esa-sceva/satcom-chunk-collection huggingface dataset:
# Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits
## 5 Machine Learning Analysis
### Preprocessing, Segmentation, and Feature Extraction  
We initially re-sampled all the data to a sampling frequency of 1 Hz. Next, the last 30 s of each task was used to extract features. Thus, one segment represents one task. For each segment, statistical features were extracted from each input signal, i.e., Heart rate, RR intervals, GSR, and TEMP, and their first differentials. The statistical features included mean, standard deviation, skewness, kurtosis, mean of the first derivative, mean of the second derivative, 25 th and 75 th percentiles, inter-quartile range, difference between the minimum and the maximum values, and coefficient of variation.  
Additional features were extracted from the GSR signal using Skin Conductance Response (SCR) analysis. This type of feature/analysis is proven to be useful for detecting stressful conditions in driving scenarios [45] and in real-life situations [11]. The GSR signal is first preprocessed using a sliding mean filter, and then fast-acting (GSR responses) and slow-acting (tonic) components were extracted. The fast-acting component was used to calculate the number of responses in the signal, the responses per minute in the signal, and the sum of the responses. The slow-acting component was used to calculate the mean value of the first differentials of the tonic component, and the difference between the tonic component and the overall signal.  
Activation of the sympathetic nervous system triggered by cognitive load leads to more equidistant heart beats. On the other hand, the rest periods between the tasks reverse this process, and the heart beats become more irregular, as \"A healthy heart is not a metronome\" [64]. Heart Rate Variability Analysis (HRV) is commonly used to quantify the dynamics of the RR intervals. The RR signal was filtered by removing the outliers, i.e., the RR intervals that are outside of the interval [0.7*median, 1.3*median], where the median is segment-specific. Next, the following HRV features were calculated: the mean heart rate, the standard deviation of the RR intervals, the standard deviation of the differences between adjacent RR intervals, the square root of the mean of the squares of the successive differences between adjacent RR intervals, the percentage of the differences between adjacent RR intervals that are greater than 20 ms, the percentage of the differences between adjacent RR intervals that are greater than 50 ms, and Poincare plot indices (SD1 and SD2) [65].
## 3 Proposed Method
### Bilinear Interaction Layer
\begin{split}\beta_{m,l}&=\mathbf{h}_{m}\mathbf{W}_{m,l}, \qquad\quad[\beta_{m,l}]_{i}=\sum_{j}[\mathbf{h}_{m}]_{j}\,[\mathbf{W}_{m,l}]_{j,i}\\ \mathbf{\alpha}_{m,l}&=\beta_{m,l}\odot\mathbf{h}_{l}, \qquad[\mathbf{\alpha}_{m,l}]_{i}=[\beta_{m,l}]_{i}\cdot[\mathbf{h}_{l}]_{i}\end{split}  \tag{14}\]  
where the first line defines the inner-product operation as the multiplication of \(\mathbf{h}_{m}\) and the weight matrix \(\mathbf{W}_{m,l}\). \(\beta_{m,l}\) is the output of the inner-product operation. The second line shows the Hadamard (element-wise) product of \(\beta_{m,l}\) and \(\mathbf{h}_{l}\). \(\mathbf{W}_{m,l}\) denotes the corresponding parameter matrix of the interaction between \(\mathbf{h}_{m}\) and \(\mathbf{h}_{l}\), with a dimension of (\(B\),\(f\),\(f\)). There are \((k(k-1)/2)\times f\times f\) learning parameters. This type is referred to as 'codeword-interaction'. By combining all second-order codeword interaction features, we obtain the output:  
\[\mathbf{H}_{BI}=[\alpha_{1,1},\alpha_{1,2},\ldots,\alpha_{k,k}] \tag{15}\]  
For the deep network layers, the input should be two-dimensional. Therefore, the output of \(H_{BI}\) is reshaped as \(\mathbf{R}\), and its dimension is (\(B\), \((k(k-1)/2)\times f\times f\)). Overall, the bilinear interaction [37] layer plays a crucial role in the steganalysis model by allowing fine-grained learning of feature interactions and extracting complex and informative interaction features. These advantages contribute to enhancing the model's ability to extract relevant steganalysis features and improve its performance in detecting and analyzing steganographic content.
# Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy
## 3 Methods
### Segmentation
#### 3.1.2 Segmentation of Femoral Cartilage from MRI  
Automatic dense labeling of MRI subjects is done by using atlas-based segmentation. An open-source SPL Knee Atlas developed by The Surgical Planning Laboratory at Brigham and Women's Hospital, Harvard Medical School [24], is elastically deformed onto our MRI volumes. The atlas is an MRI derived atlas generated through semi-automated segmentation algorithms and 3D image reconstruction techniques. Deformable registration of the atlas data onto the subject volume is performed using ANTs (Advanced Normalization Tools). Applying diffeomorphisms, the atlas based segmentation computes a reversible transformation composed of a global affine transform and a dense vector field denoting the deformation suffered by the atlas to morph onto the subject MRI image [25]. Figure 3 shows the segmentation output achieved after the deformable registration is applied to the atlas.
# An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions
## 2 Autonomous Vehicles Sensors Ecosystem
### Camera  
Self-driving vehicles may rely heavily on cameras to perceive the surrounding environment. According to the electromagnetic spectrum, most cameras can be classified as visible (VIS) or infrared (IR). VIS cameras (e.g., monocular vision [18; 19; 20; 21] and stereo vision [22; 23]) capture wavelengths that ranges from 400 to 780 nm, similarly to human eyes. They are mostly used due to their low cost, high resolution, and their capability to differentiate between colors. Combining two VIS cameras with a predetermined focal distance allows stereo vision to be performed; hence, a 3D representation of the scene around the vehicle is possible. However, even in a stereoscopic vision camera system, the estimated depth accuracies are lower than the ones obtained from active range finders such as RADARs and LiDARs.  
IR cameras work with infrared wavelengths ranging between 780 nm and 1 mm. They can be extended to the near-infrared (NIR: 780 nm-3 mm) and the mid-infrared (MIR: 3-50 mm; known as thermal cameras) [24; 25] for certain applications. IR cameras are less susceptible to weather or lighting conditions, and it can overcome some of the VIS cameras shortcomings in situations where there are peaks of illumination (e.g., at the exit of a tunnel). In addition, they can be used for warm body detection, such as pedestrians and animals [26; 27; 28; 29].  
Moreover, NIR cameras can be used for range detection by using the ToF principle and the phase difference between the transmitted and the received light pulses. Depending on the number of light emitting diodes (LEDs) utilized in the LED array, the distance ranges from 10 m for interior scenes to roughly 4 m for outdoor scenes.
# Alternative Pedicle Screw Design via Biomechanical Evaluation
## 3 Mechanical Strength of the New Design
### Numerical Testing for Pull-Out Strength  
Since pull-out strength is important in a pedicle screw design, various evaluation methods have been proposed to test it. It is noteworthy that previous researchers measured the pull-out force after applying the pedicle screw on a polyurethane block or bovine bone [4]. Others have investigated the force required after embedding in various polyurethane blocks to simulate the diverse bone density conditions of the vertebrae. The ASTM F1839 standard [18] was mainly used to assess these methods. Other testing methods using the ASTM F543-13 standard have been reported. As a vertebra model material, UHMWPE was applied. Real human bone can also be used in the tests.  
However, the polymers characteristics are not compatible with the pull-out test because they are more mechanically compliant ([PERSON]'s modulus \(\sim\)30 MPa) [19] than human bone ([PERSON]'s modulus \(\sim\)100 MPa). Additionally, it is difficult to perform every required experiment for a screw design with real vertebrae because it is difficult to obtain actual human spine bones. Moreover, it can only evaluate the results of the testing and it is not possible to know in detail the stress distributed in the screw, the lumbar spine, or both. As described above, when a pull-out or bending load is applied to the pedicle screw, static rupturing or fatigue fracturing mainly occurs in the region where the stress is concentrated. Therefore, analyzing the distribution of stress in a screw, vertebrae, or both is important for design improvement.  
There is an example study of modeling the pedicle screw via an FEA method. The researchers analyzed the pull-out strength according to the angle or depth at which the screw was mounted on the vertebrae and reported that the strength changed with the varying bone density in the spine [20]. In addition, an example of using the FEA method to predict the results after inserting intramedullary devices into the femur and hip joint to treat hip fracture was reported [21]. Based on these studies, strength analysis of the pedicle screw, vertebrae, and both together, using FEA, has become a general method. Therefore, it would be helpful to researchers in the future if a platform of modeling and experimental testing became established so that design improvement and reliability evaluation were possible.  
Therefore, in this study, an FEA model of the integrated structure comprising vertebrae (L4 and L5) and inserted screws was established and then applied to assess the structural stability of the alternative screw against the pulling-out force. A few ASTM standards and several studies on pull-out testing were consulted to define the applied load in the FEA model [4; 19; 22].
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### ISM Band-Based Technologies
#### 2.2.6 Dash7  
The DASH7 Alliance [34] is an industry consortium. They propose a full stack protocol known as DASH7 Alliance Protocol (D7 AP) based on narrow-band modulation in the ISM sub-GHz bands. D7 AP defines a complex network stack and has features such as inevitable periodic wake up of the nodes. This results in a much lower latency for the communications, but it also results in an increase in power consumption. It offers a data rate of 9.6 to 166.7 kbps.
## 2 Materials and Methods
### Network Adjustment Process
The a\({}_{\text{i}}\)\({}^{2}\) value is used as a complementary indicator to confirm the convergence and stability of the iterative adjustment process. [15; 41]. Modern network adjustments often integrate GNSS vectors with classical terrestrial measurements, such as distances and angles, using rigorous 3D least squares methods. This integration enhances network reliability, particularly in environments with GNSS signal obstructions [42; 43]. Adjustment tools like GAMIT/GLOBK and Bernese support these hybrid configurations with flexible weighting schemes and robust error modeling [10; 44]. Finally, adherence to international standards such as those of the IGS and NGS ensures the consistency and interoperability of geodetic data across regions [29; 45]. By combining rigorous adjustment theory with advanced software and standardized procedures, GNSS network adjustments provide the foundation for precise and sustainable geospatial infrastructure.
# Voltage-Based Braking Controls for Electric Vehicles Considering Weather Condition and Road Slope
## 3 Braking Controls
### Angular Velocity Control for an Electric Vehicle with DC Motors  
Considering an electric vehicle with DC motors, we present how to control \(w\), so that it converges to \(w^{o}\). In practice, the angular velocity of the wheel is measured in real time using encoders. Considering an electric vehicle with DC motors, the angular velocity can be controlled using the voltage applied in the armature windings of the DC motor [21].  
For vehicle stop, we use the relationship between the angular velocity and the armature voltage, as presented in [21]. Note that this article does not require any brake units. For braking controls, we control the voltage in the armature windings of the motor for controlling the angular velocity.  
In the Laplace domain, the relationship between the angular velocity \(w(s)\) and the armature voltage, say \(v_{a}(s)\), is  
\[\frac{w(s)}{v_{a}(s)}=\frac{0.0036}{(s+0.05)(0.005s+1)+1.44*10^{-5}}. \tag{8}\]  
See [21] for a detailed derivation of (8). We acknowledge that depending on the DC motor, the relationship between \(w(s)\) and \(v_{a}(s)\) can be distinct from (8).  
Equation (8) is simplified to  
\[50\ddot{w}+500w+10^{4}\dot{w}\approx 36v_{a}. \tag{9}\]  
One considers discrete-time systems in the development of our controls. Let \(T\) denote the sampling interval. The discrete-time system of (9) is  
\[\ddot{w}[k]=\frac{36}{50}v_{a}[k]-10w[k]-200\dot{w}[k]. \tag{10}\]  
where  
\[\dot{w}[k]\approx\frac{w[k+1]+w[k-1]}{2T}, \tag{11}\]  
and  
\[\ddot{w}[k]\approx\frac{\dot{w}[k+1]-\dot{w}[k-1]}{2T}. \tag{12}\]  
Inspired by [21], we use PID controls for controlling the armature voltage input. In discrete-time systems, the armature voltage \(v_{a}[k]\) is controlled using the following PID control:  
\[v_{a}[k]=g_{p}*e[k]+g_{i}*\sum_{j=1}^{k}e[j]. \tag{13}\]  
where  
\[e[k]=w^{o}[k]-w[k]. \tag{14}\]The DC motor allows for two-direction movement; thus, we have both positive and negative voltages. We need to limit motor damage from the overcurrent. Thus, we set the motor voltage limit such that the voltage exists in the interval \([-V_{max},V_{max}]\) (V).  
There may be a case where \(v_{a}[k]\) in (13) is above \(V_{max}\). In this case, we set \(v_{a}[k]=V_{max}\). There may be a case where \(v_{a}[k]\) in (13) is less than \(-V_{max}\). In this case, we set \(v_{a}[k]=-V_{max}\). In this way, we can limit motor damage from the overcurrent.
# Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill
## 5 Industrial Case Study and Approach Validation
### Data Gathering and Preprocessing  
The rolling mill machine has sensors on it that measure the temperature and hydraulic forces. The data used for the energy \(W\) estimation are (1) cylinder hydraulic forces in kNs and (2) segment surface temperatures in Celsius degrees. The diagram presented in Figure 5 depicts the data connections for the rolling mill machine. The data are collected from a total of four sensors implanted on the machine: two force and two temperature sensors on each cylinder. On the first level, the collected data from the hydraulic force sensors are stored to a local database in the motion controller and then transferred to a Programmable Logic Controller (PLC) database. The temperature data are directly stored in the PLC. The acquired data are transferred on a second level to the Historian database. The signals are transmitted in real-time into the processing PC via communication channels from the PLC database, once the processing of a single product has finished, to run the RUL estimation app. The incoming data are synchronized under common timestamps.  
The historical data were used to validate our model. An example of data during a single trailing arm's processing in the rolling mill machine is illustrated in Figure 6. There, the \(x\)-axis shows the processing time of the product, and the \(y\)-axis presents the hydraulic force (upper plot) and the segment surface temperature (lower plot). The collected data are being normalized before the execution of the proposed method. Normalization is performed by removing the data mean from the data values and then dividing them with their standard deviation (also see Section 3.2). Normalization is a mandatory  
Figure 4: Rolling mill process.  
Figure 5: Data connections.  
process because data values must lie within a common numerical area, in order to avoid potential numerical problems.
## 5 Discussion and Comparison to SCPT Results
### Shear Crack Kinematics and Concrete Strains in Compression Zone  
With regards to Figure 7 and the measured developments of the crack opening \(\mathrm{w_{reinf}}\) and maximum concrete compressive strain \(\mathrm{\varepsilon_{top}}\), the corresponding SCPT results generally show a very good agreement when compared to the DIC curves. For shear loads larger than \(\mathrm{V}\approx 95\) kN, the experimentally obtained crack openings and concrete compressive strains started to increase, caused by a larger shear crack rotation. However, the calculated SCPT  
Figure 8: Comparison of the calculated SCPT failure shear crack with the experimentally obtained crack pattern: (**top**) marked crack pattern and (**bottom**) digital image correlation (DIC) measurements at the opposite side.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test No.** & \(\mathbf{F_{w,exp}}\) **(kN)** & \(\mathbf{V_{u,a,exp}}\) **(kN)** & \(\mathbf{V_{u,a,SCFT}}\) **(kN)** & \(\mathbf{V_{u,a,BC2}}\) **(kN)** \\ \hline B1-1 & 195.7 & 104.8 (=100\%) & 111.4 (=106\%) & 113.3 (=108\%) \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of the experimental shear failure load \(V_{\mathrm{u}}\) (self-weight neglected) with calculated shear failure loads according to the Shear Crack Propagation Theory (SCPT) and current EC2.  
values do not show this change in slope for both measurements due to linear constitutive laws used in the SCPT algorithm (e.g., linear elastic behavior of the concrete in compression and tension).  
Apart from this deviation close to the failure load, it is stated that the SCPT in its form today using the linear constitutive behavior of concrete and linear aggregate interlock behavior [43] is capable to track the crack kinematics up to a point prior to failure, where the nonlinear behavior sets in, which may be initiated by a redistribution of stresses within the shear crack.
# Experimental and Numerical Study on UHPC-RC Decks within Hogging Moment Region
## 3 Experimental Study
### Experimental Results  
For specimen A, when a load of 60 kN was applied, the first cracking was observed, and cracks were obviously formed near the midspan when it was loaded up to 150 kN. During the cyclic loading, new cracks occurred near the midspan and the loading position, and the old cracks increased in width. When the load was increased to 335 kN, out-of-plane instability occurred on the upper flange of the steel girder and several transverse penetration cracks were formed in the concrete deck near the midspan, leading to bending damage of the specimen, as shown in Figure 6a. Thus, the specimen was loaded to its ultimate load capacity and failed.  
Specimen B had obvious cracks at the bottom of the deck near the loading position due to construction deviations. During cyclic loading, the crack width developed only slightly due to the presence of steel fibers in the UHPC layer. When the load was increased to 160 kN, several cracks occurred in the RC layer near the midspan of the specimen (no cracks in the UHPC layer). When the load was increased to 380 kN, the concrete cracks penetrated through the whole deck at the loading position, and the steel beam was loaded to yield and the specimen failed, as shown in Figure 6b.  
For specimen C, significant cracks occurred at the bottom of the deck near the loading position due to construction deviations. During cyclic loading, no obvious new cracks were found in the specimen. When the specimen was loaded to 350 kN, the width of the UHPC cracks at the loading position increased and developed into a penetration crack, while several diagonal cracks occurred in the concrete layer and penetrated the whole deck section and the specimen failed, as shown in Figure 6c.  
Figure 5: Loading arrangement: (**a**) loading positions along the specimen length; (**b**) loading arrangement of the test.  
For specimen D, when the load was increased to 220 kN, fine cracks on one side of the loading position occurred. The damage process was similar to that of specimen C, but when the load was finally increased to 405 kN, penetration cracks only occurred on one side of the concrete deck, and the development of other cracks was not obvious, as shown in Figure 6d.  
When failure occurred, more cracks penetrated through the depth of the deck for specimen A. For specimens B, C, and D, only one or two cracks penetrated through the depth of the deck, while the development of other cracks was not obvious due to the high tensile strength of UHPC.
## 3 Results and Discussion
### Aerodynamic Characteristics of Wind Turbine Blade with VG  
The CFD analysis of the viscous sub-layer was precisely performed by composing the entire grid with a hexahedron. Figure 16 shows the surface grid of the VG-mounted blade used for the simulation, and the number of grids is provided in Table 8. A pair of VGs each generates a vortex in the opposite direction and a high-energy fluid flows into the boundary layer region of the low-energy flow field. Figure 17 shows the characteristics of the fluid flow and the CFD results of the structural analysis of the vortex created by the VG. The analysis confirmed that a tip vortex was generated at the tip of the VG and a horseshoe-shaped vortex formed at the lower front of the VG. Figure 18a,b shows that the flow separation is relatively reduced on the suction side compared to the baseline blade. Figure 19a,b shows that the VG forms a vortex and pushes the separation fluid to a distance of 8 m from the root, where flow separation is dominant. The shaft power  
Figure 15: Surface plots showing the effect of three parameters on the maximum vorticity at the trailing edge: (**a**) \(l=1.5\); (**b**) \(l=2.0\); (**c**) \(l=2.5\); (**d**) \(L=1.5\); (**e**) \(L=1.75\); (**f**) \(L=2.0\); (**g**) \(h=0.75\); (**h**) \(h=1.0\); (**i**) \(h=1.25\).
## 2 Related Works
Compared with these smart energy control systems supported by RF-based communication methods, our proposed control system can provide more accurate control of devices with higher information security. In summary, the typical methods that are adopted in smart home control systems include PLC, IoT network, information fusion, ZigBee, and VLC methods. Our proposed system utilizes a combination of VLC, BLE, and VLP technologies. The details of the works mentioned above are summarized in Table 1 with a comparison of our proposed system. The performances of these methods are compared with ours in Table 2. It can be noticed that the works based on RF networks lack high-accuracy control and system security, while wireline-based systems are poor at scalability. Our proposed system can provide users with an accurate and secure control system with high-precision 3D indoor localization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year of Publication** & **Adopted Technology** & **Communication Method** & **Core Devices** \\ \hline
[19; 20] & 2015, 2014 & PLC + WSN & PLC + ZigBee & ZigBee coordinator, power line, PLC TRx \\ \hline
[21; 22] & 2017 & IoT + big data & Wi-Fi & Microcontroller, sensors, cloud server, smartphone \\ \hline
[23] & 2008 & Information fusion & Bluetooth + PLC & IF-based controller, power line, computational unit \\ \hline
[24; 25] & 2010 & ZigBee & ZigBee & RF-based devices, sensors \\ \hline
[26; 27; 28] & 2019 & VLC & VLC & Evaluation board, smart home \\  & & & & gateway, smartphone \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works on smart control systems.