esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm
## 2 Related Work
### Clustering Algorithm  
Clustering algorithms are an unsupervised learning method, which are divided into hard clustering and soft clustering. The membership degree of the hard clustering algorithm only has two values: 0 and 1. The K-means clustering algorithm [12] is one of the classic algorithms. The K-means++ clustering algorithm is improved on the basis of the K-means clustering algorithm.  
Soft clustering algorithms divide data by similarity and categorize data with high similarity. The fuzzy C-means (FCM) [13] clustering algorithm is a distance-based soft clustering algorithm that assigns each data point to multiple cluster centers and assigns a membership degree to each data point.  
Assuming user dataset \(U=\{u_{1},u_{2},\ldots,u_{n}\}\) with \(m\) features, set as \(u_{i}=\{r_{1},r_{2},\cdots,r_{im}\}\). If users are divided into \(k\) clusters with a cluster center of \(Z=\{z_{1},z_{2},\cdots,z_{k}\}\), a fuzzy matrix \(W=\)\(\left(w_{ij}\right)_{n\times k}\) can be constructed, where \(w_{ij}\in[0,1]\) represents the membership degree of the \(i\)-th user belonging to the \(j\)-th cluster. FCM uses the sum of squared errors function as the objective function to minimize the objective function and divide \(n\) users into \(k\) clusters. The definition formula and constraint conditions of the objective function are shown in Equations (1) and (2).  
\[J_{k}(W,Z)=\sum\limits_{a=1}^{n}\sum\limits_{b=1}^{k}\left(w_{ab}\right)^{\rho }\|u_{a}-z_{b}\|^{2} \tag{1}\]  
\[\sum\limits_{b=1}^{k}w_{ab}=1,a=1,2,3,\cdots,n \tag{2}\]  
where \(\rho\) is the fuzzy index, usually taken as 2. Using the Lagrangian multiplier method, calculate the extremum of the objective function, obtain the partial derivatives of variables \(w_{ij}\) and \(z_{j}\), respectively, and obtain the membership degree and cluster center according to Equations (3) and (4).  
\[w_{ab}=\left[\begin{array}{c}\|u_{a}-z_{b}\|^{\frac{2}{\rho-1}}\\ \sum\limits_{j=1}^{k}\|u_{a}-z_{j}\|^{\frac{2}{\rho-1}}\end{array}\right]^{-1} \tag{3}\]  
\[z_{b}=\frac{\sum\limits_{i=1}^{n}\left(w_{ab}\right)^{\rho}u_{i}}{\sum\limits _{i=1}^{n}\left(w_{ab}\right)^{\rho}} \tag{4}\]  
Before using FCM, we need to determine the fuzzy index \(\rho\) and the limiting iteration index \(eps\). After obtaining the final membership matrix, the partition of the cluster is obtained through membership, and users are assigned to the cluster with the highest membership, resulting in the highest similarity among users in the same user cluster.
# Landsat Surface Product Validation Instrumentation:
## 1 Big MAC Overview  
### Purpose  
The USGS EROS Center widely distributes Landsat Surface Reflectance and Surface Temperature products to the remote sensing community. These relatively new products are produced by applying an atmospheric correction algorithm (LaSRC is used for Landsat 8 and 9) to standard top-of-atmosphere products to propagate pixel reflectance values to the Earth's surface [1]. Although the calibration and validation of top-of-atmosphere, also referred to as Level 1, products is a highly developed process that has been ongoing for several decades [2; 3; 4; 5; 6; 7], validation of land surface products is still in its early stages.  
Several aspects of land surface product validation combine to make the task substantially different from the calibration of satellites [8]. First, validation must include multiple surface cover types, and the multitude of spectral signatures of surface targets requires validation at multiple locations across the planet [9; 10]. Secondly, the atmospheric correction process also needs to be validated across a variety of atmospheric optical parameters. Thirdly, instrumentation is needed to make the appropriate surface and atmospheric measurements to perform proper validation. Thus, there is a great need to optimize the validation process to ensure the maximum return of information is gained for a fixed level of investment.  
Surface product validation is not limited to only Landsat sensors, and many organizations worldwide are directing resources to improve surface product validation [9; 11]. Geoscience Australia has been leading the effort through field campaigns using portable spectrometers and sun photometers to validate products on a continental scale [12]. In Europe, the approach has developed around the concept of in situ automated hyperspectral radiometers [9; 10; 13]. Multiple approaches can be used to validate surface products; however, an optimal approach has not yet been determined.  
To address this problem, USGS EROS conducted a multi-agency field campaign (titled BigMAC) during 30 August-3 September 2021 in Brookings, SD, USA, at the Research Park for SDSU. This 125-acre facility has large uniform regions of alfalfa that provide a natural vegetative background target along with asphalt roads for a common human-made target. In addition, a number of small panels were deployed with a variety of reflectance and spectral signatures, as well as several thermal targets. Figure 1 shows the site, while Figure 2 shows a pictorial layout of the various targets.  
Figure 1: View of the BigMAC site.  
Four organizations participated in BigMAC: USGS EROS, SDSU, RIT, and Labsphere. Each organization provided targets, instrumentation, and personnel for the campaign.
## 3 Steam Condensing Performance
### Energy Conservation Equation for the Cooling Stream
Thus, CO\({}_{2}\) is a better heat-removing fluid than air as shown in Figures 6 and 7. Figure 6 shows the effect of the storage pressure on the steam vapor volume fraction. Using air as the heat-removing fluid, the steam exits the cooling device as a two-phase vapor-liquid steam. It has 53% vapor when the container pressure is 1 MPa and 10% vapor when the container pressure is 5 MPa. The results for CO\({}_{2}\) show a significant improvement. At 1 MPa, the steam contains about 44% of vapor when it exits the condensing device, and it contains a negligible vapor amount of 7% when the storage pressure is 2 MPa. When the container pressure is at 3 MPa and higher, the steam becomes a saturated-liquid water before escaping the condensing device. For example, when the steam is cooled by CO\({}_{2}\) which is expanded from the container with pressures of 3, 4, and 5 MPa, the steam becomes a saturated liquid at \(\xi\) = 0.8, \(\xi\) = 0. 66, and \(\xi\) = 0.58, respectively. In thermal power plants, the condenser is a very important component. Its function is to produce a saturated liquid water before it is pumped back into the boiler. It also maintains the back pressure on the exhaust side of the turbine. This improves the efficiency of the plant. The results shown here indicate that to meet these functions, CO\({}_{2}\) is a better heat-removing fluid than air for the condenser. Figure 6: Effect of storage pressure on the volume fraction of the steam vapor (storage container temperature of 35 \({}^{\circ}\)C (308 K)). Figure 7: Effect of storage pressure on the cooling-fluid temperature (storage container temperature of 35 \({}^{\circ}\)C (308 K)). Another benefit of using CO\({}_{2}\), based on this example, is related to the condenser surface area. The results show that when air from the container of pressure of 1 MPa is used as the cooling fluid, the steam exits the condensing device (\(\xi=1\)) with 53% vapor and 47% liquid water. This means that as the steam flows through the condensing device, 47% of the vapor content has been condensed. When the pressure of the high-pressure container is 2 MPa, the same amount of vapor can be condensed within a shorter length, \(\xi=0.77\). From Figure 5, if W is the width, the condensing surface area can be roughly calculated as WL\(\xi\) which is equal to WL when the container pressure is 1 MPa, and it reduces to 0.77 WL when the container pressure is 2 MPa. We define \(\xi\) (47%) to be the length of the condenser where 47% of the vapor content has been condensed. We report in Table 3 the values of \(\xi\) (47%) and the corresponding condensing surface areas for the various conditions used here. Depending on the container pressure, the condensing surface area with CO\({}_{2}\) as the cooling fluid could be as low as 50% of the condensing surface area when air is used. This leads to a significant reduction in the condenser size and capital costs.
## 3 Results and Discussion
### Saw-Cutting Mechanism's Simulation Analysis Results  
In the saw chain, a point P1 is selected, and the velocity variations of P1 in the X, Y, and \(Z\)-axis directions are depicted in Figure 9(a)-c. Furthermore, a point P2 is identified on the guide plate. Since the velocity of P2 remains constant in the X and \(Z\)-axis directions, the focus is on analyzing the velocity change of P2 in the \(Y\)-axis direction. The velocity variations of point P2 in the \(Y\)-axis direction are illustrated in Figure 9(d).  
As depicted in Figure 9(a), the velocity of P1 in the \(X\)-axis direction is null, indicating that the sawing device does not undergo positional changes in the \(X\)-axis direction. Based on Figure 9(c), the cutting speed of the saw chain is determined to be 5.5 m/s. Additionally, Figure 9(d) reveals that the feed speed during the work process is 20 mm/s.  
Figure 10: The variation curve of P1 and P2.  
The hydraulic cylinder external axial load change is shown in Figure 11. Between 4 s and 30 s, the axial load of the hydraulic cylinder underwent variations. During this phase, the saw-cutting mechanism was in the cutting stage, during which significant changes in the axial load were observed.
## 3 Results and Discussion
### Engine Performance  
#### 3.2.1 Brake Specific Fuel Consumption  
Figure 6 displays the brake-specific fuel consumption (BSFC) variation of the test engine while running with CPO, CPO10, CPO30, and CPO50 according to various idling engine speeds. Overall, as the engine speed increases from 750 rpm to 1500 rpm and then to 2250 rpm, the BSFC of all test fuels first decreases and then increases slightly. And the BSFC of all fuels seems to be the highest at the lowest engine speed of 750 rpm. The average BSFC of all test fuels is reduced by 10.90% and 3.89% at 1500 rpm and 2250 rpm, respectively, compared with that at 750 rpm. This may be attributed to the increase in engine speed, as more air is introduced into the engine, and air turbulence improves. Thus, the decrease in BSFC values with increasing engine speed may be explained by the increase in engine excess effective power. Similar results are noted for different biodiesel blends with various engine speeds [28]. On the other hand, the BSFC of all blended fuels is higher than that of diesel at all engine speeds. At 750 rpm, the BSFC of CPO10, CPO30, and CPO50 increased by 11.40%, 20.96%, and 24.63% compared with diesel. At 1500 rpm, the BSFC of CPO10, CPO30, and CPO50 increased by 9.16%, 8.38%, and 14.23% compared with diesel. At 2250 rpm, the BSFC of CPO10, CPO30, and CPO50 increased by 4.73%, 10.52%, and 8.39% compared with diesel. The researchers suggest that the increase of BSFC is mainly related to the low calorific value and high viscosity of vegetable oil, which requires more fuel consumption to obtain the same engine performance as diesel fuel [29].  
Figure 5: **Maximum heat release rate of all test fuels according to various engine speeds.**  
Figure 6: Brake-specific fuel consumption of all test fuels according to various engine speeds.
## 4 Kinematic Model
### Forward Kinematics (FK)  
We define the gripper's position relative to a fixed frame as \(\mathbf{x}\), which represents the set of all the point (\(x\), \(y\), \(z\)) coordinates in space that the gripper can reach. The forward kinematics (FK) function maps from the configuration space \(\mathbf{q}\) to the workspace \(\mathbf{x}\) and is given by \(\mathbf{x}=f\) (\(\mathbf{q}\)). The \(\mathbf{q}\) space contains the joint rotation angles and the MA's position along the arm, as presented in Equation (1).  
To define the kinematics equation, \(d_{i}\) and \(d_{link}\) need to be extracted from \(d\), where \(d_{i}\) represents the \(i\)-th link on which the gripper is located, and \(d_{link}\) represents the gripper position relative to the link it is on, as depicted in Figure 8. These parameters allow the FK function to map the gripper's position within the workspace accurately.  
The position of the gripper is given by  
\[\mathbf{x}=\begin{pmatrix}x\\ y\\ z\end{pmatrix}=R_{x}(\theta_{0})\cdot\mathbf{x}_{p}=\begin{bmatrix}1&0&0\\ 0&\cos\theta_{0}&-\sin\theta_{0}\\ 0&\sin\theta_{0}&\cos\theta_{0}\end{bmatrix}\begin{pmatrix}x_{p}\\ y_{p}\\ z_{p}\end{pmatrix}. \tag{2}\]  
where \(\mathbf{x}_{p}\) is the gripper position without rotation of the first joint, as follows:  
\[\mathbf{x}_{p}=\begin{bmatrix}l_{0}+\sum\limits_{i=1}^{d_{i}-1}l_{i}\cos \left(\sum\limits_{i=0}^{i}\theta_{j+1}\right)+d_{link}\cos\left(\sum\limits _{j=0}^{d_{i}}\theta_{j+1}\right)\\ \sum\limits_{i=1}^{d_{i}-1}l_{i}\sin\left(\sum\limits_{j=0}^{i}\theta_{j+1} \right)+d_{link}\sin\left(\sum\limits_{j=0}^{d_{i}}\theta_{j+1}\right)\\ 0\end{bmatrix}. \tag{3}\]
## 4 Results
### Liocc in the Study Area  
The overall accuracy of the land cover map for each year was evaluated with the testing samples, and is illustrated in Figure 5. The average accuracy of 14 years was 88.6%. Figure 6 summarizes the area fractions of built-up, water bodies, and vegetation (including grassland, cropland, EBF, and DBF) in the study area (bare land is not shown in Figure 6 due to the minimal area). It is evident that built-up areas increased rapidly after 2005, with an annual growth rate of 13.2%. On the other hand, vegetated area reduced by 0.9% per year, and water bodies decreased by 0.4% per year. While the total vegetated area was decreasing from 2000 to 2013, the fraction of forest increased slightly, which was highlighted on the plot of the relative composition of forest (Figure 7).  
Figure 4: Comparison of eight-day GPP_act against MOD17 GPP for cropland, grassland, EBF, and DBF. GPP were averaged on pixels of the same land cover type on land cover maps derived from both Landsat images and MOD12 land cover data.  
Figure 5: The overall accuracy and the average accuracy of classification of Landsat images from 2000 to 2013. The maximum likelihood classifier was applied to classify seven land cover types in the study area.  
Figure 8 illustrates the spatial pattern of land-use/cover in 2000 and LUCC from 2000 to 2013 in the study area. The urban expansion resulted in the conversion of the large area of cropland in the suburb to built-up areas. The conversion from other land-use/cover types to water bodies can be found in the northwest of the study area, where areas of cropland were transformed to ponds in pursuit of higher economic benefits. The conversion from other land cover types to EBF or DBF primarily occurred in the southeast of the study area, where croplands were used to grow saplings.  
Figure 6: Fractions of built-up, water bodies (Water), and vegetation (including cropland, grassland, EBF and DBF) in the study area from 2000 to 2013.  
Figure 7: The relative composition of EBF and DBF to the total vegetated area from 2000 to 2013. The dash line is the best-fit line of the linear regression, indicating the increasing trend of the relative composition of EBF and DBF through the study period.
## 3 Feature Selection and Methodology
### Ensemble Modeling Methods  
No single prediction model can achieve perfect accuracy because, under different data and assumptions, different algorithms have different inductive biases. Therefore, ensemble learning takes advantage of each classifier to improve accuracy and combines them to reach the final result [41; 42]. The main concept of ensemble methodology is constructing multiple individual classifiers according to each learner's performance and stability, then combining them to obtain better performance in the single classifier [43; 44]. Ensemble modeling has been successfully applied to various decision-making problems, such as feature selection, classification, and prediction [41]. It can reduce the estimated error variance and avoid overfitting problems [42; 43; 45].
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 1 Introduction
Though the above models work on an event-basis or on a local scale, they are not suitable for global scale application and statistical assessment, because rain microphysics is time-variant and depends on climatic and environmental factors. In this context, this work describes the development of an analytical model to predict rain attenuation on FSO links for a path length \(L\) of up to 5 km, whose applicability goes beyond the single site. Indeed, the proposed model represents an innovative step forward towards providing a tool enabling to plan the deployment of FSO links in sites where the local precipitation features might differ considerably. Indeed, the model introduces significant elements of novelty, which are not available all together in any of the models presently available in the literature: (1) it aims at being widely applicable; (2) it takes into account the dependence on the DSD, which the specific rain attenuation strongly depends on; and (3) it embeds the multiple-scattering effects occurring at optical frequencies at high rain rates, which, as a matter of fact, decrease the attenuation induced by precipitation. The model development relies on realistic high-resolution synthetic rain maps to properly take into account the high spatial variability of precipitation and it takes advantage of the analytical formulation of the [PERSON]'s model, originally proposed in [12] to predict rain attenuation along millimeter-/micro-wave links and adapted in this work to the optical domain. This paper is organized as follows: Section 2 describes the method proposed for the estimation of rain attenuation impairing FSO links based on synthetic rain maps. Section 3 reviews some models available in the literature to estimate rain attenuation in the millimeter-/micro-wave portion of the spectrum in order to investigate the adaptability and applicability of such methodologies to the prediction of rain attenuation on FSO links, which is compared to the results obtained in Section 2. The effect of multiple scattering is addressed in Section 4. Finally, Section 5 concludes the paper.
# Application of Spinel and Hexagonal Ferrites in Heterogeneous Photocatalysis
## 3 Single-Phase Magnetic Photocatalysts  
### Spinel Ferrites  
As presented by [PERSON] et al., pristine Fe\({}_{3}\)O\({}_{4}\) (Eg = 0.1 eV) does not show any photocatalytic activity [68]. Nevertheless, some of MFe\({}_{2}\)O\({}_{4}\) spinel ferrites are photocatalytically active themselves, without any coupling with other semiconductors (see Table 1). For example, [PERSON] et al. compared five different ferrites in terms of magnetic and photocatalytic properties [69]. It was found that materials with no or low M\({}_{\rm s}\) exhibit good photocatalytic activity for degradation of methyl orange (MO). Similarly, [PERSON] et al. observed that with an increase in the content of doped cobalt in Co\({}_{\rm x}\)Mg\({}_{\rm 1-x}\)Fe\({}_{2}\)O\({}_{4}\) ferrite, the magnetic properties increased, but photocatalytic activity decreased [70]. However, it has also been reported that manganese spinel ferrite (MnFe\({}_{2}\)O\({}_{4}\)) with a high M\({}_{\rm s}\) value (\(\sim\)60 Am\({}^{2}\)\(\cdot\)kg\({}^{-1}\)) shows high photocatalytic activity (about 56% degradation of Direct Red 81 after 2 h of solar-light irradiation) [71]. It is also worth mentioning that the photocatalytic activity of spinel ferrites seems not to depend on their bandgap energies or on the conduction band (CB) and valence band (VB) positions, as they are similar for a wide range of materials (Figure 4).  
Figure 3: Crystal structure of the BaFe\({}_{12}\)O\({}_{19}\) hexagonal ferrite.
# Broadcast Ephemeris with Centimetric Accuracy: Test Results for GPS, Galileo, Beidou and Glonass
## 8 Conclusions  
The limited accuracy of the broadcast data has been discussed in detail in several publications. Possible actions towards an improvement of the broadcast message are the focus of this paper. We have investigated the potential of the GPS and Glonass navigation messages to represent satellite position and clock with an accuracy comparable with that of the precise ephemeris delivered by the IGS within the MGEX project. The reference precise products (ephemeris and clocks) were generated by CNES for GPS, Glonass and Galileo, and by CODE for Beidou (MEO and IGSO). We have examined one satellite per constellation in one specific day, and obtained indication that the broadcast message can reproduce the precise ephemeris with a centimetric rms, provided that: (a) for GPS-like messages (GPS, Galileo and Beidou), arcs of two hours are used for the fit, complemented by one set of Helmert parameters for the day; and (b) for Glonass-like messages arcs of one hour are used for the fit. For Glonass the Helmert parameters are unnecessary because the Glonass message is based directly on Cartesian ECEF coordinates and velocities. Volume calculations on all satellites and for one month indicate that the proposed approach is applicable in general. Corrections to the clock parameters are also computed, and similar accuracy has been demonstrated. High frequency clock files from IGS have also been tested against the estimated clock polynomials. The results suggest that the rms spread is limited to fractions of nanoseconds for GPS, Galileo and Beidou, and a factor of 10 higher for Glonass. Systematic drifts in the clock differences are clearly visible, implying that our clock polynomial is likely to smooth the high frequency noise of the on board clocks, particularly for the Rubidium and Cesium clocks. Order of magnitude estimates of the perturbations caused by the higher order terms of the gravity field suggest the opportunity to modify the message to include the effect of the \(J_{3}\) component of the Earth's gravity field. Implications of our results for real time applications very much depend on the availability of predicted precise orbits and clocks which can be represented in a broadcast form. In such case, appropriate RTCM messages are available for the examined constellations, both for the broadcast message and the complementary Helmert parameters. A.C. conceptualization, paper writing, analysis; J.Z. software coding and implementation. All authors have read and agreed to the published version of the manuscript. This research is supported in part by a contract with Regione del Veneto titled 'Use of precision GNSS for applications to land monitoring of the Regione of Veneto', and by the contracts with the GSA (Galileo Supervising Authority) GISCAD-OV and GRC-MS.  
Not applicable. The authors declare no conflict of interest.