esa-sceva/satcom-chunk-collection huggingface dataset:
# TinyGS vs. SatNOGS: A Comparative Analysis of Open-Source Satellite Ground Station Networks
## 5 Discussion
### Comparison of the Number of GSs That Were Online in the Last 24 h on 29 July 2023
Observation of the Frequencies of the Satellites That Were Operational and Managed to Send Data to the GSs and That Data Was Decoded as of 29 July 2023  
In Figure 12, one can find the frequencies of the TinyGS-supported satellites operational according to SatNOGS DB and which had their data decoded. As one can see, most satellites work with frequencies around 400, 402, 436, and 437 MHz. With regard to SatNOGS, the same reasoning was used as in the section that analyzed the various transmitters without decoded data in that only the frequencies (downlink frequencies) of the \"alive\" transmitters were saved, etc. The only difference is that now, only the satellites with decoded data were studied. In Figure 13, one can find the frequencies of the satellites with at least one decoded data packet, and, as in TinyGS, the frequencies with the highest number of satellites are 436 and 437 MHz. Figure 12: Frequency of satellites supported by TinyGS and operational (according to SatNOGS DataBase) that had at least one data packet received and decoded as of 29 July 2023. Comparison of the Number of TinyGS Satellites (Operational and Supported by TinyGS) That SatNOGS Was Also Able to Communicate with and Decode as of 29 July 2023  
The purpose of this section is to determine what percentage of TinyGS satellites SatNOGS was also able to communicate with and decode. For this analysis, the operational satellites supported by TinyGS were used as a basis. To find out if SatNOGS was able to communicate with or decode data from a certain satellite, the SatNOGS DB [15] was consulted again. Initially, of the operational satellites and supported by TinyGS, the number of satellites with data received by SatNOGS GSs was observed. It is important to note that the data includes decoded and non-decoded data. As we saw earlier, TinyGS was able to receive data from 25 satellites, but SatNOGS only received data from 12 of those, that is, of the 25 satellites, only 48% of them also had data received by SatNOGS GSs. Then, from the operational satellites supported by TinyGS, the number of satellites with data received and decoded by SatNOGS was observed. As was seen earlier, TinyGS was able to decode data from 22 satellites, but SatNOGS, from those 22, could not decode any. This is because SatNOGS did not have a decoder for any of them.
## 5 Result and Discussion
### Discussion  
This paper proposes a generalized system to evaluate the three benchmark datasets on BSL. For this, the input images were segmented for feature extraction and then the images were augmented to increase the dataset for evaluation of the proposed model. However, our proposed model achieved 94% accuracy with 38 BdSL datasets, as shown in Table 4. Some other researchers evaluated their model using the same dataset. Table 5 shows the comparative performance of the proposed model with the existing model, where one paper reported 89.60% accuracy using VGG19 [11] and another article reported 91.50% accuracy using Concatenated CNN [33]. Figure 10 demonstrates our proposed method's class wise performance comparison with the existing method. Among 38 classes, in 16 classes achieved more than 99% accuracy, in 19 classes achieved more than 92% accuracy, in one class achieved 80% accuracy, and only two made below 80% accuracy. Class 'I' achieved 57% accuracy, and class 'Jha' achieved 78% accuracy. In the same way, our proposed model achieved 99.55% accuracy with KU-BdSL in both variant datasets, as shown in Table 6 and Table 7. As KU-BdSL is a new dataset, we did not find any published paper based on the KU-BdSL to make a comparison but in Table 8, we show a comparison with other dataset.  
Figure 11 demonstrate the class-wise performance accuracy for two variant of the KU-BdSL dataset. Among the 30 classes, 27 classes produced 100% accuracy for the both  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model Name** & **Segmented** & **Image Pixel** & **Accuracy (\%)** \\ \hline [PERSON] [PERSON] et al. [13] & No & \(128\times 128\) & 94.74 \\ \hline [PERSON] [PERSON] et al. [30] & No & N/A & 95.83 \\ \hline [PERSON] [PERSON] et al. [31] & No & N/A & 99.20 \\ \hline \multirow{2}{*}{Ishara-Lipi} & Proposed model (BenSignNet) & yes & \(124\times 124\) & **99.60** \\ \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: State of the Art Comparison for Ishara-Lipi Dataset.  
Figure 12: Class wise State of the Art Comparison for Ishara Lipi Dataset [30; 31]  
variants dataset. Table 10 shows the accuracy comparison of the proposed method with state-of-the-art methods using the Ishara-Lipi Dataset. Our approach with the Ishara-Lipi dataset has achieved 99.60% accuracy whereas 94.74% achieved by method [13], 95.83% accuracy achieved by BLMA [30] and 99.20% achieved by method [31]. Figure 12 shows the class-wise comparison accuracy between the proposed and the state-of-the-art methods for Ishara-Lipi dataset. Where demonstrated, 12 classes achieved 100% accuracy and the rest of the 24 classes acquired around 99% accuracy, which is better than all the state-of-the-art class-wise accuracy. Figure 13 shows the comparison of the proposed method with the state-of-the-art method. The results show that the proposed method is achieved generalized property in the BSL recognition field.
## 3 Results and Discussion
### Effect of Different Glucose Concentration on Microalgal Growth, Pigments, and Nutrient Removal
This finding is similar to the results observed in previous studies [31]. After 14 days of cultivation, the TP decreased to 3.32 mg/L, compliant with China's livestock wastewater discharge standard (GB 18596-2001; \(\leq\)8 mg/L) [60]. These results confirm the strong TP removal capacity of FACHB-8 under an optimized glucose supplementation. In this study, the significant phosphorus removal might not only result from algal assimilation through metabolic pathways but also from external environmental conditions. Previous studies suggest that phosphorus may precipitate when the pH exceeds 8.0 and dissolved oxygen levels are high [14]. Under optimized conditions, microalgae with a robust growth were likely to absorb atmospheric CO\({}_{2}\) during growth, increasing the culture medium's pH [61; 62]. Figure 6: Biomass growth (**a**) and nutrient removal capacity (**b**) of FACHB-8 before and after carbon source optimization. Note: Asterisks show significant difference from the data before optimization (\(t\) test, \({}^{*}\)\(p\) < 0.05). Figure 7: Changes in removal of TN (**a**) and TP (**b**) by FACHB-8 cultivated in ADSW with optimized glucose supplementation within 14 days. This might lead to promoted phosphorus precipitation, which was thus removed during sampling and centrifugation, leading to a significant increase in the TP removal efficiency. In other words, the notable improvement in the TP removal is not entirely due to microalgal metabolic assimilation, a phenomenon also observed in related studies [31; 57]. Taken together, these findings indicate that the glucose added to the wastewater was efficiently assimilated by _C. vulgaris_ cells, promoting biomass accumulation and nutrient utilization.
## 4 Model Building  
### Data Processing  
#### 4.1.1 Input and Output Parameters  
The core of realizing the shear stress (\(\tau\)) prediction is to accurately grasp the key test parameters that influence the shear stress. In this study, in addition to the four influencing factors (\(\sigma_{n}\), \(Tt\), \(Rsi\), \(v\)), the shear strain (\(\gamma\)) and vertical displacement (\(\mathrm{s_{v}}\)), which were automatically collected using the DRS-1 system during the test, were also used as the input parameters of the model. Finally, six input and one output parameters were selected as the training features of the model.
# Efficient Robot Skills Learning with Weighted Near-Optimal Experiences Policy Optimization
## 4 Experiments
### Experiment Setup
#### 4.1.2 Parameter Specification  
PPO has strong robustness, so the neural network structure and other hyperparameters had little influence on the training process. Various network structures, including a three-layer network and a four-layer network, were tested, and there was no significant difference in the training effect, so the default network structure commonly used by PPO, i.e., the policy network and the critic network, both use two hidden layers, with 300 and 400 nodes, respectively. The output layer of the network is six nodes, representing the moments applied to a total of six joints in both legs; the output layer of the evaluation network has only one node and is a score of the current state.  
Set the capacity of weighted replay buffer \(D\) to 600 and mini-batch size to 500. The maximum episode is 5000. The values of \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\) are 0.4, 0.5 and 0.1, respectively.  
The reward function \(r_{t}=-l(x_{t},u_{t})\) with the following expression:  
\[r_{t}=v_{x}-3y^{2}-50\hat{z}^{2}+25\frac{Ts}{Tf}-0.02{\sum\limits_{i}{(u_{t-1} ^{i})}^{2}} \tag{26}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Leg\_Radius** & **Lower\_Leg\_Length** & **Upper\_Leg\_Length** & **Torso** & **Foot** \\ \hline Value & 0.75 cm & 10 cm & 10 cm & \(5\times 8\times 8\) cm\({}^{3}\) & \(5\times 4\times 1\) cm\({}^{3}\) \\ \hline
**Parameter** & **Density** & **Torso\_Offset\_x** & **Torso\_Offset\_z** & **Joint\_Damping** & **Joint\_Stiffness** \\ \hline Value & 1.05 g/cm\({}^{3}\) & \(-\)1 cm & \(-\)2 cm & 0.001 Ns/cm & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical property parameters of the biped robot.  
Figure 3: Simulation environment. The robot’s body and ground can be regarded as the environment. The motor at the three joints (ankle, knee and hip) of each leg can be regarded as the agent. Our task is to make the motor output an appropriate torque to control the robot to walk along a straight line.  
where \(v_{x}\) is the displacement in the \(x\)-direction, \(y\) is the distance of the trajectory from the preset line, \(\hat{z}\) is the normalized vertical translation displacement of the robot center of mass, \(T_{s}\) is the sample time of the environment and \(T_{f}\) is the final simulation time of the environment. \(T_{f}\) was set to 10 s in this simulation.  
Both the policy and value networks were trained using Adam, with a learning rate of 0.0001 for the policy network and 0.002 for the value network. The order of the trajectory data in the experience pool was not scrambled in order to ensure that the complete sequence fragments could be taken for the estimation of the generalized advantage function.
# Constrained Connectivity in Bounded X-Width Multi-Interface Networks
###### Abstract  
As technology advances and the spreading of wireless devices grows, the establishment of interconnection networks is becoming crucial. Main activities that involve most of the people concern retrieving and sharing information from everywhere. In heterogeneous networks, devices can communicate by means of multiple interfaces. The choice of the most suitable interfaces to activate (switch-on) at each device results in the establishment of different connections. A connection is established when at its endpoints the devices activate at least one common interface. Each interface is assumed to consume a specific percentage of energy for its activation. This is referred to as the cost of an interface. Due to energy consumption issues, and the fact that most of the devices are battery powered, special effort must be devoted to suitable solutions that prolong the network lifetime. In this paper, we consider the so-called _p-Coverage_ problem where each device can activate at most \(p\) of its available interfaces in order to establish all the desired connections of a given network of devices. As the problem has been shown to be NP-hard even for \(p=2\) and unitary costs of the interfaces, algorithmic design activities have focused in particular topologies where the problem is optimally solvable. Following this trend, we first show that the problem is polynomially solvable for graphs (modeling the underlying network) of bounded treewidth by means of the [PERSON]'s theorem. Then, we provide two optimal polynomial time algorithms to solve the problem in two subclasses of graphs with bounded treewidth that are graphs of bounded pathwidth and graphs of bounded carvingwidth. The two solutions are obtained by means of dynamic programming techniques.  
multi-interface networks; constrained connectivity; energy optimization; treewidth; pathwidth; carvingwidth +
Footnote †: journal:  
_arxiv_
## 5 Simulation
### Simulation Setting  
We simulated 50-100 underwater node deployments with a communication range set to 700 m. The UW-sensors in the underwater network are randomly distributed, all nodes can generate data packets, and a sink node is distributed in the central water surface of the monitoring area The specific simulation settings are shown in Table 2.  
We simulated three typical attacks on internal nodes: black hole attack, gray hole attack, and DOS. In a black hole attack, the infected MNs will refuse to accept and forward any packet, affecting data communication. The malicious node in a gray hole attack will discard the accepted packets randomly; the packet discard rate of the gray hole attack set for the experiments in this paper was 0.4. The malicious node in the DOS attack simulated in this paper will forward a large number of duplicate packets or send false packets, which occupies the channel resources of the network and makes normal nodes unable to communicate. In addition to this, to verify the network performance as well as the reliability of the proposed algorithm, the experiment also evaluated the average energy consumption (AEC), average end-to-end delay (AE2 ED), and other aspects of the UWSNs.
# Influence of the Radial Gap on the External Gear Pump Performance
## 4 CFD Results and Verification
### Average and Current Values of the Flow Rate  
The average flow rate \(q\) was the main simulation performance characteristic used in the present study. It was analyzed how the size of the radial gap affects flow rate \(q\) at different rotation frequencies \(n\).  
Figure 5: Simulation results: (**a**) velocity; (**b**) pressure.  
Due to the variable angular step, the average flow rate \(q\) is calculated using the following formula applied over an exact 30\({}^{\circ}\) period:  
\[q=\frac{\sum_{i=1}^{m}\left(q_{i+1}+q_{i}\right)(\varphi_{i+1}-\varphi_{i})}{2( \varphi_{m}-\varphi_{1})}\, \tag{29}\]  
where \(q_{i}\) (\(i=1\dots m\)) is the current flow rate, \(\varphi\) is the angular position of gears, and \(m\) is number of steps per 30\({}^{\circ}\) period (typically about 1000).  
In Figure 6, one period of 30\({}^{\circ}\) is shown for each of the studied modes. For a better overview, the beginning and end of this period were chosen to be the position where the current and average flow rate are equal. The average flow rate \(q\) is given as a dashed line and as a value. The results at a 0.02 mm gap size are presented in different colors, and the results at a 0.04 mm gap size are depicted in gray.  
It can be seen from Figure 6 that when the rotation frequency \(n\) increases, the losses decrease as an absolute value, while the flow rate increases significantly. This suggests a more detailed study on the relationships between the flow rate average value, the rotation frequency, and the radial gap size.  
Figure 6: Simulated flow rate at different rotational frequencies. The gap between the gears and the housing is 0.02 mm for the colored curves and 0.04 mm for the gray curves.  
Figure 6 also does not provide a good presentation about the radial gap size and rotation frequency influence on the current flow rate variation, since the scale along the ordinate is insufficient. Therefore, Figure 7 shows a comparison between \(q_{i}\) and \(q\) at 500 and at 2500 RPM for a 0.02 mm and 0.04 mm gap size. It can be seen that both the rotation frequency and the radial gap affect the flow rate variation with time. When the rotation frequency decreases and the radial gap increases, the shape becomes more complex and differs more significantly from that described in theory.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 4 Result and Discussion
### Dataset Validation
#### 4.2.3 Removal of Outlier from Box-Cox URL Length  
All of the transformation methods discussed above can reduce the impact of the skewness of the dataset, and the skewed data negatively affect the detection performance of our model.  
Therefore, we need to remove such outliers in our dataset. To remove such outliers, we used interquartile range techniques. The skewness level of the Box-Cox transformation technique has 0.01186 outliers and the frequency of lengthy URL occurrences is reduced to 8000. This indicates that the outliers in our custom dataset removed and achieves effective detection accuracy. Moreover, with the original URL length, the distribution is heavily right-skewed with most URLs being short and with few very long URLs. After applying the Box-Cox transformation, the data become more symmetric, but some outliers remain. Finally, after outlier removal, the distribution looks much more normal (bell-shaped), which is ideal for model training as shown in Figure 8.
# Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks
## 4 Results  
The performance of the models was evaluated using MAE, MSE, and SMAPE. In both situations, 90% of the records were randomly assigned for training the models, while 10% were used for testing. The experiments were carried out in Google Colab using the Python programming language and its relevant libraries (e.g., deep KAN for implementing the Kolmogorov-Arnold network and TensorFlow for MLP and GRU-LSTM). To ensure a fair comparison between the models in predicting the house prices, the study adhered to the following principles:  
1. Exclusion of feature extraction models: No feature extraction methods, such as autoencoders or Boltzmann Machines, were used beforehand.
2. Model optimization: The most effective configuration parameters (the number of layers, neurons, optimizer, etc.) for each model were identified through hyperparameter tuning, a critical aspect of model development and validation. Hyperparameters play a pivotal role in defining both the structural and operational characteristics of a model, significantly influencing its performance. The hyperparameter tuning process employed a combination of manual trial-and-error and grid-search techniques to identify optimal settings. The tuning involved several key steps. For the learning rate, a range of values (e.g., 0.001 and 0.01) was tested to balance precision in convergence with training efficiency as smaller learning rates can lead to more precise convergence but increase training time, while larger rates may risk overshooting the optimal solution. The architecture was iteratively refined by testing different configurations of layers and neurons, starting with a simple structure and gradually increasing complexity. Performance on the validation set was closely monitored to determine the optimal depth and number of neurons. Various activation functions were evaluated to assess their impact on convergence speed and overall model accuracy, with the final choice made based on empirical performance. Additional parameters, including batch size and dropout rates, were systematically varied to enhance model robustness and mitigate overfitting. The effects of these parameters on validation performance were carefully observed to ensure stability and generalization. The optimal hyperparameter settings, summarized in Table 1, were determined based on the best validation performance across multiple runs, ensuring that the model effectively generalizes to unseen data. Table 1 below shows the optimal values for the KAN-based model on the datasets.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 4 Constrained Stochastic Game
### Constraint Function  
To keep the usage of the computational resource of observation satellite \(i\) below a specific threshold, the constraint function \(c_{C}(S_{i},A_{i})\) is defined to represent whether or not to utilize the computational resource of observation satellite \(i\). When observation satellite \(i\) compresses the observation (i.e., \(A_{i}=3\) and \(A_{i}=4\)), it consumes the computational resource. Therefore, \(c_{C}(S_{i},A_{i})\) can be defined as  
\[c_{C}(S_{i},A_{i})=\left\{\begin{array}{ll}1,\;\mbox{if}\;A_{i}=3\\ 1,\;\mbox{if}\;A_{i}=4\\ 0,\;\mbox{otherwise}.\end{array}\right. \tag{13}\]  
To keep the average utilization of the storage resources of observation satellite \(i\) below a certain level, we define the constraint function \(c_{S}(S_{i},A_{i})\) to represent whether or not to utilize the storage resources of observation satellite \(i\). To delay the transmission of the observation (i.e., \(A_{i}=0\)), observation satellite \(i\) should store the observation in its storage. Thus, \(c_{S}(S_{i},A_{i})\) can be defined as  
\[c_{S}(S_{i},A_{i})=\left\{\begin{array}{ll}1,\;\mbox{if}\;A_{i}=0\\ 0,\;\mbox{otherwise}.\end{array}\right. \tag{14}\]