esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Reusability of Catalyst  
Reusability is an important criterion for a catalytic process affecting the process cost and economics [60; 61]. Hence, the reusability of the catalysts was evaluated under identical reaction conditions. As shown in Figure 7, the oil conversion still remained above 80% after the fifth successive reuse of CaNa/AC. On the contrary, the oil conversion by Na/AC dramatically declined to 30% under the same conditions. The stability of a heterogeneous-based catalyst is critical because the bases leached during the transesterification reaction had a detrimental influence on the subsequent treatment of biodiesel substrates. The basicity loss of the catalyst was calculated by the basicity of the catalyst before and after the reaction. The results showed that the basicity losses were 30% and 6% for Na/AC and CaNa/AC, respectively, after the first transesterification. Therefore, the leached base from Na/AC could potentially act as a homogenous catalyst for transesterification, revealing a lack of chemical stability in this catalyst. In the case of the regenerated CaNa/AC, the catalytic activity decreased slightly compared to that of the fresh catalysts and still maintained an oil conversion of 80% after four cycles. These findings indicated that the introduction of calcium boosted the stability of the catalyst and enhanced its reusability, which is conducive for practical applications on large-scale operation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature (K)** & **Slope** & **Intercept** & \(\mathbf{R^{2}}\) \\ \hline
323 & 0.961 & \(-\)2.47 & 0.970 \\
328 & 0.988 & \(-\)2.26 & 0.989 \\
333 & 0.985 & \(-\)2.12 & 0.979 \\
338 & 0.980 & \(-\)1.91 & 0.982 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The fitting results of \(\lg(-\mathrm{d}C_{\mathrm{A}}/\mathrm{d}t)\) vs. \(\lg C_{\mathrm{A}}\).
## 4 Improvement of Thermal Performance
### Form-stable PCMs  
Solid-liquid PCMs, as known, present some disadvantages related to the liquid phase, such as leakage, volume change, supercooling, etc. To overcome these obstacles, composite PCMs are being investigated. A modification approach for pure PCMs, in order to enhance their performance and stability, is the fabrication of form-stable PCMs. Form-stable, or shape-stabilized, PCMs consist of a 3D matrix, or a porous structure, where the pure PCMs are entrapped. The matrix or porous structure can be in macro, micro, or nano scale [2; 33]. The advantages of this technology are the limitation of liquid phase leakage, the enhancement of the thermal conductivity, the controlling of volume change during melting/freezing cycles, the reducing of PCM interaction with the environment, the avoidance of additional device requirement, and the ability of manufacturing the thermal energy system in the desired forms and dimensions for easy application. The materials used as substrates can be polymers or inorganic porous minerals [5; 34].
# Pedestrian Behavior in Static and Dynamic Virtual Road Crossing Experiments
## 2 Materials and Methods
### Analysis
#### 3.2.3 Time-to-Passage  
An LMM of TTP with the subject ID as the random effect and vehicle initial speed, speed pattern, and experimental approach as fixed factors was also fitted. [PERSON]'s tests showed significant effects of speed pattern, F(2, 2179.55) = 27.98, \(p\) < 0.01, and an interaction effect between speed pattern and initial speed, F(2, 2168.94) = 25.93, \(p\) < 0.01. There was no significant effect of the initial speed, F(1, 2178.94) = 0.39, \(p\) = 0.53, nor of the experimental approach, F(1, 99.46) = 0.93, \(p\) = 0.34.  
The model was refitted, discarding the experimental approach factor, and the different speed patterns were compared using contrasts. The first one compared the constant and slowing down speed patterns, resulting in non-significant differences. A second contrast compared the constant and stopping patterns and showed a non-significant difference between them. Another contrast compared the constant and slowing down patterns for each one of the different initial speeds, revealing once again a non-significant difference between them. Lastly, a fourth contrast showed significant differences between the constant and stopping patterns within each one of the different initial speeds, b = 3.68, t(2183.91) = 3.16,  
Figure 13: Crossing start time and respective mean standard error as a function of the experimental approach, per initial speed and speed pattern.  
\(p<0.01\), showing that the TTP was notably longer for the stopping action when the initial speed of the vehicle was 30 km/h (see Figure 14).  
The TTP for the slowing down pattern is similar to the ones for the constant. Nevertheless, the percentage of crossings was notoriously higher, indicating that participants had made a substantially different risk assessment, believing that the vehicle would stop. The significant increase in TTP for the 30 km/h also helps to explain that. Additionally, it shows that, in both experimental approaches, the participants waited for the vehicle to almost stop before starting to cross.  
No significant differences were found between experimental approaches. However, Figure 14 shows, particularly for the stop pattern and the initial speed of 30 km/h, that the participants who performed the dynamic experiment had accepted higher TTPs. The opposite can be said regarding the stimuli corresponding to the constant speed pattern, considering both initial speeds.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 4 Significance of Communications in IoMT
### Edge Computing/Fog Computing Technologies  
Edge computing is seamlessly integrated into the communication layer of IoMT systems thanks to their ability to process data closer to the source, enabling faster response times, reduced latency, and enhanced overall system efficiency. Edge or fog computing supports real-time data processing and analysis, facilitating rapid decision-making and response to dynamic changes for both physical and virtual entities [60]. Additionally, edge computing reduces the need to transmit large volumes of raw data to centralized servers or cloud platforms, thereby alleviating network congestion and minimizing bandwidth usage. Fog computing, on the other hand, extends the capabilities of edge computing by providing a hierarchical architecture that includes intermediate fog nodes between edge devices and centralized cloud servers. Fog nodes act as intermediaries for data processing and storage, allowing for more efficient resource utilization and dynamic allocation of computational tasks based on local requirements.
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 4 Analytical Procedure
### CNN Model
#### 4.1.1 Model A  
Time domain acceleration, velocity, and displacement histories were applied to model A. Every history was evenly split into 120 signals with 100 acceleration values of time increments of 0.01 s, having 1 s of duration. The thin lines behind the graphs in Figure 5 split the signals from the duration of 120 to 1 s. Accordingly, the split time domain acceleration, velocity, and displacement histories at the bedrock are assigned to input data, and the split acceleration histories measured on the surface are to output data, as presented in Figure 5. CNN architecture A consists of a convolution layer with 100 kernels with the size (9,1) as parameters without a pooling layer. Mean squared error (MSE) was adopted to loss function and Rectified linear unit (ReLU) was to activation function.  
Figure 4: Scheme of the process of convolutional neural networks.
## 5 Sensor Signal Feature Extraction
### Signals' Feature Extraction in the Frequency Domain  
A fast Fourier transform (FFT) was applied to convert the segmented thrust force and torque signals into the frequency domain. For both thrust force and torque signals, relevant frequency peaks were found corresponding to 1\(\times\)-2\(\times\)-3\(\times\)-4\(\times\)-5\(\times\)-6\(\times\) the drill bit revolution frequency.  
Figure 14 show the FFT of thrust force signals for the drilling test performed at 6000 rpm and 0.15 mm/rev, in which the revolution frequency is 6000 rpm/60 = 100 Hz. The highest frequency peaks were observed at 100, 200, 300, 400, 500, 600 Hz, i.e., at 1\(\times\)-2\(\times\)-3\(\times\)-4\(\times\)-5\(\times\)-6\(\times\) the revolution frequency.  
Figure 12: Segmented torque signal vs. number of holes (2700 rpm–0.15 mm/rev).  
In Figure 15, the frequency peaks of the thrust force signal are reported versus hole number for the drilling test performed at 6000 rpm and 0.15 mm/rev. It can be observed that the amplitudes of some of the frequency peaks grow with increasing number of holes, suggesting a potential correlation of the frequency peak amplitude with tool wear progression.  
Figure 14: Thrust force signal single-sided amplitude spectrum (6000 rpm–0.15 mm/rev) [27].  
Figure 13: Statistical features extracted from the thrust force signals for all operating conditions vs. hole number: (**a**) arithmetic mean; (**b**) variance; (**c**) kurtosis; (**d**) skewness.
# An Overview of GEO Satellite Communication Simulation Systems
###### Abstract
In Section 2, coverage analysis is summarized. Section 3 overviews resource management and scheduling. Simulation platforms and tools are presented in Section 4. Conclusions are drawn in the last section.
## 3 Wireless Sensor Networks
### WSN Applications
#### 3.3.1 Military Applications  
Wireless sensor networks can be a part of military command, control, communications, computing, intelligence, surveillance, reconnaissance and targeting systems. The rapid deployment, self-organization and fault tolerance characteristics of sensor networks make them a very promising sensing technique for military applications. Some of the military applications of sensor networks are monitoring friendly forces, equipment and ammunition; battlefield surveillance; reconnaissance of opposing forces and terrain; targeting; battle damage assessment; and nuclear, biological and chemical attack detection (recently is considered as one of the critical types of attacks) and reconnaissance.
## 2 Materials and Methods
### Electronic Architecture  
The electronic architecture developed for this application is based on National Instrument CompactRIO system, which consists of a processor running a Linux real-time operating system and a chassis that contains a user-programmable FPGA. On the FPGA, the low level control logic and communication protocol are implemented using external modules to read sensors data and send the actuators command. The magnetic absolute encoders mentioned in Sections 2.3.1 and 2.3.2 transmit data through BiSS-C, a serial point-to-point communication protocol. Since the point-to-point communication requires a cable for each node in the network, the data of each encoder are acquired by a Texas Instrument Tiva C TM4C123 GH6 PM microcontroller, positioned at joint level, with a sampling rate of  
Figure 8: Schematic view of the actuation system. For each joint a couple of counteracting cables are routed from a linear actuator on the base up to a pulley along the robotic arm.  
16,667 Hz. This removes the need for individual wiring of each encoder into the full arm. The acquired data are filtered and processed to numerically estimate the joint velocity. The microcontrollers are connected in daisy chain and transmit the joint position and velocity to the FPGA of the CompactRIO through a CAN Bus with a sample rate of 400 Hz. A second and independent CAN Bus is implemented to establish the communication with the motor drivers. In this case, the standard CANopen protocol is employed to read the motor states and send the commands. Moreover, two microcontrollers ATmega2560, provided by Atmel, are connected to the host PC through TCP/IP protocol. One of these microcontrollers manages the motors that control the pan and tilt mechanism, while the other receives the commands from a custom-made remote controller to manually drive the end-effector. The real-time target manages the communication with both the FPGA and the host PC where a graphical user interface is implemented to drive the robot and log the data. Figure 10 shows a schematic of the electronic architecture employed in this work.
## 5 Conclusions  
In this study, a bluff-body-stabilized \(\rm CH_{4}/H_{2}/air\) flame was investigated through large eddy simulation (LES) using hydrogen ratios of up to 90%. The LES was conducted using an OpenFOAM 2.3.0 open-source code and a thickened flame (TF) model coupled with a 30-species skeletal mechanism. Our numerical results were progressively validated based on the OH profile and velocity field. Four hydrogen ratio conditions, namely 0, 30%, 60%, and 90%, were investigated for both stable conditions and the blow-off process. The results of this study can be summarized as follows:  
1. Under stable conditions, both the flame height and the inner/outer recirculation zone decrease with an increase in the hydrogen ratio. The angle between the flame edge and centerline increases linearly, and the ability of the flame to stabilize in higher-strain-rate locations is enhanced. The location of the flame moves away from the vortex concentration area. This implies that the flame tends to attach to the bluff body more strongly.
2. For blow-off conditions, a specific precursor event that triggers blow-off is identifiable by the rate of HRR decrease. The blow-off process is separated into two stages. With an increase in the hydrogen ratio, the duration of each stage decreases. The decrease in the heat release rate is one of the main factors that cause blow-off in lean \(\rm CH_{4}/H_{2}/air\) flames and also influences flame temperature.
3. An increase in enstrophy during the blow-off sequence was observed in all cases. The mean value of enstrophy decreased with hydrogen blending. Our enstrophy budget analysis showed that the vortex stretching source term was significantly reduced in magnitude with an increase in hydrogen ratio, which corresponded to a decrease in the absolute enstrophy value. The dilatation and baroclinic torque terms suddenly decreased close to blow-off, but their decline was not significant in high-hydrogen-ratio conditions.  
L.C.: Investigation, resources, writing--original draft preparation. M.Z.: Conceptualization, writing--review and editing. S.P.: Project administration. J.W.: Supervision, funding acquisition. Z.H.: Supervision. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Vortex stretching (Term 1), dilatation (Term 2), and baroclinic source (Term 3) at \(x/d=1.5\) during blow-off for different hydrogen ratios.  
**Funding:** This work was financially supported by Research on key technologies of hydrogen-mixed natural gas transportation by pipeline in service (PipeChina, project No.: DTXNY202203).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors thank [PERSON] and [PERSON] for contributing to the data acquisition. This paper also benefited a lot from the invaluable discussions with them.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 1 Introduction
[31] designed a multilayer (ZnO/Ag/ZnO) film pH sensor, which was tested as an extended gate field effect transistor. The pH detection range was demonstrated to be 2-12 pH units. Lastly, solutions using ISFET (Sensitive Field Effect Transistors) sensors, which have the advantage of being small, have been proposed. Nonetheless, with ISFET sensors, samples under study must be small, and the measuring range is limited to 10 pH units (1.5 to 11.5 pH) [32]. An essential aspect of any instrument intended for VF use is that such instrumentation must meet availability, portability, and cost-effectiveness constraints to enable global adoption. Furthermore, the global food supply chain, which by 2050 will have to increase its capacity by 60-70% due to the growth of the world population [33], would benefit greatly if connected (characteristic of _Industry 4.0_ solutions) pH sensors were available. Thus, the goal of this article is to describe the implementation of a portable, cost-effective, and connected instrument that enables accurate pH measurements for VF and, more generally, _Industry 4.0_ applications. This work is divided into six sections. Section 2 deals with the theoretical background, as required to address pH sensing for global _Vertical Farming_ applications. In Section 3, the instrument is described in the context of a three-layer Internet of Things architecture. Here, the circuit design for pH measurements is presented. In Section 4, the results of sensor characterization and pH measurements are presented. The discussion section, presented in Section 5, describes two modifications that can be implemented to the circuitry, in order to increase the precision of the instrument. Finally, the results of this work and future research directions are summarized in Section 6.
## 4 Application and Results  
### Emergency Scenario Application Result  
The hydraulic analysis results for each scenario are shown in Table 2 and Figure 5. The average A-city pressure is 39.62 m, which satisfies the Korean water supply standard of 1.53 kg/cm\({}^{2}\). In the scenarios of the water reservoir capacity loss, the water pressure decreased. However, for cases 1, 2, and 5 (Figure 5b,c,f), negative pressure was generated. Based on EPANET2's demand driven analysis (DDA), it calculates the pressure at each node under the assumption that the supply quantity satisfies  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case Number**} & \multicolumn{2}{c}{**Abnormal Scenario**} & \multirow{2}{*}{**Normal Scenario**} \\ \cline{2-2} \cline{4-4}  & **Problematic Reservoir** & & **Connected Reservoir** \\ \hline
2 & RES 2 (Block 2) & RES 1 (Block 1) & Demand Pattern (24 h) \\ \hline
3 & RES 2 (Block 2) & RES 3 (Block 3) & Demand Pattern (24 h) \\ \hline
4 & RES 2 (Block 2) & RES 1 (Block 1) & Demand Pattern (24 h) \\ \hline
5 & RES 3 (Block 3) & RES 2 (Block 2) & Demand Pattern (24 h) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Emergency connection supply pipeline operation scenarios in normal and abnormal situations.  
Figure 4: Target region information (A-city).  
all the demand quantity when it knows the demand quantity of each node. Therefore, there is a limit in that it satisfies the supply characteristic even when water cannot be supplied to the node. In order to replace these unrealistic results, [PERSON] et al. (2018) and [PERSON] et al. (2018) applied pressure driven analysis (PDA), one of the hydraulic analysis techniques using the head-outflow relationship (HOR). In addition, the method of processing negative pressure based on DDA includes the semi-Pressure Driven Analysis (semi-PDA) method (S
## 5 Experiment Simulation
### Experimental Setup  
We set up the experiment simulation based on Icarus [24], a Python-based discrete-event simulator for evaluating ICN caching performance. We implement the routing-by-resolution mechanism for caching strategies, as well as realizing the popularity-based caching strategy in Icarus. We compare PB-NCC with four widely used on-path caching strategies: LCE, LCD, CL4M, and PROB. Table 2 shows the basic parameters for the simulation. To evaluate the performance of the caching strategies in different topologies, we further select four real-world topologies: EBONE (87 nodes), TELSTRA (108 nodes), ABOVENET (141 nodes), and SPRINTLINK (315 nodes) from Rocketfuel [25]. Two types of workloads are used for simulation. The Zipf distribution workload has a content set with \(10^{5}\) contents, with popularity defined by the Zipf parameter \(\alpha\). This content size was analyzed in [26] and can be considered appropriate to simulate a realistic scenario. Based on previous research on web content [27; 28], it is reasonable for \(\alpha\) to range from about 0.4 to 1.0. We set \(10^{5}\) requests for system warm-up and another \(10^{5}\) requests for performance measurement. The YouTube workload [29] has a content set with \(5.81\times 10^{5}\) contents, and the total request number is set to \(1.46\times 10^{6}\), from which we use the first \(4.6\times 10^{5}\) requestsfor system warm-up and the remaining for performance measurement. All requests follow the Poisson distribution, and 10 requests are generated by each user per second. All caching nodes are allocated the same cache space and use the LRU [30] policy for content replacement. A unified content replacement policy is more advantageous to compare the performance of the different caching strategies.  
For each scenario, we run the experiment 10 times and and average the results. Each experiment measures three indicators: cache hit ratio, network average latency, and average server load. Cache hit ratio refers to the proportion of content requests served by caching nodes, and network average latency refers to the average latency for users to get contents. Average server load represents the average load on the servers during the experiment. In addition, we will evaluate the performance of caching strategies under different cache size ratios, which represent the ratio of cache space to the total size of the content set.