esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background and Related Work
### Diagnostics and Decision Support  
DL is also augmenting ED diagnostics, with AI models assisting in the real-time interpretation of tests and imaging [31]. For example, DL-based image analysis can detect critical findings on ED radiographs (e.g., pneumothorax, fractures) with high sensitivity and AUROC > 0.8, comparable to expert performance [34]. Similarly, DL models using vital signs and lab results can early-detect life-threatening conditions like sepsis or even cardiac arrest risk (e.g., [PERSON] et al. [35] or [PERSON] et al. [36]). By extending physicians' diagnostic capabilities, these tools accelerate emergency decision-making.
## 5 Discussion
### Land Requirement Estimation under Three Different Scenarios  
Land requirement is essential information for LULC prediction using the CLUE-S model. In this study, three different scenarios including (1) Scenario I (Trend of LULC evolution), (2) Scenario II (Maximization of ecosystem service values), and Scenario III (Economic crop zonation) are estimated based on their characteristics. The land requirement of Scenario I is estimated based on the annual change rate of LULC between 2009 and 2019 from the transition area matrix using the Markov Chain model. The land requirement of Scenario I is dictated by the accuracy of LULC data in 2009 and 2019. Meanwhile, the land requirement of Scenario II is estimated based on LULC allocation for maximizing ESV using Linear Programming with the Simplex method. The land requirement of Scenario II depends on the efficiency of linear programming for determining reclaimed and other areas changing into the wetland. At the same time, the land requirement of Scenario III is estimated based on areas of suitability classes for economic crops (paddy field, field crop, para rubber, and perennial trees and orchards) and the Markov Chain model. The land requirement of Scenario III is delimited by the economic crop zonation and the accuracy of LULC data in 2019. Consequently, the definition of each Scenario should first be well defined and local government agencies and stakeholders should be consulted before land requirement estimation.
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 4 Experimental Analysis
### Analysis  
In order to detect the internal efforts made by the capsule network in the generalization of the WMSCCN algorithm, two similar algorithms were established and compared with WMSCCN. The first half of the two algorithms is completely consistent with WMSCCN, including wide convolutional layers and a multi-scale convolutional layer. Based on the above layers, an algorithm connects three small convolutional layers (the size of the convolution kernel is 3) and two fully connected layers with 50 and 10 nodes, called WMSCNN-I. Based on the wide convolutional layers and the multi-scale convolutional layer, another algorithm accesses three fully connected layers with 500, 50, and 10 nodes which are  
Figure 5: Comparison of diagnostic performance under different degrees of noise.  
called WMSCNN-II. This section takes the influence of the capsule network on the generalization of WMSCNN as the research object. Therefore, the experimental data is consistent with Section 4.2.  
The experimental results are shown in Table 3. It can be seen that the WMSCNN-I and WMSCNN-II algorithms have achieved a relatively good generalization on the basis of wide convolutional layers and the multi-scale convolutional layer, and their average accuracy is close to or exceeds 90%. The structure of the capsule network effectively improves the generalization of the algorithm, as can be clearly seen from Table 3. Especially in the case of A\(\rightarrow\)C and C\(\rightarrow\)B, the addition of the capsule network improves the accuracy of the algorithm by about 13% and 10%, respectively. Compared with the two similar algorithms WMSCNN-I and WMSCNN-II, the proposed WMSCN algorithm improves the average accuracy by 5.58% and 8.48%, respectively. This result fully shows that the addition of the capsule network effectively improves the generalization of the algorithm.
## 4 Conclusions  
In this study, assist gas visualization experiment was performed to find the most important process variable for reducing cutting gas pressure, which is directly related to aerosol generation in underwater laser cutting. Based on gas visualization experiment, actual underwater laser cutting was performed and cut surface was analyzed to evaluate the effect of the cutting process variables. The conclusions are as follows.  
1. From the underwater gas flow visualization experiment, the top kerf width was assumed to be the dominant factor in determining the assist gas flow and the penetration depth. The assist gas penetrated deeper with increasing \(G_{\rm p}=1\) mm to 2 mm.
2. In an actual underwater laser-cutting experiment, cutting performance was determined through cut surface analysis. This is because, when the upper and middle striation lengths were long and the surface roughness was low, it was observed that the assist gas flowed well inside the cut surface. Depending on the nozzle type, the cutting performance was best when supersonic nozzles were used. However, because the top kerf width also increased, it was hard to directly compare the nozzles' effects on the cutting performance.
3. When the focus of the laser Fb was positioned deeper into the depth direction of the specimen, the cutting performance was improved. In the case of Fb = -30 mm, the specimen was able to cut well, even though the gas pressure was reduced from 10 bar to 8 bar and the striation length was not changed significantly. This is because, as analyzed in the assist gas visualization experiment, the deeper the focus position of the laser beam, the wider the top kerf and the better the flow of assist gas inside the cut surface.
4. Therefore, the top kerf width should be large enough to accommodate the easy removal of the molten metal. In underwater laser cutting, to reduce the generation of radioactive aerosols, the assist gas pressure was lowered by widening the kerf width. The kerf width was highly dependent on the laser beam focus position. Based on the results of this study, it is recommended to set the focus of the laser beam deep in the cutting object when dismantling the RVI underwater.  
Conceptualization, R.K.; methodology, R.K. and D.S.; investigation, R.K. and J.C.; resources, S.-J.L., J.C. and J.-D.K.; writing--original draft preparation, R.K.; writing--review and editing, S.-J.L. and D.S.; visualization, R.K.; supervision, D.S.; project administration, D.S.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported funded by the National Research Council of Science and Technology (Project number: NK238A, 2022, KOREA).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to an ongoing study.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Proposed Solution
### Algorithm  
Assuming that the relay node moves on a 2D surface \((X,Y)\) with the current position of the relay node as \((x^{i},y^{i})\), the RSS values at five receivers on the relay node from the server node as \(RSS_{s}(x^{i}\pm\Delta x,y^{i}\pm\Delta y)\) and from the client node as \(RSS_{c}(x^{i}\pm\Delta x,y^{i}\pm\Delta y)\) at instant \(i\), the robust stochastic optimization algorithm is expressed in Algorithm 1. The aim of the algorithm is not to reach the global optimum in one iteration, but to reach the global optimum (within a local region) progressively after several iterations. The proposed algorithm measures only the relative direction of the nodes and uses RSS measurements only in a very basic and, thus, robust way.  
Sensors spacing intervals (\(\Delta x\) and \(\Delta y\)) depend on the physical constraints of the relay node. For instance, in the youBot mobile robot that is used in the experiments, the feasible maximum values are \(\Delta x\leq 0.38\,\mathrm{m}\) and \(\Delta y\leq 0.58\,\mathrm{m}\). \(RSS_{\mathrm{threshold}}\) is an important factor that determines when to start the algorithm, because only when the current RSS is below this threshold, improving the RSS will be fruitful, as can be inferred from Figure 3. On the other hand, the algorithm stops when the difference between the RSS values from the server and client nodes reaches a threshold value \(RSS_{\mathrm{difference}}\) and the gradient norm is less than a threshold value \(g_{\mathrm{threshold}}\).  
It is important to note that we use the RSS gradient information (instead of SNR gradients used in [16]) and propose a suitable robust stochastic optimization algorithm to optimize the relay node position for receiving a relatively better RSS (relative maxima), thereby increasing the communication range and the network capacity.  
Figure 5: Schematic view of the proposed position controller implementing the robust stochastic optimization algorithm.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 2 Localization Approaches
### Distributed Localization Approaches  
In distributed localization approaches, every node in the network shares its information with the adjacent nodes and estimates the distance for its position without involving the central unit [10]. Generally, distributed approach deduces the nodes' locations from the positions of the anchor nodes. The anchor node may have GPS capabilities to find its own locations. Hence, the sensor nodes should be directly located in the comprehensive coordinate system of the anchor nodes. Distributed approaches are more efficient and well suited for complex networks due to the involvement of every node in the process of the location estimate. Distance vector (DV) hop [11], DV distance [12] and other algorithms [13] are examples of distributed localization that utilize connectivity measures to evaluate the locations of non-anchor nodes.  
The DV hop algorithm begins with the dispersal of all the anchor nodes through their positions with respect to the other nodes in the network. While broadcasting a message from one hop to another, every node preserves the information and determines the minimum number of hops needed to locate the anchor node. Whereas, DV distance algorithm focuses on broadcasting the estimated distance among the adjacent nodes rather than a number of hops in the process of the location estimate. The distributed approach correspondingly affords a degree of flexibility to the network and is also immensely suitable for movable nodes since their position changes according to the requirements. The centralized approach is suitable for smaller size networks, whereas distributed approach is chosen for larger networks. The centralized and distributed approach in the IoT-enabled wireless sensor network is depicted in Figure 2.
## 3 Materials and Methods
### Evaluation Metrics  
Four kinds of evaluation metrics are used to quantify the text generation quality from various perspectives. The first metric is bilingual evaluation understudy (BLEU) [43], which is commonly used in machine translation evaluation. It measures the word overlap between generated and reference translations based on \(n\)-gram matching and fragment accuracy evaluation. This study involves BLEU-1, BLEU-2, BLEU-3, and BLEU-4, and higher scores indicate better text matching.  
The second one is the evaluation of translation with explicit ordering (METEOR) [44]. It obtains the final score by exact word matching and semantic similarity at the word level via weighted fusion. A higher value reveals better word matching and semantic similarity.  
The third one is recall-oriented understudy for gisting evaluation (ROUGE) [45]. It calculates the score based on the length of the longest common subsequence. A higher metric score denotes the generated summary more similar in content to the reference summary.  
The fourth one is consensus-based image description evaluation (CIDER) [46]. It considers many factors such as consistency, semantic relevance and \(n\)-gram similarity comprehensively. A higher score shows better consistency and greater semantic similarity between the generated description and the reference description.  
Besides, a supplementary metric (maxTRR) is implemented in this study for evaluating the token diversity. It is defined as the maximum repetition ratio of the tokens, and a lower value indicates higher representation diversity in text generation. Assuming \(s\) tokens are generated in a candidate output (\(seq_{l}\)), the \(j^{th}\) token \(T_{j}\) appears \(t_{T_{j}}\) times, and the maxTRR can be formulated as  
\[maxTRR(seq_{l})=\frac{\max\{(t_{T_{j}})\}_{j=1}^{s}}{\sum_{j=1}^{s}(t_{T_{j}})}, \tag{12}\]  
in which the denominator represents the total number of all \(s\) tokens, and the numerator is the maximum number of times a token appears.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Security Issues
#### 3.1.7 Learned Lessons  
As this study discovered, BIoT-based applications may face a variety of security challenges, including multiple threats, trust, data availability, confidentiality, and integrity. To address these challenges, numerous interesting solutions have been developed, including leveraging a combination of private and public BCs, integrating smart contracts with lightweight encryption approaches, and message identification and authentication. While some of these techniques may provide solutions to current challenges, these solutions may not be enough if quantum computing takes place, for instance. Other solutions, such as the global reputation model [28] to ensure trust, seem to be very hard to achieve due to the heterogeneity of IoT devices and standards.
## 2 Design Aspects of the Tactile Internet
### Network Section  
This section is typically situated in the middle of the master and slave sections, and acts as a medium for two-way communication, thus interfaces the operator to the teleoperator (remote) environment.  
It comprises routers and gateways, which facilitates a medium for two-way communication. The haptic input command signals are sent from the master section. Thus, the input signals pass through routers, switches, gateways, base stations, access points, database server and tactile support engine equipped with AI and reach slave section.  
Thus, the global control loop is formed with the master and slave section, as the information is shared and exchanged between them with the help of command and feedback signals. AI is also facilitated by a tactile support engine and plays an essential job in processing latency-critical applications.  
For real-time haptic communication, the network should have ultra-reliable and ultra-responsive connectivity, which reduces RTTs. To understand the requirement and vision of the TI, the foundation of 5G connectivity is required in the architecture of the TI system, which is composed of eNBs nodes (radio access network) and a core network.  
Even though many kinds of research have been carried out on the 5G framework, there is no consistent concurrence on its architectural design. However, the consensus implies that the 5G framework must be planned in an adaptable environment. The end goal of having one network based on typical physical infrastructure is productively shared among various vertical applications. Here, the concept of NFV is taken into consideration, where the network is sliced according to the particular application's requirements.  
Subsequently, interest in NFV [17] and SDN [18] has risen to shape an envisioned architectural design. NFV facilitates a detachment of network functions from the hardware system, and hence network slicing programming can be done and deployed in any standard distributed computing framework. On the contrary, SDN facilitates an architectural system,wherein the control plane and data plane are decoupled, thus empowering the network's software coding through its controllers.
## 2 Materials and Methods
### Statistical Analysis  
The normality of the ROM data was assessed using a Shapiro-Wilk test, and a normal distribution was confirmed for the variables of interest; consequently, variables were statistically described as mean and standard deviation.  
The comparison of ROM values obtained from the marker-based MoCap and IMU was evaluated by assessing the accuracy and the Root Mean Square Error (RMSE), calculated as per the following equations:  
\[Accuracy=1-\frac{\left| ROM_{IMU}-ROM_{MoCap}\right|}{ROM_{MoCap}}*100 \tag{1}\]  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{(y_{i}-y_{i})^{2}}{n}}=\sqrt{\sum_{i=1}^{n} \frac{e_{i}^{2}}{n}} \tag{2}\]  
In Equation (2), \(\hat{y_{i}}\) represents the ROM values derived from the marker-based MoCap system, \(y_{i}\) refers to the ROM values from the IMU system, \(e_{i}\) denotes the error, and \(n\) is the total number of observations or participants.  
To evaluate the agreement between the two systems, Pearson's correlation coefficient (r) and the concordance correlation coefficient (CCC) were calculated. In fact, Pearson's correlation coefficient quantifies the strength and direction of a linear relationship between two continuous variables. The value of r ranges from \(-1\) to \(1\), where the extreme values indicate a strong (positive or negative) correlation, whereas values around zero suggest the absence of linear relationship. The strength of the correlation was classified as follows: \(|\,\)r\(|\)\(\leq\) 0.4, weak; 0.4 \(<|\,\)r\(|\)\(\leq\) 0.6, moderate; 0.6 \(<|\,\)r\(|\)\(\leq\) 0.8, strong; and \(|\,\)r\(|\)\(>\) 0.8, very strong [33]. The CCC evaluates the agreement between two continuous variables by combining measures of precision and accuracy. This coefficient provides a combined assessment of both accuracy (i.e., proximity to the values provided by the reference method) and precision (i.e., consistency of measurements); it ranges from \(-1\) to \(+1\), where 1 suggests perfect agreement, 0 indicates no agreement, and \(-1\) implies disagreement [34].  
Additionally, Bland-Altman (BA) plots were used to visualize the level of agreement (LoA) between the measurements [35].  
All statistical tests were performed with a significance level set at \(\alpha\) = 0.05.  
The methodological workflow and timeline of the study are detailed in Figure 3.  
Figure 3: Diagram illustrating the methodological workflow and timeline of the study.
# Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
## 4 Experimental Results and Discussion
### Ablation Study
Table 2 further illustrates a consistent trend in model accuracy, showing an increase of approximately 6%, from 53.98% with a token size of 8 to 60.08% when the token size is increased to 32, and then starting to decrease by approximately 3% as the token size increases to 64. We expanded our investigation into the TransIQ model through a series of comprehensive experiments. We adjusted the network's depth, kernel configurations, and the number of output channels to achieve an optimal balance between accuracy and model size. Starting with the previously implemented Transformer-encoder configuration of two heads and four layers, we explored token sizes ranging from 8 to 32, integrating a convolutional layer with eight output channels. The highest accuracy we achieved was 63.72%, with the optimal token size set to 16 samples. This represents an approximately 7% improvement in accuracy compared to the best performance of the TransDirect and an approximately 3% increase over the TransDirect-Overlapping model. The improved performance is due to the inclusion of a convolutional layer, which enhances the model's capability to extract features from each token, distinguishing it from the TransIQ and TransIQ-Overlapping models. In another set of experiments, we opted for a token size of eight to constrain the model's size. We then explored different configurations for the head and layer within our TransIQ, seeking to enhance efficiency by minimizing the parameter count. As the total number of parameters is proportional to the CNN's complexity and the embedding dimension of the Transformer encoder, we evaluated two versions: the first variant, named the large variant of TransIQ, features a convolutional layer with eight output channels, four heads, and eight layers, achieving the highest accuracy on the dataset at 65.80%. The small variant of TransIQ, on the other hand, utilizes a token size of eight and a Transformer-encoder architecture with two heads and six layers. This model has a total of 179 K parameters and achieves an accuracy of 65.39%. We further explored the impact of integrating a complex convolutional layer by employing the complex layer in the TransIQ-Complex model. This model, distinct from typical CNNs, incorporates complex weights and biases in its CNN layer. As the token size expanded from 8 to 16, we observed an accuracy improvement of approximately 1.6%. However, considering the increase in model complexity and the marginal gains in accuracy, the use of a complex layer proved to be minimally beneficial in scenarios where the model size is a critical consideration. Referencing Table 2, an analysis comparing the F1 score and parameter count across different token sizes (8 to 64) for four methods of tokenization reveals detailed insights. A
## 2 Methods of Calculation
### Boundary-Element Method (BEM)  
The Boundary-Element Method uses special (fundamental, point-load, Greens' function) solutions to establish the dynamic stiffness matrix of the soil. One version uses the explicit solution for the elastic full-space which is  
\[u_{z}/F_{z}(r*,\beta)=\left[K_{0}(ir*)-iK_{1}(ir*)/r*+b_{0}K_{0}(i\beta r*)+i \beta K_{1}(i\beta r*)/r*\right]/4\pi Gr\]  
with  
\[r*=r\omega/v_{S}\text{ and }\beta=v_{S}/v_{P}\]  
and the modified Bessel functions of the second kind \(K_{i}\). This holds for the vertical (transversal) displacement component \(u_{z}\) due to a vertical (transversal) force \(F_{z}\).  
Similar functions hold for the longitudinal displacement due to a longitudinal force and for the stresses (three functions), see [21]. The corresponding explicit solutions in three dimensions are given in [18].  
The alternative version uses the solutions (the compliances \(u_{z}/F_{z}\)) for a half-space which are calcuated by a wavenumber integral  
\[u_{z}/F_{z}(\omega,r,z_{1},z_{2})=\frac{1}{2\pi}\int_{-\infty}^{\infty}N_{zz} (\omega,k,z_{1},z_{2})\exp(ikx)dk\]  
for a line load or  
\[u_{z}/F_{z}(\omega,r,z_{1},z_{2})=\frac{1}{2\pi}\int_{0}^{\infty}N_{zz}(\omega,k,z_{1},z_{2})J_{0}(kr)kdk\]  
Figure 1: Finite-element model of a tunnel, the interior soil and a building; (the thin layers at the left and right boundary and the viscous dampers at the bottom are not shown).  
for a point load. These infinite integrals for the vertical components as well as for the horizontal components are evaluated numerically, and a proper truncation of the infinite integral is necessary [41].  
The dynamic stiffness matrix of the boundary (the outer soil region) is obtained by a superposition of a set of point-load or line-load solutions. This boundary matrix is added to the dynamic stiffness matrix of the inner region/the tunnel structure (Figure 2). The stress-free surface of the soil must be discretized if the full-space solution is used. In this contribution, the full-space method [21] is used to calculate the emission effect of a deep tunnel where only the tunnel must be discretised. The half-space method [42] has its advantages if a structure on the surface of the soil is analysed, such as the track structure in Figure 3 which has been calculated for a tunnel, bridge, and surface line in Appendix A.