esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2014**, _14_, 795-833; doi:10.3390/s140100795  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Underwater Acoustic Wireless Sensor Networks: Advances and Future Trends in Physical, MAC and Routing Layers**  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Institut ITACA, Universitat Politecnica de Valencia, Edifici 8G, Valencia 46022, Spain;  
E-Mails: [EMAIL_ADDRESS] (A.S.); [EMAIL_ADDRESS] (J.V.C.);  
[EMAIL_ADDRESS] (J.J.S.)  
\({}^{2}\) Pervasive Systems, University of Twente, P. O. Box 217, Enschede 7500 AE, The Netherlands;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-96387700 ext. 88251.  
_Received: 1 November 2013; in revised form: 24 November 2013 / Accepted: 27 November 2013 / Published: 6 January 2014_  
**Abstract:** This survey aims to provide a comprehensive overview of the current research on underwater wireless sensor networks, focusing on the lower layers of the communication stack, and envisions future trends and challenges. It analyzes the current state-of-the-art on the physical, medium access control and routing layers. It summarizes their security threads and surveys the currently proposed studies. Current envisioned niches for further advances in underwater networks research range from efficient, low-power algorithms and modulations to intelligent, energy-aware routing and medium access control protocols.  
**Keywords:** underwater sensor networks; survey; future trends; physical layer; medium access control layer; routing layer; security
## 4 Materials and Methods
### Variable Selection
#### 4.2.2 Core Independent Variable  
Our core independent variable is Green Public Procurement (GPP). This study collected original public procurement contract data from the Chinese Government Procurement Website ([[http://www.ccgp.gov.cn](http://www.ccgp.gov.cn)]([http://www.ccgp.gov.cn](http://www.ccgp.gov.cn))), which is the official platform administered by the Chinese Ministry of Finance, spanning the period from 2015 to 2023. Using R programming, we constructed a comprehensive public procurement contract dataset (\(\zeta\)) for analysis. Each contract record (\(\xi\in C\)) contains detailed fields including \"contract name,\" \"main items name,\" and \"specifications or service requirements,\" which are collectively denoted as Text(\(\xi\)). Building upon the official \"Government Procurement List of Energy-Saving Products (2019)\" and \"Government Procurement List of Environmental Labeling Products (2019),\" we developed an extensive keyword database for green products. Through the Jieba word segmentation tool in Python 3.8, we processed product names and conducted manual vocabulary refinement, ultimately establishing a repository (\(K\)) containing over 1100 verified green product names. Subsequently, we implemented multi-criteria matching procedures using Excel VBA tools and successfully identified 167,062 public procurement contracts for green products (\(\zeta_{green}\)):  
\[\zeta_{green}\ =\ \{\ \xi\in C|\exists x\in K,\kappa\subseteq\textit{ Text}(\xi)\} \tag{5}\]  
Figure 4: Distribution of carbon emission intensity in Chinese cities from 2015 to 2023.  
where \(C_{gren}\) denotes the subset of successfully matched GPP contracts, while \(\kappa\subseteq Text(\varsigma)\) indicates that the extracted keywords (\(\kappa\)) are substrings embedded within the contractual text(\(\varsigma\)).  
Through precise matching of the purchaser identity, address, and regional information contained within the GPP contracts (\(C_{gren}\)), we establish exact correspondence with China's 333 prefecture-level administrative units (comprising 293 prefecture-level cities, 7 administrative regions, 30 autonomous prefectures, and 3 leagues). The GPP for city \(i\) in year \(t\) is computed as follows:  
\[GPP_{it}=\sum_{\varsigma\in C_{gren}^{(it)}}Amount(\varsigma) \tag{6}\]  
where \(C_{gren}^{(it)}\) denotes the subset of GPP contracts for city \(i\) in year \(t\); Amount(\(\varsigma\)) represents the monetary value of contract (\(\varsigma\)).  
To address heteroscedasticity and linearize the relationship, the natural logarithm is applied to \(GPP_{it}\):  
\[LnGPP_{it}=Ln(GPP_{it}+1) \tag{7}\]
# A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning
## 2 Background and Related Work
### Background
#### 2.1.1 Cryptocurrency Manipulation  
Cryptocurrency market manipulation has different aspects. [PERSON] et al. [3] surveyed papers that have studied cryptocurrency manipulation. They listed seven different methods of cryptocurrency market manipulation, such as P&D [7], wash trading, frontrunning, order book, insider trading, Distributed Denial of Service (DDoS), and stablecoin. Wash trading involves artificially inflating trade volumes by rapid buying and selling, creating the illusion of high market activity to attract investors using multiple accounts and distorting the true value of the asset. It was especially common in less well-known cryptocurrencies and smaller exchanges [8; 9; 10]. However, nowadays, with the emergence of non-fungible token (NFT), there are new approaches to wash trading with NFTs [11]. Frontrunning refers to an advantage gained by accessing market information ahead of others; order book refers to creating false price signals by placing and immediately canceling numerous orders; insider trading is the abusive use of privileged information for trading advantages; DDoS refers to the use of distributed denial-of-service attacks to manipulate cryptocurrency markets for profit; stablecoin manipulation refers to suspicious trading practices, potentially affecting cryptocurrency markets. It is worthwhile to add two more types of manipulations: Spoofing is a technique involving placing fake buy or sell orders to create false signals of market activity and liquidity, influencing traders' decisions [12]. Stop hunting is a manipulation tactic involving deliberate actions to trigger the execution of stop-loss orders by driving the price below a critical threshold, creating selling pressure, and providing an opportunity for attackers to buy at lower prices on their analyzed manipulation.
## 3 Results
### Sensory Characteristics  
The sensory characteristics were evaluated to measure the sensory quality of the test HMR product. Slow and quick freezing methods were used to determine the best freezing method for test HMR products. Microwaves were used to reheat the frozen test HMR product prior to sensory evaluation. The color, aroma, flavor, texture, and overall acceptance of the test HMR product were evaluated by the panelists. The results of the sensory evaluation are displayed in Table 4. The color, aroma, flavor, texture, and overall acceptance values for quick freezing were not significantly (\(p\geq 0.05\)) different between fresh and frozen test HMR products. However, for slow freezing, the flavor and texture characteristics differed significantly from those of the fresh product. The slow freezing method reduced the flavor and texture characteristics by 3.8% and 4.5%, respectively. [PERSON] et al. [7] and [PERSON] et al. [6] also reported that the application of slow freezing methods significantly impaired the texture characteristics of seafood HMR products.  
Thus, the application of freezing methods affects the sensory characteristics of test HMR products. The formation of ice crystals during slow freezing can affect the sensory characteristics. According to Samples [24], ice crystals form during slow freezing and affect membrane disruption. This phenomenon can impair the texture of the product and increase oxidation. Moreover, [PERSON] et al. [25] reported that the formation of ice crystals during slow freezing causes tissue damage in frozen foods. Furthermore, the muscle fibers of frozen products can be destroyed by ice crystals [16]. In contrast, during the quick freezing process, the faster rate of heat loss does not produce ice crystals [26], which minimizes disturbance to the cell walls and maintains the sensory characteristics of the product. Therefore, we employed quick freezing to freeze the test HMR product during the production process, thereby minimizing changes in the sensory characteristics and ensuring product quality.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 4 Infrared and Visible Image Fusion Dataset
### Other Datasets  
In addition to the above datasets, there are also some public datasets, such as the OTCBVS dataset, the INO dataset, the LITIV dataset, the GTOT dataset, and the VOT-2016 dataset.  
The OTCBVS dataset [91] is a freely accessible benchmark dataset for testing and evaluating algorithms in the visible and infrared domains. The dataset is divided into 14 sub-datasets, comprising seven infrared datasets, one visible light dataset, and six  
Figure 16: Example of RGBT234 dataset.(**a1–c1**) infrared images; (**a2–c2**) visible light images.  
Figure 15: Example of RGBT210 dataset. (**a1–c1**) infrared images; (**a2–c2**) visible light images.  
vision-infrared datasets, which include pedestrians, faces, motions, weapons, vehicles, and ships.  
The INO dataset has ten image categories, each with 200 images, for a total of 2000 images with a resolution of 256 \(\times\) 256 pixels. This dataset can be used to train and test image recognition algorithms. Each image category symbolizes a different object or scene, such as a cat, dog, automobile, or airplane.  
The LITIV dataset [92] is a multi-modal dataset for computer vision and machine learning research. The collection includes a wide range of diverse sensor data, including photos, movies, infrared photos, depth photos, etc. There are numerous sub-datasets in it as well. A dataset for target detection and tracking, which includes a number of different target types and scenarios, is one of the sub-datasets.  
The GTOT dataset [93] contains about 15,800 frames of video in 50 different scenarios, such as laboratories, pools, and roads, with objects such as swans and vehicles.
###### Abstract  
Based on chirp transform and pulse compression technology, chirp transform spectrometers (CTSs) can be used to perform high-resolution and real-time spectrum measurements. Nowadays, they are widely applied for weather and astronomical observations. The surface acoustic wave (SAW) filter is a key device for pulse compression. The system performance is significantly affected by the dispersion characteristics match and the large insertion loss of the SAW filters. In this paper, a linear phase sampling and accumulating (LPSA) algorithm was developed to replace the matched filter for fast pulse compression. By selecting and accumulating the sampling points satisfying a specific periodic phase distribution, the intermediate frequency (IF) chirp signal carrying the information of the input signal could be detected and compressed. Spectrum measurements across the entire operational bandwidth could be performed by shifting the fixed sampling points in the time domain. A two-stage frequency resolution subdivision method was also developed for the fast pulse compression of the sparse spectrum, which was shown to significantly improve the calculation speed. The simulation and experiment results demonstrate that the LPSA method can realize fast pulse compression with adequate high amplitude accuracy and frequency resolution. Compared to existing digital pulse compression technology, this method can significantly reduce the number of required calculations, especially for measurements of sparse signals.  
chirp transform spectrometer; SAW filters; digital pulse compression; spectrum detection +
Footnote †: journal: Applied Sciences
## 3 Exploring Speaker-Independent Dysarthric Speech Recognition
### Experiment Setting  
#### 3.3.1 Experiment Design  
We use the Wav2 Vec2.0 model [14] as the base model, which has been commonly used in recent research. The model consists of a feature extraction network (six convolutional layers), a feature projection layer (one dense layer), twenty-four transformer encoder layers, and a final output layer dedicated to downstream tasks (see Figure 1). Specifically, we utilize the Wav2 Vec2.0 model that is self-supervised, pretrained on multilingual data, and fine-tuned for phone recognition tasks across 26 languages [61]. This base model contains approximately 315 million parameters.  
In our experiments, we kept phones as the recognition target and used Phone Error Rate (PER) as the metric for recognition performance. The base model's vocabulary includes 392 phones, which is redundant for our five-language scenario. Thus, we adopt a smaller vocabulary of 82 phones, including a <_blk>_ (blank) token for loss calculation. This reduced vocabulary was derived from phonetic transcriptions of all datasets we used. These transcriptions are expressed in the International Phonetic Alphabet (IPA) and were obtained using the _Epitran_ toolkit [62]. For the **SSNCE** database, we utilized the provided phonetic transcriptions. The final vocabulary was refined by removing rarely occurring phones, splitting diphthongs into two parts, and merging phones with identical phonological features. Greedy decoding was used during evaluation without the application of a language model. The Connectionist Temporal Classification (CTC) loss [63] was employed for model training.  
In this study, we investigate model training strategies using combinations of available data. Each strategy involves one or multiple training steps, each using only one type of source data. Models are trained sequentially on the data. In the training step using **C_rich**, only one model is trained, and it is tested with all dysarthric datasets. When the training step involves target-related data, including using language-related data **C_rich_tgt** and in-domain data **C_indm**, **D_indm**, we train one model for each language or each dataset and evaluate the model with the corresponding dysarthric dataset(s). If the training involves dysarthric speech **D** and **D_indm**, we conduct speaker-cross-validation, train one model for each fold, and test it on the target speakers in each fold. This means that for in-domain dysarthric data **D_indm**), there will be one model for each fold of each dataset.
# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
###### Abstract  
Wireless sensor networks have been considered as an effective solution to a wide range of applications due to their prominent characteristics concerning information retrieving and distributed processing. When visual information can be also retrieved by sensor nodes, applications acquire a more comprehensive perception of monitored environments, fostering the creation of wireless visual sensor networks. As such networks are being more often considered for critical monitoring and control applications, usually related to catastrophic situation prevention, security enhancement and crises management, fault tolerance becomes a major expected service for visual sensor networks. A way to address this issue is to evaluate the system dependability through quantitative attributes (e.g., reliability and availability), which require a proper modeling strategy to describe the system behavior. That way, in this paper, we propose a methodology to model and evaluate the dependability of wireless visual sensor networks using Fault Tree Analysis and Markov Chains. The proposed modeling strategy considers hardware, battery, link and coverage failures, besides considering routing protocols on the network communication behavior. The methodology is automated by a framework developed and integrated with the SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) tool. The achieved results show that this methodology is useful to compare different network implementations and the corresponding dependability, enabling the uncovering of potentially weak points in the network behavior.  
dependability evaluation; availability; reliability; wireless visual sensor networks; fault tree analysis; Markov chains 2018182629; doi:10.3390/s18082629
## 3 Results and Discussions
### SEM-EDS Analysis
#### 3.4.6 Discussion  
Hydraulic lime contains air-hardening and hydraulic phases. When hydraulic lime is used as lime mortar blocks, the initial reaction that occurs is the hydration of C2S to enhance the early strength of the material. As the hydration reaction continues, the products of the hydration reaction, C-S-H and Ca(OH)2, undergo a carbonation reaction to produce CaCO3, which further enhances the strength of the lime mortar. Its formula was shown in Equations (1)-(3):  
\[2\text{CaO}\cdot\text{SiO}_{2}+\text{nH}_{2}\text{O} = \text{xCaO}\cdot\text{SiO}_{2}\cdot\text{yH}_{2}\text{O}+(2-\text{x}) \text{Ca(OH)}_{2} \tag{1}\] \[\text{CaO}\cdot\text{SiO}_{2}\cdot\text{yH}_{2}\text{O}+\text{ CO}_{2}=\text{CaCO}_{3}+\text{SiO}_{2}\cdot\text{yH}_{2}\text{O}\] (2) \[\text{Ca(OH)}_{2}+\text{CO}_{2}=\text{CaCO}_{3}+\text{H}_{2}\text{O} \tag{3}\]  
According to the above analysis, NHL2 contains more lime and NHL5 and K3 contain more hydraulic phases. When the specimens were cured for 56d, a large amount of C-S-H and calcium carbonate formed inside SL5 and SK, which had higher strength and could better resist the erosion of the external environment. Due to the higher lime content in NHL2 and the lack of a substantial amount of CO2 in the curing environment, there is still more Ca(OH)2 present inside the NHL2 mortar. During water stability, temperature and humidity cycle, and frost-thaw cycle tests, the strength loss of NHL2 mortar is significantly higher than that of NHL5 and K3 mortars due to water erosion. In soundness and alkali resistivity tests, Na2 SO4 and NaOH crystals may fill the inside of the lime mortar to increase the strength [31]. However, the three mortars NHL2, NHL5, and K3 did not show obvious regularity, which may also be related to the pore space of each mortar and needs further study.
# Hybrid Mode Reconfigurable Antenna with V-Shaped Extrudes for Cognitive Radio Applications
###### Abstract  
As the same device may be used for various types of applications, antennas must have the capacity to perform multiple functions. Reconfiguring the polarization, operational frequency, and radiation pattern can create an outcome of multi-functionality. A hybrid, frequency- and pattern-reconfigurable antenna with two V-shaped extrudes in a patch is proposed. To increase the bandwidth of the proposed antenna, the defected ground structure (DGS) phenomenon is presented, which is significant in the bandwidth of 3200 MHz from 2.7 GHz to 5.9 GHz, providing a gain of 3.5 dB. For this design, an FR-4 material was chosen as the substrate, and CST software was used for simulation. Using two p-i-n diodes, the projected antenna illustrates switching for multi-frequency bands such as 1.5, 2.6, 3.2, 5.1, and 8.1 GHz, with a constant radiation pattern and a main lobe direction of 155 degrees. It has C-band, WLAN, cognitive radio (CR), and Bluetooth applications. The proposed antenna displays radiation pattern switching at 3.5 GHz, showing variation between \(7^{\circ}\), \(158^{\circ}\), \(173^{\circ}\), and \(175^{\circ}\), and at 4 GHz, it demonstrates switching between \(11^{\circ}\), \(15^{\circ}\), \(165^{\circ}\), and \(344^{\circ}\), which defines the hybrid reconfiguration.  
radiation pattern; p-i-n diode; reconfigurable; cognitive radio; defected ground structure (DGS); FR-4 material +
Footnote †: journal: Electronics Electronics
## 5 Errors Compensation
### Antenna Scanning Error Compensation  
The existing compensation methods for the pointing error often focus on the pointing error of the system, such as the pointing error caused by the structural deformation of the main reflector, the structural deformation of the shifting, the gravity load, and other factors, ignoring the small random scanning error that may occur in the high-speed scanning process. These methods often fail to achieve the expected compensation effect when dealing with complex scanning scenarios, especially in application scenarios that require high-precision positioning. In view of this situation, this section assumes that after obtaining the far-field data in the case of scanning error, the surface measurement distribution of the inverse solution of the far-field data with the scanning error is compensated. We use the model correction method to compensate for the surface shape measurement error caused by the scanning error.  
The main steps in using the model correction method to compensate for the surface shape measurement error caused by random error are as follows:  
1. The measurement error distribution of the surface shape is solved by using the measurement data, which is defined as \(\varepsilon_{real}\);
2. During the antenna scanning process, the angle encoder is used to record the actual elevation pointing (\(EL\)) and azimuth pointing (\(AZ\)) of each scanning point in real time. Compared with the predetermined pointing, the scanning errors (\(\Delta AZ\) and \(\Delta EL\)) of each scanning point are obtained;
3. The offset errors \(\Delta u\) and \(\Delta v\) of the far-field coordinate points (\(u\), \(v\)) are calculated by using Equation (9).
4. The offset errors \(\Delta u\) and \(\Delta v\) of each point in the far field are substituted into Equation (8) to simulate the random scanning error, and the surface shape measurement error caused by the random scanning error is solved, which is defined as \(\varepsilon_{p}\);
5. The surface shape measurement error caused by the scanning error is deducted, \(\varepsilon_{real}-\varepsilon_{p}\).