esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Mechanism of Hole Cleaning for Different Well Profiles
### The Hole Cleaning Indicators  
In drilling operations, hole cleaning indicators are essential because they track how well the drilling fluid removes drill bits cuttings and keeps the wellbore free. Pump pressure, mud returns, ROP, temperature, torque, and drag are some of these indications. A rapid rise in pump pressure or a drop in mud returns might signal ineffective hole cleaning, and an increase in temperature, torque, and drag can all point to ineffective cuttings removal [29, 30, 31]. Drilling experts may take remedial action to avoid problems such as clogged pipes, lost circulation, and wellbore instability by keeping an eye on these signs, ensuring the drilling process runs smoothly and effectively. Several works have studied the hole cleaning indicators. For instance, [PERSON] et al. showed that when drilling directional wells, the drill pipe's dynamic behavior significantly contributes to better hole cleaning [32]. [PERSON] et al. proved that a sufficiently large annular frictional pressure loss is required to obtain proper hole cleaning in deviated wells [33]. [PERSON] et al. proved that the hole cleaning efficiency enhances as the average annular velocity (AV), yield point (YP), plastic viscosity (PV), drill string rotation, and density of drilling fluid increase [34]. Moreover, [PERSON] et al. proved that the drill string eccentricity and flow rate are the drilling operation characteristics that have the most favorable effects on the cuttings transport procedure [35].  
In drilling operations, real-time hole cleaning monitoring is essential for spotting possible problems including cuttings' build-up and accidents involving trapped pipes. However, the quality of the data provided by the rig sensors has a significant impact on the dependability and accuracy of real-time hole cleaning indicators. Conventional rig sensors may not always be adequate to give the data required to run certain models in real-time, which might restrict drilling teams' ability to quickly alter course to avoid or reduce hole cleaning issues [22,36]. More importantly, real-time parameters are those that are altered while drilling is taking place. Surface operating characteristics including ROP, flow-in, revolutions per minute (RPM), open hole diameter (OH), weight on bit (WOB), and others  
Figure 2: Cutting transport mechanism for different ranges of zenith angle at different speeds in the annulus.  
are affected by these changes. Depending on how real time is defined, there may or may not be a difference between real-time and non-real-time parameters. Figure 3 illustrates the use of iterative steady-state modelling with real-time parameters as inputs while drilling in real-time. This strategy aided in the development that is detailed below.  
Thus, the next section explains a novel model which shows the existing parameters used to develop a novel cutting transport ratio for enhancing the drilling performance and continuous monitoring and assessment of hole cleaning in real-time during drilling.
# Efficient LDPC Encoder Design for IoT-Type Devices
## 6 Numerical Results
### Throughput  
The trade-offs between code parameters and the achieved encoder features can be also illustrated by means of the achieved maximum encoding throughput. This is visualized in Figure 9. We can observe the advantage of the proposed efficient QC-LDPC encoder realization over the basic LDPC, BCH and RS encodings. The throughput developed realization is rather not sensitive to the block length. The throughput in general increases with the code rate, because the portion of information bits in the code blocks increases.  
Figure 8: Encoding energy needed per bit vs. code length.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Matrix ID** & **Energy Saved** & **Matrix ID** & **Energy Saved** \\  & **[\(\mu\)J/bit]** & & **[\(\mu\)J/bit]** \\ \hline H1 & 196.81 & H2 & 197.55 \\ \hline H3 & 48.65 & H4 & 49.59 \\ \hline H5 & 76.29 & H6 & 108.94 \\ \hline H7 & 16.75 & H8 & 17.26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Energy savings per bit, achieved for QC-LDPC encoding, in comparison with counterpart Reed-Solomon encoding.
# Effect of Nonlinear Hysteresis Details of Isolation System on In-Structure Response Spectra
## 2 Consideration When Developing ISRS of Fixed and Isolated Structures
### Variables of Hysteresis Behavior of Isolator for ISRS Evaluation  
As explained previously, isolated structures behave nonlinearly, and the ISRS can be affected depending on the hysteresis behavior of the isolator. The nonlinear effect will be increased by the seismic intensity increase. When the seismic intensity is larger, the maximum displacement increases and the effective stiffness decreases, which increases the effective period and affects the low-frequency response. Conversely, when the intensity decreases, the maximum displacement decreases, which can affect the high-frequency response. Therefore, it is necessary to examine all seismic intensities under the design level intensity. [PERSON] et al. [13] analyzed the acceleration floor response of an isolated structure with high damping lead bearings by applying a seismic intensity weaker than the design intensity. Despite the weak seismic intensity, the effective stiffness of HDRB had increased significantly, shifting the resonance range, which may result in acceleration not decreasing or even increasing. Another variable considered in this study is the sharpness of the yield point of the hysteresis model. The hysteresis behavior varies depending on the bearing model and can be designed with a bilinear model and a Bouc-Wen model, as shown in Figure 5. Although the design is based on the bilinear model, the actual isolated structure behaves in a smoothed curve like the Bouc-Wen model. Although many studies have been conducted on the Bouc-Wen model and its mechanical properties have been verified, it is necessary to investigate how the differences in yield points due to the sharpness of the yield point affect the ISRS.  
There may be other variables that can affect hysteresis behavior, but in this study, the variables that directly affect the shape of hysteresis behavior were selected. We focused on analyzing the effect of the initial stiffness of the isolator, which is expected to have the greatest influence, and analyzed the ISRS variability by applying the variables of the seismic intensity and the isolator model. The initial stiffness was determined by the stiffness ratio with the secondary stiffness, and because various stiffness ratios are used depending on the research or design purposes without specific criteria, a range of stiffness ratios that can  
Figure 5: Hysteresis curve of bilinear and Bouc–Wen model.  
include the values used in various studies was applied. The aim is to analyze the ISRS variability by changing the initial stiffness for different seismic intensities and hysteresis models and to use it as a basis for developing the conservative ISRS for isolated structures.
## 2 Materials and Methods
### Data Collection  
This research was conducted as a life cycle study following the structure developed based on ISO 14040-14043, which establishes the principles, requirements, and guidelines for conducting life cycle assessments. To perform the greenhouse gas (GHG) inventory of the railway system in the state of Pernambuco, Brazil, data were collected from various sources:  
1. Fuel and traction energy consumption: The Pernambuco railway operates on both diesel and electric energy, depending on the section of the rail network. Therefore, data on traction electricity consumption by trains and the diesel fuel used by light rail vehicles were collected over the course of one year.
2. Maintenance and auxiliary activities: Auxiliary activities related to the railway, such as infrastructure maintenance, station operations, lighting, the refrigeration of facilities, and carbon dioxide fire extinguishers, were considered. Data related to the fuel consumption of auxiliary vehicles, electric energy used in stations and administrative buildings, fugitive emissions from train and facility refrigeration systems, and fugitive emissions from carbon dioxide fire extinguishers, as well as the electric energy used in train maintenance, were collected.
3. Data accuracy: To obtain accurate GHG emission calculations, specific emission factors for each source were considered. For instance, in the case of gasoline, it was taken into account that approximately 27% of this fuel in Brazil is composed of ethanol, while for diesel, the ethanol percentage is around 7%. Therefore, the amounts of ethanol, pure gasoline, and pure diesel were separated to calculate the GHG emissions using specific emission factors for each type of fuel.
4. Distance traveled: The total distance covered by trains was recorded to calculate emissions per unit of distance and enable comparisons with other railway systems.
5. Demographic and demand data: Data regarding the number of passengers were fundamental to estimate total emissions and calculate emissions per passenger unit.  
The data are reliable, up to date, and the calculations adhere to the standards of the Greenhouse Gas Protocol (GHG Protocol), recognized internationally for conducting GHG inventories.  
However, there was a limitation in data collection regarding construction materials and building maintenance, which hindered the calculation of GHG emissions associated with the construction and maintenance of the railway infrastructure in Pernambuco. Dueto the railway's long history, relevant information on construction materials over the years was lost over time.  
In this article there is a summary of all the data collected on traction and building electrical energy consumption, diesel consumption by locomotives and light rail vehicles, fuel consumption of auxiliary vehicles and maintenance logistics, fugitive emissions from refrigeration systems and extinguishers, among other factors already highlighted.
# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
## 3 Our Method
### Featured Challenges and Solutions
#### 3.1.2 System Visibility Deficits and Transparent Communication via Visual Projections  
According to [PERSON]'s usability heuristics [61], system status visibility is paramount. Such visibility, embodying communication and transparency, is indispensable for users expecting predictability and control. Users must be cognizant of their position within an ongoing task to avert undue apprehension. Beyond mere status communication, users should anticipate system behaviors via pertinent feedback. Communicating the current state allows users to feel in control of the system, take appropriate actions to reach their goal, and ultimately trust the autonomous system [62].  
Additionally, in autonomous driving technology, transparency is vital for enhancing the quality of interaction with the vehicle system and for improving trust and situational awareness. Effective and reliable communication of the system status and information has a significantly positive impact on both the behavioral and psychological aspects of users. It enhances the cooperative behavior between users and autonomous vehicles and promotes trust. Transparency not only affects the efficiency of the system but also relates to user acceptance and safety [63].  
Our proposed shared interaction communicates anticipated wheelchair movements through unintrusive visual feedback. By projecting the imminent path composed of computer-rendered virtual elements, users can juxtapose projected trajectories with real-time scenarios. This assists users in making swift adjustments based on the machine-driven decisions.
## 3 Measurement Set-Up and Prototype
### Prototype Architecture  
In this area of research for the railway sector, the interests of the companies have just begun to appear. Hence, no commercial tools are available to measure and analyse the overall behaviour of signalling systems using 4G/5G mobile networks. In this study, the goal is mainly to measure the traffic generated between an IXL and an object controller with enough detail to define the radio and network parameters required to provide reliable service to signalling systems. For this reason, this study presents a prototype developed for this purpose, which measures and processes the KPIs from the protocol-stack perspective.  
Figure 2 shows the architecture schema of the prototype and the elements to which it is connected. Following Figure 1, the lower layer represents the MNO network; the upper part represents the signalling equipment as the application layer; and, in the middle,  
Figure 1: Protocol stack design and testing approach.  
the prototype connects to the MNO and the signalling equipment as an alternative to the fibre optical network acting as the physical and network layer.  
The signalling equipment comprises a real object controller and an IXL simulator. Both connect to two E-Lins H900 fd-W-G devices, which include 4G/5G modem and routing capabilities. To these components, non-invasive measurement elements are connected based on traffic sniffers and signal meters developed for this purpose.  
The MNO network type considered in these study scenarios has been the following:  
* Prototype based on commercial equipment as a regular user.
* Prototype based on enterprise solutions with an SLA [13] that guarantees, among other parameters, a maximum latency of 80 ms, a packet loss of 0.2% and a monthly availability of 99.5%.
## 4 Applications in Optimization  
Most engineering problems can be defined as optimization problems, e.g., finding an optimal trajectory for a robot arm, the optimal thickness of steel in pressure vessels, the optimal set of parameters for controllers, optimal relations or fuzzy sets in fuzzy models, etc. Solutions to such problems are usually difficult to find, and their parameters usually include variables of different types, such as floating point or integer variables.  
Applications of the Lozi map in the now-flourishing field of optimization took longer to appear than applications in electrical devices. The main reason for this is due to a paradigm shift in optimization algorithms; instead of using deterministic algorithms such as gradient or steepest descent methods, which are not efficient in high-dimensional problem optimization involving hundred or thousand of variables, heuristic algorithms based on an imitation of Darwin's theory of the evolution of species were introduced a few decades ago. Such algorithms require easy access to random or chaotic numbers. This is why interest has only recently focused on chaotic attractors.  
Thirty years after the publication of the Lozi map, Araujo and Coelho used it as a core function for Particle Swarm Optimization (PSO) algorithms [55]. Their article was accepted on October 2007. Optimization algorithms based on chaos theory are methodologies for searching optimal solutions that differ from any of the existing traditional stochastic optimization techniques. Due to the wandering of chaos, it can carry out overall searches in the solution space at higher velocities compared to stochastic ergodic searches, which rely on probability-based computation. This remark appeared in the pioneering work of Caponetto et al. [56], who, four years before Araujo and Coelho, found that chaotic sequences improved the performance of evolutionary algorithms.  
In fact, there are many kinds of genetic and evolutionary algorithms used for optimization; see [57] for a survey. In addition to PSO, Differential Evolution (DE) is a main branch of research into metaheuristic algorithms, and a few researchers have used the Lozi map [58]. Among the less conventional approaches for enhancing the performance of metaheuristics is the incorporation of chaos as a source of stochasticity. As a key operation in metaheuristics, stochasticity is typically used for three basic operators: selection, crossover, and mutation. It is possible to implement chaotic dynamics in any of these.  
It should be noted that three years before Araujo and Coelho's landmark publication, Zelinka et al. proposed a slightly different algorithm called SOMA (Self-Organizing Migrating Algorithm) using chaotic attractors, which can be classified among the metaheuristic algorithms [59].  
Finally, the Lozi map is widely used in non-evolutionary chaotic optimization algorithms (see Section 4.5) such as the Improved Lozi map-based Chaotic Optimization Algorithm (ILCOA).  
In this section, we explain how this chaotic map is used in these algorithms.
## 3 Results
### Maximum Exposure
#### 3.2.2 Extrapolation to the Maximum Electric-Field Strength  
The maximum theoretical electric-field strength \(E_{max}\) was then extrapolated both from \(E_{RE,SSB}\) using Equation (1), with the factor \(\frac{\mathcal{G}p_{DSCH}}{\mathcal{G}_{SSB}}\) based on antenna patterns provided by the base station manufacturer, and from \(E_{RE,PDSCH}\) using Equation (2). In addition, \(N_{RB}\) = 273 (as the NR channel bandwidth was 100 MHz and the SCS 30 kHz) and \(f_{TDD}\) = 0.74 (Figure 4). A comparison of the two extrapolation methods is shown in Figure 6.  
The agreement in \(E_{max}\) was usually very good, particularly within the BS scanning range (Figure 6). In fact, the absolute relative error between the two extrapolation methods was just 1.4 dB and the correlation coefficient was 0.92. For positions within the scanning range of the BS, these values even improved to 0.9 dB and 0.99.  
Figure 5: Scatterplot of the electric-field strength per resource element \(E_{RE}\) for the Physical Downlink Shared Channel (PDSCH) signals versus the \(E_{RE}\) of the SSB signals at each measurement location. The locations were grouped by their relative position to the NR base station: within (blue dots) or outside its scanning range (red dots). Markers with a white X depict positions in non-line-of-sight. The purple line depicts a +4 dB difference between \(E_{RE,PDSCH}\) and \(E_{RE,SSB}\).  
Furthermore, the extrapolated maximum field levels ranged between 0.1 V/m and 0.6 V/m in LOS of the BS (Figure 6), which amount to less than 0.01% of the ICNIRP reference level [3] (in the far-field region, a power density \(S\) of 10 W/m\({}^{2}\) is equivalent to an electric-field strength \(E\) of 61 V/m). In NLOS, on the other hand, the maximum field levels were extremely low (\(\sim\)0.1 V/m).
## 2 Brief Review of Nanobubble-Generation Approaches
### Porous-Medium Membrane  
The first main mechanical NB-generation approach is predicated on fluid flow through porous media [21; 22; 23; 24]. Pressurised injection gas is fed through the medium membrane to make bubbles (especially in water). By reducing the sizes of pores in the medium, metastable-bubble sizes of less than 1 um can be achieved. For example, the Anzai Kantetsu, Acniti, and Molaear companies all use systems in which a pressurised gas is injected into a passage and then a fine-carbon-based porous medium with pore diameters of several nm to several tens nm. The porous medium, which is positioned horizontally or tilted downwards, should obey a defined shortest-to-longest distance ratio between the medium and the flow passage. The fluid flowrate and gas pressure are other important factors in this system.  
While most methods involve injecting the gas through a fine porous medium to create nanobubbles, some propose passing the liquid-gas mixture through a filter or porous medium to reduce the size of the bubbles and only permit bubbles smaller than a threshold to pass through. For instance, [PERSON] is offering a system that uses a Venturi section to draw raw water and water flow to create a relative pressure that allows gas to be drawn in [24]. After being pressurised, the gas-water mixture goes through three stages of filtering. In the initial stage, large bubble sizes are filtered together with foreign particles using a sponge, non-woven filter, or ceramic porous media. The bubbles are finer in the second stage, which has beads layered on top of each other, and the bubbles are filtered in the third stage, which shares the same structural design as the first stage, to produce bubbles with nanoscale order and nearly identical sizes.
## 3 Implementation and Measurement Results  
Figure 8 shows the photograph of the fabricated DPA IC using Samsung's 28 nm bulk CMOS technology. The DPA IC has an active area of 0.55 mm\({}^{2}\) and was evaluated by probing after chip-on-board (CoB) assembly. Figure 9 presents the simulated and measured small-signal characteristics of the proposed DPA. The small-signal measurements showed a gain of higher than 20 dB at the frequency band of 23.5-27.5 GHz.  
Figure 10 shows the measurement results using a continuous wave (CW) signal for the frequency range of 23.5-27.5 GHz. Figure 10a shows the measured power gain according to the output power, which shows saturated output power of 14.0-15.2 dBm at the targeted frequency band. Figure 10b presents the measured PAE performances, which are 22.8-26.7% at the peak output power and 14.6-17.6% at the 6 dB OBO. The measured CW performances show good agreement with the simulated results presented in Figure 3.  
Figure 8: Micro-photograph of the implemented DPA IC.  
Figure 9: Simulated and measured S-parameters of the proposed DPA.  
The measured results of the proposed DPA were compared to the state-of-the-art mm-wave PA ICs designed using CMOS technologies on Table 2. As intended through the dynamic power dividing technique, the designed DPA showed high power gain due to the gain boosting effect of direct power dividing. The proposed DPA also exhibited high efficiency levels at 6 dB OBO resulting from the OBO extension technique applied at the load network design.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Freq.** & **Gain** & **Psat** & **PAEpeak** & **PAE6-dB OBO** & **Core Area** \\  & **(GHz)** & **(dB)** & **(dBm)** & **(\%)** & **(\%)** & **(\%)** & **(mm\({}^{2}\))** \\ \hline
[4] & 26 & 16.2 & 18.73 & 33.2 & 17.3 & 0.094 \\
[5] & 28 & 15.8 & 15.6 & 41 & 25 & 0.24 \\
[6] & 30 & 20 & 16.6 & 34.2 & 11.2 * & 0.1 \\
[7] & 26 & 19 & 21.3 & 26.1 & 12.8 & 0.6 \\
[8] & 26 & 20 & 20.7 & 22.3 & 11.7 & 0.77 \\ This work & 25.5 & 21.9 & 15.3 & 26.7 & 17.6 & 0.55 \\ \hline \hline \end{tabular}  
* Graphically estimated.  
\end{table}
Table 2: Comparison with state-of-the-art mm-wave CMOS PA ICs.  
Figure 10: Measurement results using a CW signal at 23.5–27.5 GHz: (**a**) Power gain; (**b**) PAE.