esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Boost Converter
### Loss Resistance, Loss Voltage and Voltage Gain Determination  
At the input of the _ideal_ converter (Figure 1):  
\[P_{i}=V_{i}I_{i}=A_{Vi}V_{i}\;\;I_{o}, \tag{38}\]  
while at the output:  
\[P_{o}^{*}=V_{o}^{*}I_{o}=A_{Vi}V_{i}\;\;I_{o}. \tag{39}\]  
Of course, (38) and (39) show that in an ideal converter \(P_{i}=P_{o}^{*}\), so the efficiency \(\eta=P_{o}^{*}/P_{i}\) is 1. At the input of the actual converter (Figure 1):  
\[V_{g}=I_{g}R_{X}+V_{i}=A_{Vi}\;I_{o}\;R_{X}+\frac{V_{o}^{*}}{A_{Vi}}=A_{Vi}\;I _{o}\;R_{X}+\frac{V_{o}+V_{X}}{A_{Vi}}. \tag{40}\]  
Multiplying the previous equation by \(A_{Vi}\), and considering \(R_{L}=V_{o}/I_{o}\) the load resistance connected to the converter,  
\[A_{Vi}V_{g}={A_{Vi}}^{2}\;V_{o}\frac{R_{X}}{R_{L}}+V_{o}+V_{X}=V_{o}\bigg{(}1+ {A_{Vi}}^{2}\frac{R_{X}}{R_{L}}\bigg{)}+V_{X}. \tag{41}\]  
From here the output voltage \(V_{o}\) is given by  
\[V_{o}=\frac{A_{Vi}-\frac{V_{X}}{V_{g}}}{\Big{(}1+{A_{Vi}}^{2}\frac{R_{X}}{R_{ L}}\Big{)}}V_{g}=A_{Vi}V_{g}\;, \tag{42}\]  
where \(A_{Vi}\) is the converter actual voltage gain. That is:  
\[A_{Vi}=\frac{V_{o}}{V_{g}}=\frac{A_{Vi}-\frac{V_{X}}{V_{g}}}{1+{A_{Vi}}^{2} \frac{R_{X}}{R_{L}}}. \tag{43}\]  
At this point, we are interested in identifying the parameters \(R_{X}\), \(V_{X}\) and \(A_{Vi}\) of the model of Figure 1. From it:  
\[I_{g}=I_{i}=\frac{V_{g}-V_{i}}{R_{X}}=\frac{V_{g}}{R_{X}}-\frac{V_{i}}{R_{X}} =\frac{V_{g}}{R_{X}}-\frac{V_{o}^{*}}{A_{Vi}\;R_{X}}=\frac{V_{g}}{R_{X}}-\frac {V_{o}}{A_{Vi}\;R_{X}}-\frac{V_{X}}{A_{Vi}\;R_{X}}. \tag{44}\]  
The previous equation is then compared to (37) to determine that:  
\[R_{X}=\bigg{(}\frac{\delta-k\beta}{r+R_{S}}+\frac{\delta_{f}+k\beta}{r+R_{D}} \bigg{)}^{-1}, \tag{45}\]  
\[V_{X}=V_{\gamma}, \tag{46}\]  
and  
\[A_{Vi}=1+\frac{r+R_{D}}{r+R_{S}}\cdot\frac{\delta-k\beta}{\delta_{f}+k\beta}. \tag{47}\]  
Equations (45)-(47), respectively, allow obtaining the practical values (from \(V_{\gamma}\), \(R_{D}\) and \(R_{S}\) given by the manufacturers of the converter components) of the model parameters (Figure 1) of a generic and real boost converter.  
Then, writing Equation (45) as:  
\[\frac{1}{R_{X}}=\frac{1}{R_{X1}}+\frac{1}{R_{X2}}=\frac{\delta-k\beta}{r+R_{S}}+ \frac{\delta_{f}+k\beta}{r+R_{D}}, \tag{48}\]  
the total loss resistance \(R_{X}\) can be obtained as shows Figure 6.
## 7 Performance Analysis
### Propagation Delay  
The propagation delay (\(d_{p}=T_{2}-T_{1}\)) is determined by computing the difference between the timestamps of a message received (\(T_{2}\)) and transmitted (\(T_{1}\)). But \(d_{p}\) expects some time interval, which can be stated as in \(d_{p(V2V)}=\frac{Lsh}{f}\) and \(d_{p(V2I)}=\frac{L}{f_{SQL}}\) for \(L\) length messages (i.e., communication cost) at \(f\) transmitted data rate along with \(h\) hops through which a message is traveled [37]. Thus, the propagation delay of our protocol is the lowest, because the communication cost of the proposed protocol is the lowest.
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 5 Implementation and Performance Evaluation
### Testbed Architecture  
The testbed used to validate the proposed INC system uses real equipment and a hardware-based P4 target that implements the aforementioned data plane architecture. The validation is carried out on top of Smart Networks for Industry (SN4I) [29], a Network Function Virtualization- (NFV) and Software Defined Networking (SDN)-enabled experimental facility deployed across the Faculty of Engineering in Bilbao (EIB) and the Aeronautics Advanced Manufacturing Center (CFAA). The CFAA was launched by the University of the Basque Country along with a wide range of companies involved in manufacturing and Industry 4.0, with the aim of raising industrial competitiveness of the region through the use of novel machinery and manufacturing techniques. These two locations are interconnected through a Layer-2 SDN network at a rate of 10 Gbps. Within the EIB site network, the P4 switch is implemented using a Netronome SmartNIC, which has 2 physical 10 Gbps ports and the ability to create 128 virtual ports. Readers that would like to know more about the SmartNIC can refer to [30].  
Both sites contain data centers that are equipped with the hardware and software components that enable the deployment of virtual services in the infrastructure, leveraging NFV technology. The complete infrastructure comprises several SDN switches and servers,  
Figure 7: Flux diagram and code for the egress phase.  
amounting to a total computing and storage capacity of around 250 dual-threaded cores, 1 TB of RAM, and 25 TB of storage. Two OpenStack nodes deployed in EIB and the CFAA manage the resources of their respective locations, and three ONOS SDN controllers manage, respectively, the data plane connectivity in the EIB domain, the CFAA domain, and the Wide Area Network (WAN) between both locations. In other words, the role of SDN in SN4I is to reactively install the forwarding rules that enable the connectivity between VMs and physical equipment connected to either side of the infrastructure. Finally, an Open Source MANO (OSM) orchestrates the whole NFV infrastructure, exposed by the two OpenStack nodes, and manages the SDN controllers to control the routing.  
In this context, the CFAA contains the IIoT devices and machinery that are considered to be located at the edge of the network, in the factory itself. For the validation tests in this paper, we have simulated this equipment in a virtual machine with 4 GB of RAM and 2 cores in the OpenStack that is located in the CFAA (factory). The EIB, in turn, includes the edge MQTT broker and the connection point with the ISP that provides access to the remote MQTT broker, which in our scenario is considered the cloud server.
## 3 Methods
### Strong-Weak Contrastive Consistency
#### 3.2.3 Supervised Mechanism with Label Perturbation  
Although consistent supervision using both strong and weak perturbations can effectively enhance the learning performance of unlabeled data, the absence of direct guidance from ground-truth labels may result in the accumulation of pseudo-label errors. [PERSON] [40] computed the prediction results for both original samples and perturbed samples, applied consistency loss to ensure stable predictions under perturbations, and suggested that this approach could be extended to labeled data. Therefore, this section introduces consistency supervision for labeled data to provide indirect guidance for the learning process of unlabeled data, thereby assisting the model in correcting cognitive biases and improving its segmentation capability on real-world data. For labeled data \(x_{s}\), strong perturbation features \(q_{s}^{\text{strong}}\) are generated, and the supervised loss is calculated in conjunction with the ground-truth labels \(y_{s}\).  
\[L_{\text{consist}}^{\text{label}}=-\frac{1}{N}\sum_{i=1}^{N}\Big{(}\,\log P(y_ {s}|\,q_{s})+\log P(y_{s}|\,\,\,q_{s}^{\text{strong}})\Big{)} \tag{10}\]  
This mechanism offers indirect supervision for pseudo-label generation, thereby addressing the model's cognitive bias toward real data and further improving segmentation performance. Finally, by integrating the contrastive learning loss of unlabeled data, the consistency regularization loss from strong and weak perturbations, and the supervised loss of labeled data, the overall loss function is formulated as shown in Equation (11)\[L_{\text{total}}=L_{\text{constrast}}^{\text{strong}}+\lambda_{1}L_{\text{consist}}^{ \text{weak-strong}}+\lambda_{2}L_{\text{consist}}^{\text{label}} \tag{11}\]  
Among them, \(\lambda_{1}\) and \(\lambda_{2}\) serve as hyperparameters for adjusting the weights of the loss terms. This comprehensive loss function enables collaborative optimization in the learning process of both labeled and unlabeled data, thereby enhancing the model's robustness and accuracy in scenarios with imbalanced data distribution and complex backgrounds.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 5 Experimental Results
### Feature Selection Using Filter Method  
The feature importance supported by the tree-based model was used with DT and RF among the four classification algorithms in this study. Table 5 shows the multiclass classification evaluation results using the top 5 or 10 features with large importance values derived from the feature importance. It also shows the scores for each algorithm when using all 55 features. According to the experiment, both the DT and RF classification algorithms performed better with 10 features than with 5. When using the 10 features selected by the feature importance method, all scores, including the accuracy and F1-score, increased compared with the base model for both the DT and RF algorithms.
# ATLAS: Latest Advancements and First Observations
## 3 Results  
### Calibration  
The results from the last calibration procedure with cross-scanning using the Sun are depicted in Figures 9 and 10. Figure 9 showcases some of the steps of scanning. Initially, an azimuth scan with an 8\({}^{\circ}\) searching angle yielded an offset estimate of 6\({}^{\circ}\). Then, an elevation scan with a 4\({}^{\circ}\) searching angle provided an initial estimate of 1\({}^{\circ}\). Subsequently, finer-resolution scans were conducted around these initial offsets, and the azimuth offset was corrected to 5.4\({}^{\circ}\), and the elevation offset was corrected to 0.5\({}^{\circ}\). The convergence was obtained after the offset in azimuth stabilized within an uncertainty of 0.2\({}^{\circ}\) and the offset in elevation stabilized within an uncertainty of 0.05\({}^{\circ}\). The results in Figure 10 correspond to the last iteration on each axis after convergence was obtained.  
Figure 9: Example of the steps of the calibration procedure. In Step 1, an azimuth scan was performed with an 8\({}^{\circ}\)searching angle to obtain an initial guess of the offset, where a peak was obtained at 6\({}^{\circ}\). In Step 2, the 6\({}^{\circ}\)offset in azimuth was fixed, and an elevation scan was performed with a 4\({}^{\circ}\)searching angle to obtain an initial guess of the offset, where a peak was obtained at 1\({}^{\circ}\). Once an initial guess for both offsets was obtained, the same process was repeated but with finer resolution around the new offsets (Steps 3 and 4), where the azimuth offset was corrected to 5.4\({}^{\circ}\) and the elevation offset was corrected to 0.5\({}^{\circ}\). The process converged in Steps 5 and 6, as detailed in Figure 10. The number of scans required until convergence may vary, and for illustration purposes, intermediary scans were omitted.
## 6 Conclusions  
This study applied Char-RNN to the KNHANES VI dataset to classify five chronic diseases (hypertension, stroke, myocardial infarction, angina pectoris, and diabetes) and normal status to deal with missing values in the data. We first selected 29 of 760 variables using the stepwise selection method. We then applied Char-RNN to classify the five chronic diseases and normal status. A conventional DNN model with three hidden layers having 256, 128, and 64 nodes was applied to the same dataset for comparison. Additionally, LSTM and machine learning models, naive Bayes, and SVM were used to compare the five chronic diseases. The results show that Char-RNN performed, on average, 10% better than the other models with KNN, mode, and multiple imputation methods. Table 4 shows that LSTM was more accurate for normal status and SVM was more accurate for stroke; however, Char-RNN had higher performance for the remaining four classes. In the comparison of missing values in Table 3, we can see that Char-RNN had better accuracy than the other models on the test dataset with missing values, because it predicted the labels of partially observed instances by identifying the data patterns surrounding the missing values. In addition, the data replacement method was used to replace the missing values in the other four models; however, Char-rnn did not go through the data replacement process for the missing values. Therefore, Char-rnn can provide better results than other machine learning methods that result when analyzing missing data without passing through the data transfer process, thus reducing data preprocessing time. Characterization of char-rnn allows for more accurate prediction and classification. However, a few limitations must also be considered. First, the KNHANES dataset was only collected in South Korea. Therefore, applying Char-RNN to a dataset including respondents of diverse ethnicities and lifestyle habits can be one future research direction. This study also focused on the prediction of a dataset with missing values using machine learning methods. Identifying common and different features across chronic diseases to prevent those diseases by using machine learning methods can be studied in future work. Finally, there are several implicit methods for missing data analysis other than Char-RNN. Applying these implicit methods and comparing the results to find the best method of predicting chronic diseases can be useful.
# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
###### Abstract  
Analyzing point clouds with neural networks is a current research hotspot. In order to analyze the 3D geometric features of point clouds, most neural networks improve the network performance by adding local geometric operators and trainable parameters. However, deep learning usually requires a large amount of computational resources for training and inference, which poses challenges to hardware devices and energy consumption. Therefore, some researches have started to try to use a nonparametric approach to extract features. Point-NN combines nonparametric modules to build a nonparametric network for 3D point cloud analysis, and the nonparametric components include operations such as trigonometric embedding, farthest point sampling (FPS), k-nearest neighbor (k-NN), and pooling. However, Point-NN has some blindness in feature embedding using the trigonometric function during feature extraction. To eliminate this blindness as much as possible, we utilize a nonparametric energy function-based attention mechanism (ResSimAM). The embedded features are enhanced by calculating the energy of the features by the energy function, and then the ResSimAM is used to enhance the weights of the embedded features by the energy to enhance the features without adding any parameters to the original network; Point-NN needs to compute the similarity between each feature at the naive feature similarity matching stage; however, the magnitude difference of the features in vector space during the feature extraction stage may affect the final matching result. We use the Squash operation to squeeze the features. This nonlinear operation can make the features squeeze to a certain range without changing the original direction in the vector space, thus eliminating the effect of feature magnitude, and we can ultimately better complete the naive feature matching in the vector space. We inserted these modules into the network and build a nonparametric network, Point-Sim, which performs well in 3D classification tasks. Based on this, we extend the lightweight neural network Point-SimP by adding some trainable parameters for the point cloud classification task, which requires only 0.8 M parameters for high performance analysis. Experimental results demonstrate the effectiveness of our proposed algorithm in the point cloud shape classification task. The corresponding results on ModelNet40 and ScanObjectNN are 83.9% and 66.3% for 0 M parameters--without any training--and 93.3% and 86.6% for 0.8 M parameters. The Point-SimP reaches a test speed of 962 samples per second on the ModelNet40 dataset. The experimental results show that our proposed method effectively improves the performance on point cloud classification networks.  
deep learning; point cloud; attention mechanism; pattern recognition +
Footnote †: journal: 2024
## 4 Results
### Validation of the Omnidirectional and Translation Capabilities
Results for the 3B platform indicate that it is not controllable when there is rotation of the platform (\(\omega\
eq 0\) rad/s), so the system is stable only when translational trajectories without rotation are implemented. Both 2A-1B and 1B-2D platforms are only able to execute the E1 command (move forward without rotation), and do not successfully implement the rest of the commands, including the rotation-only motions. Both 1B-2C and 1A-2D platforms are able to implement specific translation-only trajectories and specific trajectories combining translation and rotation, but other motions requiring the same capabilities fail. Such results indicate that both platforms act like a differential-drive one, with its corresponding motion restrictions. Finally, these results obtained by simulation (Table 9) agree with the motion capabilities predicted in this paper (provided in Table 6).
## 4 Results and Discussions
### Discussion  
The neural-network models have resulted in a promising result in estimating, particularly the sagittal-plane lower-limb-joint angles. The results could have been improved if the data collection was made only for a straight walk on perfectly level ground. However, the outdoor ground had a gentle slope and subjects were taking turns at different angles. Hence, for more instructed straight walking in medical environments, the system could potentially be used to give insight into the kinematics of the lower half of the body. In particular, in a lower-limb exoskeleton robot where the robot needs to identify the patient's walking intention so that it applies a mechanical force to assist the walking movement. In addition, the before and after walking status of patients who underwent leg-joint surgery, and their progress can be tracked with this system. The main advantage of this system is its portability and reduced sensor complexity which could be used in different scenarios without too much effort.  
Even though promising results were obtained for the sagittal-plane joint angles with SID, the model has difficulty estimating ankle joint angles and coronal-plane lower-body joint angles. One reason for the difficulty of coronal-plane joint angles is that during walking our legs barely move along the coronal plane resulting in smaller angle values that are prone to noise. On the other hand, the ground-truth value of the ankle joints is affected by the foot-ground impact during heel strike which resulted in higher error. This could be improved by introducing other low-level sensors on the foot. Another general limitation of deep learning methods is the lack of explainability and interpretability. This could cause unreliable results of machine-learning methods. To be used in real-world applications, machine-learning methods must be easier to understand for unskilled personnel as well. Some studies have started the work of addressing this issue for an easier understanding of machine-learning methods. The authors in [35] employed various methods, including Local Interpretable Model-agnostic Explanations, to explain and interpret the decision-making of machine-learning methods. This could make machine-learning methods less challenging when used by less-experienced clinicians. Hence in the future, the interpretability of the system will be addressed by adopting different techniques such as the black-box explainers which will be then followed by system reliability and validity evaluation by hiring inexperienced physiotherapists and skilled people to operate the system. Eventually, the study will be extended to rehabilitation and eldercare applications. However, for more depth analysis in these fields, the addition of sensors such as insole sensors, full gait parameterization, and visualization will be implemented which will give physiotherapists a clear understanding of a patient's walking conditions.
## 4 Conclusions  
This research has demonstrated the performance of natural zeolite (clinoptilolite) to remove multiple heavy metallic ions (HMIs) commonly found in acid mine drainage. With the design and development of a novel dual-column sorption system, the lead (Pb\({}^{2+}\)) ion is removed completely and sustained throughout the analysis period. The relationships between empty bed contact time (EBCT), breakthrough capacity, and usage rate are evident. The additional ten minutes of EBCT in the second sorption column contributes to an enhancement in overall removal for Cu\({}^{2+}\), Fe\({}^{3+}\), and Zn\({}^{2+}\) by 75.67%, 99.90%, and 45.72%, respectively, from the first sorption column. This improvement is also apparent in the greater breakthrough capacity and lower usage rate in the second column, and visualized in an improved S-shape to the characteristic breakthrough curve. Based on the multi-component influent stock of 10 meq/L total concentration, the second column demonstrates a removal of 99.98%, 99.98%, 80.07%, 51.53%, and 0.00%, for Pb\({}^{2+}\), Fe\({}^{3+}\), Cu\({}^{2+}\), Zn\({}^{2+}\), and Ni\({}^{2+}\), respectively; and the final cumulative collection of effluent reports a removal of 100.00%, 99.72%, 91.71%, 79.56% and 48.97%, for Pb\({}^{2+}\), Fe\({}^{3+}\), Cu\({}^{2+}\), Zn\({}^{2+}\), and Ni\({}^{2+}\), respectively at the completion of the analysis period. These HMI sorption removal trends confirm the consistency between batch and continuous mode operations.  
The modular design inventively incorporated a 'circulation-pulse' method to distribute the flow, rather than operating on a more commonly implemented fixed flow rate. With the consideration of this unique stock feed method, the findings of the service time and flow rate with respect to the removal trends are both interesting and significant. Forthcoming works in this research project include the advancement of service life and regeneration cycles, with further design development and optimization. The potential for variable flow rate operation and automatic adjustable sampling in a packed fixed-bed dual-column sorption design reveals practicality for treatment applications.  
This study has provided greater insight into the immense potential that the natural mineral zeolite holds for the future of industrial wastewater treatment.  
This research was conducted with the financial support of a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant to [PERSON].  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] constructed the prototype, performed the experiments and analytical simulations, and analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.