esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Neural-Network-Based Interference Cancellation in LIS Systems
### Training Process  
The training data consist of received signals, detected by MRC/EGC with a single iteration, as inputs, with their corresponding transmitted signals as outputs. The output of the MRC/EGC decoder is corrupted by interferences generated in the decoding process (Equation (2)).  
The training process minimizes the Mean Squared Error (MSE) between the predicted and actual transmitted signals, as follows:  
\[\varsigma=\frac{1}{N}\sum_{n=1}^{N}\mid\mid\widehat{\mathsf{S}}_{n}^{(t)}- \hat{\mathsf{S}}_{n}^{(t)}\mid\mid^{2} \tag{5}\]  
where \(\widehat{\mathsf{S}}_{n}^{(t)}\) is the actual output signal from the one-iteration MRC/EGC detector, while \(\hat{\mathsf{S}}_{n}\) is the estimated output of the neural network (same signal clean of interference).  
Training was performed using the Adam optimizer with the following hyperparameters:  
* Max Epochs: 500
* Mini-Batch Size: 16
* Initial Learning Rate: 0.001
* Shuffling: Enabled at every epoch to prevent overfitting  
Each neural network was trained to minimize the MSE between the predicted and actual transmitted signals. The batch size of 16 was chosen as a balance between training stability and convergence speed.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 3 MAOA Algorithm
### Construction of Unique Guidance for MAOA
This helps MAOA sustain search variation longer through early and mid-phases, increasing the likelihood of discovering latent optima through \"late bloomers\" in the population. Furthermore, the proposed guidance strategy maintains population diversity across generations by preventing premature clustering around a single guide. It allows search trajectories to adapt dynamically based on fitness rank, reinforcing convergence in promising regions while ensuring broad exploration. This mechanism effectively replaces deterministic elitism with a probabilistic leadership model, allowing MAOA to strike a more effective and flexible balance between exploration and exploitation. Figure 3 illustrates the flowchart used to derive the unique guidance (\(UG_{n}\)) for each n-th solution, where \(n=1,\ldots,N\), which is subsequently used to replace \(X^{best}\) in calculating the new position of each n-th solution during the exploration and exploitation phases. Let \(UG_{n,d}\) represent the d-th dimension of the unique guidance assigned to the n-th solution. The d-th dimension of the new position vector for the n-th solution (\(X^{New}_{n,d}\)) is calculated during the exploration phase of MAOA (i.e., when \(r_{1}<MOA\)) as follows:  
\[X^{New}_{n,d}= \begin{cases}UG_{n,d}\div(MOP+\chi)\times\big{(}(X^{UI}_{d}-X^{ L}_{d})\times 0.5+X^{L}_{d}\big{)},&\text{if $r_{2}<0.5$}\\ UG_{n,d}\times MOP\times\big{(}(X^{UI}_{d}-X^{L}_{d})\times 0.5+X^{L}_{d}\big{)},& \text{otherwise}\end{cases}  \tag{20}\]  
During the exploitation phase of MAOA (i.e., when \(r_{1}\geq MOA\)), the new position of the d-th dimension for the n-th solution \(X^{New}_{n,d}\) is calculated as follows:  
\[X^{New}_{n,d}= \begin{cases}UG_{n,d}-MOP\times\big{(}(X^{UI}_{d}-X^{L}_{d}) \times 0.5+X^{L}_{d}\big{)},&\text{if $r_{3}<0.5$}\\ UG_{n,d}+MOP\times\big{(}(X^{UI}_{d}-X^{L}_{d})\times 0.5+X^{L}_{d}\big{)},&\text{ otherwise}\end{cases}  \tag{21}\]  
Compared to the original AOA, the exploration and exploitation phases of MAOA, as defined in Equations (20) and (21), incorporate valuable information from other well-performing solutions beyond \(X^{best}\) offering a unique guidance scheme for each \(n\)-th learner. Since this unique guidance is derived from a subset of population members that are superior to the n-th learner, it is expected to provide effective guidance while still preserving population diversity.
## 4 Experimental Investigation of Aeroelastic Vibration and Harvesting Performance
### Field Harvesting Performance  
To validate the energy harvesting capabilities of the airfoil-based flutter energy harvester system and to pave the way for its practical application, an energy harvesting circuit was meticulously designed, and experiments were conducted. The energy harvesting circuit, as illustrated in Figure 14, consists of a rectifier and a light-emitting diode (LED). The current generated by the airfoil-based flutter energy harvester system is rectified by the rectifier and subsequently utilized to power the LED. In Figure 14b, an experimental image showcases the airfoil-based flutter energy harvester system successfully powering six LED lamps at a velocity of 11 m/s. This demonstration serves as concrete evidence of the feasibility and functionality of the airfoil-based flutter energy harvester system, indicating its potential for practical applications in the future.  
Figure 13: Experimental results of output characteristics changing with installation angle: **(a)** output voltage versus airflow velocity; **(b)** output power versus airflow velocity.
# Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems
## 2 Overview of Information Uncertainties in Fault Diagnosis Using Bayesian Networks
These two models worked together with the fused multisource data. [PERSON] et al. [15] investigated a fault diagnosis situation in which the fault diagnosis results were affected by multiple subsystems. They fused abnormal symptoms detected by various types of sensors and different subsystems, and then updated the BN with the fused symptoms in order to diagnose the most likely fault and its cause. Other studies have highlighted online fault diagnosis/monitoring using BNs. A single fault diagnosis model only had a limited diagnosis effect in a multi-fault system and was insufficient for diagnosing all faults. [PERSON] et al. [16] constructed a purely data-driven probabilistic ensemble learning strategy and used an ensemble index to select classifiers with a better diagnostic performance. Ensemble fault classification models can capture the mixed fault characteristics of multiple faults. A BN was constructed based on selected diagnostic classifiers. The initial BN models were constructed using previously collected data and expert knowledge and may not be compatible with an actual system. To improve diagnosis accuracy by fully utilizing practical data, [PERSON] et al. [17] developed a machine learning set for one parameter learning method, and three structure learning methods for delay dynamic coupled fault diagnosis. Currently, intelligent manufacturing systems are important sources of online sensor data. The status data gathered by the distributed sensor system is crucial for defect detection in complicated multi-station manufacturing systems. [PERSON] et al. [18] focused on real-time fixture faults and part reorientation faults in a multi-station assembly, complex sensor system, established a BN-based sensor deployment strategy, and used information entropy to diagnose multi-station assembly processes. These typical research results are summarized in Table 1. In conclusion, the fault diagnosis of intelligent instruments in IoT systems must simultaneously manage online sensor data and professional expertise. However, IoT sensors are unable to gather data for the diagnosis of all fault types of a single instrument because industrial equipment has complicated fault causes and performances. In fact, only a portion of the typical parameters can be monitored. Moreover, long-distance data transfer introduces more uncertainties into the fault diagnosis of equipment in IoT systems and obtaining an exact fault probability is sometimes difficult or expensive. Among all the fault diagnosis methods, the strength of BNs lies in their ability to perform reasoning and inference in complicated systems with uncertainties, which makes it possible to reduce the complexity of fault diagnosis through probability causal inferencing. The mode proposed in this study uses a BN for fault state inferencing and can effectively handle knowledge uncertainty and sensor data uncertainty in fault diagnosis, making it meaningful for practical fault diagnosis applications in IoT systems.
## 3 Model Parameter Estimation
### Propulsion Noise
#### 3.2.1 Resampling
The algorithm described below is based on an algorithm developed for object tracking in video data [64]. Within the search section of the discrete spectrogram, the local score \(q\) is calculated by:  
\[q(m,l)=S\left(m,l\right)-\min_{m,l}\left(S(m,l)\right) \tag{33}\]  
Figure 11: Normalized spectrogram (**a**) of the measured sound pressure signal with tracked double ignition frequency (drawn as a black line) and power spectral density (**b**) of the original and asynchronously resampled sound pressure signal, respectively. The recording was conducted at the rear of the BMW with an inline, four-cylinder engine idling at 1100 rpm. for which holds \(q\geq 0\). From \(q\) for each time step \(m=\{1...M\}\) and frequency bin \(l\), the global score \(Q(m,l)\) is recursively computed by:  
\[Q(m,l)=q(m,l)+\max_{l^{\prime}\in\{l-c\leq l\leq l+c\}}Q(m-1,l^{\prime}) \tag{34}\]  
with a positive integer \(c\) realizing the requirement:  
\[|C|\leq c\frac{\Delta f}{\Delta t} \tag{35}\]  
on the absolute value of the slope \(C\) of \(F_{\text{opt}}\) given in Hertz per second. For the forward processing described by Equation (34), the starting condition is that the initial global score, \(Q(1,l)\), is set to the local score, _i.e._, \(Q(1,l)=q(1,l)\). During the evaluation of Equation (34), it is essential that the back pointers:  
\[B(m,l)=\operatorname*{arg\,max}_{l^{\prime}\in\{l-c\leq l\leq l+c\}}Q(m-1,l^{ \prime}) \tag{36}\]  
to the optimal predecessors are stored. From the global score \(Q\), the end point of the optimal path is found by:  
\[F_{\text{opt}}(M)=\operatorname*{arg\,max}_{l}Q(M,l) \tag{37}\]  
Using the back pointers \(B\), the optimal path can be traced back using a recursive procedure known as backtracking:  
Figure 10:0  
In Appendix, we provide a simple MATLAB code, which solves Equations (34) to (38). Figure 11 shows the optimal path drawn as a black line following the frequency component around 70 Hz. In the third step, the sound pressure signal is asynchronously resampled based on the course of the tracked engine order. The warped time axis is calculated by:  
Figure 10:1  
where \(F_{\text{opt}}(t)\) is the linearly-interpolated version of \(F_{\text{opt}}(m)\). For the resampling of the sound pressure signals, a windowed sinc interpolation, as described by Equation (24), is adopted. Figure 11 illustrates the effect of the asynchronous resampling on the power spectral density. In contrast to the original signal, for the resampled signal, all even engine orders from two to 12 can be clearly identified as equidistant, narrow peaks.
## 5 Experiments
### Results and Analysis
#### 5.3.2 Travel Time Interval Prediction Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case Study** & **Model** & **PICP (\%)** & **MPIW** & **NMPIW (\%)** & **CWC** \\ \hline \multirow{6}{*}{902335} & \(\rm PI_{P}\) & 88.89 & 334.46 & 63.03 & 160.73 \\  & \(\rm PI_{HC}\) & 88.89 & 368.25 & 53.97 & 137.62 \\  & \(\rm PI_{MHC}\) & 100.00 & **267.21** & **35.53** & **35.53** \\ \hline \multirow{6}{*}{902335} & \(\rm PI_{P}\) & 94.44 & 334.19 & 72.05 & 72.05 \\  & \(\rm PI_{MHC}\) & 91.67 & 305.62 & 50.61 & 50.61 \\  & \(\rm PI_{MHC}\) & 91.67 & **153.9** & **33.18** & **33.18** \\ \hline \multirow{6}{*}{16:00–19:00} & \(\rm PI_{P}\) & 94.44 & 325.36 & 71.73 & 71.73 \\  & \(\rm PI_{HC}\) & 83.33 & 262.44 & 54.12 & 1320.4 \\ \cline{1-1}  & \(\rm PI_{MHC}\) & 88.89 & **228.57** & **43.04** & **109.75** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PIs characteristics for test samples when the confidence level is 90%.
# Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks
## 4 Performance Analyses of the Cognitive IA Schemes
### Capacity Analysis  
We next demonstrate the superiority of the proposed cognitive IA schemes by proving that it can preserve the same performance as the non-cognitive IA based network when they are both feasible to transmit the same number of data streams.  
With the proposed interference nulling based cognitive IA scheme, the cross-tier interference form the macrocell has been eliminated and we have:  
\[P_{p}\mathbf{U}_{k}^{H}g_{k,p}g_{k,p}^{H}\mathbf{U}_{k}=\mathbf{0}. \tag{34}\]  
Therefore, the data rate of the \(k\)-th IoT node coexisting with the macrocell can be expressed as:  
\[R_{k}^{CIA}=\log_{2}\Bigg{|}\mathbf{I}_{d_{k}}+\frac{P_{k}}{d_{k}\sigma_{n}^{2 }}\cdot\frac{\mathbf{U}_{k}^{H}\widetilde{\mathbf{H}}_{k,k}\widetilde{\mathbf{ V}}_{k}\widetilde{\mathbf{V}}_{k}^{K}\widetilde{\mathbf{H}}_{k,k}^{H} \mathbf{U}_{k}}{\mathbf{U}_{k}^{H}\mathbf{U}_{k}}\Bigg{|}. \tag{35}\]  
Recall that in full-IA based networks, the data rate of the \(k\)-th IoT node can be expressed as:  
\[R_{k}^{full-IA}=\log_{2}\Bigg{|}\mathbf{I}_{d_{k}}+\frac{P_{k}}{d_{k}\sigma_{n}^ {2}}\cdot\frac{\mathbf{U}_{k}^{H}\mathbf{H}_{k,k}\mathbf{V}_{k}\mathbf{V}_{k}^ {K}\mathbf{H}_{k,k}^{H}\mathbf{U}_{k}}{\mathbf{U}_{k}^{H}\left(\mathbf{I}_{ \mathbf{N}_{k,k}}+\mathbf{Q}_{k}\right)\mathbf{U}_{k}}\Bigg{|} \tag{36}\]By comparing (35) with (36) we can see when the interference covariance, \(\mathbf{Q}_{k}\), becomes a zero matrix when full-IA is achieved, and these two equations have the same form with just different dimensions of the channel matrices, \(\mathbf{H}_{k,k}\) and \(\widetilde{\mathbf{H}}_{k,k}\). Note that all the precoders and decoders in both IA schemes are i.i.d. unitary matrices and are independent of the channel matrix, which means the coding process does not change the distribution of the channel matrix/vector. Therefore, the expectation of the channel gain remains the same, and the proposed cognitive IA scheme achieves the same average sum rate as the non-cognitive homogeneous IA with the same number of data streams, \(d_{k}\), \(k=1,\ 2,\ \cdots,\ K\). As the interference nulling based cognitive IA scheme becomes less effective under low and intermediate SNR, the proposed partial cognitive IA scheme aims to directly maximize the transmission rate for each data stream. Although the co-tier and cross-tier interferences are not perfectly aligned into the orthogonal subspace of the desired signal, the partial cognitive IA still achieves a better network sum rate than the interference nulling based cognitive IA scheme.
# An Automatic Participant Detection Framework for Event Tracking on Twitter
## 4 Results
### Evaluation Set-up
#### 4.1.3 Baselines  
To the best of our knowledge, the problem of APD has not been previously explored in TDT literature. Although several approaches [1; 2; 3] use participants, they are only a means to an end: Improving the performance of the over-arching TDT algorithm. In addition, these approaches address a different problem than APD: They identify named entities during--not before--the event using off-the-shelf NER tools. For these reasons, we cannot use any of these systems as baselines.  
Instead, we compare our APD algorithm with two NER libraries: NLTK's NER implementation and TwitterNER [14]. NLTK is a general-purpose Natural Language Processing (NLP) library, whereas TwitterNER is a NER method trained on tweets. In both cases, we follow the first three steps of the framework described in Section 3: We extract named entities using the NER library, score them based on frequency, and filter the ranking to retain only the top 50 most common entities.  
We also evaluate our APD algorithm twice: Once by extracting named entities using NLTK and again using TwitterNER. We refer to the two approaches as APDNLTK and APDNtwitNER respectively. Both approaches resolve as many participants as possible from the top 50 extracted named entities and use extrapolation to find missing participants. Like the baselines, at the end we retain the top 50 participants from the final ranking. The results of the baselines and of our APD algorithms after resolution are presented in Table 2. Table 3 shows the results of our APD algorithms after extrapolation. We discuss these results next.
# Enhancing Underwater Images via Color Correction and Multiscale Fusion
## 3 Method of This Article
### Contrast Enhancement
#### 3.2.2 Global Contrast Enhancement of Sub-Images  
One researcher [32] proposed a complementary relationship between the two functions in Equation (20) for enhancing low-light image contrast. The function curves are illustrated in Figure 2, indicating that function \(y_{2}\) provides a more pronounced enhancement of pixels than function \(y_{1}\).  
\[\begin{cases}y_{1}=1-(1-x)^{\gamma}&\gamma<1\\ y_{2}=(1-(1-x)^{1/\gamma})^{\gamma}&\gamma<1\end{cases} \tag{20}\]  
Therefore, we have designed a new global contrast-enhancement method for underwater images in Equation (21).  
\[V_{2}(x,y)=\begin{cases}1-(1-V_{s}(x,y))^{\gamma}&V_{s}(x,y)>T\\ (1-(1-V_{s}(x,y))^{1/\gamma})^{\gamma}&else\end{cases} \tag{21}\]  
In Equation (21), \(V_{s}(x,y)\) is the pixel value of \(V_{s}\) at \((x,y)\), and \(V_{2}(x,y)\) is the pixel value at \((x,y)\) in the image after contrast enhancement. This section uses the threshold obtained in local contrast enhancement to divide the input image for processing.  
However, in this section, each pixel of the sub-image shares the same contrast-enhancement function, resulting in the global brightness improvement of the image. Figure 3 shows the image-enhancement effect of this section and section A. To visually display the impact, Figure 3a,b show the image converted from the space of the HSV to the RGB space.  
Figure 2: The function curves.
## 2 Experimental Section
### AE Setup  
The AE signals generated during the fatigue crack growth tests were collected by a PC-based multi-channel monitoring system named SAMOS AEwin, manufactured by the Physical Acoustic Corporation (PAC). Two narrow-band piezoelectric ceramic transducers (R15\(\alpha\)), having an operating frequency of 50 kHz to 200 kHz, were used to record the AE signals. To prevent the transducer from sliding or falling, first, a plastic positioner with a hole in the middle was bonded on the specimen. Next, the transducer was stuck in the hole and the detection surface and specimen surface were attached together by using an adhesive tape and high-vacuum grease (see Figure 4).  
A preamplifier with a gain of 40 dB was used for each transducer. Before the fatigue crack growth tests were performed, a specimen without cracks was tested under the same load conditions and monitored to collect the external noises from the experimental setup. Based on this noise check test, the threshold was set as 36 dB to reduce the noise. Table 2 lists the main parameters of the AE system in the test. The AE parameter set was determined and verified using the pencil lead breaking test [41].  
\begin{table}
\begin{tabular}{c c|c c} \hline Transducer & R15\(\alpha\) & Peak definite time & 300 (\(\upmu\)s) \\ \hline Operating frequency & 50–200 (kHz) & Hit definite time & 600 (\(\upmu\)s) \\ \hline Preamplifier gain & 40 (dB) & Hit lookout time & 1000 (\(\upmu\)s) \\ \hline Threshold & 36 (dB) & Hit length & 4 (k) \\ \hline Compatible filter & 100–400 (kHz) & Sampling frequency & 3 (MHz) \\ \hline \end{tabular}
\end{table}
Table 2: Set of AE parameters.  
Figure 4: Test setup.
# PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method
## 5 Conclusions  
In this work, we proposed a regions of interest extraction procedure from the hologram phase space representation. The extracted regions are used to bound the numerical reconstruction interval and the spatial delimitation when applying the focus measure during the DFF approach. Moreover, an iterative refinement method is proposed to refine the extracted regions of interest and produce a more accurate depth estimate.  
Experimental results have shown that the use of extracted regions of interest with the DFF method can reduce the number of calls to focus measure operators while improving the initial performance. In addition, the refinement of the extracted regions of interest and the use of a fixed-size interval when applying the DFF method allows better control of the depth extent of the provided regions of interest and an improvement in the final performance. The proposed methodology halved the computation time while increasing the accuracy by a factor of three.  
The iterative method can be an alternative to the depth-from-focus method if the following two limitations are solved. First, the curvature must be directly recovered from the phase space representation without requiring any numerical reconstruction. Second, an appropriate fusing method to merge the estimations produce using different angle values \(\eta\) must be designed.  
Future works will be dedicated to the design of an efficient end-to-end learning-based-method for fast and accurate depth estimation from a hologram phase space representation without requiring any additional post- or pre-processing steps, such as numerical reconstruction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Piano** & **Table** & **Woods** & **Cars** & **Dice** \\ \hline  & & & & & Without ROI \\ \hline LAPV & 3.89 & 4.10 & 4.92 & 5.91 & 4.97 \\ \hline WAVV & 11.80 & 13.00 & 14.29 & 17.84 & 14.35 \\ \hline WAVR & 6.87 & 6.88 & 7.78 & 9.20 & 7.28 \\ \hline GLVN & 4.65 & 4.82 & 5.31 & 6.42 & 5.35 \\ \hline GLVA & 3.64 & 4.19 & 4.70 & 5.75 & 4.50 \\ \hline CONT & 2.90 & 3.16 & 3.46 & 4.30 & 3.51 \\ \hline \multicolumn{5}{c}{With ROI} \\ \hline LAPV & 1.18 & 1.17 & 1.46 & 2.98 & 1.23 \\ \hline WAVV & 2.58 & 2.38 & 3.39 & 8.64 & 2.54 \\ \hline WAVR & 1.37 & 1.29 & 1.82 & 3.85 & 1.35 \\ \hline GLVN & 1.26 & 1.31 & 1.62 & 3.09 & 1.43 \\ \hline GLVA & 1.12 & 1.10 & 1.44 & 2.71 & 1.19 \\ \hline CONT & 0.87 & 0.87 & 1.09 & 2.05 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculation time in seconds to calculate the final depth map with and without ROI.  
**Author Contributions:** Conceptualization, N.M.; Methodology, N.M.; Writing--original draft, N.M.; Supervision, A.G., P.G. and L.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Agence Nationale de la Recherche (ANR-A0-AIRT-07).  
**Data Availability Statement:** No data were generated or analyzed in the presented research.  
**Conflicts of Interest:** The authors declare no conflict of interest.