esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### EE2 Adsorption to the SBP Capsule Membrane  
We conducted an adsorption test by using sterile capsules in order to verify that EE2 concentration reduction is a result of bacterial metabolic activity and not a result of passive adsorption to SBP capsule components. The control system without any SBP capsules exhibited an EE2 concentration of 0.37 \(\pm\) 0.04 mg/L and 0.44 \(\pm\) 0.03 mg/L (T = 0 h and T = 24 h, respectively). The test system, which contained five sterile SBP capsules, showed a similar result, with 0.39 \(\pm\) 0.06 and 0.34 \(\pm\) 0.04 mg/L  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values (mg/L) a** \\ \hline pH & \(7.6\pm 0.1\) \\ COD & \(88.25\pm 29.35\) \\ Ammonia-N & \(16.42\pm 9.8\) \\ Kjeldahl-N & \(19.8\pm 11.1\) \\ TSS & \(8.33\pm 5.8\) \\ TP & \(5.42\pm 0.75\) \\ BOD & \(20.13\pm 17.67\) \\ Turbidity (NTU) & \(4.68\pm 2\) \\ \hline \hline \end{tabular}  
* Each value represents the mean of six replicates during the month of sampling. COD(Chemical Oxygen Demand), BOD(Biochemical Oxygen Demand), N(Nitrogen), P(Phosphorus), TSS(Total Suspended Solids).  
\end{table}
Table 1: Characterization of the secondary effluents from the DWWTP (Betaniya, Israel) used in this study.  
(T = 0 h and T = 24 h, respectively) of EE2. No significant difference was observed between the groups (\(t\)-test, \(p\) > 0.05, n = 6). These results show that the capsule's cellulose acetate membrane and other internal components (dead biomass) have no affinity for the EE2 molecules and that the reduction in the EE2 concentration due to passive adsorption is negligible.
## 4 Discussion
### Novelty and Innovation in Research Methods and Data Processing  
This study introduces several innovative aspects in its approach to analyzing vegetation dynamics in Aksu, particularly through its use of data integration and tailored processing techniques. First, by combining MODIS NDVI data with nighttime light data as a proxy for human activity, this study uniquely captures the combined impacts of urbanization and climate on vegetation trends in an arid region.  
Furthermore, the methodology incorporates an S-G filtering process for NDVI data smoothing, specifically optimized for Aksu's unique arid climate and vegetation characteristics. This tailored filtering technique minimizes short-term data noise without compromising the temporal trend integrity, ensuring that subtle vegetation responses to seasonal and annual changes are accurately detected. This study also applies a pseudo-invariant pixel calibration to align DMSP-OLS and VIIRS nighttime light datasets, achieving temporal consistency in human activity measures--a critical factor given the long study period.  
These methodological refinements not only improve data accuracy but also introduce a replicable framework for other arid regions where urbanization, climate variability, and vegetation dynamics intersect. By integrating these innovative techniques, this study provides a valuable contribution to remote sensing methodologies for ecological monitoring in challenging environments like Aksu.
## 3 Results
### Results of Computational Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Setting 1**} & \multicolumn{2}{c}{**Setting 2**} \\
**Algorithm** & \(E_{\text{BBC}}\) & \(E_{\text{O}}\) & \(E_{\text{BBC}}\) & \(E_{\text{O}}\) \\ \hline mQSDDE\({}_{21,01,1}^{0,1,0}\) & \(\mathbf{8.09\pm 1.29}\) & \(\mathbf{11.72\pm 1.38}\) & \(12.73\pm 1.87\) & \(18.43\pm 1.81\) \\ mQSDDE\({}_{21,01,0}^{0,2,1,0}\) & \(8.64\pm 1.45\) & \(12.38\pm 1.60\) & \(12.28\pm 1.65\) & \(17.93\pm 1.80\) \\ mQSDDE\({}_{21,01,0}^{0,3,1,0}\) & \(8.10\pm 0.96\) & \(11.80\pm 1.02\) & \(12.19\pm 1.24\) & \(17.87\pm 1.25\) \\ mQSDDE\({}_{21,01,0}^{0,4,1,0}\) & \(8.23\pm 1.08\) & \(11.88\pm 1.16\) & \(\mathbf{12.17\pm 1.39}\) & \(\mathbf{17.70\pm 1.50}\) \\ mQSDDE\({}_{21,01,0}^{0,5,1,0}\) & \(8.42\pm 1.24\) & \(12.12\pm 1.40\) & \(12.63\pm 1.52\) & \(18.20\pm 1.77\) \\ mQSDDE\({}_{21,01,0}^{0,6,1,0}\) & \(8.42\pm 1.42\) & \(11.87\pm 1.44\) & \(12.90\pm 1.30\) & \(18.36\pm 1.48\) \\ mQSDDE\({}_{21,01,0}^{2,1,0}\) & \(9.00\pm 1.45\) & \(12.48\pm 1.50\) & \(13.44\pm 1.49\) & \(19.04\pm 1.75\) \\ mQSDDE\({}_{21,01,0}^{0,1,0}\) & \(9.21\pm 1.33\) & \(12.67\pm 1.36\) & \(13.34\pm 1.63\) & \(18.65\pm 1.71\) \\ mQSDDE\({}_{21,01,0}^{0,9,1,0}\) & \(9.50\pm 1.78\) & \(13.23\pm 2.01\) & \(13.78\pm 2.02\) & \(19.30\pm 2.12\) \\ mQSDDE\({}_{21,01,0}^{1,0,1,0}\) & \(9.02\pm 1.31\) & \(12.58\pm 1.35\) & \(13.65\pm 1.70\) & \(19.13\pm 1.78\) \\ \hline \multicolumn{4}{c}{Setting 3} & \multicolumn{2}{c}{Setting 4} \\ Algorithm & \(E_{\text{BBC}}\) & \(E_{\text{O}}\) & \(E_{\text{BBC}}\) & \(E_{\text{O}}\) \\ \hline mQSDDE\({}_{21,01,0}^{0,1,1,0}\) & \(9.37\pm 1.30\) & \(12.72\pm 1.34\) & \(\mathbf{13.29\pm 1.28}\) & \(17.88\pm 1.74\) \\ mQSDDE\({}_{21,01,0}^{0,2,1,0}\) & \(\mathbf{8.92\pm 0.95}\) & \(\mathbf{12.30\pm 1.02}\) & \(13.41\pm 1.74\) & \(\mathbf{17.87\pm 2.07}\) \\ mQSDDE\({}_{21,01,0}^{0,3,1,0}\) & \(9.12\pm 1.04\) & \(12.54\pm 1.12\) & \(13.70\pm 1.61\) & \(18.21\pm 1.79\) \\ mQSDDE\({}_{21,01,0}^{0,4,1,0}\) & \(9.19\pm 1.01\) & \(12.61\pm 1.06\) & \(13.65\pm 1.39\) & \(18.09\pm 1.60\) \\ mQSDDE\({}_{21,01,0}^{0,5,1,0}\) & \(9.45\pm 1.02\) & \(12.82\pm 1.08\) & \(14.18\pm 1.68\) & \(18.92\pm 2.05\) \\ mQSDDE\({}_{21,01,0}^{0,1,0}\) & \(9.65\pm 1.40\) & \(12.88\pm 1.36\) & \(14.56\pm 1.88\) & \(19.03\pm 2.16\) \\ mQSDDE\({}_{21,01,0}^{0,1,0}\) & \(9.81\pm 1.37\) & \(13.16\pm 1.45\) & \(14.79\pm 1.63\) & \(19.04\pm 2.04\) \\ mQSDDE\({}_{21,01,0}^{0,8,1,0}\) & \(9.63\pm 1.20\) & \(12.87\pm.29\) & \(14.92\pm 2.08\) & \(19.08\pm 2.34\) \\ mQSDDE\({}_{21,01,0}^{0,9,1,0}\) & \(9.80\pm 1.00\) & \(12.98\pm 1.03\) & \(14.95\pm 1.69\) & \(19.26\pm 1.79\) \\ mQSDDE\({}_{21,01,0}^{1,0,1,0}\) & \(10.34\pm.28\) & \(13.62\pm 1.23\) & \(14.87\pm 1.53\) & \(19.03\pm 1.92\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of mQSDDE, with different scaling factor sampling parameters. Figure 2: Current error graphs of mQSD\({}_{29}\) and mQSDDE\({}_{21,01,p}\) in four benchmark settings.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 6 Evaluation  
In this section, we conduct a performance evaluation of the proposed protocols through experiments with PQC algorithms. First, we enhance the PQC algorithm implementation by adding encryption and decryption functions, enabling data encryption and decryption using both PQC standalone algorithms and the PQC-AES hybrid protocols. Second, we implement our proposed protocols (i.e., PQC standalone and PQC-AES hybrid PKE protocols). Furthermore, we measure the time required to generate PQC KEM keys and PQC signature keys, as well as the time required to perform encapsulation, decapsulation, and file encryption and decryption, among other operations. We use our extended version (which can now handle data encryption and decryption) of the PQC algorithm API implementation provided by Open Quantum Safe (Liboqs) [29], which is a collection of all the PQC algorithms selected by NIST as one standard library.
## 3 Performance Evaluation
### Base Layer Adjustment  
Our goal is to provide high QoS for video streaming before new connection establishment. Since BL includes the most basic data for playing the video, for this reason, BL is needed to transmit in advance. In the following, we discuss the simulation result of BL adjustment.  
As shown in Figures 5 and 6 let \(K_{i}\) be a constant. When the starting time is approaching the actual TTT, the shorter \(t_{r}\) can be used for transmissions and the value of \(d_{BL,0}\) decreases accordingly. While the starting time is after 71 (Figure 4) or after 21 (Figure 5), \(d_{BL,0}\) increases slightly and approaches a constant. This is because there is a shorter pre-scheduling time for transmissions after 71 (Figure 5) or after 21 (Figure 6), we need to assign a higher \(d_{BL,0}\) for maintaining high QoS for streaming video. Furthermore, because of limitative pre-scheduling time, a greater number of users leads to higher \(d_{BL,0}\) compared to a smaller number of users. On the other hand, high velocity causes a severe decrease of \(d_{BL,0}\) because of a shorter pre-scheduling time.  
Because BL has higher priority for high QoS for video streaming, while the starting time is after 75 s (Figure 7) and 21 s (Figure 8), we can see \(K_{i}\) has a severe decent rate, especially at higher velocity. This indicates our mechanism can provide more BL to meet high QoS for streaming video.  
Figure 5: Starting time for pre-scheduling _vs._\(d_{BL,0}\) (UE velocity = 30 km/h, actual TTT = 79.924 s).  
Figure 6: Starting time for pre-scheduling _vs._\(d_{BL,0}\) (UE velocity = 120 km/h, actual TTT = 25.981 s).
# A Feature Selection Algorithm Performance Metric for Comparative Analysis
## 2 Background and Related Work
### Feature Selection  
The application of feature selection on a classification dataset ideally results in the smallest set of relevant features with respect to the classification task. Feature selection is applied as a pre-processing technique in order to reduce the dimensionality of a problem by removing redundant and irrelevant features. In addition, feature selection attempts to improve the performance of classification algorithms. By reducing the number of features, the classification model complexity is reduced which results in lower computational cost to construct and use the classification model. The probability for the classification model to overfit is reduced when using less, but relevant, features since the model is simpler and noise in the form of irrelevant features is removed. Despite this advantage of simpler models, the dimensionality reduction in, and performance improvement objectives of, feature selection can often be in conflict with each other.  
Feature irrelevance is misleading since two mutually exclusive features could be irrelevant, where the union of these features could be information rich with respect to the dependant variable [7]. The utilisation of a subset of relevant features, as opposed to the set of all features, has been shown to increase classifier performance, reduce computational complexity, and lead to a better understanding of the data for machine learning [3].  
Feature selection algorithms can be categorised into three distinct categories, namely, filter, wrapper and embedded methods. This study exclusively focuses on filter and wrapper techniques. Filter methods [3] establish feature relevance based on information with respect to the dependent variable, using an information theoretic criterion such as correlation coefficient or mutual information. Wrapper methods [3] use subsets of features, for which a measure of the model performance is obtained per subset of features. Heuristic search is used by wrapper methods to determine the set of most relevant features, using the model accuracy as the objective function.  
Choosing the most suitable feature selection algorithm for a given dataset is an unsolved problem. This is due to the lack of an underlying theoretical framework and a limited understanding of the nature of the feature selection problem [7].
## 4 Key Enabling Techniques
### Dynamic Spectrum Access (DSA)  
As per 5G vision, spectrum bands have two categories: Below 6 GHz Band and above 6 GHz. The scarce and costly below 6 GHz band is exceptionally crowded as it is already been allocated to several communication systems. The challenging demand for data by a factor of 1000\(\times\) can be addressed by exploiting additional bands, licensed and unlicensed. The cellular operators find unlicensed spectrum effective, economical, and less crowded supplement to enhance the capacity of existing soon-to-be overloaded wireless networks as a radar might be operating in the same band. An advanced dynamic channel access strategy to enhance the quality of experience has been investigated in [88]. The DSA is an access technique of sharing licensed-plus-unlicensed heterogeneous spectrum, as a means to feed emerging wireless network technologies. It has the advantages of low cost, enhanced coverage, channel capacity and bandwidth, high quality of service, and less interference [99; 100; 101]. In [102], extended dynamic spectrum access, a solution proposed for the unbalanced spectrum loads and perceived capacity bottleneck. The DSA has two broad classifications: cognitive-inspired radio access and co-operative-inspired radio access. A survey of advanced schemes for spectrum sharing to a significant increase of the spectrum efficiency in 5G networks which is extensively endorsed by both the industry and the academia [103].
## 2 Materials and Methods
### Methods  
#### 2.3.1 Groundwater Flow and Heat Transport Equations  
The heat transport equation in a saturated porous medium can be written as [1,2]:  
\[\frac{\partial\rho_{b}c_{b}T}{\partial t}=-\
abla\cdot[\rho_{w}c_{w}qT-\lambda_ {b}\
abla T]+Q_{T} \tag{1}\]  
where \(\
abla T\) is the temperature gradient (\({}^{\circ}\)K/m), the main variable is the temperature \(T\) (\({}^{\circ}\)K) and the main properties or parameters influencing this transport are the bulk thermal conductivity \(\lambda_{b}\) (W/(m\({}^{\circ}\)K)) and heat capacity \(c_{b}\) (J/(kg\({}^{\circ}\)K)) of the porous medium. The other properties are linked to groundwater with the density \(\rho_{w}\) (kg/m\({}^{3}\)) and heat capacity \(c_{w}\) (J/(kg\({}^{\circ}\)K)). The groundwater flux vector \(q\) (m/s) is obtained from [PERSON]'s law. External heat or sink sources are taken into account through a positive or negative value of \(Q_{T}\) (W/m\({}^{3}\)), respectively.  
The groundwater flow is solved by taking into account the pumping and reinjection influences using the following transient equation in saturated conditions [1,2]:  
\[\
abla\cdot\rho_{w}(K\cdot\
abla h)+\rho_{w}q^{\prime}=\rho_{w}S_{s}\frac{ \partial h}{\partial t} \tag{2}\]  
where the main variable is the potentiometric \(h\) (m), \(\
abla h\) is the piezometric gradient vector (-), and the main properties or parameters influencing this groundwater flow are the hydraulic conductivity tensor \(K\) (m/s), and the specific storage coefficient \(S_{s}\) (m\({}^{-1}\)). Pumping and reinjection are taken into account through the negative and positive values of \(q^{\prime}\) (s\({}^{-1}\)), respectively.  
Feflow(r) allows solving the groundwater flow problem in saturated or variably saturated conditions, coupled with heat transport with temperature-dependent groundwater density and viscosity. This software allows very flexible meshes of finite elements.  
To link the groundwater pumping and reinjection to the thermal energy needs, we use the following expression of the power \(P\) (in W) [1,2]:  
\[P=\frac{Q\Delta T\rho_{w}c_{w}}{\left(1-\frac{1}{\zeta COP}\right)} \tag{3}\]  
where the coefficient of performance _COP_ (-) of the heat pump can be expressed as the ratio between the geothermally produced and the total required work. This equation shows that this thermal energy is indeed directly proportional to the water flow rate \(Q\) (m\({}^{3}\)/s) and the temperature difference \(\Delta T\) (\({}^{\circ}\)K).
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 2 Background
### Sensitivity Analysis in HMM  
As shown in recent studies [13; 25; 32; 33], sensitivity analysis is establishing a functional relationship that describes how output probability varies as the network's parameters change. A posterior marginal probabilities, \(y=p(v|e)\), where \(v\) is value of a variable \(V\) and \(e\) is the evidence available, is considered to represent the output probabilities of interest. The network parameter under consideration is represented as \(\theta=p(v_{j}|\pi)\) for a value \(v_{j}\) of a variable \(V\), and \(\pi\) is an arbitrary combination of the values of the set of evidences of \(V\). So \(p(v|e)(\theta)\) represents the posterior marginal probabilities as a function of \(\theta\).  
Consequently, when the parameter \(\theta=p(v_{j}|\pi)\) varies, each of the other conditional probabilities \(\theta=p(v_{i}|\pi)\) from the same distribution co-vary accordingly by the ratio between the the remaining probabilities. That is, if a parameter \(\theta=p(v_{j}|\pi)\) for a variable \(V\) is varied, then  
\[p(v_{i}|\pi)(\theta)=\begin{cases}\theta&\text{if $i=j$}\\ p(v_{i}|\pi)\frac{1-\theta}{1-p(v_{j}|\pi)}&\text{if $i\
eq j$}\end{cases} \tag{4}\]  
The co-variation simplifies to \(p(v_{i}|\pi)(\theta)=1-\theta\) for binary-valued \(V\). The sensitivity function is defined based on the stated proportional co-variation assumption. The constants in the general form of the sensitivity function are generated from the network's parameters that are neither varied nor co-varied, and their values depend on the output probability of interest. A sensitivity function for a posterior probability of interest is a quotient of two polynomial functions, since \(p(v|e)=p(v\,e)/p(e)\), and hence a rational function.  
We have the following univariate polynomial sensitivity function to define the joint probability of a hidden state and a sequence of observations as a function of a model parameter \(\theta\).  
\[p(x_{v}^{t},\mathbf{y}_{\mathbf{e}}^{\mathbf{i}:\mathbf{T}})=\sum_{i=0}^{N}c_ {v,i}^{t}\cdot\theta^{i} \tag{5}\]  
The coefficients \(c_{v,i}^{t}\) are derived from the network parameters that are not co-varied by assuming proportional co-variation of the parameters from the same conditional distribution. The coefficients \(c_{v,i}^{t}\) depend on the hidden state \(v\) and time slice \(t\) under consideration (see for details [25; 26]).
## 3 Results and Discussion
### Simple Harmonic Oscillator (SHM)
#### 3.1.2 Discussion of Results  
In this section, we test out both Adam and L-BFGS optimizers to see which of the two performs better as a source model to scale to higher frequencies. Then, the source models are trained with L-BFGS to transfer learning to the desired higher-frequency models.  
The results of the 40 Hz case without transfer learning are given in Figure 5 as a baseline. Both Adam and L-BFGS optimization algorithms are tested, leading to two versions of baseline models. In the following results, we use L-BFGS to train the network. Figure 6 shows each of the baseline model's solutions compared to the exact solution and its training curve. We make use of transfer learning and compare it with Adam and L-BFGS baseline models. In Figure 6b, it is evident that the source model for 30 Hz with Adam performed much better than that of L-BFGS. As mentioned above, this might be due to the nature of L-BFGS.  
When we compare the results of the 40 Hz case without transfer learning (Figure 5d) with those using transfer learning (Figure 6b), we observe that [PERSON] achieved a loss order of \(10^{-3}\) in about 75,000 iterations, while the one using transfer learning achieved the same order in less than 2000 epochs. This not only reduced the computation time significantly but also provided a more accurate solution.  
Now, moving on to the next set of results, we use the 40 Hz model as the source to train the model on \(\omega=50\). Similarly, we used the \(\omega=50\) model as the source to train the model on \(\omega=60\). The results are given in Figure 7. In both cases, the L-BFGS optimizer was used to train the model. The order of loss for \(\omega=60\) Hz is higher than that of \(\omega=50\) Hz because of the complexity of the solution; however, the PINN's solutions fit well with the exact solution. By comparing the relative L2 error as described in Table 1, we can conclude that although LBFGS performs more stably than Adam in training the initial model, it does not provide a \"good\" initial model for transferring to higher-frequency problems. It is difficult for Adam to handle high-frequency problems, but it provides better initial models on low-frequency problems. Using transfer learning to transfer the model trained with [PERSON] from low-frequency problems to high-frequency problems obtain more accurate solutions.  
Figure 5: Comparisons of Adam optimizer vs. L-BFGS optimizer at 40 Hz.  
Figure 6: Comparisons of 40 Hz for Adam and LBFGS.  
Figure 7: Transfer learning results for \(\omega=50\) Hz and \(\omega=60\) Hz. In both the cases, L-BFGS optimizer was used to train the model. The order of loss for \(\omega=60\) Hz is higher than that that of \(\omega=50\) Hz because of the complexity of the solution; however, the PINN’s solutions fit well with the exact solution.
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 3 Our Merging Approach: Ring Reunion Algorithm
### Coordinating the Merging Attempts in a Construct Through a Merging Probability  
In order to limit the number of merging processes to one per individual construct, instead of one per node, we introduce a certain probability for starting merging processes. Specifically, if a node is member of an overlay construct of size _size_, then each participating node's probability to initiate a merging process is \(\frac{\alpha}{size_{r}}\), where parameter Alpha \(\alpha\) defines the number of started merging operations. Thus, within a time frame any separated overlay aims at finding new contacts and initiating \(\alpha\) merging processes. If, for example, the probability is given by \(1/size_{r}\), only one node per ring starts a merger instance on average.  
While \(\alpha\) is a parameter, for which we specify good values in the evaluation, the size of the ring _size_, has to be calculated spontaneously. In the past, we have proposed several overlay statistic monitoring approaches such the tree-based over-overlay SkyEye [33] or Gossip-based approaches [34] that require more interaction with neighboring nodes (and might be hindered in a partitioned network). Optimistically, a good estimation for the size of the network is also sufficient. Thus, we opt for a quick, locally computable estimation on the network size by following an approach similar to that presented in [35] and estimate the size of the network by calculating the average responsibility range within the known contacts of a node. Therefore, we consider the id ranges which are managed by the predecessor, successor(s), and fingers of a node. Based on this information, we estimate the average responsibility id range, \(resp_{p}\), of a node. Consequently, the estimated number of nodes in the overlay is given by \(size_{r}=\frac{2^{160}}{resp_{p}}\), i.e., the size of the identifier space of the network divided by the estimated average responsibility range of a single node.  
Thus, any node initiates a merging process with probability \(\frac{\alpha}{size_{r}}\) within a given time interval. As a result, each overlay construct initiates approximately \(\alpha\) merging processes within this time interval. As shown in the evaluation, the Ring Reunion Algorithm performs well with this concept.