esa-sceva/satcom-chunk-collection huggingface dataset:
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 3 Security Challenges and Mechanisms
### IoT in Space
#### 3.5.2 Internet of Space Things  
Recently, researchers have paid attention to the Internet of Space Things (IoST). Cube satellites are new types of satellites that offer global communication at low costs. They offer advantages over normal satellites, including short development to deployment and lower cost of development and deployment due to the off-the-shelf components. These make them great tools for creating large scale networks. An automatic network slicing framework for space-ground integrated networks is proposed by the authors of [209] to use these cube satellites to their full capabilities. The design tackles challenges such as dual objectives of route computation and is able to allocate resources to avoid service level agreement violations. The system being automatic means it does not require prior information in terms of resource availability to slice. To allow for low signaling overhead, the scheme uses an online segment routing-based approach to route communications. The authors have performed benchmark testing that has shown the usefulness and effectiveness of the proposed scheme for large network communication in the IoST.  
According to our reviews, this extension has not been studied from a security perspective.
## 3 Methodology
### Image Preprocessing  
#### 3.2.1 Resizing  
The images are resized to ensure that the model receives inputs of consistent size. A standard size for all the input images is adopted in this step. This step is important to ensure that the model can effectively learn from the images. The equation for resizing an image depends upon the method used for resizing. Two common methods for resizing an image are the nearest neighbor and bilinear interpolation.  
Let us consider an image with dimensions \((M\times N)\), and we want to resize it to \((M^{\prime}\times N^{\prime})\). The new pixel value at location \((i,j)\) in the resized image can be given by the following relations using nearest neighbor interpolation:  
\[x^{\prime}=i*\frac{M^{\prime}}{M},\ \ \ \ y^{\prime}=j*\frac{N^{\prime}}{N} \tag{1}\]  
The nearest neighbor to \((x^{\prime},y^{\prime})\) in the original image is then rounded to the nearest integer and used as the value for \((i,j)\) in the resized image.  
Bilinear interpolation:  
Bilinear interpolation is a more sophisticated method for resizing an image. The resizing process employs a technique that estimates the value of a pixel in the resized image by taking a weighted average of its 4 nearest pixels. The equation for bilinear interpolation is given as follows:  
\[x^{\prime}=i*\frac{M^{\prime}}{M},\ \ y^{\prime}=j*\frac{N^{\prime}}{N},\ \ \ x=floor\big{(}x^{\prime}\big{)},\ \ y=floor\big{(}y^{\prime}\big{)} \tag{2}\]  
The value of \((i,j)\) in the resized image can then be calculated as follows:  
Figure 3: Distribution of the categories in the target variable. ’0’ and ’1’ indicate the absence or presence of invasive ductal carcinoma, respectively.  
\[\begin{split} f^{\prime}(i,j)=(1-(x^{\prime}-x))*(1-(y^{\prime}-y))*f(x,y)+(x^{\prime}-x)*(1-(y^{\prime}-y))\\ *f(x+1,y)+(1-(x^{\prime}-x))*(y^{\prime}-y)*f(x,y+1)\\ +(x^{\prime}-x)*(y^{\prime}-y)*f(x+1,y+1)\end{split} \tag{3}\]  
where \(f(x,y)\) is the pixel value at \((x,y)\) position in the original image, and \(f^{\prime}(i,j)\) is the pixel value at \((i,j)\) position in the resized image.
## 5 Result and Discussion
### Simulation Results  
To evaluate the superior performance of the proposed operation scheme, the following two cases were considered: Case 1: a bi-level optimization model that considers the tie-line flow without the energy band and Case 2: a bi-level optimization model that considers the tie-line flow within the energy band.
# Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression
## 1 Introduction
* To reduce effectively both the energy consumption and memory cost of DNN models, models are first fine-grained pruning before quantization with low bit-width. The experimental results show that fine-grained pruning does not affect the accuracy of the quantized model. It is safe and necessary to perform pruning before quantization. Figure 2: Comparisons with existing quantization work based on clipping methods for ResNet-50, Inception-v3, and DenseNet-121 on ImageNet. (**a**) ResNet-50. (**b**) Inception-v3. (**c**) DenseNet-121.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### Composite Relative Measurement Technology of Spacecraft Formation
#### 3.6.9 Laser Multiple Guidance Composite Measurement Technology  
It is important to consider that the radio measurement can easily achieve large-angle coverage, and the equipment is small and light. Laser measurement equipment has high precision but demands a substantial amount of energy resources. Among its characteristics is that the narrower the beam, the higher the precision. Laser multiple guidance composite measurement emerges as the demand increases. The scheme is shown in Figure 30, below [74].  
Figure 30: Flow chart of laser multiple guidance measurement.  
Figure 29: Schematic diagram of laser + infrared composite measurement.  
The purpose of the measurement scheme is to first achieve a coarse acquisition of the target spacecraft through the radio characteristics of all-sky coverage. The relative position and altitude information of the target spacecraft are obtained via radio, and these are used as the guidance input of the laser wide-beam measurement system or the infrared navigation system to point at the target spacecraft. The laser wide beam or infrared measurement system is guided, to obtain more accurate position and altitude information than can be established by radio detection, and then input into the laser narrow-beam measurement system to achieve precise inter-satellite measurement.
## 1 Introduction
By releasing this assumption, we further investigate the implications of parking pricing and parking capacity in alleviating the morning commute problem under a mixed environment. In this work, we aim to examine the impacts of parking on decision-making of AV users and HV users in the morning commute problem where two parking clusters exist in the central business district (CBD) area. The bottleneck model generally admits closed-form equilibrium solutions, which allows us to formulate decisions and evaluate various policies analytically. The contributions of this work are as follows. First, we extend the classic bottleneck model to formulate the combined decisions on departure time and parking location under a partially automated traffic environment. By examining the equilibrium conditions, we can identify 14 equilibrium travel patterns in total and categorize the equilibrium travel patterns into five cases. Second, we explore the parking location preferences of both AV and HV commuters. When the difference in parking fees is very low, all commuters prefer the central parking cluster. When the difference in parking fees is very high, all commuters prefer the peripheral cluster. Otherwise, we find hybrid parking preferences for the two classes of commuters. Third, based on the modeling framework, we analyze how the parking capacity of the central cluster affects parking patterns and the total travel cost at equilibrium. The analytical results show that the total travel cost decreases when the capacity of the central cluster grows, given that the capacity of this cluster can satisfy the parking demand. The impacts of the parking fees are also investigated to understand their influences on the equilibrium travel patterns. We identify the conditions under which the total travel cost does not vary with the parking prices. For most cases, we conclude that the total travel cost increases or decreases with the parking price at the central cluster. In order to optimize social welfare, we design the optimal parking pricing scheme for the central cluster. Finally, we perform numerical analysis to demonstrate the theoretical results. The remainder of this paper is structured as follows. In Section 2, we build the dynamic user equilibrium model for the morning commute by introducing two parking clusters, and we explore the parking preferences of commuters towards the two clusters. We derive the equilibrium conditions in Section 3. Section 4 analyzes the impacts of parking fees and the capacity of the central cluster on system-wide travel cost. Section 5 derives the optimal parking price charged at the central cluster to optimize the social welfare. We demonstrate the commuters' preferences and the welfare effects of parking pricing policy by performing numerical experiments in Section 6. Section 7 concludes our findings.
# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
## 3 Validation on Synthetic GPR Data
### Comparative Experiments
The formula is shown in Equation (10):  
\[\text{SSIM}(x,y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{2} +\mu_{y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{10}\]  
where \(\mu_{x}\) and \(\mu_{y}\) denote the mean pixel values of the images, while \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) denote the variances of the pixel values. \(\sigma_{xy}\) is the covariance between the pixel values of the two images. Constants \(C_{1}\) and \(C_{2}\) are introduced to prevent a very small denominator. By inputting the test set of GPR images with rebar clutter interference into the trained models, the average evaluation metrics were calculated for the results, as presented in Table 2. As seen in the table, the three evaluation metrics of our proposed model perform the best. These results indicate that our model can effectively remove rebar clutter and retain the details and structural information of the void defects in GPR images with rebar clutter interference.
## 2 Methods  
### Coherent Plane-Wave Compounding (CPWC)  
In CPWC imaging, steered plane-waves at a number of angles are transmitted to improve SNR. Consider an \(M\)-elements linear array emitting plane waves with \(N\) different steering angles, and the sound of speed is \(c\). When the \(n\)-th plane-wave steered at the angle of \(\theta_{n}\) is emitted, the time-of-flight (TOF) consists of the time taken to propagate to an imaging point (i.e., image pixel) \(p(x_{p},z_{p})\) and the time taken to propagate back to the receive element located at \((x_{m},0)\). The TOF \(\tau(p)\) is given by:  
\[\tau(p)=\frac{x_{p}sin\theta_{n}+z_{p}cos\theta_{n}}{c}+\frac{\sqrt{(x_{p}-x_{ m})^{2}+z_{p}^{2}}}{c}, \tag{1}\]  
The beamformed output for imaging point \(p\) at angle \(\theta_{n}\) obtained by DAS is given as  
\[y_{n}(p)=\sum_{m=1}^{M}w_{0}(m)s_{n,m}(\tau(p)), \tag{2}\]  
where \(s_{n,m}\) represents the signal received by the \(m\)th element located at \((x_{m},0)\), and \(\mathbf{w}_{0}=[w_{0}(1),\,w_{0}(2),\,...,\,w_{0}(M)]\) represents the receive apodization window.  
By compounding the beamformed outputs along \(N\) directions, the CPWC output obtained by DAS is expressed as the following:  
\[V_{\text{DAS}}(p)=\frac{1}{N}\sum_{n=1}^{N}y_{n}(p). \tag{3}\]  
To reconstruct CPWC images, ultrasound beamformers can be involved across different transmit angles, and it can also be involved in the receiving aperture through each transmit event. In this study, the proposed methods were implemented across different transmit apertures after the receive aperture data were synthesized using DAS.
## 2 Background
### DetNet and the Hybrid Attacks and Detection Methods  
The DetNet applies flow virtualization to satisfy time-critical flow requirements. The DetNet virtualizes heterogeneous data flows in order to (1) preserve the properties of these flows over a converged IP-based infrastructure and (2) be backward compatible with statistically multiplexed IP-based traffic. Therefore, two tasks of DetNet flow virtualization are designed. First, the DetNet duplicates and eliminates packets over noncongruent routing paths to achieve a sufficiently high delivery ratio and satisfy the application'sQoS requirement [2]. Second, the DetNet applies central controllers, i.e., Software-Defined Network (SDN) controllers, to enable a fully scheduled operation orchestrated to integrate virtualized DetNet flows in conventional IP-based implementations. The DetNet controller frequently obtains flow tables from switches with the OpenFlow protocol or measures network topology with the Link Layer Discovery Protocol (LLDP) and the Broadcast Domain Discovery Protocol (BDDP) in the control plane. These two tasks inevitably incur that DetNet flows and conventional flows share the same physical resources and interact/interfere with each other [3]. Several security issues subsequently arise [3], such as delay attacks, spoofing, reconnaissance, and, of course, Hybrid Distributed Denial of Service (Hybrid DDoS). This study focuses on the issue of Hybrid DDoS.  
Most studies focus on detecting a single type of DDoS attack, i.e., SEA or LFA, in a traditional network rather than DetNet. Reference [24] explores hybrid attacks in SDN, which launch an SEA and LFA interrelatedly. It uses correlation to cluster traffic to determine whether it is an SEA and measures the target link utilization to determine whether it is an LFA. The study also integrates two types of attacks in one system but ignores hybrid attacks that can launch an SEA and LFA at the same time. The Hybrid DDoS not only attacks the DetNet centralized controller itself but also attacks the links that DetNet flows pass through. Therefore, a new detection approach that can detect Hybrid DDoS attacks should be studied.  
The comparison of the studies mentioned above is summarized in Table 1. Type refers to the type of DDoS. Environment refers to which network environment the detection executes. Attack refers to where the attacks are detected. Method refers to the paper solution concept. Flow/packet-based refers to the solution depending on the features. Flow-based refers to packet transmission route traces such as IPFIX [25], and packet-based refers to packet payload. Dataset refers to the paper test using a real or simulated dataset.
# Model Experimental Study on De-Icing Method of Bridge Pylon Beam Based on Electric Heating
## 2 Model Test Design
### Test Procedure  
#### 2.3.1. _Icing Tests_  
Step 1: Calibrate the precipitation  
At the start of each icing test, the rainfall calibration is first required. In addition to the adjustment based on the self-contained flow meter, it is also necessary to measure the rainfall drops emitted at the outlet of the sprinkler system by using a rain barrel to ensure that the same amount of water is emitted from the spray system in each test.  
Step 2: Start up the cold chamber  
After the rainfall calibration is completed, the ambient temperature can be set as required, followed by the turning on of the cold chamber for 8 to 24 h to make the surface temperature of the test components reach the same as that of the set ambient temperature.  
Step 3: Start up the spray system  
When the surface temperature of the test component cools to the same temperature as the ambient temperature, the spray system can be started up for a continuous spraying period of 120 min. At the same time, the wind delivery system can be switched on simultaneously with the spray system according to requirements.  
Step 4: Record the data  
After 120 min of continuous spraying, the spraying was stopped. At the same time, the cold chamber was kept in continuous operation and manual access was gained to the chamber to take photographs of the icing of the beam and to measure the length, diameter, and tilt angle of the icicles using a tape measure, vernier caliper, and protractor (as shown in Figure 6), respectively. Considering that the icicles were not regular cylinders, the width of the thickest part of an icicle was used as the diameter of a single icicle in this paper. Finally,  
Figure 5: (**a**) The cold chamber; (**b**) the temperature and humidity recorder; (**c**) the dimension information of the model beam.  
the video of the icing process from the monitoring system was saved and the images were captured for relevant qualitative and quantitative analysis.  
Step 5: Measure the ice coverage  
When measuring the ice coverage, the icing photographs should be taken at the same level; then, the photographs need to be cropped out, except for the icing components; then, the cropped image is placed in ImageJ software of version 2.3.0, and the area of ice coverage can be manually framed. The software is used to measure the percentage of pixels in that area to obtain the icing coverage.
## 5 Formulation of the Optimization Problem
### Variables of the Optimization Problem  
The following variables were chosen as the appropriate design variables (Figure 3):  
\(r_{2},\theta_{2}\)--design variables determining the position of point G on the roller (2),  
\(s_{2},\psi_{2}\)--design variables determining the position of point H on the roller (4),  
\(x_{\rm K},y_{\rm K}\)--design variables determining the position of point K on the mechanism's frame,  
\(x_{\rm M},y_{\rm M}\)--design variables determining the position of point M on the frame,  
\(c_{\rm d},c_{\rm c}\)--prestress coefficients of the driving and compensation springs.  
Computed Variables  
The lengths \(\overline{\rm DG}\) and \(\overline{\rm ZM}\), Figure 3, are considered computed variables. They will be recomputed in each optimization step.  
Specified Variables, see Figure 3 where needed:  
\(k_{1}=3910\) N/m--stiffness coefficient of the driving spring,\(k_{2}=3910\) N/m--stiffness coefficient of the compensation spring,  
\(\theta_{\rm min}=0^{\circ}\)--minimum value of the angular coordinate determining the position of the grasper jaw,  
\(\theta_{\rm max}=50^{\circ}\)--maximum value of the angular coordinate determining the position of the grasper jaw,  
\(N=50\)--number of points of the movable jaw position interval \((i=1,2,\ldots,N)\),  
\(l=0.3\) m--length,  
\(R_{2}=0.0035\) m--radius of the rollers (2) and (4),  
\(R_{3}=0.002\) m--length,  
\(l_{\rm gr}=0.015\) m--length,  
\(R_{\rm f}=0.025\) m--length of the handle,  
\(F_{\rm gr}=10\) N--force acting on the movable jaw,  
\(l_{10}=0.0742\) m--free length of the driving spring,  
\(l_{20}=0.0742\) m--free length of the compensation spring,  
\(V_{\rm max}=3\) Nm--maximum allowable elastic potential energy in the springs,  
\(F_{\rm rod,max}=12\) N--maximum allowable compression force in the connecting rod,  
\(\psi_{1}=25^{\circ}\)--angle determining the handle's initial position.
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 4 Experiment and Results
### CNN Classifier Performance  
The trained CNN classifier is used to recognize the indoor scene during the evaluation process. During the training process, we used accuracy and validation accuracy to evaluate the model. The model achieved a 99.13% training accuracy while the validation accuracy is 96.22%. Figure 9 shows the loss and accuracy of CNN model during the training process. For each scene, 200 images are tested to evaluate the performance of CNN classifier. Images are re-sized to 200 \(\times\) 266 dimension and normalized for color range of [0 255]. Testing images are collected using Galaxy S8 and LG G6 to perform the evaluation. Images are captured from different angles and during different time of the day. So the collected images may vary slightly with respect to the lighting conditions of the environment. The reported testing accuracy of CNN classifier is 91.04%.  
Figure 8: Indoor scenes of the experiment area.  
Accuracy of CNN is compared with the accuracy of SVM for scene recognition. SVM achieve a testing accuracy of 83.17% for scene recognition. Table 1 shows the statistics for each class.
## 6 Sensors for Tactile Internet Applications
Wearable skin conductance sensors for real-time emotion detection and AI-based skin conductance sensors for personalized haptic feedback are being developed. * Environmental and Proximity Sensors  
For Tactile Internet, environmental and proximity sensors are going to play a crucial role in getting real-time data of surroundings to enhance safety, precision, and interactivity. They can be used for smart environments, autonomous systems, industrial automation, and haptic-enabled applications. Temperature sensitivity is one of the major concerns for several types of tactile sensors. A new triboelectric sensor is designed to detect both pressure and temperature, even in high-temperature environments of up to 200 \({}^{\circ}\)C [72]. It uses the temperature dependence of triboelectric charge transfer for temperature sensing and has low cross-sensitivity between pressure and temperature. Visual tactile sensors use camera-based computer vision to determine the required parameter with high spatial resolution. Light detection and ranging (LIDAR) and ultrasonic sensors allow depth sensing and object detection [73]. Achieving high accuracy in complex environments and real-time environmental mapping can be a challenge in developing these sensors. Infrared sensors support the detection of surroundings, such as gesture recognition and thermal mapping, which are helpful for smart haptic systems. Proximity sensors detect the presence of nearby objects and are used in touchless interfaces and medical applications. * Bio-Integrated and Implantable Sensors  
Bio-integrated and implantable sensors are designed for seamless human-machine interaction, real-time health monitoring, and neural control of prosthetics. These sensorscan be neural, optogenetic, skin-integrated, tactile, or similar, and they should have high bio-integrity to minimize intrusion and ensure accurate signal detection [74]. The neural sensors can use electrocorticography (ECoG)- [75] and EEG-based implants to measure neural signals. The development of wireless neural sensors with 6G integration for real-time neural communication is expected in the near future. The challenges could be the biocompatibility and long-term stability of the sensors. Optogenetic sensors use light pulses to stimulate specific neurons for brain-machine interactions. They achieve accurate control of neural activity; however, they are highly complex. Skin-integrated tactile sensors, like epidermal electronic tattoos, can measure touch, pressure, temperature, and motion, offering versatile sensing capabilities. These sensors are often made of flexible, biocompatible materials, like silk, and can be mounted directly on the skin for continuous monitoring. However, challenges remain, including ensuring long-term biocompatibility and stability. Additionally, the development of miniaturized wireless communication and energy supply is important for practical applications.