esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization and Formation Mechanism of GQDs
XPS analysis indicates that N atoms are introduced into GO after oxidation by HNO\({}_{3}\), as shown in Figure 6. The high-resolution XPS spectra of C 1s further confirm the chemical structure change during the oxidation and hydrothermal treatment. As shown in Figure 7, different types of C atoms exist, including C-C/C=C (\(\sim\)284.5 eV), C-N (\(\sim\)285.6 eV), C-O-C/C-OH (\(\sim\)286.5 eV) and C=O/COOH (\(\sim\)287.8 eV) [30; 36]. There are mainly carboxyl and epoxy groups in GO prepared by Hummers' method, the amount of C-O increases while C-C and C=C decreases after oxidation by HNO\({}_{3}\), which indicates that oxidation makes the C-C and C=C bonds break and attach to oxygen-containing groups. The amount of C increases and O decreases in R-O-GO, which can be explained from the fact that a lot of oxygen-containing groups are released in the form of CO\({}_{2}\), CO, and H\({}_{2}\)O during the hydrothermal treatment [23]. The decreased amount of C-O-C and C-O in R-O-GO is in good agreement with the results of FT-IR. Moreover, the high-resolution XPS spectra of N 1s (Figure 8a,b) also show that C-N exists in O-GO and R-O-GO, suggesting that N atoms are introduced into GO in different forms including C-N-C (\(\sim\)399.5 eV) and N-(C)\({}_{3}\) (\(\sim\)401.5 eV) [37; 38; 39]. The XPS results indicate that both GQD1 and GQD2 are composed of carbon, oxygen, hydrogen, and nitrogen (Figures 7 and 8). N 1s analysis shows that N exists in GQD1 in the forms of C-N-C and N-(C)\({}_{3}\), while in GQD2, besides the two forms of N, -NO\({}_{2}\) (406.4 eV) also exists [37; 39; 40; 41; 42]; see in Figure 8c,d. XPS analysis gives more information about the chemical structure of the studied systems. Element analysis of GO, O-GO and R-O-GO by FT-IR and XPS presents similar results; these techniques are complementary and exhibit more accurate conclusions when used together. Figure 5: Raman spectra of GO, O-GO and R-O-GO. Figure 7: High resolution XPS spectra of C 1s for **(a)** GO, **(b)** O-GO, **(c)** R-O-GO, **(d)** GQD1 and **(e)** GQD2. Figure 6: XPS survey of GO, O-GO, R-O-GO, GQD1 and GQD2. Based on the results mentioned above, a possible mechanism for the formation of the GQDs can be proposed. GO was initially oxidized to O-GO during the chemical oxidation with 8 M HNO\({}_{3}\), producing a lot of oxygen-containing groups and introducing N atoms. After that, the structure of O-GO was decomposed, because the oxygen-containing groups were partially removed, forming a lot of nanosheets (R-O-GO) by the hydrothermal method under the continuous high-temperature and high-pressure conditions. Meanwhile, plenty of graphene quantum dots (GQD1) were formed and released into the solution. Finally, the collected R-O-GO was further oxidized with concentrated HNO\({}_{3}\). Chemical oxidation is an effective method to release GQDs from their aggregates into solution [43]. As a result, a lot of GQD2 were released into the solution after R-O-GO was refluxed with 15 M HNO\({}_{3}\) for 12 h.
# High Speed Decoding for High-Rate and Short-Length Reed-Muller Code Using Auto-Decoder
## 1 Introduction  
Machine learning (ML) techniques are widely used in many fields, such as image recognition, natural language processing, and autonomous driving [1; 2; 3]. Auto-encoder (AE) unsupervised ML techniques are known for their capacity to extract important features of data while reducing unwanted noise, and thus are useful in generating new images with key features [4]. AEs perform roles such as dimensionality reduction, image denosing, image generation, and abnormality detection, and are used in various fields such as medical care, autonomous driving, and image recognition [5; 6; 7; 8]. In addition, various studies are being undertaken to apply machine learning technology to communication systems, such as channel coding, massive multi-input and multi-output, multiple access, resource allocation, and network security [9; 10]. In this study, we modify an AE to a new model called the auto-decoder (AD), which is suitable for reducing the noise that corrupts the transmitted information signal in channel coding. The proposed AD is used to decode Reed-Muller (RM) code of high-rate and short length, which is used in many communication systems, such as long-term evolution (LTE) and fifth-generation wireless (5G) cellular systems [11; 12], where the minimum latency delay is 5 ms. The requirement is to further reduce this delay in sixth-generation (6G) wireless systems [13; 14]. Since we consider high-rate Reed-Muller code of short length, such as \(\Re(2,4)\) with code rate 0.6875, we use a fast Hadamard transform (FHT) decoding method [15] for performance comparison instead of the recursive decoding of [16; 17] which is useful for low-rate RM code. Because the RM code has an extremely simple structure and can be decoded with maximum likelihood decoding (MLD) performance using FHT, it is especially useful in control channels in wireless communication systems. We first illustrate the key differences between the conventional AE and the proposed AD, and then show how to construct the decoder for the RM code using it. After training the AD model for the code, we found that the proposed method showed similar performance to the conventional FHT method with faster decoding speed. For improved performance, we present a parallel auto-decoder (PAD) that combines a couple of ADs in parallel.
# Impacts of Arc Length and ECOM Solar Radiation Pressure Models on BDS-3 Orbit Prediction
## 3 Results and Analysis
### Impacts of the Arc Length on Orbit Prediction
Therefore, when the fitted arc length is less than 36 h, the accuracy of BDS-3 IGSO satellite orbit predictions will diminish compared to that of the BDS-3 MEO satellites.
## 2 Initial Detection of Nodule Candidates  
### Radial Suppression Filter  
Lung regions are extracted by a threshold-based method from CT scans. Isotropic re-sampling is applied to the CT scans. Let \(f(x,y,z)\) denote the value of a voxel at a position \((x,y,z)\) in a re-sampled CT scan. A spherical VOI area is settled on each voxel whose value is \(-600\)HU and more (_i.e._\(f(x,y,z)\geq-600\)HU) in the lung region. Such a VOI area can capture the candidate region of a nodule in its middle area, whereas it often includes extra regions that do not connected with the candidate region. For example, in Figure 1(a), a nodule is captured in the middle area of the VOI, whereas a blood vessel is also observed in the bottom right area of the VOI. In Figure 1(b), a blood vessel is captured in the middle of the VOI area, whereas other blood vessels are also observed. Such extra regions often cause the degradation of recognition accuracy of nodules. In order to eliminate extra regions with preserving nodule candidates, we introduce the _radial suppression filter_ described below.  
First, a voxel value \(f(x,y,z)\) is converted to \(f^{\prime}(x,y,z)\) as follows:  
\[f^{\prime}(x,y,z)=\left\{\begin{array}{ll}0&(f(x,y,z)<-700)\\ f(x,y,z)+700&(-700\leq f(x,y,z)<200)\\ 900&(otherwise).\end{array}\right. \tag{1}\]  
This conversion is applied so that the voxel values can be regarded as mass in this filter. Figure 2 illustrates a spherical VOI area. Let \(O(x_{o},y_{o},z_{o})\) denote the center voxel of the VOI area, \(A(x_{a},y_{a},z_{a})\) arbitrary voxel and \(B^{(i)}(x_{b}^{(i)},y_{b}^{(i)},z_{b}^{(i)})\) the \(i\)-th voxel on the segment between \(O\) and \(A\). The output value of the radial suppression filter is defined by  
\[g(x,\ y,\ z)=\min_{i}f^{\prime}(x_{b}^{(i)},y_{b}^{(i)},z_{b}^{(i)}). \tag{2}\]  
By applying this minimization to all the voxels in the VOI area, extra regions can be suppressed with preserving a notable region in the middle area of the VOI. Figure 3 shows the effect of the radial suppression. In Figure 3(a), a blood vessel appears as a convex region on the intensity profile from \(O\) to \(A\). This convex region can be suppressed by the radial suppression as shown in Figure 3(b).  
Figure 4 shows the results obtained by applying the radial suppression filter to the VOIs in Figure 1. In Figure 4(a), the nodule region can be extracted successfully, whereas the extra blood vessel region can be suppressed. In Figure 4(b), the blood vessel region can be extracted successfully, whereas the extra blood vessel region can be suppressed.  
Figure 1: Examples of volume of interest (VOI) areas. (**a**) nodule; (**b**) blood vessel.
# 0.5-V High-Order Universal Filter for Bio-Signal Processing Applications
###### Abstract
The MIBD-MOST technique simplifies the CMOS structure of the OTA by using only one differential pair, thus avoiding the increase in current branches that would otherwise lead to higher power consumption. Additionally, the multiple inputs of the OTA enable support for the proposed high-order universal filter, which provides various filtering functions with both non-inverting and inverting voltage transfer functions within a single topology. For instance, first-, second-, and third-order universal filters are presented, which can be derived from the proposed \(n\)th-order universal filter. The proposed MI-OTA and the selected second-order universal filter are designed and simulated on the Cadence platform using 0.18 \(\upmu\)m CMOS technology from TSMC. The research paper is organized as follows: Section 2 presents a description of the circuit, which includes the proposed MI-OTA and the proposed \(n\)th-order universal filter. In this section, the first, second, and third-order universal filters are also presented from the proposed \(n\)th-order universal filter. In Section 3, the design of the proposed MI-OTA and  
Figure 1: Block diagram of a typical biomedical signal processing. the second-order universal filter on the Cadence platform is verified, and the performance of the proposed filter is compared with some previous works. Finally, Section 4 concludes the paper.
# A Few-Shot Learning-Based Material Recognition Scheme Using Smartphones
## 4 Few-Shot Learning-Based Material Recognition
### Few-Shot Learning for Material Recognition  
The FSMR few-shot learning model proposed in this study is designed based on ProtoNet [28]. The model's structure and hidden layers are illustrated in Figures 6 and 7. The convolutional block consists of a conv2D layer, batch normalization, and a ReLU activation layer. The conv2D layer is configured with 100 channels and a kernel size of three. The embedding network is constructed with four convolutional blocks, along with max-pooling and dropout layers. In training, training tasks augmented by scaling pass through the embedding network to form class prototypes via the clustering of the support set. These class prototypes establish the embedding space, which is then used for classifying the query set. The Euclidean distance between the class prototypes and query points is used to compute the loss. The model updates its weights in a direction that minimizes the loss. During evaluation, the trained embedding network is used. Like training, the support set of evaluation tasks forms the embedding space, and the query set is classified within this space to assess performance. The FSMR system integrates a few-shot learning model to enable material classification with minimal data. Few-shot learning in FSMR employs a task-based meta-learning framework, where each task \(\tau\) consists of a support set \(S\) and a query set \(Q\):  
\[\tau=\{S,Q\} \tag{5}\]  
\[S=\{(x_{i},y_{i})\}_{i=1}^{K} \tag{6}\]  
\[Q=\{\big{(}x_{j},y_{j})\}_{j=1}^{M} \tag{7}\]  
Here, \(S\) contains \(K\) labeld samples used for model adaptation, and \(Q\) contains \(M\) unlabeled samples used for evaluation. The purpose of few-shot learning is to optimize the model \(f_{\theta}\) to minimize classification errors on \(Q\) after adapting to \(S\). The meta-learning loss is defined as  
\[\mathcal{L}_{meta}=\mathbb{E}_{\tau}\big{[}\mathcal{L}_{query}(f_{\theta^{*}}(S ),Q)\big{]} \tag{8}\]  
where \(\mathcal{L}_{query}\) denotes the classification loss on the query set, and \(\theta^{*}\) represents the model parameters adapted after training on \(S\). The detailed procedure of FSMR is presented in Algorithm 1. In FSMR, meta-learning comprises training (meta-train) and testing (meta-test) phases. During the training phase, diverse training tasks are generated using the augmented training data, improving the model's generalization capability. During the testing phase, augmented support sets are generated for new materials, allowing the model to adapt quickly and achieve optimal classification performance. This approach enables the efficient use of data, achieving high accuracy with significantly less data compared to conventional deep learning models. The integration of few-shot learning into FSMR enhances its practicality and scalability, enabling effective material classification with minimal data, while allowing rapid adaptation to new materials. Figure 6: Structure of ProtoNet-based Few-Shot learning model. Figure 7: Structure of embedding network.
## 1 Introduction
Moreover, since the steady tracking of the single object tracker heavily relies on the quality of initialized bounding-box in the first frame in order to correctly separate the object from the background and possibly retrieve it after occlusion, the object candidates provided by a real detector under the current MOT framework are usually imperfect. With considerable noise in location and scale, this may become another major challenge. In this paper, we propose an integrated model with a binary-channel verification and delay processing model to tackle the aforementioned problems; see Figure 2 for an outline. When a new frame comes in, SOT trackers produce two scores on both the predicting location given by the motion model (the same as processing under the SOT scenario) and the region of interest provided by the detection. Combined with the previous tracking score and the trajectory, the association model produces a refined bounding box. When occlusion happens, the association model proceeds as usual, then marks the frame ID where the occlusion happens and keeps the results for later processing. A delay processing model composed with three processing parts is proposed. First, for those tracks encountering the deformation problem (as in those with a low tracker score and low detection score), re-initiation discards the non-deformed features trained before and re-initiates with new features after determination by spatial information as the same object. Second, attaching for newly initialized tracklets during the occlusion period, re-correlates them with tracks lost in the same period and re-attaches them if the correlation score is above a certain threshold. Third, re-claiming, for unattached newly initialized tracklets after the attach phase, assigns them to existing tracks in shadow tracking mode, which means the object is tracked without any verification of detection through recent frames, if the re-correlation score and overlap rate (as in the Intersection over Union (IOU) score between the newly initialized tracklets and existing tracks for several frames) between them are above specified thresholds. This paper thus presents three main contributions:  
* We propose a binary-channel verification model to deeply excavate the potential of applying SOT under a MOT scenario, specifically in refining the representation while maintaining the identities of the objects during the tracking process. * We introduce a delay processing model to improve the tracker capability of solving the drift problem under occlusion and object interaction. * We explore the proposed method on the MOT17 benchmark and perform an ablation study of each block, showing the comparable performance on result-refinement and ID consistency, and proving each block of our proposed method is indispensable. This paper is organised as follows. Section 2 briefly reviews the state-of-the-art for computer vision-based object tracking. The details of our proposed method are described in Section 3. The experimental study and benchmark evaluation are then reported in Section 4. Finally, Section 5 concludes this paper and describes opportunities for future research.
## 4 SURAA: The Proposed Method and Tool
### Execution Phase
#### 4.2.3 Vector Kernel  
Algorithm 6 illustrates the segment based vector SpMV algorithm. In this algorithm, a total of \(Y\) threads operate on each row. We assign \(Y\) as 32, 64, or 128 depending on \(npr\). \(Y\) would be the smallest multiple of 32 greater than \(npr\). The calculation of \(Y\) can be seen in lines 7, 8, and 9 in Algorithm 1. As in the Child kernel (see the explanation of Algorithm 4), we calculate the thread Id (\(tid\)), lane Id (\(lid\)), and warp Id (\(wid\)) to access the data structures. A total of \(Y\) threads are assigned to each row. These \(Y\) threads will then use segmented parallel reduction using the shuffle instruction (\(\_shfl\_down\_sync\)) to accumulate the partial results. The partial sum is then stored in the shared memory (\(shared\_mem\)for processing of all the relevant threads. We perform one more reduction to accumulate the final sum and store it in the result vector (\(y\)). This is very similar to the CSR Vector scheme. The kernels for each segment is launched with \((((segment\_size*Y)+256-1)/256)\) blocks.  
```
0:\(A_{\mathit{csr}}:(val,row\_ptr,col\_idx)\), \(vector:x\), \(vector:y\), \(vector:perm\), \(Thread\_per\_row:Y\)
0:\(vector:y\)
1:functionDCSR vector
2:\(tid\gets thread\_Id\)
3:\(lid\gets tid^{\prime}\forall Y\)
4:\(vid\gets tid/Y\)
5:\(row\_id\gets perm[wid]\)
6:\(row\_cur\gets row\_ptr[row\_id]\)
7:\(row\_next\gets row\_ptr[row\_id+1]\)
8:for\(i=lid;i<row\_next-row\_cur;i+=Y\)do
9:\(sum+=values[i]\times x[col\_idx[i]]\)
10:endfor
11:for\(i=Y;i>0;i=i>>2\)do
12:\(sum+\leftarrow\_shfl\_down\_sync(0 xff,sum,i)\)
13:endfor
14:if\(tid\&((Y>>2)-1)==0\)then
15:\(shared\_mem[tid>>5]\gets sum\)
16:endif
17:if\(lane\_id==0\)then
18:\(sum+=shared\_mem[(tid>>5)+1]\)
19:\(y|row\_id\gets sum\)
20:endif
21:endfunction
```  
**Algorithm 6** SURAA_Vector_Kernel
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 3 Hardware-Assisted Dynamic Information Flow Tracking
### Micro-Architecture-Level DIFT Design
#### 3.2.2 In-Core DIFT Design  
[PERSON] et al. in [21] presented a DIFT architecture to support a flexible security configuration at runtime. Differently from common DIFT design and the tag-based architecture, the tag in [21] was used to represent the security policy for propagation and check instead for data states, e.g., trusted vs. untrusted. In order to support flexible software programming, the authors provided two global configuration registers, i.e., the tag propagation register (TPR) and the tag check register (TCR), to configure the security policy at runtime. The configuration register could be configured only in trusted mode. Moreover, the tag propagation and check could only be disabled in trusted mode. Further, user-level exceptions were used to reduce the overhead of crashing the whole OS. The OS switches to trusted mode but keeps the address space once the DIFT exception is raised. However, the security policy is difficult to update when the architecture is deployed. The processor pipeline, register and memory system need to be extended to support DIFT operations. Similarly to [21], [PERSON] et al. in [22] also adopted global configuration registers to customize the rule of tag propagation and checking. Note that the rule customization depends on the instruction type. Different types of instructions have different rules. Their method provides more fine-grained tracking than [21]. However, it lacks enough flexibility for security policy reconfiguration for different program contexts. The DIFT schemes proposed in [21; 22] need to extend or modify the original pipeline stages to support the DIFT-related operations. For the same reason, this type of DIFT design is difficult to be deployed on modern commercial processors. The scalability of customization of security policies is also a challenge for this type of DIFT design.  
Differently to above in-core based design, [PERSON] et al. in [23] simplified the common DIFT framework to only track registers. The states of registers are analyzed to determine whether return oriented programming (ROP) attacks happened. In order to reduce the modifications to existing processor architecture, the taint related information is stored in the shadow renaming tables and reorder buffer.
# Performance Evaluation of Asphalt and Asphalt Mixtures Modified by Fuel-Resistant Admixture
## 3 Results and Discussion  
### Performance of FRMA  
#### 3.1.1 Penetration, Ductility, and Softening-Point Test  
Figure 5 illustrates the impact of FRM on asphalt penetration, softening point, and ductility. The parallel experimental results of the three experiments meet the error tolerance requirements specified for repetitive tests in Standard JTG E20-2011 [18]. The results indicate that with an increased FRM dosage, the asphalt's penetration and ductility decreased, while the softening point increased, consistent with existing literature trends [10]. The reduced penetration and increased softening point suggest enhanced viscosity and temperature stability of the asphalt, leading to improved high-temperature performance. The decreased ductility, on the other hand, indicates deteriorated low-temperature ductility, reflecting a negative influence of FRM on low-temperature performance. This can be attributed to FRM absorbing light components from the original asphalt and continuously expanding, resulting in expanded high-molecular-weight and polar components, which formed a network structure that further restricted molecular movement [7; 16]. In summary, adding FRM contributes to enhancing the high-temperature performance of asphalt but has an adverse effect on low-temperature performance.
## 2 Materials and Methods
### Methods
#### 2.3.1 Spectral Characteristics Analysis  
In order to analyze the spectral difference between infested cotton and healthy cotton in Sentinel-2A, 384 points (182 healthy and 202 infested) were selected from the eight fields based on the classification results of the airborne multispectral images. Additionally, the 384 points selected were ground checked by the field crew to confirm the infestation status, the number of these points in each field is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Field ID** & **Healthy Cotton** & **Infested Cotton** \\ \hline
1 & 26 & 41 \\
2 & 23 & 5 \\
3 & 20 & 4 \\
4 & 24 & 64 \\
5 & 21 & 30 \\
6 & 26 & 29 \\
7 & 20 & 22 \\
8 & 22 & 7 \\ Total & 182 & 202 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of sample points in healthy and infested cotton areas for Fields 1–8.  
Figure 3: Flowchart of data analysis and processing.  
These points were overlaid on the preprocessed Sentinel-2A imagery and the reflectance values were extracted. The spatial distribution of these points is shown in Figure 2. The spectral reflectance values of the 384 points are shown with box-scatterplots in Figure 4. As can be seen from the figure, the reflectance of infested cotton in the bands of 497-704 nm was higher than that of healthy cotton. The average reflectance in the bands of 740-865 nm of healthy cotton was remarkably higher than that of infested cotton with a difference of about 0.09-0.15. In the bands of 1614 and 2202 nm, which are sensitive to the response of crop moisture, the reflectance of infested cotton was higher than that of healthy cotton.