esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Implications  
A typical use case of the proposed method can be presented via the decision support model depicted in Figure 5. The model has 26 input attributes and, consequently, a huge input space (1.59 \(\times\) 10\({}^{12}\)). Using this model, the end users for whom this model was initially developed can evaluate the primary productivity of their agricultural fields. In scenarios where the output of the model is, for example, \"low productivity\", a natural question would be, \"What can I do to improve the productivity of my field?\". BAG-DSM answers that question by providing alternatives that lead to the improvement of primary productivity. In another scenario where the output of the model is \"high productivity\", a possible question could be, \"What can go wrong and degrade the productivity of my field?\". This question is also answered by BAG-DSM by providing alternatives that lead to decreased productivity. The answers are, according to our experiments, of good quality and provided in a short time.  
This study demonstrated that BAG-DSM is model agnostic, i.e., it is a general method that works with any DEX decision support model. Furthermore, the mathematical and theoretical background presented in this paper should enable the application of BAG-DSM to any other decision support models using qualitative inputs and outputs. Furthermore, the same approach can be used for generating counterfactual explanations for machine learning models.  
Thus, we envision BAG-DSM being used as a plug-in for future decision support models where the end users would receive, besides the output of the decision support models, a list of alternatives that require a minimal number of changes in the input space and yet would cause a change in the output of the decision support model.
# Chaotic Optical Communication with Wavelength-Hopping Technology Based on Tunable Lasers
## 2 Principle
For a given PD, the spectral response curve versus the wavelength is particular, and the shape of the curve is related to the device materials of PD. For a commercial PD, its spectral response can be obtained from the product manual. The synchronization error is \(<\!\!\varepsilon^{2}\!>\) and is not affected by the wavelength. It can be expressed as [23]  
\[ \begin{array}{ll}\langle\varepsilon^{2}\rangle&=\frac{1}{3} \bigg{(}\frac{\Delta T}{\tau}\bigg{)}^{2}+\bigg{(}\frac{\Delta\beta}{\beta} \bigg{)}^{2}+\bigg{(}1-\frac{\pi}{4}\bigg{)}\bigg{(}\frac{\Delta\tau}{\tau} \bigg{)}^{2}\\ &-2\bigg{(}1-\frac{\pi}{4}\bigg{)}\frac{\Delta\beta}{\beta}\frac{\Delta\tau}{ \tau}-2\bigg{(}1-\frac{\pi}{4}\bigg{)}\frac{\Delta\tau}{\tau}\frac{\Delta\tau }{\tau},\end{array}  \tag{4}\]  
where \(\Delta T\) is the mismatch of the time delay, \(\Delta\beta\) is the mismatch of the overall normalized electro-optic oscillator gain, \(\beta\) is the overall normalized electro-optic oscillator gain, \(\Delta\tau\) is the mismatch of the high cutoff response time, and \(\tau\) is the high cutoff response time. The signal to noise ratio (SNR) is also affected by the wavelength. Based on Ref. [23], the SNR of our system can be expressed as  
\[u_{\lambda}=K_{\lambda}\alpha/\sqrt{\langle\!n_{\lambda}\!^{2}\rangle}. \tag{5}\]  
where \(\alpha\) is the masking efficiency. The probability density function (pdf) of the mismatch noise can be expressed as a function of a Gaussian distribution and its derivatives. With proper tuning, the coefficients of the derivatives can converge to zero [23]. Thus, the mismatch-induced BER of the system can be given by  
\[\text{BER}=\frac{1}{2}\text{erfc}\bigg{(}\frac{u_{\lambda}}{2\sqrt{2}}\bigg{)}. \tag{6}\]When this system works normally, always set \(\Delta K_{\lambda}\) close to zero so that the BER always takes a small value. For Eve, since the secret key of the hopping sequence of the wavelength is not available, the responses of PD3 and PD4 are not the same. Even though their structures are the same as the receiver, \(\Delta K_{\lambda}\) will increase because of the detector mismatch, and then the BER of Eve will increase. This can explain why this system can improve the confidentiality of chaotic communication.
## 3 Experimental Results
### Performance Metrics for MF Class Using Imbalanced and Balanced Dataset with Feature Normalization
Figure 9: Performance metrics comparison for different models on balanced dataset with feature normalization. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Fault Type** & **Normalization** & **Rec (\%)** & **Pre (\%)** & **Spe (\%)** & **Acc (\%)** & **F1 (\%)** \\ \hline DT & HDF & No & 98.26 & 96.58 & 96.67 & 97.45 & 97.41 \\ SVM & HDF & No & 74.78 & 96.63 & 97.50 & 86.38 & 84.31 \\ k-NN & HDF & No & 98.26 & 88.28 & 87.50 & 92.77 & 93.00 \\  
GG16 & TWF & Yes & 97.83 & 93.75 & 94.00 & 95.83 & 95.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of SML and DL classifiers for different fault types using balanced dataset (without and with normalization).
# Use of Binary Classification in Non-Invasive Load Monitoring
## 2 NILM--Key Elements
### Representative Data  
The artificial intelligence methods used for building the NILM device is modeled on the basis of the data presented to them. Consequently, the quality of the results obtained strongly depends on the representativeness of the data used for learning and testing of the system. In the case of appliance monitoring, to obtain a global model, it is necessary to provide data acquired in different power systems that describe different fashions of the same appliance (hoovers, kettles, etc.).  
More than 20 data repositories are well known, mainly from European countries and North America (Table 1). In most cases, data were collected at a low frequency, with only nine databases providing high-frequency data. The authors are most likely to use data from the following databases: REDD and UK-Dale (United Kingdom--Domestic Appliance-Level Electricity), followed by REFIT (Personalised Retrofit Decision Support Tools For UK Homes Using Smart Home Technology), PLAID (Plug-Load Appliance Identification Dataset), WHITED (Worldwide Household and Industry Transient Energy Data), and LILAC (Learnable Image-Based Load Signature Construction Approach) [25] (Figure 2). The authors of [25] sometimes used the energy demand model to generate training data and, thus, eliminated the need for historical measurement data.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data Set** & **Houses** & **Country** & **Sampling [Sps]** & **Parameters** \\ \hline ACS-F1/2 & - & Switzerland & 0.1 & P, S, Q \\ AMPd & 1 & Canada & 1/60 & I, V, P, S, Q \\ BLOND & 1 & Germany & 6.4 k; 50 k; 250 k & I, V \\ COMBED & 1 & India & 1/30 & V, P \\ COOLL & - & France & 100k & I, V \\ CREAM & - & Germany & 6.4k & I, V \\ Dataport & 1400 & USA & 1/60 & P, S \\ DRED & 1 & Netherlands & 1 & P \\ EEUD & 23 & Canada & 1/60 & P \\ HES & 250 & Great Britain & 1/12–1/30 & EE \\ HERD & 5 & India & 10 k–5 M & EMI (electromagnetic interference) \\ iAWE & 1 & India & 1 & I, V, P \\ IHEPCDC & 1 & France & 1/60 & I, V, P, S, Q \\ LIFTED & 1 & USA & 50 & P \\ LILAC & - & Germany & 50 k & I, V \\ PLAID & 55 & USA & 30 k & I, V \\ RAE & 1 & Canada & 1 & I, V, P, S, Q \\ RBSAM & 101 & USA & 4 & EE \\ REDD & 6 & USA & 15 k; 1/2 & I, V, P \\ REFIT & 2 & Great Britain & 1/8 & P \\ Smart & 8 & USA & 1 & P, S \\ SustDataED & 50 & Portugal & 12.8; 1/2 & I, V, P, Q \\ SysD & - & Austria & 5 & P \\ Tracebace & 158 & Germany & 0.1 & P \\ UK-Dale & 5 & Great Britain & 16 k & I, V \\ WHITED & - & Germany, Canada & 44 k & I, V \\ \hline \end{tabular}
\end{table}
Table 1: List of data repositories used in NILM studies.
# Comparing Human Activity Recognition Models Based on Complexity and Resource Usage
## 2 Related Work
### Deep Learning
[19] for example use a Stacked Autoencoder to train their model in two stages with the first stage being about learning to reconstruct the data in an unsupervised manner before refining the model by learning on the labelled data in a supervised manner. Autoencoders consist of a decoder and an encoder with the encoder producing the code and the decoder reconstructing the input from this code. Autoencoders are typically restricted in the way they can represent the data to not be able to do it perfectly and therefore force it to find good representations, that for example reduce the dimensionality of the input data, or extract more representative features. Different types of autoencoders are used for different applications. One of those types is the Sparse Autoencoder that serves the purpose of adding sparsity (many inactive hidden units) to the input data by applying a regularization function. This forces the units that are left active to learn very latent and powerful features in the input data to then have a representation that generalizes better on unseen data. For the stacked autoencoder employed by [PERSON] et al., they use two of those sparse autoencoders to be able to find good features in an unsupervised way in phase one and then align this with the labeled data to predict activities accurately in phase two. They report an accuracy of 97.5% on the UCI HAR Smartphone data set [19]. DBNs as used by [PERSON] et al. [20] consist of multiple stacked Restricted Boltzmann Machines (RBMs). An RBM is a variant of a Boltzmann machine with the constraint that connections between units of different groups (hidden and visible units) are allowed, but no connections between units of the same group. These RBMs are then stacked with each of the hidden layers being the visible layer of the following. The hidden units of the RBMs act as feature detectors with one RBM being trained after the other in an unsupervised manner. This training phase learns a generative model that is not yet able to classify the activities. To enable the model to do that, back-propagation is used to adapt the trained units to the labeled data [20]. As shown by these highlighted papers, many different deep learning approaches are possible. The mostly used architectures are CNNs [15; 16; 17; 21; 22; 23; 24; 25] followed by RNNs [18; 26; 27; 28] and the occasional outlier with DNNs [15], Autoencoders [19], Deep Belief Networks [20] or even Generative Adversarial Networks (GANs) for specific use cases [29]. There seems to be not one single approach working especially well, as all of them achieve good results when tuned to a certain extent.
# Discrete-Time Position Control for Autonomous Underwater Vehicle under Noisy Conditions
## 4 Simulation
### The Proposed Controller Utilizing Different Sampling Times  
It is well known that different sampling times have a great impact on the performance of position tracking. For this trial, N is set to 100 and case 1 is chosen as the noise condition. Then, the proposed controller is tested for different sampling times \(T\), i.e., 0.01 s, 0.02 s, 0.05 s, and 0.1 s. Figure 5 presents the 3D position tracking for the corresponding sampling time \(T\). The latter figure highlights that the position tracking degrades as the sampling time  
Figure 4: AUV position tracking for noise case 3 (T = 0.01 s and N = 100): (**a**) 3D position tracking and (**b**) tracking in surge, sway, and heave.  
increases. This happens not only because of the discrete-time controller, but also because of TDE. Accordingly, when the sampling time increases, \(\hat{\mathbf{H}}(k)\) is not approximately equal to \(\mathbf{H}(k)\).
## 2 Detailed Analysis of the Literature
### Prosumer Communities
#### 2.1.2 Prosumer Roles  
The prosumer is one of the actors operating in the complex energy value network. As an active participant, they drive the sustainability of the energy sharing process in the PCG [7]. Although prosumers are value-adding participants in the evolving energy system, limited research exists on the subject of prosumer roles [37; 38]. Recognizing prosumer roles and aligning expectations at the first stages of smart grid implementation are important for better adoption of the technology regarding functionality, processes, business models, and opportunities for innovation [37]. There are three proposed categories of prosumer roles in smart grid based on the perspective of co-creation, or strategy to promote collaboration among different parties to achieve a shared objective. These are the \"Engineer\" who value new technologies and innovation, the \"Green User\" who are concerned with environmentally sustainable solutions, and the \"Value Seeker\" who are interested in the economic benefits and product performance, quality and security [37].  
Prosumers can also contribute to the digital energy ecosystem. Aside from being involved in energy production, prosumers can also engage in innovation and value creation processes through digital touch points such as the web, user interfaces, and mobile devices and application [38]. Some examples of such innovation and value creation are energy market processes related to data monitoring, data analytics, and other areas of physical and financial energy flows. Moreover, prosumers can also help in developing innovative solutions through sharing ideas and providing feedback [38].  
Prosumers can further participate in supporting distributed flexibility in the power market by managing their energy production and consumption schedules and providing decentralized storage capacity [39]. Different prosumer roles also contribute to the transformation from traditional to future flexible energy systems [40]. An investigation of the evolving energy system elaborates a set of exploratory propositions for six different types of prosumer roles in the smart grid [40]. These roles include participating as an early market actor, testing functionality and relative advantage, testing financial benefits, validating business models, providing feedback on technology, and engaging in co-creation [40]. Several other researchers have independently addressed some of these aspects such as early market actors [41], smart grid business models [42; 43], and prosumer co-creation [44]. Consequently, we propose the following:  
**Proposition 2**.: _Prosumers role is critical in ensuring sustainable energy provisioning in the early stages as well as ongoing operations of the smart grids. Prosumers are value-adding actors in the complex energy value network and contribute towards innovation, value-creation and distributed flexibility in the energy value network._
###### Abstract  
Polyhydroxyalkanoate (PHAs) are biodegradable plastic-like materials with versatile properties. Plant oils are excellent carbon sources for a cost-effective PHA production, due to their high carbon content, large availability, and comparatively low prices. Additionally, efficient process development and control is required for competitive PHA production, which can be facilitated by _on-line_ or _in-line_ monitoring devices. To this end, we have evaluated photon density wave (PDW) spectroscopy as a new process analytical technology for _Ralstonia eutropha_ (_Cupriavidus reactor_) H16 plant oil cultivations producing polyhydroxybutyrate (PHB) as an intracellular polymer. PDW spectroscopy was used for _in-line_ recording of the reduced scattering coefficient \(\mu_{\mathrm{s}}\)' and the absorption coefficient \(\mu_{\mathrm{a}}\) at 638 nm. A correlation of \(\mu_{\mathrm{s}}\)' with the cell dry weight (CDW) and \(\mu_{\mathrm{a}}\) with the residual cell dry weight (RCDW) was observed during growth, PHB accumulation, and PHB degradation phases in batch and pulse feed cultivations. The correlation was used to predict CDW, RCDW, and PHB formation in a high-cell-density fed-batch cultivation with a productivity of 1.65 g\(\mathrm{r}_{\mathrm{HB}}\)L\({}^{-1}\)-h\({}^{-1}\) and a final biomass of 106 g\(\mathrm{L}^{-1}\) containing 73 wt% PHB. The new method applied in this study allows _in-line_ monitoring of CDW, RCDW, and PHA formation.  
polyhydroxyalkanoate; PHA; process analytical technologies; PAT; plant oil; high-cell-density fed-batch; photon density wave spectroscopy; PDW; _Ralstonia eutropha_; _Cupriavidus reactor_; _on-line_; _in-line_
## 2 DPS Modeling of LEO-SIN
### Performance Analysis
#### 3.3.2 ACF Analysis  
The ACF of DPS of different models would be furtherly analyzed, as Figure 9 shows.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Parameter** & \(\overline{E}_{J}(P||Q)\) \\ \hline MEDS & \(N_{1}=20\), & 0.007036 \\ \hline RMEDS & \(N_{1}=20\), & 0.006969 \\ \hline MEDS-SP & \(N_{1}=20\), & \\ \(N_{2}=21\), & 0.007172 \\ \hline RMEDS-SP & \(N_{1}=20\), & 0.006807 \\ \(K=4\) & & \\ \hline \end{tabular}
\end{table}
Table 1: PDF’s modeling error of different models’ amplitude distribution.  
Figure 8: PDF of the amplitude distribution of different models.  
When the number of sinusoids are set as \(N_{1}=20,N_{2}=21,K=4\) and the number of simulation times are the same, the ACF of RMEDS-SP is more similar to the theoretical PDF of Rayleigh distribution than other models. When the relative time is 90 ms, the value of ACF of the theoretical model, RMEDS-SP, MEDS-SP is 0.01646, 0.01631, and 0.01908. We can figure out that the performance of the ACF of RMEDS-SP is better than that of the MEDS-SP.  
We define SEF for ACF \(E_{ACF}(\tau)\) to analyze the ACF error of different models, which can be substituted into Equation (19). As Figure 10 shows, when the number of sinusoids and number of simulations time are the same, the ACF error of RMEDS-SP is lower than 0.001 at a relative time from 0 to 110 ms, which have better performance than those of MEDS-SP. At the relative time from 300 ms to 700 ms, the ACF error of RMEDS-SP have an obvious improvement than at a relative time from 0 to 110 ms.
# Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests
## 4 Case Study
### Comparative Analysis
When the test is reliable, the test used in step 9 is \(t_{11}\), and when the test is unreliable, the test used in step 9 is \(t_{15}\). Additionally, the test in the last step is \(t_{4}\). The testing sequence under reliable and unreliable tests is completely different, which fully demonstrates that whether the testing is reliable or not will affect the testing sequence, and the testing sequence will affect  
Figure 5: Test sequence comparison curve. Figure 6: Fault isolation sequence comparison curve. system fault detection and fault isolation. This also confirms why we need to construct a fault diagnosis strategy for a high-speed train traction system based on unreliable testing. From the comparison curve of the fault isolation sequence, it can be seen that the sequence of fault isolation is also different under the condition of reliable tests and unreliable tests. If the test is reliable, the fault \(f_{1}\) is isolated in step 24, and the fault \(f_{28}\) is isolated in step 14. When the test is unreliable, the fault \(f_{1}\) is isolated in step 25, and the fault \(f_{28}\) is isolated in step 11. At the same time, when the test is reliable, the fault will be isolated from step 4. When the test is unreliable, the fault is isolated from step 5. In another example, when the test is reliable, faults \(f_{3}\) and \(f_{4}\) are isolated in step 28, and faults \(f_{5}\) and \(f_{6}\) are isolated in step 26. When the test is unreliable, faults \(f_{3}\) and \(f_{4}\) are isolated in step 28, the fault \(f_{5}\) is isolated in step 27, and the fault \(f_{6}\) is isolated in step 24. This fully shows that whether the test is reliable or not will not only affect the sequence of fault isolation, but also affect which step of the test is isolated. Therefore, it is of great significance to fully consider the condition of unreliable tests when implementing a fault diagnosis strategy. Using the above discussion and analysis, we conclude that, although the steps to achieve fault isolation are the same when constructing diagnostic strategies for traction systems under reliable and unreliable testing, the sequence of fault diagnosis and the specific tests selected for fault diagnosis are different. This also confirms that in engineering, considering the unreliability of testing is closely related to practical applications. It can help us better judge equipment detection and improve the accuracy of testability modeling.
# State of Industry 5.0--Analysis and Identification of Current Research Trends
## 1 Introduction
In Industry 5.0 advanced IT technologies, IoT, robots, AI, and augmented reality are actively used in the industry for benefit and convenience of human workers [5]. Industry 5.0 recognizes the capacity of industry to fulfill social objectives beyond employment and development, to become a sustainable source of development, by making production regard the limitations of our planet and prioritizing employee health first. To be a trusted system for individuals seeking a satisfying and healthy career, Industry 5.0 contributes to the technology upgrade required by industry. It prioritizes worker welfare and employs new technology to create wealth beyond employment and growth while respecting the planet's constraints. It empowers workers and meets their changing skill and training requirements. It boosts industry's competitiveness and attracts top personnel. \"An economy that works for people\", \"European Green Deal\" and \"Europe fit for the digital age\" are three goals of the Commission in implementing Industry 5.0. Therefore, Industry 5.0 is not founded on technology but on principles such as human-centricity, environmental stewardship, and social benefit. This reorientation is grounded in the notion that technology may be tailored to encourage values, and that technological innovation can be built on ethical objectives, not the other way around [6]. The overall current state of understanding of Industry 5.0 describes it as the movement to bring the human touch back to the manufacturing industry. This is driven by the consumer's desire for mass personalization. This understanding means that Industry 5.0 products provide consumers with a way of realizing their urge to express themselves, and they will pay a premium to do so [7]. To summarize, Industry 5.0 is a concept that seeks to make industry more sustainable, human centric, and resilient. Some view it as an evolutionary, incremental advancement that builds on the concepts and practices of industry 4.0 and others view Industry 5.0 as a complement to the Industry 4.0 paradigm. Table 1 contrasts objectives, systemic approaches, human factors, enabling technologies and concepts, and environmental consideration of Industry 4.0 and 5.0 [7; 8]. Since Industry 5.0 is a new concept there is little agreement on how it is defined. However, it is observed that the primary trend of Industry 5.0 is the introduction of human-robot co-working environment and the creation of smart society.