esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Design
### Design of \(q\)-Axis Current Controller  
As the inner loop of a cascade control structure, the disturbance of \(q\)-axis current loop cannot be ignored. Conventionally, compensating the disturbance of \(q\)-axis current loop can improve the robustness of the whole cascade structure.  
The differential equation of \(q\)-axis current loop is shown as:  
\[u_{q}=\omega_{e}L_{d}i_{d}+(R_{s}+pL_{q})i_{q}+\omega_{e}\psi_{f} \tag{18}\]  
where \(p\) is differential operator, \(\omega_{e}\) is synchronous speed (rad/s).  
From Equation (19), we can obtain:  
\[\dot{i}_{q}=-\frac{R_{s}}{L_{q}}i_{q}-n_{p}\omega i_{d}-\frac{\psi_{f}}{L_{q}} n_{p}\omega+\frac{u_{q}}{L_{q}} \tag{19}\]  
We define the lumped disturbance of \(q\)-axis current loop as:  
\[R_{q}=-\frac{R_{s}}{L_{q}}i_{q}-n_{p}\omega i_{d}-\frac{\psi_{f}}{L_{q}}n_{p}\omega \tag{20}\]  
Here, \(Rq\) is expended as a new state variable. The state-space form is as follows:  
\[\left\{\begin{array}{l}\dot{x}_{1q}=R_{q}+\frac{1}{L_{q}}u_{q}\\ \dot{x}_{2q}=\dot{R}_{q}\end{array}\right. \tag{21}\]  
The ESO for \(q\)-axis current loop is designed as:  
\[\left\{\begin{array}{l}\dot{z}_{1q}=z_{2q}+\alpha_{1q}\beta(x_{1q}-z_{1q})+ \frac{1}{L_{q}}u_{q}\\ \dot{z}_{2q}=\alpha_{2q}\beta(x_{1q}-z_{1q})\end{array}\right. \tag{22}\]  
Finally, the controller can be designed as:  
\[u_{q}=k_{p}(\dot{i}_{q}^{*}-i_{q})+k_{i}\int{(\dot{i}_{q}^{*}-i_{q})dt}-z_{2q} \tag{23}\]  
The block diagram of ESO-based \(q\)-axis current loop is shown in Figure 2.  
**Remark 1**.: _Some scholars may doubt the effectiveness of ESO because the quantitative stability analysis has not been given. In [34], the error analysis of second-order ESO under constant disturbance is given, and the conclusion is that the observation error of ESO can be made small enough by selecting appropriate parameters. But the premise is steady-state and constant disturbance, and the quantitative analysis is also difficult and challenging. Actually, ESO has been applied in many papers and practical system, and many cases show that ESO is an effective estimation method. Therefore, ESO is applied in this paper, and our simulations and experiments prove the effectiveness of ESO._  
Figure 2: The block diagram of \(q\)-axis current controller based on extended state observer (ESO).
## 2 Review Methodology
### Data Extraction  
Design parameters are calculated from the available mix design in the literature. The various ingredients weight in terms of kg/m\({}^{3}\) required for SCC to satisfy the fresh and hardened concrete properties is called mix design. Design parameters considered for this review are W/C ratio, W/B ratio, TA/C ratio, FA/CA ratio, SP, W/S ratio (%), % of RFA, and % of RCA. With respect to available ingredients in mixed design from the literature, the design parameters are estimated. Binder content includes the mineral admixtures that possess binding properties, which are used in the mix. The sum of fine aggregate, coarse aggregate, and recycled aggregates is known as total aggregates. Solid contents such as cement, binder, and aggregates are added to give total solid content in the mix. For W/S ratio, the water includes all liquid content in the mix. The articles without mix proportions or without any one of the ingredients are omitted from the review process.  
From the available literature, the results are grouped into six families based on the compressive strength grade. Twenty-eight days compressive strength of control or reference SCC mix is used to classify the proportions irrespective of the size of the specimen, shape of the specimen, and various international standards or codes used for testing of the specimen. For example, Family I consist of data based on the reference or control 28 days compressive strength lies in the range of 70 MPa to 80 MPa. In a similar fashion, family II, family III, family IV, family V, and family VI consists of 28 days compressive strength that lies in the range of 60 MPa to 70 MPa, 50 MPa to 60 MPa, 40 MPa to 50 MPa, 30 MPa to 40 MPa, and 20 MPa to 30 MPa, respectively. Corresponding split tensile strength at 28 days is also tabulated irrespective of nature of size, the shape of the specimen, and international standards or codes. If one of the compressive strengths is not available for evaluation in the article, then that corresponding article itself is neglected for review purpose.
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
## 5 Case Studies
### IEEE 14-Bus System  
#### 5.1.1 Case Introduction  
The IEEE 14-bus system in the literature [1; 17; 36] is used as a test case to examine the effectiveness of the proposed approach. This system includes five generators, 11 loads, 20 branches, three adjustable transformers, and one reactive power compensation device (connected to bus 9), as shown in Figure 4.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Parameter** & **Meaning** \\ \hline \(P_{G}\) & Active power outputs of generators \\ \(Q_{G}\) & The reactive power outputs of generators \\ \(P_{D}\) & Active loads of load buses \\ \(Q_{D}\) & Reactive loads of load buses \\ \(U\) & Voltage amplitudes of buses \\ \(\delta\) & Voltage angles of buses \\ \(T\) & Taps of transformers \\ \(Q_{C}\) & Switching capacities of reactive power compensation capacitors \\ \(A_{i}\) & Alarm limits of \(U_{i}\) \\ \(H_{i}\) & Security limits of \(U_{i}\) \\ \(P_{A}\) & Upper alarm limit of line active power flow \\ \(P_{H}\) & Upper security limit of line active power flow \\ \(PI_{c}\) & Security index \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main parameters in Section 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & \multicolumn{4}{c}{**Observation**} & \multicolumn{4}{c}{**Response**} \\ \hline
**Variable** & \(P_{G}\) & \(Q_{G}\) & \(U\) & \(T\) & \(Q_{C}\) & \(L\) & \(PI_{c}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Observations and responses of the Lasso algorithm.  
In the case of the 14-bus system, 19 lines are considered for the \(N\)\(-\)1 contingencies analysis. The line 7-8 (L 14) is not taken into account, because it is the only line connected to the generator. The load condition randomly varies from 50% to 150% of the base load; 950 sets are generated for the MSA-Lasso algorithm, where 20% of the sets are employed for test sets, and the others are applied as training sets [15]. It should be noted that more sets can be generated if necessary. Once the Lasso model is trained, it can be used for predicting the security indices.
## 2 Materials and Methods
### Image Analysis  
Samples were imaged with the Axioscan digital slide scanner (Carl Zeiss microscopy, GmbH, Marly Le Roi, France) and analyzed with ZEN 2 (blue edition) software (Carl Zeiss microscopy, GmbH). For safranin-O positive surface area measurements, Tagged Image File Format (TIFF) images were segmented in ImageJ software (version 1.15w, National Institutes of Health) with the Color Threshold plugin [28]. Safranin-O positive areas were isolated by setting Thresholding method to Default, Threshold color to black and white (B&W), Color space to HSB (Hue: 229/255, Saturation: 35/255, Brightness: 0/255) and Dark Background was not selected. The same parameters were applied to the total surface area of the histological section except for HSB sub-settings (Hue: 0/255, Saturation: 0/255, Brightness: 0/220). The value obtained was defined as a percentage by multiplying the ratio of safranin-O positive area on the total section area by 100. Measurements were performed on at least three entire histological sections per condition and per donor.
## 3 Analysis of Experimental Data and Results
### Model Validation
Therefore, the estimation of the true crack growth rate given by the model prediction is expected to be slightly higher. The results show that to correct the model, the predictions must be increased by a factor of 1.01. Using the experimental data points for \(log(da/dN)\) and assuming an experimental error of \(\pm 1\%\), the 45-degree solid line shows the difference between the model prediction and the \"true\" \(log(da/dN)\) (with the 45-degree line showing the perfect match). However, because of the experimental data scatter and a slight bias expressed by \(F_{m}\) (showing systematic model error) between the true \(log(da/dN)\) and regression models of Figure 9, the true \(log(da/dN)\), with a 95% confidence level, will be somewhere as high as 14% above the model prediction and as low as 10% below this prediction. As expected, the validation results showed good agreement between model predictions and experimental observations. The validated AE model can be used in real time monitoring of large cracks that subsequently improves the structural health management.
# Single Trace Side Channel Analysis on NTRU Implementation
## 2 Background  
### Algorithm of NTRU  
The NTRU is a PKC based on the shortest vector problem whose computational complexity is exponential even in the presence of a quantum computer. The encryption and decryption scheme use polynomial rings in \(\mathcal{R}=\mathbb{Z}[X]/(x^{N}-1)\), which consist of all polynomials with degree less than \(N\) and coefficients in \(\mathbb{Z}\). Thus, an element \(f\in\mathcal{R}\) can be written as \(f=\sum_{i=0}^{N-1}f_{i}x^{i}\). The polynomial multiplication in \(\mathcal{R}\) is denoted as \(\cdot\), and is performed as in Equation (1).  
\[\begin{array}{l}h=f\cdot g,\ f,\ g,\ h\in\mathcal{R}\\ h_{k}=\sum\limits_{i=0}^{k}f_{i}g_{k-i}+\sum\limits_{i=k+1}^{N-1}f_{i}g_{N+k- i}=\sum\limits_{i+j=k}\sum\limits_{(\mathrm{mod}\ N)}f_{i}g_{j}\end{array} \tag{1}\]  
Let \(\mathcal{L}_{f}\) be a set of \(f\in\mathcal{R}\) with \(d_{f}+1\) coefficients equal to \(1\) and \(d_{f}\) coefficients to \(-1\) and let \(\mathcal{B}_{g}\) be a set of \(g\in\mathcal{R}\) with \(d_{g}\) coefficients equal to \(1\) and \(-1\), where \(d_{f}\) and \(d_{g}\) are fixed parameter. We express the polynomials in \(\mathcal{L}_{f}\) and \(\mathcal{B}_{g}\) as a trinary polynomial because they consist of only three number of coefficient. The modulus values of integers \(p\) and \(q\) are used and they satisfy the conditions \(gcd(p,q)=1\) and \(p\ll q\). We define \(f_{p}\) as a polynomial in \(\mathbb{Z}_{p}[X]/(x^{N}-1)\) obtained by reducing the coefficients of \(f\in\mathcal{R}\) modulo \(p\). The inverse of \(f_{p}\) in \(\mathbb{Z}_{p}[X]/(x^{N}-1)\) is denoted as \(f_{p}^{-1}\). The \(f_{q}\) and \(f_{q}^{-1}\) are defined in the same manner.
# Experimental Validation and Evaluation of a Coupled Twist-Camber Morphing Wing Concept
## 2 Morphing Wing Concept
### Wing Skin  
The design generated in [26] was scaled up 1:3 resulting in a planar wing surface of 0.5 m\({}^{2}\), an aspect ratio (AR) of 16.9, and a wingspan of 3 m. The resulting minimum skin thickness is about 0.5 mm, which made it possible to manufacture using composite materials.  
The manufacturing process of the wing skin started with a manual layup of carbon fiber reinforced polymers (CFRP) molded and cured using vacuum bagging. The skin was manufactured as one piece.  
The outer surface of the wing was composed of a woven carbon fiber (CF) layer with fibers oriented with and perpendicular to the spanwise direction. This layer provides handling strength and reduced torsional stiffness to facilitate actuation. On the inner surface, a unidirectional CF layer is used to increase the bending stiffness without impacting greatly the required actuation power. In between layers, four layers of paper with prescribed shapes were placed as accurately as possible to obtain an approximation to the designed thickness distribution. Each layer of paper was 0.1 mm thick, and its function was to absorb the resin while maintaining the necessary separation between layers while curing the composite.  
This manufacturing solution was penalizing in terms of structural mass. Ideally, honeycomb or foam core machined to the desired shape would allow for a significantly lighter structure, however, given the reduced thicknesses involved and the available resources, it was not possible to find an alternative solution at this time. It was also difficult to guarantee a high accuracy of the placement of the paper layers during the curing of the  
Figure 4: Tip airfoil under different spanwise actuation distributions.  
material since the layers could slide relative to each other and the CF layers. Nevertheless, the resulting piece still exhibited the expected behavior and was therefore deemed suitable for the following ground and flight tests as a proof of concept.
## 2 Relationship between MWL and MASS
### Mental Workload and Safety  
[PERSON] and [PERSON] [23] defined the workload as not task-centred, but human-centred. MWL is a widely utilised concept in ergonomics and is used in various ways, including analysis of the effects of additional tasks such as the new automated design [24]. MWL can also measure the effects on elements of the safety of transportation, such as drivers' reaction time [25]. The commonly used MWL concept comprises three domains: the input of load outside the human factor, the effects inside the human factor, and performance (data output) caused by a human operator [7]. [PERSON] et al. [24] developed [PERSON]'s model [7] by adding the concept of physical and cognitive demand, elements of goals and strategies to discuss the MWL of railway signalling operators. As a partly similar model, [PERSON] and [PERSON] [6] show a unique mental process on road safety, including the MWL model, to analyse contribution factors that influence MWL and discuss the optimal situation. According to them, \"task activities\" are generated from eternal conditions such as roadway conditions and traffic situations. Then, the task activities connect to \"task demand\" through situation awareness. \"Motivated capability\" originating from physical capacity and psychological condition also influences MWL. When it comes to shipping, there are some differences from land transport. For instance, ships do not have fixed routes except in some areas such as traffic separation schemes, (TSSs), narrow channels, and in ports. Sudden crossing by small boats often happens even in fixed traffic areas. Moreover, some weather conditions (e.g., waves and wind) have effects on ships' manoeuvring. Nevertheless, the mental process is similar between ships and automobiles.  
The MWL can measure the safety level. [PERSON] [26] suggests that MWL is the \"operator's evaluation of the attentional load margin (between their motivated capacity and the current task demands) while achieving adequate task performance in a mission-relevant context\". MWL would have a potential safety risk of navigation when task demand exceeds motivated capacity and the workload margin becomes negative [6]. In this case, MWL is too high. On the other hand, the too-much margin becomes the too-low MWL that also leads to a safety risk due to a careless attitude. They suggested that adjusting these two items was necessary to keep MWL at an optimal level and drive safely. This could apply to the  
Figure 1: Flow diagram of the research.  
navigation of a ship. The too-high MWL leads to excess task demand beyond motivated capacity (negative load margin). A potential safety risk of navigation emerges in this situation. Too-low MWL leads to far less task demand than motivated capacity (too much positive load margin), where a potential safety risk of navigation also emerges (Figure 2).
# Prediction of Cardiorespiratory Fitness Level of Young Healthy Women Using Non-Exercise Variables
## 2 Methods
### Predictive Methods  
In this study, MISO models for prediction were used (multiple input single output). Ordinary least square regression (OLS), regularized regression, and stepwise regression were used in the calculations of the models. All the methods used are linear.  
The implemented methods included OLS regression, in which weights are calculated by minimizing the sum of the squared errors. The study also used stepwise forward regression (OLS-FS) [46; 47] where predictor selection based on significance tests is applied, as a result of which only statistically significant input variables remain in the model. This method makes it possible to obtain models with a smaller input structure. Regularized linear models, also known as shrinkage models, which include the Ridge and Lasso methods, were also used to calculate the models. The Ridge model was determined using the criterion which includes a penalty for increased weights. The \(\lambda\) parameter decides about the penalty: the greater \(\lambda\) the bigger the penalty [48]. The value of \(\lambda\) is always positive and for \(\lambda=0\) Ridge regression is reduced to OLS regression. LASSO regression similar to Ridge regression, adds to the criterion of performance penalty. In the Lasso model the mechanism facilitates assigning a penalty to variables, and in this way, they are eliminated from equations [49]. This model uses the norm \(L_{1}\) as a criterion of performance.  
All predictive models were calculated in R software [50] with additional packages. For Ridge regression, functions from the \"MASS\" package were used, and for LASSO, the enet function included in the \"elastic net\" package. To calculate stepwise regression, the \"olssr\" package was used.  
The methods described above were used to calculate predictive models based on all variables (Table 1). In addition, Ridge regression for subcollections of variables received after using the forward selection procedure was calculated. All models were tested using leave-one-out-cross-validation (LOOCV), for which its own functions were prepared. In the process of validation, the \(\text{RMSE}_{\text{CV}}\) was calculated, which has the form:  
\[\text{RMSE}_{\text{CV}}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{-i})^{ 2}} \tag{1}\]  
where \(n\)--sample size, \(\hat{y}_{-i}\)--model result calculated after removing the tested pair \((\mathbf{x}_{i},y_{i})\).  
This paper presents three CRF modeling solutions. In the first part, models calculated only on the basis of anthropometric variables will be presented. In the second, models calculated only for variables describing physical activity will be presented. The third type of models are those calculated for all variables, i.e., anthropometric parameters and physical activity.
## 3 Experimental Setup  
### Implementation of the ARNC System  
The MR-SISO ARNC system was implemented in an actual car for the real-time control experiment. The engine of the 4-wheel car was a Hyundai gasoline 1600 cc with 4 cylinders.  
For secondary actuation, the ESG was installed at the cowl panel to generate anti-sound against the road noise. For reference sensing, four 3-axis accelerometers (356A15, PCB Piezotronics, Depew, NY, USA), a total of 12 acceleration signals, were installed near each wheel to capture the relevant reference signals of road noise of the test car. For disturbance and error sensing, a microphone (377B02, PCB Piezotronics, USA) was located at the driver's left ear to measure the acoustic signal in the cabin.  
The MR-SISO ARNC algorithm in Equation (7) was embedded on a control unit (DS1401, dSPACE, Paderborn, Germany). The other devices included in the system were a power amplifier (Stereo Box S, Pro-Ject, Mistelbach, Austria) to operate the ESG, 13 signal conditioners (480E09, PCB Piezotronics, USA) to amplify the 12 reference signals from the accelerometers and the error signal from the microphone, and 14 low-pass filters (LPFs) for anti-aliasing and reconstruction of the input and output signals. The sampling rate of the MR-SISO ARNC algorithm was 3200 Hz, and the cut-off frequencies of the LPFs were set to 500 Hz.  
The 3-D Euclidean distance between the ESG and the error microphone was about 1.2 m, and that between each accelerometer and the error microphone was approximately 1.6-1.8 m, as shown in Figure 2.  
Before the real-time control experiment, the primary and secondary paths in the ARNC system of the test car were measured and analyzed. The car was driven on a paved road with a constant speed of 60 kph during the real-time control experiment.
## 3 Case Studies
### Sarajevo 5D
#### 3.4.2 Application Structure  
The main functionality of the AR application is showing 3D models when tracker images are being scanned from the map (Figure 9) with mobile camera. Each tracker image activates a model of different building, as well as a pin for showing additional informational content in order to deliver most important facts about selected object to the user. If the user is not at the location where the object was originally located, Google maps guidance to the site can be started directly from the application, in order to easily navigate him/her to the exact location. When the user comes to the location and loads the object model, additional content is shown. This feature was added in order to provide more interesting information and thus motivate users to come to the site and explore  
Figure 8: Taslihan AR model.  
it. Application is bilingual, so both local and foreign English speaking users can use it. Previous can be seen in the Figure 10.
## 5 Experimental Results
### Results on Speaker Models--TL from Speaker Recognition Task
* The enhanced versions of the embeddings showed more substantial improvements after the application of TL. The r-vector exhibited the highest accuracy improvement across all models, with an increase of 12.8% in UA and 13.1% in WA on IEMOCAP. Similarly, on CREMA-D, it demonstrated improvements of about 10.5% for both metrics. * The range of improvement varied between the datasets. TL led to more pronounced improvements on IEMOCAP compared to CREMA-D. The structured nature of CREMA-D, with consistent sentence use across actors (each emotion is expressed using the same 12 sentences) and well-balanced representation of emotions, may explain its higher pre-TL performance. In contrast, the mix of scripted and improvised utterances in IEMOCAP presented a more complex task, emphasizing the need for robust models and advanced techniques to achieve higher accuracy.
## 5 Performance Evaluation  
In this section, the performance of \(ETE_{FN}\) is evaluated in different prospects. The offloaded tasks are categorized in three levels of priority. Each vehicle attached with the fog node is randomly chosen to offload one to three tasks of different levels to its fog node as discussed in Section 3. The performance of \(ETE_{FN}\) is evaluated in different scenarios, and it is compared with the following three task offloading schemes:  
1. Tasks of different vehicles are offloaded to a fog node for task computation. Offloaded tasks are processed by following the smallest task first (STF) algorithm. This allows the fog node to start executing tasks from the smallest tasks and keeps on executingthe tasks to the task processing capacity of the fog node. The remaining tasks are forwarded to the cloud for computation and execution.
2. In the second scheme, a fog node executes tasks by following the longest task first (LTF) algorithm. Contrary to STF, LTF allows a fog node to start executing from the longest task execution and keeps on processing to the task processing capacity of the fog node. The remaining tasks are forwarded to the cloud for computation and execution.
3. The fog node processes offloaded tasks up to its processing capacity by applying first come first serve (FCFS) mechanism, and the rest of the tasks are forwarded to the cloud for processing and execution.  
We developed a simulation environment to evaluate the performance of the proposed technique with two other schemes. The detailed simulation setup along with its different parameter values are described in Section 5.1 and results are discussed in Section 5.2.