esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Most of the existing node depth-adjustment deployment algorithms for underwater wireless sensor networks (UWSNs) just consider how to optimize network coverage and connectivity rate. However, these literatures don't discuss full network connectivity, while optimization of network energy efficiency and network reliability are vital topics for UWSN deployment. Therefore, in this study, a depth-adjustment deployment algorithm based on two-dimensional (2D) convex hull and spanning tree (NDACS) for UWSNs is proposed. First, the proposed algorithm uses the geometric characteristics of a 2D convex hull and empty circle to find the optimal location of a sleep node and activate it, minimizes the network coverage overlaps of the 2D plane, and then increases the coverage rate until the first layer coverage threshold is reached. Second, the sink node acts as a root node of all active nodes on the 2D convex hull and then forms a small spanning tree gradually. Finally, the depth-adjustment strategy based on time marker is used to achieve the three-dimensional overall network deployment. Compared with existing depth-adjustment deployment algorithms, the simulation results show that the NDACS algorithm can maintain full network connectivity with high network coverage rate, as well as improved network average node degree, thus increasing network reliability.  
**Keywords:** underwater wireless sensor networks (UWSNs); two-dimensional convex hull; spanning tree; time marker; network reliability; full network connectivity +
Footnote †: journal: Sensors
## 2 Numerical Simulation of Ytterbium-Based Amplification
### Regenerative Amplifier: Free-Running Cavity
The absorption line at 941 nm is represented, as well as two transitions between (i) sublevels \(f_{4}\) (10327 cm\({}^{-1}\)) and \(f_{3}\) (785 cm\({}^{-1}\)), corresponding to the lasing wavelength 1048 nm, and (ii) sublevels \(f_{4}\) and \(f_{2}\) (612 cm\({}^{-1}\)), corresponding to the lasing wavelength 1048 nm. The upper level is the upper level of the lasing wavelength 1048 nm, and the lower level of the lasing wavelength 1  
to the wavelength 1029 nm. The figure also indicates the relative thermal occupancies for each manifold at a temperature of 300 K, assuming a Boltzmann distribution of the energy levels. From the rate equations for the pump process, one can arrive at the following expression for the population difference, relating the population densities of the upper manifold \(n_{\mathrm{U}}\) and lower manifold \(n_{\mathrm{L}}\), and the thermal occupancies of the relevant levels [15]:  
\[\Delta n_{4m}=f_{4}n_{U}-f_{m}n_{L},\quad m=(2,3) \tag{3}\]  
The full expressions for the population densities account for the absorption and stimulated emission cross-sections \(\sigma_{\mathrm{a}}\) and \(\sigma_{\mathrm{e}}\), the fluorescence lifetime \(\tau_{\mathrm{f}}\) and the pump rate \(W\), and may be found in the cited reference. The pump rate is defined as  
\[W=\frac{P_{pump}\ \eta\ \lambda_{p}}{h\ c\ \pi\sigma^{2}I\ n_{d}} \tag{4}\]  
where \(P_{\mathrm{pump}}\) is the pump power, \(\eta\) is the excitation efficiency, \(\lambda_{\mathrm{p}}\) is the wavelength, \(r\) is the radius of the excited region and \(l\) is the length of the Yb:YAG crystal. By replacing the values used in our simulation and making \(\Delta n_{4m}=0\), we find the pump power thresholds at which the population difference becomes positive, and amplification is possible (not taking cavity roundtrip losses into  
Figure 4: Energy level scheme and laser transitions in Yb:YAG. Figure 3: Simulation of free-running cavity based in Yb:YAG. **Left**: Output intensity \(vs\). wavelength for a range of pump power; **Right**: output energy and required number of passes \(vs\). pump power. account). We arrive at the following values: \(P_{\text{thr}}\)(1048 nm) = 11.2 W, \(P_{\text{thr}}\)(1029 nm) = 25.2 W, closely matching the results of the simulation. This shows that there is a range of powers where amplification at the main peak wavelength is effectively suppressed due to the higher absorption, and operation around 1048 nm is possible. As soon as the upper pump threshold is reached, the larger gain cross-section at 1029 nm becomes prevalent.
# Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning
## 3 Numerical Experiments
### Data Pre-Processing
#### 3.2.2 Normalization for Numerical Features  
The value ranges of three numerical features in our dataset are shown in Table 3. The values of all numerical features are integers.  
In our study, the min-max normalization method is employed to normalize the data of numerical features in our dataset. In the experiments, the training dataset \(S_{N}=\{(x_{i},y_{i}):i=1,\dots,N\}\) is first linearly mapped to the [0, 1] interval. Let \(x_{\text{max}}=\underset{i\in\{1,\dots,N\}}{\max}x_{i}\) and \(x_{\text{min}}=\underset{i\in\{1,\dots,N\}}{\min}x_{i}\); we obtain the normalized training data as follows:  
\[x_{i}^{\prime}=\frac{x_{i}-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}},\;i \in\{1,\dots,N\}.\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Num** & **Categorical Features** & **Distinct Categories** & **Encoding Method** \\ \hline \multirow{6}{*}{1} & & Bulk carrier & (0.5,0,0,0,0,0) \\  & & Container ship & (0,0.5,0,0,0,0) \\  & & General cargo & (0,0.0,5,0,0,0) \\  & & Passenger ship & (0,0,0,0,5,0) \\  & & Chemical/oil tanker & (0,0,0,0,5,0) \\  & & Other types & (0,0,0,0,0,5) \\ \hline \multirow{6}{*}{2} & & White & (0.5,0,0,0) \\  & & Grey & (0.5,0,0) \\  & & Black & (0,0,0,5,0) \\  & & Other types & (0,0,0,0.5) \\ \hline \multirow{3}{*}{3} & & High & (0.5,0,0) \\  & & Medium & (0.0,5,0) \\  & & Low & (0,0,0,5) \\ \hline \multirow{3}{*}{4} & & High & (0.5,0,0,0) \\  & & Medium & (0.0,5,0,0) \\ \cline{1-1}  & & Low & (0.0,0,5,0) \\ \cline{1-1}  & & Other types & (0,0,0,5) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Encoding methodology for categorical features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Num** & **Numerical Features** & **Value Range** \\ \hline
1 & Ship age & \{0, 1, \dots, 48\} \\
2 & Deficiencies within the \\  & previous 36 months & \{0, 1, \dots, 55\} \\
3 & Detentions within the \\  & previous 36 months & \{0, 1, \dots, 18\} \\ \hline \hline \end{tabular}
\end{table}
Table 3: The value ranges of numerical features.  
Then, we normalize the test dataset \(T_{M}=\big{\{}\big{(}x_{j},y_{j}\big{)}:j=N+1,\ldots,N+M\big{\}}\) with \(x_{\max}\) and \(x_{\min}\); that is:  
\[x_{j}^{\prime}=\frac{x_{j}-x_{\min}}{x_{\max}-x_{\min}},\ j\in\{N+1,\ldots,N+M\}.\]
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Rate of Change of Frequency:  
This method uses the equation:  
\[ROCOF=\ \frac{df}{dt}=\frac{\Delta P}{2H}\ f. \tag{1}\]  
This method's major drawback is that it cannot detect the islanding condition until the active power imbalance is higher than 15%. It detects the load and generation mismatch, which in turn affect the frequency. This change in frequency is then monitored to trip the relay, thereby disconnecting the DG and the power grid [54].
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 4 Challenges
### Integration of Communication and Computation
Even though there exist several research studies in vehicular cloud computing with resource management problems such as scheduling strategies, this research scope is another challenge that needs to be further explored.
## 4 Results and Discussion
### Shear Wave Velocity Models
Profile locations are indicated in Figure 2.  
The uppermost layer is composed of alluvium and limestone belonging to the Quaternary, with a corresponding thickness ranging from 1 to 10 m. The maximum thickness of this layer is located at P10 (Figure 2). These layers have a Vs value ranging between 150 and 350 m/s. This velocity range is in agreement with the one obtained for the city of Oran (200-380 m/s) [66] and in the Middle Chelif Basin (210-350 m/s) [6]. The composition of this formation is approximately similar for the whole Chelif Basin. The second layer, attributed to Pliocene deposits, is mainly sandy with occasional clay interbeds. Its thickness varies from 1 to 39 m. The maximum thickness of this layer is reached in point 49 (Figure 2) in the industrial zone. Its velocity ranges between 350 to 500 m/s. This variation is probably related to lateral and vertical facies transitions between sands and clays. In addition, the boreholes show that the Pliocene formations are not present in the western parts of the city, where the Quaternary deposits lie directly over the Upper Miocene layers. It should be noted that in the Middle Chelif Basin, the velocity range for the Pliocene formation is between 405 and 630 m/s. The difference is mainly linked to the lithology, where the hard sandstones are predominant within this formation in the Middle Chelif Basin [6]. The third layer from the top, which represents deposits from the Upper Miocene (Messinian), consists of silty clay, marl, limestone, and gravel. The thickness of this layer varies from 5 to 85 m in the industrial zone (P46 in Figure 2), with Vs values ranging from 500 to 850 m/s, which agrees with the Vs values obtained in Oran (460-880 m/s [41]). The deepest layer corresponds to the Middle Miocene (Serravallian) sediments, composed mainly of sandstone and marl. Its thickness varies between 45 and 280 m in P36 (see Figure 2), with Vs values between 850 and 1380 m/s. This range is in accordance with the one obtained in the city of Oran (730-1300 m/s [45]). The wide Vs range probably reflects lateral variability between the sandstone and marl deposits. These sedimentary and eruptive formations are underlain by a Mesozoic substratum that varies regionally. In some areas, it consists of Cretaceous marl, in others it consists of Jurassic pelite and sandstone. The shear wave velocity of this formation ranges from 1400 to 2600 m/s, probably reflecting the lateral transitions between the Cretaceous marl and the Jurassic sandstone. The velocity range is similar to the ones obtained in the city of Oran [45] and in the Middle Chelif Basin [32; 33]. The sedimentary column reaches a maximum thickness of 390 m in the industrial zone (point P36 in Figure 10). The thickness of the sedimentary cover significantly decreases toward the southern part near the hospital and at point P07 in the city center (Figure 10). This variation is clearly depicted in the cross-sections shown in Figure 9a.
# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 2 Background
### Human Signal Sources
#### 2.1.2 Heart Signals  
The driving force of the cardiovascular system, the heart, plays a key role in our physiology. Even characteristics that can be taken at a non-invasive level, such as blood  
Figure 1: Several physiological signals with sources.  
pressure [22], contain information that can be used to infer emotional state. The most common example of this is determining whether a person is stressed or not. When stressed, the body releases a hormone that increases heart rate and blood pressure, and affects many other cardiovascular interactions [23]. These signals can be obtained through blood pressure sensors.  
Blood volume pulse (BVP) measures how much blood moves to and from a site over time. This signal is usually captured through optical, non-invasive means using a photoplethysmogram (PPG). A PPG works through shining light, usually in the infrared wavelengths, onto the body surface of interest [24]. Since tissues reflect light more than the hemoglobin found in red blood cells [25], measuring the amount of light that returns to the device can give an idea of how much blood is in the site. These devices are so widespread now that they have been integrated into many smartphones [26] and smart watches [27] created in the last decade. Although BVP signals have successfully been used to measure peoples' heart rates in recent years [28], these signals can provide a lot more information than just that. The amplitude of the pulses and how much volume is being transferred can provide key insights into the cardiovascular health of a patient as well as their mental state [25].  
Electrocardiograms (ECG) measure the heart's electrical activity. It accomplishes this by sensing the voltage of conductors placed on the chest surface, i.e., electrodes. Historically, this has been used to evaluate how the heart functions in a patient [29]. However, due to the strong connection between heart activity and mental state, it is now a mainstream physiological measurement for effective computing.
# Food Allergies: Immunosensors and Management
## 2 Milk Allergy
### Management  
In order to protect individuals who are sensitive to cow's milk allergens, a measurement approach, designed to have excellent sensitivity, high selectivity and superior accuracy is urgently needed. Currently, biosensors, including immunosensors and aptasensors, in combination with innovative nanomaterials and a detecting platform offer cheap, facile, fast, and multiple detection of dairy allergens compared with traditional approaches such as ELISA, LC-MS, and real-time PCR [23; 24; 25; 26; 27; 28; 29; 30]. For this study, we reviewed many platforms with detectors using both immunosensors and aptasensors for milk allergens. Apart from the promise of immunosensors in the detection of milk allergens, other primary issues revolve around the instability and high cost of antibodies. Although the excellent and specific reactivity of aptamers has been applied to resolve the problem of detector cross-reactivity, the effect of the variability of allergens contained in different species on detection approaches needs to be examined. The development of aptasensors for detecting milk allergens is still in its early stages and still has a long distance to go to become an analytical tool with high stability. Current research on the design of innovative biosensors or biosensing platforms is critical in reducing the gap from research at lab scale to commercial products. The development of the biosensing platform and kit for the measurement of milk allergens is very important from the perspective of enforcing labeling regulations and directives with due regard for sensitive individuals. Therefore, the SPR biosensors for the detection of milk allergens have been utilized as a device to improve milk allergen safety in the industry. Furthermore, many works have been focused on utilizing a similar technology to develop antibody-related SPRs for the detection of other food allergens in food, including peanuts, eggs, etc. It is also of interest to evaluate the ability of the sensor for the detection of the absence of residual protein in CIP validation studies. Most importantly, from the literature, the concentration of milk proteins still needs to be standardized [22].
## 2 Existing ASs and the Related Market Design
### Categories of ASs
#### 2.1.3 Black Start  
Black start denotes the re-energization procedures implemented when the PS experiences a widespread or complete collapse due to disruptive events. Following the restoration of system stability, the re-energization process involves supplying customers with production facilities that can be activated independently without relying on an external energy source. This entails energizing the power system and commissioning additional production facilities. The restoration of the PS, signifying re-energization from a system collapse, unfolds through stages of frequency stabilization and re-synchronization [36].
# Dynamic Analysis of Underwater Torpedo during Straight-Line Navigation
###### Abstract
[23] investigated the flow characteristics of an unmanned underwater vehicle (UUV) with a commonly used Myring profile using PIV and CFD. The utilized CFD approach especially yielded excellent agreement with the PIV measurements with discrepancy. Underwater vehicles, such as torpedoes, will produce unstable pitch and yaw moments when sailing, namely the Munk moment, which will increase in proportion to the square of the speed and bring about motion stability problems for torpedoes. The Munk moment refers to the two equal and opposite forces generated by an object moving at a certain angle of attack (or drift angle) in a steady, straight line in an ideal fluid motion in the front and rear halves [24]. [PERSON] and [PERSON] [25] compared the difference between the numerical simulations and analytical formulas in calculating the additional mass coefficients of a torpedo under Munk moments. The results showed that the numerical simulations were more convenient for the calculation of the Munk moment. It was also found that the Munk moment seriously affected the motion stability of the torpedo [26]. According to various pieces of literature, despite the influence of hydrodynamic coefficients on the hydrodynamic properties of torpedoes and underwater vehicles being reported, there are few studies on the effects of Munk moments. The analysis of the Munk moment is mainly based on the analysis of other hydrodynamic coefficients. For instance, [PERSON] and [PERSON] [19] provided a possible explanation for the different hydrodynamic coefficients between the CFD and semi-empirical methods that, at small angles of attack where the inviscid nature of the flow dominates, the transverse force is generally small and the moment is predominantly a Munk moment. [PERSON] and [PERSON] [24] also believed that a major contribution to a hydrodynamic coefficient \(M_{w}\) was a Munk moment. The above investigations are conjectures about the Munk moment. Although they have been proven to be correct, there is still a lack of specific influence of the Munk moment on the dynamic properties of navigation vehicles. This is also the main goal of this article. In this paper, the torpedo is simplified as a rigid body with six degrees of freedom. The research on Munk moments is to simplify Munk moments to dimensionless coefficients and then add Munk moment coefficients to the motion equations of the torpedo. Hydrodynamic force and other forces are taken as external forces. The kinematics equations are established according to the transition matrix, and the dynamic equations of the torpedo are established based on the theorem of momentum. The linear motion mathematical model of torpedoesis synthesized, and the relationship between the traditional torpedo and the Munk moment coefficients is derived. The simulation platform used is the OracFlex software. The direct navigation model of the torpedo under different Munk moments is established, and the dynamic response of the space motion of the torpedo is analyzed.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 4 Data Description  
This dataset, called \"Macao Water Quality\", provides detailed measurements of tap water quality in a number of households in Macao. The data were collected using a multi-sensor water quality monitoring system that uses a Raspberry Pi as the core processing unit, along with various sensors to measure key water quality parameters. The dataset contains more than 5000 entries and captures important water quality metrics, including pH [25], TDSs (total dissolved solids), temperature, turbidity, and time of day readings. The trend plots for each parameter and time are shown below.  
As shown in Figure 9, the relationship between temperature and time is depicted. This figure highlights the fluctuations in water temperature over the recorded period.  
Figure 10 illustrates the trend between total dissolved solids (TDSs) and time. It provides an overview of how the concentration of dissolved solids changes over the course of the observations.  
In Figure 11, the relationship between pH and time is demonstrated. It shows the variation in the acidity or alkalinity of the water over time.  
Figure 11: pH and time.  
Figure 10: TDSs and time.  
Figure 9: Temperature and time.  
Figure 12 depicts the turbidity of the water and how it varies over time. Turbidity indicates the clarity of the water, with higher turbidity values typically corresponding to higher levels of suspended particles in the water.  
We can see the data we obtained by uploading them to the website through the Raspberry Pi, as shown in Figure 13. The dataset is stored in both CSV and XML formats for easy use in data analysis. Based on the available water quality characteristics, we assigned labels based on the composite score calculations; the dataset contains three categories of labels, which are meets physicochemical criteria, boundary conditions, and does not meet physicochemical criteria. Next, we will introduce the labeling rules in detail.
## 1 Introduction
As a realistic second-order fixed gain filter, [PERSON] [31] proposed a fixed-gain, position-velocity-measured, second-order tracking filter, described as an \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter. This filter corresponds to the \(\alpha\)-\(\beta\) filter in position-only-measured tracking problem, and the relationship between \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) and the PVM Kalman filters is similar to that between the \(\alpha\)-\(\beta\) and Kalman filters. Therefore, we believe that although the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter is underutilized at present, this filter will be widely used like the \(\alpha\)-\(\beta\) filter after the spread of PVM systems such as the UWB Doppler radar. Thus, clarifying the analytical properties and design strategy of the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter is important for tracking technology in the near future. Although [PERSON] investigated the relationship of this filter with the PVM Kalman filter using a random-acceleration model based on the tracking indices, he did not discuss their performance or any design strategies. In addition, although [PERSON] [8] described a general solution for optimal fixed-gain trackers with steady state Kalman gains, he too did not discuss filter performance or a design strategy. In this paper, a gain design strategy to compose an optimal \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter is proposed, and efficient performance indices are derived. The strategy is based on the method presented in [4] for Kalman filters, which optimizes an analytical performance index for the tracking filter. The proposed strategy provides the easy design of filter gains and accurate tracking of the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter compared with the conventional empirical design methods. Furthermore, another important objective of this paper is to demonstrate, using numerical simulations, the effectiveness of the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter obtained with the proposed strategy for a realistic UWB Doppler radar application. In this application, we show its effectiveness in the design of the PVM Kalman filter that has better steady state performance. The remainder of this paper is organized as follows: Section 2 defines the tracking problem dealt with in this paper. Section 3 reviews the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter of Sudano [31]. Its definition, relationship to conventional filters and design problems are described. Section 4 analytically derives the filter's performance indices and stability conditions for an appropriate gain design. Section 5 proposes our design strategy and explores its relationship to the Kalman filter. Section 6 analyzes the performance of \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filters designed with this strategy and compares it with the conventional filters. Section 7 presents a numerical application to realistic UWB Doppler radar tracking, and Section 8 offers concluding remarks.