esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Framework and Methodology
### Accurate Identification of Industrial Clusters Based on MDBSCAN
On the other hand, external evaluation is conducted using real labels, where the clustering results are compared against the actual labels to determine the evaluation outcome. In the process of industrial clusters evaluation, true labels are not usually available; thus, the internal evaluation methods are generally adopted. There are three commonly used internal evaluation methods, namely, the Silhouette Coefficient Index (SCI) [70], Calinski-Harbasz Score (CHS) [71], and Davies-Bouldin Index (DBI) [72]. The SCI algorithm yields evaluation scores ranging from \(-1\) to \(1\). The CHS provides the fastest clustering evaluation but is primarily applicable to the evaluation of clusters of spherical data. However, it is less accurate compared to SC and DBI when evaluating the clustering results obtained based on the density algorithm [73]. The DBI integrates the intra-class sample similarity and inter-class sample difference, resulting in superior efficiency and accuracy. The evaluation score for the DBI value is unbounded and can range from zero to any positive number. A higher DBI value indicates a better clustering effect, and it is also suitable for joint evaluation of multiple indicators. The following formula stands for the DBI, assuming that the applicant dataset \(A\) is divided into \(k\) clusters:  
\[DBI=\frac{1}{C}\sum_{i=1}^{C}\max_{i\
eq j}(\frac{\overline{S_{i}}+\overline{S_ {j}}}{\left|\left|w_{i}-w_{j}\right|\right|_{2}}) \tag{5}\]where \(\overline{S_{i}}\) represents the average distance from all internal elements of the \(i\)th cluster to the center of the cluster, which also indicates the degree of dispersion of sample data within the cluster. \(\big{|}|w_{i}-w_{j}|\big{|}_{2}\) represents the cluster center distance from the \(i\)th to \(j\)th clusters. The formula shows that the smaller the DBI index is, the smaller the distance within the cluster, the larger the inter-cluster clustering, and the better the clustering effect. In order to calculate the applicant's final clustering index, it is essential to consider both spatial and technological distances with two DBI indices, namely, the spatial distance clustering index \(DBI^{sd}\) and technological distance clustering index \(DBI^{td}\). Normalization is required to mitigate the impact of varying ranges of values on joint settlement results and prevent substantial differences between values. To achieve this, the chosen approach involves the utilization of a logarithmic function conversion method. Consequently, the overall calculation process for the complete Dissimilarity-Based Index can be outlined as follows:  
\[DBI=\frac{1}{2}\{\log[DBI^{td}]+\log[DBI^{td}]\} \tag{6}\]
## 3 Literature Reviews
### LEACH-Based Secure Routing Protocols
#### 3.3.2 SecLEACH  
SecLEACH was proposed to enhance SLEACH. Figure 3 summarizes the SecLEACH protocol. It uses the random key pre-distribution scheme proposed by [PERSON] and [PERSON] [18]. Prior to deployment each node is given a secret key (shared only with BS) and a set of key rings drawn from a large key pool. Those key rings contain pairs of key ids and keys.  
In the setup phase, every self-elected CH broadcasts its advertisement message **adv** containing its id and a nonce. After receiving all advertisement messages, a node will choose a CH that it wants to join based on the signal strength and choose the index of secret common keys that it shares with the chosen CH.  
Additional symbols denote:  
\(r\): Key index in the key rings;  
\(R_{X}\): The node \(X\)'s key ring;  
\(k_{[r]}\): Symmetric key associated with \(r\);  
\(j\): Reporting cycle within current round.  
In step 2, a node sends its join request message **join_req** to a CH containing its id, CH's id, the index of shared keys and also the MAC calculated using the shared key. The advertisement message's nonce is included in the MAC computation. After that the CH broadcasts the schedule message **sched** to its cluster members.  
In steady-state phase, each cluster member sends its sensor readings to CH on its time slots. As shown in the fourth step, SecLEACH includes sensor readings in the MAC computation. It uses the key it shares with the CH to compute MAC. It also puts nonce and the reporting cycle of the current round into the MAC computation to provide freshness. In step 5, the CH sends the aggregated data to BS along with the MAC calculated using the key shared with BS. It also puts the counter shared with BS into the MAC computation.
## 4 Discussion
During the SLM process, rapid melting and heating cause residual strain accumulation and an increase in dislocation density at the grain boundaries, similar to plastic deformation. This increase in dislocation density during the SLM process elevates the internal energy [33], which serves as the driving force for recrystallization [35; 37]. In this study, the as-built M100 samples displayed narrower laser-melted tracks owing to the smaller beam spot size of the M100. These narrower tracks induce higher KAM values (i.e., higher residual plastic strains and dislocation density) in the as-built M100 samples compared to the as-built M290 samples. This is because the number of melting and solidification steps occurring in the same area is higher in the M100 samples than in the M290 samples. The higher residual strains and dislocation density within the as-built M100 samples lead to a stronger driving force for recrystallization, and the faster recrystallization of the M100 samples reduces the required heating time after fabrication. The mechanical property values obtained in this experiment were similar to those in previous studies, demonstrating high strength and high ductility and fully meeting the requirements of the ISO standards [3; 25; 26; 38]. Under the heat treatment condition of 750 \({}^{\circ}\)C, both the ultimate tensile strength (UTS) and 0.2% proof stress (PS) are higher compared to those at 1150 \({}^{\circ}\)C, while elongation decreases [3; 26; 38]. This is attributed to the increased proportion of the hexagonal close-packed (HCP) phase at 750 \({}^{\circ}\)C. These findings suggest the importance of selecting heat treatment conditions based on the prioritized mechanical properties required for dental prostheses. Several SLM machines are commercially available, each with varying laser types, scan speeds, and focus diameters [16]. In this study, the influences of different machines with different technical specifications on the mechanical properties and microstructures were evaluated. Although the samples fabricated using EOS M100 and M290 displayed sufficient mechanical properties for use as dental prostheses, their mechanical properties and microstructures exhibited different trends. The M100 machine, being small, is suitable for fabricating relatively small devices, such as dental prostheses, and its small laser spot with good detail resolution is favorable for fabricating highly complex components. Furthermore, fine-grained microstructures and excellent elongation have been realized in this study. However, the number of M100 samples that can be fabricated in a batch is fewer than that of M290 samples; therefore, the time efficiency and order volume for dental prostheses should also be considered. Additionally, further investigations are required to compare the dimensions and fitness accuracies of the dental prostheses fabricated using both machines.
## 4 Routing Protocol
### Negotiation  
The flow chart of the negotiation phase is shown in Figure 4.  
Figure 4: The flow chart of negotiation phase.  
* The source node sends an ADV message. Then each neighbor of the source node ascertains whether it has already received the advertised data.
* If not, the receiver also assesses whether it is nearer to the sink node or not in comparison with the node that has sent the ADV message. This is the major difference between the negotiation phase of SPIN and that of ESPIN. If the hop distance of the receiving node (own_hop) is less than the hop distance received by it as part of the ADV message (rcev_hop), _i.e._, \(\text{own\_hop}<\text{rcev\_hop}\), then the receiving nodes send the REQ message to the sending node for DATA message.
* If the source node does not receive any REQ messages within a fixed time, it causes the problem that data cannot be sent to the destination, the COUNTER will be initiated and source node will randomly send the CA to its neighbor; becoming COUNTER minus one. Only if the residual energy of the receiving node is higher than the threshold, is the REQ message automatically sent to the source node and the DATA message begins to transmit. If not, the neighbor does not respond and Step 3 is repeated.
* When the COUNTER is 0, it represents the energy of neighbor nodes has been exhausted and the networks almost collapse; the source node then stops to send ADV message.
## 3 Methodology
### Real-Time Integration (Deployment)  
In the real-time integration phase, a Flask web application was developed to interface with the trained model. The app captures live traffic from the cloud environment, preprocesses it, feeds it into the ensemble model, and presents results on a web dashboard. This setup allows real-time detection of intrusions and immediate response. The deployment steps are highlighted in Figure 6.  
To deploy the SHAP-enabled ensemble IDS using PyShark v.0.6 for real-time network traffic analysis, the process began by setting up a cloud-based environment where we used an AWS EC2 instance running Ubuntu _(for cloud deployment, an AWS EC2 T2 instance was utilized: t2.medium type, which provides 2 vCPUs, and 4 GiB of memory)._  
Afterward, essential dependencies including Python libraries (Flask, PyShark, TensorFlow/Keras, scikit-learn, and SHAP) were installed to support model inference, real-time data capture, and explainability. Pre-trained models, ANN, SVM, and RF meta-classifiers were then loaded into the environment. PyShark was used to capture live packet data from the network interface, which was subsequently parsed and converted into structured feature vectors. These features were scaled and fed into both the ANN and SVM models to generate prediction probabilities.  
Later, the outputs from both models were combined and passed into the RF meta-classifier, which produces the final decision on whether the network activity represents a normal or intrusive behavior. To add interpretability, SHAP was applied to explain each prediction, offering both global feature importance and local explanations through summary and decision plots. Finally, the entire system was integrated into a Flask-based web application that provides real-time intrusion alerts and SHAP visualizations via a user-friendly interface, ensuring both operational relevance and transparency.  
During the final testing phase, specific ports were opened on the EC2 virtual server where the IDS was deployed, and simulated DoS attacks were executed using tools like hping3 to assess the system's detection capabilities under high-traffic conditions. To ensure that incoming external traffic would reach the IDS, the security group configuration of the EC2 instance was updated, and comprehensive testing was carried out. The inbound  
Figure 6: Deployment steps.  
network rules were configured as follows: TCP port 64,295 was designated for secure SSH access (restricted to the researcher's public IP), TCP port 64,297 was allocated for accessing the T-Pot web interface (also IP-restricted), and TCP and UDP ports 1-64,000 were opened to allow unrestricted external traffic specifically for simulation purposes. During this phase, the EC2 instance was deliberately flooded with malicious packets using hping3, enabling the validation of the IDS's real-time detection accuracy, responsiveness, and explainability under simulated attack scenarios.
## 3 Results and Discussion
### Interfaces Characterization
Figure 3: SEM images at different magnifications of: (**a**) bare substrate and (**b**) SC-Mg and (**c**) SC-Sr interfaces. Regarding the crystallinity of the deposited layers, the XRD patterns were recorded at grazing incidence to reduce the substrate contribution, which, even under this configuration sometimes generates separate peaks (silicon, Si, cubic structure, ICDD-00-075-0589), as can be observed in Figure 4b. The results are correlated, to a certain extent, with those presented for the targets, in the sense that a vitroceramic character is confirmed, with a larger maxima width this time, indicating the formation of smaller crystallites. Moreover, PLD-Sr-1 and PLD-Sr-2 include an important proportion of the Ca\({}_{2}\)SiO\({}_{4}\)-based phase (Ca\({}_{1.8}\)Sr\({}_{0.2}\)SiO\({}_{4}\)) with orthorhombic structure (ICDD 00-077-1621), in addition to various types of SiO\({}_{2}\)-ordered networks (ICDD-00-080-2147, ICDD-00-082-1567, ICDD-00-082-1569 and ICDD-00-083-1339). On a closer look, a changed substitution ratio can be seen in the case of calcium strontium silicate when comparing its formula before and after the laser ablation experiments. Thus, if a sintering temperature of 800 \({}^{\circ}\)C applied for the targets favours the penetration of Sr into Ca\({}_{2}\)SiO\({}_{4}\) crystalline structure, the coatings obtained at 25 or 300 \({}^{\circ}\)C are characterized by a higher Ca\({}^{2+}\)/Sr\({}^{2+}\) ratio, namely, a lower substitution degree. It can also be stated that the presence of Sr  
Figure 4: (a) Energy-dispersive X-ray spectroscopy (EDX) spectra and (b) XRD patterns of the deposited interfaces. increases the tendency towards crystallization [32]. For instance, [PERSON] et al. [39] demonstrated that the crystallinity and adhesive strength of the films grown by PLD increase with substrate temperature raising. In another train of thought, compared to the Ar atmosphere, the O\({}_{2}\) atmosphere seems to be beneficial for the PLD preparing of films with excellent osteoinductivity [47]. In conclusion, the obtained interfaces are suitable for biological applications, sustained especially by the structural characteristics. A vitreous matrix which encapsulates specific crystalline phases, easily attackable when placed in contact with the physiological fluids and prone to apatite formation, represents a material with bioactive potential. The identified crystalline silicate compounds (Ca\({}_{2}\)SiO\({}_{4}\), Ca\({}_{1.8}\)Sr\({}_{0.2}\)SiO\({}_{4}\)) also play important roles in emphasizing the biocompatible and bioactive character, while SiO\({}_{2}\) can act as a reinforcing agent, improving the mechanical properties [41].
## 7 Conclusions  
LST can be retrieved with increasing accuracy and precision, making it a suitable candidate for regional and global assessments of climate variability and change. There are attempts to use LST as a substitute for surface air temperature in areas where in situ measurements are scarce, however, it can also be used directly for change studies. AVHRR has been flown since the early 80 ies on a series of platforms. The resulting long time series--35 years--is unique. Although there are some constraints to the direct use of the data (e.g., orbital drift), the resulting time series can be used to generate additional value by extending existing time series of newer sensors with AVHRR back in time or fill gaps of these time series in areas where cloud cover is predominant. As such, AVHRR LST plays an important part in improving climatological databases.  
As the number of bands of the AVHRR instrument was not consistent, AVHRR/1 had only one band in the thermal domain, whereas AHVRR/2 and /3 have 2 bands, four different mono-window algorithms and six different split-window algorithms were assessed. For the comparison, new coefficients were generated in the form of a small look up table, accounting for different ranges of columnar water vapour, emissivity, emissivity difference (AVHRR/2 and /3 only) and first guess LST. Such sensor--specific parameters prevent producing artificial anomalies or wrong trends in the data, when processing long time series from AVHRR. For the generation of the coefficients, a radiative transfer model (MODTRAN V5) was used to model top-of-atmosphere brightness temperatures to given LST and the previously mentioned ranges. The least squares method was then used to generate the coefficients and corresponding statistical measures from the database. The statistical measures were used for the performance assessment of the different algorithms. Further, the sensitivity of the single algorithms to their input data was assessed, by simulating ranges of input values. The synopsis of the performance, as expressed by accuracy and precision measures, and sensitivity analysis revealed that the [PERSON] et al., 2001 algorithm is to be preferred amongst the mono-window algorithms due to its good performance and low sensitivity results. Amongst the split-window algorithms the Becker and Li 1990 and the Price 1984 algorithm can be suggested, as both show good performance and low sensitivity values.  
A comparison of the application of the Becker and Li 1990 coefficients and the Vegetation Cover Method (VCM) [30] to AVHRR with the MODIS LST product revealed a good agreement and confirmed the approach to be valid and physically sound.  
This work was funded by the German Aerospace Center (DLR). We thank DLR headquarters for the funding of the TIMELINE project.  
[PERSON] performed the design of the study, analyzed the data and wrote the paper. [PERSON] contributed to the design of the study and contributed to the manuscript. [PERSON] contributed to the design of the study and commented on the manuscript.  
The authors declare no conflict of interest.
## 5 Experiments
### Results in the 1-Principal-1-Assistant Scenario
#### 5.2.2 Computational Performance  
The number of served vertices and the running time of algorithms are displayed in Figure 5. In Figure 5a,b, until the graph size \(n=15\), the ILP is the optimal method, but its running time exceeds 790 seconds. Because the running time of ILP will grow exponentially as the graph size \(n\) grows larger, the ILP is not a suitable method when \(n>15\). When the graph size n ranges from 16 to 20, except for C1 and C2, the other five algorithms are close to each other. However, the running times of C2 and C2+Subjoin exceed 500 s when \(n=19\). As abovementioned, the time complexities of C2 and C2+Subjoin are greater than \(\mathcal{O}(n^{10})\), and their actual running times are also extremely large as the graph size \(n>19\). Hence, ILP and C2+Subjoin can be applied in small graphs but are not suitable in the larger graphs due to their running time. Additionally, HC, CST, CST+CB and CSC need to be employed in larger graphs to evaluate their performances. As shown in Figure 5a, the HC serves the most vertices, the CSC is only inferior to the HC, and the CST and the CST+CB serve far fewer vertices than they do. In Figure 5c, the HC costs the most time in each graph size, and when \(n=100\), its running time approaches 900 s. The CSC, CST and CST+CB cost less time, even if \(n=100\), their running time is less than 10 seconds. As a whole, when the graph size ranges from 30 to 100, the HC is the optimal choice, which although costs the most time, serves most vertices.
## 3 Load Forecasting Based on BiLSTM and the Leading Influencing Factors
### Procedure of Load Forecasting Method Based on Qgcent and BiLSTM  
The load forecasting model framework considering multiple influencing factors provides a comprehensive approach to improve the accuracy and reliability of electric load forecasting. The framework consists of four key parts: (1) the original data are cleaned and organized to ensure the quality of the data and provide a solid foundation for subsequent analysis; (2) the smoothness test is conducted on both the influencing factors and power load series to determine whether the time series of the data meets the smoothness requirement, which is crucial for the establishment of the forecasting model; (3) the Granger causality test between the influencing factors and load is carried out to identify causal relationships and support the construction of a more accurate prediction model; (4) the BiLSTM-based load forecasting is presented combining the information obtained from the previous analysis.  
To implement the framework, the first critical step is data preprocessing. The collected data of electricity load and the data of each environmental factor including temperature, humidity and wind speed are organized to judge whether there is any abnormality or not, and adjust the abnormal data.  
Moreover, with clean and organized data, the next step is to verify the statistical properties of the time series. Before conducting the causality test of each influencing factor and load change, the smoothness test of all the series should be conducted first. Comparisons are made based on the ADF statistic and the critical value to determine whether each influencing factor and load time series is smooth or not, and for non-smooth series can be differenced or otherwise transformed to make them smooth, and the series can be used for subsequent load forecasting modeling.  
Subsequently, once the smoothness of the data is confirmed, the analysis can proceed to investigate the relationships between variables. For each of the influencing factor series as well as the electricity load series obtained after the above processing, the causal relationships at different quartiles are captured by selecting the quartiles and building regression models of different influencing factors on electricity load for each selected quartile, and the Granger causality test statistic are calculated.  
Figure 1: The structure diagram of load forecasting model based on BiLSTM.  
Finally, based on the obtained causal relationships of different factors on load changes, the BiLSTM-based load forecasting model is established. The filtered key influencing factors on load changes are used as input feature quantities to train the model, so as to obtain the load forecasting results.