esa-sceva/satcom-chunk-collection huggingface dataset:
# High Linearity DC-38 GHz CMOS SPDT Switch
###### Abstract
In this work, to demonstrate that low insertion-loss, high isolation (greater than 24 dB), high linearity (IP1 dB greater than 25 dBm), and wideband (covering the sub-6 GHz and the 28 GHz bands) operation can be achieved for a CMOS SPDT, we propose a low insertion-loss, high isolation, and high-linearity CMOS SPDT switch with single-series-double-shunt transistor structure and \(\pi\)/traveling-wave input/output matching for sub-6 GHz and 28 GHz 5G communications. Traveling-wave- and \(\pi\)-matching are adopted for broadband output and input matching, respectively. The required capacitance for the traveling-wave-matching is  
Figure 1: Illustrative diagrams of the (**a**) SPST, (**b**) SPDT, (**c**) DPST, and (**d**) DPDT switch. provided by the parasitic capacitance at drain nodes of the double-shunt transistors (M\({}_{2}\), M\({}_{3}\), M\({}_{5}\), and M\({}_{6}\) in Figure 2). The required right-side capacitance for the \(\pi\)-matching is provided by the parasitic capacitance at source node of the single-series transistors (M\({}_{1}\) and M\({}_{4}\) in Figure 2). Positive/negative gate-bias and negative body-bias are used for linearity and insertion loss enhancement, respectively. The proof-of-concept SPDT switch is designed and implemented in a cost-effective 1P6M 0.18 \(\upmu\)m CMOS process (with cutoff frequency f\({}_{\text{T}}\) of 60 GHz), which has been used to design and implement a 28 GHz receiver front-end with acceptable performance [12]. Instead of the adopted 0.18 \(\upmu\)m CMOS process, insertion losses of the SPDT switch can be further enhanced if an advanced process, such as the 28 nm CMOS process with thicker top metal layer in [4], is available.
## 3 Results and Evaluations  
### Features and Properties of Ag Thin Layer  
Despite the challenges of the deposition of ultra-thin Ag films with minimal surface roughness, homogeneous layer morphology, and perfect thickness control, silver is a well-known noble metal with the lowest optical losses throughout a very broad spectral range extending from the blue to the near-infrared wavelengths [32]. Another field in today's fast-paced research and development environment is plasmonics nano-sensor development, which has lately seen a number of important breakthroughs [33; 34; 35; 36; 37; 38]. Due to their great sensitivity to the refractive index change in dielectric media placed on top of ultra-thin plasmonics metal films, these surface plasmon resonance-based optical sensors have various commercial applications in oil and gas, biochemical, medical, and chemical sensing (Ag). Figure 2 (which is partially reproduced from Ref. [32]) presents the important parameters and optical characteristics of sputtered Ag thin layers.  
The dispersion function of Ag layers' optical constants (refractive index and absorption coefficient) is found to be patchy, partly owing to film thickness dependence and partly due to investigations being confined to a small wavelength range. We gathered all available information on the optical constants of Ag layers from all accessible sources throughout our coatings development work (conducted between 2011 and 2015, mainly unpublished so far) and discovered data inconsistencies in the 1000-2000 nm range. To acquire an acceptable quality of optical constant data between 300 and 7000 nm, we experimentally modeled and fitted numerous transmission spectra of deposited Ag films of varied thicknesses (12.5-40 nm). The revised dataset is shown in Figure 2a,b, in which our fitted data are combined with data from [39; 40], resulting in close agreement between the modeled and observed transmission spectra for all film thicknesses. Figure 2c shows the fitted transmission spectrum of an as-deposited Ag layer of around 20 nm thickness obtained using OptiLayer modeling and spectrophotometry using this data set. We were able to establish that the thickness of our originally deposited layer was around 19 nm, which is correct to within 5%. Figure 2d shows the Ag layer thickness-dependent variation in color values; mainly, the lightness function (\(\Delta\)RFY). In this work, we define the value of \(\Delta\)RFY by subtracting the obtained value of Y (which is Yxy values as reported in CIE Chromaticity Coordinates) on the Ag layer from the Y value of clear glass substrate.
# Detection of SQL Injection Attack Using Machine Learning Techniques: A Systematic Literature Review
## 4 Results
### Conducting the Review
The proposed approach generated correct solutions for 67.52% of cases for Java and 41.33% of correct solutions for PHP on an independent test set. [PERSON] [47] proposed a model for the detection of SQLI attacks using artificial intelligence (AI) techniques. This model consisted of three main components: uniform resource locator (URL) generator to generate thousands of normal and malicious URLs; a URL classifier to classify all generated URLs as either normal or malicious; and a neural network (NN) model to detect whether a given URL was a malicious, or benign URL. The model was first trained and then evaluated by employing both benign and malicious URLs. URL classifiers were also used to convert all generated URLs into strings of logic (1 = malicious; 0 = benign). Q3:How can machine learning be used to generate adversarial SQL injection attacks? Adversarial machine learning (AML) is based on the threats posed by an attacker with the aim of being incorrectly classified by the victim machine learning algorithm. Generating an adversarial SQL injection dataset starts with a target malicious query that was correctly detected. And then, a set of mutation operators was iteratively applied in order to generate new queries [48]. [PERSON] and [PERSON] [48] developed a tool named WAF-A-MoLE to generate adversarial examples against web application firewalls (WAFs) by applying a set of syntactic mutations. The authors produced a dataset of SQL injection queries through an automatic procedure. To evaluate the effectiveness of the proposed tool, it was applied to different ML-based WAFs and evaluated in terms of their robustness against WAF-A-MoLE. [PERSON] et al. [49] proposed a black-box automated technique, named 4 SQLi, for generating test inputs that could bypass security filters, resulting in executable SQL queries. This technique was based on a set of multiple mutation operators that manipulated inputs to produce new test inputs to trigger SQLi attacks, making it possible to create inputs that contained new attack patterns, thus increasing the possibility of generating a successful SQLi attacks.
## 3 Data and Methods
### Overview of Machine Learning Models
#### 3.5.3 Extreme Gradient Boosting  
XGBoost, also known as Extreme Gradient Boosting, is an ensemble algorithm based on classification or regression trees, which has demonstrated excellent performance in Kaggle competitions [77]. The model incorporates several innovative strategies such as parallel processing, column subsampling, and dynamic learning rate adjustment. These techniques not only maintain computational efficiency but also effectively mitigate overfitting. Moreover, its distributed computing framework enables rapid training on large-scale datasets. The objective function of the model is shown in Equation (3).  
\[J(f_{t})=\sum_{i=1}^{n}L\left(y_{i},\hat{y}^{(t-1)}+f_{t}(x_{i})\right)+\Omega (f_{t})+C \tag{3}\]  
In this equation, \(i\) represents the \(i\)-th sample, \(y_{i}\) represents the true value of the \(i\)-th sample, \(y^{(t-1)}\) represents the predicted value of the \(i\)-th sample from the \((t-1)\) iteration model, \(\hat{f_{t}}_{(xi)}\) represents the new model added at the t-th iteration, \(\Omega(\hat{f_{t}})\) represents the regularization term, \(C\) represents some constant terms, and the outermost \(L()\) represents the overall objective function.
###### Abstract  
Most of the current research on the diagnosis of rolling bearing faults is based on vibration signals. However, the location and number of sensors are often limited in some special cases. Thus, a small number of non-contact microphone sensors are a suboptimal choice, but it will result in some problems, e.g., underdetermined compound fault detection from a low signal-to-noise ratio (SNR) acoustic signal. Empirical wavelet transform (EWT) is a signal processing algorithm that has a dimension-increasing characteristic, and is beneficial for solving the underdetermined problem with few microphone sensors. However, there remain some critical problems to be solved for EWT, especially the determination of signal mode numbers, high-frequency modulation and boundary detection. To solve these problems, this paper proposes an improved empirical wavelet transform strategy for compound weak bearing fault diagnosis with acoustic signals. First, a novel envelope demodulation-based EWT (DEWT) is developed to overcome the high frequency modulation, based on which a source number estimation method with singular value decomposition (SVD) is then presented for the extraction of the correct boundary from a low SNR acoustic signal. Finally, the new fault diagnosis scheme that utilizes DEWT and SVD is compared with traditional methods, and the advantages of the proposed method in weak bearing compound fault diagnosis with a single-channel, low SNR, variable speed acoustic signal, are verified.  
improved empirical wavelet transform; rolling bearing; acoustic signal; weak fault diagnosis Article
## 3 Results and Discussion  
### CNPs Characterization  
The morphology and diameter size distribution of the CNPs are presented in Figure 1. The SEM micrograph reveals a spherical shape for freeze-dried CNPs with a uniform size (Figure 1a). The size of CNPs ranged between 50 and 100 nm, in good correlation with results reported by [PERSON] et al. [27] and smaller with respect to those reported by [PERSON] et al. [28], very probably due to the different synthesis conditions (lower chitosan molecular weight and a lower chitosan or TPP amount in the present study).  
Figure 1b shows the particle size distribution and average size of the CNPs determined by means of DLS analysis. From the results, the polydispersity index of the synthesized CNPs was 0.191, with a mean particle size of 108 \(\pm\) 5 nm. However, the dried CNPs size was smaller due to the CNPs swelling in aqueous conditions and the aggregation of some particles in this form in solution [29]. The zeta potential is another important parameter to study the stability of colloidal materials, and it was evaluated through Zetasizer NanoZS Instrument. The zeta potential of CNPs in this study was +36 mV, suggesting their colloidal stability.
## 3 Results
### Characterization Methods  
#### 3.3.1 Carbon, Hydrogen, Nitrogen (CHN) Analysis  
Table 6 shows the CHN analysis for RPNS and the thermally activated derived adsorbents (PNS250-PNS500). Obtained results show that the %C, in general, has increased with the thermal treatment implying the conversion of PNS into activated carbon. This could be attributed to thermal destruction of all the organic matter and subsequent conversion into mainly carbonaceous material with the heat treatment. On the other hand, the concentration of hydrogen has decreased from 6% in the RPNs to 3% in PNS500. This would be attributed to the loss of hydrogen with oxygen--water evaporation--as the temperature increases.  
As will be shown later in TGA/\(d\)TGA and FT-IR analyses sections for RPNS, the raw sample was composed mainly of hemicellulose, cellulose, and lignin with some water and probably some protein. Therefore, the presence of N could be attributed to the protein content with some contaminations. On the other hand, the carbon content increases with the temperature of treatment up to 350 \({}^{\circ}\)C, then there is a change in the carbon content above this temperature. [PERSON] et al. [42], reported that the yield of gasses (H\({}_{2}\)O, CO, CO\({}_{2}\), H\({}_{2}\), CH\({}_{4}\), C\({}_{2}\)H\({}_{4}\), and C\({}_{2}\)H\({}_{5}\)), as a result of pyrolysis of hemicellulose, cellulose, and lignin materials, differs depending on the temperature and nature of the investigated  
Figure 3: Response contour plots for the adsorption capacity (\(q_{e}\), mg/g). Dark green regions represent regions where maximum adsorption capacity could be obtained using the described factorial combination.  
material. For example, and while hemicellulose produces more CO\({}_{2}\) upon pyrolysis, lignin yield is mainly H\({}_{2}\) and CH\({}_{4}\). Therefore, this could explain the observed change in %C.
###### Abstract  
The Weather Research and Forecasting (WRF) model with online coupled chemistry (WRF-Chem) is applied to study an intense Saharan dust outbreak event affecting the Italian peninsula in 15 and 16 April 2018. According to the MODIS retrievals, this intrusion was characterized by an intense aerosol optical depth (AOD) peak value in the southern Mediterranean. Measurements within the Dry Deposition Network Across the Mediterranean (XMed-Dry) are compared with the output of the WRF-Chem model. XMed-Dry samples from Lecce (Italy), Athens (Greece) and San Lawrenz/Gozo (Malta) were analysed with respect to aerosol particle size distribution, relative dust contribution, and composition. The discrepancy between the model and measured deposition indicate the need to formulate in WRF-Chem more sophisticated deposition schemes, this will need to evaluate the sensitivity of the results to the precise particle size limits chosen for the aerosol model. Moreover, satellite retrievals from MODIS sensors elaborated with the MAIAC algorithm, Aeronet stations, and measurements of PM\({}_{10}\) at the selected sites were also considered. In a numerical domain that spans the Mediterranean and the northern Saharan desert, two different dust emission schemes, namely Gocart-AFWA and the Shao-2001, were tested and compared with multiplatform observations for simulation period covering the dust outbreak. Actual results indicate that both emission schemes would benefit from replacing the static erodibility map and soil particle distribution with remote sensed and in-situ observational data.  
dry-deposition; dust contribution to PM\({}_{10}\); Gocart Aerosol model; WRF-Chem model +
Footnote †: journal: Applied Sciences
## 3 Encapsulation Protocol
### Dns
#### 3.3.2 Machine Learning  
Most of machine learning detection has been conducted to detect whether traffic is tunnel traffic, while [PERSON] [48] introduced eight features to analyze five kinds of DNS tunnel behaviors. [PERSON] [41] has integrated the above statistical features. To describe different attributes of DNS names and how they are queried, [PERSON] [41] used 15 DNS traffic features, nine of which were not previously proposed, including time-based features, DNS response-based features, TLL change features, and domain name features. When combined with a decision tree, this method can identify DNS tunnel traffic accurately, resulting in fewer false alarms than 1% and a 98% accuracy rate.  
[PERSON] [60] and [PERSON] [58] developed a way to solve domain name text detection lightly to reduce storage overhead and computational complexity. A domain name specificity score, enhanced TF-IDF, and other algorithms were used by [PERSON] [60] to detect DNS tunnel traffic in private datasets, with 99.92% accuracy in detecting DNS tunnel traffic. It is worth noting that [PERSON] [58] proposed a detection method based on CNN with a simple structure called 1D-CNN that detects 99% of normal domains and 92% of tunnel domains, even though its structure is simple. [PERSON] [59] also used 2D-CNN with a straightforward network structure to detect tunnel traffic. They used a 24-dimensional matrix to represent 22 different features, including request/response type, etc.  
[PERSON] [37] focused on DoH [66] (DNS over HTTPS) in order to detect encrypted DNS tunnel traffic because traditional website fingerprint features are insufficient for describing DoH traffic. As a result, when it is combined with RF, it introduces N-grams with TLS record lengths as new features and is able to identify DNS tunnel traffic with 84% accuracy in private datasets. A variational autoencoder (VAE) [67] was proposed by [PERSON] [35] as an E2E model for learning long sequential and structural information beyond the capability of traditional machine learning methods. VAE used bidirectional Gated Recurrent Units (BiGRU) as encoders and decoders to automatically extract latent feature representations from raw traffic. Semi-supervised training is used to train the model on normal traffic patterns. The accuracy rate is over 99% on the CIRA-CIC-DoHBrw-2020 dataset [68]. Moreover, the complexity of their model from the number of parameters and consuming time are all obviously less than others [69].
# Soft Error-Tolerant and Highly Stable Low-Power SRAM for Satellite Applications
###### Abstract
The RSP14T cell, proposed in [14] as an enhanced version of RHD12T, addressed recovery from an SEU during a transition from '0' to '1'. However, it could not recover when a sufficient charge accumulated at the '0' storage nodes and still exhibited poor read stability due to the direct connection of the '0' storage nodes to bit lines through access transistors during the read operations. Since the space environment is characterized by high-energy charged particles [15], SRAM cells intended for space applications must possess immunity to soft errors, including both SEUs and SEMNUs. Moreover, since accurate data processing is critical for the proper functioning of satellites and other space-based devices, radiation-hardened SRAM cells must ensure stable read and write operations, enabling reliable device performance in space. Additionally, minimizing power consumption is essential for memory elements to ensure the long-term operation of space devices. In summary, for SRAM cells to be suitable for space applications, they must not only possess radiation hardness but also demonstrate optimized performance in terms of stability and leakage power consumption during basic SRAM operations. This paper proposes a radiation-hardened with read/write stability and power optimized 16T (RHSP16T) cell. RHSP16T employs a polar design, incorporating two sensitive nodes among four total nodes, and has demonstrated recovery capabilities from SEUs at all sensitive nodes. Moreover, RHSP16T adjusts the minimum spacing between the transistors in the layout to avoid SEMNUs caused by charge-sharing effects. The proposed cell exhibited the highest read stability among all the compared cells and provided higher write stability than most of the other cells, ensuring high data reliability. Furthermore, RHSP16T demonstrated lower-leakage power consumption than most other cells, achieving optimized performance. This paper is structured as follows: Section 2 describes the operations and SEU recovery mechanisms of the proposed RHSP16T cell. Section 3 presents the comparison and simulation results of the proposed cell with all compared cells. Finally, Section 4 concludes this paper.
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 3 Results
### Flaring Estimates in Russia by Major Regions and Companies  
#### 3.2.1 Regional Estimates  
Upstream gas flaring was detected in 27 federal subjects of Russia as well as at 9 offshore platforms or offshore exploration vessels located on the shelf of Russian Federation, with downstream flaring occurring in 12 more regions. The histogram in Figure 5 presents a breakdown of gas flaring volumes in the major flaring regions of Russia.  
Figure 4: Histogram VIIRS Nightfire-estimated top 20 upstream gas flaring countries in 2012–2020 as per (**a**) gas flaring volumes (in BCM) and (**b**) gas flare tally. Shaded area for Russia represents the natural gas upstream sector.  
The annual analysis reveals that Khanty-Mansi Autonomous Okrug (KhMAO) remains the major APG flaring region in Russia, albeit its share in total gas flaring volumes in Russia has dropped significantly since the start of observations in 2012, from 33% to 22%. As reported, annual APG extraction volumes in the region remain consistent [31; 32], and it is evident that APG utilization in KhMAO, the major oil production region of Russia (42-50% of annual oil production [46]), has increased significantly.  
Among the other regions, Yamal-Nenets Autonomous Okrug (YaNAO) produces 10% of oil and gas condensate in Russia; however, relatively high flaring rates were achieved as new large oil fields with no or limited connection with the existing gas pipeline infrastructure were commissioned during the observation period. Other major contributors in the region are large gas condensate and natural gas producing fields where, as described in Section 1.1, flaring occurring alongside natural gas and gas condensate production is likely not subjected to increased emission fees in effect only for associated petroleum gas. Unique for this region is that fields categorized as natural gas and gas condensate upstream contribute up to 40-60% of the gas flaring volume in YaNAO, with their share dropping in the recent years. Due to natural gas upstream flaring, Yamal-Nenets AO in 2019-2020 is estimated to have been the highest flaring region in Russia.  
Substantial flaring is observed in Irkutsk Oblast and Krasnoyarsk Krai (each producing c. 3-4% of national oil output). In these regions, gas flaring mostly occurs at a small set of fields. For instance, more than 95% of estimated flaring volumes in Krasnoyarsk Krai in 2012-2013 occurred at the Vankor oil field, with the largest single flare in the world detected with VIIRS Nightfire in 2012. In Irkutsk Oblast, a continuous increase in oil production levels during the observation period led to a twofold increase in gas flaring volumes, as gas utilization capabilities in the region are limited.  
Specific flaring volumes at the largest flaring oilfields in Russia are presented in Table S2 in Supplementary Materials text section.