esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Personnel Localization Method Based on SGBM Stereo Vision Matching Algorithm and MSS-YOLO  
In order to realize the three-dimensional (3D) positioning of personnel, this paper utilized a ZED2i binocular camera for image acquisition; the Matlab2018b camera calibration toolbox was used to calibrate the binocular camera using the checkerboard grid calibration method [33]. The disparity of binocular images was calculated according to the SGBM binocular stereo vision matching method.  
Firstly, MSS-YOLO was utilized for underground coal mine personnel recognition on the left view of the ZED2i binocular camera. At the same time, disparity calculation was performed on the binocular view based on the SGBM visual matching algorithm to obtain the 3D spatial information of each pixel under the current frame, which was transformed into a disparity map. The cost calculation method, the global energy function, the cost aggregation and disparity calculation, and the disparity map calculation formulas are shown in Equations (3)-(6) [34], respectively. Finally, the rectangular pixel centers obtained from the miner recognition results on the left view were mapped with the disparity map in order to obtain the 3D spatial coordinate information of the personnel coordinates. The solution flow is shown in Figure 13.  
\[D_{b}(x,y,d)=\sum_{(i,j)\in B(x,y)}|I_{L}(x+i,y+j)-I_{R}(x+i-d,y+j)| \tag{3}\]  
where \(D_{b}(x,y,d)\) denotes the basic matching cost, \(d\) is the disparity value of the current pixel, \(x,y\) are pixel coordinates, and \(B(x,y)\) denotes a block centred on \(x,y\) with a size of \(5\times 5\).  
\[E=\sum_{x,y}D_{b}(x,y,d)+\lambda\sum_{(x^{\prime},y^{\prime})}\left|d\big{(} x^{\prime},y\big{)}-d(x,y)\right| \tag{4}\]  
where \(E\) is the global energy function, \(\lambda\) is the weight of the smoothing term, and \((x^{\prime},y^{\prime})\) denotes a pixel in the neighborhood of \((x,y)\).  
\[(x,y)=arg\;min_{d}(D_{b}(x,y,d)+\lambda\sum_{(x^{\prime},y^{\prime})}\left|d \big{(}x^{\prime},y\big{)}-d(x,y)\right|) \tag{5}\]  
where \(d(x,y)\) is the result of the disparity calculation.  
\[Z(x,y)=\frac{f\cdot B}{D(x,y)},D(x,y)e\{d_{min},d_{max}\} \tag{6}\]  
where \(f\) is the focal length of the camera, \(B\) is the baseline distance between the left and right cameras, and \(D(x,y)\) is the corresponding value in the disparity map.  
Figure 12: The results of the distribution of different environments. (**a**) The distribution of data under different cutting dust conditions; (**b**) the distribution of data under different light conditions.
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
## 1 Introduction
### Challenges in Achieving Cyber Resilience for Space Infrastructure
#### 1.2.3 Cascading Failure Risk and Systemic Vulnerabilities  
Space systems operate as part of a broader system of systems, where the failure of a single asset can result in cascading disruptions across multiple sectors [13]. For example, the failure of a satellite can impact telecommunications, navigation, and defence systems simultaneously, amplifying the consequences of a cyber-attack [13]. This interconnectedness differentiates space systems from other infrastructures where the effects of failure may be more contained. As a result, space system designers must incorporate measures to withstand and recover from attacks that could disrupt not only the asset itself but also critical downstream services [15]. This complexity demands a comprehensive approach to cyber resilience that addresses the broader implications of space system vulnerabilities [31].
## 3 Real-Coding PBIL Algorithm for Multi-Satellite Scheduling
### Encoding  
When using the traditional PBIL algorithm for multi-satellite scheduling, the binary-coding format is often used, as shown in Figure 1.  
Each bit of the chromosome represents an observation time window corresponding to a target, and its value is 0 or 1, which indicates whether the time window is selected to arrange the observation task. The length of the chromosome is the number of visible time windows for all satellites toward all targets.  
However, in the actual multi-satellite scheduling process, the number of satellites in a constellation and observation targets will be large, the planning interval will be long and the number of visible time windows will be correspondingly larger. If the binary-coding format is used to deal with the large-scale satellite scheduling problem, the chromosome will be so long that it will take a lot of time for each bit of the chromosome to do the constraints check, which will result in very low algorithm efficiency.  
To enhance the computational efficiency of the algorithm, a real-coding format is elaborated in this paper, as shown in Figure 2.  
In the real-coding format, each bit of chromosome represents a target. For one target \(i\) in the overall target set \(I\), it corresponds to the i-th bit of the chromosome.  
Assuming that the total number of visible time windows for the target \(i\) is \(n_{i}\), each time window is numbered, corresponding from 1 to \(n_{i}\).  
Figure 1: Binary-coding format of PBIL algorithm for multi-satellite scheduling.  
Figure 2: Real coding format of PBIL algorithm for multi-satellite scheduling.  
If the value of the \(i\)-th gene position of the chromosome is \(w_{i}\), one of the natural numbers from \(1\) to \(n_{i}\), it means that the time window \(w_{i}\) of target \(i\) is selected to complete the observation task of this target. Thus the mapping relationship between chromosomes and problem search space points is established.
## 1 Introduction  
Voice is the main support of communication in mankind, through which feelings, emotions and culture are transmitted. Therefore, voice is crucial to the quality of anyone's daily life, especially of professional voice users. However, because of the voice misuse or overuse, changes in laryngeal structures may lead to voice disorders. In a cure-and-care approach, clinical voice assessment is required to diagnose the dysfunction and to plan appropriate treatment strategies [1]. According to the American Speech-Language-Hearing Association [2], a recommended protocol for instrumental assessment of voice has to include laryngeal endoscopic imaging, acoustics, and aerodynamic assessments (Figure 1). However, only the laryngoscopy allows the etiology determination of an organic or functional voice disorder. Hence, visual inspection of the vocal folds vibratory characteristics is essential for diagnosis. Advanced assessment makes use of high-speed cameras. On such data, image processing is the most promising approach to investigate vocal folds vibration and laryngeal dynamics in speech and singing [3; 4; 5]. Among the image-processing tasks, the most recurrent tasks are glottal segmentation and synthesizing laryngeal videos in a family of representations named Facilitative Playbacks (FP). They are essential operations for accurate characterization of vocal folds vibrations. This process leads to identify different phonation features, i.e., periodicity and amplitude of vocal folds vibration, Mucosal Wave (MW), glottal closure, closed-state, symmetry of vibration, presence of non-vibrating portions of vocal folds, etc. Clinicians and voice scientists use the obtained information to support their clinical diagnostics about the presence or absence of a disease and also to follow the dynamics of anatomical features of interest in a more intuitive way, revealing contents, which are often hidden to human eyes [6; 7; 8].  
Reviews on laryngeal imaging research and clinical implications already exist in the literature of the past twenty years [4; 8; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. However, to our knowledge, there are no reviews devoted to analyzing the different glottal-gap segmentation and FP techniques. This paper is intended as a comprehensive state-of-the-art review on the particular issues of glottal-gap segmentation approaches and FP restricted to two widely available types of image modalities: Laryngeal High-Speed Videoendoscopy (HSV) and Laryngeal Videostroboscopy (VS) [21].  
This paper is organized into six sections. Section 2 introduces the different image modalities in laryngeal imaging. Section 3 addresses the glottal gap segmentation problem and presents the literature devoted to synthesizing vocal folds dynamics (FP). Section 4 highlights the challenges in glottal segmentation, and FP techniques. Section 5 introduces the several clinical and voice research works that make use of glottal-gap segmentation and FP approaches. We conclude with a discussion, pointing to future research directions and open problems related to glottal-gap segmentation and FP representation.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 3 State-of-the-Art
### Developmental Trends and Technical Features
#### 3.1.4 Narrow-Band IoT, IIoT (NB-IoT, NB-IIoT)  
Unlike WSN, IWSN, IoT, and IIoT, the Narrow Band (NB) NB-IoT and NB-IIoT are devices that operate in licensed frequency bands. Following the Third Generation Partnership Project (3 GPP), the NB-IoT has a history of standardization from 2005 when version R8 was released to 2016 with the release of version R14 [1]. Among the needs that necessitated the design of NB-IoT is the support for large numbers of IoT devices, extended coverage range due to high spectral density, low power consumption rate, low latency and reliability [1; 8]. The coverage range of NB-IoT has been compared with other LPWAN technologies and found to reach 1 km in urban and up to 10 km in rural settlements with a low interference rate [6; 7].  
NB-IoT has provided requirements to support a large number of devices with a low data rate demanded by industrial operations [5]. The reliability and QoS of NB-IoT is one of the requirements and needs of the industries [9]. NB-IoT also provides ease of deployment to industries, especially in Europe where the network is more established [6].
# Pullout Response of Ultra-High-Performance Concrete with Twisted Steel Fibers
## 3 Experimental and Verification
### Experimental Results
#### 3.3.3 Pullout Force Calculation  
Due to the variations of pullout forces at different fiber displacements (as shown in Figure 7, a representative pullout force is calculated for quantitative analysis and comparison study with theoretical values. The average pull-out force \(\overline{P}_{f}\) of the whole pullout process (as shown in Figure 8) is defined as,  
\[\overline{P}_{f}=\frac{E_{p}}{l_{e}}=\frac{\int P(s)\text{ds}}{l_{e}} \tag{23}\]  
where \(E_{p}\) is the total pull-out energy during the whole process, that is, the area enclosed by the pullout curve; \(l_{e}\) is the fiber embedment length; s is the fiber pullout displacement; \(P(s)\) is the pull-out force when the pullout displacement is equal to s. \(s_{1}\) and \(s_{2}\) are obtained from the two intersection points between \(\overline{P}_{f}\) and the actual pullout curve. \(s_{1}\) represents the starting point of the untwisting stage and \(s_{2}\) represents the end point of untwisting stage. Thus, the untwisting pullout force in the plastic untwisting stage can be estimated as  
\[P_{f}=\frac{\int_{s_{1}}^{s_{2}}P(s)\text{ds}}{s_{2}-s_{1}} \tag{24}\]  
The effective untwisting length \(\Delta l\) is equal to the value of \(l_{e}\) minus \(s_{2}\). Note the fiber axial extension during pullout is not considered in the calculation of \(\Delta l\) as the extension equal to the \(\overline{P}_{f}l_{e}/\left(\text{E}A_{f}\right)\) is negligible. For example, when the \(\overline{P}_{f}\) is 200 N and \(l_{e}\) is 25 mm, the extension equals 0.1 mm. According to the above calculation method, the experiment results are analyzed and shown in Table 1. These values are then compared to the those calculated by the developed formula of pullout force in Section Analytical model.
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 6 Dataset Analysis  
We analyse the annotations via visualisations. Unless otherwise specified, (Main) Behaviour represents the behaviour of an utterance. For example, if a therapist utterance consists of a reflection and a question but Reflection is annotated as the Main Behaviour, we consider the utterance to be a reflection instead of a question, in order to facilitate further analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**(Main) Therapist Behaviour** & ICC \\ \hline _Input_ & 0.975 \\ _Reflection_ & 0.991 \\ _Question_ & 0.997 \\ _Other_ & 0.996 \\ \hline
**Client Talk Type** & ICC \\ \hline _Change_ & 0.916 \\ _Neutral_ & 0.986 \\ _Sustain_ & 0.890 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Inter-annotator agreement as intraclass correlation.  
We also note that while there are clear correlations between utterance attribute distribution and MI quality in some cases, they do not necessarily point to causation, especially given the relatively low amount of data and potential sampling bias.
## 2 Materials and Methods
### Numerical Models, Solution Methods and Boundary Conditions
#### 2.2.3. Cavitation Models  
In addition to turbulence closure, the cavitation model equations need to be added to solve for the unknown covariates added due to the control equations in cavitation numerical calculations. The Zwart-Gerber-Belamri cavitation model based on the vapor phase volume fraction transport equation [30] is used, which controlling equations are shown in Equation (11) where \(\dot{m}^{+}\) and \(\dot{m}^{-}\) represent the vaporization rate and condensation rate for mass exchange between the liquid and the vapor. The transport process of the gas-liquid two-phase during cavitation is controlled by varying \(\dot{m}^{+}\) and \(\dot{m}^{-}\) to capture the phase change during cavitation.  
\[\frac{\partial\alpha_{p}}{\partial T}+\frac{\partial\big{(}\alpha_{p}u_{j} \big{)}}{\partial x_{j}}=\dot{m}^{+}-\dot{m}^{-} \tag{11}\]  
\[\left\{\begin{array}{c}\dot{m}^{+}=F_{vap}\frac{3\alpha_{nuc}(1-\alpha_{p}) \rho_{v}}{R}\sqrt{\frac{2}{3}\frac{p_{v}-p}{\rho_{1}}},p\leq p_{v}\\ \dot{m}^{-}=F_{liquid}\frac{3\alpha_{p}\rho_{v}}{R}\sqrt{\frac{2}{3}\frac{p-p _{v}}{\rho_{1}}},p>p_{v}\end{array}\right. \tag{12}\]  
where \(\alpha_{v}\) is the vapor volume fraction, \(p_{v}\) is the saturated water vapor pressure, 273.15 K water saturated vapor pressure is 2.34 kPa, and \(p\) is the local fluid pressure. If \(p>p_{v}\), water vapor condenses. If \(p\leq p_{v}\), cavitation will occur. \(\rho_{v}\) is the vapor density, \(\alpha_{nuc}\) is the gas nucleus volume fraction, which is taken as 0.0005, \(F_{vap}\) is the vaporization correction factor, which is taken as 50, \(F_{liquid}\) is the condensation correction factor, which is taken as 0.01, and \(R\) is the radius of the mononuclear cavitation bubble, which is taken as 10\({}^{-6}\) m [31].
# Assessing Blockchain Consensus and Security Mechanisms against the 51% Attack
## 5 The Majority Hash Rate Problem
### The Majority Hash Attacks  
The majority hash attacks have been a serious problem in recent times. A miner possessing a large amount of hashing power can influence the network to perform malicious activities. The 51% attack, selfish mining, and 34% attack can occur when an attacker achieves a large amount of hashing power.  
The 51% attack requires an attacker to hold more than half of the hashing power [39]. A 51% attack against cryptocurrencies enables attackers to perform malicious actions, including double-spending, controlling market price or ruling over the mining strategy. We have selected the main eight crypto-coins that have been exploited most recently. Figure 3 presents the amount of losses encountered due to the 51% exploitation from April 2018 to January 2019. Verge (CVG) has been attacked twice in consecutive months losing $1.1 million and $1.75 million, followed by bitcoin gold (BTG), which is the most affected coin, losing around $18 Million. The rest of the coins also lost a significant amount from exploitation. The total loss makes an average amount of $2.5 Million per attack on individual currencies.  
Selfish mining is another majority hash attack which is accomplished with approximately 25% of the total network hashing [40; 41]. It can be carried out by miners or mining pools which comprise a large amount of network hash. In this attack technique, the miners become very selective in broadcasting their blocks to the network. They can decide either to discard their blocks by giving up their reward or to broadcast several blocks at once. This will cancel other miners' blocks, making them losie their reward as well as the transaction fees. During the process, independent nodes intend to join the attacker to increase their profits, and attackers gain more control over the network by having more hashing power.  
Another attack is the 34% attack. This attack occurs due to a majority of network hashing. IOTA is a cryptocurrency developed for the Internet of Things. The IOTA is not based on blockchain and comprises unique storage known as Tangle [42]. Tangle is also a distributed ledger performing similar  
Figure 4: A scenario where three blocks are generated at the same block height 2053. Tow blocks became orphaned as the network chose to follow the block in the middle. Blocks 2065 to 2074 are privately mined blocks, which resulted in canceling transactions on the legit Block 2066. The network follows blocks from 2065 to 2074, broadcasted by the attacker, due to the longest chain.  
operations as the blockchain. IOTA comprises a centralized feature and us also vulnerable to 34% attack [43].
## 4 Improved Fuzzy Analytic Hierarchy Process (IFAHP)
### Normalization Calculation of Index Values  
Each attribute of the evaluation criteria is assigned a performance rating, which reflects their respective characteristics. It is a frequent occurrence for performance evaluations to utilize diverse units of measurement for various criteria, thus necessitating the quantification and standardization of these criteria in order to facilitate comparability. The method of linear scale transformation takes into account the maximum and minimum performance ratings for attributes when performing the calculations. This approach offers the benefit of ensuring that the scale measurement falls exclusively within the range of 0 to 1 for every attribute [68]. In general, the evaluation criteria can be categorized into two types: those that emphasize greater and superior relative value (referred to as benefit type) and those that focus on the opposite (referred to as cost type).  
For the performance criteria of the three types of earthquake search and rescue robots in this paper, the criteria \(C_{11}\), \(C_{12}\), \(C_{13}\), \(C_{21}\), \(C_{23}\), \(C_{25}\), \(C_{26}\), \(C_{27}\), \(C_{28}\), \(C_{29}\), \(C_{210}\), \(C_{12}\), \(C_{31}\), \(C_{32}\), \(C_{33}\), \(C_{41}\), \(C_{42}\), and \(C_{43}\) are benefit type criteria. The criteria \(C_{22}\), \(C_{24}\), \(C_{211}\), \(C_{212}\), and \(C_{41}\) are cost type criteria. To ensure ease of plotting and subsequent data processing,Equation (8) is employed to normalize the benefit type criteria, while Equation (9) is utilized for normalizing the cost type criteria.  
\[x_{ij}^{*}=\frac{x_{ij}-x_{j}^{\min}}{x_{j}^{\max}-x_{j}^{\min}} \tag{8}\]  
\[x_{ij}^{*}=\frac{x_{j}^{\max}-x_{ij}}{x_{j}^{\max}-x_{j}^{\min}} \tag{9}\]  
where \(x_{j}^{\max}\) and \(x_{j}^{\min}\) are the maximum and minimum performance ratings for attributes \(C_{j}(j=1,2,\cdots,n)\) among the alternatives, respectively.  
The specific results of the three types of earthquake search and rescue robots (Sample A, Sample B, and Sample C) after testing and normalization are presented in Table 6.
## 4 Experiments and Analysis of Results
### Construction of Entity Relationships in Pesticide Domain  
This study categorized the corpus into five types based on the product performance, technical usage requirements, precautions, storage and transportation methods, and pesticide poisoning emergency measures. Guided by domain experts and using the pesticide attribute information types published by the Department of Pesticide Administration of the Ministry of Agriculture and Rural Affairs, the pesticide domain information characteristics were thoroughly analyzed, and the correlations between various data were extensively explored. Consequently, pesticide entity relations were finely classified into 11 types of entities and 10 types of relations, as detailed in Table 2.  
For entity relation annotation, 1642 sentences were annotated in three rounds using the annotation tool Doccano, guided by domain experts and based on annotation guidelines. The results were then validated by experts. The annotation guidelines defined various entities and relationships to ensure consistency and accuracy in annotation. The annotation process was divided into three rounds: in the first round, predefined entities and relationships were annotated according to the guidelines; in the second round, the annotations were reviewed and corrected by another annotator; and in the third round, a final inspection was conducted by experts to ensure data quality. As a result, the Pesticide Registration Dataset (PRD) was generated, containing 11,361 entities and 9610 relationships. The quantity distributions of each entity type and relationship type are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Head entity** & **Tail entity** & **Relation** \\ \hline Ammonium glyphosate & Citrus & Treated crop (TRE) \\ Alloxydim & Keng Stiffgrass & Controls weed (CON) \\ Pyrazosulfuron-ethyl & DustablePowder & Pesticide Form (PES\_FORM) \\ Asulam & Herbicide & Pesticide Type (PES\_TYPE) \\ Bentazone & ADAMA & Pesticide Brand (PES\_BRA) \\ Carbetamide & Swallowing & Poisoning Route (PRT) \\ Chloramben & Vomit & Toxic Symptoms (TSYM) \\ Chloranocryl & 40–50 kg & Water Usage per Acre (WUAC) \\ Cyhalofop-butyl & Glove & Protective Equipment (PE) \\ Allidochlor & Dry & Storage Method (STMT) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of entity relation categories.  
A statistical analysis of entity lengths in the self-constructed PRD revealed that most entity lengths ranged from 2 to 5 tokens. The shortest entity length was 1 token, accounting for 2.50%, while the longest was 9 tokens, accounting for 3.37%, indicating significant variation in entity lengths. A detailed distribution of entity lengths is shown in Figure 6.