esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector
## 2 Literature Review
### Deep Learning-Based Collaborative Filtering  
The following are some of the recent few studies, with the most impact, done for the core recommendation process with deep learning. The authors of [18] proposed AutoRec, which uses Autoencoders as a predictor for missing ratings. Their experimental results showed their model outperforms Matrix Factorization and Restricted Boltzmann Machine (RBM)-based collaborative filtering regarding accuracy on MovieLens and Netflix datasets. The authors of [19] applied to user-item preferences the Neural Autoregressive Distribution Estimation (NADE) algorithm. Compared to RBM, AutoRec, and several Matrix Factorization-based approaches, NADE demonstrates to provide more accurate recommendations and to be more efficiently optimizable. The authors of [20] also utilized NADE by modeling ratings but across both all users and items simultaneously. Their algorithm, CF-UserItem-CoAutoregressive, demonstrates to be a very successful model that achieves higher accuracy than the original version. Recently, the authors of [21] utilized a hybrid algorithm, composed of a Generalized Matrix Factorization component, which has some tweaks from a classic Matrix Factorization, and a Multi-Layer Perceptron module, to non-linearly model the interactions between users and items through the usage of implicit information. This is different from the other works which use the typical ratings.
# Research on Environmental Vibration Induced by High-Speed Maglev Transportation
## 6 Conclusions and Discussion  
### Conclusions  
Through the measurements and calculations carried out in this research, several conclusions can be drawn and are listed as follows:  
1. The irregularity caused by bevel, offset, and dynamic deformation differences between adjacent girders has significant effects on the guideway structure vibration response, and the impact when maglev trains pass from girder to girder can reach up to 20 m/s\({}^{2}\). Thus, it is important to control the irregularity of the maglev track.
2. The viaduct structure is helpful in reducing the vibration propagated to the ground. The peak value of acceleration at the bottom of the viaduct column usually does not exceed 0.2 m/s\({}^{2}\). To predict the effect of column length, an empirical formula was proposed.
3. Ground vibration attenuates quickly with the increase in distance. In several zones 7.5 m away from the maglev line, the ground vibration can be lower than the human perception threshold. This phenomenon verifies that maglev transportation is suitable for use in urban transit and has the potential to be placed much closer to existing structures.
4. Like other forms of rail transportation, there also exists a vibration-amplifying zone close to the maglev line, where the ground acceleration level is much higher than in the adjacent area, with vibration magnitudes that exceed the human perception threshold. For the unique dynamic features of maglev suspension and structure characteristics, the vibration-amplifying zone is located at around the 45- to 60-degree direction, 10 m away from the maglev line.
## 7 Algorithms for HPC Systems in the Exascale Era
### Parallel Solvers for Exascale Computing
#### 7.2.5 Parallel Monte Carlo Based Methods  
Monte Carlo-like algorithms seem to have all the characteristics that make them suitable for Exascale computing systems. Taking inspiration from [191, 192], we can observe that it is easy enough to implement strategies that make them algorithms with the following attributes.  
**Communication- and synchronization-avoiding:**: To implement parallelism in an MC-like algorithm, the named dynamic _bag-of-work model_ can be used [191]. Using such a strategy, a large task, related to the computation of \(M\) states, is split into smaller independent \(P\) subtasks, where each task computes \(\frac{M}{P}\) states and where all the \(P\) subtasks are executed independently of each other in an _\"embarrassingly parallel\"_ approach.
**Fault-Tolerant and Resilient:**: To make an MC-like algorithm resilient to hard failures, an \(P\)_-out-of-\(Q\)_ strategy can be used [191]. Since MC-based applications _\"accuracy\"_ depend only on the number \(M\) of computed states and not on which random sample set is estimated (provided that all the random samples are independent in a statistical sense), the actual size of the computation is obtained by increasing the number of subtasks from \(P\) to \(Q\), where \(Q>P\). The whole task is considered to be completed when \(P\) partial results, from each subtask of size \(\frac{M}{P}\) states, are ready. In the _\(P\)-out-of-approach, more subtasks are needed than are actually scheduled. Therefore, none of these subtasks will become a \"key\" subtask and at most \(Q-P\) faulted subtasks can be tolerated. The critical aspect is properly choosing the value \(Q\) since a \"good\" value for \(Q\) may prevent a few subtasks from delaying or halting the whole computation. On the other hand, an excessive value for \(Q\) could result in a much higher calculation workload with little gain to the computation outcomes. The proper choice of \(Q\) in the \(P\)-_out-of-\(Q\)_ strategy can be determined by considering the average job-completion rate in the computing system. Suppose \(c\) is the completion probability of subtasks up to time \(t\) in the computing system. Clearly, \(Q*c\) should be approximately \(P\), i.e., the fraction of the \(Q\) subtasks finished should equal to \(P\). Thus, a good choice is \(Q=\left\lceil\frac{P}{c}\right\rceil\)[191].  
To make an MC-like algorithm resilient to a soft fault causing an error in the computation, for example, due to a change in data value in memory, strategies such as the _\"duplicate checking\"_ or the _\"majority vote\"_[191] could be considered. Subtasks are repeated and executed on separate nodes in these algorithms. Comparing the outcomes of the same subtask carried out on other nodes might help identify inaccurate computational results (e.g., by ensuring that the results fall within a suitable confidence interval). When performing duplicate checking, an incorrect result must be found by doubling computations. It takes at least three times as much calculation to find an incorrect computation result in a majority vote.
## 2 Theoretical Analysis
### The Equation of Motion of Rod-Fastened Rotor (RFR)  
The RFR is made of shaft segments clamped by several tie rods that are evenly distributed in the circumferential direction. Therefore, the contact interface and the tie rods are the most distinctive features of the rod-fastened rotor. The presence of the contact interface gives the RFR structural discontinuity so that the flexural stiffness of the RFR is smaller than the flexural stiffness of the corresponding integral rotor. The presence of tie rods makes the rotor structure more complex. First, the preload supplied by the tie rods directly affects the contact state of the contact interface, which in turn affects the bending stiffness of the shaft segment. Then, the shaft segments and tie rods are in a parallel relationship in the structure. The preload causes the shaft segment to bear the compressing force, while the tie rods are subjected to the tension force, which will affect the transmission of the displacement and load of the rotor, thereby affecting the flexural stiffness of the rotor. Finally, in the process of rotor vibration, the coordinate deformation of the tie rods will have a certain effect on the flexural stiffness of the shaft segment. This section gives the vibration equation of the RFR considering all influencing factors by the lumped parameter method. The preload of the rotor shaft segment is the compressing force, and the direction is along the direction of the rotor deflection curve. \(M_{\! {}{}_{{\mathit{r}j}}}\), \(N_{\! {}{}_{{\mathit{r}j}}}\) are the bending moment applied to the rotor by the tie rods in the \(x\)-\(z\) plane and \(y\)-\(z\) plane because of the parallel structure relationship between the rotor and rod. \(S_{rj}\), \(Q_{rj}\) are the shear force applied to the rotor by the tie rods in the \(x\)-\(z\) plane and \(y\)-\(z\) plane. \(M_{0j}\), \(N_{0j}\) are the additional bending moments that resulted from the coordinate deformation of rods in the process of vibration. The other parameters of the rotor are similar to the parameters of the tie rods in meaning. From Figure 5, the balanced equation of force and bending moment of the shaft segment of the rotor in the \(x\)-\(z\) plane can be written as:  
\[S_{j}+T_{0}\varphi_{j-1}+S_{rj-1}=S_{j-1}+T_{0}\varphi_{j}+\sum P_{xj}+S_{rj} \tag{21}\]  
\[M_{j}=M_{j-1}-l_{j}S_{j-1}+T_{0}l_{j}\varphi_{j-1}-T_{0}(x_{j}-x_{j-1})-M_{0j- 1}+M_{0j}-M_{rj-1}+M_{rj}-M_{kj} \tag{22}\]  
In Equation (22), \(M_{0j}=M_{j}\cdot\frac{4E_{A}r^{2}}{EI}\) is the additional bending moment resulted from the coordinate deformation of the tie rods. Equation (22) can be simplified as:  
\[\mu M_{j}=\mu M_{j-1}-l_{j}S_{j-1}-M_{rj-1}+M_{rj}-M_{kj} \tag{23}\]  
In Equation (23), \(\mu=1-\frac{4E_{A}r^{2}}{EI}\). In fact, \(\mu\) is the correction factor considering the coordinate deformation of the tie rods. \(\mu\)= 1 means that the coordinate deformation of tie rods is not considered, while \(\mu\
eq 1\) means that it is taken into consideration.
## 5 Fatigue Life Assessment of the Key Fatigue Detail
The relationship between the fatigue life and the initial crack size is plotted in Figure 12. It can be illustrated that when the initial crack size increases from 0 to 1 mm, the fatigue life is greatly reduced; when the initial crack size is 1 mm, the fatigue life is 20.3% of that when the initial crack size is 0 mm. When the initial crack size increases from 1 mm to 3 mm, the fatigue life still shows a decreasing trend, but the rate of decrease slows down, and the fatigue life when the initial crack size is 2 mm is 12.7% of that when the initial crack size is 0 mm, and the fatigue life when the initial crack size is 3 mm is 11.5% of that when the initial crack size is 0 mm. The reason for this phenomenon is that when the initial crack is small, the stress-intensity factor range for the tip of the fatigue crack is also small, so the fatigue-crack growth rate is small, and fatigue crack propagation requires more loading times for growing the same distance; thus, even an insignificant initial crack size also greatly reduces the fatigue life of the structure. Therefore, corrosion pits can dramatically reduce the fatigue life of weathering-steel anchor boxes. Figure 11: Relationship between crack length \(a\) and fatigue life \(N\). The above fatigue-crack growth analysis process extends the established fatigue-life evaluation method of corroded weathering steel to a multi-scale refined finite-element model. However, the fatigue life of the particular fatigue detail of the weathering-steel anchor box calculated by numerical simulation is more than 100 billion times greater, far exceeding the concerns of engineering practice, which is normally in the magnitude of millions of times. It can be concluded that the life-assessment method of the corroded weathering-steel structure established in this paper has universal applicability and generalizability, and the weathering-steel anchor box of the research case has excellent fatigue resistance.
## 4 Constitutive Model and Finite Element Implementations
### Determination of Model Parameters
#### 4.3.2 Determination of Parameters \(\gamma\) and \(r\)  
To determine parameters \(\gamma\) and \(r\), the first step was to obtain the uniaxial tensile stress-strain curve of SAF2507 stainless steel. Figure 12 shows the stress-strain curve of the material at different rates. From the figure, it can be seen that the yield stress of SAF2507 stainless steel remained almost constant under three different loading rates, and the stress value corresponding to the same strain under different strain rates changed very little.  
Figure 11: Sketch map of simulating the parameter \(Q\) and \(b\).  
Parameters \(\gamma_{i}\) and \(r_{i}\) could be determined from a uniaxial tensile curve, and the effect of strain hardening needed to be subtracted when fitting the uniaxial tensile curve. As shown in Figure 13, the monotonic stress-strain curve was divided into several segments so that each segment was linearly connected to be able to simulate the nonlinear relationship of the tensile curve, and the corresponding parameters \(\gamma_{i}\) and \(r_{i}\), could be derived from the following equations:  
\[\gamma_{i}=\frac{1}{\varepsilon_{p}^{(i)}},r_{i}=\left(\frac{\sigma_{(i)}- \sigma_{(i-1)}}{\varepsilon_{p(i)}-\varepsilon_{p(i-1)}}-\frac{\sigma_{(i+1)}- \sigma_{(i)}}{\varepsilon_{p(i+1)}-\varepsilon_{p(i)}}\right)\varepsilon_{p(i )}(i\
eq 1) \tag{56}\]  
\[\sum_{i=1}^{M}r_{i}+\sigma_{0}=\sigma_{\text{max}} \tag{57}\]  
The parameters obtained by the above method were used to simulate the uniaxial tensile curve of SAF2507 and the results are shown in Figure 14.  
Figure 12: Uniaxial tensile curve.  
Figure 13: Determination of parameters \(\gamma_{i}\) and \(r_{i}\).  
4.3.3. Determination of Parameters \(\mu\) and \(m\)  
The parameters \(\mu\) and \(m\) were closely related to the ratcheting behavior of the material. These two parameters only affected the ratcheting behavior of the material. These two parameters were derived through the trial-and-error method based on the experimental data of uniaxial ratchetings. The relevant parameters of the model used in this study are presented in Table 4.
## 2 Theory of Rock-Breaking for a High-Pressure Water Jet
### Rock-Cutting Model for a High-Pressure Water Jet  
Cutting rock with a high-pressure water jet is a complicated process, and involves many factors, such as the nozzle diameter, jet pressure, translation speed, standoff distance, cutting times, rock properties, and so on. In recent decades, several classic rock-cutting models have been established and mainly include the [PERSON] cutting model, [PERSON] cutting model, and Hashish cutting model. However, various parameters, such as the rock particle diameter, jet action time, jet dynamic viscosity, rock permeability, and so on, are required in the Rehbinder cutting model [24; 25]. Moreover, in the Hashish cutting model, the compressive yield limit, hydrodynamic friction coefficient, and damping coefficient of cutting materials must be calculated or measured in advance [26]. Therefore, the [PERSON] and Hashish models can only be applied to laboratory experiments or theoretical analysis, but cannot be effectively used to analyze the water jet slotting in field applications. In contrast, the Crow model is much simpler to a certain extent, and the required parameters can be obtained more easily. Therefore, it is more suitable for high-pressure water jet slotting field applications.  
Based on numerous rock-cutting experiments, [PERSON] proposed the general law of hydraulic rock cutting. The cutting depth can be calculated as follows:  
\[h=\frac{J\cdot(p-p_{c})}{\tau_{0}}d_{0}F(v/c_{e}), \tag{4}\]  
where \(h\) is the cutting depth; \(J\) is the translation time; \(P\) and \(P_{c}\) are the jet pressure and critical rock-breaking pressure of the water jet, respectively; \(\tau_{0}\) is the shear strength of the rock; \(d_{0}\) is the nozzle diameter; \(v\) is the translation speed; and \(c_{e}\) is the theoretical translation speed. As can be seen, the key water jet cutting parameters are considered in the Crow model. However, for water jet slotting in a coal seam, the existing Crow model is not fully applicable, owing to the difference of rock properties and working conditions, and requires further modification.
## 2 Materials and Methods
### Proposed Approach for CORS Data Quality Assessment
#### 2.2.3 Carrier-Phase Measurement Noise  
In the theory of errors, carrier-phase measurement noise is random noise. Carrier-phase measurement noise is usually very small; it should be within one millimeter in a carrier-phase measurement in consideration of the receiver tracking hardware. Double-difference zero-baseline method is commonly used to determine the receiver noise level [27; 44]. In practice, the environments of CORS stations may introduce noise into GNSS measurements, such as radio/electrical/electronic interference-induced noise. External/environmental biases and errors (including multipath and noise) are canceled out in zero-baseline analyses. Therefore, zero-baseline methods cannot be used to quantify the CORS multipath errors and measurement noises. Low-elevation-angle satellite measurements have greater noise due to greater atmospheric noise. Therefore, we only used double-difference baseline measurements collected above a 40\({}^{\circ}\) satellite elevation angle in this noise analysis. An example of using the double-difference approach in noise analysis can be found in [45]. As in Section 2.2.2, CORS administration centers/organizations and readers may use RTKLib and MATLAB Wavelet toolbox to obtain the carrier-phase measurement noise. Instead of obtaining the carrier-phase multipath errors with the MATLAB DWT and IDWT functions, we obtained the filtered noise (carrier-phase measurement noise) this time. As the DD noise involves four measurements at two stations, PPP-estimated pseudorange noise at the two stations is used to \"scale\" the DD carrier-phase noise to form the single-differencing (SD) carrier-phase noise of each of the two stations at the baseline. This step can be carried out in spreadsheets or sample programming scripts. RTKLib's \"PPP Fixed\" solution (the station coordinates are known) can be used to estimate the PPP pseudorange noise; this is the method used in this work. CORS administration centers/organizations can, of course, code up the above algorithm into a piece of software with any computer programming language.
## 3 Methodology
### Fairness and Debiasing  
Addressing algorithmic fairness is not merely a technical consideration but an ethical imperative in educational AI. To rigorously assess and mitigate potential biases, particularly those linked to sensitive attributes like gender or socioeconomic background, our methodology incorporates two distinct yet complementary fairness metrics:  
* **Demographic parity (DP)** quantifies whether the positive prediction rate is similar across different protected groups: \[|P(\hat{Y}=1\mid A=0)-P(\hat{Y}=1\mid A=1)|\leq 0.05\] aiming for equitable access to positive outcomes regardless of group membership.
* **Equalized Odds (EO)** ensures that true positive rates (TPRs) and false positive rates (FPRs) are similar across groups: \[|TPR_{A=0}-TPR_{A=1}|+|FPR_{A=0}-FPR_{A=1}|\leq 0.10,\] thereby seeking to equalize opportunities for correct classification.  
To actively mitigate these identified biases, we employ adversarial debiasing, a sophisticated in-processing technique that simultaneously trains a predictor and an adversary. The predictor aims to accurately predict the target variable, while the adversary strives to predict the sensitive attribute from the predictor's latent representations. This creates a compelling adversarial game where the predictor learns representations that are simultaneously predictive and fair by making them uninformative to the adversary. The total loss function,  
\[\mathcal{L}_{\text{total}}=\mathcal{L}_{\text{pred}}(y,\hat{y})-\lambda\cdot \mathcal{L}_{\text{adv}}(A,\hat{A}),\quad\lambda=0.7,\]  
incorporates a critical hyperparameter that balances the trade-off between predictive accuracy and bias reduction. This robust framework is meticulously trained over 50 epochs using the Adam optimizer, aligning with state-of-the-art fairness-preserving methodologies, notably those established by [PERSON] et al. (2023) [PERSON] et al. (2023).
# A Novel Approach to Incremental Diffusion for Continuous Dataset Updates in Image Retrieval
## 7 Experiments
### Experimental Setup
#### 7.1.2 Features  
It is a common choice to use global or regional R-MAC descriptors proposed by [27] for image retrieval tasks. This time, we adopt features extracted from a pre-trainedResNet101 which is directly optimized for average precision by using APLoss [10]. These features are extracted from a generalized-mean pooling [28] layer and are PCA-whitened by convention [29]. For a fair comparison, we compare our proposed method with other methods using the same features.
# Development and Simulation of Motion Control System for Small Satellites Formation
## 8 Simulation Results
### Simulation for Motion of 8 Satellites in Two PCO Orbits  
The radii of PCO orbits of 1000 m and 2000 m were set for the simulations. Each orbit contains four satellites. The phase shift between the agents located in the first orbit is \(\pi/2\). The phase shift between small spacecraft located on the first orbit is as \(\pi/2\), the second four, relative to the first \(\pi/4\). The initial positions for 8 satellites are shown in Table 1.
The approach takes inspiration from food metabolomics [16; 17; 18; 19] principles and exploits the information potential of multi-dimensional analytical (MDA) platforms that combine techniques for physicochemical discrimination/separation (e.g., gas chromatography GC) of analytes with spectroscopic detection (e.g., mass spectrometry MS). MDA technologies enable comprehensive untargeted investigations and provide a larger number of features (i.e., aggregated information for pattern recognition) that, by suitable chemometrics, give access to higher-level information [20]. Characteristic and/or discriminant patterns of chemicals might inform on: (a) food sensory quality and spoilage [4; 21; 22; 23]; (b) the impacts of environmental conditions on plant phenotype [24; 25; 26; 27; 28] and metabolome expression [29; 30]; (c) the qualities and diagnostic roles of fat fractions [31; 32]; and (d) the impact of harvesting and storage practices [19; 33; 34]. To be truly useful, an \"untargeted\" approach should detect and monitor as many features as possible by annotation and tracking across multiple samples [35; 36]. It is conceptually in contrast to purely \"targeted\" strategies, that, _a priori_, define a limited number of known analytes and therefore provide limited information with regard to the total composition of a sample. Based on preliminary results obtained by exploring the distribution of the detectable metabolome in hazelnuts of different cultivars/origins [37], this study makes a step forward in examining the impact of roasting, as an additional yet fundamental variable/process on the chemical signature of primary metabolites in hazelnut kernels. Quali-quantitative variations on known and unknown features/analytes are tracked using chromatographic fingerprinting based on peak features and template matching algorithms. In particular, the approach known as combined untargeted and targeted (UT) fingerprinting [37; 38; 39; 23] was applied to raw hazelnuts from the Tonda Gentile Trilobata cultivar (Protected Geographical Indication PGI) harvested in Piedmont (Italy) or Georgia, and from the Anakliuri cultivar, native to Georgia. Raw hazelnuts were submitted to lab-scale dry-roasting in a ventilated oven under time/temperature conditions tuned to develop the desired flavor and crunchy texture. A more intense roasting was also applied to exacerbate thermal stress and capture metabolites variations under even more drastic conditions.