esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
###### Abstract  
The planet is the most water-rich place because the oceans cover more than 75% of its land area. Because of the unique activities that occur in the depths, we know very little about oceans. Underwater wireless sensors are tools that can continuously transmit data to one of the source sensors while monitoring and recording their surroundings' physical and environmental parameters. An Underwater Wireless Sensor Network (UWSN) is the name given to the network created by collecting these underwater wireless sensors. This particular technology has a random path loss model due to the time-varying nature of channel parameters. Data transmission between underwater wireless sensor nodes requires a careful selection of routing protocols. By changing the number of nodes in the model and the maximum speed of each node, performance parameters, such as average transmission delay, average jitter, percentage of utilization, and power used in transmit and receive modes, are explored. This paper focuses on UWSN performance analysis, comparing various routing protocols. A network path using the source-tree adaptive routing-least overhead routing approach (STAR-LORA) Protocol exhibits 85.3% lower jitter than conventional routing protocols. Interestingly, the fisheye routing protocol achieves a 91.4% higher utilization percentage than its counterparts. The results obtained using the QualNet 7.1 simulator suggest the suitability of routing protocols in UWSN.  
fisheye; STAR-LORA; UWSN; average jitter; utilization rates; energy efficiency +
Footnote †: journal: Sensor and Actuator Networks
of significant amounts of resources in order to maintain the accuracy and consistency of routing information. In such designs, the finding overheads are significant because one needs to discover all of the nodes. Therefore, there will be no delay in transmission if a route has already been obtained by a node before the traffic arrives at that node. Nodes will periodically share their information with one another in proactive routing protocols as a reaction to topological changes that have been caused. The primary benefit is that it reduces the amount of time that is wasted waiting on a needed route when there is initiating traffic to a required destination and it determines in a short amount of time whether or not a destination is attainable. It is possible that a substantial number of network resources will be used up during the procedure.
# Energy-Efficient and Real-Time Wearable for Wellbeing-Monitoring IoT System Based on SoC-FPGA
## 2 Background on Biometric Signals Processing
### Eeg  
EEG is a non-invasive technique for probing the electric activity of human brain neurons by attaching electrodes on the scalp that detect voltage fluctuations upon ion flow [1]. Five major frequency bands can be identified in brain waves, depending on the neural activity--delta (1-3 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (14-30 Hz) and gamma (31-50 Hz) [1] --whose frequency pattern changes may denote a response to an external stimulus, or some brain disorder. Activities such as sleeping, exercising or meditation can also be detected in brain waves.  
The positioning of electrodes is crucial for accurate signal acquisition, given the scope of the application. The standardization is set by the International 10/20 System [2], represented in Figure 2.  
A common application of EEG is emotion classification, which maps and recognizes patterns on features of EEG signals from different known emotions. [PERSON] [3] defined arousal as the metric for awareness or unawareness during an activity, and valence as the metric for pleasure or displeasure. Both quantities are described as a 2D plane, where arousal is in the horizontal axis and valence in the vertical axis. The resulting emotion in each quartile is a combination of the two.  
Processing the EEG signal comprises several steps, namely, noise reduction, signal enhancement, feature extraction and classification. During the acquisition via the electrode, the recorded signal is attenuated by skin tissues and bones but also subject to noise caused by muscular activities, eye movements, eye blinks and cardiac signals [4]. In fact, the normal EEG signal amplitude ranges microvolts, although a single neuron promotes voltage changes of millivolts. Therefore, in order to remove this noise, the EEG signal is pre-processed and its quality is improved [5]. After signal pre-processing, features are extracted, that is, patterns are identified in order to reduce dimensional space without losing essential information. Classification is performed by, for example, support vector  
Figure 1: Proposed system architecture.  
machine (SVM), linear discriminant analysis (LDA), neural network (NN) or k-nearest neighbor (KNN) [5].
## 3 Preliminaries
### One-Class Support Vector Machine (OC-SVM)  
An OC-SVM is an ML approach that is commonly used for classification tasks where the dataset contains one class of data only. In one-class classification, an ML algorithm is trained to identify instances belonging to a specific category [44], using only data from that class as training data. As illustrated in Figure 3, the goal of one-class classification is to create a model that accurately identifies the target class's patterns so it can detect and classify instances that deviate significantly from the expected behavior [45; 46], where the arrow in the figure indicates the distance between the origin and the hyperplane. This technique is especially useful when obtaining labeled outliers is costly or challenging.  
The OC-SVM is a model used in one-class classification to define a decision boundary for a specific target class in a feature space. The training process involves creating a hyperplane or hypersphere in a high-dimensional feature space, strategically positioned to encompass a large number of normal instances while maintaining a maximum distance from the feature space's center. This technique effectively captures the support of the normal class, containing a significant portion of normal instances within the boundary [47]. In the inference or testing phase, new instances are classified as anomalous or normal depending on their distance to the established boundary. Those falling outside the boundary are considered outliers, while those within it are categorized as normal [48; 49].  
In summary, an OC-SVM is highly useful when dealing with scenarios that only include samples from the normal class, making it well suited for problems that lack a sufficient number of or unavailable labeled outliers. The approach operates under the premise that a certain class exists within a confined range of features, which allows the model to detect outliers that fall outside of this region. The OC-SVM has practical applications in  
Figure 3: One-class support vector machine (OC-SVM).  
a variety of industries, including but not limited to, identifying anomalies in industrial systems, detecting intrusions in network security, and identifying fraud. By centering solely on understanding the traits of the intended category, one-class classification presents a dependable means of detecting other class instances in practical datasets.
## 4 Discussion  
Mostly all the radar applications, like weather forecasting, military, navigation, remote sensing and so on, operate within the X-band frequency range. Metamaterials allow for the extreme downsizing of existing devices. From the given comparison table, we can deduce that the proposed structure is well suited for X-band applications with flexible and light-thickness polymide dielectric substrate material, rather than that of FR4, Rogers and etc. From [6], we can observe that the unit cell size is taken as 12 \(\times\) 12 mm\({}^{2}\), and produces only a single resonant frequency within the X-frequency band. Similarly, from [18], we can observe that the absorber is working at a single frequency within the X-band frequency. We may also deduce from the entire comparison table that no dual frequencies were present in the X-band, which is a significant benefit over all other methods of producing dual bands. In addition, in contrast to previous studies, the proposed design demonstrated left-handed metamaterial characteristics at two resonant frequencies within the X-band. Nowadays, other flexible materials are also available, such as paper, jeans and so on, with less thickness and less dielectric constant values, rather than this polymide substrate material to design the FSS. Furthermore, we are interested in designing metamaterial structures with less thickness.
# Impact of Ar/CF4 Mixed Gas Flow Rate on Silicon Etching Using Surface Discharge Plasma
## 5 Conclusions  
Our investigation delved into the impact of gas flow rate on silicon etching using surface discharge plasma. Our findings unequivocally showcased a direct correlation between escalated Ar and CF4 mixed gas flow rates and heightened etching rates of Si, spanning both width and depth. Emission spectroscopy analysis unveiled a surge in plasma emission intensities attributed to the F and CFn species, signaling an augmentation in the generation of active species pivotal for the etching process. Furthermore, amplifying the Ar/CF4 gas flow rate not only bolstered the production of these active species but also facilitated their diffusion across a broader expanse. This enhancement significantly contributed to the progression of etching in the width direction. Additionally, a higher gas flow rate deterred the deposition of etching byproducts within the plasma region, thereby smoothing the path for silicon etching. Future avenues encompass the refinement of power supplies to enable swiffer and finer processing and the optimization of surface discharge plasma generation conditions.  
Figure 10: Relationship between Ar/CF4 mixed gas flow rate and discharge power.  
**Author Contributions:** Conceptualization, T.H.; methodology, T.H. and K.N.; validation, T.H. and K.N.; investigation, T.H. and M.Y.; data curation, T.H., K.N. and M.Y.; writing--original draft preparation, T.H.; writing--review and editing, T.H., K.N. and M.Y.; visualization, T.H. and K.N.; supervision, T.H.; project administration, T.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Macroscopic Traffic-Flow Modelling Based on Gap-Filling Behavior of Heterogeneous Traffic
## 5 Simulation Results and Discussion  
The proposed model and the PW model are numerically simulated in MATLAB to evaluate and compare their performance. Both models are simulated for (i) straight road with ingress traffic, (ii) straight road with no ingress traffic, and (iii) circular road. The total length of the road for both models is selected as 100 m, and the simulation is carried out for a total time of 30 s for a straight road with ingress traffic and circular road, and 2 s for a straight road with no ingress traffic. It can be noted that, since the traffic conditions become smooth and traffic becomes uniform (or the effect of the inactive bottleneck is diminished after sometime), there is no need for additional simulation time. The maximum velocity for both models is chosen as 25 m/s (90 km/h), and the relaxation time is selected as 0.5 s, which means that the equilibrium velocity is achieved in a short time. The maximum density is chosen as 1.0 because in the simulations the density used is the normalized density, which is equal to the ratio of the actual density to the maximum density. The time step and distance step are selected so that they satisfy the Courant-Friedrichs-Levy (CFL) [36] stability condition, according to which \((dt/dx)*vMax\leq 1\). Following this condition, the maximum time step that could be chosen should be less than or equal to 0.04 s.  
The initial density conditions at \(t=0\) are such that the density is 0.1 till the distance of 50m, while that from 50m onwards is 0.6 (see Equation (33)), which means that there is a sudden increase in density.  
\[\rho=\begin{cases}0.1,&x\leq 50\\ 0.6,&x>50\end{cases} \tag{33}\]  
This sudden increase in density can be physically thought of as a bottleneck. The initial velocity conditions are according to the Greenshields velocity distribution [37]. The input parameters for the simulation of both models are summarized in Table 3.
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 3 Quantum Immune Security Solutions
### Implementation of Secrecy Coding Scheme
In other words, we use a polar code (361, 479, 184) where a single polar-encoded message consists of 361 message bits, 479 random bits, and 184 frozen (zero) bits. Thus, the code rate is 361/1024 = 0.352539 or approximately 77.0 per cent of the secrecy capacity. We use  
Figure 5: Bit-error-rate performance of (1248, 1040) low-density parity check (LDPC) code. a conventional message-passing decoder and a successive-cancellation decoder for LDPC and polar decoding, respectively. The maximum number of iterations in the LDPC decoder is set to 50. The transmitter ([PERSON]) transmits artificially generated noise and thus creates the necessary radio advantage of [PERSON] over [PERSON]. Since this noise is generated by [PERSON]'s transmitter, [PERSON] can design it in such a way that only [PERSON]'s channel is degraded. Thus, by selectively degrading [PERSON]'s channel, secret communication between [PERSON] and [PERSON] can be guaranteed. The \"selective\" degradation of [PERSON]'s channel is obtained with beamforming. Namely, [PERSON] directs the main antenna beam towards [PERSON]'s location and generates noise in every other direction. [PERSON] also injects reference signals into the transmitted signals, so [PERSON] can estimate the channel impulse response. We assume that [PERSON] uses the least-squares (LS) method to estimate the impulse response of Alice-Bob channel [47].
# Experimental Research on the Properties of Rock-Filled Concrete
## 5 Conclusions  
By combining lab tests of core specimens with in-situ tests in boreholes, the quality of RFC in the Hantang reservoir dam is evaluated and its features are listed as follows:  
1. Through the appearance of RFC core specimens, four kinds of defects exist within RFC, which are large sized voids existing in ITZ between SCC and rock block; bad cohesion in ITZ; joints within rock block; voids within SCC. The existence of those defects are essential factors that result in an obvious non-homogeneity of RFC and reduce its quality.
2. The quality of RFC dams shows an obvious non-homogeneity, whose porosity changes from 0.34% to 22.37% with great variation. The permeability mainly ranges from 2.41 Lu to 10.41 Lu, with local values exceeding 25.52 Lu. The ultrasonic velocity mainly fluctuates from 2200 m/s to 3800 m/s with a standard deviation of 650.5 m/s. The compressive strength mainly varies from 15.8 Mpa to 32 Mpa, and locally can be more than 50 Mpa. Thus the data shows a relatively great dispersion due to uneven distribution of some defects in RFC. Although obvious non-homogeneity existed, the quality of RFC can basically meet the requirements of dam design, exceeding the design strength C15. By careful analysis, the non-homogeneity of RFC is mainly influenced by uneven distribution of some defects, which are produced during pouring. Therefore, the quality of massive RFC is mainly controlled by the construction technology and only slightly influenced by the SCC and rock blocks.  
Although the quality of massive RFC can basically meet the design requirements in the Hantang dam project, for high concrete dams, RFC poured using traditional construction technology may not meet the requirements. Consequently, how to improve the homogeneity of RFC with construction technology and how to reduce defects within RFC dams will become a key subject in this field. In our next research, some valid measures will be proposed based on a large number of experiments.  
Conceptualization, G.Z. and H.W.; Methodology, H.W.; Validation, M.W., J.X. and H.W.; Formal Analysis, W.L.; Investigation, G.Z.; Resources, G.Z.; Data Curation, M.W.; Writing-Original Draft Preparation, H.W.; Writing-Review & Editing, F.S.; Visualization, W.L.; Funding Acquisition, M.W. This research and the APC were funded by Yunnan provincial natural science foundation of China, grant number KKSY20140426.  
The authors gratefully thank Yunnan provincial natural science foundation of China (No. KKSY20140426) for its financial support. All the reviewers' efforts are greatly appreciated.  
The authors declare no conflict of interest.
# Causally Remove Negative Confound Effects of Size Metric for Software Defect Prediction
## 3 Method  
### Confounding Effects of Size Metric  
The concept of confounding is popular in the field of health sciences [30]. It refers to the situation where the relationship between variables is erroneously obscured or emphasized by a third variable [31]. The third variable is usually called a confounder.  
In the field of software defect prediction, size metric is a significant confounder. Size metric may lead to overestimating or underestimating the predictive ability of software metrics to defect-proneness, depending on the direction and magnitude of its confounding effects [32].  
We use a causal graph to illustrate a confounding effect of size metric, as shown in Figure 1. To simplify the description, we use variable Z to represent the size metric, X to represent one other software metric, and Y to represent defect-proneness. Software metrics and defect-proneness are related, and software metrics are used to predict defect-proneness; therefore, there are unidirectional edges XY and ZY. The unidirectional edge represents the causal belief that means that X and Z are the antecedents to Y. The size metric and the software metric have a not weak correlation, so there is bidirectional edge ZX. The bidirectional edge represents a general association. Therefore, there are two paths connecting X and Y, which are XY and XZY. The direct path XY represents the true relationship between X and Y. The indirect path XZY contains the confounding effect of Z. When exploring the relationship between X and Y, both paths are considered. As such,the obtained conclusion includes the confounding effect. To explore the true relationship between X and Y, we must control Z to block path XZY. After that, only path XY is connected. Then, the relationship between X and Y will not be affected by Z.
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
## 2 Methods  
### Interval Data  
Interval data refers to the idea that the feature of a sample point is not a definite value but is a collection of all values contained in a range on the real number field, which can be expressed as  
\[x=\left\{t|\underline{x}\leq x\leq\overline{x},\underline{x}\in R,\overline{x} \in R,\underline{x}\leq\overline{x}\right\} \tag{1}\]  
where \(\underline{x}\) is the lower bound of interval data, and \(\overline{x}\) is the upper bound of interval data. Interval data express a range between the upper and lower bounds. Compared with discrete data, interval data can summarize the internal structural characteristics of the data from a global perspective, and they are more conducive to explaining the rules implicit in the data. Interval data can also be represented as an ordered array of upper and lower bounds: \(x=\left[\underline{x},\overline{x}\right]\).  
For an n-dimensional vector \(X=\left(x_{1},x_{2},\ldots,x_{n}\right)^{T}\), if each component in the vector is interval data, that is, \(x_{i}=\left[x_{i},\overline{x_{i}}\right]\), then \(X\) is called an n-dimensional interval vector. If each datum in the \(n\times p\) dimensional data matrix \(X_{n\times p}=\left(x_{ij}\right)_{n\times p}\) is an interval datum, it is called interval matrix:  
\[X_{n\times p}=\left(x_{ij}\right)_{n\times p}=\left(\begin{array}{c}e_{1}^{T }\\ e_{2}^{T}\\ \vdots\\ e_{n}^{T}\end{array}\right)=\left(X_{1},X_{2},\ldots,X_{p}\right) \tag{2}\]  
Each row in the matrix is an interval sample, and the number of columns \(p\) represents the sample dimension. In fault diagnosis, the interval matrix can be used to describe the data, and the observation value of each sample dimension is represented by a data interval.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 3 Methodology
### Euclidean Search
#### 3.4.2 Range Search in Euclidean Distance  
Unlike the kNN query method, the range query method searched for POIs within a specific distance range determined by the user. This method retrieves all POIs located within the specified range, and the results are also sorted based on their distance from the query point. Hence, the number of results is not limited. This study employed distance thresholds of 1 to 150 km in 5 km increments for the range search method.  
For the range search, measuring distances between query points and POIs is the same as the kNN search method. However, the key distinction lies in the result limitation, which is based on distance rather than a set number of rows. Consequently, the SQL statement conditions are adjusted to limit the distance rather than restricting the number of rows. The SQL statement in Listing 3 is provided for retrieving results within a 5 km range.  
```
1 SELECT*,
2 FROM(SELECT*,
3 ST_SETSRID(ST_MAXEPOINT(olon,olat),4326)::geography<->geom::geography
4 ist_eu
5 WRIEREdist_eu<5
6 ORDERBYdist_eu;
```  
**Listing 3** SQL statement for retrieving range query results within a 5 km for Euclidean distance.
## 5 Experiments
### Analysis of Training Convergence and Heuristic Reward Function  
To validate the feasibility and effectiveness of our SAG-MCS environment design and the heuristic reward function, we first present the learning curves of episodic reward and the global metrics over time. During the training phase, we evaluate the model for 20 episodes after every 100 training episodes, and calculate the average global metrics and accumulated reward, as illustrated in Figure 5.  
In Figure 5a, we observe the average episodic reward of ms-SDRGN improves very quickly at the beginning, and gradually converges at around 20,000 episodes. Figure 5b presents the changes of four global metrics during the training progress of ms-SDRGN. The final energy index gradually drops and stabilizes to 0.9 at 20,000 episodes, indicating that UAVs have learned to operate at an optimal cruising speed. In addition, the final coverage and fairness index quickly grow and converge at around 10,000 episodes. Correspondingly, the overall CFE score has a similar growth trend and reaches convergence rapidly. Therefore, it can be proved that ms-SDRGN has learned the policy to fulfill the overall objective of maximizing the CFE score. After convergence, the UAV swarm can continuously collect PoIs maximumly using energy-efficient flying speed. The training results have suggested the effectiveness of the heuristic reward function.  
Through visualization, we can observe that UAVs have learned to appropriately assign tasks at different remaining batteries. When its battery drops to around 25-40%, the UAV will proceed to the closest charging stations for battery exchange. In each simulation episode with 100 timesteps, the whole swarm rarely runs out of power, as such a charging process will happen two times for each UAV.