esa-sceva/satcom-chunk-collection huggingface dataset:
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
###### Abstract  
As technology scales down, digital VLSI circuits are prone to many manufacturing defects. These defects may result in functional and delay-related circuit failures. The number of test escapes grows when technology is downscaled. Small delay defects (SDDs) and hidden delay defects (HDDs) are of critical importance in industries today since they are the source of most test escapes and reliability problems. Improving test quality and creating new test methods, algorithms, and test designs requires a comprehensive study of these delay defects. This article reviews the effect and impact of SDD and HDD in logic circuits. It also analyzes the relevant fault models, automatic test pattern generation (ATFG) methods, faster-than-at-speed testing (FAST), cell-aware (CA) based delay tests, test quality metrics, diagnosis of SDDs and HDDs, and commercially available Electronic Design Automation (EDA) tools. Based on the analysis, the benefits and drawbacks of several accessible approaches are addressed.  
n acmseal delay defect; hidden delay defect; VLSI testing; test quality metric at-speed testing; fault model +
Footnote †: journal: Applied  
of the chip [8]. The reliability defects in the semiconductor chips used for safety-critical applications like automotive, avionics, medical, etc., can be life-threatening. Hence, in-field testing of these defects is also essential.  
On the other hand, systematic defects are due to process variations or crosstalk and can manifest as small delays. This is specific to a particular process technology. The major challenge in detecting SDDs and HDDs is testing them in the presence of process variations and crosstalk. It is difficult to differentiate the delay due to defects (resistive opens/shorts due to airborne particles in wafers) from the delay due to process variations or crosstalk. Also, traditional delay testing methods detect large delay defects, which will be discussed in detail in later sections. But most test escapes happen for SDDs and HDDs. Hence, there is a need to detect these defects.  
Although SDD and HDD detection is an active research field, they are hardly mentioned in a comprehensive way previously. This review article put together the different SDD and HDD detection methods and their challenges. There are four main objectives of the survey. The first objective is to elaborate on the current methods of modelling the delay defects as faults, generating patterns, the primary difference between SDDs and HDDs, and their pros and cons. The second is to survey and analyze the existing test pattern generation methods in the literature for SDDs and HDDs, the industrial practices for detecting these defects, and the low-cost alternatives. The third is to study the available test quality assessment methods and the support provided by the industrial EDA tools for testing delay defects. Finally, we identify prospective research directions that will help to guide future studies.
## 2 The Application of LDPA  
The log-periodic dipole antenna (LPDA) could be used as the conformal antenna mounted on the carrier [1]. The Vivaldi antenna is also a good choice. LPDA does not merely possess wide bandwidth and its gain is higher than the ordinary planar spiral antenna. The LPDA is a kind of frequency-invariant-dependent ultra-wideband antenna. Generally, its electrical properties remain stable in the frequency band of 10:1 or even higher because of its self-similar structure. In addition, the antenna installation is simplified, which does not destroy the mechanical structure of the carrier. Figure 1 showsthe structure diagram of LPDA, which consists of \(N\) parallel linear oscillators. The length is in proportion to the spacing of the oscillators and its definition is:  
\[\tau=\frac{d_{p}}{d_{p+1}}=\frac{L_{p}}{L_{p+1}}=\frac{R_{p}}{R_{p+1}},p=1,2, \cdots,N \tag{1}\]  
where \(d_{p}\) is the distance between \(p\)th and \(p+1\)th unit oscillator; \(L_{p}\) is the length of \(p\)th unit oscillator; and \(R_{p}\) is the distance from \(p\)th unit oscillation to the antenna void vertex. The entire structure of LDPA depends on the scale factor \(\tau\) and the structure angle \(\eta\). When \(\tau\) and \(\eta\) are decided, the geometric structure of the LPDA is fixed. Spacing-length ratio \(\sigma\) is defined as \(\sigma=d_{p}/2L_{p+1}\). The relationship between \(\eta\), \(\tau\) and \(\sigma\) is \(\eta=2\arctan\left[(1-\tau)/4\sigma\right]\). As long as two of the parameters \(\eta\), \(\tau\) and \(\sigma\) are known, the geometric structure of antenna is determined.  
Unfortunately, LPDA does not have an actual phase center; however, there is an approximate phase center, which is located in the active region, and moves along the feeder line as the frequency changes. The incident signal frequency can be measured by the digital receiver. Once the antenna installation position in the aircraft is fixed, the location of the approximate phase center with the different frequency can be determined by the HFSS software or the actual test in the microwave chamber (The step size of the frequency depends on the accuracy requirement, e.g., KHz, MHz, GHz). The frequency and the corresponding approximate phase center location are stored in the data manager. Although the data is tremendous, it can be done off-line as a preprocessing step. Figure 2 shows the position of LPDA mounted on the aircraft.  
Figure 1: The structure diagram of LPDA.  
Figure 2: The LPDA mounted on the aircraft.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 1 Introduction
### Natural Language Preprocessing Techniques in Short Text
#### 1.2.1 Tokenization  
Tokenization separates text into meaningful pieces of information, which are often known as tokens. There are two types of tokenization in text: (i) word tokenization and (ii) sentence tokenization. To interpret each word's underlying meaning, different words are separated from the raw text using word tokenization. On the other hand, sentence tokenization separates specific sentences from the source text [12]. Apart from sentences and words, punctuation and special characters are also considered tokens that can be extracted from paragraphs and documents. There are various other tokens usually treated as combined tokens that are used in abbreviations and acronyms such as BA, BS, M. Phil., and Ph.D. These tokens are considered the same as punctuation and special characters such as @, #, $, $, etc. There are many words that are used as combined words in sentences, such as [PERSON], check-in, long-term, and mind-blowing. These words are treated as a single entity in the normal space vector [12]. However, in order to check the semantics, these words can be tokenized separately. Consequently, for that, machines use various algorithms to identify the type of tokenization. Some of the well-known techniques used for tokenization are the wordpiece tokenizer, the sentence piece, and the byte-pair-encoding tokenizer. An example of tokenization is shown in the example below.  
Example:  
Text: Islamabad is the capital of Pakistan.  
Tokenized Text: ['Islamabad', 'is', 'the', 'capital', 'of', 'Pakistan'].
# Feature-Level Camera Style Transfer for Person Re-Identification
## 2 Related Works
### Person Re-Identification  
A large family of approaches treat person re-ID as a metric learning problem. Many efforts focus on the improvement of loss functions and the basic structure of feature encoders. [PERSON] et al. [6] introduce triplet loss based on hard sample mining into the re-ID theory. [PERSON] et al. [25] use a novel scheme to make instance-guided context rendering. [PERSON] et al. [7] discuss the advantage of local features through a unique partition strategy. [PERSON] et al. [8] propose the High-Order Attention (HOA) module to model and utilize the complex and high-order statistics information in an attention mechanism. These methods mainly regard person re-ID as a classification problem yet neglect camera style variance, which innately exists in the re-ID problem.  
Due to the rapid growth of GAN theory in recent years, several researchers have exploited using GAN in image-level data augmentation for person re-ID. CycleGAN [15] is applied in some works [5,26] to make cross-domain image style conversion to solve domain adaption problems. StarGAN [18] is adopted to generate pedestrian images with different camera styles due to its flexibility. [PERSON] et al. [27] manage to improve the quality of generated images by decomposing the process of transformation into a set of sub-transformation. [PERSON] et al. [13] design a generative network by exploiting and reorganizing the structure information and the appearance information in the dataset. [PERSON] et al. [25] formulate a dual condition GAN which can enrich person images with contextual variations.  
GAN-based methods can be helpful to alleviate camera style variance for the person re-ID task. However, due to the huge computing resources required by the training of GAN, image-level generative models are usually used as a pre-training or data augmentation process in re-ID. The training process of GAN is usually separated from the training of the re-ID model. In order to reduce the time and memory consumption of model training, the authors establish the generative model on a feature level and embed the generative model into the re-ID model in an end-to-end manner. Experimental results show that the proposed feature-level Camera Style Transfer (CST) approach can significantly improve the performance of multiple re-ID baselines with less time and memory cost.
# Demand-Response Control in Smart Grids
## 5 Fair Energy-Scheduling Method
### Phase I: Proportional Division-Based Energy Scheduling
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Consumer/Provider ID**} & **Electricity** & **Electricity** & **Surplus/Deficit** & \\  & **Generated (G\({}_{\textbf{i}}\))** & **Required (L\({}_{\textbf{i}}\))** & **(G\({}_{\textbf{i}}\) – L)** & **Role** \\  & **in KWh** & **in KWh** & **in KWh** & **in KWh** \\ \hline
1 & 170 & 210 & \(-40\) & Consumer \\ \hline
2 & 595 & 200 & 395 & Provider \\ \hline
3 & 850 & 165 & 685 & Provider \\ \hline
4 & 150 & 385 & \(-235\) & Consumer \\ \hline
5 & 240 & 650 & \(-410\) & Consumer \\ \hline
6 & 325 & 155 & 170 & Provider \\ \hline
7 & 188 & 420 & \(-232\) & Consumer \\ \hline
8 & 200 & 700 & \(-500\) & Consumer \\ \hline \hline \end{tabular}
\end{table} 
Table 3: System data for microgrids.
## 3 Results and Discussion
### ICEO around a Janus Micropillar Free from External Wiring
#### 3.2.2 Effect of the Radius of Janus Pillar on Frequency-Dependent ICEO Pumping
Considering the cross-influence of the post radius R and signal frequency f, the ICEO flow profile was plotted at their separate optimum frequencies of different post sizes, i.e., \(\mathrm{f}=1000\) Hz for R = 2.5 \(\upmu\)m (Figure 4A), \(\mathrm{f}=700\) Hz for R = 5 \(\upmu\)m (Figure 4B), \(\mathrm{f}=600\) Hz for R = 7.5 \(\upmu\)m (Figure 4C), and \(\mathrm{f}=500\) Hz for R = 10 \(\upmu\)m (Figure 4D). As expected, not only is the actuating range of two ICEO eddies sweeping forward is effectively extended, but the pump's flow rate is also accelerated to a great extent, as the radius of Janus post R increases gradually from 2.5 \(\upmu\)m to 10 (Figure 4A-D)), while the azimuth slip velocities maintains similar profiles under their respective ideal frequencies (Figure 4H). Thus, it is feasible to actuate net pump behavior passing by a symmetric metal-dielectric Janus micropillar free from external wiring by simply applying an AC signal of several volts and a moderate frequency range of 100 Hz \(\mathrm{\leq}\) f \(\mathrm{\leq}\) 1000 Hz across two parallel conducting DE plates, with an appropriate inter-electrode spacing of 100 \(\upmu\)m.
## 1 Introduction
The laser diffractometer method has been successfully used for many years, among other uses, in various branches of industry in order to measure the size of various particles of regular and irregular shape [22; 23; 24; 25; 26], and even for such unconventional purposes as forensic investigation [27; 28], which is why attempts are also made to use its capabilities in the field of geotechnical engineering. In practical research, various types of diffractometers are used, differing in their type of particle size measurement, and in their range and dispersion possibilities [29]. A sample for diffractometric research is most often prepared in the form of a suspension made of a representative part of the soil in distilled water, which, due to the preservation of a complete soil skeleton, makes it a fully reliable as a research material [11; 30]. Unfortunately, due to the diverse and often extremely complicated structure of soils, depending on their type and origin, no single, binding research procedure has been developed to date for research using the laser diffraction phenomenon. In general, works published on this subject most often present a complete case study, including the results of diffractometer tests and their verification using the selected method [31; 32; 33; 34; 35; 36; 37; 38]. These works mainly concern the study of mineral soils, while the information on organic soils in the literature is fragmentary or includes information on the study but only after removing organic substances from the soil skeleton, which ultimately deprives the study of practical application, because this process not only changes the structure and composition but also the geotechnical properties of the soil [6; 7; 8; 11; 39]. Research using laser diffractometers has been conducted for many years, and the overall results are encouraging, but it should be borne in mind that this method also has limitations and the research conducted today is aimed at their precise identification and, if possible, their reduction or elimination in the future [40; 41; 42; 43; 44; 45]. This work presents research that uses two types of laser diffractometers: the Helos laser diffractometer manufactured by Sympatec GmbH (Claustthal-Zellerfeld, Germany) [46; 47] and the laser particle sizer analyzer Analysette 22 MicroTech plus by Fritsch GmbH (Idaar-Oberstein, Germany) [48; 49], the results of which were referred to the hydrometric method [4; 5] and classified as sedimentation methods, the effectiveness of which has been confirmed by many years of practice in soil research for the purposes of agricultural soil science, with agricultural soil often containing significant amounts of organic substances.
## 2 Fluid Model, Plasma Chemistry, and Reactor Configuration
### Chemistry of Ar/SF\({}_{6}\) Plasma  
The Ar/SF\({}_{6}\) gas-phase chemistry and surface kinetics are given in Tables 1 and 2, respectively. The electron-impact elastic collision, excitation and de-excitation, ionization, direct attachment and dissociative attachment, and dissociation are included. The heavy species reaction types considered are neutral and ionic recombination, detachment, Penning ionization and charge exchange. The rate coefficients and cross sections of these reactions are taken from the Refs. [27; 28; 29; 30]. The surface kinetics of species considered in Table 2 include recombination and de-excitation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Reaction** & **Rate Coefficient \({}^{\text{a}}\)** & **Threshold (eV)** & **Ref.** \\ \hline \multicolumn{4}{c}{Elastic collisions} \\
1 & \(\text{e}+\text{Ar}\rightarrow\text{e}+\text{Ar}\) & Cross section & 0 & [27] \\
2 & \(\text{e}+\text{SF}_{6}\rightarrow\text{e}+\text{SF}_{6}\) & Cross section & 0 & [27] \\
3 & \(\text{e}+\text{F}_{2}\rightarrow\text{e}+\text{F}_{2}\) & Cross section & 0 & [27] \\
4 & \(\text{e}+\text{F}\rightarrow\text{e}+\text{F}\) & Cross section & 0 & [27] \\  & Excitation and de-excitation reactions & & \\
5 & \(\text{e}+\text{Ar}\rightarrow\text{e}+\text{Ar}^{*}\) & Cross section & \(11.6\) & [27] \\
6 & \(\text{e}+\text{Ar}^{*}\rightarrow\text{e}+\text{Ar}\) & Cross section & \(-11.6\) & [27] \\  & Ionization reactions & & \\  
}_{2}\rightarrow\text{F}^{-}+\text{F}\) & Cross section & \(0\) & [27] \\ \hline \end{tabular}
\end{table}
Table 1: Ar/SF\({}_{6}\) chemical reaction set considered in the model.
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
###### Abstract  
Remote areas with sparse population, disaster areas in the aftermath, and refugee camps all require communication that is not forthcoming from commercial vendors. Numerous communication system options are available, but with widely varying cost and efficacy. The goal of this work is to discuss a framework in which to consider appropriate telecommunications technology. The framework approaches sustainable development as a business, under the assumption that social/technical/environmental sustainability requires economic sustainability. The framework incorporates well known and accepted business canvas as a roadmap. Information and Communication Technology (ICT) interventions are then considered in terms of their value proposition, markets, and perhaps most important for the realm of sustainable development, the key partners. To illustrate how the framework applies, we consider three case studies and then apply the resultant principles to the consideration of these ICT projects. The case studies are chosen for their diversity. Furthermore, after verifying the decision framework, recommendations are made for three ongoing intervention projects in limited-resource settings.  
long distance Wi-Fi; TVWS; limited-resource settings; business canvas +
Footnote †: journal: Future Internet
###### Abstract  
The biopolymer levan has sparked a lot of interest in commercial production and various industrial applications. In this study, a bacterial isolate with promising levan-producing ability was isolated from a soil sample obtained from Princess Nourah bint Abdulrahman University in Saudi Arabia. The isolate has been identified and submitted to GenBank as _Bacillus subtilis_ MZ292983.1. The bacterial levan polymer was extracted using ethyl alcohol (75%) and CaCl\({}_{2}\) (1%) and then characterized using several approaches, such as Fourier transform infrared spectrometry and nuclear magnetic resonance. The IR spectrum of the levan polymer showed characteristic peaks confirming characteristics of polysaccharides, including a broad stretching peak of OH around 3417 cm\({}^{-1}\) and aliphatic CH stretching was observed as two peaks at 2943, and 2885 cm\({}^{-1}\). In addition, the FTIR spectrum featured an absorption at 2212 cm\({}^{-1}\), indicating the fingerprint of the \(\beta\)-glycosidic bond. Based on \({}^{1}\)H and \({}^{13}\)C NMR spectroscopy analysis, six unexchanged proton signals related to fructose as a forming monomer of levan were observed. Evaluation of levan's antibacterial effect against two pathogenic bacteria, _S. aureus_ (ATCC 33592) and _E. coli_ (ATCC 25922), showed inhibition zones of 1 cm and 0.8 cm in diameter, respectively, with MICs of more than 256 ug mL\({}^{-1}\) for both strains. Moreover, the antibidium property of the levan polymer was assessed and the results showed that the inhibition rate was positively proportional to the levan concentration, as the inhibition percentages were 50%, 29.4%, 29.4%, 26.5%, and 14.7% at concentrations of 2, 1, 0.5, 0.25, and 0.125 mg mL\({}^{-1}\), respectively. Levan showed a significant role in burn healing properties since it accelerated the process of healing burn-induced areas in rats when compared with those either treated with normal saline or treated with the cream base only.  
levan polymer; _Bacillus subtilis_; antibiofilm; burn healing properties
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 5 Simulation Results
### Analysis of Results  
Figure 10 shows how the connections between the nodes change according to the scheduled scenario of Figure 9. Initially, the nodes organize a tree-topological network (Figure 10a). At 3 h (Figure 10b), the nodes 10, 11, and 19 are lost as marked by '\(\times\)' and thus their descendants (nodes 2-9 and 12-14) become temporarily detached from the network. Then, the nodes 4-8 and 12-14 find detouring paths while the nodes 2, 3, and 9 fail in detouring and recognize isolation as marked by '\(\ast\)'. At 6 h (Figure 10c), the nodes 20, 21, and 27 are additionally lost, and thus the corresponding descendants (nodes 4-8, 12-15, and 22) are isolated. At 9 h (Figure 10d), the nodes 40 and 41 are newly lost, but this time, the descendants (nodes 47 and 48) all find new parents and successfully detour. At 12 h (Figure 10e), the nodes 10, 11, and 19 are repaired (sometimes they can be replaced by new ones) and the remaining isolated nodes are all connected to the network again. At 15 h (Figure 10f), the nodes 30 and 37 are newly lost and consequently the nodes 29, 36, and 43-45 are isolated. At 18 h (Figure 10g), the nodes 20, 21, and 27 are repaired and the nodes 15 and 22 adaptively reconfigure their connection in such a way that their hierarchical levels become higher. At 21 h (Figure 10h),  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Grid spacing & 1 km \\ Data rate & 2400 bps \\ Propagation speed & 1500 m/s \\ Transmission/Sensing range & 1300 m \\ \(HELLO\)/\(ACK\)/\(ISO\) packet length & 120 bits \\ \(DATA\) packet length & 1200 bits \\ Tx mode power & 20 W \\ Rx mode power & 756 mW \\ \(N_{MAX\_ACK\_FAIL}\) & 3 \\ \(N_{MAX\_RELAY}\) & 10 \\ \(N_{MAX\_LEVEL}\) & 10 \\ \(\bar{\lambda}\) & 0.001 packets/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters.  
Figure 9: Simulation scenario.  
the nodes 32 and 33 are newly lost and their corresponding descendants nodes 38, 39, 46, and 47 detour through the node 31. At 24 h (Figure 10i), the network is reorganized from the start.  
Figure 10: Cont.  
Without repairing or battery recharging, the nodes 30, 32, 33, 37, 40, and 41 are still lost and thus the associated descendants, nodes 29, 36, and 43-45 remain isolated. On the other hand, nodes 11, 21, and 28 find new parent during the reorganization. From the above scenario, we observed that the proposed self-organizing methodology works well to adapt the network variations such as node loss or node addition.