esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Findings
### Limitations  
The most common limitations observed from our review were associated with size of the empirical dataset used for analysis [27; 33; 36; 39; 41] and the annotation process which comprised few annotators [39; 41]. Some studies have considered the subjective nature of their manual coding and annotation process as a limitation [27; 39] while few other studies suggest that if the occurrence of events are not captured timely, then temporal trends cannot be captured [26; 29; 39]. Apart from these common limitations, more specific limitations were identified by each of the articles relating to the nature of their study (e.g., data screening, bias in tagging of tweets, lack of validation by experts). Finally, data collection, in particular, the collection of user data (e.g., geographical data, sociodemographic data, user characteristics) was voiced as being difficult to capture due to privacy constraints of social media platforms.
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 5 Test Results
As it can be observed, the maximum torques exerted on the lift and knee joints occur when the even tripod is in the support phase of the gait cycle; furthermore, the maximum absolute values were 1.36 Nm and 0.60 Nm for the \([\mathbf{\tau}_{L,i}]_{z}\) and \([\mathbf{\tau}_{K,i}]_{z}\) axes, respectively, which are manageable for commercially available servo motors. Figure 8: The phase shifts and torques generated when the hexapod robot traverses the lemniscate. Only the first 40 s of simulation are shown. (**a**) The phase shifts triggered during the first 40 s of the simulation. The commanded speed starts at \(v=0.02\) m/s; after the fourth phase shift (\(t\approx 22\) s), it was changed to \(v=0.04\) m/s. (**b**) For the odd tripod, the torques exerted on the \(L\) and \(K\) joints around the \(z\) axis. As mentioned in Section 4.4, the K3P algorithm can command the swing tripod to increase or decrease the maximum clearance of the robot. Figure 9 displays two different values of clearance when the robot travels in a straight line uphill: the first (see Figure 9a) with a clearance equal to 50% of \(l_{h}\) and the second with a clearance of 90%. The latter causes the support point \(SP\) to travel almost as high as the CoM \(B\). If required, the walk clearance can be updated at any moment to better adapt to the terrain's changes in elevation. Section 4.4 also shows the profile of every gait cycle that the K3P algorithm describes. Right after a phase shift is triggered, the limbs are taken to land to begin the support phase of the gait cycle.
# Exploring Pre-Trained Models for Skin Cancer Classification
## 3 Materials and Methods
### XAI Techniques
#### 3.5.2 Local Interpretable Model-Agnostic Explanations (LIME)  
LIME provides local explanations by perturbing input features and analyzing their impact on the model's output. In image classification, LIME segments an input image into superpixels and selectively alters them to observe the effect on classification probability.  
A surrogate linear model is then trained to approximate the model's decision boundary within a local neighborhood of the input. The importance of each superpixel is determined based on its contribution to the locally approximated model.  
Mathematically, LIME constructs a local surrogate model g(x) that approximates the complex model F(x) within a neighborhood \(\pi_{x}\) around the input x. The optimal explanation is obtained by solving the following:  
\[\hat{g}=\arg\,min_{g\in G}\,L(F,\,g,\,\pi_{x})+\Omega(\text{g}) \tag{6}\]  
where  
* G is the family of interpretable models;
* \(L(F,\,g,\,\pi_{x})\) is a loss function quantifying how well g(x) approximates \(F\)(x);
* \(\Omega\)(g) imposes a complexity constraint to maintain interpretability.  
By selectively modifying image regions and analyzing their influence on predictions, LIME highlights the superpixels that contribute most significantly to classification outcomes. This technique enhances understanding of model decisions and aids in distinguishing between malignant and benign lesions.
## 4 Discussion
### Flexion and Extension Test  
For the first simulation, the model was subjected to two pure moments of 1 Nm applied in flexion (positive moment) and extension (negative moment). Figure 6a shows the comparison between the results obtained from the FEM and those recorded in the in vitro tests. During extension, the behavior of the model was similar to the experimental model; however, in flexion the model assumed a linear behavior. This may be a result of the linear elastic properties of the ligaments, as they are the main components that restrict motion during flexion.  
Figure 5: Maximum contact pressure between the opposing facet cartilages in flexion, extension, axial rotation, and lateral bending.  
Figure 6: Comparison between the Moment–ROM curves of the FSU and the experimental data [34; 35] during (**a**) flexion–extension, (**b**) axial rotation, and (**c**) lateral bending.  
Output predictions were as expected, with the uppermost vertebral body leaning forward during flexion and backward during extension. During forward motion the vertebral body rotates, causing compression on the anterior edge of the intervertebral disc. As a result, stress is concentrated on the anterior region of the disc. The same occurs during extension, except that the compressive force instead affects the posterior edge, causing concentrated stress on the posterior region of the disc.  
The ligaments are fundamental in the definition of the cervical spine's ROM. They act primarily in tension, and as such develop stresses when stretched, which varies according to the moment applied. Therefore, certain ligaments contribute more to withstanding certain loads because of their location. The ALL is one example; it is not active during flexion, and produces stress during extension. The opposite occurs in the PLL, ISL, and LF, while the CL is active during both motions. Figure 7 shows the maximum stress registered by the ligaments during flexion-extension.  
Likewise, the facet joints play an important role in the movement of the cervical spine by restricting actions depending on their level [36]. After analyzing the results, it is possible to observe that there was no contact between the facet cartilages during flexion, while the opposite was the case during extension. The second scenario shows the larger role of the facet joints in restricting spinal movement, as there are fewer ligaments active in extension. Moreover, the nonlinear behavior of the model in most movements may be due to the facet joints, considering that during flexion there is no contact and the model behaves linearly,  
Figure 7: Maximum stress registered by each ligament during flexion–extension.  
while in extension, when contact is registered, the model starts to show nonlinearity. It can be speculated that in the case of extension the contact between cartilages is registered when the applied load reaches 0.5 Nm, as the behavior curve of the model changes when this moment is reached.
# Enhancing the Performance of XR Environments Using Fog and Cloud Computing
## 5 Conclusions  
This paper proposed and experimentally validated a novel approach to enhance real-time user interactions in XR environments. The proposed system distributes data processing and rendering tasks from the cloud to local environments, leveraging edge and fog computing. The experimental results showed that data compression and processing using edge computers mitigated the rendering delays significantly, highlighting the crucial role of fog systems in time synchronization and data synchronization for user interaction. These findings represent significant advances in ensuring excellent performance and real-time interactions in XR environments [6].  
Furthermore, data processing in cloud environments and network response delays can lead to increased latency, emphasizing the that leveraging edge and fog computing to address these issues is essential. Consequently, the proposed system has great potential for enhancing user experiences in XR environments, and its application and development in various systems will lead to further progress and expansion in future research and development endeavors.  
**Author Contributions:** Conceptualization, E.-S.L. and B.-S.S.; methodology, E.-S.L.; software, E.-S.L.; validation, E.-S.L. and B.-S.S.; formal analysis, B.-S.S.; investigation, E.-S.L.; resources, E.-S.L.; data curation, B.-S.S.; writing--original draft preparation, E.-S.L.; writing--review and editing, B.-S.S.; visualization, E.-S.L.; supervision, B.-S.S.; project administration, E.-S.L.; funding acquisition, B.-S.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partly supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (No. NRF-2022R1A2B5B01001553 and No. NRF-2022R1A4A1033549).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the license of contents.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Calibration of Critical Stresses and Critical Distances
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & \(\mathbf{r_{C}}\) **(mm), MTS** & \(\boldsymbol{\sigma_{C}}\) **(MPa), MTS** & \(\mathbf{d_{C}}\) **(mm), MS** & \(\boldsymbol{\sigma_{C}}\) **(MPa), MS** \\ \hline PMMA & 0.25 & \(-171.9\) & 0.79 & \(-164.4\) \\ GPPS & 0.52 & \(-82.7\) & 1.65 & \(-81.7\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Critical distance (\(r_{C}\)) and critical stress (\(\sigma_{C}\)) of PMMA and GPPS materials when following maximum tangential stress (MTS) and mean stress (MS) criteria. model for which the compressive stress at the critical distance, \(\rm{r_{C}}\), in front of the notch tip attains the critical stress, \(\sigma_{C}\). Similarly, for the MS criterion, the critical load is achieved in the FE model when the mean value of compressive stress over the critical distance, dc, reaches \(\sigma_{C}\). The experimental and theoretical LCCs and their discrepancies are reported in Table 4. It should be noted that the discrepancies for the U-notch tip radii of 0.5 mm and 4 mm were zero, because the critical stress and the critical distances were calibrated from their experimental results. Hence, in computing the average discrepancies, these zero values were not considered. Table 4 shows that MTS and MS criteria can very well estimate the experimentally achieved fracture loads of the notched polymeric samples under the closing mode loading. Predictions were seen to be less accurate in PMMA (more non-linear), with the MTS criterion providing better predictions than the MS criterion. In all cases, predictions were conservative. As mentioned in Section 2, the (compressive) critical stress of the two polymers analyzed could alternatively be determined by using the equivalent material concept (EMC), as the behaviors of these polymers under compression are nonlinear. Herein, it is verified whether the critical stress achieved from the EMC was close enough to that obtained from the experimental calibration when predicting the LCCs of the U-notched polymeric specimens using MTS and MS criteria. To combine MTS and MS criteria with EMC, the critical stresses obtained above through the experimental calibration were substituted by the compressive strength of the equivalent linear elastic material achieved by EMC. For this purpose, the area under the compressive stress-strain curve (see Figure 5) until the peak point (maximum stress) was first computed for both polymeric materials. Then, by substituting this value into Equation (3), the compressive strength (\(\sigma_{f}^{*}\)) of the equivalent material was computed, a. For the PMMA and GPPS polymers being investigated, the compressive strength values were computed to be equal to \(-175.1\) and \(-90.2\) MPa, respectively, which are significantly close to the values of the critical stress obtained from the experimental calibration. Finally, by replacing the value of \(\sigma_{f}^{*}\) instead of the critical stress, \(\sigma_{C}\), for each polymer in the MTS and the MS criteria, the theoretical values of LCC were obtained, which are reported in Table 5.
# Congestion Control in CoAP Observe Group Communication
## 4 Evaluation Setup
### Network Topologies  
In evaluating the efficiency of both of the CoAP congestion control mechanisms, the default CoAP and CoCo-RED, five different simulation network topologies were set up to conduct experiments in each scenario. Each topology differed in terms of the number of nodes, installation patterns, and hop counts for delivering the CoAP request from the source node to the destination node. The topologies used for the performance analysis were (a) a chain topology with 9 nodes, (b) a grid of 9 nodes (\(3\times 3\)), (c) a cross of 9 nodes, (d) a dumbbell topology with 9 nodes and (e) a random topology of 9 nodes. The number of CoAP nodes and the two-dimensional view of the node installation in different topologies is illustrated in Figure 8. The figure portrays the positions for installing the CoAP nodes in the five topologies. Each topology was conditioned to have a 10-m distance between nodes. The transmission range was 15 m while the interference range was 30 m. Therefore, in the chain topology, each node can select direct neighbors within the transmission range only, whereas in the other topologies, a node can have more than one neighbor.  
Figure 8: The five network topologies used for performance analysis (chain, grid, cross, dumbbell and random). The distance between the neighboring nodes is 10 m. The gray nodes are the RPL border routers. The orange nodes are the clients and the gold nodes are the groups of servers for the Obs messages.  
Due to the limitations of the memory capacity within the Z1 platform operated in the Cooja simulator, several problems occurred when we increased the number of nodes, the network density, and the congestion levels. In future, we would like to test CoCo-Red using more complicated networks.
## 2 Materials and Methods
### Description of Optimization and Algorithm
#### 2.2.2 Algorithm Architecture  
Obviously, the optimization problem in this paper is a mixed integer and nonlinear problem. In addition, conventional optimization algorithms cannot adapt to the time-varying property of MEC networks. In the following sections, how to train the network by reinforcement learning and how to simplify the model and accelerate the learning process according to the identifier provided by the user under certain network conditions will be introduced. When the optimized network is working, the MEC server can perform resource allocation directly according to the current users' tasks and network conditions. Thus, the iteration delay is eliminated, and network resource allocation is also further improved.
## 3 Experiment
### Verification on Experimental Phase II of the SHM Benchmark Data
#### 3.2.1 Experimental Phase II of the SHM Benchmark Data  
The International Association for Structural Control (IASC)--American Society of Civil Engineers (ASCE) Structural Health Monitoring Task Group published the data in 2003 as a benchmark. A four-story steel structure developed at the University of British Columbia serves as the benchmark frame. The structure was fitted with fifteen accelerometers. For more information on the laboratory structure, the readers are directed to [29]. On the benchmark frame, nine structural damage situations were simulated. Under ambient stimulation, 15 accelerometers measured acceleration output for each case. As illustrated in Table 2, structural damage progressed from undamaged (Case 1) to severely damaged (Case 9).  
It should be noted that vibration data from accelerometers 1 to 3 mounted on the structural ground was ignored due to insufficient power and information [30]. Therefore, the vibration signal of accelerometers 4 to 15 is analyzed in this paper. Due to the importance of the nature of vibrational signals, some signals are visualized to assess their nature. For example, Figure 11 shows the acceleration measurement history of the 10 th accelerometer from Case 1 to Case 4, with (a) to (d) representing Case 1 to Case 4. As can be seen from the graph below, the vibrational response of the 10 th accelerometer due to ambient vibrations appears to be non-stationary. More precisely, [PERSON] et al. [31] showed that most of the vibration signals of ASCE structures have non-stationary behavior.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structural Case** & **Description** \\ \hline
1 & Undamaged. \\
2 & On the southeast corner, a brace on the first floor is removed. \\
3 & Braces on the first and fourth levels are removed on the southeast corner. \\
4 & On the southeast corner, braces on all floors are removed. \\
5 & All braces on the east are unfastened. \\
6 & Case 5 + braces of the second-floor on the north face are removed. \\
7 & All braces are separated. \\
8 & Case 7 + loosened bolts at ends of the beam on the east face, north side, \\  & on the first and second levels. \\
9 & Case 7 + loosened bolts at ends of the beam on the east face, north side, \\  & on all levels. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the structural cases in the benchmark problem.  
Figure 10: (**a**) Graph of the AUC values of the three depth algorithms changing with the training epochs. (**b**) Average AUC of the five algorithms.
# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 6 Conclusions  
In this study, an improved routing protocol called CND-AODV was proposed to address the issue of network storms caused by the broadcast of routing control messages in mobile ad hoc networks. This protocol takes into account both the node density and the number of common neighbor nodes to determine if a node is isolated or over-dense, and then utilizes probabilistic forwarding to reduce network overhead. Through experimental simulations, it was found that under appropriate threshold settings (e.g., z = 1.645), CND-AODV outperforms traditional AODV and enhanced ND-AODV in terms of throughput, packet delivery ratio, and delay. Simulations in various scenarios, involving changes in node count, random distribution of nodes, and extremely uniform distribution, indicate that CND-AODV enhances network performance. This improvement is more significant when nodes are randomly distributed. Additionally, CND-AODV exhibits improved network performance in scenarios with varying node mobility rates. Thus, CND-AODV proves to be an effective routing protocol, successfully mitigating network storms caused by routing control information broadcasts in mobile ad hoc networks and exhibiting superior performance across diverse scenarios. This study introduces a novel approach to optimizing routing in mobile ad hoc networks, offering valuable insights for future research and practical applications in related fields.  
Note that in CND-AODV, the determination of isolated and over-dense nodes relies on the number of common neighbor nodes and the z-scores method. It is yet to be confirmed whether this determination method is applicable to all network scenarios, and further validation is needed in this regard.  
Conceptualization, X.L., M.L. and X.B.; methodology, X.L. and X.B.; validation, X.L.; investigation, X.L.; resources, M.L.; writing--original draft preparation, X.L.; writing--review and editing, X.L., M.L. and X.B.; supervision, X.L., M.L. and X.B.; project administration, M.L. and X.B.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
The work was supported in part by the National Key Research Development Program of China (Grant No. 2020 YFB2205603).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Fuel-Water Emulsions as an Alternative Fuel for Gas Turbines: A Project Summary
## 3 Research Results and Discussion
### Fuel Consumption  
The effect of adding water to the fuel on thrust-specific fuel consumption (TFSC), where the total mass of the emulsion mixture is understood as fuel, is negative (Figure 14). Figure 14 shows that for rotational speeds of 100 krpm and 120 krpm, the increase in TFSC with increasing water content in the mixture is more intense than for a rotational speed of 80 krpm. For these two rotational speeds and 12% water addition, the increase in TFSC is approximately 15%. For a rotational speed of 80 krpm, the increase in TFSC is approximately 8.8%.  
The addition of water to the fuel reduces the consumption of the base fuel (without water and emulsifier) contained in the emulsion (Figure 15). For a rotational speed of 80 krpm and 12% water addition, a reduction in the consumption of the base fuel by \(\sim\)5.5% was recorded, which was approximately linear as a function of water content. However, for a rotational speed of 100 krpm, a clear minimum was recorded for 6% water addition. At this point, the fuel consumption reduction was 2.45%. For a similar addition of water at 80 krpm, the reduction in the consumption of the base fuel was approximately 2.94%. After increasing the water content in the emulsion above 6%, for 100 krpm, the downward trend was reversed, which resulted in a reduction in the consumption of the base fuel with 12% water addition by only about 0.6%. Values recorded at a rotational speed of 120 krpm have been omitted from the graph for the reasons presented in the introduction to this section.  
Figure 14: The influence of water content in the emulsion on thrust-specific fuel consumption (calculated for the total mass of the fuel mixture).  
Figure 15: The influence of water content in the emulsion on the consumption of base fuel (after subtracting the weight of water and emulsifier).