esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method  
### Preliminary  
Before presenting our method, we first provide the structure of the entire instruction space. As shown in Figure 2, \(I\) represents all possible instruction encodings, the number of which is determined by the maximum length of the instruction. \(I_{p}\) stands for the public instruction set, containing all executable instructions indicated in the official document. Typically, a public instruction set \(I_{p}\) only covers a fraction of possible instruction encodings \(I\), and the rest of undefined instruction encodings are classified to another set \(I_{u}\). \(I_{u}\) can be further divided into two subsets, one is an illegal non-executable instruction set \(I_{i}\), and the other is the illegal executable instruction set \(I_{h}\). We aim to search the undocumented instruction \(I_{h}\) hidden in the undefined instruction space.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref\#** & **Year** & \begin{tabular}{c} **Approach** \\ **Name** \\ \end{tabular} & **Approach Target** & \
\begin{tabular}{c} **Instruction Set** \\ **Architecture** \\ \end{tabular} &
\begin{tabular}{c} **Running Time** \\ **Overhead** \\ \end{tabular} \\ \hline
[39] & 2015 & SPECS & bugs & OpenRISC 1200 & - \\ \hline
[24] & 2017 & Sandsifer &
\begin{tabular}{c} undocumented \\ instructions \\ \end{tabular} & x86 & 343 min \\ \hline
[26] & 2019 & UISfuzz &
\begin{tabular}{c} undocumented \\ instructions \\ \end{tabular} & x86 & 60 min \\ \hline
[47] & 2020 & IScanU &
\begin{tabular}{c} undocumented \\ instructions \\ \end{tabular} & ARM, RISC-V & 24 h \\ \hline
[48] & 2020 & Armshaker &
\begin{tabular}{c} undocumented \\ instructions \\ \end{tabular} & ARM & - \\ \hline
[52] & 2020 & fence.t & covert channels & RISC-V & 0.32 s \\ \hline
[53] & 2021 & RV32S &
\begin{tabular}{c} secure \\ instruction set \\ \end{tabular} & RISC-V & - \\ \hline
[28] & 2021 & Red Unlock & microarchitecture & x86 & - \\ \hline
[14] & 2021 & IMSC &
\begin{tabular}{c} undocumented \\ instructions \\ \end{tabular} & MIPS32 & real-time \\ \hline
[29] & 2021 & DFSGen & \
\begin{tabular}{c} instruction \\ decoders \\ \end{tabular} &
\begin{tabular}{c} MIPS, ARM, \\ RISC-V \\ \end{tabular} & 83 min \\ \hline
[27] & 2022 & HEAM &
\begin{tabular}{c} undocumented \\ instructions \\ \end{tabular} & X86 & 103 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between defense approaches for processors.  
To search for undocumented instructions in DSP, we designed a tool named DSPUIM. The tool contains three parts. The first part is an instruction generation method based on valid instruction information bits; it is introduced in Section 3.2. The second part refers to the precise disassembly method; it is introduced in Section 3.3. Finally, we introduce the third part in Section 3.4, presenting the automatically executing method for tested instructions.
## 2 Materials and Methods
### Meta-Vectorization Based on Text Feature Extraction
#### 2.3.1 Term Frequency-Inverse Document Frequency (TF-IDF)  
TF-IDF assigns distinct weights to words [42], accentuating their significance based on the frequency within a document and across the dataset [43]. Its effectiveness in this context is supported by previous studies [24]. _TF-IDF_ is calculated by multiplying two statistics: term frequency (\(TF\)) and inverse document frequency (\(IDF\)) [44]. Term frequency indicates how often a particular word appears in a document. It is calculated by dividing the number of word occurrences by the total number of words in the document. This method can be calculated by Equations (1)-(3).  
\[TF(t,d)=\frac{c(t,d)}{\Sigma_{i}c(t_{i},d)} \tag{1}\]  
\[IDF(t)=1+log\frac{D}{d_{t}} \tag{2}\]  
\[TF\text{-}IDF(t,d)=TF(t,d)\cdot IDF(t) \tag{3}\]  
The value of \(c(t,d)\) indicates the occurrence of the \(t\) term in the document \(d\). \(D\) represents the total number of documents, and \(dt\) is the number of documents containing the term \(t\).
## 3 Results and Discussions
### Effect of Divalent Cations on EPS Production and Compositions
The amide group with C-O (1454 cm\({}^{-1}\)), the carboxyl group (C=O, 1403 cm\({}^{-1}\)), and the polysaccharide group (1040 cm\({}^{-1}\)) were observed at C2 (Figure 5a). The S-EPS showed various patterns of FTIR spectra depending on the different salinity stress conditions (Figure 5b). In particular, the amide group with C-O (1454 cm\({}^{-1}\)) disappeared at C2 and was only observed at C3. The polysaccharide group (1040 cm\({}^{-1}\)) was only observed at C2. Although the peak of the FTIR spectra of the previous studies were different because the operating conditions and the seeding sources were not the same, several studies have shown the variation of the functional groups in the EPS under a saline environment. Polysaccharides and carboxyl groups are known to influence bacterial aggregation through cationic bridging interactions, and protein groups are known to influence the hydrophobic properties of cells required for granule aggregation [36; 37]. Although it is difficult to know exactly how the EPS functional group contributes to the alleviation of salt inhibition only with FTIR results, it can be confirmed that the change in the functional group according to the cation addition contributed to alleviate salt stress. The amount of Na\({}^{+}\) associated with EPS increased sharply under the salinity stress conditions, and it was further increased following the augmentation of divalent cations (Figure 6). Previous studies have reported a similar finding of Na\({}^{+}\) concentration increases in the EPS of granules when exposing the granules under a salt environment, which significantly inhibited the binding between the multivalent metal ions and the EPS [35]. The amounts of Na\({}^{+}\) associated with the S-EPS were approximately three times higher than those of the B-EPS. The amounts of Na\({}^{+}\) associated with the B-EPS increased from 5 mg/L at C1 to 73 mg/L at C2 and increased to approximately 100 mg/L at C3 and C4 (after divalent cations augmentation). On the other hand, the amounts of Na\({}^{+}\) associated with the S-EPS increased significantly from 46 mg/L at C1 to 240 mg/L at C2 and increased further to 285 mg/L at C3 and C4. Although the reason for why the amounts of Na\({}^{+}\) associated with EPS increased when divalent cations were augmented to granules with salinity stress is unclear, the salinity stress was alleviated by the sequestration of Na+ in the EPS and by preventing it from diffusing into the cell. Consequently, the S-EPS appears to contribute more to the alleviation of salinity stress on anammox granules than the B-EPS. Figure 5: FTIR spectra of bound EPS (**a**) and soluble EPS (**b**) at C1 (no salt and no cation), C2 (5 g NaCl/L and no cation), C3 (5 g NaCl/L and 200 mg/L Ca2+), and C4 (5 g NaCl/L and 70 mg/L Mg2+).
# Long-Term Stability of Class II Malocclusion Treated with Maximly Molar Distalization
## 4 Discussion
Nonetheless, the improvement in the molar relationship was largely maintained throughout the entire observation period. One possible explanation for this stability is that, after achieving a Class I molar relationship during treatment, dentoalveolar compensation mechanisms helped preserve this occlusal relationship during the period of maxillomandibular growth [19]. Again, this has been linked to the correct intercuspation and the skeletal growth pattern [33; 34]. Furthermore, the statistically significant extrusion of upper first molars observed during and after treatment, and the increase in the U6-X axis distance, are in line with the vertical changes previously reported by [PERSON] et al. [10] and [PERSON] et al. [17]. These changes underscore the need for careful vertical control, particularly in hyperdivergent patients. The incisor region remained relatively stable throughout, with minor inclination and extrusion changes, suggesting that anterior dental changes were primarily growth-driven. During the T2-T1 period, there was a statistically significant increase in the arch length and significant decreases in the overjet, overbite, irregularity index, and both right and left molar relationship measurements. In the long-term follow-up treatment, some relapse was observed in the overjet, arch length, irregularity index, and right molar relationship. However, the improvements achieved during treatment were largely maintained. These findings were generally consistent with those reported in similar studies in the literature [10; 13; 16; 21; 35]. Clinically, our findings emphasize the importance of overcorrection strategies, retention protocols, and growth guidance for achieving long-term stability following distalization. The small relapse values in the overjet, arch length, and molar relationship measurements support the conclusions from multiple studies indicating that, when performed with well-planned biomechanics and patient compliance, distalization is a viable long-term option for selected Class II cases. However, this study has several limitations. The absence of a control group limits the ability to distinguish treatment effects from natural craniofacial development. Additionally, the reliance on two-dimensional imaging restricts the evaluation of the three-dimensional skeletal and dental adaptations. Future studies utilizing CBCT and larger, more diverse samples are necessary to further elucidate the full effects of distalization over time.
# Yolo Based Defects Detection Algorithm for EL in PV Modules with Focal and Efficient IoU Loss
## 2 Materials and Methods
### Cascaded Detection Network  
In the realm of industrial production processes, catering to real-time detection demands requires defect detection algorithms to balance both detection speed and accuracy effectively. YOLOv5 stands out as an end-to-end one-stage model that eliminates the necessity of generating candidate regions. Instead, it conducts simultaneous regression for object localization and classification tasks. With its compact model size and rapid detection capabilities, YOLOv5 emerges as a fitting choice for EL defect detection in PV modules.  
To enhance the detection proficiency for small-sized targets, a detection model comprising two cascaded YOLOv5 detection modules is proposed. These two modules are specifically tailored to identify two categories of defects: L-type defects and S-type defects. By harnessing the synergy between upstream and downstream processes, precise detection of all defect types is achieved, as demonstrated in Figure 7. This cascaded approach ensures comprehensive defect detection by segmenting the task into specialized modules, each adept at handling specific defect types, thereby enhancing the overall accuracy and efficiency of the detection system.  
During the detection process, the input EL images \(x\) are processed through \(Network_{1}\) to determine the presence of L-type defects, as specified in Equations (1) and (2), resulting in \(Output_{1}\).  
\[Input_{1}=x \tag{1}\]  
\[Output_{1}=Network_{1}(x) \tag{2}\]  
Then, as shown in Equations (3)-(5), the EL images are segmented into 21 regions through image segmentation processing, and each region is then input into \(Network_{2}\) for S-type defect detection, resulting in \(Output_{2}\). Finally, the detection results from the cascaded networks are integrated, mapped back onto the PV module's EL image, and outputted. The final network output results \(Output_{3}\) is given in Equation (6).  
\[x\overset{split}{\rightarrow}X=\{x_{1},x_{1},\dots,x_{21}\} \tag{3}\]  
\[Input_{2}=X \tag{4}\]  
Figure 6: Image segmentation strategy.  
Figure 7: Structure of the cascaded detection network.  
\[Output_{2}=\sum_{i=1}^{21}Network_{2}(x_{i}) \tag{5}\]  
\[Output_{3}=Output_{1}+Output_{2} \tag{6}\]  
In industrial engineering, streamlining the detection process can significantly enhance efficiency. Following the PV module's EL quality standards, upon detecting an L-type defect (such as uneven current distribution and dark spots) in a PV module, the module is designated as a defective product and necessitates additional processing at the factory. During the detection process, the detection results from \(Network_{1}\) can be saved in a \(flag\) variable. If the \(flag\) is True, the detection result is directly outputted. If the \(flag\) is False, the input is passed to \(Network_{2}\) The simplified network output \(Output_{4}\) is given in Equation (7).  
\[Output_{4}=\begin{cases}Output_{1}flag=True\\ Output_{2}flag=False\end{cases} \tag{7}\]
## 4 Numerical Application  
We conducted numerical experiments to evaluate the effectiveness and robustness of the proposed method in mitigating the dependency on the initial models. We selected two velocity models: the 2D acoustic Marmousi model and the 2007 BP TTI anisotropic velocity benchmark model. For the first test, the model grid size was 100 \(\times\) 150 with a grid spacing of 5 m. In the forward modeling process, we employed a Ricker wavelet with a dominant frequency of 50 Hz as the source and applied a free surface and perfectly matched layer (PML) absorbing boundary conditions at the top and other boundaries, respectively. We uniformly deployed 85 sources along the surface with a source spacing of 8-10 m and utilized a VSP observation system for data acquisition. In addition, the 2007 BP TTI anisotropic velocity benchmark model was employed as a test case. This model, created by [PERSON] and provided by the BP Exploration Operation Company Limited, is a two-dimensional anisotropic velocity model. In this study, only the P-wave velocity (\(\mathrm{v_{p}}\)) component of the model was used to test the performance of the inversion algorithm in complex media. Figure 6 shows the velocity distribution and observation system setup for the Marmousi model and Figure 7 displays the \(\mathrm{v_{p}}\) velocity distribution and observation system setup for the 2007 BP model. All numerical simulations and inversions were performed using identical computational resources, primarily NVIDIA A800 GPUs on a Linux system with a PyTorch (version 2.4.1) deep-learning framework.  
Figure 5: Network structure of the FAFWI method.  
Figure 6: The 2D Marmousi velocity model. Red stars schematically represent the deployment locations of the 85 sources along the surface. Black dashed lines indicate the VSP receiver positions within the borehole. The color bar indicates P-wave velocity in m/s.
# A Survey on Satellite Communication System Security
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ BMD & Ballistic Missile Defense \\ cFS & Core Flight System \\ CSTN & Cognitive Satellite-Terrestrial Integrated Networks \\ CTI & Cyber Threat Intelligence \\ DCED & Distributed Collaborative Entrance Defense \\ DDoS & Distributed Denial of Service \\ DL & Deep Learning \\ DOA & Direction of Arrival \\ DoS & Denial of Service \\ DPD & Defense Plane Digest \\ DSSS & Direct Sequence Spread-Spectrum \\ DTN & Delay-Tolerance Network \\ DVB & Digital Video Broadcasting \\ DVB-S & Digital Video Broadcasting--Satellite \\ ECN & Explicit Congestion Notification \\ ETD & Entrance Traffic Digest \\ FHSS & Frequency Hopping Spread Spectrum \\ FSS & Fixed Satellite Service \\ GEO & Geostationary Earth Orbit \\ GMR & GEO-Mobile Radio \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ GSE & General Stream Encapsulation \\ HAPS & High-Altitude Platform Station \\ HELLADS & High-Energy Liquid Laser Area Defense System \\ HTTP & Hypertext Transfer Protocol \\ IoT & Internet of Things \\ LEO & Low Earth Orbit \\ LiDAR & Light Detection and Ranging \\ MEO & Medium Earth Orbit \\ MIRACL & Mid-Infrared Advanced Chemical Laser \\ MISO & Multiple-Input Single-Output \\ ML & Machine Learning \\ MSNET & Mobile Satellite Communication Network \\ NFV & Network Function Virtualization \\ NMA & Navigation Message Authentication \\ NR & New Radio \\ NTN & Non-Terrestrial Network \\ OCS & Onboard Computer System \\ OPSPF & Orbit Prediction Shortest Path First \\ PD & Power Distortion \\ PHY & Physical Layer \\ PLA & People's Liberation Army \\ PLS & Physical Layer Security \\ QKD & Quantum Key Distribution \\ QoS & Quality of Service \\ SAR & Synthetic Aperture Radar \\ SATCOM & Satellite Communication \\ SCA & Spreading Code Authentication \\ \end{tabular}
## 1 Introduction
The reconfigurable architecture, sometimes coupled with adaptable bitwidth, is a flexible solution to accommodate different types of networks and is likely to become the standard in the future. In fact, researchers have shown that networks can be compressed [22; 23] and represented on a number of bits that are being reduced over time as techniques are refined. Finally, the sparsity is a technique actively used to eliminate  
Figure 1: (**a**) How artificial intelligence has evolved over the years. (**b**) The processes of training and inference in comparison. unnecessary operations and to further lower the power envelope of the accelerators, as well as making them faster and more effective. This also instantiates the need for sparse DNN accelerators like [24; 25; 26]. This paper is organized as follows: Section 2 discusses the background related to DNNs, describing the different models and their key features. Section 3 analyzes dataflows designed for energy-efficient architectures. The discussion goes from temporal and spatial architectures to other important themes such as sparsity, variable bit-width precision and reconfigurable architectures. Section 4 shows the typical memory hierarchy used in accelerators and the methodologies to reduce the power consumed by it. Finally, the paper provides the key takeaways in the conclusion. The acronyms used in this paper are reported in Table 11.
# Transient Friction Analysis of Pressure Waves Propagating in Power-Law Non-Newtonian Fluids
## 2 Mathematical Model and Solution Method
### Methods of Solution
#### 2.4.1 Analytical Solution  
When pressure waves propagate within the drilling fluids, the flow is already stable. The amplitude of pressure waves is generally between 0.68 MPa and 1.19 MPa [26]. However, the pressure of the drilling fluid inside the drill string is generally between 20 MPa and 30 MPa to overcome flow friction [27]. Therefore, the fluctuations in velocity and shear rate caused by the pressure changes are relatively small compared to the stable flow. Based on the above reasons, it is assumed that the apparent viscosity \(\mu_{a}\) changes only with the radial coordinate, but not with time. According to Equations (6) and (7), the shear rate for the steady flow of power-law non-Newtonian fluids is  
\[\frac{\partial u_{s}}{\partial r}=-\left(\frac{Fr}{2K}\right)^{\frac{1}{n}}, \tag{10}\]  
and for a positive pressure gradient, the apparent viscosity becomes  
\[\mu_{a}=K^{\frac{1}{n}}\left(\frac{Fr}{2}\right)^{\frac{n-1}{n}}. \tag{11}\]  
Substituting Equations (11) and (5) into the Equation (4), the motion equation is written as  
\[\frac{1}{\mu_{a0}}\frac{\partial p}{\partial x}=r^{\frac{n-1}{n}}\frac{ \partial^{2}u}{\partial r^{2}}+\frac{2n-1}{n}r^{-\frac{1}{n}}\frac{\partial u }{\partial r}-\frac{\rho}{\mu_{a0}}\frac{\partial u}{\partial t}, \tag{12}\]  
where \(\mu_{a0}=K^{1/n}(F/2)^{(n-1)/n}\) is the constant apparent viscosity.  
[PERSON] et al. [28] obtained the solution in Equation (12) in the complex domain using the Laplace transform, which is  
\[V(s,y)=\left(\frac{I_{-\sigma}\left(y\sqrt{\frac{3n-1}{n+1}}\right)y^{\frac{1 -n}{1+n}}}{I_{-\sigma}\left(y_{R}\sqrt{\frac{3n-1}{n+1}}\right)y_{R}^{\frac{1 -n}{1+n}}}-1\right)\frac{1}{\rho s}\frac{\partial P(s)}{\partial x}, \tag{13}\]  
where \(s\) is the Laplace transform variable, \(V(s,y)\) and \(P(s)\) are the Laplace transforms of \(u\) and \(p\), \(I_{-\sigma}\) is the modified Bessel function of first kind, \(\sigma\) is the order of the Bessel function, which is  
\[\sigma=\left|\frac{1-n}{1+n}\right|,\]  
and \(y\) is the transformation of the radial coordinate  
\[y=\sqrt{\frac{\rho s}{\mu_{a0}}}\frac{2n}{\sqrt{(3n-1)(n+1)}}r^{\frac{1+n}{2n}},\,y_{R}=\sqrt{\frac{\rho s}{\mu_{a0}}}\frac{2n}{\sqrt{(3n-1)(n+1)}}R^{\frac{1 +n}{2n}}.\]  
When \(n=1\), the result provided by Equation (13) is consistent with [PERSON]'s result [29].
# A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
## 4 Model Construction  
Given the temporal dynamics and complex spatial correlations of OD distribution, as well as the sparsity and incompleteness of data caused by uneven flow distribution, and the susceptibility to external factors, and the coupling effect between the population radiated by the highway network and the highway flow, this section establishes a multi-task prediction model for OD flow in the highway network considering population factors (OD_MLP). The model adopts a hard parameter shared multi-task learning network structure, which is divided into sub-task learning modules for inlet inflow flow, sub-task learning modules for outlet outflow flow, and main task learning module for OD flow. Treating the highway toll station as a cross-sectional node, the historical traffic flow of the node is processed into OD matrix, entrance inflow matrix, and exit outflow matrix using networked toll data. The static features of the graph structure of the road network toll station are constructed, combined with external weather data and population data as inputs. The internal structure of each module is composed of heterogeneous neural networks combining GAT and GRU to capture the spatiotemporal patterns of the input data. At the same time, a multi-task learning framework is used to introduce stronger constraints and obtain effective embedding representations of each subtask module, thereby improving the accuracy of OD traffic prediction between stations. The model structure is shown in Figure 6. The key symbols used in the model are defined in Table 2.  
For OD traffic prediction, assuming N toll stations are given, there are N * N OD pairs that need to be predicted at a unit time step. However, the bidirectional relationship between OD pairs requires different learning to represent and dynamically changes over time. In addition, the number of non-zero elements in the OD matrix will be much smaller than the number of zero elements, as shown in the contour map of OD for time slice in Figure 7. The horizontal axis represents exsation, the vertical axis represents enstation. The number of non-zero elements only accounts for 5.22% of the elements in the OD matrix.  
Figure 6: The proposed model structure diagram.  
The high sparsity of the OD matrix makes the sensitivity and prediction accuracy of the prediction model to noise unsatisfactory. In order to better capture time-varying and bidirectional spatial relationships during the modeling process, this chapter introduces subtasks of the entrance inflow learning module and the exit outflow learning module to improve the accuracy of the main task learning module for OD traffic.