esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow Sensing-Based Congestion Detection for D2D Streaming on a 5G gNB
## 1 Introduction
Recently, resource allocation and interference mitigation solutions for D2D communications underlaying long term evolution (LTE) cellular networks have primarily been proposed for media streaming in wireless mobile networks [12; 13; 14]. However, protocols based on resource allocation and interference mitigation have problems such as frequent disconnection and traffic errors, because of their sensitivity to delay and the amount of traffic congestion under 5G applications. In particular, when a D2D streaming service is performed to a gNB channel, the quality of streaming is affected owing to limitations in traffic overflow, limited resources, and bandwidth constraints. In circumstances of limited bandwidth and long delays on a 5G gNB, D2D streaming is affected by radio interference due to traffic congestion and heterogeneous channel bandwidths. The proposed FS-CDA can overcome these constraints and limitations. If a gNB channel does not adequately control flow and traffic congestion caused by D2D streaming, it will degrade the performance of the streaming service, since the packet size of streaming flow is increasing. A control solution for flow rate and sensing-based traffic flow on 5G gNB channels is important for throughput, enhances streaming quality, and supports fairness and reliable responsiveness. The new FS-CDA for D2D streaming services on 5G gNBs is proposed to enhance the QoS. The proposed mechanism controls congestion by sensing cached traffic flows, as well as the transmission rate, rate adjustment function, underflow, and overflow of these operations. The throughput for a D2D streaming service is improved by considering a rate limitation while supporting fairness and responsiveness of the traffic rate. The proposed mechanism senses and prevents excessive increases in traffic flows in a 5G gNB channel, and the imbalance between rates for DUE and CUE pairs. This operation is crucial for the QoS of D2D streaming. The proposed FS-CDA satisfies the QoS requirement for D2D streaming on 5G gNBs and minimizes rate imbalance owing to the amount of flow congestion and delay in DUE and CUE pairs, and in DUE and DUE pairs. The simulation results show that the proposed mechanism has better performance with respect to packet loss rate, throughput, average response rate, and optimization rate than other methods. The remainder of this paper is organized as follows. Related work is introduced in Section 2. We introduce the flow sensing-based system model to mitigate congestion and improve the QoS of D2D streaming services in Section 3. The simulation results are evaluated in Section 4, and the concluding remarks are presented in Section 5.
## 3 Results
### Error analysis between HbA1c estimation models  
Comparing model (7) with the model (8) by the process described in the previous section, taking (7) as a reference, yields two error metrics: HbA1c error and SpO\({}_{2}\) error. Figure 3 illustrates the HbA1c and SpO\({}_{2}\) error for model (7), and the HbA1c error for model (8). The SpO\({}_{2}\) error is not shown in this section as the SpO\({}_{2}\) parameter cannot be directly deduced from the model (8). The SpO\({}_{2}\) approximation results and errors are described in the following sub-section.  
In Figure 3, the estimation error associated with the model (7) and (8) based systems with respect to reference values is shown. Figure 3a-b illustrate the estimation error of HbA1c and SpO\({}_{2}\) associated with the model (7), whereas Figure 3c depicts the estimation error of HbA1c with the model (8).  
The color bars drawn in Figure 3a-c show the corresponding reference SpO\({}_{2}\) level for each data point. The color bar in Figure 3b shows the reference HbA1c value for each estimation data point of SpO2.  
In Figure 3a-b, the model-(7)-based HbA1c and SpO\({}_{2}\) estimation error is in the range of \(10^{-14}\) and \(10^{-13}\), respectively. These error values can be associated with floating-point errors in computing algorithms. In Figure 3c, the HbA1c estimation error varies from \(-1.306\%\) to \(0.047\%\). This large error is due to the lack of parameters for changes in blood oxygenation level.  
Figure 3: Error analysis between HbA1c estimation models: (**a**) HbA1c error for model (7), (**b**) SpO\({}_{2}\) estimation error for model (7), and (**c**) HbA1c estimation error for model (8). For (**a,c**) the color bar represents the reference SpO\({}_{2}\) values and the color bar in (**b**) represents reference HbA1c values.  
The mean of the error levels for Figure 3a,c are found to be \(8.52^{-17}\), \(-1.60^{-15}\), and \(-0.60\), respectively. The standard deviation (SD) values of the error levels are also found to be \(2.90^{-15}\), \(3.31^{-14}\), and \(0.37\), respectively, for the corresponding plots.
# Development and Validation of a Capacitor-Current Circuit Model for Evaporation-Induced Electricity
## 5 Conclusions  
In this work, we successfully developed and validated a simplified capacitor-current circuit model to explain the behavior of evaporation-induced electricity in wood-based nano-generators. Our analysis showed that this simplified model effectively captures both the transient and steady-state electrical outputs, with fitting results yielding an initial peak current of 16.168 \(\upmu\)A, a steady-state current of approximately 9.832 \(\upmu\)A, and a time constant of 621.395 s. This work primarily focuses on understanding the key electrical behaviors of evaporation-induced electricity, providing a framework for analyzing device performance. The time constant provides us insights about the interaction between the capacitance and internal resistance, reflecting the device's charge retention capabilities. While improving device performance is not directly addressed in this study, the results provide practical guidance for the future optimization of material properties and microchannel configurations.  
This work demonstrates the applicability of the simplified capacitor-current circuit model to evaporation-induced electricity in wood-based nanogenerators. However, its broader applicability to other material systems and configurations requires further exploration. Future research should focus on extending the model to different material systems and refining its parameters to account for material-specific effects. Additionally, future studies could investigate the application of this model to other porous materials and evaluate how environmental factors, such as humidity and temperature, influence device performance.  
Conceptualization, W.Z., R.T.L. and Y.H.; model building, experiment performance, and model fitting, W.Z. and R.T.L.; writing--review and editing, W.Z., R.T.L. and Y.H.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 22372026 and the Natural Science Foundation of Sichuan Province, grant number 2022 NSFSC1207.  
Not applicable.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on reasonable request.  
We would like to thank the editors for their invaluable assistance throughout the review and publication process.  
The authors declare no conflicts of interest.
# Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers +
## 7 Conclusions  
This paper addresses the problem of planning onshore touristic itineraries for cruise passengers in the context of a smart city. Planning touristic itineraries for cruise passengers presents specific challenges due to the arrival of a high number of people visiting a city for a limited time that may cause overcrowding of tourist attractions and, at the same time, the necessity to consider unforeseen events occurring in the city that may prevent a planned tour from taking place. This is a complex problem considering the high number of variables to be taken into account. This paper proposes a planner of onshore touristic itineraries for cruise passengers. The planner is based on a deep reinforcement learning approach, allowing one to compute a city tour composed of PoIs spread in the destination city by considering more than 230 state variables and more than \(19*10^{12}\) possible states with 14 PoIs. The proposed approach provides encouraging results since it computes tours by maximizing the number of Pols to be visited in the available time window and at the same time by avoiding regrettable situations for cruise passengers, such as visiting overcrowded locations or not returning to the port in time for cruise departure. Results show that after around 20,000 episodes, the average number of visited PoIs stabilizes.  
In addition, the adoption of deep reinforcement learning allows our solution to learn to dynamically define alternative itineraries in case events happen in the city that interfere with the original plan. This point is another contribution brought by our solution with respect to state-of-the-art methods. Most works for itinerary planning provide a solution to statically plan the optimal route/itinerary at the designated time. Therefore, in case of unexpected events, the planning must be repeated. However, our solution can adapt to unexpected events and provide a novel itinerary without repeating the training process.  
Future works will extend the proposed approach to plan itineraries for multiple cruises arriving at the same time in a destination city so as to distribute passengers in different locations to further limit overcrowding conditions.  
Figure 7: Planning an itinerary with unexpected events.  
**Author Contributions:** The individual contribution of authors follows: Conceptualization, C.D.N.; methodology, G.P., P.R. and L.S.; software, G.P. and L.S.; writing--original draft preparation, C.D.N., P.R.; writing--review and editing, C.D.N., G.P., P.R. and L.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Investigation of Peptide Coupling Using UV-Visible Spectroscopy
#### 3.1.4 Detection of DPP-IV Activity Using Functionalized Au NPs  
The C/G-capped Au NPs showed a typical SPR peak at 525 nm, i.e., a red shift of 3 nm with respect to the unmodified Au NPs, and good stability in phosphate buffer saline (PBS). After carefully adjusting the molar ratio of CALNN to GPDC, the molar ratio of CALNN to GPDC at 1:10 in the reaction mixture was selected in order to evaluate the sensitivity of the method to DPP-IV/CD26, as this mixture was found to be stable (as shown in Table 3) whilst simultaneously having the highest ratio of bound functional ligand.  
A perceptible color change from red to faint purple was only obtained with 15 U/L of DPP-IV activity and it became more evident with increasing the activity to 30 U/L (see Figure 4). The ratio of absorption at 750 and 525 nm was used to evaluate the response of dipeptide-capped Au NPs to DPP-IV/CD26.  
Similar conditions were applied to measure DPP-IV/CD26 activity using the PEGylated Au NP system or the GPDCALNNC Au NP system but neither caused color changes, nor was a significant UV-visible spectral shift observed, even at high DPP-IV activities (>50 U/L). This total lack of response was attributed to steric hindrance caused by the compact structure of the remaining ligands (whether PEG or CALNN) bound to the NP and consequently preventing NPs aggregation. A probable reason for the lack of response to DPP-IV/CD26 in the case of GPDCALNNC is the presence of two cysteine residues in the sequence that may form disulphide bridges, leading to a variable percentage of dimerized products [29].  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample** & **Zeta Potential (mV)** & **Average Hydrodynamic Radius (nm)** \\ \hline GPDC: CALNN 4:1 & \(-\)46.1 & 22.88 \\ GPDC: CALNN 6:1 & \(-\)38.9 & 23.68 \\ GPDC: CALNN 10:1 & \(-\)39.0 & 35.42 \\ GPDCALNNC & \(-\)35.9 & 39.5 \\ GPG-PEG\({}_{\text{d}}\)-LA & \(-\)16.5 & 38.1 \\ \hline \end{tabular}
\end{table}
Table 3: Average zeta potential and hydrodynamic radius of different peptide-stabilized Au NPs.
## 3 Results and Discussion
### Aromatized Wine-Type Beverages Characteristics
Significant effects on the aroma substances formation/extraction in the AWTBs compared to the control were observed (Table 7) for \(\beta\)-citronellol, phenethyl alcohol, rose oxides, and geraniol. These substances were absent or present in the control wine in low amounts, and appeared in the AWTBs due to the addition of rose byproducts (solids or extracts). Phenethyl alcohol is among the compounds that contribute significantly to the favorable aroma of white and rose wines [4; 34]. Compared to the control rose, the amount of phenethyl alcohol increased significantly from 1.07 \(\pm\) 0.15 mg/L to 4.08 \(\pm\) 0.30, 4.96 \(\pm\) 0.24, and 5.77 \(\pm\) 0.28 mg/L for the ARW_E_400, ARW_M_100, and ARW_CoF_100 (the samples with the highest amounts of added rose byproducts), respectively. The same trend was observed for the concentrations of \(\beta\)-caryophyllene, \(\beta\)-citronellol, rose oxides, and geraniol. They increased significantly, and a distinctive rose aroma in all the variants was sensed (Table 7; Figure 2). The amounts of higher alcohols increased in the AWTBs compared to control rose wine, but this increase was insignificant and did not disrupt the overall balance and quality of the products. The amount of acetaldehyde increased significantly for ARW_E_400, ARW_M_100, and ARW_CoF_100 variants (the samples with the highest amounts of added rose byproducts), which confirmed the GC-FID analysis results. However, the amount of acetaldehyde is below the acceptable limits for acetaldehyde content [35]. The other aldehyde found in the AWTBs which significantly increased compared to the control was decanal. However, its amount in the ARW_CoF series was comparable to the control rose (1.43 \(\pm\) 0.11 mg/L). A slight increase in the concentration of decanal was observed in the ARW_CoF_400 samples only (Table 7). From the hydrocarbons (important aroma fixators), alkanes with more than 14 carbon atom chains predominated in the wines (control and aromatized) (Table 7).
## 6 Experimental Validation  
### Transmission Error Test Principle and Test Scheme  
In this section, the transmission errors of the unmodified and modified internal helical gear pairs in different time periods will be detected, and the results of the two transmission errors will be compared and analyzed to observe the effect of the modification. The speed is set to 1000 r/min, and the load is 3 N\(\cdot\)m. The measurement is performed every 30 min. With the extension of time, the planetary gear reducer will generate friction heat during continuous operation, and the overall temperature will gradually increase. Therefore, it is of great significance to discuss the influence of the gear temperature on the transmission error under time-varying conditions.  
The transmission error test principle is as follows: Figure 17. The test bench is composed of a drive motor, an input grating, an output grating, a measured reducer, a sensor, and a tooling base. The input gear shaft is fixed to the reducer shaft, and the actual rotation angle of the input end of the reducer is recorded by the input grating. The runout of the connecting rod of the output support disc is controlled within 0.03 mm, and then connected with the output grating angle sensor to measure the actual rotation angle of the reducer output. When the motor starts, the input gear shaft is driven to rotate, and the grating is input to record the rotation angle position at the moment. Due to the influence of the transmission error, the rotation angle position captured by the grating encoder at the output end at the same time does not conform to its theoretical position; that is, the angle signal frequencies of the two grating encoders are different. The rotation angle  
Figure 16: Transmission error under the influence of temperature field.  
positions captured at both ends of the input and output are connected according to each instantaneous, and the bearing transmission error curve can be obtained.
## 3 Results and Discussion  
### Physical Properties  
#### 3.1.1 Apparent Density  
The density of CEBs has several effects on their properties and suitability for building applications. The effect of density on strength, durability, thermal or sound insulation depends on the composition of the mixture and the compaction method. It is interesting to understand how the addition of fibers into the mix modifies CEBs' properties. In this work, the apparent density of the blocks was evaluated using the ratio between the weight of the dried specimens and their volume, which was evaluated according to their dimensions. Figure 10 illustrates the effect of fiber content and cement ratio on the apparent density of hardened blocks. It was highlighted that these values varied from 1425 Kg/m\({}^{3}\) to 1819 Kg/m\({}^{3}\). As expected, there was a reduction in apparent density with increasing fiber content for all specimens compared to the specimens without fibers. It should also be noted that for the block with 12% cement content filled with 0.2% JF, a decrease of 11.6% was noticed in comparison with that with the same content of cement without fibers. This reduction could be explained firstly by the physical properties of Juncus plant fibers, considering their lower density (1.8 g/cm\({}^{3}\)) compared to the soil-cement matrix, and secondly, the lightening of the earth blocks could also be attributed to the great capacity of fibers to absorb water (225%), which increases the composite porosity during the drying process of the produced CEB. Therefore, it can be concluded that the first benefit of incorporating natural fibers into soil-cement composites is the production of a lightweight specimen. This trend has already been reported by previous works [27; 28; 47; 48] on earth brick composites reinforced by vegetable fibers. On the other hand, it can be noticed from Figure 10 that an increase in the cement content from 4% to 8% leads to an increase in the apparent density value of approximately 5.5%, and the same behavior is observed (5%) for an increase in cement from 8% to 12%. This confirms that the increase in cement content leads to an increase in the block apparent density.
## 5 Performance Measures of PAS
### Reliability of PAS  
Reliability is a very important metric of the system's performance. It is the probability that the device will not fail over the prescribed period of time when operated under the specified conditions. Mathematically, it is the probability that the system cannot fail before time t. The mathematical equation for reliability is given below.  
\[R(t)=P(T>t) \tag{33}\]  
In order to obtain the explicit expression for the reliability, set the failure rate as \(\lambda_{mic}=0.00002\), \(\lambda_{mix}=0.00005\), \(\lambda_{amp}=0.00006\), \(\lambda_{sp}=0.00007\) in Equation (30), set all the repair rates equal to zero, and take the inverse Laplace transform of the obtained expression. One can obtain the time-dependent reliability expression of the PAS as given below in Equation (34).  
\[R(t)=e^{-0.00027\ t}+4\times e^{-0.000235\ t}\sin\text{h}(0.0000035\ t) \tag{34}\]  
Now, varying the time unit \(t\) in Equation (34), one can obtain the following Figure 5 of the reliability of the public address system.  
Figure 4: Availability of the PAS vs. time unit.  
Figure 5: Reliability of PAS vs. time unit.
## 3 Materials and Methods
### AMI-CNN Model Structure
#### 3.3.3 Classifier  
Since the preceding modules have already selected the features, ensuring a certain level of accuracy, our aim in the classifier is to reduce parameters to achieve faster classification. We use depthwise convolution before the fully connected layer to compress each channel into a single value, and then feed these into the fully connected layer for classification output. This operation significantly reduces the number of parameters, leading to faster speed and lower power consumption in practical applications. The depthwise convolution is performed as follows.  
Let \(X\) denote the input features with \(C\) channels, each channel having a length of \(L\). Depthwise convolution utilizes \(C\) convolution kernels \(K_{c}\) of length \(K\) to perform convolution operations on each channel separately, resulting in the output feature map \(Y\) with the same number of channels as the input, and each channel having a length of \(L^{\prime}\). For the output \(Y_{c}(i)\) at channel \(c\) and position \(i\), the calculation formula for one-dimensional depthwise convolution is  
\[Y_{c}(i)=\sum_{m=0}^{K-1}X_{C}(i+m)\cdot K_{C}(m), \tag{9}\]  
where \(Y_{c}(i)\) denotes the value of the \(c\)-th channel of the output feature map at position \(i\). \(X_{c}(i+m)\) represents the value of the \(c\)-th channel of the input feature map at position \(i+m\). \(K_{c}(m)\) signifies the weight of the \(c\)-th convolution kernel at position \(m\), and \(K\) is the size of the convolution kernel.  
This design not only reduces the number of parameters and speeds up the runtime but also effectively integrates spatial information into global features, contributing to the improvement of accuracy in subsequent classification tasks. Subsequently, the features are passed to a fully connected layer, where the output comprises three output nodes, each corresponding to one of the three different modulation formats.