esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Capability Evaluation of a Satellite Communication System
### Capability Demand Satisfaction Degree Evaluation  
After obtaining the capability indicators for a specific mission, we employ PHFET to obtain the PHFBPA of each indicator and then combine the results in order to evaluate the  
Figure 1: Indicator system construction process.  
Figure 2: Capability indicator system of SCS.  
overall capability demand satisfaction degree of the SCS. The process of the evaluation is shown in Figure 3.  
To evaluate the satisfaction degree of the SCS using evidence theory, it is essential to establish the appropriate frame before combining information expressed as belief functions. Each element in the frame of discernment represents a level of satisfaction with the capabilities required for the task. For example, we define \(\Theta=\left\{\theta_{1},\theta_{2}\right\}\), where \(\theta_{1}\) indicates that the SCS possesses the capacity to meet the demands, and \(\theta_{2}\) suggests that the capabilities of the SCS do not meet the demands. Hence, when the degree of satisfaction is divided into \(N\) levels, the frame of discernment is established as follows:  
\[\Theta=\left\{\theta_{1},\theta_{2},\cdots,\theta_{N}\right\}, \tag{49}\]  
where \(\theta_{N}\) represents the \(N\)th degree of satisfaction.  
Firstly, we design the simulation experiments under specific operational scenarios to obtain the experimental data of each indicator according to the mission task and indicator system. Subsequently, these experimental data are utilized to calculate the PHFBPAs of each indicator for each class of satisfaction under the established FOD. In cases where there are insufficient training data, the distance-based method proposed in Section 3.2.1 can be employed, where the ideal value of the indicator represents the desirable value that must be achieved to complete the task. Conversely, the muti-classifier based method proposed in Section 3.2.2 can be used to derive the PHFBPAs. Then, we discount the PHFBPAs by solving the uncertainty and conflicts. Moreover, the different importance of each indicator can be adjusted by adding importance weights to the PHFBPAs. Finally, combine the PHFBPA of all indicators to obtain the capability demand satisfaction degree of the SCS under a specific mission.  
It is worth noting that the ideal value of the same indicator under different sub-tasks can take different values, thus constituting multiple degrees of membership values for each element in the PHFBPA. And the probability of each PHFE in the PHFBPA can also indicate the importance of sub-tasks.
## 4 Materials and Methods
### GC and GC-MS  
Samples (10 g) of the fresh material (leaves, stems, flowers, and roots) were weighed and placed in vials with 25 mL of headspace. A fused-silica fiber covered with a 75-\(\upmu\)m-thick layer of carboxen/polydimethylsiloxane (CAR/PDMS) was used to absorb volatile compounds. It was exposed in the headspace in the vials at 25 \({}^{\circ}\)C for 20 min, then removed from vials and introduced directly into the GC injector where thermal desorption of the analysis was performed at 250 \({}^{\circ}\)C for 3 min.  
GC analysis was carried out with an Agilent 6890N GC mainframe fitted with an HP-5 fused-silica capillary column (30 m \(\times\) 0.32 mm i.d., 0.25 \(\upmu\)m film thickness; Agilent, Santa Clara, CA, USA) and a flame ionization detector (FID). The injector and detector temperatures for each analysis were 250 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively. The carrier gas was nitrogen, with a flow rate of 1.0 mL/min. The column temperature was maintained at 50 \({}^{\circ}\)C for 5 min and then programmed as follows: increase from 50 \({}^{\circ}\)C to 260 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\), increase from 260 \({}^{\circ}\)C to 280 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\), and hold at 280 \({}^{\circ}\)C for 5 min.  
GC-MS analysis was performed on a Polaris Q GC/MS (Thermo Finnigan, Waltham, MA, USA) coupled with an HP-5 fused-silica capillary column (30 m \(\times\) 0.32 mm i.d., film thickness 0.25 \(\upmu\)m; Agilent). Helium was used as the carrier gas at a flow rate of 1.0 mL/min. The mass spectra were obtained with an ionization voltage of 70 eV, trap current of 250 \(\upmu\)A, and ion source temperature of 200 \({}^{\circ}\)C. The oven temperature program was the same as described previously, and injections were made in splitless mode. Volatile compounds were separated and identified based on comparison of the mass spectra with the National Institute of Standards and Technology (NIST) atomic spectra database, Wiley Registry of Mass Spectral Data, and the related literature. Total ion current chromatograms were recorded in a mass range of 40-400 amu. The analyses were performed using three biological replicates.
# QoS Management and Flexible Traffic Detection Architecture for 5G Mobile Networks
## 3 Proposed Architecture
### EPS-Bearer with QoS Requirement Creation
Finally, as can be seen in Figures 4 and 6, the Controller creates the specific service flow entry at the OF-NB to link the traffic from the Radio Bearer with QoS with the OF-Bearer with QoS. This flow entry has its matching criteria based on the service characteristics and the source UE IP address. In this step, the uplink and downlink traffic can already be forwarded, and the EPS-Bearer with QoS is established in both directions. The specific service flow entries have an appropriate OpenFlow priority, which determines the order in which they are processed. In addition, the specific service flow entries always have higher priority than the default flow entries to UE. Then, if the specific service flow entry exists, the traffic is processed using it (see Figure 6). It is important to note that the OF-GW are standard OpenFlow switches put at the edge of a mobile network or connected to the application servers. The position where they are placed makes them appropriate to be one end of the default OF-Bearer and, in several cases, one end of the OF-Bearer with QoS, but this is not mandatory. The OF-Bearer with QoS can be created between any pair of OpenFlow switches, for example directly between two OF-NB, as is shown in the prototype (see Figure 8).
## 4 Algorithm II: Improved Algorithm
### Sort \(T_{1}\) by Inducing \(Sa_{1}\) from \(Sa_{2}\)  
Without loss in generality, we assume \(T_{1}\) is unsorted and \(T_{2}\) has been sorted to form \(SA_{2}\). Given the partitioning scheme, we have that for any \(t_{k}\in T_{1}\), \(t_{k+1}\in T_{2}\). There must exist an ordering of \(t_{k+1}\in SA_{2}\), since the indices in \(SA_{2}\) are unique. For each \(k\), we construct the set of pairs \(P\), given by \(P=\{\langle t_{k},SA_{2}^{\prime}[k+1]\rangle|t_{k}\in T_{1}\rangle\}\). Each pair in \(P\) is unique. Then, we radix sort \(P\) to generate the suffix array \(SA_{1}\) of \(T_{1}\). This step can thus be accomplished in linear time. This only works for the highest level (_i.e._, obtaining \(SA_{1}\) from \(SA_{2}\)). However, we can use a similar procedure, but with some added work, at the other levels of recursion.  
Consider a lower level of recursion, say at level \(h\). See Figure 3. We can append the values in the \(SA\) from the right tree so that we can perform successive bucket sorts. Thus, we use the \(SA\) from the right tree as the tie breaker, after a certain number of steps. In general, this will not change the number of comparisons at the lowest level of the tree. However, for the general case, this will ensure that at most \(\alpha^{h-1}\) bucket sorts are performed, involving \(\alpha^{h_{max}-h}\) symbols in each bucket sort, where \(h_{max}=\lceil\log_{\alpha}n\rceil\) is the lowest level of recursion. For instance, using the example in Figure 3, at \(h=3\), we will have \(T_{1}=a^{2}a^{8}\), and \(T_{2}=b^{4}a^{7}a^{11}a^{13}\) (for convenience, we have used superscripts to denote the respective positions in the original sequence \(T\)). Assume \(T_{2}\) has been sorted to obtain \(SA_{2}=[3\ 2\ 1\ 0]\). Using \(SA_{2}\), we now form the sequences:  
\[T[2]T[4]\circ 3\] \[T[8]T[11]\circ 1\]  
Figure 3: Asymmetric recursive partitioning for improved algorithm, using \(T=\texttt{aaaaabaaabxaaaaab}\). Recursive partitioning is performed on the right branch.  
where symbol '\(\circ\)' denotes concatenation. The last symbol in each sequence is obtained from \(SA_{2}\), the suffix array of the right tree. These sequences are then bucket-sorted to obtain \(SA_{1}\). Since bucket sorting is linear in the number of input symbols, in the worst case, this will result in a total of \(O(n)\) time at _each_ level. Thus, the overall time for this procedure will still be in \(O(n\log n)\) worst case.  
We can also observe that with the asymetric partitioning, and by making the recursive call on only one partition at each level, we reduce both the overall number of conflicts and number of comparisons to \(O(n)\). But the number of lookups could still be in \(O(n\log n)\) worst case.
## 4 Validation of Proposed Method
### Validation of Other Random Models  
To further validate the effectiveness of the equivalent calculation method proposed in this article, two models of axial flux permanent magnet synchronous motors were randomly added, as shown in Figure 18, and their electromagnetic performance was sequentially calculated using the layered equivalent calculation method. Among them, Motor 1 has a smaller size in the radius direction, a smaller pole arc coefficient, and a higher speed. Motor 2 is the model used for the detailed analysis mentioned earlier, while Motor 3 has a larger radius, a larger pole arc coefficient, and a higher speed. The specific model dimensions and calculation results are shown in Table 5.  
Through the comparison of calculation results, it can be seen that the proposed method has good calculation performance in any size random model. The main electromagnetic performance of the motor, such as no-load back electromotive force, average electromagnetic torque, and stator core loss, are all within 2% of the error, which proves the universality of the calculation method proposed in this paper.  
Figure 17: The influence of layered numbers on calculation results: (**a**) fundamental amplitudes of Back-EMF; (**b**) average torque.  
Figure 18: _Cont._Figure 18: Three random motor models: On the left is motor 1, in the middle is motor 2, and on the right is motor 3; (**a–c**) schematic diagram of the basic structure; (**d–f**) mesh generation result; (**g–i**) results of magnetic density distribution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Unit** & **Model-1** & **Model-2 (Foregoing Analyzed One)** & **Model-3** \\ \hline Inner radius & mm & 20 & 50 & 60 \\ Outer radius & mm & 60 & 100 & 150 \\ Single stator axial length & mm & 27 & 25 & 20 \\ Rated speed & r/min & 2400 & 600 & 1500 \\ Polar arc coefficient & – & 0.80 & 0.83 & 0.92 \\ Number of poles & – & 10 & 10 & 22 \\ Number of slots & – & 12 & 12 & 18 \\ Width of stator slot & mm & 8 & 20 & 16 \\ Stator slot depth & mm & 17 & 15 & 12 \\ Fundamental amplitudes of Back-EMF 3D & V & 113.52 & 286.14 & 285.32 \\ Fundamental amplitudes of Back-EMF 2D & V & 114.17 & 289.08 & 289.77 \\ error & \% & 0.57 & 1.01 & 1.53 \\ Average torque 3D & N.m & 10.76 & 47.63 & 105.71 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sizes and partial calculation results of three random motor models.
# An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies
## 3 The Proposed Method  
In methods [25; 26; 27], authentication codes are generated independently of position information and MSBs of quantized values, which can result in some tampering that cannot be detected. Moreover, these embedding techniques are not designed based on block smoothness, leading to a relatively high image distortion. In this paper, the to-be-protected contents, such as quantized values, are used to generate authentication codes to enhance the security of the image. In addition, different embedding strategies based on block smoothness are used to improve image quality. The smoothness is determined by a predefined threshold \(T\). Given an AMBTC compressed code \(C_{i}=(a_{i},\ b_{i},\ B_{i})\) of block \(I_{i}\). If \(|b_{i}-a_{i}|<T\), the block \(I_{i}\) is smooth; otherwise, it is complex. In our method, the authentication codes of smooth blocks are embedded in bitmaps using the matrix encoding, while complex blocks are embedded in quantized values using the APPM. The detailed embedding and authentication procedures are presented in the following subsections. Notice that the detection result is related to the length of authentication codes. In the proposed method, smooth and complex blocks are embedded with the same length of authentication codes, thus they have identical detection performance.
# Utilizing Soft Computing Techniques to Estimate the Axial Permanent Deformation of Asphalt Concrete
## 4 Model Development  
Artificial Neural Network (ANN) is a machine-learning tool that is widely employed due to its superior ability and high accuracy in developing robust prediction models and solving complex numerical problems. Its arithmetic consists typically of feed-forward networks of multi-layers of input, hidden, and output layers. The input layer consists of many neurons representing selected independent variables for the model. The hidden layer includes a set number of neurons assigned by the trial-and-error process, which controls the model's accuracy. A single neuron representing a response variable typically exists in  
Figure 3: Marshall properties for binder and base course mixes.  
the output layer of the model. The ANN model is also generated with calibrated weights connecting each hidden neuron, input variable, and output neuron. Other constant values, known as bias, are also developed in the model, which is associated with each neuron in the hidden layer as well as the output neuron.  
In this study, the ANN model is developed to investigate the effect of various asphalt mixes' characteristics on the axial permanent strain (\(\epsilon\)p) measurements. The model utilized twelve testing and mixture parameters as input variables for predicting the permanent strain. The testing measures include the number of applied cyclic stress (n), testing temperature (Temp.), and normal stress value (Stress) applied on testing specimens. The other variables include asphalt contents (AC), asphalt viscosity (Vis), percentage of aggregate passing \(\%\)\" sieve (PP1/2) and 4.75 mm sieve (PPNo.4), air void contents (AV), percentage of voids in mineral aggregate (VMA), theoretical maximum specific gravity of asphalt mixes (Gmm), bulk specific gravity of asphalt mixes (Gmb), and bulk specific gravity of combined aggregate (Gsb). Figure 4 demonstrates the framework of the developed model.  
The ANN tool in Matlab R2022b was used in this study to develop the model. The Levenberg-Marquardt training algorithm is used for the ANN as it is fast, converged, and suitable for a data set of intermediate size [20; 21]. To facilitate the model and improve its accuracy, performance, and generalization, the inputs and output data were initially nor  
Figure 4: Schematic diagram of the ANN model.  
malized to the scale of \((-1,+1)\) using Equation (2) [22]. It is worth noting that (Equation (3)) can be used to de-normalize the data sets.  
\[X^{norm}=\frac{(Y_{max}-Y_{min})\big{(}X^{act}-X_{min}\big{)}}{(X_{max}-X_{min})} +Y_{min} \tag{2}\]  
\[X^{act}=\frac{(X_{max}-X_{min})(X^{norm}-Y_{min})}{(Y_{max}-Y_{min})}+X_{min} \tag{3}\]  
where  
\(X^{norm}:\) The normalized values of inputs and output data set.  
\(X^{act}:\) The original values of inputs and output data set.  
\(X_{min}\), \(X_{max}:\) Each data set's minimum and maximum values (see Table 5).  
\(Y_{min}\), \(Y_{max}:\) Minimum and maximum values of normalizing data, defaulting to \(-1\) and \(+1\), respectively.
## 2 Hardware
The GPS observations, recorded at a rate of 5 Hz, are differentially post-processed in order to achieve the highest possible accuracy. Finally, the HD video camera records 30 frames per second and has a field of view of 110\({}^{\circ}\). The calibration parameters of this camera have been determined using the method outlined in Bouget [32]. Data logging and time synchronisation is performed using an on-board miniaturised computer (Gumstix Vertex pro). All other processing is performed offline. The entire sensor payload weighs 2.4 kg meeting the requirements for use on-board the OktoKopter platform, which a has maximum payload of 2.8 kg.
# The Impact of Antenna Height on 3D Channel:
###### Abstract  
Three-dimensional (3D) multi-input-multi-output (MIMO) is one of the enabling technologies for next-generation mobile communication. As the elevation angle in the 3D MIMO channel model might vary against the height of the base station (BS) antenna, it should be considered within channel modeling. In this paper, the impact of antenna height on the channel characteristics of the 3D MIMO channel is investigated by using the intelligent ray launching algorithm (IRLA). Three typical street scenarios, i.e., the straight street, the forked road, and the crossroad, are selected as benchmarks. The joint and marginal probability density functions (PDFs) of both the elevation angle of departure (EAoD) and the elevation angle of arrival (EAoA) are obtained through simulations. Moreover, the elevation angle spread (AS) and the elevation delay spread (DS) under various antenna heights are jointly discussed. Simulation results show that the characteristics of the PDFs of EAoD will vary under different street scenarios. It is observed that in order to obtain the maximum or minimum value of the AS and the DS, the BS antenna should be deployed at half of the building height.  
Keywords:3D; elevation angle; antenna height; elevation angle of departure; elevation angle of arrival; angle spread; delay spread +
Footnote †: journal: Electronics Letters
## 6 A Practical Example for GDM with IVIFPRs
### Comparative Analyses
#### 6.2.1 Comparison with [PERSON]'s Method
On the contrary, the proposed algorithm determines IVIF priority weights by extending error-analysis-based priority method to IVIFPR environment, which is a time-saving and simple calculation. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{\tau}\). & \(\mathbf{\tau}\)=0 & \(\mathbf{\tau}\)=0.5 & \(\mathbf{\tau}\)=1 \\ \hline \(\eta\) & \(\eta=(0.3888,0.3056,0.3056)^{T}\) & \(\eta=(0.4078,0.2718,0.3204)^{T}\) & \(\eta=(0.400,0.2000,0.4000)^{T}\) \\ \(w_{1}\) & \((0.1165,0.2287,\)\(|0.6715,0.6715)\) & \(([0.1151,0.2309],\)\(|0.6729,0.6729)\) & \(([0.1109,0.2435],\)\(|0.6698,0.6698])\) \\ \(w_{2}\) & \((0.3208,0.4248,\)\(|0.4754,\)\(|5.0116)\) & \((0.3262,0.4324,\)\(|0.4689,0.5034]\) & \((0.3262,0.4413],\)\(|0.4624,0.4931]\) \\ \(w_{3}\) & \((0.0881,0.1304,\)\(|0.6967,0.8696)\) & \((0.0797,0.1262],\)\(|0.8738,0.8738]\) & \(([0.0739,0.1185],\)\(|0.8815,0.8815]\) \\ \(w_{4}\) & \((0.0165,0.0527],\)\(|0.7115,0.8475]\) & \(([0.0156,0.0501],\)\(|0.725,0.8537]\) & \((0.0137,0.044],\)\(|0.7671,0.8689]\) \\ \(T(w_{1})\) & \(0.4915\) & \(0.4898\) & \(0.4957\) \\ \(T(w_{2})\) & \(0.6783\) & \(0.6836\) & \(0.6910\) \\ \(T(w_{3})\) & \(0.3711\) & \(0.3644\) & \(0.3543\) \\ \(T(w_{4})\) & \(0.3702\) & \(0.3665\) & \(0.3655\) \\ R.O. & \(x_{2}>x_{1}>x_{3}>x_{4}\) & \(x_{2}>x_{1}>x_{4}>x_{3}\) & \(x_{2}>x_{1}>x_{4}>x_{3}\) \\ \hline \hline \end{tabular} Note: 1. when \(l=0.5\), \(T(w_{i})(i=1,2,34)\) denotes the comprehensive closeness degree defined in [PERSON]’s method [12].  
2. “R.O.” is an abbreviation for “ranking order”.  
\end{table} 
Table 3: Computation results for different DMs’ weights \(\eta_{i}(i=1,2,3)\) by [PERSON]’s method [12]. Figure 2: Ranking orders of alternative by different methods with \(\text{MCI}_{0}=0.1\).
## 1 Introduction  
Recently, rapid urbanization has increased energy use demands in the building sector. The energy use of buildings has steadily increased worldwide, achieving between twenty to forty percent of total energy in developed countries.  
Due to the complexity of the energy systems of buildings, it is difficult to achieve accurate analyses. This is because of the variety of energy types and building categories. For instance, the main energy forms are consumed by condition load, hot water, lighting, and ventilation. The building categories can be classified into commercial, residential, and industrial buildings, differing from small rooms to large assets. At the same time, the energy systems of buildings are affected by different variables, such as weather conditions, building structure and properties, users' behavior, and the operation of sublevel components like cooling, heating, and lighting systems.  
The building sector has a greater potential to improve energy performance than other sectors. Therefore, reducing energy use of buildings is a main goal for various firms at local, national and global scale.  
Commercial buildings contain a wide range of building categories such as offices, health care, sport center, retail, lodging and public building. These categories have special energy requirements depending on their substantial function. In general, electricity and natural gas are the main sources of energy within commercial buildings in Japan.  
Accommodation Facilities are more energy intensive than other types of commercial buildings in a majority of countries. In addition, it is an essential element of the tourism industry along with transportation and other tourism components.  
Accommodation buildings have significant environmentally impacts because of their large consumption of natural resources and production of waste [1].  
Benchmarking energy use is a useful tool to evaluate the energy performance of buildings. It enables managers to compare the energy performance of their own buildings with that of other peer buildings. They can improve the energy use of buildings by understanding the what, why, when, where, and how of a building's energy use [2].  
This study utilizes DECC as database to model the energy performance of accommodation buildings since few studies tackled this issue in Japan because of the limitations of data and complexity of this sector.