esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Dopamine (DA), a catecholamine neurotransmitter, appears to play a central regulatory role in olfaction, retinal processes, hormonal regulation, cardiovascular processes, and cognitive functions [1; 2]. DA is an electroactive compound, and therefore, studies have investigated the use of electrochemical analysis for its detection. However, DA often coexists with ascorbic acid (AA) in biological fluids such as serum and urine [3], both of which oxidize at similar potentials when using conventional electrodes such as glassy carbon electrode (GCE) [4; 5; 6; 7; 8], Au [9; 10; 11; 12; 13; 14; 15], and Pt [16; 17]. To overcome this problem, we developed a simple and quick electrochemical method for the selective determination of DA, with high sensitivity in the presence of AA for diagnostic applications [18; 19; 20].  
Screen-printing [21; 22; 23] is a simple, robust, and cost-effective technology for the mass-production of sensing electrodes. Screen-printed electrodes can be easily modified to enhance their specificity and sensitivity by introducing DNA, enzymes, or antibodies [21; 24]. Enzyme-based electrochemical biosensors integrate a biorecognition enzyme and a mediator on the electrode's surface [25; 26; 27; 28; 29]. Furthermore, numerous protein immobilization techniques such as adsorption [30; 31], entrapment [32], and cross-linking [33] have been developed. Unfortunately, covalent enzyme cross-linking can interfere with the enzyme's activity owing to the disturbance of the enzymes' native structure. Entrapping enzymes within a matrix including gels, polymers, or inks is one approach for immobilization that does not compromise stability and biological activity. Chitosan, a natural biopolymer, is commonly used for enzyme immobilization, and shows excellent film-forming ability and biocompatibility [34; 35]. Furthermore, chitosan-reduced graphene oxide (rGO) nanocomposites have proven to be highly effective in enhancing mechanical and electric properties [36; 37; 38; 39], offering better mechanical and electrical stability. Chitosan covalently bound to graphene enhances the oxidation potential and peak current in DA and AA detection due to higher amide functionalization [40]. It has been shown that pi-pi stacking between DA and graphene enhances the sensitivity for DA detection [41]. rGOs have been widely explored in conjugation with various types of biomolecules such as antibodies, and enzymes. The carboxyl functional groups in rGOs are used for higher loading of biomolecules, because of their strong bonding with aminated groups, enzymes, and polymers [26; 42]. Tyrosine, an oxidase, catalyzes the oxidation of tyrosine. Enzyme electrodes provide great biorecognition with narrow substrate specificity. Previously, mechanisms for DA enzyme-based electroanalysis might have been be through O\({}_{2}\) reduction catalyzed by tyrosinase and mediated by DA [43]. In the current study, a tyrosinase/chitosan/rGO modified screen-printed carbon electrode (SPCE) was developed for the sensitive and selective detection of DA.
## 5 Conclusions  
In this paper, a new receiver for packet detection and signal acquisition in underwater communication and sensing applications is proposed. The proposed receiver is a ring vector sensor receiver that benefits from the multiple signals that it collects from the field. These signals are particle velocities that are vector components of the field, in addition to the scalar field component. Our experimental measurements, along with mathematical analysis, demonstrate that the signal acquisition probabilities of the vector components are higher compared to the scalar component. According to the experimental data, this could be related to the higher SNRs and lower noise powers of the vector field components. Additionally, the multichannel combining method that uses all of the channels of the ring vector sensor receiver offers the highest signal acquisition probability compared to each of its individual channels. Moreover, it is demonstrated that if the receiver channels are strong, using the strongest channel can provide nearly the same acquisition performance as the multichannel combining method, while being less complex to implement.  
To theoretically investigate how the angular behavior of the vector sensor receiver may affect its signal acquisition performance, the interested reader can refer to the analysis presented in Appendix A.  
Given the recent advances in distributed underwater acoustic sensor networks and their applications, a future research topic could be investigating signal acquisition in such networks using multichannel vector sensors.  
Conceptualization, A.A.; methodology, R.R., E.Z. and A.A.; software, R.R. and E.Z.; formal analysis, R.R. and A.A.; investigation, R.R., E.Z. and A.A.; data curation, R.R. and E.Z.; writing--original draft preparation, R.R. and A.A.; writing--review and editing, R.R., E.Z. and A.A.; supervision, A.A.; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Science Foundation, grant numbers IIP-1500123 and IIP-1340415.  
[PERSON] has registered a startup called SoundComm Technologies.
(2) A novel control strategy is proposed based on the disturbance observer and auxiliary compensator. A nonlinear extended states observer (NESO) is designed to estimate the unknown disturbances, and command-filtered backstepping is designed to stabilize the surge speed and trajectory angle independently. During this process, the command-filteredbackstepping technique can avoid the explosion caused by the derivative of the virtual control, and an auxiliary system is applied to compensate for the filter tracking errors, input saturation bias, and observer errors. The rest of this paper is arranged as follows: Section 2 presents the mathematical model and horizontal trajectory tracking problems. Section 3 introduces the nonlinear observer-based adaptive control method and stability analysis. Simulation results and conclusion are provided in Sections 4 and 5, respectively.
## 3 Numerical Analysis of Dynamic Contact Characteristics
### Variation of Dynamic Contact Normal Force  
When the screw speed is 0, friction is not considered, \(Q_{Si}=Q_{Ni}=Q_{i}\). In a previous study, it was shown that the normal forces decrease with the contact pair departing from the load end of the SNBSP. The normal forces of the ball increase simultaneously with axial force increase [12].  
However, in the dynamic state, \(Q_{Si}\
eq Q_{Ni}\). Figure 10 shows the variation of the dynamic contact force with the contact pair position and the screw speed when the axial force is 1000 N. Firstly, it is obvious from Figure 10 that the normal contact force of the nut is greater than that of the screw in the same position, and the higher the screw speed is, the greater the difference is. In different contact position, the contact force difference between the nut and screw is not the same; when the screw speed is below 1500 rpm, with the contact position moving backwards, the contact force difference between the nut and screw gradually decreases. When the screw speed is below 500 rpm, though still conforming to this rule, the relative deviation is less than 0.05%, therefore, it is considered that the screw and nut contact force are approximately the same at the speed below that speed. However, when the screw speed of the screw is higher than 1500 rpm, the contact force difference between the nut and the screw increases gradually as the contact position moves behind. The higher the screw speed, the more obvious this phenomenon.  
Secondly, it is consistent with the uneven distribution static contact force that the dynamic contact force decreases from the first contact pair to the last one. When screw speed increases, the level of uneven load distribution of screw increases slightly but that of the nut decreases. When the axial force is 1000 N, the screw speed is 500 rpm, the first and the last contact force ratio of the screw is 1.1422, and that of the nut is 1.1427. When the screw speed reaches 5000 rpm, the ratio of the screw is 1.1427, and that of the nut reduces to 1.1387.  
Figure 11a,b exhibit variation of contact normal force with the contact position and the axial force when the screw speed is 2000 rpm. It is clear that, when the axial force increases, the uneven load distribution of both screw and nut are gradually increased.  
Figure 10: Variation of the dynamic contact force with the contact pair position and the screw speed.
## 2 Materials and Methods
### Hs-Spm-Gc-Ms Analysis of Aroma Compounds  
The extraction and identification of the volatile compounds were carried out according to the analytical method previously reported in our laboratory, with slight modifications [18]. The analysis of the volatile compounds in wines was performed on an Agilent 6890 chromatograph (GC) coupled to an Agilent 5975C mass spectrometer (MS, Agilent Technologies, Santa Clara, CA, USA). A CTC CombiPAL autosampler (CTC Analytics AG, Zwingen, Switzerland) with a 50/30 \(\upmu\)m divinylbenzene/carbouxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber (2 cm, Supelco, Bellefonte, PA, USA) was used for the extraction of the volatile compounds. A wine sample of 5 mL was transferred into a 20 mL vial with 10 \(\upmu\)L of 4-methyl-2-pentanol (internal standard, 1.0086 g/L) and 1 g NaCl. Tightly capped with a PTFE-silicon septum, the samples were equilibrated at 40 \({}^{\circ}\)C for 30 min before extraction by an SPME fiber at 40 \({}^{\circ}\)C for 30 min with stirring at 500 rpm. After extraction, the fiber was thermally desorbed by insertion into the injection port of the GC for 8 min. The injection was performed in splitless mode (0.75 min) with a GC inlet temperature of 250 \({}^{\circ}\)C.  
The separation of the volatile compounds was carried out on a HP-INNOWAX capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, J&W Scientific, Folsom, CA, USA). The flow rate of the carrier gas (helium) was 1 mL/min. The oven temperature was held at 50 \({}^{\circ}\)C for 1 min after injection, then programmed to 220 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, and kept at 220 \({}^{\circ}\)C for 5 min. The temperatures of the mass selective detector transfer line, ion source, and quadrupole were 250 \({}^{\circ}\)C, 230 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C, respectively. The positive ion electron impact spectra at 70 eV were recorded with a mass range of 30-350 \(m/z\) for the full scan mode.  
The quantification process was carried out according to our previous study [18]. A synthetic model wine solution was prepared in distilled water containing 14% \(v/v\) ethanol, 5 g/L tartaric acid, and the pH was adjusted to 3.3 with a 5 M NaOH solution. The calibration curves of each aroma standard were obtained according to fifteen dilution levels in succession with the model wine solution.
# A Feature Fusion Model with Data Augmentation for Speech Emotion Recognition
## 5 Experiment Results
### Corpus Data
#### 5.1.2 Chse-Db  
CHSE-DB [47] is a speech emotion dataset recorded by the State Key Laboratory of Media Integration and Communication of Communication University of China. Four postgraduates (two men and two women) majoring in broadcasting and anchoring of Communication University of China, according to 500 different content texts, express seven levels of emotions: neutral, happy, very happy, sad, very sad, angry and very angry in the way of broadcasting performance. There are 2000 audio in each category, and 14,000 clips in total.
## 2 Materials and Methods  
The test-building where the in situ measurements (airborne and impact noise tests) were performed consists of five CLT floors (Figure 1), featuring timber frame walls, as depicted in Figure 2.  
Figure 1: Realization of the test-building.  
The third configuration features a well-established technology for impact noise reduction: a floating floor. Using this decoupling approach, vibration transmission is decreased by means of the mass-spring effect [29; 30]. It is known that this technology diminishes the transmitted noise by decoupling the covering heavyweight screed from the bare floor. In this way, vibrations are reduced and thus the transmission to the other room will be significantly reduced. Configuration 3 is represented in Figure 5.  
In order to investigate also the coupled effect of both suspended ceiling and floating floor on cross laminated timber, a further configuration (configuration 4, -Figure 6) was considered.  
In Table 1, different layers used and tested are reported, describing their thickness, density, and elasticity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **Thickness \(d\) [mm]** & **Density \(\rho\) [kg/m\({}^{3}\)]** & **Dynamic Stiffness \(s^{\prime}\) [Mn/m\({}^{3}\)]** \\ \hline Screed & 40 & 1150 & - \\ Resilient layer & 9 & 700 & 11 \\ Screed & 100 & 400 & - \\ Waterproof membrane & 0.2 & 33 & - \\ CLT floor panel. & 180 & 470 & - \\ Air layer & 62 & 1 & - \\ Rock wool panel & 60 & 70 & - \\ Gypsum board & 1.25 & 730 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Floor elements description.  
Figure 4: Configuration 2: Bare floor coupled with suspended ceiling.  
Figure 5: Configuration 3: Bare floor coupled with floating floor.
## Robustness and Vulnerability of Power Grids
### Deliberate Failures and Vulnerability  
Deliberate failures caused by intentional obstruction of targeted nodes can demonstrate the fragility of a network. Normally, the nodes with the heaviest loads are treated as the most important ones, and some measures or special protections are employed to stabilize the network, whereas the nodes with the lightest loads are normally ignored in real networks. In this section, two different forms of malicious attacks to intentionally cause cascading failures are considered, i.e., highest loads attacks (HLA) and lowest loads attacks (LLA). In terms of the proposed model, with the number \(f_{i}\) of nodes that fail during the cascading failure caused by an attack, an avalanche index is defined as:  
\[a_{m}=\frac{f_{i}}{N(0)-1}\,. \tag{8}\]  
Under HLA, the dependence of this avalanche index \(a_{m}\) on the tunable parameter \(\alpha\) and the tolerance parameters \(\beta\) and \(\gamma\) is shown in Figure 2. To quantify the extents of cascading failures, Figure 2a-c illustrates the ratios of \(a_{m}\) that fall into the subranges \(a_{m}<0.01\) (marked in blue) and \(a_{m}>0.99\) (marked in red), for the 20 values of \(\alpha\) mentioned above.  
Similarly, Figure 3a-c illustrates the ratios of \(a_{m}\) that fall into the subranges \(a_{m}<0.01\) (marked in blue) and \(a_{m}>0.99\) (marked in red), respectively, for the same values of \(\alpha\) under LLA. Interpreting Figures 2 and 3, it can be seen that:  
* For scale-free power grids, Figures 2a and 3a show that the probability of very large cascading failures (\(a_{m}>0.99\), marked in red) grows as \(\alpha\) increases under HLA, but decreases as \(\alpha\) increases under LLA. Scale-free power grids are vulnerable under malicious attacks. Further, scale-free power grids with heavy loads are more vulnerable than the ones with light loads under HLA, while the power networks with light loads are more vulnerable than the ones with heavy loads under LLA.
* For small-world power grids, Figures 2b and 3b reveal similar dependence on \(\alpha\) as for scale-free power grids. Small-world power grids are vulnerable under malicious attacks, and as \(\alpha\) increases, small-world grids are more vulnerable under HLA and less vulnerable under LLA.
* For ER power grids, Figures 2c and 3c show the same the distributions of \(a_{m}\), which indicate that parameter \(\alpha\) has no influence on \(a_{m}\) under either HLA or LLA. Hence, ER power grids are the most robust ones.  
Figure 2: _Cont._Figure 3: \(Cont\).  
Figure 2: Probabilities of the extents of cascading failures for three network types under highest loads attacks.
# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 4 Algorithm Design
### Satellite Selection  
The satellite selection problem is a mixed integer programming problem, as observed from the objective function and constraints; thus, a Monte Carlo random sampling method is considered for the solution. A Monte Carlo method-based satellite selection algorithm is proposed for satellite and task matching, where satellites move continuously according to a predetermined orbit. IoT devices generate computational tasks and obtain the optimal satellite combination for IoT device task offloading.  
Monte Carlo-Based Satellite Selection Algorithm  
Monte Carlo methods are also known as random sampling or statistical test methods. The Monte Carlo method is a computational method but is different from the general numerical computational methods. It is a method based on probabilistic statistical theory. It solves problems that are difficult to solve by numerical methods, which is why it is increasingly used in many applications.  
The UAV collects the current satellite storage resource information \((C_{1},\cdots,C_{j})\) and the IoT device task \((S_{1},\cdots,S_{i})\). According to the managed satellite resource and task information, the satellite storage resource information is randomly sampled by the Monte Carlo method to approximate the task size \(S_{i}\) and obtain the approximate satellite subsequence \(X_{t}=(a_{1},\cdots,a_{j})\).  
\[|\sum Random\big{(}C_{1},\cdots,C_{j}\big{)}-\sum S_{i}|\geq 0 \tag{31}\]  
Select the satellite subsequence \(A_{i}=(X_{1},\cdots,X_{t})\) that satisfies the task amount \(\sum S_{i}\).  
\[\sum Random\big{(}C_{1},\cdots,C_{j}\big{)}\geq\sum S_{i} \tag{32}\]  
UAV \(m\) to Satellite propagation time delay  
\[T_{m,j}=\big{(}D_{m1},\cdots,D_{mj}\big{)} \tag{33}\]  
Maximum propagation time delay is  
\[T_{max}=max\big{(}T_{mj}\times X_{i}\big{)}=max\big{(}D_{m1}\times a_{1}, \cdots,D_{mj}\times a_{j}\big{)} \tag{34}\]  
The optimal combination of offloading satellites is obtained by minimizing the maximum propagation delay of IoT devices. As shown in Algorithm 1.  
The algorithm inputs the task size processed and the satellite resources. Minimizing the propagation delay obtains the optimal offloading decision for the IoT device tasks, which are transmitted via a link between the UAV and the LEO satellite.  
```
1:Task \(S=\{S_{1},S_{2},\cdots,S_{i}\}\), Storage Resource \(C=\{C_{1},C_{2},\cdots,C_{j}\}\);
2:Selection Vector \(a_{i}\);
3:Initialize \(cost=0\);
4:Monte Carlo random sampling of a satellite combination \(X=\{a_{1},\cdots,a_{t}\}\), according to (31);
5:for\(n=1,j\)do
6:Compute distance between Satellite and UAV \(H_{m,n}\), according to (6);
7:Compute Propagation Delay between Satellite and UAV \(D=\{D_{m,1},\cdots,D_{m,j}\}\), according to \(D_{m,n}=\frac{H_{m,n}}{c}\);
8:endfor
9:for\(u=1,t\)do
10:if\(a_{u}C^{T}\geq\sum S\)then
11: Compute Propagation Delay \(T_{u}\), according to \(T_{u}=max(a_{u}\times D)\);
12:if\(cost>T_{u}\)then
13:\(cost=T_{u}\);
14:\(a=a_{u}\);
15:endif
16:endif
17:endfor
```  
**Algorithm 1** Satellite selection algorithm
## 5 Example and Discussion
Furthermore, the variation on the average packet loss rate gives the trends of the actual state of the network ISLs. Figure 9a shows the total packets received varying with the simulation time. When the network works with MST, the received packets increase slowly, especially after \(t>1000\) s. The network with FIYG\({}_{2\pi/3}\) performs slightly better compared to MST. Comparing the situation without control with RAT (\(\eta=1\) s), it is clear that the results of two conditions are nearly the same. The without control scenario receives a total of 10,125 packets, while RAT (\(\eta=1\) s) receives 10,123 packets with just two packets left. This means that the topology with RAT (\(\eta=1\) s) maintains the most reliable ISLs compared to other algorithms. Additionally, when \(\eta\) in RAT increases, the performance of received packets goes down,  
Figure 8: Topology changing results from three topology control algorithms (RAT, FIYG\({}_{2\pi/3}\), MST) showing total topology changing times (**a**) and the average topology lifetime (**b**). but it also performs better than \(\text{FIYG}_{2\pi/3}\) and MST. Furthermore, when the simulation runs at time interval \([1000,2000]\), all of the algorithms show the interruption of received data increasing, the reason being that the topology during that time changes so rapidly that the routing protocol on every satellite cannot find possible routing paths on time. Figure 9b shows the average packet loss rate compared to the simulation time. It is obvious that MST performs the worst, with up to 90% packet loss, and \(\text{FIYG}_{2\pi/3}\) comes behind, with nearly 80%. Additionally, RAT (\(\eta=1\) s) presents the best average packet loss rate with a small difference to the condition without control. Although RAT (\(\eta=200\) s) shows slightly worse performance compared to RAT (\(\eta=1\) s), its topology changes approximately 62% less, which helps create fewer topology control messages in the network. It can be summarized that RAT performs better at adapting to changing network dynamics, resulting in a lower average packet loss rate. Finally, it can be concluded in this simulation that \(\text{FIYG}_{2\pi/3}\) shows mediocre performance without any advantages. MST shows the fewest topology changing times and the longest average topology lifetime, but it performs the worst in terms of the average packet loss rate, which is up to 90%. Although RAT (\(\eta=1\) s) shows slightly worse performance compared to MST in average topology lifetime, it behaves the best with about 50% average packet loss rate. However, when \(\eta=1\) s, the topology changes frequently, which brings forward urgent demands for high accuracy of network synchronization and the satellite orbit dynamic model. And, RAT (\(\eta=200\) s) not only guarantees a proper average packet loss rate, it also makes it easier to implement in this satellite cluster network with EO missions.