esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Experimental Results
So the estimation of current bias is acceptable in the practice. Figure 13 depicts the changing in voltage and current through the cells of the cell Module 1. The current through the nine cells has the same varying as the current through the LiBP. The Figure 14 plots the comparison of SoC estimation errors of seven cell modules of LiBP using the first and second order dynamic models. The results show that when using the second order dynamic model of the cell, the SoC estimation of the cell modules in the LiBP is about 10 times smaller than using the first order dynamic model. The comparison result is illustrated in the Table A3. Figure 12: The current bias estimation of current through LiBP. Figure 13: Voltage varying and currents through the cells in cell Module 1. Figure 15 is the comparison of the SoC estimation errors of the cell modules in the LiBP tested by operating temperatures: \(-5\), \(5\), \(15\), \(25\), \(35\) and \(45\)\({}^{\circ}\)C. In general, the SoC estimation errors of the cell modules are small at the temperatures ranging from \(-5\) to \(25\)\({}^{\circ}\)C (the SoC of cell modules is estimated more accurately). At the temperature \(-5\)\({}^{\circ}\)C, the SoC estimation error is positive; this means that the SoC estimation errors will be much larger than the actual cell module average SoC, about \(2\%\). However, the SoC estimation error of cell module is negative for temperature ranging from \(35\)\({}^{\circ}\)C to \(40\)\({}^{\circ}\)C, so the SoC estimation is much smaller than actual cell module average SoC, about \(2\%\). This SoC estimation is a very significant for the SoC estimation for LiBP compared with methods presented in the literature [3,18]. The calculation time of the SoC estimation algorithm in this test is \(0.002\) s, corresponding to a sample time of T\({}_{\rm s}\) = 1 s.  
Figure 14: The SoC estimation error comparison between the two _cases_: using the first order dynamic model and the second order dynamic model of the cell at \(25\)\({}^{\circ}\)C.  
Figure 15: The SoC estimation of cell modules with respect to the temperatures: \(-5\), \(5\), \(15\), \(25\), \(35\) and \(45\)\({}^{\circ}\)C.
# Pointing Accuracy of an Operational Polarimetric Weather Radar
## 3 Solar Boxscans  
The scanning domain of a box scan is centered around the position of the sun at the beginning of the scan. The scan itself takes about 4 min to complete. During this time the scanning domain is not adjusted to account for the movement of the sun. In the analysis of the data we correct for the movement of the sun by computing the position of a ray relative to the sun using the ray time tag from which the position of the sun is computed. The domain of the box has a size of 8\({}^{\circ}\) by 5\({}^{\circ}\) in azimuth and elevation, respectively. A ray is defined by 128 pulses. With an az-speed of 2\({}^{\circ}\)/s the ray width is about 0.32\({}^{\circ}\) with a PRF of 800 Hz. In elevation we step with 0.1\({}^{\circ}\). Range resolution is set to be 250 m and data up to a range of 150 km are acquired.  
Prior each boxscan scan a noise sample is taken at the elevation of the sun, and 30\({}^{\circ}\) off the azimuth of the sun. The solar boxscan is scheduled every 10 min. Depending on the date, up to 90 box scans are acquired per day. Within a 10 min sequence the sun moves about 3\({}^{\circ}\) in elevation and azimuth. The scans were performed when no precipitation was expected during the day. Overcast situations are no limitation to the scans, but most of the time we had clear or only partial cloudy skies. The radar moments for each box scan are saved in a HDF5 format. In this analysis we consider the signal to noise ratio SNRh and SNRv (horizontal and vertical, respectively) which are computed from unfiltered signal power (no Doppler clutter filter is applied) and where only noise is subtracted.
# A Binocular Vision-Based 3D Sampling Moire Method for Complex Shape Measurement
## 4 Experimental Verification and Application  
### Experimental Verification  
After digital experimental verification of the 3D sampling moire method, we used three-point bending specimens for experimental verification of the shape measurement. The experimental equipment is shown in Figure 9a. The projector projected the fringe pattern onto the specimen surface, and a high-precision stepper motor was used to apply displacement load and generate out-of-plane displacement at the panel center. The deformed fringe images were captured by the left and right cameras, and the shape was calculated by 3D sampling moire and compared with the real value. In this experiment, the three-point bending specimen size was set to 100 \(\times\) 10 \(\times\) 2 mm\({}^{3}\), while the projector and cameras were set about 750 mm away from the specimen. It is worth noting that the  
Figure 8: Comparison results of the RMS phase error for different fringe conditions.  
stepper motors advance by a fixed step displacement (0.5 \(\upmu\)m) each time a pulse signal is provided. In each displacement loading, 2000 stepping actions of the stepping motor are used to produce an out-of-plane displacement of 1.000 mm. The projector and camera resolution were 1920 \(\times\) 1080 pixels and 1280 \(\times\) 1024 pixels, respectively, and the projected fringe spacing was 9 pixels.  
Figure 9b shows the 3D shape of the three-point bending specimen under different displacement loadings. Among them, \(L_{0}\)-\(L_{3}\) refers to the centerline of the specimen (shown by the red line). The \(x\)-\(z\) coordinates of the centerline are shown in Figure 9c. Compared with the real displacement of 1 mm, the calculated displacement at the top of the centerline was 1.047 mm, 0.966 mm, and 0.988 mm, respectively, and the average measurement error of the out-of-plane displacement was 31 \(\upmu\)m. This result was close to the measurement accuracy of the binocular projection fringe method based on phase shifting [32; 33], and sampling moire is more suitable for dynamic measurement because the phase analysis can be completed with only one image.
## 4 Discussion  
A prestress modal analysis dynamic model was established based on the structural features of the self-made RSS base-rotor system simulation test bench. The RSS base was modeled in solid form using UG software, and the mesh was discretized and post-processed in ANSYS Workbench, offering a useful reference for the dynamic analysis of similar systems. A static analysis of the base under bearing loads was conducted, where fixed support boundary conditions were chosen to accurately reflect the stress situation of the bearing outer ring on the base, identifying the location of the maximum stress.  
The first six modes of the RSS base were analyzed based on ANSYS Workbench, with the natural frequencies and mode shape contour maps of the RSS base obtained. The support plate was optimized using the equivalent static load model, which led to a decrease in the overall frequency of the base. After optimizing the support plate width from 11.5 mm to 7 mm, the first natural frequency of the base decreased to 30.34 Hz. The dynamic characteristics of the base changed from rigidity to flexibility, avoiding the shaft vibration frequency and the operating frequency of the gas turbine. This met the design requirements for vibration characteristics, prevented resonance in the flexible RSS base, and ensured the safety and reliability of the structure, serving as a useful reference for gas turbine design.  
A harmonic response analysis was conducted to study the system's response at different rotational speeds within the set sweep frequency range under a given excitation force. When the RSS base operates in the low-frequency range, the maximum deformation occurs at the center of the tangential support plate, with a peak deformation of 2.1 um, completely meeting the design requirements. It also verifies that if resonance occurs, it will be in the vicinity of the 30 Hz region.  
The constrained modal experimental testing of the RSS base was carried out using the hammering method, and the results were compared with the simulation results, with errors within 10%, thus verifying the accuracy of the finite element model. The simulation results and optimization method offer valuable reference data.
# Tablet Scoring: Current Practice, Fundamentals, and Knowledge Gaps
## 4 Tablet Scoring
### Tablet Splitting: Regulatory Guidance  
To date there have been no specific regulatory requirements that govern the practice of tablet scoring. The pediatric and geriatric patient populations are the two groups that are more susceptible to this practice [135; 136; 137]. The current practice is to score the tablet to a reduced dose; the scored tablet is then administered with food and/or beverage as a surrogate in order to mask the taste of the drug substance in some cases. The challenges with that is the risk on under dosing or over dosing, where both can lead to serious adverse drug reaction [138].  
The United States Food and Drug Administration (FDA) recently began to develop a new guidance for tablet scoring [139]. However, the concept of tablet scoring has not been fully investigated and the resulting data are not well understood. Across drug agencies worldwide, there are very few published standard data available to address this practice.  
One important factor in analyzing a final drug product is to ensure that the composite sample of the final drug products is within specification guidelines. The guidelines are determined within the scope of the United States Pharmacopeia (USP) requirements [139]. The challenge with tablet scoring is that it introduces weights and/or uniformity variations [108]. If there are weight variations within a composite sample, the potency of that composite samples may also vary [108]. There is a direct correlation between the sample size and potency [118].  
Weight and uniformity variation can expose a patient to:  
1. Drug underdose. This is a case of a patient who is continuously being exposed to a low (inefficacious) amount of a drug, hence under dosing. This can potentially create resistance to that drug, which can render the therapy ineffective. That may cause serious side effects, may prevent the drug from working properly, and/or may slow down the efficacy of the therapy [140].
2. Drug overdose. This can be very harmful to patients, especially for high potency drugs [138].  
The FDA guidance as it is written is similar to that of unscored tablets with added requirements to prevent risks associated with drug exposures upon administration [130].  
* All scored tablets should be stable at: 5 \({}^{\circ}\)C, 25 \({}^{\circ}\)C/60%RH, 40 \({}^{\circ}\)C/75%RH for up to 90 days. Stability studies should be performed in appropriate container closures.
* Scored tablets should be stable in pharmacy dispensing containers for up to 90 days 25 \({}^{\circ}\)C/60%RH.
* The label should encompass the therapeutic dose.
* Enteric coated tablets should not be scored.
* The physical characteristic criteria for scored tablets are similar to that of whole tablets.
* Scored tablet Content Uniformity and Uniformity of Dosage unit as specified in USP 37 chapter 905.
* Scored tablet Water Content as specified in in USP 37 chapter 921 [65].
* Scored tablet Dissolution as specified in USP 37 chapter 711.
* Scored tablet Microbial Examination as specified in USP 61 and 62.
# Harnessing Artificial Intelligence for Wildlife Conservation
## 2 Methodologies
### AI Models  
The foundation of Conservation AI's technology lies in its sophisticated AI models, designed to perform complex image recognition tasks with a high degree of accuracy. Central to these models are CNNs [6] and transformer architectures [7], both of which have proven exceptionally effective in the domain of computer vision. CNNs are particularly well suited for processing grid-like data, such as images, excelling at identifying patterns, edges, and textures. Conversely, transformer models are adept at capturing long-range dependencies and contextual relationships within data, making them powerful tools for interpreting more complex visual information.  
Conservation AI's models are developed using TensorFlow [11] and PyTorch [12], two of the most widely used open-source machine learning frameworks developed by Google and Meta, respectively. These frameworks provide robust and flexible platforms for designing, training, and deploying machine learning models at scale. The training process is both intensive and iterative, involving the ingestion of vast datasets comprising thousands of labelled images. These images represent a wide variety of species, human figures, and objects characteristic of typical animal behaviours, as well as potential indicators of poaching activities.  
During training, the AI models are exposed to these labelled datasets, allowing them to progressively learn the distinguishing features and patterns associated with each category. This process, known as supervised learning, enables the models to continually improve their accuracy in detecting and classifying entities within new, unseen data. The models not only learn to identify different species and human presence but also recognise specific behaviours and anomalies that might suggest threats to wildlife, such as poaching or habitat encroachment [13].  
What sets Conservation AI apart from other approaches is its careful crafting and continual refinement of these AI models. Techniques such as data augmentation [14], transfer learning [15], and fine-tuning [16] are employed to maximise model effectiveness. Data augmentation systematically varies training images--through rotations, translations, and other transformations--to help the models generalise better to different scenarios. Transfer learning allows the AI to leverage pre-trained models, adapting them to the specific requirements of conservation tasks with reduced computational costs and time. Fine-tuning further refines these models, ensuring they are sensitive to the nuances of local ecosystems and species.  
In addition to these technical advancements, Conservation AI continuously updates and enhances its models by incorporating new data and feedback from conservation partners. This iterative improvement process, which we term situated learning and precision modelling, ensures that AI remains at the cutting edge of technology, providing reliable and actionable insights that support global conservation efforts.
## 2 Materials and Methods
### Flux Data Measurement, Processes, and Gap Filling  
A close-path EC system (CPEC200, Campbell Scientific Inc., Logan, UT, USA) was used to monitor water and carbon fluxes between the land surface and atmosphere. It was positioned on a tower placed 8 m AGL within a kiwifruit orchard spanning approximately 133 hectares (30.3265N, 103.4252E, altitude 537 m). The orchard had adequate fetch and met the requirement for EC measurement. The EC system consisted primarily of a closed-path CO\({}_{2}\)/H\({}_{2}\)O gas analyzer (EC155, Campbell Scientific) to measure CO\({}_{2}\) and water vapor concentrations and a three-dimensional sonic anemometer (CSAT3A, Campbell Scientific) to measure wind velocity. The original 10-Hz flux data underwent standard processing, such as 2-D coordinate rotation, time-lag compensation, and low- and high-frequency corrections [30], and were aggregated using a data collector (CR6 Campbell Scientific) and averaged at 30-min intervals. Datapoints with latent flux (LE) and sensible heat flux (H) below \(-200\) W m\({}^{-2}\), \(H\) exceeding 500 W m\({}^{-2}\), and \(LE\) exceeding 800 W m\({}^{-2}\) were excluded from half-hour data. Nighttime friction wind speeds below 0.15 m s\({}^{-1}\) were disregarded to ensure that flux data were collected under high-turbulence conditions [6]. Subsequently, 11.5% of daytime data points and 38.7% of nighttime data points were removed.  
In instances where LE data were missing for less than 2 h, linear interpolation was used to fill the gaps, while the daily average variation (DAV) interpolation method was used for intervals exceeding 2 h [30; 31]. Net ecosystem exchange (\(NEE\)) data gaps of shorter durations (\(\leq\)2 h) were addressed using linear interpolation. For longer data gaps (\(>\)2 h), missing daytime \(NEE\) data were filled using an empirical equation calibrated with high-quality data [32]. Missing nighttime \(NEE\) data and daytime ecosystem respiration (\(R_{\text{e}}\)) were estimated using a regression equation correlating nighttime \(NEE\) with soil temperature at a depth of 10 cm [33]. Ecosystem \(GPP\) was derived as follows: \(GPP_{\text{EC}}=R_{\text{e}}-NEE\).
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Interoperability and Integration
#### 3.1.3 Interoperability with Healthcare Platform  
CoAP is employed in [11] to design and implement an international healthcare platform in conjunction with IEEE 11073 PHD [36] which is a healthcare standard developed for interoperability between medical devices and systems. An IoT device in the proposed architecture has a CoAP server, healthcare data collector and a state manager. The CoAP server performs the roles of (i) courier i.e., it handles all the requests and responses to the server, (ii) manager i.e., it performs the task of maintaining resource list, and (iii) coordinator i.e., provides harmony between state manager and data collector. The healthcare data collector is responsible for collecting data through the physical layer and converting it to IEEE/ISO 11073 PHD standard format. Finally, in the physical layer, the state manager controls the IoT device's states. The proposed system enables the application of ISO/IEEE 11073 DIM/Service Model integrated with CoAP for IoT environments.
# Theoretical Analysis on the Effectiveness of Pipe Roofs in Shallow Tunnels
## 3 Evaluation of the Effect of Pre-Support of the Pipe Roof  
### The Index of the Pipe Roof Pre-Support Effect Evaluation  
In the tunnel construction stage, the pipe shed is used for pre-support to control the stratum deformation and improve the stability of the excavation face. Therefore, it is important for the development of pipe shed design theory to select appropriate indicators to evaluate the effect of pipe shed pre-support. In shallow-buried tunnels, formation deformation is manifested by convergence of the surrounding rock and surface settlement. The effects of pipe shed advance support should also be evaluated based on these two aspects.  
The convergent deformation at each key point of the tunnel section is usually used to evaluate the ability of the supporting structure to control surrounding rock convergent deformation. However, when this index is used to measure the effect of controlling surrounding rock deformation, the results vary greatly under different geological conditions and excavation sections. It is, therefore, difficult to establish a unified evaluation standard to compare the actual ability of the supporting structure to control surrounding rock deformation under different working conditions, which restricts the development of supporting structure design theory. In this paper, the ratio of the area of the intruded excavation contour to the area of the designed excavation section caused by radial convergent deformation of the surrounding rock under pre-support of the pipe shed is defined as the stratum loss rate. This index is used to measure the effect of the pipe shed on controlling surrounding rock deformation. This relative index can be used to compare and analyze the effects of pipe shed pre-support under different working conditions with relatively uniform standard results.  
To date, the influence of shallow tunnel construction on surface settlement has not been solved theoretically. Therefore, this paper uses the Peck formula, which is often applied in subway construction, to calculate the distribution of surface subsidence deformation caused by tunnel construction. The maximum settlement of the section and the width of the settlement trough are used to evaluate the effect of the pipe shed on controlling surface settlement.
# Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
## 3 RFID Link Budget Calculation
### Uplink (Tag to Reader) Communication  
If the power received by the RFID tag is higher than the predefined threshold, the tag absorbs and re-emits a fraction \(\eta\) of this power. As defined in the ISO/IEC 18000 family of international standards, the tag will usually employ FM0 or Miller baseband encoding techniques, as well as ASK or PSK modulation schemes, to transmit the information. The procedure to calculate the power received by the reader in the vehicle is essentially the same described in the previous section.  
The presented model has been used to evaluate the reliability of the radio-link between the vehicle's reader and the RFID tag and it will be validated at simulation level in Section 4.2 and experimentally in Section 4.3, by adjusting the design parameters to the real application.
# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
###### Abstract  
This paper proposes a green computing strategy for low Earth orbit (LEO) satellite networks (LSNs), addressing energy efficiency and delay optimization in dynamic and energy-constrained environments. By integrating a Markov Decision Process (MDP) with a Double Deep Q-Network (Double DQN) and introducing the Energy-Delay Ratio (EDR) metric, this study effectively quantifies and balances energy savings with delay costs. Simulations demonstrate significant energy savings, with reductions of up to 47.87% under low business volumes, accompanied by a minimal delay increase of only 0.0161 s. For medium business volumes, energy savings reach 26.75%, with a delay increase of 0.0189 s, while high business volumes achieve a 4.36% energy reduction and a delay increase of 0.0299 s. These results highlight the proposed strategy's ability to effectively balance energy efficiency and delay, showcasing its adaptability and suitability for sustainable operations in LEO satellite networks under varying traffic loads.  
L SNs; business aggregation; double DQN; energy efficiency; green computing
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
## 2 Dual-Port Inverted T-Shaped MIMO Antenna and Analysis
### Diversity Gain  
Communication reliability and performance are improved when additional elements are used at the transceiver. Multi-path propagation, interference, and signal fading can be reduced in MIMO systems using diverse signal routes, such as spatial, polarization, or frequency diversity. Better data speed, more coverage, and improved system reliability overall are the outcomes thereof, especially in challenging wireless environments. Diversity gains of more than 9.99 were achieved for the MIMO antenna and the MIMO antenna with an FSS, as shown in Figure 15.  
Figure 12: (a) VSWR of the single-patch antenna, (b) VSWR1 of the MIMO antenna (port 1), (c) VSWR2 of the MIMO antenna (port 2), (d) VSWR1 of the MIMO antenna with an FSS (port 1), (e) VSWR2 of the MIMO antenna with an FSS (port 2).  
Figure 13: Envelope correlation coefficient of the dual-port antenna and the dual-port antenna with an FSS obtained from the scattering parameters.