esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Analysis of the Construction Land Landscape Pattern
#### 4.4.1 Analysis of Patch Size, Number, and Density  
The TA of construction land continued to increase during the study period, and the NP gradually increased, indicating the continuous expansion of the urban construction landscape. From 1972 to 2020, PD showed a floating downward trend, i.e., the number of patches per unit area gradually decreased, which indicates that the regional construction land was constantly linking and merging into large patches. This phenomenon is particularly obvious in Yuen Long and Tuen Mun districts, where the spreading expansion between municipalities constantly links small regional patches, significantly increasing the area of regional construction land. The reason for the slight increase of PD from 1995 to 2000 is that the number of construction land patches increased to a large extent due to multiple reclamation projects.  
LPI showed a downward trend from 1972 to 2020. In general, this is because the growth rate of the total area of construction land patches was slightly greater than the growth rate of the largest patch area during the development and prosperity of Hong Kong's three generations of new towns.  
During 1995-2000, there was a small increase in LPI. During this period, the construction agglomeration areas (63.59 km\({}^{2}\)) in Tuen Mun, Yuen Long, and North District  
Figure 5: Evolution map of the centre of gravity of construction land in Hong Kong from 1972 to 2020.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **TA** & **NP** & **PD** & **LPI** & **SPLIT** & **ENN\_MN** \\ \hline
1972 & 11,820 & 121 & 0.99 & 37.32 & 8.62 & 806.32 \\
1980 & 14,864 & 136 & 0.91 & 34.39 & 7.14 & 716.68 \\
1990 & 17,641 & 148 & 0.84 & 31.62 & 7.25 & 579.18 \\
1995 & 19,832 & 152 & 0.59 & 30.20 & 5.82 & 528.34 \\
2000 & 19,283 & 158 & 0.82 & 32.98 & 6.97 & 516.67 \\
2005 & 21,853 & 163 & 0.75 & 29.20 & 8.2 & 508.21 \\
2010 & 23,414 & 147 & 0.63 & 27.18 & 10.08 & 500.93 \\
2015 & 23,545 & 146 & 0.62 & 27.00 & 9.76 & 499.40 \\
2020 & 23,632 & 102 & 0.47 & 24.48 & 10.64 & 521.40 \\ \hline \hline \end{tabular}
\end{table}
Table 8: List of Landscape Pattern Indices of construction Land in Hong Kong in the Study Years.  
were integrated by roads, replacing the previous construction agglomeration areas, with Kowloon City as the core (58.23 km\({}^{2}\)) and the largest construction land patch. This also marks the conclusion of the gradual northward shift of Hong Kong's urban centre of gravity obtained from the analysis above.
## 3 Theoretical Approach
### The Formulation of the Arrhenius Form
Consequently, we assume that the translation and rotation motions do not exist, and their respective partition functions are zeroes. Furthermore, the electrical partition function is also considered negligible because its contribution is considered too low in front of the excited state of the atoms or molecules at an ordinary temperature [38]. Then, the degradation of molecules is only due to the bond breaks. The bond breaks are the consequence of the high level of vibrational energy. Finally, for solid reactions, the problem is reduced to vibration inside a solid matrix. This reduction conserves the Arrhenius form Equation (7) and changes the interpretation of the pre-exponential factor, which is only now linked to a vibrational mode. Furthermore, studies by the [PERSON] [33], [PERSON] and [PERSON] [41] and the interpretation provided by [PERSON] [42] indicate that Equation (7) is an appropriate representation of the relevant thermodynamic principles described by [PERSON] [33] when only the vibrational mode is considered. In Equation (7), the activation energy depends on temperature. To simplify the equation, [PERSON]'s approximation is frequently used. This approximation indicates that the condition that enables the approximation of \(E_{act}\) to a constant is a negligible standard heat capacity for the reaction. Through using Equations (6) and (7) (and the detailed development in Appendix B), it has been shown that the Arrhenius structure can be developed from the Boltzmann distribution for a solid. Nevertheless, the key assumptions that need to be validated when applying this approach include demonstrating a negligible standard heat capacity and the presence of an energy barrier that allows for all the differences to be included within the pre-exponential factor.
## 2 The Proactive Routing Protocol  
### Optimized Link State Routing (OLSR) Protocol  
The main function of the OLSR is to store and up-to-date its routes in the table so that whenever a route is needed, it presents the route instantly at a time. The primary control messages are employed by OLSR: Hello and Topology Control (TC). The information about the adjacent nodes and the link status is obtained using hello messages. Information about one's own advertised neighbors, at the very least the MPR Selector list, is broadcast through TC messages. By diminishing the greatest possible time frame for the transfer of periodic control messages, the OLSR may strengthen the responsiveness to topological changes [9]. Moreover, OLSR supports Several Interface Design (MID) features that allow the usage of distinct OLSR interface addresses and the relaying of external routing updates by nodes, facilitating routing to external addresses. Relying on this data, nodes in the ad hoc network potentially function as gateways toward other networks. As OLSR consistently refreshes routes to entire network destinations, the protocol is well-suited for traffic designs where a substantial chunk of nodes interacts with another group of nodes in which the source and destination pairings are progressively changing. In order to manage its routing mechanism, OLSR does not require a centralized administrative system. OLSR interpretation has been analyzed using the Web of Science Core Collection database and implemented in the VOS viewer tool as depicted in Figure 1. The figure clearly shows that the OLSR protocol in Ad Hoc Networks is diverse and ongoing, with a focus on improving the protocol's performance, reliability, and scalability in different scenarios and applications.
## 3 Results
### Evaluation of Volatile Compounds by HS-SPME-GC/MS Analysis  
Forty-four volatile organic compounds (VOCs) belonging to various chemical classes, were identified through SPME-GC/MS and reported in Table 3. The main compounds belonged to eight classes, aldehydes (13), ketones (10), alcohols (7), furans (3), acids (10), terpen (4), esters (1), and phenols (2). Notwithstanding there are few available studies concerning the organoleptic quality of gnocchi and in general fresh pasta and, in particular, very poor data on flavor characterization. Some studies suggest that VOCs pasta might be affected by raw ingredients flavors (e.g., flour, potatoes, water) and by the different processing phases, such as blending, forming, packaging, and storage [38; 39; 40]. On the other hand, the VOCs of _Gnocchi_ pasta might derive from sugar microbial metabolism that enhances by the addition of water during mixing and dough formation phases, as (alcohols, 2,3-butanedione, 3-hydroxy-2-butanone, esters, and acids), and by enzymatic oxidation or autoxidation of fatty raw materials (e.g., aliphatic aldehydes, ketones, acids and alkylfuran). The aldehydes were the most represented volatile molecules in the headspace of gnocchisamples and increased during cooking. In detail, the compound hexanal was the most abundant aldehyde in all gnocchi samples investigated; while aldehydes and ketones increase during cooking; instead, alcohols, furans, and acids decrease with cooking. During the cooking process, the starch-lipid, probably included as complexes with amylose may be released, becoming susceptible to thermal oxidation, and although the enzymes (e.g., lipoxygenase and hydroperoxidase lyase) involved in the production of aldehydes are heat denatured, the high temperatures accelerate autoxidation, a process that does not require enzymatic catalysis [38]. In accordance with some authors, in general, increases in aldehydes and ketones during handling and cooking processes are associated with a decrease in the content of alcohol and ester compounds. The loss of alcohol compounds can be due to the high solubility of alcohols in water an effect greatly enhanced by the cooking process because of the large volume of water employed [40; 41]. The terpen compounds might derive from raw ingredients. There is no evidence in the literature on the influence of water origin (SW and TW) on the production of volatile compounds in gnocchi samples.
## 4 Results and Discussion
### Heat Resistivity  
The properties regarding heat resistivity of the different materials and raster angle orientations were examined using selected process parameter sets based on the results of the experimental investigations presented in Section 4.1.1 (see Table 5). For these experiments, the specimens represented in Figure 2 and the test set-up shown in Figure 4b were utilized. The testing was carried out at ambient temperature of 21 \(\pm\) 1 \({}^{\circ}\)C and the specimens were positioned on an insulation panel. To ensure comparability with the previous experimental findings, the specimens were again directly processed on object slides.  
Figure 13: Dependencies of the resistivity of M3D regarding temperature, extrusion speed and flow rate structured by raster angle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Raster Angle (\({}^{\circ}\))** & **Extrusion Temperature (\({}^{\circ}\)C)** & **Speed (mm/s)** & **Flow Rate (\%)** \\ \hline \multirow{3}{*}{PPC} & 0 & 240 & 20 & 105 \\  & \(\pm\)45 & 240 & 20 & 105 \\  & 90 & 240 & 20 & 105 \\ \hline \multirow{3}{*}{3 dk} & 0 & 240 & 20 & 105 \\  & \(\pm\)45 & 240 & 20 & 105 \\  & 90 & 240 & 20 & 100 \\ \hline \multirow{3}{*}{FFE} & 0 & 240 & 60 & 105 \\  & \(\pm\)45 & 240 & 60 & 100 \\  & 90 & 240 & 60 & 105 \\ \hline \multirow{3}{*}{M3D} & 0 & 170 & 20 & 105 \\  & \(\pm\)45 & 170 & 40 & 105 \\ \cline{1-1}  & 90 & 170 & 20 & 105 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Utilized process parameter sets of the specimens represented in Figure 5.  
In general, compared to the experiments regarding resistivity presented in Section 4.1, the resulting resistivity of the wider specimens used for these investigations regarding resistive heating is higher. Thus, a quantitative comparison of the results in Sections 4.1 and 4.2 is not possible. However, the influence of the raster angle orientation and the different fillers is equally obvious.
# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 3 Methods
### Parameter Estimation
#### 3.2.2 Physical Model Parameters  
In the previous work [27], the physical model parameter \(U\) was estimated using the volume of the building by fitting a power function of form \(f(x)=d\times x^{p}\) to the data of the known volumes and values of parameter \(U\). This way the value of the parameter could be estimated easily for thousands of buildings. In this work, also the physical model parameter \(C\) needs to be estimated for the indoor temperature model. A similar method for the estimation of the parameter \(C\) is used as in the case of parameter \(U\) in [27]. The resulting equation is \(C=5896\times V^{0.6059}\), where \(V\) (m\({}^{3}\)) is the volume of the building.
## 3 The Setup of the Simulation
### Verification of Calculation Method
#### 3.5.2 Far Field Noise  
Some researchers have tested the aerodynamic noise of flow around tandem cylinders based on QFF wind tunnel, and three far-field microphones were arranged in the experiment. The coordinates of the three measuring points are A (9.11 \(D\), 32.49 \(D\)), B (\(\sim\)8.33 \(D\), 27.815 \(D\)), and C (26.55 \(D\), 27.815 \(D\)). The three points are located on the central section of the cylinder. Figure 9 shows the comparison of the PSD (Power Spectral Density) of evolution with \(S_{t}\) between experiment and simulation at points A, B, and C.  
Figure 8: Comparison of pressure coefficients. (**a**) Upstream cylinder (**b**) Downstream cylinder.  
Figure 9: _Cont._  
Through the analysis of the Figure 9, it is concluded that the simulation results at the three observation points are in good agreement with the experimental results, and the peak frequency and amplitude can be accurately captured. The Strouhal numbers corresponding to the maximum peaks of points A, B, and C measured by test and simulation are \(S_{t}=0.234\), \(0.227\), \(0.240\) and \(S_{t}=0.224\), \(0.221\), \(0.221\), respectively. The simulation results at the highest peak are in good agreement with the experimental results, but there are some differences in other positions. Furthermore, from Figure 8, it can be found that the downstream cylinder is affected by the upstream cylinder, and the numerical model is scaled in the spanwise direction relative to the experiment. This makes the pressure coefficient of the downstream cylinder different from the experimental results, resulting in differences between the simulation and experimental values in the far-field sound pressure level. Compared with the results of flow and acoustics, the numerical model is feasible.  
Figure 9: Comparison of PSD evolution with \(S_{t}\) between experiment and simulation. (**a**) point A (b) point B (**c**) point C.
## 3 e-Health Cloud Challenges
### Technical Challenges
In addition, all associated organizations also require certain access to records or parts of records, while trying to protect their own data. Controlling such a maze of interconnected data and entities and using it is a huge issue. Patients, healthcare providers and any other associated organization will worry about the privacy of their information and would like to see proper solutions to offer acceptable privacy levels before moving to the cloud. * **Maintainability**: Unlike having an e-health system for individual healthcare service providers, an e-Health Cloud can be used for hundreds of healthcare service providers. This increases the complexity of system maintainability in the e-Health Cloud compared to an individual e-health system. The increase is mainly due to the need to consider the requirements and characteristics of the multiple heath services providers and clients. These requirements can be completely different while maintenance in the cloud infrastructures, software, or platforms must be done without having any negative affects on any services provided for any clients. In this regard and to simplify the maintenance processes, all cloud resources and provided services must be designed for easy and reliable maintenance. In addition, testing models can be developed to simplify the process and to reduce the time needed for maintenance.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### Intelligent Algorithm-Based Methods
#### 5.2.5 Neural Network-Based Methods  
The artificial neural network (NN) concept, proposed in the 1940s [24], is constituted by a large amount of neurons that have tunable weights. The NN has some advantages such as large scale parallel processing, distributed storage, self-organization, self-learning, _etc._ The function and structure of signal neurons are simple, but the system that consists of a large amount of neurons is complex. The NN can represent the basic characteristics of human brain. Compared with a digital computer, the NN is much closer to the human brain structure and function. The NN does not compute step by step based on specific program; it can adapt to a changing environment and summarize the rules to achieve specific functions. The first step of a NN is learning, which is based on criteria. One such criterion is that if the network makes a wrong decision, then the probability that it will make the same mistake in the future should be reduced. Therefore, in general, the more neurons in the network, the better the performance is.  
In [93], each node uses the neural network to manage the routing to a sink node. The nodes use the latency, the throughput, the error rate, and the duty cycle to choose the best next hop relay node. Based on link state routing protocols and a neural network, in [94], the nodes use the number of hops, the bandwidth, the load, and the propagation delay to create metrics. The metrics use a neural network to optimize routing selection and network resource occupancy. As in a mobile _ad hoc_ network, multipath routing can improve the network reliability greatly. In [95], each node in the network will be equipped with a neural network and can be trained and used to select optimal and sub-optimal high reliable disjoint paths, which can improve the network reliability remarkably.
# Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
## 4 Experimental Results
### Comparison with the State of the Art  
The proposed model was compared with four state-of-the-art saliency-detection models on five datasets. The deep saliency models included nonlocal features (NLDF) [29], contour to saliency (C2S) [11], visual saliency detection based on multiscale deep CNN features (MDF) [37], deep saliency with encoded low-level distance map and high-level features (ELD) [30], salient-object detection in low-contrast images via global convolution and boundary refinement (GCBR) [38], and aggregating multilevel convolutional features for salient-object detection (Amulet) [39].
## 3 Results  
### Solar Cell Characteristics  
#### 3.1.1 Comparison of Sputtered In\({}_{x}\)S\({}_{y}\)- and CBD CdS-Buffered Solar Cells  
Figure 1 depicts the \(I\)-\(V\) and EQE curves of the best In\({}_{x}\)S\({}_{y}\)- and CdS-buffered CIGS solar cells fabricated within this experimental campaign. The corresponding solar cell parameters are listed in the inset of Figure 1b. With 15.3%, the efficiency of the In\({}_{x}\)S\({}_{y}\)-buffered cell is reduced compared to the reference cell with CdS buffer with 17.1% efficiency--mainly due to a lower fill factor (FF). Nevertheless, the \(V_{\rm OC}\) is only slightly lower with 661 mV compared to 680 mV of the reference cell. There is no gain in short-circuit current density (\(J_{\rm SC}\)) for the In\({}_{x}\)S\({}_{y}\)-buffered cell as there is an absorption in the short wavelength region in the EQE (see Figure 1c). This feature is the result of a too-low bandgap energy and parasitic absorption due to the pronounced thickness of the In\({}_{x}\)S\({}_{y}\) layer. In addition, the EQE values of the In\({}_{x}\)S\({}_{y}\)-buffered cell are slightly below the CdS reference in the region between 500 and 1100 nm. The gradual decrease in EQE between 700 and 400 nm would tend to an indirect bandgap energy for In\({}_{x}\)S\({}_{y}\). We observed no significant effect, neither positive nor negative, with cold-light soaking under AM1.5G conditions on solar cell parameters of CIGS solar cells with sputtered In\({}_{x}\)S\({}_{y}\) buffer.  
1.2 Dependency of Solar Cell Efficiency on Substrate Temperature During In\({}_{x}\)S\({}_{y}\) Sputtering  
Figure 2 shows the dependency of solar cell parameters on substrate temperature during sputtering of the In\({}_{x}\)S\({}_{y}\) buffer layer. Relatively low efficiency values resulted for cells with In\({}_{x}\)S\({}_{y}\) sputtered at RT whereas there is an efficiency plateau for a substrate temperature around 200 \({}^{\circ}\)C. For significantly higher substrate temperatures such as 240 \({}^{\circ}\)C there is a decrease in the efficiency values again. The difference in efficiency is mainly affected by the \(V_{\rm OC}\) whereas FF and \(J_{\rm SC}\) have minor influence. The increase in the scattering of efficiency data for solar cells fabricated with In\({}_{x}\)S\({}_{y}\) sputtered at temperatures lower and higher than 220 \({}^{\circ}\)C is mainly driven by the FF.  
Figure 1: (a) Scheme of Cu(In,Ga)Se\({}_{2}\) (CIGS) thin-film solar cell with rf-sputtered In\({}_{x}\)S\({}_{y}\) buffer layer (not to scale); (b) \(I\)-\(V\) curves of CIGS solar cells either with rf-mangetron sputtered In\({}_{x}\)S\({}_{y}\) buffer at 220 \({}^{\circ}\)C substrate temperature (red) or with a CdS buffer layer(black) grown by chemical bath deposition (CBD), both with i-ZnO/ZnO:Al on top. Corresponding solar cell parameters are listed in the inset. \(J_{\rm SC}\) values are corrected with EQE; (c) External quantum efficiency (EQE) curves of the same cells.
## 1 Introduction  
With the popularity of location-aware mobile devices, such as global position system (GPS) navigation or smart phones, Spatial Crowdsourcing (SC) [1] services have made daily life more convenient. SC is a new type of service that combines location and crowdsourcing, which is used in applications such as environmental sensing, urban planning, convenient travel and so on. In SC, users who are called requesters release their tasks to the spatial crowdsourcing server (SC-server), and other users who are called workers upload their location to the SC-server. The SC-server then assigns the tasks to the workers based on the locations of both the tasks and the workers.  
To assign tasks of the requesters to the right workers, SC-server must know the locations of the workers because tasks are time-sensitive and workers should not have to travel long distances to perform and complete a task. However, locations are privacy information, which may reveal private information, such as home address or work places, and may be used to infer personal health, political view, or religious belief information by attackers. Because the SC-server may be untrusted, it may leak the locations of workers to other parties and may also use the locations for other commercial uses. Therefore, protecting the locations of workers is an urgent issue in SC.  
In this paper, we propose a framework for protecting the privacy of workers' locations in spatial crowdsourcing based on local differential privacy. Workers' locations are obfuscated locally with local differential privacy and then sent to SC-server for task assignment. The SC-server can only access workers' noisy locations rather than the real ones. The proposed framework does not require the participation of trusted third parties and workers can customize their privacy requirements.  
Our contributions are as follows:  
1. We propose a new framework that protects the location privacy of workers in SC. In comparison with other solutions, workers' locations are obfuscated locally with noises in the proposed framework so that our framework does not require the participation of any trusted third parties for data collection and privacy processing, and the workers can customize their privacy requirements.
2. We design a task assignment algorithm that assign workers to tasks with obfuscated locations.
3. We conduct experiments on real-world datasets which show that the proposed framework has privacy guarantees and modest overhead performance.  
The rest of the paper is organized as follows. Section 2 presents related work. Section 3 introduces the threat model and privacy protection framework. Section 4 discusses the local differential privacy generation algorithm. Experimental set-up and results are presented in Section 5. Finally, Section 6 concludes the paper.