esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Tolerance Design Method for the Active Phased-Array Antenna
## 3 Statistical Analysis of the Performance of the Array Antenna
### Expectation Derivation  
The expectation of the maximum power density deviation \(\Delta P\) is determined as follows based on the mathematical definition.  
\[\mu=E(\Delta P_{1}+\Delta P_{2}) \tag{11}\]  
Figure 6: The Taylor expansion in the E plane of the \(8\times 8\) subarray. The operating frequency is 10 GHz, and the element spacing is 15 mm. The x and y directional position errors of the antenna were ignored, and only the z directional position errors were considered.  
\[\mu=E(\Delta P_{1}+\Delta P_{2}) \tag{12}\] \[E(\Delta P_{2})=\frac{1}{2}\Bigg{(}\sum\limits_{i=1}^{m}\sum \limits_{j=1}^{n}\left\{\frac{\partial^{2}P}{\partial z_{ij}^{2}}\Delta z_{ij}^{ 2}\right\}\Bigg{)}=\frac{1}{2}\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}E \left(\frac{\partial^{2}P}{\partial z_{ij}^{2}}\Delta z_{ij}^{2}\right)\] (13) \[=\frac{1}{2}\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}\left\{ \frac{\partial^{2}P}{\partial z_{ij}^{2}}E\Big{(}\Delta z_{ij}^{2}\Big{)}\right\}\]  
Based on the definition of variance, one can derive that:  
\[D\Big{(}\Delta z_{ij}\Big{)}=E\Big{(}\Delta z_{ij}^{2}\Big{)}-\left[E\big{(} \Delta z_{ij}\big{)}\right]^{2} \tag{14}\]  
Then the expectation of \(\Delta z_{ij}^{2}\) can be determined:  
\[E\Big{(}\Delta z_{ij}^{2}\Big{)}=D\Big{(}\Delta z_{ij}\Big{)}+\left[E\Big{(} \Delta z_{ij}\Big{)}\right]^{2}=\left[\left(\sigma_{ij}^{d}\right)^{2}+\left( \sigma_{ij}^{c}\right)^{2}\right]+\left(\mu_{ij}^{d}+\mu_{ij}^{c}\right)^{2}= \left(\sigma_{ij}^{d}\right)^{2}+\left(\sigma_{ij}^{c}\right)^{2} \tag{15}\]  
Then the expectation of deviation \(\Delta P\) is determined:  
\[E(\Delta P_{2})=\frac{1}{2}\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}\left\{ \frac{\partial^{2}P}{\partial z_{ij}^{2}}\bigg{[}\left(\sigma_{ij}^{d}\right) ^{2}+\left(\sigma_{ij}^{c}\right)^{2}\bigg{]}\right\} \tag{16}\]  
Finally, the expectation of deviation \(\Delta P\) is determined:  
\[\mu=E(\Delta P)=\frac{1}{2}\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}\left\{ \frac{\partial^{2}P}{\partial z_{ij}^{2}}\bigg{[}\left(\sigma_{ij}^{d}\right) ^{2}+\left(\sigma_{ij}^{c}\right)^{2}\bigg{]}\right\} \tag{17}\]
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 2 Materials and Methods
### Sensor
#### 2.3.2 Processing Unit  
Due the aforementioned development steps (initial testing, laboratory tests and filter matching as well as a field test), a Raspberry Pi was chosen as the processing unit because it is small, powerful, easy to configure and not energy hungry. It had been previously used together with an Ultramic 250 k microphone in a bat recording project [44]. The model used was the Raspberry Pi 2. The Raspberry Pi is a single-board computer consisting of an ARM processor, USB and ethernet connectors, HDMI out and an SD Card reader, which holds a Linux OS.  
Figure 10: Pseudo-code implemented in the sensor.  
Figure 9: Block diagram of the wireless sensor.  
The system is capable of processing the input acoustic signal in real-time; thus, it could be used to filter and detect thresholds (Figure 10). The principal part of the detection algorithm was configured using Linux's SoX-Sound eXchange version 14.4.2 [45].  
For each event (modt detection), the system created a resulting audio file (the acoustic signal of the moth). A cron-job (configurable recurrent process) defined on the operating system checked the creation of new audio files each minute. In a positive case, the unit connected to the database server and upgraded the event counter for the individual sensing unit.
## 2 Numerical Models  
A systematic numerical investigation was conducted in this study, using a total of three different models in order to establish the integrity of the xFEM and CZM facilities of LS-DYNA. First, a model that was analyzed by another investigator [1] was tried to validate the integrity of our approach. Then, since the configuration of the materials forming our 3D-FML is relatively complex, it was decided to initially simulate the response of the standard double cantilever beam to further home our skill in using the xFEM and calibrate the properties required for conducing such analysis. Finally, the response of a less-complex equivalent model of our 3D-FML material, subjected to an axial impact, was simulated.  
As briefly mentioned, the first trial involved simulation of the response of a simply-supported rectangular cross-section cast iron beam specimen subjected to an impact load at its mid-span using xFEM. The parameters required for xFEM simulation of the specimen were extracted from reference [1]. It should be noted that the efficient approach commonly used in simulating such simple 3D geometries is by modeling them as either 2D plane-stress or plane-strain geometry, depending on the aspect ratios of the specimen. Therefore, an attempt was made to simulate the beam's response by a plane-strain model. However, a convergent result could not be achieved when the xFEM was used in conjunction with the 2D plane strain element of LS-DYNA (even though LS-DYNA user-manual explicitly states admissibility of that element type in conjunction with xFEM). Consequently, LS-DYNA's shell elements (type 54 in conjunction with the fully integrated base element 16) were used to continue the modeling effort. It is reckoned that shell elements are not used conventionally to simulate such geometries (i.e., geometries with an appreciable thickness-to-depth ratio); nonetheless, an accurate fracture response could be successfully predicted in comparison to the experimental results reported by [PERSON] et al. [1] (who incidentally used the same approach in modeling the specimen's response). A detailed explanation of the modeling approach, as well as the discussion of the required parameters, are presented in the Appendix A. In addition, the value of the parameters used in our models are given in Table 1.  
The simulated results confirmed the integrity of the selected algorithm and element type; thus, they were used in the subsequent phases of the analysis. However, before continuing with the remaining analyses, it warrants to discuss the material model and the required parameters that will be required when conducting xFEM modeling.
## 3 Direction-of-Arrival Estimators with Known Waveforms  
In this section, we first derive the maximum likelihood (ML) DOA estimator for signals with known waveforms, then the interferometer-based DOA estimator with known waveforms is proposed intuitively.
# A Geographical Heuristic Routing Protocol for VANETs
## 1 Introduction
In [9], the authors show that any geographical routing protocol that operates under a hop-by-hop forwarding strategy can be understood as a direct application of the general \"local search\" algorithm of discrete optimization. Furthermore, they propose to adapt some other heuristic to geographical routing protocols for VANETs in order to improve the results obtained with local-search algorithms. In this work, we propose a generic Geographical Heuristic Routing (GHR) protocol that can be applied to any DTN geographical routing protocol that makes forwarding decisions at each hop. Our proposal combines all of the adaptations presented in [9] for the forwarding and recovery phases of the protocol, which are based on Tabu search and simulated annealing. Our paper offers a thorough performance evaluation of GHR with MMMR, a traffic-aware routing protocol suitable for delay-tolerant applications. Our study analyzes all of the meaningful combinations that our generic algorithm provides. Finally, to provide a complete analysis of our findings, we assess MANOVA and paired statistical \(t\)-tests to identify which performance differences are significant. This analysis takes into account the effect of the vehicle density in the appearance of differences. We found that some features of GHRbehave better than others depending on the vehicle densities or the applications' requirements. The rest of the paper is organized as follows: Section 2 summarizes some other works that use optimization techniques in wireless networks. Then, Section 3 present the Tabu search implementation used in our proposal, in addition to the simulated annealing strategy and a generic routing procedure. Section 4 describes the algorithm of our heuristic routing protocol proposal. Next, Section 5 is devoted to the performance evaluation of our contribution, which includes the description of the simulation scenario and the statistical analysis of the simulation results. Finally, conclusions and future work are drawn in Section 6.
# Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs
## Data
### Amsr-E
The SST was then retrieved using a physically based two-stage regression algorithm that expresses SST in terms of the resampled brightness temperatures (see the Appendix of [2] for an overview of the AMSR-E SST retrieval algorithm). The retrieved values for each grid point are then averaged over five along-track pixels, two on each side of the pixel of interest ([PERSON], personal communication 31 May 2018). Errors in the AMSR-E SST retrievals resulted primarily from scattering due to rain, sidelobe contamination due to proximity to land and/or sea ice, radio frequency interference (RFI), sunlight, wind speed \(>\)20 m/s, and proximity to the edge of the swath. Data flagged for any of these conditions were excluded from the analysis.
# Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications
## 3 Experimental Results
### Results and Analysis
Figures 7 and 8 imply that, when designing a MIMO underwater acoustic system, our proposed CE_IC+DFE_IC receiver is fairly suitable as an underwater acoustic channel, because the filter length of our proposed CE_IC+DFE_IC receiver can be set as long as possible, regardless of the length of the real underwater acoustic channel. In other words, our proposed method has more flexibility. However, as shown in Figures 7 and 8, the DA-DFE is very sensitive to filter length, which should be carefully tuned in one scenario and changed in other scenarios. In addition, the DA-DFE is very sensitive to the forgotten factor. In sum, our proposed CE_IC+DFE_IC offers a new approach to the design of a robust underwater acoustic MIMO communication system. Finally, we compared the computational complexity using the consumption of time. The code runs on the matlab platform in a laptop, the CPU is Intel i7-10510U, and the size of RAM is 16 GB. The sequences with a length of 187.5 ms are used for Doppler estimation, Doppler compensation, and channel estimation in our proposed CE_IC+DFE_IC receiver, while in the DA-DFE receiver, the sequences with a length of 187.5 ms were used to train the filters. After obtaining the equalizer filters, 500 QPSK symbols were equalized for both CE_IC+DFE_IC RLS_DADFE, and IPNLMS_DADFE receivers. The lengths of the feedback filters and IC filters are half the lengths of the feedforward filter. Table 3 shows the average time. It can be observed that the consumption for IPNLMS_DADFE is the shortest, since it does not need the matrix inverse process. However, it does not achieve a considerable performance. Comparing the consumption of RLS_DADFE and the proposed CE_IC+DFE_IC, when the filters length for RLS_DADFE is short, e.g., \(L_{fb}=18.7\) ms, the consumption for DA-DFE is short; however, when the filter length for RLS_DADFE is relatively long, the consumption is significantly increased. For example, when the filter length time is \(62.5/18.75=3.33\), the consumption time is \(4408.8/45.85=96.1\). However, the consumption of our proposed method almost remains the same. The key reason for this is that the RLS_DADFE performs symbol by symbol, while our proposed method performs block by block. From Table 3, it can be concluded that when the channel length is long, our proposed method has great advantages over RLS_DADFE in terms of computational complexity. Figure 8: Relationship between length of feedforward filter and the output SNR. The lengths of feedback filters and IC filters are fixed.
However, there are several shortcomings in the application of transfer learning methods for fault diagnosis: (1) Cooperation between the feature extraction model and the domain adaptation algorithm: Under complex working conditions, the feature extraction model may fail to extract effective features from the source domain, resulting in the domain adaptation algorithm's inability to effectively bridge the gap between the source and target domains. (2) Limitations of common domain adaptation algorithms: Algorithms such as MMD and MK-MMD primarily focus on reducing marginal distribution differences between the source and target domains but neglect the joint distribution of labels and features. (3) Challenges with CDAN and DANN-based algorithms: These algorithms achieve domain adaptation through adversarial training, but the classifier's decision boundary can be easily disrupted in the target domain, potentially leading to a decrease in the classifier's generalization performance on the target domain. Based on the aforementioned research status and identified shortcomings, this paper proposes a novel method for bearing fault diagnosis that is effective in conditions with high levels of noise and complex working conditions. (1) This paper innovatively integrates three types of images: MTF, CWT, and RP. The fused images encompass various features, including state transition characteristics, periodicity, autocorrelation, and time-frequency characteristics. This approach overcomes the limitations of existing single-feature processing methods, which struggle to comprehensively capture features and maintain stability under complex working conditions. (2) This paper proposes a residual network based on the Kolmogorov-Arnold representation theorem. By introducing KABlock layers into the traditional residual network and combining fixed basis functions with spline functions, the model's adaptability and robustness under complex working conditions are enhanced. This optimization enables the model to better capture the complex dynamic characteristics and nonlinear features of bearing vibration signals. (3) This paper introduces an innovative domain adaptation method that integrates the MK-MMD and CDAN+E algorithms. MK-MMD effectively aligns the distributions of the source and target domains through a multi-kernel approach, providing a solid foundation for the adversarial learning of CDAN+E, which helps improve the generalization of the CDAN+E classifier. CDAN+E introduces adversarial learning and entropy conditioning, leveraging the joint distribution of labels and features to overcome the limitations of MK-MMD, which only considers marginal distributions. The integrated domain adaptation algorithm more accurately aligns the source and target domains, harnessing their complementary advantages and demonstrating higher adaptability and robustness in complex cross-domain tasks.
# AEMB: An Automated Exploit Mitigation Bypassing Solution
## 3 Exploit Mitigations Bypassing
### Exploit-Mitigation Identification  
The first stage of _bypassing exploit mitigations_ is to identify exploit mitigations, which is mainly to determine which exploit mitigations exist in the current environment. The following takes some specific exploit mitigations, which are the most basic mechanisms DEP, GS, and SAFESEH, on Windows and Linux, as examples to illustrate. They are introduced as follows.  
* **DEP.** The DEP exploit mitigation requires the support of the scompiler and operating system under Linux and Windows, and its essence is to disable the execution permissions of the data memory page. The _AEMB_ dynamically executes the target application and then detects the permissions of memory pages that store the data, thereby judging the the DEP mechanism to be enabled, as shown in Figure 2(a).
* **GS and SAFESEH**. The GS exploit mitigation is to include stack frame protection codes, which involve saving the security cookie at the calling of the function and verifying the cookie at the return of function at compile time. The _AEMB_ statically analyzes the assembly code to judge the enablement of the GS mechanism through the existence of stack frame protection codes. As shown in Figure 2(b). The Safe Exception Handlers (SAFESEH) [18] is a Windows mechanism that can prevent the bypassing of the GS mitigation. It can produce a table of the process's safe exception handlers. This table specifies for the operating system which exception handlers are valid for the process. The _AEMB_ determines if the SAFESEH security mechanism is enabled by determining whether the current operating system supports the SAFESEH security mechanism and whether the SAFESEH flag bit is enabled in the binary's file format, which is the PE file.
# FirmHunter: State-Aware and Introspection-Driven Grey-Box Fuzzing towards IoT Firmware
## 5 Evaluation
### Evaluation of FirmHunter  
In order to evaluate the effectiveness and efficiency of FirmHunter, we tested it on eight IoT firmware images and compared it with a state-of-the-art IoT fuzzer FirmFuzz [10] and a web application scanner ZAP [11]. The purpose of this section is to test whether  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Name** & **Description** \\ \hline \multirow{4}{*}{String} & Null Value & Change the value to null to trigger potential Null Pointer Dereference (NPD) vulnerabilities. \\ \cline{2-3}  & Long String & Extending the string with a thousand character “A” to trigger Buffer Overflow (BO) vulnerabilities. \\ \cline{2-3}  & CI Attack & Using command injection statements to trigger potential Command Injection (CI) vulnerabilities. \\ \cline{2-3}  & XSS Attack & Using XSS injection statements to trigger potential Cross-Site Scripting (XSS) vulnerabilities. \\ \hline \multirow{4}{*}{Number} & Large Number & Mutate original number value to very large number for integer overflow to trigger potential BO vulnerabilities. \\ \cline{2-3}  & Boundary Number & Mutate original number value to off-by-one value for out-of-bound access to trigger potential BO vulnerabilities. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mutating strategies.  
FirmHunter can detect vulnerabilities in real-world IoT firmware images effectively with the help of our state-message queue and introspection-driven scheduling, together with whether FirmHunter outperforms other work in vulnerability detection. In short, we would answer the following questions:  
* **Q1**: Do our message-state queue and introspection-driven scheduling help to detect vulnerabilities?
* **Q2**: How effective is FirmHunter in detecting real vulnerabilities compared to other popular tools?  
Testing Images: We collected more than 500 IoT firmware images and successfully emulated no more than 30 unique firmware images with accessible WEB interfaces. Within them, FirmHunter and benchmarks found vulnerabilities in eight firmware images, including seven routers and an IP camera. The detailed specification of these images is described in Table 3.  
Testing Environment: Experiments are conducted on an 8-core Intel(R) Core(TM) i9-990 KF 3.60 GHz CPU machine with 62.8 GB of RAM. The operating system is Ubuntu 18.04 LTS.
# An Analysis of Spatial Variation in Human Impact on Forest Ecological Functions
## 3 Results  
### FEF Evaluation and Its Spatial Variation  
#### 3.1.1 Assessment of Forest Ecological Functions  
In order to analyze the spatial distribution characteristics of the county-level forest ecological function in the study area, ArcGIS 10.8 software is used to perform visualization. According to the calculated indices, they are classified into five grades with natural breaks classification at first, as is shown in Figure 2a. Meanwhile, with reference to the classification criteria developed by the Technical Regulations for Continuous Forest Inventory (GB/T38590\(-\)2020) [32], FEF can be categorized into three grades: \"Good\" [0.667, 1], \"Moderate\" (0.4, 0.667), and \"Poor\" [0-0.4], which is demonstrated in Figure 2b.  
We find that the overall ecological function of county-level forests in the YRB registers at a moderately low level, with an average score of only 0.438, and there are significant disparities in their spatial distributions. Overall, the FEF in the southern mountains is better than that in the northern plateau and desert, while the FEF in the central areas is superior to that in the western alpine zone and eastern plains. Counties with relatively better FEF occupy an extremely limited portion, and they are distributed mainly in the semi-moist areas in the middle reaches, especially along the Qinling, Ziwuling and Lvliang mountains. The counties with \"Moderate\" FEF fall into two subcategories: those abundant in natural forest resources, such as counties along the mountains, and those in river plain areas with rich arable land resources. The low-value area mainly clusters in the north-central YRB, such as the Loess Plateau and the Ordos Plateau, where there is an arid climate, limited precipitation, and, consequently, low vegetation coverage. There are also some counties  
Figure 2: Spatial characteristics of forest ecological functions: (a) distribution of forest ecological function index; (b) classification results; (c) spatial agglomeration patterns.  
with poor FEF scattered in the Guanzhong Plain and North China Plain, which are major suppliers of agricultural products in China.
## 5 Modelling Rationale
### Modelling Strategy  
We follow a modular strategy to build our models. Specifically, first we develop a model of the link reliability when transmitting one frame or several replicas of the same frame. The link model allows us to measure the probability of losing a message edition depending on the BER and the number of replicas. Besides the link model, we have developed three different models to evaluate the reliability of TSN, PTRF approach A and PTRF approach B, to which we refer as general models. We use the results of executing the link model to feed the general models. In this way, we simplify the general models and reduce both the state space (preventing it from exploding) and the computation time.  
We use PRISM to develop our models. PRISM is a probabilistic model checker that can be used to evaluate the reliability of systems [33]. PRISM allows modelling the system using different stochastic formalisms, among which we find DTMCs. In PRISM the models are developed using a modelling language that allows building a DTMC as a set of modules, which are the main composition units. On top of modules, PRISM allows using variables to define the local state of the modules. The global state of the model is determined by the local state of all the modules of the model. Moreover, PRISM provides commands to specify the behaviour of the modules. PRISM transforms the modules that conform a model into a Markov chain and solves it analytically.  
PRISM also provides a property specification language, which allows specifying which properties of the model must be checked. This language can be used to calculate the probability with which the model is in a certain state and, thus, it allows expressing reliability metrics such as the one we have defined in Section 5.1.  
For the sake of clarity and succinctness, we do not describe the models in detail. Instead, we next describe the most important characteristics of each model.