esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Performance Comparison
#### 5.2.2 Ablation Study  
In order to address RQ 2, we demonstrated the effectiveness of each module in the ExoFIA model. Table 8 shows the performance of different variants from Section 5.1.2.  
Studies have shown that integrating multi-modal information (as in ExoFIA) yields the greatest performance among all variants. If we exclude exogenous factors(ExoFIA-Trend), the performance declines. Likewise, omission of either the social network (ExoFIA-Social) or diffusion network (ExoFIA-Diff) also reduces performance.
# Spectrum Sharing Opportunities for 6G Terrestrial and Non-Terrestrial Networks +
## 4 Conclusions  
This paper analyses the coexistence between the GSO FSS E-S and the FSS WLL P-MP systems operating at 28 GHz. In order to evaluate the coexistence between an FSS Earth station and an FS P-MP link, the protection criterion has to be satisfied both at the CS receiver and at the TSs operating in the serving area of the FS CS. Some examples of the results obtained with the FUB sharing tool have been shown, considering the effect of the transmission of the FSS system on the FS CS and TS receivers. The results show that in the case of the FS CS victim receiver, the separation distances between the FSS and FS systems decreases more slowly with respect to the separation distance between the FSS and FS TS victim receiver. This is due to the antenna diagram of the FS CS. The effect of shielding on the FSS side has been also evaluated as a passive mitigation technique that can protect existing FS links; the presence of shielding can provide an attenuation of up to 30 dB that can considerably decrease the separation distances between the interfere FSS system and the receiver FS system. The analysis carried out aims to provide general indications that can be useful to operators and administrations to coordinate the development of networks.  
Conceptualization, methodology, validation, investigation, writing--original draft preparation, writing--review and editing, supervision, V.P., M.F., C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData are contained within the article.  
This work was partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on 'Telecommunications of the Future' (PE00000001--program RESTART). This work has also been carried out in the framework of the agreement between the Ministry of Enterprises and Made in Italy and Fondazione Ugo Bordoni--Project 'Studio sulle prospentive di impegno delle bande WLL'.  
The authors declare no conflict of interest.
## 3 Data Collection and Methods
### Morphometric Parameters
#### 3.1.3 Areal Indexes  
The areal aspects were estimated from different factors, including drainage frequency (F), drainage density (D\({}_{\text{d}}\)), drainage basin shape (B\({}_{\text{sh}}\)), drainage elongation ratio (R\({}_{\text{e}}\)), and drainage circularity ratio (R\({}_{\text{c}}\)) [32,33].
## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Distributed Deployment
#### 4.1.6 Based on Learning Paradigms  
Supervised learning is more successful at detecting known attack patterns when models are trained using labeled datasets. The centralized IoV IDS framework proposed in [74] makes use of supervised ensemble models, such as RF and Gradient Boosting. These techniques achieved pinpoint accuracy by spotting typical attack patterns in annotated data on network traffic. Nevertheless, the difficulty of maintaining accuracy in the face of shifting, unexplained risks was brought to light by their results. In their study, [PERSON] [69] utilized supervised ensemble learning to merge labeled data from several nodes. To improve the model's capacity to identify protocol-specific risks, they used a technique that dealt with feature variability in IoV traffic.  
Finding zero-day attacks and unexpected dangers is a breeze with unsupervised learning since it can spot trends and anomalies without labeled data. [PERSON] et al. [75] used unsupervised ensemble models featuring DT clustering approaches in conjunction with others to examine unlabeled data from IoV. Unusual occurrences that may indicate novel forms of attacks were identified with the use of this method. The significant issue of insufficient labelled datasets in IoV IDS was effectively addressed in this study. [PERSON] et al. [77] used unsupervised ensemble learning to study evolving attack patterns as part of their hybrid IDS. By focusing on anomaly identification in real-time, this approach mitigates issues caused by zero-day vulnerabilities.  
One middle ground between supervised and unsupervised approaches is semi-supervised learning, which uses both labelled and unlabeled data. To make this paradigm better, ensemble methods that take advantage of the scarcity of labelled data are used. [PERSON] et al. [57] employed semi-supervised ensemble models to facilitate learning on a bigger unlabeled dataset in conjunction with a small sample of labelled IoV traffic data. They were able to overcome the lack of data while keeping detection accuracy for unknown attacks at a high level with their method. A semi-supervised ensemble system was introduced in [61] in the context of a federated learning environment. An effective method for detecting dispersed IoV nodes was developed by combining unlabeled network traffic with labelled host-level data.  
Reinforcement Learning  
This method places an emphasis on learning by contact with one's environment and is well-suited to the ever-changing environment of IoV. Reinforcement-learning-based ensemble learning enhances both adaptation and decision-making for IoV IDS. [PERSON] and [PERSON] [69] reinforcement-based ensemble learning could adapt to new threats because it used real-time data to train its classifiers. Similarly, [PERSON] et al. [77] implemented a reinforcement-based ensemble learning IDS that can learn and adjust to new attack patterns. This solution tackled the problem of real-time flexibility and resource constraints simultaneously in IoVs.
# Investigation of Biochars in Terms of Vitamin E Adsorption Capacity
## 3 Results and Discussions  
### Characteristics of Biochars  
The biochars exhibited a widely varying particle size distribution (Figure 1). Since some biochars contained wood fragments with a particle size larger than 2 mm, an analysis of the particle size distribution using laser diffraction spectroscopy was not possible. A sieve analysis was not feasible, as some biochars appeared to be moist. A drying step prior to analysis would possibly have reduced the particle size.  
Particles varied from powdery to visibly larger elongated pieces (>10 mm); the latter could be seen for biochars A, D, E, G, H, I, and J, whereas biochars B, C, and F were powdery, with particles smaller than 5 mm (see photographic images). The SEM images reveal that these large particles were wood fragments. The SEM images clearly indicate the variation in particle size distribution within each biochar and among biochars. In particular, biochars B, C, D, E, G, H, and I have a heterogeneous particle size distribution. Although the SEM images give limited quantitative information about each sample, especially regarding the vertical dimensions [57], the irregular shape and size of the wood fragments are clearly visible. These fragments could have been a result of the pre-pyrolysis particle size and/or varying destruction during the production process [58]. In contrast, powdery biochars, such as biochar F, are the result of grinding post production [58]. During the thermal conversion from biomass to biochar, the mineral and carbon skeleton remains, preserving the porosity and, thus, the structure of the original biomass. These residual cellular structures contribute to biochar's microporosity, and can allow the identification of, e.g., woody biomasses [59]. In woody biochar, these macro-cracks generally appear due to the shrinkage stress as the surface decomposes faster than the inner part of the wood [60]. Thus, these macro-cracks are not only related to the biomass, but also to the rate of pyrolysis [61]. The SEM images reveal that the woody biomasses are more or less still intact. For instance, in the SEM images of biochars C, G, I, and J, the channels of the wood particles are visible. The channels of biochar G are app. 50 \(\upmu\)m wide, whereas the channels of biochar C, I, and J are app. 10 \(\upmu\)m wide. According to the IUPAC classification system, macropores are larger than 50 nm [19; 26], which clearly indicates that these channels are former wood cells.
# The LHAASO PeVatron Bright Sky: What We Learned
## 1 Introduction  
The origin of cosmic rays (CRs) is among the most studied topics in high-energy (HE) astrophysics. CRs are relativistic particles, mainly protons (92%) and ions, that fill our Galaxy. Their very extended spectrum spans from a few MeV to beyond \(10^{20}\) eV (see Figure 1, left). Historically, this was described by a single power law with an index \(\alpha=2.7\) up to the so called \"knee\" (\(\sim\)\(3\times 10^{15}\) eV), due to the galactic CR contribution. However, the recent results of CALET/DAMPE show that the region below 1 PeV may not be as featureless as we thought, showing hardening and steepening between 100 GeV and 100 TeV [1; 2; 3; 4; 5] (see Figure 1, right). After the \"knee\", there is a spectral steepening (\(\alpha=3.1\)) up to the so-called \"ankle\" (\(\sim\)\(3\times 10^{18}\) eV), where the spectrum hardens again, likely due to the emerging contribution of the extra-galactic component. A \"second knee\" at \(\sim\)\(10^{17}\) eV is probably due to heavy nuclei and, according to the latest experimental results, the transition from galactic to the extra-galactic component is likely between the \"second knee\" and the \"ankle\" energies [6; 7; 8].  
The complexity of the CR spectrum perfectly fits into the HE astrophysics context of recent years, especially after the latest results from the LHAASO Observatory.
# E-Textile Metamaterials: Stop Band Pass Filter
## 2 Metamaterial E-Textile Design, Simulation, and Experimentation
The measured proposed design for two SRRs provided a stop band between 3.8 GHz and 6.2 GHz with a \(-35\) dB rejection level; whereas, the one with four SRRs provided a stop band between 2.7 GHz and 4.7 GHz with more than \(-45\) dB. As can be observed, the higher the number of SRR stages, the deeper the level of rejection, exhibiting a well-defined stop bandwidth.
## 2 Methods and Materials
As a result, the multiphoton (or tunnelling ionisation by a strong laser field), inverse Bremsstrahlung and the avalanche absorptions, whose role for transparent materials has been stressed out in absorption models developed previously for ultrafast lasers [18; 24; 25; 26; 27; 28], can be reduced to a usual linear absorption. The available experimental data (see, for example, [46]) show that at relatively low \(w\) (correspondingly at low intensities), \(A_{L\text{-}P}\) first increases with \(w\), which indicates the nonlinearity of the absorption process. However, at relatively large \(w\), the \(A_{L\text{-}P}(w)\) dependence reaches saturation at about 0.80 \(\pm\) 0.05. This practically constant absorptivity is an experimental indication of the transition to linear or quasi-linear absorption. The use of the mentioned absorption models that presume a permanence of the structure of the electronic levels and also do not take into account absorption of plasma emission becomes thus strongly problematic for laser intensities applied for LFC. Besides, many other processes, such as beam propagation, excitons and defects formation, etc. can also affect the LFC. It is unlikely that the analytical model could properly take all these processes into account by a calculation \"from first principles\"--such sophisticated models have not yet been developed--and all input material parameter data required for such calculations are simply unavailable. As the most simple and reliable way of accounting for all these phenomena in the model, we use below a semi-empirical approach, where some crucial parameters required for modelling are not calculated, but rather taken from experiments. For example, these may be the energy conversion coefficients \(A_{L\text{-}P}\) and \(A_{P\text{-}H}\) as well as the introduced above filament length \(H\). This approach, although looks quite natural in laser materials processing, has not been implemented previously in other papers on laser filaments modelling. We distinguish between two different types of smoothness when separating: (a) a smoothness between different sets of filaments, as well as between any set of filaments and the free surface of the material, and (b) a smoothness between neighbour filaments of the same array. The former case has been studied previously experimentally [14], and it has been shown that in this case for obtaining sidewalls of improved smoothness some matching a filament length with a thickness of the plate to be cut is required, with this matching being different for tempered and non-tempered glasses. However, in this paper, we consider the latter case, which has not been previously studied--a smoothness between neighbour filaments of the same array.
## 3 The Design of _HeaLow_ and Implementation
### Implementation Details
#### 3.3.3 Communication Module and Delayed Response  
There are two different modules for communication in _HeaLow_. The communication module is connected to the manager, and the peer-to-peer connection module is not connected anywhere. If all messages are sent through the manager that keeps connection for all its members, the workers do not need to have any information of other members. In this manner, the number of connection to keep all devices connected is minimized. This is especially convenient when sending a message to all members, for instance when sending an offload request message. However, this doubles the number of transmissions for each message delivery, as the manager has to send the message again. This is normally not a critical issue, but, when the message size gets bigger with the offloading workload and results, the doubled network usage creates a serious network bottleneck. To avoid such bottleneck, large-size messages that include workloads or process results are sent directly to the destination. Since every offload starts with a request and an answer, the answer message includes the network address information for the requester to create a socket through the peer-to-peer connection module. This method effectively halves the network usage unless the manager acts as the wireless infrastructure.  
Again, as shown in Figure 2, the processing rate slows down for a period of time. The amount of workload offloaded proportionally increases in that period, using network bandwidth accordingly. Each workload is processed in milliseconds range, and immediately returning the result doubles the network usage. Since the competition and the congestion increase along with increased network usage, total effective network bandwidth drops below half when the usage is doubled. Therefore, if the offload process is complete, the receiving device monitors the time left until the deadline and holds on to the result if more than twice the network transmission time is left. This simple technique improves the overall performance effectively. We named this method _delayed response_.
## 2 Materials and Methods
### Soil sampling and Analysis  
Soil samples were taken in September 2020 using a hand auger from both non-tillage (NT) and conventional tillage (CT) plots. Three replicates were considered for each tillage type at five depths (0-5, 5-10, 10-20, 20-40, and 40-60 cm) and each of the three nitrogen doses. After collection, soil samples were air-dried, hand-crushed, and sieved to 0.2 mm for laboratory analysis. Texture was determined using only two samples (one per tillage type) at a depth of 0-15 cm. Organic carbon (OC), phosphorus (P\({}_{2}\)O\({}_{5}\)), potassium (K\({}_{2}\)O), and cationic exchange capacity (CEC) were analyzed before the start of the experiment, whereas total nitrogen, ammonium, and nitrates were analyzed at the end of the experiment. Soil properties were determined following international standards. OC was determined by the method of [PERSON] and [PERSON] ([PERSON] and [PERSON], 1971). Assimilable phosphorus was extracted by a sodium bicarbonate reagent (HCO3 Na) buffered at pH 8.5 with sodium hydroxide (NaOH) ([PERSON] and [PERSON], 1971), potassium was evaluated using the normal \"K exchangeable with ammonium acetate\" method at pH = 7 ([PERSON], 1971), and CEC was measured by the [PERSON] method ([PERSON], 1971). Total nitrogen was determined by the [PERSON] method ([PERSON], 1971), and ammonium and nitrates were extracted by a solution of KCl, then distilled ([PERSON], 1971).
# Wearable Vibration Based Computer Interaction and Communication System for Deaf
## 3 Materials and Methods
### Classification  
#### 3.4.1 K Nearest Neighborhood (Knn)  
In Knn, the similarities of the data to be classified with the normal behavior data in the learning cluster are calculated and the assignments are done according to the closest \(k\) data average and the threshold value determined. An important point is the pre-determination of the characteristics of each class.  
Knn's goal is to classify new data by using their characteristics and with the help of previously classified samples. Knn depends on a simple discriminating assumption known as intensity assumption. This classification has been successfully adopted in other non-parametric applications until speech definition [28].  
In the Knn algorithm, first the \(k\) value should be determined. After determination of the \(k\) value, the calculation of its distance with all the learning samples should be performed and then ordering is performed as per minimum distance. After the ordering operation, which class value it belongs to is found. In this algorithm, when a sample is received from outside the training cluster, we try to find out to which class it belongs. Leave-one-out cross validation (LOOCV) was used in order to select the most suitable \(k\) value. We tried to find the \(k\) value by using the LOOCV.  
LOOCV consists of dividing the data cluster to \(n\) pieces randomly. In each \(n\) repetition, \(n-1\) will be used as the training set and the excluded sampling will be used as the test set. In each of the \(n\) repetitions, a full data cluster is used for training except a sampling and as test cluster.  
LOOCV is normally limited to applications where existing education data is restricted. For instance; any little deviation from tiny education data causes a large scale change in the appropriate model. In such a case, this reduces the deviation of the data in each trial to the lowest level, so adopting a LOOCV strategy makes sense. LOOCV is rarely used for large scale applications, because it is numerically expensive [29].  
1000 units of training data were used. 250 data for each of the four classes were derived from among 1000 units of training data. One of 1000 units of training data forms the 1000-1 sub training cluster for validation cluster. Here, the part being specified as the sub training cluster was derived from the training cluster. Training cluster is divided into two being the sub training cluster and the validation cluster. In the validation cluster, the data to be considered for the test are available.  
The change of \(k\) arising from randomness is not at issue. The values of \(k\) are selected as 1, 3, 5, 7, 9, 11 and they are compared with the responses in the training cluster. After these operations, the best \(k\) value becomes determined. \(k=5\) value, having the best rate, is selected. Here, the determination of \(k\) value states how many nearest values should be considered.
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 4 Conclusions
However, noble metals, such as Ru and Pt, are more sensitive to the growth of a contamination film compared with Au; this contamination film makes the switch vulnerable to contamination failure. High Au content in the alloy, hermetic and reliable packaging, and contact material with CNTs could make the metal less sensitive to contamination failure. At the same time, it is observed that the contact resistance of gold alloying increased compared with the Au-Au contact. Multi-contact MEMS switches are proposed to achieve mN-level contact, leading to low contact resistances. Conclusion 4: Pull-down voltage has a strong dependence on temperature, especially at high RF power levels. The drift of pull-down voltage increased with temperature, and it was especially present in multilayer structures and clamped-clamped structures, which accelerated switch failure. Thermal compensation structures, circularly symmetrical structures, thermal buckle-beam actuators, and inverted crab topology structures have been proven to significantly reduce temperature sensitivity and residual stress sensitivity, but they are complex. At the same time, switches based on molybdenum membrane and thin-film packaging also exhibit less sensitivity to temperature. Conclusion 5: Multifrequency band functionality MEMS switches have been achieved by tailoring switch geometry and size. Switches have different inductance and capacitance values due to reconfigurable switch structure and changing membrane bridge size, resulting in different resonant frequencies. Such switches present good performance at these resonant frequencies. The following methods of improving the performance of MEMS switches in high frequency are proposed according to the above conclusions:* Utilize combinations of several switches in series and \(\pi\)-match to improve isolation;
* Cover a float metal layer on the dielectric layer, select conductor materials with low resistance, and use matching technology to achieve low insertion loss;
* Design MEMS switches based on serpentine flexure structures and use dielectric layer materials with high dielectric constant to get low pull-down voltage;
* Design MEMS waveguide switches based on reconfigurable MEMS surface and BiCMOS technology to achieve excellent switch performance for submillimeter applications;
* Select Ultra-NanoCrystalline diamond or aluminum nitride dielectric materials and apply a bipolar driving voltage, stoppers and a double-dielectric-layer structure to weaken charging effects;
* Adopt gold alloying with CNTs, hermetic and reliable packaging, and mN-level contact to minimize contact failure; and
* Employ thermal compensation structures, circularly symmetric structures, thermal buckle-beam actuators, molybdenum membrane, and thin-film packaging to reduce temperature sensitivity.