esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper introduces for the first time ECAPA-TDNN, currently popular in speaker recognition, to recognize Arabic dialects. Starting from the goal of obtaining more information on channel and time dimensions for dialect identification, we propose a multi-scale channel adaptive time-delay neural network. We also address the data imbalance problem in the ADI17 dataset by using a balanced subset fine-tuning strategy, and standardizing the scores using the Z-Score before score fusion to mitigate the score distribution differences between dialects. We evaluate our proposed models and compare them with other systems submitted to the ADI17 challenge. Our experiments show that ECAPA-TDNN is highly suitable for Arabic dialect identification, and our proposed MSCA-TDNN further demonstrates more powerful performance. To the best of our knowledge, the performance of MSCA-TDNN is superior to the results of existing single network models. Our future work will focus on optimizing network architecture from the aspects of adaptively extracting contextual information and enhancing channels to better improve dialect identification performance.  
Conceptualization, Q.L. and R.Z.; methodology, Q.L.; software, Q.L.; validation, Q.L. and R.Z.; formal analysis, Q.L.; investigation, Q.L.; resources, R.Z.; data curation, R.Z.; writing--original draft preparation, Q.L.; writing--review and editing, Q.L.; visualization, R.Z.; supervision, R.Z.; project administration, R.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The Arabic Dialect Identification for 17 Countries (ADI17) dataset used in this paper can be accessed by the following link: ([[http://groups.csail.mit.edu/sls/downloads/adI17/](http://groups.csail.mit.edu/sls/downloads/adI17/)]([http://groups.csail.mit.edu/sls/downloads/adI17/](http://groups.csail.mit.edu/sls/downloads/adI17/)), accessed on 24 March 2023).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multicolumn{2}{c}{**Overall**} & \multicolumn{2}{c}{**\textless{5 s}} & \multicolumn{2}{c}{**5\textless{}\textgreater{}20 s**} & \multicolumn{2}{c}{**\textgreater{}20 s**} \\ \cline{2-10}  & **C\({}_{avg}\)** & **Acc** & **C\({}_{avg}\)** & **Acc** & **C\({}_{avg}\)** & **Acc** & **C\({}_{avg}\)** & **Acc** & **C\({}_{avg}\)** & **Acc** \\ \hline E2E (Softmax) [23] & 13.7 & 82.0 & 18.8 & 76.2 & 10.9 & 85.1 & 6.7 & 90.4 \\ Transformer [10] & - & 82.54 & - & 76.21 & - & 86.01 & - & 90.58 \\ UKent [16] & 6.2 & 91.1 & 8.3 & 88.4 & 5.3 & 92.3 & 2.5 & 96.1 \\ DKU [16] & - & 93.8 & - & - & - & - & - & - \\ DKU (Fusion) [16] & 4.3 & **94.9** & **5.5** & **93.3** & 3.7 & **95.6** & 2.0 & 97.7 \\ MSCA-TDNN (Ours) & **4.19** & 94.28 & 5.64 & 92.23 & **3.45** & 95.22 & **1.98** & **98.12** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different systems. [\(\text{C}_{avg}\) and Acc] (%).
## 4 Experiments and Analysis of Results
### Data Expansion  
We use the data expansion rate to measure the resources occupied by marked encrypted images, which is expressed as the ratio of ciphertext data volume to original image data volume. Based on the embedding operation phase, our algorithm belongs to the category of VRIE and VRAE. We compare our algorithm with existing ones. Experimental results are shown in Table 6. An exemplary algorithm by [PERSON] et al. [14] achieves embedding via histogram shifting, categorized as a VRBE algorithm, with the resulting marked encrypted images retaining the size of the original images. Conversely, [PERSON] et al.'s algorithm [12] leverages homomorphic encryption schemes, achieving a notable data expansion rate of up to 256, with limited practical utility. The algorithm proposed in references [21; 23; 24] and the algorithm presented in this paper both perform embedding operations on sub-secret images, with the total ciphertext data volume being the number of shares \(n\). However, for each distributed storage, the data expansion rate is 1. Our algorithm embeds identifying information during secret sharing and embeds the user's secret data after encryption through bit replacement, with neither embedding process altering the image size; the only data expansion occurs during secret sharing. While our algorithm and existing RDH-EI algorithms share comparable data expansion rates, ours excels in image recovery quality and embedding rates, thereby delivering superior performance.  
Figure 9: Comparison of ERs between the proposed algorithm and existing algorithms. [5; 14; 21; 22; 23; 24].
## 3 The Deployment Models
### TLS Interception without Private Key  
As described in Section 2.1, TLS was primarily designed to establish an encrypted end-to-end session between the client and the server. However, in TLS interception, the client establishes an end-to-end TLS session with a middlebox (typically a TLS proxy in this context, but it may consist of more inspection-related functionalities, such as router, firewall, intrusion detection system (IDS)/intrusion prevention system (IPS), and content filter). As the middlebox is an endpoint of the TLS session, there is no hurdle to decrypt the application layer data. Hence, TLS interception transforms encrypted NTA into payload-based traffic analysis (also known as deep-packet inspection (DPI)), which is well established in the literature [51]. Thus, for TLS interception, TLS proxies and HTTPS proxies, which can be considered as a combination of TLS proxy and HTTP proxy, are widely deployed, especially in enterprise SOCs [27; 52].  
Notably, as shown in Figure 1 following the inspection and analysis, the middlebox forwards the data to the server via another end-to-end TLS session (i.e., with re-encryption) between the middlebox (on behalf of the client) and the server, where security policies can be enforced for the TLS traffic. This implies that although TLS is derived from a design motivated by the end-to-end argument [53], TLS interception breaks the end-to-end security property of TLS, which incurs concerns about man-in-the-middle (MITM) attacks on TLS. Furthermore, both the client and server should trust the middlebox, or the middlebox should impersonate the server (e.g., with certificate delegation [54], or forged certificates [55]). As such, TLS interception has drawn attention in various debates [27; 56; 57].
# Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
## 4 The Proposed Solution
### Reward Function  
The construction of the reward function is a critical aspect of creating the environment. It defines the optimal strategy that maximizes the cumulative reward over time, serving as the sole quality metric for an agent in the context of an unknown and abstract goal set by the environment. Careless selection of the reward function can lead to unintended agent behaviour.  
The reward is calculated following the agent's chosen action and depends on both the action and the resulting state of the simulator. Its value can also be negative and is often interpreted as a penalty. In this work, the reward was computed as a linear combination of two individual components, each aimed at capturing distinct aspects of the target strategy:  
\[R(t)=-R_{buttons}(t)-20\times R_{illegal}(t). \tag{5}\]  
The first component, denoted as \(R_{buttons}(t)\in\mathbb{N}\), represents the number of active buttons in the system. Minimizing this component is associated with reducing the number of waiting passengers. The second component is termed as \(R_{illegal}(t)\in\{0,1\}\) and represents the penalty for an illegal move, such as choosing to go up on the highest floor or to go down on the lowest floor. Penalizing the agent for an illegal action is an easy solution to the issue of the dependency of available actions on the environment's state. The scaling factor of 20 was chosen apriori to ensure that selecting an illegal action is always penalized more than choosing every other legal action. In extreme scenarios where all 19 buttons are active, selecting a legal action results in a penalty of at most \(-19\), while choosing an illegal action incurs a higher penalty of \(-20\).  
The simulator with passengers distribution, together with the state representation and reward function, formed the Markov Decision Process (MDP) model for the lift control system. It encompasses all its components, making it a suitable environment for the reinforcement learning process.
## 2 Materials and Methods
### Numerical Model and Methods  
#### 2.2.1 Geometry of Computational Model and Vocal Fold Motion  
Figure 2a shows the flow domain of the computational larynx model, the shape of which is based on the experimental model shown above. The geometry of the vocal fold models and the rectangular cross-section of the sub- and supraglottal channels were kept equal to those in the experimental model. The subglottal channel of the experimental model was shortened to 25 mm in the computational model to avoid the unnecessary computational cost. The length of the supraglottal channel was also reduced to 60 mm based on the prior measurement of the glottal jet length. Another case with a longer supraglottal channel (190 mm) comparable to the experimental model was also simulated and will be introduced in Section 2.2.3.  
The flow-induced oscillations of the experimental vocal fold models were reproduced in the computational model by externally forcing the motion of the vocal folds. This motion exactly reproduced the GAW (Figure 2) obtained from the high-speed visualization in the experiments. Furthermore, the same time point of the convergent-to-divergent shape change of the glottal duct and the same relationship between the vocal fold motion and the sub- and supraglottal flow devolution were maintained from the experiments.  
To model the mucosal wave-like motion of the vocal folds, a translational-rotational motion was applied on the medial surface of the vocal folds (blue surface in Figure 2c), generating a maximum convergent angle \(\theta_{\mathrm{max-conv}}=10^{\mathrm{o}}\) and a maximum divergent angle \(\theta_{\mathrm{max-div}}=20^{\mathrm{o}}\) of the glottal duct. These values were approximated from the high-speed footage of the oscillations of the silicone vocal folds and values reported in the literature [51; 59; 60]. The translational and rotational motions controlled the medial-lateral and the convergent-divergent motions, respectively. Finally, the translational motion was varied along the longitudinal axis of the vocal folds with a sinusoidal function to create the elliptical shape of the glottis constriction as shown in Figure 2c. More details of the modeled motion of the vocal folds can be found in [PERSON] et al. [50].  
Figure 1: Schematic of the experimental larynx model with the locations of the pressure sensors and the ROI in the supraglottal channel for the PIV measurement. The high-speed camera downstream of the supraglottal exit recorded the motion of synthetic vocal folds from a superior view. All dimensions are in mm.
# Phase Centre Corrections of GNSS Antennas and Their Consistency with ATX Catalogues
## 2 Methods  
The paper is focused on the changes of the ellipsoid height, which are measured to the ARP. The height and the horizontal component are revealed in the current international terrestrial reference frame (ITRF2014). Along with the reference frame change, GNSS receiver antenna calibration models are also changed to be consistent with the current realisation of the frame. In the epoch under analysis, the International GNSS Service (IGS) provided data on 316 GNSS permanent stations. After the introduction of the ITRF2014, 113 of these changed their reported heights due to the antenna calibration parameter changes. The values of the estimated height offset are shown in Figure 2, which are based on IGS data.  
As an example, the difference between the I08.ATX and I14.ATX calibration models for the TRM57971.00 NONE antenna is shown in Figure 3. The \(-\)1.8-mm PCO difference for the G01 (GPS L1) frequency and high PCV elevation dependent differences for the R01 (GLONASS L1) frequency are clearly seen.  
Figure 4 shows PCV variations of the TRM57971.00 NONE antenna between the ATX08 and ATX14 antenna models for two (G01 and G02) GPS signal frequencies. Azimuth elevation differences vary from \(-\)1.5 mm to 1.5 mm. The largest differences are in the northwest vs. southeast directions, which are up to 1.5 mm, while, in the perpendicular direction (northeast vs. southwest), the situation is the opposite, with a difference of up  
Figure 2: Height offset due to antenna model changes; red marks indicate positive height changes, blue marks indicate negative changes, and white indicates that no changes occurred. The ‘whiskers’ show the amount of height offset.  
to \(-1.5\) mm. Such maximum differences appear only for the nearest-to-horizon satellites, which have the smallest impact on the GNSS processing due to weighting. Differences for satellites with the highest elevation (located in the centre of the circles) are close to 0.  
In general, changes in the station heights mean that the antenna model changes or suggests an inaccurate calibration model of the antenna's PCO or/and PCV. Detailed research has been performed for the ASH701945_C antenna. This type of the antenna, with various domes, is still used at stations including IENG, KELY (NONE), BOGI, KRAW (SNOW), and SKEO (OSOD). The ASH701945C_M SNOW antenna was calibrated by Geo++ GmbH on 25 March 2011 with the use of a single antenna [4]. Six others had individual calibrations, not KRAW or BOGI. It is possible that the antenna used at the KRAW station has distinctive characteristics other than those determined by Geo++. To check on this, an experiment on the KRAW antenna was conducted. In contrast to robot or anechoic chamber experiments, the authors used an analysis of the short baselines between the different permanent (KRAW and KRA1) and auxiliary stations (KR01 and KR02).  
Figure 4: PCV variations between ATX08 and ATX14 for two GPS signal frequencies for the TRM57971.00 NONE antenna.  
Figure 3: PCO and PCV difference between I08.ATX and I14.ATX calibration data for the TRM57971.00 NONE antenna.
# Survey of Blockchain-Based Applications for IoT
## 5 Data Protection and Privacy
### Privacy  
Data privacy refers to an entity's ability to control its access to data. Blockchain technology is designed to make all transactions visible to participants. However, privacy can be achieved to a certain extent using one of the following approaches:  
* Signatures and cryptography using a different key per transacting counterparty [38];
* Private blockchains [38];
* Smart contracts [39]: Smart contracts can be used in multiple ways in IoT systems. They can be used to control the ownership of an object [40] or privacy-preserving access control using techniques such as the NFT [39]. They can also control access to resources on the blockchain using the attribute-based access control (ABAC) model [41], which is a dynamic and adaptable method that grants or restricts user access based on various attributes related to the user, resources, environment, or specific actions. This access-control mechanism is organized into four main components: users, resources, access requests, and permission removal.
## 3 Sliding Mode Path following and Control Allocation
### Attitude and Position following SMC  
Define \(s_{1}\in R^{3}\)  
\[\dot{r}_{r}=\dot{r}_{d}-\Lambda_{1}(r-r_{d}) \tag{27}\]  
and  
\[s_{1}=\dot{r}-\dot{r}_{r}=\dot{r}-\dot{r}_{d}+\Lambda_{1}(r-r_{d}) \tag{28}\]  
where \(r_{d}\) is the desired attitude and \(\dot{r}_{d}\) is the desired angular velocity.  
Now, we propose the following control for attitude following to exploit the structure of the rotational dynamics:  
\[\tau=\Psi^{-T}\Big{[}\tilde{H}\tilde{r}_{r}+\hat{C}\dot{r}_{r}-K_{1}\;SGN(s_{1 })-K_{3}s_{1}-K_{4}\tilde{r}\Big{]} \tag{29}\]  
where \(\tilde{r}=r-r_{d}\) and \(\widehat{(\cdot)}\) denotes the nominal of \((\cdot)\). \(K_{1},\;K_{3},\;K_{4}\) are positive diagonal matrices, \(sgn(\cdot)\) is the sign function, and  
\[SGN\Big{(}[x_{1}\;\;x_{2}\;\;x_{3}]^{T}\Big{)}=[sgn(x_{1})\;\;sgn(x_{2})\;\; sgn(x_{3})]^{T} \tag{30}\]  
We can now define \(s_{2}\in R^{3}\)  
\[s_{2}=\dot{p}-\dot{p}_{d}+\Lambda_{2}(p-p_{d}) \tag{31}\]  
where \(p_{d}\) is the desired position and \(\dot{p}_{d}\) is the desired velocity.  
The position following control is proposed to alleviate the chattering effects as follows:  
\[f={}^{W}R_{B}^{-1}\left(u+\begin{bmatrix}0\\ 0\\ mg\end{bmatrix}\right) \tag{32}\]  
where \(u\) is designed as follows:  
\[\dot{u}=-(K_{2}+\Lambda_{2})u+\hat{m}\big{(}(K_{2}+\Lambda_{2})\tilde{p}_{d}+ \ddot{\tilde{p}}_{d}-K_{2}\Lambda_{2}\big{(}\dot{p}-\dot{p}_{d}\big{)}- \Lambda_{3}s_{2}-\Lambda_{4}\;SGN(s_{2})\big{)} \tag{33}\]  
where \(K_{2},\;\Lambda_{2},\;\Lambda_{3},\;\Lambda_{4}\) are positive diagonal matrices.
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 4 Solutions
### Securing the Manufacturing Information Architecture
#### 4.1.4 Security Policies and Procedures  
The strongest digital security solution that can be implemented within a systems network can be rendered ineffective or even useless if there is no proper adherence to security policies and procedures. Historical analysis of cyber-attacks has shown that insider knowledge and leaked sensitive information were prominent contributing factors to the successful execution of many such attacks [2, 8, 33]. Strict adherence to security policies and procedures, in combination with strong and effective digital security measures within the systems network, should reduce the risk of both intentional and unintentional information leaks to a minimum. However, history has time and again demonstrated that an excess of security policies and procedures forced upon individuals in an industry, especially if these policies and procedures are poorly formulated, can result in non-compliance [3]. Care should thus be taken to formulate sensible and efficient security policies and procedures within an industry's security environment. Officially published security guidelines specifically aimed at securing the industrial manufacturing environment, such as NIST SP 800-82 [44] and IEC 62443 [45], can be used to aid in the formulation of these security policies and procedures [3].  
The IEC 62443 in particular is a prominent standard that defines the cyber-security lifecycle as four major phases. The first phase concerns the identification and calculation of the associated security risks that the industry faces. Once the relevant risks have been identified, suitable countermeasures can be developed and implemented in the second phase. In the third phase the security measures are maintained and monitored for performance. Any cyber-security-related incidents are also responded to and logged during the third phase. The fourth phase concerns the continuous improvement of the implemented security measures, both in terms of the incidents that could have occurred, as well as keeping up with the latest trends and developments in cyber-security measures.  
Industries can obtain different levels of IEC 62443 certification, ensuring adherence to internationally recognized cyber-security standards, which will provide peace of mind to both the industries themselves and their clients/stakeholders [45].
# Multi-Domain Modeling and Analysis of Marine Steam Power System Based on Digital Twin
## 4 Dynamic Characteristics Analysis of Marine Power System Based on Digital Model
### Dynamic Response of the System under Different Cooling Conditions
uum degree decreases, the thermal cycle efficiency decreases, and the steam turbine power decreases. It can be seen from Figure 23 that the relation between cooling water inlet temperature and condenser pressure increment is as follows:  
1. When the inlet temperature of the circulating cooling water increases gradually, the heat exchange capacity of the cooling water decreases, the pressure in the condenser increases gradually, the vacuum degree decreases, the thermal cycle efficiency decreases, and the power of steam turbine decreases accordingly;
2. When the inlet temperature of the circulating cooling water decreases gradually, the heat exchange capacity of the cooling water increases, the pressure in the condenser decreases gradually, the vacuum degree increases, the thermal cycle efficiency increases, and the power of the steam turbine increases accordingly. Through the dynamic response of the digital model under different cooling conditions, it can be concluded that increasing the flow of the circulating cooling water can improve the heat transfer performance of the condenser, thereby reducing the condenser pressure, improving the condenser vacuum degree and the steam power cycle efficiency, and thus increasing the power of the main turbine. With the gradual increase of the cooling water flow, the condenser pressure and the power of the main turbine are still limited, and increasing the cooling water flow will also increase the output power of the circulating water pump. If the horsepower of the water pump increases, the power consumed is greater than the increment of the power of the main turbine, which is often not worth the loss. Therefore, when the marine vessel is performing tasks, the flow of the circulating cooling water should be dynamically adjusted according to the sea water temperature to ensure that the condenser maintains the best vacuum degree and maximizes the efficiency of the turbine. In conclusion, the dynamic response of the system under different cooling conditions is analyzed using the digital model of the marine steam power system. The results show that the model can accurately reflect changes in the system's state parameters, realize the virtual reality mapping between the digital model and the physical equipment, and provide guidance for the safe and stable operation of the marine steam power system under different cooling conditions.