esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Distributed Systems: Concepts and Challenges
### Challenges in Distributed Systems
For instance:  
* Databases like Cassandra and DynamoDB: Prioritize availability and partition tolerance (AP), allowing them to remain available during network partitions but possibly returning stale data. * Systems like HBase and MongoDB: Often focus on consistency and partition tolerance (CP), ensuring data consistency across partitions at the cost of potential availability issues during network failures. * Relational databases with distributed architectures: Tend to focus on consistency and availability (CA), often at the expense of partition tolerance, requiring a reliable network to function correctly. **Security**: security is one of the most vital challenges distributed systems have to address in the most effective way. When talking about security, making sure the users' privacy and data's integrity are both well protected is of the utmost importance. One way of ensuring those is the system's authentication mechanisms [42]. These mechanisms need to be robust and constantly up-to-date, in order to be able to repel attempts for unauthorized access while making sure legitimate users can have access to the resources they need. The second challenge that arises, regarding security, pertains to authorization and control access. The systems need to be able to determine what is the right access level for each individual user. That's why different policies need to be defined and enforced, so that the system knows who has access to what and under what conditions. While authorization mechanisms are essential to be imposed in a system, implementing methods that ensuredata's confidentiality and integrity is equally important. This could be achieved, first and foremost, by establishing encryption techniques in the network, for both data at rest and in transit [43]. Furthermore, care should be taken into the implementation of detection and mitigation mechanisms for data tampering. Lastly, one more challenging aspect of security is the implementation of non-repudiation and auditability mechanisms in a distributed system. Non-repudiation is needed so that the participants of a transaction cannot question or outright deny their participation in it, while auditability is responsible for tracking and examining a system's activities. These two mechanisms are quite difficult to be implemented due to the need for total synchronization and secure logs across multiple locations. A notable example is the 2017 Equifax data breach, where attackers exploited a vulnerability in a distributed web application framework to access sensitive data of over 140 million consumers. This incident underscores the importance of robust security measures, including regular patching, intrusion detection systems, and secure communication protocols in distributed systems [44].
## 3 Results and Discussion
### Effect of the Roller's Rotational Speed on the Threshing Efficiency at Different Feed Rates  
As is represented in Figure 6, the threshing efficiency increased with the increase in the roller's rotational speed. It also increased with the increase of the feed rate from 600 kg/h to 800 kg/h, but it began to decrease with the feed rate increasing from 800 kg/h to 900 kg/h. The threshing efficiency increased from 95.98% to 97.03%, 96.43% to 97.37%, 96.72% to 97.64% and 96.21% to 97.25% for feed rates of 600 kg/h, 700 kg/h, 800 kg/h and 900 kg/h, respectively, which was in line with previous studies [8; 28]. These results recorded a high threshing efficiency compared with the reported results [4]. The threshing efficiency was calculated from Equation (9), which included the unthreshed seed and damaged seed percentages. As the quantity of unthreshed seed was more than the damaged seed, the threshing efficiency was more highly affected by the unthreshed seed percentage than the damaged seed percentage.  
Figure 4: Effect of the roller’s rotational speed (rpm) on the damaged seed percentage (%) at different feeding rates.
# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 2 Data
### SCADA Operating Data  
SCADA is made off a vast net of sensors, monitoring the state of the main components of a turbine, as well as the environmental conditions. Operating data are recorded at high frequency and successively down-sampled to reduce network usage and storage costs.  
The resolution of SCADA data is usually of 10 min. To reduce information loss, due to down-sampling, not only the mean value across the aggregation period is stored, but also the standard deviation, minimum, and maximum values. An example of a typical SCADA dataset is provided in Table 1.  
Operating data from a European onshore wind park, situated in Poland, is used in this research. A total of 18 turbines with nominal power of 2.3 MW is available. Approximately three and a half years of data are analyzed, starting from the beginning of 2017 to mid-2020. Data from 2017 are used to train the normality model, and the remaining two and a half years are used to test predictions. Having more than one year of data allows to control seasonal variations in environment temperature and wind-speed.
###### Abstract  
The time-delay neural network (TDNN) can consider multiple frames of information simultaneously, making it particularly suitable for dialect identification. However, previous TDNN architectures have focused on only one aspect of either the temporal or channel information, lacking a unified optimization for both domains. We believe that extracting appropriate contextual information and enhancing channels are critical for dialect identification. Therefore, in this paper, we propose a novel approach that uses the ECAPA-TDNN from the speaker recognition domain as the backbone network and introduce a new multi-scale channel adaptive module (MSCA-Res2 Block) to construct a multi-scale channel adaptive time-delay neural network (MSCA-TDNN). The MSCA-Res2 Block is capable of extracting multi-scale features, thus further enlarging the receptive field of convolutional operations. We evaluated our proposed method on the ADI17 Arabic dialect dataset and employed a balanced fine-tuning strategy to address the issue of imbalanced dialect datasets, as well as Z-Score normalization to eliminate score distribution differences among different dialects. After experimental validation, our system achieved an average cost performance (C\({}_{\text{arg}}\)) of 4.19% and a 94.28% accuracy rate. Compared to ECAPA-TDNN, our model showed a 22% relative improvement in C\({}_{\text{arg}}\). Furthermore, our model outperformed the state-of-the-art single-network model reported in the ADI17 competition. In comparison to the best-performing multi-network model hybrid system in the competition, our C\({}_{\text{arg}}\) also exhibited an advantage.  
A pacs: **Keywords:** Arabic dialect identification; multi-scale learning; channel adaptation; balanced fine-tuning; Z-Score normalization
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 2 Related Works
### Secure Storage and Query  
Since the introduction of technologies, such as storage and computing in the cloud to LBS, many researchers have conducted work on how to ensure secure storage and query of outsourced data. Based on attribute encryption, linear encryption, and RSA encryption, [PERSON] et al. [13] introduced a private-protected spatial-temporal LBS searchable framework, which effectively solved the problem of an expressive and practical search over encrypted LBS data. In [14], [PERSON] et al. designed a secure dynamic spatial keyword query (SDSKQ) structure and proposed cryptographic text-signed quad-trees to improve search security, satisfying the requirements in practical applications, such as dynamic updates and diverse query type queries. [PERSON] et al. [15] also adopted the structure of quad-trees to build the index for the POI database, solving the secure problem of linear region search, and bridging the gap in research on linear region search. In [16], [PERSON] et al. improved on the existing k-anonymity algorithm, compensating for the fact that the existing algorithm can leak some contents of POIs, providing a foundation for subsequent research on the security of the k-anonymity algorithm. [PERSON] et al. [17] proposed a fog-assisted privacy protection scheme for LBS to process the users' query in the fog server, efficiently protecting privacy security, solving the problem of double identity attacks in mobile phones. All of the above studies have addressed the issue of secure data storage and query to some extent; however, few papers have considered the pitfalls of the cloud as a potential attacker, which may leak or forge the outsourced data to reach illegal benefits.
# Attention Block Based on Binary Pooling
## 2 Related Work  
### Pooling  
Pooling operations are commonly used in convolutional neural networks to make models more robust to variations in feature positions within input images. The most common pooling methods are max pooling and average pooling. However, there are several other pooling methods in convolutional neural networks. GAP [28] helps prevent overfitting and serves as an alternative to fully connected layers, removing the black-box nature of features in fully connected layers and providing each channel with a meaningful category interpretation. Inspired by dropout, which randomly sets some activation functions to zero during model training, mix pooling [29] combines max pooling and average pooling randomly during training. Unlike max pooling, which always selects the maximum element, stochastic pooling [30] randomly selects elements from the feature map based on their probability values, giving it stronger generalization capabilities. Power average pooling [31] combines average pooling and max pooling by using a learnable parameter to determine the relative importance of these two methods. Local importance pooling [32] learns adaptive and discriminative feature maps to aggregate downsampling features while discarding uninformative features, thereby preserving image details and being particularly useful for tasks with exceptionally rich detailed information. Soft pooling [33], based on softmax weighting, aims to minimize information loss during the pooling process while preserving information features and improving the classification performance of convolutional neural networks. In contrast, the proposed binary pooling is a pooling operation specifically tailored for attention, with the aim of enhancing the representation capacity of the fundamental modules throughout the entire network.
The analysis of the selected papers and of several studies indicated in the literature shows that the adoption of efficient and effective DSSs aids clinical assessment, makes the diagnosis reproducible by eliminating inter- and intra-observer variabilities, prevents misdiagnosis, reduces mortality rates, decreases the cost of treatment, and facilitates evidence-based decision-making [14; 15; 16; 17]. Despite the manifest benefits of DSS adoption in clinical practice, there are several challenges that hamper their widespread application [18]. Validation in a clinical environment has to be carried out for DSS to have perfect integration into the clinical workflow. Moreover, dissemination of basic knowledge for their use,training of qualified personnel, usability and friendly designed user interfaces are some of the success strategies to be implemented for enhancing DSS clinician acceptance [19; 20; 21]. In conclusion, future studies and developments of DDSs for disease detection and diagnosis should try to overcome current limitations and extend the scope of their use in different application contexts. Looking towards new and emerging technologies, DSSs will assume an increasingly relevant role in shaping the feature of healthcare by changing the way healthcare services will be delivered to patients. At the end of this editorial, I am very grateful to all those who have contributed to the realization of the Special Issue. I thank all the authors of the published papers; without them, the Special Issue would not be possible, and I am grateful to all reviewers for their valuable comments and suggestions. Last but not least, a special thanks to the Editor in Chief, the Section Editor in Chief, the Editorial Board, the Special Issue Editor, the Assistant Editor, and the Editorial Office team for their support, collaboration, and useful tips. The authors declare no conflict of interest.
# ULYSSES: Automated FreqUentLY ASked QueStions for KnowlEdge GraphS
## 2 Related Work
### FAQ Generation  
The existing works in this domain work on a given textual corpus and mostly focus on auto-generating questions and answers [14]. In this regard, most approaches first extract answers from an input text and then generate the corresponding questions [15; 16]. Other approaches include additional modules to validate the question-answer pairs [17] or introducing diversity into the selected pairs, using variational auto-encoders [18].  
Another approach uses retrieval methods to directly construct FAQ lists. For example, [PERSON]. [19] introduced a customizable and partially automated method to identify and create FAQ pairs within open source projects, and [20] focused on student forums, using a hierarchical agglomeration clustering method. FAQ lists for online discussion forums are also generated by selecting past question-answer pairs, using a random forest classifier [21] to present a system that automatically generates education-specific FAQ pairs from online discussion forums. Furthermore, the latest generative methods, such as [14], combine question extraction and question generation, to create and improve FAQ lists.  
Finally, other approaches focus on answering the questions in FAQ lists by building a Knowledge Graph to train the pre-trained model [22; 23; 24; 25].  
To the best of our knowledge, however, we have not seen any work trying to generate FAQ lists over Knowledge Graphs, and, as such, ULYSSES moves the domain one step forward.
## 4 Results and Analysis
### Rate-Dependent Effect of Dynamic Tensile Strength  
The rate-dependent effect of dynamic tensile strength of the Breccia and Andesite was fitted and predicted by the incubation time criterion mentioned in Section 3. The scatters in Figure 5 are the dynamic tensile strengths determined by the dynamic Brazilian tests, also including the quasi-static strength of Breccia (\(\sigma_{0}\) = 4.05 MPa) and Andesite (\(\sigma_{0}\) = 8.13 MPa). Among them, the stress rate of the Breccia specimens was distributed in the range of 108.8-161.6 GPa\(\cdot\)s\({}^{-1}\), and that of the Andesite specimens was distributed in the range of 242.6-292.9 GPa\(\cdot\)s\({}^{-1}\). Fitting Equation (7) to the experimental data using the least squares method, we obtained the incubation times of Breccia and Andesite, which were 61.5 and 124.3 us, respectively. The predictions of the dynamic tensile strengths of the two igneous rocks under different stress rates are shown as the continuous lines in Figure 5. Their variations indicated that the tensile strength enhancement induced by stress rate was determined by quasi-static strength and incubation time.  
Figure 4: Experimental results for the Andesite specimen. (**a**) Raw signals of the Brazilian test. (**b**) Raw and filtered signal at a striking velocity of 4.88 m/s. (**c**) The incident (In.), reflected (Re.), transmitted (Tr.), and superposed (In. + Re.) waves. (**d**) Verified stress equilibrium. (**e**) Determination of dynamic tensile strength and loading rate.
## 5 Simulation Results  
We simulated energy balanced strategies EBH and DIB using MATLAB. Though EBH and DIB can apply to both moored monitoring system and our designed two-dimensional networks, we only simulate a linear sensor network here. The reason is that our proposed two-dimensional networks comprised of energy balanced chains. It is sufficient to know the characteristics of the two-dimensional networks by simulating one of its chain.  
There are several definitions on the lifetime of the sensor networks, such as the time of the first node death or the time of a given fraction of nodes run out of energy, depending on different applications [17; 20]. we adopt three lifetime levels to evaluate and compare the network lifetime of different algorithms. The three lifetime levels are: from the beginning of the monitoring system, the number of rounds when the first node depleted battery power, 10% of nodes depleted battery power, and 20% of nodes depleted battery power, which were labeled as L1, L10 and L20 respectively [17; 21].  
The node's default transmission time \(T_{TX}\) and receiving time \(T_{RX}\) are 40 ms in each round. The control packet transmission duration \(T_{CX}\) is \(10 ms\). We take the receiving power of modem as \(80 mW\) from a typical WHOI micro-modem [12]. To simplify the simulation, we just take a ocean current velocity sensor here, in which the sensor's energy power is 200 mW, and sensing duration is 50 ms [33]. The default frequency of acoustic modem is 25 kHz. The default water depth of shallow water is 70 m. The default assigned energy for each node is 2 J. When the node's residual energy is less than \(2\times 10^{6}nJ\), it is rendered died.
## 4 Simulation and Result Analysis
### Fishhook Test  
The fishhook test is used to verify the effect of MFCDC-ESP on rollover. The basic parameters of the fishhook test simulation analysis are shown in Table 11.  
Figure 11: Simulation results of DLC test. (**a–d**) are the comparison results of the steering angle, vehicle longitudinal speed, yaw angle rate, and roll angle rate, respectively. The damping forces and vertical forces of each wheel of the DLC test with P-ESP and MFCDC-ESP. (**e**) is the comparison of the damping forces of the front wheels. (**f**) is the comparison of the damping forces of the rear wheels. (**g**) is the comparison of the vertical forces of the front wheels. (**h**) is the comparison of the vertical forces of the rear wheels. The details are available in the legend. L denotes left, R denotes right, 1 denotes the front axle, and 2 denotes the rear axle.  
The main parameters of the fishhook test simulation are shown in Figure 12.  
The LTR of the NC vehicle has a step at 3 s, with the roll angle and roll angle rate increasing sharply and resulting in serious rollover. Despite the many cases in which one side wheel is off the ground (LTR reaches 1 or \(-\)1), no rollover occurs with P-ESP. The main reason is that the lateral acceleration of the vehicle is reduced due to the action of the ESP. From the yaw angle rate and sideslip angle of the mass center, the whole vehicle vibrates from 6 s to 8 s. Although the yaw angle rate is controlled in a small range after 8 s, the continuous oscillation of the side slip angle of the mass center indicates that the vehicle has entered an unstable state. The vehicle with MFCDC-ESP has no instability phenomenon in the whole movement process, and all parameters are within the controllable range. Because the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Parameters** & **Value** & **Unit** \\ \hline Longitude target speed & 80 & km/h \\ Friction coefficient & 0.85 & - \\ Steer input angle peak value & 294 & deg \\ Simulation time & 20 & second \\ Road condition & straight & - \\ \hline \hline \end{tabular}
\end{table}
Table 11: Main parameters of fishhook test.  
Figure 12: Main results of fishhook test. The result of NC is expressed with a black dotted line; the result of P-ESP is expressed with a blue chain line; the result of MFCDC-ESP is expressed with a red solid line. (**a–f**) are the comparison for LTR, vehicle sideslip angle, roll angle, roll angle rate, yaw angle rate, and lateral acceleration, respectively.  
damping forces of CDC are controlled by electrical signal, the sudden changes of damping forces often lead to the sudden change of control current. The damping force current signal can be collected and identified by appropriate methods, and the driver can be given a corresponding real-time rollover warning, which improves the rollover prediction effect.
# Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
## 2 Materials and Methods  
### Overview of Proposed Method  
The proposed crack width measuring technique uses a laser beam and image processing algorithms to accurately measure concrete crack widths in millimeters. An overview of the proposed method is illustrated in the flow chart shown in Figure 1. The steps involved in the crack width measurement process, such as image acquisition, crack segmentation, morphological operations, and skeletonization, are presented in the subsequent sections.
# Rolling Bearing Incipient Fault Detection Based on a Multi-Resolution Singular Value Decomposition
## 3 Application of the MRSVD Method in the Incipient Fault Detection of the Rolling Bearings
### Study of the Principle of the Noise Cancellation and Anti-Harmonic Interference of the MRSVD Method
The main components of the detailed signal \(\mathrm{D}_{1}\) were the noise signal and a small amount of the periodic shock signals. The total energy of the noise signal contained in the detailed signal \(\mathrm{D}_{1}\) accounted for half of the total noise energy contained in the signal \(x(i)\). From Formula (7), it also can be seen that the singular value corresponding to the approximate signal \(\mathrm{A}_{1}\) can be represented as \(\sigma_{s}\)+\(\sigma_{n}\)+\(\sigma_{hb}\). Similar to the signal \(x(i)\), the approximate signal \(\mathrm{A}_{1}\) also contained noise components, harmonic components, and periodic shock components. Continuing to the next level of decomposition of the MRSVD, the detailed signal \(\mathrm{D}_{2}\) was obtained. Similar to signal \(\mathrm{D}_{1}\), the signal \(\mathrm{D}_{2}\) also contained the noise signal and a small part of the periodic impact signal. The total energy of the noise signal contained in \(\mathrm{D}_{2}\) accounted for half of the total energy of the noise contained in \(\mathrm{A}_{1}\). With the increase of the number of decomposition layers, the energy of the noise was halved. The total energy of the noise contained in \(\mathrm{D}_{j}\) was half the amount of energy contained in \(\mathrm{D}_{j-1}\). As the noise decreased, the periodic shock components were highlighted. Nevertheless, for the harmonic signal \(h(i)\), only a very small amount of energy (\(\xi^{2}/(\sigma_{s+}\xi)^{2}\to 0\)) was decomposed into the detail signal in each decomposition level of the MRSVD. Therefore, most of the energy of the harmonic signal \(h(i)\) was retained in the last layer of the approximation signal. Based on the above analysis, for a rolling bearing signal that is affected by strong background noise and harmonic interference, the first detailed signal \(\mathrm{D}_{1}\) obtained by its decomposition will be mainly a noise signal, and its kurtosis value will be near 3. With the increase of the number of decomposition layers, the noise components will reduce layer by layer, and the periodic impact composition will increase layer by layer. Therefore, the kurtosis value will increase with the increase of the number of decomposition layers. When the kurtosis value reaches a certain maximum, it will decrease with the increase of the number of decomposition layers. This is because the periodic impact components contained in the residual approximation signal also decrease layer by layer, which will be confirmed in the simulation analysis.