esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### CNN Performance Evaluation in Challenging Conditions  
As discussed, the authors trained the CNN model using the created dataset. The training was conducted in batches with size of 32. The Keras framework for deep learning was used for implementing the CNN model. The original size of the markers in the synthetic data set were \(512\times 512\). For training the CNN model, the original images were resized to \(64\times 64\) and were given as an input to the first convolution layer. The entire dataset was divided into two parts: 75% for training and 25% for validation. Training and validation datasets were used to determine the layer parameters during the training phase of the model. Additionally, the authors tested the model with new data that had not been encountered before. The test performance of the model was carried out on the trained model. The output was produced with 29 class values. For all the experimental results, the training phase of the CNN model was carried out for 100 epochs. Experimental results were obtained for all classes. The authors also trained the model with the dataset proposed to find which marker produces better results. The training and validation plots for accuracy and loss are shown in Figures 15 and 16.  
Figure 14: Screenshots of our prototype: (**a**) A screenshot of markers after updates; (**b**) a screenshot of PVI while testing our prototype on the fourth floor.  
Figure 15: Comparative accuracy graphs after applying our model on two datasets where our dataset is drawn in red and the other dataset is drawn in blue: (**a**) Results of the training accuracy of the two datasets; (**b**) results of the validation accuracy of the two datasets.  
The results show that the proposed model can detect Aruco markers very well in challenging conditions as it provides approximately 97% accuracy for training and 99.97% accuracy for testing. When applying it to the other dataset, it gave 86% for training accuracy and 94.74% for testing accuracy. From these results, the authors improved the detection in challenging conditions and verify H3. The authors also improved the detection of markers from a long distance and verify H2. So, the authors formulate Thesis T2 and Thesis T3. T2: Aruco markers can be detected from long distances. T3: CNN models can be used to detect Aruco markers in challenging conditions such as blurring effect due to motion. However, this model still failed to detect markers under occlusive conditions. Moreover, the time required for this model to detect markers should be minimized.  
Figure 16: Comparative loss graphs after applying our model on two datasets where our dataset is drawn in red and the other dataset is drawn in blue: (**a**) Results of the training loss of the two datasets; (**b**) results of the validation loss of the two datasets.
## 4 Theoretical Results
### Hamiltonian
Proposition 4: _There exist Delaunay graphs that are not Hamiltonian._  
Proof: Let us consider the set of vertices \(V=\{v_{1},v_{2},\ldots,v_{9}\}\) and the Delaunay graph, \(DT(V)\), from Figure 6. Let \(C\) be a Hamiltonian cycle of \(DT(V)\). The neighbors of \(v_{4}\) in \(C\) must be \(v_{3}\) and \(v_{7}\), since they are the only adjacent vertices to \(v_{4}\). Similarly, the neighbors of \(v_{6}\) have to be \(v_{5}\) and \(v_{7}\) and the neighbors of \(v_{1}\) will be \(v_{2}\) and \(v_{5}\). Every vertex on a cycle has exactly two adjacent vertices. Therefore, the neighbors of \(v_{7}\) will be \(v_{4}\) and \(v_{6}\) and the neighbors of \(v_{5}\) will be \(v_{1}\) and \(v_{6}\). Consequently, \(v_{5}\) and \(v_{7}\) cannot be neighbors of \(v_{8}\) in \(C\). However, \(v_{8}\) must have as neighbors two of the vertices \(\{v_{5},v_{7},v_{9}\}\). This is a contradiction. Figure 4: Delaunay graphs with one internal triangle. Figure 5: Delaunay graphs with two internal triangles.
## 2 Study Area and Method
### The InTEC Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pool** & **ID** & **Description** & **Broadleaf** & **Configrous** & **Unit** \\ \hline \multirow{8}{*}{Biomass Carbon pool} & f\({}_{w}\) & NPP allocation coefficient to wood & 0.4626 & 0.3010 & None \\  & f\({}_{x}\) & NPP allocation coefficient to coarse root & 0.1190 & 0.1483 & None \\  & f\({}_{1}\) & NPP allocation coefficient to leaf & 0.2226 & 0.2128 & None \\  & f\({}_{tr}\) & NPP allocation coefficient to fine root & 0.1960 & 0.3479 & None \\  & K\({}_{w}\) & Wood turnover rate & 0.0288 & 0.0279 & yr\({}^{-1}\) \\  & K\({}_{cr}\) & Coarse root turnover rate & 0.0448 & 0.0269 & yr\({}^{-1}\) \\  & K\({}_{1}\) & Leaf turnover rate & 1.0000 & 0.1925 & yr\({}^{-1}\) \\  & K\({}_{fr}\) & Fine root turnover rate & 0.5948 & 0.5948 & yr\({}^{-1}\) \\  & SLA & Specific leaf area & 31.5 & 70.0 & m\({}^{2}\)kg\({}^{2}\) \\ \hline \multirow{8}{*}{Soil Carbon pool} & K\({}_{\text{sal}}\) & Surface structural leaf litter decomposition rate & 3.9-Lc *.A * & yr\({}^{-1}\) \\  & & decomposition rate & 14.8-A * & yr\({}^{-1}\) \\  & K\({}_{\text{sml}}\) & Soil structural litter decomposition rate & 4.8-Lc *.A * & yr\({}^{-1}\) \\  & K\({}_{\text{fml}}\) & Soil metabolic litter decomposition rate & 18.5-A * & yr\({}^{-1}\) \\  & K\({}_{w}\) & Woodly litter decomposition rate & 2.88-Lc *.A * & yr\({}^{-1}\) \\  & K\({}_{\text{sml}}\) & Surface microbe decomposition rate & 6.0-A * & yr\({}^{-1}\) \\  & K\({}_{\text{m}}\) & Soil microbe decomposition rate & 7.3-A *.Tm * & yr\({}^{-1}\) \\  & K\({}_{\text{s}}\) & Slow C decomposition rate & 0.2-A *Cr * & yr\({}^{-1}\) \\  & K\({}_{\text{p}}\) & Passive C decomposition rate & 0.0045:A *Cr * & yr\({}^{-1}\) \\ \hline \hline \end{tabular}  
*: A is the effect of soil temperature and moisture on the decomposition rate of soil organics, Lc is the effect of structural lignin content on the decomposition rate, and Tm is the effect of soil structure on the transfer of active Soil Organic Matter (SOM).  
\end{table} 
Table 1: Partition coefficients, turnover rates, and decomposition rates of vegetation and soil carbon.
# Deep Learning Model with Sequential Features for Malware Classification
## 2 Related Technology
### Bidirectional Gated Recurrent Unit (BiGRU)  
As a variant of RNN, gated recurrent unit (GRU) also has a recursive structure similar to that of RNN and has the function of \"memory\" in processing time series data. At the same time, GRU can effectively alleviate the gradient disappearance and gradient explosion problems that may occur during RNN training, thus effectively solving the long-term memory problem. Long short-term memory (LSTM) networks are also a variant of RNN [21] and are comparable to GRU in terms of performance, but GRU is structurally simpler and can reduce computational effort and improve training efficiency [22]. The internal structure of GRU is shown in Figure 4. GRU has two inputs, the output state at the previous time and the input sequence value at the current time; the output is the state at  
Figure 2: Dilated convolution.  
the current time. GRU mainly updates the model state through a reset gate and an update gate. The reset gate controls the degree of forgetting historical state information so that the network can discard unimportant information; the update gate controls the weight of the past state information into the present state to help the network remember the information for a long time [23]. The internal equations of GRU are as follows:  
\[\left\{\begin{array}{l}r_{t}=\sigma(\mathbf{W}_{x}\mathbf{x}_{t}+\mathbf{U}_{t}\mathbf{h}_{t- 1})\\ \mathbf{z}_{t}=\sigma(\mathbf{W}_{z}\mathbf{x}_{t}+\mathbf{U}_{z}\mathbf{h}_{t-1})\\ \tilde{h}_{t}=\tanh(\mathbf{W}_{\tilde{h}}\mathbf{x}_{t}+\mathbf{U}_{\tilde{h}}^{2}(\mathbf{r} _{t}\odot\mathbf{h}_{t-1}))\\ h_{t}=(1-\mathbf{z}_{t})\odot\mathbf{h}_{t-1}+\mathbf{z}_{t}\odot\tilde{\mathbf{h}}_{t}\\ \end{array}\right. \tag{4}\]  
The sigmoid activation function is shown in Equation (4) and Figure 4. It serves to convert the intermediate states to the range of 0 to 1; \(h_{t-1}\) and \(h_{t}\) are the output states at moments \(t-1\) and \(t\), respectively; \(x_{t}\) is the input sequence value at moment \(t\) (it is the candidate output state); \(\mathbf{W}_{r}\), \(\mathbf{W}_{z}\), \(\mathbf{W}_{\tilde{h}_{t}}\), \(\mathbf{U}_{r}\) and \(\mathbf{U}_{z}\) are the corresponding weight coefficient matrices of each component; tanh is the hyperbolic tangent function (it is the Hadamard product of the matrix).  
GRU can process the data only from forward to backward and ignores the effect of the latter moment on the data of the previous moment. To combine forward and backward data  
Figure 4: Gated recurrent unit.  
Figure 3: Residual block.  
for integrated learning, BiGRU is used for further learning of the features of the malware. In the BiGRU, which consists of a forward gated recurrent unit and a backward gated recurrent unit, the network model learns the sequence from forward to backward and vice versa. The hidden layer contains two output units with the same input and is connected to the same output. The features can be better learned to increase the time series involved in training, thus providing higher accuracy for longer time series data.
# Studies on Seismic Performance of Precast Concrete Columns with Ground Splice Sleeve
## 1 Introduction
The proposed bar splice sleeve is tested to be pulled out, showing a failure mode of rebar outside the sleeve yielding and then being ruptured, which meets the requirements. The pull-out test is demonstrated in Figure 1b and the sleeve configuration is shown in Figure 1c. The objective of this paper is to study the seismic performance of precast concrete columns with grouted splice sleeves by experiments, and validate that this kind of joint connection of precast members have the same strength as cast-in-situ members. Four 300 mm \(\times\) 300 mm prefabricated columns with grouted splice sleeve connections and two 300 mm \(\times\) 300 mm cast-in-situ columns are made. Through low cyclic repeated loading and monotonic loading tests, the seismic performance of the columns is compared between the prefabricated ones and the cast-in-situ ones, including the load-displacement curve, crack load, yield load, ultimate load, ductility, stiffness, etc. Based on the comprehensive analysis of the experimental results, some recommendations on practical seismic design pertaining to the precast concrete members connected by the grouted splice sleeve are proposed. Figure 1: (**a**) Schematic diagram of the grouted splice sleeve (distances in mm); (**b**) pull-out test of the grouted splice sleeve; (**c**) cast iron sleeve.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 2 Related Work
### NextG Architecture  
The NextG standard, starting with 5G, provides significant changes toward the existing cellular standards (up to 4G) to support enhanced performance, scalability, and coverage to support millions of users and devices. In addition to the 4G point-to-point based structure, the 5G cellular standard structure is also designed as a service-driven architecture [3; 7]. In particular, the new 5G architecture comes with a paradigm change that relies on network slicing and virtual network functions that can be utilized on-demand [8; 9].  
In addition, the 5G standard offers integration of heterogeneous networks to support enhanced coverage through various types of base stations and relays. One of the considered new portable relays is flying UAVs as a flexible and autonomous operable, handling extra users and maximizing throughput, coverage, and accommodations [10]. The UAVs' coverage boosting depends on the position selection of the UAVs within the users in need [11; 12], which as well plays a critical role in enhancing the 5G network's overall performance. For instance, in [13], the authors proposed an enhancement of UAVs' positioning likelihood for optimized cellular coverage utilizing the self-healing neural model.  
UAVs function in the 5G network will not be limited to relaying function; they can serve as short-term flying base stations to provide service in certain infrastructure failure circumstances [14]. In [15], the authors investigated a limited functionality drone (i.e., UAV) base station optimal placement in a 5G+ network backhaul, paying regard to the users' movements effect. In [16], more work was performed on providing adjusting optimal coverage positioning for base station UAVs with varying ray tracing options in different channel situations.  
Due to the increasingly demanding streaming traffic anticipated within the NextG network, it is expected to have a down or overwhelmed associated providers' base station tower with excessive traffic. That is highly expected, especially in highly demanding circumstances such as full stadiums or urgent circumstances such as disastrous situations. Building on the NextG extended dynamic structure capabilities and taking advantage of the UAV mobility; we aim to achieve seamless communication using inter-operator agility in a cooperative cellular environment. We employ UAVs as a mediating point in handling the users' traffic to an external (guest) provider's base station to complete the service request without any service interruption. That is an innovative added feature that we propose as a supplement extension to the 5G service-based agility perspective.
## 3 Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm (CPIMM-NAUKF)
### Cpiim
#### 3.1.2 Estimating the Interaction by Using CP  
Since the mixing probability varies with time, the interactive process is abstracted as a nonlinear function, and the cubature principle is used to approximate the probability distribution of the interacting random variables.  
Firstly, the nonlinear function is used to describe the interaction process. The augmented system \(\mathbf{\chi}=[\mathbf{x}_{1},\ldots,\mathbf{x}_{m}]^{\mathrm{T}}\) is constructed, where \(\mathbf{x}_{i}\sim N\Big{(}\mathbf{x}:\hat{\mathbf{x}}_{i},\mathbf{S}_{i}\mathbf{S}_{i}^{\mathrm{T} }\Big{)}\), \(i=1,\ldots,m\) is a random variable with the \(n\times 1\) dimension, and represents the state vector of the model \(i\); \(l=m\times n\); \(\hat{\mathbf{\chi}}=[\mathbf{x}_{1},\ldots,\mathbf{x}_{m}]^{\mathrm{T}}\). Assuming that \(\mathbf{x}_{i}\) is independent, and we have \(\mathrm{cov}(\hat{\mathbf{x}}_{i},\hat{\mathbf{x}}_{j})_{i\
eq j}=0\), the square root of the covariance matrix of \(\mathbf{\chi}\) is \(\mathbf{S}=diag([\mathbf{S}_{1},\ldots,\mathbf{S}_{m}])\), and we have \(\mathbf{\chi}\sim N\Big{(}\mathbf{\chi}:\hat{\mathbf{\chi}},\mathcal{S}\mathbf{S}^{T}\Big{)}\).  
The next step is choosing cubature points for nonlinear propagation. The cubature points are selected by Equation (13), and \(\mathbf{S}\) is rewritten as \(\mathbf{S}=\sigma_{r}\mathbf{V}\), where \(\mathbf{V}\) is the upper triangular matrix with all the diagonal elements being \(1\); \(\sigma_{r}=\mathbf{S}(r,r)\) is the value of the element on the diagonal of \(\mathbf{S}\), and the cubature points can be written as  
\[\mathbf{\chi}_{\pm r}=\hat{\mathbf{\chi}}\pm\sqrt{l}\sigma_{r}\mathbf{v}_{r},r=1,2,\ldots,l \tag{16}\]  
where \(\mathbf{v}_{r}\) is the \(r\)th column of \(\mathbf{V}\).  
The cubature points are calculated after the interaction by  
\[\mathbf{y}_{\pm r}=f(\mathbf{\chi}_{\pm r}). \tag{17}\]  
Finally, according to the cubature principle, the mean and covariance matrix of \(\mathbf{y}\) are obtained as  
\[\hat{\mathbf{y}}\approx\sum_{r=-l}^{l}\omega_{r}\mathbf{y}_{r} \tag{18}\]  
\[\mathbf{P}_{y}\approx\sum_{r=-l}^{l}\omega_{r}\Big{(}\mathbf{y}_{r}-\hat{\mathbf{y}})(\bm {y}_{r}-\hat{\mathbf{y}})^{T}\Big{)} \tag{19}\]  
where \(r\
eq 0\); \(\omega_{r}=1/2l\).  
For the external IMM, \(\hat{\mathbf{X}}_{j,k-1}^{o}\) shown by Equation (6) can be recalculated by Equation (18), and \(\mathbf{P}_{j,k-1}^{o}\) shown by Equation (7) can be recalculated by Equation (19); then, the newly obtained \(\hat{\mathbf{X}}_{j,k-1}^{o}\) and \(\mathbf{P}_{j,k-1}^{o}\) are used in the iteration of the IMM filter, which leads to CP-IMM.
## 4 Conclusions  
The incorporation of room-temperature ionic liquids (ILs) with different characteristics (BMIMPF\({}_{6}\) or AliquatCl) into a biocompatible regenerated cellulose (RC) film, by only 1 h immersion in the corresponding IL, permits the modification of the mechanical and electrical characteristics of the RC support, which were determined by tensile and impedance spectroscopy measurements. XPS resolved-angle measurements carried out for the RC/BMIMPF\({}_{6}\) and RC/AliquatCl films showed the presence of the ILs in the \(\sim\)10 nm film top layer, indicating a higher coverage of the RC-support by the AliquatCl IL. The coverage with the ILs practically did not modify the transmission/reflexion of the RC-support, but increased its insulating behavior (conductivity reduction and dielectric constant increase); moreover, ILs incorporation favored the mechanical stability of the RC-support, increasing the Young modulus of both RC/ILs films (with differences depending on the IL) significantly. In this context, since ILs can be easily modified, it should be possible to obtain eco-friendly or biocompatible RC/IL composite materials with characteristics of interest for specific applications (biomedical and pharmaceutical applications) by a simple process (without chemical reactions). The effect of immersion time on the parameters studied is a point for further consideration.  
**Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app122010290/s1](https://www.mdpi.com/article/10.3390/app122010290/s1)]([https://www.mdpi.com/article/10.3390/app122010290/s1](https://www.mdpi.com/article/10.3390/app122010290/s1)): Figure S1: Chemical formula of: (a) AliquatCI, (b) ILBMIMPF\({}_{6}\) IL and (c) regenerated cellulose; Figure S2: (a) Comparison of N 1s core level signal determined for a 40% CTA + 60% AliquaCI sample in dry (blue solid line) and hydrated (blue dashed dashed line) states. (b) Comparison of normalized N 1s core level signal for the RC/BMIMPF\({}_{6}\) film (red solid line) and BMIMPF\({}_{6}\) IL (red dashed line); Figure S3: Stain vs. stress curves measured with: (a) a dry RC film, (b) a wet RC film (after 72 h in distilled water); Figure S4: Impedance plots for two CTA/AliquaCI membranes with different AliquatCI content; Table S1: Atomic concentration percentages of the elements detected on the surfaces of the BMIMPF\({}_{6}\) and AliquaCI ILs and the RC-support film.  
**Author Contributions:** [PERSON] designed and coordinated the experiments; [PERSON] performed and analyzed the XPS measurements; [PERSON] carried out the optical characterization; impedance spectroscopy measurements and data analysis were performed by [PERSON] All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The date can be found in Supplementary Materials.  
**Acknowledgments:** To [PERSON] (Applied Physics I Department, University of Malaga, Spain) for his support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Numerical Results and Discussion
### Results of the Webb and Gaussian Modeled SER
As illustrated in Figure 6a, comparative analysis of SER dynamics under varying APD gain \(G\) reveals fundamentally distinct behaviors between the Webb and Gaussian models. For fixed turbulence parameters \(\sigma_{ht}^{2}\) and \(\sigma_{hs}^{2}\), the Gaussian-modeled SER exhibits a non  
Figure 5: (**a**) Comparisons of SER using Webb and Gaussian models over lossless link with \(G=40\). (**b**) Comparisons of Webb and Gaussian models estimated SER under various \(\sigma_{ht}^{2}\), \(\sigma_{hs}^{2}\), and \(\sigma_{p}\) with \(G=40\) and \(\bar{n}_{b}=0.01\). (**c**) Under different AOA fluctuations, comparisons of Webb and Gaussian models estimated SER with \(G=40\) and \(\bar{n}_{b}=0.01\). monotonic trend: initially decreasing sharply with \(G\), reaching a minimum, then gradually increasing due to noise amplification. The Webb modeled SER rapidly decreases before stabilizing asymptotically. This divergence is due to the models' contrasting treatments of signal-to-shot noise ratio (SSNR) shown in Figure 6b. At low-to-moderate gains (\(G<G_{T}\)), both models predict SER reduction dominated by signal amplification, where photon-generated electrons scale quadratically with \(G\) while thermal and dark current noise remain constant. The critical threshold \(G_{T}\), defined as the intersection point of the Webb and Gaussian SER curves, decreases logarithmically with increasing \(\sigma_{ht}^{2}\) and \(\sigma_{hs}^{2}\). Beyond \(G_{T}\), Gaussian-modeled SER degrades due to its large noise amplification, whereas Web-modeled SER stabilizes by incorporating McIntyre-distributed gain statistics that inherently bound excess noise growth. These dynamics underscore the necessity of the Webb model in gain-dominated regimes (\(G<G_{T}\)). In addition, the threshold gain \(G_{T}\) exhibits a positive correlation with channel fading, decreasing from 760 to 300 when \(\sigma_{ht}^{2}\), \(\sigma_{hs}^{2}\) decrease from (2, 3) to (0.4, 0.6), corresponding to a 60.5% mitigation. Similar trends appear under pointing errors and AOA fluctuations, as detailed in Appendix A.3. These significant results reveal that adaptive model switching based on real-time gain monitoring and channel state estimation optimizes receiver efficiency for deep space optical uplink communication under various channel impairments. In general, deep space optical uplink systems confront multi-factor channel impairments, including atmospheric and coronal turbulence, pointing errors, AOA fluctuations, and background noise. For modeling the APD output and estimating system SER performance, [PERSON] model achieves high accuracy by greatly resolving the APD gain dynamics. Gaussian approximation becomes operationally viable when the APD gain exceeds the critical threshold \(G_{T}\), where noise is dominated.