esa-sceva/satcom-chunk-collection huggingface dataset:
# Modifications to ArduSub That Improve BlueROV SITL Accuracy and Design of Hybrid Autopilot
## 2 ArduSub Alterations
### Changes to ArduSub and Motivation
#### 2.2.2 Implementation
This value is trimmed (line 16) and then converted to PWMs (line 18) to finally be sent to RC_Output. It is important to emphasize that while this new algorithm accounts for vehicle limitations, it depends on inputs from the hybrid autopilot that encode vehicle limitations as well. ```
Parameters:\(\mathbf{T_{8\times 6}^{*}},\mathbf{\gamma_{max,8\times 1}},\mathbf{PWM_{81\times 1}^{*}},\mathbf{t_{81 \times 2}}\) Input:\(\mathbf{u_{6\times 1}}\) Output:\(\mathbf{m_{8\times 1}^{*}}\)
1 Functionmotor_command_new(\(\mathbf{u},\mathbf{T^{\dagger}},\mathbf{\tau_{max}}\)): for\(i\gets 1\)to\(\mathbf{8}\)do\(m_{i}=\sum_{j=1}^{6}(\mathbf{T^{\dagger}}_{i,j}\cdot\mathbf{\tau_{max,j}}\cdot u_{i}))\);\(\mathit{lowlight}\) = find_bounding_indices(\(m_{i}\)); if\(\mathit{low}=\mathit{n}_{\mathit{high}}\)thenif\(m_{i}<0\)then\(m_{i}^{*}=-1.0\)
2 end if
3 elseif\(m_{i}>0\)then\(m_{i}^{*}=1.0\)
4 end if
5
6 end for
7
8 else
9\(m_{i}^{*}=PWM_{\mathit{low}}^{*}+\frac{PWM_{\mathit{high}}^{*}-PWM_{ \mathit{low}}^{*}}{\mathit{t}_{\mathit{high}}-\mathit{t}_{\mathit{low}}}(m_{i} -\mathit{t}_{\mathit{low}})\);
10 end if
11\(\text{constrain($m_{i}\in[-1.0,1.0]$)}\);\(\widehat{m}_{i}=1500+400m_{i}^{*}\);
12 end for
13 send \(\widehat{\mathbf{m}}\) to RC_Output channels;
```  
**Algorithm 2**New Manual Control Algorithm  
It is important to emphasize that while this new algorithm accounts for vehicle limitations, it depends on inputs from the hybrid autopilot that encode vehicle limitations as well. Figure 6: Blue Robotics experimental test results of pulse width modulation (PWM) input and resulting thrust for a single T200 thruster.
# Evaluation of Antioxidant and Anticorrosive Activities of _Ceriops_ _tagal_ Plant Extract
## 2 Materials and Methods
### Antioxidant Studies
#### 2.4.1 DPPH Free-Radical Scavenging Assay  
For the DPPH free-radical scavenging assay, 0.2 mL of 10-100 \(\mu\)g/mL _C. tagal_ extract was mixed with 2 mL of 0.5 mM 1,1-diphenyl-2-picrylhydrazyl (DPPH) prepared in methanol. With this, a blank containing only methanol was prepared. These were kept in the dark for 30 min. Absorbance was measured for test samples against a blank at 517 nm and the percentage of free radical scavenged was calculated using the following equation:  
\[\text{Inhibition of DPPH radical (\%)=(A}_{\text{control}}-\text{A}_{\text{sample}})/ \text{A}_{\text{control}}\times 100 \tag{1}\]  
Here, control absorbance, i.e., DPPH without sample is denoted as Acontrol and sample with DPPH as Asample.
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 6 Managerial Implications and Conclusions  
### Managerial Implications  
From the analytical results, we arrive at managerial insights for SOs regarding how to choose between the three channels (traditional, SataaS, and dual-channel) and when to use BCT. For the SO, the choice of channel may depend on the expected launch success rate. When th launch success rate is high, traditional sales channels may be more profitable. When it is lower, the SO should consider diversifying into SataaS or a dual-channel approach. As SataaS is an emerging business channel, whether to adopt this new channel depends on the fixed costs related to providing SataaS. If these costs are relatively low, SataaS can be a viable channel. Conversely, if the fixed costs are high, the SO should consider other options.  
Furthermore, though blockchain technology provides a method of hedging risks, it cannot be used unconditionally. The SO should adopt BCT for different channels and within the corresponding blockchain cost threshold to achieve benefits. Otherwise, using BCT will lead to a decrease in income.
## 1 Introduction
Sentiment analysis calculates the sentiment intensity of each aspect of the reviews and builds the sentiment vector with the neural network method. The semantic analysis uses hierarchical attention networks (HAN) [15] to calculate word and sentence attention in user and product reviews. The user and product aspect semantic vectors are generated based on the weights generated. In this study, user latent factors and product latent factors were generated by integrating the aspect, semantic, and sentiment vectors of users and products, and then by adopting the convolutional neural networks (CNN) method. Finally, matrix factorization (MF) [16] was leveraged to predict products that might be of interest to users in the future. The experimental results show that this study features the best prediction accuracy by using aspect, aspect sentiment, and aspect semantic features for rating prediction. Additionally, applying the deep learning method in making recommendations also leads to better performance and accuracy compared to other methods. In summary, the main contributions of this work include the following. * We propose a novel ADLRP method herein, which could effectively extract the aspect, sentiment, and semantic features from text reviews and combine these features with ratings for rating prediction. Our method integrates both implicit and explicit information to analyze user preferences and product features, and thus achieves better predictive performance. * A blend of features can achieve more accurate user preferences and product evaluation features, thereby enabling merchants to better understand users' preferences and provide them with more accurate product recommendations. * The deep learning methods and attention mechanism used in our method can effectively extract and train the user and product features to improve the accuracy of rating prediction. This paper comprises five sections. Section 2 explores the literature on deep learning, semantic and sentiment analysis, and matrix factorization. Section 3 introduces the proposedmethod, i.e., ADLRP. Section 4 presents the experimental results and discussion of this study and compares it with other related research methods. Section 5 concludes the study.
## 3 Discussion
ETS-1 is linked to liver fibrosis, where it is thought to be activated by Extracellular Signal-Regulated Kinases (ERK) signalling pathways, matrix remodelling, and cross talk with Transforming Growth Factor (TGF)-Suppressor of Mothers Against Decapentaplegic (SMAD) signalling [47]. Interestingly, there is evidence of significant cross-talk with regard to these transcription factors. For example, ETS-1 and AML-1 can mutually activate DNA binding through the intermolecular interaction of autoimmunity domains [48], while TAL1 and LMO2 function as complexes, and TAL1, LMO2, and AML-1 (Runx1) are essential for the development of hematopoietic stem cells [49]. It is also notable that these transcription factors are associated with leukaemia and that our THP-1 model is of leukaemia origin. To our knowledge, this is the first evidence suggesting the involvement of any of these five transcription factors in the cellular response to hypercapnia. Of these transcription factors, we focused on ETS-1, based on its association with all nine genes in Figure 4D. We examined ETS-1 and Lamin in nuclear lysates derived from monocytes exposed to buffered hypercapnia for 24 h. Nuclear lamin levels were relatively unchanged in this model, but ETS-1 protein expression in the nucleus was enhanced in hypercapnia. ETS-1 has a predicted molecular weight (MW) of 52 kilodaltons (kD), which is the molecular weight at which we observe our most intense bands using Western blot. We also observe some lower molecular weight bands (with the same pattern of CO\({}_{2}\)-sensitivity), which may be ETS-1 splice variants. p42 and p27 variants of ETS-1 have been described previously [50] and correspond well with the observed molecular weights of the two lower MW bands observed by Western blot (Figure 5A). In conclusion, our study indicates that _NR4A2_ and NR4A3 are CO\({}_{2}\)-responsive transcription factors, as the kinetics of their expression in response to LPS is clearly shifted by exposure to buffered hypercapnia. Furthermore, depletion of _NR4A2_ and _NR4A3_ led to reduced CO\({}_{2}\)-sensitivity of mitochondrial genes and Hsp-related genes, respectively. However, many other CO\({}_{2}\)-sensitive genes were refractory to depletion of _NR4A2_ and _NR4A3_, indicating that the NR4 As regulate selective components of the cellular response to buffered hypercapnia but that other transcription factors also contribute. Bioinformatic analysis of conserved CO\({}_{2}\)-sensitive genes implicated several novel putative CO\({}_{2}\)-sensitive transcription factors (AML1, SOX17, TAL1, ETS1, and LMO2), of which ETS-1 was validated to show increased nuclear expression in buffered hypercapnia. Taken together, these data represent a significant resource relating to CO\({}_{2}\)-dependent transcriptional regulation in monocytes and give important insights into the understanding of immune responses in patients experiencing hypercapnia.
# Review of the Research Progress in Combat Simulation Software
## 5 Development Trends of Combat Simulation Software
### Combat Simulation Software Will Provide a Much More User-Friendly Experience  
In recent years, emerging computer game rendering, extended reality, and other technologies [50] have been continuously applied to the development of combat simulation software. With the help of 3D animation effects and real-time interactive functions, participants can experience a realistic battlefield environment through multiple senses, realize immersive drills, and improve technical and tactical capabilities. The United States Army has conducted a lot of work in the development of the application of a game-based simulation training system. For instance, VIRTSIM [51] is a highly immersive virtual reality system, which can pit squads of players against each other in a realistic virtual reality combat environment, and Virtual Battlespace 3 [52] can support a variety of training objectives, including terrain reconnaissance, convoy operations, counter IED, and fire support.  
On the other hand, military games [53; 54; 55; 56] are becoming more and more practical and perform as new platforms for combat simulation. For example, the COMMAND [53] series game developed by Warfare Sims has been highly valued by the military and defense agencies of many countries. It develops expansion packages based on the changing political and military activities. For example, the \"Chains of War battlefield\" package is based on hypothetical conflicts between China, the USA, and their respective allies in the future.
# A Throughput and Energy Efficiency Scheme for Unlicensed Massive Machine Type Communications +
###### Abstract  
In this paper, the throughput and energy efficiency of an unlicensed machine type communications network is studied. If an outage event happens in the network, there is a possibility for packet retransmission in order to obtain a lower error probability. The concept of spectrum sharing is used here for modeling the network, which allows the two types of licensed and unlicensed users to share the same uplink channel allocated to the licensed users. However, it is done in a way that no harm is done to the licensed nodes' transmission for sharing the same channel with the unlicensed users, while licensed nodes' transmission causes interference on the unlicensed network. Poisson point process is used here to model the location of the nodes and the effect of interference on the network. We study how different factors such as the number of retransmissions, SIR threshold and outage can affect the throughput and energy efficiency of the network. Throughput and energy efficiency are also both studied in constrained optimization problems where the constraints are the SIR threshold and the number of retransmission attempts. We also show why it is important to use limited transmissions and what are the benefits.  
energy efficiency; massive machine type communications; poisson point process; spectrum sharing; unlicensed spectrum access +
Footnote †: Received: 19 February 2020; Accepted: 18 April 2020 ; Published: 21 April 2020
## 3 Prediction Models
### Random Forest (RF)  
Simply stated, RF consists of many Decision Trees, and the target output is predicted by considering either the average of the DIs' predicted values or the most voted value [45; 46]. To be more precise, RF is basically the combination of Bagging and the Random selection of features via the creation of various Decision Trees. [PERSON] primarily developed this method in 1995 by presenting an algorithm for random decision forests. In another research work, [PERSON] introduced an algorithm using [PERSON]'s bagging idea and the random selection of features, which was proposed by [PERSON] and [PERSON] and [PERSON] [47; 48; 49; 50]. The RF method has  
Figure 4: Histograms illustrating the distribution of the input and output variables.  
many advantages compared to other ML-based methods; the most remarkable one is its highly accurate results when a large database is used for training. Furthermore, it is simple and fast to implement [43].  
The most important parameter that should be chosen efficiently in an RF model is the number of trees. Figure 5 demonstrates the number of trees against the R\({}^{2}\)-score values for the predicted values. As can be observed in Figure 5, by increasing the number of trees up to approximately 35, the accuracy of the model increased considerably. The accuracy of the models with 35-250 trees increased insignificantly. The R\({}^{2}\)-score of the models with more than 250 trees remained almost unchanged. The optimal number of trees was 367, which is shown by a red point in Figure 5.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 3 Underwater SLAM: Background and Overview
### Underwater SLAM Challenges  
Underwater SLAM faces unique challenges due to the harsh and dynamic nature of aquatic environments. Limited visibility, caused by suspended particles, turbidity, and unpredictable lighting, significantly hinders feature extraction and matching, which are critical for SLAM algorithms [27; 28]. Additionally, dynamic elements such as marine life and water currents introduce noise and distortions, further complicating sensor data interpretation [29].  
Underwater communication is another major challenge, as acoustic signals suffer from latency, limited bandwidth, and multipath effects, making real-time data exchange difficult [30]. Computational constraints also play a role, as AUVs must operate with limited processing power and energy resources, necessitating efficient algorithms for real-time SLAM [31]. These challenges collectively make underwater SLAM a complex problem, requiring specialized solutions beyond those used in terrestrial environments.
## 4 Numerical Simulation  
### The Structural FE Model  
The accuracy of the proposed method is verified by a FE model of an eight-story steel frame structure, as shown in Figure 3. The structural height of each story is 3 m, with a total of five spans, and each span is 5 m. The material density is 7850 kg/m\({}^{3}\), and the elastic modulus is 210 GPa. The cross-sectional area of the beam and column is 0.01 m\({}^{2}\), and the section moment of inertia is 8.33 \(\times\) 10\({}^{-5}\) m\({}^{4}\). Each beam and column of the structure is a unit, and the structural FE model has 144 DOFs in total. Rayleigh damping is adopted for the structure; the first two orders of damping ratio are both 0.01. The FE model is established using MATLAB.  
As shown in Figure 3, six columns and five beams of each story are treated as a substructure, and the substructures are denoted by S\({}_{1}\)-S\({}_{8}\) and counted from the bottom. Considering that only a small part of the structure is usually damaged, two of the eight substructures are assumed to be damaged in this paper. It is assumed that the substructures S\({}_{2}\) and S\({}_{5}\) are damaged with the damage factors of 0.6 and 0.7, respectively. In order to verify the validity of the proposed method, all eight damage factors are optimized for identification. If the identified damage factor of the substructure is less than 1,the damage of the substructure can be recognized; if the damage factor is equal to 1, the substructure is undamaged.
## 2 Materials and Methods  
### Study Site  
Lake Ontario, the smallest and most easterly of the Great Lakes, ranks as the 13 th largest lake in the world [35]. It has a drainage area of around 64,000 km\({}^{2}\), a surface area of 19,000 km\({}^{2}\), and a total volume of 1650 km\({}^{3}\). The lake's mean depth is 87 m, the maximum depth 244 m. The water residence time is approximately 6-8 years, and the lake's shoreline extends over 1150 km. Our study focused on the western basin of Lake Ontario, abbreviated WLO, which represents a critical water resource for an estimated nine million people [9]. WLO covers about one-third of the entire surface area of Lake Ontario (Figure 1). Unlike for upstream Lake Erie, whole-lake assessments of RS-derived algal abundance remain limited for WLO. Also included in our study is Hamilton Harbour (HH), a 20 km\({}^{2}\) embayment at the western tip of WLO connected to the lake via a shipping channel. Since 1987, HH has been designated an Area of Concern under the Canada-US Great Lakes Water Quality Agreement. As a result, HH has a much higher density of water quality monitoring stations compared to WLO as can be seen in Figure 1.  
Historically, WLO has experienced water quality issues due to both urban and rural non-point sources of nutrients and contaminants delivered to the lake by streams and storm sewers, as well as from upstream Lake Erie via the Niagara River [36]. The lake features mesotrophic to eutrophic nearshore zones and an oligotrophic offshore core [37]. In recent years, the total phosphorus (TP) concentration in the lake ranges on average from 6 to 8 \(\mu\)g/L, while the Chl-\(a\) concentration varies between 0 and 17 \(\mu\)g/L, depending on the location [38; 39]. As expected, HH exhibits significantly higher TP and Chl-\(a\) concentrations, with Chl-\(a\) often exceeding 10 \(\mu\)g/L, that is, the threshold value commonly used to define an algal bloom [40].  
The primary drivers of eutrophication in WLO and HH include rapid urbanization, and ecosystem and climate change [6; 7; 37], while invasive deHeissenid mussels transform phosphorus into more readily bioavailable forms that promote algae growth [6; 37; 41]. The onset, duration, and intensity of algal growth are further influenced by environmental factors such as temperature, sunlight, and wind mixing [4; 40]. The harmful effects of eutrophication in the region include water supply issues, human and pet health risks, diminished aquatic life, and economic impacts on tourism, recreational activities, and lakefront property value [2].
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 2 Problem Description
### Attitude Transition Time Constraint
(**a**) shows that task OW is satisfied with the constraint, (**b**) demonstrates that task OW is not satisfied with the constraint, and (**c**) represents the situation where the linear approximation method can be used. ```
0: the end time of current task \(te_{i}\), the VTW of the latter task \(\left[wb_{j},we_{j}\right]\), the maximum number of iterations \(NumIter\), calculation time accuracy \(prc\)
0: the earliest start time of the latter task \(t_{m}\)
1:\(h_{1}=\mathrm{tidy}\big{(}te_{i},wb_{j}\big{)}\)
2:if\(h_{1}\leq 0\)then
3:return\(wb_{j}\) //Observing at the earliest visible time of latter tasks
4:endif
5:\(h_{2}=\mathrm{tidy}\big{(}te_{i},we_{j}\big{)}\)
6:if\(h_{1}>0\)then
7:return\(+\infty\) //The attitude transition cannot be completed in the entire window
8:endif
9:for\(j=1\) to \(NumIter\)do
10:\(t_{m}=\left(h_{2}\cdot wb_{j}-h_{1}\cdot we_{j}\right)/(h_{2}-h_{1})\)
11:\(h_{m}=\mathrm{tidy}\big{(}te_{i},t_{m}\big{)}\)
12:if\(\left|h_{m}\right|<prc\)then
13:return\(t_{m}\)
14:endif
15:if\(h_{m}>0\)then
16:\(wb_{j}=t_{m}\)
17:\(h_{1}=h_{m}\)
18:else
19:\(we_{j}=t_{m}\)
20:\(h_{2}=h_{m}\)
21:endif
22:endfor
23:return\(we_{j}\)
```  
**Algorithm 1** EarliestImageCal
## 2 Methods
### Mathematical Formulations
#### 2.2.3 Valve-Pump Parallel Control Motor Link  
The continuity equation for the whole system is  
\[q_{p}-q_{v}=C_{\text{m}}P_{h}+D_{\text{m}}\omega+V_{0}sP_{h}/\beta_{e} \tag{7}\]  
where \(\omega\) is the motor angular speed, \(C_{\text{m}}\) is the motor leakage coefficient, \(D_{\text{m}}\) is the motor displacement and \(V_{0}\) is the average volume of high-pressure chamber.  
The torque balance equation for the hydraulic motor is  
\[D_{\text{m}}P_{h}=Js\omega+B_{m}\omega+T_{L} \tag{8}\]  
where \(J\) is the equivalent total inertia, \(B_{m}\) is the viscous damping coefficient, \(T_{L}=D_{L}P_{h}\) is the load torque produced by the loading pump (ignoring the low-pressure chamber pressure) and \(D_{L}=D_{m}\) is the displacement of the loading pump.  
Combining (3), (5), (6), (7) and (8), the open loop dynamic equation for the LVPC [16] is given by:  
\[\omega=\frac{\frac{K_{p}q_{p0}-K_{q0}\omega}{D_{\text{m}}}-\frac{C_{l}}{D_{m} ^{2}}\bigg{(}1+\frac{s}{2\omega_{l}\xi_{l}}\bigg{)}T_{L}}{\frac{s^{2}}{\omega_ {l}{}^{2}}+\frac{2\xi_{l}}{\omega_{l}}s+1} \tag{9}\]  
where \(C_{l}=C_{p}+C  
When the PTV is closed and \(K_{v}=0,\ K_{p}=1,\) such that \(K_{vp}=0\ and\ q_{q;0}\) and \(K_{c}\) will be zero in (9), the system control mode will change from LVPC to VSVC and its open loop dynamic equation [17] is  
\[\omega=\frac{\frac{q_{q;0}}{D_{m}}-\frac{C_{t}}{D_{m}{}^{2}}\left(1+\frac{s}{2 \omega_{m}\xi_{m}}\right)T_{L}}{\frac{s^{2}}{\omega_{m}^{2}}+\frac{2\xi_{m}^{ 2}}{\omega_{m}}s+1} \tag{10}\]  
where \(C_{t}=C_{p}+C_{m},\ \omega_{l}=\sqrt{\frac{\xi_{l}D_{m}{}^{2}}{V_{0}\ J}},\ \xi_{m}=\frac{C_{t}}{2D_{m}}\sqrt{\frac{\xi_{l}}{V_{0}}},\ C_{m},\ \xi_{m}\) and \(\omega_{m}\) are the total leakage coefficient, damping ratio and hydraulic natural frequency of the VSVC system respectively. Equations (9) and (10) indicate that a VSVC may be considered a special case of an LVPC. Notice that the linearized Equations 9 and 10 are valid around a specific operating point, for the real behavior is strongly nonlinear. Moreover, in these equations, the natural frequency can be computed with fair confidence but the damping ratio especially \(\xi_{l}\) is much more variable and nebulous.  
The rotary component is simplified as a proportional element due to its fast response:  
\[K_{m}=\frac{u_{m}}{n_{m}} \tag{11}\]  
where \(u_{m}\) is the feedback voltage, \(K_{m}\) is the feedback gain and \(n_{m}=\ 30\ \omega/\pi\) is the motor's angular velocity.