esa-sceva/satcom-chunk-collection huggingface dataset:
# Phase Imbalance Analysis of GF-3 Along-Track InSAR Data for Ocean Current Measurement
## 3 Data Processing and Interpretation
### Ocean Current Measurements
#### 3.2.1 Current Measured by ATI Method  
As shown in Figure 13, the phase imbalances of the reference land scene change obviously from near range to far range, which are about 9\({}^{\circ}\)\(\sim\)15\({}^{\circ}\), and that of the ocean scene also has a similar change in range. First of all, it is considered that in the same set of SAR data, the phase imbalance caused by satellite attitude, antenna miss-pointing, and antenna phase center position error in different scenes can be ignored. Therefore, the channel phase imbalances of this set of GF-3 data can be eliminated by estimating the phase imbalances of the land scene in the same set of data. The processing steps are shown in Figure 14.  
\begin{table}
\begin{tabular}{c c} \hline \hline Radar Center Frequency & 5.4 GHz \\ Band Width & 100.0 MHz \\ Pulse Width & 30.0 \(\upmu\)s \\ Sample Rate & 133.3 MHz \\ PRF & 2107.7 Hz \\ Effective Baseline & 3.75 m \\ Sat Velocity & 7568.4 m/s \\ Center Look Angle & 20.2\({}^{\circ}\) \\ Center Incidence Angle & 22.8\({}^{\circ}\) \\ Polarization Mode & VV \\ \hline \hline \end{tabular}
\end{table}
Table 8: Product parameters.  
Figure 13: The phase imbalances of the reference land scene and ocean scene for ocean current inversion. (**a**) The GF-3 image of the reference land scene; (**b**) the phase imbalances of the reference land scene; (**c**) the GF-3 image of the ocean scene; (**d**) the phase imbalances of the ocean scene.  
Firstly, the time delay and phase imbalance between channels of the third set of GF-3 ATI data are calculated according to the steps shown in Figure 5 using the land scene data. Secondly, we perform image registration and phase imbalance calibration on the dual-channel data. Thirdly, we calculate the interferometric phase of the corresponding data. Furthermore, the obtained interferometric phase is processed by periodic pivoting filtering and averaged in the neighborhood to avoid noise, where the average window of the field is \(1024\times 1024\) pixels, and the sliding step length of the azimuth and the range is 128 pixels. The spatial resolution is about 2 km \(\times\) 2 km. Then, the phase imbalances of the ocean scene's land area are set to zero to eliminate the phase imbalances between different scenes and the constant phase imbalances between the dual-channel system. The interferometric phases that can reflect the ocean surface motion are obtained. The radial velocities of the ocean surface are shown in Figure 15a. Finally, the empirical model mentioned in Section 2.1 is used to remove the contribution of ocean surface wind and the Bragg wave to ocean surface Doppler velocity, and the ocean current retrieved by the ATI method is shown in Figure 15b, which is about \(-145\)\(\sim\)\(-30\) cm/s.
# Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview
## 4 Defense
### From Different Areas of Defense Methods  
Due to the threat of adversarial attacks to ASR and ASV systems based on deep neural networks, researchers have proposed different methods of defense against adversarial attacks from different sound research fields, mainly the including detect-rejection, filtering and retraining, as shown in Figure 3.  
Detect-rejection: This method is from the perspective of the ASV field. Since the role of the speaker authentication system itself is to confirm whether the input audio is the target speaker, it is an alternative problem. Based on this idea, the input of the adversarial sample into the ASV system is in the original In the case of prior knowledge, and the adversarial examples can be output as a type of rejection so as to protect the security of the system. Recently, in [90,96,98], the use of ASV systems to detect adversarial examples has achieved remarkable results.  
Filtering: Considering that the essential task of speech enhancement is to remove the mixed noise in the input audio, and the essence of the adversarial sample is to add disturbance to the pure audio, so the researchers target the added perturbation from the perspective of speech enhancement, using speech separation and the method of speech enhancement to eliminate the added disturbance; ref. [92] adopted the self-attention U-Net method to enhance the ASR system in the face of adversarial attacks.  
Retraing: The retraining method is to fine-tune the network by augmenting the collected or simulated adversarial audio clips in a training set with explicit labels of noise. Based on the DNN adaptation fundamentals, it can make the network more robust to similar attacks. [PERSON] et al. [99] generate adversarial examples using the fast gradient signal method(FGSM) and use these adversarial examples as augmented data to retrain the model to enhance the robustness of KWS model.
# Capacity and Delay Analysis for Large Social-Aware Mobile Ad Hoc Wireless Networks
## 5 Scheduling Scheme and Achieved Throughput Capacity
Similar analyses can employ in sub-slot B and sub-slot C. Data packets are queued at the relay nodes or reach the final destination node in sub-slot B and sub-slot C.  
Combining Lemmas 3 and 4, we get the achieved throughput of each node straightforwardly  
\[\mathcal{L}(X_{j},X_{i}) \leq r(n). \tag{17}\]4  
Theorem 2: _Based on the multi-hop scheduling and slots allocation scheme, the achieved throughput of each node in large social-aware mobile ad hoc wireless networks can be expressed as_  
\[\mu=\left\{ \begin{array}{ll}\Omega\left(\frac{1}{n\sqrt{m}}\right)&0\leq \delta<1\\ \Omega\left(\frac{\log n}{n\sqrt{m}}\right)&\delta=1\\ \Omega\left(\frac{1}{n^{2}-\delta/m}\right)&1<\delta<3/2\\ \Omega\left(\frac{\sqrt{n}\sqrt{m}}{\log n}\right)&\delta=3/2\\ \Omega\left(\frac{1}{\sqrt{n}\sqrt{m}}\right)&\delta>3/2.\end{array} \right.\]
# Spatial Multiplexing Holography for Multi-User Visible Light Communication
## 5 Experimental Results
### Experimental Results for Power Allocation
This pattern is characterized by an initial increase followed by a decline after reaching the peak, attributable to the non-linear effects inherent in the transmission and reception equipment. This trend underscores the critical balance required between signal strength and the limitations imposed by system non-linearities. The graphs also delineate the performance comparison between the SMH and MPH methods in terms of achieving target data rates: 0.5 Gbps for User 1, 1.5 Gbps for User 2, and 2.5 Gbps for User 3. It is evident that users under the SMH approach closely match their respective target data rates, demonstrating the method's effective power allocation and reduced disparity. Conversely, the MPH method exhibits significant power mismatch, failing to meet the targeted data rates across the user spectrum.
# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 2 Materials and Methods
### Programming Language and Software Packages Used  
The models were implemented using the Python (3.13) programming language. The following packages were utilized in the analysis: time, numpy, pandas, and joblib for data processing and model training. sklearn.metrics (including functions like accuracy_score, f1_score, roc_auc_score, cohen_kappa_score, precision_score, recall_score, and confusion_matrix) for model evaluation. sklearn.naive_bayes for the implementation of the Gaussian Naive Bayes classifier. csv for reading and writing data from CSV files.
# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
## 3 Approach
### Proposed Method
#### 3.2.1 Oriented Box Representation  
To account for the diverse orientations of objects in remote-sensing images, we propose a oriented box representation method to utilize the box representation. To represent the position of each oriented anchor box, we employ six regression parameters, namely, \((x,y,w,h,\Delta a,\Delta\beta)\). Among these parameters, \((x,y)\) represents the center coordinate of the outer rectangle of the oriented box, while \(w\) and \(h\) imply its width and height, respectively. The parameters \(\Delta\alpha\) and \(\Delta\beta\) denote the offsets of the top edge and right midpoint of the outer rectangle of the oriented box.  
Figure 1: Overall framework of OrientedDiffDet framework. The image encoder extracts feature representation from an input image. These is a two-stage detector built on an FPN backbone. The first stage generates oriented proposals by the diffusion model network, and the second stage uses feature alignment as input and to predict category classification.  
We can calculate the coordinates of the four vertices representing the oriented box, including the coordinates \((x+\Delta\alpha,y-h/2)\), \((x+w/2,y+\Delta\beta)\), \((x-\Delta\alpha,y+h/2)\), and \((x-w/2,y-\Delta\beta)\).  
By regressing the offsets of the midpoints of any two adjacent edges of the horizontal box, we can generate the oriented candidate boxes based on the regression of the original horizontal box. This representation enables us to capture the orientation of objects in a more precise manner while utilizing regression based on the original horizontal box. See Figure 2.
###### Abstract  
Precise intraoperative brain tumor visualization supports surgeons in achieving maximal safe resection. In this sense, improved prognosis in patients with high-grade gliomas undergoing protoporphyrin IX fluorescence-guided surgery has been demonstrated. Phase fluorescence lifetime imaging in the frequency-domain has shown promise to distinguish weak protoporphyrin IX fluorescence from competing endogenous tissue fluorophores, thus allowing for brain tumor detection with high sensitivity. In this work, we show that this technique can be further improved by minimizing the crosstalk of autofluorescence signal contributions when only detecting the fluorescence emission above 615 nm. Combining fluorescence lifetime and spectroscopic measurements on a set of 130 ex vivo brain tumor specimens (14 low- and 56 high-grade gliomas, 39 meningiomas and 21 metastases) coherently substantiated the resulting increase of the fluorescence lifetime with respect to the detection band employed in previous work. This is of major interest for obtaining a clear-cut distinction from the autofluorescence background of the physiological brain. In particular, the median fluorescence lifetime of low- and high-grade glioma specimens lacking visual fluorescence during surgical resection was increased from 4.7 ns to 5.4 ns and 2.9 ns to 3.3 ns, respectively. While more data are needed to create statistical evidence, the coherence of what was observed throughout all tumor groups emphasized that this optimization should be taken into account for future studies.  
fluorescence-guided surgery; fluorescence lifetime imaging; fluorescence spectroscopy; protoporphyrin IX +
Footnote †: journal:
## 2 The Development and Overview of the Procedure  
The history of using stool from healthy people to treat human diseases dates back to the fourth century AD [26]. [PERSON], a Chinese doctor during the Dong Jin Dynasty (AD 300-400), recorded the treatment of \"[PERSON]\" (febrile disease) and \"[PERSON]\" (tymphoid fever) by drinking the fecal suspension or fermented feces [27; 28]. Later, in the Compendium of Materia Medica, which is the most comprehensive record of resolving diseases in traditional medicine, [PERSON] described more than 20 indications that can be effectively treated with fecal suspension or fermented feces [29]. In 1958, [PERSON] et al. successfully treated patients with severe pseudomembranous enteritis using a fecal suspension, which was the first recorded instance of such a treatment in the English literature [30]. In 2011, the method was officially termed fecal microbiota transplantation (FMT) [31], and, in 2013, it was included in the medical guidelines for the treatment of refractory _Clostridiodies difficile_ infection (CDI), which represented a milestone in the history of FMT application [32]. The most successful application of FMT, to date, has been in the treatment of refractory CDI [22; 33], and there is growing evidence that FMT also has great potential for interventions in other enteric-related diseases and in neurological conditions [34; 35; 14; 36; 30].  
FMT, as a strategy to modulate gut microbes, is not only a breakthrough medical technique, but also a breakthrough in technological and theoretical research. In theoretical studies, interest in FMT has not been limited to its use as a therapeutic method (in which the fecal microflora from healthy donors is transmitted to patients to restore a healthy microbial composition to the gut), because it has also involved the transfer of bacteria from one individual to another to induce a desired physiologic effect. Potential microbial material from donors is not limited to feces, and may also comprise intestinal contents or specially modified microbiota (such as SMT) [19; 20]. Researchers have often applied FMT to studies of mouse models, which, because of their genetic proximity to humans, our ability to genetically manipulate their genomes, and the availability of many tools, mutants, and inbred strains, have become the mammalian model of choice [37; 38]. Therefore, recipients are not limited to germ-free mice and may also be genetically engineered mouse models [39; 15].  
Of all the available research methods, the most basic is the transplantation of the microbiota of target mouse donors into antibiotic-induced bacterial-depletion mouse recipients, and other research models can be modified on this basis. In Sections 3 and 4 of this review, we provide a brief overview of the selected models of FMT (mouse donors; antibiotic-induced bacterial-depletion mouse recipients), along with the literature references and considerations for each step. In the following sections, we provide some of the necessary analyses related to FMT. Finally, we discuss the contribution that FMT has made to the current understanding of disease mechanisms related to gut microbiota.
## 3 Materials and Methods
### Validation Data  
Porsmilin beach is one of the field sites regularly monitored in the framework of the long-term coastal observatory DYNAALT. For this campaign, a TLS survey was performed simultaneously to the GNSS-assisted terrestrial photogrammetry surveys. This TLS survey involved two scans from two distinct scan positions performed with a _[PERSON](r)_ VZ-400, with each scan covering 360\({}^{\circ}\) horizontally and 100\({}^{\circ}\) (from 30\({}^{\circ}\) to 130\({}^{\circ}\)) vertically with an angular resolution of 0.04\({}^{\circ}\) in both directions.  
Associated to the TLS survey, the position of reflective targets was measured via RTK GNSS. The targets are reflective cylinders (10 cm in diameter and 10 cm in height) distributed around the TLS standpoint before the survey. The processing of the TLS data was performed using the _RiScanPRO(r)_ software suite (provided by _Riegl(r)_) and _CloudCompare(r)_[31], a 3D point cloud freeware processing software. It comprised three main steps:  
Figure 4: Colored dense point clouds obtained using the photographs collected by the Nikon D800 Reflex camera (**a**) and the Huawei Smartphone camera (**b**).  
Georeferencing and individual clouds assembly. An indirect georeferencing was performed using the reflective targets (e.g., [32]).
2. Manual point cloud filtering to remove artefacts and undesirable data (people on the beach, data outside of the study area, etc.)
3. Point cloud interpolation onto a mesh, based on a Triangular Irregular Network (TIN).  
TLS datasets are often used as reference, because, contrary to SfM photogrammetry, measurement errors that are related to the accuracy of the laser are constant across the point cloud, with errors that are inherent to georeferencing being transmitted to the whole cloud [8]. Under similar survey conditions, TLS accuracy on recurrent cliff face surveys was estimated to be around 3 cm [8]. Here, the cliff face that was measured by TLS was used as large-scale validation data to assess the accuracy (represented by mean error) and precision (represented by standard deviation of error) of the SfM photogrammetric point clouds. This approach ensures a global assessment of the error, while taking into account the error spatial variability due to reconstruction artefacts or environmental parameters. This assessment was performed using the \"cloud-to-mesh distance\" tool, computing nearest-neighbor distances in _CloudCompare(r)_. Using a mesh for the TLS dataset avoids computational artefacts in quality assessment, which might be introduced due to a heterogeneous point cloud density.  
Nevertheless, the TLS point cloud is not error-free, and having a lower spatial resolution than SfM photogrammetric reconstructions, it is not fully relevant at small scale. Therefore, the consistency at small scale of the SfM reconstructions was also assessed by inter-comparing the SfM dense point clouds that were obtained using the Nikon Reflex camera and the Huawei Y5 Smartphone camera, respectively.
## 3 Methodology
### Numerical Modelling and Boundary Conditions  
Finite element modelling of groundwater considering transient seepage was conducted on the slope. The finite element mesh and corresponding boundary conditions adopted for the slope following the model proposed by [PERSON]. [3] is shown in Figure 2. Along the left- and right-side boundaries of the slope, a variable head was applied at elevation 60 m and 50 m, respectively, at the groundwater table. This study considers a free water table condition since this may rise during rainfall infiltration. It is assumed that the base of the finite element mesh applied on the slope is impermeable. The mesh element type used is four-noded quadrilaterals; the total number of elements and nodes are 3366 and 3432, respectively. Numerical simulation is executed using the Slide2 program [22].  
High rainfall intensities can lead to rapid infiltration of water into the slope, especially if the soil's air-entry value (AEV) is low. This rapid infiltration increases pore water pressures swiftly, reducing the soil's effective stress and shear strength.  
Continuous high-intensity rainfall can saturate the slope, making it more prone to shallow failures or surface erosion.  
On the other hand, lower rainfall intensities might allow for better drainage and dissipation of pore water pressures, reducing the immediate threat to slope stability.  
Figure 1: (a) Soil–water characteristic curve for different air-entry values. (b) Coefficient of permeability curve for different air-entry values.  
In this study, rainfall effect is modelled as a flux boundary (\(q\)), applied along the surface of the slope. Analysis of unsaturated suction and pore water profiles are carried out in the middle of the slope at the section denoted as \(X\)-\(X\) in Figure 2; for an unsaturated soil condition above the groundwater table, suction could be sustained if the rainfall intensity or surface flux (\(q\)) is less than the saturated coefficient of permeability (\(k_{s}\)) [3]. In this regard, the ratio of surface flux to the saturated coefficient of permeability (i.e., \(q/k_{s}\)) less than unity ensures the slope is not fully saturated during infiltration. A typical pore pressure distribution profile with increasing \(q/k_{s}\) is shown in Figure 3. Hence, a rainfall intensity value of \(1\times 10^{-6}\) m/s (i.e., \(q/k_{s}\) of 0.1) has been adopted for this study. Critical porewater profiles are analysed in this study considering both major and antecedent rainfalls. A major rainfall event is that which has a duration of less than 24 h while an antecedent rainfall is considered as that which would last for more than 24 h [23].  
Figure 3: Typical pore water pressure distribution profiles in an unsaturated soil with increasing surface flux.  
Figure 2: Slope geometry with finite element mesh and boundary conditions.
## 1 Introduction
The sensitivity matrix was used as the distance matrix and different cluster algorithms were compared using four different performance indexes; it was found that intelligence algorithms were superior to classical clustering algorithms [14]. Both the network reconfiguration and network partition help improve the power quality of the modern power grid. However, they need a well predesigned network, which is unrealistic in most rural areas. In underdeveloped areas far from the city center, it is difficult to supply enough energy from the transmission lines, resulting in the exploitation of local resources, such as hydro energy, to compensate for the load demand. However, with the development of renewable energy generators and photovoltaic (PV) poverty alleviation programs, particularly in China, a large number of RESs have been installed in such places, leading to surplus power energy. Such surplus energy is mainly provided by the large hydro station at the end of the grid, which may lead to poor power quality and a voltage overshoot. It is challenging to solve these problems because mainly runoff hydropower stations are installed in such remote areas, and the citizens are not willing to shut down their PVs. To solve the unbalanced power energy at the end of grid, a practical method that has been employed in many places in China is to add transmission lines. By doing so, a power grid is divided into several separated radial grids which are separately connected to a national grid so that the surplus energy can be directly transmitted to the national grid without causing any voltage overshoot. However, research on how to separate a particular network is limited, and the practical operation is based on subjective judgment. Instead of dividing a grid into several inter-connected clusters in the network partition, this problem requires the separation of the unbalanced part of the grid. This paper proposes a power-mileage-based network optimization algorithm to provide a guideline for how to add transmission lines. The main contributions of our work have been illustrated in the following:  
1. To save the time consumed by calculation, a performance index which denotes voltage profile is deduced and used. It has a linear relationship with the input power condition so that it can be calculated with linear algebra easily. 2. A network optimization algorithm is proposed to guide how to divide a power grid and achieve the optimal voltage condition. The algorithms automatically decide the number of clusters and when to stop the iteration. The rest of the paper is organized as follows. The problem is formulated in Section 2. In Section 3, related concepts, including the power mileage value, are introduced and a simple algorithm to calculate the mileage matrix is also presented. Section 4 elucidates the power-mileage-based algorithm, and two cases are used to illustrate the algorithm's effectiveness. Section 5 provides another case study, and Section 6 gives our conclusions.