esa-sceva/satcom-chunk-collection huggingface dataset:
# Cloud Detection and Tracking Based on Object Detection with Convolutional Neural Networks
Regarding ground-based observation, a novel deep CNN model named SegCloud was proposed and applied for accurate cloud segmentation. SegCloud showed a powerful cloud discrimination capability and automatic segmentation of the whole-sky images obtained by a ground-based all-sky-view camera [17]. Note that one challenge of artificial neural network approaches is the accessibility of large labeled datasets for training purposes. Therefore, [18] introduced selfsupervised-based training methods for semantic cloud classification, using large unlabeled datasets for pretraining. The selfsupervised pretraining is followed by a supervised training approach with a small manually labeled dataset. The selfsupervised pretraining methods increased the overall accuracy of semantic cloud classification by roughly 9%. The present work arises from and is a natural further step in the ongoing development of Hel-IoT [19; 20] as part of the HelioSun project. Hel-IoT or the smart heliostats concept is based on the application of techniques derived from industry 5.0, mainly artificial intelligence and computer vision techniques. Thanks to these techniques, Hel-IoT incorporates a new, intelligent solar tracking system capable of tracking the Sun with just one camera and one neural network trained for the purpose. The HelioSun project's main goal is to develop more efficient heliostat fields for SCR among others tools, considering the development of Hel-IoT. Due to the great relevance of knowing the availability of the solar resource in advance for the correct control of power generation plants based on solar energy, this paper presents the first implementation of a novel approach for cloud detection, trajectory, and transient remaining time prediction making use of computer vision techniques related to object detection with region proposal techniques based on deep learning with CNN. No other method is based on object detection and is able to calculate the trajectory and time remaining. The proposed approach for cloud detection is based on artificial neural networks as recommended in previously mentioned studies. Moreover, it was developed with the main goal of being operable on a wide variety of hardware, including ground-based, low-cost hardware, and having the ability to be used with smart tracker systems. This first implementation of the approach using a low-cost system is analyzed and discussed.
## 4 Precise Orbit Determination (POD)
### POD Strategy
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Model** & **Reference** \\ \hline Basic observations & \begin{tabular}{l} Undifferentiated ionosphere-free code and phase combination of B1I \\ and B3I signals \\ \end{tabular} \\ \hline Sampling interval & 300 s \\ \hline Cutoff elevation & 10\({}^{\circ}\) \\ \hline Arc length & 3 days \\ \hline Weighting & \begin{tabular}{l} Prior precision of phase and code observations is 2 cm and 2 m, \\ respectively, and elevation-dependent data weighting \\ \end{tabular} \\ \hline Satellite antenna phase & \begin{tabular}{l} C06-C14: PCO and PCV corrected with values estimated by Wuhan \\ University (except C13); \\ C31-C34: corrected with data supplied by BeiDou Operational \\ Control Center (OCC) \\ \end{tabular} & [7] \\ \hline Tropospheric delay & Saastamoinen model, global mapping function, two-hourly ZTDs & [37; 38] \\ \hline Relativity effect & Considered according to IERS Conventions 2010 & [39] \\ \hline Earth orientation parameters & Fixed according to IERS C04 & [40] \\ \hline Geopotential & \begin{tabular}{l} EIGEN\_GL04C up to degree 12 \(\times\) 12 (European Improved Gravity \\ model of the Earth by New techniques) \\ \end{tabular} & \\ \hline N-body gravitation & \
\begin{tabular}{l} Sun, Moon, and other planets: JPL DE405 (Jet Propulsion \\ Laboratory Development Ephemeris 405) ephemeris used \\ \end{tabular} & \\ \hline Solar radiation &
\begin{tabular}{l} ECOM five-parameter model with a constant acceleration bias in \\ along-track direction \\ \end{tabular} & [28] \\ \hline Attitude model & Both YS and ON models for BeiDou-2; YS model only for BeiDou-3e & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Observational models and force models used in POD processing strategy. Phase center offset (PCO); phase center variation (PCV); zenith troposphere delays (ZTDs); Empirical CODE Orbit Model (ECOM).
# An Investigation of the Temperature-Drift Effect on Strain Measurement of Concrete Beams
## 3 Strain Monitoring Tests for Beams
### Strain Monitoring Test for a Steel Beam
Although the temperature-induced drift in this test was not significant, the test results of a concrete beam to be discussed in the subsequent section showed significant temperature drift. A data-processing method was thus proposed to eliminate temperature drift interference. The proposed method was based on the assumption that temperature changes are slow and mechanical strain changes are abrupt; therefore, an adjacent data subtraction method was applied to the time-history data of mechanical strain response to obtain the relative changes in mechanical strain at adjacent times; thus, the effects of temperature drift could be significantly reduced. The adjacent data subtraction means that the current data were subtracted by their preceding data. Figure 8b displays the change in mechanical strain per unit time obtained by performing adjacent data subtraction of Figure 8a. In Figure 8b, only the four time points of load application and removal showed significant mechanical strain changes. The relative changes in mechanical strain at all other time points were within \(\pm\)2 \(\mu\epsilon\). Under the assumption that the elastic modulus of the steel beam was 210 GPa, the theoretical strain at the top and bottom of the steel beam subjected to a load of 100 g was calculated to be approximately 15.62 \(\mu\epsilon\). Comparing the positive or negative strain obtained from the four load applications in Figure 8b with the obtained theoretical strain showed that only one strain data point, 14.38 \(\mu\epsilon\), had a difference of 1.24 \(\mu\epsilon\); the differences of the other seven data points were less than 1 \(\mu\epsilon\). It is worth mentioning that the maximum absolute error (1.24 \(\mu\epsilon\)) is quite close to the strain accuracy (1.0 \(\mu\epsilon\)) provided by the measuring instrument itself. The test results showed that the proposed data analysis method could easily and accurately obtain the mechanical strain generated by the load.
## 2 Related Work
### POI Recommendation without Real-Time Performance  
POI recommendation has emerged as a new research hot spot, attracting extensive attention from researchers in recent years. The early POI recommendation methods relied heavily on machine learning methods [8], especially stochastic models based on  
Figure 1: Example of time-series trajectories with varying lengths.  
Markov chains. Additionally, inspired by the successful application of matrix factorization (MF) [11; 12] methods in other domains, researchers began integrating MF methods into the modeling of POI recommendations. The factorizing personalized Markov chain (FPMC) model [13] combines matrix factorization with Markov chains to model users' long-term preferences and sequential behaviors, aiming to achieve more accurate recommendation results.  
Currently, Recurrent Neural Networks (RNNs) [14] have become the mainstream model for modeling user check-in sequences. The spatial-temporal recurrent neural network (ST-RNN) model [15] extends the RNN model by introducing a transformation matrix of temporal and distance features, which effectively captures the cyclic effects of time and space. In addition, variants of RNNs, such as LSTM [16], show good performances. The stochastic tailor-made gradient noise (STGN) model [17] modifies the basic LSTM by adding four new gating mechanisms, including two long-term gates and two short-term gates. The Spatial-Temporal-Aware Graph Convolutional Neural Network (STGCN) model [18] extends the STGN model by adding coupled input gates and forgetting gates to reduce the computational complexity. In addition, several studies have explored the attention mechanism to better model spatial data and rich contextual information. The Personalized Long-Short-Term Preference Learning (PLSPL) model [19] combines the attention mechanism with the LSTM to recommend the next POI. In this model, the attention mechanism is used to learn the user's long-term preferences, while the LSTM focuses on capturing the user's short-term preferences. By employing personalized linear weights, the PLSPL model comprehensively takes into account the user's overall preferences, thereby providing more accurate results. The Spatiotemporal Attention Network (STAN) model [20] uses a self-attention layer to mine the relationship between non-adjacent check-in records in a check-in record sequence of the user. However, these POI recommendation models tend to perform poorly in real-time POI recommendation scenarios because they lack attention to real-time user preferences and cannot effectively model preferences over time.
## 4 Discussion
### Explanations and Limitations of the CCC Approach and the Backward Approach
The f\({}_{\text{esc}}\) and another correction factor FCVI, both designed to eliminate the effects of PAR scattering and reabsorption in the canopy, are used to downscale SIF from canopy level to leaf level, which is more directly related to photosynthesis [51; 52; 53]. The neglect of the scattering and reabsorption led to some uncertainties of the backward approach. Thus, we suggest the downscaling of canopy SIF signals with the f\({}_{\text{esc}}\) or FCVI might benefit the application of the approach we proposed here if further research was made to improve the approach.
## 4 Results and Analysis
### Effect of Transverse Bulkheads on Overall Bend Strength
This is because the transverse bulkhead at a far position will reduce the load-sharing capacity of the structure outside the bend zone, causing the load to become more concentrated in the bend zone. The deflection of SHG(0) is slightly greater than that of SHG(0.1). The reason for this is that the bend position of the transverse bulkhead at the mid-section will be offset, and these two have different deformation modes. The above results show that the distant transverse bulkhead has a significant influence on the deflection, and it is difficult to properly characterize the overall bend resistance with the parameters of a single section. Therefore, based on the moment of inertia, the equivalent moment of inertia considering the influence of transverse bulkheads at different positions was constructed for evaluation:  
Figure 14: Maximum sagging deflection of different SHGs. Figure 13: Time history curves of dimensionless deflection. \[I_{pe}=I_{s}+\sum_{l}\gamma_{l}I_{b}, \tag{11}\]  
where \(I_{pe}\) is the equivalent moment of inertia of SHGs, \(I_{s}\) is the moment of inertia of the SHGs without the transverse bulkhead section, \(I_{b}\) is the moment of inertia of the transverse bulkhead section, and \(\gamma_{i}\) is the weight coefficient of the transverse bulkhead at different positions. For this paper, \(I_{s}=80.88\) cm\({}^{4}\), \(I_{b}=1666.66\) cm\({}^{4}\). Assuming that the maximum sagging deflection of SHGs is inversely proportional to the equivalent moment of inertia when the load and ship shape is constant, the weight coefficient function of transverse bulkheads at different positions can be calculated according to Formula (11) and Figure 14, as shown in Figure 15, and the expression is:  
\[\gamma= \begin{cases}1-0.0113x^{*}&0<x^{*}\leq\frac{L_{W}}{L}\\ 0.0432-0.1376x^{*}&\frac{L_{W}}{L}<x^{*}\leq 0.3882\,,\\ -0.0087-0.0039x^{*}&0.3882<x^{*}<1\end{cases}  \tag{12}\]  
where \(L_{W}/L\) is the boundary position of the bend zone. It can be seen that, when facing a near-field underwater explosion load, the transverse bulkhead in the bend zone makes a significant positive contribution to the overall bend strength. Therefore, it can be considered to properly strengthen the transverse bulkhead in the potential bend zone in the actual ship, such as using high-strength steel. The transverse bulkhead outside the bend zone may play a positive or negative role. Although the contribution coefficient is relatively small, the influence on the equivalent moment of inertia cannot be ignored due to the large difference between \(I_{s}\) and \(I_{b}\). It should be noted that the transverse bulkhead weight coefficient function of Equation (12) is mainly for the SHG configuration used in this paper. Different SHGs with more transverse bulkheads will be more complicated and require further study.
## 2 Literature Review  
[PERSON] and [PERSON] [11] examined a hybrid metaheuristic (MH) algorithm-based clustering with multihop routing (MHR) (HMA-CMHR) protocol for WSNs. The projected method integrated data transmission, node initialization, clustering, and routing. Initially, the HMA-CMHR algorithm utilized a quantum harmony search algorithm (QHSA)-based clustering procedure for choosing a better subset of CHs. Secondarily, the improved cuckoo search (ICS) technique-based route approach was utilized for a better optimum selection of routes. [PERSON] and [PERSON] [12] presented a hybrid MH approach where optimum features of ABC and differential evolution (DE) are integrated to measure an optimum group of load-balancing (LB) CHs. Regarding energy efficiency and LB clustering, a new objective function has been developed based on average energy, intracluster distance, and delay parameter. An ABC-based MH technique was presented to the dynamic relocalized mobile sink in a cluster-based network structure.  
[PERSON] et al. [13] developed a hybridization of the MH cluster-based routing (HM-BCR) approach for WSNs. The HMBCR approach primarily contained a brainstorm optimization with levy distribution (BSO-LD)-based clustering employing an FF integrating four parameters: network load, distance to neighbors, energy, and distance to BS. The WWO-HC-based routing method was implemented for optimum route selection. [PERSON] et al. [14] concentrated on MH-based clustering with routing protocol for UWSNs--MCR-UWSNs. The purpose of the aforementioned system is to choose an effectual group of CHs and route to target. [PERSON] et al. [15] presented an improved MH-driven energy-aware cluster-based routing (IMD-EACBR) technique for IoT-supported WSNs. The presented IMD-EACBR approach aimed to achieve maximal energy consumption and network lifespan. To attain this, the IMD-EACBR technique used an improved Archimedes optimized algorithm-based clustering (IAOAC) system for CH selective and cluster organization. The TLBO-based MHR (TLBO-MHR) approach has been applied to optimal selective routes to targets.  
[PERSON] et al. [16] established an MH optimized enabled unequal clustering with MHR protocol (MOUC-MRP) for WSNs. The MOUC-MRP system's purpose is for selecting CHs and optimum routes to target from WSNs. The harmony search (HS) route-selective manner was developed to an optimum route solution. [PERSON] and [PERSON] [17] examined an improved ABC (iABC) MH with an enhanced searching formula to improve their exploitation abilities and increase the global convergence of the presented MH. An enhanced population-sampling approach was established with Student's-t distribution that needs only one control parameter for computing and storing, then enhances the efficacy of presented MH.
## 5 Conclusions  
The paper presents the full-scale testing of CFT column specimens during the top-down construction method. The study aimed to determine the structural behaviour of CFT columns when connected to the previously jacked concrete piles.  
Four specimens with different D/t ratios were tested, and the column critical buckling forces and the effective buckling lengths were determined.  
Numerical models are generated in PLAXIS and ABAQUS to include the soil-pile-column interaction. The models consider the nonlinear behaviour of steel and concrete in the CFT column and a pile and the nonlinear behaviour of the soil. The comparison between experimental and numerical results has shown good agreement.  
The main conclusions of the study are:  
* Experimental and numerical tests demonstrated that the effective buckling column length equals approximately 0.8 L;
* The preliminary numerical study on the influence of different pile sizes on the CFT column stability was demonstrated that the effective buckling column length remains within the range of 0.72 L-0.86 L. When piles with larger diameters are used compared to the CFT column diameter, the boundary conditions at the column/pile connection are closer to the fixed support. More precise conclusions will be derived from an ongoing study;
* Referring to the predictions obtained by three design codes (EC4, ACI and AS), they all give good predictions compared to the test results (with effective buckling length taken as 0.8 L). ACI and AS codes are slightly conservative, while the results obtained with the effective stiffness of the composite cross-section according to the first-order analysis (EC4) give an unsafe prediction. Therefore, it is recommended that the effective stiffness according to the second-order analysis (EC4,II) be used for accurate predictions and safety reasons.  
Conceptualisation, M.M.L.R., J.Z.N. and S.M.K.; methodology, M.M.L.R., J.Z.N., J.R.R. and S.M.K.; software, M.M.L.R., J.Z.N. and J.R.R.; validation, M.M.L.R., J.Z.N. and J.R.R.; resources, M.M.L.R.; writing--original draft preparation, M.M.L.R. and J.Z.N.; writing--review and editing, M.M.L.R., J.Z.N. and S.M.K.; visualisation, J.Z.N.; supervision, S.M.K.; funding acquisition, M.M.L.R., J.R.R. and S.M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The first, the second and the fourth author thank the Ministry of Science and Technology of the Republic of Serbia for the financial support through project number 2000092.  
The authors declare no conflict of interest.
## 5 Conclusions  
This study suggests an additive ASP algorithm with the feedforward enhancement to overcome the poor tracking performance of the pure ASP control in a car cabin at higher frequencies due to the large uncertainty included in the secondary path using the ESG-cowl pair as a secondary actuator. Two real-time control experiments using the pure and additive ASP algorithms were carried out while the engine speed was swept at the neutral mode when the test car was stationary.  
From the real-time control experiment results, the tracking performances, as the RMS of the target error \(L_{TE,\text{RMS}}\), of the C3 and C9 orders after control using the additive ASP algorithm were achieved by 1.8 dBA and 1.7 dBA, respectively, which were better than 2.9 dBA and 7.5 dBA of the pure ASP control experiment results, as described in Table 3.  
These results reveal that the additive ASP algorithm was effectively applied to the ASP system using the pair of the ESG and the cowl panel as a secondary actuator. This additive approach could also be adopted to ASP systems using other types of actuators, which makes the frequency range of the pure ASP control confined due to the uncertainty included in their secondary path.  
The above ASP control experiment results were obtained at the neutral mode with the stationary test car. That means the primary noise was the engine sound only. However, in practice, road noise will also be involved in the primary noise when the car is driven on a road. Thus, the effect of the road noise on the performance and stability of the proposed algorithm will be investigated in future work.  
Data curation, Y.-S.L.; Software, J.L.; Validation, E.Y.; Writing--original draft, S.R.; Writing--review & editing, C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Incheon National University Research Grant in 2018.  
Not applicable.  
The authors declare no conflict of interest.
# Two-Dimensional Permutation Vectors' (PV) Code for Optical Code Division Multiple Access Systems
## 2 The 2D Wavelength-Hopping/Time-Spreading System
### Properties of 2D W-T Permutation Vectors (PV) Codes  
In order to define the cross-correlation of a 2D W-T PV code, four matrices are defined as Equations (10)-(13).  
\[A_{g,h}{}^{(0)}=G_{h}{}^{T}\ G_{g} \tag{9}\]  
\[A_{g,h}{}^{(1)}=\overline{G_{h}}{}^{T}G_{g} \tag{10}\]  
\[A_{g,h}{}^{(2)}=G_{h}{}^{T}\overline{G_{g}} \tag{11}\]  
\[A_{g,h}{}^{(3)}=\overline{G_{h}}{}^{T}\overline{G_{g}}. \tag{12}\]  
The cross-correlation between any arbitrary code, \(A_{d,i}\), and characteristic code, \(A_{g,h}\), can be defined through Equation (14).  
\[R_{g,h}^{p}(d,i)=\sum_{f=0}^{L-1}\sum_{j=0}^{L-1}a_{(d,j)}(f,j)a_{(g,h)}^{(p)} (f,j), \tag{13}\]  
where \(a_{(g,h)}^{(p)}\) and \(a_{(d,i)}\) represent the elements of \(A_{g,h}{}^{(p)}\) and \(A_{d,i}\) 2D W-T PV matrix, respectively, and \(\mathrm{p}\in[0,\ldots,3]\). In accordance with Equation (11), the cross-correlation between any two codes \(A_{g,h}\) and \(A_{d,i}\) can be calculated as shown in Table 2.  
As the cross-correlation along \(G_{i}\) and \(G_{d}^{T}\) code sequences equals zero, so \(R_{g,h}^{(1,3)}(d,i)\) has no use for the new correlation function, which makes the multiple user interference (MUI) elimination process easier. Hence, the expression of a new cross-correlation function from the previous table is given as follows:  
\[R_{g,h}^{p}(d,i)=\left\{\begin{array}{ll}w,\ for\ d=g,\ i=h\\ 0\ \ otherwise\end{array}\right.. \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(R_{g,h}^{0}(d,i)\) & \(R_{g,h}^{1}(d,i)\) & \(R_{g,h}^{2}(d,i)\) & \(R_{g,h}^{3}(d,i)\) \\ \hline \(d=g,i=h\) & \(\dfrac{w}{0}\) & \(0\) & \(0\) & \(0\) \\ \(d=g,i\
eq h\) & \(0\) & \(\dfrac{w}{0}\) & \(0\) & \(0\) \\ \(d\
eq g,i=h\) & \(0\) & \(0\) & \(\dfrac{w}{0}\) & \(\dfrac{w}{0}\) \\ \hline \end{tabular}
\end{table}
Table 2: Cross-correlation values of 2D W-T PV codes.  
Figure 3: A 3-dimensional (3D) graphic representation of 2D W-T PV code sequences.