esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Authentication is a crucial security service for the wireless sensor networks (WSNs) in versatile domains. The deployment of WSN devices in the untrusted open environment and the resource-constrained nature make the on-chip authentication an open challenge. The strong physical unclonable function (PUF) came in handy as light-weight authentication security primitive. In this paper, we present the first ring oscillator (RO) based strong physical unclonable function (PUF) with high resilience to both the electromagnetic (EM) side-channel attack and the support vector machine (SVM) modelling attack. By employing an RO based PUF architecture with the current starved inverter as the delay cell, the oscillation power is significantly reduced to minimize the emitted EM signal, leading to greatly enhanced immunity to the EM side-channel analysis attack. In addition, featuring superior reconfigurability due to the conspicuously simplified circuitries, the proposed implementation is capable of withstanding the SVM modelling attack by generating and comparing a large number of RO frequency pairs. The reported experimental results validate the prototype of a 9-stage RO PUF fabricated using standard 65 nm complementary-metal-oxide-semiconductor (CMOS) process. Operating at the supply voltage of 1.2 V and the frequency of 100 KHz, the fabricated RO PUF occupies a compact silicon area of 250 \(\upmu\)m\({}^{2}\) and consumes a power as low as 5.16 \(\upmu\)W per challenge-response pair (CRP). Furthermore, the uniqueness and the worst-case reliability are measured to be 50.17% and 98.30% for the working temperature range of \(-\)40\(\sim\)120 \({}^{\circ}\)C and the supply voltage variation of \(\pm\)2%, respectively. Thus, the proposed PUF is applicable for the low power, low cost and secure WSN communications.  
Keywords:wireless sensor network; strong physical unclonable function; ring oscillator; electromagnetic side-channel attack; support vector machine modelling attack +
Footnote †: journal: Sensors
## RFI Mitigation in Microwave Radiometry Using Wavelets  
**[PERSON] * and [PERSON]**  
Remote Sensing Lab (RSLAB), Department of Signal Theory and Communications (TSC), Universitat Politecnica de Catalunya (UPC), UPC Campus Nord D4 and IEEC/CRAE -UPC; Catalonia, Spain;  
E-Mail: [PERSON]Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-934.054.153; Fax: +34-93.4017.232.  
_Received: 2 August 2009; in revised form: 2 September 2009 / Accepted: 16 September 2009 / Published: 23 September 2009_  
**Abstract:** The performance of microwave radiometers can be seriously degraded by the presence of radio-frequency interference (RFI). Spurious signals and harmonics from lower frequency bands, spread-spectrum signals overlapping the \"protected\" band of operation, or out-of-band emissions not properly rejected by the pre-detection filters due to the finite rejection modify the detected power and the estimated antenna temperature from which the geophysical parameters will be retrieved. In recent years, techniques to detect the presence of RFI have been developed. They include time- and/or frequency domain analyses, or statistical analysis of the received signal which, in the absence of RFI, must be a zero-mean Gaussian process. Current mitigation techniques are mostly based on blanking in the time and/or frequency domains where RFI has been detected. However, in some geographical areas, RFI is so persistent in time that is not possible to acquire RFI-free radiometric data. In other applications such as sea surface salinity retrieval, where the sensitivity of the brightness temperature to salinity is weak, small amounts of RFI are also very difficult to detect and mitigate. In this work a wavelet-based technique is proposed to mitigate RFI (cancel RFI as much as possible). The interfering signal is estimated by using the powerful denoising capabilities of the wavelet transform. The estimated RFI signal is then subtracted from the received signal and a \"cleaned\" noise signal is obtained, from which the power is estimated later. The algorithm performance as a function of the threshold type, and the threshold selection method, the decomposition level, the wavelet type and the interference-to-noise ratio is presented. Computational requirements are evaluated in terms of quantization levels, number of operations, memory requirements (sequence length). Eventhough they are high for today's technology, the algorithms presented can be applied to recorded data. The results show that even RFI much larger than the noise signal can be very effectively mitigated, well below the noise level.  
**Keywords:** microwave radiometers; radio frequency interference; mitigation; cancellation
## 4 Angiogenic Factors
### Angiopoietins and Thrombosphosph in 1  
The neovascularization status in CCA has also been tested through the evaluation of angiopoietins-1 and -2 (Ang) and thrombospondin-1 (TSP-1) [73]. The Ang system consists of the transmembrane endothelial tyrosine kinase Tie2 and its antagonistic circulating ligands Ang-1 and -2. It has been proved that Ang-2 can compromise the vascular integrity by facilitating the permeability to pro-angiogenic factors as VEGF. Indeed, according with a previous in vitro research [97], [PERSON] et al. detected high levels of VEGF in CCA accompanied to elevated Ang-2 expression in 57.6% of the analyzed samples together with an increase of MVD. It has been also demonstrated that Ang-2 is detectable in bile of  
Figure 3: Neovascularization in biliary tract cancer. Cartoon shows the involvement of VEGF and other pro-angiogenic factors (PIGF and Angs) in the exacerbation of tumor-driven neoangiogenesis. New vascularization contributes to cell proliferation, tumor progression, and metastasis leading to poor prognosis. Molecules that interfere with VEGF signaling (ETs, TSP-1, apatinib, pazopanib, bavacizumab, vandetanib, and tramteninib) have proved to be able to inhibit tumor evolution.  
CCA patients. This suggests that tumor cells may produce Ang-2 in order to maintain neoangiogenesis at high levels like the cellular strategy \"demand and supply\" [78]. No significant differences are found for Ang-1. These data demonstrate that both VEGF and Ang-2 act as pro-angiogenic factors in tumor-associated blood vessels.  
In addition to VEGF and Angs, TSP-1 may be another important factor involved in CCA neoangiogenesis. TSP1 is a multifunctional matrix protein which plays a controversial role in angiogenesis and tumor progression. Analyses of TSP-1 in CCA revealed that MDV is lower in TSP-1 positive patients compared to negative cases, showing a potential inhibitory role of this factor in CCA angiogenesis [98]. Findings from past years indicated VEGF as the responsible for estrogens (ERs) involvement in neovascular process, both in normal and in tumor tissues of breast cancer cells and mammary gland [99]. In 2009 [PERSON] et al. showed the impact of estrogens on VEGF and VEGFs expression on human liver biopsies and cell line (HuH-28) derived from intrahepatic CCA [100]. Furthermore, in _vitro_ experiments showed that 17\(\beta\)-estradiol, a VEGF agonist, is able to increase VEGF and VEGF levels in CCA concomitantly with the stimulation of cell proliferation. On the contrary, ER and VEGF-TRAP (a receptor-based VEGF inhibitor) showed antagonist effects by blocking the proliferative response induced by 17\(\beta\)-estradiol in HuH-28 cells [100]. These data demonstrate the key role of VEGF in arbitrating estrogens-induced proliferation on human CCA. Considering this evidence, in the last decades the attention has moved toward the role of vascular growth factors in light of their crucial involvement in the pathophysiology and in the development of CCA to draw new potential anti-angiogenic therapies, which will be less invasive and dangerous than surgery.
## 2 Experimental
### Materials Characterization  
The powder X-ray diffraction (PXRD) analysis of the photocatalysts and the membranes was conducted on a Bruker D2, 30 kV, 10 mA utilizing monochromatic CuK\(\alpha\) radiation (k = 1.54184 A) from 5-90\({}^{\circ}\). Fourier transform infrared resonance (FTIR) analyses were conducted on a Perkin Elmer spectrum 100 spectrometer. The surface and morphological properties including elemental mapping were analyzed using scanning electron microscopy (SEM), FEI Quanta 400F equipped with energy-dispersive X-ray spectroscopy (EDS). Transmission electron microscopy (TEM, FEI Tecnai T12 operating at 200 kV), was also utilized to evaluate the morphological properties of the materials. The absorption spectra of the photocatalysts were evaluated using an Ultraviolet-visible spectrophotometer (Mettler Toledo, UV5 Bio spectrophotometer, Columbus, OH, USA). The Kubelka-Munk method was utilized to determine the bandgap of the catalysts.  
The hydrophobicity of the as-prepared membranes was analyzed using the sessile drop method operated on a drop shape analyzer (DSA 100, Kruss scientific, Hamburg, Germany). The measurements were conducted in triplicates at every 20 s interval. The thickness of the membranes was measured with a Vernier caliper (Absolute Digital Caliper, Johannesburg, South Africa). Thermogravimetric analysis (TGA) of the membranes was conducted using a thermogravimetric analyzer (NETZSCH, TG 209F1, Selb, Germany).  
The membranes were cut into desired sizes and heated from 30 to 850 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min in the presence of a nitrogen atmosphere at a gas flow of 20 mL/min. X-ray photoelectron spectroscopy (XPS) operated at X-ray power of 300 W was utilized to evaluate the oxidation state and elemental composition of the membranes.
## 4 Methodology
### Land Cover Classification Framework
#### 4.2.1 Remote Sensing Image Pre-Processing  
Sentinel-2B level 2A product image was obtained on 4 October 2020 for the region of interest. Three different steps are adopted to pre-process the raw image including atmospheric correction, image resampling, and field subset. This image is atmospherically corrected based on Atmospheric/Topographic Correction for Satellite Imagery proposed by Richter [27]. Such a method is based on libRadtran radiative transfer model so that the image quality can be guaranteed [5]. Due to the difference of image spatial resolution in different bands, the resampling process is done so that a consistent image resolution can be guaranteed. Finally, the subset process allows for selecting the region of interest (ROI) from the downloaded large images. colorredAll of the pre-processing work is finished by Sentinel Application Platform (SNAP) software which is particularly designed for Sentinel series satellites.
# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 2 Related Works
### Occupancy Sensors for Smart Lighting Control
Although the temperature does not affect PIR sensors significantly in indoor environments, microwave sensors still can be used to resolve problems of PIR sensors caused by false-negative detection errors. In smart lighting control, occupant detection is crucial to control strategy and energy savings. Several sensor technologies have been utilized to detect occupants, including PIR [34], mobile devices [22], and the combinations of PIR and microwave, PIR, and ultrasonic [35]. In most studies, sensors colocated with luminaires and detected occupant presence at individual workspace level instead of zone level and room level [36, 37]. Besides, researchers proposed some methods to optimize the deployment of sensors. [PERSON] et al. [38] formulated the optimal deployment algorithmbased on adaptive binary particle swarm algorithm for sensor nodes in a plane of a building to detect targets under the full coverage and a limited cost. [PERSON] et al. [39] analyzed the feasibility of installing different occupancy sensors using a probabilistic model and a simulation software integrated with the building layout and lighting features and selected the most cost-effective one considering number of occupants, occupant presence and movement, and occupant action behavior.
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 2 Parallel Heterogeneous Computing
### Graphics Processing Unit  
The first decades in microprocessor advancements were focused mainly on serial workloads [11; 12]; whereas more recently, CPUs have evolved to provide hardware that seizes parallelism through pipelining and even multi-core architectures. However, as pointed out by ref. [11], most of the circuitry on a CPU is devoted not to arithmetic, logic, and direct parallelism execution but to control complexity, such as caches, instruction decoders, and branch predictors, among others.  
Instead, better results can be achieved with another paradigm, namely that of a GPU, which was developed using the general concept of vector processors. These vector processors allow the hardware to operate using registers that contain separate values [12]. Due in part to gaming and other consumer application demand over the last two decades, manufacturers, such as NVIDIA and AMD, now provide affordable GPU computing products on the market [11]. Some providing tools, such as CUDA, make scientific computing programming with GPUs an increasingly easier task.  
GPUs are powerful co-processors designed to exploit data parallelism based on the SIMD approach. Modern GPU architectures embed hundreds to thousands of computing cores along with dedicated units. Several of the components are part of its own memory hierarchy, such as memory banks and caches. These elements are organized into so-called Streaming Multiprocessors (SM), where there is a warp/dispatch scheduler (more generally a thread scheduler), which distributes threads to be executed at different Scalar Processors (SPs), which can be either Integer SPs or Floating Point SPs to manage integer and float operations, respectively.  
In a HC GPU structure, it is the _host_ CPU system that is able to launch tasks in a _device_ GPU system. This commonly involves data exchange (copy and paste operations) to and from the device's global memory (normally RAM comprised by the graphics card). There is also a thread-block scheduler that distributes blocks of threads to different SMs for their execution.
# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 5 Experiments
### Setup
#### 5.2.3 Datasets  
We used the public-access driving dataset POLIDriving [47] to train and test the learning models. POLIDriving contains more than 61 K observations, 32 attributes from five heterogeneous sources, and four classes (low, medium, high, and very high) representing the levels of risk of suffering a traffic accident. It includes time, speed, rpm, throttle position, engine temperature, engine load value, heart rate, current weather, visibility, precipitation, accidents on site, design speed, accidents by time, and others. For the experiments, we selected 1980 labeled observations and 14 attributes.
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 1 Introduction
To speed up the blind SS search, we propose a new PSS detection approach that explores a novel down-sampling approach resulting in a 60% reduction in on-chip memory and 50% lower search time. 4. Via detailed performance analysis, we analyze the functional correctness, computational complexity, and latency of the proposed approach for different word lengths, signal-to-noise ratio (SNR), and down-sampling factors. 5. We demonstrate the functionality of the proposed end-to-end 5G CS PHY on the GNU Radio and Universal Software Radio Peripheral (USRP) based radio frequency network-on-chip (RFNoC) platform from Ettus Research [27, 28]. We demonstrate the 66% reduction in SS search time by efficiently utilizing the Field-Programmable Gate Array (FPGA) available on USRP compared to conventional GNU Radio-based software implementation. The proposed demonstration on hardware and 3 GPP compatibility strengthens the commercial significance of the proposed work. Please refer here ([[https://tinyurl.com/A2](https://tinyurl.com/A2) AIITTD]([https://tinyurl.com/A2](https://tinyurl.com/A2) AIITTD) (accessed on 12 August 2023)) for source codes and supporting tutorials. The list of abbreviations used in the paper is given in Table 1.  
The rest of the paper is organized as follows. We discuss the 3 GPP specifications for 5G CS and review the relevant works in Section 2. The downlink transmitter and receiver physical layer architectures are presented in Sections 3 and 4, respectively. The experimental results and complexity analysis is done in Section 5. In Section 6, we discuss the realization of CS using GNU Radio based RFNoC framework followed by experimental results. Section 7 concludes the paper.
# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
## 4 Discussion  
Using cosmic occultation data and a deep neural network, a four-dimensional electron density grid model (EDG-DNN) based on longitude, latitude, altitude, and time was created in this study. Furthermore, the prediction effect has been extensively compared with IRI-2016 based on the test set. The results show that the EDG-DNN performs with good stability and error.
## 2 Related Work
For instance, [PERSON] and [PERSON] (2020) [34] shed light on the Port of Hamburg, which has harnessed the power of digitalization to seamlessly monitor parking spot availability. By employing state-of-the-art radar technology to locate ship parking spaces, the port aims to optimize the intermodal traffic involving trucks and other vehicles. By leveraging an array of sensors, including those on cranes, trucks, and carriers, the port capitalizes on AI and computer vision expertise to revolutionize port operations and traffic management. These cutting-edge technologies provide real-time insights into traffic congestion and container storage capacity, empowering the port managers to make informed decisions regarding truck routing and scheduling. Consequently, this judicious approach alleviates traffic congestion, enhancing the overall efficiency of the port. Additionally, AI and computer vision technologies can enhance port safety by promptly identifying potential hazards, such as improper container stacking, thereby alerting operators to the necessity of corrective measures. Furthermore, these transformative technologies contribute to the predictive maintenance of port equipment, ensuring optimal efficiency, minimizing downtime, and augmenting production.
## 2 Research Area and Data Sources
### Indicator Selection
#### 2.3.3 Construction of Correlation Analysis Dataset  
Due to the large scale of the study area, which complicates raster-to-vector conversion, a random point sampling method was adopted to eliminate biases arising from uneven point distribution, thereby enhancing the accuracy of correlation analyses between landscape patterns, climatic variables, and NPP. Using ArcGIS 10.8 to create a fishing net tool, we first established a grid of 300 \(\times\) 300 m, 600 \(\times\) 600 m, and 900 \(\times\) 900 m. Then, we used the random point tool to evenly distribute the grid lines and create 20,000, 40,000, and 60,000 random points, respectively. We extracted the NPP data and landscape pattern indices from the points, and used Origin 2024 and MATLAB R2022b to perform Pearson correlation analysis on the resulting data. The results show that 40,000 points are the most significant (\(p<0.05\)), while 60,000 points have overfitting. Therefore, this study used a 600 m resolution sample plot.
## 2 Materials and Methods
### Research Methodology  
#### 2.2.1 Technical Processes  
This study employed Sentinel-2A picture data from the Google Earth Engine (GEE) cloud platform to conduct a classification analysis. The data were put into groups using the support vector machine and the random forest classification methods. The accuracy of the groups was then compared. The algorithm with superior classification accuracy was chosen to extract and compute the corn planting area in Huinong District, spanning the years 2017 to 2021. The Normalized Difference Vegetation Index (NDVI) was then calculated. The NDVI difference was found using MODIS image data from May to September 2021 in the study area. Employing the method of comparing the same time period, the NDVI value of 2021 was contrasted with the NDVI value of the corresponding period in the years 2017-2020, enabling frequent monitoring of corn growth from May to September 2021. The step-by-step procedure is depicted in Figure 2.
## 4 Point Cloud Segmentation
### Graph-Based Segmentation
#### 4.3.3 Segmentation Post-Processing  
The process outlined in the previous section was used to complete the segmentation of the point groups currently used as inputs. The proposed system performed mapping and segmentation simultaneously, rather than waiting for the user to finish an action before initiating segmentation. Thus, it was necessary to integrate the segmentation results that overlapped previous frames or were from the same object. Figure 7 presents the results obtained from two frames following point cloud segmentation and integration after supervoxel generation and plane removal.  
In Figure 7, we can see that if the overlapping parts of two frames contained different groups, and the distance and normal angle were less than the threshold values, then the two groups were associated with the same object. This completed the point cloud segmentation of multiple frames during motion.  
When segmentation and integration were applied to a set number of frames, the system began shape fitting based on the segmentation results for the range in question. However, before initiating shape fitting, it was necessary to conduct a final check of the segmentation results. In previous steps, large planes had already been removed via plane detection. However, smaller planes could still result in erroneous judgments due to the fact that the number of planes failed to reach the threshold value. The segmentation results may have contained fragmented point groups or planes resulting from measurement errors imposed by depth sensors and/or the creation of rugged shapes from planes distal to the depth sensors. We sought to overcome this problem by conducting a final correction and confirmation step, in which point groups that were too small, flat, or slim, were integrated with surrounding point groups or discarded.