esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Variable** & **Equation or Value** & **Units** \\ \hline Adjusted production rate & IF THEN ELSE (\"Use DNN?\" = 1, MIN (Capacity, DNN predictions), MIN (Capacity, Judgmental production rate)) & Pcs/Month \\ \hline Backlog & INTEG (Sales orders \(-\) Fulfillment rate \(-\) Lost sales, 0) & Pcs \\ \hline Capacity & INTEG (Capacity augmentation rate \(-\) Capacity normalization rate, Normal capacity) & Pcs/Month \\ \hline Capacity augmentation rate & IF THEN ELSE (Delivery lead time overshoot \(>\) 0, Capacity flexibility/Capacity ramp-up time, 0) & Pcs/Month \(\times\) Month) \\ \hline Capacity flexibility & Maximum capacity \(-\) Capacity & Pcs/Month \\ \hline Capacity normalization rate & IF THEN ELSE (Delivery lead time overshoot \(>\) 0, 0, Excess capacity/Capacity ramp-down time) & Pcs/Month \(\times\) Month) \\ \hline Capacity ramp-down time & 2 (base case); 4 (heuristic adjustment) & Month \\ \hline Capacity ramp-up time & 2 & Month \\ \hline Delay tolerance & 2 & Month \\ \hline Delivery lead time goal & 1 & Month \\ \hline Delivery lead time outlook & IF THEN ELSE (Backlog \(=\) 0, 1, Backlog/Fulfillment rate) & Month \\ \hline Delivery lead time overshoot & MAX(0, Delivery lead time outlook \(-\) Delivery lead time goal) & Month \\ \hline Discrepancy with actual returns & Returns time series (Time/One month) \(-\) Perception of returns & Pcs/Month \\ \hline Discrepancy with actual sales & Order time series (Time/One month) \(-\) Perception of sales & Pcs/Month \\ \hline Discretionary capacity & 0.2 & Dmnl \\ \hline DNN predictions & DNN sales predictions (Time/One month) \(-\) DNN returns predictions & Pcs/Month \\ \hline DNN returns predictions & The result of RNN returns forecast is programmatically fed. & Pcs/Month \\ \hline DNN sales predictions & The result of RNN sales forecast is programmatically fed. & Pcs/Month \\ \hline Excess capacity & MAX(0, Capacity \(-\) Normal capacity) & Pcs/Month \\ \hline FINAL TIME & 96 & Month \\ \hline Fulfillment rate & MIN(Backlog, Inventory)/One month & Pcs/Month \\ \hline INITIAL TIME & 1 & Month \\ \hline Inventory & INTEG (Production rate \(+\) Return orders \(-\) Shipment rate, 0) & Pcs \\ \hline Judgmental production rate & Perception of sales \(-\) Perception of returns & Pcs/Month \\ \hline Lost sales & (Delivery lead time overshoot \(\times\) Fulfillment rate)/Delay tolerance & Pcs/Month \\ \hline Maximum capacity & Normal capacity \(\times\) (1 \(+\) Discretionary capacity) & Pcs/Month \\ \hline Net requirements & Sales orders \(-\) Return orders & Pcs/Month \\ \hline Normal capacity & 55 & Pcs/Month \\ \hline One month & 1 & Month \\ \hline Order time series & Test dataset for actual customer orders. & Pcs/Month \\ \hline \hline \end{tabular}   
**Table A2. ** System dynamics parameters.
## 3 Methodology  
### Dqn  
Reinforcement learning is employed for agents to learn how to find the optimal set of actions in decision-making problems under uncertain environments. The agent achieves transitions between environmental states by taking actions at discrete time steps: when an action is executed, the environment produces a reward and transitions to a new state, formalized as a MDP. The agent's actions are formalized as a policy \(\pi\), which is represented by the value function \(V^{\pi}\). The formula is as follows:  
\[V^{\pi}(s)=E[\sum_{t=0}^{\infty}\gamma^{t}R(s_{t},a_{t})|s_{0}=s,s_{t},a_{t}]= E_{a\sim\pi(s)}[R(s,a)+\gamma E_{s^{\prime}\sim P(s_{d})}V^{\pi}(s^{\prime})] \tag{14}\]  
Here, \(s_{t}\sim p(s_{t-1},a_{t-1})\) and \(a_{t}\sim\pi(s_{t})\), respectively, with the latter being the Bellman recursive equation. The optimal policy is derived by calculating \(Q\)-values for each state-action pair and applying the Bellman equation, then using a greedy strategy to choose the action with the highest \(Q\)-value.  
\[Q*(s,a)=E[r+\gamma\underset{a^{\prime}}{max}\;Q*(s^{\prime},a^{\prime})|s,a\,] \tag{15}\]  
DQN [47] combines \(Q\)-learning with deep networks, with key techniques being experience replay and target networks:  
* Setting up two neural networks can improve learning stability: one for determining actual values (main network) and one for predicting future values (target network). The target network is represented by a feedforward neural network consisting of three convolutional layers and two fully connected layers.
* A replay buffer D is set up to store experiences. Random sampling experiences for training can improve data efficiency and reduce the correlation between samples.
## Finite Element Models  
### General Description  
To accurately simulate the behavior of frames with L-shaped CFST columns connecting to steel beams, the main factors of the frame models, including the core concrete of L-shape CFST columns, the steel tube and its interfacial contact with concrete, the columns and their connection with steel beams, and L-shaped inner diaphragm separately, were considered and modeled. Finite element analysis software ABAQUS was adopted in this paper. To study the behavior of the L-shaped CFST frames accurately and reasonably, some key issues that need to be solved are as follows: constitutive relation model of steel and core concrete, element type of steel tube and concrete, mesh size, interface and contact model between steel tube and core concrete, boundary conditions, loading criterion, convergence criterion, and the solution.  
The span of frames was 1.8 m, the story height of frames was 1.2 m, the width of the frame columns section was 160 mm and the thickness of the steel tube was 4 mm. The size of H-type steel beam was 120 mm \(\times\) 80 mm \(\times\) 4 mm \(\times\) 4 mm and the thickness of floor slab distributed with \(\Phi\)10@100 steel meshes was 50 mm.
## 5 Simulation and Analysis
### Maneuvering Strategy Training and Testing
#### 5.2.3. PRED-RS-MADDPG Strategy vs. RS-MADDPG Strategy
Regardless of the initial air combat situations, PRED-RS-MADDPG strategy wins air combats with a high probability, thereby indicating that an air combat strategy with target maneuver prediction is significantly better than the one that does not utilize target maneuver prediction, as can be seen from Figure 13 and Table 3. When the strategy is integrated with the target maneuver prediction method, the total probability of winning reaches 78.5%.
# Dynamics of Polar Resonances and Their Effects on Kozai-Lidov Mechanism
## 3 Descriptions Concerning the Numerical Model  
Numerous simulations are carried out to study the polar resonant dynamics of CRTBP and verify the semi-analytical model results. For a particular \(k^{\prime}/k\) resonance, we randomly generate 5000 test particles at an exact resonant location with initial orbital elements \(a=a_{0}\), \(i=90^{\circ}\), \(0\leq e\leq 1,\Omega=0^{\circ},0\leq\omega,M\leq 360\). The elements of the Jupiter-mass perturber are taken as \(\mu=10^{-3}\), \(a^{\prime}=1\) au, \(i^{\prime}=0^{\circ}\), \(e^{\prime}=0\), \(\Omega^{\prime},\omega^{\prime},M^{\prime}=0\). Numerical integrations are carried out by the MERCURY6 [87] package with an accuracy parameter of \(10^{-12}\). The general Bulirsch-Stoer integrator is employed while considering the gravity of the Sun and the perturber throughout the integration. The whole integration time span is 10,000 perturber's periods with a time step of 15 days. Particles are hitting the planet or those whose heliocentric distance below the radius of the Sun or above 100 au will be removed from the integrations. We analyze the dynamical evolution of these 5000 particles at different polar resonances.  
Based on the semi-analytic model and numerical simulations, we investigate the dynamics of polar resonance and the Kozai-Lidov mechanism inside MMRs. We study the interior resonance and exterior resonance, by taking the cases of representative \(2/1\) and \(2/3\) resonances, respectively.
## 1 Introduction
Unlike the current conventional off-grid or grid-free methods, the method proposed in this paper is a new strategy used to solve the problem of algorithm performance degradation when the number of grid points is significant. The main contributions of this paper are as follows. (1) Initially, relatively sparse computational grids are established, and a statistical model describing the radiated sound field is utilized. Prior information and posterior probabilities are integrated to provide the initial values of source strength obtained from the sparse Bayesian learning. Subsequently, the previously established computational grid model is iteratively adjusted, retaining computation grids with higher weights. Fission updates are performed in their vicinity, gradually shrinking the discrete region, increasing grid density, and updating source strength values until the grid is fine enough. During the iteration process, low-rank sparse decomposition is used simultaneously to eliminate the influence of noise. This method is called VG-SBL. (2) Simulation and wind tunnel experiments verify the superiority of the proposed method in low-frequency and high-resolution sound source identification. In particular, the proposed method can generate the most accurate and clear imaging maps compared with the traditional methods in the case of far measurement distances, low source frequencies, close source spacing, and high dynamic ranges, thus showing the robustness of the method and its potential for application in wind tunnel research. The rest of the paper is organized as follows. In Section 2, the sound forward propagation model is presented. In Section 3, the principle of the VG-SBL method is presented. Simulations are performed in Section 4. Section 5 comprises the analysis of measured wind tunnel data. Finally, Section 6 concludes this paper.
## 4 Results and Discussion
### Validation with Observed PAR Data
As an example, Figure 10 shows the mean FPAR and the temporal profile of AC between paired datasets for the shrub and evergreen needleleaf forest land cover. Figure 10: Temporal profiles of shrub and evergreen needleleaf forest land cover classes over the Heihe River Basin. (**a**) mean FPAR values of inversed FPAR by the DnD model and the MODIS FPAR product of shrub land cover; (**b**) mean FPAR values of inversed FPAR by the DnD model and the MODIS FPAR product of evergreen needleleaf forest land cover; (**c**) agreement coefficient (AC) of shrub land cover; (**d**) AC of needleleaf forest land cover. Same as the mean of FPAR, AC also has seasonality for the shrub and needleleaf forest land cover. There is minimum agreement between inversed FPAR and the MODIS FPAR product in wintertime (November to February). When the FPAR value becomes smaller, the AC between two datasets is smaller. The MODIS FPAR is higher than inversed FPAR, especially in wintertime for the forest land cover (Figure 10a,b). This shows the difference between the DnD model and MODIS FPAR method. In winter, vegetation is sparse; there is a greater chance of direct light passing through the vegetation to reach the land surface. The impact of changes in the diffuse PAR proportion to the total FPAR is more obvious. In summer, the FPAR approaches saturation, and the difference between the two algorithms is also smaller. Figure 11 shows the the temporal AC for Heihe River Basin. A high agreement is found in the southern part of the region, where vegetation has a distinct seasonality. Otherwise, in the northern part of the region, there is a low temporal correlation between the two datasets for the desert and Gobi land cover. The FPAR value is zero or very low for the whole year.
## 6 Discussion  
### Mobile Sink Advantage  
In fixed sink node networks, as sinks are always away from sources, the transmission paths from areas of interest to a sink node often form certain multihop routing paths. However, in this kind of routing path, the sensors close to the sink exhaust their energy very fast. In the calculation, sensors nodes located the furtherest from the sink have 90% residual energy when the one-hop neighbor nodes drain their energy out. Finally, this uneven energy consumption will lead to energy holes, area isolation, high transmission latency, and data inaccuracy.  
With the aim to improve network performance, recent research has exploited mobile sinks. By introducing mobile sinks into sensor networks, optimization of energy efficiency, lifetime, and peer-to-peer delay can be achieved. Besides, with implemented mobile sinks, network isolation can be effectively mitigated. To realize mobile sinks in real-world implementation, special devices like gateways can be attached on taxis, animals, and humans. In this paper, one mobile sink with a trajectory along the central line of a rectangular region is proposed.
# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
###### Abstract  
Industries are rapidly moving toward mitigating errors and manual interventions by automating their process. The same motivation is carried out in this research which targets to study a conveyor system installed in soda ash manufacturing plants. Our aim is to automate the determination of optimal parameters, which are chosen by identifying the flow rate of the materials available on the conveyor belt for maintaining the ratio between raw materials being carried. The ratio is essential to produce 40% pure carbon dioxide gas needed for soda ash production. A visual sensor mounted on the conveyor belt is used to estimate the flow rate of the raw materials. After selecting the region of interest, a segmentation algorithm is defined based on a voting-based technique to segment the most confident region. Moments and contour features are extracted and passed to machine learning algorithms to estimate the flow rate of different experiments. An in-depth analysis is completed on various techniques and convincing results are achieved on the final data split with the best parameters using the Bagging regressor. Each step of the process is made resilient enough to work in a challenging environment even if the belt is placed in an outdoor environment. The proposed solution caters to the current challenges and serves as a practical solution for estimating material flow without manual intervention.  
automation; industrial process; conveyor belt; soda ash; machine learning; convolutional neural networks; computer aided design (CAD) 2023
# Enabling Multi-Mission Interoperable UAS Using Data-Centric Communications
## 3 The Data Distribution Service Open Standard in UAS Environments
### Efficient Delivery to Multiple Destinations  
A very important benefit of DDS named data exploitation for interoperable UAS applications is the possibility to easily exploit efficient delivery to multiple destinations, optimized not only in terms of consumed communication resources, but also achieved QoS, e.g., latency, bandwidth, and reliability. More traditionally, in DDS, this is based on transparent and efficient exploitation of multicast in LAN environments (for instance, for interoperable inter-GCS coordination), with the consequent scalability improvements. However, some emerging DDS solutions [18; 19] employ optimized and proprietary pluggable transport protocols for efficient delivery to multiple destinationsalso in geographic IP-based deployment environments, where the availability of multicast support is often unrealistic. This is relevant for efficiency and scalability motivations, but also to simplify the development and reduce the costs of UAS applications, thanks to the fact that group communication semantic is transparently managed by the DDS middleware, with no impact on the complexity of business logics when integrating complex multi-vendor sub-systems.
# Technology Enhanced Learning Using Humanoid Robots
## 5 Experimental Results
### Architecture Evaluation  
The software architecture of a program or computing system is the structure or structures of the system, which comprise software components, the externally visible properties of those components, and the relationships among them [21].  
Software architecture evaluation is built around a suite of three methods, all developed at the Software Engineering Institute: ATAM (Architecture Tradeoff Analysis Method) [22], SAAM (Software Architecture Analysis Method) [23], and ARID (Active Reviews for Intermediate Designs) [24]. Other methods created from their extension or mixing have appeared in the literature as well [25]. We have initially developed our architecture on a MacOS with 8 GB RAM and 2,2 GHz Dual-Core Intel Core i7 using Choregraphe 2.1.4, connected to a hot-spot. Within the same hot-spot, the Zora robot was also connected. Two Amazon EC2 instances have been employed to run the modules. Python has been the adopted programming language (for CCs and SSSCs). Given the early development stage of our architecture, for our evaluation, we have chosen to use SAAM. In fact, it was first proposed in 1994 with the basic goal to analyze system qualities early in the life cycle of the software architecture. This allows for a comparison of architectural options. Among the benefits that SAAM provides we have: (i) Potential problems can be detected in the early phase; (ii) the documentation can be improved and the understanding related to software architecture problems can be enhanced; (iii) it relates the different stakeholders (architect, maintainer, and developer); and (iv) it provides various techniques to improve the quality and classify different scenarios.  
SAAM concentrates on modifiability in its various forms (such as portability, sub-setability, and variability) and functionality. Modifiability is the ability to make changes to a system quickly and cost-effectively. Portability is the ability of the system to run under different computing environments. Subsetability is the ability to support the production of a subset of the system or incremental development. Variability represents how well the architecture can be expanded or modified to produce new architectures that differ in specific ways. Functionality is the ability of the system to do the work for which it was intended. The steps of a SAAM evaluation, which we have carried out within our analysis are the following:  
* Identify and assemble stakeholders;
* Develop and prioritize scenarios;
* Classify scenarios as direct or indirect;
* Perform scenario evaluation;
* Reveal scenario interactions;
* Generate overall evaluation.
## 3 Wireless Communication Schemes for Aircraft
### Multi-Hop Scheme  
The multi-hop network consists of a two-hop topology. Thus, two time slots are used for the multi-hop communication, which leads to the throughput reduction to more than half compared with  
Figure 4: Block diagram of the digital beamforming (DBF).  
Figure 3: Block diagram of the analog beamforming (ABF).  
Figure 2: Example of a structure of the combining block diagram.  
the single-hop communication. The outage probability [33; 41] of each link in a multi-hop scheme that is the same as cooperative and incremental cooperative schemes can be expressed by:  
\[\rho_{ij}=\Pr\left\{\sigma_{ij}<\mu\right\}\simeq\frac{1}{\Gamma\left(m+1\right) }\left(\frac{m\,N\,\mu}{P_{i}\gamma_{ij}}\right)^{m}. \tag{20}\]  
where \(\mu=2^{\theta R}-1\) where \(\theta\) is constant, which is multiplied to increase threshold level because the distance between the source node and relay node is closer than the distance between the source node and the destination node is, \(N=N_{0}B\) where \(N_{0}\) is the noise power spectral density, and \(B\) is the bandwidth. The \(\rho_{ij}\) is also used to cooperative and incremental cooperative schemes in common to get the outage probability for each channel.  
As mentioned earlier, the relay node uses the same circuit board, which means it consumed the same energy as the source, relay, and destination node. Thus, the total energy consumption [42] on the multi-hop scheme is represented by:  
\[\begin{split} E_{MH}=&\,\rho_{SR}\frac{\left(P_{ AMP}+P_{TX}+P_{RX}\right)\kappa}{\theta\,R_{b}}\\ &+\left(1-\rho_{SR}\right)\frac{\left(2P_{AMP}+2P_{TX}+2P_{RX} \right)\kappa}{\theta\,R_{b}}\end{split} \tag{21}\]  
where \(\rho_{SR}\) is the outage probability between the source node and relay node and all terms are divided by \(\theta\) because \(\theta\) is multiplied to the spectral efficiency of the multi-hop SNR threshold. The first term considers the energy consumption when the relay node fails to decode the packet from source node at the relay node, and the second term considers the energy consumption when the relay node succeeds to decode and forward it to destination node.  
The optimal transmitted power of multi-hop scheme that is also considered with the target outage probability can be obtained by:  
\[\rho_{0}\left(P_{MH}\right)^{2m}-\left(k_{1}+k_{2}\right)\left(P_{MH}\right)^ {m}+\left(k_{1}k_{2}\right)=0 \tag{22}\]  
where \(k_{1}=\frac{\left(mN\mu\right)^{m}}{\Gamma\left(m+1\right)\gamma_{SR}^{m}}\), \(k_{2}=\frac{\left(mN\mu\right)^{m}}{\Gamma\left(m+1\right)\gamma_{RD}^{m}}\). Finally, by finding the roots of the polynomial, the optimal transmitted power is given.  
In terms of throughput of the multi-hop scheme, it can be expressed by:  
\[T_{MH}=\frac{R}{2}\left(1-\rho_{SR}\right)\left(1-\rho_{RD}\right) \tag{23}\]  
where \(\rho_{RD}\) is the outage probability between the relay node and destination node, the spectral efficiency is half than that of the single-hop transmission due to using two time slots.
# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
## 2 Data and Methods
### Geomagnetic Activity Data  
Geomagnetic activity at mid/low latitudes (between \(-40^{\circ}\) and \(40^{\circ}\)) is typically characterized by the SYM-H index [37], which reflects the intensity of the symmetric ring current [38]. Therefore, to assess the impact of geomagnetic activity on whistler detection,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Band** & **Sampling Frequency (Hz)** & **Frequency Range (Hz)** \\ \hline ULF & 1024 & 10–200 \\ \hline ELF & \(1.024\cdot 10^{4}\) & 200–2200 \\ \hline VLF & \(5.12\cdot 10^{4}\) & \(2.2\cdot 10^{3}\)–\(2\cdot 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of SCM frequency bands.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Band** & **Sampling Frequency (Hz)** & **Frequency Range (Hz)** \\ \hline ULF & 125 & \(DC\)–16 \\ \hline ELF & \(5\cdot 10^{3}\) & 6–2200 \\ \hline VLF & \(5\cdot 10^{4}\) & \(1.8\cdot 10^{3}\)–\(2\cdot 10^{4}\) \\ \hline HF & \(10^{7}\) & \(1.8\cdot 10^{4}\)–\(3.5\cdot 10^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of CSES-01 EFD frequency bands.  
we utilized SYM-H data to categorize monitored days for the whistler detection as quiet, disturbed, or stormy, following the methodology outlined in [39]. Briefly, for each day within the period from January 2019 to December 2022, an average SYM-H value (\(\overline{\text{SYM}-\text{H}}\)) is calculated. The day is then classified as follows:  
\[D_{Q}:\overline{\text{SYM}-\text{H}}\in[-10\text{ nT},10\text{ nT}]\]  
\[D_{D}:\overline{\text{SYM}-\text{H}}\in[-40\text{ nT},-10\text{ nT}]\]  
\[D_{S}:\text{otherwise}\]  
where \(D_{Q}\), \(D_{D}\), and \(D_{S}\) represent quiet, disturbed, and stormy days, respectively. Data for this analysis were obtained from the OMNI database ([[https://cdaweb.gsfc.nasa.gov/index.html](https://cdaweb.gsfc.nasa.gov/index.html)]([https://cdaweb.gsfc.nasa.gov/index.html](https://cdaweb.gsfc.nasa.gov/index.html)), accessed 3 September 2024), which serves as a comprehensive compilation of solar wind data, interplanetary magnetic-field data, solar and geomagnetic indices, and energetic particle fluxes [40].