esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Preparation of Plasmid DNA  
Human ACE2 (hACE2) cDNA was obtained from Addgene (#1786; Watertown, MA, USA). To construct an expression plasmid, cytoplasmic-region- and transmembrane-lacking hACE2 tagged with a monomeric variant of eGFP carrying the A206K mutation (hACE2-eGFP), the fragment encoding hACE2, was inserted into pmeGFP-N1 with Nhel and EcoRI. To reconstruct the linker region between hACE2 and eGFP, a double-stranded synthetic oligonucleotide (5'-gaattcttttttttgggatgggataccgactggagtccatatatgcgacaggactggact-3') was inserted into the plasmid with EcoRI and Agel. A polyhistidine tag was inserted into the C-terminus of eGFP. The whole sequence of the coding region of polyhistidine-tagged hACE2-eGFP is shown in the Supplemental Figure S1. A synthetic oligonucleotide for the ER-targeting signal peptide was inserted into pmCherry-N1 (pER-mCherry) with Nhel and Agel. The DNA fragments of the spike protein 1 subunit (306-575 region) of SARS-CoV (#NC_004713) and SARS-CoV-2 (#NC_045512), including a polyhistidine tag at the C-terminus (S1 and S1-2, respectively), were synthesized by Eurofins Genomics GmbH (Munchen, Germany). The fragments were inserted into pER-mCherry-N1 with XhoI and BamHI (pER-mCherry-S1 and pER-mCherry-S1-2, respectively). The whole sequences of the coding regions of pER-mCherry-S1 and pER-mCherry-S1-2 are presented in the Supplemental Figure S1. The expression plasmid for soluble secreted yellow fluorescent protein (ssYFP)-tagged BiP (ssYFP-BiP), an ER-resident ortholog of heat shock protein 70 kDa, as an ER marker was kindly provided by Dr. [PERSON] (Fukushima Medical University, Fukushima, Japan).
# A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
## 2 Method
### Deep-Reinforcement-Learning Algorithm Combined with Manipulator
#### 2.3.1 Application of Two-Link Manipulator  
The plane of two-link manipulator simulation system is shown in Figure 2. The settings of the two-link manipulator in the simulation environment are as follows. The lengths of the rods are \(l_{1}=1.0\) m, \(l_{2}=0.8\) m, and the mass of the rods are \(m_{1}=0.1\) kg, \(m_{2}=0.08\) kg. Each joint adopts the incremental control method, that is, the joint rotates around a fixed angle \(|\Delta\theta|\) in the direction given by the control signal \(a_{t}\) at any time \(t\), where \(a_{t}:=\left[\Delta\theta_{t}^{1},\Delta\theta_{t}^{2}\right]\in\mathbb{R}^{2 },|\Delta\theta_{t}^{i}|=0.05^{\circ}\), \(i=1\), \(2\). The state of the entire simulation system is \(s_{t}:=\left[\theta_{t}^{1},\ \theta_{t}^{2}\,\ \theta_{t}^{1}\,\ \theta_{t}^{2}\,\ x_{t}\,y_{t}\,x_{t}^{*}\,y_{t}^{*}\right]\in \mathbb{R}^{8}\), where \(\theta_{t}^{i},\dot{\theta}_{t}^{i}\) are the angle and angular velocity of each joint at \(i\)th time. The (\(x_{t}\), \(y_{t}\)) is the position of the endpoint of the manipulator at time \(t\), and the desired target-point position \((x_{t}^{*}\), \(y_{t}^{*})\) is set as Equation (14).  
\[\begin{cases}x_{t}^{*}=l_{2}\cos(\omega_{1}t+\omega_{2}t)+l_{1}\cos(\omega_{1 }t)\\ y_{t}^{*}=l_{2}\sin(\omega_{1}t+\omega_{2}t)+l_{1}\sin(\omega_{1}t)\end{cases},\ \omega_{1}=\omega_{2}=1\ \text{rad}/\text{s} \tag{14}\]  
Figure 1: The framework of the robot-arm path-tracking model based on deep reinforcement learning.  
The two-link manipulator's output action has low dimensionality and can be approximated as a discrete quantity, so the algorithm uses the classical DQN algorithm. The strategy's network structure in the DQN algorithm is as follows: the input state is 8-dimensional, the output action is 2-dimensional, the hidden layer has two layers, and the number of nodes in each layer is 50. The hyperparameters are set as follows: replay buffer = \(1\times 10^{6}\), learning rate = \(3\times 10^{-4}\), discount factor = 0.99, batch-size = 64, the update between the \(Q\) network and the target \(Q\) network adopts the soft update method, and its soft parameter tau = 0.001. In addition, the setting of the reward is \(r_{t}=exp(-|p_{t}^{*}-p_{t}|)\), where \(p_{t}^{*},~{}p_{t}\) are the target path points at time \(t\) and the position of the end point of the robot arm, respectively.
## 2 Technical Implementation
### Optimization Method  
The NSGA-II algorithm used in this paper is an improvement of the NSGA evolutionary algorithm proposed by [PERSON] and [PERSON] [13], which is an efficient multi-objective optimization algorithm used in aerodynamic optimization [14; 15]. The optimization processes are as follows: (1) generate an initial population containing N individuals randomly, (2) calculate the crowding distance among the initial population individuals and perform a non-dominated sorting, (3) select, crossover, mutate and establish a subpopulation of size N, (4) merge parent and offspring populations, calculate the crowding distance for non-dominated sorting to select the best N individuals, and generate a new population, (5) judge whether the setting number of generations is reached, if not continue the process, otherwise the algorithm ends and outputs the pareto optimal solution. The parameters used in the NSGA-II algorithm of this paper are shown in Table 1.
## 3 Materials and Methods
### Finite Element Model Development
#### 3.1.2 Material Definition  
The material under scrutiny in this inquiry is the UIC 860 grade 900A, chosen for its relevance and suitability to the context of the study. The chemical compositions of the UIC 860 grade 900A materials are meticulously detailed in Table 1, providing a comprehensive overview of the elemental constituents comprising the material matrix. Additionally, Table 2 presents a comprehensive compilation of the material properties pertinent to UIC 860 grade 900A, furnishing essential information crucial for subsequent analyses and interpretations. Moreover, it is imperative to note that the material model adopted for this investigation is the Johnson-Cook model, renowned for its efficacy in capturing complex material behavior under dynamic loading conditions. This model, described by the relation elucidated in references [1; 2], encapsulates the intricate interplay between various material parameters, thereby facilitating a more nuanced and accurate representation of the material response within the simulated environment. By employing such a sophisticated material model, this study endeavors to enhance the fidelity and reliability of its analyses, thereby advancing the understanding of the mechanical behavior exhibited by UIC 860 grade 900A in the context of the deep rolling process.  
Where \(\varepsilon\) is the equivalent plastic strain, A, B, C, n, and m are the material constants where A is the yield strength, B and n are the strain hardening coefficient and exponent, C is the strain rate coefficient, and m is the thermal softening exponent. The material constants used in the Johnson-Cook equation are presented in Table 3.  
\[\sigma=(\text{A}+\text{B}\cdot\varepsilon^{\text{n}})(1+\text{C}\cdot\text{In }\ \varepsilon)(1-\text{T}^{\text{m}}) \tag{1}\]  
\[\text{T}^{\ast}=(\text{T}-\text{T}_{\text{room}})/(\text{T}_{\text{melt}}- \text{T}_{\text{room}}) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Elements** & **C** & **Si** & **Mn** & **P (Max)** & **S (Max)** & **Cr** & **Ni** \\ \hline Weight \% & 0.6 & 0.1 & 0.8 & 0.04 & 0.04 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of railway UIC 860 Grade 900A.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Density** & **Elastic** & **Moolus** & **Poisson’s** & **Thermal** & **Specific** & **Heat** & **Friction** \\
**(kg/m)** & **(GPa)** & **Ratio** & **(10\({}^{-6}\))/\({}^{\circ}\)C** & **(j/kgK)** & **Coefficient** \\ \hline
7850 & 205 & 0.3 & 11.5 & 450 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties used for simulating railway UIC 860 grade 900A.  
Figure 2: Deep rolling basic setup.
## Appendix A
**ERGAS:\(\frac{d\mu}{dl}\)** represents the ratio between the pixel size of high resolution and low resolution images; e.g., \(\frac{1}{4}\) for Pleiades data, and \(n\) number of bands. The RMSE represents root mean square error of band \(i\). **SAM:** The spectral vector \(\text{V}=\{\text{V}_{1},\text{V}_{2},\,\ldots,\,\text{V}_{n}\}\) stands for reference MS image pixels and \(\hat{\text{V}}=\{\hat{\text{V}}_{1},\hat{\text{V}}_{2},\,\ldots,\,\hat{\text{V }}_{n}\}\) stands for Pan-sharpened image pixels rep reference and both have L components. **RASE:** The \(\mu\) represents the mean of \(b^{\text{th}}\) band; \(b\) is the number of bands and RMSE represents root mean square error. **PSNR:** The L represents the number of gray levels in the image; MN is the image size, \(\text{I}_{\text{r}}(\text{i},\text{j})\) is pixel value of reference image and \(\text{I}_{\text{p}}(\text{i},\text{j})\) is the pixel value of Pan-sharpened image. A higher PSNR value indicates more similarity between the reference MS and Pan-sharpened images. **QAVG:** The \(\overline{x}\) and \(\overline{y}\) are the means of reference and Pan-sharpened images, respectively; \(\sigma_{xy}\) is the covariance and \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are variances. As QI can only be applied to one band, the average value of three or more bands (QAVG) is used for calculating a global spectral quality index for multi-bands images. QI values range between \(-1\) and \(1\). A higher value indicates more similarity between reference and Pan-sharpened image. **SSIM:** The \(\mu\) stands for mean, \(\sigma\) stands for standard deviation; \(I_{r}\) and \(I_{p}\) represent reference and Pan-sharpened image respectively. The C1 and C2 are two necessary constants to avoid the index from
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
## 2 Materials and Methods
### Experiment Site  
Fujian Province is located on the southeast coast of China and has a shortage of conventional energy resources. In recent years, with the rapid rise in demand for energy as the economy develops, it is imperative to develop nuclear energy vigorously. As of May 2021, four nuclear power plants have been built in Fujian Province, including Ningde, Fuqing, Xiapu, and Zhangzhou. Ningde Nuclear Power Plant is located in Taimushan Town, Fuding City (Figure 2) and is planned to construct six megawatt-class pressurized water reactor nuclear power units, with four units to be built in the first phase of the project using CPR1000 technology.  
The buoy with the detector is positioned 100 m southeast of the discharging outlet of Ningde Nuclear Power Station, and the water depth there is approximately 5 m to 15 m. The detector is suspended at a depth of 1.5 m from the water surface, and there is no risk of bottoming out at low tide.  
The buoy consists of two parts. The upper part is equipped with solar panels and antennas for powering the equipment and transmitting signals, respectively. The lower part is a cabin structure that houses the supporting equipment. The detector is fixed to the bottom of the buoy by a bracket. The bottom of the buoy is connected to an anchor fixed to the seabed.
## 3 Proposed Method
### Cross-Modal Feature Representation  
**Language and video feature representation:** Following most settings [21; 26; 51], we employ the GloVe [52] and pre-trained C3D [53; 54] as the feature extractors for language descriptions and input videos. Specifically, the language query is fed into the GloVe, and the output feature is denoted as \(Q=\{s_{1},s_{2},\cdots,s_{M}\}\in\mathbb{R}^{M\times D_{q}}\), where \(M\) is the number of words, and \(D_{q}\) is the feature dimension. The untrimmed video is encoded by a pre-trained C3D network to obtain the feature representation \(V=\{v_{1},v_{2},\ldots,v_{N}\}\in\mathbb{R}^{N\times D_{v}}\), where \(N\) is the number of video clips and \(D_{v}\) is the feature dimension of the video.  
As mentioned above, we randomly posit \(K\) initial candidate windows on the video. For each candidate window, the start and end positions divide the whole video into three parts, the left part, center part, and right part. In each part, we uniformly sample \(L\) features from the video representation \(V\) and thus obtain the features \(V_{left}\), \(V_{center}\), and \(V_{right}\in\mathbb{R}^{L\times D_{v}}\). In addition, we uniformly sample a global feature of the video from \(V\) with length \(2L\) and denote it as \(V_{global}\in\mathbb{R}^{2L\times D_{v}}\).  
**Cross-modal fusion representation:** Without loss of generality, we unify the subscript of the feature representation of video segments as \(V_{c}\), where \(c\in\{left,center,right,global\}\). Because of the powerful representation ability of the Transformer [55], we employ the decoder structure of the Transformer to fuse the features of video and language. Specifically, we first use fully connected layers to project video and language features into the same feature dimension,  
\[\bar{V}_{c}=FC_{1}(V_{c}),\bar{Q}=FC_{2}(Q) \tag{1}\]  
where \(FC_{1}\) and \(FC_{2}\) are fully connected layers. Then, the Multi-Head Cross Attention (MHCA) layer, Feed-Forward (FF) layer, and Layer Normalization (LN) layer are used to fuse multi-modal features:  
\[\begin{cases}Q_{c}^{\prime}=LN(MHCA(\bar{Q},V_{c}))+\bar{Q},\\ Q_{c}^{\prime\prime}=LN\big{(}FF(Q^{\prime})\big{)}+Q_{c}^{\prime}\end{cases} \tag{2}\]  
Figure 2: The framework of the proposed method. In the RL framework, the state learns the cross-modal representation from the environment (input query and untrimmed video) and inputs to the agent. The agent adopts the MMS to adjust to the candidate window according to the reward, which is defined by the GDAM.  
\[\begin{cases}V_{c}^{\prime}=LN(MHCA(\tilde{Q},V_{c}))+\bar{V}_{c},\\ V_{c}^{\prime\prime}=LN\big{(}FF\big{(}V_{c}^{\prime}\big{)}\big{)}+V_{c}^{ \prime}\end{cases} \tag{3}\]  
Intuitively, \(Q_{c}^{\prime\prime}\) can be treated as the cross-modal fusion representation in the textual space, and \(V_{c}^{\prime\prime}\) is the one in the visual space.
## 4 Evaluation
### Evaluation for Hardware Implementation
Using ASE coding, we have developed a realtime compression system of HDMI data stream. The speed of the compression is 150 MHz with 24-bit wide color pixel stream that consists of YUV444 color data. Figure 10 shows the realtime compression ratio of every frame during eight minutes of a full HD computer graphics movie that presents battles of space fighters. During the experiment, we used two compressors of ASE coding with eight- and 16-bit input symbol. We compare those with an LCA-DLT with a compressor of eight-bit symbol (16-bit input) and 256 look-up table entries. Both ratios of ASE coding become low in the beginning of video, because the first 1500 frames are almost black frame. This means that short codes are generated adaptively due to the entropy culling. Here, the ASE coding of 16-bit symbol input results better compression ratio than the one of eight-bit because the entropy culling works better in the case of 16 bits than the one of eight bits. Totally, both results of ASE coding show better compression ratios than LCA-DLT. Thus, the compressed data associated from the entropy calculation work adaptively to decide the shortest bits for the instant data frequency.
## 3 Proposed Architecture
### Network Slice Controller  
The Network Slice Controller (NSC) effectuates a transport network slice in the underlying transport infrastructure, manages and control the state of resources and topologies associated with it. The NSC receives a transport network slice request from the Operation Support System and Business Support System (OSS/BSS). The NSC runs an internal workflow for transport network slice life-cycle management and interacts with underlying IP and Optical Domain controllers via a RESTCONF client. As described in the following sections, depending on the NSC location in the architecture, the NSC will delegate to SDN Domain controllers to configure the network (Section 3.5 (a) and (b)) or make it directly through a NETCONF SBI (Section 3.5 (c)).  
The network slice controller is the key building block for control and management of network slice. It provides the creation/modification/deletion, monitoring an optimization of network slices in a multi-domain, a multi-technology and multi-vendor environment. It has two main functionalities, defined in [25; 26]:  
* _Map_: The NSC must map the Network Slice requests to the underlying technology-specific infrastructure. Accordingly, It maintains a record of the mapping from user requests to slice instantiations, as needed to allow for subsequent control functions like modification or deletion.
* _Realize_: The NSC should realize the network slice request using its SBI interface against the domain controllers in either physical or logical connectivity through VPNs or various tunnelling technologies such as Segment Routing, MPLS, etc.
# Use of Social Media Data in Disaster Management: A Survey
## 8 Applications  
In this section, we investigate the contribution of social media applications in the context of disaster management strategy, which is a discipline for dealing with disasters or avoid disasters where possible. In general, disaster management strategies consist of four phases: mitigation, preparedness, response, and recovery [65]. These four phases demand supporting tools and technologies for effective disaster management. Several recent research works have utilised social media data to address problems in different types of disasters and phases of disaster management. According to The Emergency Events Database (EM-DAT) [66], there are two general groups of disasters: natural disasters and technological disasters, with several types of disasters within each of these groups. Figure 7 depicts two significant dimensions of social media applications in disaster management studied in this paper, disaster management phase and disaster management type. The disaster management phase represents the stage in the life cycle of disaster management contributed to by social media applications, whereas the disaster management type represents the group of disaster applications. Based on these dimensions, we investigate the current coverage of existing social media applications for disaster management and the overall picture of existing applications.
# Bridging the Appearance Domain Gap in Elderly Posture Recognition with YOLOv9
## 4 Results
### Real-Time Performance Evaluation  
To evaluate the real-time performance of the proposed YOLOv9-based human posture recognition model, video frames captured from a webcam were processed on a system equipped with a Ryzen 7 7950X CPU (16 cores, 32 threads), 128 GB of RAM, and an NVIDIA  
Figure 8: Performance metrics for the VIRTUAL–REAL experiment.  
Figure 9: Performance metrics for the HYBRID–REAL experiment.  
RTX 4080 GPU with 16 GB of VRAM. The inference was performed over a period of 1 minute, and key performance metrics were measured, including pre-processing time, inference time, post-processing time, and system resource usage (CPU, RAM, and GPU).  
The results, as presented in Table 3, show that the average inference time per frame was 30.45 ms, with a minimum of 11.50 ms and a maximum of 72.58 ms. The pre-processing and post-processing times were minimal, averaging 1.93 ms and 1.63 ms, respectively. Resource usage was consistently low, with an average CPU utilisation of 0.77%, RAM utilisation of 14.24%, and GPU utilisation of 6.57%. GPU memory usage remained stable at an average of 2546.79 MB, indicating that the system maintained efficient memory management during the task.  
Based on the results, the system demonstrates strong performance in real-time posture recognition using YOLOv9. The average inference time of 30.45 ms per frame indicates that the model is capable of processing more than 30 frames per second (fps), which is suitable for real-time applications. The low CPU usage (average of 0.77%) and moderate GPU usage (average of 6.57%) suggest that the system uses the hardware resources efficiently and without significant load. In addition, the stability of GPU memory usage at around 2.5 GB out of 16 GB of VRAM shows effective memory management. Overall, these metrics suggest that the system is well optimised for real-time performance, making it suitable for use in environments that require responsive posture detection with minimal computational overhead.
# Electrochemical Biosensors Based on Conducting Polymers: A Review
## 4 Conducting Polymer-Based Electrochemical Biosensors
### Conducting Polymer-Based Enzyme Biosensors
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Active Layer** & **Linear Range** & **Sensitivity** & **Detection Limit** & **Stability** & **Real Samples?** & **Ref.** \\ \hline Polypyrrole-CNT-chitosan & 1–4.7 mM & 2860 \(\mu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 5.0 \(\mu\)M & 45 days & serum & [101] \\ \hline Polypyrrole-CNT & 1–4.1 mM & 54.2 \(\mu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 5.0 \(\mu\)M & 45 days & serum & [102] \\ \hline Polyaniline-Prussian Blue & 2–1.6 MM & 99.4 \(\mu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 0.4 \(\mu\)M. & 15 days & serum & [103] \\ \hline Polyaniline-Au NP & 1–20 mM & 14.6 \(\mu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 1.0 \(\mu\)M & — & — & [104] \\ \hline Polyaniline-Pt NP & 0.01–8 mM & 96.1 \(\mu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 0.7 \(\mu\)M & — & — & [106] \\ \hline Polyaniline-CNT & 3–8.2 mM & 16.1 \(\mu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 1.0 \(\mu\)M & 48 days & serum & [107] \\ \hline Poly(Azure A)-Pt NP & 0.02–3.2 mM & 42.7\(\mu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 7.6 \(\mu\)M & 3 month & fruit juice & [108] \\ \hline Polyaniline-Graphene-NiO\({}_{2}\) & 0.02–5.56 mM & 376.2 \(\mu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 0.5 \(\mu\)M & — & serum & [110] \\ \hline Polyaniline & 0.01–5.5 mM & 97.2 \(\mu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 0.3 \(\mu\)M & 15 days & urine & [111] \\ \hline Polypyrrole-Pt NP & 0.1–9 mM & 34.7 \(\mu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 27.7 \(\mu\)M & — & — & [112] \\ \hline Polyaniline-nanodiamonds & 1–30 mM & 2.03 mA mM\({}^{-1}\) cm\({}^{-2}\) & 18.0 \(\mu\)M & 30 days & serum & [113] \\ \hline Polycarbazole & 0.01–5 mM & 14.0 \(\mu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 0.2 \(\mu\)M & — & — & [117] \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of conducting-polymer-based glucose amperometric biosensor performances.
# Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation
## 6 Conclusions  
In this paper, we propose a data-driven method for image-based age estimation. Multiple Network Fusion with Low-Rank Representation (MNF-LRR) is designed to learn and integrate  
Figure 6: Different methods of age estimation. (**a**) Results of IMDB; (**b**) Results of WIKI.  
multi-modal features. First, multi-modal features are extracted with different neural networks. Second, these features are represented on low-rank space and fused. In this way, a robust representation of facial images for age estimation is computed. In addition, the fused features are connected to softmax, and the estimation results can be obtained. Compared with state of the art, the proposed method is based on multiple features and utilizes multiple neural networks to compute features, which improves the descriptive power of representations. We have conducted experimental evaluation on datasets from the Internet Movie Database (IMDB) and Wikipedia (WIKI). Performance comparison indicates the superiority of the proposed MNF-LRR over previous methods.  
Methodology, C.H.; Software, X.W.; Project Administration, Z.Z.; Funding Acquisition, C.H. and Z.Z.; Writing-Review & Editing, C.H. and W.Z.; Visualization, W.Z.  
This research was funded by the National Natural Science Foundation of China (61622205), the Fujian Provincial Natural Science Foundation of China (2018J01573, 2016J01327, 2016J01324), the Fujian Provincial High School Natural Science Foundation of China (JZ160472), Fujian Province Universities and Colleges (JK2015033), and the Foundation of Fujian Educational Committee (JAT160357, JAT160358).  
No conflict of interest.