esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Manufacturing the Composite Main Reflector
### Manufacturing the Composite Reflector  
The manufacturing process for the composite reflector was carried out by laminating CFRP prepreg through a hand-layup process and autoclave curing, which is the same as the general composite manufacturing process.  
Based on the initial design plan and considering the structural performance, four types of reflectors were manufactured according to process variables.  
(1) A co-bonding process in which the reflective surface is first cured at high pressure (40 psi) to minimize pin-holes on the reflective surface and then the adhesive film-honeycomb core-adhesive film-outer surface is laminated in the order of the molded reflective surface. Then, the second curing (10 psi) is performed  
(2) A co-curing process in which the reflective surface-adhesive film-honeycomb core-adhesive film-outer surface is laminated, and then the curing is performed at once at low pressure (10 psi)  
(3) A co-curing process in which the reflective surface-honeycomb core (with perforated holes)-outer surface excluding the adhesive film and the venting path are secured and the curing is performed at once at low pressure (10 psi)  
(4) A co-curing process involving the reflective surface-honeycomb core (with perforated holes)-outer surface, excluding the adhesive film. Then, the fastening interface part is additionally reinforced and laminated, the venting path is secured, and curing is performed at once at low pressure (10 psi)  
Metallic molds are manufactured considering the precise surface error of the main reflector using Invar 36 material with little thermal deformation.  
As shown in Figure 9, the prepreg and honeycomb core were machined to fit the shape of the main reflector. Then, using a Lay-up Location Projector according to the lamination angle and the order, the prepreg was laminated using a hand-layup process. The laminated structure was cured using autoclave equipment that can apply temperature and pressure.  
The main reflector after curing was checked for defects in the product through visual inspection and non-destructive testing (NDT; details regarding NDT are described in Section 3.1). Considering the interface of the deployable structure of the deployable reflector antenna attached to the main reflector, a hole was machined, and resin potting was performed to insert a metal insert.  
Figure 8: Additional laminate patch-up at the interface section of the main reflector.
# CPDet: Circle-Permutation-Aware Object Detection for Heat Exchanger Cleaning
## 4 Results
### Discussion  
We applied the proposed module to the recently introduced YOLOv8 model and commonly used methods in the industrial field such as YOLOv3 and YOLOv5. By comparing the results in Table 6, it can be noticed that the methods incorporating our proposed module showed a significant improvement in model training accuracy, and the enhancement was most significant, where the \(mAP_{50}\) increased by 5.76 percentage points for CPDet(v3), \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Methods** & \(mAP_{50}\) & \(mAP_{50}\) & \(mAP_{50.95}\) & **F1 Score** & **TPR** & **FNR** & **FPR** \\ \hline YOLOv3 [18] & 76.41\% & 63.16\% & 71.69\% & 77.82 & 0.71 & 0.13 & 0.17 \\ YOLOv5 [20] & 78.31\% & 65.64\% & 71.96\% & 74.11 & 0.75 & 0.11 & 0.14 \\ YOLOv8 [40] & 81.64\% & 67.91\% & 73.30\% & 75.95 & 0.73 & 0.08 & 0.13 \\ YOLOv3-5 [41] & 79.94\% & 63.01\% & 71.75\% & 73.62 & 0.76 & 0.09 & 0.13 \\ Gold-YOLO [42] & 80.80\% & 64.81\% & 72.80\% & 76.22 & 0.76 & 0.07 & 0.15 \\ TPH-YOLOv5 [11] & 80.98\% & 64.34\% & 71.08\% & 77.72 & 0.63 & 0.19 & 0.21 \\ YOLOv10 [43] & 82.15\% & 69.40\% & 73.84\% & 79.72 & 0.78 & 0.12 & 0.11 \\ CPDet(v3) & 82.17\% & 63.34\% & 74.13\% & 79.68(+1.86) & 0.84(+0.13) & 0.03(\(-0.10\)) & 0.05(\(-0.12\)) \\ CPDet(v5) & 83.54\% & 66.57\% & 75.40\% & 80.61(+6.50) & 0.84(+0.09) & 0.03(\(-0.08\)) & 0.04(\(-0.1\)) \\ CPDet(v8) & **84.93\%** & 68.25\% & 76.61\% & 80.30(+4.35) & 0.88(+0.15) & 0.02(\(-0.06\)) & 0.04(\(-0.09\)) \\ CPDet(v10) & 84.59\% & **68.37\%** & **76.74\%** & **81.23(+1.51)** & **0.88(+0.10)** & **0.01(\(-0.11\))** & 0.04(\(-0.07\)) \\ \hline \hline \end{tabular} Note: The blue background is to highlight the effect of adding our proposed module to baseline using the method in parentheses, inside (v3, v5, v8, v10) indicated respectively YOLOv3, YOLOv5, YOLOv8, YOLOv10, the red font represents the increment of each metric in that line compared to its corresponding baseline, and the bold indicates the highest value for this column metric.  
\end{table} 
Table 7: Experimental results of different detection algorithms of HE dataset.
## 2 Problem Formulation
### Forward Privacy  
Forward privacy focuses on preserving the confidentiality of keywords during file addition operations, ensuring that an addition query does not disclose any information about the keywords [37; 38]. An SSE scheme lacking forward privacy may unintentionally leak information when inserting a new file. Exploiting this vulnerability, \(CS\) tests the previously submitted search token during a new update, checking whether the old search token matches the new update. A file injection attack takes advantage of this behavior to extract information about the keyword. First, \(CS\) can trick the \(DO\) into injecting some files containing certain specific keywords. After \(DO\) uploads the injected files, \(CS\) employs the search token provided by \(DO\) to search within the injected files, recovering the corresponding keyword based on the search result. As forward privacy guarantees that the past search token cannot be linked to a newly added file, this type of attack can be thwarted.
## 3 Result and Discussion
### Simulation Results
Figure 7: Methane production rate of simulation and experimental results: RF (**a**) and CF (**b**). Sensitivity analysis was carried out on K\({}_{\text{dis1}}\) and K\({}_{\text{dis2}}\) parameters in mode 2 simulation. Sensitivity analysis, which combines identifiability and uncertainty analysis, is used to check if K\({}_{\text{dis}}\) parameters can be uniquely determined from available data [24]. The sensitivity function of methane production with respect to K\({}_{\text{dis1}}\) and K\({}_{\text{dis2}}\) are shown in Figures 9 and 10. In both RF and CF, the methane production was much more sensitive to K\({}_{\text{dis1}}\) than K\({}_{\text{dis2}}\). Sensitivity to K\({}_{\text{dis2}}\) was more apparent in RF than CF. The sensitivity of various other variables to K\({}_{\text{dis1}}\) and K\({}_{\text{dis2}}\) is also given in the form of sensitivity functions (SensAR) in Appendix A. The absolute-relative sensitivity function (Equation (A1)), was used to measure the absolute change in y (methane production) for 100% change in p (K\({}_{\text{dis1}}\)/K\({}_{\text{dis2}}\)). Comparison of rates for RF and CF samples showed that the rate of biomethane production is faster in particles with lower solid particles, but it also showed that solid particulates breakdown slowly resulting in a steady biomethane production over a long period. In continuous reactors, solid accumulation may occur when substrates are added continuously without efficient solid disintegration and removal. Ideally, there should be a balance between rates of solid substrate addition and solid disintegration for a stable digestion process. In granular sludge bed reactors, solid particulates are often trapped in the sludge bed for long periods, meaning solid retention times much longer than  
Figure 10: Sensitivity function of methane production with respect to K\({}_{\text{dis1}}\) and K\({}_{\text{dis2}}\) (CF). Figure 9: Sensitivity function of methane production with respect to K\({}_{\text{dis1}}\) and K\({}_{\text{dis2}}\) (RF). the hydraulic retention time can be achieved, but appreciable disintegration of trapped solid may be hindered due to various reasons among which are large particle size, inefficient mixing, and mass transfer limitation. A carefully adjusted balance between influent solids and solid disintegration kinetics has to be established and considered during reactor design in order to use the granular sludge bed for particle-rich substrates. Reactor conditions such as volume temperature, HRT (Hydraulic Retention Time) and SRT (Solids Retention Time) have to consider possible solid accumulations. In addition, a combination periodic removal of excess solids, pretreatment of solid substrates before and during reaction and continuous monitoring of reactor conditions have to be maintained.
# Analysis and Design of A PMQR-Type Repetitive Control Scheme for Grid-Connected H6 Inverters
## 4 Simulation Results and Analysis
### Controller Design
#### 4.1.1 The Proportional Gain \(k_{p}\)  
The corresponding dominant pole assignment map and Bode plot of the new control object \(P_{0}\left(z\right)\) taking different \(k_{p}\) values are shown in Figures 9 and 10, respectively. As can be seen from Figure 9, when \(k_{p}\) is taken from 3 to 11, the dominant poles of \(P_{0}\left(z\right)\) remain inside the unit circle. Figure 10 shows that when \(k_{p}\) changes from 3 to 11, the gain of \(P_{0}\left(z\right)\) in the low frequency band remains almost constant. Moreover, with the value of \(k_{p}\) increasing, the system bandwidth becomes narrower, and the  
\begin{table}
\begin{tabular}{c c c} \hline
**Symbol** & **Description** & **Values** \\ \hline \(v_{g}\) & grid voltage & 220 V \\ \(f_{g}\) & ideal grid voltage frequency & 50 Hz \\ \(U_{dc}\) & DC bus voltage & 360 V \\ \(f_{s}\) & sampling frequency & 20 kHz \\ \(L_{1}=L_{2}\) & filter inductor & 0.8 mH \\ \(C_{f}\) & filter capacitor & 4 uF \\ \(R\) & ESR & 0.1 \(\Omega\) \\ \(P\) & output power & 3 kW \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the grid-connected H6 inverter with the LC filter.  
Figure 8: Bode plot of the transfer function \(H_{d}\left(s\right)\) with different \(k\).  
filtering effect is improved. At the same time, the system dynamic response is considered. Therefore, the gain \(k_{p}\) is selected as nine in this paper.
## 1 Introduction
First, the classification accuracy varied, since the form of the chosen land surface phenology (LSP) model might be more suitable for some certain vegetation types than others. Second, the phenometrically-based classification performed well only for vegetated classes, particularly crops. Third, many cloud/snow/shadow-free observations were needed at each pixel over a year to fit the LSP model well and to avoid data gaps in the predicted land cover map. Regarding the last point, they [18] also showed that an adequate number of observations could be gathered by combining data from multiple comparable sensors, especially in sidelap zones of Landsat swaths. Finally, in addition to pointing out the challenges ofclassification based on phenometrics, [PERSON] et al. (2018) also discussed the potential opportunity to improve classification accuracy by incorporating both phenological and spectral information. Here, we explored the challenges of the phenometrically-based classification and a potential way to improve classification accuracy, as demonstrated in ([PERSON] et al., 2018). This study focused on evaluating the performance of alternative land cover classifications using either (1) only phenological metrics derived from either of different land surface phenology (LSP) models: The Convex Quadratic Model, in which EVI2 = \(f\)(AGDD) (K  
(Figure 1), cropland is a dominant land cover in Roberts County, accounting for approximately 53.3% of the county area. Other cover types in the County include grassland (25.9%), wetland (8.9%), water (5.2%), barren/developed (4.4%), and forest (2.2%). The County falls within overlap zones of Landsat paths, which allows retrieval of more cloud-free observations.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 2 Related Work
Even if the proposal seems to be similar at first sight, they do not propose a MAC protocol, and the routing they use is based on probabilities; these are very important differences. The MAC introduces a delay that depends on the kind of protocol that is used. In that paper a contention mechanism is used, which has unbounded delays in the worst case. For choosing the next hop in intermediary nodes, the authors proposed a probabilistic approach, routing the message through the path with greater probability of reaching the destination node. In order to reduce the energy consumption, alternative paths are followed only when a fault is detected. In our proposal, an epidemic routing is used to provide the maximum possible degree of redundancy in a harsh medium very prone to interference and errors. In [43] we present a real-time analysis for an underwater acoustic wireless network. The shortest path between a source and a sink node is used as routing policy, combined with a message or node slot allocation procedure in an optimized TDMA frame. Based on this, the schedulability condition is presented for the case in which messages are transmitted following a FIFO policy in the relay nodes. In order to help address the real-time communication in UW-ASN, we propose a MAC protocol and routing model for underwater acoustic sensors networks, evaluating their performance through simulations. We recognize that the submarine medium is rather difficult to model and the actual implementation of the proposal would require adjustment to accurately represent the real message exchange in such a scenario. In order to illustrate such a challenge, in [44] the authors report the deployment of an underwater acoustic network in a real-world scenario, and in [45] they emphasize the impact that the modern characteristics have on practical underwater MAC design. These researchers also indicate the acoustic modems require a long synchronization preamble, and that synchronization time depends on the modern. Therefore, the model proposed in this paper should be tuned, mainly in the slot length definition, according to the hardware used in the network implementation.
## 3 Construct the Measurement Coordinate Point
Similarly, \(\delta_{h}=\delta^{\prime}_{h}d_{h2}\) for \(\delta^{\prime}_{UH}<0\), then, \(100(1-\alpha)\%\) confidence region of \((\delta_{h},\gamma_{h})\) is \(CR=\big{\{}\left(\delta_{h},\gamma_{h}\right)|\delta^{\prime}_{Lh}\times d_{h2 }\leq\delta_{h}\leq\delta^{\prime}_{UH}\times d_{h2},\gamma^{\prime}_{Lh} \times d_{h2}\leq\gamma_{h}\leq\gamma^{\prime}_{UH}\times d_{h2}\big{\}}\) for \(\delta^{\prime}_{UH}<0\), where \(\gamma_{h}=\gamma^{\prime}_{h}d_{h2}\) and \(\delta_{h}=\delta^{\prime}_{h}d_{h2}\). Based on the above-dated, the \(100(1-\alpha)\%\) confidence region of \((\delta_{h},\gamma_{h})\) is expressed as:  
\[CR=\left\{ \begin{array}{l}\big{\{}\left(\delta_{h},\gamma_{h}\right)|\delta ^{\prime}_{Lh}\times d_{h1}\leq\delta_{h}\leq\delta^{\prime}_{UH}\times d_{h1 },\gamma^{\prime}_{UH}\times d_{h1}\leq\gamma_{h}\leq\gamma^{\prime}_{UH} \times d_{h1}\big{\}},\delta^{\prime}_{Lh}\geq 0\\ \big{\{}\left(\delta_{h},\gamma_{h}\right)|\delta^{\prime}_{Lh}\times d_{h2} \leq\delta_{h}\leq\delta^{\prime}_{UH}\times d_{h2},\gamma^{\prime}_{LH} \times d_{h2}\leq\gamma_{h}\leq\gamma^{\prime}_{UH}\times d_{h2}\big{\}}, \delta^{\prime}_{UH}<0\end{array} \right.. \tag{8}\]  
Obviously, since the intersection of zone (\(Z^{\prime\prime}_{A}\)) and the confidence region (CR) are not empty sets, it can be deduced that the value of the process capability index \(C^{\prime\prime}_{pk}\) is larger than or equivalent to \(C\) (\(C^{\prime\prime}_{pk}\geq C\)). Based on this concept, we can create the measurement coordinate point \((x_{h},y_{h})\) of quality characteristic h as follows (see Appendix A):  
\[\left(x_{h},y_{h}\right)=\left\{ \begin{array}{l}\left(\delta_{Lh},\gamma_{ Lh}\right)=\left(\left(\delta^{\prime}_{h}-\frac{t_{h/4\alpha-1}}{\sqrt{n}} \gamma^{\prime}_{h}\right)\times d_{h1},\ \sqrt{\frac{n}{\chi^{2}_{1-\alpha/4\alpha-1}}}\gamma^{\prime}_{h}\times d_{h1} \right),\delta^{\prime}_{Lh}\geq 0\\ \left(\delta_{UH},\gamma_{LH}\right)=\left(\left(\delta^{\prime}_{h}+\frac{t_{ \alpha/4\alpha-1}}{\sqrt{n}}\gamma^{\prime}_{h}\right)+d_{h2},\ \sqrt{\frac{n}{\chi^{2}_{\alpha/4\alpha-1}}}\gamma^{\prime}_{h}\times d_{h2} \right),\delta^{\prime}_{UH}<0\end{array} \right. \tag{9}\]  
Then, the process quality of the quality characteristic h is measured by the location of the coordinate point. Decision-making rules for the measurement are listed as follows:  
When \((x_{h},y_{h})\in Z^{\prime\prime}_{A}\), then the value of index \(C^{\prime\prime}_{pmh}\) is bigger than \(C\), and the process capability of quality characteristic h attains the required level. When \((x_{h},y_{h})\
otin Z^{\prime\prime}_{A}\), then the value of index \(C^{\prime\prime}_{pmh}\) is smaller than \(C\), and the process capability of quality characteristic h is under the required level. The process capability of quality characteristic h needs to be leveled up. This quality characteristic is regarded as critical to quality (CTQ) in this paper.
# An Optimization Framework for Wind Farm Design in Complex Terrain
## 2 Wind Farm Modelling
### AEP Estimation  
After the local wind speeds with and without wake effects of each wind turbine are obtained by using Equations (3)-(5), it is then easy to calculate the corresponding power outputs using the power curve of the wind turbine.  
Note that the site condition described in Section 2.1 provides wind resource parameters at each turbine site for a given far field inflow wind direction, i.e.,: \(A_{i}(\theta^{\infty})=A(x_{i},\ y_{i},\theta^{\infty})\), \(k_{i}(\theta^{\infty})=k(x_{i},\ y_{i},\theta^{\infty})\) and \(freq_{i}(\theta^{\infty})=freq(x_{i},\ y_{i},\theta^{\infty})\). Based on these parameters, the probability of each turbine site's local wind condition, i.e., \(v_{i}(v^{\infty},\theta^{\infty})\) and \(\theta_{i}(\theta^{\infty})\), with respect to the far field inflow wind condition (\(v^{\infty}\) and \(\theta^{\infty}\)) can be estimated by using the joint distribution proposed in [26] as:  
\[\begin{array}{ll}pdf_{i}(v^{\infty},\theta^{\infty})=&pdf(v_{i}(v^{\infty}, \theta^{\infty}),\ \theta_{i}(\theta^{\infty}))\\ &=\frac{k_{i}(\theta^{\infty})}{\Delta_{i}(\theta^{\infty})}\cdot\left(\frac {v_{i}(v^{\infty},\theta^{\infty})}{A_{i}(\theta^{\infty})}\right)^{k_{i}( \theta^{\infty})-1}\cdot\exp\left(-\left(\frac{v_{i}(v^{\infty},\theta^{\infty })}{A_{i}(\theta^{\infty})}\right)^{k_{i}(\theta^{\infty})}\right)\cdot freq_{ i}(\theta^{\infty})\end{array} \tag{6}\]  
Thus, for a wind farm composed of \(N_{wt}\) turbines, its gross and net AEP, i.e., AEP with and without wake effects, can be estimated as:  
\[\begin{array}{ll}\text{AEP}_{\text{\it gross}}=\sum_{i=1}^{N_{\text{\it net}} }8760\cdot\eta_{i}\cdot\iint P(v_{i}(v^{\infty},\theta^{\infty}))\cdot pdf_{i}(v^{ \infty},\theta^{\infty})dv^{\infty}\theta^{\infty},\end{array} \tag{7}\]  
\[\begin{array}{ll}\text{AEP}_{\text{\it net}}=\sum_{i=1}^{N_{\text{\it net}} }8760\cdot\eta_{i}\cdot\iint P(\overline{v}_{i}(v^{\infty},\theta^{\infty})) \cdot pdf_{i}(v^{\infty},\theta^{\infty})dv^{\infty}\theta^{\infty},\end{array} \tag{8}\]  
in which 8760 is the total number of hours in a year, \(\eta_{i}\) denotes the availability factor of turbine \(i\), and \(P(\cdot)\) represents the power curve of the wind turbine. The integrations in Equations (7) and (8) are done numerically after properly discretizing the interested ranges of \(v^{\infty}\) and \(\theta^{\infty}\). In this study, the availability factors of all the turbines are assumed to be 1.0 and the discretization used in numerical integration is \(\Delta v^{\infty}=1\) m/s, and \(\Delta\theta^{\infty}=\)5\({}^{\circ}\).
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 2 Research Methodology
### Phase 1: Planning the Systematic Literature Review (SLR)
#### 2.1.2 Rationale of the Work  
In artificial intelligence (AI), short text is an emerging field, which lacks sufficient standards to follow the proper syntax of the language. Short text (ST) contains a limited number of words, and various words affect the intent of other words, which causes inaccurate similarity in return. Words are the main parameters of any language; without words, language would not be a concept [12; 13; 14]. Understanding the formation, structure, and semantics of sentences through machine-oriented methods are a challenge for many researchers. In the absence of comprehensive analysis, this study extracts and provides a detailed systematic literature review (SLR) of existing studies. Moreover, this study helps researchers to choose appropriate solutions and propose new systems for complex queries. The techniques provided by this study can further be reused for question-answering systems, short text classification, information retrieval, and keyword analysis.
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 5 Setting up the PrECast Infrastructure
### The PrECast Algorithm  
The first step in the PrECast algorithm is to build the MCAM table dynamically. A host can either obtains its IP address dynamically from the DHCP server or its IP address is assigned manually by the system administrator. In these two cases, we describe how MCAM tables are built.  
In any corporate network, there will be few hosts that use static IP address; for example, DHCP servers, primary and secondary DNS servers, default gateway, and so on. Since our paper deals with issues that arise due to IPv4 broadcasting, we assume that hosts that are connected to our network have Layer-3 connectivity through IPv4. To gain Layer-3 connectivity to the network, every host on the network must have a unique IP address assigned to it. The IP addresses can be either assigned statically by the system administrator (in this case, the IP address will never change) or a host may obtain its IP address dynamically from a DHCP server.
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 3 Proposed Methodology
### Overview of the Algorithm  
In this study, the complete process is mainly divided into three parts, namely the PSO process, the training process and the testing process. Figure 6 is a complete flow chart of the proposed method. It should be noted that PSO is only used to optimize the sparse parameters to ensure network sparsity and speed up the optimization.  
The specific steps are as follows:  
1. Obtain the vibration signals of rotating machinery components, perform data preprocessing, convert them into frequency domain data and randomly divide the frequency domain data set into a training set and a testing set;
2. Initialize HSDAE parameters and set key hyperparameters, such as the number of neurons in each layer, batch size, number of iterations, etc.;
3. PSO parameter initialization, set the optimization boundary, inertia factor, learning factor, total number of particles and number of iterations;
4. The HSDAE network uses the training set for pre-training to obtain the initial values of the variables in the network;
5. Use the hierarchical sparse loss function as the fitness function of PSO to obtain the optimal sparse parameters. Use the optimal sparse parameters to train the HSDAE and calculate the accuracy. If the accuracy does not meet the conditions, return to  
Figure 6: The detailed flow chart of the proposed method.  
step 4 to continue iterating until the accuracy threshold is met, and then the optimal sparsity parameter is output;
6. Use the optimal sparse parameters to train the HSDAE, use the testing set to cross-validate, stop training and save the model when the accuracy meets the threshold;
7. Use the testing set to test the model and output the diagnosis result.