esa-sceva/satcom-chunk-collection huggingface dataset:
# Compressive Sensing-Based Coding Iterative Channel Estimation Method for TDS-OFDM System
## 2 Construction of TDS-OFDM System  
The TDS-OFDM system uses all subcarriers to transmit data, which effectively improves spectrum efficiency. To realize channel estimation and synchronization, the system utilizes the PN sequence with good autocorrelation as the guard interval. However, the PN sequence causes the symbol data to lose its cycle performance, bringing serious interference between adjacent symbols [14]. In addition, the autocorrelation function of the PN sequence is not an ideal impulse function, which will cause errors in channel estimation. Therefore, the processing of PN sequence pairs is key for TDS-OFDM systems. The time-domain modulation signal \(x_{\text{N}\times 1}\) for the length of \(N\) points in the TDS-OFDM system is  
\[x_{\text{N}\times 1}=\frac{1}{\text{N}}\sum\
olimits_{k=0}^{\text{N}-1}X_{k}e^{ j\frac{2\pi}{N}kn} \tag{1}\]  
where \(X_{k}\) represents the complex modulation symbol on the \(k^{th}\) subcarrier.  
After adding the PN sequence \(\phi_{\text{M}\times 1}\) with a length of M sampling points, the frame structure \(z\) of the TDS-OFDM system is shown in the following equation:  
\[z=\left[\phi_{\text{M}\times 1}\ x_{N\times 1}\right]_{(M+N)\times 1} \tag{2}\]  
The wireless channel can be modeled as a multi-delay tap filter, and its mathematical expression is given below:  
\[h(n)=\sum\
olimits_{i=0}^{L-1}a_{i}e^{-j2\pi f_{d}\tau_{i}} \tag{3}\]where \(h(n)\) denotes the total channel impulse response of all paths at n, \(a_{i}\) is the amplitude gain of impulse response in the sub-path, \(f_{d}\) is the fixed Doppler shift, and \(\tau_{i}\) is the time delay.  
To simplify the system complexity, the effect of the Doppler shift is not taken into consideration. Then, the TDS-OFDM signal, after passing through the channel \(h(n)\), is  
\[z_{R} =z(n)*h(n)+N(n)\] \[=[\varphi_{\text{M}\times 1}\ x_{N\times 1}]*h(n)+N(n) \tag{4}\]  
where \(N(n)\) denotes the noise. The PN sequence is assumed to be \(\psi=\varphi_{\text{M}\times 1}*h(n)\) and the valid data to be \(y=x_{\text{N}\times 1}*h(n)\). Due to the effect of the multipath channel, smearing and aliasing occur between \(\psi\) and \(y\), as shown in Figure 1.
# Online Optimization of Pickup and Delivery Problem Considering Feasibility
## 1 Introduction
In this paper, we propose an online pickup and delivery problem in which demand forecasting and fuel constraints are considered. First, we formulate the pickup and delivery problem. In this paper, the target area is given by an undirected graph. Constraints including fuel constraints are explained. Next, after the demand forecast is explained, the cost function is introduced. In a certain term in the cost function, we use the result of demand forecasting. Then, agents' paths can be calculated according to the result of demand forecasting. That is, based on the policy of MPC, the real order and demand forecast are used. Even if the result of demand forecasting is different from real orders, the problem can be solved. This is because the result of demand forecasting is used in the cost function, not a constraint. Hence, we can guarantee the feasibility. Third, we explain the procedure of online optimization. Finally, we present a numerical example. Through a numerical example, we demonstrate that the feasibility is guaranteed by the proposed method. The conference paper [49] is a preliminary version of this paper. In [49], the authors have considered both demand forecasting and fuel constraints. However, depending on the result of the demand forecasting, the optimization problem becomes infeasible. In this paper, this technical issue is overcome. This paper is organized as follows. In Section 2, we formulate the pickup and delivery problem studied in this paper. After a mathematical model of the target area and the definition of the orders are explained, the constraints considered in this paper are explained. Demand forecasting in this paper is also explained. Finally, the cost function is explained. In Section 3, we explain the procedure of online optimization of the pickup and delivery problem. In Section 4, we present a numerical example to show the effectiveness of the proposed method. In Section 5, we conclude this paper. The main contributions of this paper are highlighted as follows:  
1. For pickup and delivery problems, fuel constraints are introduced to utilize electric vehicles and drones as agents. 2. A pickup and delivery problem considering demand forecasting is newly formulated. 3. The effectiveness of the proposed method is clarified through a numerical example.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 2 Applications of FSSs
### FSS in Wireless Communications  
Frequency selective surfaces (FSSs) are an effective tool in wireless communication systems for controlling the beamwidth and sidelobe levels of antenna arrays. By selectively filtering specific frequencies, FSSs improve the directionality and reduce unwanted radiations or side lobes that can cause interference in adjacent frequency bands.
# Event Detection Using a Self-Constructed Dependency and Graph Convolution Network
## 3 Methods  
The event detection model proposed in this paper is shown in Figure 2. Initially, we acquire the contextual representation of the input sentence, and subsequently formulate a dependency graph to input into the improved GCN layer. Then, through the gating mechanism, we fuse the semantic representation and the dependency structure information, where the classifier is employed for event detection. In this section, we describe the proposed method in five parts: embedding layer, dependency graph construction method, multi-GCN, gating mechanism fusion and event detection.
## 5 Research Progress on Key Technologies of IPPCS  
The lightweight level of spacecraft structure depends on structural design criteria, structural design and optimization methods, structural material properties and manufacturing process capabilities. The key technologies that need to be broken through to realize the lightweight of the IPPCS include  
1. The strength criterion of the pressurized capsule structure based on shakedown: the shakedown limit of the structure is taken as the ultimate bearing capacity of the internal pressure load of the pressurized capsule structure, which improves the bearing capacity of the structure (the post-yield performance of the material is applied) and the safety of the bearing capacity of the structure whose local material is in the plastic stage.
2. The design method of the IPPCS: a design process and optimization method for the IPPCS is proposed, which can guide the design of both local and overall structures.
3. Upgrading of pressurized capsule structure material: through the application research, the promotion of material upgrades for the pressurized capsule structure to materials such as 5B70 aluminum alloy.
4. The overall manufacturing process of pressurized capsule structure: master the spinning process of large-size parts and lay the foundation for the overall manufacturing of the pressurized capsule.
# Valuing Resilience Benefits of Microgrids for an Interconnected Island Distribution System
## 2 Valuation Approach
### Frequency of Outage Events  
Reliability events are characterized based on historical outage frequency and duration during day-to-day operations. The years in which major or exceptional events occur are not used to establish the baseline for reliability events, because they greatly alter the outage data (e.g., Hurricanes Irma and Maria left the islands without power for 11 months, skewing daily operation data). Table 1 summarizes the outage frequency and total cumulative duration across different outage periods, from 5 min up to 24 h, using nine years of outage data. This work uses reliability metrics SAIDI and SAIFI (system average interruption duration index and system average interruption frequency index).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Outage Period** & **Total Outages** & \begin{tabular}{c} **Total Outage** \\ **Minutes (1000 s)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Customer** \\ **Outages** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Customer** \\ **Outage Minutes** \\ \end{tabular} \\ \hline
5 min–1 h & 13,802 & 414 & 4.63 & 139 \\ \hline
1–2 h & 6938 & 624 & 2.30 & 207 \\ \hline
2–4 h & 5848 & 1019 & 1.82 & 314 \\ \hline
4–8 h & 4793 & 1840 & 1.47 & 560 \\ \hline
8–12 h & 3067 & 1790 & 0.85 & 496 \\ \hline
12–16 h & 236 & 193 & 0.05 & 40 \\ \hline
16–24 h & 444 & 514 & 0.12 & 140 \\ \hline
**Total** & **35,129** & **6394** & **11.24 (SAIFI)** & **1895 (SAIDI)** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Outage Frequency and Cumulative Duration.  
The major and exceptional hurricane event frequencies are established based on historic records of hurricanes and the outages they caused for the islands. However, because outage data are not as well established for historic hurricanes compared to reliability events, it is assumed that hurricanes coming within 50 nautical miles of the islands have the potential to cause an outage. Table 2 summarizes the number of hurricanes at various strengths within 50 nautical miles (nm) of the islands in the past 170 years [16].  
The following assumptions are made to estimate outage frequencies based on hurricane and strength frequencies:  
* Major hurricane outage events and extreme hurricane outage events are caused by category III or higher hurricanes.
* Every hurricane category III or higher within 50 nm of the islands causes an outage.
* One in three category IV or V hurricanes that come within 50 nm of the islands cause extreme disruptions.  
These assumptions result in the following frequencies of hurricane events:  
* Major hurricane outage events occur once every 13 years (annual probability = 7.65%).
* Extreme hurricane outage events occur once every 85 years (annual probability = 1.18%).
## 2 Experimental Details
### Tribocorrosion Tests  
The tribocorrosion behavior of samples was studied by using a reciprocating tribometer. Sliding wear conditions against GCr15 steel balls with a diameter of \(6\) mm were established in [PERSON]'s solution. The performed load was \(2\) N, and the friction test reciprocating length was \(5\) mm with a frequency of \(1\) Hz for \(10\) min. In this test, the curves of open circuit potential as well as the coefficient of friction (COF) were recorded before, during and after the wear process, where the samples were immersed in [PERSON]'s solution with an exposed area of \(1\) cm\({}^{2}\). In addition, the SEM and 3D Measuring Laser Microscope were adopted to observe the morphology and measure the size of the worn surface.
## 4 Case Study
### Comparison Analysis
#### 4.4.1 Comparison of the Classic PROMETHEE Method  
For the given process, the same weights values obtained in the application of the case study were used, although it is worth mentioning that the traditional model does not provide the obtaining of weights to the criteria set, in this case, the values are attributed directly, understanding this factor as a point of evolution in the proposed approach.  
For the implementation of the case study, the Visual PROMETHEE software was used [86]. In this case, the evaluation of the alternatives in the qualitative criteria used the attributions provided by the given model, based on a scale of five points (very bad, bad, average, good, and very good). After the evaluation, Table 17 presents the final rankings obtained in PROMETHEE-SAPEVO-M1 and PROMETHEE methods.  
Through the results exposed in Table 16, regarding the PROMETHEE method, there is a variation between the obtained scores and sequence of alternatives in the final ranking. In this context, the alternative Katran placed the first ranking position, being preceded by APACHE. The helicopters Sikorsky S-70, H160M, and H215M also showed a change in their scores and positioning in the ranking. A great point of importance is that even with some alternatives maintaining their positions in the ranking, there was a relative distance between the net flows, as observed between the alternatives VIPER and T129 ATAK. The graph exposed in Figure 8 enables a clearer understanding of this distance.  
Based on this comparison analysis, the introduction of an evaluation in qualitative criteria in a more detailed way, as provided by the pairwise evaluation in the PROMETHEE-SAPEVO-M1 approach, proved to enable a better accuracy of the subjective opinion of the decision-maker, making the applied model perform a better representation of reality.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PROMETHEE-SAPEVO-M1** & \multicolumn{2}{c}{**PROMETHEE**} \\
**Complete Ranking** & \multicolumn{2}{c}{**Complete Ranking**} \\ \hline
**Ranking** & **Net Flow (\(\mathbf{\phi}\))** & **Ranking** & **Net Flow (\(\mathbf{\phi}\))** \\ \hline APACHE & \(0.136\) & Katran & \(0.324\) \\ Katran & \(0.101\) & APACHE & \(0.272\) \\ VIPER & \(0.074\) & VIPER & \(0.207\) \\ T129 ATAK & \(0.051\) & T129 ATAK & \(0.022\) \\ Tiger HAD & \(-0.018\) & Tiger HAD & \(-0.036\) \\ MI-35M & \(-0.044\) & MI-35M & \(-0.095\) \\ Sikorsky S-70 & \(-0.052\) & H160M & \(-0.162\) \\ H160M & \(-0.100\) & H215M & \(-0.248\) \\ H215M & \(-0.148\) & Sikorsky S-70 & \(-0.285\) \\ \hline \hline \end{tabular}
\end{table}
Table 17: Complete ranking in PROMETHEE-SAPEVO-M1 and PROMETHEE method.
# Autonomous Loading System for Load-Haul-Dump (LHD) Machines Used in Underground Mining
## 3 Proposed Autonomous Loading System
### Pull Back and Payload Weighing  
Once the excavation is finished, the bucket gets fully retracted and the vehicle is driven backwards, using the same \"guidance\" navigation module from the positioning step. A predefined time limit is assigned to this step (in the experiments, 60 s were used). If a timeout occurs, the system is put in the loading assistance state from Figure 6.  
After a predefined distance has been covered within the aforementioned time limit, the bucket is commanded to perform short downward and upward tilting movements, known as the \"bucket shake\". Then, the bucket is lifted up to a fixed angle (\(\beta_{weigh}\)), and the pressure sensors in the boom hydraulic line are used to estimate the bucket fill factor. A fill factor is used instead of the mass weight, since it simplifies calibration, while providing the same information for the evaluation process. The relationship between the lift cylinder pressure (\(P\)) and the fill factor was found experimentally and corresponds to the following affine model:  
\[f_{fill}=mP-n \tag{9}\]  
where \(f_{fill}\) is the relative fill factor (0 for an empty bucket and 1 for a full bucket), and \(m\) and \(n\) are the constants to be calibrated through experimentation. Table 2 shows the  
Figure 18: Flow chart of the excavation algorithm.  
parameters used for the particular conditions of the field trials presented in this work (machine and rock characteristics).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(P_{0}\) & 0.5 \\ \hline \(R_{0}\) & 1.2 \\ \hline \(skid_{max}\) & 0.2 \\ \hline \(\beta_{max}\) & 0.5 [rad] \\ \hline \(\alpha_{max}\) & 0.0 [rad] \\ \hline \(d_{max}\) & 2.0 [m] \\ \hline \(\Delta t_{max}\) & 1.0 [s] \\ \hline \(\Delta t_{min}\) & 0.5 [s] \\ \hline \(EP_{max}\) & 300 [bar] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Excavation method parameters used for the full-scale validation.  
Figure 19: Pseudo code of the excavation algorithm.
# Multi-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks
## 1 Introduction
Furthermore, the work [25] proposed some approaches to minimize the total transmit power or maximize the weighted throughput. In addition, the non-orthogonal multiple access (NOMA) technique has been incorporated to design the secure short packet communications schemes [26; 27; 28]. However, all these works only considered the secure communications of legitimate parties without an external helper, and most of them neglected the impact of cooperative jamming on the system performance, which may lead to an underestimation of the secrecy performance. Motivated by the above background, in this paper, we investigate secure short packet communications in an IoT network, where an access point (AP) sends confidential information to a connected actuator in the presence of a cooperative jammer equipped with multiple antennas and an eavesdropper. Specifically, we propose three jamming schemes, i.e., the zero-forcing beamforming (ZFB) scheme, null-space artificial noise (NAN) scheme, and transmit antenna selection (TAS) scheme, for security improvement. The main contributions of our work are summarized as follows. 1. We first derive new closed-form approximations for the secrecy throughput of an IoT network with three different jamming transmission schemes, i.e., the ZFB scheme, NAN scheme, and TAS scheme. To achieve further insights, we also investigate the asymptotic secrecy throughput in the case of infinite blocklength. 2. We present the optimal design for the three different jamming transmission schemes. The blocklength is optimally determined in terms of maximizing the secrecy throughput subject to the constraints on the latency and reliability. 3. The results demonstrate that both the ZFB scheme and the NAN scheme strictly outperform the TAS scheme in terms of secrecy throughput. Moreover, increasing the number of antennas at the jammer provides significant performance gains for the three proposed schemes, which can be used to compensate for the performance loss from short packet transmissions. The rest of the paper is organized as follows. We describe the system model of an IoT network with a multi-antenna cooperative jammer in Section 2. In Section 3, we present the analytical expressions of the secrecy throughput and asymptotic secrecy throughput and characterize the maximization of the secrecy throughput. Finally, we give simulation results in Section 4 and conclude this work in Section 5.
# SimMolCC: A Similarity of Automatically Detected Bio-Molecule Clusters between Fluorescent Cells
## 3 Automatic Detection of Bio-Molecule Clusters in a Fluorescent Cell Image
### Step 4--Bio-Molecule Cluster Filtering  
Step 4 filters bio-molecule clusters by the following four heuristic rules:  
**Step 4(a).**: \"Correct bio-molecule clusters have to have their edge (i.e., outline) in the target cell.\" Therefore, Step 4 filters out bio-molecule clusters that do not touch any Canny edges (i.e., any outline of candidates for bio-molecule clusters) in the target cell in an input TIRF image.
**Step 4(b).**: \"Correct bio-molecule clusters have not to exist in protrusions near the edge (i.e., outline) of the target cell.\" Therefore, Step 4 filters out bio-molecule clusters that touch the Canny edge (i.e., the outline) of the target cell in an input TIRF image.
**Step 4(c).**: \"The size of 1 correct bio-molecule is about 10 nm, observed as 200-300 nm (2D Gaussian, \(\sigma=\) 120-130 nm)\" because the TIRF's resolutions of \(x\)- and \(y\)-axes are diffraction-limited. Therefore, Step 4 filters out bio-molecule clusters whose area is less than 5 [pixels] in an input TIRF image of a fluorescent cell.
**Step 4(d).**: \"Correct bio-molecule clusters have to have unusually higher fluorescence intensity in the target cell.\" First, the \(n\) kinds of sampled histograms of fluorescence intensity of each pixel that has not yet been filtered out and thus whose value has not yet been \"0 (zero)\" in the filtered TIRF image of a fluorescent cell after Step 4(c) are calculated, where the number of bins, bins, is set based on the Sturges' optimal number of bins [67] from \(+0\) to \(+(n-1)\), as shown in Figure 8. Note that the number \(n\) of sampled histograms is set to 5 in this paper.  
Next, the threshold to filter out bio-molecule clusters that do not have unusually higher fluorescence intensity in the target cell is automatically searched by either of the following two kinds of ways:  
**1 st:**: The threshold flagged as \"1 st\" is set to be the average of the \(n\) fluorescence intensities of the bin that first violates \"Monotone Decreasing\" in each sampled histogram.
**3 rd:**: The threshold flagged as \"3 rd\" is set to be the average of the \(n\) fluorescence intensities of the bin that violates \"The difference of frequency (between the bin and the bin followed by it) is not 3 rd compared with its pre-difference and its post-difference\" in each sampled histogram.  
Note that the number of bio-molecule clusters at Step 4(d) (\(\texttt{kernel\_size}=3\) flagged as \"3 rd\") is calculated as 237 in Figure 8.  
Figure 7: Step 3 has four sub-steps, Step 3(a), Step 3(b), Step 3(c), and Step 3(d), to divide the regions of bio-molecule clusters in an input TIRF image of fluorescent cell #6 as precisely as possible, and finally each bio-molecule cluster is independent and assigned the sequential ID.
## 2 Methods
### Gaussian Process with a Tapering Covariance Function for Local Variability
#### 2.3.3 Parameter Estimation for \(\theta\)  
We incorporate the tapering covariance function Equation (8) into the Gaussian process Equation (5). Then, the tapering covariance matrix is  
\[\Sigma_{\delta}=\Sigma(\mathbf{\theta})\circ\mathbf{A}(\gamma), \tag{9}\]  
where \(\Sigma(\mathbf{\theta})\) and \(\mathbf{A}(\gamma)\) are the covariance matrices obtained by \(C(\mathbf{h};\mathbf{\theta})\) and \(c_{taper}(\delta s;\gamma)\), respectively; the notation \(\circ\) denotes the Schur product that is defined as the element wise matrix product. For example, two \(m\times n\) matrixes, \(P\) and \(Q\), have the Schur product \(P\circ Q~{}=~{}\left\{p_{ij}q_{ij}\right\}\).  
We use maximum-likelihood estimation (MLE) approach to estimate \(\mathbf{\theta}\). Specifically, the log-likelihood function of \(\mathbf{\theta}\) is as follows  
\[l(\mathbf{\theta})=-\frac{1}{2}\log\bigl{|}\Sigma(\mathbf{\theta})\circ\mathbf{A}( \gamma)\bigr{|}-\frac{1}{2}\mathbf{Z}^{\prime}\bigl{(}\bigl{|}\Sigma(\mathbf{ \theta})\circ\mathbf{A}(\gamma)\bigr{|}^{-1}\circ\mathbf{A}(\gamma)\bigr{)} \mathbf{Z}+d, \tag{10}\]  
where \(\mathbf{Z}=\mathbf{Y}-\mathbf{G}\) denotes the difference between the sensing observations and the global temperature profiles, and \(d\) is a constant term that is uncorrelated with \(\mathbf{\theta}\). Maximizing \(l(\mathbf{\theta})\) is to solve an unbiased estimating equation for \(\mathbf{\theta}\), i.e., \(\mathrm{E}\Bigl{[}\frac{\partial}{\partial\mathbf{\theta}}l(\mathbf{\theta})\Bigr{]}=0\). The derivative of \(l(\mathbf{\theta})\) is equal to  
\[\begin{array}{l}\frac{\partial l(\mathbf{\theta})}{\partial\mathbf{\theta}}=-\frac{ 1}{2}\mathrm{tr}\left\{\bigl{|}\Sigma(\mathbf{\theta})\circ\mathbf{A}(\gamma) \bigr{|}^{-1}\frac{\partial\Sigma(\mathbf{\theta})}{\partial\mathbf{\theta}}\circ \mathbf{A}(\gamma)\right\}\\ +\frac{1}{2}\mathbf{Z}\bigl{|}\Sigma(\mathbf{\theta})\circ\mathbf{A}(\gamma) \bigr{|}^{-1}\frac{\partial\Sigma(\mathbf{\theta})}{\partial\mathbf{\theta}}\circ \mathbf{A}(\gamma)\bigr{|}\Sigma(\mathbf{\theta})\circ\mathbf{A}(\gamma)\bigr{|}^ {-1}\circ\mathbf{A}(\gamma)\mathbf{Z}.\end{array} \tag{11}\]  
Equation (11) can be used in conjunction with a numerical optimization algorithm, such as conjugate gradients, to obtain the estimation of \(\mathbf{\theta}\)[26].