esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Split Spectrum Processing
### Signal Recombination Methods  
The signal recombination methods considered in this paper are based on order statistics and phase observation, which conclude Normalized Minimization (NORM-MIN), Mean (MEAN), Frequency Multiplication (FM), Polarity Thresholding (PT), Polarity Thresholding with Minimization (PTM) and Scaled polarity threshold (SPT), and they are expressed as described in [24]:  
\[y_{NORM-MIN}(t)=\min(|y_{i}(t)/\max_{i}(y_{i}(t))|) \tag{2}\]  
The output signal of NORM-MIN is the normalized minimum value of all sub-band signals \(y_{i}(t)\) (\(i\) =1,2,...,\(N\)) at time \(t\), which is suitable for the case where the defect signal amplitude is significantly higher than the coherent noise level; otherwise, the defect signal would not be completely preserved.  
\[y_{MEAN}(t)=\sum_{i=1}^{N}y_{i}(t)/N \tag{3}\]  
MEAN retains the average value of all sub-band signals at each time \(t\); it can reduce the coherent noise level by averaging, as the noise value varies in different sub-band signals. However, it is similar to NORM-MIN, which would get a great result when the coherent noise level is much lower than defect signal amplitude. Therefore, NORM-MIN and MEAN would not suitable for the high dispersive signals.  
\[y_{FM}(t)=\prod_{i=1}^{N}y_{i}(t) \tag{4}\]  
The sub-band signals are multiplied by each other to generate the FM output signal, and this method could enhance the defect with large amplitude and reduce the noise level with low amplitude, which implies that FM has a good processing effect on low dispersive signals.  
\[y_{PT}(t)=\begin{cases}x(t)&if\ y_{i}(t)<0\ or>0,\,\forall i\\ 0&otherwise\end{cases} \tag{5}\]  
The output signal of PT is the unprocessed signal \(x(t)\) at time \(t\) if all the sub-band signals are negative or positive; otherwise, it is zero. Thus, PT can eliminate the coherent noise, which is highly sensitive to the frequency and has a different sign in different sub  
Figure 2: (**a**) Comparison of GS and RC filters with the same bandwidth, frequency impulses of (**b**) GS, (**c**) RC, (**d**) FBR-GS and (**e**) FBR-RC filters, time impulses of (**f**) RC and (**g**) FBR-RC filters.  
band signals. Although the requirements of PT are not as strict as those of the above methods, the defect signal amplitude should not be lower than the noise level.  
\[y_{PTM}(t)=\begin{cases}\text{min}y_{i}(t)&\text{if }y_{i}(t)<0\text{ or }>0,\text{ }\forall i\\ 0&\text{otherwise}\end{cases} \tag{6}\]  
\[y_{SPT}(t)=\begin{cases}\left|\frac{N_{+}-N_{-}}{N}\right|\text{min}y_{i}(t)& \text{if }y_{i}(t)<0\text{ or }>0,\text{ }\forall i\\ 0&\text{otherwise}\end{cases} \tag{7}\]  
Both PTM and SPT are the combination of the minimization and PT, and the difference between them and PT is that the minimization of all sub-band signals at time t is retained when all the sub-band signals are negative or positive. \(N+\) and \(N-\) are the total positive and negative numbers of the sub-band signal group. However, PTM and SPT may reduce the defect signal amplitude while reducing the noise level, which would influence the further quantitative analysis of defects.
## 4 Conclusions  
From among the three different M\({}_{1}\)\({}^{2+}\)-M\({}_{2}\)\({}^{4+}\) substitutions of Mn-Ti, Zn-Ti, Zn-Zr, the Zn-Zr substitution most effectively decreased the intrinsic property of the magnetocrystalline anisotropy of the hexaferrite, although the solubility limit (\(x\) = -1.0) is smaller than that for the other substitutions. The coercivity, natural resonance frequency, and frequency range of the EM absorption via the magnetic loss mechanism decreased in large steps for an increasing substitution \(x\) of up to 0.9, and then decreased slightly with increasing \(x\) for \(1.0\leq x\leq 1.1\). All these parameters are closely related to one another, and the changes are due to the magnetic crystalline anisotropy caused by the Zn-Zr substitution. The sample with \(x\) = 0.9 demonstrated a high EM absorption in the X-band (8-12 GHz) with the lowest reflection loss of \(<\)\(-\)45 dB, and the sample with \(x\) = 0.8 exhibited a broad Ku band (12-18 GHz) absorption performance satisfying RL \(<\) -19 dB at 11 \(\leq\)\(f\)\(\leq\) 18 GHz. Herein, we report for the first time the EM absorption properties of Zn-Zr substituted M-type Sr-hexaferrites and show that they are very promising candidates for X and Ku band EM absorbers.  
Figure 8: RL vs. \(f\) plot of SrFe\({}_{12-2x}\)Zn\({}_{x}\)Zr\({}_{x}\)O\({}_{19}\) (\(x\) = 0.7, 0.8, 0.9, 1.0, 1.1) powder–epoxy composites for determining optimal absorber thickness (mm).  
**Author Contributions:** [PERSON]-M.K. designed the experiments and wrote the manuscript. [PERSON]-U.K. performed all the experiments and analyzed the data under supervision of [PERSON]-M.K. Both authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the Research Fund of the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Science and ICT (Grant No. 2017R1C1B2002394), and by the Basic Science Research Capacity Enhancement Project (National Research Facilities and Equipment Center) through the Korea Basic Science Institute, funded by the Ministry of Education (Grant No. 2019R1A6C1010047).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 The Available Optimal Quantization Function  
As described in the preceding section, the quantization function \(q(Y)\) can be considered as an estimator of the phasor \(e^{-j\varphi}\) to eliminate the dependence of the residual nuisance parameter \(\varphi\) in autocorrelator output \(A[m]\). It exhibits an important role not only in detection performance but in robustness to the CFO. Thus, in this section, we turn our attention towards developing \(q(Y)\) to achieve a benchmark that can be theoretically achieved.  
In this case, \(q(Y)\) in Equation (5) can be expressed as:  
\[q(Y)=Y^{*}=|Y|e^{-j\angle Y}. \tag{7}\]Here, \(\angle Y\) is the principal value of the argument of the measurement \(Y\), and \(|Y|\) denotes the magnitude of \(Y\). The positive nature of magnitude \(|Y|\) means it does not affect the decision result in Equation (4). Hence, Equation (7) can be simplified as:  
\[q(Y)=e^{-j\widehat{p}}, \tag{8}\]  
where \(\widehat{p}\) must be set equal to the principal value of the argument of the measurement \(Y\), i.e.,  
\[\widehat{p}=\begin{cases}\tan^{-1}\left(\frac{Im(Y)}{Re(Y)}\right),&\text{if} \quad Re(Y)>0,\\ \frac{\pi}{2},&\text{if}\quad Re(Y)=0,\,Im(Y)\geq 0,\\ -\pi+\tan^{-1}\left(\frac{Im(Y)}{Re(Y)}\right),&\text{if}\quad Re(Y)<0,\\ -\frac{\pi}{2},&\text{if}\quad Re(Y)=0,\,Im(Y)<0.\end{cases} \tag{9}\]  
Here, \(Im(Y)\) denotes the imaginary part of \(Y\). Equation (9) is referred to here as the first old estimation model, and the receiver with it is the well-known conventional _optimal_ receiver [2].  
The noise term \(\eta(1)\) in Equation (3) is not Gaussian but can be approximated as Gaussian when chip signal-to-noise ratio (SNR) is reasonably high [23]. As a consequence, we can think of the measurement \(Y\) in Equation (3) as the equivalent received signal at time \(m\) and \(\eta(1)\) as an equivalent AWGN channel. In such condition, Equation (9) can be interpreted as _data-aided maximum likelihood_ (DAML) estimates of the phase \(\varphi\) and Equation (8) a DAML estimate of the phasor \(e^{-j\varphi}\) (cf. [24], p. 166). In this case, the receiver structure is given in Figure 1[25]. In Figure 1, the switch 1 (SW 1) should open in the bit intervals \(1\leq m\leq 32\), and be off otherwise. For SW 2, the opposite occurs.  
We observe immediately from Equation (9) that the arctan operation cannot be avoided in the _postcompensation process_ of the optimal receiver. This requires a computational complexity that renders its theoretical approach impractical in WSNs, where low power and low cost are of paramount importance. Therefore, the creative approximation, thus avoiding intractable complexity in this process, is imperative for IEEE 802.15.4 WSNs where computation of the optimal detector is too difficult and uneconomical.
## 2 Results
### Intracellular ATP Levels in Response to Diosgenin  
To assess the effect of diosgenin on intracellular ATP levels in _S. aureus_, ATP was quantified using luminescence assays on cells cultured in 80 \(\upmu\)M and 160 \(\upmu\)M diosgenin media (Figure 4). ATP levels were normalized to cell density (Abs\({}_{600}\)), accounting for variations in cell growth, to provide a more accurate representation of intracellular ATP  
Figure 3: Effect of diosgenin on _relP_ (**A**) and _relQ_ (**B**) gene expression in _Staphylococcus aureus_. _Staphylococcus aureus_ cells were treated with 80 \(\upmu\)M and 160 \(\upmu\)M diosgenin, and relative expression was determined using RT-PCR analysis. Gene expression levels were normalized to that of the constitutively expressed 16s rRNA gene and expressed relative to an untreated control (None). Data are presented as means \(\pm\) the standard deviation of three independent experiments.  
content per cell. Treatment with 80 uM diosgenin resulted in a 36% reduction in intracellular ATP levels relative to the control group, indicating a significant decrease in cellular energy reserves (\(p<0.01\)). A similar reduction was observed in cultures treated with 160 uM diosgenin, where intracellular ATP levels decreased by 38% compared to the control group (\(p<0.01\)). Despite the higher diosgenin concentration, the ATP reduction was similar to that observed at 80 uM, suggesting that the inhibitory effect of diosgenin on ATP synthesis may reach a threshold at or before 80 uM. These reductions in intracellular ATP levels indicate that diosgenin disrupts metabolic processes associated with energy production, potentially contributing to its inhibitory effect on persister cell formation. Since ATP is a critical metabolite required for the survival and stress response of persister cells, the observed ATP depletion may reduce cellular viability under antibiotic stress, further enhancing the anti-persister effects of diosgenin.
## 2 Dynamic Triaxial Test of Typical Coarse-Grained Soil Filler for High-Speed Railway Subgrade  
### Test Equipment and Specim Preparation  
This paper mainly studies the long-term deformation and energy dissipation law under cyclic loading for the surface layer and bottom layer of high-speed railway subgrade. The test was carried out using a large diameter triaxial test system, as shown in Figure 1. It includes a dynamic loading system, pressure chamber, confining pressure control system, data acquisition and control operation system, etc. The dynamic loading system is controlled by the closed loop of the servomotor. The axial force is 0-64 kN; the frequency is 0-5 Hz. The displacement stroke and control accuracy are \(\pm\)100 mm and 0.20 \(\upmu\)m, respectively. The pressure chamber is suitable for triaxial specimens with a maximum diameter of 300 mm, to meet the requirements for the configuration size of coarse-grained soil specimens.  
The triaxial specimens used in the test were prepared according to the typical coarse-grained soil filler grading of high-speed railway subgrade in China. According to the code  
Figure 1: Dynamic triaxial test system.  
for the design of railway ground structures (TB 10001-2016) [51], the grading curves of filler specimens were determined, including type I- and type II-graded crushed stones for the surface layer of subgrade and group A, B, C, and D fillers for the bottom layer of the subgrade bed, with a total of six types of grading curves. The triaxial specimen used in the test was 150 mm in diameter and 300 mm in height. According to the requirement that the maximum particle size not be greater than 1/6-1/5 of the sample diameter, the equivalent substitution method was used to adjust the particle grading of the original soil sample, and the maximum particle diameter was set as 20 mm. The grading curve for each group is shown in Figure 2. The samples were mixed according to the grading requirements and were sealed and cured for 24 h to ensure a uniform water content, then the samples were compacted in five layers in the membrane cylinder. According to the compaction standard for the subgrade bed of a high-speed railway [51], the moisture content of the sample was configured according to the optimal moisture content, and the compaction degree was controlled by 0.97 of the surface layer and 0.95 of the bottom layer in the subgrade bed.
# An Energy Saving Road Sweeper Using Deep Vision for Garbage Detection
## 3 Description of The System
### Neural Network for Garbage Detection
#### 3.3.1 Mini U-Net  
Once we established an appropriate view and image size, the next step was developing a fast neural network for image semantic segmentation. We first tested the original U-Net topology defined by the paper with the same name. The traditional definition of U-Net proposes a downsampling layer (with factor two) to be added after every two consecutive 3 \(\times\) 3 convolutions. Moreover, the topology in the paper doubles the number of image features (i.e., the channels of the convolutions) after each downsampling layer.  
We ran experiments for the U-Net inference times on the Nvidia Jetson TX2, varying its architecture at each iteration, and found the following two main contributing factors to the execution times:  
* Image resolution, with a linear relationship with processing time (this holds true around our resolution range; pictures with a higher or lower order of magnitude size could hit other performance limits: DRAM size, number of processing units of the hardware);
* Number of features in the bottleneck layers (the bottleneck layers of a network are the innermost layers, where the representation, and the picture, are smaller, but the number of features is larger).  
This second factor somewhat surprised us, since we were expecting other factors to be more significant--e.g., the number of downsampling layers (with their reduction factors), and the total number of convolutional layers.  
With these observations in mind, we redesigned the U-Net to obtain a faster Mini U-Net. We choose to stick with four downsampling steps (as the original paper) but we limited the number of features per layer to a maximum of 256, as opposed to the \"full\" U-Net which grew to 512, 1024 channels in the inner layers. With this improved topology, our Mini U-Net can run its inference in around 600 ms on a Nvidia Jetson TX2. The final network topology is the one depicted in Figure 7 and detailed in Table 1.  
We should add that, to achieve these speeds, we also took advantage of TensorRT. TensorRT is a proprietary Nvidia SDK that works in a similar fashion as TensorFlow, and PyTorch, among others, but is further optimized for specific hardware, such as the Jetson.  
Another consideration arises: the linearity between picture size and execution time could lead our system to use a smaller \(d_{far}\) to obtain a smaller picture and thus higher FPS (without losing the full street coverage). This could allow us to use a different, more \"pushed\", camera and probably to have a more responsive system in general. This research line is left to be explored in future works.  
Figure 7: Our Mini U-Net for real-time dirt detection. Notably, the inner layers have just 256 features each.
# Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles
## 3 A Crossroad Passing Model
### Assumptions  
We research a driverless scenario with the following assumptions about road design, vehicle behavior rules, and communication capabilities.  
1. A control line (CL) is designed on each road at \(L\) meters from the crossroad. As soon as a vehicle enters the adjacent road, it enters the corresponding lanes to turn left and go straight, or right turn to drive according to vehicle path planning.
2. Once the vehicles enter the CL near the crossroad, each convoy stores the IoTA chain of the crossroad clearance order with the help of RSU. Under smart contract algorithms including gaming, the passing order is negotiated, agreed and strictly followed by all vehicles.
3. The vehicle has built-in sensing and computing capability, which can sense the distance between vehicles, vehicle speed, and other information.
4. Vehicles in the convoy maintain the same speed. And with the help of sensing, vehicles in the convoy can adjust the distance between each other according to moving speed, and automatically maintain the distance with the vehicle ahead.
5. Between different convoys on the same road, the first and last vehicle in the convoy can sense the driving information of other convoys, such as the speed and distance of the convoys ahead and behind.
6. Among different convoys from conflict roads around the crossroad, driving information about vehicles in front and behind the order is needed. For example, the current position of the last or first vehicle in the neighboring convoy in front of or behind the order and the time when to leave or arrive at the crossroad.  
The above is necessary for efficient convoy driving and orderly crossroad passing based on V2V.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 3 Proposed Framework
### Enhanced Counter Synchronization and Session Management
#### 3.5.1 Message Format Enhancement  
Each message is structured to include comprehensive metadata for robust verification:  
\[\text{Message}=(\text{sequence\_number},\text{counter\_value},\text{ encrypted\_payload},\text{MAC})\]
# A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network
## 4 Methodology
### Database Augmentation  
Data augmentation is a solution to increase the quantity and complexity of existing data artificially [62]. Data augmentation approaches have been applied to enrich the size of training sets, to allow developers to benefit from more representative training data [63, 64]. The main principle is to increase, artificially, the number of training examples. It can act as a regularizer in preventing overfitting in neural networks. In literature, we can classify data augmentation techniques into three types:  
* Dataset generation and expanding an existing dataset (Figure 5)
* In-place/on-the-fly data augmentation
* Combining dataset generation and in-place augmentation.  
The most known techniques are  
* Flipping: creates a mirror reflection of an original image,
* Rotation: rotating an image by an angle \(\alpha\) around the center pixel,
* Translation: involves moving the image along the \(X\) or \(Y\) direction or both.  
In our case, we applied changes using flipping, rotation, and translation techniques (Figure 6 and Algorithm 1).  
Figure 4: Normal brain images.  
```
Input :DataSet (DS) Output :Augmented images
1 DataAugmentation
2\(n\leftarrow\) number of images in DS \(i\gets 1\)
3 while\(i<=n\)do
4\(img\gets READ(DS(i))\)
5\(imgFH\gets FlipHorzontally(img)\)
6\(imgFV\gets FlipVertically(img)\)
7\(imgRFH\gets Rotate90(imgFH)\)
8\(imgRFV\gets Rotate90(imgFV)\)
9\((x,y)\gets coordinates(img)\)
10\(imgFH_{RT}\gets Translate(imgFH,x,-y)\)
11\(imgFH_{RB}\gets Translate(imgFH,x,y)\)
12\(imgFH_{LT}\gets Translate(imgFH,-x,-y)\)
13\(imgFH_{LB}\gets Translate(imgFH,-x,y)\)
14\(imgFV_{RT}\gets Translate(imgFV,x,-y)\)
15\(imgFV_{RB}\gets Translate(imgFV,x,y)\)
16\(imgFV_{LT}\gets Translate(imgFV,-x,-y)\)
17\(imgFV_{LB}\gets Translate(imgFV,-x,y)\)
18\(i\gets i+1\)
```  
**Algorithm 1**Data Augmentation  
Figure 6: Database augmentation.  
In Table 2, we detailed the performances of three models that were initially trained on a dataset without augmentation and then on a dataset with augmentation. This study was proposed in [65] and the main idea was that Cascaded Net segments the tumor following three stages: (1) locate and pick out the whole brain tumor area. (2) Remove the useless surrounding tissue area and crop a square tumor region as the input to the next network to segment the tumor core. (3) The third network divides the tumor core into an enhanced region and a non-enhanced region. The mean value is the Dice score that is mainly used to quantify the performance of image segmentation methods [66].  
As shown in Table 2, the mean values were enhanced by 2% or 3% for each of the three models. Thus, we can conclude that models with data augmentation outperformed those without augmentation.
## 3 Results and Discussion
### Soil Quality Assessment
#### 3.2.1 Comparison to National Agricultural Land Risk Control Standard Values

Table 3: Descriptive statistics of PTEs in the surface soil of the study area and the local, regional, and national background values and related guidelines. values (RSV) and risk intervention values (RIV) for soil environmental quality--risk control standards for soil contamination of agricultural land in China--are listed in Table 4. If the concentration of a PTE in agriculture land soil is lower than or equal to the concentration of RSV, then the ecological risk of this PTE in the soil is low, whereas if the concentration of a PTE in agriculture land soil is higher than the RSV, then the ecological risk for this PTE in agricultural soil is high. In the latter case, the soil should be environmentally monitored. If the concentration of PTE is higher than the RIV, strict intervention and controls should be taken [95]. The results showed that the PTEs in certain surface soil sites in the study area exceed the RSV. The site percentage of surface soils with values that exceeded the RSV were as follows: As (26.37%), Cd (17.41%), Cu (16.71%), Cr (14.04%), Ni (11.8%), Pb (3.30%), Zn (3.3%), and Hg (0.25%) (Table 4). In case of the RIV values, only a few sites with As, Cd, and Pb were above the limit. The site percentage of surface soils with values exceeding the RSV were as follows: As (0.76%), Cd (0.57%), and Pb (0.38%). To demonstrate the distribution of PTEs at an environmental quality level, figures are plotted by assigning the sample site values to 2 km \(\times\) 2 km grids (this is the sample analysis density). The spatial distribution of the sites with Cr, Ni, and Cu values that were higher than the RSV and RIV corresponds to the basalt and carbonatite distribution in the studied area. The spatial distribution of the samples with Cu, Pb, and Cd concentrations exceeding the RSV and RIV is compatible with the distribution of Cu-Pb-Zn deposits and a portion of the carbonatite rock distribution. The distribution of the sites with As and Hg concentrations exceeding the RSV and RIV is compatible with the distribution of Hg and Cu-Pb-Zn deposits, as well as a portion of the carbonatite rock distribution (Figure 2). This finding shows that soils formed from basalt and carbonatite rocks, as well as ore parent materials or those that are formed near mining areas, have higher PTE concentrations [2, 33, 34, 35, 36, 37, 54, 55, 57, 60, 62].