esa-sceva/satcom-chunk-collection huggingface dataset:
# Fractional-Order LQR and State Observer for a Fractional-Order Vibratory System
## 5 Fractional-Order LQR Method for Output Feedback Control
### Configuration of Fractional-Order State Observer  
Next, using Equation (32), a state observer is configured for the viscoelastic damper system. First, the viscoelastic damper, as the controlled object, is assumed to be given by the state equation of Equations (15) and (16). In addition, setting \(y(t)\) as the observable, the output equation for the viscoelastic damper system is assumed to be expressed as follows:  
\[y(t)=\mathbf{C}\mathbf{x}(t), \tag{34}\]  
where  
\[\mathbf{C}=\left[\begin{array}{ccc}c_{1}&c_{2}&c_{3}&c_{4}\end{array}\right] \tag{35}\]  
in which the values of \(c_{1}\), \(c_{2}\), \(c_{3}\), and \(c_{4}\) are determined by what is observable. Here, if the pair of matrices \((\mathbf{C}\), \(\mathbf{A})\) is observable, then a fractional-order state observer takes the following form:  
\[D^{0.5}\hat{\mathbf{x}}(t)=A\hat{\mathbf{x}}(t)+B\mathbf{u}(t)+\mathbf{H}(y(t)-\mathbf{C}\hat{\mathbf{ x}}(t)), \tag{36}\]  
\[u(t)=-\mathbf{F}\hat{\mathbf{x}}(t). \tag{37}\]  
The subtraction of Equation (15) from Equation (36) yields the fractional-order differential equation for the estimation error \(\mathbf{e}(t)\):  
\[D^{0.5}\mathbf{e}(t)=(\mathbf{A}-\mathbf{H}\mathbf{C})\mathbf{e}(t), \tag{38}\]  
where  
\[\mathbf{e}(t)=\hat{\mathbf{x}}(t)-\mathbf{x}(t). \tag{39}\]  
From Equation (38), the poles of the matrix \((\mathbf{A}-\mathbf{H}\mathbf{C})\) can be moved freely by the state observer gain \(\mathbf{H}\), and the convergence characteristics of the estimation error \(\mathbf{e}(t)\) can be determined freely using the pole assignment method.  
Moreover, Equations (15), (34), (36), (37), and (39) produce the following relationships:  
\[D^{\frac{1}{2}}\mathbf{x}(t)=A\mathbf{x}(t)+\mathbf{B}u(t)=A\mathbf{x}(t)+\mathbf{B} (-\mathbf{F}\hat{\mathbf{x}})=A\mathbf{x}(t)-\mathbf{B}\mathbf{F}(\mathbf{e}(t)+\mathbf{x}(t)) \tag{40}\] \[=(\mathbf{A}-\mathbf{B}\mathbf{F})\mathbf{x}(t)-\mathbf{B}\mathbf{F}\mathbf{e}(t),\]  
\[D^{\frac{1}{2}}\mathbf{e}(t)=D^{\frac{1}{2}}\hat{\mathbf{x}}(t)-D^{\frac{1}{2}}\mathbf{x} (t)=A\hat{\mathbf{x}}(t)+B\mathbf{u}(t)+\mathbf{H}(y(t)-\mathbf{C}\hat{\mathbf{x}})-A\mathbf{x}(t)-B \mathbf{u}(t)\] \[=\mathbf{A}(\hat{\mathbf{x}}(t)-\mathbf{x}(t))-\mathbf{H}(\hat{\mathbf{x}}(t)-\mathbf{C} \hat{\mathbf{x}}(t))=(\mathbf{A}-\mathbf{H}\mathbf{C})(\hat{\mathbf{x}}(t)-\mathbf{x}(t)) \tag{41}\] \[=(\mathbf{A}-\mathbf{H}\mathbf{C})\mathbf{e}(t).\]Equations (40) and (41) generate the state equation for the augmented system that includes the state observer as follows [32]:  
\[\left[\begin{array}{c}D^{\frac{1}{2}}\mathbf{x}(t)\\ D^{\frac{1}{2}}\mathbf{e}(t)\end{array}\right]=\left[\begin{array}{cc}\mathbf{A}-\mathbf{ B}\mathbf{F}&-\mathbf{B}\mathbf{F}\\ \mathbf{0}&\mathbf{A}-\mathbf{H}\mathbf{C}\end{array}\right]\left[\begin{array}{c}\mathbf{x}(t)\\ \mathbf{e}(t)\end{array}\right]. \tag{42}\]  
Figure 2 is a block diagram of the augmented system, in which the state observer is incorporated with the feedback control system.
# Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning
## 4 Results and Discussion
### Experimental
#### 4.1.2 Experiment Settings  
In the experiments for the CREMA-D dataset, we employed a ResNet-18-based [10] network as the encoder. In the MVSA dataset, we utilized M3 AE [24], a large pre-trained multimodal masked auto-encoder, as the encoder. We trained all models using a mini-batch size of 64 and the SGD optimizer [25] with a momentum of 0.9 and weight decay of \(1\times 10^{-4}\). The initial learning rate is \(1\times 10^{-3}\), with a decay step size of 70 and a decay factor of 0.1. For Differential Privacy Stochastic Gradient Descent (DPSGD), we set the clipping threshold to C = 1.0 and C = 2.0, noise standard deviation to \(\sigma\) = 2.40, corresponding to privacy budgets of \(\epsilon\) = 1 and \(\epsilon\) = 2, \(\delta\) = \(10^{-5}\)[13; 15; 24; 26]. The parameter \(\alpha\) is set within the range [0, 1] for different datasets. All experiments are implemented in Python 3.8 using PyTorch 1.9.1 on an NVIDIA A800 80 GB PCIe GPU.
# AccFlow: Defending against the Low-Rate TCP DoS Attack in Drones
## 3 AccFlow Design  
In this section, we provide an in-depth look at AccFlow. AccFlow is a transport layer protocol deployed on the SDN centralized controller. The controller's role encompasses monitoring all traversing flows, conducting flow analysis, and instructing the switches or routers to implement different routing and forwarding policies for various flows, as illustrated in Figure 3. The architecture of AccFlow is depicted in Figure 3 as well. AccFlow comprises two primary modules: Aggressive Detection and Early Drop. Aggressive Detection is responsible for blocking attacking flows that exhibit detectably aggressive behavior, whereas Early Drop is employed to safeguard the network when attackers attempt to evade detection by intelligently altering their attacking strategies. As Flow Data, computed by other layers, traverses the Transport Layer, AccFlow, equipped with these two modules, effectively safeguards the network from attacks through Flow Prioritization and Flow Analysis.
## 6 Biomedical Applications of NTP on Biofilm
aeruginosa_ biofilms grown on the surface of the glass vial for 24 h. The results showed 4.5 log\({}_{10}\) reduction of bacterial cells after 5 min of plasma treatment. This could provide an alternative approach for inner surface sterilization and decontamination in the medical device, food, and pharmaceutical industries [127]. Thus, the most commonly used plasma configurations are jet and DBD plasma. In DBD configuration, the plasma ignites from an electrode array, therefore, covering larger area and can treat larger surfaces for planktonic as well as bacteria in biofilms. This plasma is more suitable in clinical settings to treat the biofilm contaminated medical devices and implants. However, jet plasma has been widely used when compared to the DBD as previously mentioned. Jet configuration is relatively smaller and is suitable for treating bacteria in nooks and crannes of a surface, for example, in dentistry. This is possible since a plasma source with jet configuration can be customized in a desired shape and size depending upon the application. It is also possible to use different gases for plasma generation in this configuration leading to the generation of more ROS and RNS. When comparing the inactivation yield, jet configuration has demonstrated better efficacy than the DBD configuration. The jet plasma can yield complete sterilization of biofilm [56], however, the maximum inactivation yield by DBD plasma was only 7.1 log reduction [53]. In both cited studies, the bacterial strain used was _Pseudomonas aeruginosa_. On the other hand, it has been also found that the performance of NTP depends upon the parameters used. For example, the study [42] used 10 kV and 10 kHz to generate jet plasma and yielded 7 log reduction, whereas, another study [3] yielded only 4 log reduction when treating with plasma generated by 6 kV and 20 kHz source. A similar difference was observed when comparing the results of other two studies on DBD plasma. One of the studies [53] demonstrated 3-7 log reduction when treated with DBD plasma from 8-13 kV and 20-30 kHz, however, there was only 5 log reduction when treated with 8-10 kV and 37-40 kHz plasma source [54]. Therefore, it can be concluded that the antibacterial efficacy of plasma depends on several parameters like plasma generation, biological, and environmental parameters. Some of the plasma parameters are exposure time, voltage, frequency, distance between plasma source and the sample, working gas type, and the flow rate. The biological parameters that might affect the efficacy could be the bacterial species of interest and gram characteristics, initial inoculum, growth stages, biofilm growth period and mode, and the thickness of biofilm. Similarly, the environmental parameter affecting the inactivation yield by plasma could be the matrix composition, its relative humidity (RH), and the acidity [98].
## 3 Sample Collection and Data Processing
### Data Processing  
Spectral Geologist (TSG 8.0) spectral interpretation software was adopted for data processing in this study. The tested spectra were matched with those in the spectral database to identify mineral types and relative abundances. The Aux matching function of Spectral Geologist extracts characteristic spectral bands' parameters, such as the wavelength, absorption depth, full width at half maximum (FWHM), and integrated intensity. For instance, the muscovite mineral group (muscovite and illite) was interpreted within the spectral range of 2195-2200 nm (Al-OH absorption peak). The interpretation range for chlorite minerals (Fe-Mg chlorite, Mg-chlorite, and Fe-chlorite) was 2245-2265 nm (Fe-OH absorption peak). Based on field recordings and spectral absorption depths, we manually interpreted the altered minerals that had been interpreted by TSG. After selecting the minerals according to lithological sections, we finally identified the species of the altered minerals.
## 5 Results and Analysis
### Packet Delivery Ratio  
The packet delivery ratio (PDR) is defined as the ratio of the total amount of packets transmitted at sensor nodes (\(P_{\alpha}\)) to the total amount of packets received at the BS (\(P_{\alpha}\)), shown as follows:  
\[PDR=\dfrac{\sum P_{\alpha}}{\sum P_{\alpha}} \tag{14}\]  
The results of PDR under different scanning angles are given in Figure 12. Through maintaining the network connectivity, both DRS and RRP benefit from the relatively high PDR. However, this merit does not exist in the simple-link protocol that works without any reconfiguration.  
As in simple-link, the death of a node may probably trigger a great decrease of connectivity. The dead node is unable to continue forwarding data packets from its offspring even though they may still have sufficient energy to initiate data transmission. As a result, any data transmission originated from the dead node's offspring is meaningless and wastes energy. As expected, we observe that the PDR of the simple-link decreases with the growth of the scanning angle. That is because when the scanning angle increases, the probability of a node becoming the successor of other nodes increases. Once this node died of energy depletion, it might probably cause a large number of nodes to lose their contact  
Figure 12: Packet delivery ratio _vs._ scanning angle.  
with the BS, resulting in a decrease in PDR. Figure 13 gives the number of transmitted and received packets of each protocol under different scanning angles. The great difference between the number of transmitted and received packets in the simple-link protocol implies its inefficient data transmission, which is the result of poor maintenance of connectivity. Yet, both DRS and RRP indicate fairly well performance in this case compared to that of the simple-link.  
Note that within the same simulation time, the total number of delivered packets in RRP is larger than that of DRS. The reason is that nodes using RRP will die at a slower rate through the reconfiguration that combines direct and multi-hops communication, and they can transmit a higher number of packets. To better illustrate packet delivery in DRS and RRP, the results of delivered packets versus time are presented in Figure 14. RRP achieves better performance than DRS in each scanning angle. This is the merit brought by our proposed reconfiguration routing. Additionally, it is  
Figure 14: Number of delivered packets _vs._ time.  
Figure 13: Number of delivered packets _vs._ scanning angle.  
observed that the increment of delivered packets slows down as time goes by. This trend is expected because the number of transmitted packets per unit time will decrease with death of nodes.
###### Abstract  
Against the background of the development of control systems, Data Driven Control (DDC) methods are becoming more and more popular, given the system's independence from physical models and the possibility of quickly tuning the controller. The usefulness of such tuning algorithms increases with the complexity of the plants. Nonlinear models are the main class of processes for which such laws are amenable. According to the literature, a class of DDC methods exist that perform online estimation of plant behavior with an unknown structure, which is generically called Model Free. This title is assumed by two types of algorithms, which contain it in the name. One is the gradient-based algorithm, Model Free Adaptive Control, defined by [PERSON], which uses the concept of dynamic linearization through pseudo partial derivatives (PPD) and pseudo gradient (PG). The other is a non-gradient based algorithm, Model Free Control, defined by [PERSON], which uses the concept of the ultralocal model and intelligent PID controllers (iPID). For the gradient-based methods, in the compact form of dynamic linearization (CFDL), i.e., partial form dynamic linearization (PFDL), two algorithms are proposed to determine the initial value of the time-varying parameters PPD and PG from the dynamic performance perspective as they offer the best responses. The CFDL and PFDL variants of the MFAC control law, which have parameters that result from the application of the proposed algorithms, are compared with iP and iPD controllers on nonlinear control systems.  
Model Free Control; Model Free Adaptive Control; intelligent PID controllers; gradient-based Model Free Control Algorithms; non-gradient-based Model Free Control; nonlinear systems Article
## 3 Results
### Analysis of RF-EMF Studies  
In the 86 publications included in this work, 27 countries are involved: Spain, Belgium, Switzerland, France, Greece, Turkey, Poland, Sweden, Austria, Netherlands, Serbia, Asia, China, Germany, Italy, Romania, United Kingdom, USA, Amsterdam, Australia, India, Korea, Malaysia, Mexico, Norway, Palestine, and Slovenia (Table 4).  
Of the 86 studies included in this review, 61 conducted spot measurements, and 25 used a mixed method (Figure 3).  
The highest average was 200,000 \(\rm\upmu W/m^{2}\) (0.20 \(\rm W/m^{2}\)) measured in Kristiansand, Norway (Figure 5), and the lowest was 0.0600 \(\rm\upmu W/m^{2}\) measured in Palestine (Figure 4). Figures 6 and 7 show the ten lowest and highest median values of the different studies reviewed.  
Figure 6: Ten minimum median values in \(\upmu W/m^{2}\). Please see the comments section in Tables 2 and 3, where the full reference is located [1,53,69,82,95,108].  
Figure 7: Ten maximum median values in \(\upmu W/m^{2}\). Please see the comments section in Tables 2 and 3, where the full reference is located [50,86,104,108,110].  
Out of the 86 studies included in this Review, 43% of them used a Spectrum Analyzer, a device that is mostly used for spot measurements. The most commonly used model was the Narda SRM-3006, followed by the Narda SRM-3000 (Figure 8). As we know, personal exposimeters are more appropriate for measurements involving moving volunteers. In addition to Spectrum Analyzer, Exposimeters, Band probes, Databases and Antennas, we want to highlight the introduction and use of Sensors, devices that allow us to monitor at different spatial points over time.
# Forecasting Irregular Seasonal Power Consumption.
## 3 Results
### Application of Models with Regular Seasonality
#### 3.2.2 Application of ARIMA Models  
To apply the ARIMA models, MATLAB(r) is the chosen platform, using Econometrics Toolbox and SSMMATLAB [71] for double seasonal ARIMA. R is also tested with the 'forecast' package, but the MATLAB(r) results outperformed the R results. Like the previous models, models with one and two seasonalities according to Section 3.2 are tested. The best results are found using a single seasonality of one hour (ten-time units). The best model is ARIMA (4,2,4) \(\times\) (4,1,4)\({}_{10}\), for which the parameters are shown in Table 2.
###### Abstract  
This paper examines the effects of thermo-solid coupling and the influence of braking parameter changes on the vibration characteristics of high-speed train disc brakes. A multi-flexible body dynamics model of high-speed train disc brakes considering thermo-solid coupling was established to study the vibration characteristics of high-speed train disc brakes during service. The results show that the uneven distribution of temperature and stress produced during the brake disc's service was the primary cause of the warping deformation of the brake disc, which prevented the brake disc and the brake pads from making sufficient contact and caused vibration while braking. By comparing the analytical findings of whether the model was subject to the coupling effect or not, the influence of thermo-solid coupling on the braking procedure was demonstrated from the standpoint of energy distribution. The severity of the high-speed train brake disc vibration gradually increased along with the braking pressure and initial speed. In addition, vibration aggravated the instability of the braking process, which could lead to thermoelastic instability and is harmful to the braking performance of the brake. These findings provide theoretical support for designing and manufacturing disc brakes for high-speed trains.  
high-speed train disc brake; thermo-solid coupling; flexible multi-body dynamics; service vibration characteristics
## 4 Analysis
### Estimation of the Number of Sensing Rounds  
In order to estimate the number of schedule rounds in a given topology when applying the CASA scheme, the three-layer perceptron neural network is presented. For selecting the network parameters (weights and biases) that best approximate a given function, the backpropagation learning algorithm is considered to minimize the mean square error performance as described in (7).  
Figure 5 illustrates the perceptron network architecture. Note that \(J\) represents the number of input neurons, which may denote the number of 2-hop cluster members, the number of relay nodes, and the number of 1-hop cluster members. \(N^{1}\) denotes the number of hidden neurons. In the output layer, \(N^{2}\) represents the number of neurons, which may denote the network approximation results. Moreover, let IW and LW be the input weight matrix and layer weight matrix for the hidden layer and the output layer, respectively. Let \(b^{1}\) and \(b^{2}\) be bias vectors for the hidden layer and the output layer, respectively. Established upon the developed neuron network model, the behavior of the CASA scheme may be abstracted with sensible settings, which is further discussed in Section 5.
## 4 Results and Discussion
### Model Performance Evaluation
Notably, it received perfect scores of 1.00 for the akiec, bcc, df, and vasc classes, indicating immaculate detection in these categories. The mel class likewise performed well, with a precision of 0.94, recall of 0.99, and F1-score of 0.97. The nv class had slightly reduced recall of 0.89, but high precision of 0.99, resulting in a balanced F1-score of 0.94.
## 4 Experimental Validation
### Validation of Dynamic Unbalance Force  
The spindle rotor used in the experiment is handled by dynamic balancing and it produces very little centrifugal force at high speeds. An unbalanced mass is mounted at the location of pulley to generate dynamic unbalance force when it rotates, as shown in Figure 17. The dynamic unbalance force generated by unbalanced mass increases as the increasing of whirling speed. The dynamic rotational error can be measured by the DAQ system.  
In the experiment, the whirling speed of SRB system ranges from 300 to 6000 rpm. The length of sampling is set at 60 revolutions, with the sampling frequency of 15 kHz. The dynamic unbalance force generated at 300 rpm is about 0.54 N. The TIR, synchronous and asynchronous errors are selected as the initial value due to some unknown factors except unbalance force. The rotational errors generated in the process of increasing the speed are all considered to be caused by the dynamic unbalance force. The measured TIR, synchronous and asynchronous errors under different speeds, as well as the comparisons with the simulated values, are shown in Figure 19.  
It can be seen from Figure 19a that experimental results agree well with the simulated values. The simulated value of TIR varies from 14.53 \(\upmu\)m at 300 rpm to 16.78 \(\upmu\)m at 6000 rpm, while the experimental value varies from 14.41 \(\upmu\)m to 17.91 \(\upmu\)m. The difference between the simulated and experimental TIR values become large from 3000 to 6000 rpm, due to the vibration of transmission system. Figure 19b indicates that dynamic unbalance force basically has no influence on synchronous  
Figure 19: Comparing the experimental results with the simulated values.  
Figure 18: Spindle rotational error of the initial state.  
error, which is consistent with the simulated results. However, dynamic unbalance force has a significant effect on asynchronous errors. Asynchronous errors due to unbalanced mass increase with the speed increasing. The experimental value of asynchronous error varies from 3.21 \(\upmu\)m at 300 rpm to 4.69 \(\upmu\)m at 6000 rpm, while the simulated value varies from 3.22 to 4.12 \(\upmu\)m.
# Unexpected-Behavior Detection Using TopK Rankings for Cybersecurity
## 2 Intrusion Detection Systems  
Intrusion detection is the process of monitoring the events occurring within a computer system or network, and analyzing them to search for signs of possible violation of computer security policies. These events might have a malicious nature, such as malware or attackers. An IDS is a software system that automates the intrusion detection process [7].  
NIST defines three detection methodologies: (1) signature-based detection; (2) anomaly-based detection; and (3) stateful protocol analysis [7].