esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental PIV Radial Splitting Study on Expansive Soil during the Drying Process
## 2 Materials and Methods
### Testing Apparatus  
The radial splitting test equipment, based on PIV technology, consists of a photo acquisition system and a loading system, as shown in Figure 2. PIV technology was used to obtain relatively continuous displacement pictures through the camera, and image processing software was used to process the two pairs of objects before and after moving, and finally to obtain a soil deformation vector map [27,28]. The photo acquisition system included a high-speed industrial charge-coupled device (CCD) camera, a light source, and the Davis 8.0 series software StrainMaster Function module. StrainMaster is a set of professional software for strain measurement and stress analysis, with user-friendly, flexible, and powerful features. The software provides a variety of algorithms, which can calculate the stress-strain field, the displacement field, the velocity field, and other results of the acquired image, and display the results in the format of a contour map, a cloud map and a vector map. In addition, the light source has a large impact on the photography of its PIV, so it is useful to use a wide-source LED lamp. The initial size of the specimens used in the test was \(d_{0}\) = 6.18 cm and \(h_{0}\) = 2 cm. The loading system used in the test was the CMT4000 electronic universal testing machine, produced by the Mester Company in the United States [37], which can set parameters, as needed, to automatically control constant speed loading and constant speed displacement.
## 2 Materials and Methods
### Stable Isotope Analyses  
We analyzed and presented data from two separate sets of field campaigns: one that was focused on retrieving periphyton biomass samples for a stable isotope analysis, and another that was focused on measuring in situ GPP rates in FAB and non-FAB communities (described below in Section 2.4). Periphyton sample collection for the stable isotope analysis occurred in October 2019 (from the FAB site) and June 2021 (from both the FAB and non-FAB sites). The samples were stored in plastic Falcon tubes and shipped immediately to a laboratory in Berlin, Germany, where they also underwent species identification. In October 2019, FABs were sampled from four different substrates that were found at the FAB site (two types of rocks, reed, submerged macrophytes), while the samples in June 2021 were all scraped from rocks (four replicates per site). The samples were dried at 60 \({}^{\circ}\)C and ground to a fine powder. The bulk elemental content and stable isotope analyses of carbon (C) and nitrogen (N) were conducted by elemental analysis-continuous flow isotope ratio mass spectrometry (e.g., [30]), using a Flash IRMS elemental analyzer, coupled via a Conflo IV interface to a Delta V Advantage mass spectrometer (Thermo Scientific, Bremen, Germany). The results are expressed in the \(\delta\) notation, using ratios of samples and isotope standards issued by the International Atomic Energy Agency (IAEA), on the Vienna Pee Dee Belemnite (V-PDB) for \(\delta^{13}\)C, and atmospheric nitrogen (air-N\({}_{2}\)) scale for \(\delta^{15}\)N. The analytical error (mean SD from repeat measurements of in-house standards) for each run was always smaller than 0.3 %o for \(\delta^{13}\)C and \(\delta^{15}\)N.
## 4 Results
### Binary Classifiers
It is necessary to point out the importance of this step to avoid processing unbalanced datasets that easily identify patterns to make wrong predictions in later steps. * Datasets are divided into training and testing. Validation of each binary classifier is executed in a training set through Nested and Non-Nested Cross-Validation [11]. Five-fold cross-validation for inner and outer loops is selected using 30 trials. * Validation methods are compared, and finally each binary classifier is applied to the testing set to make predictions. Different metrics are calculated to show the performance. The cross-validation score measures the initial performance of the model, such that the training set is split into a selected number of folds to validate the same model multiple times on different training and validation sets. While Nested-Cross Validation performs feature selection and hyperparameter tuning to select the best combination, Non-Nested Cross-Validation considers a set of parameters previously selected by the user. The results of the Nested and Non-Nested Cross-Validation process for each binary classifier are illustrated in Figure 7, in which different scores were obtained in 30 trials. It can be appreciated that in this problem, the results of the Non-Nested cross-validation are more optimistic; however, relying solely on the information process and initial dataset without implementing Feature Engineering might result in a biased classification model. According to the cross-validation score in the training dataset, binary classifiers show a significant level of performance, which should be in the same range as the test set when evaluating final performance metrics. In a classification problem, the accuracy of the model refers to the total observations, considering both positive and negative, that were correctly predicted. Sensitivity, also known as True-Positive Rate, refers to all positive observations that are correctly classified as positive. Specificity is defined as the True-Negative Rate, which measures the total observations accurately predicted as negative [12]. Both approaches previously described in the computational algorithm were implemented using Naive Bayes, Decision Tree, and Logistic Regression. Regarding the performance metrics to evaluate that of each classifier, precision, sensitivity, and specificity are calculated and plotted in Figure 8, demonstrating a high score that is in a similar range compared to the cross-validation process. The final performance for each binary classifier shows that accuracy is around 95%, and sensitivity corresponds to a value close to 96%. On the other hand, the specificity has a slightly smaller difference, of 94%, compared to previous metrics for Naive Bayes and Decision Tree. A detailed analysis of the above results will be provided at the end of this section.
# Isolation of Circulating Tumor Cells from Glioblastoma Patients by Direct Immunomagnetic Targeting
## 3 Results
### GBM-CTC Identification Markers  
It is important to emphasize that CTC isolation methods usually deliver enrichment of CTCs rather than complete isolation, therefore further CTC identification to distinguish them from residual lymphocytes is generally required. Therefore, we also screened for alternate markers for the identification of GBM-CTCs in addition to GFAP, which is known to be heterogeneously expressed in GBM [12]. Immunocytostaining was used on our cohort of brain cancer cell lines for a range of proteins expressed in GBM (Table S2). The GLAST was found to be expressed in all GBM cell lines (-98-100% of the cell populations). Moreover, only negligible cross-reactivity was found between the \(\alpha\)GLAST antibody and healthy donor PBMCs (Figure 2), suggesting GLAST may be a suitable identification marker for brain cancer CTCs. Other potential markers had either poor expression in GBM cell lines or strong cross-reactivity of the corresponding antibodies with PBMCs (Table S2, Figure S1). Notably, GFAP was in our hands not detected in any GBM cell line (data not shown).  
Figure 1: Melanoma cell adhesion molecule (MCAM) and MCSP on glioblastoma (GBM) cells.  
Representative images of the indicated cell lines and healthy donor PBMCs stained using FITC conjugated \(\alpha\)GLAST antibody (green, left panel), Hoechst (blue, center panel) with merged images shown (right panel) are shown. Note, the nuclear localization of GLAST in most GBM cell lines, only LN-229 cells show ubiquitous GLAST localization.
###### Abstract  
The mirror unit installation error of the solar parabolic dish concentrator can adversely affect its optical performance causing optical intercept losses and hot spots on the absorber surface, which in turn affect safety. Thus, minimizing mirror installation error is considered very important. In this paper, a new method for the facet installation measurement and facet alignment of the mirror unit in the dish concentrator is presented. Firstly, a \"clean\" facet installation error measurement method using photogrammetry is presented. The photogrammetry measures the spatial coordinates of three feature points to reverse the mirror facet alignment error parameters. Next, two novel methods, a three-rotation alignment method and two-rotation alignment method for aligning the mirror facet are presented and corresponding mathematical models. The advantage of these alignment methods is that the adjustment value and order for each support bolt can be determined before the mirror facet is aligned, which could provide quantitative adjustment information to operator and avoid repeated adjustments. Finally, validity of the installation measurement and facet alignment method was verified by a numerical simulation and an experiment using a metal facet alignment. The presented methods do not rely on the geometry of the reflector mirror and could therefore have extensive uses in applications such solar tower and trough concentrator.  
solar concentrator; parabolic dish concentrator; facet installation measurement; mirror facet alignment; photogrammetry method +
Footnote †: journal: Applied Physics Letters
# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 2 Research Methods
### Svr  
An SVM uses few-shot learning to support structural risk minimization and solve classification and regression problems [34]. An SVM can be divided into classification and regression operations, where SVR is widely used to handle few-shot and nonlinear regression problems. Within a provided training set, \(S=\left\{\left.(x_{i},y_{i})\right|_{i=1}^{N}\right\}\), \(x_{i}\in\mathbb{R}\) denotes the input vector, and \(y_{i}\in\mathbb{R}\) is the corresponding output. The SVR builds an optimally separated hyper-plane in a high-dimensional (sometimes infinite) feature space, maximizing the margin among the nearest training data points. Symbol \(\varphi(x_{i})\) represents the eigenvector of \(x_{i}\) within the decision function, and empirical risk can be calculated using the \(\varepsilon\)-insensitive loss function. A regularization term is used to enhance the optimization of structural risk and achieve comprehensive regression:  
\[\mathcal{L}_{\varepsilon}=\max(0,\left|y_{i}-\mathbf{w}^{T}\cdot\varphi(x_{i})-b \right|-\varepsilon). \tag{8}\]  
When sample data are found in the \(\varepsilon\)-insensitive interval band, its loss is zero. Variables \(\xi_{i}\) and \(\xi_{i}^{*}\) are considered slack variables in this context. Specifically, \(\xi_{i}\) is used to represent the training error exceeding a certain threshold of \(+\varepsilon\), whereas \(\xi_{i}^{*}\) is used to represent a training error below a threshold of \(-\varepsilon\). Finding hyper-planes \(\mathbf{w}\) and \(b\) are convex quadratic programming problems.  
\[\min_{\omega,b\
ot\in\mathcal{L}^{*}}\frac{1}{2}\|\mathbf{w}\|^{2}+C\sum_{i=1}^{m }\left(\xi_{i}+\xi_{i}^{*}\right), \tag{9}\]\[s.t.\left\{\begin{array}{l}\mathbf{w}^{T}\cdot\varphi(x_{i})+b-y_{i}\leq\varepsilon+ \xi_{i}\\ y_{i}-\mathbf{w}^{T}\cdot\varphi(x_{i})-b\leq\varepsilon+\xi_{i}^{*}\\ \xi_{i},\xi_{i}^{*}\geq 0,i=1,2,\ldots,m\end{array}\right., \tag{10}\]  
where \(\varepsilon\) denotes the maximum deviation, and \(C\) is the trade-off factor of model complexity and error. By incorporating Lagrange multipliers \(\alpha_{i}\) and \(\alpha_{i}^{*}\), the optimization problem of Equations (9) and (10) is transformed into a dual optimization problem, and an optimal hyper-plane regression decision function is obtained using the KKT condition.  
\[\left\{\begin{array}{l}f(x)=\sum_{i=1}^{m}\left(\alpha_{i}^{*}-\alpha_{i} \right)\cdot\kappa(x_{i},x)+b^{*}\\ \kappa(x_{i},x_{j})=\exp\left(-\frac{\left\|x_{i}-x_{j}\right\|^{2}}{2\sigma} \right)=\exp(-g\big{\|}x_{i}-x_{j}\big{\|}^{2})\end{array}\right., \tag{11}\]  
where \(\kappa(\cdot,\cdot)\) is the Gaussian kernel function, and \(g\) stands for the kernel coefficient.
## 5 Automatic Monitoring and Control Method for Middle Rock Pillars  
The field monitoring method used in the past relied mainly upon manual monitoring activities, which involved coordinating the placement of measuring points and lines and conducting measurements at fixed intervals. However, this traditional method posed challenges in monitoring deformations in the middle rock pillar due to human error and the scattering of data. As a result, it was difficult to obtain an accurate and real-time reflection of the overall deformation of the structure. To address this issue, an automatic monitoring and control method was proposed to comprehensively and dimensionally reflect the application of real-time monitoring in the construction process of small clear distance tunnels. This method can identify engineering anomalies, carry out dynamic visual monitoring of the entire risk process, and achieve the purpose of early warning.  
The proposed monitoring method uses data acquisition hardware that includes microwave radar, spherical cameras, automatic laser rangefinders, computers, and processors. The automatic monitoring system, which is formed by arranging the microwave radar, spherical camera, and automatic laser rangefinder, has advantages such as high penetration, long detection distance, high precision, strong resolution, and stable operation. The system can remotely monitor and control the deformation of the rock mass in a small clear distance tunnel in real time. After analyzing and processing the monitoring data, the deformation of the rock mass was obtained, and the existing stability criterion of the rock mass was used to alert the deformation of the rock mass and control it in real time. The overall layout of the automatic monitoring scheme is shown in Figure 8. The proposed method can automatically monitor the position variation in measuring points, calculate the displacement of the surrounding rock, and judge the stable status of the middle rock pillar. The stable state of the middle rock pillar was fed back in real time at the construction site through the alarm indicator light.  
Figure 7: Situation of the working face surrounding rock: (**a**) large mileage direction; (**b**) small mileage direction.
# An Innovative Parabolic Trough Collector Design with a Twin-Cavity Receiver
## 2 Materials and Methods  
### Examined Configurations  
Two PTCs were studied in the present work, one with a twin cavity receiver (PTC-TC) and one with a flat plate absorber (PTC-F). Both receivers were enclosed inside evacuated tubes for ensuring the minimization of thermal losses. The design of the collectors is given in Figure 1. This Figure depicts the geometry of the two examined PTCs. It is important to mention that there is a specific position between the receiver and the reflector in each case. In particular, the receiver with the twin cavities is in a position where the reflector's focal distance is on the centerline axis of it, and all the reflected solar rays end up at the interior of the two cavities. The specific geometry was revealed through an optimization that took place via a ray tracing process in order to ensure the optimum useful power. This optimization is described in the next stages of the manuscript. The positioning in the PTC-F is simpler than in the PTC-TC, since the focal point here is located in the middle of the receiver between the twin tubes. All the models' dimensions are given in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimension** & **PTC-TC** & **PTC-F** \\ \hline Glass tube outer diameter & 56 mm & 56 mm \\ Glass tube thickness & 2 mm & 2 mm \\ Receiver outer diameter/width & 20 mm & 40 mm \\ Receiver thickness & 0.6 mm & 0.2 mm \\ Flow pipe outer diameter & 8 mm & 8 mm \\ Flow pipe thickness & 0.6 mm & 0.6 mm \\ Focal distance & 80 mm & 80 mm \\ Aperture width & 200 mm & 200 mm \\ Aperture length & 1000 mm & 1000 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: PTC basic dimensions for both designs.
## 4 Discussion
### Plant and Animal DNA Analysis  
Usually, during the XVII century, the stiff materials used as reinforcement of the bodices were whalebone from the jaws of the whale, wood, or horn [63]. Therefore, when we have observed the reinforcement of the bodice by microscope (Figure S1), these bucks were particular because they appeared as several fibers bonded, presumably, with animal glue. The restorers supposed that this structure was formed by horsehair, and the molecular analysis confirm such hypothesis. Although, the horsehair was used for different types of textile materials (horsehair interlining; [64]), in literature we did not find any previous study, where this kind of reinforcement material was described, so the analysis of the DNA help us to unambiguously identify the origin of these accessories of the bodice.  
DNA of Apiaceae was identified from the small portion of plant material through PCR sequencing analysis. Unfortunately, the small sequence did not permit a specific recognition of the plant species. However, excluding _Glehnia litteralis_ which is a typical plant growing at temperate sandy coasts around the North Pacific Ocean [65], and _Angelica sinensis_ which is also characteristic of different world regions other than Europe [66]. One of the others species (_Cuminum cyniminum, Anethum gravelens_ and _Petroselinum crispum_) probably were the main ornamental plants on the head of the young girl. These species present nice inflorescence [67], which can create beautiful decorative effects. Moreover, the use of these kinds of plants in burial custom was evidenced in several archaeological studies [68; 69; 70].  
The Illumina analysis on plant DNA did not confirm the results of PCR and sequencing approach, but other interesting outcomes were produced. In fact, the detection of several sequences is due by the presence of traces of plants, probably pollen belonged mainly to the species _Hordeum vulgare_ (barley), _Triticum aestivum_ (common wheat), _Brassica juncea_ (canola), and _Nicotiana tabacum_ (tobacco) which were commonly cultivated also in central Europe. We think that the identification of these plants perhaps indicates somehow the period of the funeral which could be in the late spring or summer. The register of deceased persons has been lost, therefore our molecular investigation provided at least some hypothesis about the seasonal period of the burial ceremony.
# Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators
## 6 Simulation Experiments
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Simulation Time [h]**} & \multirow{3}{*}{
\begin{tabular}{c} **Prod.** \\ **Limit PL** \\ **[Pcs.]** \\ \end{tabular} } & \multicolumn{3}{c}{**Human Operated Line**} & \multicolumn{3}{c}{**Robotic Line**} \\ \cline{3-8}  & & **Average** & & & **Average** & & \\  & & **Production** & **Adv.** & **OFE** & **Production** & **Adv.** & **OFE** \\  & & _Pavg_ [Pcs.]** & & & & **Production** & **Adv.** & **OFE** \\ \hline
**24** & 1536 & 664.6 & 8.4 & 0.432682 & 867.1 & 1.05 & 0.56451 \\ \hline
48 & 3072 & 1656.2 & 10.1 & 0.539128 & 2165.1 & 1.29 & 0.70478 \\ \hline
120 & 7680 & 4624.1 & 14.9 & 0.602096 & 6062.7 & 2.23 & 0.78941 \\ \hline
480 & 30,720 & 18,517 & 18.3 & 0.602767 & 24,243 & 3.7 & 0.78916 \\ \hline
2880 & 184,320 & 110,739 & 89.0 & 0.600798 & 145,437 & 6.8 & 0.78904 \\ \hline
5760 & 368,640 & 222,146 & 115 & 0.60261 & 290,856 & 21.2 & 0.78899 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of simulation experiments—average production value _Pavg_ for 20 simulation runs with a confidence level at 90% (8-hour warm-up per week). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Simulation Time [h]**} & \multirow{3}{*}{
\begin{tabular}{c} **Production Limit PL** \\ **[Pcs.]** \\ \end{tabular} } & \multicolumn{3}{c}{**Human Operated Line**} & \multicolumn{3}{c}{**Robotic Line**} \\ \cline{3-8}  & & **Average** & & & **Average** & & \\  & & **Production** & **Adv.** & **OFE** & **Production** & **Adv.** & **OFE** \\ \hline
24 & 1536 & 660.3 & 4.9 & 0.429883 & 855 & 34 & 0.556641 \\ \hline
48 & 3072 & 1647.8 & 10.4 & 0.536393 & 2145 & 56 & 0.698242 \\ \hline
120 & 7680 & 4605.3 & 14.6 & 0.599648 & 5974 & 211 & 0.777865 \\ \hline
480 & 30,720 & 18,453 & 36 & 0.600684 & 24,045 & 311 & 0.782715 \\ \hline
2880 & 184,320 & 110,537 & 84 & 0.599702 & 144,116 & 748 & 0.781879 \\ \hline
5760 & 368,640 & 221,156 & 134 & 0.599924 & 287,852 & 1001 & 0.780849 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of simulation experiments with human and robot failures—average production value _Pavg_ for 20 simulation runs with a confidence level at 90%. There are some outliers (the most extreme observations) represented by the minimum values of production that are related with a few random robot failures. On the other hand, the human errors occur very often and almost regularly, and therefore normal distribution can be observed in this case.
# Seasonal Monitoring of Volatiles and Antioxidant Activity of Brown Alga _Cladosteplus spongiosus_
## 3 Materials and Methods  
### Macraolga Samples  
Between May and August 2021, the samples of the brown alga _C. spongiosus_ (Hudson) [PERSON], 1817 were collected. The sampling took place in the Adriatic Sea, off the coast of the island of Civovo (43.493373\({}^{\circ}\) N, 16.272519\({}^{\circ}\) E). Each sample was collected at a depth of 20 to 120 cm from the same lagoon. The sea temperature was measured during each sampling with a YSI Pro2030 probe (Yellow Springs, OH, USA) and increased from 20.1 \({}^{\circ}\)C in May to 28.1 \({}^{\circ}\)C in August. Alga species was determined according to its morphological attributes by marine botanist. For the determination of VOCs, the samples were dried in the shade at room temperature for 10 days, while samples for the determination of non-volatile compounds were freeze-dried (FreeZone 2.5, Laboconco, Kansas City, MO, USA) prior the extraction. Algal samples were extracted in 50% ethanol using MAE in the advanced microwave extraction system (ETHOS X, Milestone Srl, Sorisole, Italy) and UAE based on the prior research [9]. The samples were pulverised and mixed with 50% ethanol at a 1:10 (\(w/v\)) algae to solvent ratio and extracted for: (1) 15 min at 200 W and 60 \({}^{\circ}\)C for MAE, and (2) 60 min at 40 kHz frequency and 60 \({}^{\circ}\)C in an ultrasonic bath for UAE.