esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Natural zeolites have been extensively studied due to their structural characteristics and physico-chemical properties. These minerals present a great porosity and a significant cation-exchange capacity [1,2]. Because of this, natural zeolites offer a wide range of applications, and one of the most notable is the removal of contaminant ions in water and soil [3, 4, 5]. It is reported that natural zeolites, specifically clinoptilolite, have been used in the removal of Mn, Cu, Ar, Pd, Cd, and Zn [4, 5, 6]. However, it is important to study the rate of adsorption of these species, in order to provide the optimum conditions for the adsorption process. The nature of the sorption process will depend on physical or chemical characteristics of the adsorbent systems and the system conditions. The most commonly used kinetic expressions to explain the solid/liquid adsorption processes are the pseudo-first-order kinetic and pseudo-second-order kinetic models [7, 8, 9, 10]. A good number of works were reported where the modifications of these natural clays with nanoparticles were produced to carry out the adsorption of metals from aqueous solutions [8, 11, 12, 13, 14, 15, 16, 17, 18]. These minerals offer low-cost, effective supports for nano-scale entities. The reactivity of nano-scale zero-valent metals such as Ag and the high cation-exchange capacity of the zeolites represent a potential alternative for remediation applications [7, 13, 14, 19]. However, specific studies about kinetics adsorption of nanoparticles on clinoptilolite have not been explored thoroughly. In this research the chemical characterization and the synthesis methodology of the composite of silver nanoparticle AgNPs-zeolite is presented. Additionally, a detailed analysis of the kinetics of adsorption of Ag nanoparticles in the clinoptilolite is given, where the study of the adsorption efficiency under optimized conditions will be carried out and, subsequently, it will be evaluated in relation to the removal of heavy metals and their antibacterial effects.
# Investigation of Rain-On-Snow Floods under Climate Change
## 2 Materials and Methods  
### Selected Catchments and Data Used  
In order to investigate climate change impacts on ROS floods we selected three meso-scale catchments located in various climate regions in Slovenia (Figure 1). The basic characteristics of these catchments are shown in Table 1. A 20-m resolution digital elevation model was used to calculate mean and maximum catchment elevations. Two of the case studies, namely the Soca and the Savinja, are typical high-altitude alpine catchments where snow is present about 6-9 months per year. We also selected the Gradascica River catchment where snow is generally present for a shorter period (i.e., 2-3 months per year) but still plays an important role in the runoff generation. The Gradascica River catchment is located in temperate continental climate whereas the Soca River and Savinja River catchments are located in mountain climate (e.g., [13]).  
Calibration and validation periods of the hydrological modelling are shown in Table 1 and were defined based on the availability of daily discharge, rainfall, and air temperature data. The goal of a data series division was to have 10 years for the validation period whereas the calibration period was defined based on the discharge data availability. Table 1 shows the basic descriptive statistics of daily discharge data used for the calibration and validation periods of all three considered catchments. One can notice that maximum and mean specific discharge values are the highest for the Soca River catchment mainly because this part of Slovenia has the highest annual rainfall amounts (i.e., more than 3000 mm). Other two catchments receive less rainfall than the Soca River catchment. For each considered catchment, the closest meteorological station with available rainfall (P) and air temperature (T) data was selected. For the Soca case study, Kredarica (altitude 2513 m; P and T available) and Soca (altitude 485 m; P available) stations were used. For the Savinja catchment, S available) and Celje (altitude 242 m; T available) stations were used. For the Gradas\(\check{\text{c}}\)ica case study, Crni vrh (altitude 827 m; P available), Lucine (altitude 639 m; P available), and Topol (altitude 662 m; T available) stations were used. To consider the impact of the difference between the meteorological station altitude and the mean catchment elevation we also considered the adiabatic lapse rate effect where a compensation of 7 \({}^{\circ}\)C/km was applied. We applied this procedure for the Soca River and Savinja River catchments whereas the Gradas\(\check{\text{c}}\)ica River catchment has roughly the same elevation as that at the Topol meteorological station, therefore, the correction was not needed.
## 3 Results and Discussion
### Environmental Fluctuation Analysis for Cold Chain
#### 3.2.2 Relative Humidity Change over Time  
The relative humidity and temperature fluctuation in the fresh fruit cold chain is illustrated in Figure 5. The green curve is the relative humidity variation over time, and the red curve is the'temperature variation over time. During the segment S1, the relative humidity slowly decreased in the beginning and then increased as a consequence of the temperature change and energy released by the respiration of fresh peaches. The relative humidity rapidly rose from 44.40% to 60.8% in the transportation segment S2. The relative humidity of segment S3 had a small fluctuation in the initial stage and then declined slowly as the temperature declined. In the segment S4, the relative humidity decreased continuously. The change of the relative humidity was the same as the change of temperature in the loading segment S5 and S6. The results show that the system worked well and largely reflects the temperature and humidity information of fresh peaches cold chain logistics, which is consistent with other literature [39].  
Figure 4: The temperature fluctuation in the fresh peach and nectarine cold chain.  
Figure 5: The relative humidity and temperature fluctuation in the fresh fruit cold chain.  
Absolute humidity is the water content of air at a given temperature expressed in grams per cubic meter, which is calculated by the equation of state of a hypothetical ideal gas and the relative humidity. It does not take temperature into consideration. The absolute humidity and temperature fluctuation in the fresh fruit cold chain is showed in Figure 6. The curve trend of absolute humidity and temperature appear almost the same in the S3, S4, S5, and S6 segments.
## 3 Results and Discussion
### The Best Values of Parameters
#### 3.2.1 Influence of Initial Concentration  
A study was carried out to determine the influence of the concentration of the feed solution on the adsorption of silver and copper ions in the fixed-bed column within the range of 3000\(-\)5000 mg/dm\({}^{3}\). Figure 8 shows the breakthrough curves for three different initial concentrations of metal cations. As expected, the initial concentration affects the breakthrough curves to a great extent. The highest sorption capacity was achieved at the highest concentration of feed solution. On the other hand, the greater the concentration, the lower the degree of adsorption. The initial slope of the breakthrough curves became more precipitous when the initial concentration was increased, showing that the reaction zone advances faster under high load conditions. In contact with a more concentrated metal ion solution, the bed is more saturated, which influences the concentration gradient (\(\Delta\)C) that drives the process. The higher the \(\Delta\)C, the faster the process. At lower concentrations, the process is characterized by a lower value of \(\Delta\)C, which results in a slower rate but a higher degree of adsorption. [PERSON] et al. investigated the effect of inlet initial Cu\({}^{2+}\) concentration for a bed height of 60 mm and a solution flow rate of 10 cm\({}^{3}\)/min [23]. This confirmed the fact that the larger the inlet concentration, the steeper the slope of the breakthrough curve.  
Figure 6: Pareto chart of standardized experimental effects for vermiculite: (**a**) For silver ions and (**b**) for copper ions.  
Figure 7: Profiles for predicted values and desirability for the removal of: (**a**) silver ions, (**b**) copper ions
## 2 Problem Background
### Existing OppNet Routing Protocols for Sensed Data Collection
#### 2.2.1 Overview of OppNet Routing Techniques
The social context-based routing scheme, namely Context Information Prediction for Routing in OppNets (CiPRO) [46], utilizes contextual information (e.g., user information, such as email address, work and home address, occupation, mobility patterns, and communities they belong to; and node information, such as battery level and storage capacity) to make forwarding decisions. Routing techniques have also been realized from a combination of two (or more) different approaches. Similar to SnW, Spray and Focus [47] (SnF) first forwards \(L\) copies of a message to any encountered node that does not have a copy. For the last copy, it selects a relay based on the time elapsed since the destination was encountered. The Sociability-Based Spray and Forward (SSF) scheme [48] also operates in two phases: in the first phase, relays are chosen based on the number of nodes they can encounter; and in the second phase, the choice is based on how often the destination is encountered. EpiPRo [49] switches from Epidemic to PRoPHET based on the hop count parameter of number of copies forwarded. The History-based Prediction Routing (HBPR) protocol [50] determines the forwarding utility of a node from the similarity of its movement history (such as location visits, time, and speed) with that of the destination. The Multi-layer Social Network based Routing (ML-SOR) approach [51] computes the forwarding ability of nodes from centrality, tie strength and link prediction.
## 2 Main Blocks for Wirelessly-Powered Cage System
### Coil Design and Optimization
#### 2.1.1 Coil Optimization for Conventional Two-/Three-/Four-Coil Inductive Links
Although previous studies provide the optimization of two-, three-, or four-coil inductive link [8; 9], they only focus on the optimization procedure for fixed Tx and Rx coils that is not simply applicable for powering large arena in the cage. If the designer uses the large Tx coil around the cage, the overall PTE will significantly drop and show large variations depending on the location of the IMD. Figure 5: (**a**) Optimized three-coil inductive link, and (**b**) PTE variation in the two-coil and three-coil inductive links vs. \(R_{\rm L}\)[9]. Figure 6: E-field distribution in V/m at skin surface for (**a**) a conventional coil and (**b**) a segmented coil [10]. Several approaches have been studying to improve the PTE from Tx to Rx coils while maintaining the homogeneity of wireless power distribution. These approaches can be mainly classified into two categories: modular design of coil array, as shown in Figure 7, and resonance-based multi-coil inductive link, as shown in Figure 8 in the following section. Figure 8: _Cont._  
Figure 7: **(a)** The configuration of the scalable multi-layer Tx coil array implemented in the wirelessly-powered cage [11; 12]. **(b)** Measured PTE distribution within the cage arena when the Rx is at the height of 70 mm from the bottom of the cage [11; 12]. **(c)** Measured PDL distribution within the cage arena when the Rx is swept at the height of 70 mm with CLPC set at 20 mW [11; 12].
## 2 Related Work
However, oversampling methods can lead to data overlap and distribution marginalization issues, potentially trapping the algorithm in local optima. (3) Mixed Sampling: Mixed sampling combines both undersampling and oversampling techniques to balance the data. For example, [PERSON] et al. [22] synthesized minority class and majority class samples separately using a generative adversarial network (GAN) and SMOTE. They compared this dual oversampling strategy to results obtained from a single oversampling approach targeting the minority class only, and found that the dual oversampling strategy outperformed the single oversampling method. While mixed sampling can alleviate information loss in undersampling and overfitting in oversampling, research suggests that the order of executing undersampling and oversampling in mixed sampling can influence predictive accuracy [23]. (4) Ensemble Learning: Different from traditional individual learners, ensemble learning combines multiple weak learners to create a strong learner [24]. The two most typical forms of ensemble learning, based on the composition of base learners, are bagging and boosting. [PERSON] et al. [25] implemented a hybrid ensemble learning model that combines CNN with CatBoost, providing a new approach for non-invasive COVID-19 detection. [PERSON] et al. [26] combined undersampling with ensemble learning and proposed a spatial undersampling model for local pattern learning. For ensemble methods, their purpose is to enhance overall accuracy, making them challenging to work effectively on imbalanced classification problems alone [27]. They often need to be used in conjunction with other algorithms. (5) Cost-Sensitive Learning: A new cost-sensitive imprecise classification decision tree has been introduced, which considers error costs by weighting instances and incorporates these costs during tree construction. [PERSON] et al. [28] proposed the nonparametric predictive inference model to improve cost-sensitive decision trees. In addition to modifying tree generation, the fusion of active learning with cost-sensitive algorithms can effectively enhance the classification performance of imbalanced data [29]. Although combining cost-sensitive learning with classification models effectively improves predictive accuracy for classification results, determining misclassification costs still requires substantial effort. These various types of methods each have their own characteristics and have made significant progress. Considering the randomness and diversity of traffic accident data [30], as well as the abundance of sparse features in the field of traffic accidents, applying them directly to address sample imbalance in traffic accident prediction can lead to suboptimal prediction performance. Given these considerations, this paper addresses the sample imbalance phenomenon in severe imbalanced traffic accident datasets from both a data-level and algorithm-level perspective, merging the two to enhance the robustness of predictions.
# Antipodal Vivaldi Antenna Arrays Fed by Substrate Integrated Waveguide Right-Angled Power Dividers
## 4 Conclusions  
A novel SIW feeding technique for planar antipodal Vivaldi arrays is introduced. It consists of right-angled power dividers that allow the phases of the output ports to be changed so that different array performances can be obtained. All circuits are designed on a single layer of Rogers 6002 substrate with relative permittivity of \(\varepsilon_{r}=2.94\) and thickness \(h=508\)\(\upmu\)m which make them low profile, compact, low cost, and easy to fabricate. The antenna array system including a two-way power divider with 8.5 dB gain provides a dual radiation pattern which is suitable for nulling/tracking applications. The proposed antenna arrays with three- and four-way dividers have a high directivity with a maximum gain of 14 dB which make them suitable for many mm-wave and microwave applications. The four-element array exhibits frequency-agile single- and dual-beam performance. Good cross-polarization levels are obtained due to corrugations in the Vivaldi elements. All proposed antenna systems provide a radiation efficiency better than 80% and polarization efficiency of 98% in end-fire directions. Two of the antenna array systems are prototyped and measured. The results validate the design approach and simulation process.  
**Author Contributions:** [PERSON] and [PERSON] conceived the initial designs of right-angled SIW power dividers. [PERSON] designed the circuits and experiments, performed the experiments and wrote the first draft of the paper, which both authors finalized.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks +
## 3 Proposed SDN-Based System
### Reactive NDN Path Selection Strategy
#### 3.1.2 Network Nodes  
The infrastructure of our proposed system consists of interconnected wireless nodes that support NDN communication. The NDN and WMN functionalities are independent and are integrated with the Controller (i.e., the Controller monitors the WMN to configure the NDN). Here, we describe the _NDN and WMN functionalities_ to illustrate the system's network nodes operation.  
In a nutshell, NDN is a future Internet architecture that follows the ICN principles and accomplishes named content retrieval by employing two types of packets (e.g., the Interest and the Data packets). In NDN, Consumers send an Interest packet in the network to fetch the corresponding Data packet that contains the requested content. Although a Data packet is originally generated from a Producer node, it may be retrieved from intermediate nodes' caches, as NDN supports in-network caching.  
Each NDN node uses three components: the Content Store (CS), the Pending Interest Table (PIT), and the Forwarding Information Base (FIB). When a new Interest packet is received, the NDN node first checks if the requested content exists in the CS (i.e., it is cached), and in that case, it responds directly with the Data packet. Alternatively, if the prefix matches a specific PIT entry (which contains the already sent Interest prefixes associated with the respective faces), then the incoming face is added to the particular entry (meaning that when the Data is fetched it will be forwarded also to that face). Otherwise, a new PIT entry is created and the Interest is forwarded to the next hop according to the FIB information [11].  
In our NDN deployment, face creation and prefix registration are triggered from the Controller node. Since NDN communication is Consumer-driven, our system performance heavily relies on the Consumer node behavior. Thus, this plane targets to fetch the data efficiently with the minimum communication delay, by exploiting the NDN features.  
The NDN consumer communicates with the SDN controller, if a new Consumer-Producer path has to be established, i.e., in cases: (i) the consumer requests a specific content for the first time or (ii) freshness period for a content (already requested) has expired. In all other cases, the requested content is retrieved to the consumer from the network, using the in-network cashing capability of NDN.  
Here, we give an example of the NDN and SDN interaction of our system, as illustrated in Figure 2. The NDN Consumer (node1) intends to fetch the content with the prefix _e.g., sensor/temperature_. Thus, it sends a request to the Controller to inform them about the specific content. If the content is not cached in the network, the Controller finds the best path to the NDN Producer (e.g., Node1-Node2-Node4) and establishes the NDN routes. Finally, the Controller triggers the Consumer to transmit the Interest packet through the created path.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 7 Conclusions  
This study proposed an integrated framework for market state prediction and stock selection, leveraging a multimodal approach that combines ontology-based stock classification, Non-Stationary Markov Chains (NMCs), sentiment analysis with BERT, and Long Short-Term Memory (LSTM) networks. The empirical results demonstrated the model's ability to achieve a 97.20% accuracy in market state predictions for the S&P 500 and generate cumulative returns of 13.67% with the State Predicted (SP) strategy, significantly outperforming the traditional Buy and Hold (BnH) strategy, which yielded only 8.62%. Additionally, the framework's application to the S&P 500 index achieved a return of 6.36%, compared to 1.08% from the Buy and Hold strategy, further showcasing its versatility across diverse market conditions.  
The key contributions of this study include the following:  
* Introducing a unified framework that integrates financial ontology, machine learning, and sentiment analysis for robust stock selection and market forecasting;
* Demonstrating the effectiveness of combining NMC with sentiment-derived inputs and exogenous variables for capturing dynamic market states;
* Providing an actionable decision-support tool for investors and quantitative traders, with empirical evidence supporting its application in real-world trading scenarios.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 4 Methodology and System Architecture
### Enhanced Security Analysis
#### 4.5.1 Attack Scenarios and Mitigations  
* Brute-Force Attacks: An adversary attempts to scan all possible frequencies to intercept or jam the communication. Mitigation: The large frequency band and rapid hopping rate make this impractical due to the required resources and time.
* Replay Attacks: Captured signals are replayed to disrupt communication or trick the receiver into accepting false data. Mitigation: Implementing time-stamped authentication tokens prevents the acceptance of outdated or replayed signals.
* Synchronization Attacks: In the context of our system, synchronization between the transmitter and receiver is crucial due to the use of true random frequency hopping sequences. An adversary may attempt to desynchronize the communication by introducing delays, jamming synchronization signals, or injecting false synchronization messages. Desynchronization can lead to loss of communication and reduced system reliability. Mitigation: To counter synchronization attacks, we could employ robust synchronization protocols that include the following features:  
* Error Correction Codes: Utilizing error correction codes (ECC) in synchronization messages allows the receiver to detect and correct errors introduced by interference or malicious activities, ensuring that synchronization data is accurately received.
* Secure Authentication: Synchronization messages can be secured using cryptographic techniques such as digital signatures or message authentication codes (MACs). This prevents attackers from injecting false synchronization signals or modifying legitimate ones.
* Periodic Resynchronization: The system could perform periodic resynchronization at predefined intervals or upon detecting anomalies in communication. This helps to realign the transmitter and receiver in case of minor desynchronization and reduces the window of opportunity for attackers.
* Redundant Synchronization Channels: Employing multiple synchronization channels or methods (e.g., combining time-based synchronization with signal-based cues) could add redundancy, making it more difficult for an attacker to disrupt all synchronization paths simultaneously.
* Monitoring and Anomaly Detection: Continuously monitoring communication metrics (e.g., synchronization error rates, unexpected delays) could allow the system to detect potential synchronization attacks early and initiate countermeasures, such as increasing the hopping rate or switching to a secure fallback mode.