esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Adsorption Kinetics  
The adsorption phenomenon can be controlled by different mechanisms of transfer of the contaminant from the solution to the adsorbent; thus, the kinetics establish the sequential factors involved for the metal retention to occur, such as diffusion of the metal from the solution to the surface contact area, diffusion from the surface to the pores, and the complexing of the metals, physicochemical adsorption, or chemisorption [7]. The adjustment of experimental kinetic data to pseudo-first-order, pseudo-second-order models, Elovich, and intraparticle diffusion is shown in Figure 4.  
The kinetic study illustrates that the lead adsorption rate in eucalyptus sawdust had a rapid stage at the beginning, achieving maximum removal followed by a slow stage approaching equilibrium. In this study, more than 96% of the metal was adsorbed during the first 30 min, then the rate decreased, and equilibrium was reached at 50 min, the latter being established as the equilibrium time. The above indicates that, due to the occupation of the bonding centers by the metal ion, the availability of these in the sawdust is reduced [51]. From the fit of Figure 4 and the parameters summarized in Table 4, it can be said that the model that fits the experimental data is the pseudo-second-order model, which suggests that the phenomenon that controls ion adsorption in sawdust is chemical adsorption [52], and that ion retention occurs on the exposed surface of the adsorbent due to its heterogeneous nature [53]. This is evidenced by structural change after the biomaterial is placed in contact with the contaminated lead solution (Figure 2) [54].  
Figure 4: Adjustment of the adsorption kinetics of Pb (II) on eucalyptus sawdust.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **GL** & **Sum of Squares** & **Mean Square** & **F-Value** & **_p_ Value** \\ \hline pH & 3 & 24.2654 & 8.0885 & 49.98 & 0.000 \\ \hline Error & 12 & 1.9472 & 0.1637 & & \\ \hline Total & 15 & 26.2301 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: ANOVA of the effect of pH on the adsorption capacity of Pb (II).  
\(>1\) is unfavorable; R\({}_{\rm L}\) = 0 is irreversible; R\({}_{\rm L}\) = 1 is linear; 0 \(<\) R\({}_{\rm L}\) \(<\) 1 is favorable and feasible [58]. In Pb (II) adsorption on eucalyptus sawdust, R\({}_{\rm L}\) values ranged from 0.63 to 0.90, which is minus 1, indicating that the adsorption was favorable and feasible under the conditions evaluated [59].
## 4 Simulation Results and Discussion
### Influence of the Size of the Reward  
Up to this subsection, the reward mechanism provides a variable size reward/punishment depending on how good the previous action was. Better/worse actions give greater positive/negative rewards. In this section the case of all rewards and punishments having the same size is analyzed. To do so, the P-N reward strategies are binarized, that is, the value +r is assigned if the reward is positive and -r if it is negative. Several experiments have been carried out varying this parameter r to check its influence. In all experiments the policy update algorithm is SAR and the window size is 100 ms.  
The results are shown in Figures 13-15, on the left the evolution of the MSE and on the right the evolution of the variance. Each line represents a different size of reward with a color code. The legend indicates the size of the reward. \"Var\" indicates that the reward strategy is not binarized and therefore the size of the reward is variable.  
Figure 12: Variation of the MSE with the size of the reward window for different reward strategies.  
Figure 13: Evolution of the MSE (**left**) and variance (**right**) for different reward sizes when velocity reward strategy (VRS) is applied.  
It can be seen how, in all cases, the MSE is much better when the size of the reward is not binarized. However, the variation is similar or even worse. It has already been explained how better MSE performance typically leads to greater variance. Another interesting result is that the speed of convergence does not depend on the size of the reward, all the curves provide similar results up to the absolute minimum. However, from this point on, oscillations appear in the MSE and vary with size.  
The VSE reward strategy is the least susceptible to variations in reward size. For \(\Delta\)MSE-RS, the oscillations seem to depend on the size of the reward: the larger the size, the greater the oscillations; however, its amplitude decreases with time. For \(\Delta\)MEAN-RS this relationship is not so clear and, what is worse, the amplitude of the oscillations seems to increase with time. Therefore, it is not recommended to use a fixed reward size with \(\Delta\)MSE-RS and \(\Delta\)MEAN-RS, it is preferable to use a variable reward size.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 5 Results and Discussion
### Comparison of the CCPD Algorithm and the ACPD Algorithm  
The performance of the ACPD algorithm is simulated and compared to the CCPD algorithm. The number \(m\) of the ZC sequences is 8, the length \(N\) is 512, and the CFOs are zero and 4 kHz, respectively. The simulation results are shown in Figure 13.  
It can be seen from Figure 13 that the existence of CFO weakened the performance of both the CCPD algorithm and the ACPD algorithm; however, its impact is negligible. Furthermore, under the same correct timing probability, the ACPD algorithm has a performance improvement of 2 dB to 3 dB, when compared with the CCPD algorithm.
## 3 Results
#### Metric Evaluation of Space Spider Sensor  
Another goal of the WP9 project is related to the metric evaluation of structured-light sensors for the 3D documentation of Near East artefacts. The Space Spider sensor, with a 3D point accuracy of 0.05 mm, is able to reach the deepest angles of cuneiform writing and to perceive the micrometric thickness of seal impressions. In this way, micro details on clay surfaces are properly recorded. For this reason, this structured-light sensor has proven to be one of the best 3D scanners for documenting small- and medium-sized archaeological finds [22; 23; 24].  
The acquired scans were cleaned by removing frames with deviation errors higher than 0.1 mm (root mean square error--RMSe). Despite the fact that declared and suggested 3D output resolution is 0.1 mm, during the scans, the fusion step can be pushed further to obtain more polygonal density and surface details. However, pushing the declared 0.1 mm resolution too much can sometimes be useless, and the process may create 3D surfaces with artefacts and anomalies.  
We tested the fusion process at 0.08 mm with a max threshold error of 0.16 mm, and the three archaeological examples presented herein experienced no anomalies and the surfaces were correctly represented. We also captured more details (Figure 15).  
Figure 14: The envelope tablet MAT 740: (**a**) external tablet (side 1) and details of the sealing as well as negative impressions of cuneiform signs; (**b**) external tablet (side 2) and details of the sealing along with chaotic fingerprint impressions.  
Figure 13: The envelope tablet MAT 740 and the extracted fingerprint (yellow circle): internal tablet. (**a**) MSII filter FVEs 3 normal; (**b**) MSII filter FVEs 3 inverted; (**c**) MSII filter FVEs 13 inverted.
## 3 Methodology  
### Temporal Convolutional Network Model with Channel Attention  
The proposed temporal convolutional network model, incorporating channel attention, primarily consists of three modules: (i) the PPG signal preprocessing module, involving frequency-domain analysis to generate spectrograms; (ii) the scale dimension attention compression module, which applies channel attention to weight each scale's data, compress it, and provides enhanced attention features for subsequent emotion recognition modules; and (iii) the emotion recognition module, utilizing TCN and GRU to extract spatiotemporal features. The architecture of the network is illustrated in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Dataset** & **Accuracy** & **Classifier** \\ \hline [PERSON]’s method [26] & DEAP & \begin{tabular}{c} Arousal 63.6\%; \\ Valence 64.40\% \\ \end{tabular} & \begin{tabular}{c} Multimodal emotion recognition \\ using EEG, PPG, GSR, and RES \\ \end{tabular} \\ \hline [PERSON]’s method [27] & DEAP & \
\begin{tabular}{c} Arousal 60.0\%; \\ Valence 61.0\% \\ \end{tabular} &
\begin{tabular}{c} Multimodal emotion recognition \\ using EOG and PPG \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy of PPG-based multimodal emotion recognition.  
As shown in Figure 2, the PPG signal preprocessing module utilizes the CWT method with multi-scale features for frequency-domain analysis, producing a spectrogram that represents the rate of change in frequency over time. This spectrogram is input to the scale dimension attention compression module. The scale dimension attention compression module employs the channel attention mechanism to weight the spectrogram's scale dimension data, enabling the network to learn the attention weights of segmented data at each scale, and differentiates and learns various scale data accordingly. The emotion recognition module takes the weighted compressed data as input and extracts spatiotemporal features using TCN and GRU, which are then passed to the fully connected layer for emotion recognition.
## 9 Future Research Directions and Challenges
### Off-Loading  
With offloading, more computational power will be available. This calls for new complex strategies and algorithms capable of running and working in parallel. For example, the onboard computer and edge server might work collaboratively and separately. While onboard computers process some part of the data to execute a rough localisation, heavy algorithms are to be implemented on the edge server separately when there is no communication between the two processors. Both algorithms then need to be merged to output the refined localisation result. At the same time, new strategies might be developed with the processing power on edge devices to focus more on accuracy and robustness instead of the computational burden. Most state-of-the-art localisation tries to balance accuracy, complexity, and computational power.
# Investigation of High-Q Lithium Niobate-Based Double Ring Resonator Used in RF Signal Modulation
## 2 Materials and Methods
\[\frac{E_{\rm H1}}{E_{\rm H1}}=t_{1}+\frac{-k_{1}^{2}t_{2}t_{3}e^{-\rho(L_{1}+L_{2}+ L_{3})+j(\theta_{1}+\theta_{2}+\phi_{3})}+k_{1}^{2}t_{2}^{2}t_{3}e^{-\rho(L_{1}+L_{2}+ L_{3}+L_{4})+j(\theta_{1}+\theta_{2}+\theta_{3}+\theta_{4})}+k_{1}^{2}k_{2}^{2}t_{3}e^{- \rho(L_{1}+L_{4})+j(\theta_{1}+\theta_{4})}}{1-t_{2}e^{-\rho(L_{1}+L_{2}+L_{3}) +j(\theta_{1}+\theta_{2}+\theta_{3}+\theta_{4})}+t_{1}t_{2}^{2}t_{3}e^{-\rho(L_ {1}+L_{2}+L_{3})+j(\theta_{1}+\theta_{2}+\theta_{3}+\theta_{4})}+t_{1}k_{2}^{2}t _{3}e^{-\rho(L_{1}+L_{4})+j(\theta_{1}+\theta_{4})}} \tag{8}\]  
Using the same method, the transmission equation of the through port of single ring resonator can be obtained, as in Equation (9):  
\[\frac{E_{\rm H1}}{E_{\rm H1}}=\frac{t_{2}-t_{2}e^{-\rho L_{4}+j\theta_{4}}}{1-t _{2}^{2}e^{-\rho L_{4}+j\theta_{4}}} \tag{9}\]  
The transmission spectra of the double ring and single ring resonators can be plotted based on Equations (8) and (9), as illustrated in Figure 2. Since the inner ring of the double ring structure is equivalent to increase the virtual length of the outer ring, which increases the length of the optical field reacting with the waveguide, the double ring structure achieves resonance enhancement. This eventually makes the resonance peak of the double ring resonator much sharper than that of the single ring resonator. The Q-factor of the double ring resonator can reach 7.05 \(\times\) 10\({}^{8}\), which is two orders of magnitude higher than that of the single ring resonator. The high Q-factor plays a crucial role in facilitating efficient interaction between the optical and electric fields, ultimately resulting in improved electro-optic tunability of the device.
## 4 Simulation Experiments: Security Analysis and Comparison
### Key Space Analysis  
Cryptanalysts often use brute-force attacks to decipher encrypted messages. To assess the vulnerability of an encryption system to such attacks, key space analysis is used. Key space refers to the range of unique secret keys that can be generated by an encryption scheme. An encryption scheme can be considered secure if its key space is equal to or greater than \(2^{100}\)[49].  
The proposed encryption scheme employs six secret keys, namely the parameters \(p,a,G,s,q\), and \(B\), which are explained in detail in Section 3. Furthermore, the decryption key for the Type IVa MP wavelets consists of the number of channels at all levels. These details indicate that the key space of our proposed text encryption scheme based on ECs and MP-wavelets is significantly larger than \(2^{100}\), meaning that the scheme has a large key space. As a result, it would be difficult for an attacker to use the brute-force method to attack the algorithm.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{4}{c}{**Key Sensitivity**} \\ \hline Plaintext \(\rightarrow\) Applied Sciences & & \\ Original key \(y^{2}\equiv x^{3}+1\pmod{227}\), \(G=(201,49)\) & & \\ Original ciphertext \(C\rightarrow\) \$FOU-5a]B3T1 SQ2[ENDFOOTNOTE] & & \\ \hline \multicolumn{4}{c}{Modified key} & Modified ciphertext \\ \hline \(y^{2}=x^{3}+b\) & \(p\) & \(G\) & \(C^{\prime}\) \\ \hline \(y^{2}=x^{3}+1\) & 227 & (193, 60) & dd*F\(>\)*TG’caj4a6, \\ \(y^{2}=x^{3}+1\) & 239 & (206, 48) & A@BrhhBlOI\%9 GG95F \\ \(y^{2}=x^{3}+7\) & 227 & (203, 22) &!!Q”wQ$Qb2I$6I$4t \\ \hline \hline \end{tabular}
\end{table}
Table 6: Key sensitivity analysis of the proposed text encryption scheme.
# Mid-State Kalman Filter for Nonlinear Problems
## 6 Conclusions  
A typical nonlinear CL problem occurs when high-resolution wide-band radar tracks very long-range targets. To solve this problem, a novel mid-state Kalman filter is proposed in this paper. The MSKF uses the observed state and its first state derivative as the mid-state vector to transform the update process of the filter into the measurement space and rederives the update equation of the Kalman filter. The state transition module can be replaced according to the actual situation without modifying the filter prediction and update module. This improves the practical engineering applicability of the MSKF. When the CL problem occurs, the MSKF has the characteristics of high state estimation accuracy and fast filter convergence compared with other filters. This will significantly help the wide-band radar system to form the target trajectory quickly and accurately.  
Conceptualization, Z.L. and Y.C.; methodology, Z.L.; validation, Z.L.; formal analysis, Z.L. and Y.C.; investigation, Z.L.; resources, Y.L.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, Y.C. and Y.L.; visualization, Y.C.; supervision, Y.L.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Analysis of Acoustic Emission Technology Characteristics
### Acoustic Emission Characteristic Parameter Analysis
#### 4.1.2 Analysis of Characteristic Parameters of P2 Beam  
The P2 beam, also prestressed, undergoes cyclic loading and shows distinct differences in the distribution of acoustic emission (AE) parameters compared to the P3 beam. Figure 9a,b show that before reaching a load of 60 kN, AE signals are only generated during the loading processes. However, during the loading process from 10 kN to 60 kN, a large number of dense AE signals are generated, with energy and ring count reaching approximately 11 V\(\cdot\)ms and 110,000 counts, respectively. Similarly, noticeable AE signals are observed during the unloading process when returning to 10 kN. In each subsequent loading and unloading process after reaching 60 kN, AE signals are generated, but their intensity significantly decreases. The energy and ring count remain below 3.7 V\(\cdot\)ms and 35,000 counts, respectively. This indicates that the P2 beam experiences severe damage when the load reaches 60 kN. When loaded to 70-75 kN, the concrete at the top of the mid-span of the P2 beam is crushed, accompanied by a significant number of AE signals, indicating complete beam failure. Additionally, through analysis, it was found that the  
Figure 8: Relationship between energy, ringing count, and load of P3 beam and P1 beam. (**a**) P3 beam energy–load changes with time; (**b**) P3 beam ringing count-load changes with time; (**c**) P1 beam energy–load changes with time; (**d**) P1 beam ringing count-load changes with time.  
distribution characteristics of energy and ring count closely follow the load. They are not significantly affected by prestress or loading methods.
## 2 Overview of Supplementary Damping Systems
### Brief History and Development of Damping Technologies
The study of [28; 29] presents the development process and initial experimental evaluations of a new energy dissipation damper that incorporates shape memory alloy (SMA) wires. The study [28] aimed to innovate this type of device and formulate a methodology for its design applicable across various applications. The core concept of our device is the implementation of a dual counteracting system featuring pre-strained SMA wire sections as the primary dissipative element. By utilizing pre-strained wires, this design seeks to optimize energy dissipation, albeit at the expense of some re-centering functionality. The experimental work was conducted on a reduced-scale prototype following this design framework.
###### Abstract  
In this research, environmental impacts associated with the harvest and processing of pineapple (fresh-packed, in syrup, and dehydrated) were determined using the life cycle assessment (LCA) tool and specialized software SimaPro(r) (version 8.4), according to ISO14040:2006 and ISO1404:2006 standards. The information used to develop inventory included field interviews and industrial visits within the study area. The functional unit was defined as one kilogram of fruit. The selected impact categories were carbon footprint, water footprint, and energy footprint; the results obtained for the agronomic stage were 0.47 kg CO\({}_{2}\) eq (equivalent), 78 L of water, and 9.09 MJ, respectively. The growth stage of the pineapple plant was found to be the one that generates greatest environmental impacts for all three categories. For packaged fruit, 0.58 kg CO\({}_{2}\) eq, 82 L of water, and 11.03 MJ were quantified; for pineapples in syrup it was 1.12 kg CO\({}_{2}\) eq, 103 L of water, and 19.28 MJ; and for dehydrated fruit, it was 5.12 kg CO\({}_{2}\) eq, 782 L of water and 97.04 MJ. This concludes that the most significant environmental impact occurred in all cases during the pineapple cultivation stage.  
pinearple production; life cycle assessment; carbon; water and energy footprint; environmental impacts +
Footnote †: journal: Applied Sciences
## 4 System Model
### Simplified Markov Chain Model  
The Markov model we have analyzed in the traditional way is slightly complicated, and we can simplify the Markov model. As can be observed from the Markov chain model shown in Figure 8 and the equations we have derived, all \(r\) states have similar stochastic properties except the first \(r+1\) states and some last states. We call the set of states with similar properties as a stage. We try to use this number of stages as a representative residual UORA back-off value.  
We use \(B_{i,n}\) as a state probability of each stage where \(i\) is the number of retransmissions as earlier and \(n\) is the number of stages. The stage-based Markov model is described in Figure 9, and each state indicates \(B_{i,n}\) we defined. For each \(i\), the maximum number of stages \(N_{i}\) and \(R_{i}\), number of states in \(B_{i,n}\) where \(n=N_{i}\), can be described as follows.  
\[N_{i}=\left\lfloor\frac{W_{i}-2}{r}\right\rfloor+1 \tag{14}\]  
\[R_{i}=W_{i}-r(N_{i}-1)-1 \tag{15}\]  
\[B_{0,0}=(1-p)\sum_{i=0}^{m}B_{i,0} \tag{16}\]  
\[B_{i,0}=pB_{i-1,0}=p^{i}B_{0,0}\text{ for }0<i<m \tag{17}\]  
Figure 8: Markov chain model of UORA channel access with 9 resource units (RUs).  
\[B_{m,0}=pB_{m-1,0}+pB_{m,0}\to B_{m,0}=\frac{p}{1-p}B_{m-1,0}=\frac{p^{m}}{1-p}B_{0,0} \tag{18}\]  
\[B_{i,n}=\{(N_{i}-n-1)r+R_{i}\}\frac{p^{i}}{W_{i}}B_{0,0}\text{ where }0\leq i<m,\ n>0 \tag{19}\]  
\[B_{m,n}=\{(N_{m}-n-1)r+R_{m}\}\frac{p}{W_{m}}(B_{m,0}+B_{m-1,0})=\{(N_{m}-n-1) r+R_{m}\}\frac{p^{m}}{W_{m}}(p+1)B_{0,0} \tag{20}\]  
From the total probability rule,  
\[\begin{array}{l}\sum\limits_{i=0}^{m}\sum\limits_{n=0}^{N_{i}-1}B_{i,n}=\sum \limits_{i=0}^{m-1}\sum\limits_{n=1}^{N_{i}-1}B_{i,n}+\sum\limits_{n=1}^{N_{m} -1}B_{m,n}+B_{0,0}\\ =B_{0,0}\sum\limits_{i=1}^{m-1}\frac{p^{i}}{W_{i}}\{(N_{i}-1)^{2}r+(N_{i}-1)R _{i}-\frac{N_{i}}{2}r\}\\ +\frac{p^{m}}{W_{m}}(p+1)\{(N_{m}-1)^{2}r+(N_{m}-1)R_{m}-\frac{N_{m}}{2}r\}+ \frac{p^{m}}{1-p}+1\}=1\end{array} \tag{21}\]  
The simplified model we derived also provides the steady-state distribution as a function of probability if specific UORA CW sizes and RU size are given.  
As in Section 4.1, the probability of transmission can be derived as follows.  
\[\tau=\sum\limits_{i=0}^{m}B_{i,0}=\frac{1}{1-p}B_{0,0} \tag{22}\]  
From Equations (13), (21), and (22), we can obtain specific values of the steady-state distribution and the transmission probability of the simplified model if we have specific system parameters.  
Figure 9: Simplified Markov chain model of UORA channel access.