esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Applications and Future Research Directions of RIS  
### RIS Assisted Unmanned Aerial Vehicles (UAVs) in IoT Networks  
Unmanned aerial vehicles (UAVs) might be considered as an alternative for providing and strengthening connection in contexts where direct communication is hampered by, for example, blockages, such as big centers, especially when IoT devices (IoTD) are resource constrained. A RIS is used to improve the UAV's energy efficiency and connectivity, especially when numerous devices are supported simultaneously and have varying channel impairments. In [10], the authors focused on enhancing the system's secure energy efficiency by optimizing the UAV's trajectory, the RIS's phase shift, user association, and transmit power all at the same time. From simulation results, authors showed their suggested algorithm converges quickly. The proposed architecture can improve secure energy efficiency by up to 38% compared to standard schemes that don't use RIS.  
In [103], the authors show that integrating a RIS with a UAV in IoT networks can significantly enhance UAV energy efficiency. For example, when employing a RIS of 100 pieces, UAV energy efficiency is boosted five times. Authors in [11] suggested a RIS-assisted UAV method, in which a RIS mounted on a building is used to reflect signals broadcast from a ground source to a UAV. The UAV is deployed as a relay to transfer the decoded signals to the destination. According to the findings, the usage of RISs can significantly increase the coverage and reliability of UAV communication systems.  
A novel framework for integrating RIS in UAV-enabled wireless networks is proposed in [104], in which a RIS is deployed to improve the UAV's service quality. While mobile users (MUs) are deemed to be traveling continually, the non-orthogonal multiple access (NOMA) technique is used to increase the network's spectrum efficiency. Minimizing energy consumption is formulated by concurrently planning the UAV's movement, RIS phase changes, power allocation policy from the UAV to the MUs, and setting the dynamic decoding order. Results revealed that by using RISs in UAV-enabled wireless networks, the energy dissipation of the UAV could be greatly reduced. Therefore, it can be stated from the literature that by integrating UAV with RIS-assisted system, coverage and reliability of UAV communication systems is improved. Additionally, by incorporating RIS in UAV, secure energy efficiency maximization, the significant improvement in processing time and throughput performance, enhanced service quality, and decreased energy dissipation of the UAV with optimized energy efficiency is possible.
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 4 Machine Learning
### Supervised Machine Learning  
Supervised machine learning uses labelled data to generate a function that maps an input to an output. The function is constructed from labelled training data.  
Supervised learning can be categorized into two types of models, classification and regression:  
* Classification models are used to put data into specific categories. From a dataset it recognizes the category it belongs to based on its features. The model will be trained on labelled input and output data to understand what the features of the input data are so as to correctly classify it. Classification models are very useful to detect attacks. For instance, for traffic coming into our network, a well-trained classification model can classify the traffic as normal traffic or as abnormal traffic. If the model performs well, the abnormal traffic can then be classified into subcategories of well-known attacks such as DoS, phishing, worms, port scan, etc. Common classification algorithms are decision tree, k-nearest neighbour, support vector machine, random forest, and neural network [6].
* Regression models are used to predict continuous outcomes. The model is trained to understand the relationship between independent variables and a dependent variable. Regression is used to find patterns and relationships in datasets that can then be applied to a new dataset. Regression models are mainly used for forecasting the evolution of market prices or predicting trends [7]. Common regression algorithms are linear regression, logistic regression, decision tree, random forest, and support vector machine.  
One of the main advantages of supervised learning is to use previous experiences to produce outputs. In addition, previous results can be used to improve the algorithm by optimizing the performance criteria to reach a precise model.  
Figure 2: Main types of machine learning methods.  
Supervised learning is used to solve many computational problems. However, the model needs precise and good input during the training phase to produce good outputs. In addition, this training requires a lot of computation time. Finally, it can be tough to classify big data, thus, if the dataset is too big, unsupervised learning is often a better choice.
## 5 Flight Tests
### Task 2: Flight Tests  
The flight tests are conducted to compare the performance of the proposed algorithm with that of the conventional PID while those two approaches applies to the prototype unmanned helicopter stability augmentation system, respectively. For a fair comparison, the attitude and position controllers remain unchanged. Our control software system, which runs on a DSP-based hardware platform with a control period of 10 ms, can provide reliable support for high precision timer and synchronization operations.  
Figure 4: Flight test results of the proposed algorithm. (**a**) \(q_{m}\) and \(q\) ; (**b**) \(e\) ; (**c**) \(\theta\); (**d**) Adjustable parameters; (**e**) \(\delta_{e}\); (**f**) The ground track view of trajectory.  
The sensors (including an AHRS unit and a DGPS unit) are used to provide the information related to the flight status, such as the angles, angular rates, velocity and position. During flight, Wi-Fi and serial-based data links provide a link to the ground station computer that allows monitoring the real-time flight information and uploading remote control commands such as way points. Testing of the proposed adaptive algorithm begins with hover, followed by simple way-point navigation due to the limitations of the test conditions such as flight safety. In consideration of measurement noises of the sensors, the results from these flight tests are provided in Figures 4 and 5.  
The performance of the adaptive stability augmentation system is first evaluated at low speeds where a square pattern is flown, as shown in Figure 4. The noisy tracking error, shown in Figure 4, is also within an acceptable range. Moreover, the model feedback strategy in the proposed algorithm contributes to the restricted variation of the control signal \(\delta_{e}\) shown in Figure 4, which explicitly improves the external command position tracking performance, and implicitly demonstrates the improvement in the control quality of the stability augmentation system.  
By comparison with the above-mentioned results, the flight test results of the conventional PID controller implemented through the widely used Pixhawk autopilot module, shown in Figure 5, appear to deteriorate. For instance, the variation of the control signal \(\delta_{e}\), together with the external command position tracking errors, increases when the prototype UH is commanded to perform the same flight mission.  
Based on the flight test results, we can conclude that the proposed adaptive model feedback control algorithm is able to adapt to rapidly changing flight conditions and effectively enhance the performance of the prototype UH stability augmentation system in the presence of parametric uncertainties and measurement noises, which results in the improvement in flying qualities. More significantly, most existing designs would require accurate models at each point, while the proposed design does not.
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## 4 Channel Sum-Capacity in LoS and Favorable Propagation Conditions
### A Lower-Bound for the Sum-Capacity  
First we define the following [PERSON]'s inequality [38]  
\[\mathbb{E}\left[\log_{2}(1+z)\right]\geq\log_{2}\left(1+\frac{1}{\mathbb{E} \left[\frac{1}{z}\right]}\right), \tag{16}\]  
where \(z=\frac{1}{u}\) for \(z>0\). Therefore, Equation (9) can be rewritten as  
\[\begin{split} C&=K\mathbb{E}\left[\log_{2}\left(1+ \frac{\rho M\eta}{d_{k}^{2}}\right)\right]\\ &\geq K\log_{2}\left(1+\frac{\rho M\eta}{\mathbb{E}\left[d_{k}^{ 2}\right]}\right).\end{split} \tag{17}\]  
Remembering that \(f_{d}(r)=\frac{1}{R-R_{\min}},\ R_{\min}\leq r\leq R\), then  
\[\mathbb{E}\left[d_{k}^{2}\right]=\frac{R^{3}-R_{min}^{3}}{3(R-R_{min})}. \tag{18}\]  
For proof of Equation (18), see Appendix C. Therefore, Equation (17) can be re-expressed as  
\[C\geq K\log_{2}\left(1+\frac{3\rho M\eta(R-R_{min})}{R^{3}-R_{min}^{3}}\right). \tag{19}\]
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 2 Preliminaries
### The Fusion of Fuzzy Logic and a Deep Neural Network  
There is an advanced concept of a type of fusion represented by the hybridization of fuzzy logic and deep learning [37]. Fuzzy logic can be combined with DNNs to provide an efficient framework for solving various complex problems. This type of combination falls under the category of hybrid techniques, which combine the characteristics of fuzzy logic and DNNs. It comprises the approximate reasoning capability of fuzzy logic and the learning ability of DNNs. It is known that DNNs significantly promote the learning ability of models. Conversely, fuzzy logic has an excellent ability to handle vague and imprecise circumstances [38]. Accordingly, the hybridization of fuzzy logic and DNNs enhances the accuracy of prediction and provides high-level abilities for interpretation and analysis. In fact, fuzzy logic has exhibited remarkable advantages in the context of DNNs and has successfully addressed aspects of ambiguity and uncertainty in various applications of DNNs. Common examples of these applications are seen in the fields of image processing, NLP, time-series prediction, and medical systems [22]. There are three different approaches to the fusion of fuzzy logic and DNNs. They can be combined in either a cooperative, sequential, or parallel structure as described in the subsections below [37; 38; 39].
## 2 System Architecture
### Control System Architecture  
The control system architecture is shown in Figure 3. This model will be used in control strategy development and simulation validation. The vehicle control unit (VCU) is the supervisory controller in this system. The driver model provides information on driving conditions, such as the demanded power, pedal position, and vehicle speed, and the battery management system (BMS) provides battery information, such as SOC, SOP, internal resistance, and open-circuit voltage, to the VCU for control strategy execution. The VCU runs the ECMS combined with PSO to solve the optimal control solution, which includes the output power of the engine-generator set and the engine combustion mode. Then, based on the BSFC map and the engine's optimal operation curve, the output power signal is split into two control outputs, torque and speed. The torque command will be sent to the engine control unit (ECU), and the speed command will be sent to the generator control unit (GCU). At the same time, the VCU also sends the torque command to the motor control unit (MCU) based on the motor speed feedback. During offline optimization, information on fuel mass flow will be sent to a fuel economy evaluation module to calculate fuel consumption for the purpose of EF optimization by the PSO.
## 4 Model Development
### Building the Initial NN (Setting the Network Architecture)  
As presented in Figure 1, building the initial NN was the first step of phase 2 of the model development. This step included defining the following three functions:  
1. scaling function layer
2. multi perceptron layers
3. unscaling layer.  
The purpose of the scaling function is to scale the inputs of the initial NN model to a suitable range. It contains some basic statistics on the inputs, including the mean, standard deviation, and minimum and maximum values. The method used for scaling the layer was the mean standard deviation method (MSDM).  
Figure 4: Target input variable correlations.  
The perceptron layers are the most important layers, which allow the NN model to learn. Two activation functions were considered when building the initial NN before selecting the optimal model: hyperbolic tangent and linear activation functions. Since the input variables of the approximation problem had a normal distribution, the perceptron layers were set using the hyperbolic tangent function, which is a sigmoid function that is the most utilized activation function with a variation between \(-1\) and \(+1\). The number of layers of the perceptron in the ANN was set to two and the number of the model outputs was one. Figure 5 depicts the architecture of the initial built NN. It had 11 inputs (in black circles) and 1 output, and it consisted of a scaling layer (yellow), a perceptron layer with two neurons (blue), and an unscaling layer (red).
## 3 Results and Discussion
### Thermal Properties: Thermogravimetric, Differential Scanning Calorimetry and Conductivity Analysis
#### 3.2.3 Thermal Conductivity  
The thermal conductivity is a prominent property that determine the application of thermal insulating materials, as function of parameters such as morphology, homogeneity and density, among others. Figure 7 depicts the average thermal conductivity results of the 5 HF/PCL biocomposites. From the results, the average thermal conductivities for AR_00 (neat), 19, 26, 30 and 38 samples are 0.2539, 0.2824, 0.2957, 0.2922 and 0.2539 W/mK, respectively. There is no difference in the thermal conductivities of neat PCL (AR_00) and highest AR_38, as both samples recorded the same value of 0.2539 W/mK. This behavior can be attributed to the longest porous structures exhibited by the sample of highest fiber AR_38, with maximum mean fiber element length of nearly 845 \(\upmu\)m. There are the presence of central hollows in the multicellular morphology of the best fibers such as hemp, as depicted in Figure 8. Notwithstanding, it is evident that the thermal conductivity depends on the sample AR. There was an initial increase in the thermal conductivity when hemp fiber was introduced into the PCL matrix, as similarly observed when same hemp fiber and other cellulosic fibers (date palm particles) were added into polyurethane matrix [32; 50]. Importantly, it is observed that the thermal conductivities of the samples increased with the hemp fiber AR to a threshold value of 26, after it began to decrease with an increase AR. Therefore, the HF/PCL sample of AR_26 recorded the maximum thermal conductivity of 0.2957 W/mK. This exhibited characteristic could be attributed to the fiber architecture, content, volume fraction as well as properties of the fiber, matrix and their ratios in the optimal sample. These properties include, but are not limited to density of the PCL matrix resin and strength of the embedded fiber present in sample of AR_26. The strength of this optimal sample in terms of thermal stability and nano-mechanical properties has been extensively and previously discussed.  
Figure 7: Comparative average thermal conductivities of the 5 samples.  
Moreover, the FRP composites are predominantly affected by fiber content, which increased with hemp fiber AR. This implies that hemp fiber possessed higher thermal conductivity than the neat PCL matrix.  
Moreover, this sample (AR_26) has the best ability to transfer heat energy through conduction. This property is required during machining operation as much tool-workpiece interfacial temperature is absorbed in order to prevent high tool wear. However, this could lead to high machining-induced damage on the workpiece such as delamination, matrix melting and fiber degradation, if not properly controlled. In addition, material of higher thermal conductivity, such as the HF/PCL biocomposite sample of AR_26 has significant applications potential for electronic packaging and thermal control.
## 4 Experimental Validation and Analysis
### Data Processing  
Due to the susceptibility of original monitoring features to noise interference, the SES algorithm was introduced to smooth [29] the monitoring information. Equation (4) was used to process the extracted features. In this paper, smoothing coefficients of 0.1 and 0.3 were selected to smooth the monitored parameters of the 14 sensors in the engine. Two randomly selected sensor parameters were plotted to show the smoothing effect before and after the smoothing process, as shown in Figure 8. The smoothing effect is the best when the coefficient is set at 0.1.  
The numerical range of monitoring parameters in aviation engines has significant differences and the dimensions of data are not unified. In order to reduce the impact of different variable value ranges on the prediction model, the MinMax Scale [30,31] normalization method was used to map the values of the data into the \([0,1]\) range.  
\[x_{scaled}=\frac{x-x_{min}}{x_{max}-x_{min}}, \tag{16}\]  
In the formula, \(x_{max}\) and \(x_{min}\) represent the maximum and minimum values of each group of monitoring parameters, respectively. Standardization was performed on the 69 th engine with the longest running cycle in the FD001 dataset. Before normalizing the monitoring parameters, the values of each parameter had a large range, making it difficult to see the change patterns of the monitoring parameters. The results after normalization are shown in Figure 9, where there is an obvious trend of performance degradation in the monitoring parameters.
# LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography
## 1 Introduction
The performance of different combination schemes was analyzed, in terms of observation ray distribution, tomographic time resolution, accuracy, and spatial resolution. This paper is organized as follows: Section 2 describes the LEO data simulation and the tomography approach. The LEO-augmented GNSS water vapor tomographic experiments and results are described in detail in Section 3. Sections 4 and 5 present the discussion and conclusion, respectively.
## 3 Numerical Results and Discussion  
### Mode-Coupling Properties  
In the proposed sensor, (a) x-polarization core FM, (b) y-polarization core FM, and (c) surface-plasmon-polariton (SPP) mode in the sensing layer were analyzed as shown in Figure 2. The way in which plasmonic mode interacted with the FM through the middle channel (direction of white arrow) was adopted. In the light-matter interaction on the gold-dielectric interface, although x- and y-polarization FMs existed simultaneously in the core, only y-polarization FM could be utilized to excite SPs. According to the dispersion relations of the y-polarization FM and plasmonic mode in Figure 3, there was a detectable loss signal at the intersection of the two RI curves. The inset indicates the resonance state of y-polarization FM and plasmonic mode. As stated in coupled-mode theory (CMT) [41; 42], when FM and SPP mode satisfy mode-resonance conditions, the energy carried by the two modes can produce a sufficient exchange. The evanescent field in the fiber could be greatly strengthened, and a sharp loss signal appears. Fortunately, thanks to its uniqueness, only y-polarization FM could resonate with SPP mode in the investigated band. There was only one loss signal at the intersection of the two curves that is very suitable to apply in the sensing field. According to the uniqueness of resonance peak in the sensor, the precise detection of the analyte RI could be achieved.
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 2 Implementation of Tracking Component  
In general, visual tracking means distinguishing the target between foreground objects and background environment in the camera's field of view (FOV) while the target is moving unrestrictedly with latent variance both in appearance and scale, additionally orienting the camera towards the target. As shown in Figure 1, the moving target is accompanied by an attached bounding box, which highlights the target without many gaps between border and target. The implementation in this paper is derived from DCF which is a classical discriminative method in correlation filter field. At the beginning of tracking, the tracking target is highlighted by a bounding box in an artificial marking manner to train and generate an optimum template called filter which can describe explicit or implicit features of the target as much as possible, as shown in Figure 0(a). A translation filter detects the areas of the target in the subsequent frames, marks with a bounding box in a suitable scale, and records the center of bounding box as the estimated result. In addition, a scale filter matches the target at the estimated position and adjusts the size of bounding box with gaps as few as possible, records as a size ratio to the original box in the initial frame. After the estimates both in translation and scale, the filters will learn some features according to the current appearance of the target and update itself to enhance the robustness. To ensure that the target always locates in the camera's FOV, the control of orientation is combined with the kinematics of CHSM which will be stated in Section 3.4.