esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Analytical Expressions of the Maximum-Rating Constraints
### RMS Current Supported by the Switch  
The RMS current through the switch is given by  
\[I_{S_{W}RMS}=\sqrt{\frac{1}{T}\int_{0}^{T}i_{S_{W}}(t)^{2}dt}, \tag{25}\]  
substituting (2) in (25), we obtain  
\[I_{S_{W}RMS}=h_{f}I_{p}=h_{f}\bigg{(}\frac{2V_{DD}\cdot g_{x}(D,q)}{R_{L}} \bigg{)}, \tag{26}\]  
where \(h_{f}^{2}\) is:  
\[\begin{split} h_{f}^{2}=&\frac{8\pi^{3}D^{3}}{3p(D, q)}-\frac{4\pi^{2}D^{2}sin(\varphi)}{p(D,q)}+\frac{2 sin(2\pi+\varphi)}{p(D,q)}- \frac{4\pi Dcos(2\pi D+\varphi)}{p(D,q)}\\ &-\frac{sin(4\pi D+2\varphi)}{4}-sin(2\pi D)+sin(2\pi D+2\varphi )-\pi Dcos(2\varphi)\\ &+2\pi D-\frac{2 sin(\varphi)}{p(D,q)}-\frac{3 sin(2\varphi)}{4}. \end{split} \tag{27}\]  
The \(h_{f}\) function was plotted in Figure 6a, in the region of interest (i.e., \(0<q<2\), \(0<D<1\)[9]). We calculate hf varying the \(D\) and \(q\) model parameters using a \(49\times 49\) grid (i.e., a linearly spaced sweep from 0.01 to 0.98 and from 0.01 to 1.98 for \(D\) and \(q\), respectively) and the math tool developed in [8], which was implemented in Maple software. These numerical data were exported to the Matlab software and were analyzed with the curve-fitting tool (i.e., cftool), in which a fitting process was performed using a non-linear least-squares method and a custom function defined by  
\[h_{f}(q,D)\approx\frac{f_{1}(q,D)}{f_{2}(q,D)}, \tag{28}\]  
where  
\[\begin{split} f_{x}(q,D)&=a_{x0}+a_{x1}q+a_{x2}q^ {2}+a_{x3}D+a_{x4}D^{2}+a_{x5}qD+a_{x6}q^{2}D+a_{x7}qD^{2}\\ &+a_{x8}q^{2}D^{2}+a_{x9}q^{3}+a_{x10}D^{3}+a_{x11}q^{3}D^{3}. \end{split} \tag{29}\]  
Figure 5: Switch current behavior. (**a**) Waveform for \(D=0.5\). (**b**) Waveform for \(D=0.4\).  
The coefficients of the mathematical expression found by the curve-fitting tool were summarized in Table 1. The \(h_{f}\) fitting process has a coefficient of determination (i.e., \(R^{2}\)) of 0.9964, and its related residuals are shown in Figure 6b. The residuals are characterized by an absolute mean error of 0.0192 and a maximum absolute error of 0.1491, which is located at \(D=0.5354\) and \(q=1.8979\). The graph illustrated that the fitting error is not focalized in a specific region, which indicates a low discrepancy in the overall design space between fitted values and the values expected under the ideal Class-E PA with FDI model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Coefficient Name** & \(f_{1}(q,D)\) & \(f_{2}(q,D)\) \\ \hline \(a_{x0}\) & 0.4532 & 3.6150 \\ \(a_{x1}\) & \(-1.2590\) & \(-7.4820\) \\ \(a_{x2}\) & 0.9159 & 3.6860 \\ \(a_{x3}\) & 1.2790 & \(-7.5180\) \\ \(a_{x4}\) & 1.3440 & 7.6720 \\ \(a_{x5}\) & \(-0.6721\) & 19.4100 \\ \(a_{x6}\) & 0 & \(-11.8700\) \\ \(a_{x7}\) & 0 & \(-15.7400\) \\ \(a_{x8}\) & \(-1.5890\) & 9.7070 \\ \(a_{x9}\) & \(-0.1598\) & 0.1672 \\ \(a_{x10}\) & 0.7299 & \(-0.06993\) \\ \(a_{x11}\) & 1.0390 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coefficients of \(h_{f}\).  
Figure 6: \(h_{f}\) graphs. (**a**) 3D plot. (**b**) Fitting residuals.
###### Abstract  
The data collection in the automated monitoring of landslides is often characterized by large amounts of data, periodic fluctuations, many outliers, and different collection intervals. The traditional method of calculating velocity and acceleration using the differential algorithm for landslide acceleration relies on experience to select thresholds and produces a large number of false early warnings. A hybrid early warning method for the landslide acceleration process based on automated monitoring data is proposed to solve this problem. The method combines the conventional warning method, based on cumulative displacement, velocity, and acceleration, and the critical sliding warning method based on normalized tangent angle according to different strategies. On the one hand, the least-squares fitting of monitoring data inside a given time window is used to calculate various early warning parameters, improving data usage and lowering calculation error. On the other hand, a dynamic semi-quantitative and semi-empirical method is provided for the determination of the thresholds, which is more reliable than the purely empirical method. The validation experiments at the Lisnanyuan landslide in southern China show that the hybrid method can accurately identify the accelerating deformation of the landslide and gives very few false warnings. The proposed method is practical and effective for systems that require automated monitoring and warnings for a large number of landslides.  
landslide; automated monitoring; early warning; threshold determination +
Footnote †: journal: Applied sciences
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 1 Introduction
Therefore, new authentication and key agreement protocols can be designed to fulfill such requirements, when used for e-health/m-health/TMIS, while being secure and light to not overload them.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 3 Proposed Speech Enhancement Algorithm
### Proposed \(\hat{v}(n,k)\) Estimation Method
With estimated \(\hat{D}_{k}^{2}(m)\), \(\hat{\lambda}_{k}(m)\) is updated as:  
\[\hat{\lambda}_{k}(m)=\eta\hat{\lambda}_{k-1}(m)+(1-\eta)\hat{D}_{k}^{2}(m), \tag{39}\]  
where the smoothing constant, \(\eta\) is set to \(0.9\). The \(\mid\)IDFT\(\mid\) of \(P_{v}(m)e^{i\phi_{k}(m)}\) yields the estimated noise, \(\vartheta(n,k)\), where \(P_{v}(m)=\sqrt{\hat{\lambda}_{k}(m)}\). To ensure the conjugate symmetry, the components of \(P_{v}(m)\) at \(me\{1,2,\ldots,127\}\) are flipped to that of the \(me\{129,130,\ldots,255\}\) of \(P_{v}(m)\) before taking the \(\mid\)IDFT\(\mid\). We can justify the improvement of \(\vartheta(n,k)\) estimation using the SPP method [26] in terms of analyzing the tuning parameters of KF in Section 3.3.
## 2 Materials and Methods
### Cytotoxicity Assay  
FBG, BG, and WG cytotoxicities in B16F10, HDF, and HaCat cells were assessed using a 3-(4,5-dimethylthiazol-2-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) colorimetric assay. In a 96-well plate, B16F10 cells were seeded at a density of 2 \(\times\) 10\({}^{4}\) cells per well. The cells for HaCat and HDF were seeded at 1 \(\times\) 10\({}^{5}\) cells/well and incubated for 24 h at 37 \({}^{\circ}\)C in a humidified atmosphere of 5% CO\({}_{2}\). The cells were incubated for 24 or 72 h with various concentrations (50, 100, 200, and 400 \(\upmu\)g/mL) of FBG, BG, and WG in serum-free media. Then, cells were added to 20 \(\upmu\)L of MTT (5 mg/mL in PBS, Life Technologies, Carlsbad, OR, USA) for 3 h at 37 \({}^{\circ}\)C. In each well, 100 \(\upmu\)L DMSO was used to dissolve the insoluble formazan. At 570 nm, absorbance was measured using a microplate reader (Bio-Tek, Instruments, Inc., Winooski, VT, USA).
## 5 Proposed Protocol
### Authentication  
When SN number i wants to interact with the CN of which it is a member, a two-way authentication must be performed by establishing an IDSNi--IDCNi secure channel between two nodes. IDSNi sends a connection request message IDSNi, encrypted message, Time to the CN of which it is a member. The CN first checks the time information. Then, using the node's own PVKSNi, it verifies the encrypted message with PBKSNi. If there is no problem so far, the CN sends IDCNi, IDSNi, IDcardCni, and Time information to the BS and requests verification. Then, the smart contract (Algorithm 3) in the BCN is triggered. First, the Time information is checked. Then, whether the CN and SN are registered in the BCN is checked. Next, IDCNi and PBKBS are used to authenticate the IDcard. Finally, it is checked whether the IDSNi is a member of the IDCNi. If verification takes place in all of these processes, a secure communication channel is established between the relevant SN-CN and CN-BS.  
Figure 4: Sequence diagram of the registration.  
```
begin iftime_check = error; Return error(); ifID\({}_{\text{CNi}}\_\)check = error; Return error(); // Is the node alive? // Is the node registered? ifID\({}_{\text{SNi}}\_\)check = error; Return error(); // Is the node alive? // Is SN a member of the CN here? ifID\({}_{\text{CNi}}\_\)check = error; Return error(); // Is the signature information correct? return true; // secure connection end
```  
**Algorithm 3** CN-SN authentication  
User U can request access to the BCN with the ID given to him. The smart contract (Algorithm 4) is triggered when the request comes. If agreed in this way, U can access the BCN. A secure channel is established between U and the BCN. In Figure 5, the sequence diagram of the authentication section is given.  
```
begin iftime_check = error; Return error(); ifID\({}_{\text{U}}\_\)check = error; Return error(); // if permission ok return true; // secure connection end
```  
**Algorithm 4** U authentication  
Figure 5: Sequence diagram of the authentication.
## 4 Convolutional Neural Network-Based Cooperative Spectrum Sensing
### Convolutional Neural Network-Based Cooperative Spectrum Sensing
#### 4.2.2 Network Training and PU Status Prediction  
The local sensing decisions from the SUs, \(D_{i}\)\(\forall i\in\{1,2,\ldots,K\}\), are used as input for the CNN. Because a CNN is mostly used for image classification, the local decisions from \(K\) secondary users are rearranged to form a grayscale image with the size of \(1\times K\times 1\), where the last figure describes the number of color channels in the image. A stochastic gradient descent (SGD) optimizer with an adaptive learning rate is used in training the network. With this algorithm, the initial learning rate of 0.01 is later reduced based on a pre-defined schedule. For instance, it can be multiplied by a factor of 0.1 after every 10 epochs. The training set is a collection of local decisions from \(K\) SUs under different environmental conditions (i.e., a wide range in the sensed channel gain).  
The FC uses the historical sensing data to train the CNN for the classification problem in advance. Thereafter, the FC determines the presence of the primary user on the licensed channel in every time slot by using the new individual sensing outcomes received at the beginning of each time slot as input for the trained network.
## 3 Proposed Method
### Adaptive Channel Feature Fusion
#### 3.4.1 Adaptive Channel Selection  
We proposed the ACS module to process the multi-channel feature maps, adaptively learn the weight coefficients of each channel, improve the overall model's discriminative ability and sensitivity to each channel feature, and strengthen the channel features that are beneficial to model classification while suppressing the useless channel feature information. Its structure is shown in Figure 2e.  
The ACS module mainly contains extraction operations and squeeze operations. Assume that the output vector \(x\) of the multi-scale convolutional layer is of size \(C\times W\times H\), where \(C\) is the number of channels, and \(W\times H\) denotes the size of the feature map of each channel. The extraction operation inputs \(x\) into a global average pooling layer and a global maximum pooling layer to compress the features, resulting in channel-level statistical information \(Z_{avg}\) and \(Z_{max}\). This information encodes the spatial features on each channel as a real number with a global receptive field representing the global features of the feature maps. The output dimensions match the number of feature channels input. The formulas for finding \(Z_{c}^{avg}\) and \(Z_{c}^{max}\) for each channel are as follows:  
\[Z_{c}^{avg}=\frac{1}{W\times H}{\sum_{i=1}^{W}\sum_{j=1}^{H}x_{c}(i,j)} \tag{12}\]  
\[Z_{c}^{max}=\max_{1\leq i\leq W}\max_{1\leq j\leq H}x_{c}(i,j) \tag{13}\]  
where \(x_{c}(i,j)\) represents the value of row \(i\) and column \(j\) in the c-channel feature map. After the extraction operation, the global description features are obtained for each channel. And the activation operation aims to obtain the relationship between the channels, which is achieved by using two fully connected layers. The first fully connected layer plays the role of dimensionality reduction, downgrading the channel dimensions of \(Z_{c}^{avg}\) and \(Z_{c}^{max}\) to \(1/16\) of their original dimensions to change the capacity and computational cost of the ACS module in the network. It is then activated by the ReLU function and upscaled to the original channel dimensions using a second fully connected layer. Finally, the normalized weights are obtained using the Sigmoid activation function after superimposing the channel dimensions computed by the two branches of global average pooling and global maximum pooling. The formula for the activation operation is expressed as  
\[s=Sigmoid\big{(}W_{1}\cdot ReLU\big{(}W_{0}\cdot Z_{avg}\big{)}+W_{1}\cdot ReLU (W_{0}\cdot Z_{max})\big{)} \tag{14}\]  
where \(W_{0}\in R^{\frac{C}{16}\times C}\) and \(W_{1}\in R^{C\times\frac{C}{16}}\); finally, the learned weights \(s_{c}\) for each channel are multiplied by the original individual channel features \(x_{c}\):  
\[y_{c}=s_{c}\times x_{c} \tag{15}\]  
The output dimensions of the extraction and squeeze operations of the ACS module are unchanged, and the whole process can be viewed as learning the weight coefficients of each channel adaptively to improve the overall model's discriminative ability and sensitivity to the features of each channel.
###### Abstract  
Critical Infrastructure Security Showdown 2021--Online (CISS2021-OL) represented the fifth run of iTrust's international technology assessment exercise. During this event, researchers and experts from the industry evaluated the performance of technologies designed to detect and mitigate real-time cyber-physical attacks launched against the operational iTrust testbeds and digital twins. Here, we summarize the performance of an anomaly detection mechanism, named AICrit, that was used during the exercise. AICrit utilizes the plant's design to determine the models to be created using machine learning, and hence is referred to as a \"design-enhanced\" anomaly detector. The results of the validation in this large-scale exercise reveal that AICrit successfully detected 95.83% of the 27 launched attacks. Our analysis offers valuable insights into AICrit's efficiency in detecting process anomalies in a water treatment plant under a continuous barrage of cyber-physical attacks.  
anomaly detection; critical infrastructure; industrial control systems; critical infrastructure security showdown; water treatment plant +
Footnote †: journal: Medical  
* **RQ 2:**_How efficient is AICrit in supporting the plant operators toward active incident response and recovery?_  
_Novelty:_ The architecture of AICrit, the use of plant design information, and its performance can be found in [2]. The key differences between the performance evaluation reported in this work and that in [2] are summarized in Table 10 in Section 5. From the data, we observe that the evaluation performed and reported in [2] was on a smaller scale than that performed during CISS2021-OL. We therefore believe that while the work in [2] is novel and worthy of sharing with the community at large, so is the large-scale experimentation reported here. The performance of AICrit in CISS2021-OL further attests to the strengths of the novel methods used in the design of AICrit.  
_Contributions:_ The contributions of this paper are listed below.  
1. Provides detailed information on the attacks launched, and their impact, on an operational water treatment plant during a large cyber exercise.
2. The performance of AICrit is compared with that of DAD--a design-centric anomaly detection mechanism.
3. Lessons learned during the exercise are reported. We believe that these lessons are useful to researchers in the design of machine learning-based defense mechanisms for safeguarding CI and translating them to large-scale systems.  
_Organization:_ The remainder of this paper is organized as follows. Information regarding the SWaT testbed along with a description of the AICrit and CISS2021-OL is in Section 2. Section 3 enumerates the attacks launched by the red teams during CISS2021-OL. The corresponding response of AICrit is in Section 4. Section 5 compares the performance of AICrit against DAD and answers the above-mentioned research questions. A summary of the recent literature on safeguarding ICS is in Section 6. Conclusions based on CISS2021-OL are in Section 7.
# InfNRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control
## 4 Discussion
Future works may try to increase the quality of the predictions, by involving both more users and by adding more signals to the dataset, in order to obtain a bigger and more balanced number of segments, which may cover a higher number of possible cases. For what concerns the network's performances, another way to improve the classification accuracy of the network is by improving its structure. In this work, we aimed at using a simple network, that has been adapted from a previous work in which the aim was not to classify the usability of a signal, but its nature [40]. The adoption of this simple network has some benefits. First, the linearity of the structure and the simplicity of the architecture allows for easy explainability--which is crucial especially when AI algorithms are used for medical data--and to rapidly modify it, to better adapt it to different scenarios (e.g., rating from more users, a higher number of samples, etc.) and computational resources (e.g., laptop, Cloud clusters, High-Performance Computers, etc.). Currently, the network trains on an average laptop (Intel i7-856 SU, 16 GB of RAM) in less than 30 min, and is able to provide predictions within seconds, making it suitable for both offline and online classification of fNIRS segments. Overall, in this work, we have demonstrated a proof-of-concepts of how a DNN model can be trained and employed to classify the usability of fNIRS signals. The developed model can help researchers estimate the quality of an fNIRS signal segment, and its usability for research purposes in a more objective way, by reducing the subjectivity introduced by a manual inspection stage. While the performances of the model are not excellent, the limits of the dataset--in terms of number of segments, and of number and expertise of the raters--and of the architecture of the network can help explain the results here reported. Future work should aim at collecting data from a higher number of raters, with different expertise levels, for a higher number of fNIRS segments, and try to use more sophisticated networks designed ad-hoc for this classification task. Moreover, future studies may aim at combining different signals (e.g., fNIRS and EEG) in order to increase the performances of the classification model.
## 1 Introduction
This paper extends some of the results presented in [12] by providing new general formulas for any even-order moments and covariance matrices of the vectors of sample moments, and by studying statistical performances of the proposed estimators with theoretical asymptotic analyses that confirm the numerical results obtained with Monte-Carlo simulations. The paper is organized as follows: in Section 2 we derive the estimators starting from a general result on the even-order moment for the given signal model; in Section 3 we derive the Cramer-Rao lower bounds for all proposed estimators; in Section 4 the asymptotic performances of the proposed estimators are derived; in Section 5 results from Monte-Carlo simulations are presented along with comparisons with the CRLBs and the asymptotic performances; finally, in Section 6 we draw the conclusions.
# Lateral Stability of a Mobile Robot Utilizing an Active Adjustable Suspension
## 5 Case Studies  
### Case Study of the Active Suspension  
The case study of the active suspension is divided as the design and kinematic modeling.  
Firstly, the design parameters of the active suspension are given. According to Equations (1)-(7) in Section 3.1, the ground clearance changes with the input of the active suspension (linear actuator length, \(L_{e}\)). The linear actuator's min length \(L_{emin}\) is achieved with the lowest ground clearance \(H_{Cmin}\)-meanwhile, \(L_{emax}\) (the max length of the linear actuator) is achieved with \(H_{Cmax}\) (the highest ground clearance). Table 1 shows the structure parameters of the active suspension used in the simulations, where, \(L_{0}\), \(l_{7}-l_{7}\), \(\beta_{2}\), and \(\beta_{4}\) are cited from the reference [27]. R is the wheel's radius; all the four wheels are the same. In addition, all the parameters listed in Table 1 can be measured from the UG/3D drawing software model. Calculations were done using MATLAB software.  
The desired ground clearance of this mobile robot is from 350 mm to 550 mm. According to Equation (7) in Section 3.1, the min and max lengths of the linear actuator are solved as \(L_{emin}=170\) mm, \(L_{emax}=221\) mm. Thus, in order to choose a normal electromotive handspike, which can be provided easily, the min and max lengths are rounded as \(L_{emin}=170\)\(mm\), \(L_{emax}=220\) mm. The change distance is 50 mm. Thus, the range of the linear actuator is:  
\[170\)\(mm\leq L_{e}\leq 220\)\(mm\]  
Then, the goal of this section is to obtain the relationship between the input and output of the active suspension. The relationship between input \(L_{e}\) (linear actuator length) and output \(H\) (height of COG) or ground clearance (\(H_{C}\)) of the active suspension can be calculated using Equations (1) and (10) in Section 3, respectively. Figure 9 shows the relationship between the input and output of the active suspension. As indicated, both the ground clearance, \(H_{C}\) and the height of the COG, \(H\), increase with the increasing linear actuator length, \(L_{e}\), when it is at its minimum value of 170 mm, \(H\) is 453 mm, and \(H_{C}\) is 348 mm. When \(L_{e}\) is at its maximum value of 220 mm, \(H\) is 650 mm, \(H_{C}\) is 545 mm, and the ground clearance increases by 197 mm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Parameter** & **Value** & **Unit** \\ \hline \(L_{0}\) & 640 & mm & \(l_{1}\) & 116.8 & mm \\ \(l_{2}\) & 160 & mm & \(l_{3}\) & 161.1 & mm \\ \(l_{4}\) & 52.1 & mm & \(l_{5}\) & 140.3 & mm \\ \(l_{6}\) & 58 & mm & \(l_{7}\) & 103.1 & mm \\ R & 150 & mm & \(H_{2}\) & 20 & mm \\ \(H_{3}\) & \(-85.5\) & mm & \(\beta_{2}\) & 23.6 & \({}^{\circ}\) \\ \(\beta_{4}\) & 16.9 & \({}^{\circ}\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the active suspension.  
Figure 9: Input–output of the active suspension.  
Generally, the height of each side increases in direct proportion to the input of the increase of the active suspension.