esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Feature Learning with Manifold Embedding for Robust Image Retrieval
## 6 Experiments
### Evaluation and Comparison
#### 6.3.4 Comparison with the State-of-the-Art  
We compare our experimental results with recent state-of-the-art image retrieval methods based on compact CNN representations. As shown in Table 5, our approach achieves superior retrieval accuracy on UKBench and comparable results on Holidays.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Network** & **Baseline** & **Fine-Tuned** \\ \hline \multirow{2}{*}{UKBench} & VGGNet-16 & 3.77 & 3.88 \\  & ResNet-50 & 3.90 & 3.96 \\ \multirow{2}{*}{Holidays} & VGGNet-16 & 0.802 & 0.821 \\  & ResNet-50 & 0.855 & 0.869 \\ \multirow{2}{*}{INSTRE} & VGGNet-16 & 0.316 & 0.529 \\  & ResNet-50 & 0.472 & 0.564 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison between baseline and fine-tuned model with different backbones. Retrieval accuracy is measured by the N-S Score for the UKBench dataset and mAP for the Holidays dataset and the INSTRE dataset.  
My approach achieves a remarkable retrieval accuracy of 3.88 with VGGNet-16 and 3.96 with ResNet-50. We also outperforms some state-of-the-art feature fusion schemes, which fuse multiple image features, including hand-crafted SIFT features and CNN features. For example, our approach exceeds Collaborative Index Embedding (CIE) [55] of N-S Score 3.86 and Query-Adaptive Late Fusion (QaLF) [56] of N-S Score 3.84. It is worth noticing that image features extracted by our fine-tuned model can be used as one of the features participating in feature fusion by CIE or QaLF, which can further boost retrieval accuracy. We also outperform RDP by 0.03 N-S Score on UKBench (3.96 vs. 3.93).  
For Holidays, our approach outperforms SPoC [20] based on an off-the-shelf pre-trained model while it is comparable to other methods using a fine-tuned model. The reason lies in the intrinsic dataset property, which has been depicted in Section 6.3.3. However, for the other methods adopting fine-tuned models, they either need another labeled training dataset or need to mine training examples from an additional dataset with plenty of images related to the target dataset. To be specific, in [35], the CNN model was fine-tuned with the Pittsburgh Dataset [57] of 250K images on street view. In [19], a labeled dataset with 213,678 images of 672-category landmark buildings was adopted as the training set. In [22; 23], a 713-category landmark dataset with 163,671 images and cleaned 586-category landmark dataset with 49,000 images were adopted to help the training process. As discussed previously in Section 1, these methods suffer from a time-consuming dataset collecting process and labor-expensive image labeling. It is notable that the best-performed method, [PERSON] et al. [22] in Table 5, adopted a different baseline of mAP 0.858 and achieved an mAP of 0.864, where the performance gain of 0.6% is marginal. My approach achieves a performance gain of 1.4% from 0.855 to 0.869 with a similar baseline based on ResNet-50.
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 1 Introduction
RUS can help simplify the algorithm complexity effectively but suffers from a loss of information, while in RUSBoost, with the combination of boosting, the disadvantage of losing information caused by RUS can be well settled. Compared with another commonly used imbalanced data classifier, synthetic minority oversampling technique boosted (SMOTEBoost), RUSBoost saves a great deal of time in training, mostly has better performance, and can better avoid the problem of overfitting. In view of the shortcomings of the existing research and the challenge of fusing different sources of data, in this paper, we propose a new algal bloom detection method that combines spaceborne GNSS-R data with meteorological factors via the ML method.After necessary preprocessing steps, we screened 2913 GNSS-R points, much more than previous studies with only hundreds of points. Not only was the data amount increased, but also meteorological factors with 10-day time memory before the observation date were added, along with introducing the RUSBoost algorithm for dealing with the imbalanced multi-source data for the first time in the field of spaceborne GNSS-R algal bloom detection. In this way, the interference of wind speed to GNSS-R data was mitigated, and the factors related to the growth mechanism of algal bloom were also considered. Relative to the previous studies, the detection accuracy is progressed significantly in this research. This paper is outlined as follows: Section 2 introduces the research area, datasets of the observed GNSS-R data, auxiliary data of the reanalysis meteorological data and reference optical remote sensing data. In Section 3, the GNSS-R observables and RUSBoost algorithm are described in detail. Section 4 shows the detection results and provides discussion. Section 5 is the conclusion of this research.
## 5 Intelligent Fault Diagnosis of Sensor Faults
### Model-Based Fault Diagnosis Methods  
The model-based fault diagnosis method was proposed by [PERSON] of the Massachusetts Institute of Technology in 1971 [16] and refers to using knowledge of the system's structure, behavior, and function to diagnose the fault of the system [84]. Based on the model, fault diagnosis methods are divided into three types: state estimation, equivalent space, and parameter estimation [85]. The approach consists of two main steps: residual error generation and residual error evaluation. This diagnosis method requires understanding the mechanism of the research object and establishing a mathematical model, using the system's structure, behavior, or function to simulate the model, and then making a decision based on the difference between the actual measured signal and the model measured signal. By generating residual signals to achieve fault diagnosis [86], the occurrence of a fault can be detected by evaluating whether the resulting residual exceeds a threshold. [PERSON] et al. [87] designed a model-based distributed fault diagnosis framework for detecting and isolating multi-sensor faults in heating, ventilation, and air conditioning systems. The adaptive threshold framework ensures the proposed method's robustness against modeling uncertainties and measurement noise. [PERSON] et al. [88] proved that a Principal Component Analysis (PCA)-based sensor fault detection method is less efficient in fault detection under minor deviation fault conditions. [PERSON] et al. [89] proposed a model-based fault diagnosis scheme that uses open-circuit voltage residuals and capacity residuals to deduce fault values of voltage and current sensors for battery fault diagnosis. [PERSON] et al. [90] proposed a Cp_PLV model combining an unsupervised learning method with change point recognition. Experiments show that the predictive effect of the model is good and can fault diagnose the sensor in complex working environments.  
Due to the fact that the model-based fault diagnosis method employs fundamental knowledge for fault diagnosis, it has improved fault interpretability. It can also meet the real-time requirements of agricultural sensor fault diagnosis. In practice, however, the model is uncertain, and it is difficult to establish a model that corresponds to other agricultural sensors. It is challenging to ensure fault diagnosis accuracy with an unmatched model, which cannot detect the fault value and is not universal.
## 5 Discussion
### The Formation Mechanism of the Muyubao Landslide  
Based on the analysis of geological conditions and monitoring data (Figure 8), the deformation of the Muyubao landslide showed a large spatial difference. Large deformation mainly occurred in the middle and upper portions, while the deformation of the uplift at the front part was smaller (Figures 7 and 10). The profile with the largest deformation is shown in Figure 15. It can be seen that the deformation of the entire section is relatively small in the period of July-September 2016. It became larger in the period of January-March 2017. At the front edge of the profile near the Yangtze River, the Muyubao landslide deformed slightly in both periods. A field investigation was conducted for further analysis of the landslide deformation mechanism.  
By analyzing the results of the field investigation, it can be seen that the deformation and failure mode is controlled by the lithology condition and the structure of the slope. Under the long-term gravitational process, the slope was creeping down along the weak mudstone layer. The front stress accumulated gradually, which resulted in the bending and uplifting in the middle and front stratum of the slope. Once the potential slip surface of the bending part connects, the accumulated energy may suddenly release, which may cause collapse and high-speed sliding. In conclusion, the evolution process can be divided into four stages: slipping stage, bending and uplifting stage, local shear stage and completely connecting stage of the shearing surface (Figure 16a) [19].  
The bending and uplifting in the front stratum limited the deformation of the front part, which occurred when the slope was creeping down along the lines of weakness. Due to the topography and geomorphology conditions, the uplift in the front increases the sliding resistance effect of the landslide. When rainfall occurs, the gullies distributed on the surface of the landslide provides favorable channels for the rapid discharge of surface water, thus reducing the threat of water seepage to the landslide stability. Meanwhile, in the front part, the sliding surface is of a gentle slope, and the rock mass is bent and dips inside, which forms a sliding resistance for the landslide as well. In short, under natural conditions, the geological characteristics are beneficial to the overall stability of the landslide. However, due to the combined influence of factors in the TGRA, the Muyubao landslide has been undergoing a continuous creep deformation.  
Figure 14: Results comparison of GPS-02 and PSI-07.  
Figure 16: (**a**) Conceptual model of the landslide evolution process (revised from [PERSON] et al. [19]), and (**b**) a photo of the front part of the Muyubao landslide.  
Figure 15: Spatiotemporal characteristics of the deformation of the Muyubao landslide: (**a**) the velocity map of Muyubao landslide, and (**b**) the velocity of geological cross-section I-I\({}^{\prime}\).
## 2 Preliminaries
### Critical Definitions  
**Definition 1**.: _For a given vector \(q=[q_{1},q_{2},\cdots q_{n}]^{T}\in R^{n}\), \(\left\|q\right\|\) is any vector norm. For any matrix \(W\!=\left[w_{ij}\right]_{m\times l}\in R^{m\times l}\), \(\left\|W\right\|\) is the matrix norm induced by the vector norm. In particular, \(\left\|W\right\|_{\infty}=\underset{1\leq i\leq m}{\max}\sum\limits_{j=1}^{l} \left|w_{ij}\right|\), \(\left\|W\right\|_{1}=\underset{1\leq j\leq l}{\max}\sum\limits_{i=1}^{m}\left| w_{ij}\right|\), and \(\left\|W\right\|_{2}=\sqrt{\rho\left(W^{T}W\right)}\), where \(\rho\left(\cdot\right)\) is the spectral radius. For all matrices \(W\in R^{m\times l}\), \(Z\in R^{l\times n}\), and \(F=WZ\in R^{m\times n}\), the following property is satisfied._  
\[\left\|WZ\right\|_{m\times n}\leq\left\|W\right\|_{m\times l}\left\|Z\right\| _{l\times n}. \tag{1}\]  
**Definition 2**.: _[_21_]_ _If a function \(f\left(x\right):R^{n}\to R^{n},\exists D\subset R^{n},L>0,0<\alpha\leq 1,s.t.\)_  
\[\left\|f\left(x_{1}\right)-f\left(x_{2}\right)\right\|\leq L\left\|x_{1}-x_{2} \right\|^{\alpha},\]  
_where \(\forall x_{1},x_{2}\in D\), then \(f\left(x\right)\) is Holder continuous in region \(D\)._
# Optimized Water Distillation Layout for Detritiation Purpose
## 3 Process Simulations
### Thermal Vapor Recompression
For the case of 20 kPa at the ejector outlet, the condition of minimum motive steam flow rate is achieved with the \(A_{2}A_{t}\) equal to 100, whereas the minimization of the power consumption is obtained with an ejector area ratio of 50. As for the case of 30 kPa, the main concerns are related to the pressure and temperature at the compressor outlet, although less stringent. It is worth noting that especially for the 20 kPa case, but also for the 30 kPa for low values of \(A_{2}A_{t}\), the power consumption remains almost constant, changing the ejector area ratio; thus, space for further optimization is available for other components (e.g., reboiler and compressor). Figure 2:2 CPS process simulator: scheme of the TVR-CMP and main results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(A_{2}A_{t}\) & \(w_{a}\) & \(w_{b}\) & \(T_{a}\) & \(T_{3}\) & \(p_{a}\) & **Condenser** & **Compressor** & **Required Heat Transfer** \\  & (kg h\({}^{-1}\)) & (kg h\({}^{-1}\)) & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & **(kPa)** & **Power** & **Area of Reboiler** \\  & & & & & & **(kW)** & **(kW)** & **(m\({}^{2}\))** \\ \hline
100 & 729.19 & 1549.71 & 963.75 & 368.82 & 1532 & 398.3 & 398.57 & 8.79 \\
50 & 672.99 & 1605.91 & 730.87 & 263.47 & 596 & 264.24 & 264.52 & 10.48 \\
25 & 775.66 & 1503.24 & 585.02 & 239.33 & 307 & 234.13 & 234.41 & 11.4 \\
15 & 902.19 & 1376.72 & 504.98 & 235.16 & 207 & 228.95 & 229.23 & 11.6 \\
10 & 1042.54 & 1236.36 & 446.86 & 235.18 & 153 & 228.98 & 229.26 & 11.6 \\
5 & 1308.18 & 970.7 & 348.08 & 222.51 & 88 & 213.29 & 213.57 & 12.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: TVR-CMP CPS: parametric analysis over the ejector area ratio for the outlet pressure of 30 kPa. Among the two ejector outlet pressures considered in this study, the case of 20 kPa ensures the power consumption minimization while keeping acceptable reboiler dimensions. Furthermore, an ejector characterized by a \(A_{2}A_{t}\) equal to 5 guarantees less stringent thermodynamic conditions at the compressor outlet. Thus, this was considered the reference case for the TVR-CMP concept, and the main results of the process simulation are reported in Figure 5. Compared to the reference MVR, the TVR-CMP allows a compressor flow rate around 54% lower.
# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 2 Study Area and Data  
### Study Area  
The study area (Figure 1) is located within latitudes 38\({}^{\circ}\)10\({}^{\prime}\) N and 38\({}^{\circ}\)45\({}^{\prime}\) N and longitudes 98\({}^{\circ}\)45\({}^{\prime}\) E and 99\({}^{\circ}\)30\({}^{\prime}\) E, in the northern Qinghai Province of western China, which lies in the Chi-lien Mountains of the northern of Qinghai-Tibet Plateau. The region has a plateau continental climate and an approximate mean elevation of 4200 m, resulting in abundant snow and ice cover for most of the year. Vegetation in this area is mainly low grass. The summer season in the area occurs from June to August with minimal snow and ice cover. Despite the increasing vegetation cover associated with summer conditions, geological materials are best exposed during this time frame [11; 49]. Therefore, this is the best period for remote sensing observations.  
Based on fieldwork and remote sensing images, the study area was selected at locations where rocks were relatively well exposed, as shown in the compiled lithological map (Figure 2a), however, vegetation was still spotted, as visible in the false color composition (Figure 2b). The main rock types in this area include gabbro, serpentinite, granite, and quartzose rock, which are well exposed and are less covered by vegetation. In this area, gabbro, serpentinite, and granite are all intrusive rocks. Gabbro and serpentinite rocks are both the major components of mafic-ultramafic rocks, and are seldom distributed in lower volcanic groups of Ordovician age. Granitoids are the representative granite rocks, which are related to rare earth metals, the origin of nonferrous metals, and radioactive element ore deposits [11]. Sandstone is the major type of quartzose rock in this area and is mainly distributed in the lower Ordovician sandstone-slate group. Carbonate rocks are not widely exposed in the study area.  
Figure 1: Location of study area.
## 3 The Study Area and Spatial Datasets
### Establishment of GIS Database, the Training Dataset and the Validation Dataset  
In the first step, a GIS database for this project was designed and established using ArcCatalog software. Accordingly, the File Geodatabase format was used due to the ability to host and process very large geographic datasets with their different data types in a only one file system [76]. Accordingly, the GIS database consists of 255 landslide polygons and eight predisposing factors (slope, aspect, elevation, land cover, soil type, lithology, distance to fault, and distance to river). These landslide polygons and factors were converted to raster format with a resolution of 25 m. In this research, the categories of the eight predisposing factors were coded and normalized, as suggested in [24; 77], to avoid the imbalance of categorical magnitudes [78].  
In landslide modeling, cross validation [79] that has proven efficient for evaluating the model performance should be used. Accordingly, in this research, 179 landslide polygons (70%, 1006 pixels) were randomly extracted [80] and used for training the landslide models, whereas the other 76 landslides (30%, 441 pixels) were used for assessing the prediction capability of the models. Because the proposed approach in this study employs \"on-off\" classification, the equal amount of non-landslide pixels were also randomly sampled in the not-yet landslide areas of the basin, area with slope angles less than 5\({}^{\circ}\), as suggested in [32]. Detailed discussions on sampling strategies can be found at [81]. In the next step, values of the eight predisposing factors for all of the aforementioned pixels were extracted to build the training dataset and the validation dataset. Finally, the coding process that was proposed in [17] was performed, in which the landslide pixels were assigned \"1\" and the non-landslide pixels were assigned \"0\".  
Because the aforementioned partition of the landslide dataset into the training and validation datasets was randomly generated only once; therefore, a further cross validation was additionally used to ensure that the modeling result is the objective. Accordingly, 10-fold cross validation was employed in the training phase with the training dataset to build landslide models. Thus, the training dataset was randomly partitioned into 10 equally sized subsets; nine subsets were used for building the landslide model, whereas the remaining subset was used for testing the landslide model. This procedure was repeated 10 times where each subset was being used once as the testing dataset. Once the model was successfully trained using the training dataset with the 10-fold cross validation procedure, the model was again validated using the validation dataset.
## 2 Structure and Operation Principle of the Proposed Converter
### Operation of the Proposed Converter  
The proposed converter can operate in two different modes: the first one is referred to as DCM. This mode occurs when the converter operates at a low duty ratio and light load during the night in energy-saving mode. Additionally, the DCM mode is activated when the converter operates at the minimum input voltage in light load applications. Moreover, the proposed DC-DC converter can switch to continuous conduction mode (CCM) when the load current increases during the day.
## 2 Mathematical Formulation
### Objectives Formulation
#### 2.3.2 Holding Cost  
The calculation of the holding costs required the holding costs per inventory unit for each storage location (\(H_{k}\)) and the conclusion from Theorem 4 showing that the holding costs are proportional to storage level. This calculation was performed as shown in expression (9). Note that the cost per unit of material in each storage location does not necessarily have to have a monetary value. If, e.g., storage location 1 has a cost per unit of 1 and storage location 2 has a cost of 0.9, this means that storage location 2 has 90% of the cost of storage location 1. Since this optimization was conducted in a many-objective way, the different objective values did not have to be comparable in absolute terms. For a correct analysis, the total stock in each storage location, \(S_{k}\), was divided by the limit of each storage location, to convert the value into a percentage of fill of each storage location.  
\[C_{Hold}=\sum_{k=1}^{n_{k}}\left(\frac{S_{k}\cdot H_{k}}{Lim_{k}}\right)\quad[ CU] \tag{9}\]  
Figure 1: Example of energy consumption evolution with the stock.  
The total stock in each storage location was calculated as shown in expression (10)  
\[S_{k}=\sum_{i=1}^{n_{i}}\left(X_{i}\cdot A_{ik}\right)\quad[IU] \tag{10}\]  
While it may seem that the holding costs and the energy consumption objectives may be similar and evolve in a similar way, it does not necessarily take place that way, e.g., a tank without substantial energy consumption per inventory unit of stock may have a very high holding cost, for business-related reasons, such as concurrency with other raw materials.
## 3 Results and Discussion  
### Influence of Phthalic Anhydride Solution Concentration on the Surface Properties of PPTA Fiber  
#### 3.1.1 Study on Microstructures of PPTA Fiber Surface before and after Modification  
PPTA fiber was modified with different concentrations of phthalic anhydride, with modification conditions as shown in Table 3. The surface morphology of aramid fiber before and after phthalic anhydride modification was observed using a Phenom Pro desktop scanning electron microscope (SEM), as shown in Figure 3. In Figure 3a, it can be observed that the surface of the unmodified PPTA fiber appears smooth. In Figure 3b, after modification with a solution of phthalic anhydride at a concentration of 0.15 mol/L (PPTA-0.15), the surface of the PPTA fiber shows a few protrusions and small grooves, indicating an increase in surface roughness. In Figure 3c, after modification with a solution of phthalic anhydride at a concentration of 0.30 mol/L (PPTA-0.30), there are more protruding structures, longer grooves, and stripes on the fiber surface. The protruding structures provide physical anchoring effects, while the grooves and stripes can increase the surface roughness and specific surface area of the PPTA-0.30 fiber. This enhances the degree of infiltration of the DGEBA matrix into the PPTA-0.30 fiber, strengthening the mechanical bonding at the interface of the composite material. In Figure 3d, after modification with 0.45 mol/L phthalic anhydride solution (PPTA-0.45), fiber delamination becomes more pronounced, indicating fiber fibrillation. This is attributed to the excessively high concentration of phthalic anhydride, which intensifies the degree of surface modification, leading to damage and detachment of the fiber cortex, and the detachment of the fiber cortex results in a decrease in the strength of the fiber itself.