esa-sceva/satcom-chunk-collection huggingface dataset:
# On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks
## 4 Channel Capacity Performance Analysis  
By comprehending the MCA noise channel as a Gaussian-mixture channel in (7), the instantaneous channel capacity per unit bandwidth \(C_{\ell}(\gamma)\) of the \(\ell\)-th link can be obtained according to the theorem of total probability ([45], p. 23), i.e.,  
\[C_{\ell}(\gamma)=\sum_{n=0}^{\infty}\alpha_{n}\cdot\log_{2}\Bigl{(}1+\frac{ \gamma}{\beta_{n}}\Bigr{)}, \tag{22}\]  
where \(\alpha_{n}\) and \(\beta_{n}\) are given in (8) and (10), respectively.  
According to the well-known max-flow min-cut theorem, the maximum achievable channel capacity of a multi-hop relay system from the source node to the destination node is bounded by the minimum of the capacities of each individual hop ([47], pp. 587-595). Therefore, the instantaneous channel capacity \(C(\gamma_{e})\) of the end-to-end full-duplex relay network can be written as  
\[C(\gamma_{e})=\min_{\ell=1,\ldots,L}\{C_{\ell}(\gamma)\}=\sum_{n=0}^{\infty} \frac{\alpha_{n}}{\ln(2)}\cdot\ln\Bigl{(}1+\frac{\gamma_{e}}{\beta_{n}}\Bigr{)}, \tag{23}\]  
where \(\gamma_{e}=\min\{\gamma_{1},\ldots,\gamma_{L}\}\) is the effective SNR equivalent to one-hop transmission channel with the same capacity as the \(L\)-hop DF relay channel.  
**Proposition 3**.: _The PDF \(f_{\gamma_{e}}(\gamma)\) of the equivalent SNR \(\gamma_{e}\) of the \(L\)-hop relay system is expressed as_  
\[f_{\gamma_{e}}(\gamma)=\frac{L}{\Gamma(m)}\cdot\left[\widehat{\Gamma}\Bigl{(} m,\frac{mN_{0}}{\Omega E_{b}P_{Rx,\ell+1}}\gamma\Bigr{)}\right]^{L-1}\cdot \Bigl{(}\frac{mN_{0}}{\Omega E_{b}P_{Rx,\ell+1}}\Bigr{)}^{m}\cdot\gamma^{m-1} \cdot\exp\Bigl{(}-\frac{mN_{0}}{\Omega E_{b}P_{Rx,\ell+1}}\gamma\Bigr{)}. \tag{24}\]  
Figure 4: End-to-end average BER of the \(L\)-hop relay network versus individual-hop average BER.  
Proof.: Under the power allocation scheme in (4), the instantaneous SNR \(\gamma_{\ell}\) (\(\ell=1,\ldots,L\)) of each hop are i.i.d. RVs with PDF and CDF given in (15) and (16), respectively. The CDF \(F_{\gamma_{\ell}}(\gamma)\) of the equivalent SNR \(\gamma_{e}\) is given by  
\[F_{\gamma_{e}}(\gamma) =\Pr[\gamma_{e}\leq\gamma]=1-\Pr[\gamma_{e}>\gamma]\] \[=1-\Pr[\min\{\gamma_{1},\ldots,\gamma_{L}\}>\gamma]=1-\prod\limits _{\ell=1}^{L}\Pr(\gamma_{\ell}>\gamma)\] \[=1-\prod\limits_{\ell=1}^{L}[1-\Pr(\gamma_{\ell}\leq\gamma)]=1- \prod\limits_{\ell=1}^{L}[1-F_{\gamma_{\ell}}(\gamma)]\] \[=1-\left[\widehat{\Gamma}\Big{(}m,\frac{mN_{0}}{\Omega E_{b}P_{Rx,\ell+1}}\gamma\Big{)}\right]^{L}, \tag{25}\]  
where \(\widehat{\Gamma}(\cdot,\cdot)\) is the normalized upper incomplete gamma function defined as  
\[\widehat{\Gamma}(\tau,y)=\frac{1}{\Gamma(\tau)}\int_{y}^{\infty}t^{\tau-1} \cdot\exp(-t)\,dt. \tag{26}\]  
The PDF \(f_{\gamma_{e}}(\gamma)\) of the equivalent SNR \(\gamma_{e}\) follows immediately by differentiating (25) and is expressed as in (24).
## 3 Results
### Experiment Results  
The experimental results generally showed a significant effect of the magnetic field, which was represented by the amount of current input, to the change of MRFs pressure in the testing channel. For instance, Figure 8 shows the pressure values obtained at a current input of 1.00 A. The current input (yellow curved line) is seen in two states; the demagnetized state (Current = 0 A) and the magnetized state (Current = on); which results in changes of MRFs pressure. The MRFs pressure (blue curved line) is at its lowest when it is under a demagnetized state. But when the current input is turned on, the MRFs pressure increases proportionally to the input current.  
As seen in Figure 9 the increase in MRFs pressure is consistent as the input current increases. At a lower current input, the change in MRFs pressure was relatively small as it only rose about 13.06 Pa to 18.39 Pa at 0.25 A current excitation. However, at a higher current input, the slope of MRFs pressure to current input is increased. For example, the average MRFs pressure was measured at about 267.80 Pa at 1.00 A of current excitation. Similar trends are still observed at input currents of 1.25 A, 1.50 A, 1.75 A, and 2.00 A with average changes in MRFs pressure of 517.16 Pa, 709.20 Pa, 904.64 Pa, 918.55 Pa, and 1020.01 Pa, respectively. The detail of overall changes in MRFs pressure during experiments is presented in Table 1.  
Figure 8: Experiment results of MRFs pressure at an input current of 1.00 A.  
Figure 7: A graph of the correlation between current and magnetic flux density in the FEMM software simulation.
###### Abstract  
Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine's condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.  
fault diagnosis; condition monitoring; induction machines; support vector machines; expert systems; neural networks +
Footnote †: journal: Electronics Letters
## 3 Results and Discussion
### MRI/fMRI-Based Hyperscanning Research
#### Decision-Making Task  
MRI and fMRI hyperscanning had four studies focusing on applications in decision-making tasks e.g., [162; 163; 8; 17]. The first hyperscanning study done was by [PERSON] in 2002. This study used a competitive two-player computer game to study decision-making during social interactions, and paved the way for future hyperscanning research [8]. [PERSON] et al. [17] had participants play an economic trust game to understand social interaction through reciprocity, while another study conducted two experiments using the economic trust game to investigate social credit during a two-person social exchange [163]. The fourth study used an ultimatum game to explore brain processes associated with bidirectional reciprocity [162].  
_Setting, Orientation, and Group Sizes._ All four experiments were conducted in a lab setting, with pairs of participants oriented side-by-side. [PERSON] et al. [17] used an economic (trust) game to explore the effect of reciprocity on another person (their partner). This study reports that that reciprocity expressed by one player strongly predicts future trust expressed by their partner--a behavioral finding mirrored by neural responses in the dorsal striatum. Recently, [PERSON] et al. [162] explored brain processes associated with the bidirectional reciprocity characterizing real-world, repeated dyadic interactions. This study revealed, for the first time, that brain signals implicated in social decision-making are modulated by these estimates of EU, and become correlated more strongly between interacting players who reciprocate one another.
## 2 Three-Leg Approach  
A recently proposed new framework for the PD-free design of insulation systems, the so-called \"three-leg approach\" [18; 22], can be exploited for the estimation of bulk, surface, or interface PDIV of an insulation system. This approach is valid for both AC and DC systems, and it is based on three steps (the \"legs\"):  
1. Electric field simulation of the insulation system/object under study;
2. Modelling of the PD-inception field and voltage, PDIE and PDIV, respectively;
3. Performance of PD measurements to validate the results predicted by leg 2.  
Specifically, the voltage at which the maximum (approximately) electric field in an insulation system (calculated at leg 1) matches the PD-inception field estimated by the model at leg 2 is the partial discharge inception voltage (PDIV) [22]. Eventually, the experimental PDIV value determined from leg 3 is used to validate the estimated PDIV thus obtained from leg 2.  
The generalized PD-inception model, namely leg 2, can be expressed as follows [21; 22] (based on [20]):  
\[PDIE\ \text{or}\ E_{i}=\left(E/p\right)_{cr}p\left(1+\frac{B}{\left(pk_{s} \right)^{1/\beta}}\right) \tag{1}\]  
which is valid for a gas at pressure \(p\). \(\left(E/p\right)_{cr}\) is the critical reduced field, \(B\) and \(\beta\) are empirical parameters (based on the discharge mechanism and Meek number, [21; 22]), and \(k_{s}\) is a scale factor that considers the field gradient in the volume/surface where the electric field is maximum. Table 1 displays the parameters of each type of discharge (surface or internal) in air.  
Before creating model-driven artificial defects on the cable, the above-mentioned three-leg approach was applied on commercial XLPE flat specimens (which is the material of insulation of the MV cable used in the present study) by the test electrode setup shown in Figure 1, under AC. The modelled value of AC PDIV was calculated by using the PD-inception model (Equation (1)) and estimating the value of \(k_{s}\) by means of the tangential electric field profile from the triple point (Figure 2). The tangential and orthogonal electric field profiles, near the triple point of the top electrode, are depicted in Figure 2. Even though the orthogonal field component (which causes gas discharges) is larger than the tangential field (which promotes surface discharges), PDIV in the case of surface PD is lower than that of gas discharges because of different model parameter values (see Table 1). As can be seen in Figure 2a, the simulated tangential field at 1.7 kV is slightly higher than the surface PDIE, satisfying the condition for the inception of surface PD. On the other hand, the simulated orthogonal field at the same voltage (1.7 kV) is significantly lower than the corresponding gas PDIE (Figure 2b). Hence, surface discharges are predominant with the type of the electrodes shown in Figure 1. The validation of the estimated surface PDIV of XLPE flat specimen with test electrode setup, by means of performing PD measurements, is discussed in Section 4.
## 4 Results and Discussion
### Experiment Environment
#### 4.1.1 Evaluation Criteria  
For the evaluation of object detection performance, the mean average precision (mAP) index following the MS COCO standard was used. For the benchmark, we compared the mAP of the proposed model with YOLOv5, faster-RCNN [23], and YOLO-Z [19]. As similar approaches of training object detectors using multi-domain datasets are not present, we compared the object detector performance by training the network for two tasks. For the human detection task, MS COCO and Teledyne FLIR dataset pairs were used in training and as benchmarks. For detecting objects in vehicle categories, we used MS COCO, VEDAI, DOTA, and the synthesized dataset jointly in training and as benchmarks. The domain adaptation training procedure was only applied to the proposed method.
## 2 Proposed Integrated Mobile VLCP Scheme  
Figure 1 illustrates the schematic diagram of an integrated mobile VLCP system model, considering both MISO and MIMO configurations. The system comprises \(N_{T}\) LEDs serving as transmitting antennas installed in the ceiling to transmit VLC signals. The coordinate of the \(i\)th LED (\(i\) = 1, 2, \(\ldots\), \(N_{T}\)) is known and denoted as (\(x_{i}\), \(y_{i}\), \(z_{i}\)). In this work, we focus on the scenario where \(N_{T}\) = 4. The mobile receiver is equipped with \(N_{R}\) PDs serving as receiving antennas, positioned in the receiving plane. For the MISO scenario, \(N_{R}\) = 1, whereas for MIMO, \(N_{R}\) = 2 is assumed. Each PD is upward-oriented to receive VLC signals. The three-dimensional (3D) coordinate of the \(j\)th PD (\(j\) = 1, 2, \(\ldots\), \(N_{R}\)) is (\(\pi_{j}\), \(y_{j\bar{\pi}}\), \(z_{\eta\bar{\eta}}\)), with its two-dimensional (2D) horizontal coordinate (\(x_{i\bar{\eta}}\), \(y_{i\bar{\eta}}\)) being unknown, and its vertical coordinate (\(z_{\eta\bar{\eta}}\)) known. At the receiver, channel estimation is conducted to recover the VLC signal. The acquired CSI is then utilized for receiver localization, enabling simultaneous VLC and VLP within the same system.
# Deep Machine Learning for Path Length Characterization Using Acoustic Diffraction
## 2 Materials and Methods
### Preprocessing  
Data preprocessing begins by randomly selecting 17 waveforms from a given round of data collection. The fast Fourier transform (FFT) is computed on the emitted and received signal for each of the 17 waveforms. While in the Fourier domain, the transfer function amplitude and transfer function phase are calculated as these values give insight into the changes that the wave has undergone as it travels through the medium. The transfer functions are calculated by taking the Fourier Transform of the received signal and dividing it by the Fourier Transform of the input signal. This allows us to remove a degree of noise from the phase and amplitude features in our training. Noise as the signal leaves the transmitter often occurs due to factors such as poor contact between the emitter and the specimen and fluctuations in the buffer water between the transducer and the ring.  
Figure 7: Pseudocode representing the process of taking all 5 collected signals at each of the 17 locations in all 4 rounds of data collection on each of the 6 specimens at both frequencies and combining them to create 60,000 sets of waveforms in the form of an array to be processed into images.  
If only the received signal is analyzed, these factors which are not useful in probing the specimen are included in the signal. However, if the transfer function is taken, such factors can be removed so that a cleaner signal is analyzed. The transfer function phase and amplitude data for all frequency ranges are then grouped into 51 bins with the maximum value representing the bin so that all neighboring frequencies are combined. As described in [9], once all FFTs are taken and binned, they are pieced together as columns of an array, such that the array has 51 rows by 17 columns. At this point, the remainder of the signal preprocessing differs depending on which model the data will go to.  
In addition to preprocessing the signals, a portion of the control data must also be preprocessed to convert the disc parameters from height to acoustic path length. The equation used to calculate the expected acoustic path length is shown below and the derivations of acoustic path length for each combination of specimen height and frequency can be found in Table 4.  
\[\text{Path Length}=2\pi\frac{\text{disc height}}{\lambda} \tag{7}\]
# NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
## 4 Discussion
### Benefits of NFT-Based Approach for Aviation Component Lifecycle Management  
The proposed NFT-based approach to aviation-component lifecycle management offers several key benefits that address critical challenges in the aviation industry:  
* Each component is represented by a unique NFT, creating an immutable digital fingerprint that tracks the component's entire lifecycle. This significantly reduces the risk of counterfeit parts entering the system.
* Real-time tracking of components through their NFTs enhances inventory management, reduces delays, and ensures timely availability of parts.
* The system simplifies the certification process by linking all relevant information directly to the component's NFT. This creates a single source of truth, reducing paperwork and simplifying audits.
* Rich historical data stored in NFTs enables informed decision-making for maintenance. Integration with digital twin technology allows for real-time monitoring and performance optimization.
* Comprehensive tracking of component history and performance data contributes to improved risk assessment and proactive maintenance.
* The hybrid approach of blockchain and decentralized storage balances security needs with system efficiency for managing vast amounts of data.
* The system facilitates better information sharing among various stakeholders in the aviation industry, fostering a more connected ecosystem.
* Optimized maintenance schedules, reduced paperwork, and improved supply chain efficiency can lead to significant cost savings.
* Complete historical records stored in NFTs can facilitate more effective recycling or repurposing of parts at the end of their lifecycle.
* The system's design allows for the integration of future technologies, ensuring long-term value and relevance.  
This NFT-based approach offers a comprehensive solution to many challenges faced by the aviation industry, potentially improving safety, efficiency, and sustainability in aviation operations.
# Linked Data Triples Enhance Document Relevance Classification
## 2 Related Work
Probabilistic approaches, like Topic Models, use corpus-word distributions to categorize documents into \(n\) different topics. Each document is represented as a probability distribution of topics, and each topic is represented as probability distribution of words in the corpus. Topic models can assist in semi-supervised document classification (with minimal labeled data) [25]. Initially, labeled and unlabeled sets are combined to represent documents with topics. Self-learning is performed by computing similarities between documents from labeled and unlabeled sets to find the best suitable label for each document. Finally, this combined labeled set is used to train a supervised classifier to make predictions on unseen data. Multi-modal architectures help to fuse different features into a single model. In Reference [26], images and their textual descriptions are combined to perform sentiment analysis. Image and text-based models are created separately and fused to perform multi-label sentiment classification. In our work, multiple features, like text, topic distributions, named entities, and triples, are fused using layer concatenation for classifying document relevance. Regarding the taxonomy of document representational approaches in Figure 1, our approach is hybrid. _Named-entity extraction_ is an example of _Linguistic enrichment_ (_Language dependent_), so it is an _open domain_ ontology technique in Figure 1. _Latent Dirichlet Allocation_ (_LDA_) topic modeling is an example of a _Probabilistic approach_ in Figure 1. _Linguistic enrichment_ is also at play with the formation of Linked data triples by analyzing the syntactic dependencies of the document sentences. On the other hand, we also deploy _Word embeddings_, considering the linked data triples as primitive sentences, and we are able to leverage _context-based word embeddings_, comparing the performance of different methods from that domain, namely GloVe, Infersent, and BERT, to baseline document relevance classification.
## 3 Results and Discussion
### Analysis of the Laws of Charge Transfer during Polarization
When the salt concentration was increased to 1 mol/L, the coefficient of 1/_b_ as opposed to 0.5 mol/L was no longer practically increased over time, maintaining a roughly constant value. In the electrolytes with 2 mol/L salt concentration, the value of 1/_b_ decreased sharply from very high values at the beginning of the process to values comparable referred low concentrations in 30 min (Figure 7). Such a reduction in 1/_b_ in the initial corrosion time period should also characterize the film thickness decrease. It also, as in the previous case, confirmed the dependence of polarization resistance on time (Figure 7b), which demonstrated the growth of film resistance in 0.5 mol/L magnesium sulfate solutions, almost constant value in 1 mol/L solutions and its decrease in 2 mol/L solution. At the same time, according to SEM, the film thickness increased distinctly with increasing the magnesium sulfate concentration from 0.5 mol/L to 2 mol/L (Figure 2). Formally, this contradicted the observed change of the Tafel's coefficient and polarization resistance in electrolytes with a high concentration of magnesium sulfate and in sodium sulfate solutions. However, it was clear that the formed film was a rather incoherent formation, which was particularly well seen in the example of magnesium sulfate solutions of increased concentration. The state of the surface in the 2 mol/L magnesium sulfate solution was quite consistent with the dissolving film. In this case, it was logical to consider the reduction of film resistance as a consequence of the replacement of the dense primary film by the incoherent secondary film, and it was more expressed for sodium sulfate solutions and high concentrations of magnesium sulfate, in which the solubility of magnesium hydroxide [29] was higher, which allowed the primary film to dissolve. Figure 7: Dependence of 1/_b_ (**a**) and resistance of the corrosion film \(R_{p}\) (**b**) on the immersion time of the electrode in magnesium sulfate electrolytes of various concentrations.