esa-sceva/satcom-chunk-collection huggingface dataset:
# Test-Retest Reliability of a 6 DoF Marker Set for Gait Analysis in Cerebral Palsy Children
## 4 Discussion
### Limitations  
The number of CP children included in similar studies varies from 5 to 20 [23; 24; 25; 26; 44] and even though this gait protocol was performed with 8 CP children, the analysis of the right and left legs imply distinguished experiments, involving independent landmark identification, marker attachment, anthropometric measurements, and data processing [17]. Consequently, the current research should be considered as an independent analysis of sixteen legs.  
Given that every gait research laboratory uses its own marker set and gait model, in order to compare gait analysis data, all the specific methodology used in each process must be considered. Regardless of the set of techniques chosen, there will always be different measurement errors that can influence the outcomes and consequently, a clinical interpretation. These differences have a greater impact in the kinematic and kinetic outcome measures (e.g., joint angles and moments). Thus, gait protocols should be described in detail to allow a contextualized interpretation of the results and comparison between similar investigations. This should be done in a critical manner on all the variables during the gait cycle, rather than only interpret the absolute values presented, regardless of the measures of repeatability or correlation used [15]. It is of great relevance when it comes to gait assessment of CP children who have an intrinsic gait variability due to their neuromuscular impairments. In these cases, it is crucial to differentiate the methodological errors (raters error) from the participants' natural variability and from the effect of a rehabilitation process.  
Due to the different gait analysis protocols used, the influence of the number of gait cycles in test-retest reliability measurements [11] remains to be determined. Although in general, repeatability increases with a higher number of gait cycles, this is true mainly for the kinematic data. All the time-distance and kinetic parameters do not reveal significant differences from the fifth gait cycle onwards. In addition, the assessment of more than five gait cycles in a clinical setting may be difficult to accomplish due to the preparation of the subject [34]. Regarding CP children, this can be a very complex and difficult task, therefore the five gait cycles used in this protocol were shown to be quite good in achieving reliable results.  
Figure 2: Scatter plots for ankle peak joint angles for dorsiflexion and plantar flexion. Subject with increased gastrocnemius stiffness values is represented with a different symbol from the rest.
# Cross-Section Shape and Asymmetric Support Technology of Steeply Inclined Thick Coal Seam Roadway
## 6 Discussion  
In the preceding analysis, the adaptability of mine main roadways with different cross-sectional shapes under uniform load conditions was investigated. It was found that in steeply inclined coal seams with a thickness greater than 3.5 m, arched roadways exhibit significant advantages over rectangular and inclined-roof trapezoidal (e.g., right trapezoidal with inclined roof) sections. Meanwhile, it should be noted that for seam thicknesses less than 3.5 m, the conclusions of [PERSON], [PERSON] [26] and [PERSON], [PERSON] [27] may be more applicable, i.e., inclined-roof trapezoidal sections show better adaptability.  
Subsequently, the influence of the coal seam dip angle on roadway stability was explored. It was revealed that with an increasing dip angle, roadways exhibit a significant asymmetric failure distribution, consistent with the findings of [PERSON] [28] and [PERSON] [29]. However, this study differs in identifying that as the coal seam dip angle increases, obvious stress-concentration zones form at the arch shoulder of the upper rib and the wall base of the lower rib, leading to asymmetric deformation and failure. Concurrently, the peak horizontal stress in these zones gradually increases, while the peak vertical stress decreases, which is likely related to the deflection of principal stress directions. In the context of this study's extra-thick coal seam, the failure mode of the surrounding rock may transition from tensile failure to shear failure. Based on these insights, an innovative asymmetric support technology for thick coal seams was proposed and achieved good engineering performance.  
Generally, in steeply inclined thick coal seam roadways, uniform support schemes based on symmetric cross-sectional shapes (such as rectangular and arched sections) are adopted, with high-strength bolt-mesh-cable systems used to improve overall support strength and maintain surrounding rock stability. The advantage of such symmetric  
Figure 14: Elastic curve of surrounding rock in 1502 level roadway.  
Figure 13: Schematic diagram of roadway surface observation.  
schemes lies in their construction convenience, but they often result in over-support in areas with relatively small deformation. By contrast, the new technology applied in the test roadway avoids this issue, optimizing support efficiency and reducing unnecessary material consumption.
## 1 Introduction
On the other hand, the consumers can also obtain several advantages, such as visualization of their consumption profile, responding to the incentives of the EM in order to increase their profit (DR), charging, etc. Thus, the information flow in the grid becomes bidirectional, where the consumer is also able to react to changes in the grid [19]. For entities involved in power and energy systems to be able to cope with the changing and growing complexity of wholesale and retail markets, as well as the consequences of DG penetration, several simulation tools have emerged, allowing the study of the various concepts and their evolution (examples are presented in Section 2). However, these tools have the disadvantage of only solving problems in specific fields, and therefore, they are not able to provide a more comprehensive study involving several domains. Thus arises the need to create solutions to perform more comprehensive studies through the interconnection of the different areas of simulation tools. However, this is a complex task, since it is necessary to create communication mechanisms between systems that have heterogeneous characteristics, such as the programming language in which they are developed, the different time resolutions, the handling of events and the synchronization of processes, among several other critical aspects, as discussed in Section 2. This paper proposes the Tools Control Center (TOOCC) framework, which has the objective of increasing the interoperability between heterogeneous agent-based systems for the simulation and study of power and energy systems' concepts, such as EM, SG operation, energy management and participant entities. TOOCC acts as a facilitator in the interaction between entities/agents with distinct natures, through the use of ontologies, which allow the sharing of a vocabulary to ensure that all systems understand concepts equally. The framework also aims to achieve more complete simulationswith different time resolutions (e.g., real-time simulation, wholesale market, etc.) in a more automated way and provides relevant information about the results to the user. After this introductory section, Section 2 presents an overview of related work in the fields of agent-based simulation in power and energy systems, decision support systems in this domain and on interoperability between systems. Section 3 addresses the characteristics, advantages, phases and also the multi-agent architecture of TOOCC framework. A case study is presented in Section 4, which illustrates how the interaction between the systems is made and also an example of the kind of results the user can analyze. Finally, Section 5 presents the most relevant conclusions and contributions of this work.
## 2 Materials and Methods
### Datasets
#### 2.2.3 Climate Models Data  
The study utilized a bias-corrected dataset created by [PERSON] et al. (2020) [45] with a high resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) for historic and projected climates for the four SSP scenarios in South Asia. The dataset was developed using the Empirical Quantile Mapping (EQM) method and output from 13 GCMs as part of CMIP6. [PERSON] et al. (2020) [36] compared the dataset against observations for average and extreme rainfall and maximum and minimum temperatures using daily rainfall and temperature data from IMD for the Indian region. The authors found a dry bias in average annual rainfall for most of South Asia, a significant cold bias in the Himalayan region for mean annual maximum and minimum temperatures, and a warm bias in average annual minimum temperature for most of South Asia, excluding the Himalayas, by calculating the multi-model ensemble mean bias in rainfall and maximum and minimum temperatures. To rectify the bias in the CMIP6-GCM output, the EQM approach was used at the daily timescale. The bias correction substantially reduced the bias in all three variables, and the bias-corrected dataset was able to accurately capture the co-variability of monsoon season rainfall and air temperature. The study predicts that South Asia will have a warmer and wetter climate in the 21 st century, with a rise of 3-5 \({}^{\circ}\)C in temperature and 13-30% in rainfall.
# Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer
## 3 Case Study
### Signal Homogenizer  
The system output vector is \(y=[y_{1}\ y_{2}\ y_{3}\ y_{4}\ y_{5}\ y_{6}]^{T}\), where the GPS output signals \(y_{1}=\text{x}\), \(y_{2}=\text{y}\), and \(y_{3}=\varphi\), are the (X,Y) position in a global framework and the orientation \(\varphi\) of the mobile robot, respectively; the encoders' output signals are \(y_{4}=\Omega_{1}\), \(y_{5}=\Omega_{2}\), and \(y_{6}=\Omega_{3}\). Hence, the set of system outputs is \(\Sigma=\{y_{1},y_{2},y_{3},y_{4},y_{5},y_{6}\}\). From the set \(\Sigma\), two subsets are generated: \(\Sigma_{1}=\{y_{1},y_{2},y_{3}\}\), which contains the GPS outputs; and \(\Sigma_{2}=\{y_{4},y_{5},y_{6}\}\) for the encoder's outputs.  
In order to generate the homogenizers \(P_{1}:\Sigma_{1}\rightarrow\Sigma\) and \(P_{2}:\Sigma_{2}\rightarrow\Sigma\), the structure depicted in Figure 2 is applied. For the homogenizer \(P_{2}\), the coordinate transformation is chosen as \(\xi_{1}^{2l_{4}}=y_{4}\), \(\xi_{1}^{2l_{5}}=y_{5}\), and \(\xi_{1}^{2l_{6}}=y_{6}\). So, the estimated input \(\bar{u}^{2}\) (see Figure 2) is computed from Equation (2) as  
\[\bar{u}^{2}=(E^{2})^{-1}\left[\begin{pmatrix}\xi_{1}^{2l_{4}}\\ \xi_{1}^{2l_{5}}\\ \xi_{1}^{2l_{6}}\\ \xi_{1}^{2l_{6}}\end{pmatrix}-\begin{pmatrix}-\lambda_{1}(\xi_{1}^{2l_{4}}- \text{Ysign}(\Omega_{1}))\\ -\lambda_{2}(\xi_{1}^{2l_{8}}-\text{Ysign}(\Omega_{2}))\\ -\lambda_{3}(\xi_{1}^{2l_{6}}-\text{Ysign}(\Omega_{3}))\end{pmatrix}\right] \tag{20}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(a_{1}\) & \(-10.3949\) & \(b_{1}\) & \(2.9695\) \\ \(a_{2}\) & \(2.5026\) & \(b_{2}\) & \(29.5783\) \\ \(a_{3}\) & \(-15.2935\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Identified parameters of the system.  
where \(\xi_{1}^{2h_{4}},\xi_{1}^{2h_{5}},\xi_{1}^{2h_{6}}\) and their derivatives are estimated by using HOSM differentiators [16]. The matrix \(E^{2}\) is given by  
\[E^{2}=\begin{pmatrix}18000\lambda_{1}&0&0\\ 0&18000\lambda_{2}&0\\ 0&0&18000\lambda_{3}\end{pmatrix} \tag{21}\]  
Then, using the system model in Equation (19), \(\Sigma^{2}\), the virtual output associated with \(\Sigma_{2}\) is computed. In a similar way, the homogenizer \(P_{1}\) and \(\bar{\Sigma}^{1}\) are computed.
## 4 Results
### Astrochronological Analysis  
Multi-window spectral analysis of the Roadian-Wordian order KTH data series from the HB1 well, which is located in relatively deep water with relatively stable deposition and minimal influence from exogenous inputs, shows that due to rapid changes in depositional rates, several signals exceed the 90% confidence curve. The signals at 3.39 and 2.59 m exceed the 95% confidence curve, and the peaks at 10.93, 6.80, 2.09, and 1.56 m exceed the 90% confidence curve. Based on the cardinality of the characteristic lithofacies distribution (Figure 5) and using the bottom boundary of the section where the event sediment was first observed as the interface, the HB1 well was divided into two phases: Phase 1 (5800\(\sim\)5902 m) and Phase 2 (5755.375-5800 m). These phases recorded signals at 10.93, 3.39, 2.59 m (4.22:1.31:1) and 6.80, 2.09, 1.56 m (4.35:1.33:1), which are close to the Middle Permian long-eccentricity to short-eccentricity cycle ratio (405:127:95 = 4.26:1.33:1), suggesting that a convincing Milankovitch cycle has been recorded for the Roadian-Wordian order deposits in well HB1 (Figure 9).  
The long eccentricity period (405 kyr) is the most stable of the astronomical orbital period parameters [37]. Therefore, using it as a standard for astronomical tuning, it is shown that relatively stable long eccentricity cycles are recorded in both Phase 1 and Phase 2. The rapid environmental changes in a short period of time cause the short eccentricity cycles to fluctuate to some extent, thus completing the conversion of the KTH data series from the depth domain to the time domain. A total of 19 long eccentricity cycles are recorded in the Roadian-Wordian order, with a duration of 8.2 Ma (Figure 10).  
Figure 8: Comparison of C isotopes in the Wordian stage of the Yangtze Caton. The C isotope value show a significant downward trend in the middle Wordian, indicating that the environment underwent drastic fluctuations. The data of Xishui, Nanchuan and Qijiang profiles are from reference [40].  
Figure 10: Time scale spectrum analysis of the 405 kyr-tuned Radian–Wordian KTH sequence in well HB1. (**a**) HB1 well profile and astronomical age scale; the Roadian–Wordian order lasted a total of 8.2 Ma, and about 19 cycles of 405 Ka can be identified, which can be divided into stage 1 and stage 2, with distinct changes in depositional rates. (**b,c**) Time-domain spectral analysis of the 5800–5902 m and 5755.375–5800 m records, respectively. They recorded the same astronomical cycle age (407 ka, 117 ka, and 102 ka).  
Figure 9: (**a**) Analysis of the 2 \(\pi\) energy MTM spectrum of the Roadian–Wordian KTH sequence in Well HB1; (**b**) sliding window analysis with a window width of 20 m. E, el, and e2 next to the white dashed line represent long eccentricity (405 ka) and short eccentricity (125 ka and 95 ka), respectively; (**c,d**) 2 \(\pi\) energy MTM spectrum analysis for 5800–5902 m and 5755.375–5800 m, respectively.
# Unstructured Data Analysis for Risk Management of Electric Power Transmission Lines +
## 4 Methodology
### Web Cravling  
ANEEL provides weekly reports available publicly on its website. These reports provide information on decisions made by ANEEL regarding the management of transmission lines. They are unstructured text files organized into paragraphs and publication dates. There is no API to extract the data, and we used web crawling to download all the reports' contents from January 2005 to June 2021.  
Figure 1: Diagram representing the methodology steps. First, the weekly reports are extracted from ANEEL’s website through web crawling. Secondly, the risks and causes are listed by an expert. Then, the unstructured reports are processed using NLP techniques, and keywords are extracted from the risks’ causes. Finally, the risk probability is estimated.  
We developed a script (PHP web-based language) to access a list of pages with the weekly reports on ANEEL's website. Each list item is composed of the publication date, title, and a link to the actual report. Each link displays the report on a single HTML page. The HTML pages have a header with the following fields: information about the meeting, date, time, and place. The content is also identified by the process ID tag, and each process is split into the relevant area, rapporteur, and final decision. The developed script traversed each report's HTML page, downloaded it, and parsed the HTML page to remove irrelevant information and store only the data or text regarding the report itself.  
The keyword search method splits each document per process. Each process was extracted, and its content was stored in the database with a respective ID. The content itself was composed of a topic and a final decision regarding the process. Aside from storing the full text of each process, each individual keyword was also stored on another table, discarding the stop words. Those keywords were used to calculate their occurrence frequency and relevance during the generation of relevant keywords.
## 3 Data Sources and Research Methods
### Research Methodology  
#### 3.2.1 Network Construction  
The gravity model was applied here; then, the passenger volume, freight volume, and transportation network distance were selected for the measurement of urban correlation strength \(R\), and the formula is as follows [40]:  
\[R=p\times\frac{c_{i}^{a}\times c_{j}^{b}}{d_{ij}^{r}} \tag{1}\]  
where \(R\) represents the correlation strength of passenger and freight transportation between two different cities; \(c_{i}\) and \(c_{j}\) represent the passenger volume and freight volume of city \(i\) and city \(j\), respectively. As urban centrality is the only indicator of city size, \(a=b=1\) can be assumed. This model was mainly used to measure the correlation strength between two cities, so \(p\) can be treated as a constant 1, and \(r\) is the distance attenuation coefficient; referring to other relevant studies, we chose \(r=2\)[41]. Further, \(d_{ij}\) represents the distance of the transportation network between city \(i\) and \(j\); it can be measured by the length of the transportation network between cities according to the high-speed, national, and provincial roads of the 12 provinces covered by the New Western Land-Sea Corridor in each year using network analysis tools in ArcGIS. Based on this, the urban passenger and freight transport network of New Western Land-Sea Corridor is constructed by the urban correlation strength \(R\).
## 3 The Proposed Protocol
### System Initialization Phase  
In the initialization phase, given a security parameter \(n\), the gateway node \(GWN\) selects a long-term key pair \(x,y\in\{0,1\}^{n}\) and generates a unique identity GID. Then, \(GWN\) saves \(\{x,y\}\) and publicizes identity GID.
## 3 Performance Analysis
### Characteristics of the Repeater  
Raspberry Pi 3 features the following specifications:  
* Model: Single-Chip BCM43143.
* Transmission power: 125.89 mW at 20 MHz and 89.12 mW at 40 MHz.
* Frequencies: 2.5 GHz and 5 GHz Wi-Fi IEEE 802.11 ac MAC/Baseband.  
Considering the radiation patterns of the hotspot in Figure 2, Raspberry Pi 3 was configured as a wireless signal repeater. For this purpose, RASPAD software was used and wireless bridge communication was implemented at a frequency of 2.4 GHz.
# Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis
## 3 Materials and Methods
### Eye Blink Extraction  
As stated above, the eye blinks are fast closing/reopening of the eyelid and can be seen as a sharp decrease in the aperture timeseries, as shown in Figure 2.  
The timeseries trough detection was performed using a custom peak detection algorithm, based on the signal's derivative and peak parameters criteria such as Signal-to-Noise Ratio (SNR), peak slope, peak distance, peak duration. These parameters were set in such a way in order to filter-out small peaks and select only timeseries with sharp decreases, as shown in Figure 1. Especially, the minimum peak duration parameter was set to 100 ms (corresponding to 5 consecutive data points), given that an eye blink typically lasts between 100-400 ms [2].  
Figure 1: Schematic representation of the eye aperture timeseries extraction. The 68 2D facial landmarks (**blue dots**) were extracted from facial videos using AAM (**left image**). Each eyeball perimeter was segmented by 6 discrete landmarks. The eye aperture is provided by the shaded blue area (**right upper image**) and a typical eye aperture timeseries is shown in the right lower graph.  
Figure 2: Time series of eye aperture and the identified blink peaks (**denoted with red points**). The IBI and the blink duration are also illustrated.
# Path Segmentation-Based Hybrid Caching in Information-Centric Networks
## 3 Path Segmentation-Based Hybrid Caching
### IP-Compatible ICN Network Architecture  
The existing Internet architecture is entrenched and challenged to change radically, and so the new architecture should be deployed as an overlay network to accommodate existing IP networks [20]. In this paper, we consider an IP-compatible ICN network architecture shown in Figure 1. IP network compatibility is achieved by extending the existing network layer and transport layer to support ICN functions. In this network architecture, the name resolution system (NRS) is an important network component aside from the routers that support caching. The NRS is often used for overlay network design, the name resolution process, and the decoupled content routing process [21]. In the network, content publishers and cache nodes register the mapping of IP addresses and content names with the NRS. ICN routers can process both IP streams (IP-based routing) and ICN streams (caching decisions).
# Variational Quantum Circuits for Machine Learning.
## 3 Quantum Approaches for Machine Learning
### Quantum Distributed or Federated Machine Learning  
One of the main limitations of quantum convolutional neural networks (QCNN) is that they are a centralized solution. The generation of deep learning models requires a huge amount of data. Some applications have public datasets for research, but in most advanced processes it is necessary to collect specific data in fields such as medical imaging or natural language processing (NLP). As sharing data from these applications can infringe on user rights, federated learning [54] can solve the problem if the model is trained directly at the user's source, and what is shared in the cloud are the parameters of the trained model. The components of a federated machine learning system are the central node and several client nodes that send the parameters calculated in the training to the central node.  
Quantum distributed training consists of performing the training of a neural network through several quantum computers (that act as central and client nodes), with the objective of reducing the time needed to train the network. Hybrid quantum-classical neural networks can be used, although the concept is equally applicable to any type of machine learning model.  
Some studies on network training [55; 56] show that similar levels of precision can be obtained more quickly with quantum distributed machine learning. However, for the integration of quantum computers in future communication systems, it is necessary to create large-scale distributed quantum networks that surpass the quantum networks currently emerging [57].