esa-sceva/satcom-chunk-collection huggingface dataset:
# Cleaner Processes for Making Laundry Soap from Vegetable Oils and an Essential Oil
## 2 Materials and Methods
### Methods and Analyses
#### 2.2.1 Analysis of Vegetable Oils Used in soap Formulation  
Determination of Saponification Value (SAP) of Oils  
The SAP is determined according to the AOCS Method cd 3-25 standard [63]. SAP values are determined for both the 2 oils used in the saponification process (olive and coconut oil) and for a blank sample. For this purpose, 1 g of oil is added to an Erlenmeyer flask, where 25 mL of 1 N alcoholic KOH solution is added. A condenser container is attached to it and the assembly is placed in a water bath, where it is kept boiling for 1 h. After cooling, 0.5 mL of 1% alcoholic phenolphthalein is added to the solution resulting from saponification when a magenta color is obtained. Using a 0.5 N HCl solution, titrate until the magenta color disappears and the liquid becomes colorless.  
The following mathematical Equation (1) is used to calculate the SAP value [64]:  
\[\mathrm{SAP}=[(\mathrm{V_{B}-V_{S}})\times\mathrm{N_{\,HCl}\times 56.1}]/ \mathrm{W}\;[\mathrm{mg\ KOH/g\ oil}] \tag{1}\]  
where \(\mathrm{V_{B}=V_{blank\ sample}}\); \(\mathrm{V_{S}=V_{sample}}\) = volume of 0.5 N HCl solution used in titration; \(\mathrm{N_{HCl}=normality\) of HCl solution; 56.1 = molecular weight of KOH used for saponification of oils (g); \(\mathrm{W=mass\ of\ oil\ sample}\) (g);  
SAP can also be expressed in (g KOH/1 g oil).
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 8 Challenges
### Challenge 3  
Smart healthcare devices may generate a high volume of data, which can lead to network congestion and result in delays in data transmission [76]. There are many techniques to handle network congestion in smart healthcare applications in the context of QoS optimization. By utilizing cloud-based services and edge computing, network performance can be improved, and network load can be spread. Finally, having a solid network architecture may help guarantee that medical data is transmitted quickly and safely. One example is 5G networks [8; 77].
## 3 Results
### TMJ Locking
#### 3.2.4 Treatments Comparison  
At the baseline, no significant difference was detected comparing the MOSP and conventional treatment methods' means (\(p>0.05\)). At T1, no significant difference was detected, where all the groups presented no TMJ locking scores for all subjects treated (\(p>0.05\)). At T3, no significant difference was detected, considering a sensible increase in pain scoring means of the conventional treatment group (\(p>0.05\)). At T4, no significant difference was detected comparing all treatment groups (\(p>0.05\)) (Table 4 and Figure 8).  
Based on TMJ locking analyses, the proportion of successful cases in three different groups can be listed as follows: exercise group 62.5%, traditional therapy 68.8%, and MOS 93.8%.  
The findings of this study demonstrated no significant difference comparing the three groups at the study time points. The TMJ locking frequency of the conventional treatment was slightly higher when compared to the MOSP and the exercise group, but no significant difference was detected (\(p>0.05\)). All of the treatments were efficient in reducing the pain score and the TMJ locking frequency from the baseline to the T1 timepoint.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**MOSP**} & \multicolumn{4}{c}{**Conventional Treatment**} & \multicolumn{4}{c}{**Exercise**} \\ \hline \hline  & **T0** & **T1** & **T2** & **T3** & **T4** & **T0** & **T1** & **T2** & **T3** & **T4** & **T0** & **T1** & **T2** & **T3** & **T4** \\ \hline Mean & 2.9 & 0 & 0 & 0.063 & 0.063 & 2.8 & 0 & 0.13 & 0.5 & 0.5 & 2.3 & 0.0 & 0.25 & 0.19 & 0.19 \\ \hline Std. Deviation & 0.34 & 0 & 0 & 0.25 & 0.25 & 0.4 & 0 & 0.5 & 0.97 & 0.97 & 0.86 & 0.0 & 0.58 & 0.54 & 0.54 \\ \hline Std. Error of Mean & 0.085 & 0 & 0 & 0.063 & 0.063 & 0.1 & 0 & 0.13 & 0.24 & 0.24 & 0.21 & 0.0 & 0.14 & 0.14 & 0.14 \\ \hline Lower 95\% Cl of
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 3 Standalone Remote Sensing (RS) Technologies  
There are a wide range of RS technologies for data acquisition on site. Each of these technologies has its capabilities and drawbacks based on the domain of applications. Selection of one particular technology can vary depending on the type of information required, the accuracy and the environment in which the data will be acquired [16]. One group of these RS technologies includes real-time location systems (RTLSs) such as GPS, robotic total station (RTS), and ultra-wideband (UWB). These technologies generally function based on the time-of-arrival (TOA) principle, where the propagation speed and the propagation time of a signal directly lead to a corresponding distance [10]. Recently, there are some efforts to localize and track people and objects using Wi-Fi-/Bluetooth/BLE technologies for the purpose of occupancy measurement. In fact, by installing some receivers in a target area, the location and number of detected items are estimated using held and/or worn smart phones, Bluetooth headsets, and smart watches. It is done by tracking the changes in the received electromagnetic wave patterns and compare them with calibrated models to count the number of workers or vehicles on site, and to provide rough information about their location and behavioral algorithms. However, due to some legal restrictions for using smart phones on site and not fully accurate results of this technology its use in construction industry is not common yet [17, 18, 19]. In a different context, barcode and radio frequency identification devices (RFID) proven to be useful technologies for material identification with applications in tracking resources for a supply chain management system [20, 21]. Vision-based technologies such as photo/videogrammetry and laser scanning have also found applications in construction industry for making 3D as-built models of site which provide sufficient information for an automated progress reporting [1]. Table 2 provides a brief overview of these technologies along with their capabilities, limitations, and accuracy.
## 2 The State of the Art
### Human Interaction and Technology with AR, VR and MRI Experiences with AR, VR, and MRI  
One of the fundamental challenges around the use of'realities' linked to the digital world is the search for maximum similarities between the real and the non-real. In this search, [PERSON] et al. [38] found that the mapping of real objects in virtual reality situations, coupled with haptic feedback, resulted in a significantly more real and participatory experience for the user, and could establish \"augmented virtuality\". The same conclusion is drawn by [PERSON] et al. [39] These authors consider that the possibilities provided by touch-related feedback are crucial to enhance user experiences, which they experimented with in a study they conducted on the use of haptic devices in VR experiences. Similarly, [PERSON] [38] considered a virtual reality experience using sensorized, physical copies of real artefacts, achieving a faithful reproduction of the appearance. More specifically, the usercould change the virtual appearance of the object with their hands from a personal physical palette printed in 3D.  
It is worth noting that the possibilities are manifold. [PERSON] et al. [40] studied over 80 VR studies in which interfaces were used, the tasks associated with them, and the analysis of the evaluation performed. The voice, the eye, and the head were the most studied interfaces, in order of most to least, although brain interfaces and facial expressions were also used. Of these, voice proved to be the most versatile in terms of interface usability.  
In any case, progress in terms of experimenting with new possibilities must go hand in hand with their evaluation [40]. It is necessary, in this sense, to establish standardized protocols and scales for this, although it may also be interesting to establish surveys aimed at the specific use of the technological solution employed, especially in the field of cultural heritage [41].  
In some cases, the absence of an evaluation associated with the experience carried out makes it difficult to improve human interaction with the \"machine\" in future experiments [42]. There is a need for evaluation, especially regarding user testing, with the aim of understanding how stimuli can change people's perceptions. The variables to be analyzed could include aspects already addressed in previous studies [42]: (a) usability, from questions about interaction, ease of use, ease of understanding, manipulability; (b) satisfaction, which encompasses excitement, likability, enjoyment, frustration/excellence, boredom/excitement, rigidity/flexibility; (c) quality of experience (\"QoE\"), which may include analyzing the sense of touch (for haptic experiences), quality of content and realism issues; (d) sense of presence, referring to engagement and immersion. Based on the variables mentioned above, it is considered that the adoption of certain evaluation models, such as the TAM model [14] can be beneficial to systematize the evaluation process.
## 6 Discussion  
Despite the benefits discussed above, UAVs still have many things to consider that need to be closely monitored. Some of these need to be addressed globally because they are capable of scaling established boundaries, and, due to the rapid emergence of commercial UAVs for anyone (hobbyists) and other professional uses, regulations are changing all over the world. It is paramount to keep people safe, and for this reason, it is important to periodically review aircraft to prevent crashes, technical malfunctions, or misuse by operators [108]. As in other countries, there is a government agency in Colombia that regulates the use of UAVs. However, due to corruption issues, there is a concern that people who want to hurt others will use them to do so. Moreover, this entity does not keep track of maintenance, which needs to be performed regularly.  
Another issue with using commercial or hobbyist UAVs is navigation and communication modules, which are vulnerable to various security breaches [91]. Generally, there is no encrypted radio signal to control drones. The IEEE 802.11 standard is used in Wi-Fi networks and some vehicles are controlled this way, which is also vulnerable to hijacking [92]. Unfortunately, UAVs can be used to smuggle drugs, contraband, or espionage. In the wrong hands, even with security breaches, it can be used as a weapon against people. Privacy is also one of the main issues associated with the use of commercial UAVs due to their remote control of embedded gadgets, raising privacy concerns related to the use of UAVs. In addition to the problems mentioned above, they can also be hacked to obtain personal information.  
Regarding the use in electrical power applications, due to Colombian regulations, they are limited to using class B and class C to perform monitoring tasks or lay power lines between towers. It is important to note that class C is not well regulated and is limited exclusively to scientific research, innovation, and development. Therefore, larger or special payloads must be considered in conceptual designs and must be properly authorized by UAEAC. Furthermore, the current regulation does not consider other classes of UAS that are currently being used in other countries. All of these considerations may make it difficult for energy companies to adopt these innovations on a massive scale.  
Using the experience of companies that have researched and implemented UAS technologies in their processes, it is possible to adapt techniques to a Colombian context, considering its climate, bioresources, topography, extension, conflicts, and demand, producing its own developments for the successful implementation of UASs in the electricity sector, combining advanced inspection techniques with the experience of its own company, in addition to some UAS projects being run in the country [109]. The proposed categorization is intended to help review the current regulation to include other categories due to its use, size, and commercial application, to address emerging uses of UASs.
## 4 Case Study
### Case route Overview
#### 4.1.2 Passenger Flow Demand Data  
Given the passenger flow demand at a granularity of 1 min from 7:00 to 9:00, taking Station 1 as an example, the arrival situation of passengers is shown in Figure 7. The arrival pattern of passengers changes over time, so it is necessary to adopt differentiated departure intervals to dynamically match the passenger flow demands.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Symbols** & **Description** & **Parameter Value** & **Unit** \\ \hline \(h_{min}\) & Minimum departure interval & 90 & s \\ \(h_{max}\) & Maximum departure interval & 600 & s \\ \(\delta_{min}\) & Minimum turnaround time & 90 & s \\ \(\delta_{max}\) & Maximum turnaround time & 360 & s \\ \(RS\) & Number of rolling stock & 50 & Group \\ \(C\) & Train capacity & 1000 & People \\ \(\phi\) & Spare train ratio & 1.2 & - \\ \(c_{1}\) & Passenger unit time value & 0.5 & Yuan/min \\ \(c_{2}\) & Average train purchase cost per unit time & 200 & Yuan/a train-h \\ \(c_{3}\) & Train cost per kilometer traveled & 40 & Yuan/a train-km \\ \(\mu\) & Passenger waiting cost weight & 0.5 & - \\ \(T\) & Research time frame & 2 & h \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters in Numerical Case.  
Figure 6: Schematic Diagram of the Case Line.  
Cross-sectional passenger flows between 7:00-8:00 and 8:00-9:00 are depicted in Figure 8. Notably, passenger flows in the up and down directions primarily concentrate between stations 6 and 12. Imbalance coefficients for both directions exceed 1.9, highlighting significant disparities in passenger flow distribution.
## 5 Results of Simulation
This outcome is primarily attributed to the incor \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **No.** & **Raising Time** & **Speed Error Area** & **Max. Jerk** \\ \hline \multirow{2}{*}{Seen 1} & MRAC & 5.91 s & 0.8569 m & 0.9971 m/s\({}^{3}\) \\  & Proposed & 5.80 s & 0.6465 m & 0.8255 m/s\({}^{3}\) \\ \hline \multirow{2}{*}{Seen 2} & MRAC & 5.87 s & 1.1346 m & 0.9527 m/s\({}^{3}\) \\  & Proposed & 5.79 s & 1.1296 m & 0.7979 m/s\({}^{3}\) \\ \hline \multirow{2}{*}{Seen 3} & MRAC & 13.49 s & 10.4628 m & 0.6860 m/s\({}^{3}\) \\  & Proposed & 10.29 s & 10.1400 m & 0.5668 m/s\({}^{3}\) \\ \hline \multirow{2}{*}{Seen 4} & MRAC & 15.32 s & 14.6642 m & 0.8050 m/s\({}^{3}\) \\  & Proposed & 12.17 s & 14.1275 m & 0.5008 m/s\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics comparison. porated predictor-based filtering mechanism, which effectively suppresses high-frequency components in the control signal. In conclusion, the proposed control framework overcomes the key limitations of standard MRAC by combining reference model modification for input-constrained environments with filtering techniques for noise suppression. As a result, it achieves both fast convergence and stable control inputs, thereby offering superior longitudinal control performance for HDV applications.
## 3 Optically Pumped Probe Design
### Electronics Unit Design
#### 3.2.2 Temperature Control Circuit  
The cesium lamp and absorption cell in the cesium optically pumped probe require constant temperature control. A schematic diagram of the temperature control circuit is shown in Figure 9. Alternating current (AC) heating was adopted in this study to avoid interference with magnetic field measurements, with a range of \(-55\)\({}^{\circ}\)C to \(70\)\({}^{\circ}\)C. The signal generator produces an AC heating signal, which drives the non-magnetic heating wire inside the probe through a power amplifier circuit. The temperature detection circuit monitors the temperature. The constant temperature control circuit implements proportional-integral (PI) control and regulates the output power of the audio amplifier by changing the signal amplitude to control the final temperature. The temperature control circuit design is shown in Figure 10. In this diagram, R1, R2, R3, R4, and the heating coil form a four-arm balanced bridge, while Q1, Q2, Q3, and Q4 form an H-bridge heating circuit that simulates PID control for maintaining a constant temperature. When the bridge is balanced, the heating coil reaches the set temperature, and the H-bridge ceases heating. If the resistance value of the sliding resistor R3 is changed, the temperature can be reset. At this point, the bridge becomes unbalanced, leading to two possible scenarios: First, if the reset temperature is lower than the current temperature, the H-bridge remains inactive, and the system must dissipate heat gradually through the cesium lamp and cesium chamber to reach the new set temperature. Second, if the reset temperature is higher than the current temperature, the H-bridge activates until the set temperature is achieved. This temperature control circuit has two notable characteristics: firstly, it uses an AC signal above 10 kHz for heating, ensuring that the generated AC magnetic field is outside the working bandwidth; secondly, it controls the heating process by monitoring changes in the impedance of the heating coil itself. This means that the heating coil functions both as an actuator in the control system and as a sensor to provide feedback signals to the control system.  
Figure 10: Design diagram of the temperature control circuit.  
Figure 9: A schematic diagram of the temperature control unit.
# Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
## 2 Methodology
### Probability Distributions
#### 2.1.1 Log-Normal (LN3)  
It represents one of the most used statistical distributions in hydrology. The three-parameter log-normal distribution (LN3) is a generalized form of the two-parameter log-normal distribution (LN2), with \(x\) shifted by a position parameter that characterizes the lower bound [18; 20; 21; 29].  
\[f(x)=\frac{\exp\!\left(-\frac{(\ln(x-\gamma)-\alpha)^{2}}{2\cdot\beta^{2}} \right)}{(x-\gamma)\cdot\beta\cdot\sqrt{2\cdot\pi}} \tag{5}\]  
\[F(x)=1-\frac{1}{2}\cdot\left(erf\!\left(\frac{1}{\sqrt{2}\cdot\beta}\cdot(\ln (x-\gamma)-\alpha)\right)+1\right) \tag{6}\]  
\[x(p)=F^{-1}(x)=\gamma+\exp(\alpha+\beta\cdot qnorm(1-p,0,1))=\gamma+qlnorm(1-p,\alpha,\beta) \tag{7}\]where \(\alpha,\beta,\gamma\) are the shape, the scale, and the position parameters, respectively; \(x\) can take any values of range \(\gamma<x<\infty\).
## 2 Related Work
Table 1 summarizes the major differences between proposed EEUCH and benchmark routing protocols. None of the benchmark protocols consider WuR-enabled SNs and they assume that as the SNs are deployed at FoI, the SNs start working (i.e., sense the surroundings, process the sensed data, and transmit them to CH). Moreover, in benchmark protocols, the BS is located within the FoI, and CHs are responsible for data reporting to BS. However, in the proposed protocol, we equipped SNs with WuR, enabling them to work in an on-demand manner. Furthermore, EEUCH is independent of the BS location and UAVs are responsible for data reporting to BS. The rounds of UAVs are application-dependent and there is no dependency among the rounds. The authors in [30; 34] proposed a hashing and partitioning approach for collecting data in WuR-enabled IoT applications that employs a scenario similar to ours, where UAVs are utilized for data collection. However, their work has notable differences and limitations. They divided SNs within the same cluster region of an UAV into different groups, and only one data packet is collected from each SN belonging to a specific group. Other SNs belonging to other groups in the same cluster region of the UAV coverage must wait for the next round of the UAV. The TDMA slot is shared with SNs through a hash function embedded in the broadcasted WuC, and the slot for data transmission assigned to an SN is based on the output of the hash function calculated by each SN. However, collisions can occur if two or more SNs produce the same output, resulting in multiple SNs using the same slot for data transmission. The likelihood of collisions increases with the number of SNs in a group. As only one packet per round of UAV per SN from a specific group is collected, this method increases latency and wastes UAV energy. SNs from the specific group with multiple packets must wait to transmit their packets for their next turn, and SNs belonging to other groups in the same cluster region must wait for subsequent rounds. Their system model in [30; 34] is at layer 2 (MAC layer), whereas ours is at layer 3 (network layer). They eliminated overlapping of clusters using a complex partition algorithm, whereas the methodology proposed in this work differs significantly and is described in the following sections. Based on the available literature work in this section, there is no hierarchical clustering routing protocol proposed for data collection from WuR-enabled ground SNs through UAVs that operates at the network layer. Therefore, we compare the proposed protocol with six benchmark routing protocols mentioned earlier, under the same scenario and network model, to evaluate its efficacy and assess its performance.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 6 Experiments and Numerical Results
### Effect of Congestion  
As we incrementally develop our model, we argue that the congestion is an important issue, which might be favored by senior managers for strategic-level decision-making. We add the congestion balance constraints to form the (HSLPT-02) model. Here, we investigate how that affects the performance and solution obtained for this model. We study the case with a congestion factor = 2 (the highest-congested hub facility should not be more than double as congested as the least-congested facility). The results are summarized in Table 4.  
Comparing the results of Tables 3 and 4 reveals that the congestion causes an increase in the objective function: from a mathematical point of view, it is an additional constraint whose addition does not expand the feasible solution space. As a result, by adding the congestion constraints, the feasible solution space either remains the same or decreases, which may result in losing favorite points and degrade the objective function (increase in our case). From the comparison of both tables, we can also learn about the effect of congestion on the solution: considering the congestion in the model results in having the same number of hubs (spokes), less, and/or a greater number of hubs (spokes). To further investigate the phenomena, we analyze the structure of the solution network and the differences with/without congestions, which gives valuable insights into the mechanism of the solution. In some cases, there is a highly congested hub, compared to the least-congested hub in the network (more than the pre-determined congestion ratio). Once the congestion constraint is introduced to the model, the model prefers to establish the network with a lower number of hub facilities, by simply removing the least-congested hub facility, and either spread the flow among the other hubs or lose it. In some other cases with similar situations, the model adds an additional hub facility to spread the flow among different hub facilities, not to violate the congestion constraint. This occurs in instances with high lost demand costs, in which the model prefers not to simply lose the demand, and also with low values of (\(\alpha\)), which further justifies adding another hub facility (in a 35-node network with lost demand = 0.5, \(\alpha=0.2\rightarrow\alpha=0.5\)). A common trend in all the tables and analyses is that a lower number of spoke facilities results in lost demand, which is not favored by the model for expensive lost demand costs. A higher number of spoke facilities, on the other hand, results in absorbing the budget and