esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 4 Experimental Setup
### Hardware Implementation of Proposed Scheme  
Experimental setup was conducted in Zhejiang Province, China, in Qingdao Lake to test the robustness and generalization capability of the proposed TL-based receiver. The entire hardware setup is depicted in Figure 12. On the transmitter side, a power amplifier is used that is a Class L2 power amplifier. Prior to being fed to the Class L2 power amplifier, a computer running MATLAB R2023a is used to generate OFDM signal. The amplified electrical signal is then fed to an UWA transducer. On the receiving side, the acoustic signals are received by a hydrophone and the received acoustic waves are converted back into electrical signals. The analog signals are then supplied to a low-noise preamplifier that carries out signal amplification as well as signal conditioning. The amplified signals are  
Figure 11: The The characteristics of the Qingdao Lake channel. Each subfigure (**a–f**) shows the time evolution of the channel impulse response, the delay-Doppler spread, and the power delay profile, highlighting the channel’s temporal and multipath variability.  
converted into digital form and are received by the receiver-side computer that executes the TL-based OFDM training and prediction.
# A Hybrid Adaptive Large Neighborhood Heuristic for a Real-Life Dial-a-Ride Problem
## 2 The DARPTW  
The DARPTW is commonly defined as a pick-up and delivery problem with maximum duration, maximum transit-time and time window constraints. The DARPTW involves a set of \(n\) customers and a set of \(m\) vehicles. The set of customers defines the set of \(2n\) vertices \(V\), connected through the arc set \(A\). Hence, the DARPTW can be defined on a graph \(G=(V,A)\). A customer \(i\) has a pick-up stop indicated by \((i^{+})\), and a drop stop indicated by \((i_{-})\). Each pick-up or drop has a non-negative service time \(s_{i}\), and a time window: \([l_{i^{+}},u_{i^{+}}]\) for a pick-up, or \([l_{i_{-}},u_{i_{-}}]\) for a drop. A pick-up stop \(i^{+}\) has a positive demand \(d_{i}\) and a maximum transit time \(r_{i}\) before arrival at its drop \(i_{-}\), with a negative demand \(-d_{i}\). A vehicle \(k\) has a work time window \([l_{k},u_{k}]\) and a maximum capacity \(q_{k}\). The total route duration of a vehicle \(k\), from the beginning until the end of its duty, cannot exceed a maximum duration \(t_{k}\). The DARPTW aims to design a set of \(m\) vehicle routes starting and ending at a depot \(D\), while satisfying all conditions. Each customer \(i\) needs to be picked-up and dropped by the same vehicle, during the respective time windows. A route executed by a vehicle \(k\) should not exceed the vehicle's load capacity \(q_{k}\) and its maximum duration \(t_{k}\). The waiting times are included in the route duration. Hence, if a vehicle arrives at a stop before the corresponding time window, a waiting time must be observed. The total duration is the sum of the total travel times, waiting times and service times.
## 1 Introduction
In the second set of reactions, glucose-6-phosphate is converted to fructose-6-phosphate mediated by phosphoglucoisomerase (hasE). Glutamine acts as an amino group donor to fructose-6-phosphate in a reaction catalyzed by an aminotransferase yielding D-glucosamine-6-phosphate (GlcN-6P). N-acetyltransferase (GNA1) catalyzes the \(N\)-acetylation of GlcN-6P, using acetyl-CoA as an acetyl donor, and produces \(N\)-acetyl-D-glucosamine-6-phosphate (GlcNAc-6P). A phosphomutase converts the last product into N-acetylglucosamine-1-phosphate (GlcNAcNAc-1P). The final enzyme, a pyrophosphoryylase, converts UTP and GlcNAc-1P to UDP-\(N\)-acetylglucosamine and pyrophosphate (hasD) [10]. The two monomers UDP-GlcNAcUA and UDP-GlcNAc are involved in co-condensation encoded by hasA, producing HA in the cytoplasm, which is translocated outside the cell membrane. The final step in HA synthesis liberates two molecules of UDP (Figure 2) [11]. Throughout the polymerization, the HA chain remains bound to HAS in the membrane until completion. Therefore, HAS controls yield and molecular weight of the polysaccharide. Endogenous HA synthesis makes use of three distinct HASes isoforms playing unique roles in different tissues, including the skin, synovial joints, arterial wall, and brain [10]. Moreover, exogenous HA is synthesized in industrial processes employing microorganisms, plants, or fungi [13; 14]. The degradation of HA occurs via free radicals and is catalyzed by proteins such as hyaluronages (HYALs), such as HYAL1, HYAL2, HYAL3, HYAL4, PH20, TMEP2, and CEMIP, that break the polysaccharide into smaller fragments of different sizes under mechanisms that are still not well understood [15]. A recent trend in wound healing is to add antioxidants like salicylic acid, oxalic acid, and tannic acid to reduce the depolymerization of HA chain length. At the same time, it inhibits the production of reactive oxygen species (ROS) [16]. Figure 2: The synthesis of HA by HASes embedded in a model of a cellular membrane with HA nascent in the formed pore of a multimer. In this image a HASes 1 tetramer is shown. The tetramer was modelled using Alpha Fold 3 server with default settings. The membrane model and nascent HA were built from CHARMM-GUI server, and for the membrane POPC molecules model was used. The ensemble of different molecular systems was performed in Pymol with homemade scripts (Retrieved from [[http://www.pymol.org/pymol](http://www.pymol.org/pymol)]([http://www.pymol.org/pymol](http://www.pymol.org/pymol)) (accessed on 2 January 2025)).
# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
## 2 Materials and Methods
### Subjective Evaluation Experiment and EEG Signal Observation Experiment under LFS Stimulation
#### 2.1.2 Evaluation Methods for Subjective Evaluation Experiments  
The subjective evaluation experiment utilized a rating-scale method, with 1-10 levels corresponding to low emotion intensity (1) to neutral state (5) to high emotion intensity (10). Participants provided evaluations based on the overall audiovisual experience and the intensity of emotions evoked. The subjective evaluation experiment proceeded in order of happy, sad, and shocked emotions. First, video signals without LFS signals were played, following the sequence of static images 1-4 and then motion pictures. Subsequently, the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Signals** & \(f_{1}\)**/Hz** & \(f_{2}\)**/Hz** & \(f_{3}\)**/Hz** & \(f_{4}\)**/Hz** & \(f_{5}\)**/Hz** & \(f_{6}\)**/Hz** & \(f_{7}\)**/Hz** \\ \hline S1 & 15 & - & - & - & - & - & 105 \\ S2 & 15 & 30 & - & - & 60 & 90 & - \\ S3 & 18 & 36 & - & 54 & - & - & - \\ S4 & 16 & - & - & 48 & - & - & - \\ S5 & 16 & - & - & - & 64 & - & - \\ S6 & 30 & - & - & 90 & - & - & - \\ S7 & 25 & 50 & - & 75 & 100 & - & - \\ S8 & 28 & - & - & 84 & 112 & - & - \\ S9 & 30 & 60 & - & 90 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Frequency composition of LFS.  
Figure 1: Happy, sad, and shocked emotion-inducing image dataset.  
audovisual signals with added LFS signals were played in the same order. The LFS signals added corresponded to the three emotion conditions in the order of S1-S3, S4-S6, and S7-S9, respectively. The duration of both video and audiovisual signals was 5 s, with a 6 s interval between them. The interval between each emotion condition was 10 s, during which a gray image with no sound was presented. Participants were required to provide evaluations of the corresponding emotion intensity after each signal ended.
## 3 Cluster-Disjoint Multipath Routing Protocol
### Overview  
Through this section, we show the overall operation process of the proposed scheme. Each node is aware of its own location by GPS [12] or other techniques [13] and knows the location of the sink node through a network initialization phase [14]. In addition, the nodes keep the information of their neighbor nodes within one-hop range by beaconing.  
In the scenario for the overview, we assume that three paths are required. As shown in Figure 1, the network is divided into a grid cluster of a certain size at the network initialization phase, and each cluster is numbered by an (x, y)-coordinate like C(0, 0), C(0, 1)..., C(x, y). In addition, each cluster has a cluster head elected (see more details in Section 3.2). In cluster C(0, 3), the sensor node detects the event, it delegates the packet to the cluster head. In order to transmit the packet to the sink node located in cluster C(3, 0) through three paths, the cluster head of cluster C(0, 3) selects the cluster C(1, 2) closest to cluster C(3, 0) and relays the packet to the cluster head of the cluster C(1, 2). The cluster head in cluster C(1, 2) selects the next cluster C(2, 2) based on the same criteria as the previous cluster head of cluster C(0, 3), and it repeats this process to transmit the packet to the cluster head where the sink node is located (the black arrow line). For transmissions over different paths, the cluster head of cluster C(0, 3) selects a cluster other than the previously selected cluster C(1, 2) and relays the packet to the cluster head of cluster C(0, 2) (the red arrow line). By performing this process once more (the blue arrow line), the packet could be transmitted to the sink node through three paths. We note that in the above-mentioned packet transmission process with three paths, the construction of multipaths and packet transmission are performed based on a cluster-disjoint approach without the path construction process (see more details in Section 3.3).
## 2 Establishment and Calculation Process of a Numerical Model
### Numerical Simulation Scheme  
Figure 2 illustrates the excavation and support process of the shaft and horsehead roadway. The shaft employs only lining support, whereas the initial support for the horsehead roadway combines bolts and anchor cables, with a spacing of 0.8 m between bolts and 1.6 m between anchor cables. The secondary support for the horsehead roadway is consistent with that of the shaft, utilizing a lining structure. To replicate the rock layers' natural settlement process within the model, an initial equilibrium state is established post-application of boundary constraints and after grouping and assigning rock layer parameters (refer to Figure 2a). Upon achieving this equilibrium, the displacement and velocity parameters are reset to zero in preparation for the next phase of the simulation. Subsequent to this reset, the shaft excavation is simulated, and the lining, representing the shaft wall, is incorporated into the model (see Figure 2c). A second equilibrium is attained, followed by another reset of the displacement and velocity parameters, facilitating  
an accurate assessment of the horsehead roadway roof subsidence and its consequential effects on the overlaying rock mass deformation [41]. The final stage encompasses the excavation of the horsehead roadway and construction of its lining (refer to Figure 2f). Based on empirical measurements, the maximum consolidation settlement displacement of the upper rock mass in the horsehead roadway approaches 1.5 m. To dynamically analyze the stress and failure evolution process and mechanisms of the shaft during settlement, the rock parameters were adjusted to achieve MSD values of 0.5 m, 1.0 m, and 1.5 m.
## 2 Background: Electron Localization in Disordered Medium
### On the Role of Interactions: Thomas-Fermi Screening  
Thus, far, we have ignored the role of interactions in our discussion. Surely the strongest such effect is screening. In fact, its impact is so large that is often cited as the reason a sea of electrons act as if they are non-interacting, or free, despite the fact that the average Coulomb interaction is as large or larger than the kinetic energy in many metals [107; 108; 109].  
As an introduction to the effect of screening on electronic correlations, consider the effect of a charged defect in a conductor [110]. Assume that the defect is a cation, so that in the vicinity of the defect the electrostatic potential and the electronic charge density are reduced. We will model the electronic density of states in this material with the DOS of free electrons trapped in a box potential; we can think of this reduction in the local charge density in terms of raising the DOS parabola near the defect (cf. Figure 8).  
This will cause the free electronic charge to flow away from the defect. We will treat the screening as a perturbation to the free electron picture, so we assume that the electronic density is just given by an integral over the DOS which we will model with an infinite square-well potential with a bare density of states:  
\[\rho(E)=\frac{1}{2\pi^{2}}\left(\frac{2m}{\hbar^{2}}\right)^{3/2}E^{1/2}\,. \tag{1}\]  
Figure 8: The shift in the DOS parabola near a charged defect causes electrons to move away from the defect.  
Figure 7: The distribution of the local density of states at the band center (zero energy) in a single-band Anderson model with disorder strength \(\gamma/t\) where \(t=1\) is the near-neighbor hopping. Near the localization transition, \(\gamma/t=16.5\) the distribution becomes log-normal (see also the inset) for over ten orders of magnitude, while for values well below the transition, \(\gamma/3\) is shown, the distribution is normal [106].  
with the Fermi energy \(E_{F}=\frac{\hbar^{2}}{2m}\left(3\pi^{2}n\right)^{2/3}\). If \(|e\delta U|\ll E_{F}\), then we can find the electron density by integrating the bare DOS shifted by the change in potential \(+e\delta U\) (c.f. Figure 8).  
\[\delta n(\mathbf{r})\approx e\delta U\rho(E_{F})\,. \tag{2}\]  
The change in the electrostatic potential is obtained by solving the Poisson equation.  
\[\
abla^{2}\delta U=4\pi e\delta n=4\pi e^{2}\rho(E_{F})\delta U\,. \tag{3}\]  
The solution is:  
\[\delta U(\mathbf{r})=\frac{qe^{-\lambda r}}{r} \tag{4}\]  
The length \(1/\lambda=r_{TF}\) is known as the Thomas-Fermi screening length.  
\[r_{TF}=\left(4\pi e^{2}\rho(E_{F})\right)^{-1/2} \tag{5}\]  
Within this simplified square-well model, \(r_{TF}\) in Cu can be estimated to be about 0.5 \(\overset{\circ}{A}\). Thus, if we add a charge defect to Cu metal, its ionic potential is screened away for distances \(r>\frac{1}{2}\overset{\circ}{A}\).
## 3 Results
Following up on the above results, the effects of the most active chelator were also evaluated on rat erythrocytes ex vivo, in order to assess whether the xanthones could protect healthy cells against the toxic effect of the metals. It is well known that copper can cause hemolysis due to its effect on erythrocytes. This undesirable effect can be suppressed by copper chelators [31], while iron is relatively non-toxic. Our previous experiments showed that the percentage of erythrocyte lysis suffers quite significantly from interindividual variability. Hence, we used a higher concentration of copper in order to (at least partly) diminish the variability. 500 uM of cupric ions induced lysis in different blood samples in the range of 28% to 51% of total red blood cells in the suspension. Regardless of that, the observed protective effects of the tested chelator (when the arithmetic difference between the percentage of the individual chelator sample and the positive blank was used) were homogenous, and in line with the cancer experiments. Substance **9** decreased the hemolysis by 15-20%, depending on the chelator to copper ratio (Figure 8A). Since iron was non-toxic, we used a very high concentration of 2500 uM. However, this concentration induced a small total lysis of about 1-8%, and this was not different from the negative control without iron. Interestingly, the tested chelator increased iron toxicity by about 10% (Figure 8B). It should be emphasized that the chelator itself without metals did not evoke any red blood cell toxicity, even at the highest tested concentration of 500 uM. \begin{table}
\begin{tabular}{l l l} \hline \hline  & \multicolumn{2}{c}{**MCF7/S0.5**} \\ \cline{2-3}  & **IC\({}_{50}\)** & **Viability at 10 uM** \\ \hline Cu\({}^{2+}\) & 299.91 \(\pm\) 1.04 & 98.15 \(\pm\) 3.02 \\ Fe\({}^{2+}\) & \(>\)750 & 94.08 \(\pm\) 4.15 \\ compound No. **9** & 218.27 \(\pm\) 1.16 & 95.03 \(\pm\) 6.04 \\ Cu\({}^{2+}\) (500 uM) + compound No. **9** & n.a. & 33.24 \(\pm\) 3.82 \\ Fe\({}^{2+}\) (500 uM) + compound No. **9** & \(>\)750 & 99.27 \(\pm\) 5.13 \\ \hline \hline \end{tabular} n.a.: not applicable.  
\end{table} 
Table 1: Cytotoxicity of Cu\({}^{2+}\), Fe\({}^{2+}\) and 4’-trifluoromethylphenyl derivate (**9**) alone and in combination. Figure 7: Cytotoxicity of 4’-trifluoromethyl derivate (**9**) alone and in combination with Cu\({}^{2+}\) or Fe\({}^{2+}\) at concentration 500 uM. Viability was determined by cell viability assay after 48 h treatment in MCF/7/S0.5 cells. Vehicle-treated cells were set as 100% viability. Viability was determined by cell viability assay 48 h after treatment in MCF/S0.5 cells. Vehicle-treated cells were set as 100% viability.
## 1 Introduction
In the equi-probable CH selection mechanism, there is a possibility of electing CH having low residual energy as compared to the one with a comparatively higher energy level. However, we are considering heterogeneous WSNs [15], where the resources of nodes may vary in terms of computational capability, residual energy, and storage capacity. In most of the existing works in heterogeneous WSNs, CH is selected based on residual energy, distance from member nodes, and distance from the sink node. However, to the best of our knowledge, all such schemes do not consider the traffic load, computational capability, and remaining storage of the CH. In the heterogeneous environment, the traffic generated by nodes may not be identical, meaning that traffic could be scalar (e.g., temperature, humidity, pressure etc.) and/or multimedia (e.g., audio, video). Therefore, the traffic load on the CHs may also vary. For instance, a CH that manages multimedia traffic would be overloaded due to high computations, and more storage requirements as compared to CH managing scalar traffic. As a result, energy consumption may also vary according to the traffic class on each CH. To address the aforementioned challenges, in this paper, therefore, we propose an energy-efficient clustering scheme for heterogeneous WSNs. The main contributions of the proposed scheme can be summarized as follows:  
* We propose an efficient CH declaration scheme to reduce the energy consumption of nodes and to prolong the network lifetime. The propose scheme provides a mechanism through which a node declares itself as a CH based on the available resources such as residual energy, computational capability, and available storage. Once the CH is declared, it remains CH until the resources fall short than a certain threshold level. * For the un-associated nodes, we employ the multi-criteria decision-making technique known as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to select an optimal CH. * We also provide mechanisms of CH-acquaintanceship and CH-friendship to reduce the energy consumption, optimize the workload, minimize the packet drop rate, and extend the lifetime of CH. In acquaintanceship mechanism, the CHs in the network may collaborate with each other for mutual benefits. Whereas, in CH-friendship, the low resources CH may request high resources CH to perform operation on behalf of low resources CH to avoid early failure and data loss. * Simulations are performed in Castalia (OMNET++) to reveal the performance of the proposed scheme with relevant and state of art scheme in terms of CH lifetime, re-clustering frequency, packets loss, control overhead and average energy consumption of the network. The rest of the paper is organized as follows: Section 2 is devoted to background and related work. Section 3 describes the problem scenario. In Section 4, we present our proposed scheme. Performance evaluations are presented in Section 5 and finally, Section 6 draws the conclusion.
## 2 Results
### RNAi Effect of DaAST and DaJHAMT
#### 2.5.4 Adult Development of the dsRNA Phenotypes  
Figure 9 shows the phenotypes of \(D\). _armandi_ adults produced by dsRNA-mediated transcript silencing. The injection of pupae with dsRNA for _DaAST_ resulted in shape abnormalities in 54.46% of the treated beetles. Many parts of their appendices, including the wings and parts of the legs, were deformed or partly covered with old epicuticle. The abnormal morphology rate in emergent adults in the control group (vector control) was significantly lower by 38.07% than in those of the dsAST groups, and the abnormal adults died within 4 days after emergence. The beetles with intact cuticles did not differ in shape from the controls. Moreover, the _DaAST_ dsDNA-treated beetles had smaller heads and bodies, but the elytra were shorter only in deformed beetles (Table 3).  
Injection of dsDNA for _DaJHAMT_ into pupae of \(D\). _armandi_ resulted in the production of about 15.45% deformed adults (Figure 9). Although there was no difference in the rate of adult emergence between intact and deformed beetles, the misshaped beetles died within two days of adult life. The elytra of the deformed beetles were much shorter in length compared with the controls and were neither tanned nor scler  
Figure 8: Survival emergence and abnormal morphology rate in \(D\). _armandi_ larvae and pupae after RNAi. Effect of bacterially expressed dsAST/JHAMT on \(D\). _armandi_ larvae and pupae. (**A**) Survival rate in larvae, (**B**) survival rate in pupae, (**C**) emergence rate in pupae and (**D**) abnormal morphology rate in pupae. There were 25 samples in each biological repetition. The data were analyzed by a one-way ANOVA test. Different letters above the bars indicate a significant difference (\(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Groups** & **Emergence** & **Adult** & **Death of Deformed** & **Days of Death** \\  & **(\%)** & **Deformity (\%)** & **Adults (\%)** & **(d)** \\ \hline Control & 72.00 & 16.39 & 66.67 & 4 \\ Non-injected & 78.67 & 1.33 & 100 & 3 \\ dsAST & 52.00 & 54.46 & 71.42 & 1 \\ dsJHAMT & 52.00 & 15.45 & 100 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Adult development of the dsRNA phenotypes.  
The frontalin content of adult males differed significantly among treatment groups (one-way ANOVA: \(F\) = 21.877, df = 2, \(p\) = 0.002) (Figure 10). Injection of _DaJHAMT_ dsRNA significantly reduced the frontalin content to zero. However, injection of _DaAST_ dsRNA had no significant effect on the amount of frontalin produced by male adults.