(z\) and was calculated in the previous iteration. Thus, using the value of the previous \(S_{2}\), the new sum can be obtained from the previous by subtracting \((n-i+1)\cdot(z_{i}-z_{i+1})\). Since \(S_{1}\) is increasing from 0 to S and \(S_{2}\) is decreasing from S to 0, the optimum is reached when \(S_{2}\) becomes less or equal to \(S_{1}\). More exactly, the optimum height of the plane is inside the interval [\(z_{\text{opt-1}}\), \(z_{\text{opt}}\)]. If the values \(z_{\text{opt-1}}\) and \(z_{\text{opt}}\) are close enough, i.e., \(z_{\text{opt}}-z_{\text{opt-1}}<\varepsilon\), where \(\varepsilon>0\) is the fixed maximum distance to the optimum, e.g., \(\varepsilon=5\) cm, then any of the values \(z_{\text{opt-1}}\) and \(z_{\text{opt}}\) are good approximations of the optimum. If \(z_{\text{opt}}-z_{\text{opt-1}}\geq\varepsilon\), then a divide and conquer strategy is applied to get closer to optimum. To do that, two initial planes \(\pi(z_{\text{opt-1}})\) and \(\pi(z_{\text{opt}})\) are considered. A new plane is placed in the middle. If the distance between \(S_{2}\) calculated for \(\pi(z_{\text{opt-1}}+z_{\text{opt}})/2)\) and \(S_{1}\) for \(\pi(z_{\text{opt-1}})\) is less than the distance between \(S_{2}\) for \(\pi(z_{\text{opt}})\) and \(S_{1}\) for \(\pi(z_{\text{opt-1}}+z_{\text{opt}})/2)\), then the optimum is further calculated in the interval [\(z_{\text{opt-1}}\), \((z_{\text{opt-1}}+z_{\text{opt}})/2\)]. Otherwise, the optimum is calculated in the interval [\((z_{\text{opt-1}}+z_{\text{opt}})/2\), \(z_{\text{opt}}\)] and so on. Algorithm 6 divide and conquer for optimum excavation/filling plane (ADCOEFP):At the end of the while loop, any of the values, a or b are good approximations of the optimum. We considered a as the optimal solution. The approximate excavation volume is \(\mathrm{V_{e}=S_{2}\cdot d_{2}}\), and the filling volume is \(\mathrm{V_{f}=S_{1}\cdot d_{2}}\). Consequently, the difference between the two volumes is:  
\[|\mathrm{V_{e}-V_{f}}|\ =\ |\mathrm{S_{2}-S_{1}}|\mathrm{d}^{2}\leq\epsilon \mathrm{S_{house}}\approx\epsilon\mathrm{md}^{2} \tag{30}\]  
So, the two volumes are very similar.
## 3 Results
As indicated by the PL spectra of Figure 4, a decrease in the concentration of Sm\({}^{3+}\) ions to 0.01 mol, while increasing that of Tb\({}^{3+}\) ions to 0.04 mol, led to a significant enhancement of the emission intensity of 489 and 544 nm lines originated from Tb\({}^{3+}\) ions, over that of the Sm\({}^{3+}\)-related features. Likewise, the Sm\({}^{3+}\)-related features can also be made prominent by increasing its concentration. Spectra (d) in Figure 4 showed that by decreasing the concentration of Sm\({}^{3+}\) ions to 0.005 mol and increasing that of Tb\({}^{3+}\) ions to 0.045 mol, the emission intensity of Tb\({}^{3+}\) -related features at 489 and 544 nm dominate over that of Sm\({}^{3+}\)-related features at 563, 607, and 646 nm. The ability to tune the peak intensity of 646 nm (red-emission) and 544 nm (green-emission), _viz._, the controllability of the color emission of CaMoO\({}_{4}\) phosphors through the adjustment of the molar ratio of the two rare-earth ions of Sm\({}^{3+}\) and Tb\({}^{3+}\)[23,24] show immense potential for the co-doped material as color-tunable phosphors for white-LEDs. Figure 5 shows the CIE coordinate of CaMoO\({}_{4}\) phosphors doped with different molar ratio of Sm\({}^{3+}\)/Tb\({}^{3+}\) ions, which are calculated from its PL spectra at 12 and 300 K, respectively [24,25]. The calculated CIE chromaticity coordinates of the synthesized samples using the distribution of the PL emissions are presented as Figure 5. The CIE coordinates for sample a (0.05 mol Sm\({}^{3+}\)-doped CaMoO\({}_{4}\)) is (0.5963, 0.4057) at 12 K and is (0.5906, 0.4087) at 300 K which are agreed to the red phosphor. On the other hand, the calculated CIE coordinates of sample e (0.05 mol Tb\({}^{3+}\)-doped CaMoO\({}_{4}\)) is (0.2956, 0.6181) at 12 K and is (0.2957, 0.6162) at 300 K which are that of green phosphor. The variations in the CIE coordinates for emission colors of Sm\({}^{3+}\)/Tb\({}^{3+}\) co-doped CaMoO\({}_{4}\) phosphors at 12 K are calculated to be: Sm\({}^{3+}\)/Tb\({}^{3+}\) = 0.025/0.025, (\(x\), \(y\)) = (0.5391, 0.4351); Sm\({}^{3+}\)/Tb\({}^{3+}\) = 0.010/0.040, (\(x\), \(y\)) = (0.5085, 0.4627); and Sm\({}^{3+}\)/Tb\({}^{3+}\) = 0.005/0.045, (\(x\), \(y\)) = (0.4205, 0.5289). For the 300 K measurements, CIE coordinates are Sm\({}^{3+}\)/Tb\({}^{3+}\) = 0.025/0.025, (\(x\), \(y\)) = (0.5518, 0.4279); Sm\({}^{3+}\)/Tb\({}^{3+}\) = 0.010/0.040, (\(x\), \(y\)) = (0.5166, 0.4560); and Sm\({}^{3+}\)/Tb\({}^{3+}\) = 0.005/0.045, (\(x\), \(y\)) = (0.4577, 0.5088). The adjustability of the chromaticity coordinates through the tuning of the molar ratio of Sm\({}^{3+}\)/Tb\({}^{3+}\) ions under UV radiation, thus showing a great potential for their use in display and white-LEDs applications. Figure 4: Spectra of 12 and 300 K PL with a molar ratio of (a) 0.05:0, (b) 0.025:0.025, (c) 0.01:0.04, (d) 0.005:0.045, and (e) 0.0:0.05.
# Antioxidant Production in _Dunaliella_
## 1 Introduction  
Microalgae are singled celled micro-bio-factories capable of producing a wide variety of high-value compounds (carbohydrates, proteins, lipids, carotenoids, phycobiliproteins, phenolic, polyunsaturated fatty acids and antioxidants) used in the pharmaceutical, nutraceuticalul, cosmetic, and food processing industries [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. Microalgal biomass can be added directly as a nutrient enhancer in animal feeds; as an enhancer for improving the quality of food; and as a stabiliser for maintaining the colour and flavour of food products [3, 11, 12, 13, 14]. The global microalgae market has gradually expanded, and worldwide sales are expected to exceed US $3.2 billion by the end of 2030 [15]. Recent studies have demonstrated the potential of microalgae to produce antioxidant molecules [16, 17, 18, 19]. The antioxidant capacity and antioxidant activity of small molecules (\(\beta\)-carotene, astaxanthin, and phenolic compounds) have also been explored extensively [10, 20, 21]. Nevertheless, antioxidant enzymes have received little attention.  
The green halotolerant microalgae _Dunaliella_ can grow in a wide range of salt concentrations, from 0.05 M to 5.5 M NaCl [22, 23]. Currently, 28 species of _Dunaliella_ have been identified, of which 23 species live in saline environments, and 5 are rare species that inhabit freshwater [22]. Commercially, _Dunaliella_ is cultivated in several countries, such as Australia, China, Israel, and India, with pilot-scale projects in Chile, Spain, Iran and Portugal [24, 25, 26] and considered one of the best sources of \(\beta\) caroten. _Dunaliella_ has also been suggested as a sustainable source for industrial bioprocessing for the production of protein [27], biodiesel [4, 28, 29], colouring agent [30], and antioxidants [31].  
Currently, antioxidant enzymes are sourced from microbial and animal sources [32, 33]. Though there is no commercial production of antioxidant enzymes from _Dunaliella_, several reports have shown that _Dunaliella_ can increase its levels of enzymatic (mainly catalase, peroxidase and superoxide dismutase) and non-enzymatic (carotenoids, phenolic, ascorbate and glutathione) antioxidants to mitigate stress induced by exogenous abiotic factors [31; 34; 35; 36; 37; 38; 39]. Its high adaptability to exogenous stressors (oxidative and osmosis) and the lack of a rigid cell wall [40] could make _Dunaliella_ an ideal natural source for antioxidant production. _Dunaliella_ can efficiently and sustainably produce large volumes of biomass without competing for cultivable land and fresh water [25].
# Detection and Evaluation of Machine Learning Bias
## 2 Bias and Unfairness  
In the machine learning literature, researchers use the terms bias and unfairness interchangeably [3; 16; 17; 20; 21; 22; 23]. We believe this is a fundamental mistake that may lead to more human intervention in machine decisions. For example, assume that in an automated hiring system, males are preferred over females. The system learns the criteria of hiring automatically from the historical training data we provided. Therefore, the algorithm is not intrinsically unfair, yet the data has a historical bias due to the fact that in this position, males are dominant [24; 25]. If we want to eliminate or mitigate model bias, we need to tune the model's parameters to obey our desires of expected outcomes [26]. Tuning the parameters is equivalent in this case to changing the hiring criteria to accredit one gender over another, which is not fair [27; 28].  
From the aforementioned example, a question arises: Who has the right to distort the model in real-world application that will impact human lives? How can we rationally justify the model's outcomes, if we intentionally tweaked it? Although we might mitigate bias, this may lead to model unfairness.  
Humans tend to deviate from rational decisions or judgments to irrational ones. This is a known fact called cognitive bias [6; 7]. Individuals make their own irrationality in decision-making due to preconceived beliefs about the topic, usually created from a cumulative subjective reality, not from the input [29; 30]. Cognitive bias is not always bad. It was found that it may expedite the decision-making process [31]. Regardless of the benefit of human cognitive bias, machine learning bias is not as desirable and thus impacts the decision-making ability of the algorithm [32]. This is due to the machines' nature. First, machines do not need this kind of expedited decision-making process. They expedite it by either increasing processing power and/or decreasing algorithmic computational complexity. Second, machine learning bias cannot be easily distinguished from error unless the data says otherwise. It is worth mentioning here that human cognitive bias is inherited in the collected data utilized for training machine learning models. Hence, it will learn bias from training data. This is the entire concept surrounding machine learning models.  
In this paper, we will tackle the bias in machine learning models, not the unfairness. Therefore, we define machine learning bias as the difference in the underlying distribution of the model learning outcome with respect to certain group(s) influenced by their affiliation to the specific group. The group could be gender, race, age, or any other protected attribute.
## 3 Proposed Online Fault Diagnosis Model
### Fault Classification Using \(k\)-NN  
The proposed online fault diagnosis model classifies unknown samples using \(k\)-NN [11; 42]. We validate the model by estimating the generalized classification accuracy through \(k\)-fold cross-validation. The \(k\)-NN classifier classifies a sample based on the majority class among its \(k_{kmn}\) nearest neighbors, which are determined using distance criteria. Several distance criteria have been used in previous studies, such as the Euclidean distance, correlation between samples, city block distance, cosine distance, and Hamming distance. In this study, we use the Euclidean distance, which is the most widely used distance criterion and can be formulated as Equation (9).  
\[dist(x_{i},x_{j})=\sqrt{\sum_{d=1}^{D}\Bigl{(}x_{i,d}-x_{j,d}\Bigr{)}^{2}} \tag{9}\]  
where \(dist(x_{i}-x_{j})\) represents the Euclidean distance between two data points \(x_{i}\) and \(x_{j}\), and samples are represented by \(d\) feature dimensions.  
Figure 12: Steps of the multivariate probability density function’s cluster distribution factor (MPDFCDF) cluster evaluation method.
# SPR-Optical Fiber-Molecularly Imprinted Polymer Sensor for the Detection of Furfural in Wine +
## 2 Materials and Methods  
### Chemicals and Instrumentation  
Divynilbenzene (DVB, CAS N. 1321-74-0), methacrylic acid (MAA, CAS N. 79-41-4), 2-furaldehyde (2-FAL, CAS N. 98-01-1), and 2,2'-azobisisobutyronitrile (AIBN, CAS N. 78-67-1), were obtained from Sigma-Aldrich. MAA and DVB were purified with molecular sieves (Sigma-Aldrich cod. 208604, St. Louis, MO, USA) before use to remove stabilizers. All other chemicals were of analytical reagent grade. Pure water was obtained by a Milli-Q system (Merck Millipore, Billerica, MA, USA). Stock solutions of 2-FAL were prepared by weighing the liquid and dissolving it in pure water.  
The wine-mimicking solution had the following composition: fructose 25 g/L, glucose 25 g/L, tartaric acid 5 g/L, glycerol 1.25 g/L, ethanol 180 mL/L (18% \(v/v\)). (pH = 3.3, \(n\) = 1.3455 RIU).  
The white wine sample (WW) was a white wine in tetra pack purchased in a local supermarket.  
The measurement apparatus consisted of a halogen lamp (HL-2000-LL, Ocean Optics) and a spectrometer connected to a PC (USB2000+UV-VIS spectrometer, Ocean Optics). The white light source presented an emission range from 360 nm to 1700 nm, whereas the spectrometer had a detection range from 350 nm to 1023 nm. The transmission spectra and data values were displayed online on the computer screen and saved by Spectra Suite software (Ocean Optics, Dunedin, FL, USA) [36]. An outline of the experimental setup based on spectral interrogation is reported in Figure 1a.
## 3 Experiments and Discussion
### Experiments on Vertical Acceleration and EDR Estimation
#### 3.2.2 Analysis of EDR Estimation
Through the computation of incremental derivatives, the influence of elevator-induced maneuvering was eliminated. Therefore, this new method only improved the accuracy of acceleration response calculation, but also effectively separated the adverse effects of aircraft maneuvering flight on EDR estimation. Figure 14: Eddy dissipation rate (EDR) calculation under elevator deflection. Figure 13: (**a**) input and recovered EDR value (**b**) statistics of two algorithms.
# Micro-Satellite Systems Design, Integration, and Flight
## 1 Introduction
### Background on Satellite Development  
In response to the rampant development of electronic systems in the early 21 st century [2], a gateway to cheaper satellite development has emerged, allowing for competition in the commercial space. Development within the areas of microchips, semiconductors, and batteries has paved the way for cheaper, lighter, and more powerful cyberinfrastructures. Complex satellites are now being fabricated at the nano/picosatellite scale. Additionally, satellite designs are becoming more modular. Commercial off-the-shelf (COTS) components have replaced custom-developed ones, allowing for mature, modular technologies and faster turnaround times.  
Background of the Alpha Mission  
The manuscript will examine systems design and analysis of the Alpha mission developed at the Cornell Space Systems Studio. The mission aims to be a proof of the concept of interstellar travel, furthering the maturity of (1) a CubeSat satellite deployer, (2) an autonomous picosatellite [3], and (3) light sail technology [4]. These technologies would parallel techniques proposed by the Breakthrough Starshot initiative. The CubeSat approach was a vital medium for this mission. Due to its modular and versatile chassis, a CubeSat was used as the capsule of our payload. Using the standardized CubeSat structure will simplify replication and further development for other developers [5]. In addition, other CubeSat missions [6; 7] could re-use the approaches outlined in this manuscript. Most importantly, the CubeSat was used to mimic the approach of Breakthrough Starshot [8]. Breakthrough Starshot proposes the first solution for space travel into another solar system [9]. The idea theorizes accelerating a diminutive payload (with a mass of about a gram) to a fifth of the speed of light propelled by high-power lasers reflecting off a light sail [10]. According to the theory, the tiny payload would be accelerated within seconds. With the payload pointed at Alpha Centauri (the closest star system outside of our solar system) at 4.37, it could be possible that this distance could be reached within 25 years. To minimize the risk of failure, thousands of payloads would be deployed with the hope of a few being successfully received by the Alpha Centauri solar system. The purpose of the Alpha mission is to successfully deploy and stabilize a single payload light sail system [11]. For this to happen, the carrier CubeSat detumbles and stabilizes itself, successfully ejects the light sail payload, and the payload must power up and communicate with the ground station [12; 13].
# GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station
## 2 Materials and Methods
### Time-Series Analysis  
PPP processing provides positions estimates (X, Y, Z) in an Earth-fixed, Earth-centred terrestrial reference frame in which the precise orbit are provided, i.e. ITRF2008 and/or ITRF2014. The Cartesian coordinates are then transformed into topocentric (or levelled) coordinates for a more intuitive and meaningful interpretation. Therefore, the coordinate time series are provided in terms of local displacements in the North, East and Up (N, E, U) directions with respect to the position at an initial epoch. We define the position of the topocentric origin in the geometrical centre of the time series.  
Typically, time series analysis includes outlier detection, offsets and displacements identification, noise characterisation, trend and seasonal estimation, and residual analysis. The most popular analysis tools are GGMatlab (TSView) [64], FODITS [65], CATS [66], and Hector [67]. TSView is written in Matlab and complements the GAMIT/GLOBK package, FODITS is embedded into the Bernese GNSS software [68], whereas CATS and Hector are C/C++ written independent command line routines. Hector ([[http://segal.ubi.pt/hector/](http://segal.ubi.pt/hector/)]([http://segal.ubi.pt/hector/](http://segal.ubi.pt/hector/))) is able to deal with missing data without the need for interpolation to fill in the gaps in the time series. Additionally, the parameter estimation is based on the Maximum Likelihood Estimation (MLE) method. Furthermore, Hector supports all the analyses mentioned earlier. Therefore, it was our choice for the coordinate time series analysis. Version 1.6 was used in this study.  
Figure 6 depicts the entire data collection, processing and analysis work flow diagram.  
Figure 6: Work flow diagram of the present study.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Year** & **Method Description** & **Datasets** & **Performance** \\ \hline \multirow{4}{*}{[PERSON] et al. [24]} & \multirow{4}{*}{2022} & Manual annotations & \multirow{4}{*}{Consider randomly selected} & IoU: 0.7651, 0.7241; dice \\  & & & backbone: VGG19, ResNet34, & full lat ceph radiographs & coefficient: 0.7794, 0.7487 \\ \cline{1-1}  & & InceptionV3, and ResNeXt50 & & \\ \hline \multirow{4}{*}{[PERSON] et al. [26]} & \multirow{4}{*}{2022} & Polygonal box annotation & \multirow{4}{*}{Consider Sella Turcica roi in} & \multirow{4}{*}{Sensitivity: 1.0; Precision: 1.0; and F-measure values: 1.0} \\  & & method performed by & & \\ \cline{1-1}  & & radiologists; U-Net & & CBCT dataset & \\ \cline{1-1}  & & architecture; Google & & \\ \cline{1-1}  & & InceptionV3 & & \\ \hline \multirow{4}{*}{[PERSON] et al. [32]} & \multirow{4}{*}{2023} & Labelme software used for & \multirow{4}{*}{Consider Sella Turcica roi in} & \multirow{4}{*}{Dice coefficients: 92.84\%} \\  & & annotation; U-Net & & X-ray dataset & \\ \cline{1-1}  & & architecture & & \\ \hline \multirow{4}{*}{SellaMorph-Net (proposed model)} & \multirow{4}{*}{-} & Full lat ceph radiographic & Data subgroups mini-batch \\  & & Annotation performed by & images are considered; & accuracy: 99.57\%, 99.51\%, \\ \cline{1-1}  & & radiologists, dental experts, & subdivide dataset into four & and 99.49\%; class-based IoU \\ \cline{1-1}  & & and researchers; a hybrid & groups based on the & score: 06.6539 + 0.03, \\ \cline{1-1}  & & \"AGM + RrCL + SE + & morphological characteristics & 0.6370 \(\pm\) 0.01, 0.6517 \(\pm\) 0.01, \\ \cline{1-1}  & & \"AGM + RrCL + SE + & of Sella; dataset group & and 0.5663 \(\pm\) 0.02; global \\ \cline{1-1}  & & \"developed from scratch & divided into subgroups & accuracy: 97.570\%; mean IoU: \\ \cline{1-1}  & & based on the structure & 0.7129; dice coefficient: \\ \cline{1-1}  & & alteration complexity & 0.7324 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Detailed overview of studies reporting Sella Turcica segmentation and classification.
## 2 Materials and Methods
### Gas-Chromography for Quantification of Beer Volatile Compounds and Olfactory of Hops
The compounds were identified based on their retention indices, odour perceptions, mass spectra of NIST 11 library, and authentic standards measured under the same conditions. The quantification of volatile compounds in beer samples was conducted using an internal standard (IS, ethyl 2-methylpentanoate 0.02 ug/mL). Seven esters (ethyl 2-methylpropanoate, 2-methylbutyl2-methylpropanoate, 3-methylbutyl2-methylpropanoate, ethyl 2-methylbutanoate, ethyl 3-methylbutanoate, ethyl 4-methylpentanoate, and ethyl cinnamate), two monoterpenes (mycrene and geranyl acetate), two monoterpenoid alcohols (linalool and geraniol), two sesquiterpenes (\(\beta\)-caryophyllene and \(\alpha\)-humulene), one sesquiterpenoid oxide (humulene oxide), one thiol/ sulphide (dimethyl trisulphide--DMTs), and two thioesters (S-methyl 5-methylpentanthioate and S-methyl hexanthioate) were selected as relevant flavouring beer compounds. Calibration curves at six concentration points were made on the beer matrix (Munich-style Helles). Odour-active volatile compounds of hops were identified by olfactory in the same gas chromatographic system. A trained GC-O analyst was asked to describe the perceived odours as well as their intensity. The method of odour intensity was used with a 4-point scale (not detected, weak, moderate, and strong). For each hop, the odour-active compounds were identified and grouped into classes according to their chemical functional group. The sums of odour intensities for each class of compounds were calculated.
# Remote Technologies as Common Practice in Industrial Maintenance: What Do Experts Say?
## 4 Results
### Category System
#### 4.1.5 Category 5--Remote Technologies  
This category contains all statements of the respondents regarding remote technologies and providers in their own companies.  
From the core statements of the category, the following can be summarised: To date, classic remote technologies have been used in companies for remote support and communication. The exchange of information on simple maintenance problems usually takes place via telephone, e-mail or video connection using TeamViewer or Skype. However, there are often limits to these types of customer support. If the complexity of the problem increases, maintenance is carried out on site by a maintenance employee. Remote maintenance, is currently only carried out sporadically for software problems. Technologies such as data glasses to use AR and VR functions have already been acquired by some companies, although these are still being tested (Interview A, F, G). The Microsoft HoloLens 2 as well as the HMT-1 from RealWear are used for testing scenarios, which were chosen due to the criteria VR/AR experience, weight and low acquisition costs (Interview A, E, G). The first findings from the companies' tests are the occurrence of motion sickness during use which can lead to headaches and nausea, among other things (Interview A). Additionally, the user's field of vision is limited and the labour law factors and influences are still unclear, as there are no extensive use cases from research in this area yet (Interview G).