esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 WLAN Integration with 6G: Challenges and Opportunities
### Enhanced Security  
Security in 6G networks is identified as a matter of paramount importance due to a multitude of reasons, such as mission-critical applications, privacy of data, and heterogeneity of the architecture. The security vulnerabilities of 6G networks typically arise from the underlying communication technologies, novel network architecture, and user application [127].  
Underlying communication technologies such as mobile cellular communication, WiFi, VLC, and OWC also have existing security vulnerabilities [128, 129]. In addition to that, the emergence of AI/ML-based attacks, intelligent network management and orchestration, intelligent transceivers, and the development of quantum computing pose greater threats on these technologies [130, 131]. While most of the communication technologies are yet to develop defenses against these threats, VLC has seen a significant improvement in security, using physical layer security (PLS) techniques [127, 132]. PLS techniques add another layer of security at the physical layer to enhance the overall security of the transmission. Further, OWC also uses PLS, time-slot coding, and chaotic phase techniques to improve the security of the links [133, 134, 135].  
On the other hand, upcoming WiFi networks support WiFi Protected Access 3 (WPA3), which provides greater security at the upper layers with the use of cryptography. However, none of the current communication technologies are equipped with mechanisms to face AI/ML-based or quantum computing-based attacks. Hence, crucial developments are required in 6G communication technologies to face future security threats.  
The 6G networks are expected to have unconventional network architectures depending on the deployment scenarios and technologies involved. For instance, spectrum sharing between cellular and WLAN networks, heterogeneous networks with different security protocols, and zero-touch deployments can give rise to new security vulnerabilities in 6G networks [123; 130]. Furthermore, with the advent of the network as a service (NaaS) concept, network function virtualization (NFV), SDN, cloudification, and deep slicing will become more and more common in 6G networks [136]. These will also bring vulnerabilities such as external attacks into the networks. While there are proposed solutions in the literature, such as encrypting signalling data and network isolation, further explorations of anomaly detection mechanism in the network and security measures are warranted to protect 6G networks from attacks on the vulnerabilities of network architectures [128].  
Finally, the novel user applications of 6G can also bring security vulnerabilities to the networks [127]. The addition of massive number of sensor nodes, as well as industrial equipment such as collaborative robots, will open doors for attackers to find more vulnerable points to enter the network. Therefore, the user applications should use security at multiple levels and support different security protocols due to the diversity of the 6G networks.
## 2 Related Work
DSS are used for managing tasks in different fields of applications, as for example in stormwater management [21], dental manufacturing production line [22], offshore wind farms [23] and healthcare systems [24,25]. Moreover, a DSS for OR scheduling was proposed in [26] based on a Mixed Integer Linear Programming (MILP) problem [27,28] that takes into account the preference order of the patients. However, this approach does not consider the variability on the uncertain parameters (surgery duration, cleaning time, etc.) and consequently can not manage the risk of exceeding the total time. The DSS proposed in this work uses an heuristic algorithm allowing to impose a minimum confidence level of not exceeding the total time in a surgical block. In this way, doctors not only canmanage the risk but also can know in advance the probability that each block exceeds the available time. The DSS proposed in this paper can also perform the management of medical teams and OR time table. Moreover, it is possible to perform iterative scheduling which is useful if, for example, after obtaining a first scheduling, some patients can not attend the day of their surgeries. Furthermore, after a surgery is performed, the DSS automatically updates and customizes the average and standard deviation of the surgery duration. Finally, in order to provide a safe environment to work and better security of the medical data, different access levels are provided to every user. The second main contribution of this paper is the presentation of a DSS that can help medical doctors not only for scheduling of surgeries, but also it can be used for management tasks of patients, medical teams, hospital resources, etc. Finally, the scheduling approach presented is compared with real historical data from the studied hospital, this being a third contribution of this manuscript.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 2 Background
### Attacks on Identity Privacy  
Privacy attacks exploiting IP addresses are not merely theoretical. By analysing real-world data on publicly exposed fingerprinting information, it has been shown that public IP addresses are often sufficiently long-lived to track a device's activity for a month or longer [5]. Even mobile devices that connected through many different networks were vulnerable, as the set of IP addresses they cycled through could be used to identify them and to track their location. Any entity that can see IP addresses can perform this kind of attack, linking all Internet activity from a node to a single identity label.  
As this attack can be performed passively and leaves no evidence, it is hard to spot it \"in the wild\". However, evidence exists of various organisations performing such an attack. The Snowden leaks exposed details of a US National Security Agency (NSA) tool used for searching collected data and metadata on Internet communications [18]. Similarly, the UK government has actively employed powers granted under its Investigatory Powers Act to collect the data necessary to perform this attack [19].
# The Progress of Cobalt-Based Anode Materials for Lithium Ion Batteries and Sodium Ion Batteries
## 3 Preparation Methods of Synthesizing Cobalt-based Active Materials
### Galvanic Replacement  
Galvanic replacement has been widely applied to synthesize nanostructured electrode materials due to its facile and low-temperature synthesis method. [PERSON] and coworkers synthesized a broccoli-like Co\({}_{3}\)O\({}_{4}\)@CNTs anode material for SIBs through a galvanic replacement route [63]. Firstly, the acidizing operation of carbon nanotubes (CNTs) can create numerous functional groups with a negative charge on the surface of CNTs. Owing to electrostatic attraction, lots of Co\({}^{2+}\) ions with a positive charge will attach to the surface of CNTs with carboxylic groups and serve as nucleation precursors. During the subsequent galvanic replacement reaction, a Co\({}^{2+}\) ion was oxidized to Co\({}_{3}\)O\({}_{4}\) at 140 \({}^{\circ}\)C. After that, Co\({}_{3}\)O\({}_{4}\) crystalline particles aggregated on the surface of CNTs owing to the electrostatic attraction and Van der Waals forces; thus, broccoli-like Co\({}_{3}\)O\({}_{4}\)@CNTs were finally obtained (Figure 5a). The TEM and HRTEM images displayed in Figure 5b-d clearly confirmed that the well-crystallized Co\({}_{3}\)O\({}_{4}\) nanocrystals were assembled on the surface of the CNTs. [PERSON] and collogues successfully synthesized a novel Sn-Co@C alloy through galvanic replacement with metal-organic framework ZIF-67 as both the template and carbon source [64]. Figure 5e-g reveal that the Sn-Co nano-alloy particles (\(\sim\)10 nm) were evenly embedded in porous N-doped carbon, forming a novel hierarchical structure. In Figure 5h-l, the distribution of C, O, N, Co and Sn elements clearly confirms the uniform structure of the Sn-Co@C alloy.  
Figure 4: Preparation methods and morphology characterization of cobalt-based composites. (**a**) Schematic illustration of the preparation of (**b**) TEM and (**c**) HRTEM images of the Co\({}_{2}\)-in-wall-N-doped carbon spheres (NCSs) [58]. (**d**) TEM image, SAED pattern (inset) and (**e**) HRTEM image of CNTs@CuCo\({}_{2}\)S\({}_{4}\)[59]. (**f**) Low-magnification TEM image of CoSe@100 CSs [60]. (**g**) SEM image of CoP nanorod arrays [61]. (**h**) Low and (**i**) high-magnification SEM images and (**i**–**k**) elemental mapping images of Co\({}_{3}\)O\({}_{4}\)/CNT nanocomposites [62].  
Mesoporous Co\({}_{3}\)O\({}_{4}\) nanowire arrays, needlelike Co\({}_{3}\)O\({}_{4}\) nanotubes [65], porous carbon/CoS\({}_{2}\)[66] and some other cobalt-based anode materials with various morphologies and structures have also been synthesized by galvanic replacement [67].
## 3 Results
### Correlation of Daily Activities in Stables with Bioaerosol Components  
Changes in the total fungal aerosol along with inhalable particulate matter concentrations in different times of day, depending on the activity in the examined stables, are shown in Figure 4A,B for the Krakow and Tarnow stables, respectively. Similarly, changes in the respirable fractions of fungal aerosol and particulate matter throughout the day, are shown in Figure 5A,B for the Krakow and Tarnow stables, respectively. The lowest values of both total and respirable fungal aerosol, as well as inhalable particulate matter, were observed when there was no activity in both stables. In the Krakow stable it was indoor background (Figure 4A), while in the Tarnow stable it was early morning--before any activity began (Figure 4B). On the other hand, the highest concentrations of particulate matter and fungal aerosol correlated only in the Tarnow stable, where the highest values were observed during indoor activities. In the Krakow stable, the highest concentration of particulate matter was observed during indoor activities, while the highest fungal aerosol concentrations (both inhalable and respirable fractions) were observed during feeding of the horses. Consequently, the correlations between the bioaerosol components and microclimatic parameters observed in the two stables are clearly different (Table 3). In the case of the Krakow stable, the components of fungal aerosol correlate positively with each other, which is not surprising. Temperature is a microclimatic parameter correlated with fungal aerosol (fraction F6, statistically significant positive correlation, 0.650 at \(p<\) 0.05). Temperature also correlated negatively with the particulate matter concentrations (statistically significant negative correlation values from \(-\)0.641 for PM\({}_{1}\) to \(-\)0.896 for PM\({}_{10}\)). There was also a positive correlation observed between relative humidity and PM\({}_{10}\) (0.786). On the contrary, in the Tarnow stable, all fractions of fungal aerosol, as well as total fungal aerosol and its respirable fractions, correlated positively not only with each other, but also with all fractions of particulate matter and temperature. For example, there were very strong positive correlations between total and respirable fractions of fungal aerosol with temperature (0.960) or between total fungal aerosol and particulate matter fractions (ranging from 0.771 for PM\({}_{10}\) to 0.804 for PM\({}_{1}\)) for respirable fraction of fungal aerosol. These correlations ranged from 0.703 for PM\({}_{10}\) to 0.749 for PM\({}_{1}\). Additionally, relative
# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Technical Improvements
#### 3.3.2 Radio Access Schemes  
One of the key enhancements needed for the network of the future is to develop ultrafast RA schemes. Their significance arises from the fact that a great part of the networks of the future will be based on IoE devices, which generate sporadic transmissions most of the time. Therefore, setting up conventional connections for IoE devices is highly inefficient in terms of spectrum utilization, which makes RA the most suitable method in this scenario. For example, the coded-ALOHA and successive interference cancellation (SIC) RA methods have been proposed in recent works [93; 94]. However, for 6G, RA can be further optimized by integrating key design elements, such as MIMO and OFDM, into the RA algorithm [95]. RA methods require constant development, because with the increasing number of devices competing for a transmission slot, the latency and packet dropping  
Figure 6: A comparison between 5G and 6G’s theoretical achievable performances.  
rate will increase. This can lead to a significant drop in the overall network performance, which can have significant consequences in some scenarios such as V2V communication or remote healthcare.
## 1 Introduction
Although this increased/decreased FC could be due to differences in disease symptoms or subtypes, we believe that part of the heterogeneity is due to comparisons made using a static connectivity measure of FC that represents an average across different dynamic modes of brain activity during an unconstrained resting state [15]. To discover the intrinsic neural changes in visual-related diseases, FC analysis has been successfully used between the primary visual cortex and patients with amblyopia [35], glaucoma [36], and retinal vein occlusion [37]. Our study focuses on the cuneus as it plays a role in both primary and secondary visual processing such as spatial frequency, orientation, motion, direction, and speed. It is located in the medial occipital gyri, part of the occipital lobe, corresponding to Broadmann area 17, which receives input from the contralateral superior retina corresponding to the lower visual field [38]. The cuneus connects to activation in the precuneus of the parietal lobe via the dorsal stream, which functionally is associated with spatial awareness and representations of object locations [39]. Activation in the ventral stream connects the cuneus to a smaller volume of activation in the inferior temporal cortex of the temporal lobe, which is functionally involved with object recognition [40] such as circles against the pattern of squares [41] and blue and red color perception [42]. One study associated greater inhibitory control among patients with Bipolar Disorder I with greater cuneus volume [43], and increased activity in the cuneus when compared with controls was found in compulsive gamblers [44] in another study. Patients with localized brain lesions are more likely to have associated with damage to the cuneus [38]. Visual functions (e.g., movement perception) are also affected by damage to more anterior parts of the cuneus, though usually for higher-level operations such as object perception and spatial processing, such as attention [45]. Different analysis tools have been used to describe brain FC using rsfMRI data, including seed-based [11; 38], independent component analysis (ICA) [15; 46; 47; 48], principal component analysis (PCA) [49], clustering [50; 51], fisher discriminant dictionary learning (FDDL) [52], centrality [53], multivariate pattern analysis (MVPA) [54; 55], and graph theory [56; 57]. In this study, we used seed-based analysis, in which the connectivity patterns are based on a selected seed region of interest (ROI). The advantage of this method is that the results are focused on particular ROIs and, hence, could be easier to understand in relation to neuropathology [58]. With rsfMRI data of 72 SZ patients and 74 HC groups from the COBRE dataset, sFC and dFC were used to analyze the whole brain in both groups, and the dynamic characteristics (standard deviation (SD) and mean strength (Str)) were used to estimates the dFC. We used the proposed seed-based FC method to obtain the FC between the cuneus and other brain regions and compare whether sFC and dFC might provide factual information by identifying dFC indicators in order to understand SZ neural mechanisms better.
# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
## 4 Result and Discussion
### Classification Method Experiment  
From Table 4, the performance of method [4] based on texture or spectral feature on the two datasets is worse deep learning. Cloud images have more texture features and deep semantic features than other images, and only by acquiring more image features can we satisfy the classification needs of such images. In recent years, CNN has been widely used in ground-based cloud images classification tasks, thanks to its powerful feature extraction capabilities. Method [9] has shown good results on both datasets. Methods [7; 11] fuse multimodal meteorological data and CNN, and achieved accuracy rates of 87.90% and 88.63%, respectively.  
While good classification accuracy is achieved on other classic CNN models, the one that achieved the highest on our model had accuracy of the MGCD dataset that reached 90.08% and of the NREL dataset, it reached 95.60%. The combined network greatly optimizes the probability distribution of the classification output vector. By comparing with the latest algorithms and single network, the results show that the method in this paper has a greater improvement in classification accuracy, and it also proves the generalization ability of the method. For ground-based cloud images collected in different regions, the model has strong robustness, which will play a positive role in the field of PV power generation forecasting.
## 3 Results and Discussion
### Batch Studies
#### 3.2.4 Effect of Contact Time on MB Adsorption  
The study examined the adsorption of MB dye at varying contact times (10-80 min) and discovered that the uptake of dye increased as contact time increased. Equilibrium was reached after 15 min (as shown in Figure 9). Based on these discoveries, the adsorption process initiates swiftly on the outer surface of the adsorbent, succeeded by a more gradual internal diffusion process, which ultimately governs the adsorption rate. Initially, adsorption occurs quickly due to the availability of numerous surface sites. However, as time progresses, accessibility of the remaining surface sites diminishes, causing the solute molecules in the solid and bulk phases to repel each other. Consequently, it takes a longer period to achieve equilibrium [51].
# Analysis of Electromagnetic Interference Effects of 5G Signals on Radio Altimeters
## 3 Simulation of 5G Signal Injection  
### Modeling the RF Front-End Circuit of the RA Receiver  
Based on the RF front-end architecture of the RA receiver, a behavioral-level simulation circuit model was developed using ADS 2022, as depicted in Figure 3. The model incorporates several key components designed to simulate the behavior of the RA receiver under 5G signal interference.  
The Vf_Triangle component generates a 100 kHz triangular wave signal, which is fed into a VCO component. The VCO produces an FMCW signal with a center frequency of 4300 MHz and a bandwidth of 100 MHz. These parameters match the operating conditions of the LRA700 RA used in the subsequent experiments, ensuring that the simulation accurately represents the signal features of the actual RA.  
The FMCW signal is then divided by a power splitter, with one channel serving as the local oscillator signal for the mixer, and the other channel generating the RA echo signal. It is crucial to note that the RA echo signal experiences energy loss during free-space transmission. To simulate this, an attenuator component is designed with a 30 dB attenuation value to match the actual power level of the echo signal received by the RA receiver.  
In the simulation, the height distance is set to 3 m, and considering the speed of light, the delay component is configured to 20 ns. These settings are derived from theoretical calculations and are consistent with the expected physical behavior of the RA system in a real-world scenario. The preliminary simulated IF signal frequency is 400 kHz, aligning with the value calculated using Equation (4), which validates the accuracy of our simulation model.  
A bandpass filter is employed to eliminate out-of-band interference and attenuate the power of the incoming 5G signal. The filter's specifications are tailored to a specific intensity, ensuring that only the desired frequency band is allowed to pass through. Following this, an LNA amplifies the input signal, which is then down-converted to an IF signal by a mixer. The IF signal is further amplified through a two-stage amplifier to achieve the necessary gain for subsequent processing.  
The 5G interference signal adheres to the 5G NR TM1.1 standard waveform defined by the Third Generation Partnership Project (3 GPP), as shown in Figure 4, ensuring that our simulation is applicable to current 5G technology standards [23]. The  
Figure 3: Behavioral-level simulation circuit model of the RF front-end.  
P_SpectrumDataset_AllFreqs component is used to read the 5G NR TM1.1 waveform data. Both the 5G interference signal and the echo signal are injected into the RF front-end of the receiver via a combiner, providing a comprehensive test of the receiver.
## 2 Related Works  
In a MANET, routing protocols can be divided into two major categories - proactive routing and reactive routing. Proactive routing protocols, also known as table-driven routing protocols, contain information on every node and update the routing table information periodically. In DSDV [6], OLSR [7], and WRP [8], typical proactive routing protocols, the source node is equipped beforehand with information pertaining to the pathway of the destination node before it send data packets there. As a fatal drawback of the proactive routing approach, however, a mass of route-updating messages flood the entire network periodically to maintain the route information fresh. Furthermore, each node unnecessarily stores the full set of route information, especially in a highly mobile environment where the routing table of a node is updated frequently for dynamic topology. Each node must find the latest broadcast routing path information periodically. Such periodic updates cause unnecessary network overhead.  
Reactive routing protocols, also known as on-demand routing protocols, do not conserve the routing table information; instead, they execute a route discovery procedure to determine a route to the destination only when the source node requires a path to the destination node. Once a route has been discovered, the route is maintained until the destination becomes inaccessible or the route is no longer desired. AODV, DSR [9] and the Temporally Ordered Routing Algorithm (TORA) [10] are representative examples of reactive protocols. Particularly with a large number of nodes, reactive routing protocols are more appropriate than a proactive routing approach.  
In the following subsections, existing routing protocols that aim to improve the data packet delivery ratio and reduce routing overhead are explained.