esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The vertically arranged ZnO NRs were successfully grown on the bare and textured Si wafers by the low-temperature solution method and employed to estimate the PV parameters by PC1D simulation for c-Si solar cell-based ZnO NRs AR. The reaction time has been optimized, in which a 5 h reaction time presented the best surface and highly uniform ZnO NRs on the Si wafer. The grown ZnO NRs exhibited a minimum reflectance of ~7.5% at 838 nm, with a ~9.5% average reflectance in the wavelength range from 400-1000 nm. The length of the ZnO NRs and the average reflectance were applied as input parameters in the PC1D simulation to instigate the PV properties of the solar cell. The optimum conversion efficiency of 14.88% has been achieved in ZnO NRs 1.1 um in length, with a minimum average reflectance of 9.5%, by using this simulation tool. From this study, it has been confirmed that the reported method (simple hydrothermal method) is an easy, facile method for the preparation of zinc nano rod structure-based Si solar cells, which has greater prospects in developing c-Si solar cells without AR layers at a low cost.  
Experiment, investigation, data curation, software, writing, D.K.S.; resources, software, validation, formal analysis, D.K.; writing--review and editing, visualization, M.S.A.; project administration, writing--review and editing, C.Y.K.; funding acquisition, supervision, O.-B.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by \"Human Resources Program in Energy Technology\" of the Institute of Energy Technology Evaluation and Planning (KETEP) and granted financial resources from the Ministry of Trade, Industry & Energy, Republic of Korea (Project No.: 20204010600470). This research was supported by technology development project sponsored by the Korean Government (Project Number: S2644695).  
The authors declare no conflict of interest.  
Figure 9: Influence of variation in the length of ZnO NRs AR on diffusion length.
# Effect of Amphiphilic Polymer/Nano-Silica Composite on Shale Stability for Water-Based Muds
## 3 Results and Discussion
### Rolling Dispersion Experiment  
In order to evaluate the hydration inhibition property of SMA/SiO\({}_{2}\), KCl and JHC as good shale inhibitors were used for comparison. The results of the rolling dispersion experiment were presented in Figure 12. The rolling recovery rate of shale in deionized water was 40.2%, demonstrating that the shale samples possessed strong hydration dispersion ability. This could be attributed to the high content of smectite in shale samples. With the increase in SMA/SiO\({}_{2}\) dosage, the recovery rate was enhanced significantly. When the concentration of SMA/SiO\({}_{2}\) was 2.0%, the shale recovery rate was up to 93.6%. In addition, compared with KCl, JHC and SiO\({}_{2}\), the higher recovery rates were observed in SMA/SiO\({}_{2}\) solutions at various concentrations. Therefore, SMA/SiO\({}_{2}\) was able to reduce the tendency of shale dispersion and exhibited strong hydration inhibition. The results can be explained by the shale hydration inhibition mechanism of amphiphilic polymer [41]. The hydrophilic segments in SMA/SiO\({}_{2}\) were capable of enhancing the adsorption between SMA/SiO\({}_{2}\) and the shale, because shale is hydrophilic. Thus, SMA/SiO\({}_{2}\) could occupy the adsorption sites in shale instead of water. As a result, the hydrophobic segments provided by St and MMA in the amphiphilic polymer could greatly change the wettability of shale. The shale surface became more hydrophobic after adsorbed by SMA/SiO\({}_{2}\), which was in favor of preventing the water invading into the shale. Therefore, the excellent hydration inhibition of SMA/SiO\({}_{2}\) was achieved.  
Figure 11: (**a**) The surface area and (**b**) pore volume of shale cores after soaked in different fluids.
# Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods
## 3 Results  
### Calendar Conversion Results  
Rainfall data were obtained from Bogor, one of the major cities in Indonesia. Bogor was chosen as the location for rainfall data collection because it is known as the \"Rain City\". Rainfall in Bogor can serve as an indicator of potential flooding in Indonesia's capital, Jakarta. Figure 1 shows Bogor, which is located in West Java, Indonesia.  
Studying rainfall in Bogor is crucial for several reasons beyond its impact on flooding in Jakarta. First, Bogor experiences high rainfall, which significantly affects local agriculture. The patterns of rainfall can help farmers better prepare for planting and harvesting, minimizing crop loss due to unexpected weather events. Second, understanding rainfall is essential for managing water resources, as Bogor's precipitation contributes to the flow of water to nearby rivers, which are vital for surrounding communities. Understanding rainfall in Bogor helps not only with mitigating the effects on Jakarta but also with ensuring the city's sustainable development, agricultural stability, and environmental health.  
The daily rainfall data were collected from Bogor and converted to monthly data using two types of calendars: the Gregorian and lunar calendars. The conversion to monthly data  
Figure 1: Bogor maps of where the rainfall data were collected.  
was performed to capture the seasonal patterns. The monthly rainfall data for both the Gregorian and lunar calendars were analyzed using the spectral regression method. The result revealed that both datasets exhibited distinct seasonal indicated l pattern, with 12 periods, indicating that the rainfall followed a consistent yearly cycle.  
Table 1 displays the outcomes of converting daily rainfall data from the Gregorian calendar to the lunar calendar. During the 22-year observation period, the Gregorian calendar consists of 273 months, while the whole lunar calendar consists of 286 months.  
After the daily data were converted to both the Gregorian and lunar calendars, the time-series data for the lunar calendar were longer than that for the Gregorian calendar. The Gregorian calendar has 365 days, while the lunar calendar has only 355 days in a year. This difference results in a longer time series when using the lunar calendar.
## 5 Discussion
### Lithofacies Characteristics of Different Sandbody Genetic Types  
The lithofacies assemblages of sandbodies are largely determined by variations in hydrodynamic conditions and sediment supply, which differ according to their genetic types and depositional environments [41]. In the delta plain subfacies situated on the landward side of the delta, sedimentation is predominantly influenced by fluvial processes that lead to the accumulation of relatively coarse sediments. Within this subfacies, the distributary channel microfacies are marked by deeply incised abrupt-transition and gradual-transition sandbodies, which primarily consist of lithofacies assemblages characterized by Type I 1 sandstone lithofacies and Type I 2 sandstone lithofacies (Figure 8; genetic types A and B). Moving toward the transitional zone between the delta plain and front subfacies, growth gradual-transition sandbodies develop within distributary channels as hydrodynamic conditions and sediment supply diminish hydrodynamics conditions and sediment supply. This change results in a distinctive lithofacies assemblage comprising Type I 1 sandstone lithofacies, Type I 2 sandstone lithofacies, and Type II 2 sandstone lithofacies (Figure 8; genetic type C).  
In the delta front subfacies, hydrodynamic conditions shift to being predominantly influenced by wave and lake flow actions as the distance from the sediment source increases. This results in fine-grained sandbodies due to limited sediment input. Progradational superimposed sandbodies that develop along the subaqueous distributary channels near the water body's edge generally consist of medium- to fine-grained sandstone, characterized by Type II 2 sandstone lithofacies and Type II 3 sandstone lithofacies (Figure 8; genetic type D). Farther from the water body's edge and sediment source, progradational mouth bars and suspension accretion sandbodies form under weaker hydrodynamic conditions. These conditions favor the deposition of thin, widely distributed sandbodies that primarily consist of fine-grained sandstone. The lithofacies of these sandbodies typically include Type III 1 sandstone lithofacies and Type III 2 sandstone lithofacies (Figure 8; genetic types E and F). The spatial distribution and lithological characteristics of these sandbodies are indicative of declining hydrodynamic forces, further supporting their occurrence within the Sha 2 member (J\({}_{2}\)S\({}_{2}\)).
## 1 Introduction
While these efforts have been comprehensive, the correlation between the simulated and experimental pressure remains a challenge. For instance, a study utilizing CLO3D's garment stress representation developed a contact pressure prediction model (CP model) that incorporated digitized stretch stiffness and simulated strain data to predict the contact pressure more effectively [6]. This model has been validated with expanded materials and showed promise in improving the simulation accuracy [16]. However, despite these innovations, there is still a lack of published comparisons between simulated and experimental strain and pressure values on human-shaped manikins using the CLO3D software (Version 7.2 or earlier). In addition, given that CLO3D's updated version (Version 7.3) includes its own pressure simulation function, it has not been thoroughly evaluated for the comparison of the simulated pressure with the experimental contact pressure using various pressure sensors alongside [PERSON]'s CP model [6; 16]. Therefore, this paper aims to evaluate the practicality of using virtual fit technology in the design of medical-grade compression garments, focusing on three aspects. * (1) Strain Measurement Validation: Evaluating the correlation between simulated strain values and experimental strain values exerted on compressional knit materials on a 3D-scanned manikin to ensure reliable strain representation in the virtual environment. * (2) Pressure Measurement Validation: Evaluating the accuracy of the new CLO3D's pressure measurement features against experimental data obtained from pressure sensors and [PERSON]'s previous CP model for materials on physical manikins and virtually scanned avatars. * (3) Practical Application in Medical Compression Band Design: Assessing the practicability of using virtual fit technology for pressure optimization in designing practical medical-grade compression garments, specifically focusing on the composite application of advanced smart materials in compression garments.
## 6 Example Analysis
### Example 1--Summer Peak  
In summer peak operation mode, in 0.073 Hz oscillation mode, the energy of some units of the power grid participates in weight \(\eta_{\lambda_{ki}i}\), and the transient energy change \(\Delta V_{t_{ki}i}\) of the units after being converted according to \(\eta_{\lambda_{ki}i}\) is shown in Table 3. It is calculated that the transient energy change of the system under this oscillation mode is \(\Delta V_{t_{k}}=523\) kW - h.  
A 2% power disturbance is applied to three DCS at the same time, and the DC modulation sensitivity \(\varphi_{m}\) is obtained by Prony algorithm identification and calculation. The DC modulation sequence is obtained by sequencing, as shown in Table 4. According to the calculation in Table 4, when \(p>1\), the energy change \(\Delta V_{\text{dc}t_{k}}\) used by DC to suppress oscillation is 1255 kW-h, and it is determined that DC a participates in modulation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode of** & **Real Part** & **Imaginary Part** & **Frequency/Hz** & **Amplitude** \\ \hline summer peak & 0.00504 & 0.44965 & 0.073 & 0.269 \\ summer base & 0.00093 & 0.38400 & 0.060 & 0.443 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Critical oscillation modes of the power grid in summer peak and summer base cases.  
Figure 4: Structure of the power system.  
Taking the power disturbance of a DC as an input, and the optimization model was built in MATLAB combined with DDPG algorithm. The learning rate \(\alpha=0.00001\), discount factor \(\gamma=0.97\), and the PI parameters of a DC FLC were iteratively obtained as \(K_{1}=1.5213\), \(T_{1}=0.4205\). When AC channel AC2 fails \(N-1\), the system frequency change curve is shown in Figure 5.  
As can be seen from Figure 5a, compared with the system without DC modulation, when DC a participates in modulation, the system frequency oscillation is significantly suppressed, which verifies the effectiveness of the proposed strategy.  
In order to further verify the correctness of analyzing the oscillation mechanism of DC modulation suppression system from the perspective of energy and the ordering of DC modulation sensitivity, the frequency variation curves of the system with three DCS participating in modulation were compared. As can be seen from Figure 5b, since the energy variation \(\Delta V_{\text{dc}_{k}}\) of the three DCS that can be used to suppress the oscillation is greater than the transient energy variation \(\Delta V_{t_{k}}\) of the system, the oscillation of the system can be suppressed when the three DCS are modulated separately. At the same time, compared with c DC modulation, the amplitude of system frequency oscillation is smaller when a DC participates in modulation. Compared with b DC, it takes the shortest time for the system frequency to stabilize when a DC participates in modulation. Therefore, choosing a DC as the DC participating in modulation is the most effective for suppressing system oscillation.
## 3 Results
### Devices Featuring Ni-ALD Dielectric-Ni Junctions  
Our experimental results obtained with Pt electrodes indicate that a fundamental issue with the use of ALD tunnel barriers in MIM junctions is that the presence of the oxidizing pulse (H\({}_{2}\)O or O\({}_{3}\)) will likely lead to partial oxidation of the metals in the SET. While the formation of native oxide promotes the nucleation and growth of the ALD dielectric, it drastically reduces the transparency of tunnel barriers making them unusable for target applications.  
In order to solve the problem of ALD nucleation and the presence of parasitic native oxide, two different approaches were considered: (1) to use a metal that is more susceptible to oxidation than noble metals but whose oxide can be chemically reduced after the ALD dielectric step; and (2) to use an ALD process in which the tunnel barrier formation does not require an oxidizing pulse. Nickel was selected for the first approach because of its relative inertness, compared to Al and Ti, and the reducibility of its native oxide at temperatures that will not damage the barrier dielectric or cause deformation of the metal layer. SiN\({}_{x}\) ALD was selected for the oxygen-free tunnel barrier formation where the N\({}_{2}\) plasma replaces the O\({}_{2}\), O\({}_{3}\), or H\({}_{2}\)O pulse and therefore parasitic formation of the native oxide can be minimized. Below we discuss the results obtained using Ni-dielectric-Ni junctions with tunnel dielectric deposited in the Oxford FlexAL PEALD reactor.
## 3 The Clustering Protocol
### Inter-Cluster Routing Mechanism
```
Inputs: (1) CHs set, \(T\) (2) the coordinate of BS, (\(BS\).\(x\), \(BS\).\(y\)) Result: (1) the set of the next hop CH of each CH, \(NH\) (2) path set from each CH to BS, \(PA\) (3) an array of path weights from remaining nodes to BS, \(dis\)
1:for \(i=1\to n\)
2: for \(j=1\to n\)
3:\(W(i\),\(j)=\) weight from Node \(i\) to Node \(j\) according to (14);
4: end for
5:end for
6:dis = [\(W(1\), \(BS)\), \(W(2\), \(BS)\), \(\ldots\), \(W(n\), \(BS)\)]T, \(NH(1)\) = BS, \(PA(1)\) = {BS};
7:sort all CHs in \(T\) in order of the distances from the BS to form an ordered set, \(CHs\);
8: initialize an array of the hop number for each CH, \(HP\) = [1,1, \(\ldots\),1]n;
9:for \(i=2\rightarrow\) length(\(CHs\))
10: for \(j=1\to i-1\)
11: if \(d(i,j)<d(i,BS)\)
12: if \(W(Path(j,BS))^{\star}HP(j)+W(i,j)<W(i,BS)^{\star}(HP(j)+1)\)
13:\(W(i,BS)\) = [\(W(Path(i,BS))^{\star}HP(j)+W(i,j)\)](\(HP(j)+1\));
14:\(HP(i)=HP(j)+1\), \(NH(i)\) = CHs(\(j\)), \(PA(i)=PA(j)+\) {CHs(\(i\))};
15: update the corresponding value, \(W(i,BS)\) in \(dis\);
16: end if
17: end if
18: end for
19:end for
```  
**Algorithm 2** Optimized Dijkstra Algorithm in WSN
###### Abstract
* **Extensive Evaluation on Diverse Datasets:** We validate our approach through extensive experiments on diverse datasets, including MOT15, MOT16, MOT17, DanceTrack, and WildTrack, demonstrating its superior performance compared to existing state-of-the-art methods. The remainder of this paper is organized as follows. In Section 1, we review related studies in the field of MOT, highlighting both traditional approaches and recent advancements. Section 2 details the proposed methodology, including our embedding structure and its integration with fine-tuned VLMs for robust object tracking. We also elaborate on the track management module and the zero-shot re-identification system, which collectively enhance tracking consistency and accuracy. In Section 3, we describe the datasets used for training and evaluation, including MOT15, MOT16, MOT17, DanceTrack, and WildTrack, and discuss the experimental setup. Section 4 presents our results, comparing the performance of our approach against state-of-the-art methods across these diverse datasets. Finally, Section 5 concludes the paper with a summary of our findings, potential applications of our work, and directions for future research.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 5 Data Validation
### Data Validation Through Anomaly Detection Algorithms
#### 5.2.1 Isolation Forest  
Isolation Forest [28] is an unsupervised anomaly detection algorithm that is particularly effective in high-dimensional datasets. It isolates anomalies by partitioning the data into smaller segments rather than profiling normal data, making it ideal for detecting rare events, such as water contamination or sensor malfunctions, in real-time monitoring systems. This method is well suited for water quality data, where anomalies are often infrequent and represent significant changes in the water's condition. Because it focuses on isolating anomalies rather than modeling normal data, it excels at identifying rare outlying samples without requiring labeled data for training, making it highly applicable to dynamic and evolving datasets like those in water quality monitoring. Figure 18 presents the Isolation Forest AUC
# Isotropic IoT-Based Magnetic Flux Density Meter Implementation for ELF Field Measurements +
## 2 Magnetic Flux Density Meter Operating Principles
### The RMS Detector  
A section of the RMS detector is required for the implementation of a magnetic field meter that can measure RMS field values instead of average or peak values. A rectifier is used to extract the DC component and measure the RMS value of the integrators' AC signal output.  
The DC component of a rectified signal is shown to be directly proportional to the RMS value of the harmonic input signal. A half-wave rectified voltage \(V_{s}\) is considered during a full cycle (0-2\(\pi\) rad):  
\[\begin{array}{cc}V_{s}(t)=V_{m}\sin(\omega t)&\quad for\;0\leq\omega t<\pi\\ V_{s}(t)=0&\quad for\;\pi\leq\omega t<2\pi\end{array} \tag{10}\]  
where \(V_{m}\) is the maximum value of the half-wave rectified voltage. The Fourier series of the previous function is as follows [36]:  
\[V_{s}(t)=\frac{V_{m}}{\pi}+\frac{V_{m}}{2}\sin(\omega t)-\frac{2V_{m}}{3\pi} \cos(2\omega t)-\frac{2V_{m}}{15\pi}\cos(4\omega t)+\ldots \tag{11}\]  
Figure 3: The block diagram of the proposed isotropic IoT-based magnetic flux density meter. An Arduino UNO Wi-Fi Rev.2 board is used to provide 3 analog inputs for measurements in the \(X\), \(Y\), and \(Z\) axes and Wi-Fi connectivity, enhancing the previous version of the sensor [30].  
and hence,  
\[V_{s}(t)=\frac{V_{m}}{\pi}+\frac{V_{m}}{2}\sin(\omega t)+\sum\limits_{N=2}^{ \infty}\frac{V_{m}\Big{[}1+\left(-1\right)^{N}\Big{]}}{\pi(1-N^{2})}\cos(N\omega t) \tag{12}\]  
Moreover, we are aware that in half-wave rectified signal:  
\[V_{DC}=\frac{1}{T}\int\limits_{0}^{T}V_{out}(t)dt= \frac{1}{T}\int\limits_{0}^{T/2}V_{m}\sin(\frac{2\pi t}{T})dt= \frac{V_{m}}{\pi} \tag{13}\]  
and,  
\[V_{rms}=\sqrt{\frac{1}{T}\int\limits_{0}^{T}V_{out}^{2}(t)dt}=\sqrt{\frac{1}{ T}\int\limits_{0}^{T/2}V_{m}^{2}\sin^{2}\!\left(\frac{2\pi t}{T}\right)dt}= \frac{V_{m}}{2} \tag{14}\]  
The first term in (11) represents the DC component of the semi-rectified signal, the second term is the 1 st harmonic (base frequency), and the remaining terms are higher-order harmonics, respectively. The following is assumed in a harmonic (sinusoidal) signal:  
\[V_{rms}=\frac{V_{m}}{\sqrt{2}},\ V_{DC}=\frac{\sqrt{2}\ V_{rms}}{\pi},\ V_{ rms}=\frac{\pi\ V_{DC}}{\sqrt{2}} \tag{15}\]  
The RMS value of the input harmonic signal is equal to the \(\pi/\sqrt{2}\) of the DC component of the semi-rectified signal. At the rectifier's output (\(V_{out}\)), except the DC component, a large number of harmonics should be rejected. A low pass filter can be utilized to reject the harmonic frequencies. To achieve perfect harmonics rejection, the low pass filter's cut-off frequency is required to be as low as possible. Consequently, the RMS detector can be realized consisting of a half-wave rectifier and a low-pass filter.
# Non-Volatile Memory Forensic Analysis in Windows 10 IoT Core
## 5 Forensic Evidence in Windows 10 IoT Core
### System Events  
These contain the logs of all the relevant actions occurred in the operating system classified into four different categories, depending on what component of the system was affected:  
* Application: activities regarding the software and components installed on the system.
* Security: data regarding the Windows system audit policies.
* Setup: data about the control of domains.
* System: mainly events related to the Windows system files [51; 52].  
They are also categorized according to the impact that the action has had on the system in errors, warnings or information messages, ordered from least to most relevant. For these reasons, the event log is a very useful source of evidence, added to the fact that the information is presented in a very detailed and structured way, making it easy to filter through the large amount of data that it stores. The logs for each category can be found in the following route of the MainOS partition: Windows/System32/winevt/Logs/.  
Other relevant events that are also stored are the ones regarding the following services:  
* OpenSSH: information about the log attempts, launch, and stop of the SSH server can be recovered.
* Network profile: data regarding the network and the connection type of the system, as well as information of when the system disconnected from it.
* Windows update: events are created when an update is found or downloaded.
* AppXDeploymentServer: information regarding the packages that have been installed and uninstalled on the system.  
In Figure 8, an example of a system event related to the Network Profile is shown, in which the disconnection of the system from the WiFi network is logged.  
Figure 8: System event informing of the disconnection of the system from the WiFi network.