esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 2 Methodology
### Transfer Learning Framework  
Due to substantial differences in meteorological and geographical conditions across regions, models trained in one area often exhibit poor performance when directly applied to another. Transfer learning (TL) is a machine learning technique that enhances model performance in a target domain by leveraging knowledge gained from a related source domain [33]. It is particularly effective when data distributions differ but the prediction tasks remain similar. In this study, a transfer learning framework based on random forest (TL-RF) was proposed to improve the generalization ability of pavement temperature prediction models across regions. The RF model was initially trained using complete  
Figure 1: Schematic diagram of the proposed methodology. (**a**) Dataset creation and random forest model development. (**b**) Transfer learning framework based on the random forest model.  
meteorological data from the source region. The learned model structure, including decision tree configurations and feature importance rankings, was then used as initialization for transfer. In the target region, a small set of representative meteorological and temperature samples was employed to fine-tune the model. This enabled rapid adaptation to new data distributions and significantly improved the accuracy and robustness of cross-regional predictions. Figure 1b depicts the implementation framework for transfer learning using a random forest approach.
# Experimental Study on Multi-Cell Counting Using an Inertial Microfluidic Device
## 2 Materials and Methods
### Experimental Setup and Procedure  
The experimental setup was carefully designed and positioned in a cleanroom environment to maintain a contamination-free setting, ensuring the integrity of the experimental results and adherence to strict safety protocols. As shown in Figure 3, the setup allowed for the controlled introduction and observation of the microparticle suspension within the microfluidic system. The experimental setup included a syringe pump for precise flow rate control, a microfluidic chip for sorting microparticles, and two imaging systems: an inverted microscope for real-time observation and a Bioimager fluorescent microscope for the enhanced visualization of particle behavior. Both microscopes were connected to a computer for image capture and analysis. During the experiments, the prepared microparticle suspension was introduced into the device using the syringe pump, and particle movement through the microchannels was recorded. The captured images were analyzed to quantify sorting efficiency and to examine how variations in flow rate affected the particle distribution and channel sizes.  
Figure 3 provides a visual representation of the experimental setup, illustrating the arrangement of the syringe pump, microfluidic chip, microscopes, and data-recording mechanism. This setup was integral to observing the particle sorting process in real-time and collecting data for analysis.
## 4 Principles and Solving Steps of the Multiplier Path Following Method
### Principles
#### 4.1.5 The Convergence Criterion  
During the \(k\)-th iteration of the algorithm, we define \(\boldsymbol{\rho}=\boldsymbol{b}-\boldsymbol{A}\boldsymbol{T}^{(\mathrm{k})}+ \boldsymbol{w}^{(\mathrm{k})}\), \(\boldsymbol{\sigma}=\boldsymbol{C}+\boldsymbol{Q}\boldsymbol{T}^{(\mathrm{k})} -\boldsymbol{A}^{\mathrm{T}}\boldsymbol{y}^{(\mathrm{k})}\), and \(\gamma=\left(\boldsymbol{y}^{(\mathrm{k})}\right)^{\mathrm{T}}\boldsymbol{w}^ {(\mathrm{k})}\), and set the permissible error \(\varepsilon>0\).  
* Condition 1: If \(\max(\|\boldsymbol{\rho}\|,\|\boldsymbol{\sigma}\|,\gamma,\|\boldsymbol{C}_{ \mathrm{e}}\boldsymbol{T}-\boldsymbol{f}\|)\leq\varepsilon\), the iteration converges, yielding the optimal solution \(\boldsymbol{T}^{\mathrm{t}}\) that meets the requirements;
* Condition 2: If \(\|\boldsymbol{C}_{\mathrm{e}}\boldsymbol{T}-\boldsymbol{f}\|>\varepsilon\) and \(\left\|\boldsymbol{C}_{\mathrm{e}}\boldsymbol{x}^{(\mathrm{k})}-\boldsymbol{ f}\right\|/\left\|\boldsymbol{C}_{\mathrm{e}}\boldsymbol{T}^{(\mathrm{k})}- \boldsymbol{f}\right\|\geq\theta(0<\theta<1)\), the penalty function \(\beta^{(\mathrm{k}+1)}=\alpha\beta^{(\mathrm{k})}(\alpha>1)\), and the Lagrange multiplier vector \(\boldsymbol{v}\) is updated;
* Condition 3: If \(\|\boldsymbol{C}_{\mathrm{e}}\boldsymbol{T}-\boldsymbol{f}\|>\varepsilon\) and \(\left\|\boldsymbol{C}_{\mathrm{e}}\boldsymbol{x}^{(\mathrm{k})}-\boldsymbol{ f}\right\|/\left\|\boldsymbol{C}_{\mathrm{e}}\boldsymbol{T}^{(\mathrm{k})}- \boldsymbol{f}\right\|<\theta(0<\theta<1)\), the penalty function \(\beta\) remains unchanged, and the Lagrange multiplier vector \(\boldsymbol{v}\) is updated. The update formula for the Lagrange multiplier vector \(\boldsymbol{v}\) is \[\boldsymbol{v}^{(\mathrm{k}+1)}=\boldsymbol{v}^{(\mathrm{k})}-\beta\Bigl{(} \boldsymbol{C}_{\mathrm{e}}\boldsymbol{T}^{(\mathrm{k})}-\boldsymbol{f} \Bigr{)}\] (30)
# Fatigue Performance of Different Rib-To-Deck Connections Using Traction Structural Stress Method
## 4 Conclusions  
Orthotropic steel bridge deck is constructed by employing groove seam welded connections at rib-to-deck and the crossbeam. This study numerically investigated the effect of the type of weld seam on the fatigue behavior of the rib-to-deck connection. Furthermore, the trends of various stresses with different geometric parameters of the weld seam were obtained for the fatigue design of the OSBD.  
Based on the present investigation, the following conclusions are made:  
1. The accuracy of the finite element method using traction structural stress method was validated and proven to be effective and accurate with the comparison to the fatigue test data.
2. The total structural stress in the middle of the bottom side of the bridge deck was the most significant with the lowest fatigue cyclic life. The location of the minimum fatigue life was in the middle of the connection between the weld seam and the bridge deck. The fatigue performance of the bridge deck with traditional weld seam was better than that of the bridge deck with weld seam of large root. Therefore, the weld seam size should be strictly controlled for better fatigue resistance.
3. The fatigue performance of the bridge deck with double-sided seam as proposed in this study was much better than that of the bridge deck with traditional weld seam. The increase in the thickness of the inner weld seam at the rib-to-deck connection optimizes the distribution of the equivalent structural stress and modifies the fatigue failure mode from the weld root of the outer weld seam to the weld toe of the inner weld seam by taking advantage of the crossbeam.
4. The trends of equivalent structural stress with the geometric parameters of the weld seam in the rib-to-deck connection were obtained. These trends can be useful for the engineering fatigue design of the OSBD. The fatigue behavior of the components and the equivalent structural stress were significantly influenced by the bridge deck thickness; other geometric parameters affected the fatigue performances of the OSBD slightly. It is recommended that the variation in equivalent traction structural stress due to the geometric parameters of weld seam should be added to the OSBD specification of Eurocode3.  
Conceptualization, H.Y. and H.Q.; methodology, P.W.; software, H.Y.; validation, P.W.; writing--original draft preparation, H.Y. and P.W.; writing--review and editing, H.Q. and P.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of China (Grant No. 51678191 and No. 51605116).  
The authors declare no conflicts of interest.
# Cost-Effective CNNs for Real-Time Micro-Expression Recognition
###### Abstract  
Micro-Expression (ME) recognition is a hot topic in computer vision as it presents a gateway to capture and understand daily human emotions. It is nonetheless a challenging problem due to ME typically being transient (lasting less than 200 ms) and subtle. Recent advances in machine learning enable new and effective methods to be adopted for solving diverse computer vision tasks. In particular, the use of deep learning techniques on large datasets outperforms classical approaches based on classical machine learning which rely on hand-crafted features. Even though available datasets for spontaneous ME are scarce and much smaller, using off-the-shelf Convolutional Neural Networks (CNNs) still demonstrates satisfactory classification results. However, these networks are intense in terms of memory consumption and computational resources. This poses great challenges when deploying CNN-based solutions in many applications, such as driver monitoring and comprehension recognition in virtual classrooms, which demand fast and accurate recognition. As these networks were initially designed for tasks of different domains, they are over-parameterized and need to be optimized for ME recognition. In this paper, we propose a new network based on the well-known ResNet18 which we optimized for ME classification in two ways. Firstly, we reduced the depth of the network by removing residual layers. Secondly, we introduced a more compact representation of optical flow used as input to the network. We present extensive experiments and demonstrate that the proposed network obtains accuracies comparable to the state-of-the-art methods while significantly reducing the necessary memory space. Our best classification accuracy was 60.17% on the challenging composite dataset containing five objectives classes. Our method takes only 24.6 ms for classifying a ME video clip (less than the occurrence time of the shortest ME which lasts 40 ms). Our CNN design is suitable for real-time embedded applications with limited memory and computing resources.  
computer vision; deep learning; optical flow; micro facial expressions; real-time processing Article
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 3 Singular Value Decomposition (SVD)
### SVD Properties
#### 3.2.2 Noise Filtering  
By zeroing out small singular values (large \(\Sigma_{i}^{-1}\)) we can low pass filter the input vector \(x\), which gives the opportunity to filter out noise in the measurement.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 2 Passive Bistatic Radar and Mie Scattering
### Forward Scatter Radar  
The forward scatter radar (FSR) configuration is achieved by modifying the bistatic geometry to where \(\beta\approx 180^{\circ}\). At this point, \(T_{x}\) and \(R_{x}\) are virtually diametrically opposed, and scattering based on [PERSON]'s principle becomes dominant. This principle states that the scattering characteristics from a target are equivalent to the radiation from a target silhouette-shaped aperture contained in an infinite perfect electrical conductor (PEC) plane [25]. Essentially, there is some signal blockage or electrical shadowing that disrupts the direct signal. This will be mathematically explored in Section 3.  
As a result of this phenomenon, the FS-RCS and scattering beamwidth are simple expressions that depend only on the target dimensions and incident wavelength as described in Equations (1) and (2)  
\[\sigma_{FS-RCS}=4\pi\left(\frac{A}{\lambda}\right)^{2}=4\pi\frac{\pi^{2}r_{lg} ^{4}}{\lambda^{2}} \tag{1}\]  
\[\sigma_{bw}\approx\frac{\lambda}{d} \tag{2}\]  
where \(r_{lg}\) is the target radius, \(A\) is the target silhouette area, \(\lambda\) is the incident or operational wavelength, and \(d\) is the largest dimension of the target, which is usually taken to be the diameter. From Equation (2), it is of note that the larger the wavelength is compared to the target, the larger the scattering beamwidth. This contributes to maximizing the coverage area of target reflections for space debris with diameters less than 10 cm.
# Design and Implementation of Model Predictive Control for a Gyroscopic Inverted Pendulum
## 2 Dynamic System Modeling
### Nonlinear Dynamic Model
\begin{array}{l}T_{b}=\frac{1}{2}m_{b}\mathbf{v}_{b}^{T}\mathbf{v}_{b}+ \frac{1}{2}\mathbf{\omega}_{b}^{T}\mathbf{I}_{b}\mathbf{\omega}_{b};\\ T_{gi}=\frac{1}{2}m_{gi}\mathbf{v}_{gi}^{T}\mathbf{r}_{gi}+\frac{1}{2} \mathbf{\omega}_{gi}^{T}\mathbf{I}_{gi}\mathbf{\omega}_{gi};\\ T_{fi}=\frac{1}{2}m_{fi}\mathbf{v}_{fi}^{T}\mathbf{v}_{fi}+\frac{1}{2} \mathbf{\omega}_{fi}^{T}\mathbf{I}_{fi}\mathbf{\omega}_{fi};\\ T_{total}=T_{b}+T_{g1}+T_{g2}+T_{f1}+T_{f2};\,i=1,2;\end{array}  \tag{14}\]  
where \(\mathbf{I}_{b},\,\,\mathbf{I}_{gi}\) and \(\mathbf{I}_{fi}\) are the mass moment of inertia of each body. The total potential energy of the system is  
\[V=\left[ \begin{array}{c}0\\ 0\\ g\end{array} \right]^{T}(m_{b}\mathbf{r}_{ob}+m_{g1}\mathbf{r}_{og1}+m_{g2} \mathbf{r}_{og2}+m_{f1}\mathbf{r}_{of1}+m_{f2}\mathbf{r}_{of2}). \tag{15}\]  
By applying the Lagrangian equation for the total kinetic and total potential energy (14), (15), the system's equations of motion are:  
\[\frac{d}{dt}(\frac{\partial L}{\partial\dot{q}_{i}})-\frac{\partial L}{ \partial q_{i}}=Q_{i};\,i=1,\ldots,5; \tag{16}\]  
where \(L=T-V\) is the Lagrangian, \(q_{i}\) is the \(i\)th generalized coordinate, and \(Q_{i}\) is the \(i\)th non conservative force applied at each revolute joint. The nonlinear equations of motion (16) can be rewritten in a matrix form as:  
\[\mathbf{M}\ddot{\mathbf{q}}=\mathbf{f}(\dot{\mathbf{q}},\mathbf{q},t); \tag{17}\]  
where matrix \(\mathbf{M}\in\mathcal{R}^{5\times 5}\) is the mass matrix of the system, \(\mathbf{q}\) is the vector of generalized coordinate \(\mathbf{q}=[\theta,\,\alpha_{1},\,\alpha_{2},\,\phi_{1},\,\phi_{2}]^{T}\), and \(\mathbf{f}\in\mathcal{R}^{5\times 1}\) is the vector of nonlinear functions. In the next section, this nonlinear model is embedded on a microprocessor board to simulate the behaviuors of the dynamic system in real time.
## 2 Measuring Method and Measuring System
### Performance Test  
To ensure that the developed measuring system meets the performance requirements for the transient voltage measurement, a series of tests including the response speed and frequency response have been carried out in the laboratory.  
Figure 4: Field arrangement of sensors. (**a**) Field arrangement of sensors for monitoring the lightning transient voltage; (**b**) Field arrangement of sensors for measuring the switching transient voltage.  
Figure 3: General connection diagram of the measuring system.  
A 5 kV 1.2/30 \(\upmu\)s lighting impulse was imposed on a circular-shape plane-plane gap with the gap distance of 10 cm and the radius of 50 cm. A 996:1 standard impulse voltage divider was employed to offer the standard measuring result. The normalized measuring results of the developed measuring system and the standard divider are shown in Figure 5a. To determine the response time of the measuring system which is in the range of nanoseconds, a bounded wave electromagnetic pulse simulator, composed of charging transformer, impulse capacitor, sharping switch, parallel-plate transmission line, and matching resistor was employed, to generate a pulse with 6 ns for the rise time and 38 ns for the half time. A 104.2:1 standard resistive divider was used to offer standard measuring results. The normalized measuring results are shown in Figure 5b, which indicates that the response time of the measuring system is less than 6 ns.  
To conduct the frequency response test, a signal generator was employed to generate a standard sinusoidal voltage from 5 Hz to 250 kHz. The frequency response of a higher frequency range is determined by a sharp voltage impulse which was imposed on a transverse electric and magnetic field (TEM) cell. The amplitude-frequency response is shown in Figure 6. Except for individual resonant frequencies, the curve of the amplitude-frequency response is flat from 5 Hz to 100 MHz.  
Based on the characteristics of transient voltages defined in [5], performances of the measuring system developed in this paper have satisfied the requirements for measuring field transient voltages in AC high-voltage power grid.  
Figure 5: Test results of response speed. (a) Lightning impulse; (b) electro-magnetic pulse.  
Figure 6: Amplitude-frequency response.
# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
## 4 Proposed Framework
### Lower-Fidelity Model Design
#### 4.2.2 Lower-Fidelity Models Design for the Discrete Event System  
In the discrete event system, lower-fidelity models are designed to simplify the time advance function of the DEVS model. We explained high- and low-fidelity models for the UTV, which is shown in Figure 8. The UTV brings passengers to their destinations via the fastest route; thus, its DEVS model computes the duration time from pickup to drop-off, which is carried out by the time advance function. In Figure 8, when the UTV model receives an input event, _Departure_, the model changes the state to _MOVE_ from _WAIT_. Then, the model computes the time of the state, _MOVE_, to generate the output event. The high-fidelity model calculates it by using the Dijkstra algorithm [37], which finds the shortest path from a start node to an end node in a map with several nodes and their connections. However, the Dijkstra algorithm has computational complexity and the increase in computational cost is exponentially related to the number of nodes. The low-fidelity model uses a simple algorithm that just calculates the travel time of the straight-line distance between the start and end nodes. The accuracy of this simple algorithm is lower than that of the Dijkstra algorithm, though its calculation speed is much faster than that of the Dijkstra algorithm.  
We measured the execution time and \(\epsilon\) for 2000 input events, as shown in Table 2. The simulation of the low-fidelity model was approximately seven times faster than the case of the high-fidelity model; however, \(\epsilon\) for the low-fidelity model was 0.21 higher than that of the high-fidelity model. \(\epsilon\), which was measured in these two simple examples, is a theoretical measurement focusing on the target model itself, not the overall simulation model. Because the target model is a sub-model of the whole model, we will evaluate the simulation accuracy from the view of the overall simulation objective in Section 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Index** & **High-Fidelity Model** & **Intermediate-Fidelity Model** & **Low-Fidelity Model** \\ \hline Execution time (sec) & 8.01 & 7.50 & 4.08 \\ \(\epsilon\) & 0 & 0.08 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Execution time and error of high-, intermediate-, and low-fidelity models for a UUV surfacing simulation.  
Figure 7: High-, intermediate-, and low-fidelity model design for the dynamic system: An example of a UUV surfacing simulation.
## 3 Q2: What Are the Challenges of the Current UIoT System
### Technical Challenges of UioT
#### 3.2.7. Network Configuration  
In the UIoT environment, since the nodes are mobile or stable, the connectivity can be easily broken or can generate a new topology, which can cause network configuration problems in UIoT networks [21].
# Analyzing Impact and Systemwide Effects of the SlowROS Attack in an Industrial Automation Scenario
## 6 Tests, Results, and Analysis
### Systemwide Effects of the Attack  
For the reasons outlined at the end of Section 3.2, the consequences of the attack presented in this paper go far beyond a disruption of the publisher-subscriber registration and notification mechanism that the ROS Master implements. If this were the case, the impact of the attack would still be significant but also somewhat limited in scope because it would just prevent _new_ publishers and subscribers from registering, without interfering with _existing_ ROS nodes that already entered the operational phase after a successful registration. So, for example, the attack would prevent a remote monitoring ROS subscriber (typically instantiated on demand) from accessing and displaying system information but would not affect the core robot control nodes.  
However, as also mentioned in Section 3.2, in ROS noetic both the ROS Master and the parameter server are part of the same process, an implementation choice that the ROS specification does not explicitly forbid. As a consequence, any DoS attack that depletes someprocess-wide resource, such as file descriptors, also prevents ROS nodes from accessing system parameters.  
Finally, the JointStateListener of ROS noetic queries the parameter server on every control cycle to retrieve the tf_prefix parameter from the closest namespace by means of the method getTPPrefix() (file joint_state_listener.cpp, line 91). Being a standard component, the JointStateListener is going to be present in virtually all ROS noetic-based systems.  
These observations are crucial to understand why an attack involving solely the ROS Master eventually leads to a full, systemwide lockup like the one discussed in [12], instead of having more localized and less severe effects.  
This discussion also calls attention to the importance of chain effects in cyber attacks, which are rather common and make their ultimate effects hard to predict. In this particular case, the combination of the following make the attack perimeter way larger than it could have been anticipated by intuition: _(a)_ A DoS attack against a very specific ROS component (the ROS Master); _(b)_ A legitimate implementation choice fully within the ROS specification boundaries (putting the ROS Master and parameter server together in the same process); _(c)_ A coding peculiarity within a crucial ROS node (leading to the retrieval of a parameter on every robot control cycle)  
By considering instead how the attack effects on the targeted server might vary depending on the actual deployment of TCP flooding countermeasures, in the next section, we discuss how a malicious user could bypass potential connection request flooding mitigation approaches.
# Automated Gluten Detection in Bread Images Using Convolutional Neural Networks
## 3 Materials and Methods
### RGB Method
#### 3.2.2 Data Labeling, Verification, and Photo Augmentation  
Data Labeling and Verification. A careful review of each selected image was conducted based on the number of followers, likes, and comments. Each image was verified manually to match the label it received, cross-referencing the title or content, such as associated recipes or ingredient lists, to ensure data credibility. Following verification, the images underwent a manual cropping and labeling process to isolate the bread, carefully focusing on relevant portions of the image while excluding background or non-target elements to enhance the dataset's quality and accuracy.  
Photo Augmentation. To improve the generalization and performance on unseen data, as well as to enhance the diversity of the training data [43], we applied data augmentation. Data augmentation was specifically used during the training phase for the Pinterest bread dataset and portions of the Instagram bread and custom datasets designated for training.  
The following methods of data augmentation were used with Keras' ImageDataGenerator module:  
* Rotation: rotating images up to 40 degrees randomly to simulate different viewing angles.
* Width and Height Shifts: shifting images along the \(x\)-axis and the \(y\)-axis by up to 20% to simulate an off-center positioning of the bread.
* Shear Transformation: using shear transformations of up to 20% to skew the image, allowing the model to learn from distorted shapes.
* Zoom: randomly zooming images by up to 20% to simulate a closer or further away shot of the bread.
* Horizontal Flip: flipping images horizontally to simulate different orientations.
* Pixel Fill: filling pixels exposed by transformations with the nearest pixel value using the 'nearest' fill mode.