esa-sceva/satcom-chunk-collection huggingface dataset:
# Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding
## 3 Modeling of HES Components
### PV Cells  
The output power of a PV is calculated using the single diode model [26]. The PV output current depends on the input data in form of the irradiance (in W/m\({}^{2}\)) and temperature (in \({}^{\circ}\)C). Figure 5 presents the single diode model of PV according to [27] while the output current of PV is given by  
\[I_{PV}=(\ (I_{SC-STC}+k_{i}\ (T-T_{STC}))\frac{G}{G_{STC}})-I_{0}\ ((e)^{(\frac{V_{PV}+I_{PV}R_{S}}{V_{T}})}-1)- \left(\frac{V_{PV}+I_{PV}R_{S}}{R_{P}}\right) \tag{2}\]  
Here, \(I_{SC-STC}\) represents short circuit current at standard test conditions (STC), \(k_{i}\) is the short circuit current coefficient given by the manufacturer, \(T\) is the temperature of the module in Kelvin, \(T_{STC}\) represents the temperature at STC and is considered 298 \({}^{\circ}\)K, whereas \(G\) refers to cell surface irradiance, \(G_{STC}\) is the irradiance at STC, which is equal to 1000 W/m\({}^{2}\), \(V_{T}=akT/q\) denotes thermal voltage, a is the ideality factor and ranges between (1\(\leq a\leq\) 2) [28], whereas \(q\) is the electron charge and is considered 1.602 10 \({}^{-19}\) C, and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**State** & **Condition for Transition (CFT)** & **Description of Event** \\ \hline
1. Grid\_Connected\_Mode & Default transition, & \\  & P\_Grid \textgreater{} 0 & Electricity grid or PV supplies the load \\
2. Islanded\_Mode & P\_Grid=0 & Renewables (PV, ESU) or \\  & & generators supply the load \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parent–child relationship and the condition for transition (CFT) for HES_Operation.  
represents the Boltzmann constant taken as \(1.381\times 10^{-23}\)\(J/K\). The \(R_{S}\) and \(R_{P}\) represent series and shunt resistances, respectively [29].  
Due to the transcendental nature of (2), the Newton-Raphson method is applied to solve the equation iteratively by considering the output voltage (\(V_{PV}\)) from 0 to \(V_{OC\_STC}\). Furthermore, the peak of the P-V curve is continuously tracked using the perturb and observe (P&O) MPPT method [30]. The implementation of the P&O method using the SF is given in Figure 6. The PV output power for a given \(G\) and \(T\) is calculated as \(P_{max}=(V_{mpp}\times I_{PV})\). Thus, the instantaneous output power of PV with \(N\) number of modules in the array can be written as [31]  
\[P\_PV(t)=P_{max}(t)\times N_{PV} \tag{3}\]  
The actual values of \(G\) and \(T\) are taken from data set for the Quetta region, which is provided by [32]. Other PV parameters are taken from [33].
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 2 Materials and Methods
### Filter Design Procedure
In Figure 8b, it is possible to observe how the response of the designed filter varied, increasing its amplitude (peak in frequency) at around 10 dB as a product of the acoustic signal of the _Lobesia botrana_. Figure 4: PSD of the sound recorded on the container without moths. Figure 5: (a) Acoustic signal of the container with 8 moths; (b) PSD of the container with 8 moths (blue) and reference (red).
## 4 Results
### Internal Validation Set  
Figure 4 shows the confusion matrix for each model variant tested on the internal validation dataset: n (Figure 3(a)), s (Figure 3(b)), m (Figure 3(c)), l (Figure 3(d)), and xl (Figure 3(e)). This dataset consists of 80 images, with 60 for myopic eyes and 20 for healthy eyes. Table 3 reports the performance metric results for each YOLOv8 variant. It can be observed that the small and medium models perform similarly, as do the large and extra-large models. The best-performing model, which combines high accuracy, recall or sensitivity, and F1 score with low computational time, is the large variant, with an accuracy of 85%, recall of 91.7%, and an F1 score of 90.2%.  
The Shapiro-Wilk test showed that all performance metrics were normally distributed (\(p>0.05\)), except for accuracy (\(p<0.05\)). Since we had only one value per model, we used the Kruskal-Wallis test, which revealed no statistically significant differences in the performance metrics across the different models. In medicine, sensitivity (or recall) is crucial for minimizing false negatives and preventing misdiagnosis. Based on the sensitivity (91.7%) and considering the F1 score (90.2%), which provides a more balanced view of the model's performance, we concluded that the best-performing model in the validation set is the large variant of YOLOv8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **ACC** & **P** & **R** & **Sp** & **F1 Score** \\ \hline YOLOv8-nano & 0.850 & 0.914 & 0.883 & 0.750 & 0.898 \\ YOLOv8-small & 0.838 & 0.851 & 0.950 & 0.50 & 0.897 \\ YOLOv8-medium & 0.838 & 0.851 & 0.950 & 0.50 & 0.897 \\ YOLOv8-large & 0.850 & 0.887 & 0.917 & 0.650 & 0.902 \\ YOLOv8-extra-large & 0.850 & 0.887 & 0.917 & 0.650 & 0.902 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results obtained by the proposed approach with all the available variants of YOLOv8 on the internal validation test set.  
Figure 4: Confusion matrix from each variant of YOLOv8 tested on internal validation set, with number of TP, TN, FP, and FN: (**a**) nano; (**b**) small; (**c**) medium; (**d**) large; and (**e**) extra-large models.
## 3 Results and Discussion
### Analysis of an Optimized Arrangement of Helmholtz Resonators
#### 3.3.3 Analysis of the Impact of Optimized Solutions on Micro-Pressure Waves  
The data measured at measurement points M-1, M-2, and M-3 for the original scheme, 72-cavity new tunnel, and new 64-cavity tunnel were compared (see Table 5) and calculated as follows:  
\[\text{Y}\%=\frac{P_{new}-P_{raw}}{P_{raw}}\times 100\% \tag{8}\]  
where Y% denotes the mitigation rate for different tunnel scenarios, \(P_{raw}\) denotes the data measured at the specified measurement points for the original tunnel scenario, and \(P_{new}\) denotes the data measured at the specified measurement points for the new tunnel scenario.  
The two types of 72-cavity new tunnel schemes with Helmholtz resonators and 64-cavity new tunnel scheme can still significantly reduce the micro-pressure wave amplitude, with the mitigation rates of 72-cavity new tunnel 1 as 27.1%, 29.28%, and 30.65% at the three measurement points, respectively, and the mitigation rates fluctuating around 30%. The mitigation rates for the 64-cavity new tunnel were 25.6%, 27.8%, and 27.4%, with mitigation rates below 30%. According to the CEN European standard [30], a comparison of the amplitude data at 20 m and 50 m from the tunnel exit in Figure 15 shows that even if the same number of Helmholtz resonators are placed, the different arrangements will have an impact on the mitigation effect, indicating that different placements of Helmholtz resonators can lead to different mitigation rates of micro-pressure waves.  
Figure 14: Comparison of wall pressures for different solutions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **M-1** & **M-2** & **M-3  
The combined wall pressure rise rate and micro-pressure wave relief rate show that the 72-cavity new tunnel 1 arrangement can ensure the micro-pressure wave relief effect while controlling the increase in tunnel wall pressure to the maximum extent. This satisfies the requirements of the project. The fitted relationship between the tunnel exit distance and micro-pressure wave amplitude for the original tunnel and 72-cavity new tunnel 1 is shown in Figure 16, and the fitted equations and correlation coefficients are listed in Table 6. In this study, the amplitude of the micro-pressure wave at the exit of the tunnel is approximately inversely proportional to the 0.78 th power of the distance from the tunnel exit.  
Figure 16: Micro-pressure wave fitting curves for different tunnel structures.  
Figure 15: Effect of different layout options on micro-pressure waves.
## 5 Machine Learning and Feature Selection Enabled IDS
### Popular Datasets Adoption for ML/FS-Enabled IDS
#### 5.3.1 KDDCup99 Dataset  
The dataset known as KDDCup99 is one of the most widely adopted datasets for anomaly detection experiments in IDS, created and distributed by MIT Lincoln Laboratory DARPA in 1999 [88]. The dataset was generated by simulating the operation of a typical US Air Force LAN with multiple attacks, where nine weeks of network data are collected in TCP dump format. The KDDCup99 dataset contains a total of 4,898,431 records, with each record consisting of 41 distinct features. The total records are categorized into five classes, where four of them are attack classes, namely Probe, DoS, U2R, and R2L, whereas one of them is a benign class.
# Management Platforms and Protocols for Internet of Things: A Survey
## 4 IoT Network Management Protocols
### Iee 1905  
IoT environments depend on several media access control (MAC) protocols. The challenge of interoperability between technologies needs to be discussed. IEEE 1905 is a standard focused on the convergence of digital home network and offers an abstraction layer to all these heterogeneous MAC protocols.  
The goal of IEEE 1905 is to define a common standard that establishes home network technologies for a data and control service access point. Each interface can transmit and receive packets, regardless of underlying technologies or layers, as shown in Figure 10[46].  
An intermediate layer used to exchange messages (Table 1), is called Control Message Data Units (CMDUs), with all standards-compatible devices. In Figure 11, all the IEEE 1905 deployed devices with Abstraction Layer Management Entity (ALME) protocol have neighbor discovery, topology exchange and rules, measured traffic, and security associations following the layers presented.  
\begin{table}
\begin{tabular}{c c} \hline
**Exchange Messages** & **Description** \\ \hline ALME-GET & This message is used by the HLEs to get a description of the HLEs device. \\ ALME-SET & This message is used by the HLEs to send a configuration of the HLEs device. \\ \hline \end{tabular}
\end{table}
Table 1: Exchanged messages at the Abstraction layer.  
Figure 10: Illustration of the protocol structure for IEEE 1905.  
The protocol introduces an intermediate abstraction layer to the logical link control (LLC) and one or multiple MAC. The service access points (SAPs) holds many networking technologies, to support advanced network management features such as auto-configuration, QoS, path selection and discovery. This layer simplifies setup, e.g., by eliminating the need for a user to enter different passwords to access each link [47].  
This ALME SAP entity can provide management services to MAC, physical layer (PHY) and higher layer entities (HLEs) [48]. It also provides advanced network management features including discovery and interface selection.  
_Pros: Interoperability and common standard model to devices._  
_Cons: No mature protocol and security feature._  
Use Case  
nVoy is an IEEE 1905 standard program [49] that provides the services to maximize and simplify the overall performance of a home network. The reliability is provided through the abstraction layer to established power line, wireless, coaxial cable, and Ethernet home networking technologies--IEEE 1901/HomePlug(r) AV, Wi-Fi, MoCA(r), and Ethernet, allowing to provide common setup procedures for establishing connected devices, secure links, and network management.
# Carbon Nanodots as Peroxidase Nanozymes for Biosensing
## 3 Carbon Nanodots (C-Dots) as POD Nanozymes
The POD-like activity of as-prepared C-dots was examined in the degradation of methyl red (MR) and MO as model azo dyes. The respective value of the rate constant and degradation efficiency of the dye were estimated to be 9.5 \(\times\) 10\({}^{-3}\) min\({}^{-1}\) and 83% after 200 min, which suggest a good catalytic activity of C-dots. These studies [38; 39] may pave the way for C-dots applications in the versatile areas of immunohistochemistry, photocatalysis, and environmental science. In subsequent work(s), the group further utilized the intrinsic POD activity of C-dots in the indirect and direct colorimetric detection of glutathione (GSH) and mercury ion [40; 41], respectively. Figure 4: (**a**) Reaction equipment for the preparation of C-dots; digital image of C-dots solution (**b**) before treatment; (**c**) after treatment; (**d**) DLS histogram of C-dots; (**e**) TEM; and (**f**) HRTEM image of C-dots. Reproduced with permission from [38]. Copyright (2012) Royal Society of Chemistry. In contrast to GSH (a detection limit of 0.3 uM), a limit of detection for mercury ion was estimated to be 23 nM, with tunable dynamic range. A facile and cost-effective preparation of water soluble C-dots was reported by [PERSON] and co-workers [42] by hydrothermal method utilizing the leaves of _Olea europaea_ as a carbon source. The as-prepared C-dots with POD-like activity were used for the colorimetric detection of H\({}_{2}\)O\({}_{2}\) and glucose with detection limits as low as 0.6 uM and 5.2 uM, respectively. The practical utility of this system was also demonstrated for glucose determination in serum samples. This work may pave the way in the exploration of low-cost carbon sources for the synthesis of highly luminescent C-dots using 'green chemistry' principles. In yet another work, high-quality C-dots were prepared from carbonized \(\beta\)-cyclodextrintrin (\(\beta\)-CD) and investigated as POD mimetics for a sensitive colorimetric assay of H\({}_{2}\)O\({}_{2}\) with a detection limit of 1 uM. Furthermore, the protocol was successfully extended to develop an excellent analytical platform for oxidative ions, namely, Fe\({}^{3+}\) and Ag\({}^{+}\). In particular, the detection limits for Fe\({}^{3+}\) and Ag\({}^{+}\) were calculated to be 0.8 and 0.5 uM, respectively [43]. Aside from their frequent use in supramolecular chemistry, this work demonstrate that the special spatial structure of \(\beta\)-CD can also facilitate the formation of high quality C-dots for POD mimic and biosensing. Very recently, reduced state C-dots have been found to catalyze TMB-H\({}_{2}\)O\({}_{2}\) system, producing a blue color, a diagnostic for PODs [44]. Based on this finding, a selective colorimetric assay was developed for detection of glucose in human serum samples. Table 1 summarizes the important details of POD-mimicking features of C-dots as discussed above.
# Deep Learning for Laryngopharyngeal Reflux Diagnosis
## Materials and Methods  
### Data Collection  
This experiment was conducted with the approval from the ethics committee of the hospital. 152 subjects from the hospital were studied prospectively from March 2015 to April 2019. All subjects were evaluated subjectively by a reflux symptom index (RSI) and a laryngoscopy. The subjects holding no LPR-related symptoms served as our control group. With their consents, the subjects with LPR-related symptoms also underwent 24 h pH probe monitoring.  
The pH monitoring results indicate that 37 subjects were confirmed with LPR, and 38 subjects were suspected with LPR but negative. The control group was composed of 77 healthy subjects. All subjects in this study were of Chinese descent and divided into three groups as follows.  
The pH-positive group consisted of 37 subjects such that proximal acid episodes \(\geq\)3, or proximal acid exposure time >1%, or impedance detected proximal acid exposure \(\geq\)4. There was no specific RSI cutoff for this group.  
The pH-negative group consisted of 38 subjects such that proximal acid episodes < 3, or proximal acid exposure time \(\leq\)1%, or impedance detected proximal acid exposure < 4. There was no specific RSI cutoff for this group.  
The control group consisted of 77 subjects who were administered laryngoscopy for routine test before thyroid surgery and without LPR-related symptoms and who had an RSI \(\leq\) 13.  
Laryngeal images were obtained during a laryngoscopy through a flexible high-resolution laryngoscope (model EVNE; XION; Berlin, Germany). The images were taken near the middle of the larynx, and the white balance function was employed to exclude other potential factors that may interfere with the true image colors. All the images were encoded with the JPEG format. In the study, we only consider the two-class problem of pH-positive cases versus control cases, which is the most distinguishable scenario with total number of examples \(n=114\).
# Performance Analysis of Pile Group Installation in Saturated Clay
## 4 Conclusions  
Through the project of continuous group pile installation in saturated clay, this paper explores the impact of subsequent pile installation on the adjacent pile based on a three-dimensional numerical model. The main conclusions are as follows:  
This paper investigates the impact of subsequent pile installation on adjacent pile in a continuous pile group installation project within saturated clay, based on a three-dimensional numerical model. The main conclusions are as follows:  
Figure 19: Effects of \(S\) and \(L/D_{\text{e}}\) on vertical bearing capacity of adjacent pile.  
Figure 18: Changes in vertical bearing capacity of the adjacent and subsequent pile with time.  
The peak vertical displacement of the adjacent pile is significantly higher than the peak lateral displacement. As \(S\) increases, the peak vertical displacement of the adjacent pile increases, while the peak lateral displacement decreases. With an increase in \(L/D_{\rm e}\), both the peak vertical and lateral displacements of the adjacent pile increase.  
As the penetration depth of the subsequent pile increases, the peak axial force of the adjacent pile initially decreases and then increases, while the peak bending moment initially increases and then decreases. The maximum peak axial force of the adjacent pile is approximately at \(h/L=0.1\), and the maximum peak bending moment is approximately at \(h/L=0.7\)-\(0.8\).  
The peak axial force of the adjacent pile increases with the increase in \(S\), while the peak bending moment decreases with the increase in \(S\). Both the peak axial force and peak bending moment increase with the increase in \(L/D_{\rm e}\).  
The vertical bearing capacity of the pile initially increases and then decreases over time, and the vertical bearing capacity of the subsequent pile is significantly higher than that of the adjacent pile. Increasing \(S\) and \(L/D_{\rm e}\) can reduce the impact of subsequent pile installation on the bearing capacity of the adjacent pile. Compared to increasing \(S\), increasing \(L/D_{\rm e}\) is more effective in improving the bearing capacity of the adjacent pile.  
Methodology, Z.L. and D.H.; Resources, F.L.; Software, W.X. and Z.L.; Supervision, D.H. and F.L.; Validation, W.X.; Visualization, Z.L.; Writing--original draft, W.X. and Z.L.; Writing--review and editing, D.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article and further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Guangdong Yudean Zhuhai Offshore Wind Power Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 5 Discussion
### Future Trends  
Many key points were identified throughout the course of this research. The most common aspect shared among studies was the relatively low number of participants. Since HMDs for AR are still in their early stages of development, they are not very comfortable for the user, especially when they have to be combined with a BCI. Hence, the rapid advancement of AR technology is expected to play a significant role in enhancing the comfort and usability of AR-BCI systems. Another future trend is the adoption of deep learning  
Figure 8: Number of system commands.  
Figure 7: Feature extraction methods.  
techniques to enhance the classification accuracy of the systems. Researchers are working on integrating different machine learning algorithms and constructing neural networks to improve the transfer rate of BCI systems. Yet another critical aspect that demands attention is finding the ideal stimulation and acquisition time for EEG signals in reactive BCIs. Various studies [19; 20; 23] that have examined the correlation between classification accuracy and different stimulation times indicate that an increase in stimulation time is associated with a corresponding rise in classification accuracy. In addition to stimulation time, ref. [39] highlighted the importance of determining the optimal color for visual stimuli. Their research findings indicated that varying stimulus colors can impact classification accuracy. One more important future trend to be considered is the development of hybrid BCI systems. This approach enables researchers to harness the benefits of each BCI paradigm while mitigating their respective limitations. As ref. [51] proposed, combining MI with SSVEP can result in decreasing the training time needed for BCIs relying on MI while expanding the range of usable classes without introducing additional visual complexity.