esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Computer Model  
To implement the model of predictive control of an asynchronous machine, shown in Figures 1 and 2, the MATLAB and Simulink R2024a simulation environment was used. The developed architecture includes both the control subsystem and the model of the control object (asynchronous machine), which is built in the form of structurally connected blocks.  
Figure 1: The top level of the structure of a predictive control computer model.  
The top level of the system architecture is shown in Figure 1. The _GRAMPC_ subsystem implements the predictive control algorithm with the subsequent generation of the control signal of the three-phase voltage _U_uvm_ in the _Motor Control_ subsystem, based on measured values and the desired speed _N_ref._ The asynchronous machine model _asm model_ receives the control signals _U_uvm_ and the load level _M_L_. In addition, the currents _I_uvm_ and the angular velocity _Omega_Rm_ are measured in the _asm model_. The _Load_ref_lookup_ block generates the load torque as a function of time. The _PWM-Interrupt_ and initialisation blocks provide system setup before starting the simulation, as well as the compilation and initialisation of the GRAMPC library.  
The _GRAMPC_ subsystem from Figure 1 is illustrated in detail in Figure 2. Here, the main component is the _MPC-Subsystem_ block, which is the GRAMPC kernel [32] that accepts the vector of the current system state, the vector of desired values, and the configuration of _userparam_ parameters. The output is the optimal control action _U_next_ (two components of the stator voltage phasor \(u_{d}\) and \(u_{q}\)), which is fed to the _Motor Control_ subsystem. _Userparam1, ramp1.mat_, and _ramp1.TLvar.mat_ provide individual optimisation parameters and desired trajectories in the form of vectors containing the values of the desired speed and load torque at each of the discretisation steps. The _Analyzer_Block_ monitors and records simulation parameters for further analysis. This allows for tracking the stability and efficiency of the model during computer experiments. The _newUserParam_ parameter generation module is responsible for determining the depth of the 'look ahead' in the predictive control task for the desired trajectories of speed and load torque. In this implementation, the desired values of the speed from the _ramp1.mat_ file and the load torque from the _ramp1_TLvar.mat_ file are fed into the _MPC-Subsystem_ as arrays of _N_horizon_ length as components of the _userparam_ vector. This allows the _MPC-Subsystem_ controller with modified _probfc.t_ not only to react to the current system state, but also to proactively take into account future operating conditions, such as expected torque fluctuations or speed  
Figure 2: GRAMPC subsystem of the predictive control computer model.  
setpoint changes. This extension to the GRAMPC made by the authors of this study is critical for improving stability and energy efficiency under dynamic operating conditions.  
Thus, the developed computer model makes it possible to evaluate the performance of commands by speed and torque in the MPC closed loop.
# Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
## 2 Dataset and Methods
### Machine Learning Algorithms
#### 2.2.3 The AdaBoost Regressor Algorithm  
Conceptualized by [PERSON] and [PERSON] [54] and further developed by [PERSON] [55], AdaBoost regression represents a significant advance in ensemble learning techniques. Initially, the algorithm assigns equal weights to all training samples, and these weights are dynamically adjusted in subsequent iterations to emphasize the importance of each sample, a method detailed by [PERSON] and [PERSON] [54]. The basis of the algorithm lies in sequentially integrating weak learners, usually decision trees; the training of each learner is based on the current weight distribution of the samples. This training allows the model to progressively concentrate on more difficult parts of the data, highlighting samples that are difficult to predict. After training each learner, AdaBoost updates the sample weights, increasing them for incorrectly predicted samples and decreasing them for correctly predicted ones; the amount of updating is determined by the accuracy of the learner.  
[PERSON]'s contribution to regression applications of AdaBoost concerns its adaptation with a customized loss function suitable for regression tasks, which led to the AdaBoost R\({}^{2}\) algorithm. This adaptation minimizes the specific regression losses, usually by using squared error or absolute error loss functions, thus customizing the algorithm more effectively for continuous outcome prediction. In this context, the overarching objective of AdaBoost is to iteratively enhance the model's focus on the most difficult-to-predict samples, thus increasing the model's overall predictive ability across the dataset. The final model, containing the weighted total of all weak learners' predictions, is robust and accurate, especially in scenarios where the data present complex and nuanced relationships. This adaptive and iterative approach of AdaBoost, especially in its specific form for regression, highlights its effectiveness in handling diverse and complex prediction tasks in various domains.
# Robust Structural Damage Detection Using Analysis of the CMSE Residual's Sensitivity to Damage
## 4 Numerical Simulation
### Description of the Truss Structure  
The example adopted in this numerical study is a 2-D clamped-clamped truss structure. As shown in Figure 2, the examined structure consists of 29 elements comprising 14 horizontal brace elements (HBs), 7 vertical brace elements (VBs), and 8 diagonal brace elements (DBs). In addition, the sectional area of each member is \(1.5\times 10^{-3}\) m\({}^{2}\) and the moment of inertia is \(3.13\times 10^{-7}\) m\({}^{4}\). The structure is made of plexiglass, with a Young's modulus of 3 GPa, linear mass density of 1300 kg/m\({}^{3}\), and Poisson's ratio of 0.3. Although only a truss structure modeled by beam elements is used as an illustrative example to validate the proposed method, it can be applied to any more general structure, such as a 3-D structure that needs to be modeled with solid elements.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 1 Introduction
### Main Contributions  
Although our proposed method leverages known building blocks such as QIM-based embedding and JND-guided perceptual modeling, the contribution of this work lies in the coherent integration, optimization, and evaluation of a full spatial-domain watermarking pipeline tailored for video content. Below we highlight the core technical novelties and distinctions from the prior literature:  
* Perceptual-driven block selection: Unlike methods that uniformly or randomly select embedding regions, we combine multiscale Laplacian-based texture analysis, spectral saliency contrast, and semantic masking (face/object detection) to create a composite perceptual map. This map drives an adaptive, content-aware selection of robust insertion blocks.
* Spatial-domain DCT estimation: Instead of computing full DCT transforms, we estimate selected coefficients directly from the spatial domain using analytical approximations. This improves computational efficiency while preserving compatibility with frequency-domain robustness.
* Adaptive QIM modulation using JND maps: The quantization step is locally modulated using a JND model that incorporates luminance, spatial masking, and contrast features. This improves imperceptibility while preserving watermark recoverability.
* Redundancy via multiframe embedding: We implement a lightweight redundancy mechanism that spreads the watermark across multiple scene-representative frames, selected through histogram-based keyframe detection. Majority voting is used for recovery, improving resilience to temporal desynchronization, noise, and compression.
* End-to-end blind architecture with low complexity: The entire method operates without access to the original video at extraction time, and is designed for low runtime overhead, enabling practical deployment in resource-constrained environments.  
To the best of our knowledge, no prior work integrates these perceptual, modulation, and temporal strategies in a fully blind, spatial-domain video watermarking system with such a favorable balance of robustness, imperceptibility, and runtime performance.
# DMS-YOLOv5: A Decoupled Multi-Scale YOLOv5 Method for Small Object Detection
## 3 Methods
### Receptive Field Module  
To improve the feature extraction ability of the network, increasing the network depth to learn more semantic information is a common practice. This method is effective for objects of regular size, as it can learn both deep-level detail information and shallow-level object position information, thus extracting more complete feature information. However, for small objects with small size and resolution, increasing the depth too much often leads to the loss of their own feature information, which affects the network's classification and regression operations. Additionally, existing spatial pyramid pooling modules often lead to problems such as decreased resolution and loss of image details while increasing the receptive field. To address these issue, this paper introduces the concept of dilated convolution and uses the receptive field module to improve the existing spatial pyramid pooling module. This approach can increase the receptive field while avoiding the problem of missing image information and decreased resolution. At the same time, different scales of receptive fields are constructed in the feature extraction process to better focus on small objects with small size and low resolution.  
Compared with ordinary convolution, dilated convolution does not increase the training volume of the model network, and the actual calculation points are the same as those of ordinary convolution. By setting the dilation rate parameter, the number of padding zeros in the convolution kernel can adjust to the size of the network's receptive field and achieve sufficient extraction of information at different scales. The comparison between ordinary convolution and dilated convolution is shown in Figure 5. In dilated  
Figure 4: Improved network model architecture diagram.  
convolution, only the red points are involved in the calculation, and the calculation of pixel values around the red points is skipped directly.  
The receptive field module is a multi-branch structure module that uses dilated convolutions to improve the performance of computer vision tasks from the perspective of simulating the human visual system. Unlike the dilated spatial convolution pooling pyramid, the convolution kernel size of each branch in the receptive field module is not the same. The small-sized \(1\times 1\) convolution kernel corresponds to a small-sized dilation rate, and as the kernel size increases, the corresponding dilation rate also increases. Finally, different sizes of feature maps are sampled in parallel, and the feature channel number is adjusted by \(1\times 1\) convolution. The use of convolution kernels with gradually increasing gradients and dilated convolution sizes in receptive field modules enables the extraction of multi-scale feature information, facilitating the extraction of small object detail information. The structure diagram of the receptive field module is shown in Figure 6.
## 2 Materials and Methods
### Workflow
#### 2.1.3 Map of Attenuation and Reconstruction  
Attenuation maps and reconstructed projections were obtained from MCID, using the resampled CT and the projections obtained from SIMIND. The reconstruction was performed using the ordered-subset expectation maximization (OSEM) algorithm, with two iterations and 10 subsets. Corrections for the collimator-detector response (CDR), attenuation, and scatter were applied, the latter obtained through the ESSE algorithm [36]. The reconstructed images had cubic voxels of 4.1 mm size and matrix size of 128 \(\times\) 128.
## 3 Datasets  
### Cro-CoV-Tweets Dataset  
The collected Twitter data capture the period between 1 January 2020 and 31 May 2021, an almost half-year period, covering the duration of the first three epidemic waves. A pandemic/epidemic wave is a graph that tracks the number of people suffering from a disease over time. Epidemics usually begin with a sharp increase in the number of patients in a short time, that number then reaches a peak, after which it begins to decline until there are no new infections. Some epidemiological experts state that if there is no new case in a population for a certain number of days (e.g., 14 days), only then can the end of the epidemic (epidemic wave) be declared. The definition of a second wave is that the first wave must end and that a certain period must pass in between. In this study, there was no complete cessation for fourteen days without a single case of infection in Croatia. Thus, there are no official dates delimiting the three epidemic waves in Croatia. However, we took approximate dates defining the periods that cover the start and end of each wave. Therefore, in this study, we determined the periods of three waves as follows: the first wave: 1 January 2020-15 May 2020; the second wave: 16 May 2020-25 February 2021, and the third wave: 26 February 2021-31 May 2021. Note that 26 February 2020 is the date when the first case of coronavirus infection was confirmed in Croatia. However, in our analysis, we wanted to capture tweets that were posted even before the official start of the pandemic in the Republic of Croatia. The data were collected using tweepy [57], a Python library for accessing the Twitter API. The retrieval of tweets was filtered with the help of a set of COVID-19-related keywords listed in Appendix A.  
The final dataset Cro-CoV-Tweets consists of 206,196 tweets. The daily frequency of tweets is shown in Figure 1. It can be observed that the highest number of tweets was posted during the first lockdown in Croatia, in March and April 2020. The second peak of tweets occurred in the autumn of 2020 during the second pandemic wave, characterised by a major outbreak of the disease. In order to better describe the Cro-CoV-Tweets dataset, we report visualisations of several other distributions of COVID-19-related tweets during the observed time period in Appendix A, Figure A1.
## 3 Results and Discussion
### The Influence of Various Factors on the Dynamic Performance of RFR
#### 3.2.4 Considering the Effects of All Factors Caused by the Tie Rods  
The RFR with circumferential tie rods depends on the tie rods to clamp the disks together. The contact interface and tie rods are the most distinctive features of the RFR. The tie rods are under pre-tension, while the discs are under pre-pressure. There is a force interaction between the tie rods and the discs in the process of vibration, which brings significant challenges to the dynamic simulation of the rotor system. To accurately analyze the rotor system, all factors caused by the tie rods are taken into consideration corresponding to situation E.  
Figure 15: The rate of change of natural frequencies of the RFR considering the parallel relationship between the tie rods and rotor.  
From Figure 16, it can be seen that considering the various factors brought by the tie rods, the tie rods have a certain influence on the natural frequencies of the rotor. In particular, when the preload of the rotor is relatively low, the influence of the contact interface and the tie rod on the natural frequency of the rotor is relatively larger. In this case, the presence of the contact interface causes the natural frequencies to decrease, and the presence of the tie rod increases the first-order and third-order natural frequencies and decreases the second-order natural frequency. When the preload of the rotor reaches a certain value, the natural frequencies of the rotor approach the natural frequencies of the corresponding integral rotor. The influence of the tie rod on the natural frequency of the rotor is small. Therefore, for the rod-fastened rotors of the gas turbine, the previous simplified calculation of neglecting tie rods is accurate.
## 3 Application examples of CAD for brain diseases
### Alzheimer's disease
We attempt to develop a CAD system for identification of patients with cerebral atrophy due to Alzheimer's disease based on a pattern recognition technique using 3-D MR images. Our CAD system consists of determination of atrophic image features and identification of AD patients. First, brain, white matter and gray matter regions were determined based on a level set method. Second, CSF regions in cerebral sulci and LVs were extracted by wrapping the brain with a level set function. Third, the cerebral cortical thickness was measured based on normal vectors as a distance from the white matter surface to the brain surface. To select mean cortical thicknesses in the most effective subregions for distinguishing AD patients from non-AD patients, we employed a Golub statistic [85,86], which can be interpreted as the degree of separation between the two classes. Finally, AD cases were classified from non-AD cases based on a support vector machine (SVM) classifier, which learned atrophic image features, _i.e._, white matter and gray matter volumes, CSF volume, and cerebral cortical thickness. The SVM classifier was constructed with a Gaussian kernel exp (\(-\gamma\|\mathrm{x}-\mathrm{y}\|^{2}\)) by using an open source software SVM light [87]. Figure 13 shows the segmentations of brain regions (outer white line) and white matter regions (inner white line) obtained by the proposed method from a non-AD patient in multiple coronal planes. As a result, the area under a ROC curve (Az value) obtained by the CAD system was 0.909 based on a leave-one-out test in identification of AD patients among 54 cases. The preliminary results showed that the proposed method may be promising for identification of AD patients.
## 4 The Performance Analysis
### The Performance Analysis of the In-VGM  
C language is used to analyze the In-VGM performance. If the FlexRay/MOST messages and command are entered in the host PC; CAN message frames are generated in a sending node and transferred to multiple protocols. When 2 Byte messages are transferred to CAN, MOST, and FlexRay  
Figure 10: The structure of the V2X based Accident Notification Service (VANS).  
protocol bus, the message transformation rate and transformation time is compared in the simulation as follows.  
In the 1 st simulation, the message transformation rate of the existing in-vehicle protocols is compared with that of the In-VGM. 2 Byte messages and 500 s transmission time are used in the simulation. The transmission message is a CAN message and the simulation result is shown in Figure 11.  
In case a CAN message was transferred to a CAN protocol, the existing in-vehicle protocol and In-VGM have transferred 8000 messages for 500 s and any message transformation was not done. In case a FlexRay message was transferred to a CAN protocol, the In-VGM did more transformations than the existing in-vehicle protocol by 1942 times and its transformation rate is higher than the existing in-vehicle protocol by 24%. In case a MOST message was transferred to a CAN protocol, the In-VGM did more transformations than the existing in-vehicle protocol by 1304 times and its transformation rate is higher than the existing in-vehicle protocol by 16%.  
In case a CAN message was transferred to a FlexRay protocol, the In-VGM is higher than the existing in-vehicle protocol by 24% in transformation rate. In case a CAN message was transferred to a MOST protocol, the In-VGM is higher than the existing in-vehicle protocol by 13% in transformation rate. This result was shown because the existing in-vehicle protocol has a disadvantage to put a header in each CAN fragment.  
In the 2 nd simulation, the transformation time of the existing in-vehicle protocols is compared with that of the In-VGM about 8000 messages and the simulation result is shown in Figure 12.  
It took about 14 min 8 s for the existing in-vehicle protocol to transform 8000 CAN message into the FlexRay protocol and about 12 min 55 s into the MOST protocol. It took about 9 min 47 s for the In-VGM to transform 8000 CAN messages into the FlexRay protocol and about 10 min 2 s into the MOST protocol. It took about 13 min 51 s for the existing in-vehicle protocol to transform a FlexRay protocol. It took about 10 min 42 s to transform a MOST message into the CAN protocol, and about 13 min 42 s to transform a MOST message into the CAN protocol. It took about 10 min 43 s for the In-VGM to transform a FlexRay message into the CAN protocol, and about 10 min 27 s to transform a MOST message into the CAN protocol. The In-VGM improves the transmission time by 1.37% compared to the existing in-vehicle protocol.  
Figure 11: The comparison of the existing in-vehicle protocols with the In-VGM in message transformation rate.
## 4 Comparison of Classical and Computer-Aided Reconstruction Results in Case 1
### Procedure of the Comparison Using Dlib-Based Face Recognition  
#### 5.1.1 Step 1: Face Detection and Localization  
To detect the presence of faces in complex scenes and accurately delineate their positions and areas, face detection must be performed as a preliminary step. Once a face is identified, the region containing the face is globally analyzed, and the positions of all faces are marked based on the input image data. The coordinates of the bounding boxes for the faces are then output [75].  
There are multiple selectable alternatives for face detection, each with its own disadvantages and advantages. One of the methods used in this study is a convolutional neural network (CNN)-based face detector, specifically the CNN Face Detection Model V1 provided by the Dlib library [80], along with the corresponding pre-trained model file (mmod_human_face_detector.dat). The advantage of this method is its ability to work well, even from odd angles [81; 82]. Convolutional neural networks (CNNs) are a prime example of classical neural networks and are increasingly being utilized across various fields. Their primary application lies in pattern recognition within images [83], making them well-suited for detecting objects in images.  
In addition to the Dlib pipeline, an alternative approach was implemented using YOLOv8 [79] as the detection module. YOLOv8 is a state-of-the-art object detection model known for its high speed and accuracy in identifying objects, including human faces [79]. From experience, YOLOv8 proves to be very reliable in detecting faces across various lighting and background conditions. However, in contrast to Dlib, YOLOv8 does not output facial landmarks, it only provides bounding boxes. This limitation directly affects the alignment step described below.
## 2 Materials and Methods
### Building a RAW Dataset  
IMU sensor data required inter-sensor calibration due to the use of different types of IMU sensors: 3-axis and 6-axis. As the 3-axis sensor had a measurement frequency of 180 Hz, and the 6-axis sensor had a frequency of 380 Hz, the data were converted to 450 Hz. This conversion involved interpolation to ensure temporal alignment, and the processed data were included in the RAW dataset. Additionally, for the KAM measurement, motion lab data were incorporated into the RAW Dataset, including both motion sensor and pressure plate data. The RAW dataset was structured to predict KAM as the target variable, using IMU sensor data as the input. Furthermore, the interpolated data were merged and synchronized with the KAM measurement data to align with the time series accurately. The structure of the dataset used for the final training and learning is shown in Table 1.