esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Framework (Module) for Short-Sea Shipping System Evaluation
## 3 Theoretical Basis of the Evaluation of Short-Sea Shipping Processes
### Mathematical SSS System Model  
The main conditions for developing an SSS system based on existing shipping lines and ports are as follows:  
* Identification of existing shipping lines and ports, as well as cargo transportation services;
* Identification of locations of cargo consignors and consignees and the need for services;
* Identification of possible cooperation schemes, assessment of technical capabilities in order to find the best solutions for the analyzed cases, and assessment of possible alternative transportation services. The following criteria are possible for assessing possible cooperation solutions:  
* Time of provision of the SSS system or other modes of transport service;
* Price of the SSS system or other modes of transport service;
* Possibility of redirecting goods to another service provider when performing transportation services in ports or other locations;
* Energy demand for providing the SSS system or other modes of transport services and environmental impact. When assessing the main SSS factors influencing the sustainability of freight transport and the ability to compare different modes of transport, the created comparative index is presented as follows:  
\[K_{L}=f(P_{L},\ T_{L},\ D_{L},\ E_{L},A_{L}\ldots), \tag{1}\]  
where \(P_{L}\) denotes the transportation services' price function using SSS or other transport systems; \(T_{L}\) denotes the time function of transportation between two ports; \(D_{L}\) denotes the costs function of goods readdressing and distribution in ports or other locations; \(E_{L}\) denotes the energy demand function, including emissions; and \(A_{L}\) denotes additional payments (costs) for environmental impact (emissions). By applying the comparative index of SSS and other transport system services, it is possible to comprehensively assess the possible main transport services, to transport goods using the SSS system, and to use direct delivery of goods by other modes of transport (taking into account price, time, aspects of distribution and routing of goods, energy consumption, environmental aspects), which is very important when it is necessary to optimally use transport means, using goods from different senders and recipients, and deliver them to final recipients. Using the SSS system and direct delivery of goods, for example, by road transport, it is possible to find the most optimal solutions. Price function can be expressed using SSS and/or other transportation system services' lump sum prices (prices of different activities within transportation services in different ports or other terminals) and the number of used ports or intermodal facilities, as is presented by Equation (2), which must include transportation prices (tariffs) by various modes of transport, quantities or units of cargo transported, and weight coefficients for transportation by different modes of transport:  
Figure 2: Possible graphical representation of short-sea shipping.
## 5 Conclusions  
A set of sentiment analysis approaches consisting of BiLSTM and attention layers based on Valence-Arousal information was proposed in this research. It demonstrates the capability of predicting the sentiment behind the text on Chinese social media. This study verifies the efficacy of our method through the data from various competitions, and the experimental results show that our method is superior to the methods used by other teams in sentiment prediction of Valence-Arousal or Chinese short text. Furthermore, through visualization, it is possible to grasp the content and the trend of public opinion. Such results can even aid large organizations and governments in decision-making and management of their social media presence. Overall, our method is superb at forecasting word-level sentiment, and it can be used to observe social phenomenon where people in general have different points of view on the same issue. It is verified that our method can easily identify problems and analyze the image of government or business units. These results can help improve the management of social media accounts in the future and provide valuable insights.  
Conceptualization, Y.-C.C.; methodology, Y.-Y.C., Y.-C.C.; software, Y.-C.C.; validation, Y.-Y.C., Y.-M.C.; formal analysis, Y.-Y.C.; investigation, Y.-Y.C., Y.-M.C.; resources, Y.-C.C.; data curation, Y.-Y.C., Y.-M.C., W.-C.Y.; writing--original draft preparation, Y.-C.C., Y.-Y.C., Y.-M.C.; writing--review and editing, Y.-C.C., W.-C.Y.; visualization, Y.-M.C.; supervision, Y.-C.C.; project administration, Y.-C.C.; funding acquisition, Y.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and Technology of Taiwan under grant MOST 109-2410-H-038-012-MY2 and MOST 107-2410-H-038-017-MY3.  
The data of Valence and arousal can be found in website CVAW ([[http://nlp.innobic.yzu.edu.tw/resources/cvaW.html](http://nlp.innobic.yzu.edu.tw/resources/cvaW.html)]([http://nlp.innobic.yzu.edu.tw/resources/cvaW.html](http://nlp.innobic.yzu.edu.tw/resources/cvaW.html))), and other data can be found in GitHub ([[https://github.com/yuyachengg/sentiment](https://github.com/yuyachengg/sentiment)]([https://github.com/yuyachengg/sentiment](https://github.com/yuyachengg/sentiment))).  
The authors declare no conflict of interest.
# Correlation Plenoptic Imaging: An Overview
## 2 Correlation Plenoptic Imaging with Chaotic Light--First Scheme  
The general working principle of correlation plenoptic imaging can be understood by considering the first setup we have developed and experimentally demonstrated.  
As reported in Figure 1, light emitted by a chaotic (in our case, pseudothermal) source is divided in two arms by a beam splitter (BS): the reflected beam propagates in arm \(a\) for a distance \(z_{a}\) from the source before being detected by the sensor array \(\mathrm{D}_{a}\); the transmitted beam travels in arm \(b\), where light impinges on a transmissive object at a distance \(z_{b}\) from the source, and is then collected by a lens \(L_{b}\) (characterized by the focal length \(F\)) for reproducing on the sensor array \(\mathrm{D}_{b}\) the image of the source. The fluctuations of the intensity detected by each pixel of the two sensor arrays are monitored in time to reconstruct their spatio-temporal correlations. By correlating the total intensity on sensor \(\mathrm{D}_{b}\) with the one retrieved by each pixel of sensor \(\mathrm{D}_{a}\), we obtain the \"ghost image\" (GI) of the object, which is perfectly focused when \(z_{a}=z_{b}\)[61; 62; 63; 64; 65]; here, the chaotic source is playing the role of a focusing element, as shown by the unfolded representation (\"Klyshko-like picture\") of Figure 2a[61; 62]. The high-resolution detector array \(\mathrm{D}_{b}\) is not required to perform ghost imaging, where a _bucket detector_ with no spatial resolution behind the object is sufficient. However, in the present scheme, the detector array \(\mathrm{D}_{b}\) is crucial: by reproducing the image of the light source, it enables joining points of the object with points of the source plane, thus giving information on the direction of light, as required to perform plenoptic imaging. Both spatial and angular information are encoded in the correlation of intensity fluctuations; hence, correlation measurements provide the necessary information for performing the typical tasks of plenoptic imaging, such as refocusing out-of-focus details of the 3D object of interest (i.e., details placed outside the DOF of the standard image, at \(z_{a}\
eq z_{b}\)).  
Figure 1: First setup for achieving plenoptic imaging by intensity correlation measurements. Adapted with the permission from [41], copyright MDPI, 2016. The lens \(L_{\mathrm{b}}\) in the transmission arm of the beam splitter collects on \(\mathrm{D}_{b}\) the image of the chaotic source, which plays the role of the focusing element. A ghost image of the object is retrieved on the detector array \(\mathrm{D}_{a}\) by means of correlation measurements between the intensity fluctuations at \(\mathrm{D}_{a}\) and \(\mathrm{D}_{b}\).
## 2 Preliminaries and Related Works
### Error-Correcting Model in John and Yi's Model  
The flowing definitions mainly refer to [21]. Consider the problem of recovering a sparse signal \(x_{0}\in R^{n}\) from highly corrupted observations \(y\in R^{m}\):  
\[y=Ax_{0}+e_{0} \tag{1}\]  
where \(e_{0}\in R^{m}\) is a sparse vector of errors of arbitrary magnitude. The model for \(A\in R^{m\times n}\) captures the idea that the messages consists of small deviations about a mean, hence the model for \(A\) likes a \"bouquet\". \(A\) are i.i.d. sampled from a Gaussian distribution:  
\[\begin{array}{c}A=\left[a_{1}\ldots a_{n}\right]\in R^{m\times n},\ a_{i} \sim_{iid}\mathbb{N}(\mu,\frac{\
u^{2}}{m}I_{m}),\\ \left\|\mu\right\|_{2}=1,\ \ \left\|\mu\right\|_{\infty}\leq\mathbb{C}_{\mu}m^{-1/2}. \end{array} \tag{2}\]  
The two assumptions on the mean force it to remain incoherent with the standard basis as \(m\to\infty\).  
**Assumption 1**.: _(Weak Proportional Growth). A sequence of signal-error problems exhibits weak proportional growth with parameters \(\delta>0,\ \rho\in(0,1),\ C_{0}>0,\ \eta_{0}>0\), denoted \(\mathrm{WPG}_{\delta,\rho,C_{0},\eta_{0}}\), if as \(m\to\infty\),_  
\[\frac{n}{m}\to\delta,\ \frac{\left\|e_{0}\right\|_{0}}{m}\to\rho,\ \left\|x_{0}\right\|_{0}\leq \mathbb{C}_{0}\mathrm{m}^{1-\eta_{0}} \tag{3}\]  
We say the cross-and-bouquet model is \(\ell^{1}-\)_recoverable at \((I,I,\sigma)\)_ if for all \(x_{0}\geq 0\) with supporting \(I\) and \(e_{0}\) with supporting \(J\) and signs \(\sigma\),  
\[\begin{array}{c}(x_{0},e_{0})=\ \
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 2 Materials and Methods
### UAV Experimental Environment Selection and Radar Parameter Settings
#### 2.2.2 Checking the Experimental Sites  
Check the surroundings according to the satellite map, and stay away from electromagnetic interference, mountains, and other dangerous areas. Select a specific area that is appropriate for the operation of the UAV, with a suitable altitude, and apply to the airspace authority with the chosen location.
## 2 Study Area and Data
### Data Preparation
#### 2.2.1 Field Data  
The field data contained soil and vegetation parameters for the entire rice growth period. In each subarea, 30 sampling points were equally distributed and surveyed on the four following dates: 4 July, (day of year (DOY) 185), 29 July (DOY 210), 28 August (DOY 240) and 17 September (DOY 260) [38]. The LAI of rice was measured by the botanical canopy analyzer (AccuPAR model LP-80). Rice and soil samples were collected, preserved in sample bags and dried at room temperature to achieve a constant weight, the collected soil samples were sited at the rooting zone according to the EQSSC, and the depth of the collected soil samples was 20 cm. Then the samples were delivered to the laboratory for testing and analysis. In particular, the dry weight of the rice roots (WRT) were calculated (Table 2), and the concentration of heavy metals in the soil and rice was measured using an atomic absorption spectrophotometer (AAS, Spectr AA 110/220, Varian, Palo Alto, CA, USA), and the nitrogen in the rice was measured by an elemental analyzer (Leco, San Jose, CA, USA) at the Chinese Academy of Agricultural Sciences [39] (Table 1).
## 3 Experimental Process and Analysis
### Taguchi Experiment  
Through the above analysis, the Taguchi experiment was carried out with \(R_{1}\), \(\theta_{1}\), \(h\), \(s\), and \(w\) as variables to be optimized. Taking the median values of the variables on both sides and their adjacent values as the impact factors, the impact factors of the optimized parameters are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & \(R_{1}\)**/mm** & \(\theta_{1}\)/\({}^{o}\) & \(h\)**/mm** & \(s\)**/mm** & \(w\)**/mm** \\ \hline impact factor 1 & 10 & 52.5 & 12.5 & 2.75 & 42.5 \\ impact factor 2 & 14 & 75 & 15 & 3.5 & 45 \\ impact factor 3 & 18 & 97.5 & 17.5 & 4.25 & 47.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters to be optimized and impact factors.  
Figure 4: Structure and parameters of the electrostatic ring.  
The orthogonal table \(\mathbf{L}_{9}(3^{5})\) is obtained by the Taguchi method, where \(\mathbf{L}\) represents an orthogonal matrix; 9 stands for the number of trials; 3 is the number of times each variable is taken, and 5 symbolizes the number of optimization parameters. According to the orthogonal table, the maximum electric field intensity can be achieved by the mutual call between MATLAB and ANSYS, as shown in the Table 3.  
From the results obtained by Table 3, the proportion of each parameter is calculated by Equation (7).  
\[EE=3{\sum_{i=1}^{3}}\big{[}m_{x_{i}}(E_{i})-m(E)\big{]}^{2}, \tag{7}\]  
where \(x_{i}\) represents \(R_{1}\), \(\theta_{1}\), \(h\), \(s\), and \(w\); \(EE\) is the proportion of each optimization parameter; \(m_{xi}(E_{i})\) is the average value of \(E_{\max}\) under the \(i\)th impact factor of \(x\); and \(m(E)\) is the average value of \(E_{\max}\). The proportion of each optimization parameter is listed in Table 4.  
Figure 5: Flowchart of insulation structure optimization of power transformer based on RSM.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of Experiments** & \(\mathbf{R_{1}/mm}\) & \(\mathbf{\theta_{1}/^{o}}\) & \(\mathbf{h/mm}\) & \(\mathbf{s/mm}\) & \(\mathbf{w/mm}\) & \(\mathbf{E_{\max}/(V/mm)}\) \\ \hline
1 & 1 & 1 & 1 & 1 & 2 & 10385.95 \\
2 & 1 & 2 & 2 & 2 & 1 & 9794.79 \\
3 & 1 & 3 & 3 & 3 & 3 & 9615.61 \\
4 & 2 & 1 & 2 & 3 & 1 & 9170.99 \\
5 & 2 & 2 & 3 & 1 & 2 & 9928.34 \\
6 & 2 & 3 & 1 & 2 & 3 & 9866.17 \\
7 & 3 & 1 & 3 & 2 & 3 & 9681.51 \\
8 & 3 & 2 & 1 & 3 & 2 & 9167.40 \\
9 & 3 & 3 & 2 & 1 & 1 & 9559.65 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Orthogonal matrix and results of \(\mathbf{L}_{9}(3^{5})\).  
It can be seen from Table 5 that \(R_{1}\), \(h\), \(s\), and \(w\) have greater influence on the maximum electric field intensity,while \(\theta_{1}\) can be eliminated from the impact variables. The Taguchi method effectively reduced the workload of the numerical analysis of the subsequent response surface. In the following experiment, for the convenience of analysis, the electrostatic ring was divided into two arcs; both of them were 90 degrees. The values \(R_{1}\), \(h\), \(s\), and \(w\) were selected as experimental variables.
## 2 Materials and Methods
### Study Sites
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline
**Classes** & \multicolumn{6}{c}{**Oak Forest Stands**} & \multicolumn{6}{c}{**Pine Forest Stands**} & \multicolumn{6}{c}{**Larch Forest Stands**} \\ \hline Species & Quercus & Quercus & Quercus & Quercus & Quercus & Quercus & Quercus & Quercus & Quercus & Quercus & Quercus & Quercus & Quercus & Quercus & Quercus \\ Species & _actist_ & _actist_ & _actist_ & _actist_ & _actist_ & _actist_ & _actist_ & _actist_ & _actist_ & _actist_ & _actist_ & _actist_ & _actist_ & _actist_ & _actist_ \\ Types & Evergreen & Declors & Declors & Declors & Declors & Declors & Escrowie & European & European & European & Declors & Declors & Declors & Declors & Declors \\ Location & Kasuya & Groupe & Solama & Schuhary & Stahary & Graug- & Woosen & Hooge- & Floibary- & Loughan & Tschurde- & Tschurde- & Tschurde- & Tschurde- & Bayrakia \\ Nation & JPN & ROK & ROK & DRPK & RUS & RUS & DRPK & CHN & RUS & RUS & CHN & MMC & MGK & RUS \\ Longitude (°) & 130,350 & 127,149 & 126,955 & 126,002 & 134,373 & 27,162 & 127,320 & 119,950 & 150,334 & 107,985 & 127,340 & 109,068 & 107,316 & 114,411 & 117,278 \\ Latitude (°) & 33,600 & 737,309 & 38,914 & 46,707 & 37,486 & 39,149 & 48,200 & 52,185 & 52,350 & 45,200 & 47,835 & 49,100 & 52,397 & 54,960 \\ Elevation (°) & 355 & 340 & 250 & 169 & 160 & 425 & 418 & 836 & 730 & 520 & 500 & 1797 & 1021 & 992 & 1621 \\ Aspect (°) & E & NW & NW & N & S & SW & NW & SW & SW & SW & SW & NW & NW & NW & NW \\ Slope (°) & 15 & 14 & 15 & 21 & 15 & 19 & 21 & 1 & 26 & 25 & 6 & 24 & 8 & 32 & 34 \\ Sand (°) & 42 & 50 & 52 & 42 & 15 & 50 & 42 & 89 & 65 & 55 & 97 & 31 & 65 & 34 & 38 \\ Sii (°) & 36 & 13 & 25 & 36 & 48 & 45 & 36 & 6 & 25 & 37 & 45 & 36 & 31 & 33 & 31 \\ Clay (°) & 22 & 37 & 23 & 22 & 37 & 5 & 22 & 5 & 10 & 95 & 19 & 30 & 24 & 20 & 21 \\ Soft depth (°) & 1,00 & 1,00 & 1,00 & 1,00 & 1,00 & 1,00 & 1,00 & 1,00 & 0,080 & 0.95 & 1,00 & 0,900 & 1,00 & 1,00 & 0,60 \\ Fast horizon (°) & 5 & 0 & 7 & 26 & 32 & 0 & 0 & 31 & 6 & 35 & 3 & 16 & 16 & 18 & 5 \\ West West West (°) & 60 & 18 & 0 & 54 & 10 & 5 & 6 & 0 & 10 & 10 & 2 & 4 & 8 & 0 & 2 \\ Forest age & 140 & 80-200 & 20-4 & - & 80 & 80+ & - & 40-00 & 95 & 60 & 40-00 & 110 & 60-150 & 100 & 100 \\ Mortality (°) & 1,7-20 & 1-2,7-20 & 20+ & 20+ & 17-20 & 1,4+ & 14-30 & 14-30 & 0.7 & 08-20 & 50-56 & 0.2 & 0.2 & 0.2-03 & 0-23 \\ Fire fraction (°) & - & - & - & 0.005 & - & - & - & 0.25 & 0.15 & - & 0.15 & - & 0.08 & 0.08 \\ Options \({}^{\circ}\) & E & N & E & C, E & P, N & C, E & P, N & C, E & P, N & E, N & C, E & P, N & P, N & P, N \\ \hline \hline \end{tabular}  
* 1: Countly code ROK, DRPK, JPN, CHN, MNC, and RUS represent South Korea, North Korea, Japan, China, Mongolia, and Russia, respectively. 2: Mainly based on [PERSON] et al. (2008) and [PERSON] et al. (2017), but also followed site-specific information. 3: Options represent historic intervention in certain forest stands (°: Planting, C: Clear-cutting, N: Naturally preserved, L: Logogu).  
\end{table} 
Table 1. Site-specific information, including mortality rates, fire fractions, and applied management options.
## 1 Introduction  
The change from two-dimensional integrated circuits (2D ICs) to 3D ICs is a trend in the electronics industry to break through the limits of very-large-scale integration circuits (VLSI) [1; 2]. The interposer structure, which is a crucial part in 3D ICs, has been served as a substrate that can provide a rigid insulating layer between stacked chips and can provide electrical connectivity between layers through the embedded vertical vias [3].  
Though silicon with through silicon vias (TSVs) has served as the interposer in 3D ICs for many years [4; 5; 6], it is far from popular in the industry because of its high cost during the fabrication process which involves deep reactive ion etching (DRIE), chemical vapor deposition (CVD), physical vapor deposition (PVD), etc. [7]. In addition, the coefficient of thermal expansion (CTE) mismatch among silicon (2.5 ppm/K), silicon dioxide (0.6 ppm/K), and copper (17.5 ppm/K) also will bring many serious reliability problems, such as cracks and delamination. Recently, glass with superior insulating property and low cost has been introduced as a replacement for silicon [8]. Unfortunately, the formation of the vias in glass with a diameter of less than 50 \(\upmu\)m was proved to be very difficult due to the frangibility of glass. Anodic aluminum oxide (AAO) films can provide natural close-packed parallel cylindrical nano-pores [9; 10; 11], thus the drilling process and other high-cost processes for via pores can be avoided.  
To endow the interposer with electrical conductivity at special areas, the AAO can be filled with copper nano-pillars (CNPs) using the electrodeposition process. The conventional electrodeposition process needs to prefabricate high-quality seed layers on the interposer surface, which involves complicated fabrication processes [12; 13; 14; 15], such as the sputtering process, as indicated in Figure 1a. Furthermore, the high pore-filling percentage in AAO is still a bottleneck [16]. The effects of technical parameters (current density and temperature) on the pore-filling percentage need to be systematically investigated.  
In this study, indium tin oxide (ITO) conductive glass, an ideal material with high conductivity and extremely flat surface was used as the conductive substrate during the electrodeposition process. As shown in Figure 1b, the AAO film was tightly fixed onto the ITO glass surface using insulating sealant and insulated adhesive tape. The ITO glass served as a frame and an electrode for the AAO film during the subsequent electrodeposition process and the uniform nano-pillars were obtained in an AAO template. After deposition, the ITO glass can be easily removed from the AAO structure. To achieve a high pore-filling percentage, electrodeposition parameters (current density, temperature) of Cu nano-pillars were investigated.
## 3 Proposed Scheduling Scheme  
### Problem Statement  
There is a lack of an overall framework that provides all of the fundamental tools to manage end-to-end resources and traffic, and the existing solutions continue to be insufficient (they only take into account latency). Although the Internet of Things (IoT) platform is very complicated, heterogeneous, and large, there is a prominent lack of cognitive processesthat may reduce the amount of human involvement in the quality-of-service management process. A decrease in quality of service (QoS) levels in service provisioning is caused by the absence of mobility-aware latency-constrained service management at the edge. This is particularly relevant for an increase in communication latency. Because of the use of architectures that were developed for the Internet before the development of wireless technologies, service provisioning is affected negatively. At that time, most of the Internet was made up of static nodes, resulting in less fluctuation in the topology, and applications were not sensitive to delays. To reduce interruptions and latency for users of services at the edge, this research aims to examine problems and theories connected to service management. Similar to the previous mark, this research aims to reduce the need for service migrations and context transfers prompted by user mobility. This is because these events substantially influence the quality of service. Regarding addressing mobility-related difficulties at the network's edge, this research focuses primarily on using software-defined networking (SDN). Allowing for the fine-grained and active management of communication flows at the edge, the global view of network entities made possible by this networking paradigm may make it easier to implement innovative solutions.  
Real-time service handling is provided to mobile users in the IoT environment by deploying the SDN. Here, concurrent service sharing is estimated for dynamic users on heterogeneous platforms. This work determines a point-of-contact-based infrastructure for concurrent processing in SDN-based IoT applications. The scope of this work is to avoid latency, failure, and outage by increasing the service distribution to the valid user. Thus, concurrent processing is estimated for the mobile devices in IoT applications concerning the SDN. A formal representation of the CS3 is presented in Figure 1. The simulation experiments are modeled using the Contiki Cooja simulator, where 200 mobile users are placed. An IoT cloud architecture with seven service providers, each with 1 TB of storage, is used for service distribution. The transmit power of the devices is set as 30 dBm, and it operates in 60 scheduling intervals.  
Figure 1 presents the proposed scheme in the IoT-SDN platform. The SDN is responsible for service scheduling and power management using this scheme's data and control plane. It operates between the users and resources throughout scheduling, queuing, and  
Figure 1: Representation of the proposed CS3.  
distribution processes. Moreover, the power allocation for the resources is governed by scheduling and prior distribution.