esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Comparison between Slosh and Motf Study
The amount of damage estimated based on the data provided by FEMA MOTF Sandy is closer to the amount of damage obtained in the SLOSH C2 hurricane. This highlights the severity of damage the WWTPs would sustain under strong hurricanes such as C2 or more. However, the study showed that six WWTPs sustained more damage as simulated by SLOSH category 2 hurricane than the observed FEMA MOTF inundation. As the SLOSH model provides the highest storm surge value considering maximum tidal elevation, it can be considered as a synthetic storm to show the maximum flooding, both in terms of depth and extent, for a certain category of hurricane. In addition, products from SLOSH inundation mapping do not include the influence of river flow or rainfall-induced flooding. Therefore, a direct comparison of SLOSH inundation maps with FEMA MOTF would not be possible. However, SLOSH can provide the information about the maximum storm surge level due to various categories of hurricanes only. Therefore, a complete flood analysis for the WWTPs needs to include the influence of storm surge and riverine flooding. On the other hand, FEMA MOTF provides the inundation maps based on observation, which were used to compare the flood depth and extent with model-simulated inundation, such as from the SLOSH C2 hurricane. Both inundation maps were generated using the high-resolution DEM, but due to the inherent difference between the data generation SLOSH C2 inundation maps were different compared to the observed FEMA MOTF maps for Hurricane Sandy.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 3 Results
### Throughput  
The throughput results of the OLSR and AODV protocols are shown in Figure 4. The figure shows that default AODV has a higher throughput value than OLSR. The default AODV protocol throughput has an average value of 21,684.532 Bps, while that of the OLSR protocol is 21,120.34 Bps. Hence, reducing the value of RREQ_RETRIES to 2 (AODV 2) increases the throughput performance by 1.6%. Next, increasing the value of RREQ_RETRIES to 4 (AODV 3) reduces throughput to 2.6%. These results also apply to reducing the value of MAX_RREQ_TIMEOUT, which increases throughput performance. By decreasing the MAX_RREQ_TIMEOUT value of 5 s (AODV 4), this increases the performance throughput to 4.57%, while increasing the MAX_RREQ_TIMEOUT value of 12 s (AODV 10) decreases the performance of throughput by 1.8%.
# Adaptive Coherent Receiver Settings for Optimum Channel Spacing in Gridless Optical Networks
## 5 Conclusions  
We presented a novel circuit and method to optimize the channel spacing in gridless optical networks. We showed the results for 100G QPSK; however, the method was successfully tested for various data-rates/modulation formats (50G/BPSK and 200G/16 QAM). When assuming 10 channels are propagating together in one MC, i.e., therefore there are nine spacings to be optimized, the experiment showed ability to gain from 13.5 GHz (9 \(\times\) 1.5 GHz) for 2 dB OSNR margin and 18 GHz (9 \(\times\) 2 GHz) for 3 dB OSNR margin in the optical spectrum. Typical optical links support 88 channels, therefore, 158.8 GHz is made available to be used to handle more services. It is expected that network providers assume at least 3 to 4 dB OSNR margin at early deployments to account for end-of-life degradation in modems and fiber aging. Our approach allows operators to take advantage of the margin to increase the capacity of their links and improve their return on investment (ROI). The method is generic and can be applied to other detectors, shown in [30], where edge frequencies are used as part of clock recovery.
# The Impact of Automated Vehicles on Road and Intersection Capacity
## 4 The Road Heterogeneous Traffic Flow Flow-Density Relationship
### Derivation of Flow-Density Theory  
Through the analysis of IDM, CACC, and ACC following models, it can be seen that, in this case, when the traffic flow reaches the equilibrium state, all vehicles have the same speed, but different vehicles have different space headway, as shown in Figure 5. Suppose that the road length is \(L\). In that case, \(h_{dI}\), \(h_{dA}\), and \(h_{dC}\) are the three respective space headways maintained by the three types of vehicles in the equilibrium state.  
In the equilibrium state, the road length covered by the traffic flow composed of different vehicle types, is shown in Equation (33).  
\[L=np_{c}h_{dC}+np_{a}h_{dA}+np_{i}h_{dI} \tag{33}\]  
According to the calculation formula of traffic flow density, the function of equilibrium speed-front spacing, and the formula derived from the proportion of various vehicles, the basic density-speed graph relationship can be obtained, as follows:  
Figure 5: Balance in a man-machine hybrid driving environment.  
\[K=\frac{n}{L}=\frac{1000}{p_{c}h_{dC}+p_{a}h_{dA}+p_{i}h_{dI}}, \tag{34}\]  
\[h_{t}=\frac{(1-p)h_{dI}+p(1-p)h_{dA}+p^{2}h_{dC}}{v_{b}}, \tag{35}\]  
\[Q=\frac{3600}{h_{t}}, \tag{36}\]  
where \(K\) is lane density in mixed environment, \(h_{t}\) is the headway in mixed environment, \(Q\) is the traffic volume in a mixed environment.  
According to the flow-density relationship of the traffic flow of mixed vehicles, the density-flow diagram of the mixed vehicles can be obtained by constantly changing the balance speed (\(v_{b}\)) of the vehicles, and the proportion of autonomous vehicles. This is demonstrated in Figure 6, where \(p\) represents the proportion of CACC autonomous vehicles in the total road vehicles.  
As can be seen from the chart above:  
1. For single-lane roads, the increase in the proportion of autonomous vehicles, contributes to the improvement of road traffic capacity. Moreover, when a road contains only autonomous vehicles, its traffic capacity is 2.42 times that of the traffic capacity of a road containing only fully human-driven vehicles.
2. The dividing line for the proportion of self-driving vehicles in the total road vehicles, is 0.6. When the proportion is below 0.6, then mixing of self-driving vehicles can improve the road traffic capacity, although the overall improvement is small; When the ratio is above 0.6, then the inclusion of autonomous vehicles greatly improves the road traffic capacity. The increase of the ratio above 0.6 was 3.19 times that of the ratio below 0.6.
# A Study on Combustion Characteristics of Insensitive Triple-Base Propellant
## 3 Results and Discussion
### NO and NH Chemiluminescence Characteristics
It can be clearly observed that the NH signal of blank propellant was strong, and the high concentration of NH was distributed in the whole flame center, while the concentration of NH of insensitive propellants was significantly lower. In the upstream region of the flame, the initial decomposition of the propellant macromolecules took place. In the upstream region of the flame, the initial decomposition of the propellant macromolecules took place, which was associated with the production of NH radicals. By researching multi-base propellants, it was speculated that the generation of NH was highly related to the decomposition of NQ, which produced the intermediate product of NH  
Figure 9: Maximum signal intensity of NO in the flame over time. soon after being heated [29], then NH\({}_{3}\) might form the NH radical by constantly capturing the OH radical, free O, or free H during the reaction process [30; 31; 32]. The reaction between NH\({}_{3}\) and NO\({}_{2}\) would also produce the NH radical [33]. The NH signal captured by LaVision system was extracted to obtain the change in maximum signal intensity of NH in flame over time, as shown in Figure 11. It can be seen from the figure that the maximum signal intensity of NH exhibited similar behavior with the flame development process that it increased with the development of flame and decreased with the decline of flame, and the maximum signal of NH mainly appeared in the stable burning period. By comparing the maximum NH signals of the five propellants, it was found that the maximum signal intensity of the four insensitive propellants was lower than that of the blank propellant, and the signal intensity of 2# was the lowest. This indicated that, in the actual combustion process, the insensitive propellant produced a lower concentration of NH during combustion. The desensitizer effect was affected not only by the concentration of desensitizers but the desensitizing methods. Since NH was derived from NH\({}_{3}\), which was obtained from the decomposition of NQ, the decrease in the concentration of NH indicated that the process of NH\({}_{3}\) produced from the decomposition of NQ might be inhibited to some extent in the combustion process of insensitive propellant. Meanwhile, compared with the signal intensity of NO in the previous figure, it was found that the signal intensities of NO and NH showed a negative correlation, indicating that there might be a competitive relationship between the formation of NO and NH radicals in the reaction process. Figure 10: NH chemiluminescence signal distribution. (**a**) 1#; (**b**) 2#; (**c**) 3#; (**d**) 4#; (**e**) 5#.
# Electrodegradation of Selected Water Contaminants: Efficacy and Transformation Products
## 3 Results and Discussion
### Development of Methods for Parent Compounds and Their Transformation Products
#### 3.1.2 Method Development for Benzotriazole and Its TPs  
The separation of BTA and its TPs, as well as the SPE method, were presented in a previous paper [39]. Methods were adjusted to accommodate samples after EO. As the electrodegradation process was performed in different supporting electrolytes, the pH of samples was measured before the injection. Samples with pH in the range of 2-8 were directly injected. Samples with pH below 2 were first neutralised with 1 M NaOH. Samples with BTA concentration below the LOD were preconcentrated by the SPE method.  
The HPLC-DAD method was used to monitor the removal of BTA and confirm the presence of TPs. The method was linear in the concentration range of 0.5 mg/L-30 mg/L of BTA with \(R^{2}\) 0.9999 and equation y = 41.7x + 1.80, while the RSD was up to 2.5%. BTA was eluted at 7.39 min. The LC-MS method was applied to identify the TPs in electro-treated samples. Transformation products were monitored in TIC mode in the \(m/z\) range of 50-320. Although most TPs, notably the chlorinated ones, displayed better ionisation in ESI\(-\), certain hydroxylated TPs still ionized better in ESI+. Therefore, all LC-MS analyses were performed with both positive and negative ESI ionisation to identify the highest number of TPs.
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 5 Remote and Environment Triggering
### Mechanical Wake-Up  
A mechanical wake-up circuit is the most straightforward way to trigger a wake-up. The mechanical sensors can detect vibration, movement, tilt, bending, pressure, skin contact. Most of these sensors consume zero power when on stand-by since their mechanism is usually contact that closes an open circuit. For example, a pressure sensor placed under the mattress, a vibration sensor placed on the bed, or a modified push-down button could trigger the bed movement sensor. Similar sensors can be placed on doorknobs, door frames, or floor mats to detect when a person is moving in and out of a room or the house, which will indicate when the IoT nodes should enter or wake up from deep sleep.  
We will briefly describe several of these mechanical switches.  
* Reed switch has two contacts that are very close to each other but normally separated. In the presence of a magnet, they will touch and close the circuit.
* Tilt switch uses a metal ball or a drop of mercury that, when tilted, drops toward two contact leads and makes a closed circuit between them.
* Vibration sensor has a sturdy metal wire in the middle surrounded by an elastic metal spring. The spring does not touch the wire even when titled but will touch if there is a vibration.
* Bi-metal thermostat detects temperature changes. Depending on the model, it makes contact closing the circuit for temperatures above or below the set value.
* Pressure switch/button creates contact on pressure.  
The main challenge with this kind of sensor is a frequent wake-up signal. For example, a vibration or pressure sensor can be placed in the person's shoes to activate when they are wearing them. However, this would mean a wake-up signal is sent with each step. The workaround is to have a secondary ultra low power timer (i.e., TPL5111) that would be placed before the mechanical switch, as shown in Figure 10.
## 3 Development of Software-Based Router and Traffic Generator
### Development of Software-Based Router
The processor which realizes the report of routing, defines a direction for message transfer and supports an urgency of tables of routing by an exchange of service packages with other knots (at construction of a network from several program routers)
5. After processing the packet header and selecting the output interface port, the packet is queued in the output channel waiting list. 6. The next step is to transfer the packets to another network device defined by the routing protocol. The designed software router has the following main functions: transfer of information packets between different subnetworks, support of static and dynamic routing protocols, filtering of information packets at the channel, network and transport layers of the open systems interconnection (OSI) model, traffic prioritization according to the requirements.
# Analytical Design of Fractional-Order PI Controller for Parallel Cascade Control Systems
## 2 Materials and Methods
### Fractional Linear Model  
Consider a linear time invariant (LTI) system with a single-input \(u(t)\), single-output \(y(t)\) (SISO), the fractional-order differential equation (FODE) can be expressed as:  
\[\sum_{i=0}^{n}a_{i}D_{0}^{a_{i}}y(t)=\sum_{j=0}^{m}b_{j}D_{0}^{\beta_{j}}u(t) \tag{4}\]Using Laplace transform in Equation (2), Equation (4) can be described by the following transfer function:  
\[G(s)=\frac{\mathrm{Y}(s)}{\mathrm{U}(s)}=\frac{b_{m}s^{\beta_{m}}+b_{m-1}s^{\beta _{m-1}}+\ldots+b_{0}s^{\beta_{0}}}{a_{n}s^{\alpha_{n}}+a_{n-1}s^{\alpha_{n-1}}+ \ldots+a_{0}s^{\alpha_{0}}} \tag{5}\]  
where \(\alpha_{i}\) and \(\beta_{i}\) are arbitrary, real, and positive.
# Development and Field Validation of Wireless Sensors for Railway Bridge Modal Identification
###### Abstract  
Bridges are strategic infrastructures which are subject to degradation during their lifetime. Therefore, structural health monitoring is becoming an essential tool in this field to drive maintenance activities. Conventional vibration monitoring systems relying on wired sensors present several limitations for continuous monitoring projects on a huge number of structures. In this work, a smart wireless monitoring system is developed for bridge modal identification with the aim of providing an alternative tool to wired sensors in this field. The main peculiarities of the designed wireless accelerometers are the low cost, the ease of installation on the structure, and the long-term autonomy granted by the use of energy harvesting techniques. To assess their measurement performance, some prototypes were installed for a field test on a railway bridge and significant data were acquired. Through the processing of the collected data, bridge main natural frequencies were estimated, and their values were in good agreement with the reference ones obtained with a conventional system. The assessment of the developed solution paves the way to the instrumentation of many bridges with the aim of performing continuous monitoring activities using simple diagnostic indicators, such as the variation of frequencies in time.  
structural health monitoring; bridges; predictive maintenance; wireless sensors; MEMS accelerometers; vibrations +
Footnote †: journal: Applied  
Article
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 3 The Proposed Method
### Optimization of SVR Parameters
#### 3.2.2 Calculating Adaptation  
In the parameter optimization process, the fitness function serves to evaluate each parameter combination, thereby guiding the evolutionary direction of the GA. As shown in Equation (12), the fitness function designed in this study incorporates both the model prediction error and multi-objective detection metrics. This dual consideration enables effective evaluation of the relative quality of population individuals and drives the algorithm toward superior solutions.  
\[f=\sqrt{\frac{1}{NK}\sum\limits_{n=1}^{N}\sum\limits_{k=1}^{K}\left[\theta_{k} (n)-\hat{\theta}_{k}(n)\right]^{2}} \tag{12}\]  
where \(N\) is the number of test samples, \(\hat{\theta}_{k}(n)\) is the angle estimation value of the sample, and \(\theta_{k}(n)\) is true angle of the sample. The smaller the value of \(f\), the better the performance of the model after parameter optimization.
## 1 Introduction
### Co-Registration and Individual Tree Matching  
Typically, a manual co-registration is performed by identifying pairs of corresponding survey trees and aerial detected trees using canopy height models (CHMs) or high spatial resolution aerial imagery [(3; 4; 19)]. Research groups have developed automatic or semi-automatic methods to overcome this time consuming and complex procedure:
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## 3 Data Generation Process
### Data Simulation  
In this section, we describe the model used to simulate the data. Recall that our primary motivation is to detect anomalies in financial time series. For that purpose, we consider the share price sample paths generated using the Black and Scholes model, i.e., geometric Brownian motions. Under this framework, the share price \(S_{t}\) is defined by  
\[S_{t}=S_{0}\exp\biggl{(}\biggl{(}\mu-\frac{1}{2}\sigma^{2}\biggr{)}t+\sigma W _{t}\biggr{)}. \tag{7}\]  
where \(W\) represents a standard Brownian motion, \(\mu\) the drift, \(\sigma\) the volatility of the stock, and \(S_{0}\) the initial stock price.  
Let \(N\) stocks \(S^{1},\ldots,S^{N}\) be simulated simultaneously from this model, where \(S^{i}:=\left\{S^{i}_{t_{0}},S^{i}_{t_{1}},\ldots,S^{i}_{t_{T}}\right\}\) is the time series of length \(T\) representing the (time-discretized) path diffusion of the \(i\)-th stock. Each stock \(S^{i}\) has its own drift \(\mu^{i}\), volatility \(\sigma^{i}\), and initial value \(S^{i}_{t_{0}}\). The paths parameters are selected randomly according to  
\[S^{i}_{t_{0}}\sim\mathcal{N}(100,1),\qquad\mu^{i}\sim\mathcal{U}([0.01,0.2]), \qquad\sigma^{i}\sim\mathcal{U}([0.01,0.1]),\quad i=1,\ldots,N. \tag{8}\]  
For the sake of realism, the Brownian motions driving the \(N\)-stocks are correlated. The contamination of the obtained time series by anomalies is described in the next section.
## 3 Proposed Method  
Spam detection in SMS messages is critical in natural language processing and cyber security and requires reliable and accurate classification methods. In this context, an ensemble learning-based hybrid learning model for spam detection in SMS messages has been developed and extensively tested. The proposed model is based on combining the most successful versions of GRU (gated recurrent unit), MLP (multilayer perceptron), and hybrid autoencoder models with a majority voting algorithm, one of the ensemble learning methods.  
GRU networks play an effective role in text data processing thanks to their ability to capture sequential dependencies in the data. MLPs offer a simple yet powerful approach to classification tasks. Hybrid autoencoder models combine the features of supervised and unsupervised learning to effectively learn the data representation. Combining these models aims to improve the classification performance by leveraging the strengths of different model architectures.  
This study vectorizes message texts separately using TF-IDF (Term Frequency-Inverse Document Frequency) and CountVectorizer techniques. While TF-IDF reflects the importance of terms in documents, CountVectorizer converts texts into numerical format by considering the frequencies of terms. In addition, nineteen different additional features derived from text statistics, such as word count, character count, average word length, and the presence of special characters, are generated. These additional features aim to improve classification accuracy by helping the model capture nuances in the text data.  
Different combinations of the models are trained and tested for different cases of vectorization and additional features. In each case, the performance of the models is evaluated, and the most effective combinations are identified. In the hybrid autoencoder model, in additionto these different combinations, results are obtained for five different classifier algorithms (e.g., logistic regression, decision trees, support vector machines). This way, the effects of different classifiers and feature sets on spam detection are comprehensively analyzed.  
Figure 1 shows the general structure of the proposed model. This structure shows how different combinations of models and features are integrated and combined in an ensemble learning framework. This approach aims to improve the accuracy and robustness of spam detection in SMS messages by combining the strengths of multiple machine learning models. Ensemble learning compensates for the weaknesses of individual classifiers, resulting in a model that can better generalize on unseen data. This hybrid approach provides an innovative solution for improving spam detection performance in text messaging applications.
# Design of Wireless Sensor Network for Agricultural Greenhouse Based on Improved Zigbee Protocol
## 3 Experimental Design and Results  
### Experimental Design  
NS2 (Network Simulator Version 2) is a professional simulation software specially used for network simulation [25]. It has very rich module components, its configuration is flexible and its scalability is relatively strong. It is favored by many scholars who study network technology. To this end, this research will randomly arrange a large number of sensor nodes in a certain area on the NS2 platform, imitating the actual system application situation, and set the node energy and packet sending rate in the network. Finally, in the same scenario, the proposed EMP-ZBR routing protocol and the traditional ZigBee routing protocol are simulated separately, and the performance of the two protocols is evaluated in four aspects: packet delivery rate, route discovery frequency, route control overhead, and end-to-end average delay.