esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Local Failure Probability Sensitivity Analysis
## 4 Local RSA in the Standard Normal Space  
In this section, we assume that after an isoprobabilistic transformation \(T\), the random inputs \(\mathbf{Z}\) have been transformed into standard normal inputs \(\mathbf{X}\), such as \(\mathbf{X}=T(\mathbf{Z})\), using the same notations as before. The dependence of \(P_{f}\) on \(\mathbf{s}\) is then necessarily expressed in the (transformed) limit state function \(g\). According to the nature of the parameters \(\mathbf{s}\), this dependence can be further derived.  
Suppose the vector \(\mathbf{s}\) contains distribution parameters only, \(\mathbf{s}=\mathbf{\theta}\). Consequently, these parameters have no influence on the limit state function in the \(\mathbf{Z}\)-space and the influence on the limit state function in the \(\mathbf{X}\)-space is actually solely contains in the transformation \(T\), denoted \(T_{\mathbf{\theta}}\) such as \(\mathbf{x}=T_{\mathbf{\theta}}(\mathbf{z})\), therefore  
\[\forall\mathbf{x}\in\mathbb{R}^{d}\quad\ g(\mathbf{\theta},\mathbf{x})=g_{\mathbf{ Z}}(\mathbf{z})\quad\text{ where }\quad\mathbf{z}=T_{\mathbf{\theta}}^{-1}(\mathbf{x}),\]  
with \(T_{\mathbf{\theta}}^{-1}\), the inverse isoprobabilistic transformation with fixed \(\mathbf{\theta}\).  
Suppose the vector \(\mathbf{s}\) contains design parameters only, \(\mathbf{s}=\delta\). Accordingly, the parameters \(\delta\) have influence on the definition of the limit state functions in both spaces. The relationship between the two limit state functions \(g\) and \(g_{\mathbf{Z}}\) can then be written  
\[\forall\mathbf{x}\in\mathbb{R}^{d}\quad\ g(\delta,\mathbf{x})=g(\delta,T( \mathbf{z}))=g_{\mathbf{Z}}(\delta,\mathbf{z})\quad\text{ where }\quad\mathbf{z}=T^{-1}(\mathbf{x}).\]  
Independently of the nature of \(\mathbf{s}\), the sensitivity of \(P_{f}\) is expressed with a surface integral: Equation (4) for \(\mathbf{\theta}\) and Equation (5) for \(\delta\). This surface integral can be estimated with various methods [29; 30] for RBDO purposes. In this article, we notably focus on methods which directly come from frameworks employed to compute failure probability estimates, as the goal is to increase as little as possible the simulation budget.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## System Model
### Mutual Information Model  
As reported in [24; 25; 26], continuous intra-body normal data transmission results in significant power wastage and reduces the network lifetime. This is because scanned information is similar or close to the previously sent data and does not contain any emergency medical information. In this paper, we adopt a mutual information approach to minimize redundant normal range data transmission. The MI, \(I(X;Y)\), can be regarded as the amount of uncertainty in \(X\) due to the knowledge of \(Y\). The expression for MI can be defined as  
\[I(X;Y)=\sum_{x,y}p(x,y)\log\frac{p(x,y)}{p(x)p(y)} \tag{3}\]  
where \(p(x)\) and \(p(y)\) are the probability distribution functions of \(X\) and \(Y\), respectively. Let \(D_{x}\) and \(D_{y}\) represent the scanned data set of an in-body sensor node at the continuous time slots \(t_{x}\) and \(t_{y}\), the mutual information then can be rewritten as  
\[I\big{(}D_{x};D_{y}\big{)}=\sum_{x}\sum_{y}p\big{(}D_{x},D_{y}\big{)}\log\left( \frac{p\big{(}D_{x},D_{y}\big{)}}{p(D_{x})p\big{(}D_{y}\big{)}}\right) \tag{4}\]  
assuming that \(D_{x}\) is within its normal range. According to [25], a higher value of \(I\big{(}D_{i};D_{j}\big{)}\) implies that during continuous time slots \(t_{x}\) and \(t_{y}\) (the next time slot), the in-body device senses similar or the same physiological information. The collected data packet \(D_{j}\) is not allowed to transmit if found to be similar to \(D_{x}\). By this means, the implanted sensor can avoid redundant continuous data transmission and therefore decrease its total energy consumption.
## 4 Galileo Augmenting GPS SFSE Precise Positioning Experiment
### Experiment Analysis
#### 4.2.1 Experiment 1
In general, the GPS/Galileo combination method can increase the number of observation equations, and the constraint method can perform a targeted length limitation on the erroneous solution. Although their emphases are different, they are both helpful in improving the precision of float solution and the success rate of ambiguity resolution. Figure 5: _Cont._  
Figure 5: Position deviation of different strategies for baseline shm1-shm4: (**a**) GPS, (**b**) GPS with baseline constraint (C-GPS), (**c**) GPS/Galileo, and, (**d**) C-GPS/Galileo. Figure 6: Mean NOS, ADOP, and bootstrapped success rates of different strategies for baseline shm1-shm4: (**a**) GPS, (**b**) C-GPS, (**c**) GPS/Galileo, and (**d**) C-GPS/Galileo.
## 3 Digitalization of Compliance Checks with RegTech LLM
### Traditional Software vs. LLM for Compliance Check  
LLM-based compliance checks offer significant advantages over traditional software in various aspects. Traditional software totally relies on rigid, predefined rules and static datasets, struggling to adapt to dynamic regulatory landscapes. In contrast, LLM-based software can learn from new data and evolve to meet changing compliance requirements.  
Traditional software requires significant manual effort and code modifications for updates, whereas LLM-based software can be easily expanded through training and fine-tuning processes such as retrieval-augmented generation (RAG). While traditional software may not provide explanations for unexpected outcomes, LLM-based software can offer more nuanced and customized explanations derived from raw data. Traditional software is often limited by its reliance on structured data sources, while LLM-based software can process a broader range of data, including unstructured text and real-time streams. Additionally, LLM-based software leverages advanced AI techniques like NLP and machine learning to perform complex tasks and provide human-friendly interactions. LLMs can identify complex compliance issues and continuously adapt to new regulations. This allows for more accurate, efficient, and adaptable compliance checks, ultimately leading to better decision making and risk mitigation. LLM, deep learning, and traditional software are compared in Figure 5, and the differences are presented in Table 1.  
LLMs mainly provide a user-friendly interface. This removes the requirement for users to be experts, integrate knowledge of operational parameters, and have a deeper understanding of what could go wrong. It also eliminates the need for humans to remember all rules in real-time assessments. Current LLM-based software may require more frequent updates and maintenance and have limitations in processing large amounts of real-time data. However, future LLMs may be explicitly created for regulatory systems.
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
## 1 Introduction
### Paper Organization  
The rest of this paper is organized as follows. The uplink NOMA VLC system model we considered is introduced in Section 2. In Section 3, we propose the channel and bit adaptive power control strategy for transmitter and develops a low-complexity ML demodulation and decoding algorithm for receiver. Simulation results of our proposed APC, GRPA, FPA, and TDMA schemes are presented and discussed in Section 4. In Section 5, we summarize this paper.
## 4 Results
### Calculation and Analysis Model of Frost-Heave Force
#### 4.2.3. Considering the Frost-Heave Force of Circular Wedge Hollowing under Drainage Conditions
When \(\beta_{0}=\zeta_{0}\), the volume of discharged water is equal to the volume of water expanded when freezing, and the frost-heave force \(P=0\). The \(\beta\) values of the excretion channel at different cavity heights can be obtained. During actual engineering use, the value can be determined by referring to the relative position \(\beta_{0}/\zeta_{0}\) of the excretion channel and the emptying cavity, as shown in Table 3. The graph of the relationship between the excretion coefficient and the excretion channel position is shown in Figure 15.  
\[V_{i}=\alpha V_{v-} \tag{18}\]0 Calculation model of frost-heave force in a circular wedge-shaped void considering the location of the excretion channel. It can be seen from Figure 15 that the magnitude of the frost-heave force is directly related to the location of the excretion channel. When the excretion channel is located at the top of the void, the water in the cavity is squeezed out during the freezing process, and there is essentially no frost-heave force in the cavity; when the excretion channel is located at the bottom edge of the void, the excretion channel is frozen first during the freezing process, and the void becomes a closed space, causing a large frost-heave force to be generated in the cavity. Both the discharge coefficient and the frost-heave force decrease as the distance between the discharge channel and the hollow bottom increases.
## 7 Discussion and Conclusions  
This study, based on literature data on 'urban planning' and related topics in the WoS system with total number of 750,000 articles since 1950, could largely represent the development feature in 'planning research' area. Academic literature is a manifestation of research investment; by analyzing the characteristics of academic literature of planning research, we could accurately understand the correlations between planning research and macro development.  
The study found the pattern evolution of planning research investment in major countries since 1950s. In past 70 years, the total number of countries investing in planning research has grown rapidly. From the perspective of participating country numbers, the number of participating countries engaged in planning research has shown a rapid rise  
Figure 5: Correlation analysis between input intensity of planning research and HDI [28; 30]. Source: based on data from Web of Science and UNDP 2020.  
since 1961, from only 8 countries involved in 1966, soaring to 193 in 2019, covering almost every country in the world. The rapid increase of planning research investment proves that countries were increasingly aware of the importance of planning and its research. Over the past 70 years, the diversity of countries engaged in planning research have also continued to grow. For diversity analysis of participating countries, the participating countries have developed rapidly from only major countries to 193 countries/regions. From the initial monopolization by developed countries to the gradual emergence of developing countries, scholars and practitioners from developing countries gradually became active and integrated into mainstream research community, finally becoming the main contributors. It is highly related to global economy and politics; the surging of developing world is because developing countries started the process of high-speed and massive urbanization after World War II, which needs the support of planning research. At the same time, the research finds that the investment intensity of planning research is consistent with the problems and characteristics of urbanization development stage, which proves that the demand of urbanization development promotes the investment of planning research.  
At the same time, this study discovers planning research characteristics of different countries in 1950-2019, according to the proportion changes of planning research investment accounts for the total world planning research investment during this period, the characteristics of investment in major countries are summarized as different investment characteristics, and further speculate its interaction with the international political and economic pattern.  
The correlation analysis between the intensity of planning research investment and HDI shows a strong correlation, indicating that the amount of research in planning and related fields is crucial in national macro development. In a sense, the more developed the society, the more attention paid to planning research, forming the virtuous cycle in this sense.
# Energy Trading Strategy for Heat and Electricity-Coupled Microgrid Based on Cooperative Game
###### Abstract
To this end, the contributions of this paper are summarized as follows:  
(1) An energy management framework for CHP-based MG integrated with PV and hybrid DR users is proposed. The energy trading process between MGO and hybrid DR users is formulated as a Nash bargaining cooperation game, where the users can bargain with MGO about the trading volumes and prices of electricity and heat. (2) The relationship between Nash bargaining solution (NBS) and social welfare maximum is studied, and a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) is adopted to achieve NBS.
## 1 Introduction
Moreover, the article initiates the development of a real-time cognitive workload monitoring system though an experiment that integrates IIoT gateways, the constrained application protocol (CoAP) [37], an AI server, and the Things Board open-source IoT platform for live data monitoring [38]. In this experiment, four virtual users participate, each connected to the AI server, which employs the DGNN model for real-time cognitive workload analysis via IIoT gateways using CoAP. Additionally, the CWMS provides real-time data on parameters such as latency, classification processing time, and cognitive workload levels. This paper offers the following contributions:  
1. This study introduces a novel deep gated neural network (DGNN) designed for classifying workloads in real-world scenarios using EEG data. 2. The proposed approach achieves superior performance in classifying various workload levels compared to previous studies. 3. This study presents a distinctive cognitive workload monitoring system (CWMS) that combines IIoT gateways, CoAP, and an AI server for real-time monitoring and analysis of workload parameters. The subsequent sections of this article are organized as follows. Section 2 presents a detailed analysis of current studies on the categorization of cognitive workload. Section 3 provides an explanation of the materials and methods used in the study. This includes the preprocessing of EEG data, the design of the DGNN model, and the implementation of the real-time CWMS. In Section 4, the results of both the proposed method and CWMS are presented, along with a thorough analysis. Section 5 provides the conclusion, while Section 6 pertains to the future works.
## 3 Results
### Evolution with the Myo-Pong Tool  
The _familiarisation session_ concluded with the _Myo-Pong_ tool working in low-difficulty mode. The _consolidation session_ concluded with a first trial of _Myo-Pong_ in low-difficulty mode and a second trial in high-difficulty mode. The low-difficulty mode has a lower ball speed than the high-difficulty one, i.e., it takes 3.7 s from wall to wall, as compared with the 2.8 s in high-difficulty. The high-difficulty mode was included in the _consolidation session_ as a new challenge after the previous training in low-difficulty mode.  
Figure 5: Box plot that summarises the four trials performed by each subject. A total of 19 patients’ timings and 62 control subjects’ timings is represented. Atypical cases, marked with asterisk, are excluded.  
Figure 6 shows the patients and control subjects' SR for low-difficulty mode. Both muscle groups are represented, flexor carpi radialis and extensor carpi radialis. Maximum, minimum, and mean value are showed for each subject. There is an important improvement in the SR between sessions for the control subjects (\(p\) = 0.009). This improvement is not achieved in the patients' group (\(p\) = 0.978).  
Figure 7 displays the SR for high-difficulty mode, achieved during the _consolidation session_. The SR gets worse for the control subjects (\(p\) < 0.001) as well as for the patients (\(p\) = 0.047). However, when comparing Figure 6 and 7, the SR decrement is slightly smaller for the patients. Both graphs show SR values close to the percentile 100, and the worst cases are around percentile 30. Thus, the _Myo-Pong_ difficulty level was achievable by all the users under trial so the applied settings are adequate. During the tests, one of the CS only performed the first trial of the _familiarisation session_. She did not enjoy the game and disagreed to participate in further trials. This is reflected under the control subject C10 in Figure 6 and 7.  
Two antagonistic muscle groups were employed during the trial. _Myo-Pong_ allows an analysis whether the dexterity is balanced in both muscle groups. The _Myo-Pong_ design is symmetric for both muscles, as both paddles have the same size and run along the same length. The required effort for each paddle to reach the full span is adapted to the DMVC of each muscle group. This adaptation was done in the calibration procedure prior to playing. Table 3 shows the statistical data obtained when comparing both muscle groups in terms of the SR. From these data, both muscle groups show similar means and variances. Thus, the performance between muscles is balanced, i.e., there are no highlights between muscle groups.  
Figure 6: Success rate (SR) for the _Myo-Pong_ at low difficulty. C is for the control subjects. P is for the patients. C10 played in the _familiarisation session_ for just a short time.
## 2 Materials and Methods
### Results of Sample ZrN B  
A different situation is found for coating ZrN B after peak-fitting separation. Here, the surface region shows a very thin top-layer (10-15 A thick) of oxides (ZrOH/ZrO\({}_{2}\), eventually oxynitride) completely screening the nitrogen signal. The oxide top-layer disappears after a short sputter-clean of 2 min, and then acquisitions of the survey scans and the high-resolution spectra of N1 s, Zr 3d, O 1s and C 1s have been performed. The N 1s region shows two singlets, one at low BE (396.46 eV) associated to a 20.81% of Zr\({}_{3}\)N\({}_{4}\) on the N 1s total signal, and the other centered at 397.35 eV associated with the ZrN compound. On the other hand, the Zr 3d region does not show the separation between the two phases (Figure 4 left side) but only the separation of satellite contribution from zirconium nitride ones. The statement [19] that a Zr 3d high-resolution spectrum cannot be separated into ZrN and Zr\({}_{3}\)N\({}_{4}\) contributions by fit is widely shared. In fact, when eventually both phases are present, they overlap. The right side of Figure 4 reports the results after a peak fit of N 1s where N-Zr bonds prevail but a low BE peak is present and attributed to Zr\({}_{3}\)N\({}_{4}\)[20]. At this point it becomes very interesting to study in detail sample ZrN B.  
Figure 3: Peak fitting results for Zr3d and N 1s high resolution (HR) fitted spectra, for level 2 of sample A.  
Figure 2: Oxygen contamination in sample ZrN A in-depth level 2.  
A complete sputtering depth profiling using theAr+ ion gun composed of 24 points from the surface was constructed at a constant temporal step. The sputtering conditions have been reported in Table 3. The in-depth acquisitions were repeated on two different areas of 9 mm\({}^{2}\). In Figure 5 the first 13 points are reported, cutting all the second part of the depth profiling for the sake of simplicity. Looking at Figure 5, the Zr\({}_{3}\)N\({}_{4}\) percentage (N1s II) stands always below the ZrN percentage that is 10% (N 1s I) and the Zr 3d nitride doublet includes both the ZrN and Zr\({}_{3}\)N\({}_{4}\) indistinguishable contributes. N 1 sI indicates the stoichiometric nitride and N 1 sII the over-stochiometric nitride separated on the N1s HR spectrum. The trend of satellite percentages (with respect to the overall Zr 3d HR spectrum) is indicated: its average value stands at 20-25% with respect to the remaining signal associated with nitride species.
## 2 Case Description
### Diagnostic Timeline
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Biochemical Target** & **Normal Range** & **Unit** & **Level** & **Value** & **Date of Measurement** \\ \hline
5-HIAA & & & & & \\ (5-hydroxyindoleacetic acid) & \(<\)2.40 & mg/g & High & 3.05 \({}^{*}\) & November 2021 \\ \hline \multirow{4}{*}{Adrenaline} & \multirow{4}{*}{\(<\)20} & \multirow{4}{*}{\(\mu\)g} & Normal & 11 & August 2012 \\ \cline{3-5}  & & & Normal & 3.5 & July 2022 \\ \cline{3-5}  & & & Normal & 10.22 & March 2023 \\ \hline B1 adrenergic receptor & & & & & \\ autoantibody & \(<\)15 & U/mL & High & 17.9 \({}^{*}\) & July 2021 \\ \hline B2 adrenergic receptor & & & & & \\ autoantibody & \(<\)8 & U/mL & High & 13.5 \({}^{*}\) & July 2021 \\ \hline Dopamine (plasma) & \(<\)85 & ng/L & Normal & 81 & March 2023 \\ \hline GABA urine/creatinine & 3–13.6 & \(\upmu\)mol/g & High & 15.4 \({}^{*}\) & July 2022 \\ \hline Glutamate urine/creatinine & 8–25 & \(\upmu\)mol/gCrea & High & 42.4 \({}^{*}\) & July 2022 \\ \hline M3-muscarinic & & & & & \\ autoantibodies ACh & \(<\)6 & U/mL & High & 8.4 \({}^{*}\) & July 2021 \\ \hline Metanephrine & \(<\)350 & \(\mu\)g & Normal & 78 & August 2012 \\ \hline \multirow{4}{*}{Noradrenalineine} & \multirow{4}{*}{\(<\)90} & \multirow{4}{*}{\(\mu\)g} & High & 427 \({}^{*}\) & March 2023 \\ \cline{3-5}  & & & Normal & 77 & August 2012 \\ \cline{3-5}  & & & Normal & 19.5 & July 2022 \\ \hline Noradrenalineine & 70–750 (lying) & ng/L & Normal & 167 & March 2023 \\ \hline Noradrenalineine & 200–1700 (standing) & ng/L & Normal & 518 & March 2023 \\ \hline Noradrenalineine & 32–58 & \(\mu\)g/gCrea & High & 64.9 \({}^{*}\) & July 2022 \\ \hline Noradrenalineine/adrenalineine & 3–6 & quotient & Normal & 5.6 & July 2022 \\ \hline Normetanephrine & \(<\)600 & \(\mu\)g & Normal & 270 & August 2012 \\ \hline Quinolinic acid & \(<\)5.50 & mg/g & High & 6.11 \({}^{*}\) & November 2021 \\ \hline Serotonin & 50–20 & \(\mu\)g/L & Normal & 39.7 & July 2022 \\ \hline Serotonin urine/creatinine & 148–230 & \(\mu\)g/gCrea & Low & 131.9 \({}^{*}\) & July 2022 \\ \hline Taurine & 5.4–31.3 & mg/L & Low & 4.8 \({}^{*}\) & July 2022 \\ \hline Tryptophan & 7.30–12.50 & mg/g & Normal & 9.86 & November 2021 \\ \cline{3-5}  & & & Normal & 10.5 & July 2022 \\ \hline Tyrosine & 38.5–84.3 & \(\upmu\)mol/L & Low & 35.18 \({}^{*}\) & November 2021 \\ \cline{3-5}  & & & Low & 36.43 \({}^{*}\) & July 2022 \\ \hline VMA (vanillyl mandelic acid) & \(<\)3 & mg/g & High & 3.14 \({}^{*}\) & November 2021 \\ \hline \hline \end{tabular} \({}^{*}\) indicates values outside the normal range.  
\end{table} 
Table 1: Summary of main altered molecular parameters. The table displays the levels of biochemical parameters, their normal ranges, and the dates of measurement (neurotransmitters, autoantibodies, amino acids, and metabolites).