esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Modulation Strategy to Eliminate Current Distortion for PV Grid-Tied H6 Inverter
## 5 Simulation Study
The PIR control method has been proposed to overcome the disadvantages of individual PR and PI control, with the system maintaining 65\({}^{\circ}\) phase margin and infinite gain margin--ensuring the system is sufficiently stable with the PIR controller [20]. Reviewing Figure 16 and Table 2, it is clear that there is a significant phase error under the PI control, resulting in a tracking error, but the proposed PIR control compensates for this deficiency. Figure 17 shows the output current \(i_{L}\) and the tracking error \(i_{err}\) when the frequency and phase of the reference grid voltage \(V_{ac}\) changes from 50 Hz to 55 Hz, with a phase of 180\({}^{\circ}\) respectively at a period of t = 0.3 s. As we can see in Figure 17a, there is a large steady error after the frequency discontinuity event of the system under the non-adaptive PIR control; while for the system which adopts the FSAPIR controller, as shown in Figure 17b, the error can be eliminated in 0.05 s after the frequency discontinuity of the system. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Controller/(50 Hz)** & **Magnitude/(dB)** & **Phase/(\({}^{\circ}\))** \\ \hline PI & 0.00573 & \(-1.9\) \\ PR & 0.0052 & \(-0.0146\) \\ PIR & \(-1.42\times 10^{-6}\) & \(-0.0031\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of errors. Figure 16: Bode diagram for closed-loop. Frequency (rad/s). Figure 15: Bode diagram for open-loop. Frequency (rad/s).
# A Novel Interpretable Deep Learning Model for Ozone Prediction
## 2 Materials and Methods
### Data
#### 2.2.2 Evaluation Metrics  
In this study, the Root Mean Squared Error (RMSE), R\({}^{2}\) and the Symmetric Mean Absolute Percentage Error (SMAPE) were used as the performance metrics. The formula of the RMSE is shown below  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n}} \tag{1}\]  
where \(y_{i}\) represents the observation of item \(i\); \(\hat{y}_{i}\) represents the prediction of item \(i\); and \(n\) represents the number of items. R\({}_{2}\) measures the fitting degree between the model and the true state. The formula is defined as follows:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{ i}-\bar{y})^{2}} \tag{2}\]  
where \(\bar{y}=\sum_{i=1}^{n}y_{i}\).  
The SMAPE measures the accuracy of the predictions based on percentage error, which is defined as follows:  
\[\text{SMAPE}=\frac{100\%}{n}\sum_{i=0}^{n}\frac{|\hat{y}_{i}-y_{i}|}{(|\hat{y }_{i}|+|y_{i}|)/2} \tag{3}\]  
Note that each evaluation metric above has its advantages and disadvantages; thus, we integrated different approaches to measure the effectiveness of the model.
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 5 Performance Evaluation
### Informal Security Analysis
#### 5.3.2 Replay Attack  
A replay attack occurs when an attacker intercepts a legitimate message, re-sends it at a later time, and attempts to deceive the recipient into processing it as if it were a new message. To mitigate replay attacks, the proposed system employs a timestamp mechanism. Each message is assigned a unique timestamp, which is verified by the recipient upon receipt. If the timestamp is older than a predefined threshold, the message is rejected as a potential replay attack.  
One potential limitation of the proposed solution is the possibility of replay attacks being launched against other nodes within the network if the attack is executed with sufficient speed. To mitigate this risk, the time window for accepting messages can be adjusted and fine-tuned to reduce the likelihood of successful replay attacks.
## 2 Literature Review  
Over the past two decades, a great number of researchers have sought new ways to efficiently design freezing walls for the AGF method of subway construction. [PERSON] et al. [5] introduced the widely employed structural mechanics method, which simplifies the freezing wall into an elastic, statically indeterminate structure. Modifying the assumptions, many researchers made improvements to these methods [6; 7; 8; 9]. Despite its safety and efficacy, the structural mechanics method suffers from several major drawbacks: (1) this method is limited to the elastic regime of the material, and the cohesion between unfrozen soil and frozen soil is omitted, which leads to a more conservative design of freezing wall thickness; (2) the thickness of the freezing wall is primarily determined by the strength requirement, with little concern for the deformation of the ground surface caused by freezing heave or thaw settlement; and (3) little attention has been paid to thermal dynamics problems during the development of freezing walls [2; 10; 11; 12; 13]. Noting the limitations of the structural analytical method, some scholars tried to develop analytical solutions for predicting the deformation of the ground surface. However, the equations derived are only applicable to limited configurations and have relatively complicated forms [14; 15]. Thus, many researchers made an attempt to examine freezing walls using numerical simulation methods. [PERSON] and [PERSON] [16], employing the three-dimensional finite element numerical simulation method, carried out research on the mechanical behaviors of the freezing wall during the excavation. Their observation showed that the bottom of the freezing wall had the largest tension zone area. Employing finite element modeling, [PERSON] et al. [17] evaluated temperature field variations during artificial freezing and observed the potential factors affecting the changes in temperature. They found that thermal conductivity had the most significant impact on the development of the temperature field, while the phase-change latent heat had little impact. [PERSON] et al. [18] undertook a study utilizing the COMSOL multi-physical platform to optimize the distribution of the freezing pipes. While the numerical simulation method has been applied to either solely mechanical or purely thermal dynamic problems when modeling freezing walls, research works on the thermal-mechanical problems induced by freezing walls, i.e., frost heave and thaw settlement, remain scarce. Based on a metro entrance construction project, [PERSON] et al. [19] investigated time variations in frost heave and found that intermittent freezing could effectively control surface deformation. Through laboratory model tests in combination with numerical simulations, [PERSON] et al. [20] examined frost heave during twin-tunnel construction; the results showed that sequential freezing could bring about less frost heave displacement than that of simultaneous freezing.
# MEMS Vaporizing Liquid Microthruster: A Comprehensive Review
## 4 Instability Control into VLMs
### Passive Control of Flow Instability  
The design of the microchannel geometry is of primary importance to mitigate the flow boiling instabilities and optimize the overall thermal performance. To this purpose, several microchannel geometries have been recently investigated, such as the saw-tooth [33], triangular [34], and divergent [35] microchannel geometries shown in Figure 10. They all exhibited an enhancement in the flow boiling heat transfer, leading to mitigate flow boiling instabilities such as flow reversal and oscillating flow.  
Figure 8: Scheme of explosive boiling [31]: (1) micro-channel; (2) area of visual observation; (3) onset of nucleate boiling point; (4) elongated cylindrical bubble; (5) liquid in front of the bubble; (6) vapor; (7) liquid droplets and clusters.  
In combination with the microchannel geometry, restrictors at the microchannels inlet have successfully hindered the onset of flow reversal instabilities and the consequent establishment of parallel channel instabilities [36; 37]. The use of artificial nucleation sites and the modification of the surface roughness and the surface porosity are also worthy of consideration for flow boiling enhancement: [29; 30] provide an in-depth literature review on the most promising solutions to mitigate flow boiling instabilities.  
It is worth underlining the further relevance of the inlet manifold geometry design: it can improve the fluid distribution between microchannels and, therefore, mitigate explosive boiling phenomena occurring into it.
## 2 Methodology
### MEMD-Based Identification Methods  
This section aims to integrate a more effective approach for accurate modal identification with the output-only multi-dimensional nonstationary signal. To this end, two modal identification methods, the SSI and FBBFT method are used and combined with the MEMD procedure to extract the modal parameters involving the natural frequency, damping ratio, and mode shapes.
## 2 Materials and Methods  
### Study Area  
The hazard and flood risk analysis presented in the current research includes a section of the Turcu River in the Olt river basin (Romania) with a length of 20,401 m out of a total of 29,059.58 m, between SH Tohanu Nou (45\({}^{\circ}\)33\({}^{\prime}\)8.679\({}^{\prime\prime}\) N, 25\({}^{\circ}\)23\({}^{\prime}\)0.314\({}^{\prime\prime}\) E) and the intra-village area of Moieciu de Sus (45\({}^{\circ}\)25\({}^{\prime}\)23.386\({}^{\prime\prime}\) N, 25\({}^{\circ}\)21\({}^{\prime}\)53.774\({}^{\prime\prime}\) E) (Figure 1). The implementation of the one-dimensional hydrological model revealed the 1% flood belt crossing from downstream to upstream in the villages of Tohanu Nou (part of the town of Zarnesti), located in the west of the Brasov intramontane depression, on the lower course of the Turcu river; Bran (commune of Bran), Moieciu de Jos, Cheia and Moieciu de Sus (commune of Moieciu); and villages located in the area of the Bran-Dragoslavele Corridor, on the middle course of the same river.  
In the area of the mentioned intramontane corridor, the hazard and flood risk maps of the national project were made along the Dambovita river, between the center of the Rucar commune and Lunca Gartii village (Stoenesti commune). The other main fluvial axis of the intramontane corridor is the valley of the Turcu River which gathers tributaries from the eastern flank of the Piatra Craiului syncline, the northern slope of the Leaota Mountains and the north-western slope of the Bucegi Mountains. This mountain valley, with an obvious flooding potential, has, so far, not come to the attention of the specialists who have made flood risk maps at a national scale, considering only that \"not covering an area of our country does not lead to the conclusion that the area in question cannot be exposed to flood risk\" [51].
## 5 Results

Table 4: Normalized input decision matrix. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & _Beneficial_ & _Beneficial_ & _non-Beneficial_ \\ \hline
**FM** & **S** & **O** & **D** \\ \hline
3 & 7 & 5 & 3 \\ \hline
16 & 8 & 6 & 5 \\ \hline
18 & 8 & 4 & 3 \\ \hline
2 & 6 & 6 & 5 \\ \hline
1 & 7 & 6 & 7 \\ \hline
17 & 7 & 4 & 6 \\ \hline
19 & 7 & 4 & 6 \\ \hline
4 & 7 & 5 & 7 \\ \hline
9 & 5 & 3 & 4 \\ \hline
11 & 10 & 1 & 2 \\ \hline
12 & 8 & 2 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Case study decision matrix with D scores modified in line with the CoCoSo method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FM** & **S** & **O** & **D** & **Pi** \\ \hline
3 & 0.42 & 0.66 & 0.76 & 1.85 \\ \hline
16 & 0.54 & 0.70 & 0.71 & 1.95 \\ \hline
18 & 0.54 & 0.61 & 0.76 & 1.91 \\ \hline
2 & 0.28 & 0.70 & 0.71 & 1.68 \\ \hline
1 & 0.42 & 0.70 & 0.00 & 1.12 \\ \hline
17 & 0.42 & 0.61 & 0.66 & 1.69 \\ \hline
19 & 0.42 & 0.61 & 0.66 & 1.69 \\ \hline
4 & 0.42 & 0.66 & 0.00 & 1.08 \\ \hline
9 & 0.00 & 0.54 & 0.74 & 1.28 \\ \hline
11 & 0.74 & 0.00 & 0.78 & 1.52 \\ \hline
12 & 0.54 & 0.45 & 0.71 & 1.70 \\ \hline \end{tabular}
\end{table} 
Table 6: Power of the weighted comparability matrix. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**FM** & **Ka** & **kb** & **kc** \\ \hline
3 & 2.40 & 4.83 & 0.898577 \\ \hline
16 & 2.64 & 5.68 & 0.987525 \\ \hline
18 & 2.54 & 5.27 & 0.949089 \\ \hline
2 & 2.13 & 4.06 & 0.796917 \\ \hline
1 & 1.65 & 3.98 & 0.615862 \\ \hline
17 & 2.12 & 4.00 & 0.79308 \\ \hline
19 & 2.12 & 4.00 & 0.79308 \\ \hline
4 & 1.55 & 3.63 & 0.579312 \\ \hline
9 & 1.46 & 2.19 & 0.546813 \\ \hline
11 & 2.25 & 5.47 & 0.840452 \\ \hline
12 & 2.16 & 4.19 & 0.809837 \\ \hline \end{tabular}
\end{table} 
Table 7: Relative weighting of the alternatives. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FM** & **S** & **O** & **D** & **Si** \\ \hline
3 & 0.24 & 0.22 & 0.09 & 0.56 \\ \hline
16 & 0.37 & 0.28 & 0.04 & 0.69 \\ \hline
18 & 0.37 & 0.17 & 0.09 & 0.62 \\ \hline
2 & 0.12 & 0.28 & 0.04 & 0.44 \\ \hline
1 & 0.24 & 0.28 & 0.00 & 0.52 \\ \hline
17 & 0.24 & 0.17 & 0.02 & 0.43 \\ \hline
19 & 0.24 & 0.17 & 0.02 & 0.43 \\ \hline
4 & 0.24 & 0.22 & 0.00 & 0.47 \\ \hline
9 & 0.00 & 0.11 & 0.07 & 0.18 \\ \hline
11 & 0.61 & 0.00 & 0.11 & 0.72 \\
## 2 Materials and Methods
### Photoelectrochemical Measurement and Stability Test  
Photochemical (PEC) measurements were performed with a three-electrode configuration that comprised the samples, saturated Ag/AgCl, and platinum as the working, reference, and counter electrodes, respectively. These cables were connected to a potentiostat (Versa stat, Ametex, Oak Ridge, TN, USA). To evaluate the effect of the pH of the electrolytes on the photocurrent generation, different solutions of 0.5 M KOH (pH 13), 0.5 M Na\({}_{2}\)SO\({}_{4}\) (pH 7), and 0.5 M H\({}_{2}\)SO\({}_{4}\) (pH 1) were prepared as the electrolytes. Prior to PEC measurement, the electrolytes were degassed with N\({}_{2}\) for 10 min. The photocurrent measurements were conducted under simulated solar light illumination at a power density of 100 mW cm\({}^{-2}\) with a Xenon lamp as the light source (ORIEL). Under similar conditions, Mott-Schottky data were collected under illumination at a frequency of 1000 Hz. The electrochemical impedance of the samples was evaluated using electrochemical impedance spectroscopy (EIS) by scanning from 1 Hz to 100,000 Hz using a similar potentiostat. The applied potential was converted into the reversible hydrogen electrode (RHE) scale using the Nernst equation; E\({}_{\text{RHE}}\) = E\({}_{\text{Ag}/AgCl}\) + 0.05196 pH + 0.1976.
## 2 Mathematical Model
### Symbol Definition  
(1) Symbols for intermodal network structure  
\(N\), \(A\), and \(S\) separately represent the sets of nodes, arcs, and transportation modes in an intermodal network; \(i\), \(j\), and \(k\) indicate the indices of nodes, and \(i\), \(j\), \(k\in N\); \((i,j)\) presents an arc from node \(i\) to node \(j\), and \((i,j)\in A\); \(r\) and \(s\) are the indices of transportation modes, and \(r\), \(s\in S\); \(N_{j}^{-}\) and \(N_{j}^{+}\) denote the sets of predecessor and successor nodes to node \(j\), respectively, and \(N_{j}^{-}\), \(N_{j}^{+}\subseteq N\); \(S_{ij}\) and \(S_{j}\) are the sets of transportation modes on arc \((i,j)\) and linking node \(j\), respectively, and \(S_{ij}\), \(S_{j}\subseteq S\).  
(2) Symbols for intermodal network parameters  
\(d_{ijr}\), \(v_{ijr}\), \(\widetilde{\eta}_{ijr}=\left(\eta_{ijr}^{-},\eta_{ijr}^{+}\right)\), \(e_{ijr}\), and \(c_{ijr}\) represent the in-transit distance in km, speed in km/h, interval fuzzy capacity in TEU, CE factor in kg/(TEU\(\cdot\)km), and costs in CNY/TEU of transportation mode \(r\) on arc \((i,j)\), respectively; \(\eta_{j}^{rs}\), \(\widetilde{\eta}_{j}^{rs}=\left(\eta_{j}^{rs},\eta_{ij}^{rs+}\right)\), \(e_{j}^{rs}\), and \(c_{j}^{rs}\) present the time in h/TEU, interval fuzzy capacity in TEU, CE factor in kg/TEU, and costs in CNY/TEU to transfer the containers from transportation mode \(r\) to transportation mode \(s\) at node \(j\), respectively; \(\widetilde{p}=(p^{-},p^{+})\) means the interval fuzzy carbon tax rate in CNY/kg; and \(c_{pen}\) and \(c^{\prime}_{pen}\) represent the penalty costs in CNY/(TEU\(\cdot\)h) resulting from the early and late delivery, respectively, of containers concerning the receiver's expected time window.  
(3) Symbols for transportation order  
\(n_{o}\) and \(n_{d}\) are the origin and destination of the transportation order, respectively, and \(n_{o}\),\(n_{d}\in N\); \(q\) is the demand in TEU of the containers of the transportation order; \(t_{0}\) represents the shipper's release time of the transportation order at the origin; \([\theta,\theta^{\prime}]\) is the receiver's tolerable time window to receive the containers; and \([\pi,\pi^{\prime}]\) refers to the receiver's expected time window.  
(4) Symbols for variables  
\(x_{ijr}\) is a binary variable on the selection of the transportation mode: if the containers of the transportation order are moved by transportation mode \(r\) on arc \((i,j)\), then \(x_{ijr}=1\); otherwise, \(x_{ijr}=0\). \(y_{j}^{rs}\) is another binary variable for the selection of the transfer type at the transfer node: if the containers of the transportation order are transferred from transportation mode \(r\) to transportation mode \(s\) at node \(j\), then \(y_{j}^{rs}=1\); otherwise, \(y_{j}^{rs}=0\). \(z\) is a non-negative variable representing the delivery time of a container to a destination. \(w\) and \(w^{\prime}\) are non-negative variables in h representing the earliness and lateness caused by early and late delivery of the containers, respectively.
# Multi-Module Combination for Underwater Image Enhancement
## 4 Experiment
### Experimental Details  
The primary objective of this section is to compare the proposed underwater image enhancement algorithm with ten widely used underwater image enhancement algorithms. In this experiment, we utilized underwater image datasets, including the UIEB dataset [6], the SUID dataset [28], and the EUVP dataset [29].  
The UIEB dataset [6] serves as a benchmark for real underwater imagery and comprises 890 sets of paired data, along with 60 sets of unreferenced challenging samples. This dataset encompasses a wide range of typical underwater imaging conditions, including natural illumination, artificial light sources, and mixed lighting scenarios. The construction process for the reference image synthesizes the outputs of 12 classical enhancement algorithms, selecting the enhancement that best aligns with human visual perception through subjective evaluation. Additionally, the SUID synthetic dataset [28] systematically simulates 900 sets of underwater images, varying in turbidity levels and types of degradation. The EUVP datasets [29] are a large number of high-quality underwater images captured by high-resolution camera equipment and specialized underwater photography.  
We randomly selected 100 pairs of images from the UIEB dataset, which included reference images, and 30 pairs of images from the SUID dataset for both quantitative and qualitative evaluations. Additionally, we selected 50 images from the EUVP dataset to conduct runtime duration tests.
## 3 Method  
### Model for Analysis by Synthesis Using Gaussian Mixture Model  
The single generalized Gaussian function is  
\[f(.)=A\exp\left(-\left(\frac{1}{2}v^{\prime}\Sigma^{-1}v\right)^{\beta}\right). \tag{1}\]  
Amplitude value \(A\) consists of amplitude and scaling factor with gamma function. The shape control is possible by the \(\beta\) parameter. The value is \(\beta=2\) for the normal Gaussian function and \(\beta=1\) for Laplace function (distribution).  
The position is defined as a vector \(v\):  
\[v=\left[\begin{array}{c}x-m_{x}\\ y-m_{y}\\ z-m_{z}\end{array}\right], \tag{2}\]  
where \(m_{x}\), \(m_{y}\), and \(m_{z}\) are parameters of the center of this function.  
Orientation and scale for 3D function requires a covariance matrix \(\Sigma\) that is \(3\times 3\) in size. The most convenient solution is the calculation of this matrix using the following formula,  
\[\Sigma=RSSR^{-1}, \tag{3}\]  
because orientation and scale parameters are available directly in the following matrices,  
\[S=\left[\begin{array}{ccc}s_{x}&0&0\\ 0&s_{y}&0\\ 0&0&s_{z}\end{array}\right], \tag{4}\]  
\[R_{x}=\left[\begin{array}{ccc}1&0&0\\ 0&\cos(\theta_{x})&-\sin(\theta_{x})\\ 0&\sin(\theta_{x})&\cos(\theta_{x})\end{array}\right], \tag{5}\]  
\[R_{y}=\left[\begin{array}{ccc}\cos(\theta_{y})&0&\sin(\theta_{y})\\ 0&1&0\\ -\sin(\theta_{y})&0&\cos(\theta_{y})\end{array}\right], \tag{6}\]\[R_{z}=\left[\begin{array}{ccc}\cos(\theta_{z})&-\sin(\theta_{z})&0\\ \sin(\theta_{z})&\cos(\theta_{z})&0\\ 0&0&1\end{array}\right], \tag{7}\]  
\[R=R_{z}R_{y}R_{x}, \tag{8}\]  
that are essential for the estimation of parameters for orientation \(\theta_{x},\theta_{y},\theta_{z}\) and scale \(s_{x},s_{y},s_{z}\). Single function \(f(x,y,z,p_{i})\) allows the synthesis of single 3D function by regular sampling, using \(x,y,z\) sampling position for particular vector of parameters \(p_{i}\):  
\[p_{i}=(m_{x},m_{y},m_{z},\theta_{x},\theta_{y},\theta_{z},s_{x},s_{y},s_{z},A, \beta). \tag{9}\]
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 1 Introduction
### Related Work  
Energy saving approaches have been proposed, including network protocols [1; 2; 4] as well as modulations schemes [3; 5]. In [6], optimum transmission time and distance for best energy efficiency are analyzed for MQAM and MFSK, taking both transmission and circuit energy consumption into account. It is shown that the optimum total energy consumption per bit is higher for MFSK when \(d\) is small due to its circuit energy consumption. When \(d\) increases, MFSK becomes more energy-efficient than MQAM. In [7], the effect of channel bandwidth and active mode duration on energy consumption of modulation schemes including OOK, MPPM, MFSK, and MQAM are analyzed and compared over Rayleigh fading channel. Results show that OOK is more energy efficient for short range and ultra-wideband scenarios. Moreover, OOK is less complex in implementation compared with other schemes.  
However, all the above works were carried out by assuming that the batteries are _ideal_, i.e., they have battery utilization factor (BUF) always equal to 1, where the BUF is defined as the ratio of the discharge current to the current actually consumed inside the battery. However, the BUF of a _realistic_ battery is always smaller than 1, meaning that some energy is wasted during its discharge process. On this reason, the battery energy consumption depends not only on the energy-consuming modules, but also on the unique battery discharge characteristic described by the BUF [8; 9; 10; 11].  
Some research works have reported on the battery energy efficiency [12; 13; 14], which can be evaluated by the battery energy consumption for transmitting per message bit (BECPB). Clearly, increase of BECPB leads to degradation of battery energy efficiency. In [8], the BECPB is also derived base on the IDC-based BUF, considering the working mode of the circuit and integrating typical WSN transmission and reception modules with realistic battery models. In [9; 10; 11], the BECPB is studied for transmitters whose BUF depends on instantaneous discharge current (IDC), assuming the amplifier's output current has the same waveform as the battery's output current waveform.  
Note that it is claimed in [15] that certain batteries may have a BUF dependent on the battery's mean discharge current (MDC) over a period which can be as long as several seconds. However, the above characteristic has not been considered in previous works to the authors' best knowledge.