esa-sceva/satcom-chunk-collection huggingface dataset:
# A Parallel Two-Stage Iteration Method for Solving Continuous Sylvester Equations
## 3 Parallel Implementation of the Two-Stage Iteration Method
### Parallel Implementation of Outer Iteration Method  
(1) Splitting process: Processor \(p_{i}(i=1,2,\cdots,p)\) computes  
\[M_{1,i}=(A_{i}+\widetilde{A}_{i})/2,\ N_{1,i}=(\widetilde{A}_{i}-A_{i})/2,\ M _{2,i}=(B_{i}+\widetilde{B}_{i})/2,\ N_{2,i}=(\widetilde{B}_{i}-B_{i})/2.\]  
(2) Cycle processor:  
Step \(1\). Processor \(p_{i}(i=1,2,\cdots,p)\) computes  
\[\Delta X_{i}^{(k)}=X_{i}^{(k)}-X_{i}^{(k-1)}\ \text{and}\ [\Delta X_{i}^{(k)}, \Delta X_{i}^{(k)}],\]  
get \([\Delta X_{i}^{(k)},\Delta X_{i}^{(k)}]\) after all-reduce. If \(\|\Delta X^{(k)}\|<\varepsilon\) stop; otherwise, turn to step 2.  
Step 2. Compute \(\widetilde{F}_{i}=N_{1;i}X^{(k)}+X_{i}^{(k)}N_{2}+F_{i}\) in each processor.  
Step 3. Use the improved parallel process of SYMMLQ algorithm to solve the new equation  
\[M_{1}X^{(k+1)}+X^{(k+1)}M_{2}=\widetilde{F}. \tag{11}\]  
This step, which improves the parallel efficiency and reduces the parallel time by reducing the frequency of communication, plays an important role in the whole parallel implementation of the two-stage iteration method, and the detailed implementation can be seen in Sections 3.3 and 3.4.  
Step 4. Let \(k:=k+1\), turn to Step 1.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 4 Dynamic Spectrum Access Model  
The presence of spectrum holes in frequency spectrum allocation means that the radio spectrum is utilized inefficiently. Static radio spectrum access with the fixed resource allocation is not suitable for coping with spectrum shortages due to little resource sharing. On the contrary, dynamic spectrum access is good at handling spectrum holes or spectrum shortage by allocating the available bandwidth according to various approaches in an efficient manner. It should be able to apply different spectrum sharing to different systems as well as assign different fixed bandwidths to different systems.  
Once media access control (MAC) or channel bandwidth allocation in LPWANs is well defined so that resource allocation can be easily made according to specific quality of service (QoS) in wireless applications, LPWANs can serve the traffic for wireless applications with strict QoS requirements. CR-LPWAN is especially efficient whenever the radio spectrum of a primary radio network (PRN)  
Figure 2: Two types of radio network architecture.  
system with a large amount of resources becomes underutilized. Hence, in CR-LPWANs, the dynamic spectrum access is essential to efficient spectrum management for densely deployed IoT sensor nodes. CR-LPWAN is distinguished from traditional WSN by spectrum sensing functionality. Cognitive radio determines the vacant bands through the spectrum sensing capability by dynamically adjusting its operating parameters, and efficiently manages these available bands for improving the overall spectrum utilization.
# A System Dynamics Approach to Optimize Milk Production in an Industrial Ranch
## 1 Introduction  
Research into increasing milk production has motivated several studies covering different aspects such as studies carried out in vitro and through different experiments on livestock race and nutrition with the aim of increasing milk production [1, 2, 3]. According to a report by [PERSON] et al. [4], milk production is associated with environmental effects such as climate change and or loss of environmental variety. Hence, studies aiming at the life cycle assessment (LCA) define the maximum milk production considering the environmental effects. The milk production model of dairy cows that is related to the energy and nitrogen dynamics is an animal growth and metabolism model that investigates livestock conditions in terms of fat, water, energy dynamic, etc. [1, 5, 6]. In addition to milk production, some issues such as livestock demographic trends, the estimation of economic loss caused by livestock mortality, and the study of major livestock diseases causing losses are related to the analysis and dynamics of the livestock population that was investigated [7, 8, 9, 10, 11].  
A potentially valuable technique for research on ranches and milk production is the development of an accurate model that takes into consideration what occurs in reality and not what the researchers develop in vitro. The simulated model is able to predict successful production under different scenarios [12, 13]. The system dynamics method studies the knowledge system related to the real world [14]. This latter is a computer-based modelingtechnique that can be used as a decision support system (DSS) to analyze management policies and can dynamically analyze the complicated economic, social, and managerial problems associated with the problem at hand.  
One of the subdivisions of the Ahrar Sepahan Corporation in Isfahan, Iran, is the Ahrar Fereidan ranch, whose management is faced with an increasing cost of milk production resulting from elevated medical costs and a reduction in the number of livestock in different classifications in recent years. It also suffers omissions resulting from livestock losses and diseases. In this paper, casual loop and stock-flow diagrams of the Fereidan Ahrar ranch were first simulated using a system dynamics approach. Then, the sensitive variables were identified and their optimal value was obtained through the design of experiment (DOE) technique. Different scenarios are defined to maximize milk production according to the optimized values resulting from the DOE technique. Such scenarios will help the management of this ranch to optimize the medical costs in order to properly concentrate these costs on sections that will improve milk production.  
The remainder of this paper is organized as follows. Section 2 provides a literature review of research in milk production. Section 3 details the research model considered in this paper. Section 4 analyzes the results and Section 5 concludes this paper.
## 5 Experimental Results
### Encoder Training and Results Using Contrastive Learning  
In deep learning, it is common to divide the created dataset into three parts: training data, validation data, and test data. During training, the training and validation data are used to search for optimal parameters, and the test data are used to evaluate the model. The dataset contains dance data collected from 20 dancers, each with 40 counts. In this study, the data from 17 dancers were split into training and validation data, and the data from the remaining three dancers were split into testing data. These three dancers were randomly selected from each group after dividing each group into three groups with low,  
Figure 16: Autoencoder learning curve.  
Figure 17: Autoencoder visualization results (blue lines indicate the correct joint positions and orange lines indicate the results of visualizing the restored positions in a human body skeleton).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Dynamics** & **Sharpness** & **Scalability** & **Timing** & **Accuracy** & **Stability** \\ \hline LMA + RF & 0.575 & 0.548 & 0.450 & 0.562 & 0.585 & 0.539 \\ AE & **0.752** & **0.752** & **0.615** & **0.566** & **0.636** & **0.674** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation model learning results (autoencoder).  
medium, and high scores, respectively. This method allows us to validate the robustness of the evaluation model for unknown dancers.  
The encoder model was trained using the supervised contrastive learning technique described in Section 4.5. The hyperparameters were searched for optimal parameters in approximately 50 trials using Optuna ([[https://optuna.org/](https://optuna.org/)]([https://optuna.org/](https://optuna.org/))), which performs a parameter search based on Bayesian optimization. Table 9 shows the details of the parameters and their values.  
As described in Section 4.5, two different methods were used to calculate the distances: Euclidean and Manhattan distances were used for learning. The learning curves and results are shown in Figure 18 and Table 10, respectively.  
From Table 10, we can see that the model using the Euclidean distance has a lower swap error and is superior. Swap Error = 0.039 represents approximately four wrong guesses when comparing 100 pairs, which seems almost practical.  
Next, the learned encoders were used to train the dance evaluation model (referenced guided) described in Section 4.5. Hyperparameters were similarly used to search for optimal parameters using Optuna. Table 11 details the parameters and their values.  
For the dance evaluation model, LMA and random forest (RF) were used as baseline models, and performance was compared with a model that used encoders with contrastive learning (CL) for feature extraction on top of them. The results of training as a class classification problem for each evaluation item  
In all items, the contrastive learning method using the Euclidean distance resulted in the highest accuracy.
# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
###### Abstract  
In network function virtualization, the resource demand of network services changes with network traffic. SFC migration has emerged as an effective technique for preserving the quality of service. However, one important problem that has not been addressed in prior studies is how to manage network load while maintaining service-level agreements for time-varying resource demands. Therefore, we propose the Resource Predictive Load Balancing SFC Migration (RP-LBM) algorithm in this paper. The algorithm uses CNN-AT-LSTM to predict VNF resource demands in advance, eliminating the delays associated with dynamic migrations and determining the optimal migration timing. It leverages the PPO algorithm's perceptual capabilities in complex environments to develop SFC migration strategies and ensure network load balancing. Additionally, it reduces the number of subsequent migrations and minimizes the service interruption rate. The simulation results show that the service interruption rate of the RP-LBM algorithm is on average 27.3% lower than that of the passive migration method. The PPO-based migration algorithm has lower SFC migration times and service interruption rates compared to the DQN algorithm, ensuring service continuity with low migration costs.  
network function virtualization; service function chaining; resource demand prediction; network load balancing; deep reinforcement learning +
Footnote †: journal:  
0000-0001-5000-7999-2248, 2019
# Improving End-To-End Latency Fairness Using a Reinforcement-Learning-Based Network Scheduler
## 3 Results
### Simulation
Through the simulation in topology 2 with the random flow, it was confirmed that the proposed algorithms reduce the maximum E2E latency and increase the latency fairness and maximum latency fairness event in an environment where divergence occurs in a bottleneck link. Table 14 shows the average maximum E2E latency for each simulation of each scheduler. The RL-based scheduler minimizes the maximum E2E latency in all simulations. NAT-HA achieves maximum E2E latency minimization in topology 2 regardless of how packets are generated. MED-HA achieved the minimization of the maximum E2E latency in all other simulations except topology 3. Additionally, looking at Table 14, it can be seen that the proposed heuristic algorithms perform better than FIFO and RR even in fixed scenarios. This is because the metadata used by the heuristic algorithm is information that can better estimate the E2E latency, so there is a high probability of transmitting a packet with a large E2E latency. The RL-based scheduler shows the same performance as these heuristic algorithms, but shows better performance in one topology. This is because the RL-based scheduler can perform scheduling by considering the future state in a fixed scenario. There is no maximum E2E latency difference between SARL and MARL in all topologies. MARL has the advantage that it can be applied in topology on a larger scale \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{6}{c}{**Latency Fairness Index [\%]**} \\ \cline{2-7}  & **FIFO** & **RR** & **NAT-HA** & **MED-HA** & **SARL** & **MARL** \\ \hline Mean & 79.46 & 77.75 & 80 & 79.8 & 79.85 & 79.81 \\ Min & 75.21 & 73.1 & 75.33 & 75.22 & 75.24 & 75 \\ Max & 82.62 & 81.01 & 83.21 & 82.73 & 83.13 & 83.28 \\ \hline \end{tabular}
\end{table} 
Table 12: Latency fairness in topology 2 with random flow. Figure 20: Box plot of latency fairness index per scheduler from 10,000 episodes in topology 2 with the random flow. than SARL, but it is expected that the performance will decrease due to the limitations of the IQL method as the scale increases. Compared to SARL, MARL can be applied on a larger scale in topology and has the advantage of being able to perform better with a smaller deep learning network. However, as the scale increases, performance is expected to decrease due to the limitations of the IQL method.
## 4 WirelessHART Scheduling Schemes
### The Han Algorithm  
The algorithm presented in [52] uses a fastest sample rate first (FSRF) policy to schedule the network transmissions. The scheduling is based on the reliable graphs presented in Section 3.1 and uses a recursive function to allocate the links in the form of a global matrix \(\mathcal{M}\). This recursive function allocates the nodes from source to destination using a depth first search algorithm (DFS). The matrix \(\mathcal{M}\) represents all of the allocations for channel/slot time for each link; its dimensions are given by the number of channel (16) _versus_ the size of the lengthiest superframe. The superframe \(\mathcal{F}_{i}\) represents a group of devices with sample rate \(r_{i}\). The variable \(l_{i}\) represents the length of the superframe \(\mathcal{F}_{i}\).  
Generally, each device is several hops away from its destination. If the network manager allocates all of the resources from the multiple paths into the scheduling, the schedulability of the network schedule would degrade rapidly. In order to avoid this, the algorithm uses a parallel \(\mathcal{F}_{i}^{\prime}\) superframe, with length \(l_{\mathcal{F}^{\prime}}=2*l_{\mathcal{F}}\), to accommodate those transmissions from nodes with two possible routes (see Figure 11). This occurs by reducing the transmission rate by one-half and determining an offset that positions the links in a communication pattern identical to the original, but by different routes. It is worth noting that reducing the publish rate means, for example, that a device that publishes once per second will be equivalent to a device that publishes once every two seconds.  
The allocation is divided by the type of link to be scheduled. The links can be an uplink or downlink and exclusive or shared. An exclusive link is one occupied by two devices for dedicated communication. Shared links allows multiple devices to compete for transmitting to the same device simultaneously. The division can be seen in Table 5.  
Figure 11: Superframes \(\mathcal{F}^{\prime}\) and \(\mathcal{F}\) (Figure from [51]). Reproduced with permission from [PERSON], “Avaliacao de Algoritmos de Roteamento e Escalonamento de Mensagens para Redes WirelessHART”; published by UFRGS, 2014.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Link Type** & **First Possible Slot** \\ \hline Uplink/exclusive slot & 0 \\ Uplink/shared slot & \(\frac{l_{i}}{4}\) \\ Downlink/exclusive slot & \(\frac{l_{i}}{2}\) \\ Downlink/shared slot & \(\frac{3*l_{i}}{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Superframe division for the scheduling algorithm presented in [52].  
Shared links may be used by stations for retries, thus enhancing the reliability of the network. If an attempt to schedule a link fails, the network manager sends a message informing the requiring node of the lack of bandwidth.  
The scheduling implementation was validated with a simulation and presented an improvement, mainly in the network utilization metric, while maintaining a scheduling success rate near 100% for lower sample rates.
# Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms
## 2 Materials and Methods
### Requirements
* A remote monitoring and management tool has been implemented, thereby enabling centralized monitoring and control of the system, even in instances of growth and increased complexity. This facilitates the process of troubleshooting and resolution, as well as the implementation of updates and improvements. * The hardware is designed with future expansion and upgrades in mind. For instance, the design incorporates space for the addition of sensors or communication modules, and the interfaces have been developed to be compatible with future hardware versions. Security: Although this product does not require specific safety measures--since its operation does not pose risks to humans or animals--the following basic security protocols typical of standard IoT systems have been implemented:  
* Device security: All nodes have been designed to prioritize security, with a protective casing that is intended to prevent damage caused by hens pecking or scratching. Additionally, the device has been bolted to the floor of the nesting box to prevent accidental displacement. * Data security: End-to-end encryption is applied to protect data in transit. Secure communication between devices and servers is ensured using protocols such as Transport Layer Security (TLS), Secure Sockets Layer (SSL), and Hypertext Transfer Protocol Secure (HTTPS). Message security: encrypted messages are used between nodes and the gateway. Network access control is facilitated by a network session key (NwkSKey), while application access control is managed through an application session key (AppSKey). * User security: User access to the system is provided through secure password authentication, two-factor authentication, and role-based access policies. * System security: the firmware and software used are updated to the latest versions to minimize the risk of security breaches and exposure to malicious software.
## 4 Case Studies
### Algorithm Performance Comparision  
#### 4.2.1 Comparison of PCCP-ADMM-FE and Centralized Algorithm Results  
In order to compare the optimization results and convergence speed of the proposed algorithm with the centralized algorithm, the optimization results of the example system under the ADMM-FE algorithm are compared with the centralized optimization results. Set \(\rho^{0}=10^{-3}\), the optimization results are shown in Table 1.  
The analysis of Table 1 is as follows:  
1. Under the constraint of convergence accuracy a = 0.001, b = 0.0001, \(\epsilon_{1}=0.001\), \(\epsilon_{2}=0.001\), both the proposed algorithm and the centralized algorithm can get a convergent optimal solution. Compared with the results of centralized optimization, the error of the calculation result of the proposed algorithm is less than \(10^{-3}\). Optimization results verify the accuracy and effectiveness of the algorithm;
2. The ADMM-FE calculation time is longer than the centralized calculation time, but the information privacy of each subsystem is guaranteed. From the scale of formulating the daily dispatch plan, it is acceptable to sacrifice a small amount of calculation speed in exchange for the decentralized autonomy of each subsystem.
# A Hybrid Mapping Method with Position and Stiffness for Manipulator Teleoperation
## 3 Motion Mapping Method for Position Teleoperation  
The objective of motion mapping for position teleoperation is to achieve ideal position trajectory tracking from the operator to the slave manipulator. Motion mapping for position teleoperation can be implemented in joint space or Cartesian space. In this paper, the Cartesian position mapping is utilized to overcome the different configuration between CyberForce and the slave manipulator.  
With the motion of robot during a specific teleoperation task, the minimum distance \(D_{min}\) between the robot (R) and the environment (E), the target (T), and the obstacles (O) is changing over the time, which can be defined by Equation (1):  
\[D_{min}=\min(\parallel R-E\parallel,\parallel R-T\parallel,\parallel R-O \parallel) \tag{1}\]  
According to the minimum distance \(D_{min}\), the workspace of robot \(WS\) can be redefined into free space for fast motion, fine space for slow motion and contact space for compliant motion, shown in Figure 2. The redefined workspace \(WS\) can be expressed by Equation (2):  
\[WS=\begin{cases}\text{Free space}&D_{min}\geq D_{1}\\ \text{Fine space}&D_{2}<D_{min}<D_{1}\\ \text{Contact space}&D_{min}\leq D_{2}\end{cases} \tag{2}\]  
where, \(D_{1}\) and \(D_{2}\) represent the predefined constant distance thresholds, and \(D_{2}\) is usually close to zero.  
Based on this redefined workspace of robot \(WS\), different mapping methods are proposed in this paper to teleoperate the slave manipulator, and these methods are switched according to the real workspace of robot.
# Wettability of Carbonate Reservoir Rocks:
###### Abstract  
Various methods have been proposed for the evaluation of reservoir rock wettability. Among them, Amott-Harvey and USBM are the most commonly used approaches in industry. Some other methods, such as the Lak and modified Lak indices, the normalized water fractional flow curve, [PERSON]'s triple rules of thumb, and the modified Craig's second rule are based on relative permeability data. In this study, a set of capillary pressure curves and relative permeability experiments was conducted on 19 core plug samples from a carbonate reservoir to evaluate and compare different quantitative and qualitative wettability indicators. We found that the results of relative permeability-based approaches were consistent with those of Amott-Harvey and USBM methods. We also investigated the relationship between wettability indices and rock quality indicators RQJ, FZI, and Winland R35. Results showed that as the rock quality indicators increased, the samples became more oil-wet.  
wettability; relative permeability; capillary pressure; rock quality; special core analysis Article