esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation and Analysis  
In this section, we explain our experiments and present the range of evaluation measures which we decided to use for experimentation purposes, then present and analyze our results. For experimental evaluation, we first collected labelled data and then made a sample window of 200 samples and then divided data into segments. We used J48, MLP, and LR to recognize the activities. We used the customized setting for tuning the parameter of each classifier. We applied three-fold cross-validation for all experiments. This works by leaving 1:3 part of data for testing and uses 2:3 part of data for training. To further evaluate the significance of our proposed approach, we used Leave-one-Subject-out (LOSO) cross-validation on  
Figure 3: Structure of MLP classifier.  
WISDM [31] dataset. We compared the results of our approach with state-of-the-art study [15] using LOSO for a fair comparison. LOSO works by leaving one participant's activities for testing and train the model on other participant's activities. As there are 26 participants in WISDM [31] dataset, this cycle repeats for all participants 26 times randomly.
## 3 Simulations
### Boundary Layer
#### 3.1.1 Wind Shear  
The wind shear used in the simulations is a logarithmic profile for the horizontal velocity (\(U_{hor}\)) vs the height (\(y\)), see Equation (5). There (\(\kappa\)) is the von Karman constant and the roughness length is \(z_{0}=10^{-4}\). The friction velocity \(u_{*}\) is calculated to provide a wind velocity of 8 m/s at hub height.  
\[U_{hor}(y)=\frac{u_{*}}{\kappa}ln\Big{(}\frac{y}{z_{0}}\Big{)} \tag{5}\]  
In the simulations two wind shear profiles are used, one with all flow in the streamwise direction and one with a turning of the wind with height.  
Figures 1a-c provide information for the cases. Figure 1a shows the total horizontal velocity together with the corresponding streamwise component \(W\) when a linear wind veer of 2 degrees over the rotor is considered, as shown in Figure 1b. Figure 1c shows the spanwise velocity \(U\) corresponding to this case. For the former case where no wind veer is considered, \(U\) is the same as \(U_{hor}\) in Figure 1.
# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 2 Related Work
### Faster R-CNN + SlowFast Spatio-Temporal Behavior Detection Method
The feature regions of interest undergo processing using the RoAlign method to ensure uniform dimensions. Subsequently, the acquired features are uniformly pooled in the time dimension, and a fully connected layer is connected through a Sigmoid activation function for behavior prediction. The pre-trained Faster R-CNN target detector is employed directly, obviating the need for training with the SlowFast behavior detection module. RoAlign (Region of Interest Align) [33], as mentioned in the preceding paragraph, represents a technique employed in target detection tasks that primarily addresses the quantization error issue inherent in RoIPooling [34]. In the realm of deep learning, target detection stands as a critical task, revolving around the precise localization and identification of targets within an image. The introduction of RoAlign seeks to enhance the performance of target detection by refining the accuracy of features within the region of interest (RoI). The primary enhancement offered by RoAlign over RoIPooling resides in the interpolation of feature points within the Region of Interest (RoI). Conventional RoIPooling involves segmenting the RoI into fixed-size grids and subsequently applying operations such as maximum pooling to the feature points within each grid. This methodology encounters an issue where quantizing the coordinates inside the RoI may result in information loss, particularly when handling small targets or requiring finer positional information. RoAlign  
Figure 3: Network structure of spatio-temporal behavior detection algorithm. addresses this challenge by intricately handling the feature points within the RoI through the utilization of bilinear interpolation. In detail, for every sampling point within the Region of Interest (RoI), RoIAlign computes its precise position on the feature map through interpolation, as opposed to approximating to the nearest integer position. This approach enables RoIAlign to extract features within the RoI more accurately, mitigating quantization errors and enhancing the precision of target detection. In practical applications, RoIAlign finds extensive use in advanced target detection models, including Faster R-CNN [35]. By enhancing the precision of object localization within the region of interest, RoIAlign has emerged as a pivotal technique in the realm of target detection, demonstrating efficacy particularly in addressing challenges posed by intricate scenes and diminutive targets. The incorporation of this technique furnishes a potent method for augmenting the efficacy of deep learning models in tasks related to target detection.
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
## 4 Performance Evaluation
### Results and Discussion  
#### 4.2.1 Prediction Error Analysis  
Aiming at the simulation experiment, the performance of the algorithm is analyzed. The influence of different mobile coordination factors on error RMSE is introduced.  
As shown in Figure 2, as the value of \(\lambda\) increases, the RMSE error gradually decreases. When the value of \(\lambda\) is greater than 0.75, RMSE shows a slow growth trend. The \(\lambda\) represents the node's mobile performance. In real life, the movement of the node requires a corresponding time to adapt to the mobile coordination performance. As can be seen from Figure 2, the value has a certain influence on predicting the speed probability of the next moment. Therefore, it is not that the greater the value of \(\lambda\), the better the performance of the algorithm.  
Figure 2: RMSE vs value of \(\lambda\).
## 4 Materials and Methods
### Parameter Extraction  
In this study, 11 entropy-based parameters were extracted from each of the decomposed (EMD, DWT, and WPD) signals using Python (Version 3.7, Python Software Foundation, Wilmington, DE, USA). The extracted parameters include: Approximate entropy (ApE), Sample Entropy (SaE), Shannon Entropy (ShE), Spectral Entropy (SpE), SVD Entropy (SVDE), Permutation Entropy (PE), Fisher Information (FI), Signal Activity (SiA), Hjorth Mobility (HjM), Hjorth Complexity (HjC), and Petrosian Fractal Dimension (PFD). A brief description of these parameters has been given below.
## 3 Results and Discussion
### Fluctuation of Environment Factors
#### 3.2.2 Time-Humidity Fluctuation  
The ambient humidity floated along with the fluctuation of temperature under both room temperature and refrigeration storage (Figure 5). During the refrigeration, the relative humidity fluctuated regularly around 40 percent with the cooling system running and the opening times of the refrigerator door. The relative humidity for eggs kept under room temperature was about 50 percent, which was obviously higher than those in refrigeration.
# Secure Monitoring System for IoT Healthcare Data in the Cloud
## 5 Proposed System
### System's Primary Goals  
The primary goals of the proposed secure monitoring system for IoT healthcare data in the cloud are listed below:  
1. _Confidentiality:_ Protect the privacy and confidentiality of sensitive healthcare data, preventing unauthorized access or disclosure.
2. _Integrity:_ Ensure the accuracy, consistency, and reliability of healthcare data by preventing unauthorized modification or tampering.
3. _Availability:_ Maintain high availability of healthcare data and systems, minimizing downtime and ensuring that authorized users can access the data when needed.
4. _Compliance:_ Meet the legal and regulatory requirements for protecting healthcare data, such as HIPAA, GDPR, or any other applicable regulations.
5. _Detection and response:_ Detect and respond to security incidents, anomalies, or breaches in a timely manner to minimize the impact on patient safety and data security.
6. _Auditability:_ Enable comprehensive logging and auditing capabilities to track and monitor user activities, system events, and data access for forensic analysis and compliance purposes.  
The primary goals of the proposed system are depicted in Figure 1. By focusing on these implementation aspects and goals, the proposed secure monitoring system for IoT healthcare data in the cloud can help safeguard sensitive healthcare information, maintain the trust of patients and healthcare providers, and ensure the delivery of quality healthcare services.
## 3 Methodology and Data Collection
### Data Pre-Processing
#### 3.2.2 Daily Aggregation  
In certain cases, it was suitable to consider even a higher level of aggregation--by days. This allowed us to create a well-arranged display of long time periods--see Figure 5 where the blue highlights signify the average values of the speed in the period from 6 January to 29 June 2020.  
The grey foundation constitutes the period of the state of emergency and ordered quarantine. We can see a clear boundary line during the course of the measured speed and 2 phases of traffic impact--absolute traffic restriction implying the complete loss of weekly profiles (dark grey) and the gradual return back to normal associated with the gradual loosening of the quarantine (light grey). Besides the average daily speed, Figure 5 also shows the development of the number of hours with the sum above the horizon (yellow).
# A Review of the Synthesis and Applications of Polymer-Nanoclay Composites
## 2 Synthesis
### In-Situ Polymerization Method
[109] showed an improvement in the mechanical properties and increased the clay basal spacing in the polymeric matrix of PP/MMT composites prepared by in-situ polymerization. Using in-situ polymerization, [PERSON] et al. [110] prepared a rubber/clay composites (_cis_-1,4-Polybutadiene/MMT) and overcame its mechanical shortcomings, such as low tensile strength, tear resistance and thermal stability. The study demonstrated that Ni-based catalyst systems within the presence of MMT carry a high efficiency and 1,4-selectivity for the polymerization of butadiene. [PERSON] et al. [37] also applied ultrasonication as part of the in-situ polymerization method to prepare poly(glycidyl methacrylate)/MMT composites. Compared to solution blending methods, ultrasound-assisted in-situ polymerization resulted in composites with improved reaction time, clay dispersion and composite yield. This review discusses a number of recent in-situ polymerization routes to the preparation of polymer/nanoclay composites with a specific focus on advanced synthetic methods, such as controlled/living radical polymerization, photopolymerization, click coupling chemistry and emulsion polymerization methods. These methods have been successfully implemented for the chemical modification of clay surfaces with low molecular or polymeric grafts [44]. Figure 4: In-situ polymerization method scheme.
## 3 Mathematical Formulation  
### Description of Geometrical Property  
Figure 1 shows the coordinate system of a thin orthotropic plate where the plate length is \(L\), with \(b\), and thickness is \(h\). The Levy-type solution is applied where two opposite edges are simply supported along the \(y\)-axis (i.e., along the edges \(y=0\), and \(y=L\)), while the other two edges may be free \((F)\), simply supported \((S)\), or clamped \((C)\), as represented in Figure 2. The material orthotropic axes of the plate are parallel in the directions of \(x\) and \(y\).  
Applying the CPT assumption, the transverse displacements of an arbitrary point of the plate are expressed by Equation (1).  
Figure 1: Levy-type solution boundary conditions with coordinate system convention.  
Figure 2: Levy-type solution boundary conditions of the orthotropic plate.  
\[u=\left\{\begin{array}{l}-z\frac{\partial u^{0}}{\partial x}\\ -z\frac{\partial u^{0}}{\partial y}\\ w^{0}(x,y)\end{array}\right\} \tag{1}\]  
where the displacement components of the plate are represented by \(u,v,w\) in \(x\), \(y\), \(z\) directions, respectively, and \(\frac{\partial w^{0}}{\partial x}=\varnothing_{y}\), \(\frac{\partial w^{0}}{\partial y}=\varnothing_{x}\) represents the rotational displacements of the \(x\) and \(y\) axes at the plate middle surface, respectively. \(w^{0}\) represents the thickness (transverse) displacement in the \(z\)-direction.  
In the case of orthotropic plates, the orientation of the constitutive material is such that orthotropic axes 1 and 2 are equal to axes \(x\) and \(y\), respectively, and the material constants, \(\mathcal{Q}_{ij}\) are given by Equation (2).  
\[\left\{\,\mathcal{Q}_{ij}\right\}=\left\{\begin{array}{l}\mathcal{Q}_{11}= \frac{E_{1}}{(1-\theta_{12}\theta_{21})}\\ \mathcal{Q}_{22}=\frac{E_{2}}{(1-\theta_{12}\theta_{21})}\\ \mathcal{Q}_{12}=\frac{E_{1}}{(1-\theta_{12}\theta_{21})}\\ \mathcal{Q}_{66}=G_{12}\end{array}\right\} \tag{2}\]  
where \(E_{1}\), \(E_{2}\) are [PERSON]'s moduli, along with the orthotropic directions 1 and 2, respectively; \(\theta_{12}\), \(\theta_{21}\) are major and minor [PERSON]'s ratios, and \(G_{12}\) is the shear modulus [43].
## 2 Preliminaries and Problem Formulation
### Adversarial Attack Model  
In this paper, we consider the adversarial attack as follows [9; 23]:  
\[\overset{\sim}{x}_{s}(t)=x_{s}(t)+a_{s}(t,\overline{x}_{s}) \tag{2}\]  
\[\overset{\sim}{u}(t)=u(t)+a(t,\overline{x}_{n}) \tag{3}\]  
where the compromise state \(\overset{\sim}{x}_{s}\), \(s=1,\cdots,n\) is the available variable after being attacked. \(u(t)\) is the unavailable control input caused by the adversarial attack. \(a_{s}(t,\overline{x}_{s})\) is defined as a time-varying, state-dependent malicious adversarial attack function, meaning it changes over time and is influenced by the system's current state.  
**Remark 1**.: _In the presence of sensor attacks, this paper proposes an NN estimation algorithm to estimate the unknown sensor attacks, essentially letting the network train itself and determine which attack values are most important. The proposed a NN estimation algorithm method does not require that the sensor attacks must be parameterized. Note that, although [10; 11; 12; 13; 14; 15] also investigate the problems of injection unknown sensor attacks, the proposed control methods in [10; 11; 12; 13; 14; 15] require that the sensor attacks must be parameterized, that is, the restrictive assumption is removed in this paper._  
In defining the reference signal \(y_{0}\), the control objectives are given as follows:  
**Control objective**. The control objective of this paper is to design a state-feedback secure control scheme based on the NN attack reconstruction strategies in an insecure network with all the information available for nonlinear CPSs (1) against adversarial attack (2) and (3), such that:  
1. The obtained NN attack reconstruction strategies in an insecure network with all information unavailable can effectively estimate unknown attacks.
2. The above secure control process reaches the expected trajectory, and all the closed-loop signals are bounded.
3. The output error of the system converges to a small neighborhood of zero.
## 3 Compact and Efficient Vision-Based Detection Frameworks  
Nowadays, whether we tackle the cross-domain on-device space involving compact and efficient solutions or more specific research focused just on the current AmI landscape, dealing with the design of vision-based object detection frameworks involves the discussion of CNN architectures. While statistical classifiers, such as Support Vector Machines (SVM) [111], Random Forest [112], Adaboost [113] and classical neural networks were for many years the computer vision (CV) standard and played a dominant role in object detection tasks, the advent of DL techniques has unquestionably represented a step forward compared to traditional detectors. The exploration of new approaches and the design of new CNN architectures, capable of automating the extraction of representative features, boosted by challenges, such as Imagenet Large Scale Visual Recognition Challenge (ILSVRC) [114] and Pascal Visual Object Classes (VOC) [115], has resulted in a plethora of novel methods with increasingly higher performance in visual recognition tasks over the last decade, producing more robust frameworks capable of addressing object localization and classification tasks in highly complex scenarios.  
Such challenges and the tremendous interest generated in the scientific community have pushed CNNs to an unprecedented evolution. Such progress, however, has led to increasingly complex architectures, as indicated in the introduction of the paper. Models, such as VGG-16 (138 M parameters) [5] or RetinaNet (built on ResNet-152 [116], with 60 M parameters) [117], while achieving high levels of accuracy, are generally based on bulky structures and multidimensional parameter spaces, resulting in a high volume of both computed intermediate products and output values. Towards a response to the reality described, a fair number of investigations have been focused on producing more efficient and effective detection frameworks during the last five years. The reduction of the computational cost of traditional detectors and the preservation of accuracy have driven the solution search process carried out by CV experts (both scientists and practitioners), resulting in a fair number of techniques and methods that, built upon existing restrained-complexity CNNs (e.g., SSD [118], the YOLO family [119, 120, 121, 122], and Faster R-CNN [123]), have shown promising performance in detection tasks and have also proven to be applicable and relevant in different disciplines, including AmI. With the support of Tables in 3, we provide an overview of the various detection frameworks devised within AmI along the described lines, covering both architectural details and traditionally related challenges together with the most relevant solutions explored.
## 1 Introduction
### Case Study A for Validation  
The proposed method in this paper is validated using data from a rock slope in Ouray, CO, USA (Figure 2). The dataset, obtained by Dr. [PERSON] in 2004 using the Optech ILRIS-3D laser scanner, consists of 1,024,521 points with a spacing of less than 2 cm. The data are publicly accessible in the Rockbench database [48]. Many scholars have extensively researched the use of this dataset for the identification of rock mass discontinuities from point clouds [49], providing a convenient basis for comparisons to validate the methods proposed in this study.  
Figure 1: Flow chart of method.  
Figure 2: Actual scene of case study A in Ouray, CO, USA.
# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 2 Related Works
### Driving Safety Enhancement Techniques by Obstacle Detection  
Various studies have investigated obstacle detection technology to enhance the safety of electric wheelchairs and personal mobility devices. [PERSON], [PERSON] designed and evaluated controllers and drive mechanisms to improve safe navigation while considering user body types and achieving performance goals such as continuous driving time, turning radius, and maximum load capacity during driving tests [4]. Despite these achievements, the authors noted that further testing is required to verify the stability of the system in outdoor environments. Additionally, [PERSON] and [PERSON] developed an obstacle detection system for electric wheelchair operation using a 3D depth camera [5]. Their system employs a KINECT depth camera, enabling real-time obstacle detection and avoidance. However, system performance can be affected by the environmental limitations of camera sensors, such as lighting conditions.  
Recent studies have concentrated on enhancing the safety and performance of electric wheelchairs through advanced detection technologies employing various approaches. Several investigations have examined the development and improvement of electric wheelchair systems, including user posture change functionalities and requirements analysis [6; 7; 8]. Innovations in specialized detection systems for electric wheelchairs encompass the development of management applications [9], torque ripple reduction in BLDC traction motors for improved ride comfort [10], and independent safety measures for wheelchair users in automated vehicles [11]. [PERSON] et al. developed an intelligent wheelchair system utilizing situation awareness and obstacle detection [12], while other studies have explored safety enhancements for wheelchair users within autonomous vehicle environments [13].  
Furthermore, previous research [14; 15] has focused on developing obstacle avoidance systems, incorporating depth cameras, fuzzy logic control, and SLAM-based navigation techniques. Additional efforts have explored hybrid approaches that integrate multiple technologies, such as the implementation of electric wheelchairs using hybrid energy storage devices [16] and the development of weakly supervised object detection models for smart city applications [17], as well as deep-learning-based 3D multi-object tracking using multi-modal fusion in smart cities [18].