esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Road and Traffic Awareness Approach
This protocol uses segments for data transmission in urban VANET. The road map is divided into different segments, and routing takes place based on the information of the next segment. This protocol has been designed based on parameters such as node position information, direction, vehicular congestion density, and link quality between the communicating nodes. The protocol is highly dependent on GPS accuracy and availability. Therefore, in case of GPS absence, like tunnels, where having a network connection is crucial, the protocol does not function properly. In the intersection-aware approach, the main challenge is its high end-to-end delay. This approach is practical to confront physical obstacles such as buildings. Data are forwarded through intersections which are selected one by one. As a result, this mechanism imposes a high processing delay. In return, a lower cost is generated in terms of routing overhead. On the other hand, protocols in the traffic density aware sub-category consider vehicular congestion as a metric to evaluate the connectivity [25]. However, a highly congested road is vulnerable to having data congestion problems.
# Low-Rank Approximation of Frequency Response Analysis of Perforated Cylinders under Uncertainty
## 5 Numerical Experiments: Trommel Screen  
By design, the perforation patterns of trommel screens are not uniform, which makes the problem more interesting since the existing asymptotic results on eigenmodes are not directly applicable here since they rely on the material uniformity assumption. In the reference case (see Figure 2), the representative parabolic profile function is \(\phi(x)=1\). In all cases, \(x\in[-\pi,\pi]\) so that the 2D computational domain is \(D=[-\pi,\pi]\times[0,2\pi]\backslash\{\text{perforations}\}\). The hole coverage is very high, \(55\%\), with one half of the shell with a regular \(20\times 10\) grid, and another with two \(5\times 5\) triangular pattern panels. Even though the mesh appears rather coarse, the \(p\)-version of the finite element method is used with uniform \(p=4\), which is sufficient to prevent numerical locking effects from dominating the solution.  
The quantity of interest is the relative accuracy of the low-rank approximation over a range of thicknesses. As a byproduct of this, one can obtain asymptotic rates for quantities of interest such as the total energy or the frequency of the dominant response as the thickness tends to zero. The size of the Krylov subspace is determined dynamically via orthogonalisation as vectors are added. Using notation of (18), \(\mathcal{K}_{4}\) was found to be adequate over the whole set of experiments.  
Material constants adopted for all simulations are: \(E=2.069\times 10^{11}\) MPa, \(\
u=1/3\), and \(\rho=7868\) kg m\({}^{-3}\), unless otherwise specified. For damping, the selected weights are simply \(\alpha=\beta=1/2\), and \(\zeta=1/2000\), which is as in earlier work [8]. The deterministic parameter ranges of \(t\in[1/100,1/1000]\) (the so-called practical range), and frequency \(f\in[5,240]\) Hz (chosen experimentally) with angular frequency \(\omega=2\pi f\), are covered. The loads or excitation functions act only on the transverse direction \(w\). The first three are Fourier modes in the angular direction and constant in the axial direction, i.e., \(\hat{f}(x,y)=C\cos(Ky)\) N, where \(C=1000\) is a scaling parameter, and \(K\in\{5,10,20\}\) is the angular wave number. The fourth loading is a concentrated load \(\hat{f}(x,y)=C^{2}\exp(-100(x^{2}+(y-\pi)^{2}))\) N. The concentrated load is not a point load, since, formally, the convergence of the finite element method is not established for point loads. The random Young's modulus depends only on the axial direction and has the form \(E(x,y,\xi)=E_{0}(x)+\sum_{m=1}^{M}E_{m}(x)\xi_{m},(x,y)\in D\), \(\xi\in[-1,1]^{M}\), where \(E_{0}(x)=E\) (constant), and \(E_{m}(x)=\sqrt{\lambda_{m}}\,E\,\sin(mx)\) with decaying sequence of coefficients \(\lambda_{m}=1/(m+1)^{4}\).  
The angular wave numbers are chosen as factors of the regular grid size \(=20\) in the angular direction. In particular, the results on free vibration indicate (Conjecture 1) that the performance profiles for \(K=10\) should mark a transition to dominant local (boundary layer) features as the thickness \(t\to 0\)[7].
## 3 Results  
### The Model Validation with Eddy Covariance Flux Data  
Monthly modelled GPP from the BEPS model was validated with eddy covariance (EC) flux data of forests in China. EC flux data were observed at three ChinaFLUX sites, Changbaishan temperate broad-leaved Korean pine mixed forest (CBS), Qianyanzhou subtropical coniferous plantation (QYZ) and Dinghushan subtropical evergreen broadleaved forest (DHS) [58]. The model performed well at the CBS and QYZ sites for GPP and ET (Figure 3a,b,d,e). The R\({}^{2}\) between modelled and observed GPP in three sites ranged from 0.47 to 0.97. The BEPS model also produced the seasonal pattern of ET well in three forest sites, which the R\({}^{2}\) values ranged from 0.75 to 0.90 (Figure 3). The R\({}^{2}\) between modelled and observed WUE was above the 0.001 significant levels at all 3 flux sites. The BEPS overestimated seasonal variations of WUE at the DHS site, while the variation of modelled WUE was a little higher than the measured values (Figure 3i). Furthermore, mean annual modelled WUE at CBS, QYZ and DHS during 2003-2010 were about 2.12 \(\pm\) 0.13 gC/mm, 3.21 \(\pm\) 0.22 gC/mm and 3.67 \(\pm\) 0.21 gC/mm, respectively. The measurements from ChinaFLUX sites showed that mean annual WUE at CBS, QYZ and DHS during 2003-2008 were about 2.53 \(\pm\) 0.23 gC/mm, 2.29 \(\pm\) 0.33 gC/mm and 2.02 \(\pm\) 0.16 gC/mm [59]. Our modelled data were consistent with previous literature [58; 59].  
Figure 3: The validation of modeled monthly gross primary productivity (GPP), evapotranspiration (ET) and water-use efficiency (WUE) with eddy covariance flux data for three forest sites in China. CBS (**a–c**), Changbaishan temperate broad-leaved Korean pine mixed forest; QYZ (**d–f**), Qianyanzhou subtropical coniferous plantation: DHS (**g–i**), Dinghushan subtropical evergreen broad-leaved forest. All monthly data were from 2003–2010, except for DHS (2003–2009).
## 3 Seismic Performance of the Frame with CFST Dampers
### Stiffness Degradation Analysis of Three Components GJ-1, GJ-2 and GJ-3  
It can be obtained from the analysis of model results that the stiffness \(K\) of the three types of components varies with the turn angle \(\theta\) in a certain way, and the entire attenuation process of the components can be roughly divided into three stages:  
1. Stiffness rapidly dropping stage: From the beginning of the first concrete plastic strain element to all the concrete on a certain line shows obvious plastic strain (corresponding to the development process from micro-cracks to visible cracks in the test).
2. Stiffness falling at medium speed stage: From the occurrence of obvious plastic strain at the main stress part of the component to the obvious yield of the entire component.
3. Stiffness slowly decreasing stage: From the obvious yield of the component to the maximum nonlinear deformation of the component.  
The \(K-\theta\) curves of the three types of components are shown in Figure 12, where the stiffness \(K\) is equal to the horizontal force \(P\) divided by the loading displacement \(\Delta\) at the corresponding time, and \(\theta=\Delta/H\) (\(H\) is the height of the component). GJ-1 has an obvious stiffness rapidly dropping stage, stiffness falling at medium speed stage, and stiffness slowly decreasing stage. The three stages correspond to the three stages of cracking, yielding, and failure, respectively. The first stage is short, while the third stage is relatively long, indicating that GJ-1 has good ductility. GJ-2 is similar to GJ-1. It also has three obvious stages, but the third stage is relatively short. This indicates that GJ-2 still has some deformation capacity after reaching the peak load-carrying capacity, but it is not excellent. It can be seen that GJ-3 has only two stages, but does not have the stiffness slowly decreasing stage. Because GJ-3 is an ordinary low-rise concrete shear wall with poor ductility, the load-carrying capacity of the member quickly drops to 85% after reaching the peak value. At this time, the component is damaged. It shows obvious brittleness in the whole failure process, so it does not have the third stage.  
Figure 11: Equivalent viscous damping coefficient.  
The initial stiffness of GJ-1 is the smallest among the three members, which is about half of that of GJ-3. The stiffness degradation rates of GJ-2 and GJ-3 are roughly the same at the initial stage of loading and both are greater than that of GJ-1. In the final failure stage, the stiffness of the three members is almost the same, but the corresponding value of the turn angle \(\theta\) is quite different. It can be concluded that GJ-1 has much more ductility than GJ-2 and GJ-3, and its energy-dissipating capacity is better than the latter two. The stiffness of GJ-1 decreases slowly after reaching the peak load-carrying capacity, which is beneficial for the structure's absorbance of energy during earthquakes.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
### CenTri as a Hybrid Route Selection Scheme: An Overview  
In CenTri, the centralized mechanism enables a CC to establish backbone routes that minimize SU interference to PUs based on network-wide information (i.e., channel availability of routes). The backbone routes can be used by different source-destination pairs. Nevertheless, the centralized mechanism has two shortcomings: (a) the CC is not sensitive to the dynamicity of the local environment (i.e., the traffic level of each source node towards its destination node) and (b) the routes predetermined and disseminated to source nodes by the CC may suggest the best route being the shortest one, which goes through the backbone route, causing an increased traffic load at the MC BS. The distributed mechanism enables nodes of small cells to revise routing decisions received from the CC to maximize traffic offload from MC BSs based on local information (i.e., available routes and traffic levels of D2D routes).  
In the small-cell layer, a source node selects a route based on four priority levels, which helps to offload traffic from the macrocell layer: the _first_ (or highest) priority is communication via the femtocell layer (through a D2D route), the _second_ priority is communication via the route with minimum interference from PUs, the _third_ priority is communication via the route with the minimum number of hops, and the _fourth_ (or last) priority is communication via the backbone route. A single multi-hop route can consist of links belonging to different layers. For example, the communication through the backbone route connects a source node of the femtocell layer to a BS of the macrocell layer. It then transmits the data to the destination node in the femtocell layer. In Figure 1, the \(fc_{s}-fc_{2}-fc_{5}-fc_{d}\) route is preferred compared to \(fc_{s}-mc-fc_{d}\) used in the traditional network.  
RL is embedded in the source nodes of the femtocell layer so that the right decisions can be made on the route selection at the local level to reduce the global workload of the CC.
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 2 Related Work
### Sensor-Based Recognition  
Recent improvements in sensor technology have significantly broadened its applications in fields such as medical health monitoring, sports science, and daily activity tracking [14; 15; 16]. In particular, sensors like accelerometers and gyroscopes can capture subtle human motions in real time, enabling the identification of basic movement patterns such as walking and running [17; 18]. These studies demonstrate that an in-depth analysis of sensor data leads to a better understanding of the complexities of human movement, which is essential for developing more efficient and precise human activity recognition systems. For example, frequency domain analysis of accelerometer data [19] enables researchers to detect variations in movement speed and gait, findings extensive applications in both sports science and rehabilitative medicine [20; 21; 22].  
Further progress in enhancing the accuracy and robustness of human activity recognition has been achieved by employing data fusion techniques. By combining information from multiple sensors--such as accelerometers, gyroscopes, and magnetometers--a more comprehensive description of motion is obtained, which improves the system's ability to recognize complex human movement patterns [23; 24]. For instance, while a single accelerometer may struggle to differentiate between stable walking and running, integrating data from gyroscopes and magnetometers allows for accurate activity classification by providing unique perspectives from each sensor [25; 26]. Moreover, these multisensor fusion techniques help to mitigate the effects of environmental noise and individual variations, thereby enhancing the adaptability and stability of the system under diverse conditions.  
Additionally, WiFi Channel State Information and radar-based have demonstrated strong capabilities in human activity recognition; they primarily rely on capturing environmental changes rather than directly sensing device states. This introduces potential limitations, such as increased susceptibility to environmental noise and occlusions, as well as privacy concerns, since these methods may inadvertently capture information about surrounding individuals without their consent. In contrast, IMU-based sensing is inherently user-centric, measuring only the movement of the device itself, thereby mitigating privacy. Additionally, IMU sensors are already embedded in most consumer IoT devices, allowing for seamless deployment without additional infrastructure.
# Research on Solidification Methods and Stabilization Mechanisms of Sulfate Saline Soils
## 5 Discussions and Conclusions  
### Discussions  
Figure 14 shows an SEM image of L3 cured for 14 d. Lime particles adhere uniformly to the surface of the saline soil particles. Lime can be turned into CaSO\({}_{4}\) and CaCO\({}_{3}\) over time via ion exchange and carbonation reactions, resulting in a stronger connection between saline soil particles. The adhesion of lime to the surface of the saline soil particles makes it easier to fill the gaps between them, resulting in a cohesive solid with higher UCS and splitting strength. [PERSON]'s research showed that, when the quicklime content was 3% and the FTs was 0, the surface pores of the sample were reduced, and most of the pores were filled with gels, making it difficult to observe the exposed soil particles [41]. This procedure eventually improves the strength of the lime-stabilized soil.  
Upon dissolution in water, FA raises the pH of the solution to an alkaline level, resulting in the production of flocculated hydration products such as C\(-\)S\(-\)H and C\(-\)A\(-\)H. As shown in Figure 15, after 7 days of FA hydration, a flocculating gel is formed, in which some of the material solidifies by interacting with sulfate to form needle-like AFt. Some of these materials react with sulfates to solidify and produce needle-like AFt. The surface of saline soil particles is wrapped by these flocculated gel materials, which fill the voids and make the microstructure of the stabilized soil denser, thus increasing the strength of FA-stabilized soil. XRD analysis showed that a large amount of unhydrated SiO\({}_{2}\) was still present, resulting in a relatively poor stabilization effect of FA on saline soils.  
Figure 14: SEM of lime solidification at 14 d: (**a**) enlarged 2000-times; (**b**) enlarged 10,000-times.  
PAM is a water-based gel material that does not react with sulfates during the curing process of saline soils, and it wraps around the surface of saline soil particles. Under the action of freezing and thawing cycles, salt expansion and freezing expansion work together to destroy the PAM gel, leading to the overall destruction of the soil particles and a reduction in strength. Therefore, PAM-stabilized soil has the worst mechanical properties and frost resistance.
## 4 Discussion of Academic Literature and Council Uses
### Planning  
In planning and development, councils are finding innovative uses for drones, such as conducting surveys for local plans, monitoring development sites, and inspecting private properties. Apart from that, drones can capture detailed 3D models of rural landscapes, aiding decision-makers in visualising and comprehending local conditions from multiple perspectives. In construction, drone-based photogrammetry provides valuable geometric data for tracking building progress and reconstructing structures like bridges. Drones also enhance the identification of small solar home systems, supporting sustainable development goals. Further, when integrated with AI, they offer innovative solutions for assessing thermal losses in buildings, revolutionising the inspection process by factoring in diverse climatic conditions. There are future possibilities of drone uses in heritage conservation, where drones facilitate the creation of digital archives and the inspection of heritage sites, offering accessible and cost-effective means to safeguard and document these elements.
## 3 Results and Discussion
### Comparative Experiments and Analysis
#### 3.5.2 Comparison of ECA-DenseNet with Machine Learning Models  
This section compares the performance of the ECA-DenseNet model with machine learning models including eXtreme Gradient Boosting (XGBoost) [29], Support Vector Machine (SVM) [30], Backpropagation (BP) [31], Random Forest (RF) [32], Gradient Boosting Machine (GBM) [33], and PLS-DA [34] on the test set. Evaluation metrics including accuracy  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Indicators** & **Model** & **P (\%)** & **R (\%)** & **F1 (\%)** & **Accuracy (\%)** \\ \hline \multirow{2}{*}{macro avg} & **ECA-DenseNet** & **99.37** & **99.12** & **99.27** & **99.42** \\  & DenseNet121 & 95.91 & 96.09 & 96.30 & 95.20 \\ \hline \multirow{2}{*}{weighted avg} & **ECA-DenseNet** & **99.36** & **99.12** & **99.28** & **99.42** \\  & DenseNet121 & 95.90 & 96.07 & 96.29 & 95.19 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Macro and weighted average evaluation metrics for ECA-DenseNet and DenseNet121.  
Figure 6: (**a**) Confusion matrix of ECA-DenseNet; (**b**) confusion matrix of DenseNet 121.  
(overall correctness rate of predictions), Kappa (agreement accounting for random chance), and MCC (a balanced measure robust to class imbalance) are employed, with results shown in Table 5 and Figure 7. In key classification metrics, ECA-DenseNet outperforms other machine learning models, with its accuracy, Kappa value, and MCC all exceeding 99%. The best-performing machine learning model, SVM, achieves corresponding metrics of 91.42%, 90.89%, and 91.12%, respectively. ECA-DenseNet outperforms SVM by approximately 8% across all the metrics.
# Improvement and Performance Evaluation When Implementing a Distance-Based Registration
## 2 System Environments and Distance-Based Registration
### Distance-Based Registration  
If DBR is adopted, whenever the UE enters a cell, it calculates the distance between the center (X, Y) of the newly entered cell and the center (Xc, Yc) of the current LA and registers its location if the value is greater than distance threshold \(D\). In other words, location registration is performed when the following expression is satisfied:  
\[\sqrt{\left(X-X_{c}\right)^{2}+\left(Y-Y_{c}\right)^{2}}\geq D \tag{1}\]  
If DBR is adopted, unlike zone-based registration, there is no ping-pong phenomenon that crosses boundaries between LAs to cause multiple location registrations. In addition, unlike zone-based registration, where location registration loads are concentrated in the boundary cell of the LA, location registration loads occur equally for all cells in the network. Furthermore, if DBR is adopted, the efficiency can be improved by setting different distance thresholds for each UE using characteristics of movement or call occurrence of each UE.  
Above all, when a call occurs, the current location of the UE is updated in the system through call processing messages, resulting in the same effect as the location registration in the current cell. On the other hand, a zone-based registration does not have the effect of an implicit location registration because location information does not change even if a call occurs. Therefore, in environments where calls are usually frequent, a DBR may show a better performance than a zone-based location registration [11; 15; 16].
# A Surprisal-Based Greedy Heuristic for the Set Covering Problem
## 2 Surprisal-Based Greedy Heuristic
### Greedy Algorithms  
As we explained in the previous section, we were interested in greedy procedures in order to produce good solutions in a very short time, suitable for real-time applications. SCP greedy algorithms are sequential procedures that identify the best unselected column with respect to a given score and then insert this in the solution set.  
Let \(I_{j}\) be the set of rows covered by column \(j\) and \(J_{i}\) the set of columns covering row \(i\). Algorithm 1 shows the pseudocode of [PERSON]'s greedy algorithm [18]. Each column \(j\) is given a score equal to the column cost \(c_{j}\) divided by the number of rows \(I_{j}\) covered by \(j\). At each step, the algorithm inserts the column \(j^{*}\) with the minimum _score_ in the solution set.  
```
1:\(S\leftarrow\varnothing\)\(\triangleright\) initially empty set
2:while\(I\
eq\varnothing\)do
3:\(j^{*}\leftarrow\operatorname*{arg\,min}_{j\in J}\frac{c_{j}}{|I_{j}|}\)\(\triangleright\) selection of the best column
4: add \(j^{*}\) to \(S\)
5:\(I\gets I\setminus I_{j^{*}}\)for\(j\in J\)do\(\triangleright\) remove the already covered rows
7:\(I_{j}\gets I_{j}\setminus I_{j^{*}}\)
```  
**Algorithm 1** [PERSON]'s greedy algorithm  
A variant of [PERSON]'s procedure for unicost problems was suggested by [PERSON] and [PERSON] [30], named _Allgreedy_. This algorithm is composed of two main steps: in the first phase, the column with the highest number of covered rows is inserted in the solution; then, in the secomd phase, some columns are removed from the solution set according to lexicographic order, as long as the number of the new uncovered rows remains smaller than the last number of new rows covered.  
More recently, [PERSON] [19] proposed a new greedy heuristic which is a recursive procedure that introduces two new terms: _valuation_ and _difficulty_. In the first step, _valuation_ is computed for all columns \(j\) by dividing the number of rows, covered by \(j\), by the column cost, as in [PERSON]'s score. For each row \(i\) is defined a parameter, _difficulty_, which is the inverse of the sum of the valuations of the sets covering \(i\), used to indicate how difficult it might be to cover that row. This is based on the observation that a low valuation implies a low probability of selection. The valuation \(v\) can be computed as:  
\[v_{j}=\frac{\sum_{i\in I_{j}}d_{i}}{c_{j}}\]  
while difficulty \(d\) will be only updated with the new valuations.