esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Personalized Recommendation Methods for Online Video Learning Resources
## 3 Preliminary Notions
### Collaborative Filtering Algorithm  
The most common algorithm in many intelligent recommendation algorithms is the collaborative filtering algorithm in commercial applications. The principle of collaborative filtering algorithm is to find the correlation between users or items according to the user's preference data for items, and recommend the items to the user based on the correlation [18]. According to different principles, collaborative filtering algorithms can be divided into two categories: collaborative filtering algorithms based on users and collaborative filtering algorithms based on items.  
Collaborative filtering algorithm based on users is to find the other users with similar interests through the interests of a user and then recommend products that similar users pay attention to. For example, if user \(A\) and user \(B\) both give a high score for item 1, item 2, and item 3, we can assume that user \(A\) and user \(B\) have similar interests. If user \(A\) gives a high score to item 4, then item 4 can be recommend to user \(B\).  
The principle of collaborative filtering algorithm based on items is to find similar items. We use the features of an item to find other items that are similar to the item, so as to recommend similar items to users who are interested in the item. For example, book 1 and book 2 were bought by readers \(A\), \(B\), and \(C\). Therefore, we think that book 1 and book 2 have strong similarity. It can be inferred that readers who like book 1 also like book 2. When a buyer purchases book 1, book 2 can be recommended to him.  
This system intends to use collaborative filtering algorithm based on items to recommend relevant video learning materials for the related users.  
Whether it is based on users or items for collaborative filtering algorithm, its principle is to find the similarity between data. There are three common methods to calculate similarity: Euclidean distance, cosine value and Pearson correlation coefficient. Here, we use Pearson correlation coefficient to find the similarity between data.
## 3 Results and Discussion
### Fatigue Limits
However, this combination of shape roughness parameters is unfavorable from a fatigue behavior perspective [62] as the deep valleys within the surface texture act as natural microstress concentrators. Previous research has experimentally proven that reducing the skewness value corresponds to a decrease in the fatigue limit of diamond-burnished CLASS specimens [50]; thus, the more negative the skewness, the lower the specimen's fatigue limit. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Finishing Process** & \(\boldsymbol{\sigma^{(1)}_{-1}}\)**, **MPa** & \(\boldsymbol{\sigma^{(2)}_{-1}}\)**, **MPa** & \(\boldsymbol{\sigma^{(3)}_{-1}}\)**, **MPa** & \(\boldsymbol{\sigma_{-1}}\)**, **MPa** & \(\boldsymbol{\sigma_{-1}}\)**, **Scattering, **MPa** \\ \hline Turning and polishing & 367.25 & 367.25 & 360.94 & 365 & \(+2/-5\) \\ DB—F & 422.20 & 417.53 & 418.57 & 419 & \(+3/-2\) \\ DB—A & 418.80 & 418.10 & 415.50 & 417 & \(+1/-2\) \\ DB—N & 420.70 & 412.75 & 420.55 & 418 & \(+2/-5\) \\ DB—D & 408.50 & 402.55 & 402.52 & 405 & \(+3/-3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fatigue limits due to different finishing processes. Figure 10: Assumed S-N curves: (**a**) turning and polishing; (**b**) DB. It should be noted that the SI characteristics in Section 3.2 were obtained for cylindrical specimens with a diameter of 28 mm. The diamond-burnished surfaces of the fatigue specimens differ significantly both in in terms of their shape (hourglass-shaped) and in size (minimum diameter of 7.5 mm). As a result, the SI characteristics of the fatigue specimens will differ. To determine the effects of the introduced residual stresses and surface defects on the fatigue limits around the minimum diameters of the fatigued specimens, for the second and third specimens of each group, the residual axial stresses were measured and the surface defects were examined by SEM at a constant magnification. The obtained results are shown in Figure 11. This figure shows the most pronounced surface defects (whose origin is from turning) for each specimen. The surface axial residual stresses were also measured in the same areas. The measured surface axial residual stresses in the fatigued specimens are smaller in absolute value  
Figure 11: SEM images of the surface texture of fatigue specimens. compared to those introduced in the cylindrical specimens by the corresponding DB processes (see Figure 9). For both fatigued and cylindrical specimens dry DB without cooling (process D) introduces the largest surface axial residual compressive stresses, followed by processes A, F, and N. Despite the pronounced surface defects observed in some of the specimens, the fatigue limits for each pair of specimens processed by the same DB process are very similar. On this basis, it can be assumed that the fatigue macroracks have been formed under the surface layer due to the introduced residual compressive macrostresses and the modified surface layers.
## 2 Matrix Factorization Based Editing Expert Recommender System  
### Problem Definition  
The proposed method attempts to recommend the optimal editing expert to a user based on the inferred user's opinion by analyzing previously collected user feedback logs to editing experts. We denote a set of users by \(U=\{u_{1},\cdots,u_{i},\cdots,u_{u_{u}}\}\), where \(u_{i}\) and \(u_{u}\) respectively represent the \(i\)-th user and the total number of users, and a set of editing experts by \(E=\{e_{1},\cdots,e_{j},\cdots,e_{n_{e}}\}\), where \(e_{j}\) and \(n_{e}\) respectively indicate the \(j\)-th editing expert and the total number of editing experts.  
Two types of user feedback, ordinal and binary ratings, are utilized for the user opinion inference. Ordinal ratings given by \(U\) to \(E\) compose an ordinal rating matrix \(O=[o_{i,j}]\), where \(o_{i,j}\) refers to the ordinal rating given by \(u_{i}\) to \(e_{j}\) and is an integer ranging from 1 to 5 in 1 point interval.  
Binary ratings given by \(U\) to \(E\) compose a binary rating matrix \(B=[b_{i,j}]\), where \(b_{i,j}\) indicates whether \(u_{i}\) liked the editing result provided by \(e_{j}\) or disliked it. \(b_{i,j}\) is 1 when \(u_{i}\) clicked the like button for \(e_{j}\) or -1 when \(u_{i}\) clicked the dislike button for \(e_{j}\) as described in Equation (1).  
\[b_{i,j}=\begin{cases}1&\text{if $u_{i}$ clicked the like button for $e_{j}$}\\ -1&\text{if $u_{i}$ clicked the dislike button for $e_{j}$}\\ \mathbf{\omega}&\text{if $u_{i}$ did not rate $e_{j}$},\end{cases} \tag{1}\]  
where \(\mathbf{\omega}\) indicates null. Note that \(o_{i,j}\) and \(b_{i,j}\) are null when \(u_{i}\) did not rate the editing service provided by \(e_{j}\).  
A feedback rating matrix \(F=[f_{i,j}]\) is constructed by combining \(O\) and \(B\), where \(f_{i,j}\) is a feedback rating indicating the degree to which \(u_{i}\) prefers \(e_{j}\) ranging from 1 to 5. When \(u_{i}\) did not give any ratings to \(e_{j}\), \(f_{i,j}\) is null. The dimension of \(O\), \(B\), and \(F\) is \(n_{u}\times n_{e}\). However, there are many unknown \(f_{i,j}\) as there are limited number of feedback from \(U\) to \(E\). In summary, given \(O\) and \(B\), we try to find the optimal editing expert \(e_{j*}\) for \(u_{i*}\) who requests an editing service by using the estimated feedback rating matrix \(\hat{F}\) which does not contain null elements and is approximated by analyzing \(F\).
# ADAfinder Tool Applied to EGMS Data for the Structural Health Monitoring of Urban Settlements
## 2 Materials and Methods
### Proposed Methodology
Then, for both orbits, the results of the ADAfinder tool are obtained (orange box of the flowchart), consisting in two maps: one with the ADA (a polygonal _ESRI shapefile_), and one with the PSs contained in each ADA (a point _ESRI shapefile_), as explained in Section 2.2. These products are spatially superimposed to the polygons of buildings, obviously, as they are obtained from the PSs previously selected on the basis of them. Finally, the ADAfinder results elaborated in order to obtain the definitive maps with information reclassified in function of the buildings, following the steps listed in the blue box of the flowchart. First of all, the ADA are overlapped to the buildings' plans. The buildings not included in an ADA or a portion of an ADA are discarded. The remaining buildings, or portions of them, are classified as \"unstable\". For them, two groups of \"unstable\" PSs are recognized: the group of ASC and the group of DES PSs. Then, for each building (with adequate numbers of PS), the average of the V\({}_{\rm m}\) and the \(\sigma\) of the ASC and DES PSs is computed only for the PSs inside the interested ADA. In this way, finally, only two values of V\({}_{\rm m}\) are associated to each building, representing the average of the mean deformation velocity along the line of sight (LoS) of the unstable PSs falling on the building itself: one along ASC (V\({}_{\rm m,ASC}\)) and one along DES (V\({}_{\rm m,DES}\)) orbits. At this step, an intersection of the results deriving from the ASC and DES datasets is performed to identify the buildings overlapped by at least one ADA from the ASC and one ADA from the DES dataset. The buildings not having both ASC and DES information are excluded from the following steps of the procedure, since the availability of both orbits is necessary for the estimation of the vertical and E-W mean deformation velocity components. Then, the vertical and E-W components of the mean deformation velocity for each building are estimated according to the formulas described in [PERSON] et al. [15]. The north-south component cannot be calculated, due to the very low sensitivity of the InSAR to this displacement component, because of the approximately north-south orbit inclination of SAR satellites. All the operations included in the dashed red contour in Figure 1 are done separately for each building included in an ADA. The final maps give a preliminary identification of constructions potentially critical from a structural engineering point of view in terms of deformation, which needs a more accurate level of SHM. The steps of the illustrated methodology can be implemented by combining simple operations to execute in QGIS and algorithms written in a code language (e.g., Matlab), as will be presented later.
## 1 Introduction
### Our Contributions  
To fully explore the benefits of the RIS-CF network, we aim to optimize the BS transmit power and BS selections for weighted sum-SINR maximization. We present the main contributions of this paper as follows.  
* Based on the considered RIS-CF network and the reflection of the signal by RISs, we derive a closed-form expression of the SINR received by the user. Then, we propose a problem of weighted sum-SINR maximization, aiming to jointly optimize the transmit power of the BSs and the BS selections.
* We present a comprehensive solution to the formulated non-convex optimization problem: we first decompose the problem into two subproblems, each of which is a sum-of-ratios problem. To address this issue, we introduce a two-layer iterative algorithm to disassemble the fraction form. For the nonlinear term after fractional programming transform, we employ the reformulation-linearization method to transform the problem into a mixed-integer linear programming (MILP) problem. This approach will offer valuable insights into challenges, such as fractional programming, involving integer variables.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Author** & **Advantage** & **Limitation** \\ \hline [PERSON] et al. [29] & The network capacity can be improved by joint design precoding at BSs and RISs in an RIS-CF network. & A lack of consideration for other performance metrics such as the energy efficiency and BS transmit power. \\ \hline [PERSON] et al. [30] & The hybrid beam-forming strategy achieves a higher energy efficiency performance than traditional ones. & It ignores the impact of different users’ selections of BSs on the system performance. \\ \hline [PERSON] et al. [31] & It balances the passive beamforming gains from all IRSs among different BS–user communication links. & It only considers the case where a single BS serves a single user and ignores the coordination between BSs. \\ \hline [PERSON] et al. [32] & It enhances the overall network performance by maximizing the minimum SINR across all users. & A lack of consideration for the influence of power allocation on network performance. \\ \hline [PERSON] et al. [33] & Reduced energy consumption with a high level of resource utilization by considering the specific characteristics of 5G green communication systems. & It may not cope with the optimization of the RIS-CF network performance directly. \\ \hline [PERSON] et al. [34] & It enables resilient and secure real-time control and monitoring in smart grids. & It may not cope with the optimization of the RIS-CF network performance directly. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of some related works.  
* The simulation results indicate that the RIS-CF network has a better performance compared to benchmark schemes including the conventional networks and no-RIS cases. The impact of the BS transmit power budget and the number of RIS-reflecting elements on the performance are also shown numerically.
# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 2 Related Works
### Multi-Modal Fusion-Based 3D Object Detection
#### 2.3.3 Intermediate Fusion  
Intermediate fusion methods perform multi-modal fusion at the feature extraction stage, balancing the fine-grained feature representation of early fusion methods with the computational efficiency of late fusion methods. In recent years, this strategy has become a popular research focus in 3D object detection. MV3D [30] combines multi-view images and point cloud data, generating candidate regions separately from images and point clouds, and achieving accurate detection through multi-view feature projection and fusion. AVOD [31] facilitates efficient and accurate 3D object detection through joint 3D candidate region generation and multi-view feature aggregation. ContFuse [32] proposes a deep continuous fusion strategy that integrates features from different sensors at multiple levels within the network, effectively combining visual and geometric information. MVX-Net [14] extends VoxelNet [33] by incorporating image features, achieving effective integration of visual and geometric information through deep fusion of multi-modal features. MMF [34] employs a multi-task learning and multi-sensor fusion strategy, optimizing multiple tasks while jointly leveraging multi-modal features. EPNet [15] enhances the expression of point cloud features using semantic information from images, proposing an augmented detection approach. BEVFusion [35] fuses multi-modal data in the bird's-eye view (BEV) perspective to achieve more accurate 3D object detection. Although intermediate fusion methods perform well in many aspects, they still face challenges such as insufficient interaction between local and global features and limited fine-grained feature extraction capability. These issues pose significant challenges for detecting small objects at long distances and recognizing targets in complex scenarios.  
To address these issues, we propose a multi-modal intermediate fusion algorithm for 3D object detection, MPVF, introducing the PVWF module to enhance interaction between multi-modal local and global features, and designing the IRFP module to improve feature extraction efficiency and capture fine-grained features across different resolutions.
## 4 Results
### Use Cases  
The control system outlined is flexible and can handle a variety of use cases involving different actors and objectives. Below are several key scenarios.  
Basic case (charging request event--CR): When a driver arrives at a charging station, they can initiate a charging request through the charging station's dashboard or mobile app. The system then calculates the optimal charging cost and provides control signals to the CS to initiate the process.  
Reservation event (RV): Drivers on the road can use a mobile app to reserve a charging slot at a station, specifying their estimated arrival time and charging preferences. The system then pre-calculates optimal charging control and provides the driver with cost estimates.  
Demand-side management (DSM) event: Retailers notify the CSCC of changes in energy tariffs or available power. The CSCC subsequently updates its control signals to the charging stations and evaluates potential cost savings for flexible drivers.  
Warning event (W): If a grid emergency occurs, a distribution system operator (DSO) may instruct the CSCC to reduce the aggregated power withdrawal from EVs. The CSCC  
Figure 1: Logical interaction between the aggregator and other actors in the reference scenario.  
quickly adjusts the charging controls and recalculates optimal strategies once the emergency has been resolved.
## 3 Theoretical Background  
### Anomaly Detection  
Anomaly detection refers to the detection of data points or instances that deviate from expected or regular behavior. These irregular instances are often referred to as anomalies, outliers, discordant observations, exceptions, aberrations, surprises, peculiarities, or contaminants in different application domains [25].  
There are three types of anomaly-detection methods, depending on the availability of class labels: supervised, semi-supervised and unsupervised methods. Supervised anomaly-detection algorithms assume that fully labeled training and test datasets are available. These algorithms develop a predictive model for abnormal and normal data groups. The primary drawback of supervised anomaly-detection algorithms is that the training dataset contains considerably fewer abnormal instances than normal instances. Semi-supervised anomaly-detection algorithms assume that the dataset includes labeled training data for the normal class only. These algorithms construct a model by using a labeled training dataset that comprises normal data. This model is used to describe normal data and is often called a one-class classifier [26]. Semi-supervised algorithms identify data that deviate from the normal data as anomalies. Unsupervised anomaly-detection algorithms do not need labels in the data, and there is no distinction between the training and test datasets. These algorithms generally assume that the vast majority of instances in a dataset are normal data and search for instances that do not seem to fit with the rest of the dataset. In phase I nonlinear profile monitoring, labeled data are usually unavailable; thus, an unsupervised method must be used in this process.
## 5 Intervalley Scattering and Franck-Condon Transition
### Evaluation of the Time Constant  
The numerically-evaluated time constants for the e-e and the elastic e-ph scatterings are given in Figures 9 and 10. In Figure 9, \(E_{\text{w}}\) corresponds to the initial electron distribution width, which is proportional to the photo-injected electron density. Since the frequency of the scattering event of two electrons in the \(\Gamma\) valley is proportional to \(E_{\text{w}}\), the time constant is inversely proportional to \(E_{\text{w}}\). The e-e scattering with sufficiently large electron density is one of the promising candidates of ultrafast \(\Gamma\)-L IVS, which can give a time constant smaller than 100 fs.  
Figure 9: The time constant by the elastic e-e scattering is given. The energy is referenced from that of the L valley bottom. Adapted with the permission from [12], copyright Journal of the Physical Society of Japan, 2016.  
As seen in Figure 10, the elastic e-ph scattering is another promising candidate. The clear difference between the above two processes are the temperature dependence. In the elastic e-ph scattering, the time constant is inversely proportional to the phonon temperature \(T_{\rm p}\), since the enhancement of the spatial randomness of the frozen phonon at the starting ground state increases the mixing rate of the eigenstates between the \(\Gamma\) valley and the L valley. On the other hand, the time constant for the e-e scattering does not depend on the temperature. The experimental results [8] have revealed that the time constant for the \(\Gamma\)-L IVS depends on the sample temperature. Thus, the elastic e-ph scattering gives a compatible result with the experiment, while the e-e scattering is also a possible process.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 1 Introduction  
Space provides a useful vantage point for monitoring large-scale trends on the surface of the Earth. For that reason, numerous Earth observation (EO) satellite missions have been launched or are being planned. By Novaspace's estimate [1], the number of EO satellites will grow by 190% between 2024 and 2033. Typical EO satellites carry multispectral or hyperspectral sensors that measure the electromagnetic radiations emitted or reflected from the surface, which are then processed to form _data cubes_. These data cubes are the valuable inputs to various EO applications.  
Many EO satellites also process the data captured by the multi/hyperspectral imagers, however, this has hitherto been limited to low-level preprocessing tasks, such as data enhancement and compression. Recently, the advent of satellite-borne hardware accelerators for machine learning inference has opened up the possibility of more advanced processing. A notable example is the PhiSat-1 mission [2], which carries the HyperScout-2 payload [3]. The payload consists of a hyperspectral imager and the Eyes of Things (EoT) \"AI on-board\" [4], which executes a convolutional neural network (CNN) called CloudScout [5, 6] to perform cloud detection on the collected EO measurements. The resultinforms whether the geographic area in the field of view is under significant cloud cover, and only clear-sky data cubes are downlinked to optimise bandwidth utilisation.  
Generally speaking, new missions that employ new sensors (e.g., HyperScout 2) typically do not have enough representative datasets to train a CNN model. An intuitive solution is to use data captured from a previous satellite mission to train the model (e.g., the CloudScout model was trained on data from Sentinel-2 [5; 7]). This workaround, however, will introduce another problem called _domain gap_ or _domain shift_[8], whereby the seemingly similar data from the training and testing domains actually differ significantly in their underlying distributions. The domain gap problem will cause the trained CNNs to perform poorly in the deployed environment. Sections 1.1 and 1.2 describe the domain gap problem in the context of satellite-borne machine learning in more detail.
###### Abstract  
In the present study, through Computational Fluid Dynamics techniques, the performance characterization of a new Stairmand-type separator cyclone was carried out using the commercial software ANSYS Fluent. Four models for the geometrical cyclone separator were built, namely model A as per the dimensions reported in the literature and models B, C, and D by applying square and circular shape cavities as a passive flow control technique on the surface of its cylindrical section. The Navier-Stokes equations with the RSM turbulence model were formulated to solve the continuous phase of the cyclone separator and, the Lagrangian approach was adopted to track the solid particles with one way-coupling. The proposed model's separation efficiency and pressure drop were compared against those recorded in the previous studies reported in the literature. Model D was the cyclone separator that stood out as the most valuable by demonstrating a separation efficiency and pressure drop decrement of 0.42% and 6.01%, respectively.  
cyclone separator; computational fluid dynamics; flow control; pressure drop; separation efficiency; turbulence