esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning-Based Password Security Evaluation Model
## 4 Experiment and Evaluation
### Experiment Results and Performance Evaluation
However, it is difficult to directly compare the zxcvbn scores and LUDS requirements in Table 7 with the proposed evaluation model because they are not binary classification models that classify passwords as those with weak security and strong security. For example, the LUDS requirement classifies security into five steps from 0 to 4. At this time, if the criterion for the low security passwords is 1, the LUDS requirement in Table 7 classifies 8394 of the leaked passwords as low security passwords and 3852 of the passwords that have not been leaked as high-security passwords, with a classification accuracy of 91.77%. However, if the criterion is set to 2, it has a classification accuracy of 86.75%. To solve this problem, the recovery operation characteristic (ROC) curve was compared. The ROC curve is a method of evaluating the performance of the trained model by using the sensitivity to judge an answer as the correct answer and the specificity to judge an answer as the wrong answer. This is as shown in Figure 11.  
Figure 11 shows the ROC curves for the evaluation model proposed in this paper, the zxcvbn score, the LUDS requirements, and the security evaluation score of Levenshtein distance. Since the x and y axes are 1, the ROC curve has a maximum area of 1. It can be seen that the wider area of the ROC curve reflects better performance of the binary classification. The proposed evaluation model displayed the best binary classification performance at 0.975, and it was 0.938 for the LUDS requirement, 0.922 for the zxcvbn score, and 0.582 for the Levenshtein distance, which was verified to be the lowest binary classification performance. Through this, it was confirmed that the proposed password security evaluation model evaluated the security of a password by considering whether the password has actually been leaked and that it was effective for dictionary attacks by password attackers. Additionally, since the evaluation model proposed in this study extracts features regardless of the password length or complexity and predicts whether a password was leaked, the time complexity is O (1). Therefore, the password security evaluation model is suitable for low-performance IoT devices.
## 2 Materials and Methods
### The Index Establishment of DBS Slip Anchoring Performance Assessment Method
#### 2.2.2 First Index of Slip Anchoring Performance Evaluation: Peak Contact Pressure Criteria  
During horizontal well fracturing operations, maintaining substantial interfacial contact pressure between slip teeth and casing wall is critical for ensuring reliable DBS anchoring integrity. This study employs the peak contact pressure at the slip-casing interface as a key performance indicator for DBS anchoring capability, establishing a corresponding evaluation criterion. The proposed criterion states that anchoring robustness increases proportionally with the maximum attainable contact pressure under applied setting loads.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 5 Hardware Architecture of the FPGA-Based System: An Application Example
### The Proposed FPGA-Based Architecture
#### 5.2.2 The ROM Block  
The ROM block is composed of three ROM memories and its symbol is indicated in Figure 10 with input and output signals. In particular,  
* clk, which is the system gated clock;
* ROM_address, which are ROM addresses of the signal to be processed
* twid_address, which are ROM addresses of twiddle coefficients
* reset
* Bit Slice 3/2/1/0 are the four bits with which each signal sample is encoded
* Twid (Real part) and Twid (img part), which are the 32 bits composing the real and imaginary parts of each twiddle coefficient
## 1 Introduction
The results show that KK transceiver can be used efficiently with performance comparable to a coherent transceiver for space communications. The KK receivers are compatible with multi-channel systems. If the bandwidth of the transmitted signal is less than or equal to the bandwidth of the PD (for example, orthogonal frequency division multiplexing (OFDM)), then the KK receiver extracts the phase, and the  
Figure 1: Free space optical (FSO) communications scenarios between geosynchronous equatorial orbit (GEO) satellites and ground station. complex signal is demultiplexed in digital domain (in the case of OFDM, FFT is used for demultiplexing). If the bandwidth of the transmitted signal exceeds the bandwidth of a PD (for example, wavelength division multiplexing (WDM)), then the received signal is demultiplexed using wavelength division demultiplexer and the demultiplexed signals pass through their respective PDs and KK receivers.
## 1 Introduction
The main contributions of this paper are summarized as follows  
* We investigate the throughput maximization of the cooperative communication system under the jamming attack, where an intermediate relay equipped with a limited-capacity battery is deployed to securely facilitate the transmission from the source to the destination. Meanwhile, due to the limited energy of the jammer, the jamming attack operation is assumed to follow the Markov chain model. * We consider the imperfection of the spectrum sensing as well as the non-RF energy harvesting model at the relay for long-term operation, which greatly affects the network performance in practice. * We propose a POMDP-based scheme at the source node to determine the optimal policy in the cooperation with the relay to improve the long-term achievable throughput of the network in the presence of the jamming attack. As a result, according to the optimal policy, the optimal action in every single time slot operation can be obtained using the proposed scheme. * We evaluate the performance of the proposed scheme in comparison with traditional schemes such as _Myopic_ and _Direct Link Only_ schemes via Matlab simulation under various network conditions. The numerical results are given to show the superior of the proposed scheme as compared with others according to the various network parameters of the cooperative communication network. The remainder of the paper is organized as follows. In Section 2, we introduce the system model and the Markov chain model of the jammer. In Section 3, we describe the optimal policy for direct and relay-assisted transmission modes. Section 4 evaluates our proposed scheme via simulation results. Finally, we conclude this paper in Section 5. To make it tractable to follow, we indicate the most frequently used symbols in Table 1.
## 3 Results
### Exafs  
To shed light on the nature of Component 2, the analysis of the EXAFS portion of the XAFS spectra was performed to provide relevant structural information. The local structure of MnHCF was refined for the most relevant states of charge, i.e., D\({}_{0}\), C\({}_{1}\), and D\({}_{1}\), considering all three components retrieved by the MCR-ALS analysis. Table 1 reports all of the most relevant fitting parameters for the fitted signals, while Figure 6 displays the experimental Fourier transform signals and the respective calculated ones, together with a pair of contour plots as examples for the error parameter determination. By taking into account the results of the MCR-ALS analysis and the agreement with the earlier spectra in Figure 3, the Mn-N ligation was differently considered--in the starting structural model used for the fitting--depending on the mixed character of the Mn state of charge. For this reason, an asymmetric environment for the Mn site, i.e., a distorted octahedron characterized by four equivalent equatorial Mn-N\({}_{\rm eq}\) and two equivalent axial Mn-N\({}_{\rm ax}\), was adopted for D\({}_{0}\). C\({}_{1}\) state and Component 3, which well describes the Mn local structure in this state, were fitted by considering an asymmetric MnN\({}_{6}\) octahedron due to the expected formal trivalent oxidation state of Mn and the respective JT distortion [11; 12; 13]. D\({}_{1}\), instead, is well described by symmetric Mn-N coordination, in line with the occurrence of divalent Mn ions, as a result of the reduction of MnHCF and the Li-ion uptake. The complete match of the experimental versus the theoretical curves of Figure 6 fitting proves the reliability of the above-mentioned structural models used for the data analysis. The EXAFS fitting results indicate: (i) C\({}_{1}\) is structurally similar to Component 3, with the Fe-C, C \(\equiv\) N, Mn-N\({}_{\rm eq}\), and Mn-N\({}_{\rm ax}\) pair distance values equal to 1.89, 1,17, 1.98, and 2.18-2.19 A, respectively. In addition, they are very close to those reported for ex-situ and in-situ measurements on charged manganese hexacyanoferrate [11; 12]. The same holds for their relative EXAFS Debye-Waller Factor. (ii) D\({}_{0}\) is structurally similar to Component 1, considering the same parameters described above, but both are very different with respect to the pristine material, especially when considering the Mn local environment [11; 12]. A large difference affects the Mn site, where the Mn-N distance quotation of 2.18 A usually found for the pristine material [11; 12] splits to 2.10 A and 2.34-2.35 A; (iii) Component 2 is characterized by an asymmetric distribution of the Mn-N first shell: four Mn-N\({}_{\rm eq}\) at 2.10 A and two Mn-N\({}_{\rm ax}\) at 2.17 A. The differences in equatorial/axial Mn-N bond lengths are lower than the respective values in Component 1, where the variation amounts to 0.26 A.
## 3 Experiment
### Development Environment and Evaluation Metrics  
#### 3.1.1 Development Environment  
The work presented in this paper was established, validated, and tested in a laboratory environment, and the experimental results are analyzed and compared. The model development configuration for the lab server includes the hardware: CPU: Intel(R) Xeon(R) Gold 5218, GPU: GeForce RTX 2080 Ti/11 GB, Software: System: ubuntu18.04, the accelerated training framework is CUDA10.2 and cuDNN7, we used pytorch1.7.0 as the training framework for the proposed and all compared models, and put all the experimental models in this paper in the same environment as above for testing, analysis, and comparison.
## 2 Materials and Methods
### Actual Plant Density Measurement from UAV Imagery Using YOLOv3  
Firstly, the number of soybean plants were accurately measured to obtain the actual plant density data of the observation fields. This data will be used as the response variable for the plant density estimation model using satellite imagery and climate data. The high-throughput detection of soybean plants was carried out using the YOLOv3 algorithm that is capable of extracting the number and exact location of the plant canopies from UAV imagery; thus, actual plant density can be evaluated by calculating the number of plants divided by the area.  
YOLOv3 is an end-to-end object detection algorithm based on a deep learning neural network [41]. The YOLOv3 network contained 53 convolutional layers, known as Darknet-53, with improved detection speed and accuracy, and was designed to be more suitable for small object detection than the previous generation [41; 42]. This feature makes YOLOv3 a suitable algorithm for detecting individual soybean plants from UAV imagery.  
The input dimensions for the YOLOv3 network were 416 \(\times\) 416 \(\times\) 3. To meet the input dimension, the orthomosaics were split into 416 \(\times\) 416 pixel images using the virtual raster function in QGIS software (QGIS Development Team 2001, version 3.14), which resulted in a total of 129,520 images. From the split images, 712 images were randomly selected for YOLOv3 model establishment. The training, validation, and test data sets contained 278 (40%), 186 (25%), and 248 (35%) images, respectively. Each soybean plant in the split images was annotated with a bounding box using LabelImg [43], as shown in Figure 3a. To establish the best YOLOv3 model for counting the number of established soybean plants, a confidence threshold affecting object detection accuracy was tested from 0.05 to 0.95 at intervals of 0.05. The epoch size for the training processes was constant at 50. The batch size was set at 4 to speed up the training process due to the limited workstation specs (Intel Core i7-7820X, Nvidia GeForce GTX 1070, 72 GB of RAM and a Windows 10 Pro 64-bit system operation). The prediction accuracy was evaluated based on the coefficient of determination (\(r^{2}\)), and root mean square error (RMSE) values obtained from calculations between the manually and automatically counted established soybean plants from the test data set by the developed models using a different confidence threshold value. The best model was determined by the highest \(r^{2}\) and lowest RMSE values.  
The final process of YOLOv3 was to conduct soybean plant detection from all available split images. The Geospatial Data Abstraction Library (GDAL) function was used in this process to extract the centroid coordinates of the bounding boxes of detected soybean plants according to the world files that contained coordinates for each split image. The detected soybean plants with coordinate data were used for further regression analysis as described in Section 2.5.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 3 Experiment and Analysis  
### Dataset  
#### 3.1.1 Object Detection Dataset  
Given the scarcity of diverse transmission tower types and instances in public datasets, this study captured 5552 images of transmission towers from various angles, different shooting distances, and under diverse lighting conditions. All collected images are in JPG format with a resolution of 2736 \(\times\) 3648 pixels. The dataset encompasses eight types of transmission tower structures, as illustrated in Figure 12. To meet the requirements for training and evaluating the transmission tower identification algorithm, all images were annotated with bounding rectangles using the LabellImg tool.  
The image dataset of transmission towers is split into training, testing, and validation subsets at a ratio of 7:2:1. Specifically, the training subset contains 3965 images, the testing subset includes 1036 images, and the validation subset has 551 images. A detailed distribution of the dataset can be seen in Table 4.
## 1 Introduction  
Soldering plays an essential role in the manufacture of integrated circuits (IC), supporting the bonding of electrical conduction in integrated circuit interconnections between electrical components and printed circuit boards [1]. Due to a series of satellite processor failures induced by tin whiskers in solder joints, the reliability of electronic device solder joints has become a very important matter in electronic packaging in the past few decades [2, 3]. With the trend of increasingly finer pins, greater demands have been placed on reliable solders with excellent resistance to specific service conditions, especially those of satellites.  
Human beings have been dawn to the glamour of the universe and stars for thousands of years, and they are the basis of mythologies and legends, but the universe intermingles glamour and danger. With scientific development, many spacecrafts have been launched since the last century, attempting to understand the origin and evolution of the universe. Cosmic radiation induced by the sun, supernovae and other galaxies, is seen as a risk factor for in-orbit satellites [4, 5]. Radiation particles including \(\gamma\)-ray photons, heavy ions and energetic electrons, could result in the performance drift and failure of electrical instruments, the majority of which is known as the total dose effect, single event effect and so on. According to the reports of National Aeronautics and Space Administration (NASA), the radiation dose rate of low earth-orbit satellites is usually 10-50 Gy(Si)/year, owing to the protection of the atmospheric layer and the magnetic field of the earth. However, for deep space satellites, the radiation dose may be remarkably increased due to the activity of the sun and other galaxies,such as solar storms and gamma ray bursts. Therefore, radiation-shielding components are currently applied to prevent in-orbit satellites from radiation damage [6]. Recently, a number of deep space exploration programs, including ExoMars of European Space Agency (ESA) and Solar Probe Plus of NASA, are in progress in order to explore vast, unknown areas of outer space. The developing trend for satellite miniaturization is proposed to meet the reliability and lightweight requirements of deep space exploration [7; 8; 9]. Therefore, investigations on the effect of cosmic radiation on satellite materials are utterly necessary for further research on the reliability and improvement of materials subjected to radiation, providing instructions and references for the fault-tolerant design of spacecrafts without radiation-shielding components.  
Owing to its outstanding cryogenic performance, tin-lead (SnPb) solder is widely used in the aerospace industry, which is unrestricted by international legislation banning Pb application [10]. In this study, the microstructure and pull force of Sn50 Pb49 Sb1 solder joints subjected to \(\gamma\)-ray irradiation were investigated and the fracture morphologies of solder joints at various irradiation doses were analyzed.
# Stressing State Analysis of SRC Column with Modeling Test and Finite Element Model Data
## 4 Stressing State Analysis Based on Correlation Method
### Correlation Modeling Analysis of Experiment  
Take SRC columns 1-100-2 and 1-200-2 as examples. The SRC column test strain data were modeled as stressing state modes and characteristic parameters by the correlation modeling analysis method described in Section 2.3. Applying the slope increment criterion to the characteristic parameter curves characterizing the overall stressing state of SRC columns, obtain the characteristic point P of 757 kN for SRC columns 1-100-2 and 727 kN for SRC columns 1-200-2. As seen in Figure 5, the correlation stressing state modes of the two SRC columns also show very obvious mutation near the characteristic point P. The correlation characteristic parameters used to characterize the evolution of the stressing state of the concrete, and spiral reinforcement in the SRC columns also show the same mutation in the turnaround near the characteristic point P.  
The failure starting point is defined as the characteristic point P obtained by correlation stressing state modeling analysis and slope increment criterion. Taking SRC columns 1-100 as an example, comparing the load values corresponding to the characteristic point P (757 kN) obtained from the modeling test data and the characteristic point P (733 kN) obtained from the modeling finite element model data, there is only a difference of 24 kN in the value corresponding to the characteristic point P. This phenomenon further illustrates the reasonableness of the finite element model established in this paper and shows that both the modeled finite element model and the experimental data can reveal the failure starting point of SRC columns. In addition, the correlation modeling analysis of the SRC column finite element model can also predict the failure starting point of the tested SRC column.  
Figure 5: Mutation of SRC test’s related characteristic pair: (**a**) Correlation parameter of 1-100-2; (**b**) Correlation mode of 1-100-2; (**c**) Correlation parameter of components; (**d**) Correlation parameter of 1-200-2; (**e**) Correlation mode of 1-200-2; (**f**) Correlation parameter of components.
# Measurement of Static Stiffness after Motion on a Three-Axis CNC Milling Table
## 4 Model of Positioning Experiments  
As experiments show, the actual table position depends on the previous load application method used. Immediate static load affects the assumed position only partially. This fact presents special demands on the mathematical model. Common mathematical models yield unequivocal results from the input values entered therein. This is not the case. We did not build the mathematical model for that reason. Only a schematic model has been built, as shown in Figure 14.  
The model is based on the sources of resistance to motion. These are friction, deformation of the contact surfaces and elastic deformation of the machine structure. All three sources cause non-linearity between load and position change [7].  
In the general case of loading, the mean position of all components is considered. In this case, the sliding determined by friction resistance is completed first. With increased load, the contact surfaces with an oil layer in between are starting to deform. Finally, the support parts are elastically deformed. This holds when the table does not move and the load is applied gradually. Following relieving and reloading in the same direction, the stiffness is considerably greater (Experiment 1). This is due to the fact that the effect of friction and contact surfaces is considerably smaller in the second round of load application. Delimitation of backlash and deformation of the contact surfaces has a certain capacity for pliability, which has decreased with the load. This capacity can be restored only by moving the elements to which the load is applied.  
A significant difference is the case with the table assuming a given position under the load (Experiments 2 and 3). In this case, the effect of friction and contact surfaces is greater. The capacity for pliability is not reduced as much as in the case of a stationary load. The stiffness is lower despite the fact that the direction of constant load (for example F\({}_{r}\)) has not changed.  
Figure 14: Table Positioning Model, inspired by the ISO 230-2 [34] standard.  
In the first experiment, a reference point R1, which remains unchanged with respect to the machine's co-ordinate system, was used. Measured displacement values A also included the influence of temperature, drive non-linearity, control errors, and the like. To assess stiffness, the slope of the load trend was essential. The absolute deviation value from that of the reference value for stiffness is irrelevant.  
In the second and third experiments the reference point R2 was used. This eliminated the effects of some sources of inaccuracy. In particular, the effect of the thermal shift was reduced.  
Measurement of the dead zone B was not investigated. Although this component has a considerable effect on accuracy, its effect on stiffness is minimal. The dead band is measured according to the ISO 230-2 [34] standard.