esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Dynamic Time Division Broadcast Protocol with Rateless Coding
## 3 Performance Analysis
### Diversity-Multiplexing Trade-off
#### 3.2.2. DMT of Sub-DTDBC-RC and DTDBC-RC
The expected rate performance comparison among TDBC-IR, DTDBC-RC, and sub-DTDBC-RC (with various number of subslots \(N\)) is presented in Figure 7. We see that DTDBC-RC outperforms sub-DTDBC-RC and sub-DTDBC-RC outperforms TDBC-IR, revealing the expected rate performance  
Figure 4: The outage probability performance of the DTDBC-RC/TDBC-IR protocol for various \(\alpha\). advantage of DTDBC-RC (or sub-DTDBC-RC) over TDBC-IR. Moreover, as \(N\) increases the expected rate performance of sub-DTDBC-RC approaches that of DTDBC-RC gradually, validating the intuition that sub-DTDBC-RC becomes DTDBC-RC as \(N\rightarrow+\infty\). Figure 5: The expected rate performance of the TDBC-IR protocol. Figure 6: The expected rate comparison performance of the DTDBC-RC protocol. As for the DMT performance, we plot the DMTs of TDBC, DTDBC-RC, and sub-DTDBC-RC in Figure 8 based on the results in Theorem 2, Theorem 3, and the Corollary. We see that DTDBC-RC outperforms sub-DTDBC-RC and sub-DTDBC-RC outperforms TDBC-IR for the same \(\alpha\). It is also obtained from Theorem 2 that TDBC-IR achieves its best DMT performance when \(\alpha\leq\frac{2}{5}\).
## 1 Introduction
Zeolite has attracted much attention as a sorbent due to its unique characteristics, including a high specific surface area, hydrophobicity and hydrophilicity, easy-to-adjust chemical properties, heat resistance, low cost, and eco-friendliness [33]. A variety of zeolites have found their application in adsorption, catalysis, construction, soil remediation, and energy storage [34]. The removal of various pollutants such as cephalexin [35], anionic dye [36], and environmental pollutants [37] by zeolites has been reported. In contrast, DSPE techniques using zeolites are rarely reported [10; 38]. Various synthetic zeolites have been produced worldwide for industrial purposes, and the price is very competitive compared to that of the currently commercialized DSPE materials. Applying this environmentally friendly, low-cost material to detect pollutants in the environment and food is of great significance based on DSPE procedures. At the same time, the applicability of being a DSPE material needs to be justified because of the structural differences and different elemental compositions of each type of zeolite. Herein, an efficient DSPE method using zeolite H-Beta as a sorbent has been first explored and verified for the determination of neonicotinoids in water and honey products, which include dinotefuran, thiacloprid, thiamethoxam, fonicamid, imidalothiz, clothial-din, imidalcoprid, and acetaminophen. The parameters (amount of the zeolites, adsorption time, desorption solvent, desorption mode, and time) affecting the DSPE procedure performance have been optimized before the determination using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). This research aims to provide a useful and economical sorbent that can be reused for detecting trace residues in bottled water and honey matrices.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 4 Evaluation and Comparison
### Computational Efficiency  
The operations executed according to the various schemes can be summarized by:  
* a pseudo-random function \(f()\),
* a permutation \(t()\),
* a MAC,
* a verification of a signature with public-key cryptography \(ver()\),
* an asymmetric encryption \(enc()\) and
* an asymmetric decryption \(dec()\).  
PPWK, PGK and EG do not require computation. In LEAP+ an FLEAP, the initiator of the handshake computes a MAC and two \(f()\) functions, while the responder computes a MAC and an \(f()\) function. RSDTMK requires that both nodes compute a MAC, an \(f()\) and a \(t()\) function. SKKE requires that both nodes compute a MAC, an \(f()\) and a \(t()\) function. Also, 2C requires that both nodes execute an \(f()\) function. In SSL, both nodes execute one \(f()\), \(ver()\), \(enc()\) and \(dec()\) function. In PCAT, both nodes execute one \(f()\), \(enc()\) and \(dec()\) function. In 1-5C, only negligible operations are involved.  
The computational overheads required by the PCAT are greater than in the state-of-the-art schemes based on symmetric encryption, but they are lower than in SSL, which requires a signature verification. Moreover, according to the energy consumption analysis executed in [17], the signature verification with public-key cryptography can require the double of the energy required for an encryption or a decryption.
## 3 Sources of Uncertainty
### Uncertainty Budget Associated with the Spectral Responsivity
An example of measured spectral responsivity curves of the three junctions together with associated standard uncertainties is shown in Figure 1.  
Figure 1: Spectral responsivity (SR) curves of the three junctions (continuous lines, left axis) with their wavelength-dependent associated standard uncertainties UC (dotted lines, right axis); the peaks are due to the fact that high relative errors occur at very low currents and poor signal/noise ratio. The functions shown in figure are those interpolated with a 1 nm step.
# Joint Balanced Routing and Energy Harvesting Strategy for Maximizing Network Lifetime in WSNs
## 4 The Proposed Strategy
### The Proposed SPT-EBR Strategy
#### 4.2.1 The Power Consumption Scheme  
In the power consumption rate scheme, time resource is allocated as a time slot that operates periodically to avoid frequent paths updating. In the beginning, the sink calculates the shortest path from all nodes to itself by the Dijkstra algorithm. The shortest path is then passed from sink to each sensor to establish a minimum spanning tree routing. Packets transmission with the shortest path will make the transmission of the entire network more energy efficient. In each time slot, each node calculates whether its own packet transmission rate is greater than the threshold value. The threshold value is determined by the sink and can be defined as the average of the overall packet forwarding rate. Then, the sink passes the threshold value to all nodes together when the routing paths are transmitted. After that, each node can update the path cost according to the threshold value.  
In each time slot, if the total amount of packet transmission value \(P\) is greater than the threshold \(T\) in each node, the path cost is increased by one. When the packet transmission amount \(P\) of the node is less than or equal to \(T\), the value of path cost keep the same. In order to avoid communication overhead, path cost can be transmitted to the sink along with the sensing data. When the sink receives the data, if the path cost changes, the sink recalculates and updates the path to the nodes with high traffic load.  
In each time slot, the node with a faster packet transmission rate can mitigate forwarding packets by increases the path cost. Let other nodes near the sink share the network traffic load and achieve the traffic load balance in each layer of the network. Repeat the load balancing operation can balance the transmission packets of the hotspot nodes since the hotspots can share the forwarding packets in turn from all the other sensors. Distributing the traffic load allows network load balancing to be achieved while letting the global nodes to fully utilize their energy. Alternatively, the shortest path transmission also minimizes overall power consumption for efficient energy utilization. Generally, the power consumption scheme achieves both energy efficiency and energy balance to extend the overall network lifecycle. The detail design flowchart of the power consumption scheme is shown in Figure 4.
## 1 Introduction
In our studies related to applying MSSI to the DM link, various DM configurations have been proposed by using non-uniform RDPSs, such as artificial distribution of arbitrary RDPSs, random distribution of various RDPSs, and an asymmetric dispersion map with respect to the midway OPC, to expand flexibility of the link configuration. On the flexibility front, the best scheme is random distribution, i.e., RDPSs with different magnitude are freely allocated for each fiber span, with no intention of deployment. However, the random distribution of RDPSs results in unsatisfactory compensation, since it is difficult to symmetrize the dispersion-power profile with respect to the midway OPC. From the viewpoint of performance, on the other hand, the best compensation is achieved by artificial distribution and predetermined RDPSs, especially the gradually ascending or descending distribution of these RDPSs in each half link. The symmetry of the dispersion profile of each half is ultimately maintained with respect to the midway OPC. From analysis of our previous studies, it was confirmed that there is a conflict of interest between the conditions of link parameters for flexibility and for the compensation effect. That is, it is difficult to simultaneously achieve the two aims of flexibility and the best compensation through the established attempts using RDPS distributions. Therefore, another method is required to increase the compensation performance and to extend flexibility. A possible and simple approach is the compromise method, i.e., the selective mixing of various distributions of RDPSs for each half link. In this study, we numerically demonstrated the compensation of the 960 Gb/s WDM signal in the MSSI-based DM link, in which RDPSs of only the former half link were randomly distributed, and RDPSs of the remaining half link were artificially distributed. The two methods of deployment of RDPSs in the latter half link were as follows: reversing or following those in the former half link. We also assessed the compensation performance in the DM link configured by all-randomly distributed RDPSs in both halves for comparison with the proposed schemes.
## 1 Introduction
The vehicle sideslip angle is the angle between the longitudinal axes of the automobile body and the automobile speed direction [16]. However, nowadays, the sideslip angle cannot be measured directly. This is one of the biggest problems for the current development of a vehicle stability control system. Therefore, it is the premise and key technology of vehicle dynamic stability control to measure accurately the actual vehicle side slip angle and yaw rate [17]. The gyro can measure the yaw rate,but there is no suitable equipment for directly measuring the vehicle sideslip angle. Estimation methods are used to get the sideslip angle. These methods are usually combined with the use of the yaw rate gyro and lateral acceleration sensor. However, these sensors usually contain a bias and noise [18]. In addition, a lateral accelerometer cannot provide a good identification of vehicle lateral acceleration and the gravity component of acceleration. The errors of these sensors will be accumulated and even diverge, when the integral is applied [19]. Therefore, errors affect the performance of the vehicle stability control system. However, the application of GPS and INS (inertial navigation system, including the gyroscope and accelerometer) can directly measure the vehicle side slip angle. GPS is the Global Positioning System. DGPS (Differential Globe Positioning System) is a kind of GPS attached to the normal differential correction signal, improving data precision to the millimeter level after processing [20].
# An Efficient Reliability Method with Multiple Shape Parameters Based on Radial Basis Function
## 4 Case Study
### Displacement Response of an Airfoil  
Example 3 is a practical engineering problem in which the proposed method is used in the reliability analysis for an airfoil. As shown in Figure 9, the airfoil is 1350 mm in length. The components of the airfoil include the main beam (made of carbon fiber), ribs (made of epoxy resin), front and rear walls (made of epoxy resin), and skin (made of glass fiber). The left end of the airfoil is fixed, and a uniform load is applied to its bottom. Then, its displacement response in the vertical direction is obtained by finite element analysis, as shown in Figure 10. Generally, when the displacement of the airfoil in the vertical direction is greater than one-tenth (135 mm in this case) of its length, the airfoil can be considered as having failed [36]. The normal random variables in example 3 are independent of each other and their distribution is shown in Table 5. In order to examine the performance of this proposed method with different failure probabilities, the coefficient of variation for a uniform load is set as 0.05, 0.1, and 0.15.  
Figure 10: Displacement contour map of the aircraft wing.  
Figure 9: The structure of the airfoil.  
The parameters for MSRBF-MCS are set as \(\varepsilon=10^{-5},n_{ini}=12,n_{mc}=1.5\times 10^{4}\). Then, MCRBF-MCS + M (\(\alpha=0.5\)) and RBF-LSH are employed to solve example 3. RBF-LSH is a one-shot surrogate model, whose DoE is generated by one-time Latin hypercube sampling (LHS) [18]. The comparation between the two methods is shown in Table 6. As one can see in Table 6, the results from the proposed method are much more accurate and efficient than those from RBF-LHS.
# Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation
## 3 Component Modeling
### SI-POF Injection  
As described in [29; 49], there is strong initial diffusion of power to higher angles at the input of large-core step-index POFs. This strong diffusion cannot be correctly captured by a fit of the Gloge power-flow equation to fiber performance at longer lengths. The strong power diffusion can be modeled via an injection matrix \(\mathbf{M_{inj}}\), in which the output intensity at some angle \(\theta_{i}\) is a weighted linear summation of the input powers at all input angles [29]. Using our discretized angles approach, the output intensity as a function of the input intensities can be represented as:  
\[p_{out}(\theta_{i},t)=\sum_{j=0}^{N}m_{inj}(\theta_{i},\theta_{j})\cdot p_{in }(\theta_{j},t) \tag{14}\]  
where \(m_{inj}(\theta_{i},\,\theta_{j})\) is a coupling coefficient between the input at \(\theta_{j}\) and the output at \(\theta_{i}\) and \(t\) indicates that the signals are in the time domain. If we represent the intensities as vectors \(\mathbf{P_{out}}(t)=[p_{out}(\theta_{i},t)]^{T}\) and \(\mathbf{P_{in}}(t)=[p_{in}(\theta_{j},t)]^{T}\), we can also represent this relationship as  
\[\mathbf{P_{out}}(t)=\mathbf{M_{inj}}\cdot\mathbf{P_{in}}(t) \tag{15}\]  
where \(\mathbf{M_{inj}}\) is the injection matrix with elements \(m_{inj}(\theta_{i},\,\theta_{j})\). \(\mathbf{M_{inj}}\) can be derived from measurements, as described in detail in [30; 32; 49]. An equation equivalent to Equation (15) can be written with the intensity vectors as functions of frequency instead of time:  
\[\mathbf{P_{out}}(\omega)=\mathbf{M_{inj}}\cdot\mathbf{P_{in}}(\omega) \tag{16}\]  
In this equation, the injection matrix is the same as that in Equation (15) because it is independent of both time and frequency. As Equations (15) and (16) are formally equal, from now on, we will write the vectors without an explicit dependence on time or frequency when we describe other components to avoid repetition of the time and frequency versions of the equation.  
The injection effects in the fiber can also be modeled via an equivalent Gloge model using a fit for \(\alpha\,(\theta)\) and \(D(\theta)\)[20; 23; 29; 30]. In this case, we ignore time-dependence of the Gloge power-flow equation since the injection matrix operates instantaneously. While the Gloge power-flow equation assumes a propagation distance \(L\), this quantity is not meaningful for the injection model. Thus, as shown in [30], because we solve the Gloge equation numerically, we need to specify a fixed number of steps \(N_{z}\) of width \(\Delta z\) to take along the \(z\) direction. The choice of \(\Delta z\) is arbitrary and, therefore, it suffices to specify the fiber injection loss factor as a function of angle, the coupling terms \(D_{0}\) and \(D_{1}\) of Equation (3) in units of radians2 (rather than radians2/m), and the number of steps \(N_{z}\). The parameters \(D_{2}\) and \(\sigma_{d}\) are also specified.
## 1 Introduction
The spatial resolution represents the smallest detecting area that the remote sensing system could recognize, and the observed physical quantity is averaged within such an area. Therefore, the spatial-dependent variability would be ignored due to this averaging, resulting in certain measurement errors. Increasing spatial resolution wo  
Figure 1: The typical observing scenario of a MERO system and the pathway of the radiation from the pixel on the Earth top of atmosphere (TOA) (hollow green rectangle) to the detector unit (solid green rectangle) of the focal plane array of the MERO. variability of the observed physical quantity, and accordingly, reduce the measurement error. Functions of the measurement error versus the spatial resolution of different remote sensing platforms differ, since the observing geometry features are different and so are the observed physical quantity characteristics. Therefore, it is necessary to evaluate the relationship between the spatial resolution and the measurement error for the MERO system, which could facilitate the determination of spatial resolution. However, current knowledge about this relationship is still limited. Similarly, the sampling interval represents the time difference that the adjacent samplings have. A shorter sampling interval would result in more samples for a certain period, thus giving a more precise observation. Besides, at a certain sampling interval, the sampling temporal sequence (the time location within the temporal sampling interval from which the observations are made) would also have consequences for the measurement error, since it could affect the final temporal interpolation that derives the continuous data. However, the effects of the sampling interval and sampling temporal sequence on the measurement error of a MERO remain poorly understood. The objective of this paper is to reveal the influences of spatial resolution and temporal sampling schemes (sampling interval and sampling temporal sequence) on the measurement error, which could facilitate the determination of the optimal spatial resolution and the sampling scheme of the MERO instrument under the specified manufacturing cost limit and accuracy requirement. Section 2 describe the methodology of measurement error computation. Section 3 presents the error-with-spatial-resolution relationship and the temporal-sampling-scheme-induced error over March 2019. Discussion and conclusions are exhibited in Sections 4 and 5, respectively.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 2 Related Work
### Deep Learning Models  
This subsection briefly presents selected deep learning models that are incorporated in the proposed MTAD method.
# A Least Squares Ensemble Model Based on Regularization and Augmentation Strategy
###### Abstract  
Surrogate models are often used as alternatives to considerably reduce the computational burden of the expensive computer simulations that are required for engineering designs. The development of surrogate models for complex relationships between the parameters often requires the modeling of high-dimensional functions with limited information, and it is challenging to choose an effective surrogate model over the unknown design space. To this end, the ensemble models--combined with different surrogate models--offer effective solutions. This paper presents a new ensemble model based on the least squares method, which is a regularization strategy and an augmentation strategy; we call the model the regularized least squares ensemble model (RLS-EM). Three individual surrogate models--Kriging, radial basis function, and support vector regression--are used to compose the RLS-EM. Further, the weight factors are estimated by the least squares method without using the global or local error metrics, which are used in most existing methods. To solve the collinearity in the least squares calculation process, a regularization strategy and an augmentation strategy are developed. The two strategies help explore the unknown regions and improve the accuracy on one hand; on the other hand, the collinearity can be reduced, and the overfitting phenomenon that may occur can be avoided. Six numerical functions, from two-dimensional to 12-dimensional, and a computer numerical control (CNC) milling machine bed design problem are used to verify the proposed method. The results of the numerical examples show that RLS-EM saves a considerable amount of computation time while ensuring the same level of robustness and accuracy compared with other ensemble models. The RLS-EM used for the CNC milling machine bed design problem also shows good accuracy characteristics compared with other ensemble methods.  
ensemble model; regularized least squares; computer simulations; augmentation strategy +
Footnote †: journal: Applied Sciences
# A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays
## 2 Methods
### Traditional Tomographic Equation
#### 2.2.1 Observation Equation  
The whole research area and the water vapor density in the discretized tomographic voxel are shown in Figure 1, and the observation equation can be established on the basis of the GPS signal crossing as follows:  
\[\text{SWV}^{q}=\sum_{i=1}^{n}s_{i}^{q}\cdot x_{i} \tag{3}\]  
where superscript \(q\) denotes the satellite index; \(n\) is the total number of discretized tomographic voxels; \(s_{i}^{q}\) represents the length of the satellite signals within the voxel, which can be calculated by the coordinates of two intersections; and \(x_{i}\) is the water vapor density of the voxel, i.
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
###### Abstract  
Cooperative communication supported by device to device (D2D)-LEO earthed satellite increases the performance of the resilient network and offloads base station. Additionally, network coding in a packet-based cooperative framework provides diversity and speedy recovery of lost packets. Cooperative communication advantages are subject to effective joint admission control strengthened by network coding for multiple interfaces. Joint admission control with network coding involves multiple constraints in terms of user selection, mode assignment, power allocation, and interface-based network codewords, which is challenging to solve collectively. Sub-problematization and its heuristic solution lead to a less complex solution. First, the adaptive terrestrial satellite power sentient network (ATPSN) algorithm is proposed based on low complex convex linearization of mix integer non-linear problem (MINLP), NP-hard. ATPSPN provides optimum power allocation, mode assignment, and user selection based on joint channel conditions. Second, a multiple access network coding algorithm (MANC) is developed underlying the D2D-satellite network, which provides novel multiple interface random linear network codewords. At the end, the bi-directional matching algorithm aiming for joint admission control with network coding, named JAMANC-stream and JAMANC-batch communication, is proposed. JAMANC algorithm leads to a less complex solution and provides improved results in terms of capacity, power efficiency, and packet completion time. The theoretical lower and upper bounds are also derived for comparative study.  
D2D; satellite; network coding; admission control; outer approximation Article