esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 2 MTD Techniques
### Discussion
#### 2.3.4 Challenges for Existing MTD Techniques  
Based on our survey, we identified several issues that require improvement in some existing MTD techniques, including:  
* Large resource consumption and high defense costs (we have highlighted this issue several times during the introduction of SDR).
* For example, in the face of the attacker's scanning, the existing MTD's countermeasure is to perform IP hopping when scanning behavior is detected, and their representative techniques include but are not limited to OF-RHM [11], SEHT [12], DDS [13], and NATD [14]. Their common problem is a lack of accuracy and efficiency in identifying attack manners, the waste of resources caused by untargeted hops, and a lack of integration with the affordable defense pursued by MTD.
* They have an incapability of balancing multi-constraints (e.g., costs, security performance, and service availability).
* For instance, routing randomization has been proven to be an effective method against eavesdropping attacks. Currently, representative routing randomization techniques include but are not limited to: RRM [53], AE-RRM [50], AT-RRM [54], and SSO-RM [55]. However, RRM and AE-RRM implement random transformations only on the routesof data transmission between nodes, without considering different attack behaviors and protecting network QoS under such circumstances. As for AT-RRM and SSO-RM, they can dynamically adjust transformation strategies to some extent, but their protection effectiveness for QoS is still unsatisfactory, and they fail to consider the varying demands of different applications for latency and bandwidth. Besides, all of their packets' granularity is too coarse, making it easy for attackers to intercept continuous data packets and render the defense ineffective.
* Relatively fixed defense strategies (easy to be reconnoitered and recognized by attackers).  
An example is the ASLR [56] deployed in Unix systems. It performs well in defending against buffer overflow vulnerabilities by randomly selecting the base address of the stack at runtime. This means that the location of each variable in memory is uncertain, making it difficult for attackers to exploit these vulnerabilities. However, ASLR is vulnerable to BROP attacks [57], which can exploit the fact that the parent process retains the same address space layout when forking a child's process. This example illustrates that fixed defense strategies are vulnerable to countermeasures applied by attackers. Therefore, there is no easy way for MTD to achieve long-term defense success.  
Although MTD is confronted with many challenges, its idea that transitioning from passiveness to activeness and affordable defense is the future trend of cyber security means it has broad application prospects in many fields. With the help of machine learning, MTD research has met a brave new world. In Section 3, we introduce the intelligent MTD techniques and how these three problems are improved (and demonstrate specific feasible solutions to the three examples mentioned above).
## 4 Results
### Classification Model Performance  
To measure the proposed classification model performance, we introduced other machine learning algorithms, including KNN, RF and single SVM, which are frequently used for classification [4; 9; 40]. Then, we used the classification matrices including the PA, UA, OA and KAPPA coefficient defined in Table 2 to evaluate the performance. Usually, the higher the values of PA, UA, OA and KAPPA, the more precise the classification model is. The PWD infection stage detection results with hyperspectral data from KNN, RF, SVM and the proposed GA and SVM classification models are listed in Tables 3 and 4.  
Compared with the KNN, RF and SVM methods, the proposed GA and SVM classification model showed the highest PA values (100%) on the testing dataset in different infection stages and the highest UA values (100%) on the testing dataset in the healthy, early and middle stages. Moreover, the proposed GA and SVM classification model provided the best accuracy (OA%: 95.24%, KAPPA: 0.9234). The results demonstrated that the collinearity existing between the spectral features usually deteriorated the performance of PWD infection stage identification, and the GA algorithm could select the optimal spectral features to present the dataset characteristics instead of using all 16 extracted spectral features. With the optimal spectral features (D\({}_{\text{green}}\), SD\({}_{\text{green}}\), D\({}_{\text{red}}\), D\({}_{\text{RE}}\), D\({}_{\text{NIR}}\), SD\({}_{\text{NIR}}\)), the SVM parameters were also optimized and achieved better performance in the PWD infection stage detection than the KNN, RF and SVM classification models with 16 spectral features.
###### Abstract  
The records of the Advanced Very High Resolution Radiometer (AVHRR) instrument observations can resolve the current lack of a long global climate data record of Reflected Solar Flux (RSF), by transforming these measurements into broadband flux at the top-of-atmosphere. This paper presents a methodology for obtaining daily mean RSF (Wm\({}^{-2}\)) from AVHRR. First, the narrowband reflectances are converted to broadband reflectance using empirical regressions with the Clouds and the Earth's Radiant Energy System (CERES) observations. Second, the anisotropy is corrected by applying Angular Distribution Models (ADMs), which convert directional reflectance into a hemispherical albedo. Third, the instantaneous albedos are temporally interpolated by a flexible diurnal cycle model, capable of ingesting any number of observations at any time of day, making it suitable for any orbital configuration of NOAA and MetOp satellites. Finally, the twilight conditions prevailing near sunrise and sunset are simulated with an empirical model. The entire day is then integrated into a single daily mean RSF. This paper furthermore demonstrates the methodology by validating a full year (2008) of RSF daily means with the CERES SYN1 deg data record, both on daily and subdaily scale. Several configurations are tested, each excluding particular satellites from the constellation in order to mimic orbital changes (e.g., orbital drift), and to assess their relative importance to the daily mean RSF. The best performance is obtained by the combination of at least one mid-morning (NOAA-17 or MetOp-A) and one early afternoon (NOAA-18) orbit. In this case, the RMS difference with CERES is about 7 Wm\({}^{-2}\). Removing NOAA-18 degrades the performance to an RMS difference of 12 Wm\({}^{-2}\), thereby providing an estimate of the impact of NOAA-19's orbital drift between 2016 and 2020. Very early or late observations (NOAA-15, NOAA-16) provide little added value, and both mid-morning orbits turn out to be almost interchangeable given their close temporal proximity.  
AVHRR; TOA; broadband; radiation; flux; diurnal cycle; daily mean Article
## 4 Methodology  
### Questionnaire Design  
This study uses a quantitative research method using a questionnaire to test the research model. This study benefits from several advantages associated with employing a survey method. First, the data required for this study are perceptual. Consumer perceptions, which encompass beliefs and attitudes about the consumption experience of cultural heritage products, are best captured through survey methods [51]. Second, using a questionnaire method also strengthens the relevance and impact of the research findings [51]. The researcher collected primary data because the type of data required for this study was consumer perception data of cultural heritage-type products influenced by AR technology, which is not available from any public source. By reviewing the experience of the related literature, the researchers developed a questionnaire for assessing the impact of AR on the willingness to purchase a cultural heritage product. The questionnaire consisted of three parts. The initial section of the survey included an informed consent form. It verified that respondents had made at least two purchases of cultural heritage products and used AR technology during the purchasing process. Respondents who did not consent to the collection of anonymized data for this study or who failed to meet the experience criteria were excluded from this study. The second section investigated demographic information about the respondents. The third section collects data about the respondent's user experience. Respondents were asked to rate these items. Each item was assessed using a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). In order to measure the impact of consumer use of AR on the cultural heritage product purchasing process, all variables and user experience factors were measured using the items listed in Table 1. These items were derived from research conducted by TAM and flow researchers and other well-established evaluation scales used to assess AR technology's relevance to the consumer sector. Figure 2: The research model of the effect of AR on the intention to purchase cultural heritage products. To ensure a wide range of respondents and the accuracy of the translation, the questionnaire, initially in English, underwent a rigorous back-to-back translation process. First, a researcher, not a native English speaker, translated all the original entries into other languages. Subsequently, a different researcher independently translated these entries back into English. Moreover, both researchers verified the semantic accuracy of the translations by comparing the two English versions. The questionnaire was finally translated into English, and the questionnaire was finally translated into English.
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
## 2 Materials and Methods
### Software Architecture  
The Linux-based operating system, Raspbian Buster, was installed on the CBM sensor. A combination of C++ programing and the open-source Python 3 (Python Core Team 2015) was utilized as the programming languages to implement all the data acquisition functionalities, onboard processing, and cloud uploading.  
Once the CBM sensor is switched ON it automatically connects with a predefined WiFi network that is available in the field, to connect with the master repository, which runs on a remote server over the internet. Inside the server, all the high-level functions of further filtering (denoising), processing, and analyzing the acquired data are scheduled immediately after injecting a new acquisition. These high-level processing nodes were implemented on the remote server because of the availability of more processing power. In the current development stage, the Raspberry Pi logs on to a password-protected hotspot upon the boot-up. In the absence of a nearby WiFi network, the CBM sensor performs in a non-networked mode, i.e., the acquired data is saved onto the device's memory and uploaded to the cloud server once an internet connection has been found. Once the data are uploaded, the system compresses the local copy of the data in an archive for safe-keeping; older data points are routinely deleted as the system space fills to free up system space.  
The LiDAR and GPS units take around 20 s to initialize, load all the required packages, and establish a serial connection with the Raspberry Pi. An indicator LED on the system that is mapped to a physical GPIO pin is used to relay the runtime status of the system to the user. At initialization, the system indicator LED remains in a'steady solid', i.e., HIGH state. Upon initialization, it starts 'flashing rapidly', i.e., HIGH-LOW state at a frequency of 20 Hz. At this'ready' state, the data acquisition from the LiDAR and GPS could be triggered using a push-button switch (active-low with internal pull-up resistance). During data acquisition, the system status LED reverts to the'steady solid' state. The sensor provides the ability to pause (inactive state) or resume (active state) multiple times,  
Figure 1: Schematic overview of the components that were used in the CropBioMass (CBM) sensor. The integrated systems consist of a solid-state LiDAR sensor, onboard Raspberry Pi 4 computer, and an autopilot controller that was used for the embedded GNSS logger.  
triggering the status LED to change response accordingly. All the critical settings, such as sampling frequency, signal strength, accumulation rate, and time tag format, are predefined as the default parameters for ease of in-field operation.
# An FDM-Based Dynamic Zoning Method for Disturbed Rock Masses above a Longwall Mining Panel
## 2 The Dynamic Zoning Method
### Determining the Zoning Criteria  
In the process of underground mining, the deformation and failure characteristics of the overlying disturbed rock masses are different, which corresponds to the different stages of the simplified complete stress-strain curves. The rock masses in the continuous deformation zone undergo elastic-plastic deformation, and they are in the elastic deformation stage (Stage I) or post-peak softening stage (Stage II). The rock masses in the surface cracking zone and the fractured zone go through the process including elastic-plastic deformation and cracking, and they are in the residual strength stage (Stage III). After the process of elastic-plastic deformation, cracking, and caving, rock masses in the caved zone fill the mined-out area; the caved rocks are in the caved material deformation stage (Stage IV).  
According to the simplified complete stress-strain curve, the zoning criteria are established by considering the deformation and failure characteristics, as well as the location of the overlying disturbed rock masses.  
The parameter \(\varepsilon^{p}\), which represents the plastic strain of the rock mass, is one of the major metrics for dynamic zoning, while'denotes the total strain of the disturbed rock mass. Details of the zoning criteria are listed as follows.  
(1) \(\varepsilon^{p}=0\) and \(\varepsilon^{t}\geq 0\).  
This means that the rock mass is in the elastic deformation stage, and if the rock mass is located above the fractured zone, it will be classified into the continuous deformation zone.  
(2) \(0<\varepsilon^{p}<\varepsilon_{residual}^{p}\).  
This means that the rock mass is in the post-peak softening stage, and if the rock mass is located above the fractured zone, it will be classified into the continuous deformation zone.  
(3) \(\varepsilon_{residual}^{p}\leq\varepsilon^{p}<\varepsilon_{loosened}^{p}\).  
This means that the rock mass is in the residual strength stage. If the rock mass is located above the caved zone, it will be classified into the fractured zone, whereas if the rock mass is located near the ground surface, it will be classified into the surface cracking zone.  
(4) \(\varepsilon^{p}\geq\varepsilon_{loosened}^{p}\).  
This means that the rock mass is in the caved material deformation stage, and if the rock mass is located above the mine mined-out area, it will be classified into the caved zone.  
It should be noted that in general, a rock mass is of low tensile strength, and if the rock mass is subjected to tensile failure, it should be considered to be in the residual strength stage.
# Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach +
## 4 AN Maximizing Relay's ASER Performance
### Assigning \(|z|\) for a Given \(E_{z}\)  
We rewrite the function \(\mathrm{ASER}(z)\) in Equation (25) as \(\mathrm{ASER}(\sqrt{x})\) to reflect the effect of AN power, where \(x=|z|^{2}\). Similar to Subsection 3.3, the optimal power assignment problem is given by replacing \(\overline{\mathrm{SER}}(\sqrt{x})\) in Equation (20) by \(\mathrm{ASER}(\sqrt{x})\), and now \(f(x)\) represents the PDF of \(x\) for Rayleigh fading channels. Since one can see that \(\mathrm{ASER}(\sqrt{x})\) in Equation (25) is a monotonically increasing function of \(x\), the distribution of the optimal AN directly follows from the PDF expression derived in Equation (21). Therefore, substituting Equation (21) into Equation (25) yields the expected ASER,  
\[\overline{\mathrm{ASER}}(x_{1},x_{2})=\frac{x_{2}-\overline{p}}{x_{2}-x_{1}} \mathrm{ASER}(\sqrt{x_{1}})+\frac{\overline{p}-x_{1}}{x_{2}-x_{1}}\mathrm{ ASER}(\sqrt{x_{2}}) \tag{26}\]  
where \(\overline{p}=\frac{E_{z}}{I_{0}}\) is the average power constraint on the AN, and \(0\leq x_{1}\leq\overline{p}\leq x_{2}\). The optimal values of \(x_{1}\) and \(x_{2}\) maximizing \(\overline{\mathrm{ASER}}(x_{1},x_{2})\) in Equation (26) are then derived numerically based on \(\overline{p}\), \(M\), \(E_{m}\), \(\sigma\), \(\sigma_{h}^{2}\), and \(\sigma_{g}^{2}\), where \(\sigma_{h}^{2}\) and \(\sigma_{g}^{2}\) represent the statistical/long-term CSI. Note that the computational complexity of the derivation has been investigated in Section 3.3.  
Similar to the analysis following Equation (24), we can also show that as the AN power increases, the expected ASER in Equation (26) converges to the non-informative SER performance.
## 2 Materials and Methods  
A narrative review was performed that synthesized literature results from PUBMED. In the search strategy, Medical Subject Headings (MeSH) terms were used. Search terms included in all possible combinations were Human Papillomavirus, HPV, Radiomics, Texture Analysis, Deep Learning, Machine Learning, Oropharyngeal Squamous Cell Carcinoma, OPSCC, Head and Neck Cancer. Identified records were first screened by title and abstract, and those judged to be not pertinent were excluded. Then, full-text articles were assessed for eligibility. In this step, systematic and narrative reviews and meta-analysis were excluded, as well as papers investigating the relationship between radiomic features and other outcomes than HPV assessment (e.g., tumor staging, risk stratification, treatment response assessment, gene expression). In the end, only retrospective mono- or multicentric series assessing the correlation between MRI, CT and 18F-FDG PET radiomic features and HPV status prediction in oropharyngeal cancer patients were included (Figure 1). Selected papers were in English and included studies on humans. The range of publication date was July 2015-April 2023. A PRISMA flow-chart illustrating the various phases of the review search and the study selection process is reported in Figure 2.  
Figure 1: **Workflow of radiomics in the assessment of HPV status.** (**A**) Data selection process: determination of imaging modalities, ROIs, target prediction (HPV status); (**B**) Segmentation: definition of target area in medical images in which radiomic features are calculated; (**C**) Extraction: automatic extraction of quantitative features through software package from ROIs; (**D**) Selection: selection of the extracted features with exclusion of unrelated or useless ones by reducing the number of variables; (**E**) Modeling and validation: modeling of the selected radiomic features by specific methods, followed by discrimination and calibration. HPV, Human Papillomavirus; ROI, Region of Interest.
# Computational Analysis and Classification of Hernia Repairs
###### Abstract
Figure 1 shows this simple classification method and its graphical presentation. Statistical analysis of significant variables in various surgical treatments enables researchers to compare different procedures and their outcomes. The methodology for mathematical data processing involves the utilization of general computational intelligence and digital signal processing methods, which share a common foundation in diverse applications [31; 32], promoting a unified approach to data analysis through similar mathematical techniques. Specific methods include machine learning, feature extraction, and classification. Machine learning holds the potential to enhance the precision and safety of hernia surgery, reduce the risk of complications, and improve patient outcomes. However, further research is necessary to validate the effectiveness of machine learning in this context and ensure its successful integration into clinical practice. The general methodology of digital signal processing finds wide applications in engineering, biomedicine, neurology, and the detection of motion disorders, among others. Figure 1: The EHS classification of incisional ventral hernias based on location. Surgery represents a crucial domain where this methodology is applied, particularly in the context of multidimensional visualization tools. Technological advancements, especially in minimally invasive and robotic-assisted surgery, rely on the utilization of vast amounts of information, including surgical videos and the prediction of instrument trajectories using computational methods and automatic data analysis. These procedures help to return hernia contents to their natural position in the abdomen, repair the muscle wall, and reinforce it with mesh. The present paper forms a contribution to the literature gap on computational intelligence in hernia surgery [15] to show the benefits of artificial intelligence in this field. The primary focus of this paper is to statistically evaluate the treatment methods in relation to patient age, body mass index, and the type of repair. Advanced mathematical techniques are employed to extract and classify relevant features, with a particular emphasis on computational and machine-learning methods. The objectives of the paper include the use of the European Hernia Society database to find facts about patients, surgery descriptions, complications, and possible statistical evaluations of its records. Specific algorithms are included in the proposed graphical user interface and are available for specialists on the web page, allowing the evaluation of selected facts in the given period of time. Objectives of the paper include (i) computational analysis of the abdominal repairs performed during the last 12 years and recorded in the European hernia database, (ii) the statistical survey of complications after the surgery, and (iii) application of machine-learning tools for classification of individuals with complications after the surgery.