esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Similarities and Differences between the Planktonic and Biofilm Surfaceomes  
The Venn diagram in Figure 2A compares all planktonic and biofilm-associated surface proteins and shows that over 97% (828 proteins) are shared by the two growth modes. The number of proteins specific to planktonic and biofilm surfaces was 19 and six proteins, respectively. Figure 2B compares the number of time point-dependent identifications between the planktonic and biofilm surfaces and indicates over 600 commonly identified proteins at each time point and growth mode. The number of biofilm-specific proteins was highest at the 8-h time point, whereas in planktonic cells, the number of specifically identified proteins was highest after 96 h. Figure 2C shows that the number of proteins shared by the majority of the time points was 121 in planktonic cells (PL26-PL96 h). In EDBF cells, the number of specifically identified proteins increased with time, whereas majority of the proteins were shared by SDBF cells at 6- and 24-h time points, and only one protein was specific to both time points. Proteins specific to planktonic and biofilm growth modes are listed in Table S3. A three-dimensional principle component analysis (3D-PCA) of detected surfaceomes indicated that each replicate samples cluster together, and the tested variables affected the protein abundance patterns (Figure 2D). The first component separates surfaceomes by total age of the cell samples with a strong positive correlation (0.896), whereas PC2 clearly divides the SD-biofilms and the corresponding planktonic cultures to the negative and positive (0.638) ends of the axis respectively. The rest of the surfaceomes arrange between the growth modes according to PC3. Thus, the detected surfaceomes show both the growth mode- and growth stage-dependent changes.  
Figure 2: (**A**) Venn diagram comparing the number of all identified proteins from biofilm and planktonic cell surfaces. (**B**) Comparison of the number of specific and commonly identified proteins between the planktonic and biofilm cells at different time points of growth. (**C**) The specific and commonly identified proteins within planktonic and biofilm cell samples at different time points of growth. (**D**) 3D-PCA score plot (varimax rotation and Kaiser normalization) of the individual samples with 3 replicates each. An initial oblimin rotation with Kaiser normalization was pre-applied to verify the absence of intercomponent correlation (\(|\)r\(|\)\(\leq\) 0.111 for all pairs).
## 2 Constellation Design
### Constellation Capacity  
On 27 February 2022, the first phase of HSC, comprising the HN-1 01/02 and WC-1 01/02 satellites, was successfully launched at an inclination of 97\({}^{\circ}\) and an orbit height of 535 km. The on-orbit status of the HN-1 01/02 and WC-1 01/02 satellites are depicted in Figures 2-4. Based on the phase differences in their orbits in late March 2022, using distinct swath widths, 110 km for HN-1 01, 115 km for WC-1 01/02, and 12 km for HN-1 02 and a 20\({}^{\circ}\) sway for each satellite, the satellite orbit simulations show that these four satellites will offer 3.5 days of observation coverage of Hainan Island and 5 days of observation coverage for regions adjacent to Hainan Island (100 km buffer).  
Figure 3: On-orbit status of the HN-1 02 satellite.  
Figure 2: On-orbit status of the HN-1 01 satellite.
## 2 Methodology  
In order to realize a multiscale 3D GIS project, in which to test the interoperability levels (see Section 3), a methodology that considers the modeling phase according to the LoDs proposed by the CityGML standard and therefore intended as \"levels of detail\" has been adopted (for research purposes in this study we refer to LoD to the Level of Detail from CityGML standard, while LOD to the Level Of Development of the IFC standard).  
It has to be clarified that when approaching HBIM the levels of development are, rather, treated as levels of detail as qualified in the CityGML standard, eventually associated to simple levels of abstraction (LoA) [32], where the objects to be modelled or exchanged according to the scale of representation are defined.  
In the GIS environment, it is possible to visualize and query data from LoDs 0-1 in 2D or 2,5D, thanks to the integration of various cartographic data (both geometric and alphanumeric) from SDIs (spatial data infrastructures) and regional and national geoporals such as shapefiles, orthophotos and DEM (digital elevation model). On the other side, to reach the LoDs 2-3-4 it has been established to model the geometries in a specific software for building modeling. In particular, an object-oriented software of the BIM domain has been used with the subsequent insertion of this model into the GIS environment.  
As mentioned above, two methods have been tested to assess the data integration between the domains (Figure 1). The first consists in using commercial solutions that can allow an easier import, but limiting the openness of the system, inserting the model in its BIM proprietary file format (_rvt_ from AutoDesk Revit, version 2019) directly into the ESRI ArcGIS Pro commercial software (version 2.4.3). While the second, properly developed for the present study, consists in converting the Revit model in a GIS standard file format (.GML) and then insert it into a GIS software. The open source QGIS software has been used (version QGIS 3.10.2 A Coruna).  
A further study on the correspondence between the two standards' data structure, in order to understand the association and relations for each entity of the IFC with those of the CityGML, divided by LoDs, has been conducted (see Section 3.2).
## 2 Literature Review
### Background
#### 2.1.2 BCSR  
The efficient storage format for sparse matrix-vector product is highly hardware-dependent, which can be problematic for heterogeneous systems [16] and also has a variety of sparse matrix storage formats to choose the most optimal one. An efficient matrix-vector product involves multiple aspects, that is, the matrix pattern, to determine if the number of elements in each row varies strongly from row to row, if the sparse matrix is near a dense matrix or not. Knowing the matrix pattern, it is easier to find the most suitable storage scheme. The performance of the SpMV is affected by the temporal locality (reuse data that brought it into the memory) and spatial locality (use of every data element brought into the memory) of an application. Temporal locality can be improved with BCRS to maximize the data access before it is replaced in the cache.  
If the sparse matrix \(X\) is made up of dense blocks of non-zeros with a regular pattern, we can change the CSR format to BCSR for the efficient exploitation of those block patterns. Block matrices usually result from the scattering of partial differential equations with certain degrees of freedom associated with grid points. The matrix is then divided into smaller blocks equal in size to the number of degrees of freedom, and each block is treated as a dense matrix, even if there are zeros. For example, if we have nxn matrix having \(n_{b}\) as the dimension of block and \(nnz_{b}\) as the nonzero per block (see Table 1), then we require total memory given by:  
\[nnz=nnzb\times n^{2}_{b} \tag{2}\]  
The dimension of block \(n_{d}\) of matrix \(X\) is defined by  
\[n_{d}=\frac{n}{n_{b}} \tag{3}\]
## 3 Research Design of the Fieldwork and Results: Comparing Three Paradigms
### Research Results: Comparative Analysis of Three Paradigms
#### 3.2.3 Algorithmic Nations [67; 68; 69]
Territoriality and Legal Layer: Hybrid Boundaries and Strategic Compliance  
The Algorithmic Nations' approach to territory and its relationship with existing nation-states are defined by their emphasis on data sovereignty, cultural preservation, and the reconfiguration of governance structures through devolution and regionalization. 1. Relationship to Territory: Algorithmic Nations are not tied to a specific physical territory but are rooted in the cultural and community development of their groups. Operating through a stateless city-regional framework, governance is shaped by community needs rather than traditional boundaries. Central to this is data devolution, where control over data and decision-making is localized, allowing communities to exercise digital sovereignty and autonomy without a contiguous landmass. This fluid, transnational approach prioritizes cultural and social development over geographical constraints. 2. Relationship to Nation-State Laws and Policies: Algorithmic Nations transform their interaction with nation-states by advocating for governance rescaling through devolution. Power is transferred from centralized structures to local levels, where communities govern themselves using digital tools and data-driven processes. By leveraging existing laws and pushing for reforms, these nations create autonomous, data-driven interoperable governance structures that reflect their cultural and cooperative ideals. This model gradually transforms nation-states into decentralized, digitally empowered entities, where sovereignty is shared across different governance levels. Additionally, they navigate legal frameworks to establish spaces of autonomy, enabling collaboration across borders.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 1 Introduction
In Section 2, we present the theoretical background for PO and MST and we introduce the mixed PO/MST model developed by the authors in [7]. In Section 3, we build the new channel synthesizer based on the models presented in Section 2. After that, in Section 4, we validate the developed synthesizer by using an artificial scenario. In Section 5, we validate the synthesizer against an actual experimental dataset obtained at 2 GHz. Section 6 then concludes this paper.
# Traffic Signal Time Optimization Based on Deep Q-Network
## 3 Problem Formulation  
The multi-intersection TSC optimization problem involves maximizing the capacity of the target intersection through cooperation with the surrounding intersections. Capacity is defined as the number of vehicles passing through an intersection during a unit of time. The waiting time denotes the amount of time it takes a vehicle to exit the intersection from the time it stops at the intersection. A large capacity at the intersection results in fewer waiting vehicles at the intersection and shorter waiting times for them.  
To increase the capacity at an intersection, it is important to optimize traffic signals. Specifically, the efficiency of green signals is important for handling many vehicles at an intersection. The more vehicles exit during a given green signal, the greater is the capacity at the intersection during the time unit. However, a fixed traffic signaling can cause green signal wastage because the green signal times are constant regardless of the traffic conditions. Green signal wastage means that although the lane has been allocated green light, there are no vehicles waiting; thus, there are no vehicles leaving the lane. Minimizing the wastage of green signals improves the capacity at an intersection.  
Figure 1 shows an example of the time wasted on green signals at a single intersection using a fixed TSC. This experiment was conducted to analyze the efficiency of green lights in a fixed signaling system. The intersection used in this experiment was in the form of a single intersection with a four-way structure; the impact from the surrounding intersections was not considered. The distribution of vehicles used in the experiment was based on data with low traffic load. This is because a lower traffic load can more clearly show the impact of green signal wastage. According to [PERSON] and [PERSON] [19], the time of day with less traffic load in urban areas is before 8 am or after 8 pm on weekdays. Therefore, we conducted the experiments using traffic data from the early hours of the day in an urban area.  
Figure 1: Green time (GT) wastage owing to the fixed green signal system.  
The experiment result showed that approximately 30% of the green signal time was wasted in the fixed signaling system. This is because more green signal time is allocated than is needed. When the duration of green signals is distributed efficiently, more vehicles can be handled at an intersection. However, dynamic green-time assignments can cause a selfish signal distribution. Intersections are chain structures that are connected to the surrounding intersections. Consequently, there can be situations in which only high-demand lanes receive signals to handle many vehicles at an intersection. In other words, the selfish distribution of signals may result in lanes not receiving green signals. A traffic signal system should distribute signals to all lanes. Therefore, a fair and reasonable signal distribution is important.
## 2 Discrete Element Modeling
### Generation of the Particle Aggregate  
To evaluate the effect of particle morphology on the mechanical response of granular materials, a numerical triaxial compression test was simulated via the particle flow code PFC 3D. A multi-shaped particle clump model, designed using Rhino 7 software, was integrated into the analysis framework, with key parameters listed in Table 1. The clump porosity command was used to generate the required porosity, and the initial porosity was set to 0.9225 (the porosity of the particle aggregate was compressed to 0.38). In consists of the following four steps:  
(1) Generate numerical simulation specimen. A cylindrical specimen (height: 1.2 m, diameter: 0.6 m) was generated by randomly distributing non-contacting clumps within the volume.  
(2) Pre-compaction. The particle friction coefficient was set to 0.1 and the gravitational acceleration was set to 0 to obtain a denser initial specimen under various isotropic states. If the circumferential pressure is set to 20 kPa, the cylinder height is about 0.6 m.  
(3) Consolidation. Modified to test target confining pressure (200 kPa; 400 kPa; 600 kPa; 800 kPa).  
(4) The particle friction coefficient was adjusted to 0.5, and a gravitational acceleration of 9.81 m/s\({}^{2}\) was applied. Loading was terminated when the axial strain reached 60%, and all mechanical data (e.g., stress, strain, force chains, coordination numbers) were recorded during compression for subsequent macro-micro analysis.  
Figure 2 shows the generation process of the specimen with the particle shape EI0.6 FI0.6 and 200 kPa, including the initial generation state and the consolidation completion state.  
The effect of particle morphology on the initial void ratio of particle aggregate after consolidation has been discussed by many scholars. The previous studies mainly focused on the effect of particle EI, while the effect on FI is less discussed. Figure 3 shows the initial pore ratios of aggregates composed of different EI, FI and EI/FI particles under different lateral pressures. As can be seen from Figure 3a, the initial pore of the specimen decreases  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & **Value** \\ \hline Particle density, \(\rho\) [kg/m\({}^{3}\)] & 2600 \\ Particle–particle friction coefficient, \(\mu\) & 0.5 \\ Wall–particle friction coefficient, \(\mu_{\mathrm{w}}\) & 0 \\ Damping coefficient, [N/(m/s)] & 0.7 \\ Particle–wall contact modulus [Pa] & \(1\times 10^{9}\) \\ Particle–particle contact modulus [Pa] & \(1\times 10^{8}\) \\ k\({}_{\mathrm{n}}\)/k\({}_{\mathrm{s}}\) & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Microscale parameters used in the DEM simulations.  
first and then increases with the increase in EI value under the same initial conditions and consolidation process. This is consistent with previous studies [29]. In Figure 3b,c, the effect of FI and EI/FI values on the initial void ratio of the particle aggregate is significantly different from that of EI. When considering FI, the initial void ratio of the particle aggregate is all small values.
# Secure Monitoring System for IoT Healthcare Data in the Cloud
## 4 Technological Challenges in Microbiology
IoT devices can help with ongoing patient monitoring by sending information to healthcare professionals regarding infection signs, the efficacy of treatments, and the progression of recovery. Antibiotic resistance surveillanceAntibiotic resistance is being tracked and addressed, which is a challenge for world health. Real-time monitoring of antimicrobial use and resistance patterns by IoT-enabled equipment can help with surveillance and well-informed decision-making. Quality control in the food and beverage industryTesting is required frequently to ensure microbiological safety in the manufacturing of food and beverages. IoT sensors can monitor important parameters like temperature and cleanliness, preventing contamination and preserving product quality. Data security and privacyStrong security measures are needed while handling sensitive microbiological data to prevent unwanted access or data breaches. Encryption, secure communication protocols, and adherence to pertinent data protection laws should all be used in IoT solutions. IoT and associated technologies, which allow real-time data collecting, automation, remote monitoring, and improved collaboration, present intriguing answers to these problems. However, data integrity, validation of IoT-generated data, and efficient integration with current laboratory operations should all be taken into account when implementing the IoT in microbiology.
###### Abstract  
This study utilized eggshell biomass as a calcium precursor for synthesizing hydroxyapatiteapatite (Hap) through a co-precipitation method assisted by a combined microwave-ultrasound (Mu/Us) crystallization process. Different milling techniques (mortar, high-energy mill, and sieving) were employed to prepare the eggshell biomass and identify the most effective calcium precursor. The precursor derived from high-energy milling, followed by sieving and thermal treatment at 750 \({}^{\circ}\)C (designated as Sample Hap-H3 750), was selected due to its higher porosity, enhanced crystallinity, and smaller particle size than other synthesized materials. This sample was subsequently used as a carrier for the plant hormone forchlorfenuron (FCF), forming the composite Hap-FCF. Comprehensive characterization was conducted using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), specific surface area analysis (BET method), zeta potential (ZP), scanning electron microscopy (SEM), and bright-field transmission electron microscopy (BFTEM), ensuring reliable and robust data. The in silico evaluation of the phytohormone FCF with two receptors, gibberellin (GA3 Ox2) and auxin (IAA7), produced notable results. Docking and molecular dynamics (MD) simulations demonstrated that the gibberellin receptor was preferentially stimulated, as shown by the higher binding affinity and the receptor's sustained stability during the MD simulations. These findings underscore the potential applications of this research, emphasizing its significance in materials science and biochemistry. Moreover, the in vitro assessment of Hap-H3 750, Hap-FCF, FCF, and the control (distilled water) on the germination and growth of butterhead lettuce seeds (_Lactua sativu_) over 30 days revealed that Hap-H3 750 and Hap-FCF promoted plant growth by 275-330% relative to the control. This effect was attributed to the preferential stimulation of the gibberellin receptors responsible for stem and root elongation. These results suggest that HAP nanoparticles could facilitate the controlled delivery of FCF in the agricultural sector, promising to be an effective nanofertilizer.  
Keywords:hydroxyapatite; forchlorphenuron; nanoparticles; eggshell; grinding; in silico This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 3 Proposed Methodology
### Real-Time Analysis of Stuck Pipe Trend Based on Inversion of Friction Coefficient
#### 3.1.2 Real-Time Inversion of Friction Coefficient  
The classical torque and drag models include soft string model [26] and stiff string model [27]. The soft string model without any consideration for the string stiffness is simple and applied earlier, but it is only suitable for vertical wells or wells with small inclination. The stiff string model with considering the stiffness of string is more complex, but it is more suitable for extended reach wells, horizontal wells and other non-straight wells. The stiff string model is used for the real-time inversion of friction coefficient.  
The stiff string model is as follows:  
\[\frac{d(-F)}{ds}=-EIk_{\text{b}}\frac{dk_{\text{b}}}{ds}-q\cos\alpha\pm\mu_{1} \cdot n_{\text{t}} \tag{7}\]  
Figure 3: Comparison of predicted and true TOB.  
Figure 2: Comparison of predicted and true WOB.  
\[\frac{dM}{ds}=\mu_{2}\cdot\mu_{\text{t}}\cdot\frac{D_{\text{bi}}}{2} \tag{8}\]  
\"\(\pm\)\": Negative for lifting condition, positive for lowering condition; \(F\): axial load on the drill string, N; \(S\): depth, m; \(q\): Gravity per unit length of drill string, N/m; \(\alpha\): inclination angle, rad; \(EI\): bending stiffness of drill string, N m\({}^{2}\); \(n_{\text{t}}\): Contact force between drill string and wellbore, N/m; \(\mu_{1}\): Axial friction coefficient; \(\mu_{2}\) Circumferential friction coefficient; \(k_{\text{b}}\): hole curvature, rad/m.  
Calculation formula of borehole curvature \(k_{b}\):  
\[k_{b}=\sqrt{\left(\frac{d\alpha}{ds}\right)^{2}+\sin^{2}\alpha\left(\frac{d \phi}{ds}\right)^{2}} \tag{9}\]  
Calculation formula of contact force \(n_{t}\) on drill string:  
\[n_{\text{t}}=\sqrt{\frac{A^{2}+B^{2}}{1+\mu_{2}^{2}}} \tag{10}\]  
\[A=EI\frac{d^{2}k_{\text{b}}}{ds^{2}}+k_{\text{b}}F-k_{n}(-k_{\text{b}}M_{ \text{T}}+EI\cdot k_{\text{b}}k_{\text{n}})+\frac{q}{k_{\text{b}}}\frac{d \alpha}{ds}\sin\alpha \tag{11}\]  
\[B=\frac{d}{ds}(-k_{\text{b}}M_{\text{T}}+EI\cdot k_{\text{b}}k_{\text{n}})+EI \cdot k_{\text{n}}\frac{dk_{\text{b}}}{ds}-\frac{q}{k_{\text{b}}}\frac{d\phi} {ds}\sin^{2}\alpha \tag{12}\]  
\[k_{\text{n}}=\frac{\sin\alpha}{k_{\text{b}}^{2}}\left(\frac{d\alpha}{ds} \frac{d^{2}\phi}{ds^{2}}-\frac{d\phi}{ds}\frac{d^{2}\alpha}{ds^{2}}\right)+ \cos\alpha\left(\frac{1}{k_{\text{b}}^{2}}\left(\frac{d\alpha}{ds}\right)^{2} +1\right)\frac{d\phi}{ds} \tag{13}\]  
\(\phi\): azimuth, rad; \(k_{\text{n}}\): borehole torsion, rad/m.  
Taking the above four basic parameters into the torque and drag model, the real-time inversion of friction coefficient can be realized by dichotomy, as shown in Figure 4. The inversion result of friction coefficient is shown in Section 3.3.  
Figure 4: Flow chart of real time inversion for friction coefficient.