esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimization Workflow in Design for Additive Manufacturing
## 4 Conclusions  
In this work, a design workflow for Additive Manufacturing was proposed, trying to overcome the limits highlighted in the literature, where it is stated that the available frameworks do not exploit all the advantages offered by AM and do not cover the entire design process. The presented workflow considers the embodiment design phase, from the definition of a design volume to the production of the part, integrating both CAD tools for the geometric modeling of the part and CAE tools for the optimization and simulation phases; more, it considers the possibility to use the size optimization to obtain lattice structures with optimized beams, and the topology optimization to obtain more organic shapes. The workflow was then applied to the remodeling and optimization of a piston rod in which both commercial and custom tools were adopted, showing its ease and universality of implementation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model/Approach** & **Mass [g]** & **\% of Mass Reduction** \\ \hline Starting design space & 104.7 & \\ Topology optimization & 29.99 & \(-71\%\) \\ Size optimization & 20.98 & \(-80\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mass reduction of the optimized models. The mass only considers the design space volume and not the rod’s ends, which were not optimized.  
Figure 9: Part orientation and support generation for the size optimized piston rod.  
As future works, the process simulation will be performed on the oriented parts. Then, a hybrid method that combines size and topology optimization is going to be developed to further expand the workflow possibilities; the 0-1 density parameter will drive the dimension of the beams, shell, or solid elements. The connection between the lattice structures and the adjacent objects will be addressed to obtain smoother links and enhance the mechanical properties of the parts. Moreover, aspects related to hybrid manufacturing technologies will be addressed.  
Conceptualization, S.R., F.U., L.G., G.S., R.M., and G.C.; Formal analysis, S.R., F.U., and L.G.; Funding acquisition, G.S., R.M., and G.C.; Investigation, S.R.; Methodology, S.R., and G.S.; Supervision, G.S., R.M., and G.C.; Writing--original draft, S.R.; Writing--review and editing, S.R., F.U., L.G., G.S., R.M., and G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a Ph.D. grant of Fondazione Cassa di Risparmio di Padova e Rovigo (CARIPARO), by the grant \"FSE 2105-37-11-2018: Hybrid manufacturing strategies through selective laser sintering for the customization of series components\" by Regione Veneto, and by the grant \"BIRD 190850--Design, geometric modeling and analysis of components with functionally graded variation for additive manufacturing technologies\" by the Department of Civil, Environmental, and Architectural Engineering, University of Padova, Italy.  
Not applicable.  
The raw/processed data required to reproduce these findings can be partially shared upon request.  
The authors declare no conflict of interest.
## 4 Measurement Results
### Download Requests  
Figures 5 and 6 show a comparison between the two connection types (with/without using Tor) from every PlanetLab node and the time it took to download 50 KB (Figure 5) and 1 MB (Figure 6). Most of the nodes have nearly the same median download speed, which is contributed by the fact that Google makes the downloaded file accessible with high download speed around the world, rendering the location of the node less significant [47, 48]. Similar to the HTTP request results, the node in Canada was the slowest one, confirming the hypothesis that it had a lower bandwidth available for this experiment. This is also indicated by the median connection times.  
Figure 4: Averages of Core Latency for HTTP Requests.  
Figure 5: Average Download Throughput (Time for 50 KB).  
Figure 6: Average Download Throughput (Time for 1 MB).  
Download measurements (from different web servers) are also conducted by the Tor metrics project [21]. During the timeframe of our experiments, the median value for downloading of 50 KB was reported as between 3 and 4 s, and for downloading of 1 MB between 9 and 15 s. Our results of downloading 50 KB were between 3 and 11 s (median), and for the download of 1 MB between 12 and 26 s (median). We see them as comparable. The difference is that the Tor metrics results were measured on the same nodes and differ in time, whereas our results differ in nodes and are averaged over time, probably causing a higher variation.
## 4 Applications of Intelligent Acoustic Monitoring
### Smart Cities and Urban Monitoring
#### 4.2.5 Emergency Vehicle Signal Detection  
In [114], [PERSON] et al. proposed an innovative system for detecting emergency vehicle signals by analysing microphones' recorded road noise in real conditions. The aim was to improve traffic management and shorten the response time of emergency services in smart cities. They developed a deep learning model as a stacking ensemble, combining MLP and DNN networks as base models and LSTM as superior models (meta-learner). The system was based on the advanced analysis of acoustic features such as MFCCs, mel-spectrogram, centroids, flux, tonnetz, or chroma. Thanks to this design, the model achieved very high efficiency, up to 99.12% accuracy and an F1 score reaching 98%, which was better than previous solutions. Experiments were conducted on a real dataset, which included road traffic and siren sounds, and the proposed approach proved to be precise and scalable for smart city monitoring applications. The authors emphasised that their method could also be adapted to other fields, such as sound recognition in smart homes, nature monitoring, or patient analysis in healthcare systems.
## Empirical Analysis
### Hypothesis Test Results
Notably, customer satisfaction (\(\beta=0.431\), \(p=0.000\)) and trust (\(\beta=0.126\), \(p=0.043\)) had a positive impact on loyalty, thereby lending support for H4 and H5. This study also examined the mediating roles of customer satisfaction and trust in the relationship between perceived value and loyalty using the bootstrap method with 500 resampling procedures, as detailed in Table 5. For utilitarian value, an indirect effect on loyalty was found through customer satisfaction (\(\beta=0.087\), \(p=0.005\)); hence, H6-a was supported. The indirect effect through trust (\(\beta=0.031\), \(p=0.070\)) did not support H6-b. This result indicated that customer satisfaction partially mediated the relationship between utilitarian value and loyalty as utilitarian value significantly influenced loyalty (\(\beta=0.216\), \(p=0.000\)). For hedonic value, an indirect effect on loyalty was observed through customer satisfaction (\(\beta=0.262\), \(p=0.004\)); hence, H6-c was supported. However, the indirect effect through trust (\(\beta=0.055\), \(p=0.095\)) did not support H7-a. This result suggested that customer satisfaction fully mediated the relationship between hedonic value and loyalty as hedonic value did not significantly impact loyalty directly (\(\beta=0.084\), \(p=0.258\)). Social value showed no indirect effect on loyalty through either customer satisfaction (\(\beta=-0.137\), \(p=0.578\)) or trust (\(\beta=0.016\), \(p=0.081\)); thus, H7-b and H7-c were not supported. In summary, customer satisfaction mediated the relationship between both utilitarian and hedonic values and loyalty, while trust did not mediate the relationship between any perceived value dimensions and loyalty.
## 2 Related Work
### Harmonisa  
The main goal of the HarmonISA research project [15, 16] is the automatic semantic integration of land use and land cover data for the three regions of Friuli Venezia-Giulia (Italy), Slovenia, and Carinthia (Austria). Some OWL ontologies were created corresponding to different land cover classification systems in Italy (Moland), Slovenia (Slovenia CORINE) and Austria (Realraumanalyse). Two other OWL ontologies were also developed, one for the USA Anderson system and the other for the EU CORINE system. These classification systems are better suited to land cover than land use and are prepared for classifying data derived from aerial photography. The land use and land cover concepts are closely related and sometimes interchangeable, but the classification systems that were harmonised are, in fact, designed for land cover.
# Feedback Stabilization of First Order Neutral Delay Systems Using the Lambert W Function
## 2 Preliminary and Problem Formulation  
The Lambert W function (hereafter denoted as \(W(\cdot)\)), is a multivalued complex function defined as the solution of the following equation:  
\[W(z)e^{W(z)}=z, \tag{1}\]  
where, \(z\) is a complex number. The Lambert W function has infinitely many complex branches when \(z\) is a complex number [27]. If \(z\) is a real number and \(z\geq-e^{-1}\), then \(W(z)\) becomes a real function with two possible real branches. By convention, the branch satisfying \(W(z)\geq-1\) is considered to be the principal branch (\(W_{0}(z)\)), while that satisfying \(W(z)<-1\) is the negative branch (\(W_{-1}(z)\)). Several function values of \(W(z)\) are \(W(-e^{-1})=-1\), \(W(0)=0\), and \(W(e)=1\).  
In this paper, we consider the first order NDDS with two time delays of the form:  
\[\dot{x}(t)+\dot{a}\dot{x}(t-\tau_{1})+bx(t-\tau_{2})=u(t), \tag{2}\]where \(b,\tau_{1},\tau_{2}\) are positive constants, \(a\) is a negative parameter, and \(u(t)\) is a control input. The autonomous NDDE is given by:  
\[\dot{x}(t)+a\dot{x}(t-\tau_{1})+bx(t-\tau_{2})=0. \tag{3}\]  
The characteristic equation associated with Equation (3) is given by:  
\[p(\lambda)=\lambda+a\lambda e^{-\tau_{1}\lambda}+be^{-\tau_{2}\lambda}, \tag{4}\]  
where, \(\lambda\in C\) are the characteristic roots. All solutions of Equation (4) are oscillatory if and only if the corresponding characteristic equation has no real roots [24].  
Here, we define two functions as follows:  
\[f(\lambda)=\lambda+a\lambda e^{-\tau_{1}\lambda}, \tag{5}\]  
\[g(\lambda)=-be^{-\tau_{2}\lambda}. \tag{6}\]  
Using Equations (5) and (6) we can rewrite the characteristic equation as:  
\[p(\lambda)=f(\lambda)-g(\lambda). \tag{7}\]  
Thereafter, \(p(\lambda)=0\) results in the following equation:  
\[f(\lambda)=g(\lambda). \tag{8}\]  
Hence, the solution set of Equation (8) is equal to the roots of Equation (4).  
Throughout this paper, we assume that \(\dot{b},\tau_{1},\tau_{2}\) are positive constants and \(\mathrm{Re}(\lambda)\) denotes the real part of a complex variable \(\lambda\).
## 6 Analysis of the Findings of This Review
### The Practicality of the Primary Cobot Program and Control Methodologies Proposed in the Literature
#### 6.2.2 Voice and Gesture Control  
Although they present an intuitive approach to controlling cobots, gesture and voice control have their limitations. Gestures can be misinterpreted by the image processor or incorrectly posed by the human operator. An example of where such errors could occur is in the subtle pose differences between _'Axis Y move forward'_ and _'Axis Z move forward'_, _where only a slight difference in left arm position separates the two commands_. Voice control is affected by noise variation and also human error in the case of an incorrect command or poor diction [91], which could be challenging in a production environment. Collectively, this method may be difficult for a human to orchestrate, having to coordinate the correct gestures and voice commands, especially when sequencing the cobot through a complex task. In addition, task parameterising is time-consuming, some of the more difficult gestures to pose cause fatigue and while the gesture recognition rate is generally high, instances of false positives and false negatives occur, of which the user is notified, following a validation process [92].
## 3 The Simulation Environment  
### The Simulation Pipeline  
In order to perform the necessary simulation steps, we developed a simulation pipeline containing eight main stages. The pipeline is depicted in Figure 2. Some of the stages with non-trivial contributions are elaborated on in the sections below.  
* In the traffic data collection stage, we received traffic count data in an hourly resolution from the Hungarian Roads. The data originated from loop detectors which are able to count the passing vehicles. Typically, one loop detector is deployed between each driveway.  
* In the traffic data selection stage, we selected traffic scenarios that were considered to represent typical traffic patterns, including, e.g., summer-time low-intensity traffic and school start high-intensity traffic. The selection is elaborated on in Section 4.
* In the traffic data aggregation stage, we calculated the average of the data of the selected road sections in the selected hours. We have also performed some cleaning on the data in this stage.
* In the traffic demand generation stage, we generated the number of entering, leaving, and passing vehicles for each driveway. The method is described in Section 4.
* In the traffic flow generation stage, we generated traffic flows from the previously generated traffic demand values using Algorithm 1.
* In the scenario selection and configuration generation stage, we selected the relevant services and simulation parameters. The configuration is elaborated on in Section 2.
* We used the Artery/OMNeT++ simulator in the supervised simulation stage to model the network and analyze the channel utilization values. The details are described in Section 3.
* We developed data processing scripts to visualize the results in the evaluation stage.  
```
0: Demand values to each driveway (D)
0: Proper flows (F)  F \(\leftarrow\) [] for all d \(\leftarrow\) D do
1\(\leftarrow\) d.next [Node satisfy demand] while d.entry \(\
eq\) d do  p \(\leftarrow\) mini/d.entry, lleaving | [Processable demand]  d.entry \(\leftarrow\) d.entry--P  l.leaving \(\leftarrow\) lleaving--P  F \(\leftarrow\) F \(\cup\) [d, l, p] [Add flow]  l \(\leftarrow\) Inext endwhile endfor
```  
**Algorithm 1** The proposed algorithm for flow generation
## 4 Enabling Technologies for FSO Communication
### Error Correction Codes and Modulated Signal Optimization
#### 4.7.3 Probabilistic Shaping
Also, in a system utilizing PCS modulation and FEC, the relationship between the system bitrate and the constellation entropy is defined as  
\[R_{b}=N_{\text{pol}}R_{S}[H_{\text{PCS}}-(1-R_{\text{FEC}})\log_{2}M], \tag{48}\]  
where \(R_{b}\) denotes the net bitrate, \(N_{\text{pol}}\) represents the number of polarizations, \(R_{S}\) denotes the operating symbol rate, and \(R_{\text{FEC}}\) denotes the FEC rate. To accurately set the PCS shaping factor for maximizing channel capacity, it is necessary to first evaluate the achievable information rate for a given SNR. For this purpose,the normalized generalized mutual information (NGMI) is often utilized as a pre-FEC performance metric in systems employing soft-decision FEC and is given by [317]  
\[\text{NGMI}=1-\frac{G(M,P_{x},\sigma^{2})}{\log_{2}(M)}, \tag{49}\]  
where \(\sigma^{2}\) denotes the noise variance in the AWGN channel. It can be demonstrated that the minimum NGMI required for error-free transmission matches the FEC rate. However, because of the imperfect performance of FEC, it is necessary to consider a slightly higher NGMI threshold, denoted as NGMI\({}_{\text{th}}\), than the theoretical minimum. To control the transmitted net bitrate, adjust the shaping parameter \(\lambda_{sp}\) according to Equation (45).
## 4 Simulation and Discussion
### Comparison of Test Results
#### 4.4.4 Discussion  
The results in Figures 12-15 are better than those in Figures 10 and 11, indicating that the addition of biological characteristics can reduce the false positives and false negatives caused by environmental factors and effectively improve the identification accuracy of essential proteins. The results of Figures 12 and 13 are similar to those in Figures 14 and 15. In particular, the results in Figure 16 show that it is difficult to improve the recognition accuracy by improving the algorithm alone. It is important to search for the relationship between essential and non-essential proteins and the internal correlation between the topology of the PPI network and various biological characteristics. In this paper, the ordinary least squares are used to supplement the missing data, and the deep neural network is used to find the correlation of each feature, which can effectively improve recognition accuracy. Especially in data training, 80% data from known essential proteins and 80% data from non-essential proteins are selected as training data, which can effectively avoid the problem of unbalanced training data.  
the Tu BP database. To solve the problem of incomplete gene expression data, the reverse operation of ordinary least squares is introduced to supplement absent data. Then, PPI network topology, Pearson correlation coefficient, and homologous correlation coefficient were constructed to reduce the convergence rate of DNN. Finally, DNN was used to find the optimal correlation among the node degree, Pearson correlation coefficient, and homology correlation coefficient, to improve the identification accuracy of essential proteins. Numerical studies show that proper selection of training data can effectively avoid the problem of unbalanced training data. At the same time, the correlation of each feature is the key to improving the accuracy of essential protein recognition.  
S.Z., conceptualization, methodology, writing--reviewing; Y.H.: software; W.Y., writing--reviewing. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Social Scientific Research Foundation of China (21 VSZ126).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Discussion  
### Moisture Retention Tests  
Several studies have investigated the effect of different biopolymer types and concentrations on moisture retention of biopolymer-treated soils [17; 28; 46; 47; 49; 51; 52]. The studies found that biopolymer treatments enhance soil moisture retention, with the biopolymer type having a significant effect on the resulting moisture retention. Most studies concluded that moisture retention increases at higher biopolymer concentrations [17; 28; 49], although one study did not confirm this effect [46]. Soil type also appears to significantly influence the moisture retention that can be achieved by a biopolymer amendment [17]. In the following, the key trends of this study's results are discussed in the context of the previous literature. A more detailed discussion is limited by the significant differences in soil, treatment, and experimental setup among the studies.
## 4 Results and Discussion
### Failure Phenomena  
Concerning the control beam A0, when loaded to a certain value, initially, vertical cracks in the pure bending section appear on the beam; subsequently, oblique cracks arise in the shear span, and the vertical cracks cease to extend or multiply. When the load is increased to a certain value, the quantity of oblique cracks ceases to increase. At this juncture, a principal crack with a relatively large width is formed in the area where the oblique cracks are densely distributed, and the stirrups intersecting with the principal crack gradually yield. After that, the main crack continued to extend to the loading point. The failure of beam A0 belongs to shear compression failure.  
The failure modes of reinforced beams A1, A2, and A3 are basically the same. When the load is applied to a certain value, vertical cracks emerge in the pure bending section of the beam. As the load increased further, the sound of concrete failure within the beam can be clearly heard. In the shear span, insufficient bearing capacity led to the tensile failure of the internal concrete, and the strains of the stirrups and the reinforcement layer started to increase. Subsequently, the stirrups of the beam successively started to yield, eventually resulting in the failure of the beam. After removing the reinforcement layer, it was discovered that the concrete cover in the shear span had been subjected to tensile damage and detached from the core concrete, and the failure location was largely consistent with the position of the main oblique crack of the comparative beam A0.  
It can be seen from Figure 11 that all four test beams experienced shear compression failure. Among them, the failure of the comparative beam A0 was more inclined to shear tension failure. However, with the increase in the thickness of the reinforcement layer, the failure pattern of the internal concrete gradually tended toward web crushing failure, and the failure area gradually expanded. The top concrete near the loading point of beams A2 and A3 suffered compression failure. Therefore, it can be concluded that the epoxy mortar reinforcement layer has obviously improved the shear capacity of the beam, but the failure mode of the beam is still brittle failure. Shear failure of the strengthening layer occurred as a consequence of shear stress redistribution within the flexural shear zone, originating from the internal concrete shear damage.  
Figure 10: Load–stirrup strain curves.
## 3 Results
### Evaluation on the Three \(C_{d}\) Models
#### 3.1.1 Validation Accuracy at Site Scale
Figure 4: (**a**) Comparison of the overall and specific validation accuracy of \(R_{nd\text{-}Cd\text{-}SM}\) calculated by using the \(C_{d}\) derived from the \(S\) Model before and after calibration. The top chart in (**a**) represents the validated RMSE (bar) and rRMSE (dash dot line). (**b**,**c**) are the overall \(R_{nd\text{-}Cd\text{-}SM}\) validation accuracy with the original and calibrated coefficients, respectively. magnitude of Bias decreasing by 41.52 Wm\({}^{-2}\). Moreover, the calibrated \(S\) Model has a more robust performance compared to its original version, yielding all R\({}^{2}\) better than 0.9, Bias of \(-\)0.85\(\sim\)\(-\)1.5 Wm\({}^{-2}\), and rRMSE of \(\sim\)17%, respectively. These results indicated the necessity of calibration for the \(S\) model.