esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 5 Evaluation and Abstraction
### Distance and Positioning Abstraction  
Distance and positioning algorithms are evaluated using KPIs, including mean distance error (\(\mathrm{E_{d}}\)) and mean positioning error (\(\mathrm{E_{p}}\)), as described in Section 2. These metrics can be obtained as  
\[\mathrm{E_{d}}=\frac{1}{J}\sum_{j=1}^{J}(d_{j}-\hat{d}_{j}), \tag{16}\]  
\[\mathrm{E_{p}}=\frac{1}{J}\sum_{j=1}^{J}\sqrt{(x_{j}^{\mathrm{UE}}-\hat{x}_{j} ^{\mathrm{UE}})^{2}+(y_{j}^{\mathrm{UE}}-\hat{y}_{j}^{\mathrm{UE}})^{2}}, \tag{17}\]  
where \(J\) is the total number of simulation seeds, \(d_{j}\) refers to the true distance from the BS to the target in the \(j\)-th seed, and \(x_{j}^{\mathrm{UE}}\) and \(y_{j}^{\mathrm{UE}}\) denote the true position coordinates of the UE.  
Figure 3a,b illustrate the Cumulative Distribution Functions (CDFs) for \(\mathrm{E_{d}}\) and \(\mathrm{E_{p}}\) under varying OFDM symbols at 0 dB SNR, with the target positioned at 64 m. The scale shift is applied at a distance error equal to 5 m to zoom in and better visualize the values of the CDFs corresponding to errors around 0 m. Fitting these CDFs to known distributions is challenging because their extensive tails extend toward the maximum estimation error corresponding to the last OFDM symbol. This implies a non-negligible probability of the employed algorithms pinpointing the target in the last OFDM symbol.  
Two different estimation conditions are valid for the abstraction proposal in the CDFs. On the left-hand side, we can find the results obtained under target-dominated conditions,  
Figure 3: CDFs of the distance errors (**a**) and position errors (**b**) for 2–1024 OFDM symbols and SNR\(=0\) dB. Here, the target is positioned at \(d=64\) m.  
where the distance and position errors are observed to be low. On the right, it reveals that for extremely large estimation errors (exceeding 10 m), their behavior exhibits a linear trend, aligning with the characteristics of a uniform distribution. Thus, the CDFs are a mix of two different distributions, one of which is the uniform distribution. Under noise-dominated conditions, distance and position estimates become uniformly distributed, indicating equal probability across all possible target locations.  
Considering all mapping levels of SNR (ranging from \(-20\) dB to 20 dB) and the full range of OFDM symbol values, the distribution curves exhibit minimal variation in distance and position error cases under target-dominated conditions. This description is illustrated in Figure 4a,b, where legends are omitted since multiple curves represent results across all possible values. This observation indicates that errors consistently follow the same distribution regardless of these two parameters.  
Results of the probability of estimation under target-dominated conditions versus SNR and the number of OFDM symbols are calculated and provided as lookup tables, which are available in [16].
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 3 Evaluation Methodology
### Structural Similarity (SSIM)  
SSIM is also an FR objective image quality metric, which results in a range of values between 0 and 1 (the higher, the better). It is a perception-based model that considers image degradation as a perceived change in structural information while also incorporating important HVS characteristics. In comparison with PSNR, SSIM quality evaluation is commonly more correlated with subjective results [25].
# A Benchmark for the Evaluation of Corner Detectors
## 3 Benchmark Evaluation Analysis
### Objective Evaluation  
The results of objective evaluation of different corner detectors obtained for the three benchmark datasets are documented in Table 1, where the _precision_, _recall_, _APR_ and _F score_ are used. Note that the evaluation results were averaged over all the test images in each dataset. It can be seen that the DMC detector [33] delivered the highest APR and F score as well as competitive precision and recall. The SODDGG [7], He & Yung [21], and SUSAN [2] methods produced the best performance for recall on Dataset 1, Dataset 2, and Dataset 3, respectively. MSRJ achieved the best performance for precision on Dataset 2. To evaluate the performance of corner detection under different situations, in total, 16,497 transformed images were obtained by applying 5 different types of attacks on each originally chosen image, which are detailed as follows:  
**Noise. **: Zero-mean Gaussian white noise was added to each image. The variance was chosen to be \([0.005,0.05]\) at 0.005 apart. **Rotation. **: Each image was rotated to different angles in \([-90^{\circ},90^{\circ}]\) at \(10^{\circ}\) apart (excluding \(0^{\circ}\), as it corresponds to the original image). **Scaling. **: Each image was scaled (uniformly and non-uniformly) with the scaling factors \(s_{x}\) and \(s_{y}\), chosen in \([0.5,2]\) at 0.1 apart, independently and respectively (excluding the case \(s_{x}=s_{y}=1\)). **Combined transformation. **: Combined transformations (by rotation and scaling) were applied to each image with rotation angles sampled in \([-30^{\circ},30^{\circ}]\) at \(10^{\circ}\) apart, excluding \(0^{\circ}\), and scaling factors \(\{s_{x},s_{y}\}\) sampled in \([0.8,1.2]\) at 0.1 apart, excluding \(s_{x}=s_{y}\), independently and respectively. **Lossy compression. **: Each image was compressed by using JPEG lossy compression with the quality factor sampled in \([5,100]\) at 5 points apart. The performance of the 18 SOTA corner detectors implemented on the degraded images is summarized in Table 2. From this table, it can be seen that the DMC method delivered superior performance in most cases, indicating that this method has strong robustness. The CPDA [22], New-Curvature [27], and MSRJ [28] methods also performed more stably than the others. SUSAN [2] had extremely high recall in some cases but produced a low APR and F score because of its extremely poor precision.
###### Abstract
However, moving from \(n\)-bit word operations to bit operations would be back to square one, since the main reason for using LFSR on extended fields is to take advantage of the capabilities of \(n\)-bit processors where bit operations are inefficient. Taking into account that the \(n\) binary LFSRs that allow generating the same sequence as the LFSR in \(GF(2^{n})\) have all the same primitive feedback polynomial in \(GF(2)\), the previous obstacle can be easily overcame. Thus, the calculation of the bit operations of the \(n\) binary LFSRs can be performed jointly, giving rise to XOR operations between \(n\)-bit words and eliminating the inefficiency generated by single-bit operations in this type of processor. The efficiency improvement provided by the proposed implementation turns it into a method especially suitable for cryptanalysis tasks where any execution time reduction in the systems under analysis is very appreciated. Therefore, security assessments performed to cryptosystems based on LFSR in extended fields must take into account the proposed implementation to report a more realistic security level. Additionally, the proposed method allows any primitive polynomial in \(GF(2^{n})\) to be used as feedback polynomial of an LFSR, thus overcoming the current limitations. The rest of the paper is organized as follows. In Section 2, the mathematical background and notation are introduced, with special emphasis on the LFSR fundamentals and the relationships between the \(m\)-sequences generated in extended and base fields, \(GF(q^{n})\) and \(GF(q)\). Section 3 describes the proposed implementation for the particular case of LFSRs defined in \(GF(2^{n})\) through a different and more efficient way. Section 4 contains the algorithm proposed to test the primitiveness of polynomials making use of the same relationships. Finally, discussion about security and efficiency of the implementations and conclusion are included in Sections 5 and 6, respectively.
## 2 ICP and NDT  
### ICP  
The basic model of the ICP algorithm is: given source point cloud \(\mathbf{P}=\{\mathbf{p_{1}},\mathbf{p_{2}},\mathbf{p_{3}},\ldots,\mathbf{p_{m}}\}\) and target point cloud \(\mathbf{Q}=\{\mathbf{q_{1}},\mathbf{q_{2}},\mathbf{q_{3}},\ldots,\mathbf{q_{n}}\}\), \(\mathbf{P}\), \(\mathbf{Q}\subset\mathbf{R^{3}}\), \(m\),\(n\) are the number of source point cloud and target point cloud, respectively. Let \(E(\mathbf{T})\) be the error between the source point cloud \(\mathbf{P}\) and the target point cloud \(\mathbf{Q}\) under the transformation matrix \(\mathbf{T}\). Then \(E(\mathbf{T})\) can be expressed by the following formula:  
\[E(\mathbf{T})=E(\mathbf{R},\mathbf{t})=\sum_{i=1}^{m}\|(\mathbf{R}\mathbf{p_{i}}+\mathbf{t})-\mathbf{q_{i}} \|^{2}\quad\ T=\begin{pmatrix}\mathbf{R}&\mathbf{t}\\ 0^{T}&1\end{pmatrix} \tag{1}\]  
where \(\mathbf{R}\) is the rotation matrix, \(\mathbf{t}\) is the translation matrix, \(\mathbf{p_{i}}\) is the point in the source point cloud, and \(\mathbf{q_{i}}\) is the nearest point in the target point cloud.  
Solve \(\mathbf{R}\), \(\mathbf{t}\) by minimizing the above error function.  
\[\mathbf{R},\mathbf{t}\leftarrow\operatorname*{argmin}_{\mathbf{R},\mathbf{t}}\sum_{i=1}^{m}\| (\mathbf{R}\mathbf{p_{i}}+\mathbf{t})-\mathbf{q_{i}}\|^{2} \tag{2}\]  
The specific process is as following:  
```
1:Input: Source Point Cloud \(\mathbf{P}=\{\mathbf{p_{1}},\mathbf{p_{2}},\mathbf{p_{3}},\ldots,\mathbf{p_{m}}\}\)
2: Target Point Cloud \(\mathbf{Q}=\{\mathbf{q_{1}},\mathbf{q_{2}},\mathbf{q_{3}},\ldots,\mathbf{q_{n}}\}\)
3: Initial Transformation \(\mathbf{T_{0}}\)
4:Output: Correct transformation matrix \(\mathbf{T}\) matching \(\mathbf{P}\) and \(\mathbf{Q}\)
5:\(\mathbf{T}\leftarrow\mathbf{T_{0}}\)
6: While the result does not converge Do
7: for\(i\gets 1\) to \(m\)
8:\(\mathbf{q_{i}}\leftarrow\text{Find}ClosestPointIn\mathbf{Q}(\mathbf{T}\mathbf{p_{i}})\)
9: if\(\|\mathbf{T}\mathbf{p_{i}}-\mathbf{q_{i}}\|\leq d_{max}\) then
10:\(w_{i}\gets 1\)
11: else
12:\(w_{i}\gets 0\);
13:endif
14: end for
15:\(\mathbf{T}\leftarrow\operatorname*{argmin}_{\mathbf{T}}\{\sum_{i}w_{i}\|\mathbf{T}\mathbf{p_{i }}-\mathbf{q_{i}}\|^{2}\}\)
16: end while
```
## 3 Wavelet Packet Transform-Based Feature Extraction
### Signal Pre-Processing  
To avoid the influence of the amplitude of the radio fuze detector on the classification and recognition results, the signal is pro-processed with normalization before feature extraction. In this paper, signal processing is performed by first subtracting the DC component of the radio fuze output signal, resulting in a zero-mean signal [29]. This can eliminate the DC bias in the output signal of the radio fuze. Then, the signal is normalized to unit variance by dividing the signal by its standard deviation. The normalization to unit variance is carried out to make the outcome of the algorithm independent of the signal amplitude, and, hence, the classification and recognition results are solely dependent on the texture of the radio fuze output signals. The specific expression for signal pre-processing is shown below:  
\[Norm\_Signal=\frac{Original\_Signal-mean(Original\_Signal)}{std(Original\_Signal)} \tag{4}\]  
where \(Norm\_Signal\) denotes the signal after pre-processing, \(Original\_Signal\) denotes the unprocessed signal, \(mean(\cdot)\) denotes the mean value of the signal, and \(std(\cdot)\) denotes the standard deviation of the signal.
# Variable Multiple Interleaved Bi-Directional DC/DC Converter with Current Ripple Optimization
###### Abstract  
In order to reduce the current ripple and improve the power density of the system, the multiple structure design is generally adopted by the traditional bidirectional DC/DC converter. However, the fixed multiplicity design can't make the DC/DC power converter always output the smallest current ripple under different duty ratios. Through this research, it is found that the current ripple is related to duty cycle and parallel multiplicity, and then a variable multiplicity bidirectional DC/DC power converter is proposed. Firstly, the relationship between the current ripple and parallel multiplicity and duty cycle is deduced, and the basic topology of variable multiplicity bidirectional DC/DC power converter is determined; Secondly, the average value model and AC small signal model of the system are established based on the topological structure, and then the state equation is obtained. Thirdly, the current compensation control method is designed based on the state equation. Finally, the experimental platform of variable multiplicity bidirectional DC/DC power converter is built.  
bidirectional DC/DC converter; current ripple optimization; constant current control +
Footnote †: journal: Applied Sciences  
0000-0001-9993-3227]J.J.J.J.
# Do Heavy Vehicles Always Have a Negative Effect on Traffic Flow?
## Impact of Heavy Vehicles on Traffic Flow
### Effect of Heavy Vehicles on Traffic Flow  
It is generally perceived that the traveling speed of a heavy vehicle is lower than that of a passenger car. However, due to an increase in driving performance of the heavy vehicles (horse power, torque, displacement, etc.), they can exhibit a driving performance which is superior, or similar, to that of a passenger car. Therefore, in a traffic flow where both heavy vehicles and passenger cars are mixed, the service level may not decrease significantly, such as a decrease in the overall speed of the traffic flow due to the effect of heavy vehicles.  
As already discussed, as the probability of a heavy vehicle being positioned at the front of a vehicle group increases, the conflict between vehicles decreases. As shown in Figures 9-14, these characteristics can appear in an actual traffic flow through a decrease in the speed variation. In other words, as the proportion of heavy vehicles increases, the speed variation (variance of speed) for each traffic flow rate decreases. Subsequently, the speed difference between the high-speed driving vehicles and the low-speed driving vehicles decreases. In this manner, heavy vehicles play a significant role in stabilizing the traffic flow, thereby reducing the risk of accidents. These results suggest that heavy vehicles can have a positive effect, unlike the existing paradigm in which the speed decreases and the effect on the traffic flow is 1.5 times or 2 times that of passenger cars, which negatively affects the service level.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Vehicle Type**} & \multicolumn{2}{c}{**Ratio**} \\ \cline{2-3}  & **Original a** & **Normalized b** \\ \hline Total vehicles & 1.000 & 1.056 \\ Passenger cars & 0.946 & 1.000 \\ Trucks & 1.716 & 1.813 \\ Recreational & 1.386 & 1.464 \\ Other & 1.023 & 1.082 \\ \hline \hline \end{tabular}  
* Original ratio = percentage of leads by vehicle type divided by percentage of total count by vehicle type;
* Normalized ratio = original ratio for vehicle type divided by original ratio for passenger cars.  
\end{table}
Table 12: Vehicle type ratio of platoon’s leader [19].
## 4 Results and Discussion
### Effect of U-Shaped Sheet Pile Section Size
#### 4.2.2 Influence of Cross-Sectional Dimensions on the Bearing Characteristics of the Retaining Walls  
(1) Horizontal displacement of the retaining wall  
Figure 11 indicates the change rule of horizontal displacement of the retaining wall at different excavation depths. The retaining wall shows a displacement pattern of horizontal movement and clockwise rotation along the wall's top, and the horizontal displacement of the retaining wall gradually decreases with the expansion of the cross-sectional area of the USCSP, and the trend of rotation gradually slows down. When the excavation deepens from 1 m to 2 m, the horizontal displacement of the top of the retaining wall increases from 5.5 mm to 9.6 mm for the type I sheet pile, from 4.2 mm to 6.0 mm for the type II sheet pile, and from 3.2 mm to 5.2 mm for type III sheet pile. This indicates that a lower bending resistance of the support pile will lead to more significant interaction between the supporting structure and the retaining wall as well as a more obvious effect between the supporting structure and the retaining wall.  
Figure 10: Stress in piles with different U-plate sizes. (**a**) Excavation at 1 m. (**b**) Excavation at 2 m.  
(2) Distribution of soil pressure  
Figure 12 indicates the distribution rule of soil pressure at the back of the retaining wall at different excavation depths. The larger the cross-sectional area of the USCSP, the smaller the horizontal displacement of the retaining wall, and the larger the soil pressure at the back of the wall; with the increase in the excavation depth, the soil pressure at the back of the wall gradually weakens. When the excavation depth increases from 1 m to 2 m, the soil pressure at the retaining wall's bottom corresponding to the type I sheet pile decreases from 38.7 kPa to 34 kPa, the soil pressure at the retaining wall's bottom corresponding to the type II sheet pile decreases from 40.7 kPa to 39.7 kPa, and the soil pressure at the retaining wall's bottom corresponding to the type III sheet pile decreases from 43.9 kPa to 41.1 kPa.
# Genesis of the Baiyangping Cu-Co and Pb-Zn Mineralizations in Lamping Basin, SW China
## 3 Analytical Methods
### Fluid Inclusions  
Twenty-two samples were collected for fluid inclusion examination. Thin, polished sections were used to microscopically assess the characteristics of inclusions in quartz and carbonates for their relative age, shape, size, and style of distribution by a Zeiss microscope. Zeiss microscope produced by Carl Zeiss Microscopy GmbH company, jena, Freista at Thuringen, Germany. Microthermometric studies were conducted at the laboratory of China University of Geosciences in Beijing, Beijing, China, the fluid inclusion microthermometry was then conducted using a Linkam THMS 600. The fluid inclusions were heated at 0.2 \({}^{\circ}\)C/min and frozen at 0.1 \({}^{\circ}\)C/min. The measured indices consisted of the first ice-melting temperature (Tm) of carbonic hydrite (Tm, cla), the ice-melting temperature (Tm, ice), and the homogenization temperature (Th). The salinity of the aqueous two-phase inclusions was calculated from the measured final melting temperature, using data for the NaCl-H\({}_{2}\)O system from [PERSON] et al. [25]. The salinity was calculated from the measured melting temperature of rock salt, applying the equations of [PERSON] et al. [29].  
Figure 3: Optical and scanning electron microscopy images of the main mineralization at the Baiyangping Cu–Co–Pb–Zn deposit showing **(a)** early chalcopyrite–tennantite–calcite paragenesis; **(b)** early chalcopyrite–tennantite–bromite–calcite paragenesis; **(c)** early cobaltite–tennantite–quartz paragenesis; and **(d)** late galena–sphalerite–pyrite–calcite paragenesis. Abbreviations: Bn = bromite, Cal = calcite, Ccp = chalcopyrite, Cob = cobaltine, Dol = dolomite, Gn = galena, Tnt = tennantite, Qzt = quartz, and Sp = sphalerite.
# The Power System and Microgrid Protection--A Review
## 2 DG and Microgrid Challenges
### Sympathetic or False Tripping  
The high penetration of various sources may change the flow of the fault current in the radial feeder of a distribution network. A traditional AC distribution system has a radial topology with a non-directional OC relay. Non-directional OC relays cannot determine the change in fault current direction, and this causes an unwanted trip in the main feeder [47, 48]. Reference [47] proposed DOC relays to protect radial distribution networks in the presence of DGs. The DOC settings are divided into OC and directional elements. A DOC relay restricts the relay maloperation due to the short circuit occurring on the adjacent feeders. The DOC element settings consist of the fault direction (forward and backward), maximum torque angle, and polarizing quantity (voltage and current). A method has been proposed in Reference [48] to avoid sympathetic tripping of non-DOC relays in the distribution network with an embedded DG. This method is offline to overcome false tripping issues and uses a genetic algorithm and a linear programming to access optimal responses. In Reference [49], a method was proposed for utilizing single-setting and dual-setting DOC relays with a minimum number of two schemes for protection. The interior-point method has been used for the optimization problem and solving the protection coordination problem. Reference [50] suggested a novel quaternary protection scheme with dual-DOC relays to protect microgrids. This scheme uses a centralized protection control strategy that can re-adjust the optimal relay setting automatically with computational intelligence optimization. Reference [51] presented a protection method in inverter-based microgrids by comparing a current-only polarity between the pre-fault current and the fault current components. The detection of fault is based on the angle difference between the pre-fault current and fault current components.  
Figure 6 depicts the basic principle of false tripping in the main feeder (DGs feeder) due to a fault on the adjacent feeder. As shown in the figure, the main feeder relay may operate by a fault on the adjacent feeder.  
Figure 5: Characteristic curve of the overcurrent (OC) relay operation zone and blinding zones.  
A DOC relay was proposed as the best solution to avoid the sympathetic tripping in the presence of DG in multi-loop systems [47; 48].