esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Advantage of the New Algorithm for Treating Functional Model Error
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise Levels** & \multicolumn{2}{c}{**E (mm/a)**} & \multicolumn{2}{c}{**N (mm/a)**} \\ \cline{2-5}
**(\(\sigma_{0}^{2}\))** & **ALSC** & **ADLSC** & **ALSC** & **ADLSC** \\ \hline
0.1 & 18.4682 & 2.3495 (87.3\%) & 3.6667 & 2.0740 (43.4\%) \\
1 & 5.4206 & 0.8802 (83.8\%) & 1.2534 & 1.1009 (12.2\%) \\
10 & 2.7854 & 0.8020 (71.2\%) & 1.0777 & 0.8921 (17.2\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The signal variance estimated results by the first Helmert VCEM calculation of the two algorithms under different noise levels. Figure 8: Comparison of the signal variance estimated results by the first Helmert VCEM calculation of the two algorithms under different noise levels: (**a**) east direction; (**b**) north direction. error of the model, and is better suited to the treatment of the functional model error. It also should be noted that in this study, we mainly focus on the stochastic signals with isotropic characteristics, even if two directions are considered: east and north. More complex anisotropic situations will be further studied in the future.
# Democratization of PV Micro-Generation System Monitoring Based on Narrowband-IoT
## 3 IoT Monitoring Solution
### Hardware
#### 3.2.1 DC-Output Current: Acs/758 Hall-Based Effect Sensor  
A breakout board for the ACS758 Hall effect linear current sensor was selected for measuring the DC current of the PV module string. The thickness of the copper conductor of this sensor allows the device to survive under high current conditions. The ACS758 provides an analog voltage output signal that varies linearly with the current value. The output voltage of the sensor varies significantly depending on the input current value. Therefore, different currents were applied within the available measurement interval, ranging from \(-\)50 A to 50 A DC. In this case, we used a calibration current range from 0 A to 20 A, in steps of 1 A. Subsequently, the sensor output voltage was measured through the FiPy microcontroller, see Figure 2. With this aim, a script was defined to convert the measured value into an estimated current value. The sensor sensitivity provided by the manufacturer was 40 mV/A, for a channel up to 5 V. In our case, the microcontroller channel input was 3.3 V, and subsequently the sensitivity to be used was 26.4 mV/A. The offset voltage was 1.65 V for 0 A, corresponding to 50% of the FiPy microcontroller analogical offset range. The relative error was estimated by comparing the real input current to the measured current values, with a maximum error of 4%. Different current sensors were calibrated and compared to evaluate their performance and accuracy within the expected current range. Figure 3 compares two current sensor calibration processes. An additional source of potential errors in the current sensor output is the sensitivity of the FiPy analog input channel, which is 20 mV/A.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### ETX-Based Solutions  
ETX indicates the quality of the node-link and it is one of the prominent routing metrics in LLNs. Following solutions take ETX as a metric for measuring the quality of the link and for solving the issue of delay caused by ETX probing and increase in hand-off delay.
## 6 Results, Analysis and Discussion
### Analysis Spatial Distribution of Performance Metrics
#### 6.2.1 Retransmissions with 2D Representation  
Figures 7-9 present the worst case of jamming, the reactive case, for the three different positions of the jammer node. Each figure shows a grid of nodes that represents the used topology, a matrix of \(7\times 7\) nodes. Through a grayscale, we exemplify the maximum (close to black color) and minimum (close to white color) values. Grids of the \"no jamming\" column show the normal behavior of the network under no attack for each of the studied variables. The other three columns in each graph show the reactive case when the jammer node is close to (near column), when it is in the middle of the topology (middle column) and when it is far (far column) from the coordinator node. These grids have the change in the reactive jamming for each of the nodes, _i.e._, the difference between the reactive jamming for each case and the value when there is no jamming for each of the nodes.  
In Figure 7, we note that in the no jamming column, we have the average number of retransmissions for the AODV, DSR and MPH protocols. In these grids, we notice that there is a greater amount of retransmissions in nodes near the coordinator, because these nodes generate and forward traffic from other nodes. For this reason, collisions are more likely to occur and, hence, packet retransmissions. However, the MPH protocol has lower average retransmissions, and the nodes that are affected by collisions are less than AODV and DSR cases. Concerning the near column, we observe the difference in values between the reactive and no jamming case for each node. We note that for AODV, the biggest change is in the farthest nodes to the coordinator, because the jammer node is close to the coordinator, and this prevents the final communication, making the number of affected nodes greater in the network. Then, this attack position alters nodes that are far from the destination for AODV. Therefore, nodes close to the coordinator node have a change between 0.4 and 0.6, which shows that they are also significantly affected by the attack. For DSR, the attack is a little less aggressive, but also affects one of the farthest nodes crowns with respect to the coordinator with a change of about 0.6. The jamming has the lowest impact in terms of the number of nodes for the MPH; the change for nodes that are closer to the coordinator is approximately 0.4. Distant nodes have a change between 0.1 and 0.2. Regarding the middle column, AODV and DSR modified approximately one half of the nodes in the topology, while MPH encapsulates the attack slightly more with less affected nodes. With regard to the far column, AODV and DSR for the jammer node have more impact with a change of 1.5 in the near node jammer nodes. The MPH protocol has a change of one in about three nodes directly connected to the jammer node, and then, the change tapers to one, _i.e._, MPH does not allow the attack to spread to multiple nodes as AODV and DSR.
# Easing DApp Interaction for Non-Blockchain Users from a Conceptual Modelling Approach
## 5 Middleware to Ease DApp User Interaction
### Security Concerns  
One of the key features of blockchain is its security. Therefore, our DApp will have to keep up this by implementing several security procedures. Hence, we propose the use of JSON Web Tokens (JWT), an open, industry standard method for representing claims securely between two parties [33] as credentials for the users as an extra layer over the blockchain security.  
When a user logs in successfully, a JWT will be created by the middleware and sent to the frontend for the user interface to be able to invoke the RESTful API. Moreover, the user's wallet will be unlocked for several seconds equal to the JWT expiration time. Hence, if a user logs out, the JWT will be blacklisted and the wallet locked. Nevertheless, if a user does not log out, the JWT will automatically expire and the wallet will be locked, thus requiring a new log in to keep on using the DApp.  
Moreover, we propose implementing an additional security layer regarding IP addresses whitelisting. With this aim, our Ethereum node will only be accessible to users whose IP addresses are whitelisted. Therefore, if an unauthorized user tries to unlock a wallet without signing in properly, the node will automatically reject the operation. For that reason, when a user logs in, his/her IP address will be whitelisted. Finally, the log out of a user will remove the IP address from the whitelist. It is worth noting that using this procedure will disable users to interact directly with the blockchain through our node. However, this issue will be only applicable to private Ethereum-based blockchains where only a few (or even one) nodes can access the blockchain.
# BGPcoin: Blockchain-Based Internet Number Resource Authority and BGP Security Solution
## 9 Conclusions  
In this work, we introduced a novel Internet resource management and route origin advertisement/authentication solution for BGP security. We designed the BGPcoin system consisting of a smart contract-based resource assignment attestation and a blockchain-based dependable repository infrastructure. We demonstrated through an extensive analysis that the deployment incentives and increased security are technically and economically viable.  
Future works: We are working on a systematic implementation of BGPcoin in a practical scenario with the Quagga Secure Routing Extension [41] Routers and some critical performance measurements including the real-time update speed from the BGPcoin blockchain to BGP border routers, as well as exploring how securely a partial and incremental deployment of BGPcoin protects the Internet BGP system.  
**Author Contributions:** [PERSON] contributed to the design of the ideas, the analysis of the results and the writing of the paper. [PERSON] and [PERSON] proofread the paper.  
**Funding:** This research is supported in part by the project of the National Key Research and Development Program of China (2017 YFB0802301).  
**Conflicts of Interest:** The authors declare that there is no conflict of interests regarding the publication of this manuscript.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Operation** & **Gas** & **USD** & **Operation** & **Gas** & **USD** \\ \hline IP register & 155,448 & 0.449 & IP revoke & 72,960 & 0.211 \\ IP allocate & 188,113 & 0.544 & ASN register & 4411 & 0.123 \\ IP assign & 183,246 & 0.530 & ASN allocate & 68,876 & 0.199 \\ IP update & 69,101 & 0.200 & ASN update & 27,691 & 0.080 \\ \hline \multicolumn{5}{c}{BGPcoin Contract Creation} & 3,985,649 & 11.524 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cost of BGPcoin trading operations in the Ropsten network.
## 3 Materials and Methods
### Validation Methods  
Commonly-used evaluation measures are precision, sensitivity, and accuracy. These measures can be defined with the help of four cardinalities of the confusion matrix, namely the truth positive (TP), true negative (TN), false positive (FP), and false negative (FN) [38].
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 3 Model
### Cell Model  
We calculate the middle beams' radius, which is the baseline, to determine the largest size beam and the smallest beam.  
Assuming the user is in the coverage area of a user beam, the users' minimum elevation angle is 85\({}^{\circ}\), denoted as \(E\), and then the, Earth Central Angle \(\alpha\) can be calculated by equation:  
\[\alpha=\arccos\left(\frac{R_{E}\cos E}{R_{E}+h}\right)-E \tag{7}\]  
The area covered by the middle-sized beam is:  
\[A_{S}=4\pi R_{E}{}^{2}\sin^{2}\frac{\alpha}{2} \tag{8}\]  
The radius of that can be derived from the equation:  
\[r=\sqrt[2]{\frac{A_{S}}{\pi}} \tag{9}\]
## 2 Study approach
### Geospatial metadata standard  
The UK GEMINI (GEo-spatial Metadata INteroperability Initiative, a.k.a. GEMINI) convention was selected to instantiate the metadata output of the prototype. As the prevailing discovery standard in the UK it offers a widely recognised, succinct format with a proven pedigree in geospatial production environments, one in which the performance of the present work can be demonstrated. Derived in accordance with _ISO 19106 Geographic Information: Profiles_ from both _ISO 19115 Geographic Information: Metadata_ and the UK eGovernment Metadata Standard (_eGMS_), GEMINI outlines thirty-two elements (Table 1) that are readily mapped to other ISO-based formats [37].  
\begin{table}
\begin{tabular}{|c|c|} \hline
**Element** & **Description** \\ \hline Title & Dataset name \\ \hline _Alternative title_ & _Alternative name_ \\ \hline Dataset language & Language used \\ \hline Abstract & Narrative summary describing the dataset \\ \hline Topic category & Main themes of dataset (high-level categories) \\ \hline Subject & Topic of the dataset content (low-level categories) \\ \hline \end{tabular}
\end{table}
Table 1: The UK GEMINI 32 metadata element set - optional elements in italics.
# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
## 3 Problem Description  
The addressed problem is an autonomic conversion of user-driven application requirements to provider-driven network requirements. This is done by first defining a simple graph that represents the application end-points and abstract topological, performance, security, and reliability demands. The graph is then stepwise converted to a virtual network that represents the physical devices and application virtual nodes with their connectivity, types, locations, and security domains. The connectivity might be simple links or composite links that are realized by adding chains of network functions that satisfy both application requirements and provider policies, and according to the topologies of the application and physical network. This virtual network is then mapped onto the physical network by first mapping its nodes according to their properties and resource requirements. Subsequently, the simple and composite links are mapped while using the shortest paths.  
The composite links with chains of network functions are mapped using a greedy proactive method that checks the remaining length and next available resources of the target path to make placement decisions in advance. This adaptive method spreads or consolidates the functions over the path according to its length. Consolidating network functions on servers saves network bandwidth and reduces the delay, even if the traffic will traverse a long remaining network path after traversing the chain. However, functions that should be placed in different locations/domains cannot be consolidated in one location/domain.  
The target use case is remote asset management in industrial environment using three ICT levels: IIoT/factory hall level, edge computing level, and cloud computing level. The used chain mapping method considers the limited resources and dense traffic at the edge computing level. The random topology that was used for evaluating the chain embedding heuristic reflects such a topology.
## 3 Results
### Adsorption Isotherms  
The effect of EPS dosage on Cu(II) and Zn(II) sorption is shown in Figure 4b. The adsorption of Cu(II) and Zn(II) increased as EPS concentration increased from 12.5 to 50 mg L\({}^{-1}\). At 100 mg L\({}^{-1}\) EPS concentration, the adsorbent got saturated with no further increase in adsorption of the metal ions. The metal removal potential for Cu(II) increased from 56.4 to 94.4% when the EPS concentration increased from 12.5 to 50 mg L\({}^{-1}\). Similarly, for Zn(II), the removal potential increased from 50 to 94% when the EPS concentration increased from 12.5 to 50 mg L\({}^{-1}\). The further increase in EPS concentration failed to increase the metal sorption. At 100 mg L\({}^{-1}\) EPS concentration, the metal removal potential was 94.8% for both the metal ions.  
The data for adsorption of metal ions by EPS at different concentrations were tested with the Langmuir and Freundlich isotherm models. The correlation coefficients (R\({}^{2}\)) of fitting the adsorption data to the Langmuir model were 0.999 for both Cu(II) and Zn(II), which showed that the adsorption of metal ions adequately fitted to the Langmuir model. The maximum adsorption capacities (q\({}_{\rm m}\)) obtained from the Langmuir plots were: 58.82 mg g\({}^{-1}\) for Cu(II) and 52.45 mg g\({}^{-1}\) for Zn(II) (Table 3). The values for the constant b were: 0.27 L mg\({}^{-1}\) for Cu(II) and 0.29 L mg\({}^{-1}\) for Zn(II). The correlation coefficients (R\({}^{2}\)) of fitting the adsorption data to the Freundlich model were: 0.95 and 0.97 for Cu(II) and Zn(II), respectively. The values for the constants K\({}_{\rm f}\) and n \({}^{-1}\) obtained from the Freundlich model fitting were: 12.59 and 2.94 for Cu(II), and 12.61 and 2.0 for Zn(II), respectively (Table 3). The R\({}^{2}\) values indicated that the Langmuir isotherm model gave a better fitting to the adsorption data than the Freundlich model. The values of R\({}_{\rm L}\) for adsorption of Cu(II) and Zn(II) by EPS were 0.13 and 0.12, respectively.  
Figure 4: Effect of increasing (**a**) pH at a constant concentration of 25 mg L\({}^{-1}\) for both metals and EPS and (**b**) EPS concentration on sorption of metals by EPS at a constant pH = 7.0 and a fixed concentration at 25 mg L\({}^{-1}\) for both metals. The error bars in the graphs represent respective standard deviation.
## 2 Materials and Methods
### Kinematic Experiment
#### 2.1.3 Data Collection and Processing  
Position and joint angle data were collected using 16 electromagnetic tracking sensors (Model: Liberty Microsensors, Polhemus Inc., Colchester, VT, USA), as shown in Figure 2b. The microsensors utilized have a diameter of 1.8 mm and a resolution of 1.27 microns. Their static position accuracy is 0.76 mm, while their static angular orientation accuracy is 0.15 degrees. The placement of each sensor on the surface of the palm is shown in Figure 2c. The orientation data from each sensor with respect to the source box were collected in the form of quaternions to avoid singularities in the range of motion. The sensors also output the position data (\(x,\ y,\ z\)) for each phalanx. Hence, the dimension of the dataset for each trial was 800 rows (8 s \(\times\) 100 Hz) and 96 columns (16 sensors \(\times\) 7 readings per sensor). The data collected were filtered with a cutoff frequency of 5 Hz using a linear 2 nd order Butterworth filter with zero lag to remove the effects of any physiological tremor. The quaternion data were filtered by converting them into exponential map format [22]. All the data analyses and processing required were performed in MATLAB (MathWorks, Inc., Natick, MA, USA; Version: R2022a). The raw quaternion data were converted into 21 joint angles by relative quaternions using the formula below from [23].  
\[q_{rel}=q_{i}^{conj}\bigotimes\ q_{j} \tag{1}\]  
where \(q_{rel}\) is the relative quaternion between quaternions \(q_{i}\) and \(q_{j}\) measured across any joint k. \(q_{i}^{conj}\) represents the quaternion conjugate and \(\bigotimes\) indicates quaternion multiplication. The relative quaternion thus obtained is converted to Euler angles using the 'eulerd' function in MATLAB using \"XYZ\" as the rotation sequence. The 3 Euler angles repre  
Figure 2: **(a) Postures and objects used in the kinematic data collection experiment involving human participants. (b) Polhemus Microsensor. (c) Sensor placement locations on the dorsal surface of the hand.**sent rotation, Ab/Ad, and F/E according to the sequence. The required angles for each joint were extracted accordingly. Thus, the dataset for each participant has 86,400 rows (36 postures \(\times\) 3 trials \(\times\) 800 data points) and 21 columns (21 joint angles).
###### Abstract  
Blasting is an effective technique for fragmenting rock in open-pit mining operations. Blasting operations produce either boulders or fine fragments, both of which increase costs and pose environmental risks. As a result, predicting the mean fragmentation size (MFS) distribution of rock is critical for assessing blasting operations' quality and mitigating risks. Due to the limitations of empirical and statistical models, several researchers are turning to artificial intelligence (AI)-based techniques to predict the MFS distribution of rock. Thus, this study uses three AI tree-based algorithms--extra trees (ET), gradient boosting (GB), and random forest (RF)--to predict the MFS distribution of rock. The prediction accuracy of the models is optimized utilizing the tree-structured Parzen estimators (TPEs) algorithm, which results in three models: TPE-ET, TPE-GB, and TPE-RF. The dataset used in this study was collected from the published literature and through the data augmentation of a large-scale dataset of 3740 blast samples. Among the evaluated models, the TPE-ET model exhibits the best performance with a coefficient of determination (\(R^{2}\)), root mean squared error (RMSE), mean absolute error (MAE), and max error of 0.93, 0.04, 0.03, and 0.25 during the testing phase. Moreover, the block size (XB, m) and modulus of elasticity (E, GPa) parameters are identified as the most influential parameters for predicting the MFS distribution of rock. Lastly, an interactive web application has been developed to assist engineers with the timely prediction of MFS. The predictive model developed in this study is a reliable intelligent model because it combines high accuracy with a strong, explainable AI tool for predicting MFS.  
Article  
Academic EditorNikolaos [PERSON]  
8 June 2025  
19 July 2025  
Accepted: 24 July 2025  
Published: 28 July 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Application of Extra-Trees Regression and Tree-Structured Parzen Estimators Optimization  
Algorithm to Predict Blast-Induced Mean Fragmentation Size in Open-Pit Mines. _Appl. Sci._**2025**, _15_, 8363.  
[[https://doi.org/10.3390/app15158836](https://doi.org/10.3390/app15158836)]([https://doi.org/10.3390/app15158836](https://doi.org/10.3390/app15158836))  
**Copyright:** 62025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
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