esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Testing BePiV on Raspberry Pi Platform  
As we have stated in our previous articles (e.g., [7]), a long-term objective of the BeePi project is to develop an open citizen science platform for researchers, practitioners, and citizen scientists worldwide who want to monitor beehives. One of the essential characteristics of a citizen science platform is scalability. Thus, it is essential to ask the question of how much physical time the BeePiV algorithm takes when executed on the raspberry pi computer functioning as the computational unit of a deployed BeePi monitor. Since a BeePi monitor, records a 30-s video every 15 min, we can re-formulate our question in a more precise fashion: Can BeePiV, when executed on the raspberry pi computer, provide the bee motion counts for a given video before the next video is taken?  
To answer this question, we executed BeePiV with DPIV_B on the 32 videos in the testbed and evaluation datasets on a raspberry pi 3 model B v1.2 with four cores, the model running in most of our currently deployed BeePi monitors. Table 5 shows the actual physical time taken by BeePiV to process each video on this raspberry pi platform. The results indicate that the processing time varies from video to video. In seconds, the mean processing time is 128.90 with a standard deviation of 61.68; in minutes, the mean processing time is 2.15 with a standard deviation of 1.03. While the longest time of 5.87 min taken by BeePiV on video 32 is significantly higher than the mean, it is significantly lower than 15 min (i.e., the length of the interval in between consecutive videos taken by the BeePi monitor). Hence, our test indicates that BeePiV can be used in situ to process bee traffic videos captured by deployed BeePi monitors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video** & **Human Count** & **BeePiV Count** & **TIME (secs)** & **TIME (mins)** \\ \hline
1 & 5693 & 4923 & 243.61 & 4.06 \\ \hline
2 & 343 & 319 & 114.35 & 1.91 \\ \hline
3 & 2887 & 2106 & 115.41 & 1.92 \\ \hline
4 & 73 & 56 & 190.04 & 3.17 \\ \hline
5 & 75 & 64 & 93.50 & 1.55 \\ \hline
6 & 239 & 285 & 85.08 & 1.42 \\ \hline
7 & 74 & 92 & 88.91 &
## 4 Results and Discussion
### Damage Mapping  
The observed alterations, namely metallic elements, cracks and fissures, detachments, and replacement of material, are identified in Figure 12. Dirt (aesthetic deterioration) and consequent biological development have not been included in this mapping, as they are extended throughout the whole surface of the spandrel.  
Figure 12: Mapping of alterations present in the spandrels of the arc of access to the STCV. (**A**) Types of deterioration present in the spandrels: (**A**) detachment and lack of repolychrome due to successive interventions, (**B**) presence of hornets, (**C**) presence of oxidized metal elements, (**D**) loss of the lower relief due to fillings with plaster/lime mortar, (**E**) detachment of part of the frame, (**F**) dirt, (**G**) detachment and hornet’s nest, and (**H**) fissure and presence of hornets (**B**).  
Regarding the metallic elements, reddish spots identified around the nails are a result of the oxidation of the metal. The loss of adhesion that makes it necessary to incorporate these metal elements is usually due to mechanical stresses that occur over time.  
From a conservative point of view, the right spandrel presents greater conservation problems than the left one. In fact, important deformations are visible in the central and upper right areas, and the latticework in some areas presents a collapse of 2 cm. To prevent this situation from going further and even resulting in the detachment of the latticework, the latticework was fixed to the support by means of plaster sinks in previous interventions.
# A Moment-Fitted Extended Spectral Cell Method for Structural Health Monitoring Applications
## 4 Numerical Examples
### Example 2
Figure 10 collects the time histories of the vertical displacements at the sensors. The simulation with the SEM in the absence of cracks (dotted line) was included for context. For the damaged case, good agreement between the XSCM and the SEM could be observed, despite the differences in these models: enrichment functions and a mass lumping error were introduced in the proposed method, while explicitly meshed details and some element distortion were encountered in the SEM. In light of the complexity of this problem, and of these fundamental modeling differences, these results speak of the quality of both approaches. \begin{table}
\begin{tabular}{c c c c} \hline
**Discretization** & **SEM Pristine** & **SEM Damaged** & **XSCM Damaged** \\ \hline \(\Delta t_{\mathrm{c}}\) (\(10^{-8}\) s) & 1.5 & 1.5 & 0.015 \\ \(n_{\mathrm{dofs}}\) (\(10^{6}\)) & 0.54 & 1.08 & 0.93 \\ \hline \end{tabular}
\end{table} 
Table 2: DOF and critical time steps \(\Delta t_{\mathrm{c}}\) for the models presented in Figures 9 and 10. A time step of \(\Delta t=5\times 10^{-9}\) was used to ensure good accuracy. In the XSCM, a local time refinement of \(p_{\mathrm{t}}=34\) enabled us to comply with the CFL condition [56]. Figure 9: Example (Section 4.2). (**a**) Aluminium plate with rivet holes, circular loading surface, and two planar through-thickness, cracks. (**b**) Conforming discretization of holes, load surfaces, and cracks for the SEM; node duplication was employed along the crack surfaces, highlighted by red lines. (**c**) Orthogonal mesh for the XSCM; mesh-independent element partitions were used to model the aforementioned details. (**d**) Close-up for the XSCM; pristine SEs versus crack- and and hole-conforming element partitions. (**e**) Scaled crack and hole deformations produced using the XSCM.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 2 Genomics in Cancer Diagnosis
Both _KRAS_ and _TP53_ pathogenic variants are typically somatic and correlate with aggressive cancer phenotypes and poor treatment response (Figure 1). Advancements in molecular diagnostic technologies have enabled rapid and precise identification of both somatic and germline pathogenic variants. Next-generation sequencing (NGS) allows for comprehensive genomic profiling of tumors, providing a detailed view of the genetic landscape of cancer. Whole-exome sequencing (WES) focuses on coding regions of the genome, thus identifying pathogenic variants that directly impact protein function. Whole-genome sequencing (WGS) analyzes the entire genome, including introns and non-coding regions, offering a comprehensive overview of potential genetic alterations. Although most clinically actionable variants lie within exons, WGS enables the detection of deep intronic variants that may affect splicing or regulatory functions. The interpretation of such intronic alterations, however, remains challenging and often requires integration with transcriptomic or functional studies to assess pathogenicity [20]. However, due to the high costs and analytical complexity of WGS, WES still represents one of the most sustainable choices for many clinical applications. In addition, one of the most commonly implemented strategies in oncogenetics is the use of gene panels. These are custom-designed tools that analyze multiple genes simultaneously, allowing for efficient and targeted identification of variants involving different cancer-relevant genes. Gene panels are typically designed to focus on genes (such as _BRCA1_, _BRCA2_, _TP53_, _EGFR_, and _KRAS_) mostly relevant to specific cancer types [21]. By analyzing several genes at once, these panels provide deeper insight into the genetic mechanisms of cancer, allow clinicians to make more informed decisions about treatment strategies, and enable faster and more precise diagnoses. Parallel to these advancements, innovative approaches, such as the use of Feulgen-Thionin staining, a DNA stoichiometric stain, have also shown promise in improving prostate cancer diagnosis, highlighting how even established diagnostic techniques can be optimized for enhanced precision [22]. Table 1 summarizes the most relevant genomic technologies currently applied in cancer diagnostics and risk assessment, highlighting their main applications, strengths, and limitations.
# Convolutional Neural Network Architecture for Recovering Watermark Synchronization
## 4 Proposed Method  
In this section, the description of the template and the watermark are presented separately without considering the insertion and extraction order. The insertion and extraction order is detailed in Section 3.  
In each step of template insertion and extraction, a resized \(K\) is used. Resizing is performed with the nearest neighbor filter, and \(K\) resized into \(r\times r\) is defined as \(K_{r}\). For example, \(K_{64}\) means that the \(K\) of size \(M\times N\) is resized to 64 \(\times\) 64. The reason for resizing \(K\) is to increase the resolution of the template to increase the accuracy in the matching step.
## 2 Results
### NMR Analysis of Dried-Hop Leaves
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compound** & **Assignment** & **1H (ppm)** & **Multiplicity [J(Hz)]** & **13C (ppm)** \\ \hline \multicolumn{4}{c}{**Sugars**} \\ \hline \(\beta\)-D-Fructofuranose & CH-3 & 4.12 * & \\  & CH-4 & 4.12 * & \\  & CH-5 & 3.82 & \\ \(\beta\)-D-Fructopyranose & CH-3 & 3.81 & \\  & CH-5 & 4.00 & \\  & CH\({}_{2}\)-6,6\({}^{\prime}\) & 3.70 & \\ \(\alpha\)-Glucose & CH-1 & 5.24 * & \\  & CH-2 & 3.52 & \\  & CH-3 & 3.73 & \\  & CH-4 & 3.40 & \\  & CH-5 & 3.84 & \\ \(\beta\)-Glucose & CH-1 & 4.65 * & \\  & CH-2 & 3.26 & \\  & CH-3 & 3.49 & \\  & CH-4 & 3.40 & \\ Sucrose & CH-1 (glucose) & 5.42 * & \\  & CH-2 & 3.55 & \\  & CH-3 & 3.76 & \\  & CH-4 & 3.48 & \\  & CH-5 & 3.84 & \\  & CH-1 (fructose) & & 62.3 \\  & C-2 & & 104.6 \\  & CH-3 & 4.22 & d (8.8) & \\  & CH-4 & 4.05 & 75.0 \\  & CH-5 & 3.90 & 82.4 \\  & CH-26,6\({}^{\prime}\) & 3.81 & \\ \hline \multicolumn{4}{c}{**Organic acids**} \\ \hline Citric acid & \(\alpha\),\(\gamma\)-CH & 2.57 * & d (16.7) & 45.1 \\  & \(\alpha^{\prime}\),\(\gamma^{\prime}\)-CH & 2.72 & d (16.8) & 45.1 \\  & \(\beta\)-C & & 76.7 \\  & COOH-1 & & 179.4 \\  & COOH-6 & & 182.1 \\ Formic acid & HCOOH & 8.46 * & s \\ Fumaric acid & \(\alpha\),\(\beta\)-CH=CH & 6.53 * & s \\ Malic acid & \(\alpha\)-CH & 4.32 * & dd (9.5; 3.1) & 71.4 \\  & \(\beta\)-CH & 2.69 & dd (15.5; 3.1) & 43.7 \\  & \(\beta^{\prime}\)-CH & 2.41 & dd (15.7; 9.6) & 43.7 \\  & COOH-1 & & 181.2 \\  & COOH-4 & & 180.6 \\ Succinic acid & \(\alpha\), \(\beta\)-CH\({}_{2}\) & 2.41 * & s & 35.2 \\  & COOH-1,4 & & 183.3 \\ Acetic acid & \(\alpha\)-CH\({}_{3}\) & 1.92 * & s & \\  & COOH & & 182.5 \\ \hline \multicolumn{4}{c}{**Amino acids**} \\ \hline Alanine & \(\alpha\)-CH & 3.78 & & 51.5 \\  & \(\beta\)-CH\({}_{3}\) & 1.49 *,\(\acute{f}\) & d (7.2) & \\  & COOH & & 176.7 \\ GABA & \(\alpha\)-CH\({}_{2}\) & 2.30 * & t (7.4) & 35.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Metabolites identified in the 600.13 MHz 1H NMR, \({}^{1}\)H-\({}^{1}\)H TOCSY, and \({}^{1}\)H-\({}^{13}\)C HMBC spectra of Bligh–Dyer hydroalcoholic extracts of hop leaves in phosphate buffer/D\({}_{2}\)O acquired at 25 \({}^{\circ}\)C. Asterisks (*) indicate signals selected for integration. Regarding the methanolic extract, the HSQC NMR experiment was chosen to see correlations between hydrogen atoms and the carbon atom to which they are attached. In fact, by means of HSQC, it is easy to assign cross peaks common to all humulones and lupulones molecules (due to methyl and methylene proton resonances of the C-4 phenyl side chain) and cross peaks typical of each series of bitter acid (due to proton resonances methyl and methylene of the phenyl side chains in C-6) [18]. Furthermore, by HSQC, it is also easy to assign unique cross peaks for adhumulone and adlupulone [19]. No bitter acids were detected since no signals of the aliphatic portion of the humulone and lupulone bitter acids series were detected in the characteristic region between 0.89 and 1.3 ppm. However, the NMR assignment of the most abundant prenylated flavonoid xanthohumol, was reported in Table 6 and Figure S8.
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 1 Introduction
[12] estimated that approximately 10% of the world's inland waters are within mixed pixels at the Landsat resolution, indicating a need to increase spatial resolution to track surface water changes more accurately. Fortunately, and through the Sentinel missions of the European Copernicus program, enhanced satellite data has recently become globally available in high spatial resolution (10 m) and with high temporal revisit time (days to weeks). Copernicus data streams are free, publicly available and covered by an operational service guarantee for many years to come. They therefore provide long-term access to enhanced high spatial resolution radar and optical observations, commencing a new era for the systematic mapping and monitoring of surface water resources [15]. The C-band Synthetic Aperture Radar (SAR) of the Sentinel-1 mission provides all-weather, day-and-night imagery, which is powerful when monitoring surface water dynamics in cloud prone and/or light constrained regions. The large footprint size of the Sentinel-2 Multispectral Instrument (MSI) along with its short revisit time and its systematic acquisition policy allows changes in the extent of water bodies to be precisely monitored. Consequently, the Sentinel missions are increasingly being used for mapping surface water resources worldwide, building on the long heritage of studies that use Landsat data. The objective of this paper is to advance satellite-based monitoring of surface water dynamics. The paper describes a simple, yet robust and operational algorithm for mapping and monitoring surface water at a large scale by fusing Sentinel-1, Sentinel-2, and Landsat-8 image data. The method is implemented and validated in mainland China--a region lacking a comprehensive high-resolution monitoring dataset of surface water bodies.
# Human-Robot Cooperative Control Based on Virtual Fixture in Robot-Assisted Endoscopic Sinus Surgery
## 2 Robot Motion Constraint Model Based on Virtual Fixture  
The narrow and complex anatomical structure of the nasal sinus makes it easy for the endoscope at the end of the robot to interfere with the nasal tissue, and the entire surgical operation space is very crowded. To ensure the safety of the surgical operation, it is necessary to restrain the movement of the endoscope. The VF can construct invisible \"clamps\" (e.g., spatial curves and surfaces) to assist the operator to improve operational efficiency and accuracy. In this section, the robot motion constraint requirements for each motion stage during endoscopic sinus surgery are determined based on the endoscope's trajectory analysis. Next, based on the obtained motion constraint requirements, three typical VF models suitable for endoscopic sinus surgery are constructed. Finally, based on the three typical VF models, a composite VF model is constructed to achieve the robot constraint for the whole endoscopic sinus surgery.
## 4 System Performance Analysis
### The Analysis of the HDT Protocol  
#### 4.1.1 Packet Timeout Probability Analysis of Single Node  
We can rewrite the CDF of the packet timeout probability by combining (6) with (7), and (12), and the expression is as follows:  
\[F_{T_{k}^{(v)}}(t)=1-\Pr\biggl{\{}G_{k1}^{(v)}G_{k2}<\biggl{[}\exp\biggl{(} \frac{\mathscr{R}_{k}}{t}\biggr{)}-1\biggr{]}\mathscr{H}_{k1}\biggr{\}}, \tag{18}\]where \(\mathcal{H}_{k2}=\frac{d_{k}^{-2k}(1-\alpha)_{k}}{\alpha_{0}\gamma_{0}\gamma_{0}k_{ 0}k_{0}}\). Combined with Lemma 1, we can derive the final expression of the CDF in the HDT protocol for the MRC and SC theories as follows:  
\[F_{T_{k}^{(\text{MRC})}}\left(t\right)=\frac{2}{\Gamma(M)}\bigg{(}\frac{\Psi(t) \mathcal{K}_{k1}}{\omega_{k}\Theta_{k}}\bigg{)}^{\frac{M}{2}}K_{M}\bigg{(}2 \sqrt{\frac{\Psi(t)\mathcal{K}_{k1}}{\omega_{k}\Theta_{k}}}\bigg{)}, \tag{19}\]  
\[F_{T_{k}^{(\text{SC})}(t)} =2M\sum_{m=0}^{M-1}\binom{M-1}{m}(-1)^{m} \tag{20}\] \[\times\sqrt{\frac{\Psi(t)\mathcal{K}_{k1}}{(m+1)\omega_{k}\Theta _{k}}}K_{1}\bigg{(}2\sqrt{\frac{(m+1)\Psi(t)\mathcal{K}_{k}}{\omega_{k}\Theta _{k}}}\bigg{)},\]  
where \(\Psi(t)=\exp(\frac{\mathcal{B}_{k}}{t})-1\), and the packet outage probability of a single sensor node for the HDT protocol can be derived combining (19) and (20) as follows:  
\[\mathcal{O}_{k}^{(MRC)} =1-F_{T_{k}^{(\text{MRC})}}(t_{out}), \tag{21}\] \[\mathcal{O}_{k}^{(SC)} =1-F_{T_{k}^{(\text{SC})}}(t_{out}). \tag{22}\]
## 3 Set-Up of Test and CFD Simulation
### Overview of CFD Simulation  
#### 3.3.1 Boundary Condition  
Ansys Fluent 24.1, a CFD program, was used to develop a mixing temperature prediction model for a single-duct VAV system. The modeling was implemented as 450 mm \(\times\) 450 mm for the outdoor air cross section, 1200 mm \(\times\) 400 mm for the return air cross section, 1400 mm \(\times\) 1000 mm for the exit cross section, and 2000 mm for the exit length. As boundary conditions for the reliability verification, the boundary conditions obtained through the experiment were used: an outdoor airflow of 483.41 CMH and outdoor temperature of 10.833 \({}^{\circ}\)C, and a return airflow of 522.4 CMH and return temperature of 24 \({}^{\circ}\)C. For CFD simulation, the mixing chamber was modeled in three dimensions using SpaceClaim 24.1. Next, Fluent Meshing was used to form the grid for the simulation before Fluent Solution was used to run the simulation with the boundary conditions. As shown in Figure 5a, the grids were polyhedral, with a maximum size of 20 mm. Figure 5b is a 3D model of a mixing chamber. The number of lattices was modeled to be about 2,000,000, and the simulation cross section was A-A'. The CFD simulation conditions were governed by the pressure-based solver, which solved the subsonic range as shown in Table 4. The gravity value was set and the simulation was performed in the steady state. The humidity was not considered and the turbulence model was solved using standard k-\(\epsilon\)[13,14]. The convergence criterion was set based on the residual value of each calculation step and was set to \(1\times 10^{-3}\) or less for continuous equations, X-Y-Z velocity, k and \(\varepsilon\), and \(1\times 10^{-6}\) or less for energy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Factor** \\ \hline Solver type & Pressure-based \\ Solver time & Steady \\ Energy & On \\ Viscous & Standard k-\(\epsilon\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: CFD boundary conditions.  
Figure 5: CFD mixing chamber modeling; (**a**) CFD grid geometry and distance; (**b**) CFD 3D modeling.
## 3 Investigation of Resilience Investment Strategies  
The framework outlined in Section 2 is employed to investigate the resilience of a test distribution system and to consider various investment strategies to enhance system resilience, with a focus on the importance of critical loads. A modified IEEE 69-bus test system incorporating DER, as illustrated in Figure 3[32], serves as the base case for this study. Each DER in Figure 3 consists of a WTG and a BES. The system displays a total active and reactive load of 2.71 MW and 1.86 MVAR, respectively, operating at a nominal voltage of 12.66 kV. The critical loads, located between bus 47 and bus 50, account for 0.262 MW and 0.187 MVAR.  
Figure 3: Modified IEEE 69-bus test system with critical loads highlighted.  
The system integrates WTGs and BESS to facilitate microgrid operations. The DER ratings for the base case are provided in the Appendix A. The WTGs operate with cut-in, rated, and cut-off wind speeds of 11 km/h, 50 km/h, and 96 km/h, respectively, and are deactivated during extreme wind events for safety considerations. The pre-warning of the extreme weather event allows the operators to set the initial state of charge (SOC) of the BESS at 95%, with a charging and discharging efficiency of 0.95. The extreme wind conditions are modeled as described in Section 2 using historical wind data from the Atlantic Region [33]. For the base case, a repair priority framework ensures that critical loads are restored before non-critical loads. The repair time is obtained following one of the scenarios outlined in Section 2. Within each category of critical and non-critical loads, the repair process is optimized to reconnect the maximum number of affected customers to the power supply as efficiently as possible in subsequent evaluation periods.  
This paper analyzes three distinct resilience enhancement strategies: infrastructure hardening, DER management, and repair management. Within each of the strategies, a number of case studies are performed to study the resiliency of the distribution system in the presence of critical and non-critical loads.
# Trace Elements and Mineralogy of Upper Permian (Zechstein)
## 6 Results
### Trace Elements Study
#### 6.2.1 Sulphate K-Mg Salts (Polyhalite)  
Calculated statistic parameters of trace element content in nine point samples, taken from the polyhalite interbeds within the core of Lower Anhydrite from M-9 borehole, are listed in Table 5.  
Content included such elements as: Ag, As, Be, Cd, Co, Cs, Ga, Sb, Se, Sn, La, and Tl at the value of their detection limits (<0.05 to <2 mg/kg; Table 5).  
Low content (mean and median: 0.07-2.0 mg/kg) characterises Ni, Rb, Mo, Ce, Pb, V, and U. Higher values (3-13 mg/kg) were noticed for Ba, Cu, Mn, Ti, and Zn.  
Lithium content is relatively high (mean and median are 20-26 mg/kg)--higher was the iron content (29-33 mg/kg)--but the most abundant element is strontium (723-1075 mg/kg) due to a sample mineral composition dominated by sulphates (polyhalite, anhydrite and gypsum; Table 4). Low content variability (standard deviation 0.03-4 mg/kg) characterises Ba, Cu, Ce, Mn, Mo, Ni, Pb, Rb, Ti, and Zn, a higher variability (13-14 mg/kg) is represented by Fe and Li but the most varied data belongs to strontium (691 mg/kg).  
The valuation of studied deposits as an economic source of required trace elements, e.g., Li, Cs, Sr, or Se is difficult because of few accessible samples taken from one borehole.  
Such a conclusion needs more data for its support, actual geochemical results indicate that the content of most analysed elements is low (up to 13 mg/kg), only lithium, iron, and strontium occur in higher amounts.
## 2 Sound Sampling and Data Transmission
### Node Implementations
#### 2.2.3 Central Processing Module  
The central processing module is crucial in managing the cluster and processing data. It comprises a Jetson Nano and a bridge ESP32, interconnected via UART for power supply and command communication. Figure 4 shows a prototype of the central processing module, where WiFi Mesh connects the bridge with the relay nodes, WiFi-1 connects the bridge to the Jetson Nano, and WiFi-2 connects the Jetson Nano to the internet. When the Jetson Nano receives a packet, several steps are needed to analyze the packet and obtain the information, including the MAC address of the source sensor node, the packet ID, and the audio. The audio is stored cumulatively until there are more than 6 \(\times\) 8192 samples (6.144 s) waiting for further processes, then the Jetson Nano uses the 6.144-s segment to compute the A-weighted SPL in a period of 1.024 s (8192 samples). If the A-weighted SPL exceeds a predetermined threshold, the corresponding 1.024-s audio segment is classified using a deep neural network, as detailed in Section 3. Consequently, the results are uploaded to a cloud server via another WiFi (namely WiFi-2) for data storage and visualization. This setup efficiently supports communication between the ESP32, Jetson Nano, and cloud infrastructure.  
Figure 3: Implementation of relay nodes.