esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Comparison with Existing Segmentation Tools  
The results achieved by the proposed pipeline were compared to those of common methods. Tools that are easy to use and well-validated in the thalamus-segmentation task were selected for reproducibility purposes. To maximize diversity and generalizability, two classes of segmentation methods were employed: atlas-based and CNN-based.  
This comparison may be considered somewhat biased, as the ground-truth was generated with a combination of T1-weighted images and dMRI, whereas all other methods only used T1. However, the masks generated with the assistance of dMRI are assumed to be equivalent to those generated only on T1, as both represent the same structure.  
FSL and FreeSurfer were evaluated as representative atlas-based methods. Their segmentations were computed in the T1 original space, and subsequently registered to the diffusion space to be compared against manual segmentation.  
The QuickNAT framework was evaluated as an off-the-shelf CNN-based method. Although the original QuickNAT weights were not fine-tuned to this specific dataset, all input data were normalized according to the author's requirements (FreeSurfer's _mri_convert -conform_ command).  
The results (Figure 10) indicate that the best evaluating metrics, excluding those exhibited by our method, were obtained by FSL. All other methods, including STAPLE, exhibited considerable differences in performance compared to the proposed method. While these methods exhibited Dice coefficients in the range of 0.75 to 0.78, our CNN model achieved DC values exceeding 0.9.  
Figure 10: Dice and average Hausdorff coefficients for each segmentation method evaluated on the test set. Star (\(*\)) denotes statistical significance (\(p<0.05\)) when compared to our optimal CNN model (all_maps). All \(p\)-values were estimated using a two-sided Wilcoxon signed-rank test.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Dice Coefficient—DC**} & \multicolumn{3}{c}{**Average Hausdorff Distance—AHD**} \\ \cline{2-9}  & **FA** & **MD** & **RD** & **AD** & **FA** & **MD** & **RD** & **AD** \\ \hline T1 & * 0.0020 & * 0.0056 & * 0.0083 & * 0.0014 & * 0.0023 & * 0.0153 & * 0.0172 & * 0.0094 \\ \hline FA & & 0.4524 & 0.6742 & 0.3683 & & 0.0973 & 0.2611 & 0.2774 \\ \hline MD & & & 0.7012 & 0.8983 & & & 0.8983 & 0.4749 \\ \hline RD & & & & 0.7285 & & & 1.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of statistical significance between CNN models through two-sided Wilcoxon signed-rank test on the test set, considering only models with a single input channel. Star (*) indicates that the pair exhibits mutual statistical significance (\(p<0.05\)).
## 4 Simulation Experiment
### Experimental Results
#### 4.2.4.4 INS + LEO2 + Unbiased Altimeter and No Altimeter Scenario Experiment under Adjacent Tracks  
According to the parameter settings in Tables 1-3, in adjacent orbits, the experimental results of the INS + LEO2 switching + unbiased altimeter algorithm and the INS + LEO2 switching algorithm in adjacent orbits are shown in Figure 6. Similarly, as a comparison, we also included the standard INS + LEO2 switching navigation and positioning algorithm, and the standard INS + LEO2 switching + altimeter navigation and positioning algorithm as a reference.  
From Figure 6 we can see that, in addition to effectively suppressing the divergence of INS, the INS + LEO2 + unbiased altimeter algorithm in adjacent orbits can also suppress the error divergence problem of the INS + LEO2 switching algorithm under a longer switching time, especially when the switching time is 90 s. The final trajectory error statistical results of the two algorithms at 60 and 90 s are shown in Figure 7.  
From Figure 7 we can obtain that, with the aid of the altimeter, when the switching time is 60 s, comparing with the INS  
altitude's errors improve by 3.13%, 49.45% and 97.58%, respectively, and the corresponding standard deviations improve by 43.27%, 31.85% and 92.21%, respectively. When the switching time is 90 s, comparing with the INS + LEO2 switching algorithm, the INS + LEO2 switching + unbiased altimeter navigation and positioning algorithm in longitude, latitude and altitude's errors improve by 59.88%, 47.96% and 99.58%, respectively, and the corresponding standard deviation performances improve by 74.00%, 60.22% and 93.41%, respectively. It can be found that the addition of an altimeter also improves the height error considerably, and the principle is the same as that under the same orbit.  
Figure 6: The experimental results of the INS + LEO2 + unbiased altimeter and no altimeter scenarios under adjacent orbits.
## 4 Application of UWSN
### Monitoring
* Coral Reef: Coral reefs are an essential source of income for millions of people and critical habitat for life underwater. They protect coastal areas by reducing the intensity of waves that reach the beach. Life on coral reefs is abundant. Tens of thousands of species can be discovered on a single reef. Thus, it is essential to monitor its ecosystem to help in taking prevention from it being corroded away. In [78], the authors developed a real-time coral reef monitoring system using LED, and AUV.
## 3 Results and Discussion
### Annual Trend of Production and Citation  
Bibliometric analysis helps to determine the level of growth in the number of articles published in a certain area of scientific studies. A yearly decline in the number of publications in an area could imply decreasing interest among researchers in the scientific field [31; 35]. Concerning the trends in the production of research documents on neurotoxicants, neurotoxic disorder, and their environmental pollution effects, a significant increase was noticed from 2011 to 2020. Precisely, 154 (48%) of the 321 research documents retrieved were published between 2011 and 2015, while the remaining 167 (52%) were produced between 2016 and 2020 (Figure 1). The year 2020 recorded the highest number of outputs, with 40 documents (12.5%), whereas the least outputs were obtained in 2012 (n = 26; 8.1%). However, some fluctuations in the annual production figure were noticed within the period of assessment. Notwithstanding, research in this field recorded an annual growth rate of 4.46% over 10 years. This is comparable to what was reported in previous studies [35; 37; 41], thus showing that research in this area is receiving huge attention worldwide. It is noteworthy that this assessment was only based on data gathered from WoS, with the exclusion of information in other databases.
## 2 Novel Contributions
### Significance of the Solution  
1. The suitability of several technologies to ITS is analyzed.
2. The need of an application-based DLT is introduced.
3. Propose a DLT that could satisfy ITS requirements without trade-offs.
4. A novel technology framework, FlexiChain 3.0, is presented as a solution.
5. The novel DLT is designed specifically for ITS as CCPS.  
In Table 1, a comparison is given between our previous versions of FlexiChain and the current work. In [30], the work is representing FlexiChain 1.0, which is combining our work with the novel MultiChain Proof of Rapid Authentication [17]. A novel block structure has been proposed with an enrollment process that creates the Accessible Secure Identification List proposed in [20]. In the next version, FlexiChain 2.0, an upgrade was presented on how to generate the file using a combined ledger of NodeChain, and how the file is created and updated, making NodeChain as Layer0 and all other blockchains as Layer1. Moving to the next version, since the proposed work is about a designed distributed ledger for Cyber-Physical Systems, FlexiChain 2.0 has been upgraded and modified to fit ITS applications as a complex CPS using a distributed offline vault (NodeChain), which is a manufacturer's predefined trust and which provides a public permissioned ledger. The rest of the table is covering other differences and similarities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features/Version** & **FlexiChain 1.0 [30]** & **FlexiChain 2.0 [31]** & **FlexiChain 3.0 (Current)** \\ \hline \multirow{3}{*}{Linked Lists} & Genesis Blockchain & Layer0 Ledger & Independent NodeChain \\  & (independent ledger) & Independent NodeChain & Layer0 Ledger \\  & DAG-Based Blocks & DAG-Based Blockchains & DAG-Based BlockX \\ \hline \multirow{3}{*}{Registration} & Pre-Installed or & Trusted Security Hardware & Entry Key Pre-Enrollment \\  & Equipped Manufacturer & NodeChain (Chain & Pre-Launched NodeChain \\  & Trusted Modules & of Narration) & Manufacturer Trust \\ \hline \multirow{3}{*}{Authentication} & ASID Proof of & NodeChain Proof of & NodeChain Proof of \\  & Rapid Authentication & Rapid Authentication & Rapid Authentication \\ \hline Type of Validation & Authentication (Minerless) & Authentication (Minerless) & Authentication (Full Nodes) \\ \hline Validators & All Virtual Nodes & All Qualified Nodes & Full Nodes \\ \hline \multirow{3}{*}{Security} & Digital Signature & Digital Signature, & Digital Signature \\  & Hash Function & Hash Function & Hash Function \\ \cline{1-1}  & \multirow{2}{*}{Secure File (ASID) and TPM} & Tokenized UID and & Tokenized UID from \\ \cline{1-1}  & & Hardware Security & Independent \\ \cline{1-1}  & & Offline NodeChain \\ \hline Design Purpose & IoT/CPS Applications & IoT/CPS Applications & Intelligent Transportation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative Perspective between FlexiChain Versions.
# A Deep Neural Network Model for Speaker Identification
## 2 Materials and Methods
### Model Overview
In a standard recurrent network layer, there is a tendency for the magnitudes of the summed inputs of the recurrent units to either grow or shrink at every time-step [49]. This could lead to exploding or vanishing gradients. In a layer normalized RNN, the  
Figure 4: The structure of the deep GRU networks. The entire architecture diagram mainly includes voice signal preprocessing, two-dimensional convolutional layer, stacked GRU layers, a fully-connected layer, and softmax layer. normalization terms normalize the inputs to the layer, which results in much more stable hidden-to-hidden dynamics. In our network model, normalization is also used in stacked GRU layers and it can speed up network training. Additionally, another difference in our experiment is that the Leaky Rectified Linear Unit (Leaky ReLU) [50,51] activation function was performed right after each layer before the output layer, many previous papers used Relu and tanh activation functions. At the same time, we chose to use categorical cross-entropy loss function in our study, and it can be computed by using the following formula:  
\[Loss=-\frac{1}{N}\sum_{i=1}^{N}\big{[}y_{i}\ln a_{i}+(1-y_{i})\ln(1-a_{i}) \big{]}. \tag{16}\]  
Here, \(N\) is the number of output size in the embedding layer, \(y_{i}\) is the expected output vector of our model for \(i\)-th class, and \(a_{i}\) is the actual output of the neuron. To obtain the output in a distributional manner across the entire subject, outputs \((y_{1},y_{2},...,y_{t})\) are computed by applying the non-linear softmax function to the output of the penultimate layer. The details of the proposed deep GRU network architecture are described in Figure 5.  
In Figure 4, the input of the feature extraction network is formed by stacking information in each training frame with that in adjacent frames. 2-D CNN can extract spectrum distribution and differential spectrum distribution in a different frequency scale. GRU layers extract features of temporal spectrum distribution changes. Then, pooling and length normalization layers generate speaker embeddings. The high-dimensional statistics are converted into a single low-dimensional feature vector that encodes speaker identity. Before we found the above proposed deep GRU network model, we also tried many different network architectures, including 2-D CNNs, a combination of CNN and LSTM cell unit network model, and a combination of the CNN and original RNN network model. Figure 5: The architecture of the deep GRU network model, showing the input and some specific parameters of other layers, the framework is also the basis of code implementation. The experimental results of these models are unsatisfactory, and they will also be shown later for comparison.
## 2 Materials and Methods
### Design, Development, and Integration of SRGs
#### 2.2.2 SRG Prototype for Post-Stroke Participants  
A revised prototype was developed to monitor the kinematics of both hands in post-stroke individuals while also providing targeted pneumatic assistance. These SRGs, integrate ten flex sensors positioned above and along the five fingers of each hand and four thin-film piezoelectric sensors mounted on each thumb and middle finger. The sensors are powered by a 5 V supply, with their output connected to the microcontroller's analog inputs and stabilized by a 10 kOhm pulldown resistor between the ground and the analog input. Additionally, two sEMG Myoware Muscle Sensors are incorporated in each SRG, with each sensor's positive terminal connected to 5 V, the negative terminal to ground, and the signal terminal linked to the corresponding microcontroller analog input. Furthermore, these SRG prototypes include two ADXL345 accelerometers interfaced with an Arduino Mega via the I\({}^{2}\)C bus (pins 20 and 21). To ensure proper communication, both sensors are wired in parallel on the SDA and SCL lines while sharing the same power and ground connections. Unique I\({}^{2}\)C addresses were assigned by connecting the AD0 pin of the first sensor to the ground (yielding the default address 0x53) and the AD0 pin of the second sensor to VCC (yielding the address 0x1D).  
Complementing the MSS suite, the system also features the SRPAS subsystem, which employs a PneuNet flexible exoskeleton to apply controlled forces to the middle, index, and thumb fingers of each hand. The pneumatic actuation is provided by a single 2.5 lpm air pump that supplies the necessary pressure and a solenoid valve that regulates the airflow for inflation and deflation of the exoskeletons. The valve is controlled by a simple TIP120 transistor circuit--with an accompanying flyback diode for inductive load protection--and its operation is triggered either via the CoMSS or manually via an integrated push button. Power for the actuation components is derived from a regulated 6 V supply, ensuring stable and reliable operation. All sensor and actuation components are integrated into antistatic gloves, as illustrated in Figure 3.
# Biomass Fly Ash Self-Hardened Adsorbent Monoliths for Methylene Blue Removal from Aqueous Solutions
## 3 Results and Discussion
### MB Adsorption Tests
#### 3.3.4 Adsorption Kinetics Studies  
The adsorption kinetic parameters using the pseudo-first-order and pseudo-second-order models were determined to further define the adsorption mechanism of MB for different initial dye concentrations. The results are shown in Table 5. Based on the obtained R\({}^{2}\) and \(q_{e}\) values, the pseudo-first-order model exhibits a better fitting with the experimental data.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Samples**} \\ \cline{2-10}  & **0C:0A** & **0C:1A** & **0C:5A** & **0C:9A** & **5C:0A** & **5C:1A** & **5C:5A** & **5C:9A** \\ \hline \(K_{F}\) mg\({}^{1-1/n}\) g\({}^{-1}\)L\({}^{1/n}\) & 0.072 & 0.103 & 0.161 & 0.261 & 0.068 & 0.080 & 0.150 & 0.239 \\ \(n\) & 1.70 & 1.71 & 1.86 & 2.14 & 1.87 & 1.91 & 2.42 & 2.16 \\ R\({}^{2}\) & 0.977 & 0.930 & 0.935 & 0.981 & 0.982 & 0.925 & 0.841 & 0.887 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Freundlich adsorption parameters calculated from plotting ln \(q_{e}\) vs. ln C\({}_{e}\)[14].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Samples**} \\ \cline{2-10}  & **0C:0A** & **0C:1A** & **0C:5A** & **0C:9A** & **5C:0A** & **5C:1A** & **5C:5A** & **5C:9A** \\ \hline \(q_{max}\) (mg/g) & 0.302 & 0.360 & 0.426 & 0.623 & 0.219 & 0.312 & 0.371 & 0.657 \\ \(K_{L}\) (L/mg) & 0.351 & 0.475 & 0.956 & 1.305 & 0.540 & 0.408 & 0.965 & 0.849 \\ R\({}^{2}\) & 0.991 & 0.971 & 0.952 & 0.989 & 0.979 & 0.999 & 0.989 & 0.966 \\ \(R_{L}\) & & & & & & & & \\
1 ppm & 0.735 & 0.672 & 0.504 & 0.427 & 0.642 & 0.704 & 0.502 & 0.533 \\
2.5 ppm & 0.523 & 0.448 & 0.287 & 0.228 & 0.416 & 0.485 & 0.285 & 0.312 \\
5 ppm & 0.365 & 0.299 & 0.174 & 0.134 & 0.272 & 0.331 & 0.173 & 0.192 \\
10 ppm & 0.235 & 0.185 & 0.101 & 0.076 & 0.167 & 0.209 & 0.101 & 0.113 \\
15 ppm & 0.165 & 0.127 & 0.068 & 0.050 & 0.114 & 0.145 & 0.067 & 0.075 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Langmuir adsorption parameters calculated from plotting \(1/q_{e}\) vs. \(1/C_{e}\) for Langmuir linearisation type 2[14].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{\(C_{0}\) **(ppm)**} & \multirow{2}{*}{\(q_{e}\) _exp_ (mg/g)} & \multicolumn{3}{c}{**Pseudo-First-Order**} & \multicolumn{3}{c}{**Pseudo-Second-Order**} \\ \cline{4-9}  & & & \(q_{e}\) **(mg/g)** & **K\({}_{1}\) (l/min)** & **R\({}_{1}\)\({}^{2}\)** & **\(q_{e}\) (mg/g)** & **K\({}_{2}\) (g/mg min)** & **R\({}_{2}\)\({}^{2}\)** \\ \hline
0C:0A & 1 & 0.04 & 0.034 & 0.00138 & 0.996 & 0.051 & 0.05027 & 0.943 \\  & 2.5 & 0.08 & 0.053 & 0.00138 & 0.970 & 0.094 & 0.04301 & 0.964 \\  & 5 & 0.16 & 0.115 & 0.00161 & 0.968
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 7 Experimental Results
### Number of Placed SFC Requests  
The rationale behind such an experiment is to verify whether the proposed smart VNF placement algorithm increases the number of placed SFC requests while overloading less resources in comparison with random and greedy approaches.  
We consider an SFC request as placed if the placement algorithm can create a feasible placement plan to allocate the VNFs of the related SFC.  
Figure 4: Consumed energy in edge nodes.  
Figure 5: Allocated CPU Resources.  
Figure 6: Allocated memory resources.  
In Figure 7, as the number of SFC requests increases in the edge environment, the percentage of requests that are placed decreases since the resources become depleted, given that the available resources of the environment are constant.  
Our approach always performs better than the other approaches, even though it is also impacted by the resource shortage. For instance, in a highly demanding scenario with 1000 SFC requests in the edge environment, our placement strategy outperforms greedy and random by approximately 50%.  
We also observe that both greedy and random support up to 100 SFC requests in the environment with 100% of placed requests, while our approach endures up to 500 requests with all requests being placed.  
With such results, we observe that our Smart Placement algorithm is able to efficiently find placement plans that can meet SFCs, even in highly demanding situations. We will see in the following experiments that this is not the only benefit that our approach brings.
## 3 Materials and Methods
### LULC Prediction of Three Different Scenarios  
LULC prediction between 2020 and 2029 for the three scenarios was conducted using the CLUE-S model. In practice, the selected driving factors of LULC change (soil drainage, distance to stream, distance to water body, distance to village, slope, distance to road, distance to fault line, annual rainfall, elevation, income per capita, and population density at subdistrict level), as suggested by [37], were first examined in terms of their multicollinearity, using the variance inflation factor (VIF) to prevent the correlation of driving factors. As a general rule of thumb, the VIF value should not exceed 10 [38,39]. Then, binary logistic regression analysis was performed to identify the significant driving factors for specific LULC type allocations (Equation (2)):  
\[Log\bigg{(}\frac{P_{i}}{1-P_{i}}\bigg{)}=\beta_{0}+\beta_{1}X_{1,i}+\beta_{2} X_{2,i}\ldots\ldots+\beta_{n}X_{n,i}, \tag{2}\]  
where \(Pi\) is the probability of a grid cell for the considered land-use type at location \(i\), and the \(X\) values are the location factors. The coefficients (\(\beta\)) are estimated through logistic regression, using the actual land-use pattern as the dependent variable [40].  
After that, two sets of local parameters for LULC prediction using the CLUE-S model--namely, the conversion matrix and elasticity of LULC change--were prepared. These parameters were considered and set up based on the transition probability matrix of LULC data between 2009 and 2019. The conversion matrix, which indicates the LULC change opportunity among LULC types, is assigned a value of 1 when it is allowed, or 0 when it is not allowed. Meanwhile, the elasticity values imply the probability of land-use change, which ranges from 0 (easily converted) to 1 (irreversible change), and are set up according to the transition probability matrix in the past period [26,41]. In this study, elasticity values were assigned according to the transition probability matrix of LULC change between 2019 and 2029 by the Markov Chain model, as suggested by [PERSON] and [PERSON] [42].  
Finally, the conversion matrix, the elasticity of LULC change, and land requirements under different scenarios were combined to predict LULC change data between 2020 and 2029, according to the driving factors of LULC change for specific LULC type location preference.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 6 Conclusions and Future Work  
This paper proposes a new patient remote healthcare monitoring system for addressing the existing issues. The proposed B-DEAH model is designed for three types of communications such as intra-WBAN, inter-WBAN and beyond-WBAN. For decentralized communication and security, blockchain is deployed in an IoT-based WBAN environment. A three factored security algorithm is proposed for patients registration, security credentials are forwarded to _KMS_. For data transmission and energy consumption reduction, the clustering process is initiated. Spotted hyena optimizer is used for clustering that computes the best CH using the fitness of prey and updates position accordingly. Then intracluster routing is implemented using the MOORA algorithm. It improves the performance of packet success rate and avoids packet losses and delays. In TBA, three operations are used such as classification and queue management, security provisioning and channel selection by TS-DNN in DRL, four-Q-curve for PBA authentication and packets encryption using the PRESENT algorithm, and multi-objective based channel selection algorithm. Then packets are forwarded to the emergency and periodical sinks in accordance with the priority. The simulation is conducted for various performance metrics as _QoS_, energy efficiency and security. Based on the results noted, the proposed B-DEAH provides better performance than previous works. In the future, we intended to focus on the following aspects,  
* Investigation of mobility since mobility is a crucial parameter of BAN. The human body parts are in motion constantly. Here, we planned to use handover mechanisms for mobility management.
* Duty cycling MAC scheduling is studied for managing the energy level of each body sensor.
* Furthermore, other emerging medical diagnosis applications are concentrated for user physiological parameter analysis, such as diabetes, asthma, Parkinson's disease, or COVID-19.  
Conceptualization, H.S.A.; methodology, H.S.A. and J.N.; software, H.S.A.; validation, H.S.A.; formal analysis, H.S.A.; investigation, H.S.A. and J.N.; resources, H.S.A.; data curation, H.S.A. and J.N.; writing--original draft preparation, H.S.A.; and writing--review and editing, H.S.A. and J.N. All authors have read and agreed to the published version of the manuscript.  
Figure 20: Reliability vs. packets per second  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.