esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Testing, Analysis, and Results
### Result
#### Average Performance
It is important to note that the migration count does not account for scenarios where a job runs on multiple CPUs sequentially, which can significantly impact performance more than a single migration. The scheduled metrics reflect the count of context switches during the simulation, happening when a job is either completed or preempted to schedule a new job. As the number of context switches rises, the simulation time extends due to the necessity of transitions with a firing time of 0.1 for each switch. This effect will become more apparent in the upcoming graphs. Consequently, a discernible pattern emerges, demonstrating a higher turnaround time with an increase in context switches, as one would reasonably expect. In the Appendices A-D, we explore turnaround time, response time, and throughput in more detail. Figure 8: Box plot of metrics with short jobs. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**FCFS**} & \multicolumn{2}{c}{**SJF**} & \multicolumn{2}{c}{**RR**} & \multicolumn{2}{c}{**MLFQ**} & \multicolumn{2}{c}{**CFS**} \\ \cline{2-10}  & **Short** & **Mixed** & **Short** & **Mixed** & **Short** & **Mixed** & **Short** & **Mixed** & **Short** & **Mixed** \\ \hline _Turnaround_ & 329.77 & 855.26 & 238.58 & 502.78 & 610.26 & 1323.56 & 714.87 & 1324.76 & 579.86 & 1260.69 \\ \hline _Response_ & 303.30 & 794.27 & 212.10 & 441.79 & 115.10 & 112.659 & 6.766 & 7.37 & 21.281 & 13.76 \\ \hline _Throughput_ & 0.122 & 0.106 & 0.365 & 0.338 & 0.059 & 0.053 & 0.064 & 0.068 & 0.142 & 0.098 \\ \hline _Jain’s F1_ & 0.423 & 0.358 & 0.690 & 0.681 & 0.414 & 0.685 & 0.278 & 0.443 & 0.302 & 0.341 \\ \hline _Migrations_ & 0 & 0 & 0 & 0 & 23 & 29 & 4 & 29 & 12 & 14 \\ \hline _Scheduled_ & 250 & 250 & 250 & 250 & 1189 & 2619 & 997 & 2056 & 970 & 2167 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Metrics evaluating the performance of both short and mixed jobs. Figure 9: Box plot of metrics with mixed jobs.
## Abstract  
As in other large Andean cities, the population in the Metropolitan District of Quito (MDQ) in northern Ecuador is growing, and groundwater is becoming essential to meet the increasing urban water demand. Quito's Public Water Supply Company (EPMAPS) is promoting groundwater research for sustainable water supply, and geophysical prospecting surveys are used to define aquifer geometry and certain transient groundwater features. This paper examines the usefulness of existing geophysical prospecting surveys in groundwater research in the MDQ. A database was built using 23 representative geophysical prospecting surveys compiled from EPMAPS' public repository, official geotechnical research reports, and the scientific literature. Fifteen EPMAPS-promoted surveys used near-surface electrical techniques (seven used electrical resistivity tomography and eight used vertical electrical sounding) to explore Holocene and Pleistocene sedimentary and volcano-sedimentary formations in the 25-500-m prospecting depth range, some of which form shallow aquifers used for water supply. Four other surveys used near-surface seismic techniques (refraction microthermor) for geotechnical research in civil works. These surveys have been reinterpreted to define shallow aquifer geometry. Finally, four surveys compiled from the scientific literature used electromagnetic techniques (magnetotelluric sounding and other very low-frequency methods) to explore Holocene to late Pliocene formations, some of which form thick regional aquifers catalogued as the larger freshwater reservoirs in the MDQ. However, no geophysical prospecting surveys exploring the complete saturated thickness of the Pliocene aquifers could be compiled. Geophysical prospecting surveys with greater penetration depth are proposed to bridge this research gap, which prevents the accurate assessment of the renewable groundwater fraction of the regional aquifers in the MDQ that can be exploited sustainably.  
geophysical prospecting techniques; groundwater research; urban water supply; Metropolitan District of Quito; Ecuador 2021
## 1 Introduction
In contrast to [25], which discards subblocks with mismatched hash values, this approach subjects unverified subblocks to an iterative process of error estimation and information reconciliation. This process continues until all subblocks are successfully verified, thereby guaranteeing that both parties obtain identical keys and avoiding any key discarding. Moreover, the performance of the integrated approach is characterized by various aspects, including the accuracy and the variability in syndrome-based QBER estimation, the information leakage and its correlation with the efficiency metric of information reconciliation, the number of interactive communications, and the success rate of obtaining the identical key. The proposed scheme significantly enhances the efficiency of classical post-processing--achieving a secret key throughput approaching the theoretical limit--compared to other schemes. The rest of this article is organized as follows. Section 2 reviews the fundamental concepts of information reconciliation and its construction into rate-adaptive reconciliation for QKD post-processing. In Section 3, the efficient integration of syndrome-based error estimation, rate-adaptive reconciliation, and subblock confirmation is presented as a unified procedure. Section 4 presents the comprehensive performance evaluation of the proposed method. Finally, the conclusions and discussions are presented in Section 5.
## 3 Materials and Methods
### Procedure  
The data collection procedure followed a structured approach. First, students were welcomed, and the context and objectives of the study were explained. Participants were then presented with an informed consent form, which they reviewed before confirming their voluntary participation. The questionnaire was administered via Google Forms to ensure efficient and anonymous data collection. Students completed the questionnaire using their personal devices--such as mobile phones or tablets--within the classroom environment to maintain consistent technological conditions. The questionnaire comprised three main sections:  
* Demographics: This section gathered information such as age, gender, educational institution, academic program, year of study, and prior experience with ChatGPT, providing context for participants' responses.
* Case Analysis: Participants were presented with a common scenario: preparing for a job interview in their field of study. They were instructed to use ChatGPT to generate examples of typical interview questions and tips for effectively answering them.
* Satisfaction Assessment: This section measured participants' satisfaction with the responses generated by ChatGPT.  
For the prompt interaction phase, students were asked to write a prompt in the questionnaire, input it into the free version of ChatGPT (3.5), and copy the AI-generated response back into the form. They were then required to evaluate their satisfaction with the response. If satisfied, the test concluded; if dissatisfied, they could rewrite the prompt up to seven iterations until they achieved satisfaction.  
Ethical considerations were central to the procedure. Participation was entirely voluntary, and students could withdraw at any time without facing any consequences. Anonymity and confidentiality were rigorously ensured, with all data collected being used exclusively for academic purposes. The entire activity lasted approximately 35 min, providing a structured and controlled environment for data collection and analysis.  
After data collection, the responses were analyzed to identify the presence of the 21 proposed techniques (circumstantial and request-based) within the prompts. Each prompt was independently evaluated by two experts in artificial intelligence, who assigned a binary code of 1 if a technique was present or 0 if it was absent (for more details see Section 3.3). The final results were consolidated and systematically tabulated for further analysis.
## 2 Basic Theory of Signal Noise Reduction Method
### Wavelet Noise Reduction Principle
#### 2.1.1 Wavelet Basis Function Selection  
The wavelet basis function has a great influence on the signal noise reduction effect. In order to obtain a better denoised signal, the basis function having the similar waveform as the original signal should be selected. Because the joint electromagnetic signal waveform is close to the dB8 wavelet basis function, the dB8 wavelet basis is adopted in this paper.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 2 State of the Art
### Point of Entry Authentication  
The task of authentication can be split up into three different approaches. First, knowledge based authentication is relying on something the users knows for verification. Common implementations of this approach would be PIN, password or pattern-based unlock mechanisms. Those systems may fail when weak passwords are chosen, or when passwords are shared with other people or written down somewhere physically [10]. Even unlock patterns can easily be recorded from a distance, as studies like [5] show.  
The second authentication approach is using objects, which are in the possession of the user, and grant them access. Systems like [11] propose a token device, serving as an authenticator for mobile devices. Problems may arise when those tokens are stolen and can not be altered or revoked.  
Finally, ID based authentication systems are using the physiological and behavioral aspects of a user to authenticate them. A paper released in 2010 by [PERSON] and [PERSON] [12] states that, compared to the previously mentioned groups of authentication systems, biometric authentication systems are doing what they are supposed to do--authenticatingusers, without relying on phrases the user must remember or some kind of token that needs to be owned and kept secure [12].
# GIS-Based Optimal Route Selection of Submarine Cables Considering Potential Seismic Fault Zones
## 2 Materials and Methods
### GIS Geodatabase and Basic Criteria  
Creating the GIS geodatabase, collecting and organizing the required spatial geodata constitute very important steps during the process of optimal route selection, since stakeholders and experts can then obtain a digital and detailed general aspect of the area under consideration. The created GIS database contains spatial geodata in the form of shapefiles and GeoTIFF. In case of insufficient digital data, existing maps are scanned/digitized and added in the GIS environment. Depending on the information they contain, geodata are organized into different thematic layers, which are subsequently utilized as basic criteria for the routing analysis.  
Generally, geodata are distinguished into vector (i.e., points, lines and polygons (areas)) and raster (matrices of pixels (i.e., cells) with rows and columns) [44]. However, since the LCPA technique requires data that change continuously across a landscape (i.e., cost surface), all of the vector geodata are further processed and converted into raster. To increase the accuracy and reliability of the developed smart tool, all of the selected geodata are reclassified to the same number of classes and resampled to have the same spatial resolution [45].  
Since optimal route selection is a complex multi-criteria procedure, several criteria are usually taken into account in real case studies, such as areas where cable alignment is a priori prohibited (e.g., military and unexploded ordnance (UXO) areas, marine parks and NATURA 2000 areas, as well as wrecks and anchorage locations) and dangerous zones, including seabed slopes with large inclination, areas of intense seismicity, geology, etc. [26].  
Figure 1: Flowchart of the developed smart decision-support tool.
## 3 Results and Analysis of the Filed Measurements
### Exhaust Fume System
#### 3.4.2 Air Velocity and Airflow Rate  
The air velocity at each measurement point of the exhaust time system should be within a reasonable range. Too low cannot ensure the effective capture and discharge of oil fume pollutants; too high will cause noise, and long-term exposure to a noisy environment will reduce the chef's work efficiency and harm their health [38; 39]. The air velocity of each measurement point and recommended standard value are shown in Table 5.  
From Table 5, we can find the air velocities of capture points 1\({}^{1}\)-1\({}^{4}\) were between 0.42 \(\pm\) 0.06-0.66 \(\pm\) 0.07 m/s, which met the requirement of the manual [40]. The air velocity of point 1\({}^{5}\) was significantly lower than the other four points. From Figure 1, there was no air inlet above point 1\({}^{5}\), and point 1\({}^{5}\) was far from air inlet 4\({}^{3}\). The air velocity of the hood face was between 0.35 \(\pm\) 0.06-0.44 \(\pm\) 0.06 m/s, which was lower than 0.5 m/s. It may be due to the uneven distribution of air velocity at the hood face. From Figure 12, we can see that the air velocity of the hood face close to the air inlet in the hood was higher than 0.5 m/s, and the maximum air velocity can reach 1.28 \(\pm\) 0.06 m/s. The air velocity of points 4\({}^{1}\), 4\({}^{2}\), and 4\({}^{3}\) was 11.41 \(\pm\) 0.25-12.86 \(\pm\) 0.17 m/s, 8.21 \(\pm\) 0.15-9.10 \(\pm\) 0.18 m/s, and 6.35 \(\pm\) 0.13-7.34 \(\pm\) 0.10 m/s, respectively, which were much higher than the standards' value. At the natural makeup air outlet, the air velocity was between 0.19 \(\pm\) 0.06-0.78 \(\pm\) 0.06 m/s, which met the standards' value [20]. The data showed that the air velocity of each measurement point meets or even exceeds the standard values, which may lead to the chefs expose to high air velocity. Paradoxically, as high as 83.33% of the chefs felt that oil fumes are overflowing from the exhaust hood during cooking. That means that the exhaust airflow rate of this test kitchen is insufficient. Therefore, we calculate the exhaust airflow rate and the makeup airflow rate of this kitchen. Figure 11: Temperature of exhaust fume system and indoor measuring points. The stoves are off and the chefs are on the workstation  
This test kitchen adopted the natural makeup air of the adjacent room to meet the makeup air requirements and used the exhaust fume system for local exhaust. The determination methods of exhaust airflow rate (L\({}_{\rm E}\)) and makeup airflow rate (L\({}_{\rm M}\)) were also given in JSCS2009 [20]. \[{\rm L_{E}=Max\ (L_{1},L_{2})} \tag{8}\]  
\[{\rm L_{1}=1000\ \times\ P\times H} \tag{9}\]
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
###### Abstract  
Today, billions of small devices that can sense things are connected, creating the Internet of Things (IoT). This major technological step has led to ideas like smart cities, smart factories, and smart countries. One important use of this technology is pervasive sensing, which could benefit from a network that covers a wide area but does not use much power. This paper looks closely at the advantages and disadvantages of using LoRa--a network technology that can reach long distances with limited energy use--in situations like this. To this aim, we have created a holistic solution to manage the considered network enabling synchronization, routing, and reliability. In particular, we have even developed an adaptive spreading factor mechanism, simple and effective in allowing the network to cope better when the connection is not very good.  
LoRa; IoT; smart cities; mesh networks; sensing; resiliency
# Study on the Time Domain Semi Analytical Method for Horizontal Vibration of Pile in Saturated Clay
## 1 Introduction
[PERSON] [18] studied the frequency domain response of horizontal vibrations of a single pile, considering the non-uniformity of the soil around the pile. [PERSON] [19] conducted vibration table experiments to test the vibration response of pile foundations in clay foundations. The above research has made important contributions toward revealing the dynamic impedance and frequency domain characteristics of horizontal vibrations of pile foundations in saturated soil. However, it can also be seen that the above studies all use frequency domain analysis methods and obtain steady-state solutions without considering the viscous rheology of saturated soil. In practical engineering, soil particles undergo rearrangement and skeleton dislocation under load, resulting in significant rheological properties (relaxation, creep) [20; 21; 22]. Rheology leads to a time-dependent stress-strain relationship in the soil and also alters the time-domain response of pile foundation vibrations. To investigate the time-domain response of pile foundation horizontal vibrations under the influence of saturated clay rheological effects, this paper uses a fractional order Kelvin rheological model to describe the stress-strain relationship of the soil skeleton, establishes a coupled vibration model of saturated clay and pile foundation, and uses Laplace transform to solve the control equation to obtain a semi-analytical time-domain solution. Based on case analysis, the displacement and internal force response of pile foundations in a saturated clay foundation under horizontal vibration loads were studied, and the influence of soil physical parameters was discussed.
## 4 Models and Algorithm Design
### Battery System Representation  
In this section, the model for BESS will be presented [30; 51]. Each BESS has capacity limit. These limit capacities are usually given as a percentage, for example, 20% and 80% or 10% and 90% from the maximum amount of the energy. This restriction is introduced mainly to maintain the most extended possible lifetime of BESS [32]. Next, it is advisable to introduce the maximum value of BESS charging and discharging rate:  
\[0\leq P_{b}^{ch}(t)\leq P_{b}^{ch_{\max}},\] \[0\leq P_{b}^{dis}(t)\leq P_{b}^{dis_{\max}},\]  
where \(P_{b}^{ch}(t)\) and \(P_{b}^{dis}(t)\) denotes the charge and discharge rates of the \(i\)-th BESS in the considered time \(t\).  
Another important condition guarantees that the BESS cannot be charged or discharged at the same time; only one operation can take place at a given time:  
\[P_{b}^{ch}(t)\cdot P_{b}^{dis}(t)=0. \tag{20}\]  
The currently stored energy in the BESS \(E_{b_{i}}(t)\) is described by Equation (5). Parameter \(E_{b_{i}}^{\max}\) symbolizes maximum capacity of the \(i\)-th BESS. On this basis, the variable SOC(\(t\)) can be introduced which denotes the state of the charge (in %) at the given time:  
\[\text{SOC}(t)=\frac{E_{b_{i}}(t)}{E_{b_{i}}^{\max}}.\]The next conditions are the BESS storage constraints, which were defined in Equation (6). In the case of the \(\text{SOC}(t)\) it will be defined as a percentage. For instance, if \(E_{b_{i}}^{min}=10\%\) and \(E_{b_{i}}^{max}=90\%\), then  
\[E_{b_{i}}^{min}\leq\text{SOC}(t)\leq E_{b_{i}}^{max}.\]  
Sometimes, it is necessary to take into account the efficiency of charging or discharging. In the next step, Equation (5) will be supplemented by the considered charging or discharging efficiency \(\eta_{\text{ch}}\) and \(\eta_{\text{dis}}\).  
\[E_{b_{i}}^{0}+\sum_{t=k}^{k+T_{d}}\left(\left(P_{b}^{ch}(t)\cdot\eta_{\text{ ch}}\right)\cdot h+\left(P_{b}^{dis}(t)\cdot\eta_{\text{dis}}\right)\cdot h \right)=E_{b_{i}}(t),\]  
Due to the condition given in Equation (20), it is ensured that only one operation will take place at a time. Furthermore, the index \(k\) indicates the \(k-\)th step of the algorithm.  
The last point will be the definition of the price function for BESS. The \(i\) index guarantees multiple BESS definition options with different price functions:  
\[\sum_{i=1}^{N_{b}}C_{b_{i}}(t)=\sum_{i=1}^{N_{b}}\pi_{b_{i}}(P_{t^{\text{ch}}_ {i}(t)}+P_{t^{\text{dis}}_{i}(t)}),\]  
where the parameter \(\pi_{b_{i}}\) denotes the \(i\)-th consumption coefficient of the BESS. Such a parameter indicates the costs associated with the operation of the BESS as well as its wear and tear; in essence, it serves to penalize the use of the BESS. Used parameters for BESS will be presented in the Section 5 simulation examples.
## 2 Study Area  
The study area was located on the northern slope of Changbai Mountain in Jilin Province, Northeast China (41 deg41'49''N-42 deg25'18''N, 127 deg42'55''E-128 deg16'48''E) (Figure 1). The annual average temperature decreases with elevation and ranges from 2.8 \({}^{\circ}\)C at 750 m above mean sea level (AMSL) to -0.75 \({}^{\circ}\)C at 1400 m AMSL. Annual precipitation increases with elevation, with an annual mean of 670 mm at 750 m to 870 mm at 1400 m AMSL Eighty percent of the total annual precipitation occurs between June and September [37]. The soil in the study area is classified as dark brown forest soil originating from volcanic ashes [38]. The vegetation near the tower is an approximately 200-year-old, uneven-aged, mixed forest of Korean pine (_Pinus koraiensis [PERSON]. et [PERSON]_), Amur linden (_Tilia amurensis Rupr.),_ Painted map (_Acer mono Maxim._), Manchurian ash (_Fraxinus mandashurica Rupr._), Mongolian oak (_Quercus mongolica Fisch. [PERSON]_), and 135 understory species. The study area includes additional land use types, e.g., residential, commercial, and agricultural.  
The EC flux tower is located at the Chinese Academy of Sciences' Forest Ecosystem Experimental Station of the Changbai Mountains (42 deg24'9''N, 128 deg05'45''E, elevation 738 m AMSL). Mass and energy fluxes were measured continuously at the site with an EC system mounted at a height of 40 m. The system consists of a triaxial ultrasonic anemometer (CAST3, Campbell Scientific, USA) and a fast response open-path CO\({}_{2}\)/H\({}_{2}\)O infrared gas analyzer (Li-7500, LiCor Inc., Lincoln, NE, USA) mounted on an arm extending 3 m from the tower. The anemometer and the infrared gas analyzer (IRGA) provide digital outputs of the three wind components, sonic temperature, water vapor, and CO\({}_{2}\) density. Raw data were recorded at a frequency of 10 Hz and stored every 10 s as the 30 min averages into separate files [39].  
**Figure 1.** Location of the flux tower study area and the research station on the northern slope of Changbai Mountain in Jilin Province in Northeast China. **(a)** shows a China province map; **(b)** shows a Hyperion true color image with a non-forest land cover mask (white areas); **(c)** is an image of forest landscape adjacent to the CBS EC flux tower during the growing season.