esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Materials  
### System Configuration  
The schematic of the PA imaging system is provided in Figure 1. A pulsed light from OPO is pumped by the Nd:YAG laser (Surelite I-10, Continuum) with wavelengths of 680-1064 nm, pulse durations of 5-10 ns, and a repetition rate of 20 Hz. The laser beam from OPO is divided into two parts according to different paths, and the two-light beams (each beam has the energy density of 8 mJ/cm\({}^{2}\)) then go through Convex Lens 1 and Convex Lens 2, and finally are focused into two fiber optics bundles. The optical fiber bundles are custom-made silica fibers with one input fiber and two output fibers. The four output optical fibers and two ultrasound transducers (focused transducer: central frequency of 10 MHz, bandwidth range from 6.28 to 12.72 MHz, V327-SU-CF-1.00-IN, OLYMPUS NDT; unfocused transducer: central frequency of 10 MHz, bandwidth range from 6.80 to 12.31 MHz, V311, OLYMPUS NDT) are fastened on the holder. The holder and the sink are placed on the rotator. The element size of the focused transducer is 0.375 in., while the element size of unfocused transducer is 0.5 in. The focal length of the focused transducer is 1.014 in. For each cross-section imaging, we scanned 480 positions, and the interval of each step was 0.75\({}^{\circ}\). The pulser/receiver (5073 PR, Olympus Inc., Waltham, MA, USA) has the functions of reception, so the PA signals are finally received and amplified by the pulser/receiver apparatus. The output signals were shown on the oscilloscope with an average of 8 times and then stored on the computer for further data processing. For the present work, the delay-and-sum beam forming algorithm is used to generate the PA images of adult zebrafish.
## 5 Problem Solving and Algorithm Designing
```
0: Episode length \(T_{\max}\), learning rate \(\beta\), greedy factor \(\epsilon\), discount factor \(\gamma\), iteration number \(\Delta\), and randomly initialize parameters \(\omega_{j,main}\) and states \(s_{j}^{1}\), let \(\omega_{j,target}=\omega_{j,main}\), \(\delta=0\), \(D_{j}=\Phi\), and \(D_{j}^{batch}=\Phi\);
0: Optimal strategy for each LEO satellite
1:for\(t=1\) to \(T_{\max}\)do
2:for\(j=1\) to \(J\)do
3: The \(j\)-th LEO satellite takes action \(a_{j}^{t}\) according to \(\epsilon\)-greedy strategy, where the optimal action is \(\underset{a\in A_{j}}{\max}\widehat{q_{j}}\Big{(}s_{j}^{t},a,\omega_{j,target} \Big{)}\);
4: Interact with environment to get the rewards \(r_{j}^{t}\) and the reached states \(s_{j}^{t+1}\);
5:endfor
6: Form the global state \(\mathbf{s}^{t}=\left[s_{1}^{t},\cdots,s_{J}^{t}\right]\), the global action \(\mathbf{a}^{t}=\left[a_{1}^{t},\cdots,a_{J}^{t}\right]\), and the global reached state \(\mathbf{s}^{tr}=\left[s_{1}^{tr},\cdots,s_{J}^{tr}\right]\);
7:for\(j=1\) to \(J\)do
8: Store \(\left(\mathbf{s}^{t},\mathbf{a}^{t},r_{j}^{t},\mathbf{s}^{t+1}\right)\) into the replay buffer \(D_{j}\);
9: Randomly sample an experience batch \(D_{j}^{batch}\) from \(D_{j}\);
10: Update \(\omega_{j,main}\) based on \(D_{j}^{batch}\)according to (27);
11:endfor
12:\(\delta=\delta+1\);
13:if\(\delta==\Delta\)then
14:\(\omega_{j,target}=\omega_{j,main}\) for \(j=1,\cdots,J\);
15:\(\delta=0\);
16:endif
17:for\(j=1\) to \(J\)do
18: Send the trained value network \(\widehat{q_{j}}\big{(}s_{j},a_{j},\omega_{j,target}\big{)}\) to the \(j\)-th LEO satellite;
19: The \(j\)-th LEO satellite trains its own policy network based on \(s_{j}\) and \(\widehat{q_{j}}\big{(}s_{j},a_{j},\omega_{j,target}\big{)}\);
20: Develop the optimal strategy of the \(j\)-th LEO satellite based on its trained policy network. 21:endfor
22:endfor
```  
**Algorithm 1** Matching approach based on multi-agent deep reinforcement learning
### Applications of Macroalgal Proteins and their Derivatives
#### 1.3.1 Bioactive Proteins  
Among all the macroalgal proteins, lectins and phycobiliproteins have received particular attention due to their reported biological activities [42]. The main biological activity associated with lectins is their hemagglutinating activity against erythrocytes [42], whereas phycobiliproteins are used in biomolecule labeling for fluorescent immunoassay, immunohistochemistry assays and fluorescence microscopy, and as natural colorants for food and cosmetic applications [56]. Different phycobiliproteins have exhibited antioxidant, anti-inflammatory, neuroprotective, hypocholesteroiclemic, hepatocyte, antiviral, antitumor, liver-protecting, atherosclerosis treatment, serum-lipid-reducing and lipase-inhibition activities [56]. The bioactivities of several macroalgal proteins are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **Type of Protein** & **Bioactive Activity** & **Ref.** \\ \hline _Bryothanmion triquetrum, Bryothanmion seaforthii_, and _Anansia multifida_ & \multirow{2}{*}{Lectins} & \multirow{2}{*}{Antinociceptive effects.} & [42] \\ \hline \hline \multirow{4}{*}{_Codium fragile_} & \multirow{2}{*}{Lectins} & Blood typing; characterization of & \\  & & cell-surface polysaccharides; & \\  & & lectinsorbent assays for & \\  & & cell-binding-pattern examinations. & \\ \hline _Codium intricatum_, _Codium latum_, and _Codium divaricatum_ & \multirow{2}{*}{Fibrinolytic enzymes} & Preferentially hydrolyzing & \\  & & fibrinogen Aa chain. & \\ \hline \hline \multirow{2}{*}{_Eucheuma serra_} & \multirow{2}{*}{ESA-2 (lectin)} & \multirow{2}{*}{\
\begin{tabular}{c} Colonic carcinogenesis suppression \\ in mice; growth inhibition of 35 \\ human cancer cell lines. \\ \end{tabular} } & \multirow{2}{*}{[42]} \\  & & & \\ \hline _Eucheuma serra_ and _Galaxaura marginata_ & \multirow{2}{*}{Lectins} & Antibacterial activity against the & \\  & & fish pathogen _Vibrio vulnificus_. & [57] \\ \hline _Griffithsia_ spp. & Novel lectin & Strong anti-HIV activity. & [42] \\ \hline \hline \multirow{4}{*}{_Hypnea cervicornis_ and _Saleria robusta_} & \multirow{2}{*}{Lectins} & Anti-inflammatory and mitogenic & \\  & & activities in mice spleen & \\ \cline{1-1}  & & lymphocytes; growth inhibition of & \\ \cline{1-1}  & & mice leukemia cells L1210 and mice & \\ \cline{1-1}  & & FM3A tumor cells. & \\ \hline \hline \multirow{2}{*}{_Hypnea cervicornis_} & \multirow{2}{*}{Mucin-binding agglutinin} & \multirow{2}{*}{
\begin{tabular}{c} Antinociceptive and \\ anti-inflammatory activity. \\ \end{tabular} } & \multirow{2}{*}{[42]} \\  & & & \\ \end{tabular}
\end{table}
Table 1: The bioactivities of several macroalgal proteins.
###### Abstract  
**Teamporomandibular disorders (TMD) represent a complex disease with a multifactorial etiology. Despite several studies on the subject, a causal relationship between orthodontic treatment and different forms of TMD has not been established. The aim of this study was to analyze the effect of orthodontic treatment on two aspects of TMD: myofascal pain and disc displacement. This retrospective cohort study followed 224 orthodontic adult patients at three points in time: before treatment (T0), immediately after treatment (T1), and one year after treatment (T2). Disc displacement and myofascal pain were evaluated through a clinical assessment and with a semi-structured interview, along with headache, neck, and shoulder pain parameters and behavioral and somatic accompanying symptoms. Multivariate logistic regression was used to identify risk factors that could influence the development of TMD in these patients. There was a non-significant increase in disc displacement during orthodontic treatment, which mostly resolved after completion of treatment. Myofascal pain scores worsened during treatment, but improved when compared with the baseline once treatment was complete (T0 = 51.3%, T1 = 64.6%, T2 = 44.9%). Female gender (aOR = 1.9, CI 95%, 1.23-2.36), the presence of somatic symptoms (aOR = 3.6, CI 95%, 2.01-5.84), and symptoms of anxiety or depression (aOR = 2.2, CI 95%, 1.14-4.51) were significant risk factors associated with the development of TMD. There is a low and not significant risk of TMD development during orthodontic treatment. When TMD occurred, they resolved within 1 year of the end of treatment.**  
**Keywords: occlusion; orthodontics; temporomandibular disorders; anxiety; depression**
## 4 Performance Degradation of MICP-Treated Soil Under Wet-Dry Cycling
### Volume Expansion  
The results of the volume expansion tests conducted on the MICP-treated soil specimens after various wet-dry cycling intervals are presented in Figure 14. The results show that the volume expansion of the specimens increased with the number of wet-dry cycles, exhibiting a rapid initial increase followed by a more gradual reduction.  
After 15 wet-dry cycles, the total volume expansion of the MICP-treated soil specimens was 6.53%. However, the majority of the volume expansion, 71.36% of the total, occurred within the first three wet-dry cycles, where the volume expansion reached 4.66%. Furthermore, after 15 drying-wetting cycles, the stabilized expansive soil exhibited a 33.20% reduction in its volumetric expansion rate compared to the untreated soil.  
Figure 14: Volume expansion behavior.  
Figure 13: Loss of quality.  
This behavior can be attributed to the changes in the soil microstructure during the wet-dry cycling. Initially, the MICP treatment had effectively reduced the swelling potential of the expansive soil by altering the internal particle structure and reducing the pore spaces, as observed in the earlier microscopic analysis. However, the subsequent wet-dry cycles caused the progressive degradation of the MICP-induced cementation, leading to an increase in the volume expansion of the soil. After the initial rapid volume expansion during the first three wet-dry cycles, the rate of volume expansion gradually decreased. This can be explained by the stabilization of the soil structure, where the remaining cementation bonds became more resistant to the effects of wet-dry cycling, limiting further volume expansion.
## Materials and Methods
### Methods  
The shards were analysed by SEM-EDS (scanning electron microscopy coupled with energy dispersive X-ray analysis) to obtain both qualitative and semi-quantitative information on the chemical composition, using a methodology already set up [15]. The instruments employed were a Hitachi TM-1000 equipped with a secondary electrons detector (SE) and an energy dispersive X-ray spectrometer (Oxford Instruments SwiftED); a JEOL 5500 LV equipped with secondary electron (SE) and a backscattered electrons detector (BSE); and a IXRD EDS 2000 microprobe.  
In both cases the fragments were analysed without any sampling from the surface and excluding any coating application as well, the latter not being required as the samples were analysed under low vacuum conditions. The shards were attached to the sample holder with double-sided tape or removable glue pads. That is to underline that no polished cross-sections could be prepared due to the fact that it was not possible to get the permission to take sample fragments for cross-section preparation.  
Basically, the Hitachi instrument was utilised to perform both the preliminary morphological observations of the samples surfaces with a secondary electron detector and the qualitative/semi-quantitative determinations as well, whereas the JEOL one, due to its higher spatial resolution, was employed to better investigate some details as in the cases of samples 1 and 6, for which some punctual analyses have been performed.  
ATR-FTIR analyses were performed by a Nicolet 380 spectrophotometer coupled with an ATR accessory Smart Orbit equipped with a diamond crystal. The spectra have been acquired from the samples in the range 450-4000 cm\({}^{-1}\) with a 4 cm\({}^{-1}\) resolution. Each spectrum was obtained as the result of 64 accumulations.  
Raman analyses were carried out by a Thermo Fisher Raman iXR spectrophotometer equipped with a microscope and a solid-state laser emitting at 785 nm as an excitation source, and a BWTek i-Raman EX device provided with a fibre optic probe and an Nd-YAG laser emitting at 1064 nm. All the  
Figure 2: Example of a stucco moulding.  
measurements were performed directly on the fragments, without any sampling, and the spectra were obtained as averages of 20 scans in the spectral range between 200 and 2000 cm\({}^{-1}\).  
The identification of compounds was performed by comparing the FTIR and Raman spectra recorded on the samples to those available in the literature.  
Finally, visible reflectance analyses have been performed by means of a Konica Minolta CM 2300d portable spectrophotometer. The instrument was calibrated using its 100% reflective white reference and a 0% reflective calibration box in the 400-700 nm range. All the measurements were conducted straight on the samples' surfaces.
## 2 Materials and Methods
### RSSI Localization  
The experimental testbed for the RSSI localization algorithm consisted of three configurable wireless routers (Figure 4) transmitting in the 2.4 and 5 GHz spectra. During the localization tests, the routers were configured to transmit in the 2.4 GHz spectrum with a transmission power of 6 dBm. On the receiver side, the user carried the UPD with an integrated wireless card able to scan the available wireless networks and determine their signal strengths. Figure 4 illustrates the experimental configuration, in which three routers formed an equilateral triangle ABC with edge lengths of d = 20 m. The user was located inside the triangle at an unknown position, shown as the blue dot in Figure 4. The wireless router on the left side (Figure 4) was mounted on a wooden pole at a height of 2 m and was powered by an external battery placed on the ground.  
Figure 4: Wireless router on a pole (simulating an autonomous vehicle) and the experimental configuration.  
Three tests were performed using the testbed during which the user's orientation with respect to the routers was varied. The first set of localization tests was performed with the user and the UPD facing the router A (Figure 4). In the second set of tests, the user faced router B, and in the last test, the user faced router C. The tests were designed in this manner because the user always faces at least one autonomous tractor. Both the multilateration and min-max localization algorithms were employed simultaneously during each test. The tests lasted 120 s, and the data were sampled at a rate of 0.5 Hz. The collected results were processed and are presented in the next section.
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
## 1 Introduction
The paper reviews the concepts needed to design new equalization algorithms for nonlinearity mitigation, and discusses the properties of several such methods that have been previously reported in the literature. Section 2 introduces the time-varying ISI model of NLIN, and discusses briefly its origin and properties. Methods of characterizing the statistical properties of the ISI process are also reviewed, as they are of importance for designing equalization algorithms. The different types of NLIN correlations are discussed in Section 3. These correlations are the key feature that enables NLIN mitigations, and so exploiting as many types of correlations as possible can dramatically improve an equalizer's performance. Section 4 reviews the main types of algorithms that have been used in the literature, comparing their performance and computational cost in an exemplary link. In particular, in Section 4.3 we introduce the concept of Turbo-equalization, which shows a high potential for NLIN mitigation. Section 5 is devoted to conclusions. Figure 1: Schematic classification of NLIN types, and methods of mitigating them.
## 2 Materials and Methods  
In this research work, the data for nearly the past five years (i.e., from 1 January 2017, until 22 August 2021) were downloaded from the international database Meteobleu ([[https://www.meteoblue.com/en/historyplus](https://www.meteoblue.com/en/historyplus)]([https://www.meteoblue.com/en/historyplus](https://www.meteoblue.com/en/historyplus)) (accessed on 2 February 2022)). This website provides meteorological data, with hourly updates relating to the twenty-four parameters shown in Table 1, starting from 1985 for worldwide locations. We have analyzed Jordan's west-central region data to forecast solar radiation as accurately as possible; the final dataset comprises 40.676 samples for each parameter. According to the Pareto principle, the dataset needs to be split up into train and test subsets with an 80:20 ratio; in other words, the learning model will use 80% of the dataset for training, while the remaining 20% (test subset) will be used for the solar radiation prediction. Figure 1 shows the main phases of the prediction process. The data are pre-processed by applying a standardization technique; afterwards, a Principal Component Analysis (PCA) noise filter is used. The most significant input variables are then selected using Pearson Correlation Coefficient (PCC) to predict solar radiation, while the remaining variables are removed from the learning set. Once the selected training data are prepared, the deep learning LSTM and machine learning ANFIS models can be trained to predict solar radiation. Finally, the trained models are evaluated by calculating the root-mean-square error (RMSE) and compared according to their prediction performance for the total period of 5 years and for each season (summer, autumn, winter, and spring).  
Figure 1 presents the flow chart of the proposed prediction process. We conducted two different sets of experiments related to meteorological parameters influencing solar radiation, the first one with the ANFIS model, the second one with LSTM; then, the outcomes were evaluated based on the obtained RMSE values.
# Dynamic Response of a Heavy-Haul Railway Tunnel's Bottom Structures in Hard Rock
## 4 Dynamic Response under Surrounding Rock Pressure and Train Load  
### Maximum Principal Stress of Tunnel Structure  
Under the action of the surrounding rock pressure and train-load coupling, there were no wheel marks in the tunnel's maximum principal stress contour because the static stress caused by rock pressure was much higher than the dynamic response caused by train load, as shown in Figure 15.  
The tunnel structure was in a high-static and low-dynamic stress state with a maximum value of principal stress on the tunnel' feature lines of 1.27 MPa, located at the bottom surface of the structure, below the side drain.  
The results in Section 3.1 reveal that the maximum dynamic response appeared at the point P3\({}_{\text{b}}\). Under the action of different axle loads, the time-history curve of the maximum principal stress at point P3\({}_{\text{b}}\) is shown in Figure 16. The initial value of the time-history curve was caused by a static load, only related to the surrounding rock pressure, and the  
Figure 14: Vertical displacement characteristics: (**a**) horizontal distribution; (**b**) time–history curve.  
Figure 15: Maximum principal stress characteristics: (**a**) contour; (**b**) horizontal distribution.  
amplitude of the time-history curve was caused by a dynamic train load, which increases as the axle load enlarges.  
Under the action of the surrounding rock pressure and 30 t axle train-load coupling, the maximum amplitude position of the dynamic response was consistent with that under the action of an individual train load; the maximum principal stress amplitude was 129.3 kPa. When the axle load increased by 1 t, the dynamic amplitude of the maximum principal stress increased by about 4.14 kPa.
## 4 Protection Mechanism  
The protection mechanism of the proposed scheme can be characterized by two categories: fault-A and fault-B. The fault-A protection mechanism can be considered when a single working feeder fiber fails between any two RNs, i.e., \(RN_{j}\) and \(RN_{j+1}\); then, the \(OS_{w1\sim 0}\) or the \(OS_{w2\sim RNj}\)_at RN\({}_{j}\)_transfers the load to the second port that is connected with the protection fiber. Similarly, the optical switch at RNj+1 transfers the load to the switch's second port, which accepts the data from the protection fiber. Therefore, normal operation can be easily restored after repairing the working feeder fiber by utilizing the protection fiber. This platform can work even for numerous single-feeder fiber faults, as it easily transfers the transmissions to the protected fiber. As shown in Figure 7, three cases, I, II, and III, show the faults at three single feeder fibers at different positions. The simulation results of these cases can be studied in Figure 8. The receiving powers across the downlink transmissions show us the power loss during the transmission and during the fiber failure cases.  
Figure 8: Simulation results: (**a**) multiplexed downlink signal at each RN, (**b**) received signal from each RN during class-A failures, and (**c**) received signal from each RN during a class-B failure.  
Figure 7: Fiber failures at different positions.  
The fault-B protection mechanism is used when the feeder fiber fails and \(OS_{w2\sim\alpha\text{/}RNj}\) cannot restore normal operation. In the situation that occurred at I and II, the fault protection mechanism works as we described earlier. For the fault at IV, the state of the binary switch \(OS_{w1\sim\alpha}\) is changed before the \(OC_{irc2\sim\alpha}\) is turned on, and the signal is transmitted across the \(OC_{irc}\). This signal is connected to the \(OS_{w2\sim\alpha}\). Here, the direction of the signal is changed, and the signal at \(OS_{w1\sim\alpha}\) is directly received by the RN1, whereas the RN1 and RN5 are no longer connected due to a failure of the feeder and protection fiber, and the \(OS_{w2\sim\alpha}\) changes the flow and transmits the signal to RN5. The working mechanism of the upload transmission is the same for RN1; however, the uplink signal generated by RN5 can no longer connect to RN1. Therefore, the uplink signal is also received by the \(OS_{w2\sim\alpha}\). All the switches in the CO (OS\({}_{w1,2,3\sim\alpha}\)) and ONUs realize the working conditions in normal and protection mode.  
Similarly, the proposed model can also work in the dual feeder fiber failure, i.e., if both the normal mode fiber and the protection fibers fails, as shown in Figure 7 by IV between the RN1 and RNj. In this scenario, the switch in the CO (OSCO-1) can manage both the downlink and uplink transmission, and both the branches will work in the two tree networks for the time being, until the repairing of the feeder fibers occurs.