esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Materials  
All the chemicals and solvents were purchased with high purity and were used as is unless otherwise specified. Yttrium-89 foil (0.127 and 0.25-mm thick, 99.9%) and yttrium-89 powder 40-mesh (99.6%) with different metal composition (Table 1) were bought from Alfa Aesar (Ward Hill, MA, USA/VWR International, CA).  
Hydrochloric acid (99.999%), oxalic acid (99.999%), Na\({}_{2}\)CO\({}_{3}\) (99.999%) trace metal basis, sodium hydroxide pellets (\(\geq\)98.0%), diethylenetriaminepentaacetic acid (DTPA, \(\geq\)99%), and deferoxamine (DFO) mesylate salt (\(\geq\)92.5%) were purchased from Sigma-Aldrich (Saint-Louis, MO, USA). High-purity water (Optima LC/MS, ultra-high performance liquid chromatography ultraviolet grade, 0.03-mm filtered) and acetonitrile (HPLC grade, 99.9%) were purchased from Fisher Scientific  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Metal (ppm) 1** & **Fe** & **Al** & **Mn** & **Ca** & **Cu** & **Gd** & **Mg** & **Nd** & **Zr** & **Mo** \\ \hline \({}^{89}\)Y-foil 2 & \(<\)110 & \(<\)100 & \(<\)100 & \(<\)100 & \(<\)100 & \(<\)100 & \(<\)100 & \(<\)100 & np & np \\ \({}^{89}\)Y-powder 3 & \(\sim\)2000 & 45 & 46 & \(\sim\)2000 & 55 & 100 & 540 & 200 & 2.8 & 4.7 \\ \hline \end{tabular} 1 Not all elements are mentioned in this table. Fe: Iron, Al: Aluminum, Mn: Manganese, Ca: Calcium, Cu: Copper, Gd: Gadolinium, Mg: Magnesium, Nd: Neodymium, Zr: Zirconium, and Mo: Molybdenum. 2 Y-Foil (99.9%).  
3 Y-Powder (Alfa Aesar, 99.6%). Not present (np).  
\end{table}
Table 1: Trace metal analysis for \({}^{89}\)Y-foil and powder (Certificate of analysis).  
(Ottawa, ON, CA). All the glass vials were cleaned while using chromic sulfuric acid (Fisher Scientific, Ottawa, ON, CA). Instant thin layer chromatography-silica gel (ITLC-SG) was acquired from Agilent Technology (Santa Clara, CA, USA). The labeling efficiency of \({}^{89}\)Zr-DFO was determined while using radio-ITLC-SG with 100 mM DTPA as the mobile phase. The radio-ITLC plates were scanned using an instant imager scanner (Bioscan, DC, USA). Radioactivity measurements were performed in an ionization chamber (CRC-25 PET; Capintec) on the \({}^{89}\)Zr setting to control process efficiency and by \(\gamma\)-ray spectrometry with a calibrated high-purity germanium detector (GMX HPGe; ORTEC, Oak Ridge, TN, USA) for analytic quantitation. Experimental samples were counted for 10 min by using dynamic energy windows of 1-2000 keV. All of the radioactive detection devices were calibrated and maintained in accordance with our routine quality control procedures.
## 5 Discussions  
### Major Findings  
We employed three key methods to estimate crop yields based on satellite data. First, there are clear benefits to retrieving subpixel areas of different crop types and other vegetation based on land-cover maps at a fine resolution. Although some studies concluded that coarse spatial resolution satellite data (0.05 degree or approximately 5.5 km) were advantageous over higher spatial resolution data for estimating the yield because individual pixels will likely shift between crop types on a year-to-year basis [15], we observed that a combination of fine and moderate resolution data was preferable for accurately estimating yield because retrieving information related to crop types is essential to yield modeling.  
Second, our analysis of corn and soybeans reveals that applying crop-type specific RUE values in the PEMs is necessary to estimate crop yields properly. Global-scale PEMs mainly focus on quantifying vegetation productivity for a general biome type [38]. For example, the default RUE value in MOD17 is the same for all crops and grasses [36,48]. However, it would be inappropriate to apply flat RUE values to estimate the yields for different crops, especially for productive C4 plants such as corn.  
Finally, it is necessary to deduct the GPP contributed by vegetation other than crops when estimating the yield at the spatial resolution of MODIS. As illustrated, crop yields are likely to be overestimated without such considerations when applying RUE values measured in field studies. Our findings are consistent with other studies, which observed that the direct use of field-measured RUE values in global-scale PEMs would result in an overestimated GPP [35].
## 3 Results
### E. coli Flushing Test  
#### 3.2.1 Breakthrough Analysis in Small Column Experiments  
Figure 5 presents the BTCs obtained from the small column flushing experiments. All BTCs showed a clear pattern, consisting of a rising limb, a plateau phase, and a declining limb. The supplementation of raw hydrochar in the sand media resulted in early _E. coli_ breakthrough. In contrast to the rising limb of the sand-only column observed after 15 min, the one from the raw hydrochar-amended column was observed already after 10 min. This was similar in the declining limbs: the one in the sand-only column started 5 min later than the column supplemented with the raw hydrochar. It was apparent that the decrease in the pore space of the sand media induced by the filling of pores by raw hydrochar amendment facilitated the _E. coli_ transportation. While the effect of raw hydrochar addition in the sand media for the _E. coli_ removal was insignificant, the amendments with the activated hydrochar showed an important increase in the removal efficiency. The C/C\({}_{0}\) ratios in the plateau phase of both the sand-only and raw hydrochar-amended columns were similar at \(\sim\)0.9. However, the C/C\({}_{0}\) ratio of the sand column with the activated hydrochar supplement was only around 0.1. The average _E. coli_ removal efficiencies of the sand and the raw and activated hydrochar-supplemented columns were 9.2%, 9.6%, and 90.1%, respectively. The measurements on the vertical hydrochar distribution in the column showed no significant migration of hydrochar particles during the flushing.  
Figure 4: SEM images of raw (**left**) and activated (**right**) hydrochar.
## 4 Results and Discussion
### Energy Absorption Capability of Uniform TPMS Structures  
The stress-strain curves and energy absorption curves for axial compression are shown in Figure 14. As the displacement increases, the structure is compressed from a pre-damage phase to a progressive damage phase, and finally to a densification phase. The stress and energy absorption values increase with increasing compression displacement. Figure 14b. It was found that the energy absorption of Sk-G is lower than that of Sk-D by 33.52% under the same volume fraction, while that of Sh-D and Sh-G is higher than that of Sk-D by 53.85% and 38.11%, respectively. It is worth noting that the stress-strain curves of the sheet TPMS structures are higher than those of the skeletal TPMS structures, and the sheet TPMS structures exhibit more stable deformation patterns and energy-absorbing effects.
# A Lesson for the Future: Will You Let Me Violate Your Privacy to Save Your Life?
## 2 Related Work
### Contact Tracing Protocols and Applications  
In the last few years, several contact tracing applications have been developed to stop the spread of the coronavirus [4, 9, 10, 11]. From the software architectural view, CTAs employ either \"centralized\" or \"decentralized\" architecture solutions. While centralized apps store anonymized data on a central server, decentralized apps keep the data on the users' devices, providing more control over data. However, there is also a third model, the hybrid model, that benefits from both centralized and decentralized worlds [12, 13].  
In particular, DP-3T [13] is a decentralized privacy-preserving proximity tracing protocol designed by researchers from European institutes and universities. The protocol leverages Bluetooth technology and symmetric encryption techniques, such as SHA-256 and AES-128. The contact history logs are stored on the users' devices and will not be transmitted to a central authority unless a user tests positive.  
TraceTogether was one of the first CTAs that was developed and released by Singapore's government [12]. TraceTogether employs a privacy-preserving protocol called BlueTrace. BlueTrace adopts Bluetooth technology, in which devices exchange digital beacons (i.e., digital IDs) and store all encounters in their history logs. Although BlueTrace employs decentralized architecture, identifying and notifying the exposed individuals are performedin a centralized center. Hence, BlueTrace is considered a hybrid protocol. Moreover, it leverages a symmetric key encryption scheme (AES-256-GCM) for efficiency considerations.  
Google and Apple [14] also made a joint effort to help health and government agencies to reduce the spread of the COVID-19 virus. Both companies agreed on a decentralized approach using Bluetooth technology. In their approach, users' ephemeral IDs are stored on the users' devices and are generated using AES-128 and HMAC-SHA-256. However, their introduced protocol is an Application Programming Interface (API) that can be leveraged by public health authorities to develop their own mobile applications. SwissCOVID app, Protect Scotland app, and Immune are just a few CTA examples that are developed using Apple and Google API in Swiss, Scotland, and Italy, respectively.  
Alipay Health Code is the CTA app that was developed in China. It leverages a centralized architecture and Global Positioning System (GPS) technology. The GPS-based system collects and stores individuals' geographic locations on a 24/7 basis. South Korea also employed the centralized architecture to develop their contact tracing app, Corona 100 m (Co100). Besides GPS technology, the app uses government-collected data, such as surveillance camera footage and credit card transactions. The app alerts users when they are in a close vicinity, 100 m, of a location visited by an infected person.  
Thus far, 120 contact tracing applications have been developed worldwide [15]. Table 1 shows a short list of CTAs, including the employed technology and software architectures.
## 4 Interval Analysis Method for Consideration of Uncertainties
#### Possibility of Damage Existence (PoDE) and Damage Measure Index (DMI)  
The PoDE is calculated by comparing the ESPs vectors in both healthy and damaged modes. As shown in Table 1, the vectors are the lower and upper boundaries of ESPs, which are also the output of WWLS-SVM models (refer to Table 1), respectively. The terms are as follows:  
\[\alpha_{u}^{I}=\left\{\alpha_{u1}^{I},\alpha_{u2}^{I},\ldots,\alpha_{uk}^{I} \right\}^{T}, \tag{15}\]  
\[\alpha_{d}^{I}=\left\{\alpha_{d1}^{I},\alpha_{d2}^{I},\ldots,\alpha_{dk}^{I} \right\}^{T}, \tag{16}\]  
where \(\alpha_{u}^{I}\) shows the interval bound for the undamaged ESP (\(\left[\overline{\alpha_{uk}},\underline{\alpha_{uk}}\right]\)) and \(\alpha_{d}^{I}\) shows the interval bound for the damaged (\(\left[\overline{\alpha_{dk}},\underline{\alpha_{dk}}\right]\))  
Figure 2 illustrates the intersection of the intervals of the damaged and undamaged ESPs on the same axis, where the shaded region indicates the PoDE. The middle value (\(x^{c}\)) disparity between the two states will increase as the damage increases. The PoDE ranges from 0 to 100%, with 100% indicating a relatively high possibility of damage to that particular element and 0% indicating no damage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Surrogate Model** & **Training Input** & **Testing Input** & **Output** \\ \hline Lower bound & WWLS-SVM 1 & \(\dfrac{E_{p}^{lr}=E_{p}^{lr}\) of Acc. response with negative \(\omega\) & \(\underline{\alpha_{ck}}\) \\ Upper bound & WWLS-SVM 2 & \(\dfrac{E_{p}^{lr}=E_{p}^{lr}\) of Acc. response with positive \(\omega\) & \(\overline{\alpha_{ck}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input and output variables for training and testing of WWLS-SVM.  
The areas of damage and undamaged ESP on two separate axes are shown in Figure 3. The possible damage area is shown by a single solid rectangle where the failure plane for both states is equal. The shaded region reflects the ESP damage. Compared to the undamaged ESP, since the ESP damage is greater, the PoDE is defined as the ratio of the area of damage region to the total area of the entire region. The quantitative measurement of the PoDE can therefore be described as below [9]:  
\[PoDE=\frac{A_{damage}}{A_{total}}\times 100\%, \tag{17}\]  
The interval value of damaged and undamaged ESP will have significant variations in reality, as the PoDE would be 100%. Therefore, using PoDE alone will not provide an accurate or direct indication of the damage. Accordingly, the damage measure index (DMI) is calculated as below [9]:  
\[DMI=SRF\times PoDE. \tag{18}\]
## 7 Results
### Optimization Process with In-Laboratory Measurements
#### 7.1.6. Impedance Head  
A study to investigate the advantages provided by the interposition of a metallic interface on top of the impedance head was conducted and reported in Figure 15a.  
Without the interface (in red), the measurement is less stable and accurate because of the hole in the impedance head. Indeed, there are three bands in which the standard deviation is greater than 10%. In particular, the 1 kHz band shows very high variability, which can be attributed to the presence of resonance. As expected, the metal interface makes the DS spectrum 'cleaner' and the measurement more accurate, avoiding the problem of disturbance due to the presence of the hole.  
The influence of the impedance head fastening methods to the sample/pavement was also investigated, comparing screwed fastening and magnetic fastening. The results are reported in Figure 15b.  
Figure 14. Effect on DS measurements of the tip and mass extender. (**a**) Tip effect on the DS results. (**b**) Extender influence on DS results.  
Based on the results presented, it is possible to draw up the following list of practical indications for carrying out the measure:  
* Concrete or plaster fastening should be preferred based on the external condition (for in-laboratory measurements);
* Measurement should be performed between 24 and 48 h from the sample laying (for in-laboratory measurements);
* The screwed fastening of the impedance head must be utilized;
* An appropriate metallic interface must be installed on top of the impedance head;
* A rubber tip should be preferred;
* A target for the impressed force should be set to 100 N;
* The use of a mass extender is not recommended;
* The impact hammer must be properly aligned, thus, leveling the system before each measurement is mandatory.  
These guidelines were followed to obtain the results presented in the following subsections.
## 1 Introduction  
Dextran has been regarded as one of the very significant exo-polysaccharides (EPS) with several established applications in various industrial interests. EPS are important metabolites generally synthesized by some bacteria in bioprocesses. Although not all EPS are useful, beneficial polysaccharides are produced by certain bacteria characterized as probiotics in nature. Therefore, the selection of appropriate strain/s to perform a fermentation process is of utmost importance to synthesize extracellular polysaccharide products suitable for commercial application/s. EPS are widely studied, and their valuable effects correlate with the sustainability of gut health and wellness by alleviating certain animents [1]. Food products containing EPS produced by probiotic bacteria are reported as dietary therapeutic representatives to re-establish the weakened gut microbiota, which is necessary to ease inflammation in the gastrointestinal tract, IBD, or IBS, and can avert the initiation of colon cancer [2]. EPS produced in fermented milk beverages have been studied in detail for their properties of improving shelf-life and boosting nutrition with functional properties in dairy products [3; 4]. Furthermore, exopolysaccharides produced as the secondary metabolites of bacteria, employed in food fermentation, have many potential and well-established functions in products that are classified as nutraceuticals. These products have been studied for their therapeutic activity, improving gut health in certain consumers who experience allergy and discomfort responses to some foods or their ingredients and additives used in products [5]. Valuable polysaccharides are produced by probiotic cultures in the GI tract through the support of dietary fibers, ingested as diet ingredients that are prebiotic in nature, and available in functional beverages and foods [6; 7].  
Polymeric carbohydrate structures are high molecular weight polysaccharides that are formed of monosaccharide units linked by glycosidic bonds. Dextran is one of those polysaccharides of commercial importance. A bacterial strain of _Leuconostoc mesenteroides_ has been studied for its potential to produce its extracellular metabolite, a glucose polymer, which is biodegradable in nature. This EPS, characterized as dextran, has been reported as a value-added product useful in a range of industrial applications [8].  
The aim of this article is to review a few important applications of dextran, a significant material used in different sectors such as blood transfusion, clinical, pharmaceutical, and food. Hence, the requirement of dextran preparations of diverse molecular weights and structural configurations has become essential for their specialized utility in each industry. For that purpose, the studies conducted for the biosynthesis of a diverse range of dextran products, employing specific microbial species as efficient and productive biocatalysts, have been presented in this article.
# Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints
## 2 Modular Bus Scheduling Model
### Preliminaries
Another sequence \(o-k_{2}-k_{4}-k_{5}-f_{9}-k_{7}-s\) encompasses the process of trip execution and charging, where all the arcs involved satisfy the connectivity condition \(v_{j}-t_{b}\leq w_{i}+t_{ij}\leq v_{j}-t_{a}\). For example, considering the arc \((f_{9},k_{7})\), the modular unit arrives at node \(k_{7}\) at time \(11+1\), which is both earlier than the start time of node \(k_{7}(14-0.5)\) and not earlier than \(14-2\), as expressed by \(14-2\leq 11+1\leq 14-0.5\). The cost of a modular bus passing through arc \(c_{ij}\), is also discussed by case depending on the node type, as defined below, where \(c_{m}\) represents the dispatch cost of one modular unit, \(c_{p}\) is the charging cost, and the remaining parameters \(c_{t}\), \(c_{s}\), and \(c_{w}\) are the idling cost, operating cost, and waiting cost per unit of time, respectively. \[c_{ij}=\left\{ \begin{array}{ll}c_{m}+c_{t}\cdot t_{ij}+c_{s}\cdot(w_{j}-v_{ j}),&(i,j)\in A,i=o\\ c_{t}\cdot t_{ij}+c_{w}\cdot(v_{j}-w_{i}-t_{ij})+c_{s}\cdot(w_{j}-v_{j}),&(i,j) \in A,j\in K\\ c_{t}\cdot t_{ij}+c_{w}\cdot(v_{j}-w_{i}-t_{ij}),&(i,j)\in A,j\in D,\\ c_{t}\cdot t_{ij}+c_{w}\cdot(v_{j}-w_{i}-t_{ij})+c_{p},&(i,j)\in A,j\in F\\ c_{t}\cdot t_{ij},&(i,j)\in A,j=d\end{array} \right.\]
# Automatic Quantitative Coronary Analysis Based on Deep Learning
## 1 Introduction
However, the dataset used for segmentation was limited, resulting in many redundant branches, and the results merely referred to the classification of stenosis degrees instead of quantification. [PERSON] [30] used deep learning to quantify coronary artery disease according to computed tomography angiography images from 156 patients. Similar to the work in [28], [PERSON]'s method was not entirely automated, as the majority was realized by a semi-automated software named Autoplaque, where manual operations were required. In this study, we propose a fully automatic workflow for a QCA-based deep learning framework, named AI-QCA, with the purposes of achieving an accurate assessment of CAD in an intelligent manner, minimizing the time cost, and eliminating manual intervention. Our proposed QCA workflow mainly consists of three parts: First, a boundary-aware segmentation architecture is proposed to segment the main vessels and primary branch vessels. Then, the coronary artery tree is constructed on the basis of deep learning models, where the root node is auto-located via a proposed searching algorithm. Finally, by applying techniques such as Gaussian smoothing, branch separation, reference diameter fitting, and stenosis detection, the system can automatically generate a fitting line for the diameters and detect stenosis regions with quantitative parameters. The main advantages of our proposed AI-QCA can be summarized in three aspects as follows: (1) We realize a fully automatic QCA process, which can effectively avoid the interference of personal preference and experience. (2) AI-QCA is able to quantify stenosis parameters as accurately as the senior experts, benefiting from all of the boundary-aware segmentation based on AI, the proposed methods of coronary artery tree (CAT) construction, and the efficient utilization of fitting algorithms. (3) AI-QCA can reduce the time cost from tens of minutes to several seconds compared with manual QCA. All the above is significant for the intelligent diagnosis and treatment of coronary artery disease.
# Seismic Behavior of Steel Plate-Concrete Shear Walls with Holes
## 5 Conclusions  
Steel plate-concrete shear walls are studied widely. However, the influence of holes and axial loading on the behavior of steel plate-concrete shear walls are neglected in most studies. Thus, the seismic behavior of steel plate-concrete shear walls is completely different when the influences are considered. Thus, a series of low-cycle reciprocal loading tests were conducted on SPSW specimens with holes in this study. The failure modes and hysteretic curves of SPSW structures under different working conditions were also studied. On the basis of failure modes and hysteretic curves, the seismic behavior of SPSW structures was discussed. Influences of axial compression ratio, hole size, hole position, and the thickness of stiffening plate on the seismic behavior of SPSW structures were considered in the tests. The ultimate bearing capacity of SPSW structures with holes was estimated by AIJ codes and Ono reduction rate calculation methods. The major conclusions are indicated as follows:  
1. SPSW structures without holes mainly develop failures at the roots of shear walls, accompanied with the concrete and bending failures of steel plates. SPSW structures with small holes also develop failures at the roots, but several steel plates crack along the corners of the holes. The failure mode is brittle to a certain extent. SPSW structures with large holes develop significant deformation and better ductility than SPSW structures with small holes but low ultimate loads.
2. Concrete cracks early under high axial compression ratio. The ultimate strength of SPSW structures increases, but the deformation capacity and ductility decrease to a certain extent. Deformation capacity is enhanced, but the ultimate bearing capacity decreases as hole size increases. With an increase in the thickness of stiffening plates, the ultimate bearing capacity of SPSW specimens increases. Eccentric holes decrease the earthquake-resistant energy-dissipation capacity of SPSW structures, and such a reduction is disadvantageous to seismic design. Therefore, eccentric holes on SPSW structures should be avoided.
3. The calculated results of AIJ codes are safe, but those of the Ono formula have high accuracy because it considers the influences of loading directions. In summary, both calculation formulas are feasible to calculate the ultimate shear capacity of SPSW structures with holes.  
[PERSON] and [PERSON] carried out the experimental research and the academic paper writing. [PERSON] provided assistance during the experimental research.  
This research received no external funding.  
There is no conflict of interest.