esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Adsorption Modelling  
#### 2.5.1 Adsorption Kinetics  
Adsorption kinetics is one of the key factors that must be understood prior to the application of any adsorbent material. It is an important consideration in understanding the adsorption process as it provides information on the rate of adsorption, the performance of the adsorbent used, and the mass transfer mechanisms. The adsorption kinetics of CV and Cr (VI) on ACSCGs was studied using three models, namely the pseudo-first order (PFO) model in Equation (4) [50], the pseudo-second order (PSO) in Equation (5) [51], and intraparticle diffusion [52] as given by Equation (6):  
\[\mathrm{q_{t}}=\mathrm{q_{e}}\Big{(}1-\mathrm{e^{-k_{t}t}}\Big{)} \tag{4}\]  
\[\mathrm{q_{t}}=\frac{\mathrm{q_{e}^{2}k_{2}t}}{1+\mathrm{q_{e}k_{2}t}} \tag{5}\]  
\[\mathrm{q_{t}}=\mathrm{K_{dit}t^{1/2}+\mathrm{C_{i}}} \tag{6}\]  
where q\({}_{e}\) is the estimated equilibrium adsorption quantity in (mg/g); k\({}_{1}\) is the adsorption rate constant of PFO in (min\({}^{-1}\)); k\({}_{2}\) is the adsorption rate constant of PSO in (g/mg\(\cdot\)min); k\({}_{d}\) is the intraparticle diffusion rate constant; C\({}_{i}\) is the thickness of the boundary layer in (mg/g).  
The pseudo term is added to differentiate between the equations for solution concentrations and adsorption capacities.
## 2 NB-IoT System Detailed Design and Implementation
### NPUSCH Transmitter Chain Implementation
#### 2.1.8 Narrowband Demodulation Reference Signal (NDMRS)  
In NB-IoT Uplink channel, NDMRS are used as pilots for channel estimation in the receiver chain. The channel estimation block's main function is to estimate the changes that occur in the data bits while they go through the channel so that the change can be reversed and the data can be corrected. This channel correction is performed by sending pilots--NDMRS--with the data bits which will be affected also by the channel, and the receiver generates these pilots using upper layer parameters. Following that, if the received pilots are divided by the generated ones, this creates the channel effectwhich will be delivered to the equalizer to reverse it [10].
## 2 Materials and Methods
### Data Collection  
Overall, 20 one-hour-long in-depth face-to-face interviews were conducted with 20 selected fisheries managers who manage salmonid streams with sustainable self-reproducing native wild brown trout populations. The fishery managers restock fish, check the reporting of fish yields and fishing visits, communicate with anglers, and ensure that the streams and rivers are not littered and polluted. They check for littering and pollution by visiting the fishing sites weekly and checking for litter, trash, or signs of pollution (foam, dead fish, and unnatural water colour). We contacted the managers by telephone in cooperation with the headquarters of the Czech Fishing Union. Five fishery managers refused to participate (allegedly for time reasons), so we contacted 25 fishery managers altogether to schedule 20 full interviews. Altogether, there are 478 fishery managers who manage self-sustainable salmonid streams in the Czech Republic. In addition, one interview was conducted with each of the following specialists: a state water manager, fish biologist, meteorologist, disaster prevention expert, a geography expert, a conservationist, an aquaculture expert, and a fisheries regulations expert. The interviewees were selected randomly from a list of experts in each field.  
The anonymised interviews took place from January 2021 to January 2022 according to a preprepared scenario. The questions were drafted several times by a sociologist, anthropologist, and a fisheries expert. This should ensure their validity. The interviews always took place during work hours in the office of each of the fishery managers. The interviews were not recorded, but rather, the interviewer was taking notes in-hand. The interviewed fishery managers were asked to list all the important fishery management adjustments that they use to keep a sustainable native wild brown trout population in their streams. Then, they were asked to elaborate on each of these adjustments and to explain why it is important and how specifically it should be done. The questions for the interviews were prepared based on scientific literature that describes the management of streams with trout populations [1; 2; 4; 5; 6; 7; 9]. For more information, check the in-depth interview scenario in the Supplementary Materials (Table S1).  
We also reviewed and analysed state and local policies that regulate management on small salmonid streams (fishing regulations, water regulations, species conservation laws, and aquaculture regulations). Specifically, we analysed the Act No. 99/2009 Coll. (Act on Fishery) that describes fishing regulations and aquaculture regulations, the Act No. 254/2001 Coll. (Act on Water) that describes water regulations, and the Act No. 144/1992 Coll. (Act on Protection of Nature and Landscape) that describes species conservation. We connected the expert knowledge to the policies to provide specific recommendations.
## 2 Project Overview
### Theoretical Calculation of Support  
We use the roof support strength quasi-measurement to theoretically investigate the effectiveness of roof support in the retracted roadway. The support force (\(P_{t}\)) required to keep the direct roof structure stable is simplified into the weight of rock body within the height of the direct roof.  
\[P_{t}=\gamma H \tag{1}\]  
In the formula, \(\gamma\) is the average capacity of the direct top rock, and we take \(\gamma\) = 47 kN/m\({}^{3}\); \(H\) is direct top thickness, and we take \(H\) = 8.1 m.  
The support force provided by anchor rods and anchor cables is \(P_{z}\).  
\[P_{z}=P_{g}+P_{s}=\frac{\delta_{g}n_{g}N_{g}}{a_{g}}+\frac{\delta_{s}n_{s}N_{ s}}{a_{s}} \tag{2}\]  
\(P_{g}\) is roof anchor support resistance, kN;  
\(P_{g}\) is the top plate anchor cable support resistance, kN;  
\(\delta_{g}\), \(\delta_{s}\) are the anchor rod and anchor cable support efficiency respectively, and we take \(\delta_{g}\) = 50%, \(\delta_{s}\) = 60%;  
\(n_{g}\), \(n_{s}\) are the number of anchor rods and anchor cable support per unit length of the roadway roof, respectively;  
\(N_{g}\) and \(N_{s}\) are the breaking force of anchor rods and anchor cables, respectively, taking \(N_{g}\) = 60 kN and \(N_{s}\) = 200 kN;  
\(a_{g}\) and \(a_{s}\) are the spacing between anchor rods and anchor cables.  
The design principle of roof support is  
\[P_{z}\geq KP_{t} \tag{3}\]  
Figure 3: Original supporting structure.  
\(K\) is the engineering safety coefficient, not less than 2.  
According to theoretical calculations and field experiments, it is determined that the thickness of the direct roof is taken as \(H=7.9\) m, the width of the roadway is 6 m, the average capacity of the overlying rock layer is taken as \(\gamma=19.2\) kN/m\({}^{3}\), and the minimum support force required to maintain the stability of the direct roof structure per meter of the roadway is \(P_{t}=151.68\) kN, which is calculated according to Equation (2):  
\[P_{\rm z}=\frac{50\%\times 5\times 60}{1\times 1}+\frac{60\%\times 4\times 200}{2 \times 2}=270\;\text{kN} \tag{4}\]  
From the design principle of roof support of Formula (3), it can be obtained that \(P_{z}<2P_{t}=303.36\) kN, so from the theoretical analysis, it can be seen that the support design of the original retracted roadway does not meet the requirements.
## 3 Method
### Overall Pipeline
#### 3.1.2 The Bidirectional Long Short-Term Memory Network  
The BiLSTM [37] serves as an extension of the LSTM and is extensively utilized for the processing of sequence data and time-series analysis. LSTM, a specialized variant of Recurrent Neural Networks (RNNs), is primarily designed for the processing and forecasting of time-ordered data. A significant advantage of LSTM lies in its capacity to effectively address the vanishing and exploding gradient issues that traditional RNNs encounter when dealing with lengthy sequences, thereby enabling the model to handle long-term dependencies.  
BiLSTM improves this capability by employing a bidirectional structure, which enables the model to process temporal data in both the forward and backward directions, thus enhancing its ability to capture a broader range of contextual information. The BiLSTM architecture consists of two parts: the forward LSTM, which handles the sequence from start to finish; and the backward LSTM, which processes the sequence in reverse. This dual flow of information allows BiLSTM to simultaneously learn the dependencies in both directions, overcoming the limitations of unidirectional LSTM models. The structure of BiLSTM can be succinctly represented as follows:  
\[\overset{\rightarrow}{h}_{t}=LSTM\bigg{(}x_{t},\overset{\rightarrow}{h}_{t- 1}\bigg{)} \tag{2}\]  
\[\overset{\leftarrow}{h}_{t}=LSTM\bigg{(}x_{t},\overset{\leftarrow}{h}_{t- 1}\bigg{)} \tag{3}\]  
In this context, \(\overset{\rightarrow}{h}_{t}\) is the hidden state of the forward LSTM, \(\overset{\leftarrow}{h}_{t}\) is the hidden state of the backward LSTM, and \(x_{t}\) is the input data at the current time step.  
In a BiLSTM network, the outputs generated by both the forward and backward LSTM components are combined to create a bidirectional output vector:  
\[h_{t}=\begin{bmatrix}\overset{\rightarrow}{h}_{t},\overset{\leftarrow}{h}_{t }\end{bmatrix} \tag{4}\]  
Consequently, the BiLSTM model has the capacity to utilize information from both the preceding and subsequent time steps, rendering it especially effective for applications that necessitate a comprehensive understanding of the global context.
## 8 Application and Discussion of the Results
### Application on SSI
#### 8.1.5 Network Layer  
The vulnerabilities and attacks found in the network layer can be divided into blockchain- or P2P-specific issues (for P2P, see, for example, [PERSON] et al. [146]) and general network issues. The blockchain-specific and network-related items can be applied to SSI. However, some issues, such as impersonation, Sybil attack, and Eclipse attack, might be mitigated due to governance and configuration.
# Real-Time Monitoring of Passenger's Psychological Stress
## Materials and Methods
### Preliminary Signal Processing  
The SC signal has been processed in two different ways. The first one is a slight signal smoothing, performed by applying a 2 nd order low-pass Butterworth filter with a cut-off frequency set to 0.2 Hz. This allows removing peaky artefacts from the signal, such as pressure waves, or zeros caused by one-time contact losses between electrodes and the skin--and nonetheless, to preserve the main skin conductance responses (SCRs). The second processing is a harder signal smoothing, also performed by a 2 nd order low-pass Butterworth filter, with a cut-off frequency set to 0.05 Hz: the remaining information corresponds to the skin conductance level (SCL). SCL is the main tonic component of SC: it is related to the current physical properties of the skin and to individual's general arousal level. SCRs consist in punctual rises in the skin conductance that are potentially triggered by emotional events, with a progressive return to SCL; they represent the main phasic component of SC. They are the part of interest in the first processed signal, which includes both the tonic and phasic information.  
The IBI signal is derived from BVP by an Empatica(tm) proprietary algorithm that performs a real-time recognition on the BVP waves. It is not regularly sampled, and the poor signal-to-noise ratio brought on BVP by ambulatory conditions makes wave recognition difficult, generating sometimes a sparse IBI signal. The HR signal is inversely proportional to the IBI.  
Only the Euclidian norm was considered for the 3-axis accelerometer data. No post-processing was applied to the skin temperature signal: raw data were used.
## 5 Conclusions  
This study proposes and evaluates a DL peephole LSTM model-based CSI estimator. The proposed model provides outstanding learning and generalization capabilities. It also can improve future communication systems, including 5G. The suggested estimator is trained offline and then applied online to monitor channel statistics in the real-life communication system so that channel information can be approximated, and hence the transmitted data can be recovered successfully. The performance of the suggested estimator was evaluated in a comparative study using Adam, RMSProp, and SGDm, three different DL optimization techniques, and different pilots (64, 8, and 4). The suggested estimator outperformed LS and MMSE estimators when just a few pilots were available. The results demonstrate that the SGDm model with 64 and 8 pilots performs worse than the Adam and RMSProp models. The RMSProp model has a better SER with only four pilots than its traditional competitors.  
Using RMSProp at different pilots, 4, 8, and 64, the peephole LSTM-, LSTM-, and GRU-based CSEs were compared. The results demonstrate that the peephole LSTM outperforms GRU when the number of pilots rises (8 and 64). With four pilots, GRU outperforms the peephole LSTM and LSTM for high SNRs.  
The suggested estimator is promising for OFDM 5G communication system channel state estimation with few pilots without the prior channel knowledge required. For future work, the authors suggest the following research plans: Examine the efficiency and accuracy of the suggested estimator using various cyclic prefix kinds and lengths. Evaluate the performance of CNN, GRU, and peephole LSTM-based CSIEs utilizing robust loss functions with robust statistical estimators like M-estimators and traditional loss functions with traditional statistical estimators like crossentropyvex, MAE, and SSE.  
Conceptualization, M.H.E.A., A.R.A. and A.K.; methodology, M.H.E.A. and G.S.A.; software, M.H.E.A., G.S.A. and A.R.A. validation, A.R.A., M.H.E.A., A.M. and H.A.A.; formal analysis M.H.E.A., G.S.A. and A.R.A.; investigation, A.M., M.H.E.A. and H.A.A.; resources, A.K.; data curation, M.H.E.A. and G.S.A.; writing--original draft preparation M.H.E.A. and G.S.A.; writing--review and editing, A.R.A., M.H.E.A., A.M. and A.K.; visualization, M.H.E.A., A.R.A. and H.A.A., A.M. and A.K.; supervision, M.H.E.A.; project administration, A.M. and A.K.; and funding acquisition, A.M. and A.K. All authors have read and agreed to the published version of the manuscript.  
The studies at St. Petersburg State University of Telecommunications, prof. [PERSON] were supported by the Ministry of Science and High Education of the Russian Federation by the grant 075-12-2022-1137.  
Not applicable.  
The article contains the data, which is also available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 3 Proposed Cumulative QoE Model
### Degree-of-Interest  
For modeling QoE, there have been numerous studies that take into account video content-related factors (e.g., type of video, the complexity of video, etc.). However, most of them neglected the  
Figure 5: An example of the memory weight in a session under different values of parameters \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\).  
user's interest, in other words, Degree-of-Interest ( DoI). In fact, influenced by video content and viewer preferences, the user possibly has different DoI on different videos or different parts of a video. Intuitively, the user seems to provide higher QoE scores for the video with interesting content and vice versa. Typically, DoI [44] is defined as the interestingness of the video content, or the ability of the video content to attract the user and keep the user's interest [26].  
To make this clear, in this study, we investigated the correlation between DoI and the overall QoE by conducting a subjective test. In this test, 18 undistorted videos from the LFOVIA Database [12] were utilized. The video content varied upon nature, wildlife, outdoor, marine, sports, animation, and gaming [12] among every video, each with a duration of 120 s. This guaranteed that the subjects would retain their interests as they watched. The referenced videos were randomly divided into six collections and encoded using FFmpeg [45] under the default settings with the resolution of 1920 \(\times\) 1080 and were displayed on a 15-inch monitor with a resolution of 1920 \(\times\) 1080 and a black background. The Absolute Category Rating (ACR) [46] method was used and 60 subjects agreed to participate in this experiment. Each video was assessed by at least 10 subjects. At the end of each video, the subject was asked to give an overall score representing his/her interest in the entire video content, ranging from 1 (worst or not at all interested) to 5 (best or extremely interested), following the general principle of the ITU-T recommendation P.913 [46]. A 3-min break was provided to each subject between each video to minimize the effects of viewer fatigue. The average of subjects' scores or Mean Opinion Score (MOS) for each video was utilized as the DoI of video. These values were then linearly scaled up to the range of 0-100 and compared with the corresponding overall QoE in the LFOVIA Database.  
Figure 6 illustrates the obtained correlation between DoI and the overall QoE, which achieved the Pearson Correlation Coefficient (PCC) of **0.601**. The correlation was modest. We speculated this was due to the small number of subjects participating in the experiment. However, it is shown that the DoI has an influence on the final decisions of the users when they provide the overall QoE. In the future, a larger number of subjects will be considered for further investigation. Based on the conclusion of this experiment, we introduce DoI as one of the potential influence factors in the proposed cumulative QoE model.
## 3 Results
### Characterization
The absorption peaks of Si-O-Si bonds were observed at \(\sim\)1090 and \(\sim\)1240 cm\({}^{-1}\) for all samples. The stretching absorption vibration of Si-OH was allocated at \(\sim\)3460 and \(\sim\)970 cm\({}^{-1}\). The absorption peaks of C-H stretching bands were assigned at \(\sim\)2940 cm\({}^{-1}\) and \(\sim\)2840 cm\({}^{-1}\) for as made Fe\({}_{3}\)O\({}_{4}\)@MSNs, where were disappeared after the CTAB extraction process. Compared with Fe\({}_{3}\)O\({}_{4}\)@MSN-Br, a new peak was observed at \(\sim\)1730 cm\({}^{-1}\) after the polymerization process, referred to as C = O groups. \begin{table}
\begin{tabular}{c c c} \hline \hline
**adsorbent** & **BET Surface Area (m\({}^{2}\).g\({}^{-1}\))** & **Pore Volume (cm\({}^{3}\).g\({}^{-1}\))** \\ \hline Fe\({}_{3}\)O\({}_{4}\)-NP & 61.75 & 0.30 \\ Fe\({}_{3}\)O\({}_{4}\)-MSNs & 568.28 & 1.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brunauer–Ermett–Teller surface analysis of Fe\({}_{3}\)O\({}_{4}\)-NP and Fe\({}_{3}\)O\({}_{4}\)-MSN. Figure 3: N\({}_{2}\) adsorption–desorption BET isotherm for (**a**) Fe\({}_{3}\)O\({}_{4}\)-NP and (**b**) Fe\({}_{3}\)O\({}_{4}\)@MSNs. The insets show the BJH pore size distribution. Figure 2: XRD patterns of the magnetite Fe\({}_{3}\)O\({}_{4}\) nanoparticles and Fe\({}_{3}\)O\({}_{4}\) modified by silica shell. The successful decoration of Fe\({}_{3}\)O\({}_{4}\)@MSNs with polymer layers was evaluated by X-ray photoelectron spectroscopy (XPS), as shown in Figure 5. The C1s spectrum of non-modified PDEAEMA brush on the surface of Fe\({}_{3}\)O\({}_{4}\)@MSNs was fitted with four peaks, at 285 eV, 285.9 eV, 286.8 eV, and 288.3 eV with peak-area ratios of 4.5:3.8:1:1, related to the C-H, C-N, C-O, and O = C-O, respectively. This is in good agreement with the theoretical value of 5:3:1:1 (Figure 5a). After quaternization, the tertiary amino groups in the polymer chains, the C1s spectrum was fitted with three peaks, at 285 eV, 286.3 eV, and 288.9 eV, related to the C-H, C-N/C-O, and O = C-O, respectively (Figure 5b). As expected, there was an increase in peak intensity \(\sim\)286.3 eV, due to the increase in the amount of C-O bonds present in the organic layer. This observation confirmed the successful attachment of iodoethanol molecules. The surface charges of Fe\({}_{3}\)O\({}_{4}\)@MSN-PDAAEMA and Fe\({}_{3}\)O\({}_{4}\)@MSN-QPDAAEMA were also investigated using a zeta potential analyzer, and the results are shown in Figure 6. Both materials had similar zeta potential at pH below 7. However, above pH 7 the Fe\({}_{3}\)O\({}_{4}\)@MSN-QPDMAEMA potential remained almost constant, whereas a significant drop in the potential of Fe\({}_{3}\)O\({}_{4}\)@MSN-PDMAEMA material was observed. Figure 4: FT-IR spectra: (**a**) as-made Fe\({}_{3}\)O\({}_{4}\)@MSN; (**b**) Fe\({}_{3}\)O\({}_{4}\)@MSN-Br (without CTAB); (**c**) Fe\({}_{3}\)O\({}_{4}\)@MSN-PDEAEMA.
# FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation
## 3 Hardware Architecture of DDFS
### Single-Core DDFS
The input of the entire DDFS is an N-bits FCW, which is accumulated by the phase accumulator to produce an N-bits phase \(x\), \(x\in[0,4]\), with the 2 most significant bits being the integer part and the remaining N-2 bits being the fractional bits. In order to take advantage of the symmetry of the sine function, truncation operations and 2's complement operations are required. We truncate the N bits phase to N-2 bits, keeping only the fractional part. The truncation operation maps the phase to the first quadrant by a 2's complement operation. Whether or not to perform the 2's complement operation is determined by the most significant bit (MSB). We illustrate this process with an example below. For example, \(x=12\)'b010111000111, it is in the second quadrant. Its 2 most significant bits are the integer part, the remaining 10 bits are the fractional part, so \(x\) is expressed in decimal as 1.4443359375. After the truncation, \(x=10\)'b0111000111 (0.4443359375 in decimal), then take the complement \(x=10\)'b1000111001 (0.5556640625 in decimal). By the symmetry of the sin function, \(sin(\frac{\pi}{2}*1.4443359375)=sin(\frac{\pi}{2}*0.5556640625)\). Thus we can get the approximate value of \(sin(\frac{\pi}{2}x)\) in the interval [0,2]. Similarly, we can obtain the absolute value of the approximation of \(sin(\frac{\pi}{2}x)\) in the interval [3,4]. The approximate values obtained also need to be converted to the correct encoding format in a format converter, not the simple one with signed numbers.
## 3 MALDI Non-Imaging MS Analysis of GSLs
### MALDI Non-Imaging MS Analysis of GSLs in Pathology  
Given the fact that GSLs take part in different aspects of cell biology (cell proliferation, cell-to-cell signaling, apoptosis, etc.), they are, therefore, implicated in several diseases in humans, including metabolic diseases, neurological diseases, and cancer [185]. While they have a high diversity regarding their structure, it is still a tremendously difficult task to identify the structure, functions, and distribution of GSLs in specific tissues. For this purpose, research is ongoing, and MS has proved to be an essential instrument for determining the structures of GSLs and their levels in biological samples. In terms of our focus on MALDI non-imaging MS, we will next address the findings regarding pathological human tissues (see Appendix A, Table A2). Most studies focus on neural tissue samples (human brain with various diseases), and of prime interest are gangliosides that are involved in the pathologies, but there are also a few findings in cancer research regarding aberrant levels and distribution or accumulation of GSLs.