esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Energy Efficient Protocols, and Scheduling in WBSNs
* Long Term Evolution (4G LTE): 4G LTE is a technology that optimizes communications with transmission speeds of 100 Mbit/s for high mobility and 1 Gbit/s for low mobility. This technology uses transmission with Multiple Input-Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) techniques, transmitting more data in the same time period. On the negative side, 4G LTE is less power efficient than 3G and Wi-Fi for small data transfers, while for bulk data transfer is more power efficient than 3G [38]. * Bluetooth Low Energy (BLE): BLE was adopted and developed by the Bluetooth Special Interest Group aimed at novel applications in healthcare, fitness, security, and home entertainment industries [39]. The most important fact that guarantees Bluetooth presence in the market is its support by every major operating system. In the latest BLE improvement, a new stack protocol was proposed to offer energy efficiency, latency, piconet size, and throughput parameters. In the medical device area, BLE has the advantage that is implemented in majority of medical devices.
# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 1 Introduction
### Evolution of MPC Algorithms  
MPC technology is presently used across a range of industries, such as aerospace, petrochemicals, automotive manufacturing, food processing, and many more. Figure 2 displays the evolution of the major industrial MPC algorithms since the 1960s. These evolutionary MPC algorithms are discussed below.  
**1. Linear quadratic Gaussian (LQG):** The linear quadratic gaussian (LQG) approach determines appropriate control for a stochastic system that minimises a performance metric. LQG helps to address some of the most difficult control problems in stochastic systems. As a result, it can be viewed as the stochastic counterpart to the deterministic LQR problem. In order to apply the LQG formulation, it is prudent to first construct a solution to the deterministic LQR problem and then develop a Kalman filter to deal with the LQG stochastic problem [14; 19].  
**2. Identification and command (IDCOM):** This differs from the previous approach in that it analyses the plant's impulse response model, which includes linear inputs or internal variables. The model's performance objective is quadratic with respect to the input variables across a finite prediction horizon. A reference trajectory as well as input and output restrictions are included to the formulation to characterise future plant output behaviour. An iterative heuristic strategy (the polar opposite of identification) is used to calculate the ideal inputs [15; 20].  
**3. Dynamic matrix control (DMC):** A linear step response model for the plant is linked with a quadratic performance target across a finite prediction horizon in the DMC control strategy. The plant's future output behaviour is characterised by the plant's attempt to match the set point as closely as feasible in the future output behaviour. The least-squares problem is used to determine which inputs are the most effective/significant [16; 21].  
**4. Quadratic dynamic matrix control (QDMC)-** The original IDCOM and DMC algorithms were particularly effective in regulating non-limited multivariable processes. Constraint handling, on the other hand, remained unpredictable. The Shell Oil corporation solved this issue by applying a quadratic programming technique to achieve the DMC technique using well-defined input/output constraints in QDMC [22; 23].  
Figure 1: MPC development milestones [6; 13; 14; 15; 16; 17; 18].
## 1 Introduction  
Railways are a primary mode of public transportation, and reliable maintenance is essential to prevent inconvenience and traffic congestion for passengers due to train failures during operation. Therefore, the maintenance of railway vehicles is a crucial factor in preventing performance degradation and failures. Maintenance of railway vehicles involves tasks to restore the vehicles to their original state due to issues such as performance degradation, aging, and failures, requiring high adaptability and expertise. Maintenance costs account for 70% of the total lifecycle cost, unlike automobiles [1]. Hence, railway operating agencies are considering specialized maintenance training to minimize personnel errors and prioritize reliability while minimizing maintenance costs.  
Recently, many industrial sites are emphasizing predictive maintenance using condition-based maintenance (CBM) and smart maintenance. CBM monitors the actual condition of equipment using sensors to determine maintenance needs. Smart maintenance integrates technologies like artificial intelligence (AI) and the Internet of Things to predict and prevent equipment failures, transitioning industries towards smart factories--highly digitized and connected environments [2; 3; 4]. AI involves creating systems capable of tasks requiring human intelligence, such as learning and problem solving. Sensor technologies detect and measure physical properties, converting these into data for analysis. This digital transformation enhances efficiency and productivity in manufacturing and maintenance. Core technologies of the Fourth Industrial Revolution include big data, AI, augmented reality (AR), and virtual reality (VR). Big data involves analyzing large volumes of data for insights. AR overlays digital information onto the real world, while VR immerses users in a virtual environment. Both AR and VR provide valuable information without time and space constraints, improving remote assistance, visualization, and interactive training in maintenance and manufacturing.
## 4 Enabling Technologies for FSO Communication
### Adaptive Detection Thresholds
#### 4.10.3 Benefits of ADTs  
ADTs offer several advantages in FSO communication systems:  
1. Improved Signal Detection: By continuously adjusting to the optimal threshold, the Rx can better distinguish between the signal and noise, leading to lower BER and enhanced data integrity [418; 427].
2. Enhanced System Reliability: Adaptive thresholds help maintain consistent communication quality even under adverse weather conditions and AT, thereby increasing the system's reliability [413].
3. Efficient Use of Power: Adaptive thresholds can lead to the more efficient use of power by ensuring that the system only uses the necessary amount of power to maintain communication, which is particularly beneficial in power-constrained applications [189; 413; 422].
# Task-Oriented Adversarial Attacks for Aspect-Based Sentiment Analysis Models
## 3 Related Work
[32] introduced the TextFooler method, which utilizes two core Natural Language Processing tasks: (i) text classification and (ii) textual entailment to generate adversarial examples. According to the authors, this combination of tasks helps to create modified opinions that preserve proper semantics and grammar, making the modifications imperceptible to humans. [PERSON] et al. [33] proposed a gray-box adversarial attack and defense framework for sentiment classification, which addresses challenges related to differentiability, label preservation, and input reconstruction, offering a unified approach for both adversarial attacks and defenses. Current attacks on sentiment analysis have shown success in misleading models while preserving the semantic of the input and the imperceptibility of modifications, by employing several metrics to control the perturbations. However, most existing attacks on sentiment analysis have addressed it as a document-level analysis, applying a generalized modification approach for all analysis levels. This type of method does not necessarily provide an optimal design for adversarial examples, as the specific challenges at each analysis level must be considered to stabilize the input modification process. A recent effort to target sentiment analysis models on a more specialized level was presented by [PERSON] et al. [19]. This method incorporates aspect-level characteristics to generate adversarial examples, ensuring that modifications affect only the non-aspect terms in an opinion while leaving the aspects intact. Taking into account the evaluated aspects, the proposed attack maintains high semantic similarity and syntactic correctness. However, it assumes that only one aspect is evaluated, even when multiple aspects are included. We argue that the generation of adversarial examples should be particularized, focusing on altering the elements that contribute to input classification. In aspect-based sentiment analysis, it is critically important to consider the different aspects within an opinion, as each has a different sentiment expressed. To determine the user's opinion on these aspects, it is important to identify the opinion terms corresponding to each aspect, thus establishing the aspect term relation and classifying the sentiment as positive or negative. Modifications to generate adversarial examples should then be applied to these identified terms. Our hypothesis is that by focusing modifications on the aspect terms, the adversarial examples can be generated with minimal modifications and focus only on the specific key terms that determine the opinion on the aspect. This approach attempts to mislead the model by focusing on task-specific features rather than analyzing patterns in a text.
## 3 Error Modeling and Analysis
### Influence of Assembly Error on Pointing Accuracy
\[\varepsilon=\arccos{\left(\frac{{A^{\prime}}\cdot{A^{\prime\prime}}}{{|{A^{ \prime}}||{A^{\prime\prime}}|}}\right)} \tag{15}\]  
According to Equation (15), we can derive the pointing error angle between \({A^{\prime}}_{out}\) and \({A^{\prime\prime}}_{out}\), \({A^{\prime}}_{i}\), and \({A^{\prime\prime}}_{in}\). The beam-pointing error under different assembly errors is performed. The results can be viewed in Figure 5a-f. The control variate method is adopted in the simulation, assuming we take one of the assembly error values from 0.001\({}^{\circ}\) to 0.005\({}^{\circ}\) and set other element's assembly errors to 0.001\({}^{\circ}\), 0.003\({}^{\circ}\), and 0.005\({}^{\circ}\) in different groups. For example, to obtain the point error, we take the assembly error of \(M_{1}\), \(Az\) axis, \(Az\)_FM_, \(El\) axis, and \(El\)_FM_ as 0.001\({}^{\circ}\), rotate \(M_{2}\) by 0.001\({}^{\circ}\), 0.003\({}^{\circ}\), and 0.005\({}^{\circ}\), and then record the deviation angles of the incoming light and outgoing light in the corresponding states. Figure 5a-f shows that the assembly error has a nonlinear influence on beam-pointing error, and that the influence of the azimuth axis and azimuth folding mirror, as well as the elevation axis mirror and elevation folding mirror, on beam-pointing accuracy is almost uniformly distributed. This proves that the folding mirror is rigidly connected to the axis. Moreover, in each set of data, there exists an intersection point where the pointing error has the same value, and the position of the intersection point will be increased if the assembly error increases. Furthermore, before the intersection point, the closer the elements to the light source, the greater the effect of the assembly error on the beam offset error angle; however, after the intersection point, the opposite tendency was observed.
## 3 Experimental and Results
### Results  
Equations (20) and (21) were verified by experimental data and the parameters of different experimental rectangular open channels are presented in Table 1. The experimental verification consisted of two parts. The experimental data for Run 1-Run 3 were obtained at the UOW, Australia, whereas those for Run 4-Run 11 were obtained at the CAU, China.  
Figure 5: Rectangular inclined water tank at the Fluid Mechanics Laboratory in the University of Wollongong (UOW), Australia.  
Figure 6: Schematic of the experimental device at China Agricultural University (CAU), China.  
The values of _b/h_ may have an effect on the empirical coefficient \(B\) in Equations (14) and (15), which may cause error between the calculated and measured values.  
In order to demonstrate the performance of the location of depth average velocity (LDAV), the relative error between LDAV calculations (subscript _l_) and measurements (subscript _i_) is defined as: \(E=|d_{l}-d_{i}|/d_{i}\), where \(d\) refers to the depth average velocity along the same vertical lines from the bedform to the water surface under different water depths, as shown in Table 1. It should also be noted that the calculated and measured values are consistent with the calculated and measured values.  
Figure 8: Comparison of the location of depth average velocity between the calculated and measured values along different vertical lines from the bedform to the water surface (_b/h_ = 3.297).  
Figure 9: Comparison of the location of depth average velocity between the calculated and measured values along different vertical lines from the bedform to the water surface (_b/h_ = 2.727).  
that LDAV relied on Log-Law, and the errors may be affected by secondary currents. According to the comparison, it is found that Equations (20) and (21) produced more realistic results both in Regions I and II, especially in the middle section of the channel. The relative differences of error between calculations and measurements increased from 11.9% to 13.8% as the aspect ratio decreased from 4.62 to 1.88. This further proved that this model is more suitable for use in a wide channel.  
Through the experiments at the UOW, Australia, the location of the depth average velocity along the center line, as calculated by Equations (20) and (21), turns out to be highly consistent with the measurements. Therefore, in order to further verify the application of Equations (20) and (21) at the center line, a series of experiments concerning flow rate were conducted at different depths of the center line at the CAU, China. The results of experiments at the UOW and the CAU are shown in Figure 10.
# Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems
## 3 Proposed Algorithm
### Power Allocation Algorithm
#### 3.2.1 Particle Swarm Optimization (PSO)  
We suppose the particle swarm is \(D\) dimensional and the number of particles is \(K\). The position of the particles \(i\) during the \(i\)th iteration can be represented by vector \(X_{i}(t)=\big{[}x_{i}^{1}(t),x_{i}^{2}(t),\ldots,x_{i}^{D}(t)\big{]}^{T}\). We can express the velocity of the particle \(i\) during the \(i\)th iteration in terms of vector \(V_{i}(t)=\big{[}v_{i}^{1}(t),v_{i}^{2}(t),\ldots,v_{i}^{D}(t)\big{]}^{T}\). When particle \(i\) reaches the historical optimum position in the iterative process, the particle is called the individual optimum particle, which can be expressed as  
\[\text{Pbest}_{i}(t)=\Big{[}\textit{pbest}_{i}^{1}(t),\textit{pbest}_{i}^{2}(t ),\ldots,\textit{pbest}_{i}^{D}(t)\Big{]}^{T} \tag{15}\]  
Similarly, the global optimal position of all particles in the particle swarm traversing, namely the global optimum value, is expressed as  
\[\text{Gbest}(t)=\Big{[}\textit{gbest}^{1}(t),\textit{gbest}^{2}(t),\ldots, \textit{gbest}^{D}(t)\Big{]}^{T} \tag{16}\]  
The iterative formulas for updating the moving speed and position of each particle are as follows:  
\[v_{i}^{j}(t+1)=\omega\cdot v_{i}^{j}(t)+c_{1}\cdot r_{1}\cdot\Big{(}\textit{ pbest}_{i}^{j}(t)-x_{i}^{j}(t)\Big{)}+c_{2}\cdot r_{2}\cdot\Big{(}\textit{gbest}^{j }(t)-x_{i}^{j}(t)\Big{)} \tag{17}\]  
\[x_{i}^{j}(t+1)=x_{i}^{j}(t)+v_{i}^{j}(t+1) \tag{18}\]  
where \(\omega\) is the inertia weight coefficient, and \(c_{1}\) and \(c_{2}\) are both acceleration coefficients, which usually take a constant. \(r_{1}\) and \(r_{2}\) are random variables uniformly distributed on the interval (0, 1). The specific flow of particle swarm optimization is shown in Figure 2 below.
## 3 Results and Discussion
### Determination of Residual Stress  
The variation in residual stress is shown in Figure 8. The indices for the T1, T2, and T3 groups were reduced by 74.0%, 44.5%, and 5.7%, respectively, owing to the reduction in MC during drying. The MC distribution in the thickness direction exhibited uniformity (as shown in Figure 7d), resulting in a decrease in the MC gradient and further reducing the residual stress.  
At the stage with 30%-20% moisture, the residual stress index of T1 decreased from 3.38% to 2.11%; T3 decreased from 3.68% to 3.30%; T2 showed no significant change but considerably increased within the 3.87%-3.95% range, which might be attributed to the deposition of modifiers inside the conduit during drying that hindered the outward migration of moisture. In addition, the MC gradient still existed or even slightly increased; thus, the residual stress at this stage slightly increased. At the 20%-12% stage, T1 decreased from 2.11% to 0.88%, T2 decreased from 3.95% to 2.15%, and T3 increased from 3.30% to 3.47%. For T1, at the end of drying, no residual stress was observed. For T2, a decrease in residual stress was observed, which could be attributed to the increase in the filling amount of the modifier, which could absorb the stress released from the internal part of the wood. For T3, the decrease in MC caused no apparent change in residual stress, which could be explained by the following two points: 1) the modifier was solidified in the form of a film on the pit surface of the wood conduit that affected the migration of moisture inside the wood; 2) the quantities of the curing agent in T3 were not as high as that of T2; thus, the solidified part did not contribute to the absorption of residual stress. The residual stress index was not reduced and even exhibited an upward trend.  
Figure 7: MC distribution of T1 (**a**), T2 (**b**), and T3 (**c**) in the thickness direction and MC deviation of T1, T2, and T3 (**d**) during drying. Lay 1–5 corresponds to MC section 1–5 in Figure 2.  
In summary, T1 maintained the minimum index at each MC stage. At the stage with 30% and 20% moisture, the MC of T2 was higher than those of the other two groups; T3 had the second highest MC. This results demonstrated that the modifier was impregnated in the wood; the greater the internal curing, the more uneven the MC distribution (Figure 7d), resulting in a high stress index in the thickness direction of the wood. T3 exhibited an irregular change in residual stress because of poor impregnation.
# Operational Temperature Effect on Positioning Accuracy of a Single-Axial Moving Carrier
## 3 Results and Discussions
### Positioning Accuracies under Different Operational Temperature Conditions
The heat sources of a moving carrier come from the friction of the moving carrier and the driving linear motor. In the case of an environmental temperature decline, the temperature differences between the environment and the heat sources are larger than those in the case of an environmental temperature rise. The larger temperature differences in the case of an environmental temperature decline cause greater positioning accuracy variations. This is the reason why the positioning accuracies for an environmental temperature decline are greater than for environmental temperature increases, as shown in Tables 1 and 2. In this study, each experiment was performed at least five times, and the uncertainties of the measured positioning accuracies and temperatures were 4.6% and 1.01%, respectively.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 3 Concepts of the CPS Communication Hub
### Structure and Operation of CPS Communication Space  
An example of the logical structure of a CPS communication hub is depicted in Figure 3. Each owner has a virtual communication space, represented as a colored oval in the dashed rounded rectangle in the middle of Figure 3. The dashed green lines represent the related ownership relations, for example, person X owns the blue virtual communication space. The physical CPS resources (e.g., devices, server) of a specific owner register their presence in the communication space (green lines). For example, the devices and server of company A can register in the white communication space. The red lines connecting virtual communication spaces represent agreements between the owners. The referred agreements are negotiated online between the resources of the owners via the communication spaces. The blue, black, and orange lines represent the example information flows. The main operation processes related to the referred information flows are _presence management of the physical CPS resources_ (green lines), _information sharing_ (black lines), and _data plane operation_ (orange line). The operation of physical CPS resources and trust checking with CPS communication spaces represent the key enablers of the CPS hub, which are described in the following sections.  
Figure 3: An example logical structure of a CPS communication hub.
# World-Models for Bitrate Streaming
## 5 Results and Discussion
### Sample Complexity Bounds  
#### 5.3.1 Rainbow  
We compare our results with the Rainbow algorithm to make fair comparisons on sample complexity [20]. Rainbow is considered to be state-of-the-art with regard to sample complexity, and was used recently in comparisons with a model-based algorithm, SimPLe [6]. We used a Rainbow implementation from the Dopamine package; further information is provided in Appendix B. We reduced the number of actions in the real environment to 1,000,000 (1M) for world-model construction and model-free training. For hyperparameter tuning, we used a combination of grid search and autotuning in order to optimize Rainbow. The grid search uses a nearly identical parameter space to [6]; these parameters are listed in Appendix B. We additionally completed 100 iterations of hyperparameter autotuning with the Optuna framework [33] to optimize the hyperparameter search space with the Tree-Structured Parzen Estimator method (TPE). TPE attempts to balance exploration versus exploitation by iteratively selecting hyperparameters with the largest expected improvement on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature** & **Dream Score** & **Real Score** & **Test Score** & **Real (Single Sweep)** \\ \hline
0.1 & \(2064.1\pm 1.7\) & \(-22,010.7\pm 694.1\) & \(-18,215.6\pm 632.6\) & \(-22,010.75\pm 10,986.6\) \\ \hline
0.5 & \(1036.9\pm 253.1\) & \(-3801.2\pm  
model loss. For more information on TPE, this work refers readers to the original paper [34]. The best Rainbow model trained for 1M actions received an average score of \(306.35\pm 316.34\) in an evaluation of 500 k actions.