esa-sceva/satcom-chunk-collection huggingface dataset:
external-spraying injection dedusting device was developed and discussed. The device can greatly reduce the amount of dust produced in the cutting process of a roadheader on a fully mechanized mining face. [PERSON] et al. [3] proposed a foam technology for dust control and applied it in an underground coal mine. Foam technology was also used for suppressing the rock dust driven by roadheaders [7,8]. In a study conducted by [PERSON] et al. [9], dry-type filtration dust removal technology was designed for use in large tunnel construction. [PERSON] et al. [10] designed an atomization device to suppress the coal dust in underground roadways. The above-mentioned dust suppression technologies played an important role in dust control. The dust suppression efficiencies of these technologies ranged from 60% to 90%. However, to take a traditional method--water spraying--it has the shortcomings of high consumption of water, low efficiency for respirable dust [11,12] and high resistance [13]. Although foam technology can effectively reduce respirable dust, the foam covered on the dust source will hinder the workers' attention [11]. Additionally, the cost of a foaming agent is high, which severely limits its application to roadway excavation [8,14,15]. Ventilation methods are also very poor at eliminating dust in coal mine roadways [16,17].  
Currently, some efficient wet dust collectors, such as Venturi wet dust collector, are generally used to control the dust in metal mines in China. However, due to its high resistance (1500-4000 Pa) and high energy consumption (8-35 kJ/m\({}^{3}\)), the application effects are limited [18]. Besides, although the total dust removal efficiency of wet dust collectors is as high as 95%, the corresponding suppression efficiency of respirable dust is only around 80% [19]. The throat of Venturi wet dust collector is easily blocked and needs to be cleaned regularly, which does not satisfy the current requirements of energy saving and environmental protection [20]. Due to the continuous improvement of national environmental awareness and dust emission standards, the current wet dust removal equipment can not satisfy the requirements of the current national dust emission standards. It is necessary to develop new technology to effectively solve the problem of dust pollution in the mining process.  
In this study, a new dust collection device which is combined with swirling atomization and water-spray curtain technology was proposed. The performance of the swirling curtain dust collector (SCDC) was tested. This study will provide an important basis for the application of swirling curtain dust collection technology in mines, making it an effective method to improve the health and safety of miners.
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 3 The Proposed Scheme
### Online-Signing Phase  
* When \(BS\) obtains \((C_{1}^{\prime},C_{2}^{\prime},C_{3}^{\prime},h,z,\hat{z})\) from \(DS_{i}\), it could perform verification of these cipher-texts. If they are valid, then \(BS\) decrypts them with \(\gamma^{-1}\) as follows: \[\begin{split} C_{1}&=C_{1}^{\prime}*\tau^{2}* \gamma^{-1}\\ C_{2}&=C_{2}^{\prime}*\gamma^{-1}\\ C_{3}&=C_{3}^{\prime}*\gamma^{-1}\end{split}\] (3)
* After decrypting the above cipher-texts successfully, \(BS\) computes the signature as follows with a QR number \(\lambda\): \[\begin{split} C_{3}^{\prime}&=C_{3}^{-2}*(\frac{ \beta}{\alpha})^{-2}*(\lambda)^{2}\\ C_{3}^{\prime\prime}&=C_{3}^{\prime}*y_{1}\pmod{ n}\\ C_{2}^{\prime\prime}&=C_{2}^{\prime}*y_{2}\pmod{ n}\\ C_{1}^{\prime\prime}&=C_{1}^{\prime}*y_{3}\pmod{ n}\end{split}\] (4)
* The signer \(BS\) finishes the signing operation and generates the signature \((C_{1}^{\prime\prime},C_{2}^{\prime\prime},C_{3}^{\prime\prime})\) to the data sender \(DS_{i}\). When the node \(DS_{i}\) has received this signature, it could unblind the signature by computing the following operations: \[\begin{split} C_{1}^{\prime}&=C_{1}^{\prime\prime}* y_{3}^{-1}\\ C_{2}^{\prime}&=C_{2}^{\prime\prime}*y_{2}^{-1}\\ C_{3}^{\prime}&=C_{3}^{\prime\prime}*y_{1}^{-1}\\ C_{3}^{*}&=C_{3}^{\prime}*(\frac{1}{\alpha})^{2}\\ &=C_{3}^{-2}*\beta^{-2}*(\lambda)^{2}\end{split}\] (5)
* Then, the \(DS_{i}\) computes the final encrypted cipher-text messages \((C_{1}^{\prime\prime\prime},C_{2}^{\prime\prime\prime},C_{3}^{\prime\prime \prime})\) to the \(BS\) in the following and enters the unsign-cryption phase: \[\begin{split} C_{1}^{\prime\prime\prime}&=C_{1}^{ \prime\prime}*\gamma\\ C_{2}^{\prime\prime\prime}&=C_{2}^{\prime\prime}* \gamma\\ C_{3}^{\prime\prime\prime}&=C_{3}^{*}*\gamma\end{split}\] (6)
## 2 Preliminaries
### Controller Design
#### 2.2.2 Outer Loop Controller  
In order to formulate a partitioning-based control scheme for the outer loop, first considering altitude state and replacing \(U_{1}\) with \(f\), we can write the following from Equation (9c):  
\[(\cos\phi\cos\theta)U_{1}=m\ddot{z}+mg \tag{19}\]  
\[f=(\cos\phi\cos\theta)^{-1}\ m\ddot{(}\ddot{z}+g) \tag{20}\]  
\[f=\alpha f^{\prime}+\beta \tag{21}\]  
with \(\alpha=m(\cos\phi\cos\theta)^{-1}\) and \(\beta=mg(\cos\phi\cos\theta)^{-1}\).  
Thus, we can derive the following affine nonlinear controller equation:  
\[\ddot{z}=f^{\prime} \tag{22}\]  
Altitude error is defined as \(e=z_{d}-z\), where \(z_{d}\) is desired altitude and \(z\) is current altitude. We can write the servo portion of the control law as:  
\[f^{\prime}=\ddot{z}_{d}+K_{d}\dot{e}+K_{p}e \tag{23}\]  
\[\ddot{z}=\ddot{z}_{d}+K_{d}\dot{e}+K_{p}e \tag{24}\]  
The final controller equation will assume the following form:  
\[f=m(\cos\phi\cos\theta)^{-1}\big{(}\ddot{z}_{d}+K_{d}\dot{e}+K_{p}e\big{)}+mg( \cos\phi\cos\theta)^{-1}\]  
In order to formulate a correspondence between translational states Equation (9a,b) and rotational states Equation (10a,b), a linearization assumption is made by considering small roll and pitch angles, i.e., it is assumed that the UAV is around hovering state. This assumption leads to following from translational dynamics represented by Equation (9a,b):  
\[\ddot{x}=g(\Delta\phi\sin\psi+\Delta\theta\cos\psi) \tag{25}\]\[\ddot{y}=g(\Delta\theta\sin\psi-\Delta\phi\cos\psi) \tag{26}\]  
Furthermore, we can extract the following desired roll and pitch commands:  
\[\phi_{d}=\frac{1}{g}\big{(}\ddot{x}\sin\psi_{d}-\ddot{y}\cos\psi_{d}\big{)} \tag{27}\]  
\[\theta_{d}=\frac{1}{g}\big{(}\ddot{x}\cos\psi_{d}+\ddot{y}\sin\psi_{d}\big{)} \tag{28}\]  
In order to force the system to follow desired trajectory commands, the following controller equations can be formulated:  
\[\ddot{x}=\ddot{x}_{d}+K_{d}\dot{e}+K_{p}e \tag{29}\]  
\[\ddot{y}=\ddot{y}_{d}+K_{d}\dot{e}+K_{p}e \tag{30}\]  
The final forms of Equations (27) and (28) are:  
\[\phi_{d}=\frac{1}{g}\big{(}(\ddot{x}_{d}+K_{d}\dot{e}+K_{p}e)\sin\psi_{d}- \big{(}\ddot{y}_{d}+K_{d}\dot{e}+K_{p}e\big{)}\cos\psi_{d}\big{)} \tag{31}\]  
\[\theta_{d}=\frac{1}{g}\big{(}(\ddot{x}_{d}+K_{d}\dot{e}+K_{p}e)\cos\psi_{d}+ \big{(}\ddot{y}_{d}+K_{d}\dot{e}+K_{p}e\big{)}\sin\psi_{d}\big{)} \tag{32}\]  
In order to track a specified trajectory, a complete trajectory profile must be generated from waypoints in an autonomous system. For a minimum snap trajectory, the desired profile is \(\Big{(}x_{d},\dot{x}_{d},\ddot{x}_{d},x_{d}^{(iii)},x_{d}^{(ir)},y_{d},\dot{y }_{d},\ddot{y}_{d},y_{d}^{(iii)},y_{d}^{(iv)},z_{d},\dot{z}_{d},\ddot{y}_{d}, \dot{\psi}_{d},\ddot{\psi}_{d}\Big{)}\).
# Measurement and Analysis of Industrial Forklifts Vibration Levels for Unit Load Testing Purposes
## 3 Results and Discussion  
The overall G\({}_{\rm rms}\) (1-200 Hz) and the kurtosis values for distributions of G\({}_{\rm rms}\) values are presented in Table 1 while the trends in overall G\({}_{\rm rms}\) for the carriage and the tip of the forklift times are presented in Figures 4 and 5. Out of the three locations investigated, the data obtained for the carriage and the back of the fork tine heel were identical. Thus, only the data obtained for the carriage and the tip of the fork tines were further analyzed. Based on the overall G\({}_{\rm rms}\) values, forklifts experience higher vibrations on the tip of the fork tine compared to the carriage. The maximum acceleration level observed while recording measurements was 1.73 G (with a duration of 8 ms); it was observed for the electric forklift on asphalt while carrying a payload of 1134 kg and driven at 4.8 km/h. The kurtosis values of the probability distribution functions of recorded acceleration events at the back of the fork tine heel, fork tine tips, and forklift carriage were 0.51 to 6.59, 1.05 to 13.81, and 1.26 to 10.28, respectively. It should be noted that timer-triggered data should not automatically be considered random vibration with normal distributions as stated in previous studies [24; 25; 26; 27; 28]. This study revealed that, similar to other modes of transportation, the random vibration observed for forklifts was non-Gaussian in nature. A second analysis was done using the PSD plots to analyze the effects of speed, road quality, forklift type, unit load weight, and sensor location on forklift vibration levels in more detail. Furthermore, a comparison of vibration levels was performed between the ISTA truck vibration profile and these forklifts (Figure 6). The PSD plot with the highest G\({}_{\rm rms}\) value for each forklift type is also presented in Figure 6. The highest intensity peaks were observed for the gas and electric forklifts and were between 3-4 Hz. Meanwhile, the peak shifted to 4-5 Hz for the reach truck, indicating that its suspension system is much stiffer than the suspension of the other two forklifts. When compared to the PSD plot of the ISTA-recommended vibration test spectrum, it was observed that trucks experienced higher vibration levels compared to forklifts, especially for the higher frequency regions above 10 Hz.
## 3 Results and Analysis
### Influence of Spatial Resolution on LAI Inversion
Generally, the performances of the LAI inversions between the HJ-1 CCD data and the up-scaled results of the ZY-3 MUX or GF-1 WFV data were preferable for both pure and mixed pixels. The accuracies of the LAI inversions for pure pixels are shown in Figure 8b,c, and these results presented a better agreement between the up-scaled LAI inversions from ZY-3 MUX (R\({}^{2}\) = 0.87, RMSE = 0.42 and BIAS = 0.12) or GF-1 WFV (R\({}^{2}\) = 0.89, RMSE = 0.66 and BIAS = \(-\)0.30) and HJ-1 CCD. In contrast, the up-scaled LAI inversions from ZY-3 MUX and GF-1 WFV, as shown in Figure 8a, did not perform as well for the pure (R\({}^{2}\) = 0.58, RMSE = 0.67 and BIAS = \(-\)0.45) or the mixed (R\({}^{2}\) = 0.69, RMSE = 0.66 and BIAS = \(-\)0.48) pixels. The up-scaled LAI inversions for mixed pixels from ZY-3 MUX (R\({}^{2}\) = 0.52, RMSE = 0.52 and BIAS = 0.06) and GF-1 WFV (R\({}^{2}\) = 0.63, RMSE = 0.58 and BIAS = \(-\)0.24) at 30 m spatial resolution were both substantially different from the HJ-1 CCD inversion (Figure 8b,c). In particular, the ZY-3 MUX sensor had much higher performance than the HJ-1 CCD, which demonstrated that the differences between the LAI inversions increased with the increasing spatial resolution between the two sensors.
## 3 Target Response Model and Analysis
### Acquisition Geometry and Response Model
These per-element diagrams are assumed known. They will typically have been derived from the precise on-ground characterization of the antenna array [22]. The power of the complex diagram is the antenna gain. The azimuth variant propagation direction towards the target is defined in the so-called _element coordinate system_, which is related to the instrument coordinate system by a rotation \(\mathbf{R}_{I\to e}\). The rotation accounts for, and is defined in terms of, the _antenna element mount angles_  
\[\Delta\mathbf{\overline{A}}_{e}=\left(\Delta\textit{roll}_{e},\Delta\textit{pitch}_{e}, \Delta\textit{yaw}_{e}\right), \tag{7}\]  
which represent the mispointing of each antenna element \(e\). These angles are initially unknown and assumed zero. The expected target backscatter amplitude \(R^{s}_{pq}(az,f_{r})\) is accounted for in a similar fashion to the antenna pattern. The target amplitude varies as a function of the azimuth-variant propagation direction towards the sensor. It also depends on the polarization state of the transmit and receive antenna elements and varies as a function of the range frequency. It may also be complex valued if the reference target is known to be not an ideal point-like scatterer. The power of the modeled target backscatter is the radar cross section in m\({}^{2}\). The backscatter model of each target is assumed known and may be directly measured, derived from electromagnetic simulations or analytically from geometric optics [23]. In addition to the backscatter model, the computation of \(R^{s}_{pq}(az,f_{r})\) requires geometric transforms into a suitable reference target coordinate system. This coordinate system is the analog of the antenna element coordinate system introduced above. Details of the model used and the coordinate systems adopted are, however, beyond the scope of this article.
# Aggression Detection in Social Media from Textual Data Using Deep Learning Models
## 3 Methodology
### Feature Extraction
#### 3.4.1 Discrete Emotions  
A group of researchers [42; 43; 44] at the National Research Council Canada (NRC) used word choice to identify certain positive and negative emotions in tweets. Using NRC, we extracted eight emotional features: anticipation, joy, surprise, trust, anxiety, sadness, anger, and disgust. Anticipation, joy, surprise, and trust are deemed to be positive discrete emotions, whereas the analysis revealed that anxiety, sadness, anger, and disgust are considered negative discrete emotions. To the best of our knowledge, these novel features have not yet been used for cyberbullying problems in previous work. All emotional features were extracted using the following mathematical expression.  
\[F_{x_{i}}=\frac{(W_{mt}*W_{n})}{100} \tag{5}\]  
where \(F_{x_{i}}\) is the emotional score of the specific emotional feature \(X_{i}\) and \(i\) belongs to the emotional features set, including anticipation, joy, surprise, trust, anxiety, sadness, anger, and disgust, \(W_{mt}\) is the number of matching words in the dictionary for specific features \(X_{i}\), e.g., (anger, sad...) and \(W_{n}\) is the total number of words in a sentence.
# Study on Corrosion Monitoring of Reinforced Concrete Based on Longitudinal Guided Ultrasonic Waves
## 3 Finite Element Simulation of Corroded Reinforcement
### Results of Finite Element Simulation
#### 3.2.2 Damage Index
\[r_{xy}(\tau)=\int_{-\infty}^{+\infty}x(t)y(t-\tau)\mathrm{d}t. \tag{10}\]  
To further investigate the effect of different excitation frequencies on the characterization of each DI at different corrosion degrees, the DIs at different corrosion degrees are shown in Figure 8, and the waveforms are analyzed at three excitation frequencies (50, 80, and 100 kHz). It can be seen from Figure 8 that each DI at different frequencies has a monotonically increasing relationship with the mass loss rate \(\eta\). The sensitivity (change degree) of the five DIs to the mass loss rate \(\eta\) from strong to weak is DI-SAD > DI-MSD and DI-NCM > DI-TDC and DI-TDD. Therefore, the next section preferentially adopts the changing pattern between DI-SAD and the corrosion degree of the reinforcement. Another advantage of DI-SAD is that its calculation process does not involve mode separation. The normalized DI-SAD increases with the increase in the frequency; that is, the ability to characterize the change in the corrosion degree increases with the increase in frequency. Hence, selecting the center frequency of 100 kHz sine function excitation signal is superior.
## 3 Task Planning Model and Solving Algorithm
### Task Planning Model
#### 3.1.1 Main Constraints of the Planning Model
\(\varepsilon_{uv}\) represents the energy consumed per unit time from the end of task \(\mathbb{C}^{c}_{i\_u}\) to the start of task \(\mathbb{C}^{c}_{i\_v}\). \(E\) represents the total energy available in one orbit of the satellite. \(x_{uv}\) represents the transition from task \(\mathbb{C}^{c}_{i\_u}\) to task \(\mathbb{C}^{c}_{i\_v}\), \(1\) represents the transition, and \(0\) represents no transition. * Storage capacity constraint: \[\sum\limits_{u=1}^{N}\mathbb{C}^{c}_{i\_u}*ci\leq M,\] (9)  
where \(M\) represents the total storage capacity of the satellite in one orbit. \(ci\) represents the storage capacity consumed per unit observation time.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 2 Literature Review
### Centralized and Semi-Centralized Trust Evaluation Methods  
Centralized and semi-centralized trust evaluation methods offer structured and coordinated approaches to managing trust in IoT systems.  
The authors of [31] propose a centralized trust evaluation mechanism for Underwater Wireless Sensor Networks (UWSNs) called TEUC. This system utilizes the C4.5 decision tree algorithm to classify nodes based on multi-dimensional trust evidence, enabling better detection of malicious nodes and efficient energy consumption. In [32], a lightweight centralized trust management system is proposed for IoT-based edge nodes called lightTrust. The system divides trust management into Trust Agent and Trust Developer components, where trust is evaluated based on parameters like compatibility, cooperativeness, and delivery ratio, ensuring secure communication among edge nodes.  
[PERSON] et al. [25] present a semi-centralized trust management model that integrates blockchain technology with centralized cloud servers. This model combines direct trust from historical interactions with indirect trust from recommendations, achieving a balance between the efficiency of centralization and the security of decentralization. A hybrid trust management model for IIoT environments has been proposed in [26], which combines centralized and distributed approaches. Trust is managed within clusters, with continuous updates to ensure real-time accuracy. This hybrid model addresses scalability and energy consumption while enhancing the reliability of trust metrics.  
Although they provide structured and coordinated frameworks for managing trust in IoT environments, centralized and semi-centralized methods may encounter issues with scalability, adaptability, and complexity, as well as security and privacy.
## 4 The Proposed Method
### DGNDP (Synthetic Directed Graph Based on Node Differential Privacy) Algorithm
#### 4.2.1 GSEM (Generating Degree Sequence Based on Exponent Mechanism) Algorithm
If in case 1, when the larger cluster is cluster \(m_{i}\), the change of centroid of \(m_{i}\) is less than \(\Delta/k\). While if the larger cluster is another cluster, nothing will happen and it will meet the requirements of the theorem. If in case 2, a changed cluster is the larger cluster, one of the fractions in the third term of expression shown above has a denominator that is greater than \(k\) and the overall sum is less than \(\Delta/k\). Therefore, the lemma is set up; if the larger cluster is not affected, the lemma also holds. In the ADPRA algorithm, given a query function  
\[=RE(Seq_{out}(\text{S}_{Ga}))+LE(Seq_{out}(\text{S}_{Ga}))\]5  
because the node differential privacy is used in this algorithm, according to the analysis mentioned above, the sensitivity of the query function \(f\) is  
\[=RE(Seq_{out}(\text{S}_{Ga}))+LE(Seq_{out}(\text{S}_{Ga}))\]6  
Therefore, due to the ranking micro-aggregation, the sensitivity of the query function decreases, so that the noise added to the degree sequence is reduced. To sum up, the ADPRA algorithm provides differential privacy for the degree sequence while maintaining data utility. As illustrated in the Algorithm 3, line 1 sorts an out-degree sequence from small to large. After this sequence is aggregated in line 2, line 3 adds the Laplace noise to obtain a differential private out-degree sequence. In the same way, from line 4 to line 6, a differential private in-degree sequence is also got. ```
0: an out-degree sequence \(S_{dout}\), an in-degree sequence \(S_{din}\), a privacy budget \(\epsilon_{2}\), the number of elements in a cluster, k
0: a noised out-degree sequence \(NS_{dout}\), a noised in-degree sequence \(NS_{din}\)
1: Sorting \(S_{dout}\) from large to small
2: \(AS_{dout}\leftarrow\) micro-aggregating \(S_{dout}\)
3: \(NS_{dout}\leftarrow\) adding the Laplace noise ((\(\bigtriangleup f\))/k*\(e_{2}\)) on \(AS_{dout}\)
4: Sorting \(S_{din}\) from large to small
5: \(AS_{din}\leftarrow\) micro-aggregating \(S_{din}\)
6: \(NS_{din}\leftarrow\) adding a Laplace noise ((\(\bigtriangleup f\))/k*\(e_{2}\)) on \(AS_{din}\)
7: Return a noised out-degree sequence \(NS_{dout}\), a noised in-degree sequence \(NS_{din}\)
```  
**Algorithm 3** ADPRA algorithm
## 4 Discussion
### Extending Techniques to Determine the Viability
#### 4.2.4 Fluorescence Determination in the Viability Assessment  
Nowadays, the least information regarding a possibility of misinterpretations of the \(H\). _pylori_ viability concerns fluorescence methods determining cell membrane permeability. For this purpose, SYTO9/propidium iodide (so called Live/Dead) and NPN staining are most commonly used in \(H\). _pylori_ studies [69]. It seems that a basic criterion for cells with the VBNC phenotype, an integrity of cellular structures, contributes to an effective determination of the viability of coccoid \(H\). _pylori_ forms using the aforementioned methods [80]. It should be borne in mind, however, that morphological transition may somehow affect the adsorption or retention capacity of some fluorescent dyes. Since the loss of membrane potential has been seen in coccoid \(H\). _pylori_ forms, fluorescent dyes determining this parameter may have limited use in assessing their viability (Figure 6) [15]. Research in this area is undoubtedly needed.  
Difficulties in Determining the Viability of H. _pylori_ in the Context of Applied Research Techniques  
The reservations presented above are reflected in the heterogeneity of the results obtained in some of the analyzed articles, in which several research methods determining the viability of _H. pylori_ were used.  
In the study by [PERSON] et al. [55], using microscopic observations and PCR, the existence of morphobstructural changes and DNA degradation were noticed. On the other hand, bacterial cells maintained a high level of culturability and a high ratio of green/red fluorescence, indicating their high viability. In the article by [PERSON] et al. [58], a high number of culturable cells was also observed, while other techniques indicated a different situation, i.e., rRNA degradation and reduced level of iATP. In the study by [PERSON] et al. [61], a total decrease in the culturability and morphobstructural changes using TEM were noticed, however, a high green/red fluorescence ratio was still present.  
The indication of the above publications in no way detracts from the quality of these results. On the contrary, it illustrates well the difficulties faced by scientists involved in analyzing the viability of antimicrobials-treated \(H\). _pylori_. These seem to be, at least in part, associated with the presence of coccoid \(H\). _pylori_ forms, which are characterized by changed physiology and morphobstructure in relation to spiral forms (Figure 6).