esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### State-Space Representation  
In order to facilitate both the formal analysis and computer implementation, a state-space model was introduced. According to (5), the dynamical dependencies in the considered network may be grouped into:  
\[\mathbf{l}(t\ +\ 1)=\ I(t)+\sum_{\beta=1}^{\mathbf{B}}\mathbf{M}_{\beta}(t\ -\ \beta)\mathbf{o}(t\ -\ \beta)+\ \mathbf{M}_{0}(t)o(t)-\mathbf{s}(t), \tag{7}\]  
where:  
\(\bullet\)\(\mathbf{l}(t)\) is the vector of on-hand stock levels.  
Figure 1: Operational sequence at a controlled node.  
* \(\mathbf{s}(t)\) is the vector of satisfied demands.
* \(\mathbf{o}(t)\) is the vector of stock replenishment orders.
* \(\mathbf{\mathrm{M}}_{0}(t)\) is the hollow matrix representing the internal trans-shipments: \[\mathbf{\mathrm{M}}_{0}(t)=\left[\begin{array}{cccc}0&\alpha_{12}(t)&\cdots& \alpha_{1N}(t)\\ \alpha_{21}(t)&0&\cdots&\alpha_{2N}(t)\\ \vdots&\vdots&\ddots&\vdots\\ \alpha_{N1}(t)&\alpha_{N2}(t)&\cdots&0\end{array}\right].\] (8)
* \(\mathbf{\mathrm{M}}_{\beta}(t)\) are the diagonal matrices describing the node interconnections, i.e., for each \(\beta\in[1,\,\mathrm{B}]\): \[\mathbf{\mathrm{M}}_{\beta}(t)=\left[\begin{array}{cccc}\sum\limits_{i\
ot\in_{ 1}=\beta}\alpha_{i1}(t)&0&\cdots&0\\ 0&\sum\limits_{i\
ot\in_{2}=\beta}\alpha_{i2}(t)&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&\sum\limits_{i\
ot\in_{\mathrm{B}N}=\beta}\alpha_{iN}(t)\end{array}\right].\] (9)  
For the sake of further derivations, a matrix \(\mathbf{\mathrm{M}}\) is defined as (with the time reference dropped to indicate the nominal operating conditions):  
\[\mathbf{\mathrm{M}}\ =\sum\limits_{\beta=1}^{\mathrm{B}}\mathbf{\mathrm{M}}_{\beta} =\ \mathbf{\mathrm{I}}_{N}+\mathbf{\mathrm{M}}_{0}, \tag{10}\]  
where \(\mathbf{\mathrm{I}}_{N}\) is an identity matrix of size \(N\times N\). \(\mathbf{\mathrm{M}}\) is invertible and one can show [55] that \(\mathbf{\mathrm{M}}^{-1}\) is a positive matrix.
###### Abstract  
This paper proposes a novel joint precoding and artificial noise design framework for generalized spatial directional modulation (AN-GSDM) in unmanned aerial vehicle (UAV) communications, aimed at enhancing the physical-layer security of downlink UAV communication systems. The key innovation lies in the dynamic co-optimization of multi-beam control and artificial noise (AN) power allocation under mobility constraints, enabling real-time adaptation to varying channel conditions. This approach jointly optimizes the precoding matrix and power-control factor, facilitating the effective management of multi-beams and AN to maximize the secrecy rate. The secrecy rate expression is derived, and the corresponding joint optimization problem is formulated. Due to the non-convex nature of the problem and the lack of a closed-form solution, an alternating iterative algorithm is proposed. This algorithm alternates between optimizing the precoding matrix using gradient descent and deriving a suboptimal closed-form solution for the power-control factor. Simulation results confirm that the proposed algorithm significantly enhances security by maximizing the secrecy rate, reducing eavesdroppers' achievable rate to near zero, while simultaneously maintaining legitimate user's rate. The approach not only strengthens security but also preserves system effectiveness, demonstrating robust convergence properties. This makes it a practical and promising solution for secure UAV communication.  
generalized spatial and directional modulation; artificial noise; secrecy rate optimization; enhancing security; joint precoding and power-control factor design +
Footnote †: journal:  
Article
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 2 Communication Model  
Protocols of this category communication takes place from neighbor to neighbor, usually via single-hop routing. These data-centric protocols can convey more data for a certain quantity of energy. However, data delivery is not guaranteed. Protocols of this kind are classified into three subcategories depending on the method used in order to exchange data, which namely are: Query based, Coherent/Non-coherent, and Negotiation based.
When organic molecules absorb IR radiation, the radiation is converted into molecular vibrations. Although the IR spectrum is not characteristic of the entire molecule, the energy required to vibrate a functional group such as a carbonyl (-C=O) is very similar in different compounds so the peaks occur at or near the same frequency regardless of the structure of the rest of the molecule. Thus, IR spectroscopy can be used to elucidate organic structure by providing structural/chemical characterization based on functional group vibrations. Characteristic absorption bands can be used for compound-specific detection or to verify quality markers in plant extracts or distillates while a fingerprint IR spectrum can be used to identify unknown compounds. Attenuated total reflection (ATR) is a sampling technique which is considered a universal sampling accessory. Spectra are recorded directly in either the solid or liquid state, without sample preparation. The only sample preparation required is to apply pressure with the pressure applicator, if solid samples are analyzed. There is no need to apply pressure when liquid samples are analyzed. The quality of ATR-FTIR spectra is sufficient to allow identification when examined and compared to the reference spectra of pure compounds, on different portions of the spectrum. Despite a small interference from solubilized silica to sample identification, compounds isolated from certain chromatographic bioactive bands can be identified. The search-match results can be used to compare the ATR-FTIR spectrum of the compound from certain chromatographic bands with the ATR-FTIR spectrum of a standard compound. The open access SpectraBaseTM(John Wiley & Sons, Inc., Hoboken, NJ, USA) [8] is a free database of IR, NMR, Raman, UV, and mass spectra, containing hundreds of thousands of spectra provided by Wiley. Spectra can be searched by name, International Chemical Identifier (InChI) key, and Chemical Abstracts Service (CAS) number. Spectra of unknown compounds isolated from chromatograms can be superimposed with the spectrum from the database for comparison. Combined with effect-directed assays and ATR-FTIR spectroscopy, hyphenated HPTLC allows an effect-directed high-throughput screening and fast characterization of the active compound. HPTLC combined with effect directed analysis has been previously used to detect biological active compounds on chromatograms [9]. Combined with effect-directed assays and attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, hyphenated HPTLC has allowed a fast characterization of the compound responsible for \(\alpha\)-amylase inhibition in Mediterranean culinary herbs [10]. Compounds with biological activities from five native Australian plants were identified via ATR-FTIR spectroscopy [11]. ATR spectral analysis of this bioactive band on chromatograms has tentatively identified an oleanolic acid derivative to be responsible for \(\alpha\)-amylase inhibition. Figure 1: Overview of hyphenated (HP)TLC with ATR-FTIR identification as a tool in the Effect-Directed Discovery of bioactive compounds in plant extracts.
## 4 Experimental Results
### Model-Based Approach Based on Spectral Features and Conventional Machine Learning Algorithms
RMSEs for the valence dimension using (**c**) principal component regression models and (**d**) partial least square models. and it was 0.156 using the baselined features alone. Using the PLS method, the lowest RMSE was 0.151 using the combined features, and it was 0.156 using the baselined features alone. Using the feedforward method, the lowest RMSE was 0.158 using the combined features, and it was 0.169 using the baseline features alone. Again, the inclusion of the STC-based features shows an improvement in prediction error. It is believed that the proposed STC-based features are more suitable for capturing the quality of audio signals, such as timbre, harmonicity, etc. Since the quality of audio signals, especially timbre, plays a major role in affecting the perception of emotional valence in music [22], the inclusion of the STC-based features was more effective in improving the prediction accuracy of the valence dimension than the one of the arousal dimension. Similar improvements were demonstrated when the Pearson's correlation coefficients between the ground-truth labels and the predicted labels were calculated. The overall results are shown in Table 2, and it is shown that the best results were obtained when the baseline features were combined with the STC-based features.
## 4 Chemical Deterioration Symptoms in RC Structures  
Understanding the nature and magnitude of deterioration necessitates a comprehensive exploration of the symptoms associated with each deterioration type within the structure. These symptoms are derived from inspections and are compiled by examining existing concrete technology literature. This research aims to encompass the complexities arising from deterioration, considering both unique symptoms associated with specific deterioration types and those shared across multiple types. In total, 21 symptoms (which are explained in the following) are indicative of chemical deteriorations in reinforced concrete structures or are directly attributable to them:  
1. Concrete strength reduction.
2. Concrete durability reduction.
3. Aggregate pop-outs.
4. Concrete disintegration.
5. Increase in chlorine ion concentration.
6. Concrete spalling.
7. Concrete porosity/permeability.
8. Interfacial transition zone (ITZ) strength reduction.
9. Increase in salt/ion concentration.
10. Increase in sulfate ion concentration.
11. Irregular cracks.
12. Map cracking.
13. Alkali-silica gel leakage.
14. Alkali-carbonate gel leakage.
15. Efflorescence.
16. Steel rust production (corrosion).
17. Increase in cement Na2O equivalent alkali percentage.
18. Existence of potentially active aggregates for ASR.
19. Existence of potentially active aggregates for ACR.
20. Appearance of purple region after spraying phenolphthalein (concrete carbonation).
21. Structural deformations.  
The aforementioned 21 indicators, crucial for diagnosing the type and extent of chemical deteriorations, are fraught with uncertainty, inaccuracy, and ambiguity. Several factors contribute to the unreliable nature of this information, including the inspector's experience, timeliness, and expertise, as well as the definition and severity of the complications involved. Additionally, the complex environmental conditions further complicate the interpretation of these indicators [25]. Schematic representations of the study framework can be observed in Figures 1 and 2.  
Figure 1: Symptoms and types of chemical deteriorations.
## 3 Methodology
### Transmission
#### 3.1.5 Channel Authentication  
Here, the channel was authenticated following \(\partial_{\text{mod}}\) using CHMAC-EPQH, to protect the channel from attacks and for secure transmission. The CHMAC-EPQH algorithm was mentioned in Section 3.1.1. The authenticated channel was denoted as \(\Omega\).
## Appendix C  
The lower bounds of NDA-EVM over the \(\eta-\mu\) channels can be obtained by substituting Equations (4) and (11) into Equation (18), and letting \(t=b^{2}k^{2}\alpha^{2}/4\sigma_{n}^{2}\), it can be rewritten as  
\[\xi_{\eta-\mu}(k)>\int_{0}^{\infty}{\frac{\tau(k)e^{-t}}{\theta_{1}^{\mu} \theta_{2}^{\mu}\Gamma(2\mu)}\beta(k)^{2s}t^{2\mu-1}\Phi_{2}^{(2)}(\mu,\mu;2 \mu;\frac{-\beta(k)t}{\theta_{1}},\frac{-\beta(k)t}{\theta_{2}})dt}, \tag{12}\]  
where \(\tau(k)=\frac{\sigma_{n}+bk}{(2\pi)^{1/4}}\exp(-\frac{b^{2}[(1+k)^{2}+2k(1+k)] }{4\sigma_{n}^{2}})\) and \(\beta(k)=\frac{4\sigma_{n}^{2}}{b^{2}k^{2}}\). Without the loss of generality, \(p\) is introduced, and Equation (11) is transformed as follows:  
\[\xi_{\eta-\mu}(k)>\int_{0}^{\infty}{\frac{\tau(k)e^{-t}}{\theta_{1}^{\mu} \theta_{2}^{\mu}\Gamma(2\mu)}\beta(k)^{2\mu-p}\frac{1}{k^{2p}}(\frac{2\sigma_{ n}}{b})^{2p}t^{2\mu-1-p}t^{p}\Phi_{2}^{(2)}(\mu,\mu;2\mu;\frac{-\beta(k)t}{ \theta_{1}},\frac{-\beta(k)t}{\theta_{2}})dt}. \tag{13}\]  
When \(\alpha\geq 1/k\) and \(t^{p}(2\sigma_{n}/b)^{2p}=(k\alpha)^{2p}\geq 1\), Equation (34) can be rewritten as:  
\[\xi_{\eta-\mu}(k)>\int_{0}^{\infty}{\frac{1}{k^{2p}}\tau(k)\beta(k)^{2\mu-p} \exp(-t)\frac{t^{2\mu-p-1}}{\theta_{1}^{\mu}\theta_{2}^{\mu}\Gamma(2\mu)} \Phi_{2}^{(2)}(\mu,\mu;2\mu;\frac{-\beta(k)t}{\theta_{1}},\frac{-\beta(k)t}{ \theta_{2}})dt}. \tag{14}\]  
According to the identity [28]  
\[F_{D}^{(n)}[a,b_{1},\cdots,b_{n},c,x_{1},\cdots,x_{n}]=\frac{1}{\Gamma(a)} \int_{t=0}^{\infty}{e^{-t}t^{\mu-1}\Phi_{2}^{(n)}(b_{1},\cdots,b_{n},c,x_{1}t,\cdots,x_{n}t)dt}, \tag{15}\]  
Equation (14) is equivalent to  
\[\xi_{\eta-\mu}(k)>\frac{1}{k^{2p}}\tau(k)\beta(k)^{2\mu-p}\frac{\Gamma(2\mu-p )}{\theta_{1}^{\mu}\theta_{2}^{\mu}\Gamma(2\mu)}F_{D}^{(2)}(2\mu-p,\mu,\mu;2 \mu;\frac{-\beta(k)}{\theta_{1}},\frac{-\beta(k)}{\theta_{2}}). \tag{16}\]  
In Equation (16), by setting the optimal \(p=p^{*}\), we obtain  
\[p^{*}=\arg\max_{\begin{array}{c}p^{*}\in p\\ p\in(0,2\mu)\end{array}}\{\beta(k)^{2\mu-p}\frac{\Gamma(2\mu-p)}{\theta_{1}^ {\mu}\theta_{2}^{\mu}\Gamma(2\mu)}F_{D}^{(2)}(2\mu-p,\mu,\mu;2\mu;\frac{- \beta(k)}{\theta_{1}},\frac{-\beta(k)}{\theta_{2}})\}, \tag{17}\]  
where \(p\in(0,2\mu)\).
## 7 Proposed Solution
### ANN-Based Detection System
#### 7.1.3 Feature Selection  
Feature selection is a technique used to select the most relevant features for the model and reduce the dimensionality of the dataset. This is important because having too many irrelevant or redundant features can lead to overfitting, slower training time, and reduced model performance. Feature selection techniques include filter methods, wrapper methods, and embedded methods. Filter methods use statistical or machine learning measures to rank the features and select the top features. Wrapper methods use a subset of features to train and evaluate the model and select the best subset. Embedded methods use regularization techniques to penalize irrelevant features and select the most relevant features during training. By using a random forest algorithm, a correlation matrix, and hyperparameter selection, the best and most important features are selected. It is worth noting that source, destination IP, and machine addresses were removed because the attackers can spoof them. Also, the category field was removed since this work is processing the IoT architecture by default and it is not needed.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 2 The Construction of RC-NB-LDPC Codes
### Design and Implementation of the RC-NB-LDPC Code
#### 2.3.3 Coding Module  
The coding module is the core module of the encoder. The block diagram of the structure is shown in Figure 7. It includes the SSRAA (serial shift register accumulator unit) operation module, the source information cache module, the parallel-to-serial conversion module, the output module and the control module.  
The SSRAA operation module completes the coding operation process, and the four sets of SSRAA operation modules work in parallel, which improves the coding efficiency. The parallel-serial  
Figure 6: The hardware structure of the NB-QC-LDPC code encoder.  
Figure 7: Coding module structure.  
conversion module converts the verification sequence encoded by the four parallel SSRAA circuits into a serial form for sequential output. The source information cache module is used to store the source sequence. The output module mainly completes the output of the encoded result.  
After the processing of the upper input module, the input data of the encoding module is a piece of truncated source information, and the data length corresponds to the current encoding rate. At the time of data entry, the SSRAA module is also constantly calculating the check sequence. When a piece of information is entered, the SSRAA module also completes the calculation of the check sequence. The output of the encoded result is then performed, and the state machine of the output module is shown in Figure 8.  
After the encoding is completed, the state machine jumps from the original IDLE state to the SRTART state and begins reading the encoded result. In the READ_CHECK state, the output module reads the parallel-converted 192-bit check sequence from the parallel-to-serial conversion module and then jumps to the READ_INFORMATION state. In the READ_INFORMATION state, the output module reads the source information of the corresponding length from the information cache module according to the code rate information of the current frame, and completes the output of the encoded result. The state machine then jumps back from the OVER state to the IDLE state, waiting for the encoding of the next frame to complete.
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
## 3 FRR Port to ns-3 DCE
### DCE Extensions to Support FRR  
As previously mentioned, DCE does not support all existing glibc functions; therefore, when porting a new application to DCE, it is possible that multiple errors appear due to unrecognized function symbols, since they were not declared in DCE. Therefore, the process of adding support for a new application is very cumbersome, and it is mostly based on trial and error until all needed functions are detected and correctly implemented in DCE.  
During the process of porting FRR to DCE, we found several (10) functions that were not declared in the POSIX layer of DCE. For seven of them, it was necessary to implement their functionality inside DCE because they are related to memory allocation, timing, file operations, disk allocation and threading. The other three are functions where their by FRR does not involve system resources, therefore, it is enough to indicate DCE to use the original glibc implementation (use the NATIVE macro). These extensions can be found in our public repository [22].  
In addition to these added functions, we detected two bugs in memory management in functions already implemented under the DCE macro. Two Pull Requests were performed in the ns-3 DCE project due to the correction of these bugs [28; 29]. In addition, another Pull Request was made with the necessary functions to execute the code of FRR. At the time of writing this paper, the Pull Requests are pending review.
# Numerical Simulation of Non-Linear Models of Reaction--Diffusion for a DGT Sensor
## 2 The Model  
The proposed algorithm was developed to solve the reaction-diffusion equations that model the operation of a Diffusive Gradients in Thin Films (DGT) sensor [6], submerged in an aqueous solution containing different metal species. Nevertheless, the algorithm does not require major modifications to consider new theoretical or experimental configurations.
## 5 Networked Control Systems with Minimal Attention
For each index \(i\) where \(i=1,\dots,N\), the state error \(e_{i}(t_{k})\) represents the difference between the OCXO state and the IEM. The modeling error matrices, \(\tilde{A}_{i}=A_{i}(\delta)-A_{i}(h)\) and \(\tilde{B}_{i}=B_{i}(\delta)-B_{i}(h)\), indicate the difference between the actual clock of the ensemble member and its corresponding remote IEM realization model. It is important to note that the local IEM realization model for each index \(i\) is updated every \(h\) seconds, which means that \(e_{i}(t_{k})=0\) for \(k=0,1,\dots\). This resetting of the state error at each update interval is a crucial feature of the networked control platforms discussed here. Furthermore, it can be demonstrated that the dynamics of the local IEM realization system for index \(i\) in the time interval \(t\in(t_{k},t_{k+1})\) can be described by  
\[y_{i}(t_{k+1})=\Lambda_{i}\,y_{i}(t_{k})\,, \tag{86}\]  
where  
\[\Lambda_{i} =\left[ \begin{array}{cc}A_{i}(\delta)+B_{i}(\delta)K_{i}&-B_{i }(\delta)K_{i}\\ \tilde{A}_{i}+\tilde{B}_{i}K_{i}&A_{i}(h)-\tilde{B}_{i}K_{i}\end{array} \right] \tag{87}\] \[y_{i}(t_{k}) =\left[ \begin{array}{c}\hat{x}_{i}(t_{k})\\ e_{i}(t_{k})\end{array} \right]. \tag{88}\]  
Moreover, the discrete state-feedback system (86) is globally exponentially stable around the solution  
\[y_{i}=\left[ \begin{array}{c}\hat{x}_{i}\\ e_{i}\end{array} \right]=\left[ \begin{array}{c}0\\ 0\end{array} \right] \tag{89}\]  
Figure 10: Local time scale realizations via networked control systems. if and only if the eigenvalues of  
\[M_{D}=\left[ \begin{array}{cc}I&0\\ 0&0\end{array} \right]\Lambda_{i}\left[ \begin{array}{cc}I&0\\ 0&0\end{array} \right] \tag{90}\]  
are inside the unit circle. The essential implication of results (89) and (90) relates to the necessary and sufficient conditions for the stability of system (86). This outcome specifies the maximum transfer time or the minimum frequency for updating the state in the remote steering controller, denoted as \(i\), associated with the separate orbital platform \(i\), where \(i=1,\ldots,N\). Specifically, it establishes an upper bound for \(h\), referred to as the update time for its local reference time scale, pLEOT \(i\), which is aligned with the global IEM time scale operating on the anchor orbital platform \(j\).
# Removal of Cesium from Radioactive Waste Liquids Using Geomaterials
## 4 Conclusions  
Based on the static adsorption test, the adsorption performance of Cs on geomaterials was studied, and the influence of various factors on the adsorption performance was discussed. Constructing the foundation for the further solidification of waste containing radioactive elements was examined in this work. The geomaterials were synthesized from fly ash and slag, and the synthesized geomaterials were characterized by XRD, SEM, and N\({}_{2}\)-BET. The adsorption efficiency of the fly ash-based geomaterials and the geomaterials doped with slag as adsorbents for removing Cs was evaluated. The effects of the pH, adsorbent dosage, and contact time on Cs removal were investigated. Furthermore, the Langmuir and Freundlich models were used to describe the adsorption equilibrium mathematically. From this work, the fly ash-based geomaterials showed a maximum Cs adsorption capacity of 89.32 mg\(\cdot\)g\({}^{-1}\) and a high PC of 31.02 mg\(\cdot\)g\({}^{-1}\cdot\)mM\({}^{-1}\) for Cs. Additionally, the applicability of the kinetic model to the adsorption process was also discussed.  
Through this work, it was proved to a certain extent that the fly ash-based geomaterials adsorb Cs more efficiently and that they have considerable potential to be applied as adsorbents for removing Cs. Furthermore, the immobilization of Cs in geomaterials was explored from a chemical perspective. From the perspective of environmental protection, fly ash can be used to treat nuclear waste containing radioactive nuclear waste liquid. In conclusion, fly ash based-geomaterials may be a promising option for the treatment and disposal of nuclear-contaminated waste.  
Experiment, data evaluation, and writing, H.Z.; instruments of measurement, M.Z.; experiment and data evaluation, X.D.; experiment and data evaluation, S.F.; instruments of measurement and data evaluation, N.M.; supervision and writing, N.K. All authors have read and agreed to the published version of the manuscript.  
The present work was partially supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (Research Program (C), No. 21K12290).  
Not applicable.  
The authors are grateful to [PERSON] from the Office for Environment and Safety and to [PERSON] of the Common Facility Infrastructure Center. The authors are also grateful to [PERSON]; [PERSON]; [PERSON]; and [PERSON] from the Facility of Engineering at Niigata University for permitting the use of the ICP-MS, XRD, FT-IR, SEM-EDS, and the surface area analyzer.  
The authors declare no conflict of interest.