esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning
## 3 Material and Experimental Methods
### Methodology
Temperature, a potent determinant of water phase transitions and evaporation rates, plays a pivotal role in dictating flow patterns. Precipitation, on the other hand, directly affects inflow rates into the river system. By assimilating these features, we harness the comprehensive interplay between meteorological elements and water resource dynamics, thereby enhancing our models' accuracy in prognosticating future water potential. Model hyperparameters: the efficacy of our predictive models hinges upon the precise calibration of model hyperparameters. For an in-depth understanding, we illuminate the hyperparameters adopted for each method, offering insight into their roles in shaping model performance. SVR leveraged kernel functions as a foundational parameter. The choice of kernel, be it linear, polynomial, or radial basis, profoundly influences the model's capacity to capture non-linear relationships. DTs were modulated by the depth parameter, which dictates a tree's branching complexity. The balance between depth and interpretability was meticulously calibrated. RF and ETR voyaged into the realm of ensemble learning, demanding meticulous orchestration. The number of trees, a quintessential hyperparameter, was discerningly determined. This multitude of trees encapsulates diverse insights from various angles, fortifying the ensemble's collective predictive power. Validation strategy: the crucible of model validation and hyperparameter tuning stands as a testament to the robustness of our methodology. Our chosen validation strategy incorporates multiple facets, ensuring the stability and integrity of our predictive endeavors. To navigate the intricate landscape of model selection and hyperparameter calibration, we meticulously employed cross-validation techniques. This stratagem entailed partitioning the dataset into training and validation subsets, facilitating iterative validation across distinct segments. This process augments our confidence in model generalizability, curbing overfitting tendencies that could compromise accuracy. A multifaceted metric array was deployed to assess model performance. Mean squared error (MSE), a quintessential measure of prediction accuracy, elucidated the dispersion between predicted and actual values. Additionally, the coefficient of determination (R\({}^{2}\)) was wielded as a yardstick to quantify predictive efficacy. Intricately weaving these validation threads, we navigated the labyrinth of model selection and hyperparameter tuning, ensuring our model's resonance with real-world phenomena. The vigilance against overfitting was further reinforced by our judicious validation strategy. Thus, the triumvirate of feature engineering, hyperparameter modulation, and validation strategy not only shapes the foundational pillars of our methodology but also lends it the credibility and accuracy indispensable for unraveling the enigmatic tapestry of water resource dynamics.
# Optimization of Inventory Management to Prevent Drug Shortages in the Hospital Supply Chain
## 3 System Model
### Problem Formulation
Consequently, the objective function in our model is to minimize the cost incurred when the hospital makes a refilling decision. The objective function is expressed as follows:  
\[\min\sum_{t=1}^{T}\sum_{i\in\mathcal{I}}\alpha_{i}p_{i}(t)x_{i}(t)+R_{i}(v_{i} (t),r_{i}(t))+\psi(v_{i}(t)) \tag{3}\]  
We next formulate the set of constraints that the decision variables should respect. First, we consider the capacity constraint to ensure that the refilling decision will not exceed its storage capacity \(C_{i}(t)\). Furthermore, the system has to ensure the lowest volume \(\underline{C}_{i}(t)\) of each drug \(i\) to avoid a shortage. Hence, we formulate the constraint as follows:  
\[\underline{C}_{i}(t)\leq v_{i}(t)=v_{i}(t-1)-\lambda_{i}(t)-e_{i}(t)+x_{i}(t )\leq C_{i}(t),\forall i\in\mathcal{I},t=1,2,\ldots,T. \tag{4}\]  
Second, we consider the budget constraints of the hospital for purchasing drugs during time \(T\) as follows:  
\[\sum_{t=1}^{T}\sum_{i\in\mathcal{I}}x_{i}(t)p_{i}(t)\leq\mathbb{B}. \tag{5}\]  
In addition, there is a budget for each medicine \(i\) at time \(t\), which is considered based on the following constraint:  
\[x_{i}(t)p_{i}(t)\leq B_{i}(t),\forall i\in\mathcal{I}. \tag{6}\]  
Figure 1: A quadratic function of the penalty cost. Finally, at each timeslot \(t\), the hospital could have a specific refilling range for each drug \(i\) in order to provide for a specific requirement in the hospital. We formulate this constraint as follows:  
\[\underline{\rho}_{i}(t)\leq x_{i}(t)\leq\bar{\rho}_{i}(t),\forall i\in\mathcal{I },t=1,2,\ldots,T, \tag{7}\]  
where \(\underline{\rho}_{i}(t)\) and \(\bar{\rho}_{i}(t)\) are the lower and upper bound refilling volumes, respectively, of drug \(i\) at time \(t\).
## 2 Materials and Methods
### Plasma Deposition in LP and Aerosol-Assisted AP
A three-step plasma deposition process has been designed to produce coatings exhibiting a vertical cross-linking gradient in their subsurface and to improve, in turn, their adhesion to the underlying substrate. As reported in Table 1, the three steps are: step 1, plasma deposition of a hydrocarbon polyethylene (PE)-like coating (\(\phi\)C\({}_{2}\)H\({}_{4}\) 8 sccm, \(\phi\)He carrier 8 slm, applied voltage, 8.5 kV\({}_{\rm p-p}\), frequency 16 or 26 kHz, deposition time, t\({}_{\rm d}\), 60 s); step 2, plasma deposition of a highly cross-linked PEO-like coating obtained by mixing C\({}_{2}\)H\({}_{4}\) and TEGDME as precursors (\(\phi\)C\({}_{2}\)H\({}_{4}\) 8 sccm, (\(\phi\)He-TEGDME aerosol 3.15 slm, He carrier \(\phi\)He 4.85 sccm, applied voltage 8.5 kV\({}_{\rm p-p}\), frequency 16 or 26 kHz, deposition time 10 s); step 3, the uppermost PEO-like layers exhibiting different cell adhesive or repulsive properties were deposited using He/TEGDME feed mixtures by changing the flow of the carrier gas (\(\phi\)He-carrier) from 4.85 slm to 6.85 slm (deposition time 300 s) to increase the total He flow rate from 8 to 10 slm, respectively. By reducing the \(\phi\)He-carrier, with the aerosol flow rate kept constant at 3.15 slm (i.e., by increasing the \(\phi\)He-TEGDME aerosol/\(\phi\)He-carrier ratio), the concentration of the TEGDME aerosol in the feed mixture and the residence time of the gas feed in the discharge increase. Thus lower monomer fragmentation is expected and consequent high retention of the PEO-character could be achieved for the films deposited with \(\phi\)He carrier of 4.85 slm [28]. These films from now on will be named PEOA and distinguished from the films deposited at a higher carrier flow rate (PEOB). All the PEO-like coatings of the (third step) were deposited at 16 and 26 kHz except for the PEOB coatings, which were deposited only at 26 kHz. The frequency remained the same for the whole duration of the multistep process. As an example, if the first step is carried out at 16 kHz, the same value is kept constant until the third step. The Va value of 8.5 kV\({}_{\rm p-p}\) was used in the first two processes to ensure high degree of cross-linking of the interlayers. The third step was performed both at 6.5 kV\({}_{\rm p-p}\) and at 8.5 kV\({}_{\rm p-p}\) to study the effect of the applied voltage on the chemical composition of the deposited coating. All conditions are listed in Table 1.
## 1 Introduction
### Problem Statement  
The dramatic growth of the IoT networks results in numerous challenges like: illegitimate data sharing, unauthorized access control, unauthenticated users, single point of failure due to centralization, and many others. To solve the aforementioned issues, many systems are presented in the existing literature. In these systems, blockchain technology is used that is beneficial due to its decentralized, immutable, transparent, and secure nature. The authors in [1] proposed a smart contracts-based access control framework to provide distributed and trust-based access to the data. However, authorization and authentication of users are not considered, which affect security of the IoT networks. Furthermore, cost and complexity of the system are increased due to multiple smart contracts. Furthermore, the authors in [7] proposed a blockchain-based key management scheme for access control. This scheme is intended to achieve privacy, efficiency, decentralization, and scalability in IoT network access. However, unauthorized access affects validity, reliability, trustfulness, and authentication of the system. Several schemes are proposed for data sharing in IoT networks. The authors in [9] proposed a blockchain-based service sharing system to protect IoT terminals from unauthorized service providers. Despite authorization, trust-based sharing is not taken into consideration, which highly affects the reliability and validity of data sharing. Furthermore, the authors in [10] proposed a blockchain-based data sharing in AI-powered networks. This system works on trust-based sharing mechanism and is managed by smart contracts. However, the use of the system in all sharing scenarios is a challenging task. Furthermore, it does not provide trustfulness, and authentication-based data sharing. Hence, the aforementioned literature work lack to provide authenticated, authorized, and trust-based data sharing and access control among IoT devices.
# Software Architectures for Adaptive Mobile Learning Systems: A Systematic Literature Review
## 4 Third Phase: Review Results
### What Requirements Must Be Considered for the Development of an AMLS? (RQ1)
#### 4.2.2 Main Services Considered in the Learning Adaptation  
Based on the results obtained, most of the selected primary studies [P1][P2][P3][P4][P5][P6][P9][P10][P12][P13][P14][P15][P16][P18][P19][P20][P21] presented a proposal for the design or implementation of an AMLS focused on providing content adaptation services. The studies [P2][P3][P9][P10] that performed content adaptation first requested that the users resolve a knowledge test of the subject they want to learn. Based on the grade obtained in the exam, the AMLS adapts the appropriate content to the student's level of knowledge. Concerning the content adaptation in [P1][P5][P12][P13][P14][P15][P16], the way to provide this service is by using educational-level information of the student and their learning style. In these studies, in addition to applying a knowledge test to the learner, they provide a set of questions that will help the AMLS obtain the necessary information to create a user profile. This information adapts content when the student accesses the educational topics. Finally, in the AMLS proposed in [P4][P6][P18][P19][P20][P21], the learner is provided with a content adaptation service based on the information of the learner's level according to their degree of studies and their learning styles, which are determined based on a questionnaire.  
Studies [P3][P7][P8][P9][P10][P11][P14][P16][P17][P18][P19] present AMLSs with format adaptation services, i.e., how the presented information is adapted to the user. The approaches proposed in [P7][P14][P16] adapt the interface elements concerning the learning styles and knowledge level of the student. On the other hand, in [P3][P8][P9][P10][P11][P17][P18][P19], a format adaptation is produced in function of the types of files available in the applications, e.g., PDF, MP3, and MP4, among others. In this regard, the systems collect the learner's learning style, access device information, and context information to identify their environment and offer the type of file that best adapts to the conditions of their context.  
Finally, the proposals presented in [P5][P7][P22] provide students with learning path adaptation services based on their learning style, context, and levels of study. Cases were identified in which a questionnaire was first carried out to measure the learner's knowledge and the learning styles used to generate a learning path. Diverse assessments are applied as the learner progresses through the course to measure their progress and adapt to the learning path.
# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 6 Applications to 3D Printer Data
### CURE and Bootstrap-CURE  
The first step toward the CURE strategy is to shuffle the original data of size \(n\) records into a sample of size \(n^{\prime}=pn\), with \(p\) being a certain proportion of \(n\). For the _bootstrap-CURE_, this \(n^{\prime}\) size sample is split into \(r=10\) datasets of size \(s=n^{\prime}/r\) records each, drawn at random without replacement. The index of each record from the original dataset was preserved to use them at the later stage of the clustering and to keep the reproducibility of experiments. All 10 sample datasets share the same set of variables as used in work [9] to enable comparisons.  
This work continues the application of both CURE and _bootstrap-CURE_ to data according to Section 5.1.1.  
For \(n=46821\) and \(s=2000\) (and \(r=10\), so that almost half of the data size is involved in the bootstrap phase and the bootstrap samples have sufficient variability). The dendrograms of some of the samples in the bootstrap process are shown in Figure 6. The proposed approach resulted in a total of 39 centroids (3 centroids from sample_0 and 4 centroids from each of the rest). These 39 centroids merge in a new dataset of final representative points of the clusters. This new dataset of centroids would be subject to the super-clustering step (see Figure 7) in such a way that all centroids across all samples representing the same cluster group are merged. Without going into detail, it is interesting to see that all samples provide more or less similar structures, which are aligned with a good sample representation, even with smaller sample sizes as compared to the size of the complete dataset. As is evident from the dendrograms (Figure 6), all samples (1-9) exhibit 4 cluster structures, except sample_0, which shows 3 clusters.  
The final _bootstrap-CURE_ dendrogram (Figure 7) indicates a two-cluster solution; however, as a general practice, a human expert in such a situation often considers the second-best solution which shows four clusters. This strengthens the fact that a four-cluster solution (also established in [9]) is sufficient to represent the sensor behavior in the 3D printer.  
Figure 6: Sample dendrograms in bootstrap method.
## 2 Related Works
### Security Analysis for Satellite Networks  
Several studies have investigated the overall security landscape of satellite systems and systematically categorized attack techniques to facilitate the security analysis of satellite networks. [PERSON] et al. [4] conducted an in-depth analysis of the architecture and cybersecurity landscape of SCSs, categorizing them into three primary segments: space, ground, and link. Meanwhile, [PERSON] et al. [31] expanded the MITRE ATT&CK framework to systematically classify adversarial tactics and techniques targeting satellites. However, they identified significant challenges, such as the difficulty in deploying advanced security solutions because of limitations in computing resources, as well as the obstacles in implementing new security measures stemming from restrictions on upgrading satellite systems. [PERSON] et al. [17] analyzed firmware vulnerabilities, an area that is relatively underexplored in satellite security research. Three satellite firmware images were subjected to an experimental security analysis, which revealed serious flaws and insufficient access control measures. [PERSON] et al. [20] investigated the security vulnerabilities of satellite modems, a critical component of satellite communication networks. [PERSON] et al. [32] examined security vulnerabilities in CubeSat systems and explored potential attack vectors through attack tree analysis. The overall security posture of CubeSat systems, including spacecraft, GCS, and communication signals, was evaluated in their study. However, different CubeSat models' unique configurations and functionalities create a challenge, as a single attack tree cannot universally apply to all CubeSat systems.
## 4 Review: Slant Links Rain Fade Models
### Statistical Models
#### 4.2.2 Modified Genetic Algorithm-ARIMA (MARIMA) Model  
This model [91] combines GA concepts with the autoregressive integrated moving average (ARIMA) filter. The detailed steps of theARIMA model are given in Algorithm 11. The validated results of theARIMA model show a good agreement between measured and predicted attenuation.  
Figure 4: Memoryless non-linear device used in M-K model, where \(\beta\) is the dynamic parameter of the model, which signifies the rate of change of attenuation; \(g\) is the gain; \(X(t)\) is the low-pass filtered output; and \(\gamma(t)\) is the attenuation.  
Advantages  
The model can determine fade at millimeter-wave bands dynamically, and also, it is an eight-parameter-based model where each value of parameter depends on a scheme (based on inter-parameter dependency).  
Limitations  
The local developing procedure is not easy to fit with the model; consequently, the parameters of this model have not been developed worldwide.
## 2 Materials and Methods
### Preparation of Graphene Oxide (GO)  
GO was prepared from pure graphite using the Modified Hummer's method [21]: 5 g of diphosphorus pentoxide (P\({}_{2}\)O\({}_{5}\)) was weighed and immediately added to 5 g of pure graphite powder and 5 g of potassium peroxdisulfate (K\({}_{2}\)S\({}_{2}\)O\({}_{8}\)) to avoid the adsorption of water vapour. Subsequently, 100 mL of sulphuric acid (H\({}_{2}\)SO\({}_{4}\)) (98%) was slowly added to the Erlenmeyer flask containing the mixture, while the flask sat in a cooling bath filled with ice and cold water, to prevent temperature spikes during the process. After the addition of the acid, the flask and the cooling bath were placed on a hotplate, and the temperature was gradually increased until the temperature of the mixture reached 80 \({}^{\circ}\)C and stirred for 5 h. The heat was then turned off and the mixture was left to cool to room temperature (25 \({}^{\circ}\)C) overnight. The next day, the mixture was placed on the magnetic stirrer and, while stirring, 1 L of distilled water was added. To prevent any sudden reaction between the mixture's acid and distilled water, the first 500 mL of the distilled water was added dropwise under slow mixing conditions. After the addition of the total 1 L of distilled water, the mixture was stirred on a magnetic stirrer at room temperature (25 \({}^{\circ}\)C) for 24 hrs.  
The next day, the homogenous mixture was filtered by a vacuum-filter pump, and the residual was washed with 6 L distilled water until the pH of the filtered water reached approximately 5. The residual was then transferred to Petri dishes and dried in an oven at 40 \({}^{\circ}\)C for 24 h. The next day, the dried black materials were scrubbed and transferred to a beaker, where they were stirred with 250 mL sulphuric acid (98%) on a magnetic stirrer for 10-15 min at room temperature. Next, 35 g of potassium permanganate (KMnO\({}_{4}\)) was slowly added at room temperature under continuous mixing conditions (400 rpm), to prevent accumulation of KMnO\({}_{4}\) in the mixture. The mixture was maintained at 35 \({}^{\circ}\)C and stirred for 6 hrs. Subsequently, 500 mL of distilled water was added dropwise to the mixture, with an additional 2 h of mixing. Due to water addition in this step, the mixture was observed to change from a black to brown colour, indicating material oxidation. Next, 1 L of distilled water was added to the mixture and stirred for an additional 30 min, followed by slow addition of 40 mL hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) (30%), with further stirring for 24 h. The final solution was centrifuged and washed with 1 L of 0.1 N hydrochloric acid (HCl) followed by distilled water, until the pH reached 5. The washed mixture, graphene oxide, was dried at 40 \({}^{\circ}\)C for 24 h.
###### Abstract  
Thirty-two ambient cured alkali-activated engineered composites (AAECs) were developed by incorporating MgO, multi-walled carbon nanotubes (MWCNTs), reduced graphene oxide (rGO), and polyvinyl alcohol (PVA) fiber with a one-part dry mix technique using powder-based activators/ reagents. The effects of material variables, namely binary or ternary combination source materials (fly ash C or F and ground granulated blast furnace slag 'GGBFS'), two types of reagents with varying chemical ratios and dosages of additives (from 0 to 5% MgO and from 0 to 6% MWCNT/rGO), on the physical (slamp flow, flow time, flow velocity, and density), hardness (compressive strength from 0 to 180 days and 28-day ultrasonic pulse velocity 'UPV'), and micro-structural (SEM/EDS, XRD and FTIR) properties were evaluated. All these variables, individually or combined, influenced the properties and microstructural aspects of AAECs. Problems associated with the dispersion and agglomeration of nanomaterials, which could disrupt the microstructure and weaken its mechanical/physical properties, were avoided through the use of defined ultra-sonication with a high-shear mixing protocol. All AAECs achieved a 28-day compressive strength ranging from 26.0 MPa to 48.5 MPa and a slump flow > 800 mm, satisfying the criteria for flowable structural concrete. The addition of 5% MgO and up to 0.3% MWCNT/rGO increased the compressive strength/UPV of AAECs with MgO-MWCNT or rGO combination provided an improved strength at a higher dosage of 0.6%. A linear correlation between compressive strength and UPV was derived. As per SEM/EDS and XRD analyses, besides common C-A-S-H/N-C-A-S-H or C-A-S-H/C-S-H gels, the addition of MgO led to the formation of magnesium-aluminum hydrolattice (Ht) and M-S-H (demonstrating self-healing potential), while the incorporation of rGO produced zeolites which densified the matrix and increased the compressive strength/UPV of the AAECs. Fourier transform infrared spectrometer (FTIR) analysis also suggested the formation of an aluminosilicate network in the AAECs, indicating a more stable structure. The increased UPV of MWCNT/rGO-incorporated AAECs indicated their better conductivity and ability of self-sensing. The developed AAECs, incorporating carbon-nano materials and MgO additive, have satisfactory properties with self-healing/-sensing potentials.  
alkii-activated engineered composites; MgO; MWCNT; rGO; slump flow; workability; compressive strength; ultrasonic pulse velocity; microstructure +
Footnote †: journal: Applied sciences  
Article  
Incorporating MgO, MWCNTs, and rGO  
[PERSON] and [PERSON]  
Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]
## Abbreviations  
* Cuckoo Search
* Integral Square Error
* Integral Absolute Error
* Integral Time Square Error
* Integral Time Absolute Error
* Proportional and Integral
* Proportional, Integral and Derivative
* Proportional and Derivative
* Particle Swarm Optimization
* Particle Swarm Optimization-Cuckoo Search
* Parameter Model
* Unmanned Aerial Vehicles  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**ISE Errors for Output**} \\ \cline{2-7}  & \(x\) & \(y\) & \(z\) & \(\phi\) & \(\theta\) & \(\psi\) \\ \hline PSO & 0.072 & 0.048 & 0.163 & 1.079 & 2.606 & \(2.001\times 10^{-34}\) \\ \hline CS & 0.756 & 0.188 & 0.172 & 1.155 & 6.084 & \(3.721\times 10^{-36}\) \\ \hline PSO-CS & 0.009 & 0.009 & 0.007 & 0.010 & 0.023 & \(1.128\times 10^{-35}\) \\ \hline RM & 55.221 & 53.052 & 0.932 & 2.016 & 6.745 & \(2.291\times 10^{-33}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Errors (ISE-Integral Square Error) obtained with Particle Swarm Optimization (PSO), Cuckoo Search (CS), Particle Swarm Optimization-Cuckoo Search (PSO-CS), and Reference Model (RM) for the quadrotor with disturbance (helical trajectory).