esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Analysis of Algorithm Results
#### 4.3.2 UCI Datasets
Overall, these algorithms exhibit better clustering effects compared to the K-means, DBSCAN, and SNNDPC algorithms. In summary, the clustering results of the ADPC and CDPC algorithms are consistent on the whole. In general, both the ADPC and CDPC algorithms outperform the K-means, DBSCAN, and SNNDPC algorithms. By comparing the performance of the ADPC, CDPC, K-means, DBSCAN, and SNNDPC algorithms on different UCI datasets, the effectiveness of the CDPC algorithm, which employs dichotomy to determine the truncation distance, is verified once again. When comparing the clustering indicators of the DP-CDPC and DP-ADPC algorithms, it can be concluded that the clustering of the DP-CDPC is better than that of the DP-ADPC on the whole. When comparing the clustering indicators of the DP-CDPC and CDPC algorithms, it can be concluded that they do not necessarily weaken the effectiveness of the algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Algorithm** & \(\mathbf{\Delta}\)**\(\mathbf{RI}\)** & \(\mathbf{\Delta}\)**\(\mathbf{MI}\)** & \(\mathbf{FMI}\) \\ \hline \multirow{8}{*}{banknote} & K-means & 0.013 & 0.010 & 0.509 \\  & DBSCAN & 0.611 & 0.568 & 0.784 \\  & SNNDPC & \(-\)0.003 & 0.013 & 0.704 \\  & ADPC & **0.962** & **0.932** & **0.981** \\  & CDPC & **0.962** & **0.932** & **0.981** \\  & DP-ADPC & 0.886 & 0.859 & 0.940 \\  & DP-CDPC & 0.961 & 0.929 & 0.980 \\ \hline \multirow{8}{*}{abalone} & K-means & 0.136 & 0.163 & 0.430 \\  & DBSCAN & 0.149 & 0.136 & 0.501 \\ \cline{1-1}  & SNNDPC & 0.031 & 0.105 & **0.538** \\ \cline{1-1}  & ADPC & **0.197** & **0.183** & 0.530 \\ \cline{1-1}  & CDPC & 0.189 & 0.176 & 0.525 \\ \cline{1-1}  & DP-ADPC & 0.195 & 0.181 & 0.529 \\ \cline{1-1}  & DP-CDPC & 0.185 & 0.170 & 0.523 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._Noise is volatile and, after selecting an appropriate privacy budget, the effectiveness of the algorithm will fluctuate within a certain range after adding noise.
###### Abstract  
Covert channel methods are techniques for improving privacy and security in network communications. These methods consist of embedding secret data within normal network channels, making it more difficult for unauthorized parties to detect such data. This paper presents a new approach for creating covert channels using the Message Queuing Telemetry Transport (MQTT) protocol, widely used in the context of the Internet of Things (IoT). The proposed method exploits storage channels by altering the field length of MQTT messages. Our solution leverages well-known one-way mathematical functions to ensure that data remain hidden from third parties observing the MQTT stream. In this way, we ensure that not only the content of the communication is preserved but also that the communication itself takes place. We conducted a security analysis to show that our solution offers the above-mentioned property even against severe threats, such as an adversary being able to observe all the messages exchanged in the network (even in the clear). Finally, we conducted an overhead analysis of our solution both in terms of the time required to perform the required operations and of the bytes to send. Our study shows that our solution adds no significant time overhead, and the additional overhead in terms of transmitted bytes remains within acceptable limits.  
MQTT; cover channel; censorship Article
# Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition
## 2 Method Overview
### Noise Reduction Process  
In this paper, the \"analysis-optimization-contrast\" model is used to study the noise reduction process. The flow chart of this model is shown in Appendix A. It can be seen from this flowchart that the noise reduction model in this paper is mainly divided into four parts. It can be seen from the flow chart that the noise reduction model of this paper is mainly divided into four parts:  
Part I: Adding the noise into the signal. Four Gaussian white noises obeying normal distributions with different intensities are added into the experimental signal.  
Part II: Building a model. Based on the MRSVD, different noise reduction models are established according to the different values of the decomposition method L and the decomposition type N.  
Part III: The selection of the optimal model. Taking the experimental signals adding different noise as the example, the noise reduction analysis on the various models of MRSVD is carried out, and through the comparison between two evaluation indicators, the noise reduction effect of the method is analyzed and discussed under different application scenarios and different parameters, and the optimal model of MRSVD is obtained.  
Part IV: Comparison on multiple models. Under the condition of selecting the optimal parameters, the traditional noise reduction model such as EMD, EEMD, VMD, SVD and wavelet decomposition are used to process the simulation signal, which are compared with the MRSVD optimal model obtained in the previous step, and then the conclusion is obtained by an example.
# Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks
## 5 Coalition Formation Game for MPC Creation
### CFG Model  
The hedonic CFG is a coalitional game that satisfies the following two hedonic conditions. In this study, the PSPs can join and leave any MPC freely according to their preference, e.g., collateral cost reduction for the MPC coalition formation. Such a gain is decided only by the MPC in their participation.  
**Definition 1**.: _(Hedonic Conditions): A CFG is classified as hedonic if (i) the payoff of any player depends solely on the coalition members to which the player belongs; (ii) the coalitions are partitioned according to players' preferences over coalitions._  
For the mathematical model for MPC creation, we first define the notions of the coalition structure and transferable utility based on the CFG theory [51; 52; 53].  
**Definition 2**.: _A set of players in this CFG is a set of directional PCs in a PCN, \(\mathcal{L}\), which form a cooperative MPC coalition as a subset, \(S_{k}\subseteq\mathcal{L}\). However, the actual players are PSPs, who determine coalitions for their directional PCs by executing the formation algorithm. Therefore, the disjoint coalitions constitute the coalition structure, \(\Pi=\{S_{1},S_{2},...S_{k}\}\), \(\mathcal{L}=\cup_{k=1}^{K}S_{k}\), which can be denoted by a set of \(\mathcal{V}\), \(\{\mathcal{V}_{1},\mathcal{V}_{2},...,\mathcal{V}_{K}\}\). The transferable utility value \(v(S_{k})\) of the coalition is determined solely by the coalition members in the characteristic form of this CFG, and it is fairly divided by SPCs._  
To derive the aggregate utility of the coalition, we address the revenue and cost of the MPC as below.  
* Revenue: sum of transaction fee over PBT flows served by an MPC coalition, \(v_{r}(S_{k})\).
* Cost: deposit sum of PSPs of an MPC, \(v_{c}(S_{k})\). The collateral cost of individual PSPs is reduced by deposit sharing among multiple SPCs, which is limited by increasing payment delays from the SPC turnaround. Such an additional delay to the legacy PC, \(\mathbb{E}(D_{ij}^{MPC}-D_{ij}^{PC}),ij\in S_{k}\), demands a larger deposit, thereby increasing the collateral cost. Furthermore, the delay cost will be \(-\infty\) if it exceeds the \(T_{exp}\), which can be a constraint for coalition formation.
* Utility: with the above revenue and cost, the net payoff of the coalition formation can be \(v(S_{k})=v_{r}(S_{k})-v_{c}(S_{k})\).
# Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications
## 4 SA with Incomplete Cost Evaluation Applied to EIT
### The Inverse Problem  
Reconstructing the conductivity distribution is an inverse problem. A valid solution would be a conductivity distribution that produces a given set of electric potential measurements. The EIT reconstruction problem can be solved by minimizing the difference between a simulated impedance domain and the one implied by the measured electric potential [40]. This is solved using SA to optimize the simulated impedance domain, using FEM to evaluate the solution at each iteration of the SA algorithm.  
This optimization problem is in general strongly ill-conditioned and often nonconvex. Gradient-based optimization methods can converge to local minima and require special regularization terms to converge properly, which lead to the loss of detail in the reconstructed image.  
A maximum a posteriori (MAP) probability estimate for the conductivity distribution based on Bayes' rule can be performed using the measurements taken on the surface of the body, as well as the error estimates at each optimization iteration and some prior information of the expected conductivity distribution.  
A MAP probability estimate of the conductivity distribution is one that maximizes  
\[\pi(\sigma|V)\propto\pi(\sigma)\prod_{i=1}^{n}\chi(\Phi_{m}^{i}-\Phi_{e}^{i}) \tag{7}\]  
where \(\pi(\sigma)\) is a prior distribution for \(\sigma\) and \(\chi(\[PERSON]_{m}^{i}-\[PERSON]_{e}^{i})\) is the probability density function (PDF) for a random error vector \(e\), \(\chi(e)=\chi(\[PERSON]_{m}^{i}-\[PERSON]_{e}^{i})\), and \(\[PERSON]_{e}^{i}\) is the vector of expected potentials at the electrodes. The optimization problem  
\[\hat{\sigma}=arg\ \min\ E(\sigma) \tag{8}\]  
is used to obtain a MAP estimation, where  
\[E(\sigma)=\sum_{i=1}^{n}\|\[PERSON]_{m}^{j}-\[PERSON]_{e}^{j}\|^{2}+R(\sigma), \tag{9}\]  
and \(R(\sigma)\propto ln(\pi(\sigma))\) is the regularization term, related to the prior knowledge of \(\sigma\). Since the EIT problem is generally ill-posed, reconstruction is often impossible without prior knowledge [41].
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 5 Performance Evaluation
### Slide Behavior with Different Mobility Models
#### 5.2.2 SLIDE Scalability  
Table 4 displays the number of actual conflicts, missed and false alarms, the probability of missed and false alarms, and the average maneuver time under a different number of drones. As the experiments from the previous section show that the performance of SLIDE is affected directly by the tuning parameters \(T_{l}\) and \(T_{b}\), their values should be carefully selected to ensure acceptable performance for conflict detection. The considered value of \(T_{b}\) is 2 s, and \(T_{l}\) is 8 s.  
Table 4 shows that, regardless of the growth of the number of drones from 40 to 120 and the growth of the number of actual conflicts from around 8 to 80 s, the probability of missed and false alarms remains almost constant for all mobility models. The same applies to the average maneuver times, in which it stays stable. For instance, with GM, SLIDE maintains a stable and low probability of missed alarms (around 0.2), an acceptable and stable probability of false alarms (around 1.5), and a stable average maneuver time of approximately 2 s. With EGM, also, SLIDE keeps a stable and lower probability of missed alarms (around 0.15), an acceptable and stable probability of false alarms (around 1.5), and a steady average maneuver time of approximately 2.5 s. Indeed, Table 4 shows that SLIDE is still scalable to an increasing number of drones even with nonlinear mobilities.
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
## 6 Discussion
The scalability of the microservice architecture allows the framework to dynamically adapt to the demands of IoT sensor data streams, which is a critical requirement for CM and AM applications where uninterrupted data flow and rapid analytics are essential. By integrating Kafka, Spark Streaming, and a microservice architecture, the proposed nine-layer framework delivers unparalleled performance gains for modern industrial applications. The high-throughput durability of Kafka supports seamless data flow across real-time and archival tiers, whereas the in-memory processing of Spark Streaming provides actionable insights in near real time. The microservice architecture ensures that the framework remains robust and scalable, and is capable of handling both real-time alerts and long-term resource planning tasks. These combined features make the framework highly adaptable to the evolving needs of CM, with particular emphasis on the AM sector. By enabling precise control over critical manufacturing parameters, such as temperature and humidity, during the filament extrusion process, the framework ensures the superior quality and consistency of the 3D-printed parts. Therefore, we demonstrated the effectiveness of the nine-layer framework in addressing the complexities of modern industrial ecosystems. The performance gains achieved through Kafka, Spark Streaming, and microservices underscore the framework's potential to transform decentralized manufacturing systems. Future work could focus on extending the applicability of the framework by incorporating advanced predictive analytics and optimizing cross-tier communication, further validating its role as a transformative solution for CM and AM.
###### Abstract  
Soil moisture plays a significant role in surface energy balance and material exchange. Synthetic aperture radar (SAR) provides a promising data source to monitor soil moisture. However, soil surface roughness is a key difficulty in bare soil moisture retrieval. To reduce the measurement error of the correlation length and improve the inversion accuracy, we used the surface roughness (_Hrms, root mean surface height_) and empirical correlation length _lopt_ as proposed by [PERSON] to introduce analytical equations of the backscattering coefficient using the calibrated integral equation model (CIEM). This empirical model was developed based on analytical equations to invert soil moisture for _Hrms_ between 0.5 and 4 cm. Experimental results demonstrated that when the incidence angle varied from 33.5\({}^{\circ}\) to 26.3\({}^{\circ}\), R\({}^{2}\) of the retrieved and measured soil moisture decreased from 0.67 to 0.57, and RMSE increased from 2.53% to 5.4%. Similarly, when the incidence angle varied from 33.5\({}^{\circ}\) to 26.3\({}^{\circ}\), R\({}^{2}\) of the retrieved and measured _Hrms_ decreased from 0.64 to 0.51, and RMSE increased from 0.33 to 0.4 cm. Therefore, it is feasible to use the empirical model to invert soil moisture and surface roughness for bare soils. In the inversion of the soil moisture and _Hrms_, using _Hrms_ and the empirical correlation length _lopt_ as the roughness parameters in the simulations is sufficient. The empirical model has favorable validity when the incidence angle is set to 33.5\({}^{\circ}\) and 26.3\({}^{\circ}\) at the C-band.  
synthetic aperture radar (SAR); calibrated integral equation model (CIEM); soil moisture retrieval; surface roughness (_Hrms_); empirical correlation length (_lopt_)
## 2 Generalities and Classification of Cannabis Metabolites
### Alkaloids of Cannabis  
The classification of alkaloids is complex, and several approaches exist, each with its own rationale. Some classify alkaloids according to their chemical structure, others based on the taxonomic group of the producing organism, or as is often preferred, according to their biosynthetic origin pathway. Alkaloids are molecules containing at least one nitrogen atom and generally have a basic nature, hence their name--alkali--from Arabic, _al-qali_[36]. Generally, alkaloids are derived from amino acids, but some alkaloids are biosynthesized via transamination reactions [36].  
According to the chemical structures' classification, heterocyclic alkaloids can be distinguished from aliphatic ones [37]. Aliphatic alkaloids have their nitrogen atom in an open carbon chain, while heterocyclic alkaloids have theirs in a closed chain (a cycle). Plants can generate several alkaloids, which is what the taxonomic classification of alkaloids is based on. According to this classification, alkaloids produced by a common taxon can be identified as such, for example _Cannabinaceous_ alkaloids, referring to any alkaloid produced by members of the _Cannabaceae_ plant family, including _Cannabis_[37]. However, this approach, nonetheless useful for phylogeny, is less appropriate when considering alkaloids as a chemical species, since alkaloids produced by the same organism can be wildly different from one another.  
Classification according to the biosynthetic pathways provides more precision when compared to the two approaches described above. It could allow for the distinguishment of alkaloids according to their nature (nitrogen of amino acid or transamination) and their chemical type (type of carbon chain). This then allows for the differentiation of purine alkaloids from true alkaloids. A true alkaloid is defined as an alkaloid whose biosynthesis involves the catabolism of amino acids, where the nitrogen atom of the latter is included in the heterocyclic of the alkaloid. Purines are not derived from amino acids; despite the nitrogen heterocycles, they would not be considered true alkaloids. Thus, alkaloids obtained from transamination would be referred to as pseudo-alkaloids. There also exists what would be referred to as proto-alkaloids, derived from amino acids, but with an aliphatic nitrogen, which are fairly common in _Cannabis_ (Table 2) [36].  
In the _Cannabis_ plant, we find, among others, piperidine, pyridine, pyrrolidine, and spermidine alkaloids, phenylethylamines, and tertiary/quaternary amines. In plants, alkaloids and other specialized NC metabolites play critical roles in defense against pathogens, herbivores, and other predators, often repelling or killing these threats. In some cases, alkaloids also act as regulators of growth and development [38]. The role of _Cannabis_ alkaloids for the plant remains a mystery.
# Influence of Silane-Modified Coal Gangue Ceramsite on Properties of Ultra-High-Performance Concrete
## 3 Results and Discussion
### Micro-Mechanism Analysis  
#### 3.5.1 XRD Analysis  
X-ray diffraction (XRD) was conducted to investigate the alterations in the crystalline phases of UHPC mixtures (M0, M20, and M100) after 28 days of standard curing, as depicted in Figure 11.  
The XRD pattern reveals that the UHPC incorporating hydrophobic-modified aggregate largely retains the same crystal structure as the control group. The most prominent characteristic peaks in all three samples are observed at 29.5\({}^{\circ}\) and 32.6\({}^{\circ}\), corresponding to tricalcium silicate (C\({}_{3}\)S) and dicalcium silicate (C\({}_{2}\)S), respectively, which are due to the existence of incompletely hydrated cement particles and the low water-cement ratio of UHPC. Calcium hydroxide is detected at 18.1\({}^{\circ}\) and 47.5\({}^{\circ}\), while ettrinigite is identified at 8.9\({}^{\circ}\). Although the peak values of these hydration products exhibit some changes with the addition of modified aggregate, the variations remain relatively subtle. The most notable peak change occurs in the Calcium-Alumina-Silicate-Hydrate (C-A-S-H) gel at 27.8\({}^{\circ}\). C-A-S-H gel is the primary binding phase formed during the hydration of cementitious materials, contributing significantly to the strength and durability of concrete. The peak  
Figure 11: XRD of different UHPC formulations.  
intensity significantly increases with the incorporation of modified aggregate, suggesting an augmentation in the amount of UHPC hydration products. Therefore, the data indicate that the addition of hydrophobic modified aggregate will not change the crystal structure in UHPC, but only affect the amount of hydration products.
## 3 Component Selection and Criteria
### Estimation of \(C_{L},C_{D}\), and Moment of Inertia
#### 4.3.2 Forward Maneuvering  
From the conventional free-body force inputs of multirotor UAV, for the forward speed, it can be determined that the angle \(\alpha\) between the horizontal plane and the reference plane of the UAV arm is essential for estimating the forward speed and its subordinate specifications. The calculations are as follows:  
\[\text{L}\times\cos(90-\alpha)-\text{Drag}=0; \tag{12}\]  
\[\text{L}\times\cos(90-\alpha)=\text{Drag};\]  
\[\text{L}\times\sin(90-\alpha)-\text{Weight}=0;\text{ and}\]  
\[\text{L}\times\sin(90-\alpha)=\text{Weight}.\]  
Four different angles (15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\)) underwent aerodynamic performance estimations; the performance was better at 30\({}^{\circ}\)than at other angles. Therefore, the same angle was used in this work for the estimation of aerodynamic performance and stability analysis, \(\alpha=30^{\circ}\), as follows:  
\[\text{L}\times\sin(30)=\text{Weight}; \tag{14}\]  
\[\text{L}\times\cos(30)=\text{Drag}; \tag{15}\]  
\[\text{Upward Force }(L)=\frac{\text{Weight of the octocopter}}{\sin(30)}; \tag{16}\]\[\begin{split}\text{Forward Force }(\text{T})=\frac{\text{Drag over the octocopter}}{\text{cos}(30)}\ ;\\ \text{Upward Force }(\text{L})=\frac{4.4268}{0.5}=8.8536\text{ kg}; \text{ and }\\ 8.8536\times\text{cos}(30)=\text{Drag}\Rightarrow 8.8536\times 0.866= \text{Drag}=7.6672176\text{ kg}\.\end{split} \tag{17}\]  
[PERSON]'s Second Law is as follows:  
\[\text{F}=\text{m}\times\text{a} \tag{18}\]  
Here, \"F\" is the resultant force in Newtons, \"m\" is the mass of the UAV in kg, and \"a\" is the acceleration of the UAV in \(\text{m}/\text{s}^{2}\).  
\[\text{F}=8.8536-7.6672176=1.1863824*9.81=11.638411344\text{ N}.\]  
Thus,  
\[\text{a}=\frac{11.638411344}{7.6672176}=1.518\Rightarrow\int\text{a}=\text{v} \Rightarrow\int_{0}^{\text{t}}1.518.\text{dt}\Rightarrow\text{v}=1.518\times \text{t}.\]  
In the above, \"t\" is the time taken at the phase, assumed to be 20 s; therefore, the velocity is calculated as follows:  
\[\text{V }(\text{forward speed})=1.518*20=30.36\text{ m}/\text{s}.\]  
From Equation (14), we have calculations as follows:  
\[\begin{split}\frac{1}{2}\rho\text{v}^{2}\text{SC}_{\text{L}} \times\sin(30)=4.4268\times 9.81;\\ [0.5\times 1.2256\times(30.36)^{2}\times 8\times\pi\times(0.0719655 )^{2}]\text{C}_{\text{L}}\times 0.5=4.4268\times 9.81;\\ \text{C}_{\text{L}}=\frac{4.4268\times 9.81}{0.5\times 1.2256 \times 30.36\times 30.36\times 30.36\times 30.36\times 30.36\times 3.14\times 0\times 0\times 19655\times 0.0719655};\text{ and }\\ \text{C}_{\text{L}}=\frac{4.4326908}{73.484}=0.5911.\end{split}\]  
From Equation (15), we have calculations as follows:  
\[\text{L}\times 0.866=\text{D}=7.6672176\text{ kg}=7.6672176\times 9.81=75.22\text{ N};\]  
\[\begin{split}\frac{1}{2}\rho\text{v}^{2}\text{SC}_{\text{D}}=75. 22;\text{ and }\\ \text{C}_{\text{D}}=\frac{75.215404656}{0.5\times 1.2256 \times 30.36^{2}\times 0.1935}\Rightarrow\text{T}_{\text{D}}=0.6882. \end{split}\]