esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
## 2 SSA Forecasting
### Decomposition and Reconstruction of Time Series  
In SSA, we embed the time series \(\{x_{1},x_{2},\ldots,x_{N}\}\) into a high-dimensional space by constructing a Hankel structured trajectory matrix of the form:  
\[\mathbf{X}=\left(\begin{array}{ccccc}x_{1}&x_{2}&x_{3}&\ldots&x_{n}\\ x_{2}&x_{3}&x_{4}&\ldots&x_{n+1}\\ \vdots&\vdots&\vdots&\ldots&\vdots\\ x_{m}&x_{m+1}&x_{m+2}&\ldots&x_{N}\end{array}\right)=\left[\mathbf{x}_{1}\quad \ldots\quad\mathbf{x}_{i}\quad\ldots\quad\mathbf{x}_{n}\right], \tag{1}\]  
where \(m\) is the window length, the \(m-\)lagged vector \(\mathbf{x}_{i}=(x_{i},x_{i+1},\ldots,x_{i+m-1})^{\prime}\) is the \(i\)th column of the trajectory matrix \(\mathbf{X}\), \(n=N-m+1\), and \(m\leq n\).  
The singular value decomposition (SVD) of the trajectory matrix \(\mathbf{X}\) can be expressed as  
\[\mathbf{X}=\mathbf{S}_{k}+\mathbf{E}_{k}=\sum_{j=1}^{k}\sqrt{\lambda_{j}}\mathbf{u}_{j}\mathbf{v}_{j} ^{\prime}+\sum_{j=k+1}^{m}\sqrt{\lambda_{j}}\mathbf{u}_{j}\mathbf{v}_{j}^{\prime} \tag{2}\]  
where \(\mathbf{u}_{j}\) is the \(j\)th eigenvector of \(\mathbf{X}\mathbf{X}^{\prime}\) corresponding to the eigenvalue \(\lambda_{j}\) and \(\mathbf{v}_{j}=\mathbf{X}^{\prime}\mathbf{u}_{j}/\sqrt{\lambda_{j}}\).  
If \(k\) is the number of signal components, \(\mathbf{S}_{k}=\sum_{j=1}^{k}\sqrt{\lambda_{j}}\mathbf{u}_{j}\mathbf{v}_{j}^{\prime}\) represents a matrix of signal, and \(\mathbf{E}_{k}=\sum_{j=k+1}^{m}\sqrt{\lambda_{j}}\mathbf{u}_{j}\mathbf{v}_{j}^{\prime}\) is the matrix of noise. We apply the diagonal averaging procedure to \(\mathbf{S}_{k}\) to reconstruct the signal series \(\widetilde{x}_{t}\) such that the observed series can be expressed as  
\[x_{t}=\widetilde{x}_{t}+\widetilde{e}_{t}, \tag{3}\]  
where \(\widetilde{x}_{t}\) is the less noisy, filtered series. A detailed explanation of decomposition in Equation (3) can be found in [28; 29].  
To construct the trajectory matrix \(\mathbf{X}\) in Equation (1) and to conduct the SVD in Equation (2), we have to select the Window Length \(m\) and the number of signal components \(k\). Since our aim is not to demonstrate the selection of SSA choices (\(m\) and \(k\)), we opt not to reproduce the selection procedures for SSA choices, as these are already covered in depth in [12; 28]. As our interest is in examining the effect of transformation on the forecasting performance of SSA, we select \(m\) and \(k\) such that the Root Mean Squared Error (RMSE) in forecasting is minimised.
## 4 Results
### Comparisons between Satellite-Based and in Situ ET  
Time series of satellite-based ET and _in situ_ observations at the flux towers (US-ARM, US-Twt, Seolmacheon, and Chungmicheon) within the four study sites are summarized in Figure 11. At the four sites, both satellite-based and _in situ_ ETs showed a similar seasonal trend, with a peak season around July and a low season in December at Sites 1, 2, and 4, and ET peak was found to be in the middle of the growing season at Site 3. Compared to the other sites, the flux tower at Site 2 (US-Twt) observed greater ET in growing seasons. This is because the flux tower is located within a rice paddy that consumes a greater amount of water than the wheat does at Site 1 (similarly rice paddy at Site 4). Each year, the field is flooded after rice is planted in spring, and drained when rice is harvested in early fall. Thus, ET experiences a rapid rise from April to June and a sharp descent from September.  
Although satellite-based products captured the temporal trend of ET variation at the four study sites, the values of ET had large discrepancies from the _in situ_ ET measurements, especially during growing seasons. Figure 11a, b, and d show a large underestimation of ET at the crop sites, especially for the MODIS-based, Landsat modeled 1 km, and Landsat aggregated 1 km products, with the bias as great as \(-20\) mm/8 days. The downscaled 30 m ET is similar to MODIS ET at US-ARM (Site 1, during the year 2013) and Cheongmicheon (Site 4), while at US-Twt (Site 2) it is on average 13 mm/8 days higher than MODIS ET during growing seasons, which is closer to _in situ_ ET. Further examination  
Figure 11: Comparison with _in-situ_ ET at: (**a**) Site 1 (US-ARM flux tower); (**b**) Site 2 (US-Twt flux tower); (**c**) Site 3; and (**d**) Site 4. (**e–h**) The close view of the tower sites in July 2013 for Sites 1–3 and May 2013 for Site 4, with a black square denoting 1 km grid around each tower location: (e) Site 1; (f) Site 2; (g) Site 3; and (h) Site 4.  
indicates that compared to Sites 1 and 4, the crop patches are more heterogeneous within a 1 km grid around the flux measurement tower at Site 2 (Figure 11f). The measurement tower is located within a rice patch, with higher vegetation greenness and wetness values and lower LST observation compared to the corn, alfalfa and fallow/idle crop patches surrounding the tower (S
# Identification of Water Hammering for Centrifugal Pump Drive Systems
## 2 Methodology
### Application of Linear Regression  
Linear regression is a statistical model-based ML approach that may be applied when there is a linear relationship among variables, and it is necessary to study the impact of the independent variable on the dependent variable. The causes of water hammering problems in pumping systems are variations in flow rate, speed, pressure, and velocity, and they have strong relationships between one other. Thus, the linear regression method, which shows a more or less constant or proportional relationship among variables, may be applied for water hammering problem identification in pumping systems. Moreover, linear regression is an ML-based approach, and under the supervised learning category, so it is possible to make a predictive control model with the help of linear regression to predict the outcome of the system. Here, vibration-based technology has been used to collect vibration data using the sudden opening and closing of valves of a VFD-based pumping system, using the Vib Sensor app. This mobile-based app is used through a mobile phone, and the mobile phone is connected to the pumping system to collect continuous data through this app. All the acceleration data was collected, and the relationship between velocity and acceleration simultaneously formed, by the flow sensor flow rate of the pump. From the panel speed change, pressure and current value also have been noted.  
Acceleration head loss is sometimes ignored when calculating the Net Positive Suction Head Available (NPSH\({}_{\text{A}}\)). The liquid mass in the suction line to the pump must be started and stopped with every pump stroke. The pump must expend energy to accelerate the liquid into the pump during the suction stroke, and then stop the inlet flow on the discharge stroke. This is the acceleration head component of NPSH\({}_{\text{A}}\) for reciprocating pumps and can be calculated using the formula below.  
\[\text{h}_{\text{a}}=\text{L}\text{V}_{\text{N}}\text{C/Kg} \tag{4}\]  
where ha is acceleration head loss in meter, L is pipe length, V is the velocity of the pump, N is the rotational speed of the pump at a particular velocity, C is constant which depends on pump type, K is a factor representing the reciprocal of the fraction of the theoretical acceleration head, which must be provided to avoid a noticeable disturbance in the suction line, and g is the gravitational acceleration.  
After collecting all the data, linear regression is used in the system for vibration signature analysis to detect the water hammering fault.  
Figure 1: Pressure and velocity waves in a single-conduit, frictionless pipeline following its sudden closure.
# Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy +
## 4 Experiments and Results  
In this work, we carried out all our experiments on our test track under natural lighting conditions. Our proposed approach was tested in a track whose length is 850 m. The track is composed of two lanes of width of 3.5 m each. The current position of the vehicle is measured using a low cost GPS receiver mounted on the top of a testing car. When conducting the experiments, we do not have a high precision GPS to be used as a ground-truth data. This way we measured the deviation of distance from the middle lane to the center of the vehicle. By assuming that the vehicle should most of the time be in the middle lane, we measured vehicle's position relative to the median lane as our performance metric. Table 1 shows the obtained results of the comparison between the GPS positioning and the estimated position obtained using our proposed method. The qualitative results of the experiments can be seen in Figure 8.  
Figure 8: Qualitative localization results of the proposed method.  
comparison, we plan to release (GitHub, under construction) all the code and material for the research community.  
All the co-authors contributed to the article. The implementation is mainly done by [PERSON] with the help of M.A. The results are then discussed between all the co-authors during several rounds. The first draft of the manuscript was prepared by [PERSON] The rest of the co-authors participated in revising and improving the draft. All authors have read and agreed to the published version of the manuscript.  
This research was funded (1). _European project: InDiD: Infrastructure Digitale de Demain_ (2). _La chaire d'excellence RIVA de la region Hauts de France._ (3). _L'AGGLO de Valenciennes et Valenciennes metropole._  
This article does not involve data about humans or animals requiring approval.  
Not applicable.  
The authors declare no conflict of interest.
## Methods
### Mass Flux (Ice Discharge)  
We estimated the mass flux through a flux gate near the terminus (08/02/2014) of the main branch of the glacier during 2011-2014 (see Figure 1). Ice thickness was calculated by subtracting the bed topography data [13] from the respective TanDEM-X DEMs. However, we restricted our mass flux estimations to the main branch since the bedrock reconstruction could be validated against field data for this branch only. In order to reduce the influence of seasonal variation of the X-band signal penetration, we approximated the ice thickness decrease with a linear trend based on an interpolation of the summer elevations between 07/21/2011 and 07/03/2014. The surface velocity was taken from the respective velocity fields year around. The gate was split into a number (\(n\)) of sections of constant length (\(L_{n}\) = 20 m) and variable ice thickness (\(H_{n}\)) to compute the mass flux (\(F\)) as  
\[F=\sum_{n}f*\rho_{ice}*L_{n}*H_{n}*\vartheta_{n}*sin(\alpha_{n}), \tag{5}\]  
where (\(f\)) is the ratio of surface-to-depth averaged velocity (\(f\) e [0.8,1]). For most tidewater glaciers the terminus velocity is driven by basal sliding [9] keeping the factor (\(f\)) closer to unity. We used a value of 0.93 \(\pm\) 0.05 from the study by [PERSON] et al. (2015) [32] which covers fast flowing tidewater glaciers in Greenland. We assumed a mean ice density (\(\rho_{ice}\)) of 917 kg m\({}^{-3}\), (\(\vartheta_{n}\)) is the magnitude of surface velocity and (\(\alpha_{n}\)) is the angle of glacier flow direction with respect to the cross-sectional plane of the flux gate. Linear interpolation was applied where velocity data (\(\vartheta_{n}\)) and the ice thickness (\(H_{n}\)) at a cell were missing for a given time step. Outliers of (\(\alpha_{n}\)) were eliminated by setting a threshold of 90\({}^{\circ}\) with reference to its deflection from the gate's perpendicular direction. Applying error propagation, the error of the mass flux (\(F\)) is defined as  
\[\varepsilon_{F}=|F|*\sqrt{\left[\frac{\delta_{f}}{f}\right]^{2}+\left[\frac{ \delta_{H}}{H}\right]^{2}+\left[\frac{\delta_{\vartheta}}{\vartheta}\right]^ {2}} \tag{6}\]  
where (\(|F|\)) is the magnitude of mass flux and uncertainty in ice thickness (\(\delta_{H}\)) is 26 m. The mean ice thickness (\(H\)) at the flux gate of the main branch is 105 m on the first sample date (07/21/2011) with values as high as 305 m at some points. The error in velocity (\(\delta_{\vartheta}\)) \(<\)0.25 m day\({}^{-1}\) in more than 90% of the data pairs, (\(\vartheta\)) is the mean velocity at the flux gate. No errors are included for the constant length (\(L_{n}\)) of 20 m and the assumed ice density (\(\rho_{ice}\)). Therefore, the error amounts to \(\sim\)25%.
## 4 Results and Discussion
### Scenario III: Unbalanced and Distorted Source Voltage
Figure 20: Simulation results obtained under Scenario III for Load B, showing source voltage \(v_{\text{Salc}}\), load current \(i_{\text{Labc}}\), injection current \(i_{\text{in,j,j,c}}\), source current \(i_{\text{Salc}}\), and neutral current before and after mitigation, demonstrated by SAPF while applying the (**a**) enhanced-ADALINE and (**b**) STF-dq0 algorithms.
## 4 Principle of Operation
Furthermore, in the frequency range from 1.16 THz to 1.634 THz, the Y component of the transmitted wave is ahead of the X component hence the transmitted wave is LHCP, whereas in the frequency range of 3.935 THz to 5.29 THz, the Y component of the transmitted wave lags the X component, hence the transmitted wave is RHCP. In addition, the proposed unit cell behaves equally well for the incident Y polarized wave resulting in RHCP and LHCP for the two frequency bands. Total transmission in the X direction can be computed as \(T_{all}=\left|t_{xy}\right|^{2}+\left|t_{xx}\right|^{2}\)[46]. Figure 5 shows the axial ratio for the incident X polarized and Y polarized waves along with total transmission. For the sake of simplicity, total transmission in the X direction is only shown in Figure 5. A similar tendency is observed for transmission in the Y direction. It is clear from Figure 5 that the proposed structure has an axial ratio of 3 dB from 1.16 THz to 1.634 THz and 3.935 THz to 5.29 THz for both X polarized and
# Bioactivity and Delivery Strategies of Phytochemical Compounds in Bone Tissue Regeneration
###### Abstract  
Plant-derived secondary metabolites represent a reservoir of phytochemicals for regenerative medicine application because of their varied assortment of biological properties including anti-oxidant, anti-inflammatory, antibacterial, and tissue remodeling properties. In addition, bioactive phytochemicals can be easily available, are often more cost-effective in large-scale industrialization, and can be better tolerated compared to conventional treatments mitigating the long-lasting side effects of synthetic compounds. Unfortunately, their poor bioavailability and lack of long-term stability limit their clinical impact. Nanotechnology-based delivery systems can overcome these limitations increasing bioactive molecules' local effectiveness with reduction of the possible side effects on healthy bone. This review explores new and promising strategies in the area of delivery systems with particular emphasis on solutions that enhance bioavailability and/or health effects of plant-derived phytochemicals such as resveratrol, quercetin, epigallocatechin-3-gallate, and curcumin in bone tissue regeneration.  
bone regeneration; phyto-bioactive compounds; molecular signaling pathways; bone-devices +
Footnote †: journal: Medical  
resveratrol, oleuropein, quercetin, etc., have often been incorporated into biomaterials as a natural and non-toxic therapeutic alternative to traditional treatments [7; 8]. Phyto-bioactive substances, defined as non-nutritive plant secondary metabolites, could interact with enzymes, proteins, and membrane receptors modulating cell-signal transduction cascade and specific molecular pathways leading to bone anabolic effects and decreasing bone resorption [9; 10]. In addition, epidemiological studies have reported a correlation between a diet rich in biologically active factors such as fruits, vegetables, and olive oil and the reduced risk of bone loss and bone-related trauma [11; 12]. However, these compounds present in vivo limited biological activity, lack long-term stability, and are subject to oxidation overtime under exposure to oxygen, light, moisture, and heat [13; 14]. Therefore, a nanotechnological approach that involves controlled drug delivery systems for natural bioactive molecules could be a solution to avoid invasive procedures and minimize off-target cell behaviors. In addition, these alternative strategies could provide phytochemicals better performance, enhance their low water solubility or very short circulating half-life, improving the functionality and clinical utility [3; 15]. This review attempts to summarize the recent works involving delivery systems (i.e., synthetic ceramic, scaffolds, nanoparticles) and phytochemicals to guarantee the protection of natural biomolecules from environmental degradation, to modulate compounds release, and to prolong delivery at localized injury sites. A short chapter on the structure of bone tissue, its functional activities, and the regulatory mechanisms of bone remodeling/regeneration will help in understanding the results discussed.
# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
## 5 Experiments
### Evaluating the Neighborhood of Set-Valued Datasets
#### 5.1.2 Data Privacy and Neighborhood Distance  
In this section, we investigate the effect of the level of privacy on the neighborhood generation. From the first analysis, we recognized that the same privacy constraint can generate re-associated datasets that have a close radius to the original dataset. What is shockingly interesting is that higher privacy (\(k=4\) and \(m=4\)) generate neighbor datasets closer to the original dataset than lower privacy constraints (\(k=2\) and \(m=2\)) as shown in Figure 5. This can be explained by the reality that higher privacy is harder to achieve while disassociating the original dataset. In fact, imposing an extra check on associations between items in a cluster, will force dissimilar subsets to be separated into distinct record chunks, thus creating less diversity in a record chunk. Accordingly, the probabilistic wheel re-association algorithm will have less diverse subsets to choose to re-associate together between record chunks. We can notice another behavior related to the maximum cluster size \(\delta\), smaller \(\delta\) generate datasets that are closer to the original dataset and this is due to the fact that when fewer records are allowed in a cluster, diversity in terms of associations is minimized. Therefore, giving less choice to probabilistic wheel re-association by imposing higher privacy (\(k\), \(m\)), and lower number of records to re-associate (smaller \(\delta\)) can lead to generating datasets that are less distant from the original dataset.  
To conclude, the above experiments prove the efficiency of the Probabilistic Wheel Re-association algorithm for its ability to generate different solutions, yet taking into account the privacy level imposed in the disassociation phase. The last question we investigated in our experiments is: what can we learn from those disassociated datasets compared to the original dataset? The next section tackles this problem and evaluates the knowledge discovery on a neighbor dataset.
## 3 Results and Discussion
### Influence of Atomizing Gas Type
#### 3.3.2 Peak Surface Temperature  
The peak surface temperatures for the HRR conditions of 1.17 and 5.23 kW are shown in Figure 11a,b, respectively. Overall, the peak surface temperatures for HRR of 5.23 kW were higher than those for HRR of 1.17 kW. For both HRR conditions, increases in water and gas flow rates led to a decrease in peak surface temperatures. One of the reasons for this is the cooling effect of the discharged flows. Under low water and gas flow rate conditions, the effects of the atomizing gas type on the peak surface temperatures appeared to increase. The air case showed slightly higher peak surface temperatures when compared with those of the nitrogen case. This may be due to the fact that the discharged air slightly increases the combustion rate by supplying oxygen to the fire [29]. However, overall, the differences between the peak surface temperatures of nitrogen and air were quite small.  
In Figure 12, the relation between the extinguishing time and peak surface temperature for nitrogen and air, obtained from Figures 10 and 11, is shown. Increases in extinguishing times resulted in increasing the peak surface temperatures, which were similar to Figure 6. However, their relations in the nitrogen case were not much different from those in the air case.  
Figure 11: Peak surface temperature for nitrogen and air: (**a**) HRR = 1.17 kW and (**b**) HRR = 5.23 kW.  
Figure 12: Extinguishing time vs. peak surface temperature for atomizing gas type effect (note that the scales of \(x\)-axis in both figures are different): (**a**) HRR = 1.17 kW and (**b**) HRR = 5.23 kW.
## 1 Introduction
Axial turbomachinery was used for compression and expansion, requiring a total of four machines. Isentropic Ltd. developed a cycle with lower maximum temperatures of around 500 \({}^{\circ}\)C and envisaged using a bed of spheres as the storage inventory. The beds can be \"segmented\" to improve performance and flexibility. Isentropic Ltd. developed reciprocating machines that could be operated as both compressors and expanders [8]. A 150 kW Brayton cycle was successfully tested in 2019 [1; 8]. Recently, Malta Inc. has begun developing a regenerative closed-loop system [9; 10]. Malta Inc.'s goal is to use commercial or near-commercial technologies for each component [11]. Air-based turbomachinery is used for compression and expansion, and temperatures are kept below 600 \({}^{\circ}\)C to control  
Figure 1: (**a**) Flow sheet of a Brayton battery; (**b**) associated T-s diagram. steel costs. A molten salt storage system is used for the hot storage, while a coolant such as glycol or isopropanol is provided for the cold storage. GridScale [12], developed by the Danish company Stiesdal, is a scalable concept for electrical storage that utilizes a reversible closed Brayton battery using natural stone beds as thermal storage, aiming to balance the production of photovoltaic and wind farms. Stiesdal's GridScale project, supported by various partners including Andel, Aarhus University, and the Technical University of Denmark, plans a demonstration project in Reddy, Denmark, with a charging/discharging capacity of 4 MWe/2 MWe and a storage capacity of 10 MWhe. Enolcon GmbH and Storasol GmbH [13] are also developing a closed-loop Brayton battery, which can optionally be supplemented with an ORC cycle to increase RTE. Proof-of-concept is currently being developed, aiming to achieve an RTE of about 40-45%. There are plans to develop reversible turbomachinery with nitrogen as the working fluid in collaboration with Atlas Copco to achieve higher pressures and temperatures, thus achieving an RTE of about 63-67%. Simple system models for Brayton batteries with ideal gasses can be established analytically, and several correlations have been developed, see for example [14; 15; 16; 17; 18]. These correlations typically illustrate the significance of certain parameters for the efficiency of the cycle process, which is particularly sensitive to the isentropic or polytropic efficiency of the compression and expansion machinery. Another important parameter is the pressure ratio, which represents the ratio between the compression and expansion work during the charging process, with larger values improving the electricity-to-electricity efficiency [19]. Brayton batteries have relatively low pressure ratios, and the design of efficient compressors and expanders is necessary to achieve reasonable cycle efficiencies. The simplest implementation of a Brayton battery is shown in Figure 1, but different technologies and materials can be used in each phase of the process, leading to a variety of concepts proposed in the literature.