esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Candidate Genes for Aldehyde Dehydrogenases Involved in Sesquiterpenoid Biosynthesis  
From the plant _Artemisia annua,_ the aldehyde dehydrogenase ALDH1 was recently biochemically characterized and shown to catalyze the oxidation of dihydroartemisinin aldehyde into dihydroartemisinin acid [37]. Thus, homologues of this enzyme could potentially be involved in the modification of oxygenated sesquiterpenes that are required for formation of a lactone moiety (Figure 1). A survey on NCBI using the ALDH1 protein sequence (gb: ACR61719.1) using BLASTp, followed by Neighbour joining tree of this (data not shown), showed that ALDH1 formed a small clade, along with one otheruncharacterized aldehyde dehydrogenase from _Saussurea medusa_ (gb: AAT44126.1). This sub-clade formed part of the more universal F2 clade of cytosolic plant ALDHs [37]. The sequences of these two proteins were therefore used to search in our transcript data for sequence encoding similar enzymes that could be involved in terpenoid biosynthesis.  
Figure 4: Alignment of _A. annua_ ALDH and three _T. laciniata_ contigs. Alignment of proteins encoded by contigs 645, 2331 and 10370 with _A. annua_ ALDH1 (gb: ACR61719), illustrating the high amino acid sequence similarity. The consensus sequence is shown below the alignment and the sequence logo is at the bottom. Contig 645 contains a mitochondrial targeting sequence at the \(N\)-terminus.  
In this clade, we find two _T. laciniata_ sequences, contig 2331 and contig 10,370; these two transcripts are of major interest in the future search for genes potentially involved in sesquiterpene lactone biosynthesis. Contigs 2331 and 10,370 encode proteins that exhibit 69% amino acid identity with _A. annua_ ALDH1, and both group in the same sub-clade of F2 ALDHs. Contig 645 encodes a protein that groups in the related clade of mitochondrial F2 ALDHs and contains a targeting peptide at the _N_-terminus (Figure 4). Other ALDH encoding transcripts in our _T. laciniata_ transcriptome include contig 155 in the F2 mitochondrial family, contig 24,667 in the 3H1/311 family, and contig 8241 in the plant specific 22A1 family (data not shown). Three contigs, 23,206, 27,004, and 20,996, did not fall into a described clade. Other than contigs 2331 and 10,370 (Figure 4), no other _T. laciniata_ ALDHs exhibited greater than 52% sequence identity with _A. annua_ ALDH1. The family annotations described here have been adopted from previous published results [38].
# Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques
## 5 Evaluation of the Performance of MARS Technique and the Old Models  
In this section, a comparison has been made between the models developed in this study and the old models developed by previous scholars [6; 11; 77; 90; 91]. Table 7 shows the previous models with their unique characteristics that have been extracted and presented for the earth slopes in the effects of an earthquake.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Basis Function** & **Values/Expression** \\ \hline BF1 = & C(x3\(|\) + 1,0.02,0.04,0.07) \\ BF2 = & C(x3\(|\) + 1,0.07,0.1,0.11) \\ BF3 = & BF2 * C(x6\(|\) + 1,0.42,0.6,0.65  
These relations are based on the Newmark block model. For comparison, the relative error (RE) of all the models was obtained using the following relation:  
\[RE=\frac{D_{ave-p}-D_{ave-m}}{D_{ave-m}}\times 100 \tag{11}\]  
where \(D_{ave-p}\) and \(D_{ave-m}\) are the mean deformations predicted by different models (piecewise-linear method of MARS model and the existing relationships) and the real deformations arising from earthquakes. Results of this comparison have been presented in Figure 10. As it is observed in this figure, the newly developed model provides a more appropriate performance than the other models. Compared to the conventional old models, using the new methods can increase the precision of calculations and designs. One of the major reasons for the high accuracy of the artificial intelligence methods is considering the various complications of the earth structures affected by earthquakes. These models are quicker in calculations and can be a better substitution for the previous models. Finally, the new models can be a positive step in order to reduce uncertainty in the geotechnical problems and particularly the slope deformations of the earth dams.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Equations** & **Limitation** \\ \hline \multirow{2}{*}{Hynes-Griffin and Franklin} & \(\log(D_{(cm)})=-0.287-2.854(\frac{a_{y}}{a_{\max}})-1.733(\frac{a_{y}}{a_{\max }})^{2}\) & \(0.01\leq a_{y}/a_{\max}\leq 0.6\) \\  & \(-0.702(\frac{a_{y}}{a_{\max}})^{3}-0.116(\frac{a_{y}}{a_{\max}})^{4}\) & \(M_{\varpi}\leq 8\) \\ \hline \multirow{2}{*}{Ambraeys and Menu} & \(\log(D_{(m)})=0.9+\log[(1-\frac{a_{y}}{a_{\max}})^{2.53}\times(\frac{a_{y}}{a_{ \max}})^{-1.09}]\) & \(0.05\leq a_{y}/a_{\max}\leq 0.95\) \\  & \(6.6\leq M_{\varpi}\leq 7.2\) \\ \hline \multirow{2}{*}{Jibson} & \(\log(D_{(cm)})=-2.710+\log[(1-\frac{a_{y}}{a_{\max}})^{2.335}\times(\frac{a_{y}} {a_{\max}})^{-1.478}]\) & \(0.05\leq a_{y}\leq 0.4g\) \\  & \(+0.424M_{\varpi}\) & \(0.53\leq M_{\varpi}\leq 7.6\) \\ \hline \multirow{2}{*}{Saygili and Rathje} & \(\log(D_{(cm)})=5.52+0.72\ln(a_{\max})-4.43(\frac{a_{y}}{a_{\max}})\) & \(0.05\leq a_{y}\leq 0.3g\) \\  & \(-20.93(\frac{a_{y}}{a_{\max}})+42.61(\frac{a_{y}}{a_{\max}})^{3}-28.74(\frac{a_ {y}}{a_{\max}})^{4}\) & \(a_{max}\leq 1g\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The models developed for determining the value of slope deformation (Dave).
## 2 Data  
### Study Area  
We selected several fire-prone regions at a global scale as our study area. The fire-prone regions are characterized by very frequent fire activities [31; 32]. In this study, a total of six fire prone regions (i.e., North America, South America, Africa, Indonesia, Siberia, and Australia as shown in Figure 1) were chosen based on active fire detection density that was calculated using global VIIRS 750 m daily active fire observations in 2018. Although all these regions are affected frequently by fires every year, they have very different fuel types, fire intensities, smoke plume sizes, smoke aerosol densities, and smoke aerosol size distributions [33; 34]. Specifically, North America and Siberia are dominated by forest fires with large smoke plumes, while Africa, South America, and Australia are affected mostly by savanna fires or grass fires with relatively small smoke plumes [35; 36]. In general, forest fires emit smoke with coarse particulates while the fires from grasses and/or savannas usually release smoke with fine particulates [37]. Particle size in smoke aerosols is also impacted significantly by combustion phases with different particulate size distributions for smoldering and flaming combustion. For example, smoke particles are usually coarser in Indonesia than other regions due to a higher proportion of smoldering from peatland fires [33]. Particulate sizes in smoke aerosols affect scattering from incident radiation [34]. Hence, the smoke plume samples selected from these fire-prone regions should represent well the typical spectral characteristics of global fire smoke. Crop fires in Mainland Southeast Asia (such as Thailand) were not selected in the algorithm development because most of the smoke plumes there are so thin that they are easily affected by other cover types (i.e., mixed pixels).
# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 3 Detailed Description of the Proposed Method
### Point Cloud Tracking and Pose Optimization  
#### 3.3.1 Initial Value Calculation in the Point Cloud Coordinate System  
In Section 3.2.2, the relative motion relationship between the front and back moments in the left camera coordinate system is acquired. We can obtain the motion relationship in the point cloud coordinate system. Assume that \(\mathbf{R}_{\mathrm{c}},\mathbf{T}_{\mathrm{c}}\) is the rotation matrix and translation vector in the left camera coordinate system and \(\mathbf{R}_{\mathrm{cp}},\mathbf{T}_{\mathrm{cp}}\) is the rotation matrix and translation vector from the camera coordinate system to the point cloud coordinate system obtained in the joint calibration. For a point set \(\mathbf{M}\) in space, the following motion holds at time \(t\) and \(t+1\):  
\[M_{\mathrm{c}}^{t+1}=\mathbf{R}_{\mathrm{c}}M_{\mathrm{c}}^{t}+\mathbf{T}_{\mathrm{c}} \tag{16}\]  
\[M_{\mathrm{p}}^{t}=\mathbf{R}_{\mathrm{cp}}M_{\mathrm{c}}^{t}+\mathbf{T}_{\mathrm{cp}} \tag{17}\]  
\[M_{\mathrm{p}}^{t+1}=\mathbf{R}_{\mathrm{cp}}M_{\mathrm{c}}^{t+1}+\mathbf{T}_{\mathrm{cp}} \tag{18}\]Combined with the above formula, we obtain:  
\[M_{\rm P}^{i+1}=R_{\rm cp}R_{\rm c}R_{\rm cp}^{-1}M_{\rm p}^{i}+R_{\rm cp}(T_{ \rm c}-R_{\rm c}R_{\rm cp}^{-1}T_{\rm cp}) \tag{19}\]  
\[\left\{\begin{array}{c}R_{\rm p}=R_{\rm cp}R_{\rm c}R_{\rm cp}^{-1}\\ T_{\rm p}=R_{\rm cp}(T_{\rm c}-R_{\rm c}R_{\rm cp}^{-1}T_{\rm cp})\end{array}\right. \tag{20}\]  
where \(R_{\rm p},T_{\rm p}\) are the rotation matrix and translation vector in the point cloud coordinate system to be solved.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 5 Code Construction and Simulation Results
### Encoder  
First, a key \(s\) of 128 bits length is generated uniformly at random. The secret-key is then encoded using an error-correcting code, resulting in codeword \(c^{n}\). As the error-correcting code we use a CRC (cyclic-redundancy check) code concatenated with an LDPC code as defined in the 5G NR (fifth generation new radio) standard [26]. For our simulations, we use the LDPC coding-chain implementation from the MATLAB 5G Toolbox [27]. The codeword \(c^{n}\) is XORed with \(t\) SRAM-PUF observation vectors of length \(n\) and the results are added together, resulting in helper data \(m_{t}^{n}\). This concludes the encoder part of the scheme, where the helper data \(m_{t}^{n}\) is stored for later usage and all other variables are discarded.  
The secret-key rate of our implementation is  
\[R=\frac{128}{n}\quad\text{key bits per SRAM cell.} \tag{26}\]  
Furthermore, for \(t\) enrollment observations of \(n\) SRAM cells the required number of bits for storage of the helper data are \(n\lceil\log_{2}(t+1)\rceil\), and thus the helper data rate is  
\[R^{\text{hd}}=\frac{\lceil\log_{2}(t+1)\rceil}{R}\quad\text{helper data bits per key bit.} \tag{27}\]
###### Abstract  
The aim of this study was to evaluate and compare the effectiveness of three different intra-articular injective treatments: hyaluronic acid (HA), autologous conditioned serum (ACS) and bone marrow aspirate concentrate (BMAC) for the treatment of knee osteoarthritis (OA). A Level III retrospective comparative clinical study was performed on 505 consecutive patients treated with HA (\(n=171\)), ACS (\(n=222\)) or BMAC (\(n=112\)) for knee OA. The mean patient age was \(52\pm 13\) years; 54.5% were males. Collected data included patient demographics, symptoms, visual analogue scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and radiographic classification of osteoarthritis grade using plain radiographs and advanced imaging. Clinical outcome was assessed at 3 and 12 months post treatment. Significant improvement in VAS and WOMAC was seen for all three treatments at the 3-month follow-up. At 12 months, VAS was improved in all three treatment groups, yet only BMAC sustained the improved WOMAC even in patients with more severe degenerative changes. This study shows that BMAC is more effective than HA and ACS in the treatment of symptomatic knee OA, especially in the patients with more severe degenerative changes.  
osteoarthritis; knee; hyaluronic acid; autologous conditioned serum; bone marrow aspirate concentrate; clinical outcome
# Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs
## 4 Evaluation
### Number of Handovers  
We counted the total number of handovers that occurred with different values of \(v\). Reducing the number of handovers is one of the main contributions of the proposed scheme, as it can prevent control message flooding. The results are depicted in Figure 7. In Figure 7a, RND corresponds to the right \(y\)-axis, while the left \(y\)-axis is for the other algorithms. As it can be clearly seen in Figure 7a, when \(v\) is small, RND incurs a significant number of handovers overall. In fact, that of RND does not change much throughout simulations with different values of \(v\). This is because RND randomly chooses RRHs to associate with.  
On the other hand, the other algorithms show gradual change as \(v\) increases. Obviously, ALGO-2/3 caused the fewest handovers due to the \(\alpha\)-weighted penalty given to handovers. Moreover, ALGO-2/3 with the largest value of \(\alpha\) resulted in having the most handovers among the same algorithms with different values of \(\alpha\). This is because its penalty to handovers is the least. It is worth mentioning that ALGO-1 resulted in more handovers than SDF. As SDF associates IoT devices with the closest RRHs, it suppresses handovers to some degree. On the other hand, ALGO-1 does not have such function reducing handovers. As long as the object function results in the optimal solution, it does not matter which IoT devices associate with which RRHs, as discussed in Section 3 (Figure 2).  
Figure 6: Average fairness performance with respect to different number of IoT devices when \(v=20\). (**a**) Average maximum load; (**b**) Fairness performance.  
Figure 7: Total number of handovers with respect to different upper bounds on the speed of 50 mobile IoT devices. (**a**) When IoT devices are moving at a slow speed; (**b**) When IoT devices are moving at a fast speed.  
When there are more IoT devices in the network, both RND and ALGO-1 show an increasing trend in the number of handovers, yet that of ALGO-1 is much less than that of RND, as shown in Figure 8. On the other hand, ALGO-2/3 and SDF keep the number of handovers small and stable. It is worth noting that SDF yields a smaller number of handovers than the other algorithms in most cases. When the network becomes overloaded, the effect of maximizing the fairness performance at the expense of handovers becomes noticeable. However, ALGO-2/3 result in the the number of handovers comparable that of to SDF in many cases with large number of IoT devices.
# Is Malware Detection Needed for Android TV?
## 3 Methods
### Malicious Payload Injection  
We observed that all of the Android TV applications collected from the APKMirror market were labeled as benign based on the analysis results. In order to obtain TV malware, we injected a malicious payload into some benign TV applications and converted them into malicious TV applications. On the other hand, we kept some of the TV applications as benign.  
The Metasploit Framework [26], an open source penetration testing and development tool that identifies security vulnerabilities for many operating systems and platforms, was used in our study to obtain malicious applications. The malicious payload of Meterpreter in the Metasploit Framework is a command-line program that runs in the memory of the target device. It is possible to execute the desired commands on the target device using the Meterpreter payload. A malicious person can capture a screenshot of the TV  
Figure 3: VirusTotal scan results for a malicious TV application.  
Figure 2: VirusTotal scan results for a benign TV application.  
screen, take a picture via the webcam, record the audio, and run an application using the Meterpreter payload.  
Most of the Android TV devices on the market are not rooted out of the box, but attackers can exploit their vulnerabilities to gain root privileges. Meterpreter can be used with vulnerabilities like Dirty Cow, Zygote, or Stagefright in older Android versions to escalate privileges [27]. If attackers gain root access, they can modify system applications to include the Meterpreter payload and enable the malware to launch when a system application is launched. Attackers can set up scheduled tasks to invoke malicious code at specific intervals to ensure persistence and evasion. To make detection even more difficult, Meterpreter establishes reverse shell connections that allow the malicious payload to hide behind normal network traffic.  
In order to inject a malicious payload into a TV application, first, we generated malware including the Meterpreter payload using the Metasploit Framework [26]. It is possible to use 1 of 3 Meterpreter payload types, named reverse_http, reverse_https, and reverse_tcp, which use different connection protocols for the Android operating system. We also defined the local host and port numbers of our machine at this stage. Secondly, we decompiled a benign application to obtain its Smali code. Similarly, we decompiled the malware and obtained the Smali code for the payload. Thirdly, we copied the payload files into the benign application. Then, we injected the hook into the benign application by invoking the start command of the payload at the start of the benign application in the Smali file. Finally, we added the permissions needed by the malware into the Manifest file of the benign application and recompiled the application. As a result, we obtained a payload-injected malicious TV application. We repeated these steps to obtain more types of TV malware in our dataset. Since the Metasploit Framework [26] uses random characters in its functions and class names, the generated payloads are not identical.
# Design of a Broadband and High-Isolation CMOS Active Quasi-Circulator
## 4 Conclusions  
An idea for a compact design has been devised with the objective of creating an operational quasi-circulator by TSMC 90 nm CMOS process. By using a cascading structure, the main circuit consists of three CS FETs, which play a crucial role in delivering the active balun and phase inverter functionalities. The active quasi-circulator has both of these features, leading to excellent isolation of the S31 signal and enabling broadband operation. This article provides a thorough analysis that seeks to establish the relationship between \(g_{m1}\), \(g_{m2}\), and \(g_{m3}\) in order to obtain precise amplitude imbalance and isolation of S31. Based on the measured results, the created active quasi-circulator exhibits exceptional performance of wide operating band across C- to K band, good insertion loss about \(-10\) to \(-4.8\) dB S21 and \(-9.1\) to \(-3.4\) dB S32, and less power consumption of \(41.25\) mW while consuming a little chip space of \(0.37\) mm\({}^{2}\). This feature makes it a relatively efficient device for the integration of wireless communication networks.  
Conceptualization, S.T. and Y.J.; methodology, S.T.; software, X.L.; validation, X.L.,and J.G.; formal analysis, S.T.; investigation, J.G. and P.L.; resources, P.L.; data curation, X.L.; writing--original draft preparation, S.T. and X.L.; writing--review and editing, P.L. and J.H.; visualization, X.L.; supervision, Y.J.; project administration, S.T, and P.L.; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Wuxi University Basic Research Project 2022R016.  
The data that support the findings of this study are available from the corresponding author.  
Author [PERSON] is employed by the company Hongyuan Green Energy Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 AI-Based Solving Methods for V2G Optimization
### Agile Optimization  
V2G systems involve complex interactions between electric vehicles and the grid, considering factors such as varying energy demands, charging station capacities, fluctuating energy prices, and environmental considerations [37]. In this context, agile optimization (AO) can address these challenges inherent in V2G optimization, providing flexibility, parallelization capabilities, and the rapid generation of solutions for dynamic environments. Additionally, the ability of AO to operate effectively with minimal intensive parameters further enhances its applicability in optimizing V2G systems. AO is an optimization approach that combines the principles of heuristic algorithms with controlled randomness to address complex problems. In this method, a deterministic constructive heuristic is augmented with randomness introduced through skewed probability distributions [38]. This randomness allows for the exploration of diverse solution spaces without fundamentally altering the underlying logic of the heuristic. By incorporating a biased-randomized behavior, AO can efficiently generate high-quality solutions for dynamic optimization problems, such as those encountered in V2G systems. The biased-randomized heuristic in AO typically involves selecting building steps based on skewed probability distributions, where certain steps are favored over others [39]. This controlled randomness enables AO to adapt to changing conditions, explore alternative solutions, and effectively address the complexities of real-world optimization problems [40]. Figure 3 illustrates the AO framework. The main body of the AO algorithm is the circle representing the AO algorithm's iterative process. Within this circle, the constructive heuristic addresses the problem by employing biased-randomized techniques. In each iteration, the solution generated is evaluated.  
Figure 1: Simheuristic framework to address V2G optimization problems under uncertainty.  
Consequently, the algorithm generates multiple solutions at the same time. Ultimately, the best-found solution emerges as the output.