esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2012**, _12_, 12814-12835; doi:10.3390/s120912814  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Benchmarking the Performance of Mobile Laser Scanning Systems Using a Permanent Test Field**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 2,3**  
1 Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, P.O. Box 15, FI-02431 Masala, Finland; E-Mails: [EMAIL_ADDRESS] (J.H.); [EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (A.J.)  
2 Helsinki Metropolitan University of Applied Sciences, FI-00079 Metropolia, Finland; E-Mail: [EMAIL_ADDRESS]  
3 School of Science and Technology, Aalto University, FI-00076 Aalto, Finland  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +358-9-2955-5202.  
_Received: 25 June 2012; in revised form: 14 August 2012 / Accepted: 10 September 2012 / Published: 19 September 2012_  
**Abstract:** The performance of various mobile laser scanning systems was tested on an established urban test field. The test was connected to the European Spatial Data Research (EuroSDR) project \"Mobile Mapping--Road Environment Mapping Using Mobile Laser Scanning\". Several commercial and research systems collected laser point cloud data on the same test field. The system comparisons focused on planimetric and elevation errors using a filtered digital elevation model, poles, and building corners as the reference objects. The results revealed the high quality of the point clouds generated by all of the tested systems under good GNSS conditions. With all professional systems properly calibrated, the elevation accuracy was better than 3.5 cm up to a range of 35 m. The best system achieved a planimetric accuracy of 2.5 cm over a range of 45 m. The planimetric errors increased as a function of range, but moderately so if the system was properly calibrated. The main focus on mobile laser scanning development in the near future should be on the improvement of the trajectory solution, especially under non-ideal conditions, using both improvements in hardware and software. Test fields are relatively easy to implement in built environments and they are feasible for verifying and comparing the performance of different systems and also for improving system calibration to achieve optimum quality.  
**Keywords:** GNSS/INS; reference data; systems; laser scanning; point cloud; accuracy; mobile; geometric
# Title-to-Length Coupling Analysis of an Off-Axis Optical Bench Design for NGGM
## 3 Local TTL Coupling
### Piston Effect  
For the off-axis layout, imaging systems are employed to minimize the beamwalk on the photodiodes, and with this setup the lever arm effect is significantly suppressed. We will only consider the piston effect in what follows. Depicted in Figure 6, piston noise is generated due to the offset between the geometric centre and the pivotal point of the FSM [14].  
Figure 5: Results of the local TTL coupling simulation. The figures depict the local TTL coupling in yaw and pitch degrees of freedom.  
Figure 6: Illustration of the piston effect. The pivot of the FSM is shifted longitudinally by d1 and laterally by d2 against the reflection point. Beam path 1 represents the nominal propagation when there is no offset exists. Beam path 2, drawn in dashed lines, represents the reflective beam when the offset exists [14].  
The RX beam is rotated around the RX-RP point in yaw and pitch degrees of freedom for \(\pm\)2 mrad. Local TTL coupling noise results are shown in Figure 7. For a local piston noise budget of 3 \(\upmu\)m/rad in both rotational degrees of freedom, the longitudinal offset between the rotation point and its nominal location should be kept under 200 \(\upmu\)m, while the lateral offset must be kept under 800 nm. From the experience in gravitational wave detection, the piston effect can be drastically reduced by a novel design of the FSM actuator, which minimizes the offset between the pivot and the reflection point [15].
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 3 Network and Energy Model
### Energy Model  
The energy model of this paper is the same as literature [33] and [34]. The energy consumption of nodes mainly occurs at the transmitter, the power amplifier, and the receiver to run the radio electronics. The model adopts the free space and the multi-path fading channel, depending on the distance between the transmitter and receiver. The energy consumption of a node is proportional to \(d^{2}\) when the propagation distance \(d\) is less than the threshold distance \(d_{0}\), otherwise it is proportional to \(d^{4}\). The total energy expended to deliver an \(l\)-bit packet from the transmitter to its receiver over a link of distance \(d\) is calculated by Equation (1):  
\[E_{T}(l,d)=\left\{\begin{array}{l}l\times E_{dec}+l\times\varepsilon_{fs} \times d^{2},d<d_{0}\\ \times E_{dec}+l\times\varepsilon_{mp}\times d^{4},d\geq d_{0}\end{array}\right. \tag{1}\]  
where \(E_{dec}\) is the energy consumed by a sensor node to transmit or receive 1-bit data, and \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are two amplifier coefficients of free-space model and multi-path fading model respectively. The threshold distance \(d_{0}\) is calculated by Equation (2):  
\[d_{0}=\sqrt{\varepsilon_{fs}/\varepsilon_{mp}} \tag{2}\]  
where \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are two parameters of the amplifier. When \(d<d_{0}\), the energy consumption of the sensor nodes uses the Free-space model, and the amplifier parameter is \(\varepsilon_{fs}\). When \(d\geq d_{0}\), the energy consumption of the sensor nodes uses the multi-path fading model, the amplifier parameter is \(\varepsilon_{mp}\).  
In this paper, the maximum transmission distance of a node is controlled not to exceed \(d_{0}\), which ensures that nodes in the same cluster are within the transmission range of the proposed PSO-based uneven dynamic clustering method.
# FTIR Characterization of Asphalt SARA Fractions in Response to Rubber Modification
## 3 Results and Discussion  
### Analysis of Asphalt Fractions  
The SARA fractions of neat binder and A-RBs are depicted in Figure 3. The coefficient of variation (COV) for saturates varied from 0.02 to 1.12%, for aromatics from 0.75 to 4.00%, for resins from 0.58 to 1.77%, and for asphaltenes from 3.21 to 5.51%. A-RBs had lower saturates plus aromatics and higher resins plus asphaltenes compared to the neat binder. Saturates showed the highest reduction, reaching the lowest value for A-RB 220-3000 (13.12%). A-RBs 190-600 and 190-3000 showed a decrease in saturates by 17.31 and 15.59%, respectively, compared to the neat binder. Aromatics decreased by 2.89 to 15.08% for A-RBs as compared to neat binders. This reflects the role of saturates and aromatics in rubber swelling during the interaction with asphalt. Resins showed minimal variations. They decreased slightly in A-RB 190-600 and then increased in A-RBs 190-3000 and 220-3000. However, asphaltene increased across A-RBs. Asphaltene content increased by 30.65 and 30.21% in A-RBs 190-600 and 190-3000, respectively, and by 56.25% in A-RB 220-3000 relative to the neat binder. The \(I_{c}\) value of the neat binder was 0.43 and increased for A-RBs 190-600 and 190-3000, reaching a value of 0.46. For A-RB 220-3000, the \(I_{c}\) was 0.53, indicating colloidal instability (\(l_{c}>\) 0.5) [35]. The COV values for \(l_{c}\) ranged from 2.24 to 4.35%.
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 4 Design of Applications
### Fiber Optic Oxygen Sensor Based on Phosphorescence Lifetime Measurement
#### 4.2.2 Measurement Method  
The proposed method involves measuring the lifetimes of the phosphorescent emission, in which oxygen acts as an attenuator of the emission. The lifetimes are not affected by variations in the intensity of excitation light, in the amplitude of light emitted by the decomposition of the luminescent molecule, drifts in the gain of the detection system, or by optical interference in the optical path; therefore, it is not necessary to correct for the emitted light as a function of the excitation light. In addition, the system is insensitive to dirt in the optical elements. For the measurement of phosphorescence lifetimes, the sensor is excited with a pulse of high-intensity light and at a certain wavelength. The sensor emits light at another wavelength, the intensity and lifetime of which depends on the concentration of the substance being measured (in this case the concentration of oxygen is being measured). The sensor acts by attenuating the emission of the luminophore, as we noted in Section 2.
## 3 Optimization Modeling for the Uniformity Design of Accuracy Lifetimes
### Determination of Design Variables  
The objects to be designed are the accuracy loss laws of the system and its components. However, these time-varying accuracy loss functions have too many variables to be designed. It is unrealistic to use the accuracy loss functions as design variables.  
For general engineering designers, it is relatively easy to gain the average velocities \(\Delta\overline{v}_{ei}\) (\(i=1,2,\ldots,n\)) of the accuracy loss of some components under their working conditions. Therefore, the average velocity of the accuracy loss of each component is taken as a design variable in the optimization model, and the measurement system can be improved according to the designed values \(\Delta\overline{v}_{ei}^{\prime}\).
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 5 Proposed Energy Efficient and Reliable Routing Approach  
In this section, we first present a general idea of the network model and assumptions related to the energy-efficient routing approach. Following this, we describe the routing metric and OF to support the traffic differentiation for the IIoT network, employing the RPL routing strategy.
# Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
## 3 Higher Education Evaluation System
### Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)
#### 3.4.2 Calculation of Weighted TOPSIS Method  
The detailed calculation process is as follows.  
Assume that there are \(m\) research objects and \(n\) evaluation indicators (attributes) and the \(i\)-th evaluation value of the \(j\)-th evaluation indicator is \(x_{ij}\). Then, we set up the initial data matrix \(X\).  
\[X=\left(\begin{array}{cccc}x_{11}&x_{12}&\cdots&x_{1n}\\ x_{21}&x_{22}&\cdots&x_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ x_{m1}&x_{m2}&\cdots&x_{mn}\\ \end{array}\right) \tag{5}\]  
Considering that the dimension of each index may be different, the initial judgment matrix \(X\) is normalized, and then we have the standardization matrix \(V\).  
\[V=\left(\begin{array}{cccc}v_{11}&v_{12}&\cdots&v_{1n}\\ v_{21}&v_{22}&\cdots&v_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ v_{m1}&v_{m2}&\cdots&v_{mn}\\ \end{array}\right) \tag{6}\]  
Then, we define \(W\) as the evaluation indicators' weight matrix and construct the weighted judgment matrix \(F\).  
\[F=VW=\left(\begin{array}{ccccc}v_{11}&v_{12}&\cdots&v_{1n}\\ v_{21}&v_{22}&\cdots&v_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ v_{m1}&v_{m2}&\cdots&v_{mn}\\ \end{array}\right)\left(\begin{array}{ccccc}w_{11}&&&\\ &w_{22}&&\\ &&\ddots&w_{mn}\\ \end{array}\right) \tag{7}\] \[=\left(\begin{array}{ccccc}f_{11}&f_{12}&\cdots&f_{1n}\\ f_{21}&f_{22}&\cdots&f_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ f_{m1}&f_{m2}&\cdots&f_{mn}\\ \end{array}\right)\]  
According to the weighted judgment matrix \(F\), we determine the ideal best plan and the ideal worst plan.  
The ideal best plan:  
\[f_{i}^{+}=\left\{\begin{array}{ll}\max_{j}\bigl{(}f_{ij}\bigr{)},&\text{the positive indicators}\\ \min_{j}\bigl{(}f_{ij}\bigr{)},&\text{the negative indicators}\\ \end{array}\right. \tag{8}\]  
The ideal worst plan:  
\[f_{i}^{-}=\left\{\begin{array}{ll}\min_{j}\bigl{(}f_{ij}\bigr{)},&\text{the positive indicators}\\ \max_{j}\bigl{(}f_{ij}\bigr{)},&\text{the negative indicators}\\ \end{array}\right. \tag{9}\]  
The Euclidean distance between a research object and an ideal plan is calculated as follows.  
\[C_{i}^{+}=\sqrt{\sum_{j=1}^{m}\bigl{(}f_{ij}-f_{i}^{+}\bigr{)}^{2}} \tag{10}\]  
\[C_{i}^{-}=\sqrt{\sum_{j=1}^{m}\bigl{(}f_{ij}-f_{i}^{-}\bigr{)}^{2}}\]  
The relative closeness of each evaluation object is calculated as follows.  
\[C_{i}^{*}=\frac{C_{i}^{-}}{C_{i}^{+}+C_{i}^{-}},i=1,2,\ldots,m \tag{11}\]  
\(0\leq C_{i}^{*}\leq 1\), the closer \(C_{i}^{*}\) is to 1, the better the research object is.
## 3 Results and Discussion
### Pigments
#### 3.2.2 Grey  
The spectrum for the greyish colorations shows bands at 1337 and 1582 cm\({}^{-1}\), suggesting the use of black carbon pigment, and, once again, at 276 and 1085 cm\({}^{-1}\) (calcite). The Raman spectrum of amorphous carbons is characterized by a band at 1582 cm\({}^{-1}\), given to the G (graphite) band in-plane aromatic ring CC stretching vibrational mode with E\({}_{\rm 2g2}\) symmetry [37], and an additional D band at around 1350 cm\({}^{-1}\), known as a \"disorder\" band [38; 39]. Most likely, the pigment has a vegetable origin and consists of charcoal obtained from the carbonization of wood. This statement is corroborated by the absence of the additional phosphate stretching band at 960 cm\({}^{-1}\) related to the presence of bone/ivory black pigment [37]. The bands of calcite detected by Raman indicate that the black pigment was mixed with calcite or lime.  
Data obtained from Raman spectroscopy, performed on the greyish pictorial film, are, once again, supported by SEM-EDS analysis on the black particles within the pictorial layer in sample S_4. Peaks of carbon and oxygen were detected, corroborating the presence of the carbon black pigment (Figure 10, Table 4).  
EDS analyses punctually performed on areas other than the black inclusions of the greyish pictorial film indicated calcium as the main element, followed by silicon, aluminum, sulfur and potassium. No evidence of copper was found, keeping out the presence of copper-based pigments such as azurite (basic copper(II) carbonate) and Egyptian blue (artificial pigment consisting of cuprorivaite, CaCuSi\({}_{4}\)O\({}_{10}\)) [40]. In addition, as evidenced by the autoptic observations made by restorers and the optical microscopy investigations, the greyish pigment was mostly applied to fresh and wet plaster, according to the \"fresco\" technique. This assumption lets us discard the use of indigo/wood blue. Even if the presence of this pigment is quite rare in wall painting, its use is considered incompatible with strictly alkaline conditions, and its lightfastness is inadequate [41]. Based on the EDS  
Figure 10: EDS spectrum punctually acquired on the greyish pictorial layer (sample S_4) (highlighted by the red square) and related SEM image.  
and Raman spectroscopy examinations, it is possible to conclude that blue chromophores have not been unequivocally detected.  
As known from the past literature, the formula for the execution of the blue backgrounds consisting of the application of a black carbon layer was widespread after the iconoclastic period. The procedure was followed by the addition of a second layer of pure lime to achieve a lighter tone [42]. It is highly probable that the same executive methodology was applied in this case.
# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
## 4 Proposed Framework
### Lower-Fidelity Model Design
#### 4.2.1 Lower-Fidelity Models Design for the Dynamic System  
As mentioned in Section 2, the fidelity of the DTSS model is determined by the state transition function. Therefore, lower-fidelity models are designed to simplify this function of the current target model that already has a high fidelity. For simplification of the function, this paper suggests two methods: elimination and projection. The elimination method deletes the terms that have little effect on the accuracy of output in the state transition function and the projection method fixes the values of variables in the state transition function.  
Figure 7 shows an example of lower-fidelity model design for a UUV surfacing simulation. The target model is the maneuver model for UUV motion in six degrees of freedom. We assume that the input of the UUV maneuver model is an elevator \({}_{S}(t)\), the output is a depth \(Z(t)\), and one of the state variables is a pitch \(q(t)\). The state transition function of the model is a differential equation, which consists of many terms. Further explanations for the maneuver equation are found in [36]. Depending on the level of eliminating terms, an intermediate-fidelity model and a low-fidelity model can be developed by using the elimination method, which is used to derive \(f_{M}\) and \(f_{L}\) from \(f_{H}\) and \(f_{M}\), respectively. The more terms that are eliminated, the more the accuracy of the state and out variables decreases.  
Table 1 shows empirical results regarding the simulation execution time and simulation accuracy for the above example. We first measured the elapsed time for a single simulation. The simulation of the high-fidelity model took about twice as much execution time as the low-fidelity model. For example, if the number of iterations of this simulation is more than 500,000, the high-fidelity model's simulation takes twenty-three more days than that of the low-fidelity model. We next calculated \(\epsilon\) derived from Equation (1). In this experiment, the desired result is obtained from the high-fidelity model (this means that the reference model is the high-fidelity model \(f_{H}\)). For simulation accuracy, the high-fidelity and the intermediate-fidelity models had similar \(\epsilon\) values; however, the low-fidelity model had an \(\epsilon\) value with a significant difference.  
Figure 6: Examples of interest regions: (**a**) two regions defined from one interest region variable (IRV); (**b**) one region defined from two IRVs.
# Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
## 4 Determining the Voltage Ratio and Measurement Uncertainty
### Uncertainty in Estimating the Input Resistance \(R_{s}\) of the Signal Acquisition Card  
According to the technical specification of the signal acquisition card used, the input resistance of the analog channel is 1 M\(\Omega\)\(\pm\) 0.5%. Therefore, the standard uncertainty can be estimated from the equation:  
\[u(R_{s})=\frac{R_{s}\cdot 0.005}{\sqrt{3}} \tag{19}\]
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
## 2 Model and Problem Formulation  
### PMSG-WT Configuration  
In Figure 2, a gearless WPCS equipped with a PMSG is connected to the power grid through full-rate back-to-back voltage source converters. Wind power extracted by the WT is transmitted to the direct-driven PMSG. Then, the mechanical power is converted to electrical power by the PMSG. Then, the electrical power is supplied to the power grid via a machine-side converter (MSC) and a grid-side inverter (GSC). The main objective of the MSC is to extract power from wind by controlling the mechanical rotation speed and electromagnetic torque or q-axis stator current, and produce the required stator voltage, whereas the GSC has to enable decoupled control the active and reactive power required by grid codes. The operation control of these two converters can be decoupled by a DC voltage link [16].
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 2 Applications of Personalized Video Summarization
### Personalized Sport Highlights  
Sports footage is produced using manual methods; editing requires experience and is performed using any video editing tool [37]. The personalized video summary is useful for creating sports highlights so that each viewer can view the snapshots that interest them from each sport activity. Examples of such works are presented below.  
[PERSON] and [PERSON] [18] proposed a flexible framework to produce personalized basketball video summaries in real time, integrating general production principles, narrative user preferences on story patterns, and contextual information. [PERSON] et al. [38] generated video highlights by summarizing full-length matches. [PERSON] et al. [39] proposed a complete autonomous framework production process for personalized basketball video summaries from multisensor data from multiple cameras. [PERSON] and [PERSON] [40] described how the user interaction of previous viewers could be used to create a personalized video summarization, computing more interesting plays in a game. [PERSON] et al. [41,42] produced a hybrid personalized video summarization framework by summarizing a broadcast soccer video that combines content truncation and adaptive fast forwarding. [PERSON] et al. proposed a personal video summarization system that integrates the global positioning system and the radio frequency identification information for marathon activities [43]. [PERSON] and [PERSON] [44] presented an approach that can be applied by multiple matches to create complex summaries or the summary of a match. [PERSON] et al. [45] proposed a framework that uses neural networks to generate a personalized video summarization that can be applied to any sport in which games have a sequence of actions. To select the highlights of the original video, they used the actions of the players as a contract. To evaluate the method, this project uses the case of Kendo.
# Sentiment-Aware Word Embedding for Emotion Classification
## 4 Experiments
### Description of Datasets  
The data in this paper comes from Weibo (a popular Chinese social networking site). The training corpus for word embedding learning includes 10 million unlabeled blogs of COAE 2014 (The Sixth Chinese Opinion Analysis Evaluation) and 40 thousand blogs labeled using the emotion category in the task of NLPCC2018. The text of Microblog is labeled \"none\" if it does not convey any emotion. If the text conveys emotion, it is labeled with emotion categories from happiness, trust, anger, sadness, fear, disgust, or surprise. The number of sentences for each emotion category in training data and test data are described in Table 1. We can see from the tables that the distribution of different emotion classes is not balanced.  
This paper selected DUTIR, which is a Chinese ontology resource collated and labeled by the Dalian University of Technology Information Retrieval Laboratory, as emotional lexicons. There are seven kinds of emotion in the lexical ontology, happiness, trust, anger, sadness, fear, disgust and surprise, a total of 27,466 emotional words, and five emotional intensities, 1, 3, 5, 7, and 9, where 9 indicates the maximum strength and 1 is the least strength. This resource describes a Chinese word or phrase from different perspectives, including lexical categories, emotional categories, emotional intensity, and polarity of words.  
In the Chinese Emotion Word Ontology, the general format is shown in Table 2:  
The emotional categories are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Emotion Type**} & \multicolumn{2}{c}{**Training Data**} & \multicolumn{2}{c}{**Test Data**} \\ \cline{2-5}  & **Number** & **Percentage** & **Number** & **Percentage** \\ \hline HAPPINESS & 5200 & 13\% & 3000 & 15\% \\ TRUST & 5200 & 13\% & 2200 & 11\% \\ ANGER & 3600 & 9\% & 2000 & 10\% \\ SADNESS & 2800 & 7\% & 1600 & 8\% \\ FEAR & 2400 & 6\% & 1000 & 5\% \\ DISGUST & 4800 & 12\% & 2000 & 10\% \\ SURPRISE & 4000 & 10\% & 2200 & 11\% \\ none & 12,000 & 30\% & 6000 & 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of the emotional vocabulary ontology format.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Word** & **POS** & **Sense** & **Sense Number** & **Emotional Subcategory** & **Strength** & **Polarity** \\ \hline Happy & adj & 2 & 1 & happy & 5 & 1 \\ sad & adj & 1 & 1 & sad & 5 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of the emotional vocabulary ontology format. Part of speech (POS).