esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Adaptive Time-Frequency Analysis
### Empirical Wavelet Transform and Optimization Models
#### 3.3.3 Empirical Fourier Decomposition
First, the components obtained through EFD decomposition typically possess clear physical meanings, reflecting the natural modes of vibration systems and the modulation characteristics of signals. This attribute renders EFD a powerful tool for understanding the essence of signals and accurately identifying structural damages or anomalous states. Second, EFD exhibits strong adaptability, being capable of processing nonlinear and non-stationary signals, which is particularly crucial in the analysis of vibration signals in practical engineering. By conducting local spectrum analysis, EFD can better capture the instantaneous features of signals, providing accurate information for the analysis of structural dynamic behavior. Furthermore, EFD possesses excellent modal separation capability, effectively separating different modal components in signals, which is greatly beneficial for identifying the multi-modal vibration characteristics of structures. However, EFD also has some disadvantages that cannot be ignored. Its computational complexity is relatively high, necessitating detailed analysis of the local spectrum of signals and involving complex processes of modal identification and component extraction. This may lead to prolonged computation times, especially when dealing with large-scale data or conducting real-time monitoring, where computational efficiency may become a bottleneck. Additionally, EFD is relatively sensitive to noise, with environmental or measurement noise potentially interfering with local spectrum analysis and affecting the accuracy of decomposition results. Meanwhile, the performance of EFD depends on the selection of parameters, such as the window size for local spectrum analysis, and improper parameter selection may result in unsatisfactory decomposition results, increasing the difficulty of use. Finally, in some cases, EFD may exhibit modal aliasing, where components of different modes fail to be completely separated, further reducing the accuracy of decomposition results and adversely affecting subsequent analysis and diagnosis.
###### Abstract  
The increasing availability of satellite Synthetic Aperture Radar (SAR) images is opening new opportunities for operational support to predictive maintenance and emergency actions. With the purpose of investigating the performances of SAR images characterized by different geometric resolutions for post-earthquake damage detection and mapping, we analyzed three SAR image datasets (Sentinel-1, COSMO-SkyMed Spotlight, and COSMO-SkyMed StripMap) available in Norcia (Central Italy) that were severely affected by a strong seismic sequence in 2016. By applying the amplitude and the coherent change detection processing tools, we compared pairs of images with equivalent features collected before and after the main shock on 30 October 2016 (at 06:40, UTC). Results were compared against each other and then measured against the findings of post-earthquake field surveys for damage assessment, performed by the Italian National Fire and Rescue Service (Corpo Nazionale dei Vigili del Fuoco--CNVVF). Thanks to the interesting and very rare opportunity to have pre-event COSMO-SkyMed Spotlight images, we determined that 1 \(\times\) 1-m nominal geometric resolutions can provide very detailed single-building damage mapping, while COSMO-SkyMed StripMap HIMAGE images at 3 \(\times\) 3-m resolutions return relatively good detections of damaged buildings; and, the Sentinel-1 images did not allow acquiring information on single buildings--they simply provided approximate identifications of the most severely damaged sectors. The main outcomes of the performance investigation we carried out in this work can be exploited considering the exponentially growing satellite market in terms of revisit time and image resolution.  
Synthetic Aperture Radar; earthquake; change detection; natural hazard; emergency assessment; IRIS software; disaster monitoring 202214210. [[https://doi.org/10.3390/rs14092210](https://doi.org/10.3390/rs14092210)]([https://doi.org/10.3390/rs14092210](https://doi.org/10.3390/rs14092210))
# Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer
## 3 Case Study
### Sensor Fusion  
This subsection shows how to compute the merged output of the Kalman filter \(\bar{\Sigma}^{/_{2}}\); a similar procedure should be used for \(\bar{\Sigma}^{/_{1}}\).  
At the sensor fusion stage, the entries of \(\Sigma\) are smartly merged with \(\bar{\Sigma}^{2}\) by using the EKF. The augmented measurement vector is  
\[\bar{y}_{k}^{2}=[(y_{k})^{T}\,(y_{k}^{\bar{\Sigma}^{2}})]^{T} \tag{22}\]  
where \(y_{k}=[y_{1k}\ y_{2k}\ y_{3k}\ y_{4k}\ y_{5k}\ y_{6k}]^{T}\) are both the real GPS and encoder's outputs, and \(y_{k}^{\bar{\Sigma}^{2}}=[\bar{y}_{1k}^{2}\ \bar{y}_{2k}^{2}\ \bar{y}_{3k}^{2}\ \bar{y}_{4k}^{2}\ \bar{y}_{5k}^{2}\ \bar{y}_{6k}^{2}]^{T}\) are the virtual outputs taken from \(\bar{\Sigma}^{2}\). Based on Equation (5), the augmented covariance of the measurement noise is  
\[\bar{R}_{k}^{2}=\begin{pmatrix}R_{k}^{y}&0\\ 0&R_{k}^{\bar{\Sigma}^{2}}\end{pmatrix} \tag{23}\]  
where  
\[R_{k}^{y}=\text{diag}(0.0517,0.0138,0.1035,4237,5269,5408) \tag{24}\]  
\[R_{k}^{\bar{\Sigma}^{2}}= \text{diag}(0.0279,0.0057,0.0214,27777,27306,39973) \tag{25}\]  
Note that the entries of Equation (23) are selected based on the variance noise, as well as the entries of the parameter \(Q_{k}^{2}\), which in this case are chosen as  
\[Q_{k}^{2}=\text{diag}(0.005\ 0.005\ 0.005\ 0.5\ 0.5\ 0.5\ 0.5\ 0.5) \tag{26}\]  
Then, the augmented measurement vector in Equation (22), the augmented measurement noise covariance matrix in Equation (23), and the process noise covariance matrix \(Q_{k}^{2}\) in Equation (26) are used by the known EKF algorithm to produce the merged signal \(\bar{\Sigma}^{/_{2}}\).
## 4 Conclusions  
Optical switch can play an important role in data communication by replacing electronic circuit switching, reducing the energy consumption of data centers, and be agnostic to both the data rate and modulation format. For this transformation to truly happen, the switching speed of the current 3D MEMS technology needs to be increased to at least the \(\mu\)s range. A potential solution is to reduce the size of the micro mirrors, and use the MEMS as a diffractive element. For this configuration to be efficient, the MEMS element should modulate the phase of the light. Three critical steps in this direction have been demonstrated so far: first, the use of the Texas Instruments DLP to prove the concept of diffractive switching and assess the switching speed; second, the use of LCoS SLM technology to determine the ability of the phase optical switch to meet existing telecommunication infrastructure  
Figure 9: Illustration of the fine pitch diffraction MEMS phase modulation array. (**a**) Isometric view of the diffractive MEMS phase modulation array; (**b**) Cross-sectional view of the device showing the hidden vertical combdrive structure.  
requirements for efficiency, cross talk, and encoding format support; and third, the development of prototypes of optical phase array MEMS to establish the architecture.  
All the experimental measurements so far have demonstrated that the diffraction-based phase modulation MEMS approach is a viable solution for high speed, low loss, and low power consumption optical switching.  
The authors would like to thank Dr. [PERSON] and [PERSON] for discussion on diffractive switch architecture, as well as [PERSON] for the access to the NSF TOAN lab testing facility at the University of Arizona. The authors acknowledge support from the National Science Foundation through CIAN NSF ERC under grant EEC-081207Z, and PIFA-AIR-TT 1640329, as well as from the Department of Energy under grant SBIR DE-SC0015178.  
P.-[PERSON] did the experiment with the DLP and LCoS, and wrote Sections 1, 2, and 4. [PERSON] did the LCoS QPSK experiment. [PERSON] was involved in the phase modulation MEMS manufacturing and testing, and wrote Section 3. [PERSON] was involved in the phase modulation MEMS manufacturing and testing.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DLP & Digital Light Processing \\ DMD & Digital Micromirror Device \\ HCG & High-Contrast Grating \\ LCoS & Liquid Crystal on Silicon \\ MEMS & Micro Electro Mechanical System \\ QPSK & Quadrature Phase Shift Keying \\ SLM & Spatial Light Modulator \\ SRG & Surface Relief Grating \\ SVC & Staggered Vertical Combdrive \\ \end{tabular}
# RF Energy Harvesting Wireless Communications:
## 2 Urban and Semi-Urban RF Environment
### RF Distribution Features
#### 2.2.1 Nonuniform Spatial Distribution
In the downtown and midtown areas such as Sharpstown, Houston, the strength of FM and TV signals becomes negligible while the Global System for Mobile Communications (GSM) and Long-Term Evolution (LTE) cellular bands (i.e., 740 MHz and 890 MHz in the top graph of Figure 3) are significant.
## 2 Material and Methods
### Methods  
#### 2.3.1 Annual Average Values of NDVI, EVI, and SIF, and Land Use Type Extraction and Classification  
The annual NDVI, EVI, and SIF data were processed into an overall 20-year average using the maximum synthesis method for temporal and spatial analysis. In addition, yearly data were analyzed temporally using the NDVI, EVI, and SIF averages from raster images. Additionally, land use types were classified into nine categories: tree cropland, woodland, shrub, grassland, water body, snow/ice, wasteland, impervious surface, and wetland.
## 5 Application to Rotating Machinery Fault Diagnosis  
When gear or rolling bearing is operated with local faulty, the measured vibration signal generally is a multi-component AM-FM signal, and the high-frequency vibration components, such as natural frequency vibration and mesh frequency vibration, are modulated by shock pulse caused by local faults [27; 28]. The RS-LOD method can decompose the multi-component AM-FM signal into a series of single-component signals, while performing the Hilbert transform on each single-component signal can obtain an envelope signal with a physical meaning. Theoretically, the modulation information caused by fault can be extracted from the spectrum of these envelope signals. Therefore, RS-LOD is very suitable for extracting characteristic information from fault vibration signals of the rolling bearing and gear.  
The main steps of extracting fault characteristics based on the RS-LOD method can be summarized as follows: Firstly, the vibration signal is decomposed into a serious of the MOC components by RS-LOD method. Secondly, the envelope signal of MOC component with fault characteristics is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Number of Sifting Times** & **IO** & **Consuming Time(s)** \\ \hline EMD & 38 & 0.0834 & 4.0328 \\ LOD & 27 & 0.0574 & 0.0381 \\ RS-LOD & 30 & 0.0347 & 1.3226 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three assessing indicators for comparison among EMD, LOD, and RS-LOD methods.  
Figure 9: Rational spline based-LOD (RS-LOD) results of the simulation signal \(x(t)\) (\(p=5\)).  
obtained by the Hilbert transform. Finally, the frequency spectrum of the envelope signal is analyzed for effectively extracting the fault characteristic frequency.
## 1 Introduction
PuC is extensively used in air-coupled UT [19; 20], medical sonography [21; 22], and for the UT inspection of high-attenuating materials [23; 24]. PuC is also used in guided waves [25; 26], thermography [27; 28; 29], and it has been also successfully applied to eddy current testing [30]. In this work, PuC is combined with PA ultrasonic testing to inspect a thick CFRP sample. The main contribution of this paper is neither pulse-compression, nor PA-based imaging--both being well known--but their contextual use for the NDT of composites and the comparison of the sensitivity and resolution obtained with those of a standard, commercial pulse-echo system. To our knowledge, while PuC-based PA imaging is widely used in medical ultrasound, its use in NDT is still almost unknown. We hope that the results reported here will stimulate more investigations on the potentialities of such a technique. The paper is organized as follows: Section 2 briefly resumes the PuC theory, while Section 3 describes the hardware system used for driving the PuC-PA system and the imaging procedures implemented. In Section 4, the experimental results collected on a 60 mm thick CFRP sample are reported for both the PuC-based system and a standard commercial one. Analysis of the results and conclusions are presented in Section 5.
## 3 Results and Discussion
### BGPs Outputs and Performance
#### 3.2.2 Outputs and Performance of CN BGPs  
CN1 had a daily OLR of 0.78 kg\({}_{\text{GDM}}\)/m\({}^{3}\)/d, a HRT in the heated system of 36 days, and a HRT in the entire gas-tight system of 74 days. [PERSON] et al. (2017), in a 10-day monitoring to two BGPs (mono-digestion with CSM) in Penglai City of Shandong Province in China, showed a daily OLR of 0.97 \(\pm\) 0.4 kg\({}_{\text{GDM}}\)/m\({}^{3}\)/d, a HRT in the primary digester of 30 days, and a HRT in the entire system of 40 days, for a BGP with a total digester volume of 3300\(\times\) m\({}^{3}\) and working temperate of 37 \(\pm\) 2 \({}^{\circ}\)C; and a daily OLR of 0.85 \(\pm\) 0.3 kg\({}_{\text{GDM}}\)/m\({}^{3}\)/d, a HRT in the primary digester of 45 days, and a HRT in the entire system of 55 days, for another BGP with a total digester volume of 3300\(\times\)12 m\({}^{3}\) and working temperate of 37 \(\pm\) 2 \({}^{\circ}\)C [41]. In our study, CN1 produced a total of 157,202 m\({}^{3}\)/a biogas to supply households. It had a productivity of 0.17 m\({}^{3}\)CH4/m\({}^{3}\)/d and a yield of 222 m\({}^{3}\)CH4/t\({}_{\text{GDM}}\).  
CN2 had a daily OLR of 0.91 kg\({}_{\text{GDM}}\)/m\({}^{3}\)/d, a HRT in the heated system of 30 days, and a HRT in the entire gas-tight system of 47 days. It produced a total of 212,206 m\({}^{3}\)/a biogas to supply the households. It had a productivity of 0.18 m\({}^{3}\)CH4/m\({}^{3}\)/d and a yield of 199 m\({}^{3}\)CH4/t\({}_{\text{GDM}}\).  
CN3 had a daily OLR of 1.55 kg\({}_{\text{GDM}}\)/m\({}^{3}\)/d and a HRT of 16 days. It produced a total of 33,333 m\({}^{3}\)/a biogas to supply the households. It had a productivity of 0.17 m\({}^{3}\)CH4/m\({}^{3}\)/d and a yield of 106 m\({}^{3}\)CH4/t\({}_{\text{GDM}}\).  
Currently, China has no regulation yet to restrict the HRT in an anaerobic digestion system. Unlike the co-digestion BGPs, a pure livestock manure BGP usually has a much lower HRT, both in DE and CN [41]. There were nine BGPs under the BMP I, applying predominantly livestock manure (>95% of the total FM), which resulted in a HRT range of 17-43 days, a daily OLR range of 1.0-5.6 kg\({}_{\text{GDM}}\)/m\({}^{3}\)/d, and a productivity of 0.50-1.41 m\({}^{3}\)CH4/m\({}^{3}\)/d [10]. Therefore, the productivity of these three CN BGPs was much lower than the comparable DE BGPs, which may be caused by (1) lower substrate input and mono-digestion; (2) lower working temperature, especially during the wintertime; and (3) sedimentation in digesters caused by improper mixing in CN1 and CN3, which reduced the working volume.
# Precise Channel Estimation Approach for a mmWave MIMO System
## 8 Conclusions  
In this paper, an extended version of ADMM (Ex-ADMM) was proposed for mmWave channel estimation. In the proposed scheme, a joint optimization problem was formulated, exploiting the sparsity and low rank properties of the channel matrix. The proposed Ex-ADMM algorithm exploits both the properties of targeted optimization problems by breaking them into several subproblems. These subproblems are then solved effortlessly, by acquiring their closed-form solutions independently. A relaxation factor was introduced to converge the proposed algorithm to smaller NMSE values for all training symbol lengths. Comprehensive simulation experiments were performed to validate the performance of Ex-ADMM. The proposed Ex-ADMM algorithm outperformed other benchmark algorithms in terms of ASE, NMSE and convergence rate.  
Conceptualization, P.S.S.; methodology, P.S.S. and A.H.W.; validation, P.S.S. and A.H.W.; writing, review and editing, L.C., A.H.W. and P.S.S.; funding acquisition, L.C.; supervision, L.C. and A.H.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Major Special Funding (NMSF) under grant 2018 ZX03001006-002.  
The first author, [PERSON], hereby acknowledges the University of the Chinese Academy of Sciences (CAS) and the World Academy of Sciences (TWAS) for financial support for Ph.D. studies under the CAS-TWAS Fellowship. The first author would also like to acknowledge [PERSON], Post-Doctoral Fellow, Y.M.C., Tsinghua University, Beijing, China for his help in mathematical expressions.  
The authors declare no conflict of interest.
## 3 Results
### Quantitative Results of BOP, PI, and PPD  
Eight studies [8; 11; 18; 20; 21; 22; 23] were included in the quantitative evaluation, considering the effects of probiotic therapy on the mean reduction of BOP, PI, and PPD at 3 and 6 months. The overall effect for both was calculated using the weighted mean difference (WMD).  
The random effects model was used for PPD, BOP, and PI analysis, as heterogeneity was statistically significant at 3 and 6 months, respectively. For BOP (Chi\({}^{2}\) = 42.35, \(p<0.00001\), I\({}^{2}\) = 88% and Chi\({}^{2}\) = 13.22, \(p<0.004\), I\({}^{2}\) = 77%), PI (Chi\({}^{2}\) = 63.95, \(p=0.02\)\(\mathrm{I}^{2}=92\%\) and \(\mathrm{Chi}^{2}=18.47\), \(p<0.0004\), \(\mathrm{I}^{2}=84\%\)), and for PPD (\(\mathrm{Chi}^{2}=50.15\), \(p<0.00001\), \(\mathrm{I}^{2}=92\%\) and \(\mathrm{Chi}^{2}=96.39\), \(p<0.00001\), \(\mathrm{I}^{2}=94\%\).  
The overall effect for the reduction of BOP at 3 and 6 months was statistically significant in favor of the probiotic group, (WMD = \(-1.13\), 95% CI = \(-1.95\) to \(-0.30\), \(p=0.007\); Figure 2) and (WMD = \(-1.32\), 95% CI = \(-2.15\) to \(-0.48\), \(p=0.002\); Figure 3), respectively. Similarly, the overall effect on the reduction of PI at 3 months (WMD = \(-1.22\), 95% CI = \(-2.25\) to \(-0.19\), \(p=0.02\); Figure 4) was statistically significant in favor of the probiotic group. However, at 6 months, there was no statistically significant difference between the groups (WMD = \(-0.71\), 95% CI = \(-1.65\) to \(0.22\), \(p=0.14\)
## 2 Material and Methods
### Sample Implantation  
The animals were anesthetized via administration of an intraperitoneal injection of ketamine chloride (14 mg/kg body weight) and acepromazine (10 mg/kg body weight). The tibas were shaved, and the skin disinfected with 5% iodine in alcohol. The implantation technique was performed under sterile conditions according to the procedures described in a previous report [28]. Briefly, with a scalpel, a 1.5 cm longitudinal incision was made at the lateral aspect of the anterior border of the tibias. After the incision, muscles, ligaments, and the periosteum were displaced by blunt dissection, thus exposing the underlying bone. To avoid overheating, a cylindrical opening 1.5 mm deep was prepared by manually rotating a sterilized 1.2 mm diameter end-cutting bur in the diaphysseal bone of each tibia at approximately 8 mm from the lateral external side. Bleeding was controlled by rinsing with saline and the openings were lightly dried with sterile gauze. Then, two silicone tubes were placed in each rat. One tube with ESHF was placed in the right tibia while the other side received AHPS. The tubes were placed so that the open ends contacted the marrow space while the closed ends were level with the cortical bone. After the wounds were closed with silk sutures, the animals were placed in cages maintained at a temperature of 22-24 \({}^{\circ}\)C with 12 h light/dark cycles and access to food and water ad libitum. After 10, 30 and 90 days the animals were euthanized in groups of 8 (\(n=8\)) by means of an overdose of the anesthetic solution. The tibias were dissected and fixed in 10% neutral buffered formalin. The solution was replenished after 48 h, and the samples fixed for 8 days. Following decalcification in ethylenediaminetetraacetic acid (EDTA), the specimens were processed for routine paraffin embedding. Longitudinal sections approximately 6 \(\upmu\)m thick were obtained from the center of the implants and stained with hematoxylin and eosin (H&E). From these sections, three of them were randomly selected and used for histological evaluation.
## 3 Passive Phased Arrays with Integrated Metallic and Dielectric Waveguide Phase Shifters
### Electronic Steerable Horn Antenna Array
The minimum time for a \(360^{\circ}\) phase change is in between 16.1 and 138 s, while the maximum phase change rate reaches \(5.1^{\circ}\)/s to \(45.4^{\circ}\)/s, depending on the applied voltages. As a consequence, when losses could be reduced, the concept would become a viable option for the desired application [76].
# Engineering Resource-Efficient Data Management for Smart Cities with Apache Kafka +
## 4 Data Corpus-as-a-Service
### User Authentication  
Authentication refers to verification of the system or user identity for accessing the system or, in other words, it is the procedure of confirming whether the user is the person they claimed to be. Two common authentication technologies are: (i) Lightweight Directory Access Protocol (LDAP) for the directory, identity, and other services and (ii) Kerberos. Authorisation is the process of determining the access rights of the user, specifying what they can do with the system. As Hadoop mixed various systems in its environment, it required numerous authorisation controls with different granularities. In Hadoop, the process of setup and maintenance of the authorisation control is simplified and can be performed by dividing users into groups by specifying them in the existing LDAP or Active Directory (AD). Other than that, the authorisation can also be set up by providing role-based access control for connection methods that are alike. The most popular tool for authorisation control is Apache Sentry. Currently, user authentication for the H2020 MARVEL project data corpus is performed through MARVdash, thus allowing for an external, secure access to the Data Corpus API services by integrating a web proxy service. The transmission of data is secured with HTTPS and the underlying TLS. The authorisation level is defined during the registration of the user account (e.g., simple user, administrator, etc.).  
HBase can be configured to provide User Authentication, which ensures that only authorised users can communicate with HBase. The authorisation system is implemented at the RPC level and is based on the Simple Authentication and Security Layer (SASL) [6], which supports (among other authentication mechanisms) Kerberos. SASL allows authentication, encryption, negotiation, and/or message integrity verification on a per connection basis. The most popular mechanism for authentication is Kerberos, which is also the primary authentication for Hadoop developed by MIT [30]. The Kerberos protocol provides secure communications over a non-secure network by using secret-key cryptography. The protocol of Kerberos is shown as below:  
* The client first needs to request Ticket Grant Ticket (TGT) from the Authentication Server (AS) of the Key Distribution Centre (KDC).
* After the client receives the TGT, the client must request a Service Ticket (ST) from the Ticket Grant Server (TGS), which comes from the Key Distribution Centre (KDC) (the Authentication Server and Ticket Grant Server are the components of the Key Distribution Centre).
* The client can use the ST to authenticate a Name Node. The TGT and ST are renewed after a long run of jobs. The greatest benefit of Kerberos is that the ticket cannot be renewed if it was stolen (ref). Kerberos provides powerful authentication for Hadoop. Instead of using a password alone, the cryptographic mechanism is used when requesting services [31; 32].