esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Transmitters without Driver Amplifiers--Optimal Operation Conditions
## 3 Investigation of the Effect of Clipping on the Electrical Eye-Opening  
A reason for the roll-off factor dependence of the eye-opening as discussed in the previous section is the peak-to-average power ratio (PAPR). The different PAPRs can be seen in the simulated eye diagrams of Figure 1b. Especially, in the context of multicarrier systems [22], e.g., orthogonal frequency-division multiplexing (OFDM) [23; 24; 26; 55], high PAPR is a well-discussed fundamental issue. Clipping is a solution to handle waveforms with a large PAPR [21; 22; 23; 24; 25; 26; 27]. Clipping limits the maximum amplitude to a certain level. This can be either achieved using a nonlinear driver amplifier or by clipping the values in the digital domain [22]. Clipping reduces the dynamical range, and thus, allows the reduction of the number of required bits for the analog-to-digital conversion [21]. Clipping, however, induces additional noise [21], and therefore, needs to be applied wisely.  
Figure 2 shows a simulation for the impact of clipping on SRRC signals. We generated waveforms with one million PAM-2 symbols, oversampled with two samples per symbol, and SRRC as a pulse shape. The waveforms were clipped at different amplitude levels and received with a matched filter; finally, the SNR penalty was evaluated. Eight different clipping values between 0% and 33% clipping were chosen. These clipping values were chosen because they allowed an analysis of up to an SNR penalty of slightly more than 3 dB. A value of 0% clipping means no clipping at all, whereas 33% clipping means that the amplitude was saturated for values above 67% of the positive and negative maximal amplitude. Figure 2a shows the root-mean square (RMS) value of the signal amplitude normalized to the maximum value as a function of different roll-off factors. For a wide range, the RMS amplitude increases with clipping and roll-off factor. Figure 2b shows the resulting SNR penalty from clipping. The SNR penalty increases significantly with the roll-off factor. This is contrary to the optimum in RMS amplitude. Therefore, the ideal ROF lies in the area of 0.4, which allows a large RMS amplitude, but limits the SNR penalty due to clipping. The black crosses in Figure 2 indicate examples at ROFs of 0.2 and 0.4. The power for an ROF of 0.2 with 33% clipping is similar to the power for an ROF of 0.4 with clipping of only 14%. However, the SNR penalty at 0.2 with 33% of clipping is 1 dB, and for 0.4 with 14% clipping, the penalty is only 0.27 dB. This leads to the conclusion that clipping can help increase the RMS amplitude, and thus, the modulation efficiency of the EO conversion. However, a smart choice of ROF does further increase the signal quality. The ROF should not be blindly chosen and just compensated for by applying clipping.
# Power Control Techniques for Interference Management-
## 3 Analysis and Findings
### Dataset  
#### 3.1.1 Main Information  
After merging and removing duplicates from the Scopus, Web of Science, and IEEE Xplore databases, we obtained 54, 324, and 392 journal articles, respectively. These documents were collected from 85 (combined) sources published between 2018 and 2023 (until October 2023). Biblioship captured a total number of 2734 authors. Table 5 presents additional statistics of the key data used for bibliometric analysis.
# SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification
## 4 Proposed Technique 3: Efficient Feature Selection and Machine Learning Classification
### Kelm
#### 4.6.1 Arc-Cosine Kernel Function  
Choosing the kernel function is quite important in the KELM. The most used kernel functions are sigmoid, wavelet, polynomial, RBF, etc. In this study, the arc-cosine kernel function is used. Depending on the cosine angle, the computation of vector input similarity has been conducted, and some good results have been obtained [49]. With the help of integral representation, the \(n^{th}\) order kernel is expressed as follows:  
\[K_{n}(p,q)=2\int dw\frac{e^{-\frac{\left\|w\right\|^{2}}{2}}}{\left(2\pi \right)^{d/2}}\Theta(w\cdot p)\Theta(w\cdot q)(w\cdot p)^{n}(w\cdot q)^{n} \tag{73}\]  
where the Heaviside step function is indicated as follows:  
\[\Theta(l)=\frac{1}{2}(1+sign(l)) \tag{74}\]  
With \(n\) degrees, an arc-cosine kernel family is expressed as follows:  
\[k_{n}(p,q)=\frac{1}{\pi}\|p\|^{n}\|q\|^{n}A_{n}(\theta) \tag{75}\]where the angular dependence is computed by \(A_{n}(\theta)\) and is expressed as follows:  
\[A_{n}(\theta)=(-1)^{n}(\sin\theta)^{2n+1}\bigg{(}\frac{1}{\sin\theta}\frac{ \partial}{\partial\theta}\bigg{)}^{n}\bigg{(}\frac{\pi-\theta}{\sin\theta} \bigg{)},n=1,2,3,\ldots \tag{76}\]  
The first 3 expenses of \(A_{n}(\theta)\) are expressed as  
\[\left\{\begin{array}{c}A_{0}(\theta)=\pi-\theta\\ A_{1}(\theta)=\sin\theta+(\pi-\theta)\cos\theta\\ A_{2}(\theta)=3\sin\theta\cos\theta+(\pi-\theta)\big{(}1+2\cos^{2}\theta\big{)} \end{array}\right. \tag{77}\]  
The similarity of vectors \(p\) and \(q\) is measured by the angle \(\theta\) and is expressed by the following:  
\[\theta=\cos^{-1}\bigg{(}\frac{(p\cdot q)}{\|p\|\|q\|}\bigg{)} \tag{78}\]  
When the activation factor has arbitrary values, it implies that various geometric properties are considered by the arc-cosine kernel. There are no corresponding kernel parameters for the arc-cosine kernel function, so no tedious and meticulous pre-setting is required here.
# FOG De-Noising Algorithm Based on Augmented Nonlinear Differentiator and Singular Spectrum Analysis
## 1 Introduction
However, it must be pointed out that since the tradeoff always exists between the immunity to measurement noise and the speed of state recovery in observer theory, the differentiators must compromise between the noise filtering, and state reconstruction speed, i.e., better noise filtering property in signal differentiation is often achieved at the expense of noticeable time delay. Motivated by above analysis, to develop a novel de-noising algorithm for FOG with advantages of strong noise suppression ability, simple structure, and delay-free, an SSA-AND algorithm is proposed. In addition, the superiority and effectiveness of the proposed de-noising algorithm in significantly reducing the noise is demonstrated through experiments. The main contributions of this article are summarized as follows:  
* The idea of combining singular spectrum analysis (SSA) and augmented nonlinear differentiator (AND) for de-noising is firstly proposed, based on which the signal delay caused by AND is eliminated, and the de-noising ability is enhanced effectively. * A parameter determination method for the proposed hybrid de-noising algorithm is proposed based on the correlation coefficient of each decomposed component. The rest of paper is organized as follows: the description of proposed algorithm is given in Section 2; the simulation is shown in Section 3; Section 4 is experimental and verification; and at last the conclusion is given in Section 5.
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Virtual Tutors and Chatbots
#### 3.2.2 Technology Used: MLLMs in Virtual Tutors and Chatbots  
The deployment of MLLMs in virtual tutors and chatbots involves several key technologies. Transformer-based language models, such as GPT-4 [4; 9], serve as the foundation for generating coherent and contextually relevant textual responses. Their capacity to understand and produce human-like language makes them suitable for conversational agents.  
Multimodal integration allows MLLMs to extend traditional language models by incorporating additional modalities. For example, models like LOVA3[51] integrate visual information into language understanding and generation, enabling chatbots to reference and generate content based on images. This is particularly valuable in educational settings where visual content is integral to learning.  
Advanced dialogue systems utilize MLLMs to manage context, maintain conversation flow, and handle multi-turn dialogues [52]. These systems enable virtual tutors to provide coherent and contextually appropriate responses over extended interactions. LOVA3, in particular, enhances these capabilities by introducing tasks that foster the skills of asking and assessing questions in the context of images, thereby enriching the interactive experience.  
Personalization and adaptive learning are enhanced by MLLMs, which can analyze user inputs and learning patterns to tailor responses and instructional strategies [53]. The ability to ask and assess questions, as enabled by frameworks like LOVA3, allows virtual tutors to engage learners more effectively, encouraging deeper engagement with the material and fostering critical thinking skills.
# Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic
## 5 Conclusions  
Two new multi-rate loss models are proposed in this work, namely the f-MC-SC and the f-MC-MC models, for the analysis of a C-RAN that accommodates different service classes of calls which arrive in the system according to a quasi-random process. The f-MC-SC model describes a C-RAN of a single cluster of RRHs while the f-MC-MC model includes multiple clusters of RRHs (clusters are distinguished via the RRH capacity in radio RUs). We proved that a PFS exists for both models and proposed convolution algorithms for the efficient determination of TC probabilities. The accuracy of these algorithms was verified via simulation. The main conclusions of our work are (a) that the behaviour (in terms of TC probabilities) of the proposed f-MC-SC model cannot be captured by the existing f-SC-SC model and (b) that the total TC probabilities increase as the offered traffic-load increases.  
As a future extension of this paper, we intend to study similar C-RAN structures but for bursty traffic and elastic type calls [60; 61; 62; 63; 64]. In the case of bursty traffic, a possible idea is to express the notion of burstiness via the compound Poisson process where calls arrive in the RRHs as batches (a batch contains one or more calls of the same service class) while batches follow a Poisson process. Regarding the case of elastic type calls we may consider two different scenarios: (a) a call may have different (elastic) resource requirements during the call set-up phase and use certain (fixed) RUs while in service and (b) a call may have certain (min and max) resource requirements during the call set up phase and use, while in service, RUs that fluctuate between these min and max values. Following these two main extensions, a possible future direction can be the study of more complicated resource sharing policies in C-RAN architectures such as the multiple fractional channel reservation policy and the probabilistic threshold policy which may provide a better handling of congestion probabilities from the network planning point of view [65; 33].  
An additional aspect that can be included in our models is the notion of quality of experience (QoE). The QoE concept has emerged due to the progress of multimedia services, such as online 4K streaming video and virtual reality, along with diverse capabilities of devices through which such services are used [66]. The NFV may enable network management to be automated and at the same time ensure that the QoE/QoS requirements of the end users are fulfilled [67]. In any case, and to the best of our knowledge, there are no C-RAN teletraffic models that provide congestion probabilities formulas which consider both QoS and QoE requirements.  
Conceptualization, all authors; methodology, all authors; software, I.-A.C. and I.M.; validation, I.-A.C. and I.M.; writing--original draft preparation, all authors; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 2 Nature and Characteristics of _A. altissima_
### Utilization in Bioenergy Production
[PERSON] and [PERSON] [100] investigated the potential of hemicelluloses hydrolysate from _Ailanthus excelsa_ wood as a feedstock for ethanol production, evaluating the chemical composition of the hydrolysate and its suitability for ethanol fermentation. They found that the hemicelluloses hydrolysate had a high sugar content and was potentially fermentable to ethanol. It had a low inhibitory effect on yeast growth, indicating its potential as a feedstock for ethanol production. Overall, the study highlighted the potential of _A. excelsa_ wood as a feedstock for ethanol production and provided useful information for researchers and engineers interested in developing sustainable biofuels. However, further research is needed to explore the long-term sustainability and commercial viability of this process. The study of [PERSON] and [PERSON] [101] investigated the potential of _A. altissima_ as a feedstock for the pyrolysis process and found that it had a high yield of bio-oil and a low ash content, which constitute desirable properties for a biofuel feedstock. However, the study indicated that this species presented a slightly lower calorific value compared to other biomassfeedstocks, which could affect its economic viability. [PERSON] and [PERSON] [102] investigated the in silico temperature sensitivity of the pyrolysis of tree-of-heaven, beech, and spruce, using the ASPEN Plus v8.8 software and simulated the pyrolysis process at temperatures ranging from 300 to 1000 \({}^{\circ}\)C, and analyzed the yields and properties of the pyrolysis products, including bio-oil, char, and gas. They found that the yield of bio-oil decreased with increasing temperature for all three species, with _Ailanthus_ showing the highest bio-oil yield at all temperatures. The char yield was found to be increased with increasing temperature, while the gas yield decreased. The properties of the bio-oil produced varied with temperature and species, with _Ailanthus_ showing the highest calorific value and the lowest acidity. [PERSON] et al. [103] investigated the potential of _A. altissima_ for use in anaerobic digestion, a process that converts biomass into biogas, which can be used to generate electricity or heat, and reported that this species' biomass presented a high potential for methane production and could potentially be used as a feedstock for anaerobic digestion. Generally, although there is significant evidence to suggest _A. altissima_ to be used as a feedstock in energy production, further research is necessary to fully understand the potential of this species for such applications. In addition, it is important to consider the potential environmental impacts of using an invasive species in this way and to ensure that any use of _A. altissima_ is in line with sustainable biomass production practices.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 4 4G and NSA-Based 5G V2X Services
### Network Support
#### 4.2.2 Control Function Support  
The core part of the V2X communication network requires many important functions and protocols. The V2X control function is one of the key functions supported by 4G & 5G V2X communication technologies of 3 GPP standardization. In this regard, the 3 GPP specification (_TS 24.386 Rel-16_) provides details for a V2X control function, protocols, and other procedures enabling the V2X authorization and communication for 4G & 5G V2X based V2X applications. From an abstract view, the V2X control function supports secure communication, provides configuration parameters, helps with authorization procedures, and keeps an IP address for UEs to be discovered for communication. Similarly, the protocols are used by various interfaces i.e., 4G-Uu, 4G and 5G PC5, V3, etc., of the enhanced architectures discussed in the specification document (_TS 23.285 Rel-16_). This dictates the effective and efficient: V2X authorization, which is between UE and V2X control function; and V2X communication, which is between UEs, and/or between UE and V2 Xapplication server. The other procedures also help control the function for transmission and reception of V2X communication messages over the interfaces discussed above.
## 3 SDWSNs and IEEE 802.15.4e TSCH
### IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH)
#### 3.2.2 Scheduling  
TSCH provides the concept of scheduling. Each pair of timeslot and channel offset is called a cell. There are three types of timeslots [10]: (i) shared, where all the transmit and receive processes are achieved; (ii) dedicated, where only a single process is achieved; and (iii) idle, where no process is achieved. The scheduling determines, for each node, when to transmit, receive, or sleep. It is considered, thus, one of the most important aspects concerning TSCH. The minimal mode of operation for TSCH is called 6 TiSCH minimal scheduling [12], which consists of only one shared cell. Various scheduling approaches were proposed in the literature, and TSCH proved its efficiency with the limited resources of the WSNs [23].
## 2 Materials and Methods
### Procedure for Measuring OC Using the GCS  
For OC determination, an analytical curve was constructed by measuring the current peak intensity as a function of analyte concentration. Ten points were obtained by adding known amounts of a 0.01 M stock OC solution to 10 mL of BR buffer (0.5 M H\({}_{2}\)SO\({}_{4}\)). Before each measurement, the GCS surface was immersed in the electrolyte for 10 min. All experiments used to construct the analytical curves (via chromatographic and electrochemical methods) were performed in duplicate under ambient conditions and following the experimental procedures outlined in Section 2.3.  
OC quantification in NaCl solutions and swimming pool water samples was carried out using the standard addition method to minimize potential matrix effects. Known volumes of a stock OC solution were added to the samples (chloride solutions or water matrices), generating three additional data points. Experiments were performed in quintplicate to reduce experimental error, ensuring a confidence level greater than 95% for all measurements. Only the most reliable data set was selected for analysis.  
Figure 2: The electroanalytical setup used for quantifying OC in NaCl solutions and swimming pool water samples by the standard addition method consisted of the following components: (1) stirring plate, (2) electrochemical cell, (3) counter electrode, (4) reference electrode, (5) working electrode, (6) potentiostat/galvanostat, and (7) computer.
## 4 NFV and SFC Frameworks Taxonomy
### Performance Tuning Frameworks
#### 4.2.4 Nethricks Framework  
NetBricks is an NFV framework proposed by [PERSON] et al. [18], which offers a platform for building and running virtual network functions that provide software isolation between NFs. The NetBricks framework differs from other approaches by (1) limiting the set of processing modules to core functionalities, which helps to reduce the number of modules that network application developers must deal with, and (2) allowing the customization of modules using user-defined functions, which makes the modules more flexible and optimised for better network function(s) performance.  
NetBricks eliminates overheads resulting from context-switching by enforcing memory-level isolation in software and reducing I/O related overheads by introducing zero-copy software isolation [18]. Using zero-copy isolation, the cost of packet I/O is greatly reduced by NetBricks, which means that chains of network functions can be run as a single process.  
NetBricks provides a major distinction in providing fault and memory isolation for NF implementations by utilising operators designed for parsing, de-parsing, transforming, and filtering packets. In addition to packet operators, the framework also provides abstractions for processing byte-streams, abstractions for control flow, and for state and scheduled events. To evaluate the performance of the NetBricks framework, two example network functions were used: the first is a simple network function that decrements the TTL of a packet and discards any packet that has a TTL of 0, and the second is a stripped down implementation of the Maglev load balancer [86], which splits ingress traffic among servers and also provides failure recovery for back-end servers.  
The measurements evaluated include simple NF overheads, array bound overheads, and how _general_ the NetBricks programming abstractions can be. For the latter part of the evaluations, that is, the programming abstractions, five network functions were implemented: NAT, firewall, Maglev load balancer, and a Snort-like NF that performs signature matching on ingress packets. Improved performance was observed for scenarios where (1) CPU cores and chain lengths were varied, (2) the load was varied with respect to CPU cycles and chain length, and (3) throughput measurements for single network functions with a variable number of CPU cycles for multiple isolation approaches.
## 4 Model Description
### Model Learning  
We define \(\Theta\) as the parameters associated with the rating prediction model (\(P,Q\) and parameters in the score function Equation (5)) and \(\Phi\) as the parameters in subset selection (\(P^{\prime},Q^{\prime}\) and parameters in SaPN) and rating aggregation (position-aware weights \(W\)). The optimization model of SaSLM is formulated as follows:  
\[\operatorname*{arg\,min}_{\Theta,\Phi}\sum_{u\in\mathcal{L}}\sum_{s\in \mathcal{S}}\mathcal{L}(\hat{r}_{u}^{s}(\Theta,\Phi),r_{u}^{s})+\Omega(\{ \Theta,\Phi\}), \tag{8}\]  
\(\mathcal{L}(\cdot,\cdot)\) is the point-wise loss function. Although pair-wise [40] or list-wise [41], loss functions are also utilized in collaborative filtering; the goal of SPL is to reveal individual item preferences from the set preference, rather than ranking items. Moreover, this loss  
Figure 4: An illustrative example of position-aware rating aggregation. Suppose items \(i_{2},i_{4},i_{5}\) are selected from a set of items \(\{i_{1},i_{2},i_{3},i_{4},i_{5}\}\) by SaPN. The set-rating predictions \(\hat{r}_{u}^{s}\) are obtained by aggregating \(i_{2},i_{4},i_{5}\) with personalized positional weights \(w_{u1},w_{u2},w_{u3}\).  
function is also consistent with our experimental evaluation where we evaluate whether the predicted item scores can correctly fit the true rating scores provided by user to the item.  
\(\Omega(\cdot)\) is the regularization function. While various forms of regularization have been studied, we consider the simple but widely used \(\ell_{F}\)-norm regularization to regularize user and item embeddings and the positional weights:  
\[\Omega(\{\Theta,\Phi\})=\alpha(\|P\|_{F}^{2}+\|Q\|_{F}^{2})+\beta(\|P^{\prime} \|_{F}^{2}+\|Q^{\prime}\|_{F}^{2})+\lambda\|W\|.\]  
We propose to optimize \(\Theta,\Phi\) in Equation (8) alternatively. This is because the learning of subsets can be relatively unstable due to the discrete selection. Therefore, we isolate the learning of subsets with the learning of rating prediction. The positional weights \(W\) is contained in \(\Phi\). Learning \(W\) together with the policy network helps to relieve the problem of instability. On the other hand, by fixing \(W\) and the parameters of SaPN, the model can focus on learning item ratings.