esa-sceva/satcom-chunk-collection huggingface dataset:
# An FPGA-Based Four-Channel 128k-Point FFT Processor Suitable for Spaceborne SAR
## 4 Experimental Evaluation and Results
### Hardware Performance Comparison  
In order to evaluate the hardware performance of the proposed processor, we also compared it with some previous work and Xilinx IP. The performance comparison is shown in Table 9, where it is indicated in the relevant references. In the table, BRAM is used for storage, which is often proportional to the size of the data (width \(\times\) length), and DSP is a multiplier. The real-time performance of Xilinx IP is very close to our design. If we use Xilinx IP to implement 128k point FFT, we need to use at least four identical IPs, that is, the processor will occupy four times the hardware resources as in the table. Therefore, using Xilinx IP to achieve the same function of the processor will require at least 408 DSP and 590 BRAM, which is far larger than our design. In Reference [29], the author proposed an efficient FFT processor which used a method of pre-saving all twiddle factors. Therefore, this FFT processor did not need to generate factors in real time, which saved the use of Slice LUTs resources with a good real-time performance. However, the pre-stored selection factors took up a lot of BRAM resources. When a large-point FFT processor was implemented on FPGA, BRAM resources were the most limited, so this strategy is not suitable. In Reference [31], the author proposed a configurable FFT processor with a 16-bit fixed-point number, so the processor occupied a few BRAM and Slice LUTs. Although the transform length in Reference [31] was longer than that in Reference [29], the FFT processor in Reference [31] still occupied less BRAM. However, a too-small width also brought the problem of insufficient calculation accuracy. In addition, because the Reference [31] adopted the radix-22 algorithm, it obviously took up more DSP per unit transform length, compared with our design. In Reference [32], the author proposed a floating-point FFT processor. Due to the use of floating-point number, the FFT processor can process a wider range of data. However, the use of floating-point numbers may cause the processor to take up more hardware resources than our design, especially in Slice LUTs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Proposed** & **Pipelined** & **Radix-4** & **Radix-2** & **Radix-2 Lite** \\ \hline Transform cycles & 131,218 & 131,284 & 262,367 & 655,741 & 1,179,691 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Transform cycles of the proposed processor and four architectures in Xilinx IP.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Our Design** & **Xilinx IP** & [29] & [31] & [32] \\ \hline Transform length & 128k & 64k & 256 & 1024 & 4k \\ Data type & 32-bit fixed & 32-bit fixed & 24-bit fixed & 16-bit fixed & 32-bit floating \\ Transform cycles & 131,218 & 131,284 & 298 & 48 & - \\ Slice LUTs & 39,519 & 11,182 & 6043 & 5174 & 44,071 \\ BRAM Tiles & 282 & 147.5 & 8 & 4 & 179 \\ DSPs & 146 & 102 & 24 & 16 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The performance comparison of our design and previous works.
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 4 Testbed and Evaluations
### Performance of Central Control Platform  
The main idea of central control platform is to create a trusted authority that verifies the attack definitions received so that intermediate controllers do not have to go through the laborious task of individually verifying and forwarding the attack definitions. The result of this approach is quite similar to the one achieved in the Mesh approach. In this section we evaluate the efficiency and scalability of the central control platform. This includes the effect of payload size, dissemination delays and throughput of the flows and system benchmarks (CPU and memory usage). For the performance evaluations, we used a low end machine with Intel core i3-4010U 1.7 GHZ \(\times\) 4 CPU with 4.0 GB of RAM as the central controller.
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 6 State-of-the-Art NB-IoT WUR
### Experimental Results  
The WUR was fabricated in 28 nm CMOS. It occupies an area of 2 mm\({}^{2}\) and consumes 2.4 mW. Figure 18 shows the power breakdown. The digital wake-up processor consumes 27 \(\upmu\)W and the ADCs consume 30 \(\upmu\)W each, in total contributing only 4% of the total WUR power.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Traditional 12-Point DFT** & \begin{tabular}{c} **Pipelined Cooley–Tukey** \\ **16-Point FFT** \\ \end{tabular} & \begin{tabular}{c} **Optimized 12-Point FFT** \\ \end{tabular} \\ \hline \begin{tabular}{c} Number of Complex \\ Multipliers per FFT \\ \end{tabular} & 144 & 64 & 48 \\ \
\begin{tabular}{c} Number of Twiddle Factors \\ \end{tabular} & 144 & 16 & 16 \\
\begin{tabular}{c} Area Estimate \\ Power Estimate \\ (Normalized) \\ \end{tabular} & 144 \(\upmu\)m\({}^{2}\) & 85 \(\upmu\)m\({}^{2}\) & 72 \(\upmu\)m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different FFT architectures.  
Figure 18: Power breakdown of the NB-IoT WUR.  
Figure 19 shows the test setup. An Agilent power supply was used to provide supply voltages to the WUR chip. An Agilent function generator was used to provide a 12 MHz reference clock signal for the on-chip PLL. A Keysight noise source and a Keysight signal analyzer were used for noise figure (NF) measurement. A Tektronix oscilloscope was used to observe the chip output. The RF front-end NF was measured to be 6 dB over the 750-960 MHz RF input. Standard specified NWUS sequences (discussed in Section 4.1) were used to measure the required \(\mathrm{SNR}_{\mathrm{min}}\) of the digital baseband detector. The measured NF and \(\mathrm{SNR}_{\mathrm{min}}\) result in a sensitivity of \(-107\) dBm according to Equation (1).  
Figure 19 shows the test setup and the measured WUR output. Two different input signals were tested: the \"correct\" WUS is a 132-length Zadoff-Chu sequence specified by the 3 GPP standard [13], while the \"incorrect\" WUS is a 132-length random sequence that is not supposed to wake up an NB-IoT WUR. Upon receiving the correct NWUS input, the WUR successfully detected it and sent a wake-up instruction to the main radio (in Figure 19, the orange signal goes high). The incorrect NWUS, on the other hand, did not trigger the WUR wake-up response (Figure 19).  
Figure 20 compares the power-sensitivity performance of the presented NB-IoT WUR (starred) compared to other receivers supporting coherent modulation schemes (blue dots).
# Compressive Ghost Imaging of the Moving Object Using the Low-Order Moments
## 1 Introduction
By solving a \(l_{1}\)-norm convex optimization problem, CS recovers the most sparse image in a certain compressive basis, with the background noise being strongly suppressed by the sparsity constraint [21], which also benefit a lot to the application of the low-order moments. In this paper, we combines the advantages of CS and low-order moments to propose a new method of tracking and imaging the moving object in GI. The proposed method relies on the CS to reduce the number of measurements for faithful motion estimation in each image period, and replaces the time-consuming \"global search\" operation by a simple moment calculation, which would theoretically speed-up the tracking and imaging process of GI. Experimental results show that, when the object is moving, the reconstructions of the conventional normalized GI would suffer from severe motion blur. Even with the help of the cross-correlation-based ghost imaging (CBGI), the reconstructions using the second-order correlations still not satisfactory. Our experiment also demonstrate that, based on the faithful image reconstructions of CS, the low-order moments could effectively extract the motion information, and the GI system would effectively obtain the clear image of the moving object. Details in algorithm operation and optimal parameters are also investigated.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 7 Sequential MO Helper Data Scheme
### Simulation Results  
We evaluate the performance of the Sequential MO helper data scheme through Monte Carlo simulations. The encoder and decoder constructions and LLR calculations are similar  
Figure 8: Reconstruction phase of the Iterative MO helper data scheme: the helper data \(m_{t}^{n}\) is updated to \(m_{t+1}^{n}\) after each successful reconstruction.  
to the construction presented in Section 5. We evaluate two key rates, \(R=1/6\) and \(R=1/7\) with \(t_{0}=4\) and \(t_{0}=2\) enrollment observations, respectively. We have chosen the number of enrollment observations s.t. the achieved initial error probability is larger than, but close to, the target \(\text{FER}<10^{-6}\) (based on the previous results in Figure 6). We simulate 4 consecutive reconstruction attempts of the SMO scheme and plot the FER as function of the number of reconstruction attempts (time) in Figure 9. Please note that the helper data are only updated when the reconstruction attempt is successful. Therefore, the'reconstructions' in the current simulation are different from '\(t\) enrollment observations' plotted on the x-axis in Figure 9.  
First, we can directly observe that the FER reduces with each reconstruction attempt. Therefore, indeed the SMO scheme supports an improved reconstruction performance over the lifetime of the devices. More specifically, for the simulated rates and selected number of enrollment observations, we see that the average error probability already reduces below the required threshold \(\text{FER}<10^{-6}\) after the first reconstruction attempt. Therefore, in both cases, the initial cost (larger FER) of reducing the number of enrollment observations by one, was already nullified after one reconstruction.
# Use of Smart Technology to Improve Management of Utility Tunnels
## 3 Smart System Design
### Inspection and Maintenance Layers  
The inspection and maintenance of the utility tunnel constitute major concerns for the tunnel safety and performances. Requirements and procedures for the smart inspection and maintenance of each component of the tunnel and hosted utilities should be established and coordinated by the concerned manager. Each inspection and maintenance operation should be controlled and result in an online report, which is transmitted to concerned staff and authorities.  
Figure 7 illustrates how smart technology can help in improving the quality of inspection and maintenance of utility tunnels. In addition to the digital identification and geo-localization of the tunnel and utilities components, each component is equipped with inspection and maintenance RFID tags, which are connected to the \"smart management platform\" via mobile application.  
Figure 6: Main utilities monitoring.  
Figure 7: Inspection and maintenance data of the tunnel and hosted utilities.  
The inspection tags provide the inspection procedure and documents, as well as historical data. They allows controlling the inspection quality, as well as online reporting, via specific protocols. The inspection report could include sheets, images, and videos. The report is automatically transmitted to staff concerned by the inspection, with alerts in case of need for urgent intervention.  
RFID maintenance tags are used in the same way as the inspection tags to improve and control the quality of maintenance.  
Robots are equipped with sensors to measure some parameters such as air quality, temperature, and humidity and to take videos in restricted-access areas. They can also be used to conduct regular and automatic inspections of the tunnel and its equipment to detect anomalies such as cracks, water infiltration, presence of smoke and gas, etc.
## 5 Discussion  
This study innovatively combines the improved reciprocal velocity method with ArcSAR technology to achieve high-precision prediction of landslide occurrence times. Compared to traditional methods, this model precisely captures the subtle dynamic changes during the accelerated deformation stage of landslides by utilizing the normal distribution confidence interval of velocity parameters, significantly enhancing prediction accuracy. Furthermore, we creatively constructed a \(\Delta\)t-\(\log(T)\) prediction model and successfully achieved the unitization of monitoring data dimensions through coordinate transformation, effectively solving the previously existing problem of non-convergence in data fitting and further consolidating the stability and predictive performance of the model. When compared to the research findings of [PERSON] et al. [23], our model demonstrates superior prediction accuracy in practical case applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time/\(T\)** & **Prediction Time/h** & **Actual Failure Time/h** & \(\triangle\)**h** \\ \hline
85.57 & 94.2 & 94.74 & \(-\)0.54 \\
86.52 & 95.1 & 94.74 & 0.36 \\
87.56 & 95.48 & 94.74 & 0.74 \\
88.52 & 95.28 & 94.74 & 0.54 \\
89.57 & 94.88 & 94.74 & 0.14 \\
90.52 & 94.56 & 94.74 & \(-\)0.18 \\
91.57 & 94.56 & 94.74 & \(-\)0.18 \\
92.53 & 94.56 & 94.74 & \(-\)0.18 \\
93.57 & 94.56 & 94.74 & \(-\)0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Error analysis of landslide prediction time and actual landslide time.  
Although the data for this study originate from a specific mining environment, meaning the generalization of its findings may be limited, the slope landslide type studied conforms to the general law of creep deformation stage. This suggests that, with further research and applications in diverse settings, this study could possess potential universal application value. However, it is important to note that future research should deepen this perspective through additional case studies and practical applications to fully assess the method's universality and effectiveness. It undoubtedly provides a novel perspective and powerful tool for landslide prediction. It is worth noting that the precise fitting of model parameters still relies on a large amount of high-quality monitoring data support. For regions where data acquisition is difficult, targeted research may be required to adapt to local conditions. Looking ahead, the methods proposed in this study have broad application prospects and await deep integration with other monitoring technologies (such as GNSS monitoring, satellite remote sensing, water level monitoring, etc.) and artificial intelligence algorithms (such as deep learning, machine learning, etc.), in order to improve the accuracy and automation level of landslide predictions on a larger scale. At the same time, researchers can actively explore adaptive adjustment mechanisms for model parameters, enabling them to flexibly adapt to different geographical environments and types of landslides, further enhancing their universality and practicality.
# GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station
## 3 Results
### Environmental Loading  
The four time series that were used are depicted in Figure 15. The black curves are computed by the EOST. Their atmospheric loading is computed using the ECMWF operational model with 3 h resolution, averaged to daily values. The non-tidal loading is from the ECCO2 ocean bottom pressure model [72], which provides daily values. The red curves were interpolated from the GFZ loading grid using bilinear interpolation. The atmospheric loading is computed from the same 3 h ECMWF operational model, but for the non-tidal ocean loading the MPIOM model [73] is used with 3 h resolution. The atmospheric loading shows a variability of few millimetres in the horizontal components and a few centimetres in the vertical. The non-tidal ocean shows a smaller variation both in vertical and horizontal.  
We studied the effect of the environmental loading on the time series rms and trends. We did the time series analysis before and after removing the atmospheric and non-tidal ocean loading. The effect was unremarkable, as removing the loading affected the trend below the 0.01 mm level and increased the residual rms in all three components. For example, using the EOST products, the trend of the up-component decreased from 0.79 to 0.74 mm/year and the uncertainty for the trend increased from 0.35 to 0.48 mm/year. The residual rms increased from 5.7 to 6.1 mm.  
Figure 14: Signal decomposition for the vertical direction at Aboa between 1 February 2003 (2003–032) and 8 December 2017 (2017–342) [unit: mm].
## 3 Materials and Methods
### Data Annotations  
The extracted frames for training were divided into two groups. One group was composed of engaged students and the other one included non-engaged students' frames. We annotated approximately 3000 frames after removing repeated frames for both categories for training data. To annotate the data into engaged and non-engaged categories, we took the features from the literature [24]. Moreover, we also surveyed different classes while asking the students about their gestures when they were engaged or non-engaged. We listed the most salient features to annotate the data. Figure 3 shows a sample of annotated data from engaged and non-engaged features.  
Figure 2: A visual diagram of experimental setup to analyze students’ engagement state.  
The incorporated features for engaged and non-engaged are briefed as follows:  
1. Engaged Frames: The frames in which the student is looking towards the teacher or board, taking notes, or discussing with a teacher are labelled as engaged.
2. Non-Engaged Frames: The frames in which the student seems not interested in the lecture, is looking away from the teacher, barely opening or closing their eyes, yawning, leaning on the desk, using a mobile phone, or talking with fellows are labelled as non-engaged.
## 2 Materials and Methods
### Study Data
#### 2.2.2 Climate Data  
The climate data used in this study were sourced from ERA5 post-processed daily statistics on single levels from 1940 to the present ([[https://cds.climate.copernicus.eu/datasets/derived-era5-single-levels-daily-statistics?tab=download](https://cds.climate.copernicus.eu/datasets/derived-era5-single-levels-daily-statistics?tab=download)]([https://cds.climate.copernicus.eu/datasets/derived-era5-single-levels-daily-statistics?tab=download](https://cds.climate.copernicus.eu/datasets/derived-era5-single-levels-daily-statistics?tab=download)) (accessed on 1 March 2025)), with a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\). This globally available dataset provides daily temporal resolution, allowing for detailed characterization of climate variability over time. These data were synthesized from a daily temporal resolution to a 16-day resolution, where the clear-sky direct solar radiation at the surface and the surface solar radiation downwards were combined into total radiation. The climate data were also reprojected, resampled, and clipped to align with the Landsat NDVI data, preparing the data for GPP estimation using the LUE model.
## 2 Related Work
### CAV Trajectory Optimization for Saturated Heterogeneous Traffic Flows  
Optimal CAV trajectory control at intersections based on dedicated CAV lanes can ensure traffic safety and improve the operational efficiency of saturated heterogeneous traffic flows [12; 14; 37]. CAV trajectory planning at intersections based on dedicated CAV lanes includes planning to reduce energy consumption and shorten traffic delay [38; 39; 40; 41; 42; 43; 44]. [PERSON] et al. [42] suggested a model predictive control method to achieve mixed CAV platoons and HV platoons to minimize fuel consumption through signalized intersections. [PERSON] et al. [43] created an analytical model for controlling CAV trajectory and solved the model from the standpoint of severe safety constraints. Additionally, various cutting-edge control techniques for vehicles, such as numerical computation, are suggested. [PERSON] [44] proposed a decentralized planning approach for CAV trajectories. Based on a two-layer model, [PERSON] optimized the longitudinal and lateral trajectories of individual CAVs. In the objective function, [PERSON] considered vehicle delays, fuel consumption, and lane-change cost. It employed a parallel Monte Carlo tree-search technique and a lane-change strategy tree. Some numerical solution algorithms, including gradient-based methods [41] and metaheuristic algorithms [45], also provide new perspectives for solving CAV trajectory optimization problems. Unfortunately, CAV trajectory optimization near intersections often needs to be considered in conjunction with signal phasing because of vehicle motion continuity. [PERSON] [11] proposed a dedicated CAV lane segregated intersection control model based on a shared phase dedicated lane (SPDL) for a mixed traffic environment, which is based on a dedicated CAV lane and unifies and optimizes CAV trajectory planning and signalized phase control to reduce the delay of heterogeneous traffic flow. However, the above problems in CAV trajectory planning for saturated heterogeneous traffic flow ignore the value of the waiting zone, and no method has been developed for CAV trajectory planning regarding signal phasing and having a waiting zone, which may play an important role in the above strategies.
## 3 Results
### Effects of CWP on Concrete Durability Properties
#### 3.3.2 Chemical Resistance  
The use of CWP in concrete significantly improves its resistance to chemical degradation, particularly in acidic environments. This property is critical for concrete structures exposed to aggressive conditions, such as industrial zones and wastewater treatment plants. [PERSON] et al. [67] demonstrated that concrete with a 30% CWP replacement combined with 2% sodium silicate admixture exhibited remarkable durability under exposure to hydrochloric acid (HCl) and sulphuric acid (H2 SO4). Their study showed that sulphuric acid caused more severe durability loss than hydrochloric acid; however, the concrete mix retained strength longer due to the denser microstructure created by the pozzolanic activity of CWP, which limits aggressive ion penetration.  
[PERSON] et al. [82] highlighted that CWP reduces chloride ion penetration in concrete, enhancing resistance to chloride-induced corrosion across various compressive strengths (25 MPa, 50 MPa, 75 MPa). This improvement is attributed to the denser microstructure and refined pore system provided by the CWP.  
[PERSON] et al. [68] also noted the impact of CWP in reducing permeable pores, further aiding the resistance of concrete to chemical attacks. However, they cautioned that higher replacement levels beyond 30% may increase porosity and reduce durability because of dilution effects.  
These findings underscore the importance of optimal CWP replacement levels in achieving chemical resistance in concrete. The improved resilience to chemical attacks, whether from acids or chloride ions, highlights CWP's potential for use in aggressive environmental conditions where long-term durability and structural integrity are critical.