esa-sceva/satcom-chunk-collection huggingface dataset:
# RF Frequency Selective Switch by Multiple PMIM Conversions
## 3 Results
### The Complex Channel Selection Function  
When we turn on more than one of the 21 taps randomly, the RF frequency response of channels corresponding to the above distribution can be observed at the output. As shown in Figure 4a, from low frequency to high frequency, the 1 st, 2 nd, 4 th, 5 th, 6 th, 9 th, 11 th, 12 th, 13 th, 16 th, and the 18 th channels can be output at this port, while other channels are suppressed. Note that the channel spacing is 50 MHz. Because the switches of optical channels correspond to RF channel switches one by one, the complex channel selection function can be realized easily in our scheme and high resolution can be realized. This is more advantageous than the traditional FIR filter. With the help of the excellent performance of optical WSS, we can also perform more precise operations on the amplitude frequency characteristics of each RF channel instead of simply selecting on and off. There are 21 individual channels with a frequency interval of 50 MHz from 9.5 GHz to 10.5 GHz. The amplitude response of these channels is set to linear increase with a discrete step of 0.6 dB. Figure 4b shows the amplitude frequency response (expressed in dB) from 9.5 to 10.5 GHz, which follows the RF frequency approximately linearly. Moreover, the amplitude difference of 21 individual channels is also mapped to the final output. The realization of the frequency response only needs to set the transmission rate of each channel in the optical WSS accordingly. Note that although the RF channel interval is 50 GHz, we require far less resolution than 50 MHz for optical devices.
# Context-Based Energy Disaggregation in Smart Homes
## 4 Disaggregation Algorithm  
This section describes the proposed new energy disaggregation algorithm. First, we briefly mention the principles of the state of the art approach we adopted and then we describe how we enhanced this approach to leverage context information (e.g., timing usage statistics and user presence).
## 3 Problem Analysis  
### Problem Statement  
Information of a recommendation system consists of data of users and items, including user features, item attributes, and the relation of user and item. A recommendation system involves users,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Recommendation Technique** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Collaborative filtering} & easily realized, handle unstructured & rely on massive historical data, \\  & complex objects, high automation, & data sparseness problem, \\  & personalized & cold-start problem \\ \hline \multirow{3}{*}{Content filtering} & more personalized, domain knowledge & data sparseness problem, difficult \\  & required, well-understood results & to extract and describe feature \\  & & from complex structured objects \\ \hline \multirow{3}{*}{Social filtering} & feature-distinguished, easily-accepted & user privacy problem, complex \\  & & social relationship \\ \hline \multirow{3}{*}{Association rule base} & high automation, discover interest point & \\  & without domain knowledge, no & difficult to extract rules, less \\  & cold-start problem & personalized \\ \hline \multirow{3}{*}{Demographic based} & discover interest point without domain & difficult to obtain demographic \\  & knowledge, no cold-start problem & information of users \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of recommendation techniques.  
Figure 1: Two-phase recommendation process.  
items, and their relations. Here, relations refer to user behaviors on items, such as searches, purchases and ratings. User features refer to the basic user information submitted during the user registration process. Item attributes include all the information about an item. As indicated in Figure 2, there are three kinds of relations in the recommendation system: user-user feature (U2F) relation, user-item (U-I) relation, and item-item attribute (I2A) relation.  
U-I relations are the most important relations which are used by a collaborative filtering algorithm to generate recommendations. Traditional CF methods rely on user-item rating matrix, and recommendations are generated through similarities of users or items. However, their accuracy of recommendations is not always satisfactory due to the sparsity problem of the matrix. A user often gives ratings only for items that he/she is interested in. The low possibility of rating leads to a serious lack of rating data, and further influences the quality of recommendation based on the CF methods.  
Based on the analysis above, we will make full use of objects and information on recommendation systems such as metadata of items, information of user behaviors. The extracted data will be used to supplement the feature matrix.
###### Abstract  
Metal Organic Frameworks (MOFs) are a unique family of tailor-made porous materials that have gained significant attention for their properties and their applications in various fields, including agriculture and agrifood. The aim of this review is to explore the potential of MOFs as smart carriers and delivery mediums of essential oils (EOs) and/or aromatic volatiles. Emphasis is given to their potential to be applied in crop protection and fresh food preservation. MOFs indeed present highly promising physicochemical characteristics in order to be applied in such sectors. To name a few, their high surface area, tunable porosity, and customizable functionalities, make them ideal carriers for EOs, which are established for their antimicrobial properties but their wider practical applications are limited by their volatility and chemical sensitivity. The encapsulation of EOs in MOFs enhances their stability, controlled release, and bioavailability, providing effective solutions for sustainable agriculture and food safety. Furthermore, in this review we discuss various MOF types, emphasizing the most recent literature references, including cyclodextrin-based MOFs, Cu\({}^{2+}\) based MOFs, Zn\({}^{2+}\) based MOFs as well as Zr\({}^{4+}\) MOFs. In this work, we attempt to highlight the interactions and physicochemical characteristics (e.g., pore size and pore functionality), that contribute to the encapsulation of different EOs within MOFs. We focus on a detailed discussion of the external stimuli that can trigger the targeted release of EOs, such as pH changes caused by pathogenic microbial activity. Additionally, we examine the potential benefits of the EOs encapsulation in MOFs, including the reduction of premature evaporation due to their volatile nature and their improved delivery to targeted sites. These aspects are explored within the frameworks' food safety enhancement, extended shelf life and the promotion of sustainable food preservation alternatives. Furthermore, we address MOFs' limitations such as biocompatibility, scalability and chemical stability under field conditions to further comprehend their potential as EO carriers in agrifood applications, emphasizing food preservation and protection. Finally, this work aims to contribute to global challenges in nutrition and sustainable agriculture.  
metal-organic frameworks; essential oils; agrifood; food packaging; smart delivery 2025155479
## 3 Experimental and Results  
It has always been known that stone quality is the main parameter affecting buildings and constructions' long-term sustainability. Taking into account the fact that the pathological defects have appeared in the Panorama Sea Wall (\"exposed in an open place\") only a few years after the erection of the wall, one could assume that the qualitative method of assessment proposed by [PERSON] ([PERSON] [16] wrote 2000 years ago: \"... All these soft kinds have the advantage of being easily worked as soon as they have been quarried. Undercover, they play their part well; but in open and exposed situations, the frost and crime make them crumble, and they go to pieces. On the seacoast, too, the salt seats away and dissolves them, nor can they stand great heat either. But traverine and all stone of that class can stand injury whether from a heavy load laid upon it or from the weather; exposure to fire, however, it cannot bear, but splits and cracks to pieces at once [...] Let the stone be taken from the quarry two years before building is to begin, and not in winter but in summer. Then let it lie exposed in an open place. Such stone as has been damaged by the two years of exposure should be used in the foundations. The rest, which remains unhurt, has passed the test of nature and will endure in those parts of the building which are above ground. This precaution should be observed, not only with dimension stone but also with the rubble which is to be used in walls.\") This suggestion proposed two thousand years ago looks to be an entirely true one. However, there is no chance that anybody involved in the tight timetables of the modern construction process will implement this method of natural two-year-long aging to evaluate the durability of the natural stones and reveal their compatibility with the specific service conditions. Currently, there is not any necessity to wait for such a long period (two years) to assess the stone's quality. A combination  
Figure 10: An efflorescence of kurkar siding in the Panorama sea walls. Captured by the author on 27 November 2018.  
of the modern comprehensive macro-and microstructural analyses with the accelerated weathering tests could provide an architect, a civil engineer, and a contractor with essential information on the stone's durability. Unfortunately, the contractor carried very scarce tests before the construction of the Panorama Sea Wall. Thus, the current investigation was carried out to answer two main questions:  
* Was it realistic to predict kurkar stone durability trends before its implementation in the Panorama Sea Wall?
* What factors have led to the quick deterioration of kurkar stone sliding in the contemporary Panorama Sea Wall, although the kurkar stones in Acre's historic sea wall were less deteriorated?
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 4 Performance Metrics for the SCAR-Phase of Spectrum Sensing
### Mean Computational Complexity  
In this section, the computational complexity is measured in terms of the total number of real operations (additions and multiplications) needed to decide the presence of the PU's signal. As shown in the block diagram displayed in Figure 2, enhanced ST-based detection is only performed if the energy detector does not detect a PU's signal. Using the energy detector, deciding the absence or presence of the PU's signal can be modeled as a Bernoulli r.v. denoted by \(B\), whose probability of deciding the absence of the PU's signal (denoted by \(P_{H_{0}}\)) is given by \(P_{H_{0}}=\Pr\left\{\mathcal{H}_{0}\right\}(1-P_{\rm fa}^{ed})+\Pr\left\{ \mathcal{H}_{1}\right\}(1-P_{d}^{ed})\), where \(\Pr\left\{\mathcal{H}_{0}\right\}\) and \(\Pr\left\{\mathcal{H}_{1}\right\}\) are the prior probabilities denoting the absence and presence of the PU in the frequency band, respectively. Hence, carrying out the second phase of sample processing depends on the value that this r.v takes (i.e., its expected value, given by \(E[B]=P_{H_{0}}\)). Hence, the total mean computational complexity of the SCAR-Phase of spectrum sensing is:  
\[\bar{C}=C_{sync}+C_{ed}+P_{H_{0}}C_{st}, \tag{43}\]  
where \(C_{sync}\), \(C_{ed}\) and \(C_{st}\) are the number of real operations required by the synchronization process, the energy detector and the enhanced ST-based detection. Note that \(C_{sync}\) and \(C_{st}\) are related to the method used to carry out the synchronization process and the enhanced ST-based detection. To obtain the number of real operations, it is considered that in a modern digital signal processor (DSP), the time consumed by a real multiplication and a real addition is the same. Moreover, it is considered that a complex addition requires two real additions and a complex multiplication needs four real multiplications and two real additions. Hence, for the synchronization method previously described, which requires \(P^{3}+2P+3\) complex operations [18], \(C_{sync}=216P^{3}+12P+18\) real operations. Moreover, the energy detector requires\(N\) complex multiplications and \(N-1\) real additions. Hence, \(C_{ed}=7N-1\) real operations [19]. For the enhanced ST-based detection, the ST-related metric of the ST-Det, \(\Phi_{t}\), needs \(N+1\) complex multiplications and \(N-1\) complex additions, i.e., \(4N+4\) real multiplications and \(4N\) real additions. Therefore, \(\Phi_{\text{ST}}(\mathbf{x})\) requires \(8N+5\) real multiplications and \(7N\) real additions. Thus, \(C_{st}=15N+5\) real operations. Finally, the mean computational complexity of the SCAR-Phase of spectrum sensing is:  
\[\bar{C}=216P^{3}+12P+17+7N+P_{\mathcal{H}_{0}}(15N+5). \tag{44}\]
## 2 Materials and Methods
### QCM Method  
A\(\beta\) accumulation was measured using a QCM [23]. As described in Section 2.2, the planar membrane was immobilized on an AT-cut quartz crystal with a gold electrode (diameter 5.1 mm, resonance frequency 7.995 MHz; BAS Inc., Tokyo, Japan). The quartz crystal was connected to ALS/CHI electrochemical QCM equipment (400; BAS Inc.). A\(\beta\) solution (10 \(\upmu\)M) was loaded onto the immobilized membranes to monitor the frequency change. In general, the frequency decrease (\(-\Delta f\)) relates to both mass increase per unit area and viscoelastic change. However, as the device used in this study provides only the frequency change, the plateau value of the frequency change was adopted, and not the time course of frequency which sensitively depends on the viscoelastic change.
## 4 Discussions
### Leakage ErrorsAnalysis  
All water storage observations from GRACE represent average values, in both space and time. Temporally, GRACE data are approximately monthly averaged quantities. Because of truncation and filtering in the spectral domain, GRACE data are also spatially averaged, with spatially varying weights [59]. This results in a time series that differs from the true, i.e., uniformly weighted, time series; this difference is often referred to as \"leakage\". The leakage error depends on the filtering process as well as the characteristics of the signal [59; 60]. The truncation of GRACE SHCs and needed spatial filtering and smoothing to suppress the longitudinal stripping noises will introduce leakage bias and degrade the spatial resolution of GRACE-determined surface mass variations [59]. In order to reduce this leakage error, the research of [59] derived gain factors (basin-scale gain factors and grid-point gain factors) by minimizing the misfit between the unfiltered, true, and filtered GRACE water storage time series through a simple least square regression.  
Right now, the methods handling the leakage error effects are mainly based on region (basin) scale and are mostly used in the regional water storage analysis. For the leakage errors at one point, we try to correct the GRACE leakage error by adding the corrections derived from the Global Land Data Assimilation System (GLDAS) Noah outputs (transformed into SHC with degree and order up to 60) by calculating the differences between the original GLDAS modeled VD and the truncated and filtered GLDAS VD at each IG2 station. However, the differences between GLDAS filtered and unfiltered solutions display largely temporal variations and can reach up to tens of centimeters, which is far larger than the non-tidal loadings effects. Meanwhile, the comparison results between GNSS and GRACE derived solutions with these leakage errors corrections are far from satisfactory. Moreover, the research of [20] investigated the leakage error effects by comparing the GRACE SHC solutions and the most recently released GRACE mass concentration (MASCON) solutions which have been confirmed to have a high resolution and fewer leakage errors. However, all the indicators considered in their study show that the GRACE MASCON and SHC solutions have a similar ability to interpret the GNSS signals, suggesting that the VD signals are insusceptible to the smoothing. Mean annual amplitude reduction of the 11 GNSS-Observed VD solutions corrected with CSR SHC modeled solutions and with CSR MASCON modeled solutions are both 0.32 [20]. The conclusions of the leakage errors effects on the comparisons between GNSS and GRACE are equally valid for this study.
## 2 Dataset  
The Swarm constellation is composed of three satellites (Swarm A, Swarm B, and Swarm C), which were launched on 22 November 2013. During the period from 2014 to 2021, Swarm A and Swarm C flew side by side at an altitude of 500 to 435 km with an orbital inclination angle of 87.35\({}^{\circ}\), whereas Swarm B cruised at 520 to 500 km with an 87.75\({}^{\circ}\) inclination angle. It took about 130-140 days for each Swarm satellite to cover all local time.  
The in situ electron densities (Ne) measured by the Langmuir probe onboard Swarm constellation are used to investigate the variations of the ionospheric irregularity. In addition, the upward-looking TEC data obtained from Global Positioning System (GPS) observations measured by the Swarm constellation are used to determine the scintillation. The detailed procedure for the upward-looking TEC retrieval was introduced in [PERSON] et al. [18] and [PERSON] et al. [19]. Note that only nighttime data are used to study the ionospheric irregularities, though the measurement error of Ne could occur at daytime [20].  
In this study, we use the Swarm electron density with a sampling rate of 2 Hz and TEC data at a time resolution of 1 s. The study covers a long period from 2014 to 2021. The Swarm data are divided into different seasons (March equinox, June solstice, September equinox, and December solstice) and solar activities. The data within a 90-day window centered on solstice or equinox are selected for each season. The conditions of F107 < 100 and F107 > 100 are defined as low and high solar activities, respectively. Note that due to the data limitation, the averaged altitude of the Swarm satellites was higher under high solar activity. In the following, the ionospheric irregularities are detected by RODI, and phase fluctuations of the GPS receiver onboard Swarm are determined by ROTI.
# Observing APOD with the AuScope VLBI Array
## 2 Experiments
### The APOD-A Nano Satellite
The BACC generated and provided two kinds of orbit solutions for our APOD experiments, both solely based on the GPS coordinate data: (1) predicted orbits and (2) final orbit solutions. The predicted orbits, which were provided about 12 h before the experiments started, were used to calculate the antenna pointing data required to steer the antennas throughout the observations (see Section 3). The final orbit solutions do not contain any predictions and were calculated some weeks after our experiments took place. They were used for the calculation of a priori delays (see Section 4.1) required \begin{table}
\begin{tabular}{l c c} \hline \hline
**Label** & **Band** & **Frequency [MHz]** \\ \hline Carr\({}_{S}\) & S & 2262.010 \\ DOR\({}_{S1}\) & S & 2256.869 \\ DOR\({}_{S2}\) & S & 2260.982 \\ DOR\({}_{S3}\) & S & 2263.038 \\ DOR\({}_{S4}\) & S & 2267.151 \\ \hline Carr\({}_{X}\) & X & 8424.040 \\ DOR\({}_{X1}\) & X & 8404.894 \\ DOR\({}_{X2}\) & X & 8420.211 \\ DOR\({}_{X3}\) & X & 8427.869 \\ DOR\({}_{X4}\) & X & 8443.186 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Carrier and Differential One-way Ranging (DOR) tone frequencies of the APOD VLBI signal in the S- and X-band. for the correlation, as well as for the data analysis. As mentioned above, these final orbits show post-fit residuals of 10-20 m. A comparison of all available predicted and final orbits revealed differences of several 100 m (up to 1 km) in the along-track and cross-track directions. In the radial direction, the discrepancies are much lower with values in the range of several meters. Hence, when we assume that the final orbits are accurate on a level of 10-20 m, we can conclude that the predicted orbits used for satellite tracking were only accurate on the level of several hundred meters. The rather low accuracies of the predicted as well as of the final orbit solutions have consequences on different stages of the observation and data processing schemes discussed in this paper.
## 3 Simulations and Experiments
In contrast, in the present work our aim is to achieve higher antenna gain and radiation efficiency by inserting an air-gap layer for applying to remote RFID systems. Figure 8: (**a**) Simulated and measured AR of the RHCPA and the LHCPA versus frequency; (**b**) AR optimization with varied short stub length corresponding to the RHCPA. Figure 10: Simulated and measured gain of the RHCPA and the LHCPA and simulated gain of the optimized EPA as a function of frequency. Figure 9: Radiation patterns of (**a**) RHCPA and (**b**) LHCPA in simulation; and (**c**) RHCPA and (**d**) LHCPA in measurement, at frequency 2.438 GHz and 2.37 GHz, respectively.
# Bearing Fault Diagnosis Approach under Data Quality Issues
## 3 Materials and Methods  
### DFA Method  
The DFA method was introduced to study the long-range correlation in the DNA nucleotides [30]. It is a powerful method in estimating the power low scaling, especially in non-stationary time series. As a result of detrending in the DFA method, the present non-stationary can be removed prior to estimating the scaling exponent (\(\alpha\)). The method has been used in assessing the correlation properties in various research fields.  
The scaling exponent (\(\alpha\)) provided a reliable measure of the stationarity and the degree of long-range correlation in any time series. The time series was stationary when the scaling exponent (\(\alpha\)) was below 1. Moreover, the stationary time series could be either anticorrelated (\(0<\alpha<0.5\)), uncorrelated (\(\alpha=0.5\)), or long-range correlated (\(0.5<\alpha<1\)). In the anticorrelated series, the increases were likely to be followed by decrease, and vice versa. On the other hand, in the long-range correlated series, the increases were expected to be followed by further increase, and vice versa.  
The non-stationary time series had a scaling exponent higher than 1. For \(\alpha<1\), the series had long-range correlation not following the power law. The steps to compute the scaling exponent (\(\alpha\)) of any time series, \(x(t)\), are summarized below:  
1. Compute the integrated time series, \(y(t)\), as shown in Equation (1). \[y(t)\ =\ \sum_{i\ =\ 1}^{t}(x(i)-\overline{x})\] (1)
2. Divide the integrated series into equally spaced boxes with size of \(n\). Inside each box, detrend the series segment by subtracting it from its best linear fit, \(y_{n}(t)\).
3. Compute the fluctuation function, \(F(n)\), by calculating the RMS value of the detrended time series for different box sizes, as shown in Equation (2). \[F(n)\ =\ \sqrt{\frac{1}{N}\sum_{j\ =\ 1}^{N}\left[y(j)-y_{n}\right]^{2}}\] (2)
4. The scaling exponent (\(\alpha\)) represents the slope of the best linear fit of the fluctuation function on the log-log scale.