esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Inhibition of Alternative \(\sigma\) Factors by Anti-\(\sigma\) Factors
### Anti-\(\sigma\) Factors that Wrap around \(\sigma_{2}\) and \(\sigma_{4}\)  
An alternative mechanism is to stabilize the naturally compact structure that free \(\sigma\) factors are thought to adopt, while simultaneously occluding the RNAP-binding regions (Figure 3). The Group 3 _Aquifex aeolicus_\(\sigma^{\text{Fia}}\)/FlgM structure revealed that the \(\sigma^{\text{Fia}}\)\(\sigma_{2}\), \(\sigma_{3}\) and \(\sigma_{4}\) domains are organized as a compact unit with the extended helical FlgM wrapped around the outside, blocking the major RNAP binding determinants in \(\sigma_{2}\) and \(\sigma_{4}\)[64]. Recently, a new class of ECF anti-\(\sigma\) factors was proposed to act in a similar way [65]. The structure of the cobalt and nickel resistance regulator \(\sigma^{\text{CnrH}}\) from the \(\beta\)-proteobacterium _Cupriavidus metallidurans_ in complex with the cytoplasmic domain of its anti-\(\sigma\) factor, \(\text{CnrY}\), revealed that the \(\sigma_{2}\) and \(\sigma_{4}\) domains are embraced in a closed conformation by two \(\text{CnrY}\) helices, such that the \(\sigma_{4}\) domain is buried against the \(-10\) interaction surface in \(\sigma_{2}\) and \(\text{CnrY}\) blocks the \(\sigma^{\text{CnrH}}\) RNAP-binding determinants [65] (Figure 3). Remarkably, despite no primary sequence similarity, this structure can be superimposed on that of a complex between the \(\sigma\) factor mimic PhyR and the anti-\(\sigma\) factor NepR from \(\alpha\)-proteobacteria [66, 67] suggesting that this might be a widespread mechanism.
## 1 Introduction
These sensors send every piece of information to the utility server via a communication network, which has resulted in a new challenge for cybersecurity in the smart grid. The smart grid network is susceptible to a wide variety of cyberattacks, the most prevalent of which include false data injection, denial of service attacks, and botnets. The algorithms for machine learning have a very high degree of accuracy when predicting these types of attacks [2; 3; 4]. In the field of healthcare, machine learning is utilized to make predictions regarding the early stages of cancer, the prediction of outbreaks, the performance of drugs, etc. In a manner analogous to this, there are other industries like entertainment, e-commerce, autonomous driving, social networks, email service providers, cloud service providers, and others that use machine learning for a variety of activities. The following are the primary contributions of this paper: (1) an in-depth analysis of the current machine learning technique that is being applied to solve a wide range of classification, regression, and clustering issues. (2) The machine learning engineer can understand the reasoning behind all machine learning algorithms by reading this paper. This will allow them to determine which machine learning algorithms they can apply to their problem based on the type of data they have. (3) In this study, we also studied the current issues in machine learning, which, if not addressed, can deteriorate the performance of any machine learning model. (4) Finally, we provide a method for dealing with the difficulties encountered while developing a machine-learning model. The rest of the paper is organized as follows. In Section 2, we discussed the types of machine learning, followed by Section 3, which presented the need for data analysis before ML model building and various tools for data analysis and visualization. In Section 4, we presented various machine learning problems. In Section 5, we described the machine learning algorithms for regression, classification, and clustering along with their mathematical models. Section 6 is about the challenges and solutions faced during the training of machine learning models; this is followed by Section 8, which gives an overview of the machine learning tools available for building the model. Section 9 provides current research in machine learning and use cases. Lastly, Section 10 concludes our work.
# Creep Characteristics of Soil in the Sliding Zone of Huangtupo Landslide
## 6 Conclusions  
In this paper, based on the influence of reservoir water level changes in the Three Gorges Reservoir area of the Yangtze River on the force of the landslide and the unloading effect on the sliding zone soil under the water when the reservoir water level rises, a direct shear creep test of the sliding zone soil in the Huangtupo landslide under unloading condition was carried out, and the Burgers model was used to fit the creep test curve. The following conclusions were obtained.  
1. The direct shear creep of the sliding zone soil was mainly divided into the attenuation creep stage and the steady creep stage; the accelerated creep stage was not observed. At the moment of the application of shear stress, the sliding zone soil produced instantaneous deformation, and the degree of deformation was positively correlated with the shear stress. The sliding zone soil was in the attenuation creep stage, and the creep rate decayed rapidly to zero with the low shear stress; the sliding zone soil was in the steady-state creep stage, the creep rate decayed to a constant value, and the visco-plastic creep characteristics were more obvious with the higher shear stress.
2. With the increase in the pre-consolidation pressure applied to the sliding zone soil, the creep displacement was correspondingly reduced. When the level of loading stress was high, the shear strain of the sliding soil was also at a high level. Additionally, the strain rate of the sliding zone soil was positively correlated with shear stress and negatively correlated with the previous consolidation pressure. In this study, shear shrinkage occurred during the shear process.
3. We found that the Burgers model can accurately fit the creep process of sliding zone soil. The shear modulus of the Burgers model was positively correlated with the applied shear stress. When the applied shear stress increased, the viscosity coefficient increased first and then decreased. The long-term strength value of the sliding zone soil was obtained using the steady-state creep rate method, and the value was about 72% of the peak strength. The long-term strength also increased with the increase in consolidation stress.  
Conceptualization, M.L. and D.C.; methodology, D.C.; software, X.B.; validation, M.L., Z.Q. and C.Z.; formal analysis, D.C.; investigation, M.L.; resources, D.C.; data curation, M.L.; writing--original draft preparation, M.L.; writing--review and editing, D.C.; supervision, X.B.; funding acquisition, D.C. All authors have read and agreed to the published version of the manuscript.  
The work was financially supported by the National Natural Science Foundation of China (No. 42277171 and No. 41772304).  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the author.  
The authors declare no conflict of interest.
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 2 Preliminaries and Related Works  
This section contains the preliminary knowledge and work related to our proposed system. We first describe AAN services then give explanations about various learning techniques.
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
## 4 Proposed Defense System
### Design Goals of a Defense System  
To counteract the adversarial attacks in an SV system, we attempt to design and implement a defense system that achieves the following goals:  
* **Simplicity**. The defense system is easy to implement and can be compatible with an existing SV system. That is, it does not require any change to the internal structure of the currently used SV system.
* **Light weight**. It does not significantly increase the computation load of the SV system. The defense method only slightly increases the processing time for an input audio.
* **Effectiveness**. The defense algorithm should be able to greatly increase the time for an attacker to generate a successful adversarial audio and significantly reduce the ASR of adversarial attacks such as FakeBob. On the other hand, the defense method should minimally affect the normal operations of an SV system, such as slightly increasing the EER of the SV system.  
Figure 3: An adversarial audio in the time domain and its perturbations in both the time domain and the mel spectrogram. (**a**) Adversarial audio in time domain; (**b**) perturbations in time domain; (**c**) perturbations in mel spectrogram.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 4 Results and Discussions  
### Experimental Setup and Parameter Settings  
The trials were performed on a high-performance computing workstation equipped with an Intel architecture CPU including 24 physical and 32 logical cores, together with 32 GB of RAM, which exhibited a 69.0% usage during training. However, the NVIDIA GeForce RTX 4070 Ti GPU enhanced this system's computing power, providing 28.3 GB of VRAM and facilitating mixed-precision training with the Ampere architecture, essential for managing large flood detection datasets and complex model architectures. Furthermore, the deep-learning framework was developed with Python 3.10, utilizing TensorFlow 2.9.1 and the Keras API, chosen for its efficiency in GPU resource utilization and suitability for computer vision applications. The model's architecture employed Sentinel-1 SAR images, utilizing 3 \(\times\) 3 spatial patches to facilitate temporal comparisons of circumstances pre- and post-flood, hence enabling flood detection via a binary classification approach.  
For training, the Adam optimizer was employed with standard adaptive learning rates and key parameters set as Beta1 = 0.9, Beta2 = 0.999, and Epsilon = 1 \(\times\) 10\({}^{-7}\), which aids in achieving stable convergence even amid noisy data. We selected the binary cross-entropy loss function to ensure appropriate gradient signals for binary classification tasks. The dataset contained 54,733,367 labeled pixels, highlighting a significant class imbalance (with a 1:31 ratio of flood to non-flood pixels) and was split into 70% for training, 10% for validation, and 20% for testing. Data preprocessing involved normalizing pixel values and extracting 3 \(\times\) 3 patches from co-registered SAR imagery, explicitly avoiding data augmentation to preserve the integrity of radar backscatter characteristics. The model underwent training for 100 epochs with a batch size of 64, and it was evaluated using various metrics, including overall accuracy (OA), Kappa coefficient, \(F1\)-score, and mean Intersection over Union (mIoU), to ensure a comprehensive performance assessment despite the existing class imbalance. Additionally, memory management techniques were implemented to optimize GPU memory usage, with batch processing tailored for the RTX 4070 Ti, and random seed initialization was performed to ensure the reproducibility of results.  
\[bce\_loss(x,y)=-1\;*\;(y\;*\;math.log(x)+(1-y)\;*\;math.log(1-x)) \tag{13}\]
## Materials and Methods
### Real-time Reverse-Transcription Polymerase Chain Reaction (qRT-PCR)  
Total RNA was extracted from cultured cells with an RNeasy\({}^{\circledR}\) mini kit (Qiagen, Inc., Hilden, Germany) combined with Trizol\({}^{\circledR}\) (Thermo Fisher Scientific Inc., Waltham, MA, USA). Briefly, cells were dispersed and homogenized in Trizol\({}^{\circledR}\) using Powermasher (Nippi, Inc., Tokyo, Japan) and Biomassher II (Nippi, Inc., Tokyo, Japan), and total RNA was extracted according to the manufacturer's instructions. cDNA was generated with a ReverTra Ace\({}^{\circledR}\) qPCR RT Master Mix with a gDNA Remover (Toyobo Co., Ltd., Osaka, Japan) according to the manufacturer's instructions. qRT-PCR was performed using the Thunderbird\({}^{\circledR}\) SYBR\({}^{\circledR}\) qPCR Mix (Toyobo Co., Ltd., Osaka, Japan) on an ABI PRISM 7900 HT Sequence Detection System (Applied Biosystems, Inc., Foster City, CA, USA). The relative quantification was calculated via the \(\Delta\Delta\)Ct method and normalized against the housekeeping _GAPDH_. The following primers were used: _COL1A1_ (5'-gtcgaggcacaagaga-3' (sense) and 5'-cagatcacqgtcatcgcacaac-3' (antisense)) [41], _COL2A1_ (5'-ggcaaatcagaggtcacagttacgtacagttacgtacagttacgcccatta-3' (antisense)) [42], _COL1A1_ (5'-gtctgctttactgtattattctccaaa-3' (sense) and 5'-tgctgtgtccgtgtattacaaaatttt-3' (antisense)) [43], _ACAN_ (5'-agcctgcgctctcaatgact-3' (sense) and 5'-taatggaacacagtgcttta-3' (antisense)) [44], _SOX9_ (5'-ttccgcgcagtggacat-3' (sense) and 5'-tcaaactcgtgtgacatcgaaggt-3' (antisense)) [45], and _GAPDH_ (5'-agcaagagcacaagagagaaga-3' (sense) and 5'-gaggggagattcagtgtgtg-3' (antisense)).
# Spectrum Demand Forecasting for IoT Services
## 5 Study Case Implementation: Colombia  
In this section, the above-proposed methodology is followed step by step. All studies and evaluations were carried during 2019 and most of the data was collected from 2017. However, IoT and digital dividend bands in Colombia were recently allocated after a long-awaited bidding process. This spectrum allocation has severely delayed the introduction of many IoT services in the country. The main purpose is to forecast IoT spectrum needs in the following ten years.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 4 Experimental Results
### Test Results
#### 4.4.3 Averaged Top-x Accuracy Comparison Between Competitors  
Moreover, Figure 8 compares the averaged test accuracy of the three models across top-x predictions. The results indicate that introducing the MSA mechanism in the CNN-MSA-GRU model yields noticeable performance gains compared to the CNN-GRU model. At each top-x level, the accuracy of CNN-MSA-GRU surpasses that of CNN-GRU, highlighting the MSA ability to capture richer temporal dependencies and improve feature extraction from sequential data. Additionally, the ELP-Net model consistently achieves the highest test accuracy at every top-x level, demonstrating its effectiveness in improving prediction accuracy and outperforming the baseline models. The consistent improvements of ELP-Net can be attributed to its integration of GNNs for spatial dependency modeling and transformer module for capturing temporal relationships. By leveraging these advanced components, ELP-Net effectively combines spatial and temporal information, enabling more robust and accurate location predictions in complex wargaming scenarios.  
Figure 6: Top-x prediction accuracy for all pieces using ELP-Net.
## 1 Introduction
In this paper, PS-InSAR and SBAS-InSAR methods are used to obtain real-time surface deformation data of the Kunming urban area from 2020 to 2022, and cross-verification is carried out. Based on the surface deformation information monitored by TS-InSAR, a new surface subsidence prediction model (GWO-LSTM) is proposed. To guarantee the precision of the forecasting model, root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R\({}^{2}\)) are introduced as evaluation indexes. The accuracy and applicability of the new method are verified. This paper adopts new methods to monitor and predict land subsidence, which provides a scientific basis for the planning and development of cities and the prevention of geological disasters. Despite the advancements in monitoring techniques, the ability to accurately predict land subsidence trends remains a significant challenge. The proposed research introduces a novel approach by integrating the strengths of Time Series-Interferometric Synthetic Aperture Radar (TS-InSAR) with LSTM by the Grey Wolf Optimizer (GWO). This study is the first to apply the GWO-LSTM model to predict land subsidence, specifically in the context of Kunming City, where the complex geological conditions and rapid urban expansion require reliable monitoring. The novelty in the development of a hybrid methodology leverages the global search capabilities and convergence characteristics of the GWO, which enhances the parameter selection of the LSTM model. This optimization significantly improves the accuracy and stability of subsidence prediction, which addresses the limitations of traditional monitoring and prediction methods. Furthermore, the application was used for a real-world case study in Kunming City. With its unique geological setting and rapid urban development, this provides a critical test bed for the model's effectiveness and applicability.
## 4 Measurement/Calibration of \(R_{s}\)
### Laboratory Calibration  
#### 4.1.1 Static Depletion Design  
The design of a static depletion experiment was used to monitor a single peak of the target compounds and subsequent reduction of analyte concentration in the exposure solution [112; 113]. Using first-order dissipation kinetics (Equation (1)), researchers detected, with time, the decrease of analyte concentration in water; thus, the \(R_{s}\) of the passive samplers was estimated [20]. If there was no competing sorption phase (e.g., particulate or dissolved organic matter) in the exposure system and the concentration in the sampler was much lower than its equilibrium value, in a short time, Equation (1) could be converted to Equation (4) for the calibration of the \(R_{s}\)[114].  
This calibration method possesses specific advantages, such as simple procedure, low cost, and easy operation to alter environmental variables. It should be noted that the control experiment in this calibration had to be performed to avoid the analyte losses in the processes other than sampling accumulation. For instance, there was evidence that a large amount of analytes might have remained in the membrane [34; 59], which was always ignored in the calibration, causing a measurement error.
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 2 Materials and Methods
### Feature Extraction Based on PRPD Data
#### 2.3.2 Conversion of PRPD into Grayscale Feature Maps  
The PRPD plots obtained in the previous section were converted to PNG-format images for training and recognition. Firstly, the horizontal axis was divided into 60 equal intervals and the vertical axis was divided into 50 equal intervals, crossing the intervals in the horizontal and vertical axes to create cells on the map. The 360\({}^{\circ}\) phase of the horizontal axis was divided into 60 equal parts, with each cell occupying 6\({}^{\circ}\) of phase, and the voltage range of 2 V on the vertical axis was divided into 50 equal parts, with each cell representing an amplitude range of 0.04 V, resulting in a total of 3000 cells. The value of each cell was determined based on the number of discharge points within each respective cell; the conversion process is illustrated in Figure 7. Finally, the pixel values of the grayscale map are normalized to a range of 0 to 255, based on the number of discharge points, resulting in the characteristic grayscale map for the final input model.  
The number of discharge points contained within each array represents the grayscale value per pixel of the PNG image, and the generated image and parameters are shown in Figure 8 and Table 1.  
Figure 6: PRPD plots corresponding to three different discharge types: (**a**) tip model single discharge data; (**b**) PRPD mapping corresponding to tip discharge; (**c**) single discharge data for ball-plate model; (**d**) PRPD mapping corresponding to ball-plate discharge; (**e**) single discharge data for board model; (**f**) PRPD mapping corresponding to board–board discharge.  
Figure 7: Grayscale map conversion.
# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 4 Solution Approach
### Simulated Annealing Chimp Optimization Algorithm with Sine-Cosine Operator
#### 4.2.6 SACHOA Algorithm Steps  
To sum up, the calculation steps for SACHOA are as follows:  
Step 1: Initialize parameters and population. \(a\) is obtained according to Formula (24), as the sequential crossover probability, and \(c\) is obtained according to Formula (25), as the mutation probability.  
Step 2: Initialize the group and divide it into four populations.  
Step 3: Calculate the fitness of all individuals in each population and determine the leader in the population.  
Step 4: Update the position of the leader in each population and determine the attacker based on the fitness.  
Step 5: Optimize the position of the attacker by incorporating the sine-cosine operator.  
Step 6: Use a simulated annealing mechanism to further search the solution space.  
Step 7: If the algorithm reaches the termination condition (the maximum number of iterations), output the global optimal solution; otherwise, repeat Steps 2-6.
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 3 Methodology
### Step 1--Platoon Identification  
To identify HCV platoons at intersections, headway and spacing thresholds are used to evaluate the distance between moving and stationary vehicles. Both connected and conventional heavy vehicles are considered in the platooning process, but conventional heavy vehicles are only assessed based on their spacing. The process of identifying HCV platoons is divided into two main steps: identifying stationary platoons by estimating queue length and identifying moving platoons based on data input from connected HCVs.
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 3 Results  
### Drought Characteristics of the YRB  
#### 3.1.1 SPEI Multi-Time Scale Change Trend  
The temporal variation characteristics of meteorological drought were calculated at different time scales (SPEI-1, SPEI-3, SPEI-6, SPEI-9, and SPEI-12) from 2001 to 2020 (Figure 3). The results show that the number of drought events under the five time scales is not identical. SPEI-1 and SPEI-3 have the largest fluctuations, the most frequent fluctuations, and the cyclical law is not obvious. SPEI-9 and SPEI-12 dry and wet events began to show periodic changes, and there was no obvious positive and negative alternation in the short term. The results show that the larger the SPEI time scale, the more negative values. The 1-month scale is 55.83%, the 3-month scale is 60.83%, the 6-month scale is 61.67%, the 9-month scale is 63.33%, and the 12-month scale is 65.83%. Among them, the negative value of SPEI at the 12-month scale is the most (65.83%), and the cyclical change is the most obvious. Therefore, with the gradual increase of SPEI time scale, the fluctuation frequency of SPEI decreases gradually and the periodic change is obvious.  
Using SPEI-3 and SPEI-12 to analyze the drought temporal variation characteristics of YRB in annual and different seasons, the results are shown in Figure 4. From 2001-2020, the annual SPEI has shown a gradually increasing trend at a rate of 0.0228/a. The fluctuations in the early stage of the year are violent, and the fluctuations in the later stage tend to be stable. SPEI showed a linear downward trend (\(-\)0.2147/a, R\({}^{2}\) = 0.0171) in spring. The fluctuation range was large before 2010 and had a small amplitude after 2010, indicating that YRB showed a gradually drought trend in spring (Figure 4b). SPEI showed an increasing trend in summer and autumn (Figure 4c,d), with an upward rate of 0.0442/a and 0.0061/a respectively. However, the overall fluctuation range in summer is large, and the regularity is not obvious. SPEI in summer reached its minimum in 2015. The rising rate in summer is significantly higher than that in annual, spring, and autumn, indicating that summer presents a wetting trend, which may be related to more rainfall in summer.  
Figure 3: Temporal variation of meteorological drought at different time scales in YRB from 2001 to 2020. (**a**) SPEI-1; (**b**) SPEI-3; (**c**) SPEI-6; (**d**) SPEI-9; (**e**) SPEI-12.