esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Active Vibration Control Based on a Smart Exciter with an Optimized Electrical Shunt Circuit
## 4 Experimental Verification of Noise Reduction Potential
### Experimental Results  
To verify the simulated results (shown in Section 3.2), the acceleration over time has been recorded. Figure 15 shows the acceleration of position P2 with the SE coupled at position P2. Moreover, for better and persuasive comparison, the input forces of the impulse hammer for different situations are shown in Figure 16. In these two figures, the black curve represents the panel alone.  
Figure 14: Experiment’s setup: (**a**) back, and (**b**) front, with the SE mounted on position P3.  
The blue curve is for the panel coupled with the SE but without the RL-shunt. The red and green curves are for the panel with the SE which was connected with series or parallel RL. As shown in Figure 16, the impulse hammer acts on the panel in the time span of 0.020 to 0.025 s. The peaks after 0.03 s may be caused by slowly withdrawing the hammer. The biggest input force was at the panel without the SE, and the smallest at the panel with the SE and the parallel RL-shunt. In Figure 15, the blue curve shows that the SE can control the vibrations effectively (acting as a classical dynamic absorber), even without an RL-shunt. Already after 0.04 s, the acceleration amplitude of the blue curve was significantly reduced. However, the SE with an RL-shunt performed even better. Between 0.04 and 0.06 s, the acceleration of the panel with the SE coupled with an RL-shunt was more reduced than the panel with an SE alone. This is due to the fact that the amplitude of the red and green curves is much smaller than amplitude of the blue curve.  
To see the wideband vibration reduction effect of the SE and the RL-shunt more clearly, the sum of acceleration reduction and sound pressure reduction compared to the panel without a SE up to 400 and 800 Hz were calculated. Equations (6)-(8) show the calculation of the sum of acceleration reduction up to 400 or 800 Hz. \(L_{\sum~{}no-SE}\) and \(L_{\sum~{}with-SE}\) are the sums of the acceleration level of the panel without the SE and with the SE, respectively. The method which is shown in Equations (6) and (7) can also be used in all other situations  
Figure 16: Input force of impulse hammer vs. time for different situations.  
Figure 15: Acceleration vs. time at position P2 for different situations.  
RL-shunt. With the SE connected to the shunt, a reduction up to 25 dB is possible at this position. However, the acceleration reduction effectivity was better at a lower frequency, since the bars in Figure 17b are lower than the bars in Figure 17a. Meanwhile, the maximum sum of the sound pressure level reduction was 4.0 dB, and the minimum was 2.8 dB. The lower blue bars indicate that the SE with the RL-shunt can reduce the sound pressure level more effectively than the SE alone. According to only small differences between the red and orange bars at sound pressure in Figure 17a,b, the RL-shunts in series or parallel connection showed almost the same reduction effect.
# Laser Spot Centering Algorithm of Double-Area Shrinking Iteration Based on Baseline Method
###### Abstract  
High-precision laser spot center detection occupies an important position in optical measurement technology. In this paper, we propose a laser spot centering method to improve positioning accuracy. This method is an iterative double-area shrinkage approach based on the baseline method. The background noise baseline is calculated from the noise statistics of multiple background image frames acquired, and then the background noise is subtracted during the calculation while retaining the effective information of the spot region. The real spot area is located in the end by double-area shrinkage iteration to calculate the position of the spot center. Simulation and experimental results showed that our proposed method has strong anti-background noise interference ability, as well as higher positioning accuracy in locating the spot center than commonly used approaches; the maximum localization accuracy could reach 0.05 pixels, meeting the real-time requirements of the algorithm. The fluctuation range of measurement results was small when continuously detecting the center of the same laser spot, which could reach 0.04 and 0.03 pixels in the x- and y-directions, respectively. The result indicates that the method can meet the requirements of laser high-precision positioning.  
laser spot center; baseline method; double-area shrinkage
# Trust Management for Artificial Intelligence: A Standardization Perspective
## 4 AI Trust Management Framework
### Artificial Intelligence Systems Trust Framework
#### 4.2.1 Trust Analysis and Management in Collector  
Trust management is required to ensure that high-quality, error-free, and unbiased data is delivered from data sources and data vendors being collected as mentioned in Section 3.1. In addition, whether an error occurred in the transmission process through the network should be verified.  
\"Collector Data Extraction and Identification\" extract the data required for trust analysis and verification for SRC and network through the interface with the Collector and delivers it to the \"SRC Trust Analysis\" and \"Network Trust Analysis\" functions.  
\"SRC Trust Analysis\" analyzes the occurrence of errors and omissions in the data transmitted from each SRC, derives them as quantitative numbers and delivers them to the \"SRC Trust Information Management\" function. In this case, the SRC may be a data generating node such as an IoT sensor directly built by an AI system operator or a seller through data exchange.  
Figure 2: Trust framework for artificial intelligence systems.  
The \"Network Trust Analysis\" function receives information related to retransmission and errors that occur in the data transmission and reception process from the network protocol functions connected to the Collector such as Transmission Control Protocol/Internet Protocol (TCP/IP), Constrained Application Protocol (COAP), and Message Queuing Telemetry Transport (MQTT). Furthermore, it is transmitted to the \"Network Trust Information Management\" function after analyzing the status information of the network channel. The \"Network Trust Information Management\" function manages to trust information about communication connection status and errors with the SRC over time.
## 1 Introduction  
With the increasing complexity of industrial use cases, it becomes necessary that agents (humans and robots) cooperate in teams to reach more elaborate goals. The complexity of these goals is given by the actions that must be carried out by the agents in order to achieve them. Repetitive actions that must be executed in a fixed order and by specific agents are replaced by flexible processes where more agents cooperate in a shared environment. The complexity of such scenarios is further increased if the teams of agents are heterogeneous, consisting of humans and robots. To coordinate the actions of these agents, considering their capabilities, the status of the world, and the complexity of the goals, automated task planning approaches (also known as AI planning) can be used as high-level control strategies. These approaches offer the planning flexibility required by real-world use cases in which changing initial and goal states are expected. With these approaches, a high autonomy of the involved robots can be achieved.  
In order to deploy AI task planning approaches in such industrial use cases, task planning models must be formulated and validated. In the related literature, a couple of such models have already been presented [1; 2; 3; 4]. However, for each industrial use case, an individualized planning model is formulated. This individualized solution complicates the transfer of planning models among diverse industrial use cases and the development of a generic validation methodology. Such a methodology is used to guarantee that the task planning models cover a large number (if not all) possible planning situations. A new planning situation for an industrial scenario can occur when new orders (e.g., goals) are received, and the high-level control module must generate a series of actions from a new state of the world to a state in which the new goals are reached.  
To overcome the shortcoming presented above, in this work, a generic industrial scenario description is determined based on a set of industrial scenarios surveyed from the literature. For this generic industrial scenario, a generic planning problem is developed. To demonstrate the generality of this model, it is configured for a new industrial scenario in which customized grippers are assembled. All planning models are formulated as temporal planning problems in the Planning Domain Definition Language (PDDL). Lastly, a generic validation method is presented that checks a set of criteria (e.g., solvability, makespan) for many possible variations of the planning problems for a configuration of the generic planning problem, for a specific industrial scenario.
## 2 Materials and Methods
### ANN Model  
Artificial neural networks have recently become powerful tools for machine learning that mimic the human brain's biological neural networks, which make them a powerful method for solving a large variety of complex problems. A typical ANN architecture is completely well-defined by three layers (an input layer, one or more hidden layers, and an output layer), where each layer has a specific number of neurons that are linked to each other [29].  
In this work, the ANN architecture consisted of the PCs obtained from PCA model as input variables; the number of neurons in the hidden layer was optimized from 1 to 1000 by iterative tuning, configuration, and three output layers (concentrations of each dye in the mixture in mg/L). Figure 2 shows the optimal ANN architecture.  
We divided the dataset into training samples (75%) and testing samples (25%). To improve the ANN stability and prediction performance, the PC's input data were standardized with Equation (3) so that the mean of observed values is 0 and the standard deviation is 1.  
\[Z=\frac{x_{i-\mu}}{\sigma} \tag{3}\]  
With mean:  
\[\mu=\frac{1}{n}\sum_{i=1}^{n}x_{i} \tag{4}\]  
And standard deviation:  
\[\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\mu)^{2}} \tag{5}\]  
where \(n\) is the number of data points, \(x_{i}\) denote the input data, \(\mu\) and \(\sigma\) are the mean and standard deviation of input data, respectively.  
The accuracy of the ANN model was measured by the coefficient of determination (r\({}^{2}\)) and the mean absolute prediction error (MAPE) of both training and testing datasets, as shown in Equations (6) and (7), respectively.  
\[MAPE=\left(\frac{1}{n}\sum_{i=1}^{n}\frac{\left|y_{i}-y_{pred,i}\right|}{y_{i} }\right)\times 100 \tag{6}\]  
\[r^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-y_{pred,i}\right)^{2}}{\sum_{i=1}^{n} \left(y_{i}-\overline{y}\right)^{2}} \tag{7}\]  
where \(n\) is the number of data points, \(y_{pred}\) denotes the estimated obtained from the ANN model, \(y_{i}\) and \(\overline{y}\) are the observed and mean of the data, respectively. A summary of the parameters used in the ANN model is presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Number of iterations & 200 \\ Learning rate & 0.001 \\ Hidden layer neurons activation function & ReLU \\ Number of hidden layer neurons & 500 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of learning parameters of the ANN model.  
Figure 2: Architecture of the ANN in this study.  
ANN method was carried out with the use of the sklearn.neural_network module and MLPRegressor function of the language programming Python.
# Automatic Symbol Resolution on Embedded Platforms by the Example of Smart TV Device
## 3 Contribution  
This paper focuses on retrieving the information about symbols removed from firmwares by Smart TV manufacturers. During the conducted studies, the authors focused on two systems: Orsay, used in Samsung TV, and Netcast, used in LG. The choice of these systems was not accidental--in these two cases, companies have removed the names of the functions from the binary file to prevent using firmware for hacking [27]. Furthermore, Samsung and LG cover almost 50% of the Smart TV market and naturally hackers are interested in breaking the security systems of the most popular devices instead of attacking vendors who supply only a small percentage of the market [15; 16; 17]. We evaluated two methods for automatic symbol resolution:  
* Symbol resolution for Samsung firmware based on search of characteristic binary values in previous firmwares (Section 5.2).
* New framework for symbol resolution on a Samsung TV, based on locating string references (Section 5.3).  
Note that all of the methods for working with software with removed symbols mentioned in the last paragraph of the related works section were tailored down for specific use cases and could be applied only for single cases and cannot be generalized for usage in the whole OS. Moreover, it has to be stressed that those methods cannot be directly applied to IoT devices. The most popular OSs use hundreds of small dynamically loaded libraries that make localizing specific functions much easier. On the Samsung Orsay Smart TV however, there are big blob binaries (exeDSP or exeAPP/exeTV in newer devices) that have thousands of private functions and are between 100 and 200 megabytes in size. At the same time, the processing power of Smart TVs is much lower in comparison to modern computers and, as a result, a need for custom solutions arose.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 9 Conclusions and Research Opportunities
### Potential Non-Filtration Applications  
The submerged HF concept is widely used in membrane filtration (MF and UF) applications. The concept is also amenable to other membrane separations, such as membrane distillation (MD) and forward osmosis (FO). Indeed 'high-retention' MBRs based on MD and FO with submerged HFs have been developed [333; 334]. Further work is required to optimize these systems, including development of externally skinned FO hollow fiber membranes to minimize fouling. An interesting extension of FO is pressure-retarded osmosis (PRO), used for harnessing the salinity gradient for the generation of electricity, that can use hollow fibers with pressurized 'draw solute' on the lumen side [335; 336]. It would be feasible to use the submerged HF arrangement for PRO with the low salinity feed in the tank.
## 2 Impact Test and Results
### Experimental Results and Discussion
#### 2.2.2 Collision Process Analysis  
Tool wear state can be obtained indirectly based on the changing law of impact force, the state of the machined surface of the energy-absorbing tube, and chip deformation mode during the test [31]. Figure 3 illustrates the collision force-displacement curve of the impact test and structural deformation, divided into four stages as follows.  
The AB segment (0-30 mm) illustrates the incipient stage, from the start of the collision to the entry of steady-state cutting. At the crash moment, the impact force reached an initial peak of 437 kN. In the subsequent 30 mm displacement, the force decreased to 280 kN and  
Figure 3: Force–displacement curves and structural deformation in various stages: (**a**) incipient stage, (**b**) steady-state cutting stage, (**c**) slight wear and breakage stage, (**d**) severe wear and breakage stage, and (**e**) after the collision.  
maintained oscillation. The unstabilized cutting process resulted in pronounced folds on the machined surface, as seen in Figure 3a.  
The BC segment (30-102 mm) illustrates the steady-state cutting stage. During this period, the cutting state becomes stable, and the continuous stripe chips flow out along the tool rake face to curl, as seen in Figure 3b. The accumulated curled chips obstruct the tool movement, and the force continues to increase. After the chip curls to a certain extent, the force no longer significantly increases and stabilizes at 340 kN.  
The CD segment (102-220 mm) stands at the slight wear and breakage stage. A large number of broken chips are produced, as seen in Figure 3c. Meanwhile, the impact force continues to increase, and the force oscillation amplitude is significantly larger than that of the BC segment. The tool wear is gradually severe at this time and deteriorated cutting performance leads to increased impact force. In contrast, the CF segment presents an ideal trend line (blue dashed line in Figure 3) without tool wear.  
The DE segment (220-360 mm) indicates the severe wear and breakage stage. The tool is severely broken and fails to cut normally, generating many fragmental and broken chips and causing violent force oscillation, as seen in Figure 3d. After the curled chips break, the force decreases slightly. Finally, the curled chip is squeezed by the supporter and tool to the back of the anti-climb creeper device, as seen in Figure 3e. The force increases abruptly, and the collision ends.
## 3 Model and Preliminaries
### LSTAR Model Construction
Like the traditional STARIMA model, LSTARIMA makes use of spatial weight matrices \(W\) and \(U\) to model the influence of the spatiotemporal neighborhoods. However, it relaxes the globally fixed temporal dependence for all locations by using different AR and MA parameters according to location. Furthermore, it accounts for the temporal non-stationarity by allowing the matrix elements value and size of the spatial neighborhoods to vary with time. According to [PERSON] [37], the ARMA model can be properly approximated by the high-order AR model. As AR only has one type of parameter to be estimated, ARMA and ARIMA have two or three types of parameters to be estimated, and the parameters estimation of AR is easy even when the order is a little bit higher. There exist plenty of studies that have used a high-order AR model to approximate many processes of interest [37]. Furthermore, traffic flow has complex dynamics and may not exactly match an ARIMA model. In addition, many studies have removed MA and used only different AR models to conduct traffic prediction and obtain good results with limited AR order [38]. For reducing computational complexity and real-time prediction purpose, in this paper, we proposed the LSTAR model for traffic flow prediction. With the MA component removed, the LSTARIMA prediction model (Equation (4)) is changed into the following LSTAR model:  
\[z_{i}(t)=\sum_{k=1}^{p_{i}}\sum_{h=0}^{\lambda_{i,k}}\sum_{j=1}^{N_{i}}\phi_{i,kh}w_{ij}^{(h)}(t-k)z_{j}(t-k)+\varepsilon_{i}(t) \tag{5}\]  
where \(\varepsilon_{i}(t)\) is white noise, \(\phi_{i,hk}\) is the parameter for each link \(i\) (\(i=1,\ 2,\ \ldots,\ N\)), and \(w_{ij}^{(h)}(t-k)\) are the elements of the dynamic spatial weight matrix \(W_{i}^{(h)}(t-k)\) pertaining to link \(i\) at temporal lag \(k\).
# Enhancing Efficiency and Cost-Effectiveness: A Groundbreaking Bi-Algorithm MCDM Approach +
## 4 Conclusions  
### Conclusions  
The selection of welding machines in practical industries can be effectively carried out using the DEX approach integrated with the EDAS technique. This approach considers essential factors such as operating capacity, costs, welding speed and the precise thickness of the workpiece. The ranking results obtained from the proposed approach align well with established methods, enhancing its reliability and robustness. It is simple to observe that the suggested EDAS method is as follows: The best alternative is WM4, which was classified first (by MOORA and our proposed method), second (by CODAS) and third in VIKOR. WM 2 and WM 1 take second and third place, respectively. The final two options are WM 5, the worst-case scenario, with three fifth-place rankings and one first-place ranking (WM5 consistently ranks lower in all methodologies, indicating rejection), and WM 3, with the fourth-place potential selection.  
There are several key advantages of the proposed approach. First, it employs a consistent weight derivation process, ensuring a reliable basis for decision making. Second, it requires fewer pair-wise comparisons, reducing the complexity and cognitive burden on decision makers. Third, it is user-friendly, providing an easily understandable framework for decision making. Finally, the proposed approach entails lower computing expenses, making it more efficient and accessible.  
The dual-MCDM approach, specifically the DEX-EDAS method, enables the rating of welding machines based on both qualitative and quantitative factors. This comprehensive approach is a valuable tool for decision makers, offering practical solutions to issues with relatively simple selection criteria for industrial ranking alternatives.
## 4 Experimental Results
### Video Test Sequences  
The experimental results over all video coding schemes are reported for fourteen standard test sequences based on YUV color format with 4:2:0 color sampling and 8 bits of precision per sample,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sequences**} & \multicolumn{4}{c}{**MCTF Level**} \\ \cline{2-5}  & **1 st** & **2 nd** & **3 rd** & **4 th** \\ \hline Football & 0.9921 & 0.9894 & 0.9951 & 0.9975 \\ Foreman & 0.9860 & 0.9954 & 0.9967 & 0.9982 \\ Soccer & 0.9984 & 0.9962 & 0.9981 & 0.9993 \\ Crew & 0.9976 & 0.9983 & 0.9989 & 0.9991 \\ Ice & 0.9787 & 0.9899 & 0.9935 & 0.9980 \\ City & 0.9843 & 0.9894 & 0.9932 & 0.9982 \\ Johnny & 0.9885 & 0.9891 & 0.9964 & 0.9989 \\ KristenAndSara & 0.9932 & 0.9967 & 0.9970 & 0.9985 \\ Stockholm & 0.9924 & 0.9962 & 0.9984 & 0.9990 \\ Basketball & 0.9787 & 0.9812 & 0.9899 & 0.9988 \\ Cactus & 0.9949 & 0.9950 & 0.9966 & 0.9990 \\ Park\_joy & 0.9815 & 0.9843 & 0.9957 & 0.9981 \\ Traffic & 0.9870 & 0.9932 & 0.9960 & 0.9991 \\ PeopleOnStreet & 0.9893 & 0.9919 & 0.9949 & 0.9993 \\
**Average** & **0.9888** & **0.9919** & **0.9957** & **0.9986** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The R\({}^{2}\) values of each MCTF level for test video sequences.  
including five different resolutions: CIF (352 \(\times\) 288, 30 fps), 4 CIF (704 \(\times\) 576, 30 fps), 720p (1280 \(\times\) 720, 60 fps), 1080p (1920 \(\times\) 1080, 50 fps), and 2K (2560 \(\times\) 1600, 30 fps), which are listed in Table 3. We also plot the temporal information (TI) and spatial information (SI) indices [51; 52] of all source sequences in Figure 3. It demonstrates that the test sequences cover a wide range of video contents in terms of motion and spatial details. For each of the resolutions, the test sequences are decoded at different target bitrates (CIF and 4 CIF: 256, 384, 512, 640, 768, 896, 1024 kbps; 720p: 384, 512, 640, 768, 896, 1024, 1536 kbps; 1080p and 2K: 2048, 3072, 4096, 5120, 6144, 7168, 10240 kbps).