esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of the System with Base Generation (Proposed Study)
### Capacity Design of the PV Power Generation System and ESS  
The power of the middle and peak loads is supplied by the PV power generation and the ESS, excluding the base load supplied to the fuel cell. In this subsection, the capacity of the PV power generation and ESS responsible for the middle and peak loads are designed. The design method is the same as the referenced research method. The capacity of the PV power generation is calculated using (1) to (3), and the capacity of the ESS is calculated using (4).  
Because the average daily power load is 2197 kWh, it is sufficient to construct a PV power generation facility for the remaining power load (\(P_{remain}\)) of 1357 kWh, excluding the daily base power generation (\(P_{base}\)) of the fuel cell, which is 840 kWh. The PV power generation capacity (\(PV_{0}\)) calculated using (2) was 398.99 kWh. From the referenced work, the average daily sunlight time (\(T_{as}\)) was 3.56 h, the inverter efficiency (\(I_{eff}\)) was 96.5%, and the cable loss (\(C_{L}\)) was 1%.  
The capacity of the PV power generation system was calculated by reflecting the loss of the ESS device to design the capacity of power systems with the PV module and ESS. The charge loss (\(E_{CL}\)) and discharge loss (\(E_{DL}\)) of the ESS device were applied as 6% and 4%, respectively, to add a margin to  
Figure 4: Average monthly power load of the residential area and industrial complexes.  
the power generation capacity. Equation (3) was used in the calculation process, and the scale of the PV facility was adjusted to 442.15 kW.  
The daily generation amount of the PV power generation system, the inverter efficiency (\(I_{\mathit{eff}}\)), and the charge/discharge depth (\(E_{SOC}\) and \(E_{DOD}\)) of the ESS device were considered when calculating the capacity of the ESS with (4). The inverter efficiency (\(I_{\mathit{eff}}\)) was 96.5%, the charge depth (\(E_{SOC}\)) was 90%, the discharge depth (\(E_{DOD}\)) was 90%, and the set-off power factor (\(C_{CTP}\)) was 0.47. The capacity of the ESS was designed as 1375.96 kWh.  
All capacities of the PV, ESS, and fuel cell were derived using the system design method proposed in this study, and three energy sources were plotted in one graph. Figure 5 shows the portion of each power generation source (PV, ESS, and fuel cell) that were in charge of the day loads. The black line on the graph indicates the hourly average load, and the area under the black line is equal to the average daily load of 2197 kWh. The base power source (fuel cell) is installed with a rated capacity of 35 kW and produces an average of 840 kWh per day, which is 38.23% of the average daily load of 2197 kWh. PV and ESS are in charge of the remaining power to supply for the middle and peak loads at 1357 kWh.
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 5 Evaluation
### Results and Discussion  
This section presents and discusses the performance evaluation results in two parts detailed in Sections 5.2.1 and 5.2.2 The first part (i.e., Section 5.2.1) presents results regarding contact information overhead. Specifically, simulation results about the amount of contact information maintained by PRoPHET and the reduction achieved by the proposed PoiFord are presented. Then the impact of increasing network size on the amount of contact information maintained by PRoPHET and PoiFord is presented and compared with theoretical estimates given by the proposed contact information model. The section concludes by presenting simulation results regarding the impact of contact information size on message forwarding, particularly in terms of successful contact information exchanges and energy consumption.  
The second part (i.e., Section 5.2.2) evaluates the performance of PRoPHET, PoiFord and HERO in terms of the selected metrics, namely throughput, average delivery delay, message transmission overhead, and average energy consumption. Specifically, PoiFord's ability to improve network performance while reducing contact information overhead is measured by comparing its results with those of PRoPHET. The failure to implement the process of exchanging summary vectors in simulation experiments and its impact on the validity of performance evaluation is also discussed.
## 1 Introduction
Due to their different power sources, electric buses and diesel buses have their own driving styles and precautions. The energy consumption varies with drivers' approaches to driving practices. In this study, a BMS was used to obtain vehicle driving data. Although existing works have addressed the problem of analysis of the relationship between energy consumption and driving behaviors, they have not involved records collected from a battery via a BMS. As mentioned earlier, a BMS is an electronic system that manages a rechargeable battery and monitors the state of the battery. Thus, we can directly observe the actual cumulative driving power consumption within a certain time period. However, existing works have either estimated mechanical energy consumption using the driving distance [6] or utilized a simulation model to approximately evaluate energy consumption [11]. As a result, they could not completely verify their proposal. Therefore, it is desirable to utilize the records collected from a battery via a BMS to build an interpretable model which precisely classifies driving behavior and also provides an in-depth analysis of the relationship between driving behavior and energy consumption. Therefore, random forest is a type of interpretable machine learning model that was applied to construct analytical models of driving energy consumption and summarize the factors influencing energy consumption. According to our proposed method, eco-driving indicators can be analyzed to provide driving references for drivers. The data analysis method used in this study could provide decision rules for saving energy, and therefore maintenance cost of the electronic buses could be reduced, and the results would reflect the actual driving conditions of the vehicle, particularly in the case of vehicle operations of longer duration (the longer the operation of a vehicle, the more the driving data). The eco-driving behaviors would promote the efficient use of electric buses and optimize the benefits of saving energy and carbon reduction, as well as could be used as reference information for the education and training of drivers and for passenger transport operators to manage their energy expenditure. To the best of our knowledge, this is the first work that involves records collected from a battery via a BMS to explore machine learning for building an interpretable model. This paper is arranged as follows: First, we introduce some related work in Section 2; in Section 3, we explain the machine learning-based approaches for constructing analytical models for driving energy consumption of electric buses; in Sections 4 and 5, we present the experiment and discussion, respectively; and finally, in Section 6, we present the conclusions of this work.
# A Vibration Based Automatic Fault Detection Scheme for Drilling Process Using Type-2 Fuzzy Logic
## 5 Conclusions  
This work shows how to find faults in the drilling process using an interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy-based observer fault detection system. In the face of system uncertainty, the stability of the system using the fault detection estimator is validated. This system uncertainty is tackled using the type-2 fuzzy logic system. Theorem 1 is developed to validate the stability of Type-2 fuzzy modeling. The system states of the process are proved to be bounded, which also validates the stability of the fault detection estimator using Theorem 2. The stability requirements of Theorem 1 and Theorem 2 are fulfilled using the Lyapunov stability candidate. The defect detection system's effectiveness is verified using numerical analysis, which also establishes theoretical features. The main intention of this paper is to develop a control-based fault algorithm to detect an induced fault at the correct instant with assured stability of the system. This type of unique way for detecting a flaw in a drilling system has never been used before. The effective methodology can be implemented in real-time for detecting faults during drilling operations. This is critical for increasing the productivity and quality of the machining process, and it also helps improve the surface finish of the work piece satisfying the customer needs and expectations. Future work is intended towards the computation of Remaining Useful Life (RUL) of the drilling devices.  
Figure 11: Fault detection scheme along the \(y\)-axis.  
Figure 9: Drilling Tool vibration along the y-direction with induced fault.  
**Author Contributions:** Conceptualization, S.P., R.T. and D.K.; Methodology, S.P., R.T. and D.K.; Software, S.P.; Validation, M.L. and S.P.; Investigation, S.P., R.T. and D.K.; Writing--original draft preparation, S.P., R.T. and D.K.; Writing--review and editing, S.P.; Supervision, M.L.; Project administration, M.L.; Funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Study did not report any data.  
**Acknowledgments:** [PERSON] gratefully acknowledges VINNOVA research funding in the project A digital twin to support sustainable and available production as a service and Swedish Mining Innovation funding in the project. A general digital twin driving mining innovation through statistical and logical modelling.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Antimicrobial Studies
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & **Inhibition Zone (mm)** & \\ \hline  & _S. epidermidis_ & _S. aureus_ & _E. coli_ & _A. baumannii_ \\ \hline Met & 23 \(\pm\) 0.5 & 20 \(\pm\) 0.5 & 13 \(\pm\) 0.5 & 18 \(\pm\) 0.5 \\ met@MOF-5 & 21 \(\pm\) 0.5 & 15 \(\pm\) 0 & 10 \(\pm\) 0 & 10 \(\pm\) 0 \\ met@PLGA & 43 \(\pm\) 1 & - & - & - \\ met@PCL & 5 \(\pm\) 1 & - & - & - \\ MOF-5@PLGA & 73 \(\pm\) 1.5 & 83 \(\pm\) 0 & - & - \\ MOF5@PCL & & & & \\ PLGA-met@MOF-5 & - & - & - & - \\ PCL-met@MOF-5 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Zones of inhibition of Met, Met-loaded MOF and polymer composites against four bacterial strains. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & **Inhibition Zone (mm)** & \\ \hline  & _S. epidermidis_ & _S. aureus_ & _E. coli_ & _A. baumannii_ \\ \hline Ceph & 79 \(\pm\) 1 & 66 \(\pm\) 1 & 34 \(\pm\) 0.5 & - \\ ceph@MOF-5 & 34 \(\pm\) 0.5 & 31 \(\pm\) 1.5 & 10 \(\pm\) 0 & 13 \(\pm\) 0.5 \\ ceph@PLGA & 73 \(\pm\) 0.5 & 42 \(\pm\) 0.5 & 25 \(\pm\) 1 & - \\ ceph@PCL & 16 \(\pm\) 0 & 32 \(\pm\) 0.5 & 16 \(\pm\) 0 & - \\ PLGA-ceph@MOF-5 & 75 \(\pm\) 0 & 7 \(\pm\) 0 & - & - \\ PCL-ceph@MOF-5 & 6 \(\pm\) 0.5 & 11 \(\pm\) 1 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Zones of inhibition of Ceph, Ceph-loaded MOF and polymer composites against four bacterial strains. previous studies [63]. However, in relation to the polymer composites, PLGA-ceph@MOF-5 inhibited bacterial growth better than PCL-ceph@MOF-5 for same reasons that were discussed above. Additionally, the drugs and MOF (without being loaded) were incorporated together into both polymers and tested against the bacterial strains. The measured zones of inhibition were all larger than those of their corresponding composites with MOFs loaded with drugs (Figure 9 and Tables 1-3). This is attributed to the immediate release of the MOF and drug molecules from the polymer composite immediately after degradation relative to the gradual release of drug molecules as a result of degradation of the polymer and MOF in the case of loaded MOFs incorporated into polymers. This confirms that the release of the drug molecules from the loaded MOFs incorporated into the loaded polymers was relatively gradual and released at a slower controlled rate, and this was the main aim of this study.
## 3 Results
### Capability for Monitoring Water Stress via Diurnal Variation Characteristics
The results indicated that NMR\({}_{\Phi_{\mathrm{F}}-\mathrm{F}\mathrm{CVI}}\) and NMR\({}_{\Phi_{\mathrm{F}}-\mathrm{N}\mathrm{R}\mathrm{v}}\) exhibited weak correlations with \(\Delta\mathrm{T}_{\mathrm{noon}}\). Notably, NMR\({}_{\Phi_{\mathrm{F}}-\mathrm{F}\mathrm{CVI}}\) showed a slight negative correlation with \(\Delta\mathrm{T}_{\mathrm{noon}}\). In contrast, the structurally based vegetation index, NMR\({}_{\mathrm{NDVI}}\) demonstrated a highly negative correlation with \(\Delta\mathrm{T}_{\mathrm{noon}}\). The regression slopes of NMR\({}_{\mathrm{NDVI}}\) were similar to those of NMR\({}_{\Phi_{\mathrm{F}}-\mathrm{F}\mathrm{CVI}}\), but NMR\({}_{\mathrm{NDVI}}\) exhibited overall lower values.NMR\({}_{\mathrm{SIFy}}\), which incorporates both structural and physiological information, exhibited negative correlations, and compared with those of the other observations, the slope was higher. Compared with that of the NMR, the relationship between the AMR and \(\Delta\mathrm{T}_{\mathrm{noon}}\) differed. The slope of the relationship between the NMR and \(\Delta\mathrm{T}_{\mathrm{noon}}\) was higher than that of the relationship with the AMR for the corresponding canopy remote sensing observations. Except for the NMR of \(\Phi_{\mathrm{F}}^{\mathrm{N}\mathrm{R}\mathrm{v}}\), the NMR of the other observations decreased to a greater extent with increasing \(\Delta\mathrm{T}_{\mathrm{noon}}\). Under mild water stress (with \(\Delta\mathrm{T}_{\mathrm{noon}}\) less than 2 \({}^{\circ}\mathrm{C}\)), the AMR of the different types of canopy remote sensing observations remained close to 1 compared with the NMR, showing smaller changes (Figure 16b). This finding indicates that the sensitivity of the AMR to water stress is lower than that of the NMR.
## 3 Data and Methodology
### Rock Mass Quality Evaluation  
#### 3.5.1 Fracture Data Collection  
At present, the window sampling method is extensively employed in field surveys for collecting fissure data [32]. The window sampling method involves assessing the geometric attributes of all fracture features within a specified area of a natural outcrop or the exposed surface of an excavation in the same geological zone and describing the geological characteristics of each fracture according to the requirements of engineeringgeological criteria [33; 34]. In addition, the model-based method proposed by [PERSON] et al. [35] was used to automatically identify fracture sets.  
Due to challenging site conditions, acquiring direct measurements for all fracture data in some well-exposed rock faces can be difficult. In recent years, rapid advancements in remote sensing technology have significantly improved the accuracy of UAV 3D models, essentially meeting statistical requirements. Therefore, image data were captured using UAV and camera to construct 3D modes of sampling windows to carry out fracture mapping indoors. Finally, filed measurements were combined with in-door mapping to enhance the interpretation of fracture data (Figure 10b-f).
## 2 Materials and Methods
### Protocol of Stage 1  
This study applied a randomized crossover design (which was randomized through the sequential arrangement of each intervention, including four treatments, controlling for the effects of VT, FFR, and ES) to investigate the effectiveness of the 3D-based VT program with or without FFR generated by visual cortex-activated auditory frequency entrainment and bilateral orbital ES. A total of 27 school children with myopia were randomized through sequential arrangement into four groups: (1) sham control with no VT, FFR, or ES (control group); (2) 3D-based VT only (VT group); (3) VT with FFR (VT-FFR group); and (4) VT with FFR and ES (VT-FFR-ES group). Each participant completed all three interventions and the control test (30 min per intervention) with a washout period of at least 3 days between interventions. For each intervention, the pre-test and post-test visual acuity was measured using the Snellen chart, and the best effective multicomponent physiotherapeutic single intervention of myopic improvement was identified.  
Inclusion Criteria  
(1) myopia is defined as the equivalent spherical (SE) degree of at least one eye being below - 0.50D;  
(2) without using cyclologic agent and any ophthalmic drugs (such as cyclogel or atropine) currently or for at least seven days;  
(3) without using orthovetatology lens currently or for at least seven days;  
(4) could answer the questionnaire;  
The exclusion criteria: the eye currently had/has experienced trauma or irreversible eye diseases.  
Figure 2: Flow chart depicting the total number of participants and how the final study enrollment was achieved. VT: Vision training; VT-FFR: Vision training and frequency following response; VT-FFR-ES: Vision training, frequency following response and electrical stimulation; Control group: No function group of VT-FFR-ES.
## 4 Discussion
In contrast, this study is the first to achieve the atmospheric correction and unconstrained positioning of SAR imagery without depending on satellite and ephemeris parameters, or requiring ground control points. It only requires satellite imagery and RPC parameters, demonstrating a broader potential for practical application. However, this study also has certain limitations. Due to the confidentiality of Chinese satellite data, the scope of the dataset in this study is limited, and it does not fully account for topographical variations or broader atmospheric conditions. When conditions permit, further benefits could be gained from a broader dataset that includes images from multiple satellites and different geographical locations, expanding the analysis to include regions with varying topographies, especially mountainous areas, to enhance the model's applicability and robustness. To improve the effectiveness of atmospheric correction, multiple atmospheric datasets can be compared for specific regions, identifying the most suitable datasets, or integrating different atmospheric datasets. Future research could involve testing imagery from multiple study areas, focusing on analyzing and removing the influence of terrain factors to further improve the accuracy of unconstrained positioning. Additionally, the model developed in this study could be extended to a data processing operating system module, creating an integrated data processing system. This would make SAR imagery atmospheric correction more convenient, while also increasing its practical application value.
# Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards
## 2 Materials and Methods
### System Architecture  
The network was based on a simplified communications architecture. A star topology was followed, based on which the Coordinator was positioned at a central location and all the End-nodes were placed in surrounding positions within the communication range. In that sense, the End-nodes were responsible for the data collection, while the Coordinator was responsible for collecting the data sent by the End-nodes, and sending them to the cloud server (Figure 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Measurement Range** & **Accuracy** &
\begin{tabular}{c} **Monitored** \\ **Parameter(s)** \\ \end{tabular} \\ \hline Soil moisture sensor & & & \\ (WATEMMARK) & 0–239 cb & - & Soil water content \\ \hline Soil/water & & & \\ temperature sensor & & & \\ \hline Leaf wetness sensor & 5 k\(\Omega\)\(\sim\)\(>\)2 M\(\Omega\) & - & Leaf wetness \\ \hline Temperature, & 0–100\% RH & \(\pm\)3\% RH & Relative humidity \\ humidity, and & \(-\)40–\(+\)85 \({}^{\circ}\)C & \(\pm\)1 \({}^{\circ}\)C & Ambient temperature \\ pressure sensor & 30–110 kPa & \(\pm\)0.1 kPa & Atmospheric pressure \\ \hline \multirow{2}{*}{Weather station} & - & 0.28 mm resolution & Precipitation \\  & 0–240 km/h & 2.4 km/h & Wind velocity \\ \cline{1-1}  & 0–360\({}^{\circ}\) & 22.5\({}^{\circ}\) & Wind direction \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensors, their characteristics, and attributes measured by the developed system.
## 2 Aggregates and Test Design  
### Aggregates  
Two kinds of typical aggregates with different morphological characteristics were used to investigate the effects in this study. The crushed stone and manufactured sand were selected as aggregates with good angularity, denoted as crushed stone (CS), and the gravel and natural sand were selected as aggregates with poor angularity, denoted as gravel (G). The change of particle surface curvature can quantitatively reflect the angularity of particles. In order to quantitatively investigate the difference of angularity between crushed stone and gravel particles, 3-D scanning method [42] was used to scan particles with sizes of 2.36 mm and above. The one side 3-D images of particles with sampling interval of 0.5 mm in two horizontal directions were obtained. Figure 1 shows the typical particles and the corresponding 3-D scanning results. The curvature of a point on a 3-D surface is a direction-dependent parameter. In this paper, the average value of the maximum curvature and the minimum curvature at a point is taken as its mean curvature. The average curvature (\(C_{m}\)) of given point on surface \(T\):\(f\) (\(x\),\(y\)) can be calculated by Equation (1) [43]. The surface function of can be obtained by local quadratic polynomial fitting. Equation (2) can be used to calculate the variance of mean curvature (\(S_{Cm}^{2}\)) of the surface of each particle. In this paper, \(S_{Cm}^{2}\) is used to characterize the angularity of aggregates. Figure 2 shows the comparisons of \(S_{Cm}^{2}\) between crushed stone and gravel with different particle sizes.  
\[C_{m}=-\frac{(1-q^{2})r-2 pqs+(1+p^{2})t}{2(1+p^{2}+q^{2})^{\frac{3}{2}}} \tag{1}\]  
where \(p=\frac{\partial f}{\partial x}\), \(q=\frac{\partial f}{\partial y}\), \(r=\frac{\partial^{2}f}{\partial x^{2}}\), \(s=\frac{\partial^{2}f}{\partial x\partial y}\), and \(t=\frac{\partial^{2}f}{\partial y^{2}}\).  
\[S_{Cm}^{2}=\frac{1}{n-1}{\sum_{i=1}^{n}}\Big{(}C_{m}^{i}-\overline{C_{m}} \Big{)}^{2} \tag{2}\]  
where  
\[\overline{C_{m}}=\frac{1}{n}{\sum_{i=1}^{n}}C_{m}^{i} \tag{3}\]  
Figure 1: _Cont._Figure 1: Typical particles and the corresponding 3-D scanning test results: **(a)** 13.2 mm crushed stone (CS); **(b)** test result of 13.2 mm CS; **(c)** 13.2 mm gravel (G); **(d)** test result of 13.2 mm G; **(e)** 2.36 mm CS; **(f)** test result of 2.36 mm CS; **(g)** 2.36 mm G; **(h)** test result of 2.36 mm G.  
The mechanical properties of the aggregate were determined according to \"Test Methods of Aggregate for Highway Engineering (JTG E42-2005)\" of China [44]. Table 1 lists the bulk density values of the aggregates with different size.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 4 Bounds on Orientation Estimation
### Three-Dimensional CRLB on Orientation
The first-order partial derivatives in the rotation can be simplified using  
\[\frac{\partial}{\partial\alpha}\mathbf{R}_{x}(\alpha) =\mathbf{S}[1,0,0]\,\,\mathbf{R}_{x}  
\[\frac{\partial^{2}}{\partial\bar{\beta}\partial\gamma}\mathbf{R}( \theta) = \mathbf{S}[0,0,1]\mathbf{R}_{z}(\gamma)\;\mathbf{S}[0,1,0]\mathbf{R} _{y}(\beta)\;\mathbf{R}_{x}(\alpha), \tag{46}\] \[\frac{\partial^{2}}{\partial\alpha\partial\gamma}\mathbf{R}( \theta) = \mathbf{S}[0,0,1]\mathbf{R}_{z}(\gamma)\;\mathbf{R}_{y}(\beta)\; \mathbf{S}[1,0,0]\mathbf{R}_{x}(\alpha). \tag{47}\]  
To the best of our knowledge, this is also the first closed-form, three-dimensional CRLB on orientation estimation in a TWR system.