esa-sceva/satcom-chunk-collection huggingface dataset:
# Super-Frequency Sampling for Thermal Transient Analysis
## 2 Super-Frequency Sampling  
### Periodic Non-Uniform Sampling  
When sampling a time-dependent temperature signal, \(T(t)\), at discrete times, \(t_{i}\), two parameters determine the accuracy of the observed signal. One is the sampling period, \(\Delta t_{\mathrm{s}}=t_{i+1}-t_{i}\), which describes the time between two neighboring measuring points. The other one is the maximum frequency, \(f_{\rm max}\), present in the signal. If the sampling frequency, \(f_{\rm s}=1/\Delta t_{\rm s}\) is larger than \(2f_{\rm max}\), the signal is reconstructed with good accuracy. This is the essence of the Nyquist-Shannon sampling theorem.  
However, for a sampling frequency that is too low, a reconstruction is still possible. The approach is to periodically repeat the signal with a period, \(\tau_{\rm set}\), which is chosen such that \(\tau_{\rm set}\) mod \(\Delta t_{\rm s}\
eq 0\). Then, the time series is collapsed, i. e., each time \(t_{i}\) is mapped to a point within the first period. These new times are denoted \(t_{i}^{\prime}\) and are calculated as  
\[t_{i}^{\prime}=t_{i}\,\text{mod}\ \tau_{\rm set}\,. \tag{1}\]  
As an example, Figure 1 (left) shows a thermal transient with a period of \(\tau_{\rm set}=20\,\)s, which is recorded at a sampling period of \(\Delta t_{\rm s}=6\,\)s. Each transient is sampled at three to four points, \(T(t_{i})\). Applying (1) results in a reconstructed signal with a sampling period, \(\Delta t_{\rm r}=\Delta t_{\rm s}/3\) and \(11\) points per period, as shown in Figure 1 (right).  
To measure the thermal transient with an \(N\)-times increased sampling frequency, the following approach is appropriate: considering a sampling period, \(\Delta t_{\rm s}\), as given by the recording device, \(\tau_{\rm set}\) has to be chosen according to (2). Experimentally, a minimal sampling period \(\tau^{\prime}\) is usually required to capture the entire transient. Here, \(\tau^{\prime}\) has to fulfill the additional constraint, \(\tau^{\prime}\) mod \(\Delta t_{\rm s}=0\). The period allowing for super-frequency sampling is then calculated as  
\[\tau_{\rm set}=\tau^{\prime}+\frac{\Delta t_{\rm s}}{N}\,. \tag{2}\]
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 3 Related Work
### Edge Computing for AI  
All the studies reviewed previously in this section used edge-cloud computing but for the non-machine learning task. The edge processing needs to accommodate the final machine learning computation for machine learning activities while decreasing the required network bandwidth and traffic ([PERSON] et al., 2018). Our work focused on using edge-cloud computing for machine and deep learning applications. Hence, it is essential to review a few works in this category.  
[PERSON] et al. (2018) addressed the mapping of deep learning and information-centric networks (ICN) with IoT at edge computing and their potential benefits. They also introduced an effective component that enhances IoT by making the edge more intelligent. As a result, a convolutional neural network (CNN) model can be employed in the IoT area to use data from a complicated environment reliably. Furthermore, reinforcement learning (RL) and recurrent neural networks (RNN) have recently been integrated into IoT, which can be utilised to account for data multimodality in real-time applications. [PERSON] et al. (2018)proposed a distributed framework, DeepDecision, that links computationally weak devices (assuming that they're smartphones) to more robust back-end devices that let deep learning in choosing local or remote execution. Any device that delivers the required computing power can be worked as the back-end helper. They are especially interested in running a CNN developed for recognising objects in real-time for augmented reality applications. Another work by [PERSON] et al. [48] presented a unique distributed deep learning framework (DDL). The DDL enables mobile edge nodes in the network to cooperate and interchange information in order to decrease the error of content demand estimation without compromising mobile users' private information. They demonstrated through simulation findings that their proposed approach can improve accuracy by lowering the root mean squared error (RMSE) by up to 33.7 percent and minimising service latency by 47.4 percent compared to other machine learning techniques.  
All the papers reviewed in this section show that cloud computing is the computing platform that is currently being used to analyze and monitor the quality of water. Therefore, we decided to take advantage of distributed computing and apply edge computing to monitor water quality and make real-time decisions.
## 3 Experimental Results and Discussion
### Hysteresis Curves  
One of the important indicators to examine a structure's seismic behavior is to analyze the deformation capacity and the energy dissipation capacity of the links using the hysteresis curve. Figure 9 shows the hysteresis responses of Specimens L1-L8 under the cycle loading. All specimens first went through the elastic stage, and then the link web began to yield. As a result, the plastic deformation of the structure gradually developed and the rigidity decreased as the loading cycles increased. Comparing the hysteresis curves of the specimens, the hysteresis loops of Specimens L1, L2, and L6 are quite different from those of the others, which were induced by the brittle failure of the link flange-to-end plate weld. The residual stress had a great influence on the link weld, non-ductile failure occurred on the welds, and the plasticity of the specimens was not fully developed. These specimens exhibited different levels of pinching in the hysteresis loops due to slippage of the high-strength bolts. On the contrary, Specimens L3, L4, L5, L7, and L8 with the welding access holes showed full and stable hysteresis loops with stable energy dissipation. It showed that the welding access holes of the link  
Figure 8: Yielded points on the web of each specimen. (**a**) L1; (**b**) L2; (**c**) L3; (**d**) L4; (**e**) L5; (**f**) L6; (**g**) L7; (**h**) L8.  
reduced the influence of residual stresses on the welds, and the energy dissipation behavior of the link was improved.  
Figure 9: Hysteresis curves of specimens. (**a**) L1; (**b**) L2; (**c**) L3; (**d**) L4; (**e**) L5; (**f**) L6; (**g**) L7; (**h**) L8.
# Improving Very High Plastic Clays with the Combined Effect of Sand, Line, and Polypropylene Fibers
## 2 Research Significance  
From examining the literature, it can be observed that most experimental studies on the topic of fiber reinforced soils have been conducted on granular or low plasticity, fine-grained soils [14; 15; 16; 17]. However, the fiber-sand contact mechanism differs greatly from the respective fiber-clay mechanism. The first is purely frictional, while in the second, in addition to friction, the contact mechanism involves adhesion, thus the latter is stronger for high plasticity clays, although the mechanism has not yet been fully understood [14]. Furthermore, previous works indicate a rather limited effect of sand addition on the strength of clay. However, the presence of lime significantly improves the strength but decreases the ductility of clay [11; 13].  
The present study aims to investigate the potential beneficial effect of the use of PP fibers along with lime and sand to enhance the engineering properties of a typical problematic soil and, in particular, to remove the restrictions associated with the use of a single additive (the limited increase in strength and the drop of ductility for the case of sand and lime, respectively). The experimental procedure presented herein consists of the following successive steps: optimizing the sand content based on the UCS test results, determining lime demand for the clay-sand mixtures based on the pH value, increasing the fiber content progressively in the mixture from 0 to 1.2 wt% (at 0.3% intervals) to quantify its effect on the fundamental geotechnical properties of clay.
# A Calibration Method for Time Dimension and Space Dimension of Streak Tube Imaging [PERSON]
## 5 Conclusions  
In conclusion, we proposed a calibration method for the time dimension and space dimension of STIL. Through this method, we obtained two calibration arrays, and more accurate time and angle information can be indexed through these two arrays. Finally, we conducted a validation experiment. The experimental results indicate that the time and angle calibration method proposed in this paper is effective. The overlapping effect and the range accuracy are all improved, especially with a 34% reduction in the range error. Higher-ranging accuracy is more conducive to improving the practicality of the 3D point cloud, such as target recognition, plant growth status monitoring, vehicle quantity statistics, etc. Therefore, reducing the error by 34% is of great significance for the application of STIL. The correction method proposed in this article can effectively reduce the errors caused by  
Figure 12: (**A**) Three-dimensional point cloud of a town, (**B**) the section at profile line (a) before calibration, (**C**) the section at profile line (a) after calibration, (**D**) the section at profile line (b) before calibration, and (**E**) the section at profile line (b) after calibration.  
Figure 13: Three-dimensional point cloud of the measuring area.  
the temporal and spatial coupling caused by the STIL special mechanism. The improvement of detection accuracy will play a positive role in the promotion of STIL.  
Conceptualization and methodology, Z.C. and R.F.; software, X.W. and C.D.; validation, Z.C., F.S. and Z.D.; data analysis, Z.C., X.W. and D.C.; writing--original draft preparation, Z.C. and F.S.; writing--review and editing, Z.C. and Z.F.; project administration, D.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 62305085 and No. 62193277012).  
Not applicable.  
According to the requirements of the project regulatory department, streak images and point cloud data cannot be provided.  
The authors declare no conflict of interest.
## 1 Introduction  
In general two main approaches are distinguished to estimate forest inventory attributes from airborne laser scanner (ALS) data [1]: (1) Area based methods, also described as canopy height distribution (CHD) approaches; and (2) Individual-tree-detection (ITD) methods. Often the CHD approaches are associated with low-resolution and the ITD methods with high-resolution data.  
In Central Europe, the majority of studies have concentrated on CHD approaches to estimate forest inventory attributes such as stand heights, basal area or timber volume per hectare. Usually geo-referenced plots from operational inventories were utilized as reference data. Metrics related to canopy height and densities have been used as predictors in regression models and nonparametric methods (e.g., [2-5]).  
In addition, a number of single tree delineation algorithms were developed. Possibilities to detect individual tree crowns in deciduous and mixed temperate forests in Germany are described in [6]. In [7] a 3D single tree modeling procedure was developed and tested in a mixed forest in southern Germany. A 3D segmentation technique using full-waveform ALS data was presented by [8]. The procedure was tested in the Bavarian Forest National Park in south-eastern Germany. The potential of full-waveform ALS for tree species classification in a mixed forest in Austria was presented by [9].  
However, there are fewer ALS studies in central European forests that explored the estimation of individual-tree-based information relevant for forest inventories such as tree height, stem diameter and stem volume. Experiments have been carried out in the Bavarian Forest National Park in south-eastern Germany [10-12]. A first European-wide attempt to derive single tree information from ALS data was carried out within the HIGH-SCAN project [13].  
The main reason why CHD approaches have been favored is due to the fact that these methods are considered to be more robust than ITD techniques [14,15]. Particularly in dense deciduous forests in the temperate climate zones it can be difficult to delineate individual trees with satisfying accuracy [6]. Another reason why the estimation of single tree attributes has not been studied in detail is that appropriate reference data is often missing. The positions of trees on the ground have to be measured with high precision. Otherwise it is very difficult and often impossible to link crown segments which were delineated based on remote sensing data with trees measured in the field.  
The objective of this study was to analyze the potential of high-resolution ALS and multispectral line scanner data for the extraction of individual-tree-based information for pine trees (_Pinus sylvestris_) in a study site in the south-west of Germany. The main objective was to assess the accuracy of several allometric models for stem volume estimation of automatically delineated trees. The results were analyzed and compared to other studies carried out in Germany and Scandinavia.
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
## 2 Multi-Symbol OFDM-Based IRC Systems and Signal Model  
In this section, the multi-symbol OFDM-based IRC signal model is first introduced. Then, the IRC system framework is illustrated.
## 5 Conclusions  
This paper investigated ACDM-based VLPC networks. We first proposed OPRC to enhance the orthogonality of VLP signals under asynchronous transmissions, thereby improving the reliability and efficiency of VLP systems. Through experiments, the OPRC-VLP scheme demonstrated its capability to achieve sub-centimeter positioning accuracy without requiring synchronization among transmitters. Furthermore, we extended the OPRC concept to simultaneously provide VLC and VLP services through the SICD-OPRC-VLPC scheme. This scheme combines the advantages of OPRC with SICD to eliminate MAI caused by asynchronous transmissions, enhancing data transmission quality and positioning accuracy in ACDM-based VLPC systems. Numerical results showed that the SICD-OPRC-VLPC system significantly reduces BER and PE compared to existing VLPC approaches like OZCZ, nearly matching the performance observed in synchronized systems. The system achieves an average BER of 4.3 \(\times\) 10\({}^{-4}\) and PE of 2.7 cm, with BER meeting the FEC standard at around 96% of test points and PE staying below 5.6 cm for 90% of test points. This study offers insights into VLPC for future 6G infrastructures, paving the way for enhanced multi-service capabilities such as lighting, communication, and positioning. Our future work will optimize the VLPC network architecture to accommodate various real-world conditions, including multipath reflections from the walls, receiver mobility across cell boundaries, and different LED layouts. In addition, the trade-off between data rate, system complexity, and positioning accuracy under higher-order modulation formats will be investigated.  
Conceptualization, Z.L. and X.Y.; methodology, Z.L. and X.Y.; software, Z.L.; validation, Z.L.; formal analysis, Z.L.; investigation, Z.L.; resources, Z.L.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L., X.Y. and C.Y.; visualization, Z.L.; supervision, C.Y.; project administration, X.Y. and C.Y.; funding acquisition, X.Y. and C.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Statistical results of: (**a**) BER for the synchronous system; (**b**) PE for the synchronous system, (**c**) BER for the asynchronous system; and (**d**) PE for the asynchronous system.  
**Funding:** This research was supported by Suzhou Science and Technology Bureau-Technical Innovation Project in Key Industries (SYG202112), HK RGC GRF (15212720), National Natural Science Foundation of China (62001319), and Open Fund of IPOC (BUPT) (IPOC2020A009).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Formulation of Exergy Analysis  
Thermodynamics and energy transference principles are fundamental in chemical process design, also applicable to bio-based thermochemical technologies. From an energetic viewpoint, exergy assessment means a methodology to surpass the limitations of the 1 st law of thermodynamics to recognize critical energy outflow sources [45]. This feature defines its capacity to screen energy hotpots to improve overall process efficiency. Figure 3: Process flowsheet diagram of the pyrolysis process. Exergy analysis quantifies chemical process system performance from a thermodynamic perspective. Exergy gives the maximum theoretical useful work (or energy) once a system reaches its thermodynamic equilibrium with the surroundings [46]. Some considerations are considered for performing exergy analysis, and these include the following aspects:  
* Steady-state conditions;
* Kinetic and potential (energy) exergy flows as neglected;
* Reference temperature and pressure of 25 \({}^{\circ}\)C and 1 atm, respectively. Table 2 shows governing equations of the exergetic analysis. The exergy balance does not follow the energy/material conservation principle. There is always a loss of exergy connected to unavoidable irreversibilities. These are an estimation of destroyed exergy flows within a chemical process [47]. For ideal gases, the physical exergy flow is determined by (7). Here, heat capacity (C\({}_{\text{p}}\)) is considered as a constant. For a solid-liquid mixture, physical exergy is calculated using (7). \[\text{Ex}_{\text{phy}}=\text{C}_{\text{p}}(\text{T}-\text{T}_{0})-\text{T}_{0} \big{(}\text{C}_{\text{p}}\ln\text{T}/\text{T}_{\text{o}}-\text{R}\ln\text{P} /\text{P}_{\text{o}}\big{)} \tag{7}\]  
The physical exergy is estimated using ASPEN PLUS. The exergy balance formulation contains all inlet exergy flows by material streams or utilities. The balance is formulated with the following:  
\[\dot{\text{Ex}}_{\text{in}}=\dot{\text{Ex}}_{\text{stream}}+\dot{\text{Ex}}_{ \text{utilities}} \tag{8}\]  
\(\dot{\text{Ex}}_{\text{in}}\) gives the total inlet exergy,\(\dot{\text{Ex}}_{\text{stream}}\) is the exergy flow of process streams, and \(\dot{\text{Ex}}_{\text{utilities}}\) represents the exergy of utility. The total outlet exergy embraces products and waste streams produced throughout production. Equation (9) gives the mathematical formula of this variable. \[\dot{\text{Ex}}_{\text{out}}=\sum\dot{\text{Ex}}_{\text{product}-\text{out} }+\sum\dot{\text{Ex}}_{\text{residues}-\text{out}} \tag{9}\]  
\(\dot{\text{Ex}}_{\text{out}}\) is the total outlet exergy flow, \(\dot{\text{Ex}}_{\text{product}-\text{out}}\) is the summation of product exergy, \(\sum\dot{\text{Ex}}_{\text{residues}-\text{out}}\) represents the exergy of waste flows. This formulation of the exergy
## 4 Conclusions  
In this study, the biocompatible Zn-based MOF-5 was loaded post-synthetically with two antimicrobials, Ceph and Met, respectively. The pristine MOF-5, drug-loaded MOF-5 and loaded polymer composites were characterised to confirm that the MOFs were successfully synthesised and loaded with the drugs using efficient methodologies. The loaded MOF-5 was incorporated into two biodegradable polymers, PLGA and PCL, and the release of the drug from the polymer composite and pores of the MOF-5 was studied. PCL-ceph@MOF-5 demonstrated the most gradual and extended-release profile. In a descending order, this was followed by PCL-met@MOF-5, PLGA-ceph@MOF-5 and PLGA-met@MOF-5. The steady rise in the drug concentration of PCL-ceph@MOF-5 and PCL-met@MOF-5 over the six-day study period provides a more extended sustained release, which is an advantage over the PLGA comparatively, achieving the main objectives of this study. That notwithstanding, further studies are ongoing, which seek to use the PCL-ceph@MOF-5 and PCL-met@MOF-5 composites in medical devices, such as patches, to provide the extended release of incorporated drugs. This study has successfully brought out a comparative analysis for the provision of the more efficient and sustained delivery of Ceph and Met antimicrobials, as stipulated in our initial objectives, and this can be a potential tool to control the rise in antimicrobial resistance by providing a means of sustained localised applications.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app131910611/s1](https://www.mdpi.com/article/10.3390/app131910611/s1)]([https://www.mdpi.com/article/10.3390/app131910611/s1](https://www.mdpi.com/article/10.3390/app131910611/s1)), Figure S1: Calibration curve for cephalexin; Figure S2: Calibration curve for cephalexin; Figure S3: Lambda max for metronidazolezo; Figure S4: Lambda max for cephalexin; Figure S5: EDS analysis of MOF-polymer composites; Figure S6: PXRD of the drug-loaded MOF composites before and after exposure to PBS for 24 h: (A) PCL composites (B) PLGA composites.  
**Author Contributions:** A.A., execution of concept and idea, experiments, characterisation of materials, data collection, data analysis and manuscript preparation; L.A.M.M., manuscript preparation, data analysis, illustration of figures and Supporting Information; M.G.K., supervision and data analysis, review and editing of manuscript; A.L.K., supervision and data analysis, review and editing of manuscript; S.N., supervision, conceptualisation, data analysis and manuscript preparation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data can be available on request from the authors.  
**Acknowledgments:** A.A. acknowledges a scholarship from the Ghana Scholarship Secretariat, Accra-Ghana. L.A.M.M. acknowledges an Erasmus exchange studentship.  
**Conflicts of Interest:** The authors declare no conflict of interest.