esa-sceva/satcom-chunk-collection huggingface dataset:
# Modified Smith Input-Shaper Crane-Controller for Position Control and Sway Reduction
## 5 Conclusions
\begin{tabular}{l l} \(\mathbf{r}_{p}\) & center mass of the cargo \\ \(\Phi\) & sway angle of the cargo \\ \(\Phi_{m}\) & measured sway angle of the cargo \\ \(\Phi_{s}\) & simulated sway angle of the cargo \\ \(\omega\) & natural frequency of the cargo \\ \(m_{p}\) & mass of the cargo \\ \(f\) & force \\ \(d\) & disturbance \\ \(T_{osc}\) & cargo sway time constant \\ \(l\) & length of rope \\ \(W_{lin}\) & kinetic energy of the system \\ \(W_{pot}\) & potential energy of the system \\ \(L_{l}\) & Lagrange function \\ \(l_{l}\) & functional \\ \(g\) & gravitational acceleration \\ \(t\) & time \\ \(L_{4,em}\) & armature inductance of the motor \\ \(R_{a,em}\) & armature resistance of the motor \\ \(u_{a,em}\) & armature voltage of the motor \\ \(i_{a,em}\) & armature current of the motor \\ \(T_{a,em}\) & armature time constant of the motor \\ \(K_{v,em}\) & voltage constant of the motor \\ \(n_{s}\) & simulated the rotation speed of the motor \\ \(n_{m}\) & measured the rotation speed of the motor \\ \(\omega_{1}/\omega_{2}\) & gearboxes ratios of the motor \\ \(J\) & inertia of the motor and cart \\ \(T_{m,em}\) & mechanical time constant of the motor and cart \\ \(G_{PD}\) & transfer function of position PD controller \\ \(G_{PC}\) & transfer function of cart's position \\ \(G_{d}\) & transfer function of disturbance \\ \(G_{P}\) & transfer function of error controller \\ \(G_{PC}\) & transfer function of identified position \\ \(G_{IST}\) & transfer function of ZV input shaper \\ \(u_{p}\) & position control value \\ \(u_{\Delta}\) & error control value \\ \(u\) & overall control value \\ \(u_{m}\) & measured control value \\ \(u_{s}\) & simulated control value \\ \(K_{P}\) & proportional gain of the PD controller \\ \(K_{D}\) & derivative gain of the PD controller \\ \(T_{st}\) & sample time of the PD controller \\ \(T_{f}\) & filter time of the PD controller \\ \(N\) & the number impulses/samples of PD controller and input shaper \\ \(K_{P,POS}\) & proportional gain of PD position controller \\ \(K_{D,POS}\) & derivative gain of PD position controller \\ \(T_{st,POS}\) & sample time of PD position controller \\ \(N_{POS}\) & number of samples of PD position controller \\ \(K_{P,e}\) & gain of the error controller \\ \(T_{st,e}\) & sample time of the error controller \\ \(B_{i}\) & i-th amplitude of impulse \\ \(\Delta_{i}\) & i-th time of impulse \\ \(s\) & Laplace variable \\ \(q^{-1}\) & backward shift operator \\ \(\delta(t)\) & Dirac delta function \\ \end{tabular}
# SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs
## 7 Conclusions and Future Work
### Future Potential
#### 7.2.2 GraphDB Multi-Tenant Support  
The second item is that currently, one GraphDB tenant is used in the cloud to collect the data of all customers, which could become an issue for scalability and data privacy. A potential solution may be to have separate GraphDB tenants for each customer and then create a common GraphDB instance to collect anonymized and aggregated data from the other customers' instances. Managing these tenants and synchronizing them will be big challenges.
## 3 Results and Discussion
### Plastic Deformation of Grains, and Slip Line Angles  
The angles of slip lines were measured after the test. In Figure 7 the straight red lines represent the direction of the parallel slip lines in each grain. As can be seen, slip lines were found in most grains, and some grains had two sets of slip lines with different angles. However, no mechanical twinning, which is usually observed as a lenticular region, was found. In the present specimen, therefore, we suspect that plastic deformation mainly occurred via crystallographic slip.  
Figure 6: Relationship between average height change per unit load under elastic and plastic loading conditions. (**a**) Between the first (abscissa) and third (ordinate) load steps; and (**b**) between the third (abscissa) and seventh (ordinate) load steps [(24)].  
In order to investigate the collective behavior of the grains under plastic deformation, the slip lines were categorized according to their angle relative to the loading direction, as shown in Figure 8. The angle ranges were (a) \(10^{\circ}\leq\theta<30^{\circ}\), (b) \(30^{\circ}\leq\theta<50^{\circ}\), (c) \(50^{\circ}\leq\theta<70^{\circ}\), (d) \(-10^{\circ}\leq\theta<-30^{\circ}\), (e) \(-30^{\circ}\leq\theta<-50^{\circ}\), (f) \(-50^{\circ}\leq\theta<-70^{\circ}\), and (g) \(-70^{\circ}\leq\theta<-90^{\circ}\), with the anticlockwise direction set as positive. Since there were no grains with a slip-line angle between \(70^{\circ}\) and \(90^{\circ}\), the corresponding figure is omitted. As Figure 8 shows, slip lines with similar angles were found in neighboring grains, which suggests that the plastic deformation of each grain was interdependent on that of the others, and that the deformation occurred collectively. We found that a large proportion of the grains had slipped at an angle between \(\pm 30^{\circ}\leq\theta<\pm 50^{\circ}\), implying that slip mainly occurred near the maximum shear direction.  
Figure 8: \(Cont\).  
Figure 7: Slip-line directions in each grain.
# Content Adaptation and Depth Perception
## 3 System Description
### Software  
We modified and employed different libraries (see Figure 2). Following our low-cost and easily accessible automatic content adaptation objective, the software is open-source and cross-platform. More explanations and images are provided in Section 6.  
SliceViewer is a ThreeJS component that slices the 3D scene at different depths, and renders the final images to be projected by the monitor. Different settings can be tuned, such as the number of layers, perspective of the camera, and occlusion behaviour. It can run on multiple devices through a web browser. SlicePainter is a web browser multi-depth canvas painting software developed with the P5 JS library. We have modified the MegaDrive (aka Genesis) emulator Helios so that it renders each layer of the videogames separately, therefore many of the video games are suitable to be played in SliceView. VLC Media Player has been configured to test the use of overlaid subtitles at different depths into video.  
Some basic transformations are required to correctly render the final image (see Figure 3). Firstly, the layers are mirrored vertically to counter the image inversion from the reflective sheets. Secondly, as the layers are not completely parallel, there could be misalignment and deformations in the projected images, so keystone correction was employed in such cases. Afterwards, it is necessary to re-scale the layers proportionally to their distance. We made calibration templates to facilitate end-users' assembly of their SliceView. OBS Studio is used to apply transformations and calibration into the slices. The use of an OBS plugin known as StreamFX provides mirror and keystone transformations.  
Figure 2: Component diagram showing modified and employed software.  
Stretch deformation is required when layers are not placed at 45\({}^{\circ}\) over the screen. When placing them at narrower angles, the projected images should be vertically shrunk. Conversely, when placing the layers at wider angles to stack more layers on the same surface area, images should be enlarged vertically.
# Adversarial Attack for Deep Steganography Based on Surrogate Training and Knowledge Diffusion
## 3 Adversarial Attack Method for Deep Steganography
### Adversarial Attack Based on Surrogate Training and Knowledge Distillation  
The attacker does not have access to the decoder \(F\), training dataset, model structure, hyperparameters, etc., which is more in line with real-world situations. The most critical detail is that the attacker does not have knowledge of decoder \(F\); therefore, the attacker cannot calculate the gradient accurately through backpropagation as in a white-box scenario to optimize the objective functions. Furthermore, in the usual threat models of black-box attacks, the attacker is assumed to be able to send many queries to the model [46; 47; 48]. However, this is unreasonable in deep steganalysis. Therefore, defining a suitable black-box threat model for deep steganalysis is necessary.  
First, assume that an attacker can obtain multiple instances created by the target model. [PERSON] et al. [11] investigated whether an attacker could train a network to reveal secret images without having access to the original network after the deployment of a deep steganographic system. They proposed a possible attack scenario wherein an attacker is able to obtain multiple container image instances \(C^{\prime}\) created by the target system, with at least one of either the cover \(C\) or secret image \(S\) included in each instance.  
```
input :Decoder \(F\), Container image \(C^{\prime}\), Target image \(T\), Allowable magnitude \(\epsilon\) output :Attacked image \(C^{\prime\prime}\)
1 if\(T==NULL\)then// Initialization
2\(T\leftarrow C^{\prime}\);
3\(L\gets L^{ut}\);
4 else
5\(L\gets L^{t}\);
6
7 end if
8\(C^{\prime\prime}\gets C^{\prime}+\alpha\cdot random\);
9 foriteration \(=1,\ldots,N\)do
10\(g_{C^{\prime\prime}}\leftarrow\
abla_{C^{\prime\prime}}(L^{(\prime\prime},T)\); // Calculate Gradient
11\(\delta\leftarrow\alpha\cdot sign(g_{C^{\prime\prime}})\);
12\(C^{\prime\prime}\leftarrow\
abla^{\prime\prime}+\delta\); // Update
13\(C^{\prime\prime}\gets Clamp(C^{\prime\prime},C^{\prime},-\epsilon,\epsilon)\); // Clamping
14
15 end for
```  
**Algorithm 1**Container Image Attack  
Moreover, thanks to its transferability, DS can be used to reduce the difficulty of obtaining instances created by the target model. As with the transferability of adversarial examples [25], DS also exhibits transferability, i.e., the decoder of one model can partially recover the secret image encoded by another model (Figure 2). The attacker can not only roughly know the secret image but also may find the original secret image \(S\) corresponding to the revealed image \(S^{*}\). This will help the attacker to collect enough instances \((C;S\;\;or\;\;S^{\prime})\). Therefore, the following variant of the black-box scenario is proposed as a threat model for deep steganalysis.
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 2 Background
### Apache Spark
#### 2.2.1 Resilient Distributed Datasets  
Resilient distributed datasets (RDDs) are the underlying data structure and outstanding feature of Spark. They are a type of distributed collection that can be temporarily stored in RAM with a high fault tolerance and the capability of parallel computation. Each RDD is divided into multiple logical partitions and can be computed on different nodes of a cluster. In this research, we use the Scala language, since Apache Spark is built mainly on Scala, so it has the best speed and support for this language. RDDs basically support two main types of operations.  
* Transformation: Through a transformation, a new RDD is created from an existing RDD. All transformations are \"lazy\" operations, meaning that these transformation operations will not be performed immediately, but the steps taken are only memorized and saved as pending scripts. This process can be understood as a job-planning process. Those operations can only be performed when an Action is called.
* Action: An Action performs all transformations related to it. By default, each RDD will be recalculated if an Action calls it. However, RDDs can also be cached in RAM or on a disk using the persist command for later use. The Action will return the results to the driver after performing a series of computations on the RDDs.  
It would be time-consuming if we encountered an RDD being reused many times because each RDD will be recalculated by default. Therefore, Spark supports a mechanism called persist or cache. When we ask Spark to persist with an RDD, the nodes that contain those RDDs will store those RDDs in memory, and that node will only compute once. If the persist fails, Spark will recalculate the missing parts if necessary.
# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
## 4 Results and Discussion
### Intelligent Optimization Algorithm to Optimize XGBoost Parameters  
XGBoost has a large number of parameters, so in this paper, intelligent optimization algorithms are used to tune the XGBoost parameters, including the sparrow optimization algorithm [32], the whale optimization algorithm [33] and the dung beetle optimization algorithm. The accuracy of each model for inversion of Chl-a concentration in Wuliangsu Lake was analyzed with the introduction of month features, and the results are shown in Table 8. It is obvious that the R\({}^{2}\) of the XGBoost model after the optimization of the intelligent optimization algorithm is higher than 0.83 in both the training set and the test set, which shows good fitting and generalization abilities. Secondly, the RMSEs were all lower than 4.0 \(\upmu\)g/L, the MAEs were all lower than 2.8 \(\upmu\)g/L, and the deviation between the inversion values of the optimized models and the measured values was small. Among them, the R\({}^{2}\)s of the DBO-XGBoost model on the training and test sets were 0.8578 and 0.8490, the RMSEs were 3.6259 and 3.3987 mg/L, and MAEs were 2.1346 and 2.7216. Figure 7 demonstrates the fitting degree of fit of the four models. As can be observed, the DBO-XGBoost model's Chl-a concentration predicted value is closest to the 1:1 line and has the least variation from the measured value, suggesting that its inversion accuracy is the best. In summary, in the task of Chl-a concentration inversion in Wuliangsu Lake, using DBO for tuning the parameters of the XGBoost model can effectively improve the inversion accuracy of the model and reduce the inversion error.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{2-6}  & **R\({}^{2}\)** & **RMSE (\(\upmu\)g/L)** & **MAE (\(\upmu\)g/L)** & **R\({}^{2}\)** & **RMSE (\(\upmu\)g/L)** & **MAE (\(\upmu\)g/L)** \\ \hline XGBoost & 0.8183 & 4.0982 & 2.5775 & 0.7711 & 4.1845 & 3.3462 \\ SSA-XGBoost & 0.8515 & 3.7048 & 2.2391 & 0.8396 & 3.5027 & 2.7974 \\ WOA-Xgboost & 0.8445 & 3.7912 & 2.1312 & 0.8434 & 3.4611 & 2.7737 \\ DBO-XGBoost & 0.8578 & 3.6259 & 2.1346 & 0.8490 & 3.3987 & 2.7216 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Inversion results of the XGBoost model after optimization by different intelligent optimization algorithms.  
Figure 7: Prediction results of the model for (**a**) XGBoost, (**b**) SSA-XGBoost, (**c**) WOA-XGBoost, and (**d**) DBO-XGBoost.
# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 5 Multi-Modality Data Representation and Event Detection
### Topic Model Based Methods  
Probabilistic models have been widely used in event detection. However, most probabilistic model based research detects events in documents. In these methods, each document is represented as the distribution on a set of topics, and each topic is represented as the distribution on a set of topic words, as discussed in Section 4.1.2. For multi-modality event detection, due to the fact that the multi-modality data not only contain texts, but also contain visual information such as image and video. Therefore, topic models for multi-modality event detection had been researched by some researchers.  
Several methods have been proposed to apply topic models to deal with the multi-modality data [94; 95]. In multi-modality data based event detection, not only words but also other modalities need to be taken into consideration. [PERSON] et al. [95] proposed a novel topic model that models the text, image, location, timestamp and hashtag feature of Twitter to discover events. In this model, each tweet is assigned to a topic and each topic has a mixture of several important Twitter features distributions. [PERSON] et al. [96] proposed a boosted multimodal supervised Latent Dirichlet Allocation (BMM-SLDA), which is used for the event classification task. The model integrated the multi-modal supervised Latent Dirichlet Allocation (mm-SLDA) [97] in the boosting framework. The BMM-SLDA can effectively exploit the multi-modality and the multiclass property of social events, as well as make use of the label information to classify multiclass social event directly. [PERSON] et al. [98] also proposed a novel multi-modal event topic model (mmETM), which can effectively model social media information such as text with images. It also learns the correlationsbetween textual and visual modalities to separate the visual-representative topics and non-visual-representative topics.
## 2 Methods
### Ecosystem Models  
We used five terrestrial ecosystem models: VEGAS (Vegetation Global Atmosphere Soils), OCN (Organizing Carbon and Hydrology In Dynamic Ecosystems (Orchidee)-Carbon Nitrogen), LPJ (Lund-Potsdam-Jena), JULES (Joint UK Land Environment Simulator) and CLM4.5 (Community Land Model Version 4.5), from the TRENDY (Trends and drivers of the regional scale sources and sinks of carbon dioxide) project [25]. Description of these models and the TRENDY dataset are given in Table 1. Although the chosen five models contain different dynamic vegetation sub-models, there are fundamental commonalities in their carbon cycle representation. In all five models, plants are generally categorized into different plant functional types (PFTs). Some models show higher number of PFTs (e.g., OCN with 12 PFTs) than others (e.g., JULES with 5 PFTs). Across the models, the processes of photosynthesis (P), maintenance respiration (MR) and growth respiration (GR) are broadly similar for each PFT. However, their specific parameters and conditions may differ from model to model [26]. The sum of MR and GR is categorized as the autotrophic respiration (AR). In each model, gross primary productivity (GPP) is a measure of carbon absorption by plants per unit time (T) and space. NPP is determined as the difference between GPP and AR. Mathematically, NPP = GPP \(-\) AR = GPP \(-\) MR \(-\) GR.  
We used primarily the simulated NPP estimates from these five models for the period of 1982 to 2012. The models' simulations were performed using the protocol established by the TRENDY project, including time variant CO\({}_{2}\) concentrations, climate, and land use change. The CRU (Climate Research Unit) at the University of East Anglia, United Kingdom, and NCEP (U.S. National Center for Environmental Predictions) climate forcing data for the period of 1901 to 2010 was used in these simulations. The global atmospheric CO\({}_{2}\) for the period of 1860 to 2012 was assembled at annual time steps from ice-cores and other sources by NOAA. The land-use change data for the S3 case (constant crops and pasture distribution since 1860) scenario of TRENDY simulatio  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Resolution (Degrees)** & **Period** \\ \hline CLM4.5 & 192 \(\times\) 288 & 1860–2012 \\ JULES & 192 \(\times\) 112 & 1860–2012 \\ LPJ & 720 \(\times\) 360 & 1901–2012 \\ OCN & 360 \(\times\) 150 & 1901–2012 \\ VEGAS & 720 \(\times\) 360 & 1860–2013 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fundamental features of the five trends and drivers of the regional scale sources and sinks of carbon dioxide (TRENDY) ecosystem models used in this study [25].  
Hyde (History Database of the Global Environment) database. Further detail about models simulations can be found at [[http://dgvm.ceh.ac.uk/node/9](http://dgvm.ceh.ac.uk/node/9)]([http://dgvm.ceh.ac.uk/node/9](http://dgvm.ceh.ac.uk/node/9)).
## 6 Worst-Case Latency Analysis of the FWOS Model
### Length of the Contention-Free Interval
#### 6.1.2 Closing-Edge Overlapping  
Similar to the opening-edge overlapping case and as depicted in Figure 8, the length of the overlapping interval between \(Q_{k}^{h}\) and any other TT queue (\(Q_{m}^{h}\)), at the closing edge, can be found easily as  
\[L_{k,m}^{h,E,i}=\left\{\begin{array}{cc}t_{k}^{h,c,i}-\ t_{m}^{h,\rho,i}& \cdot,t_{m}^{h,\rho,i}<t_{k}^{h,c,i}<t_{m}^{h,c,i}\\ 0&\cdot,otherwise\end{array}\right. \tag{13}\]The relevant overlapping ratio between \(Q_{m}^{h}\) and \(Q_{k}^{h}\) windows is  
\[OR_{k,m}^{h,E,i}=\frac{L_{k,m}^{h,E,i}}{W_{k}^{h,i}} \tag{14}\]  
Higher-priority overlapping at the closing edge: The strongest influence from higher-priority overlapping (\(OR_{k,k^{-}}^{h,E,i}\)) (\(1\leq k^{-}<k\)) at the closing edge, as shown in Figure 8, is included by considering the maximum overlapping ratio, as follows:  
\[OR_{k,H}^{h,E,i}=\underset{1\leq k^{-}<k}{max}\Big{\{}OR_{k,k^{-}}^{h,E,i} \Big{\}}\ =\ \frac{max}{\frac{1\leq k^{-}<k}{\left\{L_{k,k^{-}}^{h,E,i}\right\}}{W_{k}^{h, i}}}=\frac{L_{k,H}^{h,E,i}}{W_{k}^{h,i}} \tag{15}\]  
In the same manner as in the opening-edge consideration, as the \(Q_{k^{-}}^{h}\) frame has the priority to be transmitted before the \(Q_{k}^{h}\) frame, during the overlapping interval, the service is not guaranteed for \(Q_{k}^{h}\) frames. Thus, the ending time of the \(i\)-th \(Q_{k}^{h}\) contention-free interval under the higher-priority overlapping at the closing edge can be given by  
\[i_{k,H}^{h,E,i}=t_{k}^{h,c,i}-L_{k,H}^{h,E,i}=t_{k}^{h,c,i}-OR_{k,H}^{h,E,i} \cdot W_{k}^{h,i} \tag{16}\]  
Lower-priority overlapping at the closing edge: Similarly, we can determine the largest OR between \(Q_{k}^{h}\) and \(Q_{k^{+}}^{h}\) windows (\(k<k^{+}\leq N_{q}\)) at the closing edge as  
\[OR_{k,L}^{h,E,i}=\underset{k<k^{+}\leq N_{q}}{max}\Big{\{}OR_{k,k^{+}}^{h,E,i }\Big{\}}\ =\ \frac{L_{k,L}^{h,E,i}}{W_{k}^{h,i}} \tag{17}\]  
As \(Q_{k}^{h}\) has a higher priority than \(k^{+}\) queues, the overlapping at the closing edge does not affect the contention-free interval (\(\overline{W}_{k}^{h,i}\)). However, in a particular case of frame arrival, the lower-priority overlapping can affect the maximum waiting time of the associated frame, as we discuss next.  
Figure 8: Closing-edge overlapping with higher- and lower-priority windows.
## 4 Optimization Methods
### Genetic Algorithm (GA)  
Unlike gradient-based algorithms, genetic algorithm (GA) is a gradient-free optimization technique that is used to find global or near-global optima without specifying initial values. The algorithm is based on a random population evolution technique using some set of operators. In this study, we employed GA to estimate stiffness and damping values while fitting the measured and calculated transmissibility. GA was executed using the 'ga' function in MATLAB with a population size of 90, 9 elites, crossover probability of 0.8, function tolerance of 1 \(\times\) 10\({}^{-12}\), 100 maximum generations, adaptive feasible mutation function, and defaults values for the creation, fitness scaling, selection, and crossover functions. At this time, the algorithm obtained poor fitting of the transmissibility plots with a mean _FVAL_ of 0.1078 returned after 880 s.  
According to [PERSON] [28], a larger population size improves the capability of GA to comprehensively search for optimal values close to the global optima, albeit at the expense of the computation speed. The initial population size was increased to 180 and then 270, while other computation parameters were retained, resulting in much-improved fits of the transmissibility plots as presented in Section 5.  
However, the optimized parameters obtained by GA were characterized by high instability, making it difficult to perform parameter sensitivity analyses on the optimized stiffness and damping coefficients' dynamic response to the excitation.