esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study proposed a one-step method for the preparation of microcrystalline glass using the iron smelting slag of SACs-3. We investigated the effects of crystallization temperature and crystallization time on the physical and chemical properties of microcrystalline glass prepared using the proposed method. Furthermore, the toxicity and leaching characteristics of heavy metals in the microcrystalline glass were investigated. The main conclusions of the study are as follows:  
1. The differential thermal analysis of the base glass and the XRD analysis of the microcrystalline glass samples indicated that the main crystalline phase of the microcrystalline glass was constant and constituted by chalcogen feldspar. A suitable range of variables was determined. The crystallization temperature varied between 900 and 950 \({}^{\circ}\)C, and the crystallization time varied between 0.5 and 4 h.
2. Single-factor experiments were performed to analyze changes in the degree of crystallization, number of crystalline phases, grain shape, and physical and chemical properties of the microcrystalline glass, with crystallization temperature and time as  
Figure 16: Effects of crystallization time on the acid and alkali resistances of the microcrystalline glass.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Zn** & **Cr** & **Mn** & **Pb** \\ \hline Base glass & 1 & 0.072 & 4.7 & BDL \\ Microcrystalline glass & 0.28 & BDL & 2.48 & BDL \\ Limit value & 500 & 5 & 5 & 5 \\ \hline \end{tabular} BDL: below instrument detection limit.  
\end{table}
Table 3: Heavy metal leaching characteristics of the microcrystalline glass prepared under optimal conditions (mg/L).  
variables. The optimal parameters for the heat treatment process were determined as follows: a crystallization temperature of 950 \({}^{\circ}\)C and a crystallization time of 3 h.
3. The physicochemical properties of the microcrystalline glass prepared with the proposed one-step method were determined: the bulk density was 2.72 g/cm\({}^{3}\), the water absorption rate was 1.55%, the porosity was 4.2%, the Vickers hardness was 618 HV, the acid resistance was 2.6%, and the alkali resistance was 0.04%. The leaching concentrations of heavy metal ions were lower than those of the base glass and were considerably lower than the stipulated limits, and the harmful components were stabilized for disposal.  
X.Z.; Methodology, Y.H.; software, H.W.; validation, F.L.; formal analysis, S.S.; investigation, C.L.; resources, X.Z.; data curation, X.Z.; writing--original draft preparation, R.Z.; writing--review and editing, R.Z. and X.Z.; All authors have read and agreed to the published version of the manuscript.  
The authors received the financial support from National Key Technology R&D Program (No: 2019 YFC1907504), Liaoning Educational Committee General Program (LJKZ0166).  
Not applicable.  
The author thanks the members of the research team for all their help in the process of testing, research and thesis writing.  
The authors declare no conflict of interest.
# An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
## 3 Tool Description
### Satellite Links  
#### 3.5.1 Antenna Characteristics  
In this step, the user is required to input the antenna positions in geographic coordinates. Upon completing the insertion of data, an antenna entity is displayed in a Cesium window provided on the user page.  
Using the orbit later selected, the elevation and azimuth angles of each antenna are also outputted in order to be directed to the satellite position.
## 3 Preliminaries
### Multi-Hop Networks  
The significant advantage of network calculus is that the concatenation of servers becomes easier in concatenated systems, e.g., multi-hop store-and-forward networks. Using the associativity of the (min, +) convolution, it is possible to replace servers in tandem with one system whose service curve is the (min, +) convolution of their service curves [18].  
For given \(n\) tandem servers with through and cross traffic flows, as shown in Figure 3, the network service process \(S^{net}(s,t)\) is provided by  
\[S^{net}(s,t)=\{S^{1}\otimes S^{2}\otimes S^{3}\otimes S^{4}\otimes...\otimes S ^{n}\}(s,t), \tag{14}\]  
where \(S^{i}(s,t)\), for any \(1\leq i\leq n\), represents the leftover service process of the \(i^{th}\) server.  
The MGF of the end-to-end service process, written as \(\overline{M}_{S}(\theta,s,t)\), of \(n\) independent tandem servers, is bounded by  
\[\overline{M}_{S}(\theta,s,t)\leq\{\overline{M}_{S^{1}}(\theta)\,*\,\overline{ M}_{S^{2}}(\theta)\,*....*\,\overline{M}_{S^{n}}(\theta)\}(s,t). \tag{15}\]  
where \(*\) is a univariate operator such that  
\[(x*y)(\delta)=\sum_{\tau=0}^{\delta}x(\delta-\tau)y(\tau).\]  
Figure 3: Tandem servers with cross traffic.  
Figure 2: Traffic model of a single node.  
Then, the MGF for the (min, +) deconvolution is bounded as follows:  
\[M_{A\O\!S}(\theta,\delta)\leq(M_{A}(\theta)\,o\,\overline{M}_{S^{1}}(\theta)\,o...o\,\overline{M}_{S^{n}}(\theta))(\delta), \tag{16}\]  
where \(o\) is a univariate operator such that  
\[(x\,o\,y)(\delta)=\sum_{\tau=0}^{\infty}x(\delta+\tau)y(\tau).\]  
Using these network calculus basics, we derive the delay and PDV bounds for multi-hop networks in the following section.
## 1 Introduction  
Portfolio management (PM) is a multifaceted domain where investors strive to optimise financial asset returns to achieve long-term goals. It can be differentiated into passive or active management, whereby each branch adopts unique strategies that fulfil these objectives. Active PM [1, 2], characterised by dynamic trading to secure higher profits, contrasts with the conservative nature of passive PM, which aims to mirror market indices for steady, long-term gains. Prevailing practices in active PM often rely on predefined trends, which increasingly fall short against the complexities of volatile financial markets. This limitation has catalysed a shift toward incorporating advanced machine learning and artificial intelligence technologies [3, 4]. Reinforcement learning (RL), renowned for its dynamic adaptability to market changes, has been applied in various PM models, such as iCNN [5], EIIE [6], SARL [7], AlphaStock [8], and GPM [9]. These models utilise historical asset prices and external information such as financial news, extracting features to guide portfolio rebalancing and decision-making. However, PM still grapples with challenges that include the extraction of non-stationary temporal sequential features, integrating macro- and microeconomic knowledge, and diversifying portfolios to minimise risk exposure.  
To tackle these challenges, our study introduces a unique algorithmic approach that combines a non-stationary transformer with deep reinforcement learning algorithms (NSTD). This innovative approach is designed to reconstitute non-stationary features post-stationary processing of time-series data, merge multimodal information from diverse sources, and enrich portfolio diversity to mitigate concentration risks. The proposed model integrates macroeconomic indicators and sentiment scores from financial news into the non-stationary transformer framework, enhancing its ability to navigate the complexities of PM with data heterogeneity and environmental uncertainty. The model's enhanced performance, validated through rigorous experimentation, demonstrates the efficacy of combining diverse knowledge sources for PM.  
This paper is structured as follows: Section 2 reviews related work in PM, focusing on the evolution of active and passive strategies and the role of machine learning in this domain. Section 3 elaborates on our methodology, detailing the integration of macroeconomic features and sentiment scores from news data into the non-stationary transformer and its synergy with DRL. Sections 4 and 5 present our experimental setup and findings, offering a comparative analysis of the NSTD model against traditional and modern deep-learning financial strategies. Finally, Section 6 concludes the paper, summarising our key findings and suggesting avenues for future research in PM.
## 1 Introduction
### Multiple Authentication Mechanisms Based on ECDH Protocol  
During the presented protocol process, the private parameters of communication nodes and ECDH protocol are employed to implement an efficient authentication mechanism. Network entities examine each other's identity by using private and secure items with multiple parameters, which increases the security of the provided protocol against network attacks.
## 3 Results and Discussion  
### X-ray Diffraction (XRD) Analysis  
The XRD pattern of the biosynthesized ZnO NPs using leaves extract of _M. spicata_ was shown in Figure 1. The 20 values of XRD at 31.76\({}^{\circ}\), 34.44\({}^{\circ}\), 36.24\({}^{\circ}\), 47.56\({}^{\circ}\), 56.66\({}^{\circ}\), 62.90\({}^{\circ}\), 66.42\({}^{\circ}\), 67.96\({}^{\circ}\), 69.14\({}^{\circ}\), 72.64\({}^{\circ}\), and 76.98\({}^{\circ}\) were assigned to (100), (002), (101), (102), (110), (103), (200), (112), (201), (004), and (202) planes, respectively. All the diffraction peaks were indexed in the ZnO wurtzite structure (JCPDS card number 01-089-1397) and agree with XRD peaks of biosynthesized ZnO NPs by _M. spicata_ plant extract [25]. The sharp and narrow peaks indicated that the biosynthesized ZnO NPs were highly crystallized. Additionally, ZnO NPs were free of impurities where no characteristic XRD peaks other than zinc oxide peaks were observed [50; 51].
## 2 Related Research  
### Stop-Bang  
In 2008, [PERSON] and her research team published \"STOP questionnaire: a tool to screen patients for obstructive sleep apnea,\" which detailed the development and validation of a questionnaire tool aimed at distinguishing patients with sleep apnea among surgical patients. The STOP-BANG questionnaire consists of eight items, including questions about snoring, tiredness, observed apnea, and blood pressure, with additional criteria considering BMI (BMI \(>\) 35 kg/m\({}^{2}\)), age (age \(>\) 50 years), neck circumference (>41 cm), and gender (male). Each 'yes' answer scores one point, with a total possible score of eight. A score of three or more is considered indicative of being at risk of sleep apnea. The STOP-BANG questionnaire, composed of questions related to snoring, daytime fatigue, observed apreas, and hypertension, was administered to 2467 patients. Sensitivities for Apnea-Hyppone Index (AHI) thresholds of greater than 5, 15, and 30 were 65.6%, 74.3%, and 79.5%, respectively, for the STOP questionnaire cutoffs. Therefore, the STOP-BANG questionnaire can be considered useful for identifying patients at high risk of sleep apnea. [8] In 2011, [PERSON], M.D., undertook an evaluation of the STOP-BANG questionnaire's capability to stratify obstructive sleep apnea (OSA) severity into four distinct categories, spanning from none to severe. The diagnostic utility of the STOP and STOP-BANG questionnaires for OSA was studied through logistic regression analysis on the study subjects, demonstrating the significance of classification prediction based on numerical regression analysis by predicting the severity of sleep apnea based on the AHI [9]. A 2019 study conducted by [PERSON] established a statistically significant correlation between the prevalence of obstructive sleep apnea (OSA) and body mass index (BMI). This investigation reviewed literature spanning the previous decade, distinguishing between community-based populations and individuals seeking evaluation at sleep clinics, and it evaluated the efficacy of various diagnostic questionnaires. The findings revealed that the STOP-BANG questionnaire exhibited the highest sensitivity in diagnosing OSA among domestic patients, whereas the Berlin questionnaire demonstrated superior specificity. Consequently, it was proposed that the STOP-BANG questionnaire could be refined to improve its specificity by integrating additional physical measurements and physiological indices for individuals who are preliminarily diagnosed with OSA [10].
## 2 Methods
### Satellite Observation Probability
#### 2.1.2 Geosynchronous and Geostationary Earth Orbit Satellites  
Different from MEO satellites, inclined geosynchronous orbit (IGSO) satellites, which sit fixed at the crossover point of a figure-eight shape (over the equator), are relatively static to Earth in the ECEF system [32]. Therefore, the observation probability \(P_{I}\) of an IGSO satellite depends on both \(\omega_{\phi_{j}}\) and \(\omega_{\lambda_{i}}\), which can be calculated as follows:  
\[P_{I}(\lambda_{i},\phi_{j})=\begin{cases}\frac{\kappa}{\omega_{\text{max}}}& (\lambda_{i},\phi_{j})\in(\lambda_{\text{IGSO}},\phi_{\text{IGSO}})\\ 0&(\lambda_{i},\phi_{j})\
otin(\lambda_{\text{IGSO}},\phi_{\text{IGSO}})\end{cases} \tag{6}\]  
where \(\omega_{\text{max}}=\text{max}\Big{\{}\omega_{\lambda_{i}},\omega_{\phi_{j}} \Big{\}}\) is the maximum of \(\omega_{\phi_{j}}\) and \(\omega_{\lambda_{i}}\)[30], and \((\lambda_{\text{IGSO}},\phi_{\text{IGSO}})\) is the ground track of an IGSO satellite. The calculation of IGSO satellite trajectories is described in [30].  
The observation probabilities of the IGSO satellites for all grids satisfy:  
\[\sum_{(\lambda_{i},\phi_{j})\in(\lambda_{\text{IGSO}},\phi_{\text{IGSO}})}P_{ I}(\lambda_{i},\phi_{j})=n_{\text{IGSO}} \tag{7}\]  
where \(n_{\text{IGSO}}\) is the total number of IGSO satellites.  
Equation (6) can also be used to express the observation probability of a quazi-zenith orbit (QZO) satellite whose trajectory is an asymmetric eight [33]. Likewise, the total number of QZO satellites \(n_{\text{QZO}}\) satisfies:  
\[\sum_{(\lambda_{i},\phi_{j})\in(\lambda_{\text{QZO}},\phi_{\text{QZO}})}P_{Q}( \lambda_{i},\phi_{j})=n_{\text{QZO}} \tag{8}\]Geostationary Earth orbit (GEO) satellites are also relatively static to the Earth in the ECEF system. Therefore, the GEO satellite observation probability \(P_{G}\) can be expressed as:  
\[P_{G}(\lambda_{i},\phi_{j})=\begin{cases}1&(\lambda_{i},\phi_{j})\in(\lambda_{ \text{GEO}},\phi_{\text{GEO}})\\ 0&(\lambda_{i},\phi_{j})\
otin(\lambda_{\text{GEO}},\phi_{\text{GEO}})\end{cases} \tag{9}\]  
where \((\lambda_{\text{GEO}},\phi_{\text{GEO}})\) is the designated position. For instance, three BDS-3 GEO (BDS-3G) are located at 80\({}^{\circ}\)E, 110.5\({}^{\circ}\)E, and 140\({}^{\circ}\)E, respectively [9].
# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 5 Experiments
### Experimental Settings
#### 5.1.3 Baseline Models  
To ensure a diverse and representative evaluation of our model, we compare our HKRec model with various baseline models: conventional collaborative filtering (BPR, LightGCN), embedding-based knowledge-aware recommendation (CKE), GNN-based knowledge-aware recommendation (KGAT, KGIN), self-supervised learning recommendation (SGL), knowledge-aware self-supervised learning recommendation (KGCL, KGRec).  
* BPR [56]: A widely used recommendation model that employs Bayesian analysis to rank items and generate user preference predictions.
* LightGCN [41]: A simplified collaborative filtering method that integrates GCN.
* CKE [11]: A typical method that integrates collaborative filtering with knowledge graph feature learning, incorporating three key components to learn representations including structural, textual, and visual information.
* KGAT [19]: This model initially utilizes the TransR model for KG vectorization learning and a GNN for information propagation and aggregation.
* KGIN [21]: KGIN explores the relational modeling of user intents and provides explainable semantics captured from knowledge graphs for recommendation tasks.
* SGL [25]: This method introduces contrastive learning into recommendation data by generating multi-views through techniques such as node removing, edge removing, and random walk.
* KGCL [27]: This model generates auxiliary self-supervised signals through data augmentation strategies to construct contrastive views on both KG and UIG.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Dataset** & & **Last-FM** & **MIND** & **Alibaba-iFashion** \\ \hline \multirow{3}{*}{User–Item} & \#Users & 23,566 & 100,000 & 114,737 \\  & \#Items & 48,123 & 30,577 & 30,040 \\ \cline{1-1}  & \#Interactions & 3,034,796 & 2,975,319 & 1,781,093 \\ \cline{1-1}  & \#Density & \(2.7\times 10^{-3}\) & \(9.7\times 10^{-4}\) & \(5.2\times 10^{-4}\) \\ \hline \multirow{3}{*}{Knowledge} & \#Entities & 58,266 & 24,733 & 59,156 \\ \cline{1-1}  & \#Relations & 9 & 512 & 51 \\ \cline{1-1}  & \#Triplets & 464,567 & 148,568 & 279,155 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets.  
* KGRec [31]: This method generates rational scores for knowledge graph triplets and designs a self-supervised learning framework based on the scores.
## 5 Discussion
### Results per Scenario  
The first scenario of simulation corresponds to the military application environment. In this type of network, the nodes' reliability and security tend to be high. Additionally, the attacker's technical strength is the strongest, and often regardless of cost; for these reasons, the infection intensity in these networks is high. As shown in Figure 3a, the MCC maintains consistency with the normal state for a large proportion of the time and is 0 in the rest time. Aggregation and separation of two curves mean that these nodes can maintain the connection relationship as long as the central node functions correctly, but it is all over if the central node fails. In the lattice-2d topology, the number of infected sensors increased slowly in the early stage. The stability and consistency of the number of normal nodes and MCC are maintained after the spread of malware. The random topology shows a slightly worse MCC than that of lattice-2d; in general, both can maintain the connectivity of the remaining normal nodes when a considerable number of sensors fail.  
The simulation environment has been performed in the industry in the second scenario. In these networks, the sensors may have high reliability and medium security level;  
Figure 7: Simulation results of a (**a**) scale-free network, (**b**) small-world network.  
attackers are willing to attack industrial infrastructure to demand ransom from businesses. In Figure 4, it is observed that the last two topologies maintain acceptable MCC performance. However, different from the military scenario, the MCC performance of lattice-2d topology is worse than that of random topology, which shows that after the proportion of failure nodes in all nodes rises to a certain extent, the lattice-2d network may be \"divided\" by failure nodes. In contrast, random topology with more global connectivity will not be so split. Not surprisingly, the problem with star topology is the same as in the previous scenario--dramatic mutation.  
The third scenario of simulation has been designed as an environment of daily activities and Smart Home. In these networks, the nodes' reliability and security tend to be low. The attacker also has no interest in attacking individuals. In this case, all the performances of the three topologies are unacceptable, especially star topology. Only random topology networks have been able to maintain a low-quality MCC (see Figure 5).
## 4 Case Studies
### Additional Investigations on the Synthetic Dataset
#### 4.3.1 The Robustness of the IMCS Algorithm  
To evaluate the robustness of the proposed IMCS algorithm, classification accuracy is obtained through a dynamic feature selection scheme. The basic idea is to run the IMCS process for several rounds and exclude the features that were selected in the previous round from the next round in order to observe the impact of different feature subsets on classification performance.  
In this subsection, the training and testing datasets used in Section 4.2 are adopted. A total of five rounds was run. In each round, IMCS classifiers based on the reference MD space constructed from healthy samples (SMD-R1), defective samples (SMD-R2), and both SMD-R1 and SMD-R2 are considered.  
The classification accuracies of the testing dataset, based on the reference MD space constructed from healthy samples, defective samples, and both healthy and defective samples, are reported in Figure 15 for the five consecutive rounds. Considering that the most promising features are selected first, it is clear that subsequent rounds will yield lower classification accuracies. This can be clearly seen in Figure 15.  
Figure 14: (**a**) Classification on the training dataset; (**b**) classification on the testing dataset.  
Figure 15: Testing accuracy on the testing dataset for five consecutive rounds, with dynamic feature selection over 20 independent runs.  
Furthermore, the result indicates that the IMCS\({}_{\text{RF}}^{\text{BPSO}}\) classifier with the reference MD space of both healthy and defected parts (SMD-R1 + SMD-R2) consistently exhibits the highest accuracies over the five rounds, followed by the IMCS\({}_{\text{PSO}}^{\text{BPSO}}\) classifier with the reference MD space of healthy parts (SMD-R1) and, finally, the IMCS\({}_{\text{PSO}}^{\text{BPSO}}\) classifier with the reference MD space of defected parts (SMD-R2), with the lowest accuracies in all rounds.
## 4 Discussion
### Fragmentation at the Landscape Scale  
Since some questions remain unanswered in the footprint scale area, a larger scale was also analyzed. On the landscape scale, the proportion of the edge area compared to the total forest area of the landscape was analyzed as a measure of fragmentation state. We suspect that landscapes with a higher degree of fragmentation were deforested a longer time ago [41; 42].  
One result of our analysis shows that the gross primary productivity has lower values for highly fragmented landscapes in comparison with lower-fragmented landscapes. The results of net primary productivity at the landscape level are surprising because, in contrast to the GPP, it shows higher values in highly fragmented areas when compared with areas with a moderate or low fragmentation. There may be several reasons for this, but one main reason could be a different species composition in favor of fast-growing pioneer species. We also derived LAI values from the forest model. Typical values are in agreement with the MODIS measurements [43]. Please note, there could also be differences in LAI values. This is because the FORMIND forest model does not take into account the leaf area of shrubs or grasses, only the leaf area of trees. Please note, the workflow presented here has already been extensively tested for the Amazon against field data on forest biomass (114 plots, [16]) and against forest productivity (GPP and NPP from MODIS [3; 16]). The quality of the used lidar simulator has been tested against field data from a large forest plot [16; 44].  
At the same time, it should be noted for the results at landscape level, that the recognizable differences between the fragmentation types are not only influenced by fragmentation itself, but regional effects can also have an influence. For example, forest biomass in the Andes is lower due to elevation and temperature. Biomass and productivity are also low in the Arc of Deforestation region because of the amount of deforestation that has occurred and is still occurring. Biomasses and productivities in the central Amazon are higher overall, as there is undisturbed rainforest here. Therefore, it would be interesting to analyzethe results at landscape level in more detail in the future, as several factors may play a role in why we observe a low biomass and productivity in some areas (Figures S3 and S4). For follow-up studies, it would therefore be interesting to investigate the correlations with climate conditions (e.g., temperature and precipitation) and topological conditions (e.g., elevation) to analyze their effect on biomass differences.