esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis  
Completeness Magnitude (\(M_{\text{C}}\)) and Linearity Assessment of Frequency-Magnitude Distribution (FMD)  
As shown in Figure 3, the results of the corrected MaxCurvature method show that the \(M_{\text{C}}\) of the earthquake catalog used in this work is \(M_{\text{C}}=1.5\). This result is consistent with the results of previous studies on the Wenchuan earthquake zone [30; 50]. [PERSON] et al. [51] described in detail the aftershock performance and analysis of the Lushan earthquake based on the combined data from permanent and temporary seismic stations. They concluded that the minimum complete magnitude was \(M=1.0\). To unify the consistency of the \(M_{\text{C}}\) of the Wenchuan \(M_{\text{S}}\) 8.0 and Lushan \(M_{\text{S}}\) 7.0 earthquakes in the Longmenshan fault zone, we selected events with magnitudes of \(M\geq M_{\text{C}}=1.5\).  
We performed a linearity check on FMD, and the results are shown in Figure 4. The NLIndex (red) is shown for different cutoff magnitudes (upper inset) and the NLIndex \(\leq\) 1 for all cut off magnitudes; thus, the linear FMD is accepted as the best \(M_{\rm C}\).
# Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage
## 3 Model Proposal and Solution Method  
### Proposed Model  
To use the FFT to achieve a faster algorithm computation speed, it is assumed that the image satisfies a periodic boundary condition. Moreover, the model is expressed as a matrix, and the gradient difference is expressed in the form of a convolution to avoid large-scale matrix multiplication operations, thereby facilitating the subsequent discussion. Combining the Lp-pseudo-norm with the TGV regularization, this study proposes a new regularization model for image restoration to more  
Figure 1: Contours of the Lp-pseudo-norm: (**a**) \(p=0.5\); (**b**) \(p=1\).  
Figure 2: Feasible domain under the Lp constraint: (**a**) \(p=0.5\); (**b**) \(p=1\).  
thoroughly extract the structural characteristics of the first-order and second-order gradient matrices of the image, and obtain a sparser solution. The model is named the TGV-Lp model, described below.  
\[\begin{split}&\underset{F,\mathbf{V_{h}},\mathbf{V_{v}}}{min}\left\|\mathbf{H \ast F-G}\right\|_{p}^{p}+\mu\mathrm{TGV}_{2}(\mathbf{F})\\ &=\underset{F,\mathbf{V_{h}},\mathbf{V_{v}}}{min}\left\|\mathbf{H\ast F-G} \right\|_{p}^{p}+\mu[\alpha_{0}(\left\|\mathbf{K_{h}\ast F-V_{h}}\right\|_{1}+ \left\|\mathbf{K_{v}\ast F-V_{v}}\right\|_{1})\\ &+\alpha_{1}(\left\|\mathbf{K_{h}\ast V_{h}}\right\|_{1}+\left\|\mathbf{K _{v}\ast V_{v}}\right\|_{1}+\left\|\mathbf{K_{v}\ast V_{h}}+\mathbf{K_{h}\ast V_{v}} \right\|_{1})]\end{split} \tag{4}\]  
where \(\mu\) is the regularization parameter, \(\left\|\cdot\right\|_{p}^{p}\) represents the Lp-pseudo-norm, \(\alpha_{0}\) and \(\alpha_{1}\) serve as the balance coefficients, and \(\mathbf{V_{h}},\mathbf{V_{v}}\) represent the horizontal and vertical gradients of the processed image.
# Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights
###### Abstract  
This paper presents a comprehensive overview of the frequency-domain filtered-x least mean-square (FxLMS) algorithms for active noise control (ANC). The direct use of frequency-domain adaptive filters for ANC results in two kinds of delays, i.e., delay in the signal path and delay in the weight adaptation. The effects of the two kinds of delays on the convergence behavior and stability of the adaptive algorithms are analyzed in this paper. The first delay can violate the so-called causality constraint, which is a major concern for broadband ANC, and the second delay can reduce the upper bound of the step size. The modified filter-x scheme has been employed to remove the delay in the weight adaptation, and several delayless filtering approaches have been presented to remove the delay in the signal path. However, state-of-the-art frequency-domain FxLMS algorithms only remove one kind of delay, and some of these algorithms have a very high peak complexity and hence are impractical for real-time systems. This paper thus proposes a new delayless frequency-domain ANC algorithm that completely removes the two kinds of delays and has a low complexity. The performance advantages and limitations of each algorithm are discussed based on an extensive evaluation, and the complexities are evaluated in terms of both the peak and average complexities.  
Active noise control; frequency domain adaptive filter; computational complexity; delayless; causality +
Footnote †: journal: Applied Sciences
## 3 Results
### PBX1 and PCCA Genes in a Family with AF  
For Sanger sequencing, 18 samples identified as putative carriers of at least one of the four imputed genome-wide significant variants were supplemented with an additional six samples selected as possible carriers based on their GPP. A total of 94 samples, chosen randomly from samples that had not been identified as carriers of any of the four variants of interest, were included as controls.  
Overall, two out of the four genome-wide significant SNPs were confirmed by Sanger sequencing: rs745582874 next to the _PBX1_ gene on Chr1, and rs768476991 within the _PCCA_ gene on Chr13 (Figure 2).  
Briefly, rs745582874 was confirmed in all 14 previously identified carriers and in an additional individual of the family identified by familial aggregation analysis (III-3, Figure 3 and Supplementary Figure S2), who is an obligate carrier. Furthermore, rs768476991 was confirmed in three out of four previously identified carriers but also in an additional seven individuals: five of whom belong to the family just mentioned above (III-2, III-3, IV-2, IV-4, and IV-6, Figure 3 and Supplementary Figure S2), and one of whom is a relative of an AF sporadic case carrying the same SNP, and one is a control.  
Both of the confirmed SNPs are very rare. However, they show a threefold higher frequency in the CHRIS cohort (Table 3) than in the gnomAD database (European, non-Finnish), showing a MAF of 0.019% and 0.006%, respectively. Of note, the two risk alleles are enriched in the family identified by familial aggregation analysis, and both co-segregate with the phenotype in the three AF cases (Figure 3). The individual IV-3 (Figure 3) is a young man with a medical diagnosis of AF at the age of 31 years. During study enrollment, he showed aP wave = 0 at 10 s ECG. The other two AF cases, one man (III-3) with medical diagnosis of AF at the age of 68 years and a woman (III-2) of 78 years (without a reported AF diagnosis), showed a P wave = 0 at 10 s ECG (Figure 3), and both take specific cardio drugs. The woman also takes antithrombotic medication. We have also analyzed the 20 min ECGs for all family members, and only the three AF cases exhibited P wave = 0 along the entire 20 min. None of the other family members carrying the confirmed SNPs have AF signs.  
Almost all AF cases carrying the confirmed variants belong to the family here described; however, an additional three sporadic AF cases were identified to be positive carriers. For a summary of demographic characteristics and common risk factors in carrier and non-carrier AF cases, see Supplementary Table S4.  
Figure 2: Schematic representation of genomic localization of 4 significant SNPs. The red circles indicate the confirmed SNPs by Sanger sequencing.
# Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
###### Abstract  
Precise monitoring of respiratory rate in premature newborn infants is essential to initiating medical interventions as required. Wired technologies can be invasive and obtrusive to the patients. We propose a deep-learning-enabled wearable monitoring system for premature newborn infants, where respiratory cessation is predicted using signals that are collected wirelessly from a non-invasive wearable Bellypatch put on the infant's body. We propose a five-stage design pipeline involving data collection and labeling, feature scaling, deep learning model selection with hyperparameter tuning, model training and validation, and model testing and deployment. The model used is a 1-D convolutional neural network (1 DCNN) architecture with one convolution layer, one pooling layer, and three fully-connected layers, achieving 97.15% classification accuracy. To address the energy limitations of wearable processing, several quantization techniques are explored, and their performance and energy consumption are analyzed for the respiratory classification task. Results demonstrate a reduction of energy footprints and model storage overhead with a considerable degradation of the classification accuracy, meaning that quantization and other model compression techniques are not the best solution for respiratory classification problem on wearable devices. To improve accuracy while reducing the energy consumption, we propose a novel spiking neural network (SNN)-based respiratory classification solution, which can be implemented on event-driven neuromorphic hardware platforms. To this end, we propose an approach to convert the analog operations of our baseline trained 1 DCNN to their spiking equivalent. We perform a design-space exploration using the parameters of the converted SNN to generate inference solutions having different accuracy and energy footprints. We select a solution that achieves an accuracy of 93.33% with 18\(\times\) lower energy compared to the baseline 1 DCNN model. Additionally, the proposed SNN solution achieves similar accuracy as the quantized model with a 4\(\times\) lower energy.  
w _et al._ Article
Remote Sens._**2013**, \(5\), 2973-3006; doi:10.3390/rs5062973  
_Remote Sensing_**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
**Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data**  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Department for Earth Observation, Friedrich-Schiller-University Jena, L\(\ddot{\rm o}\)dergraben 32, D-07743 Jena, Germany; E-Mails: [EMAIL_ADDRESS] (C.B.); [EMAIL_ADDRESS] (C.T.);  
[EMAIL_ADDRESS] (C.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +49-0-3641-948-871; Fax: +49-0-3641-948-882.  
_Received: 18 April 2013; in revised form: 30 May 2013 / Accepted: 30 May 2013 / Published: 13 June 2013_  
**Abstract:** This study presents a method for the reconstruction of pixels contaminated by optical thick clouds in multi-spectral Landsat images using multi-frequency SAR data. A number of reconstruction techniques have already been proposed in the scientific literature. However, all of the existing techniques have certain limitations. In order to overcome these limitations, we expose the Closest Spectral Fit (CSF) method proposed by [PERSON] _et al._ to a new, synergistic approach using optical and SAR data. Therefore, the term Closest Feature Vector (CFV) is introduced. The technique facilitates an elegant way to avoid radiometric distortions in the course of image reconstruction. Furthermore the cloud cover removal is independent from underlying land cover types and assumptions on seasonality, _etc._ The methodology is applied to mono-temporal, multi-frequency SAR data from TerraSAR-X (X-Band), ERS (C-Band) and ALOS Pulsar (L-Band). This represents a way of thinking about Radar data not as foreign, but as additional data source in multi-spectral remote sensing. For the assessment of the image restoration performance, an experimental framework is established and a statistical evaluation protocol is designed. The results show the potential of a synergistic usage of multi-spectral and SAR data to overcome the loss of data due to cloud cover.  
**Keywords:** cloud cover; SAR; data fusion; image reconstruction; missing information
## 5 Case Study
### Second Scenario (Mode 2: Hybrid Energy Supply, without Storage, EVs and DSM)  
In this mode, the heat load is met by the micro gas turbine and gas boiler together, while wind and solar power ought to supply the power load of the micro grid. In this mode, daily operation costs are 2840 RMB. The purchased electricity from the system is 143 kWh with a cost at 177 RMB; the natural gas purchase is 1065 m\({}^{3}\) with a cost of 2663 RMB.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \(\mathbf{P_{wt}}\) & \(\mathbf{P_{pv}}\) & \(\mathbf{P_{gt1}}\)  
Third Scenario (Mode3: Hybrid Energy Supply, Storage Batteries Included, without EVs and Demand Response)  
In this mode, the heat load supply is the same as Mode 2; wind and solar power ought to supply load in the micro grid, while the residual is supplied by energy storage and the power grid system. In this mode, the daily operation cost is 2674 RMB; the purchase of electricity from the grid is 15.2 KWh, costing 19 RMB; the gas purchase volume is 1011 m\({}^{3}\), costing 2527 RMB; and the cost of battery charge and discharge is 128 RMB.  
Fourth Scenario (Mode4: Hybrid Energy Supply, Storage Batteries and EVs Included, the Demand Response Not Considered)  
In this mode, the heat load supply is the same as Mode 2, and wind and solar power ought to supply the load to the micro grid. The residual is supplied by energy storage, EVs and the power grid system. The daily operation cost is 2674 RMB, but the purchase of electricity from the grid is not involved. The gas purchase volume is 1010 m\({}^{3}\), costing 2626 RMB; the cost of battery charge and discharge is 128 RMB; the cost of EV charging and discharging is 28 RMB. As electric vehicles are connected to the micro grid system, the total system load has increased, followed by a slight increase of the system operating expenses compared to Mode 3.
## 2 Materials and Methods
### Preparation of Fe\({}_{3}\)O\({}_{4}\) NPs  
1.17 g of FeCl\({}_{3}\)\(\cdot\)6H\({}_{2}\)O (purity 97%) and 0.93 g of Fe(NH\({}_{4}\))\({}_{2}\)(SO\({}_{4}\))\({}_{2}\cdot\)6H\({}_{2}\)O (Mohr salt, purity 99%) were dissolved in 50 mL of double distilled water (DDW), deoxygenated by N\({}_{2}\) bubbling. A 10% excess of Mohr salt with respect to the stoichiometric Fe(II)/Fe(III) ratio 1:2 was used to compensate for possible oxidation of Fe(II). The solution was heated at 60 \({}^{\circ}\)C and 22 mL of 1 M NaOH (prepared in deoxygenated water) were rapidly added. The reaction mixture was kept for 15 min at this temperature and let to cool to room temperature (RT) in ca. 1 h under N\({}_{2}\) bubbling. The black precipitate of magnetite NPs was separated from the solution by means of a magnet, washed several times with DDW and dried in a flux of N\({}_{2}\). Hereafter, the sample prepared in this way is indicated as A.  
For the coating of the Fe\({}_{3}\)O\({}_{4}\) NPs with citrate, 0.5 g of NPs were dispersed in 100 mL of DDW and 1.90 g of sodium citrate dihydrate (purity 99%) were added (citrate ion concentration: 0.065 M) corresponding to 1:1 molar ratio of Fe to citrate. The pH was adjusted to 6 with drops of a HCl solution and the reaction was carried out for 5 h under mechanical stirring. The pH was measured by means of a pH meter (pH700, Eutech Instruments). The NPs were separated magnetically and dried under a N\({}_{2}\) flux. This sample of NPs with the citrate coating after the co-precipitation will be referred to as sample B (or ex situ).  
Magnetite NPs were co-precipitated by the same procedure as for sample A and B but adding 1.90 g of sodium citrate dihydrate to the Fe(II)/Fe(III) solution (1:1 molar ratio of Fe to citrate). The colour of the iron salts solution changed from orange to pale green upon addition of citrate indicating the formation of complexes with the iron ions. The NPs were separated from the solution by means of a magnet and by centrifugation (2 h at 10,000 rpm, 12,000\(\times\)\(g\)). Subsequently the NPs were dried under a N\({}_{2}\) flux. Hereafter the NPs precipitated in the presence of citrate are indicated as sample C (or in situ). For samples B and C the coating with citrate ions was monitored by means of Fourier Transform Infrared spectroscopy (Figure S1, Supplementary Materials).
# Energy-Efficient Region Shift Scheme to Support Mobile Sink Group in Wireless Sensor Networks
## 3 Efficient Mobility Support for Sink Group Utilizing Region Shift
### Guard Region  
Since we update region information by deviated member sinks, it may cause frequent updates rather than periodic ones in the case of fast varying sink group. In order to prevent the frequent updates, we introduce a supplement functionality, called the guard region. The guard region is defined as a circle also covering group region with radius \(R+\alpha\). If a member sink deviates out of the group region but exists in the guard region, the member sinks do not report to a pivot. Instead of that, cache  
Figure 4: Relation between DGR and estimated region.  
nodes surrounding the group region support local movement of the sinks. Cache nodes are selected on eight sides as shown in Figure 5.  
When a new region is updated, a previous pivot node includes positions of cache nodes. Note that the cache nodes are not explicitly designated. A member sink on the guard region selects an agent node and then it registers the member sinks to the closest cache position. The closest sensor node to the position keeps the information of the sink as a cache node and relays all packets in the group region via the agent. The cache node maintains its role until it receives a release message from the sink (when the sink re-enters into the region or re-registers to another close cache position) or new region information is updated.
## 4 Functionalization of Electrospun Nanofiber Mats for Electromagnetic Shielding
### Magnetic Nanofiber Mats
described electrospinninging PAN with Ni(CH\({}_{3}\)CO)\({}_{2}\cdot\)4H\({}_{2}\)O from an N,N-dimethylformamide (DMF) solution, followed by calcination at 550 \({}^{\circ}\)C in air and finally reduction under H\({}_{2}\) flow at 400 \({}^{\circ}\)C to prepare porous Ni nanofibers [80]. They found high magnetization at saturation of 50-55 emu/g, depending on the porosity, and reflection losses of around 40 dB at 10 GHz. Similarly, [PERSON] et al. prepared Ni\({}_{0.5}\)Zn\({}_{0.5}\)Fe\({}_{2}\)O\({}_{4}\) nanofibers by electrospinning polyvinyl pyrrolidone (PVP) with Fe(NO\({}_{3}\))\({}_{2}\cdot\)9H\({}_{2}\)O from DMF, followed by calcination at 650 \({}^{\circ}\)C [81]. Depending on the frequency between 0 and 18 GHz, they found maximum losses of 42-60 dB for different absorber thicknesses.
# Survey of Smart Parking Systems
## 5 Algorithms or Methods Implemented in SPS
### Real-Time Algorithms  
In these cases, it is necessary to re-plan the route while driving as new information is received [298]. This information that is being received is generally from the particular method of detection for available parking slots that are being used. The system is fed with updated information on the status of each parking in real time and can also incorporate information about traffic, the state of the routes, accidents, detours, etc. Their implementation complexity is greater, and they need more processing power. However, this type of algorithm is probably the best solution to implement in an SPS, since it will keep drivers constantly informed, thus increasing the efficiency of such systems.  
The detection process in an SPS can be coupled with a pre-analysis algorithm that allows the construction of a topology to be implemented in a distributed way that allows the driver to be informed about the available parking places in real time. The routing algorithm for the end-user is implemented as an incremental algorithm, i.e., it starts from a general point or position at the beginning and, as it increases, advances with more precise available parking positions as the driver approaches their final destination [34].  
The main difference with dynamic algorithms is the availability of information. The information in the dynamic algorithms depends on the refresh rate in the set of data available for processing. This presents a limitation in case the route changes, and the information is not updated. Many of these applications show availability at a certain time and, although this is updated with some frequency, users can take the indicated route without being aware of the changes in the availability of the space [46]. In real-time algorithms, however, drivers are assured of up-to-date information, thus further enhancing their decision-making [209].  
While the complexity of implementation is greater, this feature makes them more suitable for modern systems whose real objective is to reduce the amount of time drivers spend looking for a parking slot.  
Other types of real-time algorithm are convolutional neural networks (CNN). These are a type of deep learning algorithm, which extends from ANNs. The main difference between these two methods is the use of convolutional layers in CNNs. CNNs were shown to be an effective model, especially in the analysis of image and video content [170].  
CNNs are composed of several layers of neural networks, and their training can be used to extract features automatically.  
In the context of SPS, CNN algorithms are used to detect and locate vehicles in parking lots. The algorithm is suitable for its performance and real-time responses, benefiting the management of parking spaces and information on their availability [212].  
In addition, CNN algorithms are used in private and closed parking lots to detect the license plates in real time and recognize the user who wants to park their car [133].
## 4 Experimental Results and Discussion of IHSSAO Performance Verification
### Experiment I: Classical Benchmark Function
#### 4.1.2 Assessment Standards of Performance  
In this section, we introduce two metrics for evaluating the algorithm performance, namely, the average fitness value (Avg) and standard deviation (Std). The Avg visually characterizes the convergence effectiveness and search capability of the algorithm. Additionally, the Std indicates the degree of deviation of the experimental results from the mean. The expressions of Avg and Std are as follows, respectively.  
\[\text{Avg}=\frac{1}{n}\sum_{k=1}^{n}S_{k} \tag{26}\]  
\[\text{Std}= \sqrt{\frac{1}{n-1}\sum_{k=1}^{n}(S_{k}-\text{Avg})^{2}} \tag{27}\]  
where \(n\) denotes the number of times the algorithm has been run and \(S_{k}\) is the result obtained after each execution.