esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Main Constraints and Load-Bearing System  
The design process started by identifying the main boundary constraints.  
The initial criterion for satisfying technical feasibility was the span, equal to 45 m; the position of supports was influenced by the topographic characteristics of the site and by the profiles of obstacles (Figure 3).  
The first selected longitudinal profile was an arch lower-chord truss scheme (Figure 4a), which was chosen to provide suitable structural heights. It easily allowed the foundation to reach from the river level up to the road. Nevertheless, the resulting structure presented three main disadvantages: (i) it was very light, susceptible to oscillations (vertical and horizontal) and had little torsional rigidity [22]; (ii) maximum bending forces reached excessive values at midspan; (iii) the arch truss system was unprotected against weather conditions. Therefore, this proposal was abandoned.  
The obvious solution was to adopt an arch lower-chord strut system with V-struts (Figure 4b). On the one hand, this second solution better addressed the environmental impact of the foundations and led to smaller bending moment effects, but on the other hand, it was not able to satisfactorily fulfill the robustness and durability requirements. In fact, the V-struts were excessively exposed to weather, especially to rainfall, with the consequent risk of a reduction of their bearing capacity.  
For these reasons, a covered Pratt-type truss system was finally selected, by assuming the lower chord as cycle path level and the upper chord as the roof (Figure 5). Such a scheme fits well in the natural environment around the Gravina torrent, which is dominated by the concept of horizontality. In order to both increase the stiffness and improve durability, a secondary steel truss was placed on the main laminated-timber Pratt-type truss.  
Figure 3: Topographic characteristics of the site in proximity of the Gravina torrent.  
Figure 4: First longitudinal profiles for the timber bridge: (**a**) arch lower-chord truss scheme; (**b**) arch lower-chord strut system with V-struts.  
Figure 5: Final longitudinal profile chosen for the bridge.  
As for the installation aspects, the bridge is located in an area where common construction site vehicles cannot be used, i.e., crane trucks and/or telehandlers; the use of a specifically designed crane would risk being as expensive as the entire work. For these reasons, assembly via helicopter would be appropriate.  
The structure should therefore be divided into parts (to be assembled on site) with a weight equal to the maximum capacity of the chosen vehicle (for example the maximum capacity of the AS 332 Super Puma mode, a helicopter for aerial work, is 45 kN).
## 3 Optimal Dispatch Model for DHN under CF-VT Control Strategy
### Constraints of the Optimal Dispatch Model
#### 3.2.1 Pipeline Temperature Decay and Delay Constraints  
Considering the transmission delay and heat loss of mass flow in the pipeline, the pipeline temperature decay and delay constraints in the supply and return networks are established as shown in (6) and (7), respectively [25]:  
\[T_{mn,t}^{\text{S,tail}}=T_{t}^{\text{am}}+\Big{(}T_{mn,t-\tau_{mn}}^{\text{S, head}}-T_{t}^{\text{am}}\Big{)}e^{-\frac{\lambda_{mn}Lmn}{f_{mn}^{\text{S,tail}}Amn \rho_{w}c_{w}}},\ \forall mn\in L^{\text{H}} \tag{6}\]  
\[T_{mn,t}^{\text{R,head}}=T_{t}^{\text{am}}+\Big{(}T_{mn,t-\tau_{mn}}^{\text{R, tail}}-T_{t}^{\text{am}}\Big{)}e^{-\frac{\lambda_{mn}Lmn}{f_{mn}^{\text{S, max}}Amn\rho_{w}c_{w}}},\ \forall mn\in L^{\text{H}} \tag{7}\]  
\[\tau_{mn}=\frac{l_{mn}A_{mn}\rho_{w}}{f_{mn}^{\text{S}}},\ \forall mn\in L^{\text{H}} \tag{8}\]  
where \(L^{\text{H}}\) is the set of pipelines in DHN; \(T_{mn,t}^{\text{S,head}}\) and \(T_{mn,t}^{\text{S,tail}}\) are the temperature at the head and end of pipeline \(mn\) in supply network at period \(t\), respectively; \(T_{mn,t}^{\text{R,head}}\) and \(T_{mn,t}^{\text{R,tail}}\) are the temperature at the head and end of pipeline \(mn\) in return network at period \(t\); \(T_{t}^{\text{am}}\) is the outdoor temperature at period \(t\); \(f_{mn}^{\text{S}}\) and \(f_{mn}^{\text{R}}\) are the mass flow rate of pipeline \(mn\) in supply and return network, respectively; \(\tau_{mn}\) is the transmission delay of pipeline \(mn\); \(\lambda_{mn}\) is the heat conductivity factor of pipeline \(mn\); \(l_{mn}\) is the length of pipeline \(mn\); \(A_{mn}\) is the cross-sectional area of pipeline \(mn\); \(c_{w}\) is the specific heat capacity of water; \(\rho_{w}\) is the density of water.
# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
## 3 Proposed Methodology
### Fine Tuning of GENDIS and Classification Models  
This stage refers to the training phase of GENDIS and the Logistic Regression classifier using the synthetic data. The trajectory generator is used to create enough and meaningful data for the tuning of the models' parameters and the cross-validation evaluation of the resulting models. Since we have five different maneuver types, we choose to train five different binary classifiers instead of a single multi-class classifier. Each classifier is trained using a different synthetic dataset comprising trajectories that contain a certain maneuver and trajectories containing a random movement. As already depicted in Figure 2 the synthetic trajectories are converted to time-series, are normalised and standardised and used to fit the GENDIS shapelet model. We train our models using: (i) only the vessel bearing time-series, (ii) only the vessel bearing change time-series and, (iii) both time-series (mixed). This last case, supports the multi-timeseries model depicted in Figure 3.  
The GENDIS algorithm has several parameters that allow to set the number of individuals in the population, the number of generations to use, the existence of noise in the generation of shapelets and so forth. The optimal values for all these parameters are subject of a hyperparameter tuning process. In order to improve the discriminating power of our classification model, we experiment with several parameters of the trajectory generator, the GENDIS algorithm, as well as with parameters of the Logistic Regression classifier. This hyper-parameter tuning process, allowed us to better simulate the real-trajectories that comprise SAR maneuvers and consequently to adapt the extracted shapelet features to the characteristics of each pattern.  
GENDIS fits the training data to its genetic extractor and finds the best shapelets that constitute its model. The training data are then transformed to distance matrices (one for each class) containing the distances of each training sample from the selected best shapelets. A similar distance matrix is generated for any trajectory in the test dataset during the shapelet transformation step. The resulting matrices can be fitted to a plethora of classifications algorithms. As shown in the experimental evaluation section, even with a simple classifier, such as Logistic Regression, high accuracy scores can be achieved in a cross validation setup that comprises synthetic trajectories only.
## 3 Results and Discussion
### Artificial Neural Network Model Results
#### 3.3.1 Artificial Neural Network Performance in Training and Validation  
Using the model overfit index (Equation (3)), it emerges from the results (Table 6) that the ANN models are generally not overfit except for only one model (No. 19) that is overfit. This implies a non-overfit rate of 95%.  
Since the methodology used runs the same ANN model across 10 different partitions of the training data, a review of model results indicates that almost all the models post an \(R^{2}\) of at least 0.7 in all the partitions of the training data except for two models (No 20 and 21 in Table 6). Model 21 from the GAM process that was earlier marked as a special case (with lag of the dependent variable) is shown to post a low performance (\(R^{2}\) = 0.66) in the ANN process. The best model from the ANN process is different from that of the GAM process. In fact, the best ANN model (\(R^{2}\) = 0.83) (No 1 in  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multicolumn{2}{c}{**Model**} & \multicolumn{2}{c}{**Training (\(R^{2}\))**} & \multicolumn{2}{c}{**Validation (\(R^{2}\))**} & \multicolumn{2}{c}{**Overfit**} & \multirow{2}{*}{**Overfit**} \\  & **Min** & **Max** & **Mean** & **Min** & **Max** & **Mean** & **Index** & \\ \hline  
& 0.64 & 0.71 & 0.68 & 0.60 & 0.73 & 0.66 & 0.02 & No \\ \hline \hline \end{tabular}
\end{table}
Table 6: ANN model performances in training and validation datasets. The only overfitting model (No. 19) is indicated using an asterisk (*) and the min, max and mean performances are calculated over 10 partitions of the training data.  
Table 6) was ranked the third best model in the GAM modelling process in Table 5 (\(R^{2}\) = 0.85). Figure 8 illustrates the performance of the ANN models as compared to the GAM models.  
In general, as indicated in Figure 8, the GAM models outperform ANN models except for model 16, for which the ANN slightly out-performs GAM by an \(R^{2}\) of 0.01. This is an important property since the GAM process is proved to be more optimistic in performance as compared to ANN, and therefore fewer deserving models would be excluded from the ANN process. In the training and validation, the best ANN model has the best subset performance of \(R^{2}\) = 0.86 as shown in Figure 9.  
Figure 8: Performance of the ANN models in validation as compared to similar GAM models.  
Figure 9: The best ANN model with the 1-month lag of the variables VCIDekad and RFE1M. The plot is from the 4 th partition of the training data that recorded the best performance. Blue nodes and lines indicate bias nodes and bias terms respectively, the red nodes are the input nodes. The yellow nodes are the hidden nodes while the output node is in green.  
A detailed analysis of the lag-time performance of the ANN models in model training is provided in the Appendix A which has the results for the 102 possible ANN models similar to the GAM process.
## 3 Method: Massive MIMO Network Design
### Problem Description
#### 3.1.2 Tri-Objective Optimization Problem Formulation  
As the three objectives can scarcely be met simultaneously during the operation of the network, a utility function is built by linearly combining \(f_{1}^{3}\), \(f_{2}^{3}\) and \(f_{3}^{3}\) to easily solve this tri-objective problem and find the possible solutions with the best compromises. Thus, this tri-objective problem can be formulated as follows:  
\[\mathbf{P1:}\min_{\{y,\mathbf{x},p\}} \omega_{1,3}f_{1}^{3}+\omega_{2,3}f_{2}^{3}+\omega_{3,3}f_{3}^{3}\] s.t. \[C_{1}:y_{k}\in\{0,1\},\forall k\in\mathcal{K},\] \[C_{2}:y_{k}\in\{0,1,2,\cdots,p_{t}\},\forall k\in\mathcal{K},\] \[C_{3}:x_{kn}\in\{0,1\},\forall n\in\mathcal{N},\forall k\in \mathcal{K},\] \[C_{4}:\sum_{k=1}^{K}x_{kn}=1,\forall n\in\mathcal{N},\] \[C_{5}:\sum_{j=1}^{3}\omega_{j,3}=1,\] \[C_{6}:\frac{\sum_{j=1}^{K}\sum_{i=1}^{N}x_{ij}}{N}\geq 0.95,\]  
where \(\omega_{1,3},\ \omega_{2,3},\ \omega_{3,3}\) are the weights indicating the relative importance (preference) of the objective functions and are determined arbitrary based on the prior knowledge on the priority of either objective. The constraint \(C_{1}\) denotes the operation or not of the \(k\)-th BS. Similarly, the constraint \(C_{2}\) denotes the transmission power. The constraint \(C_{3}\) denotes the association or not between user \(n\) and BS \(k\). Moreover, the constraint \(C_{4}\) expresses the unique association between one user \(n\) with BS \(k\) at the same time. Constraint \(C_{5}\) denotes that the weights sum is equal to one [35]. Additionally, the constraint \(C_{6}\) does not allow the percentage of users served by a BS to be below 95%, as per the radio network planning requirements.  
Given that a linear function is convex, the utility function which is a linear combination of convex functions \(f_{1}^{3},f_{2}^{3}\) and \(f_{3}^{3}\) is convex as well. Therefore, the problem can be treated as a multi-objective optimization convex problem since the utility or the global fitness function is minimized towards the constraints which are convex as well. The higher the global fitness function, the better the network is optimized as the individual objectives are minimized. We then apply the traditional weighted sum method to resolve the multi-objective optimization problem formulated earlier as the decision maker may have a knowledge of the objective's priority. For each new combination of the weights, the 5G networks are designed by means of system-level simulations using the capacity-based network deployment tool of [36; 37] extended with the appropriate optimization algorithm described further in this section. The Pareto optimal concept is then used to compare the different solutions obtained in order to identify the ones with better trade-off between the constraints.
# Deformation Study of Strongly Structured Clays Considering Damage Effects
## 4 Conclusions  
To clearly illustrate the unique deformation characteristics of structured clay, the authors conducted a series of one-dimensional compression tests on Zhanjiang structured clay samples with varying disturbance degrees. The main conclusions are as follows:  
1. Before the soil reached structural yield, the compression index significantly decreased while the compression modulus sharply increased. After reaching structural yield, the compression index stabilized. Concurrently, the change in damage, \(\Delta W\), exhibited a dynamic evolution pattern characterized by an initial increase, followed by a decrease, and then a subsequent increase. Under identical overburden pressure conditions, samples with higher disturbance degrees demonstrated larger compression coefficients, resulting in greater compressibility.
2. For samples with the same damage degree, those exhibiting greater disturbance degrees showed larger compression coefficients, whereas their compression moduli were relatively smaller. Furthermore, the compression coefficient increased exponentially with the rise in damage degree, while the compression modulus decreased according to a quadratic function as the damage degree increased.
3. By analyzing the variation characteristics of the compression modulus and damage degree of disturbed soil samples during one-dimensional compression tests and integrating the conversion relationship between the deformation modulus and compression modulus, this study established an empirical formula for calculating the deformation modulus that accounts for soil sample damage. This formula was validated using two additional sets of experimental data, confirming its effectiveness and providing a solid theoretical foundation and practical reference for the settlement calculations of disturbed, damaged structured clay foundations.  
Writing--original draft, writing--review and editing, and methodology, Y.S.; writing--review and editing and supervision, B.T.; software, Y.S. and Y.W.; data curation, Y.X.; writing--original draft, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Natural Science Foundation, China [Grant No. 2024 GXNSFAA010450].  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 6 Factors Affecting the Adsorption Process
### Aerogels
The sorption of A800 and A900 (for 800 and 900 C) was reduced due to a decrease in the number of nitrogen atoms in the pyridinic and pyridonic rings, as well as the positively charged surface of the samples, which repel positively charged Cu (II) ions. [PERSON] et al. [241] employed a viable green technique to study graphene/polydopamine modified multiwalled carbon nanotube (MWCNT-PDA) hybrid aerogels for effective heavy metal ions adsorption. They discovered that the hybrid aerogel exhibited a high adsorption capacity of 318.47 mg/g for Cu (II) and 350.87 mg/g for Pb(II) due to its appropriate active adsorption sites and interconnect porous structure. [PERSON] et al. [242] developed three-dimensional (3D) double network graphene oxide/polyacrylic acid (GO/PAA) hybrid aerogels under mild conditions utilising a one-pot in situ solution polymerisation techniquethat involved the polymerisation of AA as well as the self-assembly of functional GO sheets. The results of the GO/PAA aerogels demonstrated a high rate of Cu\({}^{2+}\) ion adsorption, good recyclability, and a high adsorption capacity (390.34 mg/g). The Langmuir isotherm model characterised the adsorption equilibrium data well, and the Cu\({}^{2+}\) ions adsorption kinetic data were well-fitted by the pseudo-second-order kinetic model. [PERSON] et al. [243] published another GO hybrid investigation in which a porous calcium alginate/graphene oxide composite aerogel (mp-CA/GO) was used to evaluate the efficient removal of lead (Pb), copper (Cu), and cadmium (Cd) ions from water. Pb\({}^{2+}\), Cu\({}^{2+}\), and Cd\({}^{2+}\) clearance rates were determined to be 95.4%, 81.2%, and 73.2%, respectively. [PERSON] et al. [244] described how graphene oxide aerogels (GOAs) were used to adsorb lead (II) (Pb (II)) metal ions from aqueous solutions. They used a unidirectional freeze-drying approach to create the aerogels from graphene oxide (GO) colloidal suspensions. Their findings revealed a high adsorbent capacity (q\({}_{\text{mqv}}\) = 158 mg/g) and a rather quick adsorption process. [PERSON] et al. [245] investigated the adsorption behavior of copper ions on graphene oxide chitosan (GO-CS) aerogels and found that adsorbent separation may be easily performed after adsorption using filtration or low-speed centrifugation. Furthermore, their findings revealed that GO-CS is an effective Cu\({}^{2+}\) adsorbent, with a substantial adsorption capacity (254 mg/g) and a low binding strength.
## 2 Proposed System
### Intelligibility Criteria  
Sound reproduction setup in end users' living rooms can vary in their specifications. It may be unfeasible to build a system which is capable of making accurate speech intelligibility predictions for all different audio systems from high end down to low end, and in particular making adaptions to accommodate each system automatically. However, as aforementioned, down-mixing, from 22.2 to a stereo system for example, effectively leads to the gradual loss of spatial cues such as relative source separation and distance between objects. As such, given a dialogue scene with a default mixing level, intelligibility may decrease monotonically from a 22.2 to a mono system, with the worst intelligibility in the latter case. In turn, if the intelligibility of the mono system is guaranteed, with the same mixing level there should not be any further intelligibility issue for high-end systems in which spatial cues may be preserved.  
The BiDWGP metric is in the form of a numeric index like other OIMs (e.g., SII and STI), which falls into the range of 0-1 with larger numbers indicating better intelligibility. The output does not directly provide any information about the SBR, however, and so a transformation from index to SBR is needed. What SBR is optimal for speech? To answer this key question, an assumption is made that the optimal SBR should guarantee that all the information conveyed by the speech will be well understood by listeners. The next section details an experiment to measure the minimum SBR leading to 100% intelligibility for normal hearing listeners in a range of background sounds when listening monaurally.
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 3 MLE Discriminator
### Parameters Estimation by LM Algorithm
Otherwise, \(d\) must be increased again. Step 6 adjusts the convergence rate by increasing or decreasing \(d\). When \(\hat{\theta}\) is away from the extreme point (gradient values are large), \(d\) is decreased to achieve fast convergence. When \(\hat{\theta}\) is close to the extreme point (gradient values are small), \(d\) is increased to achieve slow convergence. The inequality \(G\) < 0.25 denotes every element in \(G\) is less than 0.25, the same as below. Step 7 ends the iteration process when gradient is smaller than the threshold \(\Gamma\) or the iteration number exceeds the threshold \(M\). Figure 3: Block diagram of the LM algorithm. LM: Levenberg–Marquardt.
## 2 Theoretical Background for Spherical Localization Based on Attenuation of EM Waves
### Cramer-Rao Lower Bound  
The mobile receiver unit computes its ranges to the beacons by using the measurements of the RSS values on different channels. The ranges are then fused to estimate the receiver's position, i.e., with a Bayesian filter, such as EKF or PF.  
As a measure for the achievable accuracy of the localization system the Cramer-Rao lower bound (CRLB) can be computed to express a lower bound on the variance of the estimated receiver position. The CRLB is the inverse Fisher information matrix and can be computed as:  
\[\mathbf{CRLB}=\mathbf{F}^{-1}. \tag{6}\]  
Define a mean vector of received signal strengths:  
\[\mathbf{\mu}=[\text{RSS}_{1}\text{ RSS}_{2}\cdots\text{RSS}_{N}]^{\top}. \tag{7}\]  
We assume the noisy signal strength measurements to be zero-mean Gaussian with covariance matrix \(\mathbf{C}=\text{diag}(\sigma_{\text{RSS},i}(R_{i}))\) and combined in the vector:  
\[\mathbf{\tau}\sim\mathcal{N}(\mathbf{\mu},\mathbf{C}). \tag{8}\]  
The Fisher information matrix \(\mathbf{F}\in\mathbb{R}^{n,m}\) has the elements:  
\[F_{m,n}=\frac{\partial\mathbf{\mu}^{\top}}{\partial p_{m}}\mathbf{C}^{-1}\frac{ \partial\mathbf{\mu}}{\partial p_{n}}+\text{tr}\left(\mathbf{C}^{-1}\frac{\partial\bm {C}}{\partial p_{n}}\mathbf{C}^{-1}\frac{\partial\mathbf{C}}{\partial p_{m}}\right). \tag{9}\]  
The two-dimensional localization case yields \(p_{m}=x\) and \(p_{n}=y\).  
The CRLBs for a localization scenario with four emitting beacons are illustrated qualitatively in Figure 6. Thereby, the covariance \(\sigma_{\text{RSS},i}(R_{i})\) is modeled as a linearly-increasing function of the range to the \(i\)-th beacon. The CRLBs indicate that the lowest position estimation covariance lies within the convex hull spanned by the beacons.  
Figure 5: Identification of two beacons transmitting at 432.6 MHz and 433.0 MHz, respectively. The receiver unit identifies the beacons and corresponding RSS values by determining the frequencies and peak values.