esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Smart Feasant Farming System and Data-Mining Process
### Cloud-Computing Platform
#### 3.4.2 Data Storage Service  
A data storage cloud service typically consists of a NoSQL database (a nonrelational database), either an object database or a JavaScript Object Notation (JSON)-based database. The importance of this component within the proposed architecture lies in the possibility of storing all data collected by sensors composing the WSN in a secure and fully scalable manner.  
In addition, some cloud-computing solutions from this category that are part of platforms aimed at developing serverless web and mobile applications such as Google Firebase and Amazon AWS Amplify allow multiple client devices to directly connect to databases through bidirectional channels. This enables real-time synchronization of data on all devices directly connected to a database in response to changes made on each of the devices, which would partially eliminate the need for a cloud messaging service. Nonetheless, there would be a need for a service that allows for sending messages from the cloud back to the connected devices because of the execution of functions hosted in the cloud, which would be the case of the crop yield prediction task in our system.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Drone Revocation Phase  
To dynamically deploy a new drone (\(D_{j}^{new}\)), the following steps are required.  
**Step 1:**: \(D_{j}^{new}\) chooses a new identity (\(ID_{D}^{new}\)), random number (\(r_{D}^{new}\)), and challenge (\(C_{j}^{new}\)).
**Step 2:**: \(D_{j}^{new}\) computes \(PID_{D}^{new}=h(ID_{D}^{new}\parallel r_{D}^{new})\) and \(R_{j}^{new}=PUF(C_{j}^{new})\) and sends \(\{PID_{D}^{new},C_{j}^{new}\}\) to the GCS.
**Step 3:**: The GCS checks if \(PID_{D}^{new}\) is in the record. If not found, the GCS selects a new random number (\(r_{1}^{new}\)) and computes \(D_{j}^{new}\)'s secret key \(K_{D}^{new}=h(PID_{D}^{new}\parallel k\parallel r_{1}^{new})\). Then, the GCS stores \(\{PID_{D}^{new},r_{1}^{new},C_{j}^{new}\}\) and sends \(K_{D}^{new}\) to the \(D_{j}^{new}\).
**Step 4:**: \(D_{j}^{new}\) computes \(K_{D}^{new}=K_{D}^{new}\oplus R_{j}^{new}\) and stores \(\{K_{D}^{new},r_{D}^{new}\}\) in its memory.  
the proposed protocol achieves mutual authentication. Table 3 shows the notation and description of the BAN logic. Then, this work describes the rules, goals, idealized forms, and assumptions for the BAN logic proof.
# Potentiality of Rod-Type Chitosan Adsorbent Derived from Sewage Sludge
## 4 Conclusions  
The purpose of this study was to develop a high-performance biosorbent from non-living activated sludge generated in the wastewater treatment process. The problem of elution of organic matter from the sludge was solved through the immobilization method using chitosan. Based on the As(V) removal performance, the optimal conditions for the preparation of chitosan-based biosorbents were derived: 6.0% dry sludge, 4.0% chitosan, and a rod shape with a diameter of 0.2-0.3 mm. Investigation of the removal rates of various cationic and anionic heavy metals showed that the biosorbent adsorbed anionic metals more effectively than cationic metals. The maximum removal capacities of RCS were determined to be 42.25 mg/g for As(V), 28.69 mg/g for Cd(II), 70.79 mg/g for Cr(VI), and 284.11 mg/g for Mn(VII). Since this is a basic study, it is not appropriate to mention the practical applicability of RCS at this point. That is, a solution to the problem of non-acid resistance is required for the commercialization of RCS. Studies related to adsorbent regeneration and disposal should be also performed. In addition to these follow-up studies, the results of this study will be useful for commercially producing adsorbents for heavy metal removal using wastewater sludge in the future.  
Figure 3: Adsorption isotherms of (**a**) As(V), (**b**) Cd(II), (**c**) Cr(VI), and (**d**) Mn(VII) by the rod-type biosorbent. The continuous lines were predicted by the Langmuir model; the dotted lines were produced by the Freundlich model (biomass dosage = 2 g/L, agitation rate = 200 rpm, temperature = 25 \({}^{\circ}\)C, Vw = 200 mL, and pH non shift (pH 5.5)).  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13042055/s1](https://www.mdpi.com/article/10.3390/app13042055/s1)]([https://www.mdpi.com/article/10.3390/app13042055/s1](https://www.mdpi.com/article/10.3390/app13042055/s1)), Figure S1: Stability of chitosan-immobilized biosorbents as a function of solution pH; Figure S2: Fourier transform infrared absorption spectra of raw sludge and sludge-immobilized chitosan; Figure S3: Mn concentration profiles during Mn(VII) biosorption by RCS.  
**Author Contributions:** J.H.S.: methodology, formal analysis; N.K.: conceptualization, writing--original draft preparation; M.P.: investigation, methodology; D.P.: supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2021R1 II1A1A01050092).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Data and Methods  
### Data  
The locations of ground-based GNSS stations in this paper are derived from the IGS network, and after excluding the co-located stations, a total of 339 globally distributed ground-based GNSS stations are selected, as shown in Figure 1. The LEO constellation is designed with multiple orbital altitudes (300 km, 500 km, 800 km, and 1000 km). At each altitude, different satellite configurations are considered, with 15, 30, 60, 96, and 192 satellites, respectively. The satellites are evenly distributed across multiple orbital planes, increasing in number as the total satellite count increases. Table 1 presents the design parameters for the 300 km orbital altitude as an example, and the same configuration applies to other altitudes.  
Based on the orbit parameters of GNSS satellites and simulated LEO satellites, the coordinates of the corresponding LEO/GNSS satellites are computed, and the sampling interval is set to 1 min. The NeQuick2 model is applied to simulate the STEC on the ray paths of the ground-based GNSS stations to GNSS satellites, GNSS stations to LEO satellites, and LEO satellites to GNSS satellites [33]. The observation noise is added based on the elevation angle weighting. First, a Gaussian noise with a mean of 0 and a standard deviation of 1.5 TECU is generated [22]. Random errors are controlled to \(0\pm 3\) TECU, within a 95% confidence level. Then, the noise is scaled by a factor of \(1/(1+sin^{2}(e))\); \(el\) is the elevation angle. This ensures that signals at lower elevation angles receive higher noise levels due to increased ionospheric effects. Due to the limitation of the altitude of the tomography region (1000 km in this paper), the STEC of the rays outside the tomography region may affect the results of the tomography inversion, especially during the night at the local time, when the percentage of the top ionospheric TEC increases. Therefore, the ionospheric information from the altitude of the top of the tomography (1000 km) to the altitude of the GNSS satellites is deducted from the simulated STEC data to ensure the reliability of the results of the tomography inversion.
## 8 Scalability
### Integration in Federated Learning Workflows  
Federated learning (FL) enables decentralized devices to collaboratively train a shared global model by processing local data and aggregating the results. Frameworks like TensorFlow Federated and PySyft are available but are mainly designed for more powerful devices, such as cloud servers.  
TFLite offers limited support for on-device learning on resource-constrained edge devices. While on-device learning solutions are available for Android and iOS, the capabilities are still restricted on ultra-constrained devices like microcontrollers.  
Nevertheless, custom solutions are being developed to address on-device learning limitations. In [46], the authors implement a lightweight feed-forward neural network with a single hidden layer of 25 nodes, allowing backpropagation to be run on-device and model weights to be updated. The federated learning server is implemented on a PC that communicates with client nodes via a serial port.  
Another custom implementation is presented in [17], where a neural network with 187 input neurons, two hidden layers with 25 neurons each, and an output layer with 5 neurons is used. In this case, the MQTT protocol is employed, with different topics configured for tasks like adding new nodes, sending model weights, and receiving updated model weights from the central server.  
The proposed ML attestation solution can be incorporated into the previously discussed FL system by embedding the EAT tokens in the connection request to the MQTT broker. With the EAT, the broker can assess whether the client is authorized to subscribe to or publish messages on specific topics. The broker can also leverage the information in the EATs to enforce access control policies, such as topic restrictions or rate limiting, based on the client PSA and ML-EAT tokens.  
The EAT tokens are secured using ECDSA cryptographic signing, meaning the transmission medium does not need to provide additional security. As a result, these tokens can be sent over protocols like HTTP or CoAP without jeopardizing their integrity. For communication protocols like TLS and SSL, the EAT can be incorporated into the client certificate during the handshake stage using X.509 certificate extensions. For example, the Trusted Computing Group's DICE Attestation Architecture defines X.509 certificate extensions for attestation and endorsements using EAT tokens [47].  
Additionally, since the ML model deployed on devices is a custom solution tailored for on-device learning, rather than being built with frameworks like Keras or TensorFlow, the ML-EAT model subclaims must be defined using a custom serialization schema that the central FL server can process and understand or to opt for string-encoded claims.
## 3 Results  
### Chemical Analyses  
The weld metal compositions are summarised in Table 3. The weld metals from the two test runs are compared: one without metal powder addition (BC) and one with metal powder addition (MP7). Confirmation of the alloying effect of the cobalt and aluminium metal powders in the MP7 weld metal was affirmed by the EDS analyses shown in Table 4. The area selected for EDS analyses shown in Figure 1 is positioned at the geometric centre of the weld metal cross-section. Although the SEM-EDS equipment cannot analyse %C and %O, the alloying element levels from SEM shown in Table 4 support the detailed bulk chemistry analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **\%C** & **\%Si** & **\%Mn** & **\%O** & **\%A1** & **\%P** & **\%S** & **\%Ti** & **\%Cu** & **\%Cr** & **Balance** \\ \hline Plate & 0.120 & 0.155 & 1.340 & 0.0007 & 0.067 & 0.019 & 0.007 & 0.005 & 0.030 & 0.160 & Fe \\ \hline Wire & 0.110 & 0.137 & 0.990 & 0.0003 & 0.000 & 0.009 & 0.023 & 0.000 & 0.140 & 0.000 & Fe \\ \hline \end{tabular}
\end{table}
Table 1: Base plate and weld wire compositions (mass%).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**MnO** & **CaO** & **SiO2** & **Al2O3** & **CaF2** & **MgO** & **FeO** & **TiO2** & **Na2O** & **K2O** \\ \hline
6.8 & 0.1 & 19.6 & 24.9 & 17.9 & 22.2 & 2.4 & 1.0 & 1.6 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flux bulk chemical composition (mass%).  
Table 3 displays the bulk chemical analyses of the BC and MP7 weld metals. It is clear that the %Mn and %Si increased in the MP7 weld metal. This effect was due to the aluminothermic reduction of SiO\({}_{2}\) and MnO from the flux into the weld metal, as displayed in Equations (2) and (3). It is notable that the weld metal total ppm O was significantly lower in MP7 compared with the BC weld metal, at 230 ppm O vs 499 ppm O, respectively. These ppm O values confirm the de-oxidiser role of aluminium at the weld-pool-slag interface [15; 16; 17; 18; 19; 20].  
As confirmed in previous studies, FeO in slag is formed from excess oxygen transferred from the arc cavity to the weld metal. The FeO was reduced by aluminium powder via reaction (4) to control the oxygen potential at the weld pool-slag interface [23; 24; 25; 26; 27; 28]:  
\[3(\mathrm{SiO}_{2})+4(\mathrm{Al})=3(\mathrm{Si})+2(\mathrm{Al}_{2}\mathrm{O} _{3})\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \
# Novel Boundary Edge Detection for Accurate 3D Surface Profilometry Using Digital Image Correlation
## 2 Methodology of the Random-Speckle Optical Detection and Processing Algorithm
### Search Strategy for Accurate Edge Points  
In view of the fact that the speckle pattern measured along a contour edge does not match the image in the reference template database, two new types of speckle subsets, namely boundary and corner subsets, were proposed to search the accurate edge points. The boundary subset had a condition that the split line was either of a horizontal or vertical boundary. Figure 10a shows the difference in edge detection between the proposed boundary subset and the traditional central subset when an inspection point was located on the contour edge. The overlapping region shown as a blue rectangle is the difference between the surface edge and the center of the boundary subset. Similarly, the corner subset had a condition that the split line is of a tilting boundary. Figure 10b shows the comparison between the corner subset and the traditional subset when an inspection point was located on the contour edge. The overlapping region shown as a blue square represents the potential difference in object edge detection. When the inspection point was scanned toward the eight directions shown in Figure 10c, eight speckle subsets could be formed either by the boundary subset or corner subset. More speckle subsets could be formed when more scanning directions were taken, for higher accuracy in the surface edge detection. In addition, the higher the scanning resolution, the more accurate the reconstruction of the object contour.  
Figure 9: Estimation of initial edge point: (**a**) edge point, (**b**) non-edge point, (**c**) an example of the measured point cloud, and (**d**) an initial edge link established for the example in (**c**).  
The search direction of the edge point was specified to be opposite to its composite vector, as shown in Figure (a)a. When an inspection point was set along the search direction, the composite vector of the inspection point was calculated to determine the subset type for the edge point search. Figure (b)b shows the boundary speckle subset to be defined for a horizontal or vertical search direction.  
On the other hand, the corner subset was defined for a titling search direction. Figure (a)a shows an inspection point set in the horizontal direction and another in the vertical direction. These two inspection points can define a corner subset for the edge search, as shown in Figure (b)b.  
Figure 11: Boundary subset for horizontal or vertical edge search.  
Figure 10: Proposed speckle subsets for contour edge detection.
## 3 Performance Analysis  
This section provides the statistical properties of the Rayleigh fading channel and SR fading channel. The exact expressions of EDP at \(S\) in the ISMTRN are obtained. Further, we also derive ACR to measure this covert performance of our considered system.
## 3 Results
### Butyrate Stimulates Insulin Transcription via Inhibition of HDAC  
Since butyrate is a known class I and II HDAC inhibitor (HDACi), we tested the HDAC activity of butyrate-treated NPICCs in fluorometric enzyme assays. Butyrate and trichostatin A (TSA) inhibited class I HDAC activity in a cell-free assay and decreased HDAC enzyme activity in NPICCs (Figure 4A,B). To further elucidate which HDAC enzyme may induce a proendocrine effect, we tested several specific small molecule HDACis including class I inhibitor MS275 and mocetinostat as well as class IIa inhibitors MC1568 and TMP269.  
Figure 3: GPR41 and GPR43 are not involved in up-regulation of insulin gene expression by butyrate. (**A**) Expression of _GPR41_ (119 bp) (lanes 1, 2), _GPR43_ (197 bp) (lanes 3, 4) and _GAPDH_ (lanes 5, 6) in NPICCs assessed by RT-PCR and agarose gel electrophoresis. Samples without reverse transcriptase (\(-\)) were included as controls (**B**) NPICCs were cultured in B-IC medium with 1000 \(\upmu\)M butyrate for six days containing GPR41 inhibitor (5 mM \(\beta\)-hydroxybutyrate, BHB), or GPR43 inhibitor (200 nM GPLG0974) or both as indicated. The mRNA levels of _INS_, _GCG_ and _PDX1_ were examined by RT-qPCR. Cells without butyrate exposure were used as controls. Data are presented as mean \(\pm\) SD of three independent experiments. *\(p<0.05\) vs. control group. ns = not significant.  
Exposure of NPICCs to MS275 and mocetinostat strongly increased the expression of insulin, recapitulating the effect of butyrate in NPICCs (Figure 4B). Treatment with MC1568 and TMP269 had no significant effect. Peak insulin gene expression was observed after treatment with mocetinostat (3.1-fold increase) providing evidence that inhibition of class I HDACs enhanced differentiation of neonatal porcine islets. These findings suggest that butyrate may induce maturation of neonatal porcine islets towards a beta cell phenotype through its HDAC inhibitory activity.
## 4 Experimental Design and Result Analysis
### Evaluation Indicators  
This experiment employs precision (\(P\)), recall (\(R\)), and mean Average Precision (\(mAP\)) as metrics to gauge the model's performance. The calculations are shown in Equations (8)-(11). Additionally, metrics such as model parameters, model complexity (GFLOPs), model file size (Weight), and frames per second (FPS) are also used. These indicators give a comprehensive overview of the model's performance, reflecting its performance in detection tasks from various perspectives.  
\[P=\frac{TP}{TP+FP} \tag{8}\]  
\[R=\frac{TP}{TP+FN} \tag{9}\]  
\[AP=\int_{0}^{1}P(r)dr \tag{10}\]  
\[mAP=\frac{\sum_{1}^{N}AP_{i}}{N(class)} \tag{11}\]  
In this section, \(TP\) signifies the number of samples detected correctly, \(FP\) signifies the number of samples detected falsely, \(FN\) signifies the number of samples that were missed, \(AP\) is the precision for a single category, and \(mAP\) is the mean Average Precision across all categories. [EMAIL_ADDRESS] calculates the mean of \(AP\) for each category when the IoU threshold in the confusion matrix is 0.5. [EMAIL_ADDRESS]:0.95 represents the mean of \(AP\) when the IoU varies from 0.50 to 0.95 with a step size of 0.05.
## 2 Materials and Methods
### Automated Feature Inference and Extraction from Non-Etched Metallographic Microstructures
#### 2.2.2 Classification of Nodules  
Once the nodules are segmented, they must be classified. The designation of the microstructure is a valuable characteristic, providing a basis for classifying the shape, distribution, and size of graphite in castings. Graphite classification through visual analysis during the casting process is a common method for categorizing the graphite microstructure in castings using comparative visual analyses. The structure of the metallic matrix (e.g., ferrite, perlite) has a significant effect on the material's properties.  
Graphite can be classified as UNE EN ISO 945-1 [4] and described by the following:  
* Its shape (numbers I to VI, shown in Figure 5). The graphite size is determined by measuring the graphite and comparing it with the dimensions and reference numbers given in Table 1 of UNE EN ISO 945-2 [5]. Types III, V, and VI will be considered in the study, and IV and V are considered together as type V.
* Its distribution, uniform or clustered, represents the homogeneity with which it distributes the graphite throughout the metal matrix. It affects properties like thermal conductivity and wear resistance. A homogeneous distribution is essential to avoid stress concentrations.
* Its size. It is classified according to standards (grades 1 to 8), where a lower grade indicates coarse graphite (specifically, gray cast iron with low strength), and a higher grade corresponds to fine graphite (in other words, superior mechanical properties).  
For the purpose of this study, the classification of graphite will be carried out based on its shape, as described in criterion (a).
## 2 Mud Ring Optimization Algorithm
### MRA-Based MPPT Technique  
By altering the terminal voltages, the maximum power point of a multi-modular TEG power source with a centralized MPPT control scheme is monitored. At MPP, the terminal voltage is changed so that the power may be delivered to the load more effectively using a DC-DC converter (boost). To change the output voltages and achieve MPP, the duty cycle of the boost converter can be changed. This modification of the control action is carried out by an intelligent controller built on the MRA. The duty cycle is between 0 and 1.  
The MPP tracking is performed by generating the reference voltage as Figure 5. The process is randomly initialized by four random duty cycles depicted by D1, D2, D3, and D4. Any large voltage fluctuation is caused by the abandonment of the least performing duty cycle, as D1 discovered at 0.01 s as a result of D2's effect on the greater fitness score. [PERSON] is detained for 0.09 s while search agent [PERSON] successfully completes the GMPP. Within 200 ms, the remaining population has already begun to swiftly concentrate around D4. The voltage transients and power variations first exhibit extremely explorative activity.  
The population size is one of the most important aspects that must be properly tuned when adjusting the meta-heuristic optimization algorithm's parameters. Low efficiency, sluggish convergence, and reduced volatility are caused by smaller populations. The exploratory behavior is maximized by using more particles, but the setup time is extended by the longer computing time. Therefore, there must be a careful balance between the  
Figure 5: Tracking mechanism of MRA in TEG.  
setting time and power tracking efficiency. As a result, \(\text{Dn}=4\) is selected for this study. The effectiveness of tweaking this parameter is examined with various population sizes.  
The MRA procedure for MPPT is shown using a flowchart in Figure 6 as an example of an engineering problem for the TEG system.  
Effective MPPT control requires the proper tuning of parameters to follow the global maxima. The sensitivity analysis is performed to determine the optimal parameter ranges. Poor parameter tuning leads to power loss as MPPT control fails to track the GMP. The PSO algorithms experience fluctuations and oscillations from randomized location and velocity vectors, thus hindering the steady convergence to the global maximum power point (GMPP). The proposed method reduces the random variables, leading to fewer oscillations and higher energy production. Despite these challenges, the method effectively controls the TEG systems using MPPT.