esa-sceva/satcom-chunk-collection huggingface dataset:
# Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model
### Transfer Learning  
Transfer learning is to apply the prior knowledge obtained in primary fields to other target fields. [PERSON] [10] published a survey paper on transfer learning in 2010, summarized the history of the existing transfer learning, and defined the classification of different transfer learning methods. In fact, transfer learning can not only be used in machine learning but also has outstanding performance in data mining and other fields. [PERSON], [PERSON] et al. summarized the application of transfer learning in data-mining-related tasks such as classification, regression and clustering, and further standardized the definition of terms related to transfer learning in different fields.  
Generally, the network comprising feature extraction layers and classifier layers has been trained in the primary field. Then the feature extraction part is reused and fixed in the target field training where only the classifier will be trained based on the samples in target fields.  
In this way, it can solve the problem of insufficient sample size in the target field. It has been proved to be one of the effective methods to deal with small sample learning. It has been widely used in natural language processing [11; 12; 13], computer vision field [14; 15].  
The biggest advantage of transfer learning is that it can be further trained based on existing high-performance models to adapt to different situations. This usage is widely used in the field of natural language processing. BERT [16] is one of the most advanced natural language processing models. Based on it, researchers have produced a large number of research results [17; 18; 19] using transfer learning. Based on the original Bert model, they retrained the model with data sets in different target fields. In the case of retaining part of the original network structure, some parameters are added or deleted to optimize the effect in the corresponding target field.  
Based on this feature, transfer learning can solve the problem of small sample learning, especially fault-related problems. Transfer learning can extract features from massive source data and apply them to similar but smaller fault data. [PERSON] et al. [20] use the CNNnetwork based on transfer learning to solve the problem of the small amount of mechanical fault data in industrial applications, and uses motor fault data to verify the accuracy of its fault analysis model.  
This paper takes Shanghai Metro as an example and uses the transfer learning method based on LSTM to establish a prediction model for metro transportation emergency passenger flow. The problem of small samples in the training process is solved by using transfer learning. The results show that the model has good accuracy under normal and emergency conditions.  
The rest of this paper is organized as follows. Section 2 introduces the relevant knowledge and work; Section 3 introduces the data set and the method used in this paper; Section 4 shows the experimental results. Finally, Section 5 summarizes and discusses the future research.
## 4 Discussion
### Data Acquisition  
* Both the analyzed case studies confirmed the need to acquire satellite images over several seasons. In such circumstances, it is of the utmost importance that the PCARA begins at least two years before the final decision to commence the dam construction. Assuming that this timeframe is allowed, the first year would be dedicated to satellite image acquisition and archive building. During the first part of the second year, image processing and then remote sensing analysis would be carried out in order to provide preliminary mapping of potential sites. When permitted and feasible, ground-truthing activities would take place during the second half of the second year.
* In the case of tight deadlines or if the construction operations unexpectedly or suddenly accelerated, change detection analyses could not be performed, unless archive images were already available. The study of both Halabiyeh and GERD dams showed that most of the potential archaeological sites are generally identified by manual remote sensing using satellite imagery taken during humid periods. This evidence is confirmed by the analysis of both the Sentinel-2 false-colored infrared and COSMOSkyMed StripMap imagery. Therefore, in cases of limited time or tight deadlines, it is possible to reduce acquisitions to humid seasons.
* In the case of GERD, Google Earth imagery was only partially helpful and could not support the assessment of damage to local heritage as it was possible to achieve with Sentinel-2 and COSMO-SkyMed. The available images did not record some key phases of the dam construction. Therefore, it was not possible to comprehensively reconstruct the recent history of the dam prior to its filling process without incurring in high uncertainty (see Figure 5). At the time when the satellite-based assessment was done and the present paper was completed, the Google Earth time-lapse did not include any satellite mosaic covering the whole study area at the same date of acquisition. Even the most recent satellite images collected at the end of 2020 were spatially limited to the area of the Main Dam and the Saddle Dam (see examples in Supplementary Materials, Figures S2-S4). This lack of imagery prevented a full assessment of the progressive flooding of villages and sites through PCARA to PFAP protocols.
# Channel Estimation for UAV Communication Systems Using Deep Neural Networks
## 2 System Model and Problem Formulation
### Problem Formulation
\[W_{ij} \stackrel{{\leftarrow}}{{=}} W_{ij}+\eta\left(\left\langle\frac{1}{\sigma_{i}^{2}}v_{i}h_{j}\right\rangle_{d}- \left\langle\frac{1}{\sigma_{i}^{2}}v_{i}h_{j}\right\rangle_{m}\right), \tag{8a}\] \[b_{i} \stackrel{{\leftarrow}}{{=}} b_{i}+\eta\left(\left\langle\frac{1}{\sigma_{i}^{2}}v_{i}\right\rangle_{d}- \left\langle\frac{1}{\sigma_{i}^{2}}v_{i}\right\rangle_{m}\right),\] (8b) \[P(v,h)=\frac{1}{Z}e^{-E(v,h)}, \tag{2}\]0  
where \(\eta\) denotes the learning rate. The GBRBM is updated and trained efficiently by updating (8a)-(8c). The RBM has been used as a universal approximator. Nevertheless, the number of hidden nodes is always limited, which leads to the inability to model some discrete-valued probability distribution [19]. In practice, it has been proved that not all the hidden nodes are active, and some are purposeless, where the weight associated with \(m\) cannot be incorporated into the bias term. We introduce the adaptive learning rate and a new enhanced gradient in the next part.
###### Abstract  
The dysfunction of the tumor suppressor gene TP53 has been associated with the pathogenesis of the majority of the cases of cancer reported to date, leading the cell to acquire different features known as the cancer hallmarks. In normal situations, the protein p53 protects the cells against tumorigenesis. By detecting metabolic stress or DNA damage in response to stress, p53 can lead the cell to senescence, autophagy, cell cycle arrest, DNA repair, and apoptosis. Thus, in the case of p53 mutations, it is reasonable to assume that the reestablishment of its function, may restrain the proliferation of cancer cells. The concept of cancer gene therapy can be based on this assumption, and suitable biotechnological approaches must be explored to assure the preparation of gene-based biopharmaceuulicals. Although numerous procedures have already been established to purify supercoiled plasmid DNA (sc pDNA), the therapeutic application is highly dependent on the biopharmaceutical's activity, which can be affected by the chromatographic conditions used. Thus, the present work aims at comparing quality and in vitro activity of the supercoiled (sc) isoform of the p53 encoding plasmid purified by three different amino acids-based chromatographic strategies, involving histidine-agarose, arginine-macroporous, and histidine-monolith supports. The B-DNA topology was maintained in all purified pDNA samples, but their bioactivity, related to the induction of protein p53 expression and apoptosis in cancer cells, was higher with arginine-macroporous support, followed by histidine-monolith and histidine-agarose. Despite the purity degree of 92% and recovery yield of 43% obtained with arginine-macroporous, the sc pDNA sample led to a higher expression level of the therapeutic p53 protein (58%) and, consequently, induced a slightly higher apoptotic effect (27%) compared with sc pDNA samples obtained with histidine-monolithic support (26%) and histidine-agarose support (24%). This behavior can be related to the mild chromatographic conditions used with arginine-macroporous support, which includes the use of low salt concentrations, at neutral pH and lower temperatures, when compared to the high ionic strength of ammonium sulfate and acidic pH used with histidine-based supports. These results can contribute to field of biopharmaceutical preparation, emphasizing the need to control several experimental conditions while adapting and selecting the methodologies that enable the use of milder conditions as this can have a significant impact on pDNA stability and biological activity.  
affinity chromatography; chromatographic conditions; p53 encoding plasmid; supercoiled pDNA bioactivity; gene-based cancer therapy
## 3 Materials and Methods
### Simulations
#### 3.1.2 CoMTUS Imaging Depth  
The dependence of CoMTUS performance on imaging depth was investigated by changing the local orientation of the arrays and using the same control propagation medium (only soft tissue). For a given effective aperture (fixed gap \(\sim\)45.3 mm), each probe was rotated around its centre by the same angle, but in the opposite direction. In that way, a certain given rotation, for example negative in T1 and positive in T2, will result in a deeper common FOV, and the opposite for the counter rotation. Figure 4 shows the imaging depth dependence on the transducer orientation (defined by the position of the common FOV of both arrays). Using this scheme, four different imaging depths were simulated: 57.5 mm, 75 mm, 108 mm and 132 mm.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Tissue** & **SOS** & **Density** & **Attenuation** & **Nonlinearity** \\
**Type** & **(m/s)** & **(kg/m3)** & **(dB/MHz/cm)** & **B/A** \\ \hline Soft tissue & 1540 & 1000 & 0.75 & 6 \\ Fat & 1478 & 950 & 0.63 & 10 \\ Muscle & 1547 & 1050 & 0.15 & 7.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tissue map properties.  
Figure 3: (**a**) Schematic representation of the spatial location of the two linear arrays, T1 (red) and T2 (blue), three point-like targets and the anechoic lesion in the local coordinate system of T1. Different geometrical parameters are shown: angle between probes (\(\theta\)) and the gap (\(Gap\)) in the resulting effective aperture (\(Ef\)). (**b**) Associations between \(\theta\), \(Gap\) and the resulting \(Ef\).
## 3 Research on Alkali-Activated Grout Materials
### Study on UCS and Fluidity of Alkali-Activated Composite Grouting Material  
#### 3.2.1 Alkali-Activated Composite Grouting Material UCS and Flowability Test Plan  
This study delves into the impact of various parameters on the strength and fluidity of grouting materials, with a focus on the alkali-activated composite solidification characteristics of in situ soil in the Shenzhen area. It systematically examines the influence of three critical variables: the soil-to-binder ratio, slaked lime content, and water-to-solid ratio on the UCS and fluidity of alkali-activated grout materials. To reveal the interactions between the factors, a three-factor, four-level orthogonal test protocol is designed (as shown in Table 6). Three representative specimens from each set of test samples were selected for UCS tests after a standard 28-day curing age. The test results will aid in determining the optimal proportioning and mechanical properties of alkali-activated grout materials.
## 4 Data Quality Checks and Experimental Corrections
### Correction for Microphone Directivity and Frequency Response  
The experimental data were then corrected to account for the characteristics of microphones, such as microphone directivity (for the actual incident angle determined by \(\theta_{t}\)) and frequency response. The characteristics are provided by the manufacturer.
## 4 Results
### Fan-Delta Front
#### 4.1.2 Braided-River-Delta Depositional System
(**a**) Grayish-brown fine conglomerate with red gravels floating randomly within the matrix, exhibiting massive bedding (Well SM1, 5377.5 m, T3h); (**b**) Yellowish-gray medium sandstone displaying trough cross-bedding, featuring a sourcing surface in the upper core section (Well ST1, 6799.5 m, T3h); (**c**) Yellowish-gray pebbly coarse sandstone with calcareous cementation and massive bedding (Well ST1, 6673 m, T3h); (**d**) Dark gray carbonaceous mudstone showing specular luster on fracture surfaces, containing coal streaks (Well ST1, 6674.8 m, T3h); (**e**) Dark gray carbonaceous siltstone containing carbonized plant rhizomes marked by yellow dashed lines (Well ST1, 6673.2 m, T3h); (**f**) Gray fine sandstone exhibiting calcite-filled veins that effervesced upon hydrochloric acid application (Well ST1, 6799.1 m, T3h); (**g**) Gray siltstone with wavy bedding containing flaser-like carbonaceous fragments (Well ST1, 6673 m, T3h); (**h**) Yellowish-gray fine sandstone displaying horizontal bedding (Well ST1, 6798.1 m, T3h). Braided-River-Delta Front  
The braided delta front deposits are predominantly characterized by gray-colored sand-conglomerates with relatively limited depositional scale and finer grain sizes compared to proximal delta plain facies, primarily consisting of sandstones and pebbly sandstones frequently interbedded with black to grayish-black mudstones, while coal seams are rarely observed. This subfacies domain comprises two principal microfacies associations: subaqueous distributary channels and interdistributary bay deposits [60]. Subaqueous distributary channels form the dominant depositional elements within the braided delta front, predominantly composed of medium- to fine-grained sandstones with subordinate siltstones, coarse sandstones, and pebbly sandstones. Vertical lithofacies successions display a fining-upward trend from coarse basal units to finer upper intervals. Wireline log responses typically exhibit blocky, bell-shaped, or composite patterns reflecting these grain size transitions (Figure 7). The channel fills are further characterized by frequent lateral migration and vertical stacking under highly dynamic hydrodynamic conditions, evidenced by multiple internal erosional surfaces and channel-on-channel incision geometries. Interdistributary bay deposits, in contrast, consist of thinly laminated mudstones and siltstones with rare wave ripples or bioturbation features, representing low-energy suspension settling between active channels (Figure 6g). This architectural complexity underscores the interplay between high-energy fluvial sediment delivery and wave-modification processes within the delta front environment, where recurrent channel avulsion and reworking processes govern the spatial heterogeneity of reservoir-quality sand bodies. Figure 7: Typical logging facies from the Triassic unit in the Wushi Region.
## 1 Introduction
[16] proposed a range split spectrum interferometry assisted phase unwrapping (R-SSIaPU) method to monitor large gradient mining-induced surface movements, this method agreed with GPS with differences of approximately 4.2 cm in the Tangjiahui mine. Due to numerous parameters, an extremely complex velocity and acceleration prediction function, and an unreasonable parameter calculation methodology, most of the prediction models are not only different from the actual values but also not conducive to field application. Hence, a dynamic surface subsidence prediction model with high accuracy and few parameters is necessary. In this paper, a new model, based on the Knothe time function model, to predict the dynamic surface subsidence is proposed, and a simple, feasible parameter determination method is provided. Finally, the model accuracy is verified by comparing to the 1301 working face monitoring data in the Guotun coal mine in Shandong Province, China.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 3 Material and Methods
### Data Preparation
#### 3.3.2 Feature Extraction  
We calculate spectral indices after the gapfilling step for each element of the pixel series. Spectral indices are commonly used in addition of spectral bands as the input of the supervised classification system in the remote sensing literature [3]. They can help the classifier to handle some non-linear relationships between the spectral bands [4]  
More specifically, we compute three commonly used indices: the Normalized Difference Vegetation Index (NDVI) [65], the Normalized Difference Water Index (NDWI) [66] and a brilliance index (IB) defined as the norm of all the available bands [63].  
In the experiments, as we want to quantify the contribution of the spectral features on the different algorithms, we use a set of three different configurations: 1) NDVI only, 2) the spectral bands only (SB), and 3) the spectral bands and all indices (SB + NDVI + NDWI + IB). Note that we also decided to analyse NDVI separately, as it is the most common index for vegetation mapping. Table 2 summarizes the total number of variables for our SITS as a function of the temporal strategy and the spectral features that we use.
# Study on the Elimination Method of Wind Field Influence in Retrieving a Sea Surface Current Field
## 3 Situation of Data Acquisition  
The GaoFen-3 satellite sea current measurement experiments were organized by the National Satellite Ocean Application Service (NSOAS), and the experiments were conducted in Qing Dao on 29 January 2022. The radar parameters were specially set by the Institute of Remote Sensing Satellite (IRSS, China Association for Science and Technology (CAST)). For the GF\(-\)3 satellite, the ATI mode was taken as the experimental mode. The radar echo imaging process was completed by NSOAS based on a modified GF\(-\)3 data processor. The HFSWR data of Qing Dao and sea current meter data in Beibu Gulf were provided by the North China Sea Marine Forecasting Center, Ministry of Natural Resources (NCSFC, MNR) and Beihai Marine Environment Monitoring Center Station, Ministry of Natural Resources (BHMEMC, MNR). Sun Yat-sen University, as one of the participating institutions in the project, worked with the satellite center to retrieve the sea surface current field.  
To further verify the accuracy of correcting the Doppler current field based on SAR wind field retrieval and ECMWF wind field retrieval, an experiment was conducted on 29 January 2022 in Qing Dao, Shandong Province, China. Five images were obtained in the experiment. The specific times and regions of the observations are presented in Table 1.  
Figure 1 shows the observation area in the yellow sea of China, near Qing Dao. The area observed by GaoFen-3 is within the red rectangle. There are five satellite observation areas, with a latitude and longitude ranging from 120.48\({}^{\circ}\) E to 120.88\({}^{\circ}\) E and 34.41\({}^{\circ}\) N to 36.38\({}^{\circ}\) N, namely, regions (a), (b), (c), (d), and (e), corresponding to the index in Table 1. Meanwhile, the HFSWR observing area is in the blue area. This paper chose regions (b) and (c), which can cover the satellite observation area, to compare the retrieved sea surface current of the SAR images.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Index** & **Observation Time (UTC)** & **Observation Area** & **Direction** & **Antenna Direction** \\ \hline a & 2022-01-29 21:53:10 & 120.88\({}^{\circ}\) E, 36.38\({}^{\circ}\) N & Descending & Right \\ b & 2022-01-29 21:53:18 & 120.78\({}^{\circ}\) E, 35.90\({}^{\circ}\) N & Descending & Right \\ c & 2022-01-29 21:53:26 & 120.69\({}^{\circ}\) E, 35.42\({}^{\circ}\) N & Descending & Right \\ d & 2022-01-29 21:53:34 & 120.59\({}^{\circ}\) E, 34.95\({}^{\circ}\) N & Descending & Right \\ e & 2022-01-29 21:53:43 & 120.48\({}^{\circ}\) E, 34.41\({}^{\circ}\) N & Descending & Right \\ \hline \end{tabular}
\end{table}
Table 1: GF\(-\)3 satellite data obtained from the experiment conducted in Qing Dao.
## 2 Theoretical Foundation
### Topology Optimization
There are mainly four parameters involved:  
1. Optimization region: seek the optimal material distribution within a given design area by applying the following three parameters. 2. Objective function: the objective function is the optimization target. It is defined as the quantity to be minimized or maximized based on the parameters in the design response. The objective function can also be expressed using multiple design responses, and in the case of multiple objective functions, weighting factors can be used to define the influence of each objective function on the system. 3. Response constraints: constraints imposed on the design response to fulfill the analysis or practical requirements, such as volume or weight reduction by a certain percentage. 4. Geometric constraints: also known as manufacturing constraints, they refer to the limitations imposed directly on design variables. These constraints are primarily imposed to ensure that the analyzed structure aligns with real-world requirements. They encompass factors such as member size, pull-out direction, extrusion limits, and symmetry limits. Applying the SIMP and RAMP methods in ANSYS Workbench involves specific configurations for design variables and objective functions. Below are the general steps for utilizing the SIMP and RAMP methods in Workbench: 1. Design variable setting: open ANSYS Workbench and import or create the structural model in SIMP, access the topology optimization module, and select the structural domain for optimization. Designate the material density of each element as a design variable and specify the upper and lower limits for density, which can be adjusted during optimization. The RAMP method is similar to SIMP: treat the material density of each element as a design variable, and set upper and lower limits for density. 2. Objective function definition: choose the performance index for optimization in SIMP, such as minimizing structural deformation or volume. The objective function typically involves density distribution and material properties. By using a penalization factor to adjust the relationship between density and material, apply [PERSON]'s modulus for optimization. Unlike SIMP, when using the RAMP method, introduce a rational approximation of the material properties and configure the objective function to minimize structural deformation or volume while considering the influence of rational approximation. 3. Constraint setting: set constraints based on specific requirements, such as maximum stress, degree of freedom limitations, etc. The constraints setting may ensure that the optimization results comply with the design requirements. 4. Select the optimization algorithm: in Workbench, choose an appropriate optimization algorithm, such as sequential quadratic programming (SQP), genetic algorithm (GA), etc.