esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of a Coupled Eulerian-Lagrangian Approach to the Shape and Force of Scientific Balloons
## 4 Dynamic Fluid-Structure Coupling Shape and Force Analysis
### Aerodynamic Shape and Force Analysis of Wind-Driven Balloons
The Eulerian boundary conditions are defined as non-reflecting, allowing the airflow to exit the Eulerian domain without reflecting back, which could otherwise interfere with subsequent calculations. The simulation results, as shown in Figure 18, reveal the balloon's shape after the membrane fully rises under the buoyant force. Compared to the zero-pressure balloon in a windless environment, the balloon under the influence of a crosswind exhibits notable differences: helium is more concentrated at the top of the balloon, while its distribution at the bottom is relatively reduced. The crosswind causes the balloon's leeward side to bulge outward, while the windward side, particularly the lower-middle part, is indented. Additionally, the maximum stress on the leeward side occurs at the apex of the bulging region. The balloon's dynamic behavior also changes significantly under the wind field. It oscillates within the wind-facing plane and rotates laterally around its axis of motion. The membrane stress at the base of the balloon increases significantly, especially near the fixed point, where stress concentration becomes more pronounced. These results highlight the profound impact of crosswinds on the balloon's shape, internal gas distribution, stress distribution, and overall motion, providing valuable insights for the optimization of scientific balloon designs in complex flow conditions. In addition, we simulate a zero-pressure balloon in a windless environment with a zero external flow field and identical settings, and we plot the tension curves at the bottom end in both cases. From the curves, as shown in Figure 19, in the early stages of the simulation, the helium inside the balloon membrane rises rapidly. When it reaches the highest point, the rise stops due to the membrane's resistance, causing a large impact on the fixed bottom end, and the tension briefly peaks. Afterward, the tension curve gradually decreases. In the windless environment, about 2 s later, the tension at the bottom of the zero-pressure balloon stabilizes and fluctuates only slightly within a small range. In contrast, in the windy environment, after a brief period of stability, the tension at the bottom of the zero-pressure balloon begins to oscillate upward, with a significant increase in amplitude. Figure 18: Wind-scopping figures of a scientific balloon ((**left**): front view; (**right**): bottom view)).
## 3 Method  
### Multiple Linear Regressions  
Multiple Linear regression (MLR) is one form of Linear Regression which is used to describe the variations of a dependent variable (which varies under the influence of other system parameters) associated with the variations of several independent variables (which varies without being influenced by the other system parameters) [31]. The truth data in use include in-situ measurements of rice height, rice biomass, Day after Sowing (DaS--which is counted from the first day when farmers sow in their plots), backscattering coefficients VV and VH polarizations. The purpose is that using the ground measurements to predict rice height and biomass values based on DaS with single polarization (VV or VH) and dual-polarization (VH and VV). Therefore, the application of MLR equation to estimate rice height and rice biomass in each polarization is different. For example, the equation below was used in case of dual-polarization:  
\[ biomass=x_{0}+x_{1}\sigma_{VH}+x_{2}\sigma_{VV}+x_{3}DaS \tag{1}\]  
where \(\sigma_{VH}\) and \(\sigma_{VV}\) are the backscattering coefficients in the decibel unit, in VH and VV polarization, respectively; DaS stands for Day after Sowing. To estimate rice height, we substitute rice biomass by rice height in Equation (1). In VV polarization model, we just exclude the term \(\sigma_{VH}\) in Equation (1). Similarly, in VH polarization model, we just exclude the term \(\sigma_{VV}\) in Equation (1). We use the same input parameters for SVR and RF methods.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 5 Evaluation
### Metrics  
The performance metrics presented in this section allow meeting Requirement R7 described in Section 3.1.  
1. _Acceptance Ratio (AR)_: This metric measures the number of services that are processed. If \(P_{A}\) is insufficient to meet all power demanded (\(P_{D}\)), one or several services should be rejected (_RejServ_) [19]. The \(AR\) can be expressed as: \[AR=\frac{N-RejServ}{N}\times 100\%\] (23)
2. _Available Energy Utilization (\(E_{A_{U}}\))_: This metric measures the amount of energy allocated to processed services respect to the total amount of available energy (\(E_{A_{U}}=100\%\), if \(P_{D}=P_{A}\)). The \(E_{A_{U}}\) can be defined as: \[E_{A_{U}}=\frac{\sum_{i=1}^{L}\sum_{j=1}^{M}\sum_{k=1}^{N}p_{k,j}^{i}\times q_{ k,j}^{i}\times d_{k,j}^{i}}{P_{A}\times W}\times 100\%\] (24)
3. _Missing Power (\(P_{LACK}\))_: This metric measures the amount of power needed to process all demands under the current conditions of the system (i.e., for a given value of \(u_{k,j}^{i}\) and \(q_{k,j}^{i}\)). The \(P_{LACK}\) can be expressed as: \[P_{LACK}[t]=\left\{\begin{array}{ll}|P_{A}[t]-P_{D}[t]&\text{ if }P_{A}[t]<P_{D}[t],\\ 0&\text{ otherwise.}\end{array}\right.\] (25)
# DWPIs: Dynamic-Weight Parallel Instance and Skeleton Network for Railway Centerline Detection
## 4 Experiments and Analysis  
### Experimental Setting  
**Datasets.** A total of 3235 overhead-view images of railways were collected in Ma'anshan, Nanjing, Qinghai-Tibet Railway and our laboratory. These images were divided into 2277 images for the training dataset, 655 images for the validation dataset, and 303 images for the test dataset at a ratio of 7:2:1. Figure 5 shows some examples.  
**Implementation details.** Our network was implemented using PyTorch and was run on three NVIDIA TITAN RTX GPUs (24 GB RAM). For training, this paper used the stochastic gradient descent (SGD) optimizer with an initial learning rate of 0.01, a momentum of 0.9, and a weight decay of 0.0001. The final model was trained for 360 epochs and the models for ablation experiments were trained for 36 epochs. Pre-processing operations included resizing, random flipping, normalization and padding. After inference, this paper performed erosion as the post-processing method to remove the noise points or lines for skeleton detection.  
**Evaluation protocol.** Average precision (AP), average recall (AR) and mean average precision (mAP) were utilized as evaluation metrics for instance segmentation. For skeleton detection, the F-measure score (F-score) was used as the evaluation metric. A weighted-mean metric \(W_{mean}\) was designed for our dynamic-weight parallel instance and skeleton network, which consisted of mAP for the instance-segmentation branch and F-score for the skeleton-detection branch, defined as:  
\[W_{mean}=\gamma mAP+(1-\gamma)F_{score} \tag{5}\]  
where \(\gamma\) is the weight value of 0.2 for mAP.
# Agnostic Envelope Linearization of Dynamically Supplied Power Amplifiers for Mobile Terminals
## 2 Envelope Tracking PA Linearization  
The proposed ET PA digital linearization approach that relies only on the observation of the RF PA output is presented in this section. Figure 1 shows the block diagram of the forward path for the proposed ET PA linearization system. It is composed of three major blocks: (i) a shaping function for the generation of the supply envelope; (ii) an envelope optimizer in the supply path that contributes to the ET PA linearization; and (iii) an optional I-Q digital predistorter that can be used if further linearization is required. The shaping function generates the original supply envelope \(Env_{gen}\) which determines the efficiency and initial linearity of the ET PA system. The envelope optimizer takes the baseband I-Q input signal envelope (i.e., \(|u_{BB}|\)) and generates the optimized supply envelope \(Env_{opt}\), which, properly combined with \(Env_{gen}\), results in the supply envelope \(Env_{in}\), oriented at improving the linearity of the ET PA. An I-Q DPD can be included to further enhance the linearity of the ET PA.  
In the following subsections, the linearity versus efficiency trade-off in the shaping function responsible for the generation of the supply envelope is first introduced. Then, for the sake of completeness, the EGMP model proposed in [19] and the direct learning approach to extract the EGMP coefficients, assuming that ETM output is available, is presented. Next, to generate the optimized supply envelope \(Env_{opt}\) without having access to the ETM output, a slow envelope-dependent (SED) PA behavioral model is used, and an exploration scheme is proposed to help the PA model converge to a global optimal. Finally, the procedure to extract the coefficients of the EGMP model through the SED PA behavioral model is explained. It is important to note that the proposed EOPT method is agnostic on the ETM behavior (i.e., the access to the ETM output is not required) and, thus, it only requires monitoring the RF PA output and a precise ET PA behavioral model to extract the coefficients of the EGMP model.
###### Abstract  
Security of quantum key distribution (QKD) protocols rely solely on quantum physics laws, namely, on the impossibility to distinguish between non-orthogonal quantum states with absolute certainty. Due to this, a potential eavesdropper cannot extract full information from the states stored in their quantum memory after an attack despite knowing all the information disclosed during classical post-processing stages of QKD. Here, we introduce the idea of encrypting classical communication related to error-correction in order to decrease the amount of information available to the eavesdropper and hence improve the performance of quantum key distribution protocols. We analyze the applicability of the method in the context of additional assumptions concerning the eavesdropper's quantum memory coherence time and discuss the similarity of our proposition and the quantum data locking (QDL) technique.  
quantum key distribution; quantum information; quantum superadditivity; quantum data locking +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Stain Defect Classification by Gabor Filter and Dual-Stream Convolutional Neural Network
## 1 Introduction
This paper proposes a CNN for stain defect classification using inspection images and Gabor filter images intending to improve the performance of stain defect classification. First, a Gabor filter image is created using a Gabor filter on the inspection image obtained from the camera. A GLCM is constructed for the Gabor filter image, and an IDM value, a local homogeneity feature, is extracted from GLCM. The Gabor filter image is selected based on the IDM, and the inspection image and the selected Gabor filter image are used as input to the CNN. The contribution of this paper is as follows. 1. Gabor images with Gabor filters applied were used to improve the accuracy of stain defect detection. A method of adding features for spot detection using Gabor images was used rather than a method without Gabor filter images. It was confirmed through experiments that the method using the Gabor image improved the accuracy. 2. The stain defect classification performance was improved through a Dual-stream extraction based on inspection image and Gabor filter images, compared to the existing Single-stream extraction. The most suitable image for stain defect classification from the extracted Gabor filter image was selected and used as an input to a Dual-stream extraction that connects two streams in parallel. Through this, the features of each input image were extracted independently and merged in a fully connected layer to improve the stain defect classification accuracy. This paper is structured as follows. Section 2 describes the structure of the stain defect classification system proposed in this paper. Section 3 describes the Gabor filter image creation and selection process. Section 4 describes the structure of a Single-stream extraction and a Dual-stream extraction. Section 5 demonstrates the performance of the proposed method, the Dual-stream extraction, by conducting experiments on the MT and CCM datasets. Lastly, Section 6 describes the conclusion of this paper and future research.
# Multi-Layered Satellite Communications Systems for Ultra-High Availability and Resilience
## 1 Introduction
We provide a thorough assessment of multi-layered satellite systems covering markets, regulations, and technology enablers. 2. We define the MLSS concept and the system architecture enabling ultra-high availability and resilience both for commercial and governmental users. 3. We have implemented a simulation model and testbed and show with the initial results the achievable performance with implemented routing policies. 4. Finally, we define potential future research directions, taking into account the validation results obtained from our testbed work. This paper is structured as follows: Section 2 provides an analysis of markets and regulatory factors affecting the use and adoption of MLSS systems. Section 3 is the technology analysis, describing what kind of benefits and challenges there are. The system model is presented in Section 4, and selected results from our simulations and testbed work in Section 5. Future research directions are identified in Section 6, and this paper is concluded in Section 7.
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 3 Corrosion and Degradation of Structural Materials in Farm Environment
### Activity of Fungi and Bacteria  
According to the information presented in the preceding sections, bacteria-induced corrosion has a negative effect on the durability of concrete structures. The risk of bacteria-induced corrosion is most pronounced if concrete structures are reinforced with steel, which in turn leads to the formation of FeS and FeP as the main products [16]. Emerging evidence has provided contrary evidence concerning the impact of bacteria on the corrosion of concrete structures.  
Based on emerging evidence, bacteria and fungi have a mixed effect on concrete and metallic structures. The positive effects are linked to the presence of non-aggressive microbes, while the negative effects are linked to the proliferation of undesirable microorganisms [7; 16]. The activity of bacteria and fungi in mediating corrosion is summarized in Figure 3 The image shows that heterotrophic bacteria and fungi operate optimally during mechanical and chemical corrosion. The process is dependent on the availability of sulfate salts (such as Gp, Anh, Bas, and Ett), silicate/carbonate aggregates, and cement (C-S-H-phases, Ca(OH)). In addition, the most active bacterial species in this phase include are _Acidithiobacillus thiooxidans_, _Acidithiobacillus ferrooxidans_, _Thiobacillus intermedius_, _Halothiobacillus neopolitanus_, _Starkeya novela_, and _Thiobacillus tioparus_[17] as shown in Figure 3; it is of note that corrosion cannot proceed unless the environmental conditions are appropriate--high relative humidity, average temperature, and higher concentrations of H\({}_{2}\)S and CO\({}_{2}\). The durability of concrete and effects of acid-induced corrosion are discussed below.
## 3 The Fruit Fly Optimization (FOA)-Based Parameter Tuning Method of VSI Output Control
### The FOA-Based Online Parameter Optimization Tuning Method  
FOA-based online optimization tuning refers to the search for optimized computation after each control step-size of the step-response and change in the controller parameters in real time according to the adjustment principle, so as to achieve the goal of increasing the response speed of the controller and improving its performance. As shown in the control diagram of online parameter optimization tuning in Figure 7, the tuning process involves real-time optimizing and self-tuning of parameters during the step-response. According to the rising characteristic of the step-response curve, the online parameter optimization tuning process shall conform to the following tuning rules:  
1. In the rising stage of the system (the set value of the curve ranging from 0 to 0.9), the variation slope of the real-time sampling point should be the maximum error change ratio (IE) value identified during the optimizing process, in order to shorten the rising response time of the system.
2. In the oscillation and adjustment stages of the system, the slope of the real-time sampling point should be the minimum IE value (as the following Equation (34) expresses) in order to reduce the oscillation and overshot of the system, and gradually smooth the system curve. \[IE=\frac{d(error(t))}{dt}.\] (34)  
According to the above-mentioned tuning rules, and compared with the offline tuning process, the difference lies in the smell function in Step 3 consisting of the IE values after each control step, rather than the integral errors after each entire simulation process. The online self-tuning parameter flow chart is shown in Figure 8.  
**Step 3*:** Update the controller parameter and operate for one step, then compute the IE index:  
\[D\text{ist}1^{\prime}=\frac{de_{i1}}{dt},D\text{ist}2^{\prime}=\frac{de_{i2}}{dt},D\text{ist}3^{\prime}=\frac{de_{i3}}{dt},D\text{ist}4^{\prime}=\frac{de_{i4}}{ dt}, \tag{35}\]  
\[Si1^{\prime}=1/Dist1^{\prime},Si2^{\prime}=1/Dist2^{\prime},Si3^{\prime}=1/Dist3 ^{\prime},Si4^{\prime}=1/Dist4^{\prime}. \tag{36}\]  
Figure 8: The online optimization process flowchart of the FOA step, then computation of the IE index.  
Figure 7: Online parameter optimization diagram.
# Rock Crack Types Identification by Machine Learning on the Sound Signal+
## 4 Conclusions  
Rock failure is closely related to crack development. The identification of crack type (tensile or shear crack) can provide beneficial information to understand crack development and further reveal the rock failure mechanism. In our study, using sound signals, a method for automatically identifying the type of crack that develops in rock is proposed. This method takes advantage of the deep learning method and the shallow machine learning method. In this proposed method, sound signals corresponding to tensile cracks and shear cracks are first collected from Brazilian splitting tests and direct shear tests, respectively, to establish the training sample dataset. Abstract features that distinguish the corresponding spectrograms of sound signals are extracted by using the pooling layer of a pre-trained ResNet-18. GPC, a supervised learning method adaptable to the analysis of small samples, is trained to establish the crack type identification model.  
Application in biaxial tests indicates that the proposed method is feasible. In addition, the proposed method is used to investigate crack development during rockburst. The study results indicate that the early stage of rockburst is dominated by tensile cracks, while shear cracks dominate the late stage of rockburst. The obtained result is consistent with those of previous studies, which further indicates the feasibility of the proposed method.  
In this paper, the proposed method has the advantages of low cost and easy implementation. Compared with the traditional identification method using AE signals, the proposed method overcomes limitations of objective factors such as the number of sensors or unstable contact between the acoustic emission sensor and the rock mass. In addition, the proposed method based on sound signals provides a new way to deepen the understanding of the mechanism of rock failure and can help further realize the early warning of rock failure. However, the method in our study is proposed based on lab testing, and many influencing factors, such as the sample shape and also position of the microphone, have not been considered in the preliminary stage of this study. The application of the proposed method to on-site practice will be conducted in our future work. In addition, it should be pointed out that the proposed method is a supplement instead of a replacement of the pre-existing methods to crack type identification.  
Conceptualization, [PERSON]; methodology, [PERSON]; validation, [PERSON]; investigation, [PERSON] and [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52169021, 51869003) and the High-Level Innovation Team and Outstanding Scholar Program of Universities in Guangxi province. (Grant No. 202006).  
Figure 26: Tensile and shear crack development process during the rockburst test.
## 3 Results  
### MSC and S-MSC Characterization  
After culture condition restoration post 24 h starvation, S-MSCs like MSCs were plastic adherent and showed the typical spindle-shaped morphology (Figure 1). They maintained the in vitro differentiation capacity towards adipocytes and osteoblasts as demonstrated by the appearance of lipid droplets and the detection of phosphatase alkaline activity and calcium depositions as their corresponding MSCs (Figure 1). S-MSCs entered senescence at P10, medium passage, (range 8-11) similar to MSC (P11, range 8-12).  
S-MSCs expressed \(\geq\)95% of CD90, CD73, and CD105 surface antigens and \(\leq\)5% of CD34, CD45, CD19, and HLA-DR molecules as MSCs (Figure 2).  
Figure 1: MSC and S-MSC characterization. Morphology, differentiation, and senescence of representative MSC and S-MSC samples.  
Moreover, S-MSCs maintained the in vitro immunomodulatory properties as those shown by their counterparts, without any significant difference between the groups. The capacity to downregulate the immune response, in particular, the activation of T lymphocytes by a polyclonal activator, is evidenced by the reduction in the stimulation index (Figure 3a).  
For the metabolic activity detected by the MTT assay, no significant difference was found between the two groups (mean value for MSCs and S-MSCs: 576 \(\pm\) 283 and 641 \(\pm\) 209, respectively), as described in Figure 3b.  
Figure 2: Flow cytometer analysis of MSCs and S-MSCs. Gate A defines MSCs according to their physical parameters (FSC-A and SSC-A). MSC and S-MSC stained with specific antibodies were positive for CD73, CD105, and CD90 and negative for CD34, CD19, HLA-DR, and CD45.