esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Ketones  
Ketones constitute only a restricted fraction of the headspace of the samples in terms of both the number of analytes identified and their abundance. Their number strongly decreased after 24 months (2 analytes against the initial 8), but the total abundance increased from WDF_t0 to WDF_t1 (30 days) samples and subsequently remained almost constant in WDF_t2 (24 months). All data related to ketones is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & **WDF\_t0** & **WDF\_t1** & **WDF\_t2** & \\ \hline
**Analyte** & **Aroma** & **LRI** & **Area (\(\times\)106)** & **Area (\(\times\)106)** & **Area (\(\times\)106)** & **Ref** \\ \hline Acetone & Solvent, apple, pear & 495 & 31.1 \(\pm\) 0.2 & 32.5 \(\pm\) 0.3 & 166 \(\pm\) 0.5 & - \\ \hline
3-Methyl-2-butanone & Camphor & 588 & 6.14  & 6.43  & - & - \\ \hline
1-Penten-3-one & Pepper, mustard, onion & 689 & 45.0 \(\pm\) 0.3 & 52.7 \(\pm\) 0.4 & - & [23,26,29,39] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of the VOC fraction of Gavina® watermelon pomace WDF identified using HS-SPME-GC-MS analysis: ketones class.  
3,5-Octadien-2-one, 1-penten-3-one, and 2,3-pentanedione are involved in the degradation of polyunsaturated fatty acids (PUFAs) [37]. The abundances of 3,5-octadien-2-one and 1-penten-3-one increased after 30 days (WDF_t1) and then reset to zero after 24 months (WDF_t2). Each of them imparts specific notes that contribute to enriching the aroma profiles of WDF_to and WDF_t1.  
In contrast, acetone was detected in higher quantities in WDF_t2 than in the previous aging steps. This was probably due to the degradation of complex molecules with different natures present in the initial matrix.  
Acetoin has a pleasant yogurt smell, with buttery and creamy notes, and is used as a food additive to enhance the aroma of some preparations [40]. It is often detected together with its precursor species on HS-SPME-GC-MS analysis. Among these, 2,3-butanediol was identified in the WDF samples. They are molecules with similar biological and biosynthetic pathways, and they also showed a similar behavior over time, as they were detected only in WDF_t0 and WDF_t1.
###### Abstract  
The alignment stability and controllability deteriorate considerably when the air spring vibration isolation system for the high power density main engine (HPDME-ASVIS) is in a special running state of high power and high output torque. The coupling characteristics among the alignment components are obtained by analyzing the mechanical characteristics of HPDME-ASVIS. A control response model is established, which can predict the alignment state and the vertical displacement of air springs after k times of inflation/dellation. By solving the linear programming problem, the alignment controllability judgment models are constructed for two conditions, which are under hard constraints and soft constraints, respectively. Furthermore, alignment reconfigurability is described and an autonomous control method is established. The experimental results confirm that high output torque will cause large and serious coupling of horizontal offset, self-rotation angle, and vertical deformation of air springs. Meanwhile, hey cannot reach the given control precision range. The traditional control method under hard constraints will result in a state of oscillation and may bring many reliability problems to HPDME-ASVIS. The alignment reconfigurability analysis can judge the alignment control influence degree based on different torque accurately. The autonomous control can use the control parameters autonomous adjustment and the spatial configuration to realize the alignment control reconfigurability for two different alignment control fault states. The stability of the alignment control system in HPDME-ASVIS can be improved observably, which is of great significance to the engineering application of the high power density main engine (HPDME) in the field of marine propulsion technology.  
alignment control reconfigurability; autonomous control; high power density; air spring vibration isolation system; soft constraints +
Footnote †: journal: Applied Sciences
## 3 Results
### Primary Endpoint  
Daytime cough and nighttime cough scores were significantly improved following the treatment with the active product. Results of the Cough Clinical Score (CCS) are shown in Figure 2. The administration of the active product resulted in a significant reduction of both daytime cough and nighttime cough already after 5 days of treatment (\(p<0.001\)) with a complete resolution after 10 days (\(p<0.001\)). An improvement of at least 2 scores (CCS) was found in all subjects treated with the investigational product. In particular, the mean CCS at baseline for the active group was 4.9 for daytime cough score and 4.8 for nighttime cough score. After 5 days of treatment (visit 3), the mean score decreased to 1.7 for daytime cough and 1.8 for nighttime cough, respectively. At the end of treatment (visit 4/day 10) both daytime and nighttime cough scored 0.  
On the contrary, the mean CCS at baseline for subjects on the placebo was 4.7 for daytime cough score and 4.6 for nighttime cough score. After 5 days, in the placebo group, the mean CCS reached 4.9 for daytime cough (\(p=0.89\)) and 4.8 for nighttime cough (\(p=0.324\)) remaining unchanged at the end of the study. No significant reduction was observed for CCS in the placebo group at the end of the treatment (\(p=0.573\) for daytime; \(p=0.347\) for nighttime).
## 7 Discussion Section
### Stability Considerations  
To analyze the stability of the dynamic system and proposed OENN + AOG control logic, the MATLAB LMI Control Toolbox maybe utilized [32]. LMI stability criteria are a powerful tool for matrix operations based on linear matrix inequality techniques. If there is a solution to the matrix inequalities, then the MLS system (and the corresponding intelligent control algorithm) can be considered stable [33]. However, it is challenging to  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Control Algorithm** & **Training Time (s)** & **Tracking RMSE** \\ \hline OEI Controller & 143.260 & 1.4730 \\ LQ Controller & - & 1.5949 \\ DNN Controller & 58.236 & 1.8301 \\ OENN + AOG Controller & 98.976 & 1.4761 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summarized tracking performances.  
Figure 8: Control signals.  
check the algebraic inequality method due to numerous adjustable network and gland parameters considering a lack of information on how to tune these parameters in every new training iteration. The exceedingly complex LMI-based stability results are valuable only for numerical purposes, and the theoretical meaning is deprived.  
Further, the stability of OENN + AOG intelligent control without global feedback may be analyzed layer by layer. A single Lyapunov function can be used for each layer and generated in independent runs of the LMI solver. For example, for OENN with one hidden layer without global feedback, it should be determined if the layer is stable. This can be performed by running the LMI solver to solve LMI for this layer alone. If a solution is feasible, the stability of other hidden layers or the output layer can be analyzed. Due to the non-zero connection matrix between the layers and non-zero outputs of the first layer at its equilibrium, biases of the second layer should be adjusted accordingly before invoking the LMI solver for another layer. If a solution for the first layer is not feasible, then nothing can be concluded about the stability of the second layer either. Generalization to cases with more than two layers is straightforward. The LMI method can easily incorporate adjustable parameters into stability criteria and decrease conservativeness. In the sense of total performance evaluation of the desired results, LMI-based results are considered very effective.  
Finally, the more complex the stability condition is, the less it will influence physical and theoretical meaning for analysis and will become useful only for numerical purposes. It can further lead to challenges in comparing different conditions among the LMI-based stability results. Therefore, the efficiency of exploited stability conditions can only be compared by means of a specific MLS example and not in a universally analytical way.
# Double Deep Q-Network with a Dual-Agent for Traffic Signal Control
## 3 Deep Reinforcement Learning Model with Dual-Agent
### State Representation  
Taking into consideration the difficulty in real traffic information acquisition, the method of defining the vehicles' speed as part of a state in [6; 17] is discarded in this paper. Instead, we define the state based on the position of vehicles at an intersection. Through vehicle detection technology [22], or other tools such as induction-loop and vehicular network, information of vehicle positions can be easily collected. In addition, vehicle positions reflect the traffic environment adequately and offer some detailed information, such as the queue length. The open source simulator SUMO provides the aforementioned traffic information. In the preset intersection environment, each observable incoming lane is divided into same-size small cells. According to the vehicle length and the minimum safe distance, the cell length is set to be \(c\). The length of observable lanes starting from the stop line is set to be \(l\). The state of each road is a matrix with a binary number. If the midpoint of a vehicle fills the cell, the corresponding coordinate of a matrix is set to be 1, else 0.  
For example, Figure 5 shows a state matrix at incoming road 2. The observation area of road 2, starting from stop line, is divided into the same discrete cells with length \(c\). Lanes from \(L0\) to \(L3\) represent rows of state matrix. At each time step, we obtain four state matrices from four incoming roads. Therefore, the agent state is represented by four vertically concatenated matrices, each value of which represents the relative vehicle position in each incoming lane.  
Figure 4: Operating mode to ensure the fixed phase sequence. The red and green color indicates waiting and busy state respectively.
# DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection
## 4 Experiment Results and Discussion
### Ablation Study
#### 4.3.2 Analysis of the Proposed FPN Structure  
Table 7 shows the ablation study results of the proposed model vs. FPN. The proposed FPN produced 2.5M fewer computations and improved the mAP by 2.0%. The FPN has a bottom-up structure, making it difficult for low-level information to reach deeper layers. A path aggregation network (PAN) has both top-down and bottom-up structures, which improves the low-level information shortage problem as the network depth increases. However, considering PAN's side connections, the number of computations is much higher than that of the proposed method. Therefore, the proposed method minimizes feature loss better while achieving a good balance between efficiency and accuracy.
## 1 Introduction
While it is effective to have such a state estimator in a single level model, and eliminating post-processing detection algorithms, the work in [26] assumed errors in parameters are varied in a small range, not considering the possibility of R2U and U2R attacks that enables an adversary to alter those parameters in any range. In addition, the final estimate is sensitive to Gaussian noise in the measurements set and extended redundancy due to the increase size of the state vector. The aforementioned solutions come with the cost that a non-linear system is linearized using Taylor series expansion and solved in an iterative process to estimate the system parameters. In addition, a simultaneous parameter attack would result in estimating all suspicious parameters under attack in a sequential order. Thus, the correction of one attack depends on the other. Hence, the choice of what attack to correct first might influence the result while there is no guarantee of convergence to the correct physical solution. In this work, a simultaneous cyber-attack detection and correction bi-level model is presented, towards the solution of previously mentioned state-of-the-art limitations. The bi-level model combines the two steps in a single optimization framework. The presented framework takes advantage of the temporal and spatio characteristic of the grid. In addition, the formulated optimization problem eliminates the effect of the presence of measurements Gaussian noise on parameter correction. Hence, the contribution of this paper towards the state-of-the-art are two-fold:  
1. An explicit mathematical bi-level model for detecting and correcting cyber-attack pertaining state estimator static data. 2. Using the temporal and spatio characteristics of the grid to eliminate non-linearity in parameter correction and providing a sliding-window for an online monitoring scheme of the measurement model parameters. The remainder of this paper is organized as follows. Section 2 presents background theory on the SE and measurement and parameter attack modelling. Bi-level model and framework is presented in Section 3. Section 4 presents a case study and concluding remarks are provided in Section 5.
# High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios
## 3 Proposed Timing Recovery Scheme  
In general, to obtain more accurate time delay estimates, symbol synchronization is divided into two steps: coarse and fine synchronization. For coarse synchronization, we utilize a CP-based estimation method that estimates the STO while estimating the residual fractional CFO. It should be noted that the code type information of PRS is determined through the 5G new radio (NR) higher-layer configuration information. And blindly capturing the PRS signal undoubtedly increases the complexity of the system. In addition, information regarding base station location and subcarrier interval cannot be obtained from the positioning signal itself. Therefore, it is necessary to perform the coarse synchronization process using the synchronization signal and physical broadcast channel (PBCH) block (SSB). This process helps obtain auxiliary information used for localization, such as cell ID, base station location, and subcarrier spacing. For fine synchronization, path delay estimation is necessary, and the signal is converted to the frequency domain to track the code phase. Considering that the Co-Band PRS is superimposed on the communication signal, pre-processing is required to eliminate the influence of the communication signal on the positioning signal prior to fine synchronization. In this paper, the overall architecture of timing recovery scheme for the Co-Band PRS is shown in Figure 2.
# Dual PID Adaptive Variable Impedance Constant Force Control for Grinding Robot
## 6 Experimental Study
### Flat Surface Constant Force Control  
The force control experiment was carried out on a flat surface, as shown in Figure 12a. The end of the robot moved 0.3 m in the \(x\) direction, and the force control direction was in the \(z\) direction of the end effector. The desired force was set as \(f_{d}=-5\) N, and the running time was 10 s. The control parameters of CIC, AVIC, and DPAVIC were set as follows:  
CIC: \(m=1,b=10,k=0\).  
AVIC: \(m=1,b=8,k=0,\sigma=0.003\).  
DPAVIC: \(m=1,b=5,k=0,\sigma=0.003\), \(k_{p}=0.4\), \(k_{d}=0.1\), \(k_{i}=0.001\), \(r=100\), \(h=0.01\).  
Figure 13 shows the robot end contact force control and end compensation displacement. It can be found that when CIC and AVIC are adopted, the overshoot of the system is large, 34.31% and 13.54% respectively, the force control error is 0.409 N and 0.25 N, and the adjustment time is long. When the DPAVIC force control strategy is adopted, the overshoot is 1.35% and the force control error is within \(\pm\)0.2 N.  
Figure 11: Experimental setup for robot grinding force control.  
Figure 12: Force control platform.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 4 Mathematical Model
### Energy Consumption
#### 4.1.2 Energy Consumed by Dpe  
Data packet is forwarded to selected node which is reliable in terms of distance; therefore, energy consumed by \(DPE\) is calculated as:  
\[E_{1}=DPE \tag{33}\]  
For \(TD\) and \(N\), energy consumption is calculated as:  
\[E_{N}=N(TD*DPE) \tag{34}\]  
Combining Equations (32) and (34).  
\[TE=N(TD*DPE)+N(HPS+2 HPR+HPRA) \tag{35}\]  
_HPS_ and _HPRA_ energy consumption remain same and _HPR_ is taken one over fifth of _HPS_. Now, Equation (35) is written as:  
\[TE=N(TD*DPE)+N(HPS+\frac{2 HPS}{5}+HPS) \tag{36}\]  
\[TE=N(TD*DPE)+N(24*HPS) \tag{37}\]  
\[TE=N(TD*DPE)+N(24*HPS) \tag{38}\]  
\[TE=N((TD*DPE)+2.4*HPS) \tag{39}\]  
Energy is also affected by ship turbulence, water current movement, wind speed, biological noises, etc. Therefore, Signal to Noise Ratio (SNR) is also added into total energy. _SNR_ is calculated by the following equation.  
\[TE=N((TD*DPE)+2.4*HPS)+SNR \tag{40}\]  
\[SNR=SL-TL-NL+DI \tag{41}\]  
where, _SL_ is the target noise level of source node, _TL_ is transmission loss, _NL_ is noise level from receiver plus environment, and _DI_ is directive index. Values of _NL_, _DI_, and _SNR_ are taken from [34]. Their values for deep water are given in Table 3. _TL_ for deep sea is given by the following equation.  
\[TL=20 logd+\alpha*d*10^{-3} \tag{42}\]  
_TL_ is directly proportional to distance dependent attenuation \(d\) and frequency dependent absorption \(\alpha\). [PERSON]'s expression calculates \(\alpha\) for frequencies above few hundred Hertz through following equation.  
\[\alpha=\frac{0.1*f^{2}}{(1+f)^{2}}+\frac{40*f^{2}}{4100+f^{2}}+2.75*10^{-4}f^{ 2}+0.003 \tag{43}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline NL & 70 dB \\ DI & 3 dB \\ SNR & 20 dB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical layer model.  
For lower frequencies. \(\alpha\) is given by equation.  
\[\alpha=\frac{0.11*f^{2}}{(1+f)^{2}}+0.011f^{2}+0.002 \tag{44}\]  
where, \(\alpha\) is measured in dB/km and \(f\) is in kHz. SL is directly related to signal intensity at 1 m from where the signal is generated and can be calculated, as follows.  
\[SL=10 log\frac{I_{T}}{1 vP_{a}} \tag{45}\]  
For deep sea source, transmission power \(P_{T}(d)\), which is in \(Watts/m^{2}\), is calculated as:  
\[P_{T}(d)=4\pi*(1m)^{2}*I_{T} \tag{46}\]  
By Equation (40), the total energy consumption of a network having \(N\) number of nodes at different depth and \(TD\) are calculated.
## 4 Implementation of Energy Forecasting and Power Theft Detection Models
### Power Theft Detection Results Analysis  
The proposed scheme to detect the illegal practice of power theft is simulated in this section in order to show its effectiveness.  
The range of theft detection is shown in Figure 13, where hourly data samples are evaluated, and no power theft occurs. The figure shows the threshold limit to detect power theft as 7.5 kW mean difference, as proposed in [34; 35]. If the power consumption crosses the mentioned threshold, then users will receive a message regarding power theft that is occurring in their connection line. The utilization of a conventional distribution network and energy meters in the city of Vellore in India leads to the possibility of power theft events. Therefore, in order to provide the power theft case, the authors refer to a 727 day data set from Vellore city. In particular, Figure 14 shows the case of power theft detection where two power theft periods are highlighted.  
Figure 12: Comparison of Errors for accuracy analysis.  
Figure 13: The optimal range of power theft (kW), no power theft detected.
# Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz
## 2 Channel Measurement  
### Measurement System Setup  
The investigations were based on channel measurements in an open-air hall at Beijing Jiaotong University. In the campaign we employed a 128-element virtual uniform linear antenna array (ULA) and uniform cylindrical antenna array (UCA) at the base station (BS), which was realized by moving an omni-directional antenna in 128 fixed positions of a rail track. A 2-element virtual antenna array was employed at the MS (mobile station). Element spacing was chosen to be a half of a wavelength in order to reduce the mutual coupling effect, which deteriorates the performance of the angle of departure (AOD) estimation algorithm.  
We placed the BS antenna array on the 6 th floor platform, with a height about 30 m from the ground, and set the receive equipment in the hall, at eight different MS positions. Figure 1 shows the measurement scenario. In Figure 1a, TX means the BS, where the antenna array lies in, and the RX1 to RX8 represent the eight spots of MS pointed out with eight red stars. Figure 1b,c provides views of the measurement scenario in-field.  
Figure 1: Massive MIMO channel measurement system. (**a**) The measurement scenario sketch map; (**b**) sight from the receiver in-field; (**c**) sight from the transmitter in-field.  
The complex-valued Zad-Off Chu (ZC) sequence worked as the excitation signal, which performs well in orthogonality and constant amplitude. A vector signal generator (R&S SMBV100A) was used as the transmitter. A pair of bi-conical antennas worked as the elements of the virtual antenna array at the BS and MS side. The antenna was assumed to provide an ideally omnidirectional radiation pattern in the azimuth plane, so idealized equal gain can be obtained in the H-plane. The data collector was composed of an RF down-converter, a high-speed digitizer card and a data storage unit. The rubidium clock trained by the GPS was used to maintain the time synchronism of the measurement system. At remote end the accurate synchronization performance was promised by a fiber unit to transmit the frequency standard. The measurement system is shown in Figure 2.  
The measurement environment was a typical outdoor micro-cell scenario. This configuration can represent a mobile station surrounded by large obstacles. Only a few operators were allowed in field, so we can assume that the channel is time-invariant. The signal bandwidth was 100 MHz, which was up-converted to radio frequency of 1.4725 GHz. The measurement parameters are listed in Table 1.
Various simple crowns showed an ability to interact with enzymes, such as R-chymotrypsin, lipases, and subtilisin Carlsberg, benefited by the presence of these CEs [15]. The CEs came into the limelight due to their unique structure and applications in organic synthesis and drug delivery [16]. Ionophoric properties allow CEs to transport through membranes and interact with the living system [17]. Stability constants can be improved by selecting CEs for various ions and via an extraction method [18]. Polyfunctional crown compounds act as photo-controlled ionophores or optical molecular sensors. The crowns have been entirely used to produce ion sensors that identify the presence of target ions [8,19]. Crown ethers are specifically designed and integrated for their interaction with DNA [20]. By using crown ether compounds, DNA intercalation and binding mechanisms have been studied [21,22]. In addition to the biological and catalytical activities, crown ethers also show toxicity. For example, 18-crown-6 can absorb through the skin and cause harmful effects on Central Nervous System (CNS) [23]. In terms of toxicity, simple CEs of 18-crown-6 were found less toxic than aspirin [24,25]. The CEs derivatives with the least toxicity can be synthesized to inhibit cell proliferation and kill pathogens [26]. The CEs analogs are also widely used in chromatography procedures [27] and during capillary electrophoresis. The CEs resins are used especially for chiral compounds [28]. They proved their significance in ion-exchange chromatography during both mobile and stationary phases [29]. Modern studies highlight different uses of CEs derivatives; fluorescent probes [30, 31, 32] chemosensing of bioactive species [33], chemo-sensors for bioactive molecular detection [34,35].