esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[36] proposed a new method for objectively evaluating the accuracy of digitized full dental arch models using a CMM and compared the accuracy of four different digitization methods, including true model intraoral scans, impression CBCT scans, cast CBCT scans, and cast extraoral scans, by performing 12 automatic linear measurements on the models. [PERSON] et al. [37] presented a study on the uncertainty analysis of measurements performed by a ZEISS CenterMax CMM, focusing on the variables of parallelism, angularity, roundness, diameter, and distance. The study found that the interaction effects among stylus diameter, step width, and speed were active at a 95% confidence level. These studies highlight the importance of measurement accuracy and precision in the digitalization of industries. In this study, we present a method for generating DCC using a reference standard hole plate to measure CMM. We begin by examining the related research papers on PTB and NIST in the Introduction Section, which focus on the formulation and implementation plans for measurement standards and the advantages of DCC for the development of the measurement field in response to the digital transformation. Section 2 provides relevant background information on DCC, including the Norms and Standards, Hierarchical Structure, and XML as Exchange Format. In addition, we present an example of administrative information in the XML. Section 3 describes the experiment of measuring CMM using the hole plate. The results are compared with traditional standards such as laser interferometer, autocollimator, and angle square. To make the measurement results readable by both humans and machines, we convert them into XML and PDF formats using XMLSpy software, respectively. This approach combines software and hardware devices in the manufacturing industry. Finally, in Section 4, we provide a conclusion to our work.
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 3 Methods
### SAR Image Preprocessing
#### 3.1.2. Speckle Noise Reduction  
In general, speckle noise exists in SAR images, and speckle noise is a factor that degrades the image quality. Therefore, the noise must be reduced before image analysis. In this study, noise in the image was reduced using a median filter and multi-looking technique [33]. First, median filtering was  
Figure 3: Flow chart of proposed SAR image preprocessing in this study.  
performed. If the median filter size is too large, the pixels of the ship in the image may be replaced by pixels of the sea. Therefore, the size of the filter should be determined based on the size of the ship. In addition, if the size of the ship becomes too small due to multi-looking, the ship may not be detected. Therefore, the number of looks in the multi-looking should be determined based on the size of the ship, such as the size of the median filter.
# Low-Power Two-Phase Clocking Adiabatic PUF Circuit
###### Abstract  
Internet of Things (IoT) has enabled battery-powered devices to transmit sensitive data, while presenting high power consumption and security issues. To address these challenges, adiabatic-based physical unclonable functions (PUFs) offer a promising solution for low-power and secure IoT device applications. In this study, we propose a novel low-power two-phase docking adiabatic PUF. The proposed adiabatic PUF utilizes a trapezoidal power clock signal with a time-ramped voltage to achieve an improved energy efficiency and reliable start-up PUF behavior. Static CMOS logic is employed to produce stable challenge-response pairs (CRPs) in the adiabatic mode. The pull-down network is designed to control the PUF cell to charge and discharge its output nodes with a constant supply current during secure key generation. The body effect of PMOS transistors, ambient temperatures, and CMOS process variations are investigated to examine the uniqueness and reliability of the proposed work. The proposed adiabatic PUF is simulated using 0.18 \(\upmu\)m CMOS process technology with a supply voltage of 1.8 V. The uniqueness and reliability of the proposed adiabatic PUF are 49.82% and 99.47%, respectively. In addition, it requires a start-up power of 0.47 \(\upmu\)W and consumes an energy of 15.98 fj/bit/cycle at the reference temperature of 27 \({}^{\circ}\)C.  
physical unclonable function (PUF); secure keys; low-power; adiabatic; reliable and unique identity; IoT security; SRAM PUF  
**2021**, 10, 1258. [[https://doi.org/10.3390/electronics1011258](https://doi.org/10.3390/electronics1011258)]([https://doi.org/10.3390/electronics1011258](https://doi.org/10.3390/electronics1011258))
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 1 Introduction
### Natural Sources of Radio Noise  
In quiet rural areas, where the population is sparse, most of the radio noise received originates from remote sources. For example, the noise from distant lightning discharges transported via the ionosphere [7], emissions from extraterrestrial sources such as Jupiter [8, 9], the Milky Way [10] and the Sun [9, 11]. The noise from lightning discharges [12] is impulsive in nature, although the impulses may have spread in time due to dispersion in the ionosphere when they arrive at the sensor. The emissions from Jupiter, the Milky Way and the Sun appear as band-limited Gaussian noise, with a wideband butdistinct frequency profile. Additionally, Jupiter shows random emissions at frequencies between 20 and 40 MHz, generated in its magnetosphere, which sounds like a hiss, with incidental popping modulations.
## 9 Prevention and Management of Diabetic Foot Complications  
Prevention is the primary management strategy to control any lower limb injury linked to DM. Providing basic guidance to patients regarding the choice of suitable footwear and wound treatments, including a specific emphasis on foot care, has been found to help delay the occurrence or re-occurrence of DFU [188]. The use of inappropriate footwear and walking barefoot are the main contributors to foot trauma resulting in ulcers in diabetic patients with sensitive feet. Thus, people with a loss of protective sensation (LOPS) should have unrestricted access to suitable footwear and should be advised to wear them at all times, both indoors and outdoors [53; 189].  
Some other important aspects involved in the prevention of DFU include identifying the foot likely to be at risk of ulceration and inspecting and examining it on a regular basis, as well as treating pre-ulcerative symptoms [53]. To identify individuals who are at risk of developing foot ulcers, all diabetic patients should get their feet examined at least once annually, and those who have been identified should be checked more frequently. Diabetic patients who show symptoms of pre-ulcers should be treated accordingly. The pre-ulcerative treatments include the removal of excess calluses, the treatment of blisters and ingrown or thickened nails, the administration of antifungal medications for fungal infections, and in case of foot deformities non-surgical techniques such as orthosis may be applicable. These interventions should be carried out by an experienced foot care expert until the pre-ulcerative signs subside and do not reappear [53; 189].  
To control and manage DFU, it is crucial to heal the wound first. This mostly depends on the ulcer's grade and vascularity, as well as the presence of infection. The affected foot should be given adequate relaxation and pressure relief [32]. Controlling hyperglycemia accelerates wound healing and also helps to avoid negative effects on cellular immunity and infection [190]. Many antiseptic and antimicrobial agents are being investigated as topical preparations for the treatment and management of DFU. Honey, which hasantibacterial properties, has recently gained popularity as an alternative treatment for diabetic foot wounds. It provides a moist environment for wounds, and its high viscosity helps to form a protective barrier against infection. Its immunomodulatory effects are also essential for tissue repair. Much work, including performing well-designed randomized double-blind controlled studies, is still required to properly evaluate the potential of honey, design evidence-based practice guidelines, and assess the risks and benefits of such therapies [191; 192]. A summary of the preventive measures and treatment strategies used for diabetic foot conditions is depicted in Figure 6.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 4 Recent Advancement on UAV Tracking Antenna
### UAV Tracking Techniques
#### 4.3.4 UAV Tracking Based on Monopulse
\begin{table}
\begin{tabular}{c c} \hline \hline
**Reference** & **Observation** \\ \hline
[119] & Conducted optimal numerical analysis of electronically-steered arrays onboard electrically-large platforms based on FEKO software. \\ \hline
[120] & Implemented an analog beam tracking utilizing Cramer–[PERSON] lower bound algorithm that enables high tracking speed and data rate for UAV. \\ \hline
[121] & Proposed a 7-dBi-high gain steering beam consisting of three circular array of two Yagi–Uda antennas resulting in seven beams which cover the 0–180-degree azimuth plane. \\ \hline
[122] & Designed array antenna at the receiver side to detect the Angle of Arrival (AoA) of the target. \\ \hline
[123] & Deployed frequency difference of arrival (FDOA) for UAV localization which employs several weighted least-squares minimizations only and does not require initial solution guesses to obtain a location estimate. \\ \hline
[124] & Proposed an architecture consisting of attitude heading and reference system (AHRS) for marine satellite tracking antennas (MSTAs) to overcome attitude disturbance due to ship vibration and rotation motion. \\ \hline
[125] & Fault tolerant control (FTC) system was used for the satellite tracking antenna which aligns the onboard antenna toward a chosen satellite while the high sea waves disturb the antenna. \\ \hline
[126] & MATLAB-based tracking system simulator was also developed to test the control system performance of a rooftop antenna tracking system. \\ \hline
[127] & Leveraged on the desirable attributes of slotted wave guide array (SWGA) technology, an innovative dual polarization antenna working at Ku band (14–14.5 GHz) was fabricated. \\ \hline
[128] & Design, fabrication, and testing of a helical antenna using 3D printing technology operating at 5 GHz. Several commercially-available dielectric printers and materials (e.g., PLA, ABS, PC) were evaluated. \\ \hline
[129] & Achieved a blind beam tracking for Ka-band UAV satellite communication system, where UAV is equipped with a large-scale antenna array. \\ \hline
[130] & A priori knowledge aided channel tracking method was proposed in for Teraherz massive MIMO system, where a linear motion is adopted to derive angles of the incident signals and then the complex gains are estimated by pilots. \\ \hline
[131] & Presented a scheme referred as an angle division multiple access (ADMA) based channel tracking scheme was proposed in for massive MIMO systems. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The other strategies that have been proposed. Figure 12: Monopulse UAV tracking [114].
## 3 Multi-Objective Uncertain Adjacent Only Quadratic Minimum Spanning Tree
#### 3.1.1 Chance-Constrained Model of the Proposed mUAQMSTP  
[PERSON] and [PERSON] [36] proposed the chance-constrained model (CCM) as an alternative method for optimizing uncertain programming problems. At a predetermined confidence level(s) and subject to chance constraints, the goal of CCM is to minimize or maximize the objective function(s) with respect to a deterministic target value(s) such that the optimized value(s) of the objective function(s) does not surpass the corresponding target value(s). The CCM permits the restrictions to be disregarded. Nevertheless, it guarantees that the limitations are feasible at specific confidence levels. The following is the formulafor the related CCM of mUAQMSTP if the decision maker wishes to optimize it under the limitations.  
\[\left\{\begin{array}{l}\underset{\begin{array}{l}\text{Min }\overline{F}_{1}\\ \text{Min }\overline{F}_{2}\\ \text{subject to}\\ M\left\{\sum_{i=1}^{n}\xi_{i}x_{i}\leq\overline{F}_{1}\right\}\geq\alpha_{1} \\ M\left\{\sum_{i=1}^{n}\sum_{j=1}^{n}\bar{\zeta}_{ij}x_{i}x_{j}\leq\overline{F}_{ 2}\right\}\geq\alpha_{2}\\ \text{constraints of }\eqref{eq:mUAQMSTP}.\end{array}\right. \tag{6}\]  
The first two constraints in Model (6) have target values denoted as \(\overline{F}_{1}\) and \(\overline{F}_{2}\), respectively. The predefined confidence levels for the first constraint are denoted as \(\alpha_{1}\) and for the second constraint as \(\alpha_{2}\). Additionally, when it comes to finding the adjacent only quadratic minimum spanning tree (AQMST) for G, the first constraint defines the total linear cost while heading between the cities at the chance level \(\alpha_{1}\), and the second constraint defines the total interactive cost of the adjacent cities going between all the connected cities at the chance level \(\alpha_{2}\).  
The graphical representation of the proposed mUAQMSTP model from its formulation to the solution methodologies involved to solve it is shown in Figure 1.  
Figure 1: Graphical illustration of the model formulation to solution methodologies employed for the proposed mUAQMSTP.
###### Abstract  
In recent years, the Smart Grid (SG) has been conceptualized as a burgeoning technology for improvising power systems. The core of the communication infrastructure in SGs is the Synchrophasor Communication Network (SCN). Using the SCN, synchrophasor data communication is facilitated between the Phasor Measurement Unit (PMU) and Phasor Data Concentrator (PDC). However, the SCN is subjected to many challenges. As a result, the components, such as the links, PMUs, PDCs, nodes, etc., of the SCN are subjected to failure. Such failure affects the operation of the SCN and results in the performance degradation of the SG. The performance degradation of the smart grid is observed either temporarily or permanently due to packet loss. To avoid dire consequences, such as a power blackout, the SCN must be resilient to such failures. This paper presents a novel analytical method for the resiliency analysis of SCNs. A graph-theoretic approach was used to model SCN from the resiliency analysis perspective. Furthermore, we proposed a simulation framework for validating the analytical method using the Network Simulator-3 (ns-3) software. The proposed non-intrusive simulation framework can also be extended to design and analyse the resiliency of generic communication networks.  
smart grid; synchrophasor communication system; resiliency; network simulator +
Footnote †: journal:  
0000-0001-4699-2223-2231][PERSON]  
1 School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India 1  
2
## 4 Results
### Results of Data Augmentation Methods on the Balanced Datasets  
We compared the performance of various data augmentation methods on two class-balanced datasets. These methods include Jit, Rot, Flip, CS, Inv, SP, Sca, FCS, and SigAugment. Experimental results are shown in Tables 3 and 4.  
On the benchmark dataset 2016A, SE-MSFN achieves an optimal recognition accuracy of 62.96%, whereas using the SigAugment proposed in this paper, a SOTA mean average accuracy of 64.44% can be easily achieved on the LSTM2 model without changing the model structure. The average accuracy of the DAE and SE-MSFN models also improved from 58.45% and 62.96% to 62.63% and 63.94%, respectively. The improvement was greater for lightweight models than for larger models such as the SE-MSFN (4.18% vs. 0.98%). Individual augmentation transforms, for the most part, improve the model's recognition accuracy; however, there are some that have a negative effect on a specific model; for example, training SE-MSFN on the 2016A dataset with Rot and FCS decreased the accuracy by 0.24% and 0.68%, respectively. Individual transformations perform differently across models and datasets. Individual transformations that are most helpful for DAE, SE-MSFN, and LSTM2 on the 2016A dataset are Rot, CS, and FCS, in that order. FCS, SP, and Rot are the most helpful individual transformations for DAE, SE-MSFN, and LSTM2 on the 2016A-1 dataset, while our proposed SigAugment method consistently works, the two SigAugment transformation selection modes are only slightly different for different datasets and models. This demonstrates that our proposed SigAugment method can be applied effectively to a variety of datasets and deep neural network models.  
On the 2016A-1 dataset, the gains from the three baseline models using SigAugment are 5.73%, 3.98%, and 5.48%, respectively, which are 1.55%, 3.00%, and 3.14% higher than the gains obtained on the 2016A dataset. The 2016A dataset is widely used in existing studies, and it achieves the best available average recognition accuracy of 64.44% without changing the model structure and using only SigAugment-C training LSTM2. At 0 dB, the three experiments achieve accuracies of 91.05%, 91.60%, and 91.85%, respectively, compared to 88.30%, 87.05%, and 87.80% without using any data augmentation method. When used to train SE-MSFN, the two augmentation methods, Rot and FCS, performed significantly differently on the two datasets. Rot and FCS have a negative impact on SE-MSFN recognition performance on the 2016A dataset, which has extremely low SNRs (SNR \(<\) 8 dB), but they perform well on the 2016A-1 dataset. We suspect that this is due to interference in the model's training after using the Rot and FCS transforms on signals with very low SNRs, which results in poor performance.
## 4 Hydrocarbon Potential of formations in Malaysia
### Organic Geochemical Characteristics of Formations in Peninsula Malaysia (PM)
#### 4.1.5 Hydrocarbon Generation Potential of Rock Formations in PM  
The Penyu basin and Batu Arang formation stood the highest chance of generating hydrocarbons from all the formations studied in Peninsula Malaysia. This was due to the high TOC, high GP and high hydrocarbon yield (S2) values, which showed that the formations had high organic matter present. As the samples were still immature, most of the hydrocarbons were still intact and stood a high chance of generating hydrocarbons when subjected to high temperatures. The Penyu basin and Batu Arang formation also generate type I and type II kerogens, respectively. The Semantan and Semanggol formations had a low chance of generating hydrocarbons. These formations had relatively average TOC and low GP and matured organic matter, indicating that most hydrocarbons had already been generated. The Semantan and Semanggol formations generate type III kerogen, which is gaseous in nature.  
The Kroh and Belata formations have the least possible chance of generating hydrocarbons among the formations studied in Peninsula Malaysia. This was because of the very mature organic matter in those formations and poor GP as well. The formations' high maturity and low GP represented low hydrocarbon generation potential. The Belata and Kroh formations also generate type III kerogen. A fair assessment of the Batu Gajah shale  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Formation**} & \multicolumn{2}{c}{**Generic Potential (GP)**} & **([PERSON], 1995), GP** & \multirow{2}{*}{**Reference(s)**} \\  & & **S1 + S2 (mg HC/g)** & & **Interpretation** & \\ \hline
**1** & Belata & 0.02 & Poor & [8] \\ \hline
**2** & Semanggol & 0.21 & Poor & [13; 14; 52] \\ \hline
**3** & Kroh & 0.05 & Poor & [53] \\ \hline
**4** & Semantan & 0.84 & Poor & [13; 52] \\ \hline
**5** & Batu Arang & 143.07 & Very good & [54; 55] \\ \hline
**6** & Penyu & 86.46 & Very good & [56] \\ \hline \hline \end{tabular}
\end{table}
Table 7: Interpretation of average GP values of formations in PM according to [PERSON] [75].  
Figure 8: Tmax vs. HI plot showing level of kerogen maturity of formations in PM.  
was not possible, due to inadequate data for its assessment; however, the formation is known to have good TOC.
## 2 Materials and Methods
### Analysis of Volatile Compounds by GC-MS  
Volatile compounds were extracted and analyzed using headspace solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) [12]. Briefly, 2.0 g tea and 0.0864 g ethyl decanoate were added to a 40 mL headspace vial and quickly sealed. After preheating for 15 min at 80 \({}^{\circ}\)C, a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber (50/30 \(\upmu\)m inner diameter, 2 cm length) (Supelco, Darmstadt, Germany) was inserted into the headspace vial, and extraction proceeded for 40 min at 80 \({}^{\circ}\)C. After extraction, the SPME fiber was injected into the GC inlet for 3 min at 250 \({}^{\circ}\)C.  
GC-MS was carried out on an Agilent 1890B gas chromatograph coupled with a 5977A mass spectrometer (Agilent, Santa Clara, CA, USA). Compounds were separated on an HP-5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness). Samples were injected in splitless mode. The carrier gas was high purity helium (purity \(\geq\) 99.99%) that was maintained at a column flow rate of 1.0 mL/min. The heating program had an initial temperature of 50 \({}^{\circ}\)C for 1 min, increase to 220 \({}^{\circ}\)C for 5 min at the rate of 5 \({}^{\circ}\)C/min. The ion source temperature was 230 \({}^{\circ}\)C and the electron impact (EI) ionization was 70 eV. The solvent delay time was 4 min. The scan range was 30-400 atomic mass unit (amu). The analyses were performed in triplicate.  
To qualitatively identify the volatile components, retention index (RI) and mass spectral match factors were used. RI was calculated by comparing retention times of a series of \(n\)-alkanes (C\({}_{9}\)-C\({}_{21}\)) to the values provided in the NIST 11 database that used the same capillary column as:  
\[\text{RI}=100\text{n}+100\ [\text{RT(x)}-\text{RT(n)}]/[\text{RT(n}+1)-\text{RT(n )}]\]  
where RT(x) is the retention time of compound x, and RT(n) and RT(n + 1) are the retention times of the alkanes with carbon number n and n + 1 immediately eluting before and after compound x, respectively. Compound matches were accepted when the calculated RI was less than 15, or if the mass spectral match factor was more than 90.