esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Future Work  
Future work will focus on three main areas: first, developing a preamble-based SNR estimator to address mobile environments, where factors like Doppler shifts and time-varying channel conditions impact SNR estimation. One potential application is in UAV communication systems, where knowledge of exact UAV communication channels is required [52]. In these systems, the Doppler effect arises due to flight speed, making it challenging for traditional methods. Employing orthogonal time frequency space (OTFS) modulation with a preamble could significantly improve SNR estimation in high-mobility scenarios like UAVs by enhancing Doppler resilience, synchronization accuracy, and channel estimation. Second, the study could be extended to massive MIMO systems, where two potential approaches can be considered for adapting the proposed SNR estimation method. First, decentralized estimation can be utilized, where clusters or individual antennas independently estimate the SNR. This approach reduces computational demands and enables the system to scale efficiently as the number of antennas increases. Second, exploiting spatial correlation between antennas can further enhance the estimation process. By sharing similar SNR estimates across neighboring antennas, this method improves the accuracy of SNR estimations while also reducing computational complexity, leveraging the natural similarity in signals received by spatially proximate antennas [53]. Lastly, an extension of this work could involve conducting a comparison study between the simulation results and real-time experimental measurements to provide a more comprehensive evaluation of the proposed methods, offering valuable insights into their performance in practical, real-world settings.  
Conceptualization, S.M. and N.S.O.; methodology, S.M. and N.S.O.; software, S.M., N.S.O. and M.W.M.; validation, S.M., N.S.O. and M.W.M.; formal analysis, S.M. and N.S.O.; investigation, S.M. and N.S.O.; resources, S.M., N.S.O. and M.W.M.; data curation, S.M. and N.S.O.; writing--original draft preparation, S.M., N.S.O. and M.W.M.; writing--review and editing, S.M., N.S.O. and M.W.M.; visualization, S.M., N.S.O. and M.W.M.; supervision, N.S.O.; project administration, N.S.O.; funding acquisition, S.M. and M.W.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the authors due to privacy concerns.  
The authors would like to express their sincerest gratitude to the Universiti Tenaga National/UCSI University for providing Matlab(R) licensed software for this research.  
The authors declare no conflicts of interest.
## 3 Proposed DRL-Based Optimization Algorithm
Then, the analytic expression for the policy gradient with baseline is denoted as  
\[+\omega_{1}\bigg{[}\psi_{1}\sum_{m=0}^{M}\sum_{k=1}^{K}\mu_{k,m,t }\left(E_{m,k}^{0}[t]+E_{m,k}^{c}[t]\right)+\psi_{2}\sum_{m=0}^{M}\sum_{k=1}^{ K}\mu_{k,m,t}\Gamma_{k,t}l_{k,t}(\beta_{k,t})\bigg{]}\Bigg{\}} \tag{6a}\]5  
Off-policy method adopts an exploratory behavior policy \(\psi[\mathbf{A}(t)|\mathbf{S}(t)\,]\) to generate behavior, while the target policy \(\pi_{\mathbf{\theta}}[\mathbf{A}(t)\,]\,\mathbf{S}(t)\,]\) learns about the behavior and finally become the optimal policy. Importance sampling technique is widely used by off-policy methods, which weights the returns by importance-sampling ratio [28]. The parameter \(\mathbf{\theta}\) is updated as  
\[+\omega_{1}\bigg{[}\psi_{1}\sum_{m=0}^{M}\sum_{k=1}^{K}\mu_{k,m,t }\left(E_{m,k}^{0}[t]+E_{m,k}^{c}[t]\right)+\psi_{2}\sum_{m=0}^{M}\sum_{k=1}^{ K}\mu_{k,m,t}\Gamma_{k,t}l_{k,t}(\beta_{k,t})\bigg{]}\Bigg{\}} \tag{6a}\]6  
where \(\alpha\) is the learning rate, \(G\) is the return following time-slot \(t\), \(\mathbf{\theta}_{t}\) is the estimate of \(\mathbf{\theta}\) at time-slot \(t\). We adopt the estimate of the state value \(v[\mathbf{S}(t);\mathbf{w}]\) as the baseline, where \(\mathbf{w}\) is the weight vector of the state value function. Then, the DRL-based algorithm is summarized in Algorithm 2. ```
1:Initialization:
2:\(\mathbf{\theta}\), \(\mathbf{w}\), target policy \(\pi_{\mathbf{\theta}}[\mathbf{A}(t)|\mathbf{S}(t)]\), behavior policy \(\psi[\mathbf{A}(t)|\mathbf{S}(t)]\)  maximum number of iterations \(K_{\max}\), discount factor \(\gamma\), the learning rate of policy \(\alpha_{p}>0\), the learning rate of the baseline \(\alpha_{b}>0\);
3:for\(k=1:K_{\max}\):
4: Using \(\psi[\mathbf{A}(t)|\mathbf{S}(t)]\) and Algorithm 1 to generate trajectory \(\mathbf{S}(0),\mathbf{A}(0),R(1),\mathbf{S}(1)\ldots\mathbf{S}(T-1),\mathbf{A }(T-1),R(T)\), \(\mathbf{S}(T)\) by action policy \(\psi\);
5:for\(t=T-1,T-2,\ldots,0\):
6: Update \(G\): \(G\leftarrow\gamma G+R_{t+1}\);
7: Update \(\mathbf{w}\): \(\mathbf{w}\leftarrow\mathbf{w}+\mathbf{\alpha}_{b}[G-v[S(t);\mathbf{w}]]\
abla v[ \mathbf{S}(t);\mathbf{w}]\);
8: Update \(\mathbf{\theta}\) by (18) with \(\alpha=\alpha_{b}\);
9:endfor
10:endfor
```  
**Algorithm 2** The proposed DRL-based algorithm.
## 6 Conclusions  
In this paper, a signal processing technique to classify 17 voluntary movements from EMG signals is proposed. The major novelty of this study is the use of a small set of low-cost EMG sensors (low sampling rate) to classify a reasonably large number of hand movements. Only six electrodes were used for EMG recording. In the proposed method, power spectrum densities are used as features and the motions are classified using artificial neural network. Online classification experiments were conducted on 12 participants to evaluate the validity of the proposed method. The results show that the proposed method is capable of achieving 42-83% overall accuracy (average of 63.8% across subjects). Statistical analysis results suggest that improvement in accuracy may be due to the fact that the user had gained more experience from the training on the previous day. Results also indicate that the accuracy drops significantly after one hour of continuous usage and re-training of the classifier is recommended. Based on the above results combined with the fact that users can promptly modify any incorrect classifications by looking at the actual output of the prosthetic hand, the proposed algorithm demonstrates the potential to classify 17 voluntary movements from 6 consumer grade EMG sensors. Furthermore, classifying 9 motions using this method could achieve 43-92% accuracy (average of 72.9% across subjects). Based on this result, if the prosthetic hand is intended for a specific task, limiting the number of motions can significantly increase the performance and usability. Overall, the findings reported in this paper have given us an idea of the extent of how well the current state-of-the-art classification scheme can perform with limited hardware. The discussions have also revealed the challenges towards developing a practical and multi-functional prosthesis. An extension to this study would be to raise the overall performance by improving the feature extraction algorithm as well as devising a training method in order to improve the accuracy.  
[PERSON] and [PERSON] conceived, designed and performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] developed the hardware and software of the myoelectric hand system; [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 2 Technical Enablers
### Deploying Intelligence on the Network Edge
#### 2.1.2 Fog Computing
This problem becomes more obvious when considering the functional requirements of many industrial or safety-critical systems such as patient monitoring platforms [58; 59; 60], automated production lines and road traffic optimization applications [61], which only tolerate just milliseconds of end-to-end latency and therefore remain based on obsolete architectures. In addition, a certain type of autonomous operational capacity is important for reaching the aforementioned level of service continuity. All safety-critical systems need to stay operational and secure besides any potential and unexpected connectivity issues. Data reporting is to be restored, as soon as connectivity is available, as normal. This example showcases the desired behaviour of a dedicated node in such unfortunate, but definitely expected situations. Sensor data need to be temporarily stored and even pre-processed in the intermediate layers of the application producing notifications and warnings as normal. Preliminary system responses are generated at a smaller distance from the data producers and in a more direct manner without the need for always-on connectivity. Figure 2: Introducing fog computing in the communication stack [52]. Enhanced reliability and platform security prerequisites: As the actual size of the transmitted data increases, so do the error rates, packet droppings and transmission times. However, increased error rates cannot be tolerated in the case of emergency and safety-critical applications. Successful IoT deployments need to rely on uninterrupted service delivery in combination with elevated security. As the fog computing layer is placed between the cloud and end-user devices, it provides the necessary guarantees to ensure service cohesion [55]. Its nodes are intermediates for providing security and sensor management capabilities, such as deep packet inspection or message encryption and benefiting from context and location-related information to easily detect threats [62]. Without a doubt, resource-constrained device protection and security update delivery to the very end of distributed system nodes, in a trustworthy manner without causing intolerable disruptions, increase the overall efficiency and performance of the system to a whole new level [63]. In conclusion, fog computing is an architecture that distributes computation, communication, control and storage closer to the end-users, along the cloud-to-things continuum [64], and will at the same time unveil a novel architectural concept that will most likely also enable fascinating business models for computing and networking.
## 6 The 6 GCloHE System Architecture
### Application Layer  
In this layer, state-of-the-art applications of the 6 GCIoHE system-related technologies in the healthcare domain are presented. Healthcare applications have different requirements; therefore, the five-layer architecture is chosen to directly affect the application performance. Applications with similar themes and scope are therefore grouped into categories as follows: (i) real time monitoring and alerts generation; (ii) telemedicine; (iii) chronic disease detection and prevention; (iv) home and elderly healthcare. Each of these healthcare application categories and the role of the 6 GCIoHE system and the associated technologies are as follows: (i) through the IoHE, WBS can be deployed on a human body to measure different parameters, and data can be analyzed for prescribing the necessary medication to the patients in emergencies; and (ii) an e-health system is used for continuous monitoring of ECG, temperature, foot pressure, and heart rate.
## 2 Fundamentals and Terminology
### Hardware-in-the-Loop (HiL), Software-in-the-Loop (SiL), and Digital Twin (DT)  
The concepts of HiL and SiL are integral parts of the V-Cycle development process, which is commonly used in the automotive industry.  
HiL is a standard method for testing Electronic Control Unit (ECU) software in automotive development [23]. SiL testing involves generating code from Simulink models and deploying it on hardware devices [2].  
DTs are used to create virtual representations of physical systems, allowing for comprehensive testing and validation in a controlled environment. This approach is particularly useful for testing complex systems like automotive lighting under various simulated conditions [24].  
In the context of this review, we define a digital twin (DT) in alignment with the Basyx Asset Administration Shell (AAS) type 2 classification. This definition characterizes the DT as more than a static data container (type 1, i.e., a serialized AAS); rather, it is an active software entity deployed on a server. It exposes an interface--referred to as the front end in the Basyx framework--that allows interaction with other parts of the system, in our case, the operational environment. This interface provides access to both properties and operations, enabling runtime interaction. While a metamodel may also be included in accordance with the AAS type 2 specification, it is not essential for the discussion presented in this paper. Importantly, unlike a type 3 AAS, the DT defined here is not autonomous in initiating negotiations with other components. This distinction is critical because autonomous behavior in safety-critical systems--such as vehicle subsystems governed by ISO 26262--requires extensive validation and formal guarantees of functional safety. Limiting the DT to type 2 ensures that it remains predictable and verifiable, avoiding the introduction of runtime uncertainties that could compromise system integrity [25].  
Figure 1 presents a V-Cycle representation adapted for automotive systems engineering with integrated DT functionality. The left branch of the V reflects the development workflow, beginning with system requirements and progressing through system-level design, component specification, and software implementation. Each layer incrementally refines the system architecture in preparation for integration and testing. The right branch represents the validation process, starting with HiL and SiL evaluations, advancing to integrated HiL/ViL (Vehicle-in-the-Loop) scenarios, and culminating in complete vehicle testing to ensure regulatory compliance and real-world performance. At the center, the DT serves as a cross-domain enabler, synchronizing data between virtual models and physical systems throughout the development lifecycle. This integration supports continuous monitoring, traceability, and predictive analytics, enhancing both early design validation and post-deployment feedback. The figure also emphasizes the shift toward a data-driven, simulation-supported validation strategy that aligns with current trends in automotive system digitization.
## Appendix B Choice of \((\rho,\lambda,\rho)\) Based on a Case Study
The table includes information on the interpolation methods utilized, Lanczos (L) and Nearest (N), which are shown in Column 1. The remaining columns present the following data: _Column 2:_ the number of adversarial images used for testing noise blowing-up technique, _Column 3:_ the number of images classified in the target category, _Column 4:_ the number of images that remained adversarial in the untargeted category, _Column 5:_ the number of images classified in the ancestor category after employing the noise blowing-up technique, and _Column 6:_ the resulting average loss in target category dominance. Table 14 indicates that there are no significant differences observed when using different combinations of (\(\rho\), \(\lambda\), \(\rho\)) in relation to \(L_{p}\) norms (where \(p=0,1,2,\infty\)). However, Table 14 demonstrates that the combination of L-L-L produces optimal results in terms of both the loss function (\(\mathcal{L}\)) and the number of adversarial images remaining in the target category (\(c_{t}\)) when utilizing the noise blowing-up technique for generating high-resolution adversarial images. Therefore, in our experiments (see Scheme (11)), we employ the **L-L-L** combination for (\(\rho\), \(\lambda\), \(\rho\)). Figure 11: Evaluating the performance of the noise blowing-up method for **AdvGAN** in **untargeted** scenarios with the increased strength of adversarial images per each CNN. The charts display \(\Delta\) values at the bottom, along with the corresponding number of images used for the tests at the top. Figure 14: Evaluating the performance of the noise blowing-up method for **AdvGAN** in **target** scenarios with the increased strength of adversarial images per each CNN. The charts display \(\Delta\) values at the bottom, along with the corresponding number of images used for the tests at the top.
# Underwater Electromagnetic Sensor Networks--Part I: Link Characterization +
## 2 Experimental Section
### Setup Procedures  
#### 2.4.1 Launching Procedure  
Before performing underwater experiments, some measurements are carried out on the pier in order to test air propagation between antennas and verify testbed behaviour. Underwater measurements start with the launching of each chamber with its antenna connected (Figure 5). For this purpose, a small marine crane is required to hold the containers and throw them into the sea, as each chamber weighs approximately 80 kg. Then, a diver locates the container and the antenna on the seabed at a specific separation from the other one with the help of a measuring rope.  
Three types of antenna scenarios are of great interest in this work (the first two take place underwater):  
* Variation of horizontal separation between the centres of antennas;
* Variation of vertical separation;
* Sea to air measurements.  
These scenarios require different antenna movements. Nevertheless, only the horizontal separation measurements are finished and explained in this work. The others are currently in progress. Horizontal variation implies both transmitter and receiver on the seabed and the separation between them is increased or decreased by locating one of them farther. However, dipoles and loop antennas are always on the seabed. Dipoles are set to use horizontal polarization and are parallel to each other, while loop antennas are both lying on the seabed. Regarding the alignment, both loops have been placed on the seabed, ideally a horizontal plane in order to have nearly omnidirectional coverage. A measuring rope assisted the placement of the antennas. However, high precision is not achieved due to the unevenness of the seabed, especially at very short distances.  
By contrast, vertical variation is planned to be performed when antennas are separated a fixed distance horizontally, and a reference measurement is carried out when both of them are on the seabed. Subsequent measurements will consist of lifting one of them metre by metre to analyse the behaviour of the channel under this condition (vertical separation). The last measurement will be conducted with  
Figure 5: Launching procedure.  
one antenna still on the seabed and at the same horizontal distance while the other is suspended 0.5 m above the sea surface. Afterwards, they will swap places to check channel isotropy.
## 3 Results and Discussion
### State of Charge Soft Sensor
Other major instances numbered approximately 2000, 1900, 1800, and 1700 corresponding to error values of 1.43 \(\times\) 10\({}^{-4}\), 2.47 \(\times\) 10\({}^{-4}\), 1.7 \(\times\) 10\({}^{-4}\) and 2.7 \(\times\) 10\({}^{-4}\), respectively. The remaining testing error instances measured approximately 1500 or lesser for bins corresponding to 3.51 \(\times\) 10\({}^{-4}\), 4.55 \(\times\) 10\({}^{-4}\), 3.8 \(\times\) 10\({}^{-4}\), and 4.8 \(\times\) 10\({}^{-4}\). Thus, the majority of testing error instances were found at very low values of deviations, confirming the high prediction accuracy of the developed SOE soft sensor. Figure 17 shows the training state indicators for the SOE datasets. The gradient settled at a value of 9.574 \(\times\) 10\({}^{-8}\) at epoch 32, triggering the stopping criterion of the training algorithm. The mu finished at 1 \(\times\) 10\({}^{-10}\), whereas there were zero validation fails till the epoch 32. Figure 18 indicates that the best validation performance for the FSEV SOE soft sensor was achieved at 7.4901 \(\times\) 10\({}^{-8}\) corresponding to epoch 32. The validation performance of the SOC soft sensor (8.30 \(\times\) 10\({}^{-8}\)) was superior than that of its SOE counterpart. As discussed in the case of SOC modelling, the FSEV SOE was also modelled using regression and system identification methods for performance comparisons with the proposed ANN-based soft sensor: Regression-Linear  
\[\text{SOE}=0.9722-0.0002885*t+1.258\times 10^{-6}*I \tag{21}\]  
Regression--Nonlinear  
\[ \begin{split}\text{SOE}=0.9718-0.0002669*t+2.022\times 10^{-5}*I- 1.695\times 10^{-7}*t^{2}\\ -1.005\times 10^{-7}*t*I-2.25\times 10^{-7}*I^{2}\end{split}  \tag{22}\]  
ARX 441 \(A(z)y(t)=B(z)u(t)+e(t)\)  
\[ \begin{split} A(z)&=1-0.3732z^{-1}-0.198z^{-2}-0.334 2z^{-3}-0.09464z^{-4}\\ B(z)&=-2.078\times 10^{-7}z^{-1}-2.821\times 10^{-8}z^{-2 }-4.796\times 10^{-8}z^{-3}+3.061\times 10^{-9}z^{-4}\end{split} \]  
ARMAX 2221 \(A(z)y(t)=B(z)u(t)+C(z)e(t)\)  
\[ \begin{split} A(z)&=1-0.3427z^{-1}-0.6573z^{-2}\\ B(z)&=-2.076\times 10^{-7}z^{-1}-8.8\times 10^{-9}z^{-2 }\\ C(z)&=1+0.06146z^{-1}-0.469z^{-2}\end{split} \]  
BJ 22221 \(y(t)=[B(z)/F(z)]u(t)+[C(z)/D(z)]e(t)\)  
\[ \begin{split} B(z)&=-2.236\times 10^{-7}z^{-1}+3.515 \times 10^{-8}z^{-2}\\ C(z)&=1-0.3365z^{-1}-0.2003z^{-2}\\ D(z)&=1-0.7011z^{-1}-0.2989z^{-2}\\ F(z)&=1-0.5538z^{-1}-0.4461z^{-2}\end{split} \]  
OE 221 \(y(t)=[B(z)/F(z)]u(t)+e(t)\)  
\[ \begin{split} B(z)&=-1.591\times 10^{-7}z^{-1}+1.517 \times 10^{-7}z^{-2}\\ F(z)&=1-1.939z^{-1}+0.9392z^{-2}\end{split} \]Figure 16: Error histogram of FSEV SOE soft sensor. Figure 17: Training states of FSEV SOE soft sensor. Figure 15: Derived versus soft sensor outputs of FSEV SOE.