esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multithreaded Algorithm for the Computation of Sample Entropy
## 4 Parallel Approaches  
In order to investigate parallel approaches for the bucket-assisted algorithm, we examined opportunities for parallelization on a simple multithreaded home computer processor. A number of threads share the same address space. Each thread works on a different subset of points and is responsible for the similarity checks for those points. The similarity check is the computationally intensive part of the method, where parallelization emerges. The first part of the algorithm, i.e, the computation of the integrated signal and the distribution of the points in the buckets, is not expensive. We chose to execute this part sequentially, even though some parallelism could also be extracted here.  
In the parallel part of the algorithm, each thread computes the local values for similarity counters \(A\) and \(B\). After all threads complete their tasks, all local values of \(A\) and \(B\) are summed up, yielding the total number or similar points for the whole time series. Three parallelization policies were considered, each expecting to work better in different conditions.  
* _Static load balancing_: In Static load balancing, buckets are cyclically assigned to the available threads. It is a low overhead policy and can lead to optimal speedup when the number of points are evenly distributed in the buckets or, at least, if neighboring buckets have almost equal numbers of points. Since we expect the number of points in neighboring buckets not to differ significantly, we investigated if we could gain from the low overhead of the policy.
* _Dynamic load balancing_: In the case in which static load balancing fails to achieve a satisfactory distribution of the work, dynamic load balancing could offer better parallelization opportunities. Each thread is assigned one bucket at a time. When the thread completes the similarity checks for this bucket, a new bucket is assigned to the thread until there are no more buckets left unassigned. The additional effort to distribute the load and synchronize the tasks increases the overhead, but it is expected this overhead not to be significant.
* _In-bucket-based balancing_: When points are not well balanced in the buckets, both previous approaches might be proved insufficient, especially the static balancing. We investigated an additional policy, in which points in each bucket are equally distributed to the number of the available threads, i.e., every thread works on every bucket and is assigned a part of the points in this bucket. This approach seems also promising, since theoretically it achieves the optimal load balancing, with each thread assigned almost equal number of points. However, the compiler technology avails large loops and splitting large loops into smaller ones might lead to lower performance.  
Which policy is better, and when, for the examined problem will be decided by the experimental analysis. In the next section, we will present and compare execution times and speedups from all the above policies.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 3 Wireless Network Architectures for Energy-Efficient CFL
### Terrestrial-Based Architectures
#### 3.1.1 CFL in Centralized Wireless Networks
To further address communication challenges posed by wireless channel fading in centralized networks, recent studies have explored CFL along with multiple-input multiple-output (MIMO)/massive multiple-input multiple-output (mMIMO) technology as a promising approach for efficient parameter transmission between multiple wireless devices and servers. This integration enables communication-efficient CFL by improving both spectral and energy efficiency, thereby addressing key requirements for scalable and sustainable wireless communication [58; 59; 60]. Studies like [60] proposed synchronous, asynchronous, and session-based designs for energy-efficient CFL in mMIMO networks. However, research in this area remains limited.
## 4 Discussion and Conclusions
### Clinical Impact  
Despite the overwhelming clinical evidence that an FFR-guided revascularization strategy improves patient outcome, still the number of coronary interventions preceded by FFR measurements is relatively low due to the limitations of invasive pressure measurements [76]. Hence, a virtual functional index would increase the adoption of physiology-guided coronary interventions, while drastically reducing the requirement for invasive pressure measurements.  
The proposed method is potentially well suited for a clinical setting, given the real-time prediction performance of the DL model. Certain manual steps are required in the current pipeline, but these can be automated using algorithms for image quality assessment, e.g., to exclude slices with blood artifacts, and more accurate lumen contour detection [77]. The approach only requires knowledge of the coronary luminal geometry, which can be extracted directly from OCT.
## 1 Introduction
Our main contributions can be summarized as follows:  
* We aim to exploit the aforementioned information of interaction among users and develop a QoS-aware and TS-aware routing protocol to provide video-streaming services over MANETs. Our routing protocol considers both the relationship among users while providing a good performance in terms of quality of service. * We claim that this idea can be useful to deal with possible trust issues in MANETs, since with our approach users' information is routed through paths formed by users with high interaction and trust with each other. Therefore, with our proposal, packets are preferably forwarded by trusted nodes rather than by strangers that are not trusted, which leads to increased trust in communication. * In this work we find a trade-off between (i) the user performance in terms of QoS parameters (focusing mainly in the percentage of packet losses and the end-to-end packet delay) and (ii) a good level of trust or confidence from the user's point of view, represented by the average TS of the forwarding nodes. * We include simulation results under different scenarios that show the benefits of our proposal. The rest of the paper is structured as follows. Section 3 introduce the basics of our framework. In Section 4 we summarize the main features of our proposal of a QoS-aware multimetric routing protocol. Section 5 gives a brief explanation of the TS concept and its application in a social-aware routing protocol to select the best forwarding path. Simulationresults are shown and analyzed in Section 7. Finally, conclusions and future work are given in Section 8.
###### Abstract  
This study aims to identify personal, clinical, and acoustic predictors of therapy outcomes based on changes in Korean voice-related quality of life (K-VRQOL) scores, as well as to compare the predictive performance of traditional regression and machine learning models. A total of 102 participants undergoing voice therapy are retrospectively analyzed. Multiple regression analysis and four machine learning algorithms--random forest (RF), gradient boosting (GB), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost)--are applied to predict changes in K-VRQOL scores across the total, physical, and emotional domains. The Shapley additive explanations (SHAP) approach is used to evaluate the relative contribution of each variable to the prediction outcomes. Female gender and comorbidity status emerge as significant predictors in both the total and physical domains. Among the acoustic features, jitter, SFF, and MPT are closely associated with improvements in physical voice function. LightGBM demonstrates the best overall performance, particularly in the total domain (R\({}^{2}\) = 32.54%), while GB excels in the physical domain. The emotional domain shows relatively low predictive power across the models. SHAP analysis reveals interpretable patterns, highlighting jitter and speaking fundamental frequency (SFF) as key contributors in high-performing models. Integrating statistical and machine learning approaches provides a robust framework for predicting and interpreting voice therapy outcomes. These findings support the use of explainable artificial intelligence (AI) to enhance clinical decision-making and pave the way for personalized voice rehabilitation strategies.  
Korean voice-related quality of life; Shapley additive explanations; voice therapy; voice disorders; machine learning; regression analysis; acoustic parameters; explainable AI +
Footnote †: journal: Applied Sciences  
Article
## 4 Simulation Experiment of IABCBAS Algorithm Path Planning Application
### Establishment of Environmental Models
Two-dimensional fixed-area and three-dimensional simulation map models are established based on different obstacle distributions in various environments to verify the universality of the proposed method, as shown in Figure 5. For the 2D environment, a simple map (20 \(\times\) 20) and a complex map (30 \(\times\) 30) are constructed, where obstacles are represented by black grids and free spaces by white grids. For the 3D environment, simple bimodal and complex multimodal elevation maps are built using elevation data. In Figure 5a,b, the top left corner of the map is the starting point of the path planning and the bottom right corner is the endpoint. The fence side length is set as \(B\), the fence space is divided into \(M_{x}\) rows and \(M_{y}\) columns with B unit length. The \(n\)-th fence grid coordinates are \((x_{n},y_{n})\), and the formula is shown in  
\[\left\{ \begin{array}{l}x_{n}=\text{B}\times\left((i-1)\text{mod}M_{x}\right) +\frac{B}{2}\\ y_{n}=\text{B}\times\left(M_{y}-\text{ceil}\left(\frac{i}{M_{y}}\right)\right) +\frac{B}{2}\end{array} \right., \tag{18}\]  
where \(mod\) is the redundancy function and _ceil_ is the upward rounding function. The real coordinates are scaled down to one-tenth of their original values for the axes in Figure 5c,d to facilitate the 3D environment modeling. The slope model is generated based on the average slope between adjacent path points, as shown in Equation (19) and Figure 6.  
\[\theta_{1 xy}=\text{arc}\frac{b_{y}-b_{x}}{d_{xy}}, \tag{19}\]  
where \(x\) and \(y\) are the coordinates corresponding to adjacent elevation path points in the B-dimensional space, \(\theta_{1 xy}\) is the average slope of the path point \(b_{y}\) relative to path point \(b_{x}\), and \(d_{xy}\) is the planar straight-line distance between the two path points. Figure 5: Establishment of environmental map model. (**a**) 20 \(\times\) 20; (**b**) 30 \(\times\) 30; (**c**) twin peaks terrain; (**d**) multimodal terrain. Figure 6: Schematic diagram of slope model.
## 2 II-36, IL-37, and IL-38: A Complex Group of Pro- and Anti-Inflammatory Cytokines
### Receptors and Intracellular Signaling  
The common structure of the receptors of the IL-1 family cytokines is characterized by three extracellular Ig domains and an intracellular Toll/IL-1 Receptor (TIR) domain. Similarly to TLR, the IL-1 family receptor can recruit the adaptor protein myeloid differentiation primary response protein 88 (MyD88) following the dimerization into a complex of signalization. So far, four distinct complexes have been described: IL-1R (IL-1R1 and IL-1 receptor accessory protein (IL-1 RaCp)), IL-33R (ST2 and IL-1 RaCp), IL-18R (IL-18R\(\alpha\) and \(\beta\)), and IL-36R (IL-1 receptor like 2 (IL-1 RL2) or IL-1 Rrp2- and IL-1 RAcP). IL-1R2 and IL-18 binding protein (BP) lack the intracellular domain and act as decoy receptors by competitively linking to IL-1\(\beta\) and IL-18, respectively, and preventing their binding to IL-1R1 and IL-18R. The recently discovered TIR8 (also known as IL-1R8) is another exception to the characteristic structure as it contains one extracellular domain and one mutated TIR intracellular domain. This particular domain competes, in a decoy fashion, with the activated IL-1R or TLR complex, eventually leading to a decreased intracellular signaling [23]. Figure 1 summarizes and graphically represents the receptors and intracellular pathways activated by IL-1, IL-36, IL-37, and IL-38.
## 3 Results  
### Drought Index Model Accuracy and Validation  
Based on the mISDI model constructed in Section 2.3.2, the cubist package of R software was used to test the six combinations in the Table 4, and the June data from 2001 to 2020 were chosen as an example. As June is summer, the ISDI in summer has a better model accuracy in [PERSON]'s study [21], and the results are shown in Table 6.  
The results showed that combination 3 of the mISDI model had the highest accuracy, as the MAE and the RMSE were the lowest and the R\({}^{2}\) was the highest (Table 6). Comparing the results of combinations 1, 2, 4, and 5, the addition of spatial variables (longitude (LON) and LAT) greatly improved the accuracy of the model, which was better than that reported by [PERSON] et al. [22]. When LAT, LON, and time variables were not included (combinations 1), the errors were higher and the R\({}^{2}\) was lower. Adding LAT and LON to combinations 2 increased the R\({}^{2}\) by 0.20 and the further addition of the year increased R\({}^{2}\) by another 0.17 to reach the very high value of 0.987 (Table 6), and the model accuracy greatly improved [21; 30]. The comparison of combinations 1, 3, 4, and 6 revealed that the performance of the three-month scales of the SPI (SPI03) as an independent variable was better than that of the SPI. Finally, comparing the different combinations, this study chose combination 3 as the input variable combinations for the mISDI model.  
By further analyzing the importance of the independent variables in the model, it was found that LC and IAA barely contributed to the model, as shown in Figure 4. Within the study area, the proportion of land surface types varied greatly, the distribution of various surface types was uneven, and the cultivated land accounted for approximately 83% of the total study area. Similarly, according to the Asian irrigation area map, the type of irrigation in the HHHP was extracted, and it was found that the irrigated area accounted for 62.45% of the total area, the rainfed area accounted for 20.33% of the total area, and the non-agricultural area and the water body accounted for 14.98% and 2.24% of the total area, respectively. The data were sufficient to show that the data distributions of LC type and irrigation type in the HHHP were uneven, so we chose to delete the two variables of LC and IAA on the basis of the first group of models and obtained mISDI data in other months according to the modified model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**1 Combination** & **2 MAE** & **3 RMSE** & **4 R\({}^{2}\)** \\ \hline
1 & 1.057 & 1.384 & 0.619 \\
2 & 0.725 & 0.980 & 0.819 \\
3 & 0.156 & 0.253 & 0.987 \\
4 & 1.069 & 1.387 & 0.618 \\
5 & 0.712 & 0.959 & 0.827 \\
6 & 0.162 & 0.265 & 0.986 \\ \hline \hline \end{tabular}  
* Combinations as described in Table 4; 2 MAE, mean absolute error; 3 RMSE, root-mean-square error; 4 R\({}^{2}\), coefficient of determination.  
\end{table}
Table 6: Model accuracies of different data combinations.  
Figure 4: mISDI variables importance.
# Reliability Analysis of Hybrid Laser INS Under Multi-Mode Failure Conditions
## 3 Reliability Analysis Model for the Hybrid INS Based on Kriging Surrogate Model  
As critical components of an inertial navigation device, the base, platform, and inner and outer frames significantly impact the accuracy and stability of the INS system due to their static and dynamic properties. Under high-overload and large-shock mechanical conditions, elastic deformation in these structures can more easily cause the coordinate system of the inertial group body to deviate from the virtual body coordinate system. Based on the conclusions in Table 3, the base, platform, inner frame, and outer frame all experience some degree of elastic deformation under shock conditions, where \(\delta\)_Platform > \(\delta\)_Inner Frame > \(\delta\)_Outer Frame > \(\delta\)_Foundation. The stability of the inertial measurement system's reference coordinate system is primarily affected by deformation, angle sensor accuracy, motor control precision, and gyroscope accuracy. However, improving the angle sensor accuracy, motor control precision, and gyroscope accuracy can be costly and challenging. Thus, as the inner and outer frames are the direct structural components connecting the platform assembly, shaft end assembly, and base assembly, increasing their structural stiffness and strength to reduce elastic deformation under overload conditions is the most direct method to minimize the deviation of the inertial measurement unit's coordinate system from the true coordinate system.  
When the inertial measurement unit body is mounted on the projectile without installation errors, the virtual body coordinate system of the inertial group is parallel to the projectile coordinate system. When the frame is in its initial strapdown lock state, the inertial group body coordinate system coincides with the virtual coordinate system, as shown in Figure 9.
## 3 Proposed Methods
### Improved Grey Wolf-Harris Hawks Hybrid Algorithm
#### 3.3.5 Algorithm Execution Framework  
To systematically present the procedural logic of the proposed IGWOHHO algorithm, the pseudocode is provided in Algorithm 1, accompanied by a flowchart. These offer a clear and structured overview of the algorithm's initialization, dynamic phase transition, hybrid search mechanism, and elite retention strategy.  
```
1:population size \(N\), max iterations \(T\), perturbation adjustment coefficient \(\theta\), minimum switching threshold \(\varepsilon_{min}\), maximum switching threshold \(\varepsilon_{max}\), elite retention ratio proportion \(P\)
2:optimal solution \(X^{\star}\)
3:Initialize parameters \(N\), \(T\), \(\theta\), \(\varepsilon_{min}\), \(\varepsilon_{max}\), \(P\)
4:Generate initial population \(X_{0}\) using Tent chaotic mapping
5:Evaluate fitness of each individual in \(X_{0}\)
6:Set the best individual as \(X^{\star}\)
7:for\(t=1\) to \(T\)do
8:Evaluate fitness for all individuals
9:Compute \(\varepsilon(t)\), change rate \(\Delta f(t)\), and dynamic weights \(\lambda\_\)GWO, \(\lambda\_\)HHO
10:Compute ratio R(t) = \(\lambda\_\)HHO/\(\lambda\_\)GWO
11:if\(R(t)<0.5\)then
12:Perform the global search using GWO
13:Apply elite retention and perturbation
14:Update \(X^{\star}\) if better solution is found
15:elseif\(0.5\leq R(t)\leq 2.0\)then
16:Perform hybrid search
17:Generate candidate solutions using both GWO and HHO
18:Compute weighted combination
19:Apply multi-source selection
20:Apply elite retention and perturbation
21:Update \(X^{\star}\) if a better solution is found
22:else
23:Perform local search using HHO based on \(E\) and \(r\)
24:Apply Levy-based dynamic refinement
25:Apply elite retention and perturbation
26:Update \(X^{\star}\) if a better solution is found
27:endif
28:Merge elite and non-elite groups, sort by fitness
29:Select top \(N\) individuals for next generation
30:endfor
31:return\(X^{\star}\)
```  
**Algorithm 1**Framework of the IGWOHHO algorithm  
Figure 6 presents the flowchart of the IGWOHHO algorithm, outlining the key steps from initialization to adaptive strategy selection and solution update. It illustrates the dynamic switching mechanism among GWO, HHO, and hybrid search phases.
# Path Segmentation-Based Hybrid Caching in Information-Centric Networks
###### Abstract  
Information-centric networks (ICNs) have received wide interest from researchers, and in-network caching is an important characteristic of ICN. The management and placement of contents are essential due to cache nodes' limited cache space and the huge Internet traffic. This paper focuses on coordinating two cache metrics, namely user access latency and network resource utilization, and proposes a hybrid caching scheme called the path segmentation-based hybrid caching scheme (PSBC). We temporarily divide each data transmit path into a user-edge area and non-edge area. The user-edge area adopts a heuristic caching scheme to reduce user access latency. In contrast, the non-edge area implements caching network content migration and optimization to improve network resource utilization. The simulation results show that the proposed method positively affects both the cache hit ratio and access latency.  
ICN; hybrid caching; cache placement; content migration +
Footnote †: journal:  
on-path caching, content is cached on nodes on the data return path [10], while off-path caching [11] provides additional caching at a strategic location. On-path caching is more flexible and scalable than off-path caching.  
This paper focuses on combining the advantages of on-path caching and cooperative caching and proposes a caching scheme based on path segmentation referred to as the path segmentation-based hybrid caching scheme (PSBC). We choose content migration between nodes to implement cooperative caching. Our goal is to coordinate the two cache targets described above with good performance in user access latency and network resource utilization. The main contributions of this paper are as follows:  
* We propose a caching idea of path segmentation. We divide the cache nodes on the data return path into a user-edge area and a non-edge area according to the data transmission distance. Different caching schemes are adopted for nodes of different areas.
* We adopt a heuristic mechanism to implement on-path caching in the user-edge area and use a lightweight mechanism based on path competition to select one node in the non-edge area as the migration node to implement content migration of the user-edge area.
* We use real-world network topologies to evaluate the performance and scalability of the proposed caching scheme. The result shows that our scheme has better performance in different network topologies.  
The rest of this paper is organized as follows. Section 2 reviews the related research of the ICN caching scheme. Section 3 introduces our proposed scheme PSBC, including the cache placement strategy and content migration mechanism. In Section 4, we carry out simulation experiments and analyze the results and performance of PSBC. Finally, we conclude and look into future research.
# Multi-Temporal InSAR Structural Damage Assessment: The London Crossrail Case Study
###### Abstract  
Spaceborne multi-temporal interferometric synthetic aperture radar (MT-InSAR) is a monitoring technique capable of extracting line of sight (LOS) cumulative surface displacement measurements with millimeter accuracy. Several improvements in the techniques and datasets quality led to more effective, near real time assessment and response, and a greater ability of constraining dynamically changing physical processes. Using examples of the COSMO-SkyMed (CSK) system, we present a methodology that bridges the gaps between MT-InSAR and the relative stiffness method for tunnel-induced subsidence damage assessment. The results allow quantification of the effect of the building on the settlement profile. As expected the greenfield deformation assessment tends to provide a conservative estimate in the majority of cases (~71% of the analyzed buildings), overestimating tensile strains up to 50%. With this work we show how these two techniques in the field of remote sensing and structural engineering can be synergistically used to complement and replace the traditional ground based analysis by providing an extended coverage and a temporally dense set of data.  
multi-temporal InSAR; InSAR; tunneling; subsidence; relative stiffness method; structural engineering; damage assessment; soil-structure interaction +
Footnote †: journal: Remote Sensing  
18 December 2017; Accepted: 7 February 2018; Published: 13 February 2018