esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Experiment Implementation  
The accelerometer has the advantages of high precision, high sensitivity, low power consumption, small size and good linearity. It is widely used in applications, such as dynamic testing, parameter identification, health monitoring, of practical structures [25; 26]. In the experiment, accelerometers (model 991C, developed by the Institute of Engineering Mechanics of Chinese Earthquake Administration) were mounted on the slabs of each story to collect the horizontal accelerations of each floor as the observations, while the exciter was anchored on counterforce wall exerting force on the slab of the 5 th story, as shown in Figure 13. As shown in Figure 14, the model 991C accelerometer applied in this experiment had two output interfaces, one for acceleration and the other for velocity, but only acceleration output was collected and analyzed here. It inherits the excellent characteristics of passive servo vibration sensors, possessing good impact resistance with no need for zero adjustment before testing, and is suitable for vibration measurement in many occasions. The main performance specification of the accelerometers is listed in Table 4. Acceleration data were collected by the Quantum X data acquisition system produced by HBM Co. Ltd. The sampling frequency of the signal was set to be 50 Hz for all cases. The total masses of the five stories including the sensor weight were: m\({}_{1}\) = 24.99 kg, m\({}_{2}\) = 24.94 kg, m\({}_{3}\) = 24.93 kg, m\({}_{4}\) = 24.75 kg, and m\({}_{5}\) = 24.80 kg.  
Figure 14: Model 991C accelerometer.  
Figure 13: The five-story steel frame.
# Study of a Miniaturizable System for Optical Sensing Application to Human Cells
## 3 Results
### Tumor Cell Fluorescent Time-Lapse Analysis
#### 3.2.2 LoVo Fluorescence Analysis  
We investigated the fluorescence of LoVo tumor cells labeled with Ru(bpy)\({}_{3}\)\({}^{2+}\) across time and cell generations. This experimental condition was typical of time-lapse fluorescence analysis for cell biology, which allows for the determination of the kinetics of fluorophores through tissue or cells and investigation of different features relative to the intracellular system, an important issue in tumoral cells analysis.  
Results of MTT and FM analysis on LoVo at 1, 2, and 5 days after Ru(bpy)\({}_{3}\)\({}^{2+}\) labeling are reported in Figure 9.  
MTT assay (Figure 9A) showed that the labeled cells vitality did not change with respect to the controls, not treated HDFs, for each time point of analysis.  
Figure 7: MTT assay plot of HDF cells vitality: Controls (0) and exposed samples to 0.14; 1; 1.4; 13.5 uM of Ru(bpy)\({}_{3}\)\({}^{2+}\). Significance of data is reported (*).  
Figure 8: Uptake of Ru(bpy)\({}_{3}\)\({}^{2+}\) in epithelial cells of colon adenocarcinoma, observed by fluorescence microscopy: Intrinsic green fluorescence of LoVo controls (**A**) and red fluorescence of samples treated with 0.4 uM of fluorophore (**B**), after 24 h labeling, with a high density in the cells central region. Fluorescence was acquired with a 488 nm light source and collected at 620–630 nm.  
Figure 9: **(A)** MTT assay and fluorescence analysis of LoVo cells at (**B**),1 day, (**C**) 2 days and (**D**) 5 days after 0.14 uM Ru(bpy)\({}_{3}\)\({}^{2+}\) labeling. New generations of cells appeared in green, proving the cell proliferation and detailing the kinetics of fluorophore segregation. Fluorescence was acquired with a 488 nm light source and collected at 620–630 nm.  
Fluorescence analysis revealed cell proliferation, as confirmed by the green population appearing in Figure 9C,D. Moreover, Ru(bpy)\({}_{3}\)\({}^{2+}\) segregation from cell to cell across time was, also, proven by the orange and light orange cells in the figures. Controls reported only the growth of cells keeping the same green intrinsic fluorescence (data not shown).  
The figure clearly shows that the dye was transferred to daughter cells maintaining its functionality. The results indicated that there was no decomposition of the dye over time or because of the cell proliferation. In fact, if the fluorescence is reported as a function of the number of cells, a linear decrease in the luminescence intensity by increasing the number of cells across generations is observed (see Figure 10).
# Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography
## 3 Methodology
### Reinsertion and Volumetric Interpolation of Motion-Corrected Patches into 3D CT Volume  
Lastly, we reinsert the 2D patches generated by the CycleGAN model into the CT volume to obtain the motion-corrected scan. We employ the methodology outlined in our earlier work [14] and, therefore, provide only a concise overview here.  
For a smooth and continuous appearance of the reinserted patches and the volume, we perform volumetric interpolation. Given our prior knowledge, of the center point \(\vec{q}_{j}\) and the normal \(\vec{n}_{j}\) of each patch through the patch extraction process described in Section 3.1, we reinsert the output patches \(\mathbf{P}_{j}\) using the inverse of the known projection transforms.  
For two adjacent patches \(\mathbf{P}_{j}\) and \(\mathbf{P}_{j+1}\), volumetric interpolation is on the bounding box enclosed by the reinserted patch coordinate grids \(\mathbf{Q}_{j}\) and \(\mathbf{Q}_{j+1}\). For each voxel with coordinate \(q\) within the bounding box, and in the volume between the two patches, we determine the vector \(v^{k*}_{j,j+1}\) among \(\{v^{k}_{j,j+1},1\leq k\leq R^{2}\}\) that is closest to the voxel coordinate, i.e., that has minimum point-to-line distance \(d_{p2l}(q,v^{k}_{j,j+1})=\frac{\left|(q-q^{k}_{j})\times(q-q^{k}_{j+1})\right| }{\left|q^{k}_{j+1}-q^{k}_{j}\right|}\). The final value for voxel \(q\) is the weighted average defined as \(I(q)=w^{q}_{j}\mathbf{P}_{j}(\vec{q}^{\prime}_{j})+w^{q}_{j+1}\mathbf{P}_{j+1 }(\vec{q}^{\prime}_{j+1})\), where \(\mathbf{P}_{j}(\vec{q}^{\prime}_{j})\) is the intensity obtained from bilinear interpolation on \(\mathbf{P}_{j}\) at 2D non-integer coordinate \(\vec{q}^{\prime}_{j}\), and \(w\) is the weight defined by the point-to-plane distances \(d_{p2p}\) of \(q\) to the planes containing \(\mathbf{Q}_{j}\) and \(\mathbf{Q}_{j+1}\).
# Zirconium-Containing Metal-Organic Frameworks (MOFs) as Catalysts for Biomass Conversion
## 6 Conversion of Lipids
### Glycerol Valorization: Formation of Glycerol Acetates  
The increasing production of biodiesel in recent years has generated large stocks of glycerol, since this molecule is stoichiometrically formed from glyceride transesterification by 10 wt% of total biodiesel production [30]. Therefore, there is a great interest in developing novel routes for converting this largely abundant biodiesel by-product into commodity chemicals, such as reduction, oxidation, esterification, or acetalization, among others. In particular, glycerol reacts with carbonyl compounds to yield a mixture of 5- and 6-membered ring compounds, viz. 1,3-dioxan and 1,3-dioxolan, respectively, as shown in Scheme 7 [65; 66; 67]. The resulting glyceryl acetates find applications as fragrances [68], pharmaceutical products [69], surfactants [70], or fuel additives [66]. In this sense, we have recently reported on the use of Zr-UiO-66 as a heterogeneous catalyst for the synthesis of hyacinth and other glyceryl acetal fragrances [71].  
We have found that the catalytic activity of UiO-66 increases with the number of missing linker defects (see Figure 7), similar to the case of LA esterification discussed above. However, the intrinsic activity per defective Zr\({}^{4+}\) site was roughly constant at a TOF around 350 h\({}^{-1}\), thus behaving as a single site catalyst. Interestingly, we found that the dioxolane-dioxane ratio in the final product can be adjusted between 2.8 and 4.6 by introducing -NH\({}_{2}\) in the MOF catalyst used and by controlling the amount of missing  
Figure 6: Pseudo-first order reaction rate constant (k) of esterification of saturated and unsaturated fatty acids with EtOH over UiO-66-NH\({}_{2}\).  
linker defects. This affords a means to fine-tune the organoleptic characteristics of the final hyacinth fragrance mixture, which is highly desired for the fragrance industry.  
Similar to the hyacinth fragrance, other acetals are indeed widely used in the fragrance industry, since acetalization can modify vapor pressure, aroma, and solubility of the starting aldehyde or ketone flavor. Interestingly, UiO-66 catalysts were also active for the acetalization of other carbonyl compounds, and the results obtained are summarized in Table 2.  
Figure 7: Turnover frequencies (TOFs) calculated for UiO-66 and UiO-66-NH\({}_{2}\) catalysts containing a variable amount of missing linker defects. In part (**a**), all the Zr in the catalyst is considered for TOF calculation, while in part (**b**), TOF is obtained only for Zr\({}^{+4}\) sites associated with missing linker defects. Reproduced from reference [71].
# Design for Additive Manufacturing: Tool Review and a Case Study
## 2 The Additive Manufacturing Workflow  
The approach to the additive manufacturing processes establishes an orderly workflow, so that by relying on different tools, processing files are generated for subsequent printing and post-processing. There are five fundamental stages in the additive manufacturing process, which are listed below and shown in a diagram in Figure 1.  
1. Design: For the development of the parts and products that will be manufactured by additive processes, the first stage consists of the generation of three-dimensional designs by means of computer-assisted design tools (CAD). The characteristics of the processes allow the generation of complex geometries, however, not all shapes are always viable and they are closely linked to the technology used. The construction of 3D models can be done with conventional solid and surface modeling programs; likewise, due to the flexibility of the process, the use of advanced modeling tools, such as polygonal meshes or NURBS surfaces, is very common.
2. Verification: Once the design process is completed, the next step is to export the files to the 3D printing standards. The most common file format is STL, which consists of  
Figure 1: Additive manufacturing (AM) workflow. Note: CAD (Computer-Assisted Design); CAE (Computer-Aided-Engineering).  
a triangulated surface mesh that defines the complete geometry [23]. The export of files is done for their correct inclusion in the lamination tools or slicers. However, a previous step of surface verification is recommended, since sometimes discontinuities or incorrect orientations of the normals that define the surface triangles could be produced.
3. Slicing: The lamination programs or slicers previously mentioned are the tools that generate the machine code with the characteristics of the process and the parameters, as well as the trajectories. It is important to define the correct parameters of the process according to the characteristics of the design and materials. The main parameters shared by most processes are: layer height, manufacturing speed, temperature and percentage of filling. The result of this process definition is the machine path files, which are transferred to the printing equipment for process activation.
4. 3D printing: This is the physical process in which the materialization of the parts using the 3D printers in question is done. Its characteristics vary depending on the technology selected. A common feature in all the technologies is that this is a long process and usually requires post-processing compared with traditional manufacturing processes such as injection molding.
5. Post-processing: Depending on the characteristics of the process, more or less intervention is necessary to achieve the specific finish and properties of the printed parts. Tasks common to all technologies are the removal of parts from the printing surface, the removal of supports and mechanical processes of surface finishing, as well as heat and surface treatments, if necessary.
## Abbreviations  
* Global Positioning System
* Signal to Noise Ratio
* Line-of-Sight
* Non-Line-of-Sight
* Open Sky
* City street
* Urban Canyon
* Extended Kalman Filter
* Pseudo Random Number
* The new model, named after the first author's initials
* Root Mean Square Error
## 3 Building Blocks of Vehicular Networks
### Base Stations  
Even though some vehicular networks do not need to rely on pre-existing infrastructure for operation, many employ devices that can be denoted as base stations. These are known as roadside units in networks of ground vehicles, and in UAV networks, those are labeled as ground control stations or base stations [36].  
RSUs are designated to extend the communication range in vehicular networks by forwarding information to either onboard units or other RSUs, thereby providing access to networks such as the internet. In some systems, they serve as edge computing devices enabling the reliable operation of time-critical applications by processing data before they are sent to a cloud or by running applications locally, thus ensuring a lower latency.  
Similarly, base stations or ground control stations are used to extend the range of communication among UAVs, offload tasks from onboard units to enable time-sensitive applications, establish the connection of vehicular network to other networks, and preprocess data before they are moved to a cloud [37]. Both RSUs and base stations are intended to be placed in geographical proximity to vehicles. They can be installed at a fixed location, or other vehicles may operate in the role of base station/RSU. It may be a bus moving on a scheduled route at a scheduled time or a UAV hovering at a set altitude, which would then be known as an aerial base station [38].
## 3 Results and Discussion
### Gap Width Effects on the Finger Width  
We expect that the finger width is significantly restricted by the gap width \(b\). Therefore, we varied the gap width \(b\) from 0.4 to 0.8 mm and analyzed the results. Figure 5a shows a diagram of fingering patterns under four conditions with the change in gap width \(b\) and injection rates. In both gap width \(b\) conditions, the number of fingers increases when the injection flow rate increases, which is the same result as that discussed in the previous section. The typical fingering pattern is similar even when the gap width changes. However, finger width is reduced when the gap width \(b\) is smaller, whereas increasing the gap width \(b\) leads to a larger finger width. A larger finger width is accompanied by a decrease in the number of fingers. Figure 5a shows that approximately 25 fingers appear at a gap width \(b\) of 0.4 mm and an injection rate of 15 mL/h. However, at the same injection rate, the number of fingers decreases to only five when the gap width \(b\) is 0.8 mm. The decrease in  
Figure 4: Relationship of injection rate \(Q\) with respect to the number of fingers \(N\) at a citric acid concentration \(c=15\) wt% (when the volume of the injected aqueous citric acid solution is 2 mL and the gap width b is 0.4 mm). The transition zone represents the region at which the fingers begin to merge.  
the number of fingers compensates for the increased finger width at the same volume of the injected aqueous citric acid solution.  
The proportional relation between the gap width \(b\) and finger width is subject to the tendency of the aqueous citric acid solution to have a sphere-like shape because of surface tension. When the gap width \(b\) is wider, the sphere-like shape of the aqueous citric acid solution also becomes larger and, thus, the finger width, as illustrated in Figure 5b. When the gap width \(b\) is smaller, however, the finger width also decreases. This result confirms that the finger width is significantly limited by the gap width \(b\).
# Cotton Terry Textiles with Photo- and Bio-Activity in a Model Study and Real Conditions
## 2 Materials and Methods
### Antimicrobial Activity of Textiles--Model Study  
The antimicrobial activity of textiles under model conditions was determined by the AATCC Test Method 100-2004 [40] according to previous studies [41]. The densities of inoculums were established by the culture method. The densities for the tested microorganisms were: \(5.7\times 10^{7}\) CFU/mL (_B. subtilis_), \(7.3\times 10^{7}\) CFU/mL (_A. niger_), \(2.3\times 10^{8}\) CFU/mL (_C. albicans_), \(7.2\times 10^{8}\) CFU/mL (_S. aureus_), \(4.4\times 10^{9}\) CFU/mL (_E. coli_). The suspension of microorganisms (0.1 mL) was inoculated on the surface of the modified and control samples (area 4 cm\({}^{2}\)). Next, the samples were incubated at temp. 30 \(\pm\) 2 \({}^{\circ}\)C and RH: \(80\pm 1\%\) without lighting (in a Binder-720 climate chamber, Tuttlingen, Germany). An additional sample with _E. coli_ was incubated in room conditions (RH = \(43\pm 5\%\), temp = \(21\pm 2\) \({}^{\circ}\)C, light intensity \(69.5\pm 10\) lux, lighting time 420 min). Samples were collected after time \(t\) = 0 h and 24 h, transferred into saline (50 mL sterile 0.85%) and shaken (5 min). Then, the method of serial dilutions in saline (from \(10^{-2}\) to \(10^{-6}\)) and the culture method were performed. The microorganisms were cultured in the following conditions: MEA medium (Malt Extract Agar, Merck, Darmstadt, Germany), temp. \(27\pm 2\)\({}^{\circ}\)C, 48-72 h (number of fungi); TSA medium (Tryptic Soy Agar, Merck, Darmstadt, Germany), temp. \(30\pm 2\)\({}^{\circ}\)C, 24-48 h (number of bacteria). After incubation, the colonies were counted and the result was given as colony forming units per 4 cm\({}^{2}\) of sample (CFU/sample). The tests were performed in three independent replicates for each variant. Survival index (N) after 24 h incubation and antimicrobial biostatic activity (A) were determined.  
The microbial survival index was determined using Equation (3):  
\[\text{N}=(\text{N}_{t}/\text{N}_{0})\times 100\% \tag{3}\]  
where:  
N\({}_{0}\)--number of microorganisms on textile material (CFU/sample) at time \(t\) = 0 h  
N\({}_{t}\)--number of microorganisms on textile material (CFU/sample) at time \(t\) = 24 h Biostatic activity was determined using Equation (4):  
\[\text{A}=\text{log N}_{\text{C}}/\text{N}_{\text{m}} \tag{4}\]  
where: N\({}_{\text{C}}\)--number of microorganisms on textile material (CFU/sample) after incubation on the control textile material; N\({}_{\text{m}}\)--number of microorganisms on textile material (CFU/sample) after incubation on modified textile material.  
The results of biostatic activity were compared to criteria using normative findings in order to determine the biostatic effect of disinfectants against microorganisms [42]. Activity below 1 was considered low, activity at the level of 1-3 was considered medium, and above 3 it was high, assuming that the value of 1 means a decrease in the number of microorganisms by one logarithmic scale.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Analysis of Statistical Distributions
#### 4.4.2 Length of Stay and Process Times  
Length of stay (LOS) modeling shows greater complexity in distribution selection. [PERSON] [50] demonstrated that lognormal distributions effectively capture the right-skewed nature of emergency department LOS patterns. Their analysis of over 15,000 patient visits revealed that lognormal distributions provided the best fit for ED LOS data (\(p<0.05\) in Kolmogorov-Smirnov tests).  
Studies by [PERSON] and [PERSON] [51] in surgical units found the following:  
* General surgery: Weibull distribution best fitted post-operative stays.
* Orthopedic procedures: Gamma distribution showed superior fit.
* Day surgery: Triangular distribution adequately represented shorter stays.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 4 Experimental Comparison
### Disturbed Scenario
#### 4.2.1 Conventional Convolutional Neural Network Architecture  
The navigation dataset serves as input information, and the training process is depicted in Figure 10.  
The measurement error obtained through training is shown in Figure 11.  
Figure 11: Training process LOSS RMSE error graph.  
Figure 10: CNN training process.  
The final training outcome indicates a loss error of RMSE_error = 1.2106.
# A CP-ABE Scheme Based on Lattice LWE and Its Security Analysis
###### Abstract  
In recent years, the research on ciphertext-policy attribute-based encryption (CP-ABE) scheme design based on learning with errors (LWE) has been a challenging problem, and many researchers have made a lot of attempts at it. At EUROCRYPT 2021, [PERSON], [PERSON], and [PERSON] proposed the first provably secure direct CP-ABE construction that supports NC1 circuit access structures. Improving on their work, we propose a CP-ABE scheme. Technically, we use the new lattice two-stage sampling technique of [PERSON], [PERSON], and [PERSON] (EUROCRYPT 2021) in the key generation phase instead of the extended trapdoor sampling technique. In this way, we obtain a CP-ABE scheme, which is at least as secure as the original scheme under the same conditions, and has a shorter ciphertext; we provide an innovative design idea for designing CP-ABE schemes only based on LWE, although this is only a partial theoretical work.  
CP-ABE; lattice; DNF; linear secret sharing scheme (LSSS); LWE Article
# Study of a Small Robot for Mine Hole Detection
## 4 Hardware Circuit and Program Design
### Control Program Design  
The control program of the small robot for mine hole inspection is designed to facilitate the real-time management of the robot's operational status and to collect environmental parameters within the mine hole. The process of designing the control program is shown in Figure 11: first, the communication protocol is configured and the corresponding mapping serial port and baud rate are selected. Upon completing the configuration, commands can be dispatched, and data can be obtained with the host computer. After receiving instructions from the host computer, the small robot will analyze and recognize the instructions and adjust its motion posture. It provides feedback when motion control instructions are received, and when data query instructions are received, it transmits data--including parameters such as the crawling speed, methane concentration, temperature, and humidity--to the host computer, which the host computer then utilizes for subsequent processing.  
Figure 11: Control program flow chart.