esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Description
### Hardware
\begin{table}
\begin{tabular}{c c} \hline
**Component/Device/Reference** & **Main Specifications** \\ \hline Pressure Sensor & \begin{tabular}{c} measures pressure range between 0 and 50 kPa; 2.5\% maximum \\ error relative to V\({}_{\text{FSS}}\), whose typical value is equal to 2.7 V; \\ sensitivity equal to 54 mV/kPa; response time equal to 1.0 ms and \\ temperature compensation capabilities in the temperature range \\ between \(-\)40 \({}^{\circ}\)C and \(+\)125 \({}^{\circ}\)C \\ \end{tabular} \\ \hline Electro-Pneumatic Pressure Regulator (EPPR) & \begin{tabular}{c} pressure range between 0 and 5 p.s.i.; control voltage of 0–5 V; \\ monitor output voltage 0–5 V; pressure accuracy of \(\pm\)1.5\% of \\ full-scale maximum; response time lower than 15 ms; linearity \\ better than 1.5\% of full-scale maximum and availability of \\ internal vent \\ \hline Pressure Calibrator & pressure range between \(-\)1 and 1 bar; accuracy of 0.0185\% of FS \\ DRUCK DPI611 & \begin{tabular}{c} and total uncertainty of 0.025\% of FS \\ \end{tabular} \\ \hline Release Valve & exhaust time lower than 6.0 s for a pressure reduction from \\ KOGE & 300 mmHg to 15 mmHg; resistance 100 \(\Omega\)\(\pm\) 10\% and leakage \\ KSV05 & maximum of 3 mmHg/min for a pressure equal to 300 mmHg \\ \hline Data Acquisition Board & \
\begin{tabular}{c} two differential analog input channels with 16-bit resolution; \\ maximum sampling rate of 200 KS/s; timing resolution of 10 ns; \\ analog input range \(\pm\)2 V and \(\pm\)10 V and typical accuracy of \\ 4.9 mV for analog input range \(\pm\)2 V \\ \end{tabular} \\ \hline Digital Multimeter &
\begin{tabular}{c} a total of 6 \(\%\) digits; minimum voltage resolution of 0.1 \(\mu\) for \\ 100 mV scale; linearity for 10 V DC range equal to \(\pm\)(1 ppm of \\ 2000 SERIES \\ \end{tabular} \\ \end{tabular} \\ \hline
2000 SERIES &
\begin{tabular}{c} a total of 6 \(\%\) digits; minimum voltage resolution of 0.1 \(\mu\) for \\ 100 mV scale; linearity for 10 V DC range equal to \(\pm\)(1 ppm of \\ reading + 2 ppm of range) and accuracy for a DC voltage range \\ from 100 mV to 1 kV equal to 0.002\% \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the specifications of the main electrical components and devices. Figure 6: Hardware block diagram of the proposed measurement system (SC—signal conditioning; AGC—automatic gain control; PS—pressure sensor; DUT—device under test; CAL—calibrator; EPPR—electro-pneumatic pressure regulator).
_Article_  
**Influence of Three-Dimensional Coral Structures on Hyperspectral Benthic Reflectance and Water-Leaving Reflectance**  
**[PERSON] \({}^{1,\bullet}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\) and [PERSON] \({}^{2}\)\({}^{\copyright}\)**  
\({}^{1}\) Numerical Optics Ltd., Tiverton EX16 8 AA, UK  
\({}^{2}\) Department of Marine Sciences, University of Connecticut, Groton, CT 06340, USA; [PERSON]ESS] (M.M.); [EMAIL_ADDRESS] (H.M.D.)  
\({}^{3}\) Materials Science and Engineering Department, University of Connecticut, Storrs, CT 06269, USA; [EMAIL_ADDRESS]  
\({}^{\bullet}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +44-1884-675070 check for updates  
Received: 31 July 2018; Accepted: 30 September 2018; Published: 19 December 2018  
**Featured Application: Hyperspectral remote sensing of coral reefs. We present simplifying factors and guidance on handling or avoiding the potential variability in spectral reflectances caused by the structural nature of corals and illumination conditions. Results presented here can be implemented in remote sensing algorithms for bottom mapping of coral reefs, and guide data collection practice.**  
**Abstract:** Shading and inter-reflections created by the three-dimensional coral canopy structure play an important role on benthic reflectance and its propagation above the water. Here, a plane parallel model was coupled with a three-dimensional radiative transfer canopy model, incorporating measured coral shapes and hyperspectral benthic reflectances, to investigate this question under different illumination and water column conditions. Results indicated that a Lambertian treatment of the bottom reflectance can be a reasonable assumption if a variable shading factor is included. Without flexibility in the shading treatment, nadir view bottom reflectances can vary by as much as \(\pm 20\%\) (or \(\pm 9\%\) in above-water remote sensing reflectance) under solar zenith angles (SZAs) up to \(50^{\circ}\). Spectrally-independent shading factors are developed for benthic coral reflectance measurements based on the rugosity of the coral. In remote sensing applications, where the rugosity is unknown, a shading factor could be incorporated as an endmember for retrieval in the inversion scheme. In dense coral canopies in clear shallow waters, the benthos cannot always be treated as Lambertian, and for large solar-view angles the bi-directional reflectance distribution functions (BRDF) hotspot propagated to above water reflectances can create up to a 50% or more difference in water-leaving reflectances, and discrepancies of 20% even for nadir-view geometries.  
**Keywords:** remote sensing; hyperspectral; shallow water; coral; derivative; radiative transfer; canopy
## 3 Methodology
### Validation and Accuracy Evaluation Methods  
In this study, 7 methods were applied, including single-band threshold method, water body index method, knowledge decision tree classification method, supervised classification method, unsupervised classification method, SMDPSO, and SMDPSO+LFE.  
A sensitivity analysis was carried out to evaluate the accuracy of the extraction of surface water using the standard water spectrum, block size, and iteration times. The standard water spectrum, block size, and iteration times are input parameters for the selected water extraction method. Three sampling areas were selected to evaluate the sensitivity of the selected water extraction method to the water standard water spectrum. These three sampling areas were identified as Lake Superior, the Congo river, and the Persian Gulf, and their maps are shown in Figure 10.  
Figure 9: Linear feature enhancement result of the NDWI.  
According to Figure 11, all the 7 methods can clearly show the large area of lakes. However, for rivers, especially relatively small tributaries, SMDPSO+LFE performed significantly better than the other six methods.  
Figure 12 shows the detailed results of the partial image captured from the whole image. As the threshold value of the water index method was set too high, almost all the tributaries on the southwest shore of Poyang Lake were not extracted. Other methods can be used to extract the central pixel of the southwest shore-based river with a large width. However, for the river edge and tributaries with a width of fewer than two pixels, only the SMDPSO+LFE method is nearly perfect, and the extraction effect is better for small tributaries and discrete land surface waters.  
In order to effectively use satellite data for flood monitoring during the flood season, we used the satellite data from July and August 2020 to extract the water body information of Poyang Lake area. As can be seen from the diagram, in the Poyang Lake region, since the beginning of this season in July, there was distribution of large water bodies, and in August, after the flood receded gradually, water area decreased significantly in the region. The water distribution maps are shown in the Figure 13.  
Figure 12: Extraction results of tributary water in the study area: (**a**) FY-3/MERSI image; (**b**) single-band threshold method; (**c**) water index method; (**d**) decision tree method; (**e**) supervised classification; (**f**) unsupervised classification; (**g**) SMDPSO; (**h**) SMDPSO+LFE.  
Figure 13: Water distribution in Poyang Lake region in July and August.
## 4 Simulation Studies and Performance Assessment
### Dynamic Timing Regularization  
As mentioned earlier, the curves obtained from SNN prediction still need dynamic adjustment between peak values to improve the accuracy of frequency regulation. Figure 10 shows the dynamic timing regularization effect of DG1 and its comparison with other cases. As can be seen from the figure, after dynamic regularization, compared to using SNN alone, the red line has become very close to the black time series line, even almost completely overlapping in some positions. Although, as shown in the magnified inset, there is still not absolute overlap, it has moved closer to the desired situation.  
The implementation results when applying the time-regularized curves to frequency regulation are shown in Figure 11. Compared to Figure 9, the frequency fluctuations are  
Figure 10: Comparison of DG1’s dynamic timing regularization effects with other scenarios.  
Figure 9: System performance using SNN curve fitting. (**a**) frequency, (**b**) power sharing.  
significantly reduced, indicating that this method can more effectively resist the impact of dramatic changes in common load.  
Taking DG1's case for comparative analysis, as shown in Figure 12, the results further clearly demonstrate that better frequency regulation effects were achieved after applying SNN and dynamic timing regularization.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 11 Discussion on Private Campus Networks
### Private Campus Network--New Possibilities for Industry 4.0
The fundamental parameters are throughput, latency, and high transmission rate together with extremely high reliability and availability (99.999% to 99.99999999%). On the other hand, in the current state of industrial networks, these have not been well defined yet.
## 3 Case Study  
### Participants  
To study drivers' attitudes and willingness to use the VP-ADD, we conducted a questionnaire survey using the Questionnaire Star app. A total of 562 questionnaires were collected, and 59 questionnaires were removed due to missing and incomplete data, so 503 valid questionnaires were obtained after removing invalid ones. Among the subjects, 306 (60.8%) were male and 197 (39.2%) were female. Most of the subjects were 18-45 years old (85.6%), and 72 (14.6%) were over 45. The primary educational attainment is a bachelor's degree (62.0%). The distribution of driving experience is as follows: Participants with 1 to 5 years of driving experience constitute the largest proportion, accounting for 40.8% of the total sample; those with 6 to 10 years of driving experience account for 41.7%; while experienced drivers with over ten years of experience make up 11.1% of the sample. Novice drivers with less than one year of experience represent 6.4%. Specific information is shown in Table 1. This study was conducted under the guidance of the ethics committee of the College of Civil Engineering, Fuzhou University. All participants voluntarily participated in the experiment and signed a written consent form.
# ALPINE: Active Link Prediction Using Network Embedding
## 4 Query Strategies for ALPINE
### Variance Reduction
#### 4.3.3 Prediction Variance Reduction with V-Optimality
: _The V-optimality utility function is given by:_  
\[u_{\mathbf{A},\mathbf{X}^{*}}(i,j) =\sum_{k:\{ik\}\in T}u^{ik}(i,j)+\sum_{l:\{jl\}\in T}u^{il}(i,j),\]  
_where:_  
\[u^{ik}(i,j) =(\gamma P_{ik}^{*}(1-P_{ik}^{*}))^{2}(\mathbf{x}_{i}^{*}-\mathbf{x}_{k}^ {*})^{T}(\mathcal{I}(\mathbf{x}_{i}^{*})^{-1}-\mathcal{I}^{j}(\mathbf{x}_{i}^{*})^{-1 })(\mathbf{x}_{i}^{*}-\mathbf{x}_{k}^{*}),\] \[u^{il}(i,j) =(\gamma P_{jl}^{*}(1-P_{jl}^{*}))^{2}(\mathbf{x}_{j}^{*}-\mathbf{x}_{l}^ {*})^{T}(\mathcal{I}(\mathbf{x}_{j}^{*})^{-1}-\mathcal{I}^{j}(\mathbf{x}_{j}^{*})^{-1 })(\mathbf{x}_{j}^{*}-\mathbf{x}_{l}^{*}).\]  
Due to the fact that the [PERSON] information update in Equation (5) is rank one, we can apply the Sherman-Morrison formula [39] to \(\mathcal{I}(\mathbf{x}_{i}^{*})^{-1}-\mathcal{I}^{j}(\mathbf{x}_{i}^{*})^{-1}\) and rewrite \(u^{ik}(i,j)\) (and similarly for \(u^{il}(i,j)\)) as:  
\[u^{ik}(i,j)=\frac{\gamma^{4}P_{ij}^{*}(1-P_{ij}^{*})}{1+\gamma^{2}P_{ij}^{*}(1 -P_{ij}^{*})d_{jj}(\mathbf{x}_{i}^{*})}[P_{ik}^{*}(1-P_{ik}^{*})]^{2}d_{kj}(\mathbf{x} _{i}^{*})^{2},\]  
where \(d_{jj}(\mathbf{x}_{i}^{*})=(\mathbf{x}_{i}^{*}-\mathbf{x}_{j}^{*})^{T}\mathcal{I}(\mathbf{x}_{ i}^{*})^{-1}(\mathbf{x}_{i}^{*}-\mathbf{x}_{j}^{*})\) and \(d_{kj}(\mathbf{x}_{i}^{*})=(\mathbf{x}_{i}^{*}-\mathbf{x}_{k}^{*})^{T}\mathcal{I}(\mathbf{x}_{ i}^{*})^{-1}(\mathbf{x}_{i}^{*}-\mathbf{x}_{j}^{*})\). Unsurprisingly, the variance reduction is always positive.
## 2 Theory of Fractional Calculus  
Here, we will introduce the rudimentary ideas and terminology of fractional theory, which will be utilized for the analysis of the system. Additionally, it has several beneficial uses across a variety of scientific areas. The essential concepts of the fractional derivative (FD) of Liouville and Caputo, introduced in [21; 22; 23], are as follows:  
**Definition 1**.: _Let us take a function \(g(r)\) such that \(g(r)\in L^{1}([c,d],R)\), then, the fractional integral of Liouville-Caputo (LC) is denoted by 1 and is given by_  
\[\xi l_{c+}^{c}(g(r))=\frac{1}{\Gamma(\xi)}\int_{0}^{r}(r-s)^{\xi-1}g(s)ds, \tag{1}\]  
_where \(0<\xi\leq 1\) is the order of the fractional derivative and \(r\) is assumed for time._  
**Definition 2**.: _Consider \(g(r)\) in a manner that \(g(r)\in C^{n}[c,d]\); then, the fractional derivative of \(g(r)\) in the LC form is denoted by \({}^{LC}D_{0^{+}}^{\xi}g(r)\) and is_  
\[{}^{LC}D_{0^{+}}^{\xi}g(r)=\frac{1}{\Gamma(n-\xi)}\int_{0}^{r}(r-s)^{n-\xi-1} g^{n}(s)ds, \tag{2}\]  
_subject to the constraint that \(n-1<\xi\leq n\)._  
**Lemma 1**.: _Let us take the setup described below_  
\[\left\{\begin{array}{c}{}^{LC}D_{0^{+}}^{\xi}g(r)=\ \ w(r),r\in[0,\tau],n-1<\xi<n,\\ g(0)=w_{0}\end{array}\right. \tag{3}\]  
_the above system has a solution given by_  
\[g(r)=\sum_{j=0}^{n-1}d_{j}r^{j},\text{ where }d_{j}\in R,\ \ j=0,1,\ldots,n-1. \tag{4}\]  
**Theorem 1** ([23]).: _Select \(X\) to be a Banach space and \(T:X\to X\) to be compact and continuous. Then, \(T\) has a fixed point if_  
\[\mathrm{G}=\{L\in X:L=\eta TL,\eta\in(0,1)\},\]  
_is bounded._
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
## 3 RARZ Protocol
### RARZ Protocol Phases
#### 3.2.2 Data Communication Phase  
Whenever a node has data to send, it broadcasts them. The next relaying node will be selected on the fly. This is totally different to SMAC and IEEE 802.11, wherein a node can route its data to a particular node as suggested by the routing algorithm in advance. The nodes in the n-th ring send their data to the BS via nodes located in the (n \(-\) 1)th or (n \(-\) 2)th rings. The next relaying node can only be selected from the lower rings; the nodes in the same ring as well as located in higher rings will delete the packet. As data are communicated to the BS in a multi-hop manner, the node at each hop that is to relay the packet modifies the ringID field of the packet by adding its own ringID, so that only lower ring nodes will relay it further to the BS. The routing technique used in RARZ is described in Figure 3.  
Figure 2: The network configuration phase of RARZ.
# Utilization of Phosphogypsum and Sediment in Subgrade Material for Pavement Construction
###### Abstract  
(1) Background: The construction industry continuously seeks sustainable alternatives to traditional materials for subgrade material in pavement construction, aiming to mitigate environmental impact while maintaining performance standards. This study investigates the feasibility of incorporating phosphogypsum (PG) and contaminated sediment into subgrade materials, focusing on their physico-chemical and physico-mechanical properties. (2) Methods: The physico-chemical and physico-mechanical properties, performance, and mechanisms of solidified sediment with phosphogypsum (3% and 5% of phosphogypsum in mixture) were studied using long-term leaching tests (ANS 16.1), uniaxial compressive strength (UCS), California Bearing Ratio (CBR), X-ray fluorescence (XRF), and thermogravimetric analysis (TGA). (3) Results: Based on the pseudo-total metal content (Cr, Ni, Cu, Zn, As, Cd, Pb), the sediment is classified as third- and fourth-class, indicating it is polluted and requires treatment before disposal in the environment. To assess the long-term behavior of the sediment treated with phosphogypsum (S/S), a semi-dynamic ANS 16.1 leaching test was performed. The results showed that the metals exhibit moderate mobility, with average diffusion coefficients (De) ranging from \(10^{-8}\) cm\({}^{2}\)/s for Zn (in both mixtures) to \(10^{-12}\) cm\({}^{2}\)/s for Cr (in mixture F-3). The leaching index (LX) values for both mixtures were above 9 for most metals, confirming their suitability for \"controlled\" use. Granulometric analysis indicated a predominance of fine particles, which enhances the material's plasticity and mechanical properties. Afterberg consistency tests showed that increasing phosphogypsum content improved both the Liquid Limit and Plastic Index. However, UCS tests indicated that neither the 3% or 5% phosphogypsum mixtures met the minimum strength requirements for subgrade material. On the other hand, CBR values demonstrated promising performance, with 12.5% for the 3% phosphogypsum mixture and 22.9% for the 5% phosphogypsum mixture. Overall, phosphogypsum positively influenced the strength development of the sediment-PG mixtures, as confirmed by XRF and TGA analyses. (4) Conclusions: Environmental considerations, such as leachability of contaminants, were investigated to ensure the sustainability of the proposed subgrade materials. Leaching tests indicated minimal pollutant release, suggesting the potential for safe utilization of PG and sediment in subgrade material. This study provides valuable insights into the physico-chemical and physico-mechanical properties of pavement mixes incorporating PGand sediment, supporting the feasibility of using these alternative materials in sustainable subgrade material for pavement construction and offering a viable solution to mitigate waste generation while enhancing pavement performance.  
sediment; phosphogypsum; solidification/stabilization; toxic metals; subgrade material for pavement construction
## 2 Literature Review
### Pick-Up and Delivery Vehicle Routing Problem (PDVRP) and Multi-Objective Optimization
Subsequently, they employed two multi-objective genetic algorithms (Strength Pareto Evolutionary Algorithm [30] and nondominated sorting genetic algorithm [31]) to optimize paths for each cluster. Their objectives were derived directly from the paths. In contrast, the problem considered in this paper involves various possible combinations of bike allocations along a given path, in addition to the distance derived from the path. Hence, this paper introduces a hybrid genetic algorithm to effectively tackle these complexities. Despite the extensive literature on PDVRP in BSS and similar fields, there are still some areas that need further exploration. One challenge is the prioritized selection of bike stations to visit. It is reasonable to reduce the number of depots to visit, and this process is usually performed by prioritizing certain depots based on specific criteria. However, in a rebalancing problem setting, it may be more natural to pick up and deliver bikes along travel paths based on demand forecasting. Another deficiency is the choice of objectives. Minimizing travel distance is very common in the literature, and another frequently adopted objective is maximizing customer satisfaction. Usually, satisfaction is a resultant metric, which is not only difficult to define but also not easily estimated during the planning phase. Instead, it might be possible to set an optimal service level and, along with the delivery plan, use the gap between the target inventory and the actual inventory along the pick-up and delivery paths as a metric to evaluate the plan. Our approach aims to address these gaps to provide efficient and comprehensible bike-rebalancing operations.
# Analysis and Comparison of Electromagnetic Microrobotic Platforms for Biomedical Applications
## 3 The Electromagnetic Microrobotic Systems
### Three-Dimensional Manipulation
#### 3.2.6 Platform 8E  
To achieve the 3D independent control for multiple microrobots, an EMA system including eight iron-core electromagnetic coils has been developed [95]. Figure 18a illustrates the proposed setup, where the iron-core electromagnets are all pointing to the common workspace center. The 3D motions of microrobots are directly actuated using the magnetic gradient generated by the eight completely independent coils. The differentiation of magnetic forces applied to each microrobot is accomplished by inducing the unique viscous drag on each microrobot when placed in a rotating magnetic field. The different rotational responses result in the different orientations of microrobots, and thence the forces are generated in different directions. Therefore, the proposed EMA system allows different magnetic forces to be exerted on the microrobots, that is capable of enabling the independent remote control and path following of multiple microrobots along arbitrary 3D trajectories. Such electromagnetic microrobotic systems can be used to expand the microrobot techniques for the clinical manipulation, drug delivery, remote sensing and localized therapy in microfluidic channels or in the human body.  
Indeed, EMA setups with a 3D configuration are more useful for a wider range of biomedical applications than a 2D placement, and other 8 electromagnets configurations such as the well-known OctoMag [59] and MiniMag [60] have been also investigated. The OctoMag platform have been designed with a hemispheric organization of 8 stationary coils. Similarly, the MiniMag system occupies a hemisphere with a tilted arrangement. Compared to OctoMag, the MiniMag setup has been designed to restrict the locations of the electromagnets to a single hemisphere that has a smaller volume. Specifically, they are both developed for the ophthalmic MIS procedure in the retinal veins.