esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of a Geotextile in the Treatment of Post-Subsidence in Karst Areas
## 4 Results and Discussion
### Effects of Buried Depth of Geotextile
#### 4.2.3 Soil Displacement
The minimum anchorage ratio of the geotextile for buried depth \(H\), being the ratio of anchorage length of geotextile to the subsidence width, is shown in Figure 19.  
The minimum anchorage ratios of the geotextile are 0.65, 0.60, 0.55, 0.50 and 0.50, respectively, for depths of 2 m, 3 m, 4 m, 5 m, and 6 m, indicating that the buried depth of the geotextile has less influence on the construction workload of transverse excavation. Although a deeper buried depth for the geotextile achieves a better treatment effect regarding the treatment of subsidence, it requires deeper longitudinal excavation. For example, when the buried depth of the geotextile is \(H\) = 2 m or \(H\) = 5 m, the minimum anchorage lengths of geotextile are 0.65\(B\) and 0.5\(B\) (\(B\) = 1.5 m), respectively. A buried depth of 5 m has a better effect on the treatment of subsidence and would save 0.225 m of transverse excavation compared to a buried depth of 2 m, although it requires a further 3 m for longitudinal excavation. Considering the treatment effect of subsidence and the construction workload, the buried depth of the geotextile is suggested to be chosen as \(H\) = 3 to 4 m for the treatment of subsidence. Figure 17: Surface settlement for different buried depths of the geotextile (\(H\)) at the final state of subsidence. Figure 18: Decrement ratio of surface settlement against different buried depth values (\(H\)).
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 3 Completion Time of the Operation
### Impact of PM on Operational Completion Time
The PM activities have two influences on the initial schedule \(\delta_{p}\): 1 PM can reduce the possibility of machine failure and increases the stability of schedule; 2 PM can reduce the idle time between operations and may increase the makespan of schedule.
# The Application of Nano Titanium Dioxide for Hydrogen Production and Storage Enhancement
## 2 Titanium Dioxide
### Exploiting the TiO\({}_{2}\) Properties for Hydrogen Storage
The MgH\({}_{2}\)-fl-TiO\({}_{2}\)@C composite allowed a hydrogen desorption of 6.0 wt% to be obtained in 7 min at 250 \({}^{\circ}\)C under a hydrogen pressure of 1 kPa. Concerning the absorption, the MgH\({}_{2}\)-fl-TiO\({}_{2}\)@C composite showed a hydrogen absorption of 6.3 wt% at 150 \({}^{\circ}\)C within 40 min under 5 MPa of hydrogen pressure. [PERSON] et al. [67] synthesized graphene-supported TiO\({}_{2}\) nanoparticles (TiO\({}_{2}\)@FOO) using the solvothermal method. It was observed that the MgH\({}_{2}\)-TiO\({}_{2}\)@FOO composite desorbed 6.0 wt% hydrogen within 6 min at 300 \({}^{\circ}\)C and absorbed 5.9 wt% hydrogen within 2 min at 200 \({}^{\circ}\)C. The enhancement of the catalytic activity can be due to fine, uniform TiO\({}_{2}\) nanoparticles that were obtained by exploiting the combined effect of ethylene glycol and graphene during the solvothermal process. In addition, rGO can act as an electronic conductive channel to enhance the catalytic effect. [PERSON] et al. [68] synthesized TiO\({}_{2}\) nanosheets with exposed [001] facets in order to exploit the nanometer-size and highly active {001} facets of anatase TiO\({}_{2}\) to enhance the reaction kinetics of MgH\({}_{2}\). The MgH\({}_{2}\)-TiO\({}_{2}\) nanosheet composite allowed a hydrogen desorption of 6.0 wt% to be obtained at 260 \({}^{\circ}\)C within 192 s and, in addition, a hydrogen desorption of 1.2 wt% within 300 min at 180 \({}^{\circ}\)C was also observed. Furthermore, the composite absorbed 6.1 wt% hydrogen within 10 s at 150 \({}^{\circ}\)C. Also, a hydrogen absorption of 3.3 wt% hydrogen at 50 \({}^{\circ}\)C within 10 s was obtained. The general strategy to improve H\({}_{2}\) storage performance of MgH\({}_{2}\) using TiO\({}_{2}\)-based catalysts are reported in Table 2.
# Event-Based Emergency Detection for Safe Drone
## 4 Case Study
### Discussions  
It is important to be aware of emergencies that have already occurred due to the failure to respond in advance as quickly as possible. PID control methods seem to show sufficient performance for control within the normal range, but it is difficult to recognize situations outside the normal range of the drone, and even if it is recognized, the cases in which it can be dealt with are quite limited.  
Therefore, we conducted experiments to prove that risk factors can be reduced by recognizing emergency situations outside of the normal range and making an emergency landing by mounting an event-based controller on the upper layer that operates simultaneously with the PID controller. In fact, we were able to witness expected results in case studies through simulations, as shown in Figure 9.  
Figure 8: (**a**). Drone movement 2D log on abnormal case: too late. The vertical red line marks the moment when the drone detected an emergency and proceeded with an emergency landing. Due to a motor failure, the drone far exceeded the expected path, which could possibly have led to secondary collisions. (**b**). Time-window check results. The drone did not make it in the normal arrival time-windows on the path from d to e. The yellow line after the drone left d indicates the moment of the motor failure. The red line indicates the moment where the event-based controller detected an abnormal situation due to max time-window being exceeded.  
As shown in Figure 9 below, the difference can be seen numerically by comparing the abnormal running time according to the presence or absence of an event-based controller. Abnormal operation time is a concept that expresses flight time in a space outside the normal range or a time outside the normal range. In Figure 9 below, when comparing the case of air collision, it showed a better performance of about 300%. It took 5.5 s when there was an event-based controller and 16.8 s when there was no event-based controller. We believe that reducing abnormal operation time as much as possible is essential for safe drone-service operation.  
The main purpose of our approach is to immediately recognize and detect abnormal situations caused by malfunctions or external environmental factors. The experiments above proved that it is capable of handling emergency situations without diagnosing the specific cause and situations with time-window mechanisms.  
However, this approach is not a sophisticated control method through intelligent control. It is also not a technique capable of failure diagnosis for analyzing the cause of an abnormal situation. In order to apply this technique, spatiotemporal statistical analysis of the predicted path must be conducted first.  
In the future, through convergence research with existing control and diagnostic techniques, it will be possible to use this method not only for risk detection, but also for precise control and diagnosis.
# Valorization of Date Fruit (_Phoenix dactylifera_ L.) Processing Waste and By-Products: A Review
## 4. Valorization Approaches of Date Processing Waste
### Extraction of High-Value Compounds
#### 4.4.3 Minerals  
Similarly, the date processing waste is a rich source of essential minerals such as K, Mg, Ca, and Fe, and the recovery of these minerals from date processing waste can offer a sustainable and cost-effective solution for the food and supplement industry. The extraction of minerals from date processing waste can be achieved through different methods, such as acid extraction, enzymatic hydrolysis, and microwave-assisted extraction [27]. The extracted minerals can be used in various applications, such as in the production of dietary supplements or as food fortifiers to increase the nutritional value of processed foods.  
Extracting valuable compounds from date processing waste is a promising method to produce value-added products using this abundant waste stream. Using green extraction methods and identifying novel bioactive compounds offer opportunities for economic and environmental sustainability in the date industry. Further research and development in this field are needed to optimize the extraction processes and to identify new applications for the extracted compounds.
# Progress in the Development of Electrodeposited Catalysts for Direct Liquid Fuel Cell Applications
## 2 Progress in the Electrodeposition of Carbon Supported Pt and Pd Catalysts
### Electrodeposition of Pt
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of DLFC** & **Pt/C** & **Pt-M \({}^{1}\)/C** & **Reference** \\ \hline \multirow{6}{*}{DMPC 2} & Pt + CH\({}_{3}\)OH \(\rightarrow\) Pt\({}_{3}\)CH\({}_{2}\)OH + H\({}^{+}\) + e\({}^{-}\) & Pt + CH\({}_{3}\)OH \(\rightarrow\) Pt-CO + 4H\({}^{+}\) + 4e\({}^{-}\) & \\ \cline{2-4}  & Pt\({}_{3}\)-CH\({}_{2}\)OH + H\({}^{+}\) + e\({}^{-}\) \(\rightarrow\) Pt-CO + 2 Pt + 3H\({}^{+}\) + 3e\({}^{-}\) & M + H\({}_{2}\)O \(\rightarrow\) M-OH + H\({}^{+}\) + e\({}^{-}\) & \\ \cline{2-4}  & Pt + H\({}_{2}\)O \(\rightarrow\) Pt-OH + H\({}^{+}\) + e\({}^{-}\) & Pt-CO + M-OH \(\rightarrow\) Pt + M +CO\({}_{2}\) + H\({}^{+}\) + e\({}^{-}\) & \\ \cline{2-4}  & Pt-CO + Pt-OH \(\rightarrow\) Pt-COOH + Pt & & \\ \cline{2-4}  & Pt-COOH \(\rightarrow\) Pt + CO\({}_{2}\) + H\({}^{+}\) + e\({}^{-}\) & & \\ \hline \multirow{6}{*}{DEFC 2} & CH\({}_{3}\)CH\({}_{2}\)OH \(\rightarrow\) CH\({}_{3}\)CHO + 2H\({}^{+}\) + 2e\({}^{-}\) & CH\({}_{3}\)CH\({}_{2}\)OH \(\rightarrow\) CH\({}_{3}\)-CHO + 2H\({}^{+}\) + 2e\({}^{-}\) & \\ \cline{2-4}  & Pt + CH\({}_{3}\)CHO \(\rightarrow\) Pt-(CO-CH\({}_{3}\)) + H\({}^{+}\) + e\({}^{-}\) & Pt + CH\({}_{3}\)CHO \(\rightarrow\) Pt-(CO-CH\({}_{3}\)) + H\({}^{+}\) + e\({}^{-}\) & \\ \cline{2-4}  & Pt-(CO-CH\({}_{3}\)) + Pt \(\rightarrow\) Pt-CO + Pt-CH\({}_{3}\) & Pt-(CO-CH\({}_{3}\)) + Pt \(\rightarrow\) Pt-CO + Pt-CH\({}_{3}\) & \\ \cline{2-4}  & 2 Pt + H\({}_{2}\)O \(\rightarrow\) Pt-H + Pt-OH & 2M + H\({}_{2}\)O \(\rightarrow\) M-H + M-OH & \\ \cline{2-4}  & Pt-CH\({}_{3}\) + Pt-H \(\rightarrow\) 2 Pt + CH\({}_{4}\) & Pt-CH\({}_{3}\) + Pt-H \(\rightarrow\) 2 Pt + CH\({}_{4}\) & \\ \cline{2-4}  & Pt-CO + Pt-OH \(\rightarrow\) Pt + CO\({}_{2}\) + H\({}^{+}\) + e\({}^{-}\) & Pt-CO + M-OH \(\rightarrow\) Pt + CO\({}_{2}\) + H\({}^{+}\) + e\({}^{-}\) & \\ \hline \multirow{6}{*}{DFAFC 2} & HCOOH + Pt \(\rightarrow\) Pt-CO + 2H\({}^{+}\) + 2e\({}^{-}\) & HCOOH + Pt \(\rightarrow\) Pt-CO + 2H\({}^{+}\) + 2e\({}^{-}\) & \\ \cline{2-4}  & Pt + H\({}_{2}\)O \(\rightarrow\) Pt-OH + H\({}^{+}\) + e\({}^{-}\) & M + H\({}_{2}\)O \(\rightarrow\) M-OH + H\({}^{+}\) + e\({}^{-}\) & \\ \cline{1-1} \cline{2-4}  & Pt-CO + Pt-OH \(\rightarrow\) 2 Pt + CO\({}_{2}\) & Pt-CO + M-OH \(\rightarrow\) Pt + M+ CO\({}_{2}\) & \\ \hline \hline \end{tabular}  
* M—second metal, 2 DMFC—Direct methanol fuel cell; DEFC—Direct ethanol fuel cell; DFAFC—Direct formic acid fuel cell.  
\end{table} 
Table 2: A comparison of the anodic oxidation of DLFCs employing Pt/C and Pt-M/C catalysts. Figure 1: A summary of the progress in development associated with Pt-Ru catalysts utilising electrodeposition. Pt(dba)\({}_{2}\)—Bis(dibenzylideneacetone)platinum; Ru(COD)(COT)—organization Ru precursor; COD—1,5-cycloocadiene; COT = 1,3,5-cycloocatriene [35; 36].
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 6 Analyses of Simulation Experiments
### Location Analysis Between Relay Node and Receiving Point  
Figure 20 shows the variation in field strength with increasing distance between the relay and receiving point when the height difference between the relay node and receiving point is 0 m, 0.1 m, and 0.2 m. The transmitting frequency of the antenna is 0.3 GHz, the relative dielectric constant of the roadway wall is 6, the distance from the relay node to the plane \(m_{5}\) is \(w_{3}\) = 0.3 m, the distance from the receiving point to the plane \(m_{6}\) is \(w_{2}\) = 0.3 m, and the height of the relay node is \(v_{3}\) = 0.3 m. The antenna transmits electromagnetic waves from the relay node, and the receiving point moves parallel along the roadway walls from the relay node, passing through the second corner until it stops moving at 1.7 m from the second corner., i.e., the distance between the relay node and the receiving point.  
From Figure 20, it can be seen that the overall trend of attenuation of the field strength at the receiving point is observed with the increase in the distance between the relay node and the receiving point. When the height difference between the relay node and the receiving point increases, the attenuation of the field strength at the receiving point becomes more significant. This is because a large height difference leads to changes in the propagation path of electromagnetic waves and increases the multipath effect and propagation loss, thus reducing the field strength at the receiving point, which is consistent with the conclusion of Figure 14. Therefore, in the actual mine U-shaped roadway, the location of the sensor and the radiation source should be at different heights to increase the field strength attenuation and decrease electromagnetic interference.  
Figure 19: Flowchart of HFSS simulation steps.
# mIoT Slice for 5G Systems: Design and Performance Evaluation
## 2 The Massive Internet of Things Use Case
### mIoT Deployment Scenarios and Traffic Models  
Together with the analysis of communication requirements of mIoT, it is also of critical importance to envision possible deployment scenarios (i.e., number and geographical distribution of user equipment and UEs) and reasonable traffic models for different categories of services. These estimations, albeit speculative, help identifying bottlenecks, design flaws and unsuitable resource dimensioning criteria of current communication systems.  
Models commonly used by a wide proportion of related work, formalized in [17; 18; 19], consider a device density between \(10^{3}\) and \(10^{5}\) UE/km\({}^{2}\), leading to up to \(10^{6}\) devices per cell, obviously depending on the scenario (e.g., central, urban, and rural). As per device traffic patterns, models have been defined aiming at stressing the radio access network (RAN) capabilities. Synchronous and asynchronous device transmission is considered, predominantly device triggered, with different access distributions in time (e.g., uniform, and beta-distributed) over periods ranging from tens of seconds to tens of hours. The UP traffic generated is expected mostly on the uplink (UL), each device activation requiring the transmission of data ranging between \(10^{2}\) and \(10^{4}\) bits. A quantitative comparison between the figures provided in this subsection and those available from data traffic reports for deployed 4G networks highlights mIoT, even at its early stage, will require networks to handle number of connections 3-4 orders of magnitude higher than Mobile Broadband (MBB) use cases, while each connected device will lead to a data traffic 5-8 orders of magnitude lower than 4G smartphones [17]. The above quantitative estimation should be carefully considered in the design of an E2E architecture: it should be emphasized that a region covered by, e.g., \(10^{3}\) cells (which would be very likely handled by a single CN instance), would lead to up to one billion registered mIoT devices, although the load per cell would be 1-4 orders of magnitude lower than those generated by 4G smartphones. This leads to an essential design consideration: if such expected deployment scenarios and traffic models will be proven to be accurate on field, legacy 4G architecture would be challenged not on Access Network, but rather on the Core Network side, where novel solutions will be required.
# On the Influence of Data Imbalance on Supervised Gaussian Mixture Models
## 5 Results
### Simulation Studies  
In the first simulated scenario, data have been generated by drawing from a mixture of bivariate Normal distributions with fixed means and covariance matrices. Following the notation in Murphy [21], the data generating process (DGP) can be described as follows:  
\[y=\{\mathbb{C}_{0},C_{1}\} \sim\text{Cat}(\mathbf{\tau})\] \[\mathbf{x}\mid(y=\mathbb{C}_{k}) \sim\mathcal{N}(\mathbf{\mu}_{k},\mathbf{\Sigma}_{k}).\]  
According to the above DGP, classes are generated from the categorical distribution with probabilities \(\mathbf{\tau}=(\tau_{0},\tau_{1})^{\top}\), where \(\Pr(y=\mathbb{C}_{k})=\tau_{k}\) for \(k=\{0,1\}\), and such that \(\tau_{0}+\tau_{1}=1\). The majority class follows a bivariate standard Gaussian distribution, so \(\mathbf{\mu}_{0}=\mathbf{0}_{2}=\left[ \begin{smallmatrix}0\\ 0\end{smallmatrix} \right]\) and \(\mathbf{\Sigma}_{0}=\mathbf{I}_{2}=\left[ \begin{smallmatrix}1&0\\ 0&1\end{smallmatrix} \right]\). Also, the minority class follows a bivariate Gaussian distribution, but with mean \(\mathbf{\mu}_{1}=\left[ \begin{smallmatrix}1\\ 1\end{smallmatrix} \right]\) and covariance matrix \(\mathbf{\Sigma}_{1}=\left[ \begin{smallmatrix}1&-0.5\\ -0.5&1\end{smallmatrix} \right]\). In this simulation study, we consider samples of \(n=5000\) observations from the above DGP with the probability of the minority class \(\tau_{1}\) taken to be either \(0.1\) or \(0.01\). After fitting models described in Section 2.2, their evaluation is based on a test set of size \(m=50,000\) generated from the same DGP. Results for \(100\) replications are reported in Tables 3 and 4, while Figure 2 shows a sample of simulated data with decision boundaries for classification using different models and methods dealing with class imbalance. Based on the results of this simulation study, it is clear that addressing class imbalance is important for both the EDDA model and the more flexible MclustDA model. The unbalanced distribution of classes significantly impacts the performance of these models, emphasizing
# A Compact Monopole Wideband Antenna Based on DGS
## 1 Introduction
A unique patch of octagon shape connected to a 50 \(\Omega\) microstrip feed line on a top plane in addition to the partial ground on the bottom plane. The patch tapering results in bandwidth improvement [21]. It has simulation results as bandwidth from 2.8-13 GHz and a gain of 4.64 dBi. Particle swarm optimization (PSO) is involved in determining the distribution of control points both on the radiator and ground plane. The antenna configuration that uses a straight CPW feedline is then changed by applying a tapered CPW feedline to suppress the notch response that occurred, thereby yielding a single wide bandwidth response to accommodate UWB applications [22]. This shows bandwidth is from 1.76 to 20 GHz. This study presents a compact monopole wideband antenna based on DGS, designed to enhance communication efficiency through a compact form, increased bandwidth, and improved impedance matching. The design process encompasses three key stages: reconfiguration, characterization, and evolution. Initially, the conventional rectangular radiator was reconfigured into asymmetric U-shaped and G-shaped designs. Characterization of these designs identified the G-shaped antenna as superior due to its wider bandwidth and better impedance matching. In the evolution stage, DGS was incorporated to further enhance performance by strategically adding an L-slot and a semi-circular slot, resulting in a bandwidth increase from 2.71 to 14.84 GHz. The novelty of this antenna lies in its dual-slot configuration that effectively defects the ground plane, enabling multi-frequency operations suitable for modern wireless communication systems. With overall dimensions of 34 mm \(\times\) 28 mm, this compact design can be seamlessly integrated into various portable devices. The iterative design process, illustrated in the accompanying figures, showcasessignificant enhancements and systematic improvements in the antenna's performance. The detailed evolution performance analysis presented in Table 1 highlights various design stages and their outcomes, showcasing the systematic approach towards enhancing the antenna's characteristics based on the DGS methodology. The remainder of this paper is organized as follows: Section 2 presents antenna design methodology and optimization process; Section 3 discusses the simulation results and analysis; Section 4 presents fabrication measurements and a comparison of the antenna with previous works; and Section 5 summarizes the paper.
# Influence of CeO\({}_{2}\) Addition to Ni-Cu/HZSM-5 Catalysts on Hydrodeoxygenation of Bio-Oil
## 3 Results and Discussion
### Characterization of Catalysts  
#### 3.2.1 XRD  
The diffraction peaks for each catalyst near 6.7\({}^{\circ}\)and 24.8\({}^{\circ}\) were due to the catalyst support (HZSM-5, PDF# 42-0305), and the peak near 46.5\({}^{\circ}\) was due to the Ni-Cu/HZSM-5 catalyst without CeO\({}_{2}\) (Figure 4). These peaks were expected to be the Ni-Al compound formed between the metal and the catalyst support (Ni-Al PDF# 20-0019). However, the addition of CeO\({}_{2}\) into the Ni-Cu/HZSM-5 catalyst inhibited the presence of the Ni-Al compound phase, in which the intensity of the peak (near 46.5\({}^{\circ}\)) was remarkably decreased. The addition of CeO\({}_{2}\) can seemingly reduce the bond between the active metal and the catalyst support and can improve the catalytic activity of the catalyst. The crystal size was calculated by the Scherrer formula (Table 4). The crystal size decreased first and then increased with the increase of the CeO\({}_{2}\) content. When the content of CeO\({}_{2}\) was 15%, the average particle size of the catalyst was the smallest at 19.2 nm.
# Improving GNSS Landslide Monitoring with the Use of Low-Cost MEMS Accelerometers
## 4 MEMS Sensor Features and Calibration Results  
Particularly interesting are the features of TE Connectivity's silicon MEMS DC model 4030 triaxial accelerometer, used for inclinometric measurements. The device has a small size and weight, is rugged and cheap (approx. \(\copyright\)130), and is usually recommended for structural monitoring.  
This device has a variable voltage output for each axis, from 0.5 to 4.5 V, which corresponds to a dynamic range of \(\pm\)2 g. The value of 1 g corresponds to a voltage of 1000 mV and 90\({}^{\circ}\) tilt. It is possible to convert the voltages into acceleration and angular values on the three axes and determine the corresponding tilt values, under the hypothesis of linearity between voltage output and acceleration [14].  
For example, if a sensitivity and angular accuracy of 1\({}^{\circ}\) is required for tilt measurements, a corresponding accuracy of 1000 mV/90\({}^{\circ}\) = 11.1 mV for degree is required. The residual noise of the 4030 inclinometer is declared by the manufacturer as RMS = 0.24 mV, which corresponds to a value in degrees equal to 0.24 mV \(\times\) 1\({}^{\circ}\)/11.1 mV = 0.022\({}^{\circ}\).