esa-sceva/satcom-chunk-collection huggingface dataset:
# SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs
## 7 Conclusions and Future Work  
### Learned Lessons  
Conducting this study helped us to learn several lessons. The first lesson is that the use of semantic web technologies to solve complex industrial problems is still a largely unexplored area. Even today most of the solutions on the market focus on the enterprise and IT side than on the operational side of large industries. This means that there are mature solutions that use semantic web technologies to bridge siloed enterprise data in RDBMS (Relational Database Management System) and unstructured data like documents but there is no mature solution that can do the same for data described in operation technology protocols, e.g., OPC-UA (Open Platform Communications--Unified Architecture) [35].  
The second lesson is that technologies such as triplestore are not easily adoptable to typical industrial use cases. Almost all triplestores are focused on big data and huge numbers of triples but, as our work demonstrates, there are several use cases where an efficient solution is needed for typical industrial gateways. Industrial PCs are an option but they are expensive and can only be used by large companies whereas small devices have a very large user base. While machine failures are reality, they do not occur every minute, and so there is no need to use a complex solution that supports billions of triples. Outside the vendor space, the open source community has some options like RedStore [36] but most are not in active development.  
The third lesson is that the development of industrial grade ontologies is still a herculean task and the existing tool set continues to act as a barrier to entry. In our experience, experts want to formalize their domain knowledge but they have no motivation to learn complex tools such as Protege that do not support collaborative ontology development. WebProtege is a possibility but lacks query, visualization and documentation capabilities. New efforts such as Modom.io [37] and Zazuko [38] take a more simplified approach for non-experts to create ontologies but they are still works in progress.  
Regarding ontology governance, there is no standard framework that can be applied to design and develop modular ontologies on an industrial scale. The evolution of ontologies is another area where no clear recommendations and no industrial tools are available to manage the required documentation, evaluation, release and versioning. While some academic works such as [39] exist, they are not mature and often not easy to deploy and use in industrial settings. The Semantic Web community as a whole needs to address these points and improve the developer experience in order to mainstream these useful technologies.
# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 4 Proposed Approach for Lower Limb Activity Detection
### Preprocessing Phase
#### 4.1.1 Filtering  
Applying a band-pass filter, which retains frequencies within the designated range and eliminates frequencies outside of it, is one of the most used methods for filtering EMG data. Our raw data are run through a band-pass filter at 10-400 Hz using the following functions. One of the easiest and most straightforward ways to improve the fidelity of the sEMG signal is to filter as much noise as possible while keeping as much of the required EMG signal frequency spectrum as feasible. This is in addition to employing efficient techniques for identifying and attaching the sEMG sensor to the skin. Figure 10a,b illustrates an example  
Figure 9: Hierarchy for the proposed HAR approach.  
of filtering raw EMG that represents fast walking activity and filtering raw IMU signal, respectively. Commonly used sensors can record sEMG signals with a frequency spectrum ranging from 0 to 400 Hz, depending on the electrode spacing, the quantity of fatty tissue between muscle and skin, and the geometries of the action potentials. To preserve the desired information from the sEMG signal, band-pass filtering always strikes a compromise between reducing noise and artifact contamination.  
The sensor's bandwidth is typically higher if it is positioned above the muscle's innervation zone or where the muscle fibers insert into the tendons. When the amplitude of the noise components exceeds that of the sEMG signal, the low-pass filter corner frequency should be placed near the high-frequency end of the sEMG signal spectrum. There should therefore be a low-pass corner frequency in the 400-450 Hz range at the top end of the sEMG frequency spectrum.
## 4 Control Design
### Robust Optimal Vibration Control  
Aiming at designing the optimal control law using the quadratic form, the model of SFS is rewritten as  
\[\dot{X}_{k}\ =\ A_{k}X_{k}\ +\ B_{k}I_{f2} \tag{69}\]  
where \(X_{k}=\begin{bmatrix}y_{f2}\\ \frac{dy_{f2}}{d\tau_{2}}\end{bmatrix}\), \(A_{k}=\begin{bmatrix}0&E\\ -D_{4,g2}(\theta,\varepsilon_{2}y)\widetilde{K}&0\end{bmatrix}\), \(B_{k}=\begin{bmatrix}0\\ D_{3,g2}(\theta,\varepsilon_{2}y)\widetilde{A}^{-1}\widetilde{C}\end{bmatrix}\).  
The control purpose is that the appropriate control law is proposed to adjust the system state to zero, that is, suppress elastic vibrations in SFS. It is easy to verify that it is completely controllable, and the optimal control method can be used, since SFS is a linear system.  
The quadratic performance index function is selected as follows:  
\[J_{k}\ =\ \frac{1}{2}{\int_{0}^{\infty}}\left[X_{k}^{\mathrm{T}}QX_{k}+I_{f2}^{ \mathrm{T}}RI_{f2}\right]dt \tag{70}\]  
The algebraic Riccati equation can be written as  
\[A_{k}^{\mathrm{T}}P+PA_{k}-PB_{k}R^{-1}B_{k}^{\mathrm{T}}P+Q=0 \tag{71}\]Then, the optimal feedback control law is  
\[I_{f2}=-K_{f}X_{k}=-R^{-1}B_{k}^{\rm T}PX_{k} \tag{72}\]  
A robust optimal control law with great dynamic performance is given below to ensure the stability of the system while there are uncertainties in SFS. Therefore, we obtain the dynamic equation with uncertainties  
\[\dot{X}_{k}\ =\ (A_{k0}+\Delta A_{k})X_{k}+B_{k}I_{f2} \tag{73}\]  
**Theorem 2.**_For the second fast subsystem with parameter uncertainty, if the uncertainty satisfies_  
\[\left\|\Delta A_{k}\right\|_{2}<\frac{\lambda_{\min}}{2\rho(P)}\]  
_where \(\lambda_{\min}\) is the minimum eigenvalue of \(Q+PB_{k}R^{-1}B_{k}^{\rm T}P\) and \(\rho(P)\) is the spectral radius of \(P\), and \(I_{f2}\) is elected optimal control law (72), the closed loop asymptotic stability of SFS can be guaranteed._  
**Proof.** Considering the [PERSON] theory, define the second positive definite energy function  
\[V_{2}\ =\ X_{k}^{\rm T}PX_{k} \tag{74}\]  
Substituting (69), (72) and (73) into the time derivative of Equation (74), we obtain  
\[\dot{V}_{2} =\dot{X}_{k}^{\rm T}PX_{k}+X_{k}^{\rm T}P\dot{X}_{k} \tag{75}\] \[=X_{k}^{\rm T}(A_{k0}^{\rm T}P+PA_{k0}-2 PB_{k}R^{-1}B_{k}^{\rm T}P )X_{k}+2X_{k}^{\rm T}P\Delta A_{k}\dot{X}_{k}\] \[=-X_{k}^{\rm T}(Q+PB_{k}R^{-1}B_{k}^{\rm T}P)X_{k}+2X_{k}^{\rm T} P\Delta A_{k}\dot{X}_{k}\]  
Because both \(P\) and \(Q+PB_{k}R^{-1}B_{k}^{\rm T}P\) are symmetric matrices, we have  
\[\begin{array}{c}2X_{k}^{\rm T}P\Delta A\dot{X}_{k}<2\rho(P)\cdot\|AA_{k}\|_ {2}\cdot\|X_{k}\|_{2}^{2}\\ \frac{X_{k}^{\rm T}(Q+PB_{k}R^{-1}B_{k}^{\rm T}P)X_{k}}{X_{k}^{\rm T}X_{k}}> \lambda_{\min}\end{array} \tag{76}\]  
Substituting inequality (76) into Equation (75), we have  
\[\dot{V}_{2}\ <\ [2\rho(P)\cdot\|\Delta A_{k}\|_{2}-\lambda_{\min}]\|X_{k}\|_{2}^ {2} \tag{77}\]  
When the inequality \(\left\|\Delta A_{k}\right\|_{2}\ <\ \frac{\lambda_{\min}}{2\rho(P)}\) holds, \(\dot{V}_{2}\) will be less than 0. Hence, the second fast closed-loop subsystem is stable.  
This completes the proof. \(\square\)
## 2 Literature Review
### Machine Learning and Performance Prediction Applications  
Artificial intelligence-based algorithms are used to explore the relationship between TBM performance and TBM performance parameters. An adaptive neuro-fuzzy inference system (ANFIS) that is more accurate than statistical models in predicting PR [9] has been developed. A new approach, group modeling of data handling (GMDH), was introduced to predict TBM PR accurately [46]. A gene expression programming equation was developed to estimate TBM PR accurately [15]. Several new optimization methods, namely hybrid harmony search, differential evolution, and the grey wolf optimizer, have been introduced to estimate TBM PR [14]. In another study, a hybrid SVM technique was used to estimate the energy consumption of cutterhead drives in shield tunneling [17]. Many ANN-based models have been developed for solving geotechnical engineering problems [46; 47]. Hybridized models have been developed to predict TBM penetration and advance rates [7; 8]. Researchers have performed various studies, developed modeling techniques, and identified input parameters to predict TBM performance in hard rock. However, AI-based models alone lack the understanding and visualization capability of tunnel progress and performance. With the assistance of DTs, AI can perform better in the overall performance prediction of TBMs.
# Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records
## 1 Introduction
### Sensor Networks  
Many devices produce data. These devices contain sensors. Sensors may exist alone or coupled with other sensors. This grouping of sensors on a single device could identify a connection. Sensors in a sensor network may share similar data bounds with other sensors of the same type. This may identify another connection. Finally, sensors may be influenced by or send data to and from one another for data fusion. This may identify a third potential connection.  
How you define a sensor network will determine how the sensors are connected. For instance, a person's home may contain many IoT devices (smart televisions, smart lights, robotic vacuum, health devices, smart speakers, and utility monitoring devices). Some of these devices may naturally interact with one another (a user's smart speaker may link all the home smart devices for a user's control). Others may only be associated because they are on the same computer network.  
The industrial IoT contains many devices working together to monitor industrial systems [16]. These systems generally report information to a monitoring system or the industrial device in case of faults or errors. The medical IoT contains many health-related sensors to notify patients and medical staff of health-related issues [17]. The environmental IoT contains devices that monitor environmental conditions, including weather and atmospheric composition [10]. This can include weather systems, atmospheric particle sensors, and light and noise sensors. The urban monitoring IoT contains devices that monitor conditions in an urban environment [8]. This can include environmental IoT, cameras, traffic sensors, counters for vehicles, people, entrances/exits, etc. Marine Monitoring sensor networks work similarly to the urban monitoring IoT, with cameras for tracking boats, but they also include ocean monitoring buoys, acoustic sensors, and various undersea sensors [18]. In all these cases, each network requires the ability to detect faults, uncommon readings that indicate uniquely sensed data, or malicious attacks. For networksthat produce time-series data, anomaly detection approaches focusing on time-series data should be considered.  
This work focuses on utilizing the Chicago Array of Things (Chicago AoT), a set of Internet-connected nodes along the coast of Chicago, IL. Each node in the AoT contains a set of sensors housed on circuit boards. The sensors on each circuit board are independent of other circuit boards and their sensors. This is explained in detail in Section 2. This work focuses on sensor-level anomalies, so the AoT network's exact structure is out of scope.
# Pixel Circuit Designs for Active Matrix Displays
## 1 Introduction
### TFT Devices
#### 1.3.3 Metal Oxide TFT  
Metal oxide (MO) TFT [35] represents a compromise between a-Si TFT and LTPS TFT. Its representative device is IGZO TFT. The mobility of the MO TFTs lies in the middle of the a-Si TFT and the LTPS TFT. Both a-Si and MO TFTs have amorphous microstructures, in contrast to the polycrystalline microstructure of LTPS TFIs. Thus, a-Si and MO TFTs share similar fabrication, scaling, and cost characteristics [29; 30]. Moreover, MO TFTs have a low leakage current (about 1 pA) compared to LTPS. Leakage current is a critical indicator of TFT performance [36; 37]. When the leakage current is high, even if the pixel circuit is not operating, the charge stored in the storage capacitor is constantly consumed, resulting in a decrease in panel brightness. Pixel circuits with a diode-connected structure may increase the brightness due to additional current injection when the leakage current is high. However, this undoubtedly increases the complexity of the circuit design. Therefore, a high leakage current leads to increased power consumption, making it challenging to achieve low frame rate displays and fine grey-level adjustments [38]. The leakage current of MO TFT is several orders of magnitude smaller than that of a-Si TFT and LTPS TFT [39]. Thus, MO TFTs currently offer a suitable low-cost solution for large-size high-resolution displays [40]. In addition, MO TFT has the characteristics of flexibility and transparency. The combination of MO TFTs with flexible substrate can achieve flexible and transparent displays [41; 42; 43]. This technology has broad application prospects in wearable devices.
# Incorporating Epistemic Uncertainties in Ship Operability Study
## 2 Methods
### Seakeeping Analysis
#### 2.1.2 Wave Spectrum  
Short-term stationary irregular sea states are for engineering purposes traditionally described by wave energy spectrum. The formulation of the wave energy spectrum depends on the geographical area under consideration with local bathymetry and the severity of the sea state. The Pierson-Moskowitz spectrum and JONSWAP spectrum are frequently applied for wind seas. The former was originally proposed for a fully developed sea in the open ocean, while the latter is appropriate for fetch limited seas, describing developing sea states.  
The shipping route in the Adriatic Sea is considered in the present study. For such an enclosed sea, modification of the JONSWAP type of wave spectrum is used. Tabain's single-parameter wave spectrum, developed specifically for the Adriatic, has been used for naval architecture and marine engineering applications [29]. Recently, Tabain's spectrum was optimized using a wave database obtained by combining satellite measurements and numerical re-analysis [30]. Thus, the following one-parameter spectral formulation relevant to the Adriatic Sea is used:  
\[S(\omega)=0.8626\frac{5}{16}\frac{H_{s}^{2}\omega_{m}^{4}}{\omega^{5}}exp \left[-\frac{5}{4}\left(\frac{\omega_{m}}{\omega}\right)^{4}\right]\,1.78\,c \
u p[-\frac{1}{2}(\frac{\omega-\omega_{m}}{\sigma\omega_{m}})^{2}], \tag{8}\]  
\[\omega_{m}=0.52+\frac{1.4}{H_{s}+0.7}, \tag{9}\]  
\[\sigma\left\{\frac{\sigma_{a}=0.06\,za\,\,\omega\,\,\leq\,\omega_{m}}{\sigma_{ b}=0.08\,za\,\,\omega>\omega_{m}}.\right. \tag{10}\]  
where \(\omega_{m}=2\pi/Tp\) is the peak of the wave energy spectrum.  
Obviously, that one-parameter formulation cannot cover all combinations of significant wave heights and zero-crossing periods occurring in the Adriatic [24].
## 3 Results  
### Absolute Spectral Irradiance Measurements  
Figure 3 shows an example of the absolute spectral irradiance measured. UV irradiance was present within the UV-A (400 nm to 320 nm) and UV-B (320 nm to 280 nm) ranges but not within the UV-C (280 nm to 200 nm) range. However, the spectrometer was not capable of measuring absolute irradiance below 200 nm wavelengths.
# Classification of Bugs in Cloud Computing Applications Using Machine Learning Techniques
## 1 Introduction
### Significance of Work  
Bugs in cloud computing applications can have significant consequences, as they can potentially compromise the system's security, reliability, and performance. In addition, bugs can lead to data loss or corruption, resulting in unexpected user downtime. Additionally, attackers can exploit bugs to gain unauthorized access to the system or steal sensitive information. Therefore, it is important for developers to thoroughly test and debug their cloud computing applications to ensure that they are free from bugs before they are deployed to production.  
The outline of the paper \"Classification of Bugs in Cloud Computing Applications Using Machine Learning Techniques\" presents a comprehensive outline for utilizing machine learning techniques to classify and categorize bugs in cloud computing applications in the introduction section, along with problem statements and the significance of work. The paper begins by introducing the concept of cloud computing and highlighting the importance of identifying and addressing bugs within these applications. It then discusses the machine learning algorithms used for bug classification, including decision trees, support vector machines, and neural networks. Section 2 explains the previous study that delves into the various types of bugs in cloud computing applications, such as resource allocation, configuration, and security. Section 3 shows the detailed design and implementation with different classifiers. The paper concludes by outlining a step-by-step process for bug classification using machine learning techniques, including data collection and preprocessing, feature selection and extraction, and model building and evaluation. Overall, this paper serves as a valuable resource for those looking to improve the quality and performance of cloud computing applications by identifying and addressing bugs through machine learning techniques.
## 3 Results and Discussion
### Anthocyanin Profiles and Related Gene Expressions
These two genes play an important role in the biosynthesis of anthocyanins, so the high expression levels of _VdGST_ and _VdOMT_ may be the main reason for the high anthocyanin content of these grapes [18]. Transcriptomics revealed that the difference in gene expressions between varieties might be the direct cause of the differences in anthocyanin composition [29]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Anthocyanins** & **G2** & **XZZ** & **G4** & **BY** \\ \hline Delphinidin-3-glucoside & \(21.20\pm 7.64\)\({}^{\text{ c}}\) & \(122.91\pm 0.97\)\({}^{\text{ a}}\) & \(88.42\pm 3.19\)\({}^{\text{ b}}\) & nd \\ Cyanidin-3-glucose & \(2764.26\pm 2.13\)\({}^{\text{ c}}\) & \(4232.11\pm 7.79\)\({}^{\text{ a}}\) & \(3935.36\pm 0.29\)\({}^{\text{ b}}\) & nd \\ Petunidin-3-glucose & \(8.22\pm 11.63\)\({}^{\text{ b}}\) & \(19.08\pm 0.36\)\({}^{\text{ b}}\) & \(35.84\pm 0.17\)\({}^{\text{ a}}\) & nd \\ Peonidin-3-glucose & \(114.69\pm 1.90\)\({}^{\text{ c}}\) & \(211.47\pm 1.43\)\({}^{\text{ b}}\) & \(242.04\pm 6.99\)\({}^{\text{ a}}\) & nd \\ Malvidin-3-glucose & \(144.05\pm 0.57\)\({}^{\text{ c}}\) & \(151.29\pm 1.11\)\({}^{\text{ b}}\) & \(159.65\pm 0.04\)\({}^{\text{ a}}\) & nd \\ Peonidin-3-acetyl-glucose & \(31.38\pm 20.81\)\({}^{\text{ a}}\) & \(28.48\pm 0.32\)\({}^{\text{ a}}\) & \(29.21\pm 0.02\)\({}^{\text{ a}}\) & nd \\ Malvidin-3-acetyl-glucose & \(2807.84\pm 13.63\)\({}^{\text{ c}}\) & \(5571.95\pm 2.90\)\({}^{\text{ a}}\) & \(3015.96\pm 1.36\)\({}^{\text{ b}}\) & nd \\ Peonidin-3-coumayl-glucose & \(15.17\pm 0.07\)\({}^{\text{ a}}\) & \(10.12\pm 0.12\)\({}^{\text{ c}}\) & \(14.57\pm 0.01\)\({}^{\text{ b}}\) & nd \\ Malvidin-3-coumayl-glucose & \(156.44\pm 1.56\)\({}^{\text{ c}}\) & \(435.45\pm 0.04\)\({}^{\text{ a}}\) & \(226.41\pm 0.01\)\({}^{\text{ b}}\) & nd \\ \hline \hline \end{tabular} Note: All values shown are mean \(\pm\) SD, \(n=3\); Lowercase letters indicate significant (\(p<0.05\)); nd means not detected; G2: Gaoshan #2; G4: Gaoshan #4; XZZ: Xiangzhenzhu; BY: Baiyu.  
\end{table} 
Table 2: Anthocyanin profiles of Chinese wild spine grapes (mg/L).
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 5 Experimental Results and Analysis
### ED Sensitivity-Based SF Allocation in an Unconfirmed Mode  
The \(SR\) of ED sensitivity, GW sensitivity, and the random SF allocation-based schemes in an unconfirmed mode with log-normal shadowing and Okumura-Hata propagation models are presented in Figure 9. In comparison with the confirmed mode, the unconfirmed mode performs exceptionally well under a traffic load of 24 packets/day. In fact, with confirmed communication, the network traffic is increased by both the ACK messages sent in RWs and the retransmissions. This causes an avalanche effect, as a packet loss causes retransmissions that lead to an increase in the interference. Furthermore, in the unconfirmed mode, an unsuccessful message delivery could only be caused by a data packet loss.  
Figure 7: The success ratio and SF assignment ratio of the ED sensitivity, GW sensitivity, random-based, probability-based SF allocation schemes by varying the distance in an urban environment: (**a**) \(SR\) under LOG and OH; (**b**) SF assignment ratio of ED sensitivity; (**c**) SF assignment ratio of GW sensitivity; (**d**) SF assignment ratio of probability-based SF; (**e**) SF assignment ratio of random-based SF.
## 4 Discussion
### Perspectives on Real Application  
#### 4.1.1 Belgrade City Air  
Non-exhaustive but indicative testing suggests that the level of HE contamination in the experiments with Belgrade city air was below detectable limits, and the suitability of the method for mitigating NO\({}_{x}\) levels to below those of even the lowest recorded values in the city suburbs was confirmed. In Figure 34, official publically available environmental agency data are compared with the test data from the application of the experimental filter.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Water Soluble Fraction,**} \\  & & \multicolumn{2}{c}{\(\boldsymbol{c}\) (mg-kg\({}^{-1}\))} & \\ \hline  & **Pb** & **Cd** & **Hg** & **As** & **Sb** & **Cu** \\ \hline Filter exposed & 0.42 & \(<DL\) & \(<DL\) & 0.60 & \(<DL\) & \(<DL\) \\ \hline Blank non-exposed & 0.42 & \(<DL\) & \(<DL\) & 0.66 & \(<DL\) & \(<DL\) \\ \hline  & \multicolumn{4}{c}{**Acid Soluble Fraction,**} \\  & & \multicolumn{2}{c}{\(\boldsymbol{c}\) (mg-kg\({}^{-1}\))} & \\ \hline  & **Pb** & **Cd** & **Hg** & **As** & **Sb** & **Cu** \\ \hline Filter exposed & 0.84 & \(<DL\) & \(<DL\) & 0.48 & \(<DL\) & \(<DL\) \\ \hline Blank non-exposed & 0.36 & \(<DL\) & \(<DL\) & 0.75 & \(<DL\) & \(<DL\) \\ \hline  & \multicolumn{4}{c}{**Total HE Content,**} \\  & & \multicolumn{2}{c}{\(\boldsymbol{c}\) (mg-kg\({}^{-1}\))} & \\ \hline  & **Pb** & **Cd** & **Hg** & **As** & **Sb** & **Cu** \\ \hline Filter exposed & 42.00 & \(<DL\) & \(<DL\) & 0.51 & \(<DL\) & 72.00 \\ \hline Blank non-exposed & 54.00 & \(<DL\) & \(<DL\) & 0.66 & \(<DL\) & 96.00 \\ \hline \multicolumn{4}{c}{\(<DL\)—below detectable limit.} \\ \end{tabular}
\end{table}
Table 13: Heavy elements (HE) content in extracted fractions.