esa-sceva/satcom-chunk-collection huggingface dataset:
# Camouflaged Object Detection That Does Not Require Additional Priors
## 4 Experiments and Analysis
### Comparison with States of the Art
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **CAMO-Test** & **COD10K-Test** & **NC4K** \\  & \(S_{\mathbf{x}}\uparrow E_{\mathbf{\phi}}\uparrow F_{\mathbf{\beta}}^{w}\uparrow\mathbf{\mathcal{ M}}\downarrow\) & \(S_{\mathbf{x}}\uparrow E_{\mathbf{\phi}}\uparrow F_{\mathbf{\beta}}^{w}\uparrow\mathbf{\mathcal{ M}}\downarrow\) & \(S_{\mathbf{x}}\uparrow E_{\mathbf{\phi}}\uparrow F_{\mathbf{\beta}}^{w}\uparrow\mathbf{\mathcal{ M}}\downarrow\) \\ \hline
2019 GCNet [42] & 0.732 0.800 0.604 0.109 & 0.736 0.810 0.517 0.061 & 0.777 0.841 0.639 0.075 \\
2019 SCRN [52] & 0.779 0.797 0.643 0.090 & 0.789 0.817 0.575 0.047 & 0.830 0.854 0.698 0.059 \\
2020 F3 Net [47] & 0.711 0.741 0.564 0.109 & 0.739 0.795 0.544 0.051 & 0.780 0.824 0.656 0.070 \\
2020 CSNet [53] & 0.771 0.795 0.642 0.092 & 0.778 0.809 0.569 0.047 & 0.750 0.773 0.603 0.088 \\
2020 BASNet [54] & 0.749 0.796 0.646 0.096 & 0.802 0.855 0.677 0.038 & 0.817 0.859 0.732 0.058 \\
2020 SINet [19] & 0.745 0.804 0.644 0.092 & 0.776 0.864 0.631 0.043 & 0.808 0.871 0.723 0.058 \\
2021 PFNet [36] & 0.782 0.841 0.695 0.085 & 0.800 0.877 0.660 0.040 & 0.829 0.887 0.745 0.053 \\
2021 S-MGL [21] & 0.772 0.806 0.664 0.089 & 0.811 0.844 0.654 0.037 & 0.829 0.862 0.731 0.055 \\
2021 C\({}^{2}\)FNet [37] & 0.799 0.851 0.710 0.078 & 0.811 0.886 0.669 0.038 & 0.843 0.899 0.757 0.050 \\
2022 LSR+ [18] & 0.789 0.840 0.751 0.079 & 0.805 0.880 0.711 0.037 & 0.840 0.896 **0.801** 0.048 \\
2022 BGNet [20] & 0.807 0.861 0.742 0.072 & 0.829 **0.898 0.719** 0.033 & 0.849 **0.903** 0.785 0.045 \\ \hline CCGNet (Ours) & **0.827** **0.877** **0.754** **0.069** & **0.833** 0.888 0.710 **0.033** & **0.859** 0.902 0.785 **0.045** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison with state-of-the-art methods for COD on three benchmarks using four widely used evaluation metrics (i.e.,\(S_{\mathbf{x}}\), \(E_{\mathbf{\phi}}\), \(F_{\mathbf{\beta}}^{w}\), and \(\mathcal{M}\)). The best scores are highlighted in **bold**, and the symbol \(\uparrow\) indicates that a higher score is better. Figure 5: Visual comparison of the proposed model with five state-of-the-art COD methods. (The images are from the CAMO [23] dataset).
# Seismic Performance of Reinforced Concrete Short Columns Subjected to Freeze-Thaw Cycles
## 2 Experimental Program  
### Test Specimens  
Specimens with the same geometry and structural details were designed and subjected to different numbers of FTCs. The centerline of the lateral loading point was 100 mm away from the top face of the specimens, and therefore the shear span-to-depth ratio of the specimen was 2.5. Cross-section and reinforcement details of the test columns and footings are shown in Figure 1. The specification of the longitudinal bar and reinforcing hoop adopted were HRB335 and HPB300. Three 16 mm diameter longitudinal bars were used in both sides of the columns, and 6 mm diameter transverse reinforcement was uniformly spaced at 50 mm over the top 200 mm of the column to prevent crushing of the local concrete, and at 80 mm over the rest of the column. The specific design parameters are presented in Table 1. It is noted that the axial force \(N\) was kept as a constant value instead of the axial force ratio because the actual axial force ratio changed with the decrease of concrete strength initiated by the freeze-thaw attack.  
The control specimen was designed to be governed by flexure shear failure according to the ratio of theoretical flexural strength \(V_{p}\) and nominal shear strength \(V_{n}\) calculated conforming to Chinese code [25], as presented in the following equations:  
\[V_{p}H=\frac{1}{2}\alpha_{1}f_{c}^{\prime}bc(h-c)+\frac{1}{2}(f_{y}A_{s}+f_{y }^{\prime}A_{s}^{\prime})(h_{0}-a_{s}) \tag{1}\]  
\[V_{n}=\frac{1.75}{\lambda+1}f_{l}bh_{0}+f_{yv}\frac{A_{sv}}{s}h_{0}+0.07N \tag{2}\]  
where, \(\alpha_{1}\) is a factor relating the width of the equivalent rectangular compressive stress block to the compressive stress of concrete; \(f_{c}^{\prime}\) is the compressive strength of concrete; \(b\) is the width of column section; \(c\) is the distance from the extreme compression fiber to the neutral axis; \(h\) is the length of column section; \(f_{\rm t}\) and \(f_{\rm y}^{{}^{\prime}}\) are the yield strength of tension reinforcement and compression reinforcement, respectively; \(A_{\rm s}\) and \(A_{\rm s}^{{}^{\prime}}\) are the area of tension reinforcement and compression reinforcement, respectively; \(h_{0}\) is the distance from the extreme compression fiber to the centroid of longitudinal tension steel; \(a_{s}\) is the distance from the extreme compression fiber to the centroid of longitudinal compression steel; \(\lambda\) is the shear span ratio;\(f_{\rm t}\) is the tensile strength of the concrete;\(f_{\rm yy}\) is the yield strength of the transverse reinforcement; \(s\) is center-to-center spacing of the transverse reinforcement; \(A_{\rm sw}\) is the area of the transverse reinforcement; and \(N\) is the axial load. In ASCE 41-13 [26], it is pointed that the column would be vulnerable to flexure-shear failure when \(1.0\geq V_{\rm p}/V_{\rm n}>0.6\). Therefore, the controlled column was designed to have a ratio around 1.0.
## 4 Discussion
In the next few years, it is reasonable to expect that the quality of these sCT images could be further improved. Augmenting the number of training cases or exploring alternative AI architectures, such as diffusion models, are some strategies currently under investigation [28; 29]. High-quality sCT images generated from CBCT are not only useful for improving the accuracy of dose calculation but also for allowing more precise delineations for clinicians, as reported in a recent publication [30]. Furthermore, implementing a robust quality assurance protocol is crucial for promptly identifying local inaccuracies in synthetic CT images, thereby issuing warnings in the event of errors in HU attribution. Some experiences have been recently reported in the literature, demonstrating the need for new QA solutions [31; 32].Once these points are properly addressed, the implementation of a clinical workflow without the simulation of CT acquisition could become a reality, significantly speeding up the time needed to offer a radiation treatment to patients who are subject to palliative care, maintaining high standards in terms of clinical quality, and offering the possibility of fully online adaptive treatments.
# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 2 Related Work
### Current Issues in Multi-Pedestrian Tracking  
In today's day and age, multi-pedestrian tracking still presents many challenges. For example, the following are three of the more common challenges:  
* Robustness: In complex scenarios characterized by rapidly changing lighting conditions, frequent occlusions, and dynamic blurring, the robustness of multi-pedestrian tracking algorithms tends to be compromised. To tackle this, we constructed the KC-YOLO detection model as the detector in our research. This model optimizes the selection of detection frames and implements adaptive feature refinement, thereby enhancing the robustness and accuracy of the detection algorithm.
* Long-term tracking: Tracking targets in long temporal sequences presents several challenges, as it requires addressing cross-frame target re-identification and scene updates. To tackle this, we employ cascade matching for target re-identification, which effectively reduces instances of target loss caused by occlusion and scene updates [28].
* Algorithm efficiency: In real-world applications, multi-pedestrian tracking algorithms often need to process a large volume of data in real time. Overcoming these challenges is crucial for improving multi-pedestrian tracking methods and ensuring their effectiveness in diverse and complex practical environments. In our research work, we introduced an identity validity discrimination module into the tracking algorithm. This module is designed to assess and remove erroneous data resulting from incomplete or unclear features, reducing the unnecessary data-processing workload.
## 1.2 1.1 Introduction
tion of an HPLC analysis utilizing a photodiode array detector to estimate and evaluate the stability of DUV in a combination made in a laboratory. In order to determine the amount of DUV in its capsules, [PERSON] et al. [17] established a microwell-based spectrophotometric method depending on the charge transfer reaction of DUV with chloranilic acid. Another HPLC approach with UV detection was reported by [PERSON] et al. [18] for DUV quantification in rat plasma samples. Nevertheless, the method required a time-consuming liquid-liquid extraction step and had relatively low accuracy (about 88%). UPLC-MS/MS technique was developed by [PERSON] et al. [19] for the estimation of tissue-to-plasma distribution ratios of DUV and other basic compounds in mice. Even though, this method required lengthy run times and laborious sample pretreatment procedures to fully separate DUV from the other co-administered substances. For the purpose of evaluating DUV in beagle dogs, [PERSON] et al. [20] created a new UPLC-MS/MS procedure with improved sensitivity and precision. Recently, the HPLC approach with fluorescence detection (HPLC-FD) for DUV quantification in plasma and its application to a pharmacokinetic investigation in rats were disclosed by our lab [21]. This review highlights that the existing methods are mostly chromatography and rely on extensive and expensive instruments. In addition, these methods have been developed for either copiktra capsules or plasma samples; none of them can be used for both. For these reasons, a new alternative simple technique with high throughput, and high sensitivity, which requires minimum sample pretreatment and is environmentally/health safe is urgently needed for the analysis of DUV in both copiktra capsules and plasma samples. Fluorimetry is widely used in both pharmaceutical and biomedical analysis. The wide applications of fluorimetry are because of its inherent simplicity, high sensitivity, and low analysis cost [22; 23]. This work detailed, for the first time, the development and validation of an innovative microwell-based fluorimetric (MW-FLR) method for DUV quantitation in various samples including dosage form as well as plasma samples. The procedure of the proposed MW-FLR method was carried out in 96-microwell plates and involved measuring the native fluorescence of DUV by a fluorescence plate reader. The method involved no pretreatment for copiktra capsule samples and simple one-step protein precipitation for plasma samples prior to the analysis.
## 3 Results and Discussion
### Extraction and Evaluation of Antioxidant Power
#### 3.1.1 Total Phenolic Content  
The experimental results, presented in Figure 2, showed that the highest content of phenolic compounds was recorded in the ethanol 80% leave extracts of CA and CF. This outcome was expected as ethanol is a very efficient solvent for antioxidant extraction; in fact, there is a certain correlation  
Figure 1: Yield (g/100 g) determined on the matrix of dried leaves of four Calendula taxa (CM: _C. maritima_; CF: _C. fulgida_; CI: _C. hybrid (maritima \(\times\) fulgida)_; CA: _C. arvensis_), extracted with different solvents (Results of the statistical analysis are shown in Supplementary Table S1).  
between polarity and polyphenolics extraction yield. Instead, for extracts obtained with SFE, a lower content of polyphenols related to the yield is observed.  
As expected, the hybrid CI shows intermediate values between the two parents, CM and CF.
# Protection of Personal Data in the Context of E-Commerce
## 3 Protection of Personal Data on the Internet
### Basic Personal Data Concepts  
In 1948, the United Nations Universal Declaration of Human Rights highlighted the importance of ensuring individuals' freedom from unwarranted interference in their personal lives, encompassing family, residence, and private correspondence. The principle discussed here applies to safeguarding personal data, which includes any information that can be used to identify a person. Primary personal data includes information such as name and address, while personal data encompasses details like race, religion, and political affiliation. Biometric data, on the other hand, refers to characteristics such as fingerprints and behavioral patterns. Ensuring the security of these data is crucial for safeguarding individual privacy and identity.  
The Personal Data Protection Agency (AZOP) emphasizes the importance of organizations providing stakeholders with clear information regarding their data usage. The paper covers various aspects, such as the purpose of data use, the legal basis for processing, the duration of data storage, the sharing of data with third parties, and the rights of individuals about their data. It is essential to provide individuals with information regarding the transfer of their data outside the EU, their ability to file complaints, the process of withdrawing consent, and the contact details of the data processor.  
When personal data is shared online, particularly during e-commerce transactions, individuals are entitled to certain rights. These rights encompass being informed about how their data is being processed, having the ability to request corrections for any inaccuracies, and, under specific circumstances, having their data completely erased, commonly referred to as the \"right to be forgotten\". This condition applies when the data become unnecessary, when consent is revoked, when objections are raised without a valid reason for continuation, or if the data were unlawfully processed or collected. Maintaining trust and privacy in the digital age requires these protections to be in place.  
The general workflow of an online shop and how the data move through its subsystems is shown in Figure 1:  
Many websites allow users to write product reviews when setting up an account to make an online purchase. When users provide feedback, the website collects data, including comments, usernames, customer network addresses, and information about the user's Internet browser and operating system. As a result of this data collection, an individual's name may become associated with a webshop in an Internet search result.  
This phenomenon may arise from misrepresenting an individual's name or the user's deliberate decision to exclude their personal information from search results. In such cases, individuals are entitled to contact the data controller, like Google, and ask to remove any hyperlinks containing their personal information in search results.
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 6 Evaluation
### Discussion  
In this section, we will discuss the limitations of DGF-CFGConstructor and future directions of research to further improve the completeness of CFGs.  
Although our DGF-based approach resolves more indirect jumps than traditional approaches, it still cannot guarantee that all indirect jumps can be exercised. Many factors affect the coverage of DGF, such as the initial seeds and the complexity of the target programs. In future work, we can try to improve the coverage of indirect jumps by improving the seed selection and further analyzing the structure of the target programs. In addition, our approach might benefit from other improvements of DGF.  
The scalability of our approach depends on the two techniques, i.e., static CFG construction and dynamic directed gray-box fuzzing (DGF). The two techniques have been proved to be scalable in analyzing large-scale programs in previous researches. Therefore, our approach is also scalable. Specifically, in the static part, LLVM can construct the static CFG for any real-world program in the benchmark in a few minutes, which is trivial compared to dynamic part. On the dynamic part, directed gray-box fuzzing continues to generate test cases until it is aborted manually. Like existing fuzzing works, we cannot give an exact end time. However, intuitively, our algorithm can be aborted when the DGF-based test case generation gets _stuck_, i.e., the _stuck time_ is reached. In our evaluation on eight real-world programs, the average stuck time' is about 9.7 h.  
At present, DGF-CFGConstructor is still a prototype. It is implemented to analyze source code. However, our proposed DGF-based test cases generation and iterative feedback mechanism can also be applied for binary code. We are extending DGF-CFGConstructor to the binaries.  
In addition, the CFG is applied in many practical analyses. However, in this paper, we only preliminarily evaluate the application of our approach in vulnerability detection. In the future, we will extend the approach to other applications, such as malware analysis, code similarity analysis, and so on.  
Figure 12: Example of updating the distance incrementally.
# Blockchain-Based Decentralised Authentication in Closed Environments
## 2 Background Knowledge and Related Work
### Blockchain  
One way to solve the problem of centralised authentication systems is to have more than one authenticator. Therefore, instead of one centralised authenticator, an ideal architecture would have many authenticators acting in a distributed or decentralised manner.  
Blockchain is a technology with several appealing properties [6; 9]. First, blockchain allows for decentralisation. A blockchain functions as a network of many nodes, each of which possesses a ledger in the form of blocks that contain information and transactions. Second, blockchain allows immutability, meaning that once data and transactions are recorded in a block, they cannot be changed or deleted. The third important property is transparency, i.e., the result of a ledger is distributed among the nodes. In this manner, every node can verify all information and transactions recorded in the ledger.  
Due to the mentioned properties of blockchain, the technology will be applied to our proposed decentralised authentication protocol. The distribution of the ledger across multiple nodes is a feature that helps enhance both reliability and security. In other words, when data are distributed among nodes, it ensures that no single point of failure exists within the system. If one node is unavailable, other nodes can still continue to operate. This maintains the availability of the authentication service.  
This decentralised nature also improves security by making it more difficult for an attacker to compromise the system [10]. This is true in both the context of attacking and taking control of a server. That is, in a decentralised system, an attacker would need to attack and gain control over a majority of nodes to disrupt the service and operation. Moreover, in blockchain-based applications, each node maintains a complete copy of data or ledger, which allows for transparency, which not only builds trust but also facilitates compliance with regulatory requirements related to data integrity and accountability.
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 2 Methods  
### Transformer Model  
The transformer model, a network architecture first proposed by [PERSON] et al., in 2017 [36], is a network that eschews recursion and convolution and is based entirely on attention mechanisms, as shown in Figure 1.  
Specifically, the transformer model is still a network structure from encoder to decoder, but with the abandonment of recursion and convolution, a multihead self-attention mechanism is added to the encoder and decoder modules, respectively. The multihead self-attention mechanism is a key component of the transformer model in that the multihead attention mechanism is able to capture remote dependencies between elements and to encode interactions between sequential tokens. As shown in Figure 2b, the multihead self-attention mechanism allows the model to jointly attend to information from different subspaces at different locations. The objective of the multihead attention mechanism is to simultaneously perform multiple parallel attention functions. A single attention (Figure 2a) function can be represented as a function consisting of a query and a set of keys and values corresponding to an output, where the query, key, value, and output are all represented as vectors. The input is composed of a query and key with dimension \(\mathrm{d_{k}}\) and a value with dimension \(\mathrm{d_{v}}\). The dot product of the query is calculated by using all the keys divided by \(\sqrt{d_{k}}\), and the weights of the values are obtained by the function. During the calculation, the attention function of a set of queries is simultaneously computed, which is defined as a matrix \(Q\). The keys and values are defined as matrices \(K\) and \(V\), respectively, and the attention function can be expressed as follows:  
\[Attention(Q,K,V)=softmax\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}\bigg{)}V \tag{1}\]  
Figure 1: Transformer model structure.  
Figure 2: (**a**) Self-attention structure, (**b**) multihead attention structure.  
The multihead self-attention module can acquire information from different representation subspaces at different locations. This cannot be done with single-head attention. The output of the multihead attention module can be expressed as follows:  
\[MultiHead(Q,K,V)=Concat(\ heat_{1},\dots,\ heat_{h})W^{O} \tag{2}\]  
\[wherehead_{i}=Attention\left(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}\right) \tag{3}\]  
The multihead self-attention mechanism plays a crucial role in the transformer model that can not only improve the efficiency of remote-sensing image classification but also more accurately acquire the global and local features of remote-sensing images. However, if the network does not contain an attention layer, the model-based network cannot be implemented unless the network is changed, which defeats the original purpose of experimenting with high efficiency and accuracy.
## 3 Some Simple One-Dimensional Transport Scenarios
### Start-Stop Scenarii
Quantitatively, the variation in \(W_{start\_stop}(M,L_{AB},\tau_{AB},\delta)\) with the acceleration and deceleration length \(\delta\) is given by  
\[W_{AB\_start\_stop}(M,L_{AB},\tau_{AB},\delta)=W_{AB}(M,L_{AB},\tau_{AB})\left\{ \frac{1-\frac{4}{3}\left(\frac{\delta}{\tau_{AB}}\right)}{\left[1-\left(\frac{ \delta}{\tau_{AB}}\right)\right]^{2}}\right\}, \tag{8}\]  
and drawn as a function of \(\delta/\tau_{AB}\) in Figure 7.  
Turning to the corresponding energy inputs and energy withdrawals, we note that the transport-initiating kinetic energy is  
\[T(M,L_{AB},\tau_{AB},\delta)=\frac{1}{2}M\upsilon(\delta)^{2}=\frac{1}{2}M \frac{\left[\frac{L_{AB}}{\tau_{AB}}\right]^{2}}{\left[1-\left(\frac{\delta}{ \tau_{AB}}\right)\right]^{2}}. \tag{9}\]  
Recalling that this same energy needs to be withdrawn again from mass \(M\) as it approaches its destination \(B\), the energy efficiency of the \"start-stop scenario\" becomes  
\[\eta_{E\_AB}(M,L_{AB},\tau_{AB},\delta)=\frac{W_{start\_stop}(M,L_{AB},\tau_{ AB},\delta)}{T(M,L_{AB},\tau_{AB},\delta)}=\tau_{AB}\left[1-\frac{4}{3} \left(\frac{\delta}{\tau_{AB}}\right)\right]. \tag{10}\]  
This latter equation shows that the parameter of energy efficiency does no longer turn towards infinity as in the \"fly-through scenario\" above, but that it stays finite, matching the total transport time \(\tau_{AB}\) when the transport-initiating energy is instantaneously introduced at point \(A\) and instantaneously withdrawn close to point \(B\). Extending acceleration and deceleration lengths \(\delta\), on the other hand, towards \(\delta=\tau_{AB}/2\), \(\eta_{E\_AB}(M,L_{AB},\tau_{AB},\delta)\) is lowered to \(\tau_{AB}/3\). Considering that the energy efficiency parameter \(\eta_{E}\) takes on the physical dimension of time, the parameter \(\eta_{E}\) can be regarded as an energy relaxation time \(\tau_{E}\), which effectively measures the time during which the invested energy had stayed with the moving mass while it approached its destination \(B\).