esa-sceva/satcom-chunk-collection huggingface dataset:
# Decoupled Monte Carlo Tree Search for Cooperative Multi-Agent Planning
## 4 Background
### Action Selection Policies for Monte Carlo Tree Search
#### 4.2.2 Stochastic Policies  
The stochastic action selection policies use the statistics of each action to calculate a random event and choose the action as a result of this. Most commonly, a stochastic policy may calculate a probability distribution over actions and sample an action according to this. The stochastic action selection policies are best suited for adversarial and stochastic bandit problems [37]. For such problems, the reward distributions may change at every simulation in unknown ways. In this study, we investigated two stochastic action selection policies:  
* \(\epsilon\)-greedy
* EXP3
* \(\epsilon\)-greedy action selection policy chooses actions uniformly with the probability of \(\epsilon\). The action having the maximum average cumulative reward is selected with the probability of \(1-\epsilon\). Therefore, there is direct control on the exploration and exploitation behaviors.  
EXP3 (Exploration-Exploitation with Exponential weights) [34] is one of the well known stochastic action selection policies. The policy maintains a weight value for each action and calculates a probability from it. The probability of an action is calculated by the Equation (2):  
\[p_{i}=(1-\gamma)\frac{w_{i}}{\sum_{j}^{K}w_{j}}+\frac{\gamma}{K} \tag{2}\]  
\(p_{i}\) represents the probability of action \(i\). \(w_{i}\) represents the weight of the action \(i\). All weights are initialized to 1 at the beginning. \(K\) denotes the number of actions. \(\gamma\) is a parameter that determines how much uniform action selection is favored. As \(\gamma\) gets closer to 1, the action probabilities get closer to uniform probabilities (\(1/K\)). After an action is selected and executed, a reward \(r_{i}\) is taken. The weights are updated according to the Equation (4):  
\[\dot{r_{i}} =\frac{r_{i}}{p_{i}} \tag{3}\] \[w_{i} =w_{i}e^{\gamma r_{i}/K} \tag{4}\]  
EXP3 is considered to be a pessimistic policy and shown to have a provably good performance for adversarial multi-armed bandit problems [34]. However, it is a complex method and numerically unstable when used with rewards that are not scaled to 0-1 range. In addition to the returns, weight values are stored for each action. The Equation (4) grows weight values boundlessly with the help of an arbitrary scale of rewards. To contain the weight values in practical limits, we normalize and clip the values at each calculation step. Compared to EXP3, \(\epsilon\)-greedy is a simple method that directly controls the exploration rate with a single parameter. As its strategy is not affected by the scale of the reward, it can be used directly for every problem. There is also a difference in the parameter optimization. The \(\epsilon\)-greedy is sensitive to the parameter because the \(\epsilon\) parameter adjusts the exploration strategy. However, \(\gamma\) parameter of EXP3 has more attenuated effect that makes the parameter selection harder.
# Probabilistic Resilience Analysis of the Icelandic Power System under Extreme Weather
## 1 Introduction  
The continuous supply of electricity is an essential facility for modern life. This essential facility is guaranteed by designing and operating electrical power systems, so that they are able to function within a certain range of likely conditions, formally expressed by reliability criteria, such as the widely adopted N-1 criterion. In recent times, the increasing occurrence of extreme weather events, the aging of the system infrastructure, and the unclear valuation mechanisms for grid security in the market-based regime are bringing power systems outside the 'comfort zone' created by reliability criteria. The modern power system is therefore additionally required to withstand a much wider range of atypical, yet not entirely unlikely, operating conditions characterized by the simultaneous failure of several system components, or at least to deteriorate moderately and promptly recover. This additional system property of interest is commonly termed electrical power system resilience [1, 2].  
The Icelandic power system provides a clear example of the stakes for power system resilience. Electricity is indispensable to the local community, and even more so at times of extreme weather conditions characterized by strong winds, sub-zero temperatures and lack of natural light. However, at such times, the infrastructure of the islanded power system is much more likely to fail, as the intensity of weather phenomena may simply outgrow the component design specifications. The system should therefore be able to fail in a contained manner whatever the initiating event, in order to avoid extremely impactful service interruptions. The recent storm in December of 2019 represented one of the most extreme weather events experienced by the Icelandic power system. Despite suffering numeroussimultaneous outages, the consequences were largely contained to radially connected communities, practically validating the resilience of the Icelandic power system.  
Motivated by the real-life data and observations that were collected from the operation of the Icelandic power system while facing the extreme storm of December 2019, this paper introduces a probabilistic resilience assessment methodology. The methodology integrates weather-dependent component failure rates serving to express failure probabilities in function of forecasted wind speed [3] with a bi-level attacker-defender model serving to quantify the extent of the worst-case outage in terms of the consequences potentially realized by the system end-users. We combine these two building blocks in a Monte Carlo framework to assess in advance the expected impact of the worst-case outage at several characteristic phases of the wind storm, subject to uncertainty on the set of surviving transmission assets that remain online as the storm develops further.
## 1 Introduction
First, the results of numerical calculations, interpretations, and empirical formulas show that the simulation results are slightly different from the actual test data. To reduce this error, numerous commissioning activities must be performed, which increases the cost considerably. Next, the results of studies on bulk transfer using computational fluid dynamics are rather difficult to apply to SILS. The present study aims to realize this application. In addition, the integration with the target controller is often inadequate, and the study becomes unrepeatable and often results in high cost and low effect without sufficient practicality. Nowadays, the use of HILS for MCSs is not compulsory in the shipbuilding and marine sectors but is mainly undertaken by owners. The demands of ship owners with respect to drilling systems are changing, and the complexity of the integrated systems is increasing. Many of the studies on the BTS are based on numerical calculation, analysis, and estimation. Until now, only few studies have considered the application of SILS to the pressure loss of the BTS in the drilling system and the optimization of the pressure loss using a genetic algorithm. Therefore, in this study, the overall pressure loss of the BTS is modeled and simulated. Real test data and simulation results are compared and analyzed for the verification of the simulation model. In addition, an air booster is installed and used to compensate for the pressure loss and thereby prevent clogging. A genetic algorithm is used to determine the number of air boosters required to optimize pressure loss and the distance between the boosters. The use of the proposed SILS testbed is expected to improve the BTS in drilling operations. This paper considers an optimization strategy to minimize the pressure loss of the BTS in the drilling system. Section 2 explains the typical characteristics of the BTS and the pressure loss modeling procedure used to prevent frequent pipeline clogging. Section 3 presents the verification and validation of the proposed model using real test data. It also focuses on the number of air boosters required to minimize the pipeline's pressure loss and the optimal distance between the boosters using a genetic algorithm. Section 4 summarizes the simulation results and its usefulness.
# Precursory Tidal Triggering of the 2011 Mw 9.0 Tohoku Aftershocks
## 1 Introduction
and minimum values of the tidal load in a tidal cycle, respectively [17; 18]. A focused distribution of earthquakes occurring near a certain phase angle indicates a tidal correlation and the presence of some dominant phase angles. If the tidal phase angles show a random distribution, then there is no tidal correlation [14; 17; 18]. This test method is suitable for study areas with homogeneous seismicity, while nonuniform seismic activities (such as aftershocks) may affect the judgment of the correlation [33; 34]. In one study [34], a new method to analyze the possible periodic characteristics of aftershocks was proposed. In this study, we aim to investigate the correlation between Earth tides and earthquake occurrences on the basis of the 2011 Mw 9.0 Tohoku earthquake aftershocks. Previous work has revealed tidal triggering in the aftershock sequence of the Ms 8.0 Wenchuan earthquake on 12 May 2008 (e.g., [35]). The Wenchuan earthquake was an inland event, while the Tohoku earthquake occurred offshore in the Pacific Ocean. The tide of solid Earth and the ocean were both included. First, the Schuster test is used to explore the tidal correlation. Then, the Schuster spectrum method is used to analyze the seismic sequence to obtain possible periodicity in the Schuster spectrum of aftershocks. Furthermore, it is discussed whether the tidal forces corresponding to these periods have played a significant role in triggering earthquakes. On this basis, a preliminary discussion is provided on the characteristics of the possible tidal triggering of the Mw 9.0 earthquake aftershocks.
## 2 Objective Evaluation Test
### Volume Test
#### 2.3.1 Power Test  
The procedure of the power test is shown in Figure 6.  
The defined stereo test signal is used as input signal for the tested upmix. The power \(P_{FL}\), \(P_{C}\), \(P_{FR}\), \(P_{SL}\) and \(P_{SR}\) of the generated upmix output signals \(x_{FL}\), \(x_{C}\), \(x_{FR}\), \(x_{SL}\) and \(x_{SR}\) are considered. The maximum power of the three front signals \(P_{max}\) is compared with the power of both surround signals \(P_{SL}\) and \(P_{SR}\) each. The ratios \(P_{L}\) and \(P_{R}\) serve as the basis for evaluation. The evaluation scores of the left and right surround signal, \(score_{LT1,L}\) and \(score_{LT1,R}\), are calculated from the means of the quotients across all \(N_{L}\) and \(N_{R}\) times in which the power of the particular surround signals is greater than the power of the front channels, with \(P_{max}(n)=max[P_{FL}(n),P_{C}(n),P_{FR}(n)]\):  
\[\begin{split} score_{LT1,L}=1-\frac{1}{N_{L}}\sum\limits_{n=1}^{N _{L}}P_{L}(n)\;\forall\;P_{SL}(n)\geq P_{max}(n)\\ score_{LT1,R}=1-\frac{1}{N_{R}}\sum\limits_{n=1}^{N_{R}}P_{R}(n)\; \forall\;P_{SR}(n)\geq P_{max}(n)\end{split} \tag{8}\]  
The evaluation scores \(score_{LT1,L}\) and \(score_{LT1,R}\) represent the relative deviations of the considered power \(P_{SL}\) respectively \(P_{SR}\) from the maximum power of the three front signals \(P_{max}\). The evaluation score of the power test \(score_{LT1}\) results from the evaluation scores of the power test for the left and right surround signal, \(score_{LT1,L}\) and \(score_{LT1,R}\), weighted with \(g_{LT1,L}\) and \(g_{LT1,R}\), and is given by  
\[score_{LT1}=\frac{g_{LT1,L}\cdot score_{LT1,L}+g_{LT1,R}\cdot score_{LT1,R}}{g_ {LT1,L}+g_{LT1,R}} \tag{9}\]  
The evaluation allows the comparison of front with surround channel power (see Figure 7). For reasons of clarity only the left surround channel power is used in the following figures.  
Figure 6: Block diagram: Power test.  
Upmix 1: In mode (a), the power of the left surround channel is basically lower and in some areas partly as high as the power of the front channel with the greatest power. In mode (b), the power of the left surround channel is mostly lower and in some areas partly higher than the power of the front channel with the greatest power. While in mode (a), the power of each front channel is more or less relatively similar, they are mostly considerably different in mode (b). The strong emphasis on the center channel can especially be recognized.  
Upmix 2: In mode (a), the power of the left surround channel is basically lower and in some areas partly as high as the power of the front channel with the greatest power. In mode (b), the power of the left surround channel is lower and in some areas partly as high as or slightly higher than the power of the front channel with the greatest power. While in mode (a), the power of each front channel is more or less relatively similar, they are mostly considerably different in mode (b). The strong emphasis on the center channel can especially be recognized.
## 3 Experiments and Discussion
### Parameters Setting  
In this proposed method, there are two parameters that may affect the algorithm performance, including the length of sub points set \(length=2L+1\) (fitting curve length) used in Equation (1), and the total candidate number \(N\) (sample number) used in Equation (6). In order to figure out how these two parameters influence the performance of the proposed method, two corresponding experiments were introduced.  
The experimental results of the relation between curve length and location of the local maximums are shown in Figure 10. For the same input histogram image, when the length was small, the curvature curve was as chaotic as the histogram and many of false positions were detected. However, the local maximums were getting more and more obvious in the curvature curves as the length got larger. When the length was up to 151, the second peak that was detected in length 101/121/131 was not detected anymore. As a result, the best choice of the length should depend on the application requirements: if in the application the false positives are not allowed, then a larger length should be used, while in same applications if a small percentage of false positives are allowed, the middle range of the length (101/121/131) is better. In this work, the length is set to 131.  
The sample number in Equation (6) indicates how many random control points will be used to generate the corresponding Bezier curves. In this experiment, random uniform distribution was used. General speaking, the more the sample number, the more accurate the fitting result, and also the more time that was consumed, but actually, when the sample number is larger than a certain number, the difference in accuracy is relatively small. As seen in Figure 11, there were some small differences of the fitting results between six different sample numbers (20, 40, 60, 80, 100, 120), but all of them can locate the local maximums correctly with very small position errors. It can be proved that, in a sample range of \(N_{w}\times N_{h}\), where \(N_{w}\) is the width of the sample area, and \(N_{h}\) is the height of the area, if a control point falls within a 5 \(\times\) 5 neighborhood centered on the correct control point position, and it is considered to be approximately correct, then the minimum sample number is \(N\geq\frac{N_{w}\times N_{h}}{5\times 5}\). The detailed proof can be found in Section 2.4. According to this result, if \(N_{w}=20\) and \(N_{h}=20\), then the least sample number is \(N=16\), which was well verified by the experiments. In this work, \(N=60\) was used.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Type A** & **Type B** \\ \hline Length & 800 mm & 200 mm \\ Width & 50 mm & 10 mm \\ Height & 2 mm & 1 mm \\ Speed of Conveyor & 2 m/s & 2 m/s \\ Interval Time & 500 ms & 333 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of Test Tubes.  
Figure 11: Bezier curve fitting results using different sample number.  
Figure 10: Bezier curve fitting and curvature results using different sub curve length.
# Development and Proposal of a Novel Scoring System to Classify Dry Month Severity
## 4 Discussion  
### Gaps in the Literature  
There is no consensus or gold standard for the inclusion of dry mouth patients into clinical studies; thus, comparing different clinical trials on dry mouth products is challenging or even impossible. In the present study, we have proposed a dry mouth severity score, the DMSS, to classify the severity of dry mouth based on five objective and subjective parameters that may be used as a common basis for patient inclusion in studies.  
Several examples from the literature show that the inclusion criteria vary extensively in clinical trials concerning dry mouth. [PERSON] et al. tested a commercial dry mouth product on two groups with different age categories solely [5]. [PERSON] et al. tested four different commercial dry mouth products on members from the Sjogren's Syndrome Foundation (Bethesda, MD, USA) with self-identified disease [34]. Another study from [PERSON] et al. tested an experimental moisturizing mouthwash on subjects answering at least 3 on a 0 to 10 scale on the question I have dry mouth, while the inclusion criteria in a study from [PERSON] et al. was a minimum of 4 on the same scale [6; 35]. [PERSON] et al. included patients with a mean salivary flow rate of \(\leq\)0.16 g/min or any medical condition or treatment associated with xerostomia when testing the efficacy of a new oral saliva equivalent [7]. Other studies have included specific patient groups, such as the study of [PERSON] et al. that included HNC patients and excluded patients using drugs associated with anticholinergic side effects or other medical conditions associated with xerostomia [36]. Furthermore, [PERSON] et al. used wider inclusion criteria when including patients with PSS, non-SS, and others who answered at least agree a little to two out of four questions from the Xerostomia Inventory when testing an experimental moisturizing mouthwash [37; 38]. Taken together, these examples clearly demonstrate a need for standardized inclusion for dry mouth patients.
# Telehealth Using PoseNet-Based System for In-Home Rehabilitation
###### Abstract  
The increasing cost of healthcare services is accelerating the development of the telehealth system to fulfill the necessity of delivering an efficient and cost-effective remote healthcare services. Moreover, the ageing of the global population and the disruption of the COVID-19 pandemic are creating a rapid rise of demand for healthcare services. This includes those who are in need of remote monitoring for chronic conditions through rehabilitation exercises. Therefore, this paper presents a telehealth system using PoseNet for in-home rehabilitation, with built-in statistical computation for doctors to analyze the patient's recovery status. This system enables patients to perform rehabilitation exercises at home using an ordinary webcam. The PoseNet skeleton-tracking method is applied to detect and track the patients' angular movements for both elbows and knees. By using this system, the measurement of the elbow and knee joint angles can be calculated and recorded while patients are performing rehabilitation exercises in front of the laptop webcam. After the patients complete their rehabilitation exercises, the skeleton results of four body parts will be generated. Based on the same actions performed by patients on selected days, the doctors can examine and evaluate the deviation rate of patients' angular movements between different days to determine the recovery rate.  
Keywords:telehealth; PoseNet; in-home rehabilitation; angular movement; skeleton result +
Footnote †: journal:  
_Article_  
Telehealth Using PoseNet-Based System for In-Home Rehabilitation  
1
## 3 Introduction to Related Technologies of the Scheme  
### Spatiotemporal Big Data Technology  
Spatiotemporal big data represent a voluminous data set encompassing both temporal and spatial dimensions, which has found broad applications across multiple disciplines. The data can be categorized as government and corporate data, search data, cyberspace data, spatial media data related to location, and human geography data, as illustrated in Figure 6. This type of big data is primarily composed of information relevant to national security, industry, meteorology, transportation, medical care, social networking, among other areas. To analyze and process spatiotemporal big data, it is essential to investigate reliable, effective, and practical techniques for data management and processing. For specific spatiotemporal data, such as those involving national defense, medical care, and industry, personal privacy and the vitality of national energy resources must be safeguarded. Therefore, the integration, cleaning, transformation, and extraction of spatiotemporal big data are crucial preprocessing steps requiring effective approaches.  
The secure storage of spatiotemporal big data is also a significant challenge. To ensure data security, technologies such as encryption, backup, recovery, access control, and authentication can be employed. Moreover, blockchain technology is a promising solution for secure data storage, offering a tamper-proof distributed database that guarantees  
Figure 6: Classification of spatiotemporal big data.  
the security and integrity of spatiotemporal big data. To summarize, the processing of spatiotemporal big data necessitates a comprehensive consideration of data management, processing, and secure storage, requiring corresponding technical means and measures to ensure the data's integrity and security. Additionally, the utilization of blockchain technology can be explored to address the security storage issue.
# One-Layer vs. Two-Layer SOM in the Context of Outlier Identification: A Simulation Study
## 3 The Computational Experiment
### Structure and Parameters of One- and Two-Layer SOMs  
The one-layer SOM was a \(10\times 10\) map. For the two-layer SOM, four \(10\times 10\) maps were built in the first layer, one for each stratum. Then, the 400 nodes of these maps were used as the sample for the second (final layer) SOM, which produced a \(10\times 10\) map as well, in such way that the comparison of the one-layer SOM was performed on equal footing. The topology in both cases was hexagonal.  
Euclidean distances were used to select winning nodes. The values of the parameters were used for weight updating, in accordance with \(\alpha_{1}=0.05\) and \(\sigma_{1}=0.5\)