esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Design of the Goaly3 Multi-Mirror Telescope System with a Wide Field of View
## 2 Design of the Goaly3 Multi-Mirror Telescope System  
### The Fill Factor of the Golay3 MMT  
The Cassegrain configuration is adopted for the Golay3 MMT; the spherical primary mirror consists of three identical sub-mirrors, which are arranged in the Golay3 form as shown in Figure 1.  
The fill factor \(F\), which is defined as the ratio of all sub-mirrors' area to that of the surrounding aperture, can be written as:  
\[F=3\cdot D_{sub}^{2}/D^{2} \tag{1}\]  
where \(D_{sub}\) and \(D\) are the diameters of the sub-mirrors and the surrounding aperture, respectively. For the sparse aperture system, the fill factor \(F\) varies from 0 to 1 (i.e., the monolithic aperture), and a larger \(F\) allows more light to enter the image plane through the sub-mirrors. Compared with the system with a monolithic primary, the Modulation Transfer Function (MTF) of a sparse aperture system suffers a contrast loss in the mid frequency range as shown in Figure 2, in which \(\rho_{r}\) denotes the first cutoff frequency. The angular resolution \(\vartheta\) of the Golay3 sparse aperture telescope is given by:  
\[\vartheta=1.22\frac{\lambda}{D_{eq}} \tag{2}\]  
Figure 1: The Golay3 aperture.  
In Equation (2), \(\lambda\) is the wavelength and \(f\) is the focal length. \(D_{eq}\) is the equivalent aperture for the Golay3 MMT and defined as [7]:  
\[D_{eq}=\frac{3L+\sqrt{4D_{sub}^{2}-3L^{2}}}{2} \tag{3}\]  
where \(L\) is the distance between the centers of the surrounding aperture and the sub-mirror. Combining Equations (1)-(3), the relationship between the fill fact \(F\) and the angular resolution \(\theta\) is obtained:  
\[F=\frac{3\left(2\frac{44\lambda}{\theta}-3L\right)^{2}+9L^{2}}{4D^{2}} \tag{4}\]  
For diffraction-limited telescopes, the diameter of the Airy spot is proportional to the focal length provided that the aperture is determined. In addition, the F-number (the ratio of focal length to aperture) determines the compactness of the telescope, based on which the resolution of the telescope is set to 0.6 arcsecond and the F-number is given as 7. Since the aperture is 300, focal length is 2100 mm.  
It can be deduced that the fill factor of the sparse aperture system is 29.6% and the semi-diameter of the sub-mirror is 46.2 mm. The detailed parameters of the Golay3 MMT are listed in Table 1.
###### Abstract  
The Internet of Things (IoT) concept has emerged to improve people's lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT environments. Data from the published studies were retrieved from five databases (IEEE Xplore, Scopus, Web of Science, Science Direct, and MDPI). Out of 2116 identified records, 26 relevant studies were selected to answer the research questions. This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security challenges in the IoT ecosystem. It is also found that supervised deep learning techniques offer better performance, compared to unsupervised and semi-supervised learning. This analysis provides an insight into how the use of data types and learning methods will affect the performance of deep learning techniques for further contribution to enhancing a novel model for anomaly intrusion detection and prediction.  
Keywords:systematic literature review; anomaly intrusion detection; deep learning; IoT; resource constraint; IDS +
Footnote †: journal: _Sciences_
## 3 Establishment of Numerical Model
### Assumptions in the Model  
During the course of this study, several assumptions have been made to enable more efficient numerical simulations. However, these assumptions may introduce certain limitations:  
(1) The assumption of rigid spherical particles is made. This assumption serves to simplify the geometric configuration of simulations and reduce computational complexity. However, it significantly diverges from the irregular shapes found in actual soil particles. Irregularly shaped soil particles can enhance shear strength through interlocking, and their motion and rotation induce internal structural adjustments, thus affecting the macroscopic shear strength and deformation characteristics of the soil. This can lead to an elevation of the dynamic shear modulus and dynamic damping ratio.  
(2) The hypothesis of elastic collisions is employed. It assumes that collisions between particles are purely elastic, neglecting energy dissipation post-collision. Such an assumption is reasonable in many instances, particularly for low-speed collisions or minor deformations. Nevertheless, real particle collisions might entail energy loss and plastic deformation, especially when high-speed impacts or intricate interparticle interactions are present.  
(3) The supposition of an absence of internal structure is invoked. It presumes that particles lack internal structure, rendering mechanical and thermal analyses more straightforward. This postulation reduces computational costs in simulations. Yet, in scenarios where the internal structure of particles significantly affects mechanical and thermal properties, accurate emulation of actual phenomena might be compromised.
# Crop Prediction Model Using Machine Learning Algorithms
###### Abstract  
Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential benefits of integrating machine learning algorithms in modern agriculture. The main focus of these algorithms is to help optimize crop production and reduce waste through informed decisions regarding planting, watering, and harvesting crops. This paper includes a discussion on the current state of machine learning in agriculture, highlighting key challenges and opportunities, and presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, incorporating online IoT sensor data that were obtained in a real-time manner, farmers can make more informed verdicts about factors that affect crop growth. Eventually, integrating these technologies can transform modern agriculture by increasing crop yields while minimizing waste. Fifteen different algorithms have been considered to evaluate the most appropriate algorithms to use in agriculture, and a new feature combination scheme-enhanced algorithm is presented. The results show that we can achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99.46% using Naive Bayes Classifier and Hoeffding Tree algorithms. These results will indicate an increase in production rates and reduce the effective cost for the farms, leading to more resilient infrastructure and sustainable environments. Moreover, the findings we obtained in this study can also help future farmers detect diseases early, increase crop production efficiency, and reduce prices when the world is experiencing food shortages.  
crop prediction; machine learning; feature selection; artificial intelligent; smart farming +
Footnote †: journal: Applied sciences  
Article
# Coverage Fulfillment Automation in Hardware Functional Verification Using Genetic Algorithms
## 1 Introduction
### The Steps in Manufacturing of Integrated Circuits  
Most electronic devices are controlled by an integrated circuit (IC), which vary in complexity from the simple ones embedded, for example, into a bicycle safety light to the very complex ones found, for example, in smartphones; these are known as System-on-Chip [1] which either can (in the case of, e.g., microprocessors) or cannot (e.g., Application-Specific Integrated Circuits, or ASICs) be programmed by the user.  
Development of an ASIC can last from about a year to several years; in semiconductor factory, the minimum time for manufacturing is about six months. In addition, this process involves tens of persons and, in most cases, several companies. Considering this information, every improvement which might be represented by, for example, automating a step in the logical design or the manufacturing process can save important resources and permit reduction of the time-to-market period of the developed product. In order to provide the reader with a short overview of the manufacturing flow of ICs as well as to introduce the role of functional verification in this process, the principal manufacturing steps of ASICs are enumerated below; see also Figure 1. The blue color indicates relation to the design stage and the green color relation to the foundry stage, which is not in the focus of this paper.  
As observed in Figure 1, manufacturing of ASICs consists of two main parts: design flow (which implies several types of simulations performed involving the design in question) and physical realization of the product (which implies manufacturing of the integrated circuit in the semiconductor factory). The IC design stage can be divided into two parts, front-end and back-end. Front-end design focuses on implementing the functional characteristics of the design according to its specifications, while back-end focuses on the steps required for building a physical implementation of the digital version of the design such as floor planning, placement of logic cells to design a routable chip that meets timing and other constraints, and routing [2]. In this step, multiple aspects such as timing analysis, design rule checks, electromagnetic compatibility (EMC), thermal analysis and other checks are considered. The functional characteristics of the ASICs are mainly implemented in the front-end stage, which implies as principal steps the creation of system specifications, development of Register Transfer Logic (RTL) design, implementation of functional verification, and synthesis and pre-layout verification.  
The behavior of a digital design is commonly assessed through conceptual validation of using a physical set-up, e.g., validation with a Field Programmable Gate Array (FPGA) [3].
## 4 Experiments and Results
### Hydra Heads Performance  
The trained SISR model (_Hydra_ trunk) can be used as a shared backbone to solve any number of target tasks that operate on CXR modality. To solve a new visual task, we only need to append the shared SISR model with task-specific layers followed by training these layers. In this paper, we experimented with segmentation and classification because they represent the key tasks in most medical image analysis applications [38]. However, the _Hydra_ is flexible and can be easily extended to include any number of tasks (e.g., detection). We describe next task-specific heads for CXR abnormality classification and lung segmentation. In all experiments, the Shenzhen, Montgomery, and JSRT datasets are used alternately for training and testing the task-specific heads.
## 4 Materials and Methods
### Calculation of Vgreenup  
To remove outliers of GPP data, a Savitzky-Golay filter was adopted to smooth the daily GPP time series [67]. The width of the moving window was set to be 91 days, and the iteration time was set to be 1 according to a previous study [68]. Then, a modified double logistic function was used to fit the daily GPP for each year [32; 69]:  
\[f(t)=\alpha_{1}+\frac{\alpha_{2}}{1+e^{-\partial_{1}(t-\beta_{1})}}-\frac{ \alpha_{3}}{1+e^{-\partial_{2}(t-\beta_{2})}} \tag{1}\]  
where \(f(t)\) is the daily GPP at day of year (DOY) \(t\); \(\alpha_{1}\) is the background GPP; \(\alpha_{2}-\alpha_{1}\) and \(\alpha_{3}-\alpha_{1}\) represent the amplitude relative to the background for the early summer plateau and the late summer plateau, respectively; \(\partial_{1}\) and \(\partial_{2}\) are the transition curvature parameters (normalized slope coefficients); and \(\beta_{1}\) and \(\beta_{2}\) are the midpoints in DOYs of these transitions for greenup and senescence, respectively.  
The relative threshold method is a simple but practical method to extract phenological metrics [70]. In this study, the start and end of greenup period (SOG and EOG) were defined as the dates when the fitted GPP curve first reach 20% and 80% the seasonal amplitude, respectively [19]. Dgreenup was defined as the difference between EOG and SOG. Agreenup was calculated as the difference in GPP values at dates of EOG and SOG. At last, Vgreenup was calculated as the ratio of Agreenup to Dgreenup [19]. The process for determining Dgreenup, Agreenup, and Vgreenup is illustrated in Figure 7.  
Figure 6: Spatial distribution of eddy covariance flux sites used in this study. DBF, deciduous broadleaf forest; ENF, evergreen needle-leaf forest; MF, mixed forest; GRA, grassland; CRO, cropland; WET, wetland.
## 2 Experimental  
Single side polished 2-inch diameter silicon wafers were used as substrates. Dry silica spheres (SS) sized at 1.57 \(\upmu\)m were obtained from Bang Lab (10% possible size variation indicated by Bang Lab, Fishers, IN, USA). The common solvents (acetone and methanol) used in the experiment were purchased from Chemical Strategies, Inc. (Los Angeles, CA, USA). H\({}_{2}\)O\({}_{2}\) (30 wt% in water) was purchased from Honeywell (Morris Plains, NJ, USA), H\({}_{2}\)SO\({}_{4}\) (purity 96.0%) from KMG Chemicals, Inc. (Fort Worth, TX, USA), and \(N\),\(N\)-dimethyl-formamide (DMF, Purity \(>\) 99%) from Sigma-Aldrich (Saint Louis, MO, USA).  
Prior to SS spin-coating, the Si substrates were cleaned with acetone, methanol, and de-ionized (DI) water in that order, and submerged in a piranha solution (H\({}_{2}\)O\({}_{2}\):H\({}_{2}\)SO\({}_{4}\) = 1:4 volume ratio) for 15 min followed by a DI-water rinse and then dried in a spin rinse dryer at 1000 rpm for 10 min to provide clean and hydrophilic Si surfaces. The substrate when placed on the spin-coater is exposed to air under normal ambient laboratory conditions (Temperature: 20-23 \({}^{\circ}\)C, Humidity: 25-35%). The spin-coater was programmed with an acceleration speed of 100 rpm/s and maximum speed of 2000 rpm/s for 120 s. 300 \(\upmu\)L of SS solution (630 mg/mL SS in DMF) was dispensed on the Si surface for spin-coating.  
SS spin-coated Si were then moved to the reactive ion etching(RIE) chamber (Oxford PlasmaLab 80+) and the SS size reduction was performed using a CHF\({}_{3}\)/Ar mixed gases at 25/25 sccm flow rate with 200 Watts of RF plasma power under 75 mTorr chamber pressure. After RIE, ultra-violet ozone (UVO) surface cleaning treatment was followed to remove an organic passivating layer on the Si surface resulting from the CHF\({}_{3}\)/Ar RIE, and a Cr mask layer was deposited by using an e-beam evaporator (Lesker PVD-75). The SS layer on Si surface were then lift-off by dipping in a buffered oxide etch (10:1 BOE) solution for 10-min. The Cr masked Si substrate was then etched to fabricate inverted pyramids using a 1% KOH solution at 85\({}^{\circ}\)C with 4% iso-propanol (IPA) added to improve etching uniformity [18; 19].  
Scanning electron microscopy (SEM, FEI XL-30, Pillips, Amsterdam, The Netherlands) was used to observe the Si surface and the SEM image was investigated to calculate the coverage percentage of the SS monolayer by direct counting of the SS coated area through \"Image J\" (National Institutes of Health, Bethesda, MD, USA) image analysis software [20], which has been widely used for particle related surface analysis [21; 22; 23; 24; 25; 26]. Then, the contact angle of solvent on a Si surface was also measured for wettability analysis by using the EasyDrop contact angle measurement system (KRUSS, Hamburg, Germany).
# Improved YOLOv7 for Small Object Detection Algorithm Based on Attention and Dynamic Convolution
## 2 Related Work
### Attentional Mechanisms  
The application of attention mechanisms in deep learning has increased recently. Among them are excellent performances in image detection, speech processing, and natural language [20; 21; 22; 23]. In the realm of computer vision, attention mechanisms have also had a significant impact, particularly on target identification. Of these, the Squeeze-and-Excitation Network (SENet) [22] and the Convolutional Block Attention Module (CBAM) [23] have displayed the most-traditional performance.  
The SENet is an attention model that enhances the expressiveness of the model by introducing a \"squeeze and excitation\" module. It learns a weight from each channel of the base model, which is used to adjust the importance of that channel in the next layer, thus making the model more focused on the important features. This allows the SE attention mechanism to directly enhance the expressiveness of the model and improve the model performance; moreover, the small number of SE model parameters makes it easy to train and deploy, but this may lead to an increase in model complexity due to its additional computational cost to calculate the weights of each channel.  
Convolutional neural networks have an attention mechanism called the Convolutional Block Attention Module (CBAM), which can enhance the network's representation and generalization capabilities and excels at tasks such as image classification, target recognition, and picture segmentation. The benefit of the CBAM is that it can implement attention techniques on both channels and spaces, enhancing the network's expressiveness and generalization by causing it to pay closer attention to critical features while suppressing less-essential ones. It also has the apparent disadvantages of lacking spatial information at various levels to enrich the space of features with a focus on space, only taking into account the data in local areas and failing to determine long-range dependence.  
In this paper, we conducted controlled variable experiments on the Backbone, Neck, and Head of YOLOv7 and found that much medium and shallow texture and contour information, which is important for small target detection, is somewhat lost in all three parts of the feature extraction process, with the Backbone network having the most-serious loss. This situation has a certain degree of impact on small target detection, and it easily results in target missed detection.  
Figure 1: YOLOv7 network architecture diagram.  
Therefore, this paper starts by enhancing the attention of the network to small targets, taking into account the relationship between the input and input to reduce the occurrence of missed detection.
## Distributed Efficient Similarity Search Mechanism in Wireless Sensor Networks  
**[PERSON] \({}^{\dagger,\star}\) and [PERSON] \({}^{\dagger}\)**  
School of Electrical and Computer Engineering, RMIT University, Melbourne, VIC 3000, Australia;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +61-4262-40101.  
Academic Editor: [PERSON]  
_Received: 15 December 2014 / Accepted: 25 February 2015 / Published: 5 March 2015_  
**Abstract:** The Wireless Sensor Network similarity search problem has received considerable research attention due to sensor hardware imprecision and environmental parameter variations. Most of the state-of-the-art distributed data centric storage (DCS) schemes lack optimization for similarity queries of events. In this paper, a DCS scheme with metric based similarity searching (DCSMSS) is proposed. DCSMSS takes motivation from vector distance index, called _iDistance_, in order to transform the issue of similarity searching into the problem of an interval search in one dimension. In addition, a sector based distance routing algorithm is used to efficiently route messages. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries.  
**Keywords:** Wireless Sensor Networks; distributed data centric storage; similarity search; range query; K-nearest neighbor query; sector based distance routing
###### Abstract  
A novel empirical approach to categorize oil slicks' sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear discriminant analysis (LDA) to try accuracy improvements from our previously published methods of discriminating seeps from spills that achieved \(\sim\)70% of overall accuracy. Analyzing 244 RADARSAT-2 scenes containing 4562 slicks observed in Campeche Bay (Gulf of Mexico), our exploratory data analysis evaluates the impact of 61 combinations of SAR backscatter coefficients (\(\sigma^{\circ}\), \(\beta^{\circ}\), \(\gamma^{\circ}\)), SAR calibrated products (received radar beam given in amplitude or decibel, with or without a despeckle filter), and data transformations (none, cube root, \(\log_{10}\)). The LDA ability to discriminate the oil-slick category is rather independent of backscatter coefficients and calibrated products, but influenced by data transformations. The combination of attributes plays a role in the discrimination; combining oil-slicks' size and SAR information is more effective. We have simplified our analyses using fewer attributes to reach accuracies comparable to those of our earlier studies, and we suggest using other multivariate data analyses--cubist or random forest--to attempt to further improve oil-slick category discrimination.  
ocean remote sensing; satellite image classification and segmentation; RADARSAT; synthetic aperture radar (SAR); linear discriminant analysis (LDA); physical oceanography; oil slicks; oil spills; oil seeps; Campeche Bay (Gulf of Mexico)
## 4 Design Trade-Offs of Distributed Open Systems
### Eventual Consistency  
Eventual consistency is defined as consistency among the nodes of a distributed system once all messages have been delivered. The proposed Conflict-Free Replicated Data Types (CRDTs) enable eventual consistency without coordination, such as reaching consensus or rolling back [38]. A data type is said to be a CRDT if the possible concurrent operations are commutative.  
**Guideline 3**.: _Eventual consistency can be achieved without coordination in open distributed systems by ensuring that concurrent operations are commutative._  
Note that with eventual consistency, statements that are considered true in a given time can become false after receiving new messages. Thus, this consistency may not be sufficient for systems with strong consistency requirements, such as crypto-currencies.  
CRDTs achieve eventual consistency once all messages have been delivered. Different systems may tolerate different delays of these messages. For instance, while a Q&A system may ignore a vote for a long period of time, for a collaborative document, incorporating relatively old updates may be problematic, regardless of eventual consistency.