esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Acquisition System Based on the Bluesky Suite in the Shanghai Synchrotron Radiation Facility
## 1 Introduction
Therefore, we adopt Bluesky suite to develop a uniform and universal data acquisition system for SSRF Phase-II beamlines. Bluesky suite contains three main parts: _Bluesky_, Python abstractions of hardware (_ophyd_) and _databroker_[6]. _Bluesky_ and _ophyd_ are the co-developed acquisition software. _Bluesky_ orchestrates the execution of an experiment and communicates with low-level hardware via _ophyd_. _Ophyd_ is a hardware abstraction library and it represents hardware in Python frame, as hierarchical objects grouping together related values form the underlying control system; it interfaces with EPICS via _PyErics_. Many facilities use _ophyd_ to integrate with control systems that use EPICS. _Ophyd_ is typically used with the _Bluesky Run Engine_ for experiment orchestration and data acquisition. _Bluesky_ collects measurements and metadata into documents which are stored in _databases_ and can be used in any live visualization or processing. _Databroker_ can access these documents and export them into any desired format. So far, data acquisition systems based on SSRF Bluesky suite have been developed and applied at the BL07U beamline and the BL16U1 beamline of SSRF. In particular, the BL07U data acquisition system has been highly evaluated after it opened to users [16; 17].
## 3 Test Stand and General Experimental Methodology
### Process of Preparing a Soil Sample for Testing
Figure 9: Example of graphical representation of the fulfillment of dynamic stress equilibrium condition (reprinted from Ref. [59]). In accordance with the conclusions of the publication [60], the compaction process of the sample was carried out on a vibrating shaker. The soil was compacted by vibrating the shaker for 60 s. The tests carried out in [60] proved that a time of 60 s was sufficient--the vibration process for longer periods of 120 s and 180 s did not result in a significant increase in soil compaction values. A sinker weight of approximately 1000 g was applied to the sample to improve the effectiveness of the shaking compaction. During the shaker operation, the air voids were filled by the moving soil grains--the compaction process took place. Figure 10 shows the soil sample casing (rigid ring) and the auxiliary components necessary for the compaction process of a soil sample, with their markings:  
For each soil sample variant, 5 mini-samples of 0.2 g were extracted. This mass value is due to the specifics of the test in this model of the Bettersizer S3 Plus laser particle size analyzer--feeding a larger mass of soil sample causes a process of flushing the excess sample out of the circulation tank until the desired mass effect of approximately 0.2 g is achieved. Distilled water was added to each of the 5 vessels containing an equal variant of the sample. Over a period of 24 h, the sample underwent a process of disentangling and loosening the clumps of the silt and clay fraction. The combined silt and clay particles (soil aggregate) were separated in the water into individual soil particles. After waiting for 24 h and the particle separation process, the soil sample was transferred to the circulation tank together with the water in the vessel. The remaining sediment in the vessel was also washed into this circulation tank--this ensures that no soil grains remain on the vessel walls. The laser particle size analysis was then commenced according to the methodology discussed in Section 3.2. Each sample from the vessel was subjected to a cycle of 5 analyses in the instrument. A total of 13 variants of the silty sand sample were distinguished. For each sample variant I-XIII, 5 mini-sample vessels of that variant were extracted (an example set of 5 mini-sample vessels is shown in Figure 11a,b). In order to obtain a meaningful statistical sample for both variants of the silty sand with a silt and clay fraction of \(f_{Si+Cl,1}=15.14\%\) and \(f_{Si+Cl,2}=20.48\%\), a cycle consisting of 5 analyses in the device was carried out. Finally, 650 analyses were carried out in the laser particle size analyzer.
## 2 Experimental Setup  
A schematic of the discretely tunable multiwavelength visible laser based on cascaded SRS and SHG/SFG is presented in Figure 1. The whole system contains a 1064 nm pumping laser, an optical isolator, an external Raman laser oscillator, a nonlinear conversion part, and a prism for color separation. To start, a Q switched flash-lamp pumped 1064 nm laser is utilized, delivering a maximum energy of 400 mJ with a pulse width of 7.7 ns at a repetition rate of 10 Hz. For energy adjustment and prevention of residual return laser, a half wave plate (HWP), a polarization beam splitter (PBS), and a Faraday rotator (FR) are placed directly after the 1064 nm laser. Another HWP follows the FR and is used to change the polarization of the pumping laser. Next, the laser beam, with p-polarization, is focused by a plane-convex lens L1 (f = 400 mm) and then injected into the Raman oscillator.  
The cavity of the external Raman oscillator is composed of a high reflection mirror (M1) and a partial reflection mirror (M2). M1 is coated with a high reflection film from 1150 to 1400 nm and antireflection at 1064 nm. M2 is a partial reflection mirror with transmittance of 80% from 1000 to 1450 nm and high transmission at 1064 nm. Meanwhile, two axis-orthogonal KGW and one Ba(NO\({}_{3}\))\({}_{2}\) crystals are placed in the Raman oscillator in sequence, as shown in Figure 1. The KGW1 crystal is cut along the b-axis with dimensions of \(10\,\times\,10\,\times\,50\) mm without coating and is placed after M1 with the N\({}_{\text{m}}\) optical axis parallel to the pumping polarization (E\(\parallel\)N\({}_{\text{m}}\)). The KGW2 crystal is cut along the b-axis with dimensions of \(10\,\times\,10\,\times\,70\) mm without coating and is placed after the KGW1 with the N\({}_{\text{g}}\) optical axis parallel to the pumping polarization (E\(\parallel\)N\({}_{\text{g}}\)). The Ba(NO\({}_{3}\))\({}_{2}\) crystal, with dimensions of \(10\,\times\,10\,\times\,80\) mm without coating, is placed after KGW2 crystal. Since the 1064 nm pumping lasers are focused into the oscillator by L1, the beam diameter was measured at four points--A, B, C, and D--which are about 3, 2, 1.25, and 2 mm respectively. Two dichroic mirrors, DM1 and DM2, are placed after the SRS oscillator to separate the output Stokes lasers from pumping lasers.  
After being reflected by DM2, the multiorder Stokes lasers are focused by a plane-convex lens, L2, with a focal length of 100 mm. For type-I SHG and SFG, an uncoated \(5\,\times\,5\,\times\,30\) mm KDP crystal is placed at the focus of the laser beams. Moreover, for accurate azimuth adjustment, the KDP crystal is mounted on a rotation stage. As calculations suggest that the phase-matching angle, \(\theta\), for several output Stokes laser is around \(42^{\circ}\), the crystal is initially cut for the type-I phase matching with cutting angles of \(\theta=42^{\circ}\) and \(\varphi=45^{\circ}\).
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 4 Discussion
### Research Trends
Corresponding with car-following behavior, it is the process by which drivers pursue their desired car-following state. Based on the characteristics of car-following behavior affected by various sources of information, it will be a major and significant issue of future research to explore in depth the drivers' decision-making and behavior mechanisms in the car-following process in the V2X environment. Updating the Application Methods of Car-following Models  
Application of the theoretical models is a necessary process to achieve their value. As the core of traffic flow theory, the status of car-following models is determined by their role as the theoretical basis in most traffic flow studies. In the V2X environment, the improved informatization degree makes the present-day traffic environment different from that of the past, which has made profound changes in the application of car-following models. The first issue in future works is to construct a large-scale open-source data set collected from the V2X environment. On the one hand, widely used data sets such as the NGSIM cannot be employed in the V2X environment, and there is an urgency to construct a large-scale high-precision open-source data set about the car-following process in the V2X environment, which is vital for applying the established models. On the other hand, when dealing with emerging technologies, opportunities and challenges usually coexist. With the application of V2X technology, it is easier to collect massive amounts of information, especially under the natural driving condition, about the units in the system to form the data set. In addition, there are other new technologies, such as the intellectualization and the new energy technology, along with the V2X, that will significantly influence the transportation system. Application of the intellectualization technology will further impact the operating and stability characteristics of traffic flow, and applications of the new energy technology will change the characteristics of energy consumption and emission. It is another major issue in future research to update the research on car-following models' applications, represented by analyzing the operating and stability characteristics of traffic flow, and estimating energy consumption and emission, which may combine with the industry standard with the penetration of the aforementioned technology.
# Towards a Hybrid Security Framework for Phishing Awareness Education and Defense
## 5 Accuracy of GPT-3  
GPT-3 has rapidly gained popularity among public users, not just those in the AI and NLP communities who may be more acquainted with LLMs. One of the primary factors is the abundance of GPT-3 use cases that are published online by both academic and non-academic users, in addition to social media. It is therefore important to analyze the accuracy of the system itself.  
A study found that on several tasks, GPT-3 scores better than several cutting-edge LLMs, and on a few tasks, it even beats fine-tuned models. Although GPT-3 works well in many of the tasks, some instances of failure do occur for each job. Sometimes GPT-3  
Figure 1: GPT-3 acting as a Linux Terminal.  
Figure 3: GPT-3 acting as an AI Writing Tutor.  
Figure 2: GPT-3 acting as a Football Commentator.  
produces a summary for a summarization job that is even lengthier than the original text. When performing machine translation, GPT-3 occasionally renders an erroneous translation for some phrases, subtly changing the meaning. Consequently, handling these special situations is a difficult but crucial job [18].  
In terms of multilingualism, GPT-3 performs admirably in a few high and medium-resource languages. However, GPT-3 is still unable to comprehend and produce sentences in low-resource languages. Also, despite the high resource demand for non-Latin script languages, GPT-3 is unable to translate phrases in such languages. This brings up the issue of how languages are represented in GPT-3 ([PERSON] et al., 2023). The accessibility of the technology is thus constrained by the difference in performance when dealing with low-resource languages [19].  
When asked questions on certain topics, GPT-3 gave confident replies that may seem absurd to experts on the subject. \"Artificial hallucination\" has been used to characterize this occurrence [20]. While GPT-3 may produce convincing scientific articles, the data it produces are a mixture of real data and made-up data. This raises questions regarding the integrity and correctness of utilizing extensive LLMs, like GPT-3, in academic writing. To uphold strict scientific standards, it is suggested that the procedure for assessing papers for journals and conferences be reconsidered. Moreover, full disclosure of any usage of these technologies in the writing process is recommended [21].
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 7 Conclusions  
This study proposed an EMR storage and sharing solution based on Hyperledger Fabric and IPFS. Storing patient information and medical data in separate channels mitigates the privacy threat posed by data mining that could leak patient identities. Introducing an IPFS alleviates the deficiency in the blockchain storage performance. zk-SNARKs are used as an implementation of decentralized authentication, avoiding third-party trustworthiness issues. The efficient and secure transmission of medical data is ensured through AES-PRE. In addition, creating composite key improves the efficiency of queries in the Hyperledger Fabric network. Security analyses and experimental results show that these efforts provide significant advances in EMR sharing, offering solutions to the privacy, security, and efficiency challenges.  
Our scheme can be further improved in several aspects. Firstly, we will explore how to strengthen patient key management to further enhance privacy protection in our future work. Secondly, creating composite keys results in a large amount of redundant storage, and the introduction of indexes can be considered in the future to reduce redundancy. These efforts can further improve system security and efficiency.  
Conceptualization, methodology, software, and writing, J.G.; validation, supervision, and revision, K.Z., Z.L. and K.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under Grant No. 2020 YFB1805400.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Acute Respiratory Distress Identification via Multi-Modality Using Deep Learning
###### Abstract  
Medical instruments are essential in pediatric intensive care units (PICUs) for measuring respiratory parameters to prevent health complications. However, the assessment of acute respiratory distress (ARD) is still conducted through intermittent visual examination. This process is subjective, labor-intensive, and prone to human error, making it unsuitable for continuous monitoring and early detection of deterioration. Previous studies have proposed solutions to address these challenges, but their techniques rely on color information, the performance of which can be influenced by variations in skin tone and lighting conditions. We propose leveraging multi-modality data to address these limitations. Our method integrates color and depth data using deep convolutional neural networks with a late feature fusion scheme. We train and evaluate our model on a dataset of 153 patients with respiratory illnesses, 86 of whom have ARD of varying severity levels. Experimental results demonstrate that multi-modality data combined with simple late fusion techniques are more effective with limited data, offering higher confidence scores compared to using color information alone. Our approach achieves an accuracy of 85.2%, a precision of 86.7%, a recall of 85.2%, and an \(F_{1}\) score of 85.8%. These findings suggest that multi-modality data provide a promising solution for improving ARD detection accuracy and confidence in clinical settings.  
acute respiratory distress; action recognition; deep learning; multi-modality; transfer learning; two-stream network; video classification Article
# Hash Based DNA Computing Algorithm for Image Encryption
## 3 Methods
### Content-Aware Encryption Module
```
Input: Initial Key \(K\), DNA Encoded Sequence \(Q_{1}\) Output: DNA Permutation Sequence \(Q_{1}^{p}\)
1\(TA=\) DNA ADD Table;
2\(TX=\) DNA XOR Table;
3\(length=\) Length(\(Q_{1}\));
4\(AR=Q_{1}(0);XR=Q_{1}(0)\);
5 For(\(i=1\)to length);
6\(AR=TA(AR,Q_{1}(i))\);
7\(XR=TX(XR,Q_{1}(i))\);
8\(HASH_{\rm D}=\) SHA256(\(|AR,XR\));
9\(HASH_{\rm K}=\) SHA256(\(K\));
10\(HASH_{\rm DK}=HASH_{\rm D}\oplus HASH_{\rm K}\);
11\(A_{1}=HASH_{\rm DK}(0:63)\); \(A_{2}=HASH_{\rm DK}(64:127)\); \(A_{3}=HASH_{\rm DK}(128:191)\); \(A_{4}=HASH_{\rm DK}(192:255)\);
12\(x_{1}=\left(\text{mod}\left(\text{fix}\left(A_{1}/10^{8}\right),80\right)-40 \right)+\left(A_{1}/10^{14}-fix\left(A_{1}/10^{14}\right)\right)\);
13\(y_{1}=\left(\text{mod}\left(\text{fix}\left(A_{2}/10^{8}\right),80\right)-4 0\right)+\left(A_{2}/10^{14}-fix\left(A_{2}/10^{14}\right)\right)\);
14\(z_{1}=\left(\text{mod}\left(\text{fix}\left(A_{3}/10^{8}\right),80\right)+1 \right)+\left(A_{3}/10^{14}-fix\left(A_{3}/10^{14}\right)\right)\);
15\(w_{1}=\left(\text{mod}\left(\text{fix}\left(A_{4}/10^{6}\right),500\right)-250 \right)+\left(A_{4}/10^{14}-fix\left(A_{4}/10^{14}\right)\right)\);
16 Put \(x_{1},y_{1},z_{1},w_{1}\) into HCLS to generate a sequence \(X=[x_{1},x_{2},...,x_{n}]\) by iterating;
17\(Q_{p}=mod(floor\big{(}X\times 10^{15}\big{)},length\big{)}\);
18 If(This is the encryption process);
19\(i=0,j=length/2\);
20 Else;
21\(i=length/2,j=0\);
22 For(\(k=i\)to \(j\));
23\(Q_{1}(Q_{p}(k)\leftrightarrow Q_{1}(Q_{p}(length-k)\);
24\(Q_{1}^{p}=Q_{1}\);
25 Output DNA Permutation Sequence \(Q_{1}^{p}\);
26
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4933
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5011
5111
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511111
51111 Similarly, we describe the calculation process of the DNA Sequence diffusion algorithm in Algorithm 4. Specifically, we first calculate key parameters \(X_{1}\) and \(p\) based on values of \(H_{1}\) and \(H_{2}\) transmitted from the last module. Then, we input these parameters into the PWLCM algorithm to generate a diffusion control sequence. With the input rule selection sequence, we adopt DNA rules to map from numbers to DNA bases, thus generating DNA diffusion sequence. Finally, we apply DNA-based calculations on the generated permutation sequence and diffusion sequence, thus obtaining the merged sequence as the final output of the DNA permutation and diffusion sequence.
# Topology Optimization Based on SA-BESO
## 4 Conclusions  
This paper introduces the idea of the SA algorithm topological optimization and provides a fresh perspective on the application of the BESO algorithm to topological optimization. Numerical experiments on two-dimensional simple support beams and L-shaped supports verify the feasibility and effectiveness of the proposed method. The contributions of our approach can be summarized as follows:  
1. The combined SA-BESO algorithm takes into account the change in structural compliance in the iterative process, and improves the structural compliance mutation process caused by large deletion rate in BESO method.
2. Compared with the BESO algorithm, the SA-BESO algorithm can obtain a topology configuration with lower structural compliance without sacrificing too much computational efficiency. The number of iterations of the latter is generally lower than the former.
3. The semi-random solution of the SA-BESO algorithm is semi-directional and converges to highly similar results with a high probability, that is, the results obtained by this method are stable and robust, and have the potential and value to be applied to the optimization design of actual engineering structures.
4. The value of the minimum variation rate has an important influence on the number of iteration steps of the SA-BESO joint optimization method, and a structure with a lower structural compliance value can be obtained if it is properly set.  
In this paper, the SA-BESO method is mainly used in a single stress model. In the future, models with a variety of stress and boundary conditions can also be studied to make the application of the joint algorithm more extensive.  
Conceptualization, L.C. and Q.Z.; methodology, W.W.; software, W.W.; validation, Q.Z.; formal analysis, H.Z.; investigation, H.Z.; resources, L.C.; data curation, W.W.; writing--original draft preparation, H.Z.; writing--review and editing, L.C.; visualization, W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Commission (No. 2021-JCXX-JX-0872), the Central Universities Basic Research Business Fund of China (No. 22 qntd1604).  
Not applicable.  
The data used to support the findings of this study are available within the article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline
**SA-BESO Algorithm** & **Mean Iterative Step** & **Average Compliance of Structure** \\ \hline
1 & 25 & 37.8189 \\
2 & 27 & 37.7605 \\
3 & 26 & 37.8063 \\
4 & 30 & 37.5806 \\
5 & 25 & 37.5854 \\ \hline \end{tabular}
\end{table}
Table 3: The SA-BESO algorithm for the loaded knee structure—the optimization result data when deletion rate being 3.5%.
# Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks
## 2 Materials and Methods
### DHRECA-Net Structure
#### 2.1.3 Eca-Aspp  
ASPP is mainly used to solve the scaling problem in semantic segmentation tasks, in which each pixel in an image needs to be categorized into different classes, while different objects and structures may have different scales in the image. Traditional convolutional  
Figure 4: The network structure before and after the improvements: (**a**) network structure prior to enhancement; (**b**) network structure following enhancement. Different colors represent various levels of feature maps, with Level 1 at the top and Level 4 at the bottom.  
neural networks can only operate with a fixed-scale convolutional kernel when extracting semantic information and thus cannot capture the contextual information at different scales well.  
To address this issue, this paper introduces the ASPP module after the second- and third-level features of the DHRECA-Net, which is used to further enhance the network's ability to detect small-scale landslides. ASPP captures different levels of contextual information by introducing multiple parallel branches in the network, where each branch uses different scales of void convolution and pooling operations. By using different expansion rates for null convolution, the sensory field can be expanded to obtain a broader range of contextual information. The ASPP consists of a 1 \(\times\) 1 convolutional layer, a pooling pyramid layer, and a pooling layer. In this paper, the layers of the pooling pyramid are used with expansion rate (rate) settings of 6, 12, and 18. In order to enhance the network's ability to differentiate between landslides and backgrounds, this paper also adds the ECA module to the ASPP structure and constructs an ECA-ASPP model. This module further enhances the network's ability to extract features from different categories, the structure of which is shown in Figure 5.  
By introducing the ECA-ASPP structure, the DHRECA-Net is able to better capture contextual information at different scales, which improves the network's ability to detect small-scale landslides and further enhances the segmentation of landslide areas.