esa-sceva/satcom-chunk-collection huggingface dataset:
# When Operation Technology Meets Information Technology: Challenges and Opportunities
## 2 Related Works  
IT and OT have evolved independently over time to solve different problems by employing different system architectures and communication protocols. IT systems were designed to connect applications and share data; as a result, they tend to be open and standards-based. Operational control systems were designed as standalone entities and were not originally intended to be connected or even accessed remotely [14]. As a result, OT systems tend to be closed and proprietary: OT alone monitors and controls physical devices only where operators are located. If OT can be accessed from different locations and by management, it will improve decision-making, optimize industrial control processes and business flows, lower operating expenses, accelerate business results, and reduce risks. This can be achieved by the integration of IT and OT [15].  
Considering the related works in the literature, the majority of the academic research regarding Eclipse BaSyx for IT/OT integration is tailored to vertical use cases, focusing on the achievement of context-specific objectives. These works include data collection and visualization from existing \"off-line\" machinery [16]; resource monitoring in an open, interoperable, and vendor-neutral manner [17]; real-time control of a production line by shifting analytical capabilities to edge components, avoiding high volume data transfers, and reducing latency [18]; and robust data exchange between enterprise and control applications in a motor control scenario [19].  
In contrast, [20] applies Eclipse BaSyx Asset Administration Shells in the context of service robots for intra-logistics tasks such as transportation and order picking. The AAS allows at runtime to command a service robot and to collect individual performance indicators such as kilometers traveled, success rates of task completion, the time needed for tasks, etc. In the end, the authors admit that the AAS of a service robot system can also hold accumulated operational data to support decisions such as maintenance but the idea is not supported by any real simulation.  
Considering security-related scenarios, the only relevant related work can be found in [21], where authors propose a safety and security reference architecture to provide a firm ground for developing safe and secure AAS-based applications in the context of Industry 4.0.  
Regarding instead the application of PTP in visual camera synchronization scenarios, [22] describes a frame synchronization scheme for high-speed networked vision systems. In this work, multiple vision sensor nodes, each comprising a camera and a PC, are connected via Ethernet for data communication and clock synchronization. The clocks of the PCs are synchronized over the network by PTP, while the trigger of each camera's shutter is locally controlled based on the PC's clock that is locally provided inside the node and globally synchronized over the network.
# Ionospheric Remote Sensing with GNSS
## 4 Ionospheric Remote Sensing
Adapted from [27] (**a**) S4 measurement; (**b**) SigmaPhi (\(\sigma_{\phi}\)) measurement. Besides ground-based GNSS ionospheric remote sensing, there are space-based approaches that utilize the spaceborne GNSS receivers on satellites for ionospheric radio soundings. For example, the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission uses the radio occultation technique (a bending effect on the GNSS signals propagating through the Earth's upper atmosphere) to measure space-based TEC and scintillations, detect ionospheric irregularities, and reconstruct global electron density profiles using ionospheric tomography techniques [31]. Using low-Earth-orbit GNSS receivers sensors in proximity together with spacecraft formation flying techniques, the ionospheric TEC, electron density, and scintillation index can also be measured globally with high flexibility [32, 33, 34].
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 4 Robotic Maintenance Technology in Other Industries
### Mobile Manipulators for Inspection and Repair Tasks
#### 4.2.4 Maintenance in Civil and Construction Sites  
Civil infrastructures such as buildings, roads, bridges, etc., require scheduled inspection and repair to ensure their asset life, safety, and user comfort. Inspection and repair technology for civil and construction infrastructure requires human involvement. Although robots have been used in civil infrastructure since the early 2000s [16; 109; 110], they have mostly been used for inspections, but not to perform any repair work. The earliest mobile manipulator with repair capability found in this review was published in 2013. In this work, [PERSON] et al. demonstrated a mobile manipulator for dry friction-based drilling for bridge deck repair tasks [111].  
[PERSON] et al. designed a civil infrastructure inspection and evaluation with a mobile manipulator with a combination of computer vision and thermal camera technologies for detection, shown in Figure 15[112]. Apart from a ground-based system, an aerial mobile manipulator can be used for civil infrastructure inspection and repair, as many places are hard-to-reach with a wheeled robot. Therefore, collaborative aerial mobile manipulators were proposed, in which one can detect cracks using artificial intelligence, and the other  
Figure 14: Mobile manipulator in space for maintenance application: (a) DAFSAR space mobile manipulator [99; 100]; (b) AMODS [101].  
can localize and repair cracks by depositing material [113]. Usually, commercially available drones and UAVs can only carry a light payload, typically one of less than 1 kg. However, repair work in civil infrastructure regularly requires a larger payload capacity. To solve this issue, research was carried out to design and fabricate an aerial manipulator with a coaxial tri-rotor arrangement capable of carrying a high payload [114].
## 4 Experiments and Analysis
### Experimental Environment  
As shown in Figure 12, the information received from MPU6050 entered the Raspberry Pi through I2C communication [42] using a connected wire. Afterward, the Raspberry Pi was configured in such a way that it judged the posture via the IMU sensor using Algorithm#1 and Algorithm#2, which were inside the Raspberry Pi, to output the visual results on the monitor. The communication time cycle of the raw data received through I2C communication was very short, taking about 10 s to connect the Raspberry Pi to the MPU6050 to obtain 1000 acceleration values; therefore, the time required to retrieve one value was about 0.01 s. Noise was detected in the raw data. In this experiment, the time sampling method was used to reduce the noise effect. The sampled value was the average of the incoming values with a time interval of 0.5 s. In addition, if a received value was significantly different from the immediately preceding value, it was classified as a missing value to reduce the sampling error. As a result, the value was updated every 0.5 s. To observe the difference between the raw and sampled data, the MPU6050 was tilted for about 10 s and the sensor angles were changed. As a result, 1000 raw data values were obtained, which were quite noisy, as seen in Figure 13. After time sampling every 0.5 s, it can be seen that the sensor \(\theta\) versus time graph was smoother and less noisy.  
Figure 11: Relationship between the CVA and forward tilted angle (\(\theta\)).  
Figure 12: Experimental device system with an MPU6050, Raspberry Pi, and monitor. (**a**) System design. (**b**) Real device connection.  
All subsequent experiments were conducted by attaching MPU6050 to the cervical spine, as shown in Figure 14.  
Table 5 shows the accepted standard CVA for \(\sim\)10 s that was obtained while maintaining a good posture by each participant.  
In Table 5, the CVAs of Person#2 and Person#4 was more than 5\({}^{\circ}\) lower than those of Person#1 and Person#3. If the _baseline__CVA_ was set to 50\({}^{\circ}\), the system was determined to be in the FHP state even though Person#2 and Person#4 were in good posture. Therefore, the _baseline__CVA_ should be set differently for each person.
# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 4 R2-D2 Case Study Results  
This section presents example results of the capability analysis of satellite imagery for supporting emergency services during incident management for the three selected challenges of snow, IWS fire and electricity transmission.
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 3 User-Based Integrity with LEO-PNT Formulation (Option 2)
### Performance Assessment
#### 3.5.2 LEO Constellations  
In earlier studies, these strategies were analyzed over full LEO constellations containing 200-400 satellites, such as the HIGH_LEO constellation (Table 5). It would be interesting to evaluate the different algorithms in a less standard constellation, one that can be tested in orbit without spending millions of dollars. For that, the \"IoD constellation\" has been defined, as shown in Table 5.
# Feasibility Study on Color Grading of Blue Iolite Based on GemDialogue Color Comparison Charts
## 3 Result and Discussion
### Application and Feasibility Study  
Twenty-nine elliptic faceted blue iolites were selected for color test with the integrating sphere handheld spectroscopy, and color parameters \(L^{*}\), \(a^{*}\), \(b^{*}\), \(C^{*}\), and \(h^{\circ}\) were recorded. Due to the pleechroism of iolite, the table facet was selected as the color measurement surface in this paper. The color data were substituted into the discriminant functions of 9 categories, respectively, to obtain the category of each sample, and the results were shown in Table 7.  
The 29 samples were divided into three categories and classified into four color levels, among which 1 sample belonged to light purple with high lightness and medium chroma, 11 samples belonged to bluepurple with high lightness and medium chroma, 2 samples belonged to grayblue with low chroma, and 15 samples belonged to bluepurple with black and gray tones. After observation, it was found that the classification results were  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster Center** & **Color Level** & **Discriminant Function** & **Classification Accuracy (\%)** \\ \hline \multirow{4}{*}{1} & A\({}^{++}\) & F\({}_{1.1}=0.265L^{*}-1.285C^{*}+14.807h^{\circ}-2270.23\) & 100 \\  & A\({}^{+}\) & F\({}_{1.2}=0.803L^{*}-1.441C^{*}+14.497h^{\circ}-2201.61\) & 100 \\  & A & F\({}_{1.3}=0.419L^{*}-1.048C^{*}+13.589h^{\circ}-1926.30\) & 100 \\ \hline \multirow{4}{*}{2} & B & F\({}_{2}=0.37L^{*}+0.954C^{*}+2.954h^{\circ}-480.89\) & 100 \\  & D & F\({}_{3}=0.622L^{*}+0.173C^{*}+2.529h^{\circ}-334.51\) & 98.8 \\ \cline{1-1}  & F & F\({}_{4}=0.335L^{*}+0.25C^{*}+3.067h^{\circ}-475.767\) & 100 \\ \cline{1-1}  & E & F\({}_{5}=0.359L^{*}+0.278C^{*}+2.576h^{\circ}-339.71\) & 97.3 \\ \hline \multirow{4}{*}{6} & C\({}^{+}\) & F\({}_{6.1}=-0.794L^{*}+0.996C^{*}+5.09h^{\circ}-692.16\) & 100 \\ \cline{1-1}  & C & F\({}_{6.2}=-0.565L^{*}+0.958C^{*}+4.727h^{\circ}-606.29\) & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fisher discriminant analysis results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Category** & **Color Level** & **Color Quality** \\ \hline \multirow{4}{*}{1} & 1.1 & A\({}^{++}\) & Purple \\  & 1.2 & A\({}^{+}\) & Light purple \\  & 1.3 & A & Bluish purple \\ \hline \multirow{4}{*}{6} & 2 & B & Bright blue \\  & 3 & D & Grayblue \\  & 4 & F & Bluish-purple with blackgray tones \\  & 5 & E & Dark blue \\ \hline \multirow{4}{*}{6} & 6.1 & C\({}^{+}\) & Blue \\  & 6.2 & C & Light blue \\ \hline \hline \end{tabular}
\end{table}
Table 5: Color grade and description of color charts.  
consistent with the visual observation, and the color comparison between some samples and the cluster centers is shown in Figure 7.  
Although not all categories were covered due to the limited number of samples, the results proved that this method is still feasible and can provide a more objective and stable basis for color classification of blue iolite than the visual evaluation.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Patient Number**} & \multirow{2}{*}{**Implant Position Characteristics**} & \multirow{2}{*}{**Symptons**} & \multicolumn{4}{c}{**Patient Characteristics**} \\  & & & & **Gender** & **Age** & **Smoker** & **Anannesis** \\ \hline
4 & 1.6 \({}^{\ast\ast}\) & 3 \({}^{\ast}\) & moderate pain on chewing & F & 55 & Moderate & Negative \\  & 1.6 \({}^{\ast\ast}\) & 1 \({}^{\ast}\) & severe spontaneous pain & & & & \\
13 & 1.2 \({}^{\ast\ast}\) post-extractive & 1 \({}^{\ast}\) & severe spontaneous pain & M & 66 & Non-smoker & Negative \\  & 2.3 \({}^{\ast\ast}\) post-extractive & 3 \({}^{\ast}\) & none & & & & \\ \hline
20 & 1.6 \({}^{\ast\ast}\) post-extractive & 48 \({}^{\ast}\) & moderate pain on chewing, peri-implantitis & M & 59 & Non-smoker & Negative \\
22 & 1.3 \({}^{\ast\ast}\) & 3 \({}^{\ast}\) & none & F & 65 & Heavy smoker & Diabetes treated with OHAs \\ \hline
37 & 4.2 \({}^{\ast\ast}\) & 48 \({}^{\ast}\) & none, peri-implantitis & M & 57 & Non-smoker & Diabetes treated with OHAs, previous ischemic attack treated with warfarin \\  & 2.2 \({}^{\ast\ast}\) post-extractive, & 3 \({}^{\ast}\) & moderate pain on chewing & & & & \\
47 & torque \(<\) 35 Ncm & 3 \({}^{\ast}\) & moderate pain on chewing & & & & \\
2.5 \({}^{\ast\ast}\) & 36 \({}^{\ast}\) & none, peri-implantitis & F & 66 & Moderate & Negative \\  & 1.5 \({}^{\ast\ast}\) & 72 \({}^{\ast}\) & none, peri-implantitis & & & & \\
50 & augmented with bone & 3 \({}^{\ast}\) & none & M & 71 & Non-smoker & Negative \\  & + membrane & 3.5 \({}^{\ast\ast}\) post-extractive & 1 \({}^{\ast}\) & severe spontaneous pain & & & \\
53 & 4.2 \({}^{\ast\ast}\) post-extractive & 1 \({}^{\ast}\) & severe spontaneous pain & F & 59 & Non-smoker & Negative \\  & 4.5 \({}^{\ast\ast}\) post-extractive & 1 \({}^{\ast}\) & severe spontaneous pain & & & & \\ \hline
56 & 4.3 \({}^{\ast\ast}\) & 24 \({}^{\ast}\) & none, implant fracture & M & 69 & Heavy smoker & Diabetic and hypertensive \\
75 & 1.5 \({}^{\ast\ast}\) & 48 \({}^{\ast}\) & moderate pain on chewing, peri-implantitis & F & 66 & Non-smoker & Hypercholesterolememia \\ \hline
89 & 3.1 \({}^{\ast\ast}\) post-extractive & 3 \({}^{\ast}\) & none & F & 65 & Non-smoker & Negative \\
94 & 3.6 \({}^{\ast\ast}\) & 12 \({}^{\ast}\) & none, implant fracture & M & 57 & Heavy smoker & Negative \\ \hline
99 & 2.2 \({}^{\ast\ast}\) post-extractive & 3 \({}^{\ast}\) & moderate pain on chewing & F & 48 & Non-smoker & Negative \\ \hline
103 & 4.5 \({}^{\ast\ast}\) & 24 \({}^{\ast}\) & none, peri-implantitis & M & 62 & Non-smoker & Negative \\  & 2.5 \({}^{\ast\ast}\) & 36 \({}^{\ast}\) & none, peri-implantitis & M & 62 & Non-smoker & Negative \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of the implant failures that occurred up to 7-year post-loading. Peri-implant mucositis due to plaque accumulation occurred in 35 patients (Table 5). They were solved with professional cleaning and chlorhexidine.
## 5 IETF ROLL Working Group
### Leveraging upon RPL to Realize the IoT  
#### 5.3.1 Real Life Use Cases  
The RPL framework is currently used in different research projects. However in most cases the protocol is adapted to the specific requirements of the network. Most often, the RPL framework is used as a basis for the development of a specific protocol. Examples of such adaptations can be found in the previous evaluation section.  
Additional examples include the following: in [72] the implementation of a smart monitoring system over a wireless sensor network is presented. The implementation focuses on the use of RPL to create an efficient and reliable routing structure. The paper also shows how, by changing some key parameters, the performance of RPL routing in a smart grid scenario can be influenced. In [73] a case study for the usage of RPL in an agricultural context is presented. Further, in [74] an example of the usage of RPL in transport logistics can be found.
# Multimodal Emotion Recognition Using Visual, Vocal and Physiological Signals: A Review
## 4 Human Emotional Expression Recognition
### Single Modality
#### 4.1.1 Visual Modality
A single VGG-Face model takes the data of a 3D optical flow image, 3D optical strain converted from 2D optical strain or 3D grayscale image converted from the 2D grayscale image, and each VGG-Face model outputs a 4096-fixed length vector \(\phi(x_{t})\). The data in the last fully connected layers are fused before being fed into the LSTM (12288-length spatial feature vector). Each video sequence was interpolated using the temporal interpolation model with the aim of keeping the length of the LSTM input constant. The learning rate is tuned to be smaller than typical rates because of the subtleness of micro-expressions, which poses difficulties for learning. The temporal dimension enrichment (TE) variant outperforms its spatial dimension enrichment (SE) variant counterpart, demonstrating the importance of fine-tuning separate networks for each type of data (for a small dataset). SE performs better than TE for larger training datasets composed of the CASME II and SAMM datasets. SE needs more data than TE, given that it has a large input dimension (curse of dimensionality). The use of optical flow is more beneficial than the use of raw pixel intensities in providing a proper characterization of the input data to the network, according to the results of the ablation study in ref. [130]. Instead of optical flow, neural spikes can be encoded from human facial and physiological data to capture emotion. Considering these spikes' timing, a three-dimensional representation of human emotion is generated. This process uses brain-inspired spiking neural networks (SNNs), such as KEDRI's NeuCube, which belong to the third generation of artificial neural networks (ANN). SNNs consist of 3D spatio-temporal structures applied to facial and physiological emotion recognition. This method achieves classification accuracy comparable to state-of-the-art deep learning approaches that utilize facial expressions and physiological signals. Table 5 presents a categorization of deep learning methods specifically designed for micro-expression extraction. A deep learning model's discriminative power is primarily determined by the quality of the features it generates. The model must also exhibit strong generalization across a wide range of subjects. Moreover, the classifier plays a crucial role in both discriminative power and generalization [130]. Data augmentation techniques are crucial to enhance generalization and improve model performance. Various methods of data augmentation exist, with temporal interpolation being particularly useful for spatio-temporal methods. Temporal linear interpolation
# Application of Canola Oil Biodiesel/Diesel Blends in a Common Rail Diesel Engine
## 3 Results and Discussion
### Combustion Characteristics
#### 3.1.1 Combustion Pressure and Rate of Heat Release (ROHR)
In contrast, with increasing BMEP, the ROHR increased and the ignition delay period shortened, which occurs because the air and fuel are mixed fully at higher values of BMEP, leading to faster-starting combustion. In order to further study the effect of ignition delay of ULSD, BD20, and PCO with the variations of BMEPs, the results are presented in Figure 5. It can be seen that the ignition delay of BD20 and PCO is shorter than that of ULSD. Increasing the biodiesel blend will lead to increasing the oxygen concentration of fuel and then promoting chemical reactions, reducing the chemical delay. Those results are similar to [43]. Although increase the canola oil in their blends will cause increased the density and viscosity of mixed fuel and then delay the ignition time. However, the cetane number of canola biodiesel (61.5) is higher than that of diesel (55.8). The high cetane number has an appreciable effect on ignition delay for canola biodiesel compared with high density and viscosity. On the other hand, the ignition delay of the test fuels is shortened with increased BMEP, because the temperature and pressure of the surroundings of test fuels are increased as engine load increases. From the Figure 5, It can also be seen that ignition delays of PCO and BD20 are shorter than that of ULSD by about 5.5\({}^{\circ}\) CA and 3.25\({}^{\circ}\) CA. It is very similar to the results of ignition delay in Figure 3 by ROHR (see the result of ignition delay in the Section 3.1.1). Figure 4: Effect of BMEP on combustion pressure and ROHR according to test fuels. (**a**) ULSD (ultra low sulfur diesel); (**b**) BD20 (20% canola oil blended with 80% ULSD by volume); (**c**) PCO (pure canola oil).
# Frequency Analysis of Extreme Events Using the Univariate Beta Family Probability Distributions
## 2 Methods
#### 2.2.7 Lomax (LMX)  
The equations needed to estimate the parameters with MOM have the following expressions:  
\[\mu=\gamma+\frac{\beta}{\lambda-1} \tag{42}\]  
\[\sigma^{2}=\frac{\beta^{2}\cdot\lambda}{(\lambda-2)\cdot(\lambda-1)} \tag{43}\]  
\[C_{s}=\frac{2\cdot\lambda\cdot(\lambda+1)}{(\lambda-3)\cdot(\lambda-2)\cdot( \lambda-1)^{3}\cdot\left(\frac{\lambda}{(\lambda-2)\cdot(\lambda-1)^{2}} \right)^{1.5}} \tag{44}\]  
The parameter \(\lambda\) can be obtained approximately depending on the skewness coefficient, using the following rational functions (\(2<C_{s}\leq 9\)):  
\[\lambda=\frac{3.9284194-3.0914822\cdot C_{s}+6.9762716\cdot C_{s}^{2}}{1-5.27 06252\cdot C_{s}+2.385356\cdot C_{s}^{2}} \tag{45}\]\[\beta=\sigma\cdot(\lambda-1)\cdot\sqrt{1-\frac{\lambda}{2}} \tag{46}\]  
\[\gamma=\mu-\frac{\beta}{\lambda-1} \tag{47}\]  
The parameter estimation with the L-moment method is carried out numerically (definite integrals) based on the equations using the quantile of the function.  
\[L_{1}=\gamma+\frac{\beta}{\lambda-1} \tag{48}\]  
\[L_{2}=\int\limits_{0}^{1}\frac{(\gamma+\beta)\cdot qbeta(1-p,1,\lambda)-\gamma }{qbeta(1-p,1,\lambda)-1}\cdot(1-2\cdot p)\cdot dp \tag{49}\]  
\[L_{3}=\int\limits_{0}^{1}\frac{(\gamma+\beta)\cdot qbeta(1-p,1,\lambda)-\gamma }{qbeta(1-p,1,\lambda)-1}\cdot\Big{(}6\cdot p^{2}-6\cdot p+1\Big{)}\cdot dp \tag{50}\]  
An approximate form can be adopted based on the parameter estimation depending on L-skewness, as follows (\(\tau_{3}>0.35\)):  
\[\lambda=\frac{7.7817436+257.3810019\cdot\tau_{3}+330.3408356\cdot\tau_{3}^{2} }{1-296.9737484\cdot\tau_{3}+882.1944808\cdot\tau_{3}^{2}} \tag{51}\]  
\[\beta=\frac{L_{2}}{z} \tag{52}\]  
\[\gamma=L_{1}-\frac{\beta}{\lambda-1} \tag{53}\]  
where the exact form of the \(z\) parameter is:  
\[z=-\int\limits_{0}^{1}\frac{qbeta(1-p,1,\lambda)}{qbeta(1-p,1,\lambda)-1} \cdot(1-2\cdot p)\cdot dp \tag{54}\]  
or a simplified form can be adopted using a rational function:  
\[z=\frac{-0.3686598+0.4493708\cdot\lambda-0.0010038\cdot\lambda^{2}}{1-1.8121 107\cdot\lambda+0.8536406\cdot\lambda^{2}} \tag{55}\]