esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### IRS-RFL Algorithm  
#### 2.2.1 Fingerprint Database Construction  
Suppose \(N\) fingerprint reference points were uniformly distributed in the IRS-RFL system. Let the coordinate value of the \(n\)th fingerprint reference point be \(\mathbf{LOC}_{n}=(x_{n},y_{n},z_{n})\). Then, the signal emitted from this point after reflection by \(IRS_{m}\) is received by \(BS\) and expressed as \(\mathbf{Y}_{nUS}\in\mathbb{C}^{W\times 1}\). The corresponding fingerprint data \(\mathbf{f}_{n}\) can be represented by Equation (15)  
\[\mathbf{f}_{n}=[\mathbf{f}\mathbf{p}_{1n^{\prime}}\cdots,\mathbf{f}\mathbf{p}_{mm^{\prime}}\cdots,\mathbf{f}\mathbf{p}_{Mn}], \tag{15}\]  
where \(\mathbf{f}\mathbf{p}_{mn}=[E(|\mathbf{Y}_{mn}|)]^{T}\), \(|\cdot|\) denotes the modulo operation for each item in the matrix, \(E(\cdot)\) denotes the expectation of the matrix, and \([\cdot]^{T}\) denotes the transpose of the matrix. Therefore, \(\mathbf{f}_{n}\) is a one-dimensional row vector in the column \(M\times W\), which, when combined with the coordinate value of the point forms the complete fingerprint \(\mathbf{f}_{n}\) of the point, is expressed as (16)  
\[\mathbf{f}_{n}=[\mathbf{LOC}_{n}\quad\mathbf{f}_{n}^{\prime}]. \tag{16}\]Let \(\mathbf{LOC}=[\mathbf{LOC}_{1},\ldots,\mathbf{LOC}_{n},\ldots,\mathbf{LOC}_{N}]^{T}\) denote a summary of the coordinate values of the \(N\) fingerprint reference points, which are expressed in Equation (17)  
\[\mathbf{LOC}=\begin{bmatrix}\mathbf{LOC}_{1}\\ \vdots\\ \mathbf{LOC}_{n}\\ \vdots\\ \mathbf{LOC}_{N}\end{bmatrix}=\begin{bmatrix}(x_{1},y_{1},z_{1})\\ \vdots\\ (x_{n},y_{n},z_{n})\\ \vdots\\ (x_{N},y_{N},z_{N})\end{bmatrix}_{N\times 3.} \tag{17}\]  
\(\mathbf{F}^{\prime}=[\mathbf{f}_{1},\ldots\mathbf{f}_{n^{\prime}},\ldots\mathbf{f}_{N}]^{T}\) denotes a summary of the fingerprint data from the \(N\) fingerprint reference points, which is expressed by Equation (18)  
\[\mathbf{F}^{\prime}=\begin{bmatrix}\mathbf{f}_{1}\\ \vdots\\ \mathbf{f}_{N}\end{bmatrix}=\begin{bmatrix}\mathbf{f}\mathbf{p}_{11}&\cdots&\mathbf{f}\mathbf{p}_{ M1}\\ \vdots&\ddots&\vdots\\ \mathbf{f}\mathbf{p}_{1N}&\cdots&\mathbf{f}\mathbf{p}_{MN}\end{bmatrix}_{N\times(M\times W).} \tag{18}\]  
Consequently, the constructed fingerprint database \(\mathbf{F}\) is expressed by Equation (19)  
\[\mathbf{F}=\begin{bmatrix}\mathbf{LOC}&\mathbf{F}^{\prime}\end{bmatrix}=\begin{bmatrix} \mathbf{LOC}_{1}&\mathbf{f}_{1}\\ \vdots&\vdots\\ \mathbf{LOC}_{N}&\mathbf{f}_{N}\end{bmatrix}. \tag{19}\]
# A Voting-Based Star Identification Algorithm Using a Partitioned Star Catalog
## 1 Introduction
Initially, a method for uniform partitioning of the star catalog is introduced. Building on this, a navigation feature library using partitioned catalog neighborhoods as a basic unit is constructed. During star identification, a method based on a voting decision is employed for feature matching in the basic unit. Different from other star identification algorithms, the proposed algorithm significantly simplifies the navigation feature library and narrows the retrieval region during star identification, markedly enhancing identification speed while effectively reducing the probability of redundant and false matching. The paper is organized as follows: A method for uniform partitioning of the star catalog is put forward in Section 2. Based on the partitioned star catalog, the construction of the navigation feature library is explained in Section 3.1, and then a voting-based star identification algorithm is proposed in Section 3.2. A simulation experiment and nighttime star observation experiment are conducted in Sections 4 and 5, respectively, to validate the performance of the proposed algorithm. Finally, in Section 6, the methodology and experimental results are summarized.
# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 6 Conclusions  
The proposed wavelet-based current signal analysis method aims to offer a cost-effective, sensorless solution for obstacle detection in train doors. By leveraging a current sensor and discrete wavelet transform (DWT), this system efficiently detects obstacles, differentiating between soft and hard objects, and reverses door movement in 0.3 s, complying with EN14752 standards. The proposed approach was validated on a downscaled test bench for forces between 100 and 200 N. It provides benefits for train manufacturers by reducing costs, enhances operational efficiency for operators, and improves passenger safety. Future advancements could focus on integrating the algorithm with AI, optimizing detection time, and transitioning to microchip implementation for enhanced performance.  
Conceptualization, Y.S.; Methodology, Y.S. and C.-Y.L.; Software, P.G.; Formal analysis, P.G.; Investigation, Y.S., P.G. and C.-Y.L.; Data curation, P.G. and C.-Y.L.; Writing--original draft, P.G.; Writing--review & editing, Y.S.; Supervision, Y.S.; Project administration, Y.S.; Funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Industrial Technology Research Institute (ITRI) grant number 211P19.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Comtrend Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results and Discussion  
### Surface Section of the Cracks  
First, to thoroughly check for precipitates formed by self-healing in the surface portion of a crack, observations were made with an optical microscope. White precipitates of self-healing were visible in both the CN series (immersed in Ca(OH)\({}_{2}\) solution with a continuous supply of nanobubbles) and CM series (immersed in Ca(OH)\({}_{2}\) solution with a continuous supply of microbubbles) of test bodies. Figure 6 shows images of the surface of a crack in test bodies exposed to microbubbles (CM-20) and nanobubbles (CN-20) at a temperature of 20 \({}^{\circ}\)C and pH of 9.0. As shown in the figure, the crack surface was almost completely filled with white precipitates in both CN-20 and CM-20, making it difficult to confirm the presence of the crack itself and indicating that the crack surface was closed by the self-healing of the cement paste.  
Meanwhile, to check for precipitate crystals generated by self-healing, Raman analysis was conducted by using test bodies immersed in Ca(OH)\({}_{2}\) solutions with microbubbles (CM-20) and nanobubbles (CN-20), subjected to self-healing at 20 \({}^{\circ}\)C and a pH of 9.0. Figure 7a shows the experimental setup, and Figure 7b shows the Raman spectra of the specimens containing the cracked section with attached white precipitates and the non-cracked section in the test bodies. The spectra of CaCO\({}_{3}\) and Ca(OH)\({}_{2}\) powders are shown at the bottom of Figure 7b for comparison. The peak corresponding to CaCO\({}_{3}\) is almost non-existent in the spectrum of the specimen containing the non-cracked section. However, the peaks corresponding to CaCO\({}_{3}\) in the spectra of specimens containing the cracked section in CN-20 and CM-20 are almost identical, indicating that, regardless of the size of the CO\({}_{2}\) bubbles in the Ca(OH)\({}_{2}\) solution, most of the white precipitates produced after the self-healing of each test body were CaCO\({}_{3}\).  
\begin{table}
\begin{tabular}{c|c|c|c} \hline \multirow{2}{*}{**Step**} & \multirow{2}{*}{**Experimental Sequence**} & \multicolumn{2}{c}{**Subject and Method of Evaluation**} \\ \cline{3-4}  & & **Surface and Internal Sections of Cracks** & **Self-Healing Substances** \\ \hline A & Prior to self-healing & & Raman spectroscopic analysis, \\ \hline B & After self-healing & & TG-DTA, SEM analysis \\ \hline \end{tabular}
\end{table}
Table 2: Experimental procedure and evaluation of self-healing performance.
## 2 Scenario Overview and Organization of the Paper  
The target MASSIVE scenario is depicted in Figure 1, showing an uncoordinated (grant-free) population of IoT terminals which transmit a low volume traffic with a low duty cycle, a concept often referred to as sporadic transmission, towards a gateway via a satellite return link. Both geostationary and non-geo-stationary satellites were considered in this study.  
The rest of the paper is organized as follows. Section 3 provides a description of the physical (PHY) layer used by each IoT terminal for the uplink transmission on the return link direction from the user terminal towards the gateway via the satellite. Section 3 also highlights the new features implemented in the MASSIVE test-bed, compared to the E-SSA based air interface [5].  
Section 4 describes the signal receiver at the gateway that shall be able to demodulate the transmitted bursts by a large population of satellite IoT transmitters. In particular, Section 4.1 gives an overview of the receiver structure. Sections 4.2 and 4.3 focus on the two main innovative blocks of the ME-SSA receiver which were studied, implemented and assessed during the project; the preamble searcher (PS) and MMSE detector based on MSD approximation, respectively.  
Section 5 describes the test-bed used for the validation of the results and gives the implementation guidelines to efficiently execute the most computationally demanding block, that is, the MSD receiver.  
Section 6 provides a summary of the main results and outcomes from the performed simulations, highlighting the causes of implementation loss in a real-time receiver compared to the theoretical results available from the literature and the adopted solutions to minimize such losses.  
Finally, concluding remarks and future perspectives are drawn in Sections 7 and 8, respectively.
## 3 Estimation of Lateral Stiffness Ratio
### Estimation of Maximum Inter-Story Drift Ratios (IDRmax)  
This study presents the maximum inter-story drift ratios obtained for the proposed model (\(\theta\) = 1/N) and the cases where the base rotation is 0, as illustrated in Figure 3. In particular, the lateral deformation profiles obtained using the average value of \(\alpha\) = 12.5 recommended by [PERSON]'s approach (\(\theta\) = 0) for frame buildings with the estimated \(\alpha\) value for the \(\theta\) = 1/N condition proposed in this study were compared. The procedures were repeated for sample-building models in each set (group), and the maximum relative inter-story drift ratios (IDR\({}_{\max}\)) were determined and compared with the SAP2000 (FEM) results. The results are compared on a one-to-one function graph provided in Figure 3. The circular data points labeled as A in the graph represent the solution obtained using [PERSON]'s approach (\(\alpha_{\text{ort}}\) = 12.5, \(\theta\) = 0), while the starred data points labeled as B represent the proposed model approach in this study (\(\alpha\) determined through trial and error, \(\theta\) = 1/N). All predictions from the continuous model were overestimated from the SAP2000 (FEM) result values.  
Figure 2: _Cont._  
It can be observed that the analytical results obtained for cases where the base rotation was different from 0 significantly improved the FEM results. This improvement was particularly pronounced in low-rise models; however, as the number of stories increased, the rate of improvement diminished. This can be attributed to the fact that the base rotation decreases with respect to N and approaches 0, leading to an approximation of the fixed-end beam model solution at the base. Although continuous models are more suitable for analyzing multistory buildings, including the base rotation angle in the continuous model solution also allows for its applicability to low-story buildings.
## 3 Results and Discussion
### Feasibility for Practical Use in Anaerobic Digestion Operation  
#### 3.2.1 Semi-Continuous Operation of an Anaerobic Digester Treating De-Oiled Grease Trap Waste  
A laboratory-scale mesophilic anaerobic digester was operated through gradually increasing the organic loading rate to take digestate samples that include different concentrations of LCFAs. Since the operation performance of the digester treating de-oiled GTW has been detailed in several studies [7; 11; 31], this study focuses on the evolution of LCFA concentration and methanogenic activity; i.e., the biogas production rate. Figure 4a shows the time courses of gas production rate and feeding rate of de-oiled GTW and Figure 4b shows the shifts of pH and LCFA concentration during the semi-continuous experiment. The main compositions of LCFA species during days 30-79 are summarized in Table 1. The feeding rate is elevated in stages from 0.010 to 0.040 L-de-oiled GTW/(L-reactor-day). The gas production rate gradually increases accordingly during feeding rates of 0.010-0.027 L/(L-day). Methane content in biogas ranges from 56% to 72% throughout this period. After the feeding rate changes to 0.040 L/(L-day), a constant level of gas production rate is maintained for the first 20 days and then the gas production sharply decreases. Finally the gas production is completely inhibited, even when the feeding stops. On the other hand, the pH is within the range 7.4-7.8 until day 64 and slightly drops to 6.6 when the inhibition occurs on day 77. During the first three stages of the feeding rates, LCFA concentration slightly increases with time from 42 mg/L (day 0) to 616 mg/L (day 48). At the last stage, LCFAs show a sharp increase from 620 mg/L to 2602 mg/L during days 70-79, which is consistent with the period when gas production is inhibited. In more detail, a significant drop in the gas production rate occurs on day 77, when the LCFA concentration is 1686 mg/L. Actually the increase in LCFAs slightly precedes the decrease in biogas production. There is no great difference in the composition of LCFAs except on day 79 as shown in Table 1. Palmitic acid always has the highest percentage in C8-C22 fatty acids, and accounts for 47.5 \(\pm\) 15.7 wt % of the total C8-C22 fatty acids on average throughout the experiment. Oleic acid follows palmitic acid and accounts for 26.7 \(\pm\) 12.4 wt %. During the sudden increase in LCFAs at days 73 and 77, the percentage of palmitic acid increases to 75 wt %. As Table 1 shows, the total LCFA concentration consists majorly of palmitic acid from the initial period to day 77; hence palmitic acid should be monitored as a priority in view of these above findings.
## 3 Semi-Physical Experiment and Analysis
### Experimental Environment Setup  
The satellite model was considered a non-cooperative target, with its motion simulated by controlling the slide's translation and the turntable's rotation. The slide's position control error was kept below 0.1 mm, while the rotary table's angle control error was maintained under 0.01\({}^{\circ}\), as illustrated in Figure 7.  
Figure 6: Schematic of joint alignment.  
The servicing spacecraft's vision system was equipped with a ToF camera, whose specifications are detailed in Table 1. The point cloud of the target mockup was captured by the ToF camera at a frequency of one frame per second during the experiment.  
To validate the robustness of the algorithm, we compared the method proposed in this paper with classical methods (ICP, GICP). All the algorithms mentioned above were implemented on a PC (I7-2.8 GHz, I7-7700 with 8 GB RAM) using Visual Studio 2019.
## 2 Materials and Methods
### Measurement of IL-1\(\beta\) and IL-10 in Skin Tissue  
IL-1\(\beta\) and IL-10 contents in the dorsal back skin tissue were measured using mouse IL-1\(\beta\) (ab100705; Abcam, Cambridge, UK) and IL-10 (ab108870; Abcam) ELISA kits, following the manufacturer's protocol. Briefly, the homogenized tissue sample was centrifuged (1236R, Labogene, Daejeon, Republic of Korea) at 14,000\(\times\)\(g\) for 20 min with 0.1 M phosphate-buffered saline containing 1% Triton X-100. The supernatants were incubated in the plate for 2 h, washed, and further incubated with biotinylated IL-1\(\beta\) or IL-10 antibody for 2 h. The absorbance was measured using a microplate reader (Sunrise, Tecan, Mannedorf, Switzerland) at 450 nm.  
Evaluation of Antioxidant Activities (Measurement of Glutathione (GSH) Level, Lipid Peroxidation and Superoxide Anion Production)  
To determine GSH level in skin tissue, the sample was homogenized in 100 mM NaH\({}_{2}\)PO\({}_{4}\) (pH 8.0) containing 5 mM EDTA. The homogenates were treated with 30% trichloroacetic acid and centrifuged twice (at 1940\(\times\)\(g\) for 6 min and at 485\(\times\)\(g\) for 10 min). The supernatant was mixed with _o_-phthalaldehyde (1 mg/mL in methanol). The fluorescence intensity of the supernatant was measured using a fluorescence spectrophotometer (RF-5301 PC, Shimadzu Corp., Tokyo, Japan) (\(k_{exc}\) = 350 nm; \(k_{cm}\) = 420 nm). Results are expressed as \(\upmu\)M of GSH/mg of protein as compared with a standard curve prepared with GSH (0.0-75.0 \(\upmu\)M). Thioobarbituric acid reactive substances (TBARS) assay for detecting malondialdehydedehyde (MDA), a commonly used biomarker for lipid peroxidation, was conducted to determine lipid peroxidation. Briefly, the skin tissue homogenate was mixed with trichloroacetic acid (10%, Sigma-Aldrich) to precipitate proteins and centrifuged (1000\(\times\)\(g\), 3 min). The extracted clear protein-free supernatant was incubated with thio-barbituric acid (0.67%) at 100 \({}^{\circ}\)C for 15 min. MDA was determined by computing the difference between absorbances at 535 and 572 nm by a microplate reader (Tecan). Results were expressed as nM/mg of protein. To assess superoxide anion production in skin tissue (10 mg/mL in 1.15% KCl), the nitroblue tetrazolium (NBT) assay was employed. Briefly, the homogenate of skin tissue was incubated with NBT (1 mg/mL) at 37 \({}^{\circ}\)C for 1 h. The supernatant was removed, and the reduced formazan was solubilized by adding 2 M KOH and dimethyl sulfoxide. The NBT reduction by superoxide anion was observed at 600 nm using a microplate reader (Tecan). Data were normalized with the protein content.
## 6 Simulation Results
### Enlarging the Network  
Till now, we have evaluated HECTOR by comparing it to other existing algorithms by running them in a restricted area and by making the node density grow. In this section, we fix the node density to \(\delta=15\) and the maximum node range radius to \(R_{max}=200\) and expand the network area size.  
Indeed, in such a scenario, the energy consumption will necessary grow since nodes may be further one from the others and more hops are needed to connect them than in previous scenarios. This section allows us to check the scalability of HECTOR (in terms of growing area rather than increasing node density) in very large networks by being sure that we still ensure a low ECO.  
Figure 14 draws the ECO of the routes taken by each algorithm when the network size grows, for \(5\) landmarks. The results are similar for \(3\) landmarks. The abscissa axis plots the factor by which the network size has been multiplied. We only plot results where VCost fails since these runs represent the most energy costly results and the longest paths, as seen in previous sections.  
One can notice that for homogeneous networks, even if the network grows as well as the route length, the energy overhead compared with the optimal shortest path consumed by HECTOR grows slowly with the network size. This is because HECTOR can follow \(V\) coordinates, and thus can have an energy  
Figure 14: ECO when the network grows.  
consumption close to the optimal. Therefore, the energy consumed by paths followed by each algorithm is within a constant factor of the optimum. Also note that for distributions with a hole, the energy overhead tends to increase with the network size. This is due to the fact that the bigger the network (and longer the routes), the more likely greedy routing encounters a dead end and thus HECTOR has to follow the \(T\) labels and the tree, which gives longer paths and thus consumes more energy.
## 3 Results
### Category-Specific Method Results  
In Figure 5, the LSTM-estimated MAP and measured MAP for one patient per category are shown. Figure 5, 5, and 5 show the results obtained for patients in categories 1, 2, and 3, respectively. Variations in estimation accuracy can be observed, specifically for Patient 15 (weak or no correlation category) and Patient 59 (weak correlation category); the results demonstrate a significant difference between estimated and real MAP values. This highlights the failure of this method for accurate estimation in weak correlation situations. For patient 28 (mild correlation category), the results show good estimation. This emphasizes the impact of correlation strength on predictive accuracy, as milder correlations present additional challenges in capturing the dynamics of physiological responses.  
Figure 5: Results of LSTM-estimated MAP vs. actual MAP values for one patient from each category using the category-specific method.  
For categories 4 (moderate correlation) and 5 (strong correlation), the results indicate a very good estimation of MAP using LSTM category-specific methods (Figure 5d,e). This demonstrates the effectiveness of the method in capturing the intricate relationships between drug concentrations and MAP profiles in patients with strong correlations.