esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 2 Methods
### Improving Matching-Point Location Estimation by NCC and LSM  
With the translation parameters obtained by OPC, the position of the conjugate points was predicted. The conjugate points can be searched for in a relatively small window around the predicted position. For remote sensing image matching with small rotation and scale variances and initial matching values with good quality, Harris-supported NCC and LSM methods have been shown to be highly efficient and precise [38]. Thus, in our approach, feature extraction based on the Harris detector and matching based on NCC and LSM were adopted for precise image matching [14; 39]. The Harris detector is known for its high speed and stability. We used this detector to extract apparent features and refine feature points to the sub-pixel level by using the Forstner operator. To extract an appropriate number of evenly distributed feature points, the image was segmented into 50 by 50 pixel grids. The features with the strongest Harris value in each grid were then extracted. To enhance the positioning precision of the ZY-1-02C by image matching, the feature points needed to correlate with the original ZY-1-02C image. In this study, feature points with positions that corresponded to those of the original image were extracted from the resampled ZY-1-02C image. The corresponding points were then searched from the ETM+ orthoimage. Thus, extracting feature points from the resampled ZY-1-02C images, which were connected to the original ZY-1-02C images, and searching for the corresponding points in DOMs of ETM+, was a good approach for the case study. After feature extraction, the coordinates of the corresponding points on DOMs of the ETM+ images were estimated using the following equation:  
\[\left\{\begin{array}{l}x_{ETM}=x_{ZY}+x_{0}\\ y_{ETM}=y_{ZY}+y_{0}\end{array}\right. \tag{5}\]  
where \((x_{ETM},y_{ETM})\) is the point on the DOMs of the ETM+ images, \((x_{ZY},y_{ZY})\) is the feature point in the resampled ZY-1-02C images, and \((x_{0},y_{0})\) represents the translation parameters.  
A small search window was needed around the estimated position to find the conjugate points, which ensured high efficiency and reliability of the matching results when an NCC-based approach is used. To achieve sub-pixel matching precision, the corresponding points obtained by NCC were used as the initial values for LSM. Proposed by [PERSON] [40], LSM uses adequate information in the image window that used a second-degree polynomial model for the adjustment calculation.
## 1 Introduction
How to improve the matching efficiency is the key problem of the TAN system, but traditional terrain matching algorithms fail to meet the requirements of high precision and real-time. Therefore, this paper proposes a terrain matching algorithm based on PSO to improve the matching accuracy and reduce the matching time. Since PSO was proposed, it has garnered the attention of many researchers for its advantages of simple principle, fast solving speed, and strong global optimization ability. PSO has been widely used in TAN. However, due to the instability of intelligence algorithms, a single algorithm fails to meet the requirements of matching accuracy. Among these intelligence algorithms, PSO and artificial bee colony (ABC) can solve numerical optimization problems well and can be applied to terrain matching. Unlike other traditional terrain matching algorithms, PSO performs heuristic searches on the entire domain within the error range and has a stronger global searching ability. However, its diversity deteriorates in the later search period, and it can easily fall into a local optimum. By contrast, the scout bee search in ABC is a guided random search, which considers randomness, convergence, and diversity. Most importantly, it can search locally. The combination of these two algorithms can not only increase the diversity of the population but also find the optimal solution. Considering the high-precision positioning requirement of INS in a short time, this paper proposes an improved PSO search strategy based on ABC (F-WAPSO) for terrain matching. The mean Hausdorff distance (MHD) was introduced as the fitness function for terrain matching. The MHD could effectively eliminate noise and improve the stability of F-WAPSO, thus improving the matching accuracy. The optimization steps are as follows:  
1. The traditional terrain matching algorithm has limitations on the initial position error and matching time. To this end, the matching algorithm combining ABC and PSO is proposed for INS terrain matching. 2. In this study, PSO is analyzed in depth, and its ability to find the global optimal solution is improved by modifying the acceleration factor \(c_{1}\). The improved PSO is called adaptive PSO (APSO). Meanwhile, linear decreasing inertia weights are introduced based on APSO to improve the search speed. The new algorithm is named WASPO, and its convergence is proved. 3. To strengthen the particle optimization ability of WASPO, ABC is combined with WASPO to avoid it falling into the local optimum in later iterations, and the final algorithm is termed ABC search-based WASPO (F-WAPSO). Subsequently, it is used for terrain matching. 4. The search area and fitness function are determined according to the requirements of terrain matching navigation. The algorithm's validity is verified by experiments with real bathymetric data obtained in Weihai.
# Physical Side-Channel Attacks on Embedded Neural Networks: A Survey
## 4 Physical SCA Attacks on DNNs
### Recovering Input Data  
#### 4.3.1 Micro-Controller Targets  
**EM:** [PERSON] et al. [21; 36] target a three layer-MLP pre-trained model by using floating-point numbers for inputs by normalising the MNIST database between 0 and 1 and con ducted their experiments on an ARM Cortex-M3 32-bit micro-controller. The authors assume the network architecture is known by the attacker, including weights, activation function and number of layers and neurons. They target the multiplication operation in the first hidden layer where several known weights \(w\_i\) are multiplied by each unknown single input \(x\). The authors note that as the multiplication can be processed by an ALU or by dedicated floating-point units, a solution in order to generalise their approach is to target the multiplication result update in the registers or memory. However, as \(x\) will change between two measurements, it is not possible to leverage the information learned from one measurement with subsequent measurements. Therefore, the authors propose to use a single EM trace-based technique, called _Horizontal Power analysis (HPA)_[45], but using EM. The weights in the first layer are multiplied one by one with the same input \(x\) and the single trace is split into \(i\) sub-traces for \(i\) weights in order to isolate them and to treat them separately. Then, the value of the input is statistically deduced through a DPA standard technique on the sub-traces applying [PERSON]'s correlation. **Discussion and limitations:** In order to be reliable, this attack technique requires a large number of multiplications to recover inputs. Therefore, it cannot be applied on small networks consisting of less than 40 neurons in the first hidden layer according to their experiments [21]. Accordingly, the authors repeated the experiments for a network architecture that encompassed 500 neurons in the first layer [36]. When evaluating the accuracy of this attack, the authors consider it a success when the recovered input value matches two decimal places.
## 3 Research Methods
### NISDL Algorithm
#### 3.3.5 Algorithms for Comparison  
Two algorithms are used for comparison in this paper: (1) DL algorithm. The commonality between NISDL method I and II is that they all use SSI for model training. For validating the effectiveness of NISDL algorithm (with SSI), we remove the SSI and corresponding hidden layers for SSI features extraction from NISDL method II, so that it will be another deep learning network (without SSI) and is named the DL algorithm hereinafter. (2) NIPST algorithm. DL algorithm, NISDL method I and II are all nonlinear methods and data driven methods with deep learning networks. For further validating NISDL method I and II, the NIPST algorithm is also used for algorithm comparison, which is a linear and model driven method.  
\begin{table}
\begin{tabular}{l l} \hline \hline Algorithm: the NISDL algorithm \\ \hline
**Output:** NISDL model (\({}^{*}\)_h5_), skin temperature (\({}^{\circ}\)C) \\
**Step:** \\
1. & Video pre-processing \\  & (1) & De-noise and handle subteness magnification for captured video. \\  & (2) & Magnification coefficient \(\zeta\) is 10 (formula (3)). \\  & (3) & Extracting region of interest (ROI) from each frame of video, the size is 150\(\times\)150. \\
2. & Making label \\  & (1) & Making numerical interpolation for skin temperatures captured by iButton. \\  & (2) & Uniform interpolation is adopted, plus 11 points/min. \\  & (3) & Establish a correspondence table between ROI images and skin temperatures (after interpolation). \\
3. & Algorithm training \\  & (1) & Commonality between NISDL method I and II \\  & 1) & Training set and test set ratio: 12: 4. \\  & 2) & Validation set: 500 images. \\  & 3) & During network training, 32 images/batch, epoch is 8. \\  & 4) & Training \(\sim\)30000 images, validate once. \\  & 5) & Activation function: ReLU \\ (2) & NISDL method I \\  & 1) & SSI values were concatenated with ROI images in the first steps. \\  & 2) & Convolutional kernel: \(1\times 1\). \\ (3) & NISDL method II \\  & 1) & Features are extracted from SSI values and ROI images, respectively. \\  & 2) & Concatenating the two kind of features in the second half of network. \\  & 3) & Convolutional kernel: \(3\times 1\). \\
4. & Optimizing model parameters \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contactless measuring method of skin temperature based on skin sensitivity index.
## 3 Signal Level Compatibility Analysis
### Analysis of the Effect of Interference on the Tracking Performance of BeiDou
#### 3.2.2 Code Tracking Performance  
In the tracking process of the BeiDou receiver, the tracking loop continues to complete the tracking of the code phase after the carrier tracking is finished. The code tracking loop commonly used in the receiver is the Delayed Phase-Locked Loop (DLL), and the measurement error sources of the code loop mainly include the code phase jitter due to thermal noise and the dynamic stress error in two parts. Representing the mean square error of the code phase measurement error due to thermal noise, the formula for the measurement  
Figure 6: Carrier tracking error varies with carrier-to-noise ratio.  
error for the code loop of the normalized early minus late power law discriminator is as follows:  
\[\sigma_{DLL}=\begin{cases}\sqrt{\frac{B_{L}}{2\cdot C/N_{0}}d(1+\frac{2}{(2-d)T \cdot C/N_{0}})},d\geq\frac{\pi}{W\Gamma_{c}}\\ \sqrt{\frac{B_{L}}{2\cdot C/N_{0}}(\frac{1}{W\Gamma_{c}}+\frac{W\Gamma_{c}}{ \pi-1}(d-\frac{1}{W\Gamma_{c}})^{2})}(1+\frac{2}{(2-d)T\cdot C/N_{0}}),\frac{1 }{B_{fa}T_{c}}<d<\frac{\pi}{W\Gamma_{c}}\\ \sqrt{\frac{B_{L}}{2\cdot C/N_{0}}\frac{1}{W\Gamma_{c}}(1+\frac{1}{T\cdot C/N_ {0}})},d\leq\frac{1}{W\Gamma_{c}}\end{cases} \tag{6}\]  
where \(B_{L}\) represents the code loop noise bandwidth, \(B_{fe}\) represents the RF front-end bandwidth, \(T_{c}\) represents the pseudo-distance code width, \(D\) and \(T\) represent the correlator spacing and pre-detection integration time, respectively, and \(C/N_{0}\) represents the equivalent carrier-to-noise ratio.  
When the receiver code loop noise bandwidth is 2 Hz, and the correlator spacing D is 1 code chip, the relationship between the code tracking error and the equivalent carrier-to-noise ratio can be calculated as shown in Figure 7, and it can be seen that the longer the pre-detection integration time, the smaller the code tracking error. Increasing the pre-detection integration time can reduce the effect of interference on the code tracking performance.
## 5 System Overview
### Nodes  
One of the essential elements of the system is node elements, and there are two types: data nodes and replica nodes. Distributed systems usually use head name nodes and data nodes. In this work, all the functions of the head node are inherited to the data node, so only data node is used. Because, in other systems, when head node is crushed, all the system will be unavailable usually, perhaps it causes data loss [19; 20; 21; 22; 23]. These nodes have quite functional properties in that they can collect data node and server node functions. In this way, the survivability and availability of the system is very good. In the whole system, even if only one node remains, the system can continue to operate. This is one of the features our system possesses.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 4 Integration of Various RS Technologies
### Integration of the Vision-Based Technologies Together and with Other RTLS
#### 4.3.1 Photogrammetry Integration with Laser Scanning
The integration of a laser scanning system with photogrammetry would provide a timely, cost effective, and accurate 3D model of a structure which can decrease the human exposure costs and risk exposures caused by the lack of information during construction [38]. In fact, more accurate visual information results in less amount of rework and change order which would improve the productivity indexes [50]. Moreover, these 3D models are used for making accurate rehabilitation, maintenance, and renovation plans during the structure operation. However, the registration and alignment of these two sensory data matters. On one hand, both digital images and scanning point cloud data have internal errors that affect the registration process. On the other hand, factors such as the required accuracy, resolution and accessibility of the object may affect the chosen of the optimal method [28; 33; 34].
## 3 Results and Discussion
### Seasonal Variation of Estimated Rain Heights Using KDE
#### 3.3.4 Rain Height Distribution in Spring  
The peak rain height in Polokwane increased from 4500 m in winter to 5000 m in spring, with a probability density of 0.0016. The range remained constant, but the extremes rose to 3500 m and 6000 m, as shown in Figure 7a. Nelspruit, Pretoria, and Mahikeng also maintained similar peak rain heights but with probability densities of 0.001, 0.006, and 0.0012, respectively. Nelspruit has the widest rain height range, which indicates high variability during the season. According to Figure 7e-g, Upington, Bloemfontein, and Durban have a peak rain height of 4500 m, with a probability density of 0.008 and a range of 2500-5500 m. These imply that the peak rain height is higher than winter heights. The further decrease in Port Elizabeth and Cape Town rain heights, as presented in Figure 7h,i, is famously attributed to the coastal characteristics of these regions, as discussed previously.  
Figure 6: Probability density functions of rain heights during winter for (**a**) Polokwane, (**b**) Nelpruit, (**c**) Pretoria, (**d**) Mahikeng, (**e**) Upington, (**f**) Bloemfontein, (**g**) Durban, (**h**) Port Elizabeth, and (**i**) Cape Town.  
Figure 7: _Cont_.  
The spring season in South Africa shows a clear transition from the lower rain heights of winter back to the higher rain heights associated with the summer. Rain events become more frequent or intense, as indicated by the increased probability density in several locations. The rain height distributions also broaden, suggesting more variability in the intensity and altitude of rain events as the region moves into the wetter part of the year.
# Visual Performance Assessment of Videos--A Case Study of the Game \"Spot the Difference\"
## 3 Results and Discussion
### Number of Fixations  
As shown in Table 1, the ANOVA results for the number of fixations indicated that background (F = 4.160, \(p\) < 0.05), video type (F = 4.391, \(p\) < 0.05), arrangement (F = 6.823, \(p\) < 0.05), and the interaction between background and arrangement (F = 5.832, \(p\) < 0.05) were the significant factors. For the interaction between background and arrangement, as shown in Figure 5, no significant difference was found with a background in the two arrangements. However, a significant difference (\(p\) < 0.05) was found for the two arrangements without a background, where left-to-right arrangement without a background had a small number of fixations. One possible reason for this may be that the participants are used watching left-to-right screens when watching less complex backgrounds, resulting in a lower number of fixations. On the other hand, the top-to-bottom arrangement generated a short scanning distance due to its convenient search from the top down. Although inducing more fixations, participants gave positive comments on it.  
Figure 4: The interaction between film type and arrangement for accuracy.  
The results also indicated that the background was a significant factor. The number of fixations of the video without a background (M = 1694.954) was significantly lower than that with a background (M = 1815.030). [PERSON] et al. [8] showed that the structure of the background (lateral or radial) had an impact on the duration of fixations. The participants also reported that they had to concentrate on complex images, which resulted in more fixations. [PERSON] and [PERSON] [28] also concluded that the use of more visual cues (such as 3D images) induces a greater number of fixations than the use of less visual cues (such as 2D images).  
The results also indicated that the video type was a significant factor. The number of fixations of the animation (M = 1676.024) was significantly lower than that of the text (M = 1833.960). [PERSON] and [PERSON] [13] indicated that the total fixation counts and average fixation duration of texts were more than those of pictures, whether for EFL beginners or intermediate level readers. In this study, the participants also reported that reading texts required more concentration and resulted in more fixations.  
The results further demonstrated that the arrangement was a significant factor. The number of fixations of the left-to-right arrangement (M = 1668.364) was significantly lower than that of the top-to-bottom arrangement (M = 1841.620). The reason for this may be that participants are used to watching left-to-right screens, which results in fewer fixations. In addition, participants also considered the top-to-bottom arrangement convenient to scan. Although it induced more fixations, the participants made positive comments about it. [PERSON] [24] showed that vertical phonetic arrangement has better operation performance than a vertical one in sending Chinese text messages.
## 3 Distributed Optical Fibre Sensing Applied to Geo-Hydrological Applications
### Soil Leves and Embankments Monitoring
Unfortunately, these processes often proceed without any surface evidence, and even when external signs became visible, the structure would already be irremediably compromised. On these premises, levees and embankments--as they are very long structures requiring high spatial resolution monitoring--represent ideal scenarios for the application of temperature and strain distributed fibre sensors. A significant number of applications of DOFSs in the literature regards the monitoring of levees and embankments, mostly using Raman-DTSs. Other approaches deal with Brillouin-based DTS, or with the measurement of anomalous strain field, exploiting Brillouin or Rayleigh scattering. Physical measurands are the temperature variations as tracers of seepage, soil moisture, and the strain.
## 2 Methods
### Laboratory Experiments and Mie Scattering Calculations to Evaluate LISST-VSF
#### 2.1.2 Experimental Procedure
For 400 and 700 nm bead suspensions, two different dilutions were measured. For each polarization state (i.e., perpendicular and parallel) of incident light, we acquired 1440 measurements with a sampling frequency of 8 Hz over 3 min. For a given sample, this data acquisition protocol was repeated three times. Each of these three replications was made with a different randomly-chosen orientation of sample cylindrical vial within the instrument. The sample was gently mixed between these replicate measurements. The baseline measurements of 0.2 \(\upmu\)m filtered water were acquired using the same protocol. As mentioned above, the optical measurements were made on sufficiently-diluted samples to ensure negligible effects of multiple scattering over a pathlength used by a given instrument. A criterion for a single scattering regime is generally defined in terms of small optical thickness of the sample, \(\tau<<1\), where \(\tau\) is a product of the beam attenuation coefficient, \(c\), and pathlength, \(r\)[51; 66]. Also, a simple practical test for ensuring that multiple scattering effects are negligible is to verify a direct proportionality between the measured optical signal and the concentration of particles in suspension by conducting a series of measurements on the same sample with different dilutions [51]. Our measurements on bead samples with different dilutions showed an excellent 1:1 relationship between the LISST-VSF measurement and the bead concentration over the range of beam attenuation coefficient up to at least 2 m\({}^{-1}\). The single scattering regime can also be determined by the condition \(\tau\)(1-\(g\)) \(<<1\), where \(g\) is the average cosine of the scattering angle of the volume scattering function [23; 66]. For the 100 nm polystyrene beads, the \(g\) value is 0.115, which yields the most restrictive condition in our study, \(\tau<<1.13\). For all bead samples measured with LISST-VSF, including all bead sizes and sample dilutions, \(\tau\) was always less than about 0.3. This condition was also satisfied for samples measured with a spectrophotometer. For the measurements with DAWN-EOS, the \(\tau\) values were even smaller. For the natural seawater samples examined in our study (which is described below in Section 2.2), the \(g\) values (for the total volume scattering function including the contribution by pure seawater) were about 0.9 or somewhat higher, which yields less restrictive criterion \(\tau<<10\). Our measurements of natural samples clearly satisfied this single scattering condition, as the highest value of \(c\) for the natural samples measured with LISST-VSF was about 2.6 m\({}^{-1}\), so \(\tau\) was always less than about 0.45, given that the maximum pathlength for LISST-VSF is 17.5 cm for the scattering angle of 150\({}^{\circ}\).