esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cloud Microservices Architecture for Data Integrity Verifiability Based on Blockchain
## 3 The Architecture Model
### The Cloud System  
From the final user perspective, cloud computing can be divided into two parts: the client and the server [38]. The client part comprises the software used by the final user. The user does not have to install complex software to use services provided by the cloud; usually, a web browser, mobile application, or terminal are used. The server side has to do with the back-end, it is an abstract layer with the hardware and the connectivity; it means various computers, servers, and data storage.  
On the other hand, according to [PERSON] et al. [39], the cloud computing architecture consists of four layers: the hardware, the infrastructure, the platform, and the application. The hardware layer, typically implemented in data centers, manages the physical resources such as servers, routers, switches, power, cooling systems, etc. The infrastructure layer shares physical resources using virtual technologies, such as VMware, XEN, and KVM. The platform layer consists of operating systems and application frameworks. The application is the highest layer consisting of the cloud applications; it is the view of the final user.  
In the case of our model, we locate The Cloud System in the platform layer according to cloud computing architecture of [PERSON] et al. [39]. The Cloud System will establish communication with VBSI using the TCP/IP protocol. In particular, it will be over the hypertext transfer protocol (HTTP) [40], and its secure version HTTPS [41]. The methods that The Cloud System will be requesting to VBSI will be GET, POST, PUT and DELETE.  
Figure 1: General Model: interaction among The Cloud System, The Verifiability Blockchain Service Interface and the blockchain.
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 4 Machine-Learning-Based Models
### Random Forest Regression Model  
Random forest (RF) is one of the learning algorithms that uses the tree as a base learner. The RF is introduced since a single regressor is not enough to predict the correct fit. The reason is that, based on sample data, the regressor cannot distinguish between noise and pattern, so it performs sampling with changes such that the given \(n\) trees to be learned are based on the dataset samples after taking the averages. The RF model sets up several trees to address the regression challenge, where each tree contains a root node, leaf nodes, and internal nodes. The root node has a set of training samples, and leaf nodes correspond to the final result. The internal nodes are split by features, and the criterion used to obtain features or split nodes is the MSE. Moreover, in the proposed setup, each tree is learned using four features selected randomly. After creating \(n\) trees, when the testing data is used, the decision regarding the majority of trees that come up is considered the final output. The number of trees adopted for this work in our RF model was 1090, with a maximum of 17 tree nodes.
## 1 Introduction
The ratio of the red signal (\(AC_{Rol}/DC_{Rol}\)) to the infrared signal (\(AC_{IR}/DC_{IR}\)) reflects a ratio similar to that of oxygenated Hb, which can be converted to oxygen saturation through Equation (2) [10]. \[RoR=\frac{\frac{AC_{Bol}}{DC_{Rol}}}{\frac{AC_{IR}}{DC_{IR}}} \tag{1}\]  
\[SpO_{2}=A\cdot RoR+B \tag{2}\]  
Reviewing existing studies summarized in Section 2, the method of calculating the AC and DC components was also used in the non-contact oxygen saturation measurement. However, since IR wavelengths are not observed in RGB cameras, as shown in Figure 1, a visible light wavelength band with light absorption characteristics similar to IR was used. However, unlike pulse-oximetry, this method is highly sensitive to factors such as skin color and ambient lighting conditions. Therefore, the methods for calculating the AC and DC components are different in each existing paper, and this diversity can affect the accuracy and reliability of the results. In fact, existing studies have not been able to produce satisfactory results. Therefore, in this study, the oxygen saturation was estimated using the signals obtained from the video as the input of the model without calculating the AC and DC components. Specifically, we separated the signals obtained from videos into 10 color spaces and predicted the oxygen saturation using a ResNet-based deep learning model. We investigated the feasibility of various color components, rather than specific wavelength color components, in reflecting the oxygen saturation values. Considering the time-consuming nature of accurately measuring the oxygen saturation, we diversified the model inputs into 10 s, 20 s, and 30 s intervals to compare performance. Additionally, we analyzed the color channels contributing to oxygen saturation prediction on a per-subject basis using SHAP (Shapley additive explanations).
# Evaluation of Two Self-Fitting User Interfaces for Bimodal CI-Recipients
## 3 Procedures
### Expert-Fitting Procedure  
For the duration of the study, all participants were fitted with an _Audeo Marvel R 90 RIC_ (Phonak) HA on the hearing-impaired ear, and with a _Neptune_ (Advanced Bionics) processor in their implanted ear. For the CI side, all settings, as well as the most used map, were exported from the patient's private processor and imported to the study _Neptune_ processor. This ensured that the hearing impression was close to that experienced by the patients in everyday life. Participants were asked whether the new program sounded familiar, and changes were made in the case that participants were not satisfied. This final setting reflected the expert setting for the CI in the subsequent tests. For the expert setting on the HA side, however, a completely new hearing program was created. Due to the large variety of HA manufacturers used by the participants, it was decided not to export the settings from the private HAs. Instead, an expert setting was newly created by an audiologist using the _Target 7.0_ fitting software (Phonak ). This fitting was carried out based on the measurements of the _AudiosymDirect_ functionality and the use of the _Adaptive Phonak Digital Bimodal_ (APDB) formula. This fitting formula takes into account that, in bimodal CI users, especially those with limited hearing on the contralateral side, the CI often dominates the understanding of spoken language [19]. Due to its design features, the CI mainly encodes the frequencies (1-4 kHz) that are critical for speech intelligibility. For bimodal patients, the APDB fitting formula emphasizes the audibility of low-frequency information, which complements the CI and provides temporally fine-grained structural information to support speech understanding in noisy environments. In addition, the volume enhancement and automatic gain control (AGC) features were matched between the CI processor and the HA [20]. Nevertheless, according to [PERSON] et al. [21], there is no consensus on the fitting strategy for HAs in bimodal users. However, there are two common approaches that prevail in the calibration of HAs for bimodal users. The first approach is also the approach followed by the APDB fitting formula. The HA is fitted to support the CI by improving audibility in low frequencies and reducing amplification in higher frequencies [22]. The second approach is to optimize the CI and HA individually for speech perception. Regardless of the fitting approach, a compensation procedure is applied, to match the perceived loudness between the CI and the HA. In this study, the loudness of the new fitting on the HA side was matched to the loudness of the CI side. This loudness adjustment was performed spontaneously and without further measurements in consultation with the participant. Subsequent fine tuning was performed, according to the needs of the participant. For both devices, only the omnidirectional microphones were activated. The CI and the HA expert settings also served as the baseline settings from which changes were made with the UIs by the participants themselves.
# Design of a Compact Planar Magnetic Levitation System with Wrench-Current Decoupling Enhancement
## 7 Conclusions and Future Work  
In this work, a compact planar maglev prototype was developed to be capable of both precision and long-range motion. The stator was designed as a compact module. The coil array consisted of ten optimized square coils arranged in a zigzag pattern. The single magnet mover was deployed with five-DOF motion. The experiment began with the evaluation of the decoupling enhancement approach. The 2-D linear interpolation was able to reduce the oscillations which occurred when the mover was at the switching location, by estimating the data at that location to smooth the motion. Next, for step and microstep motion, the experiment results demonstrated the mover was moved with steps of 10 and 20 \(\upmu\)m for translation and 0.5 and 1 mrad for roll and pitch. Finally, full-range motion tests were conducted. The mover was moved following the trajectory within the range of 20 mm \(\times\) 20 mm \(\times\) 8 mm with a maximum air gap of 26 mm. The rotation range was \(\pm\)10 degrees for both roll and pitch. Levitation results for microstep and full-range motion demonstrated that the proposed planar maglev design had the potential to increase flexibility for manufacturing systems as a machining workstation and an expandable modular omnidirectional conveyor.  
Future work of this research will mainly aim to achieve six-DOF motion with unlimited horizontal motion range. The stator design will be further developed into a modular structure to provide flexibility in its arrangement. Accurate real-time wrench-current transformation solutions and improved motion-tracking techniques will elevate the levitation performance.  
**Author Contributions:** Conceptualization, C.T., Y.W. and M.B.K.; methodology, C.T. and Y.W.; software, C.T. and Y.W.; validation, C.T., Y.W. and M.B.K.; formal analysis, C.T. and Y.W.; investigation, C.T. and Y.W.; resources, C.T. and Y.W.; data curation, C.T. and Y.W.; writing--original draft preparation, C.T. and Y.W.; writing--review and editing, M.B.K.; visualization, C.T. and Y.W.; supervision, M.B.K.; project administration, M.B.K.; funding acquisition, M.B.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Canada Foundation for Innovation (CFI) and the Natural Sciences and Engineering Research Council of Canada (NSERC).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available from the authors upon request.  
The authors declare no conflict of interest.
## 3 Mechanical Overview
### Design Principles  
The module-design of _Serpens_ is inspired by the following principles:  
* _Principle of minimalism_. To make the robot inexpensive, easily customisable, and fast to fabricate, each module is equipped with the simplest mechanical structure, the minimum number of actuators (only a single SEA per module with one degree of freedom (DOF) and the simplest set of sensors. During the design process, the main focus is to keep the amount of parts as low as possible, and at the same time minimise the number of assembly operations.
* _Principle of symmetry_. To facilitate the interaction with the environment, a symmetric design is adopted for each module with a flat profile for the interaction with the terrain. The symmetric design is also selected to store and release energy in a balanced manner.
* _Screw-less assembly mechanism_. To ease the connection and reconfiguration of the robot modules, a screw-less assembly mechanism is designed in a very reliable and robust manner.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.11 Respiratory Rate Monitor  
Respiratory rate monitors [17] track the number of breaths taken per minute, offering crucial insights into respiratory health. In particular, they find applications in monitoring respiratory conditions such as asthma, chronic bronchitis, and pneumonia. These monitors detect changes in respiratory rate that can indicate symptom exacerbation or complications. Continuous monitoring enables healthcare professionals to evaluate respiratory status, make informed treatment decisions, and enhance patient care. Common examples of these devices include the ResMed AirSense 10 AutoSet CPAP Machine and the Philips Respironics DreamStation CPAP Machine.
# Side Channel Leakages Against Financial IC Card of the Republic of Korea +
## 3 Evaluation Methodology for the Financial IC Card Protocol
### Security Evaluation Methodology of Side Channel Analysis Under Black Box Model
#### 3.2.1 Enumeration For Intermediate Variables  
To evaluate the security against side channel analysis, the underlying scheme is performed as first and second-order analyses. Thus, the intermediate variables should be enumerated.  
Figure 2: Experimental setup for security evaluation of financial IC card protocol.  
1. First-order correlation power analysis 1.1.1 Each of the four S-boxes in the G-function 1.1.1 Second-order correlation power analysis 1.1.1 Combination between two of the four S-boxes in the G-function 1.1.1 Combination between two out of the four outputs of the G-function 1.1.1 First-order correlation power analysis is a fundamental attack when the countermeasure is not applied. Sometimes the first-order leakage may be exposed in spite of applying the countermeasure, due to unintended phenomena, such as optimization in the computer language compiler and human error. Therefore, item 1a should be required. Additionally, the four S-boxes in the G-function should be chosen as intermediate variables to recover the single round key. To conduct second-order correlation power analysis, pre-processing logic must be employed [18]. In addition, two intermediate variables should be required to release the leakage of the round key. In particular, the identical masking value is usually applied to each of the S-boxes to reduce the cost of pre-computed tables when assuming the general first-order Boolean masking scheme. For this reason, item 2a is needed to disclose the masking information. Moreover, the novel leakage in connection with block cipher SEED was recently suggested in [19]. According to this proposal, second-order leakage can be revealed with low attack-complexity, despite first-order Boolean masking and restricted shuffling countermeasures being employed, although a main target is the output of G-function. In other words, first-order Boolean masking and shuffling countermeasures can usually be utilized as reasonable countermeasures in software implementation due to certain merits, such as time performance, memory size, and security. However, in terms of block cipher SEED, side channel leakage can occur via 2b. In particular, authors in [19] only explained that one combination of total six cases was showed.
## 3 Results
### Experimental Comparison  
The CSP is the most popular method in BCI systems, and all CSP-based methods are on the basis of the CSP method. The proposed MDCCSP is based on the original CSP method. Therefore, it is first supposed to compare the results with CSP-based methods. The CSP [32], CCSP [35], and FBCSP [36] methods are chosen to validate the improvements of the proposed method. To compare with the CCSP and the FBCSP, the effectiveness of  
Figure 5: The average engagement recognition results of MDCCSP using different regularization parameters \(\alpha\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & P-CC & K-CC & S-CC \\ \hline
**Mean \(\pm\) std** & **87.74 \(\pm\) 5.98\%** & 87.68 \(\pm\) 6.40\% & 87.61 \(\pm\) 6.52\% \\ \hline \(p\)**-value** & / & \textgreater{}0.1 & \textgreater{}0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The average engagement recognition results of MDCCSP using different CCSPs where the best result is marked bold.  
the main idea of the MDCCSP is validated, that is, integrating multi-domain information, including frequency information, spatial information, and phase information. The filter bank used in the FBCSP in the experiments is the second filter bank in Table 3. In addition, the proposed method is also compared with [PERSON]'s minimum distance to the Riemannian mean method (MDMR) [41], [PERSON]'s method [40], and [PERSON]'s method [42] to validate the effectiveness of the MDCCSP. [PERSON] et al. projected data onto the Riemannian space and extracted features in the space to realize classification. [PERSON] et al. employed a shrinkage linear discriminant analysis to classify the features extracted by the CSP. An extreme learning machine was utilized to extract high-level features in [PERSON]'s method. The recognition accuracies are presented in Table 6. It can be observed that the proposed MDCCSP method achieves the best performance compared with existing methods, with an average accuracy of 87.74 \(\pm\) 5.98%, which provides a 4.03% improvement on the second-best method. In detail, for 24 out of 29 subjects, the MDCCSP achieves the best accuracy, which validates the effectiveness of the proposed method.
## 2 The Theoretical Basis of the Algorithms Used in This Research
### Dnn
#### 2.2.5 Optimization Algorithm  
Neural network optimization algorithms are techniques used to effectively train neural networks by adjusting the weights and biases of the network connections in order to reduce error or loss function. Choosing the best optimization algorithm for a neural network depends on various factors, including the nature of the problem, characteristics of the dataset, network architecture, and available computational resources. There is no one-size-fits-all answer that can support all issues, as different algorithms perform differently in certain situations. In this paper, optimization algorithms of Stochastic Gradient Descent (SGD) [48; 49], Adaptive Moment Estimation (Adam) [50], and Root Mean Square Propagation (RMSProp) [51] were used to search for the best results.
# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
## 3 Results and Discussion
### Compacted Behavior  
The compaction test procedure was performed according to the ASTM D698 standard [47], and the results are graphically presented in Figure 7 by showing the variation of maximum dry density (MDD) and optimum moisture content (OMC) for untreated soils and soils treated with different amounts of WMP by weight of dry soil.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 6 Experimental Results of Decoding by Proposed Multi-class NN-Based Decoder
### Experimental Results  
#### 6.2.1 Results for BCH (15, 7) Code  
The code length of BCH (15, 7) is \(n=15\), the message length is \(k=7\), the error-correcting capability is \(t=2\) and the code rate is \(7/15=0.46\). As shown in Figure 11a,b, the BER of the proposed decoding framework with \(l=2\) is better than that of the Chase-II algorithm and approaches that of OSD (\(w=2\)). The numbers of candidate codewords for the proposed method, Chase-II and OSD algorithms are 2, 21 and 121, respectively. Figure 11a,b also reveals that the proposed decoder also outperforms the DNN-based or assisted decoder in [13,14].
## 3 Results
### Experiment  
To efficiently validate the reliability of the numerical model, a prototype was manufactured via 3D printing (Figure 10). Stereolithography (SLA) was selected for this study due to its high resolution and sufficient mechanical strength [31]. Key process parameters were set as follows: layer thickness: 50 \(\upmu\)m, UV curing wavelength: 405 nm, and post-processing curing time: 90 min. Coordinate measuring machine (CMM) results confirmed machining errors of critical dimensions within \(\pm\)0.15 mm and surface roughness \(\text{Ra}\leq 6.3\)\(\upmu\)m. The UV post-curing treatment ensured sufficient mechanical strength to meet experimental requirements.  
Figure 9: Performance of centrifugal pumps with dimple-type non-smooth structures at different locations: (**a**) head; (**b**) efficiency.  
Figure 11 compares the experimental and simulated head and efficiency values of the original model and the bionic non-smooth impeller. For head characteristics, the experimental and simulated head-flow curves exhibit consistent trends, though the experimental head values demonstrate a systematic overestimation with a maximum relative deviation of less than 3%. Among these, deviations were particularly pronounced under low-flow conditions, primarily attributed to unaccounted wall roughness effects and clearance leakage in the numerical simulations. The optimized impeller achieved a maximum head improvement of 11.5% at 130 m\({}^{3}\)/d, showing a 7.9% relative deviation compared to the simulated prediction of 12.98%.  
Figure 12 presents the experimental and simulated efficiency curves for the original model and bionic non-smooth impellers. Comparative analysis reveals that the experimental and simulated efficiencies deviate by less than 2% in the low-flow regime (50-100 m\({}^{3}\)/d). However, the deviation increases to 4% at 150 m\({}^{3}\)/d directly correlated with the reduced predictive accuracy of the turbulence model under high Reynolds number conditions. The optimized impeller achieved a peak efficiency of 50.67% at 130 m\({}^{3}\)/d, representing a 4.6-percentage-point improvement over the prototype but falling 45.8% short of the simulated prediction (8.55%). This discrepancy indicates insufficient characterization of secondary flow losses in the numerical model. The efficiency enhancement exhibits distinct flow dependency: gains remain below 1.2% in the 50-100 m\({}^{3}\)/d range but increase significantly to 2.8-4.6% at 100-150 m\({}^{3}\)/d.  
Figure 11: Head comparison curves.  
Figure 10: The 3D-printed impeller prototype.
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
## 5 Broadcasting in Path-Connected Cliques
### Broadcasting in Path-Connected Cliques When \(k=2\)  
**Lemma 9**: _Let \(G_{k}=(P,C_{1},C_{2},\ldots,C_{k})\) be a graph of path-connected cliques. If \(k=2\), then \(b(u_{1},G_{2})=\max\{\lceil\log(l_{1}+2^{l_{1}-a_{1}})\rceil,a_{1}+\lceil\log l _{2}\rceil\}\)._  
In any broadcast scheme for originator \(u_{1}\) in \(G_{2}\), \(u_{1}\) must inform \(u_{2}\) at some time unit \(a_{1}\) to ensure broadcasting is completed in both cliques. In an optimal scheme, \(u_{1}\) waits until the very last moment to inform \(u_{2}\) in such a way that the broadcasting process in \(C_{2}\) completes either simultaneously with or before the broadcasting process in \(C_{1}\). Additionally, \(u_{1}\) tries to minimize the number of vertices in \(C_{1}\) that it does not inform because it is busy informing \(u_{2}\). Once \(u_{2}\) is informed, \(C_{2}\) can finish broadcasting in \(\lceil\log l_{2}\rceil\) time units. Thus, all vertices in \(C_{2}\) will be informed by time unit \(a_{1}+\lceil\log l_{2}\rceil\).  
Let \(t_{1}=\lceil\log l_{1}\rceil\) be the time unit at which broadcasting in \(C_{1}\) would finish if \(u_{1}\) does not inform \(u_{2}\) at time unit \(a_{1}\). If \(2^{l_{1}}-l_{1}<2^{l_{1}-a_{1}}\), then \(C_{1}\) may require an extra time unit to complete broadcasting; since vertex \(u_{1}\) informs \(u_{2}\) instead of a vertex in \(C_{1}\) at time unit \(a_{1}\), \(2^{l_{1}-a_{1}}\) vertices could remain uninformed at time unit \(t_{1}\). In any case, all vertices in \(C_{1}\) will be informed by time unit \(\lceil\log(l_{1}+2^{l_{1}-a_{1}})\rceil\). Hence the broadcast time of \(G_{2}\) from originator \(u_{1}\) is given by \(b(u_{1},G_{2})=\max\{\lceil\log(l_{1}+2^{l_{1}-a_{1}})\rceil,a_{1}+\lceil\log l _{2}\rceil\}\). A similar result can be shown for broadcasting from vertex \(u_{2}\) in \(G_{2}\).