esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Opportunities, Challenges and Suitability
### Suitability  
RDMA can perform one operation in each _Round Trip Time_ (_RTT_), however traditional protocols provide more flexibility by fulfilling multiple operations. Thus, it makes RDMA more suitable for environments with single and quick operations. Furthermore, RDMA is more suitable for small and alike message sizes. Additionally, exploiting RDMA with dynamic connections cannot be beneficial due to the heavy initial cost of RDMA. Moreover, although RDMA provides high-speed communication, it limits scaling in both distance and size, meaning that the distance among nodes and the number of nodes cannot be arbitrarily large [15].  
RDMA offers simple READ, WRITE and atomic operations (CAS and FAA) as well as an operation to send messages (SEND). However, it does not support sophisticated operations such as dereferencing, traversing a list, conditional read, on-the-fly operations (e.g., compression and de-compression), histogram computations, results consolidation [16]. Thus, exploiting RDMA in such environment requires precise and fine mechanisms to achieve the best performance.
## 4 Conclusions  
In order to improve the contrast, sharpness, color fidelity, and visual effect of images under hazy weather conditions, this paper proposes a hybrid dark channel prior. (1) The original image was first processed by Retinex in order to remove the interference of the illumination component and make the enhanced image more natural. (2) In order to improve the DCP, the iterative method was employed instead of the conventional method to calculate the atmospheric light intensity. (3) The variant genetic algorithm was employed to enhance the guidance image to optimize the transmittance. (4) Tolerance was introduced to prevent color distortion during the process.  
This paper conducts subjective analysis and objective evaluation of different fog images. From the subjective analysis, it can be seen that the new algorithm has improvements in image enhancement and detail preservation. The sharpness and contrast are significantly improved. The visual effect is excellent. In terms of the O-HAZE dataset, compared with other DCP algorithms, the average \(MSE\) by the proposed method decreases by 26.98%. The average \(SSIM\) increases by 10.298%. The average _entropy_ increases by 7.780%. At the same time, the color fidelity of the enhanced image is high and closer to reality. The proposed algorithm can effectively enhance foggy images, improving the visual effect and visibility, contrast, and clarity of the image. The image is more realistic and natural.  
Conceptualization, L.W. and J.C.; methodology, J.C.; software, J.C.; validation, L.W. and J.C.; formal analysis, L.W.; investigation, X.Y.; resources, X.Y.; data curation, L.X.; writing--original draft preparation, J.C.; writing--review and editing, L.W.; visualization, S.C.; supervision, S.C.; project administration, X.Y.; funding acquisition, L.X.; Validation, Y.Z. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61801429 and the Natural Science Foundation of Zhejiang Province under Grant LY20F010001 and LQ20F050010. This work was also supported by the Fundamental Research Funds of Zhejiang Sci-Tech University under Grant 2021Q030.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Average MSE** & **Average PSNR** & **Average SSIM** & **Average Entropy** \\ \hline DCP & 234.36 & 24.43 & 0.7026 & 7.4116 \\ FDCP & 230.58 & 24.50 & **0.8503** & 7.4222 \\ GDCP & 152.22 & 26.31 & 0.6649 & 7.2812 \\ MDCP & 179.09 & 25.60 & 0.7251 & 7.5161 \\ FFA & 154.55 & 26.24 & 0.7845 & 7.4289 \\ HDCP & **96.43** & **28.29** & 0.7780 & **7.6365** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the overall objective evaluation results of different algorithms on the enhancement of the NYU2 dataset.
# Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes
## 7 Discussion  
Through the experiments, it was observed that the Bi-LSTM is more suitable for estimating the resources in the Kubernetes cluster than statistical ARIMA and the feed-forward LSTM. More specifically, the Bi-LSTM prediction model was compared with the ARIMA prediction model in Table 4. It is shown that the Bi-LSTM has a higher prediction accuracy as compared to ARIMA. It reduces prediction error by 3.28% in one-step prediction and 6.64% in five-step prediction on RMSE metric. The prediction speed is 530 and 55 times faster than ARIMA in one-step and five-step predictions.  
In addition, the Bi-LSTM is compared with LSTM in Tables 5 and 6. It is shown that the Bi-LSTM achieves higher prediction accuracy than LSTM. It reduces prediction error by 7.79% in one-step prediction and 20% in five-step prediction on RMSE metric. It also achieves comparable prediction speed to LSTM in one-step and five-step predictions. It is observed from Table 6 that Bi-LSTM has more errors in prediction when decreasing resources, and has less error in prediction when increasing the resources as compared to LSTM. Obviously, Bi-LSTM outperforms LSTM in the overall prediction accuracy as shown in Table 5. It can also be observed from the elastic speedup results in Table 6 that the Bi-LSTM autoscaler has a higher autoscaling gain than the LSTM autoscaler in both one-step and five-step predictions.  
The proposed architecture in this work is based on the existing components of the Kubernetes. However, this architecture can also be implemented in other container-based platforms, such as using Docker Swarm for orchestration and other container engines like OpenVZ, LXC. This is because the Bi-LSTM forecasting service and the Adaption manager service can analyze and process the monitored data (networking data, and pods or containers information) to calculate appropriate scaling decisions and are independent not only from the container virtualization technology but also from the cloud providers. Furthermore, we can change and update the Bi-LSTM forecasting service to learn and predict more hardware metrics such as CPU utilization and memory usage for more accurate scaling decisions.
## 2 Materials and Methods
### Results Analysis
#### 2.3.2 Radiological Evaluation  
For radiological evaluation, CBCT was performed for all the implants, under the same imaging conditions (FOV diameter, 10 cm; FOV height, 5.6 cm; acceleration voltage, 90 kV; beam currency, 8.0 mA; voxel size, 0.2 mm), preoperatively and immediately and six months (just before the second surgery) after the first surgery. The radiographic measurements were conducted by a single independent investigator (I-O.C) who was not involved in the surgery and blinded to the group allocation. Cross-sectional CT images across the center of the implant in the metrology program (OnDemand 3-DIM; Cybermed, Seoul, Korea) were used to compare the amount of bone acquired from the non-contained horizontal defects, and the line perpendicular to the long axis of the implant was extended from the platform level to the buccal side to measure the distance to the outermost bone on the extension line (Figure 2). The length of the implant was measured, and measurement errors were corrected based on the magnification of the image in proportion to the actual length of the implant. To assess intra-examiner reliability, 20 randomly selected CT radiographs were measured twice with a 4-week interval between measurements. They were compared using the Pearson correlation coefficient (r). A reliable repeatability frequency (r > 0.90) was also determined. All measurements were performed three times, and the average value was used. The amount of bone graft resorption (BR) was defined as the amount of grafted bone mass measured immediately after the first surgery (BA, bone augmentation immediately post-surgery) minus the amount of new hard tissue mass after 6 months (HG, hard tissue gain), and hard tissue gain rate (HGR) was defined as the percentage of HG compared with BA.
###### Abstract  
Polysaccharide biopolymers have been shown to be alternatives to established dust suppressants. This study investigates the potential of 14 polysaccharides and proteins from diverse botanical (corn, pea, wheat, cellulose, potato, and fava bean) and animal (pig, chicken, and cow) sources as dust suppressants on two mine soils (medium-grained sand and fine-grained silica sand). Results of laboratory tests demonstrate that the type of biopolymer and its concentration have a significant effect on penetration resistance, moisture retention, and crust thickness. Depending on biopolymer type, concentration, and soil type, moisture retention range from 1.0 to 19.5 wt% (control, C\({}_{\text{medium-gr.}}\) = 2.5 and C\({}_{\text{fine-gr.}}\) = 6.9 wt%), penetration resistance from 1.7 to 37.9 N (C\({}_{\text{medium-gr.}}\) = 1.5 and C\({}_{\text{fine-gr.}}\) = 1.7 N), and crust thickness from 0.3 to 18.1 mm (C\({}_{\text{medium-gr.}}\) and C\({}_{\text{fine-gr.}}\) = 0 mm). Proteins form crusts with penetration resistances similar to polysaccharides but mainly require higher concentrations than polysaccharides. Based on the test results, xanthan gum, carboxymethyl cellulose, corn starch, fava bean protein concentrate, and plasma protein exhibit the highest potential to act as dust suppressants. This research contributes to evaluating biopolymers as environmentally friendly soil amendments that may be used to control fugitive dust emissions from large, barren surfaces.  
dust suppressant; dust control; biopolymer; penetration resistance; crust strength; moisture retention; mine soil; protein; polysaccharide
## 6 Discussion and Conclusion
First, the method is highly accurate, the results are close to 100% (recognition rate of Arabic numerals was slightly better than the recognition rate of Hindi numerals). Second, the proposed method exhibits high performance: the proposed approach performs recognition in only a few milliseconds. It was obvious from the previous results that some of the images were misrecognized due to unclear and misleading handwriting. This issue can be solved in the future by adding \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**REF** & **Architecture** & **Goal** & **\# of Layers** & **Accuracy** \\ \hline \multirow{3}{*}{[42]} & \multirow{3}{*}{AOCT-Net} & Classification of & \multirow{3}{*}{5 levels (18 layers)} & \multirow{3}{*}{95.3\%} \\  & & optical coherence & & \\  & & tomography into five & & \\  & & classes & & \\ \hline \multirow{3}{*}{[46]} & \multirow{3}{*}{New Structure of CNN} & Classification of aerial & \multirow{3}{*}{6 levels (18 layers)} & \multirow{3}{*}{92.2\%} \\  & & images & & \\ \hline \multirow{3}{*}{[47]} & VGG19 [48] is a & \multirow{3}{*}{Extracting deep & \multirow{3}{*}{19 levels (25 layers)}} & \multirow{3}{*}{90\%} \\  & pretrained CNN on & & features & \\ \cline{1-1}  & ImageNet & & & \\ \hline \multirow{3}{*}{[49]} & Deep Recurrent & \multirow{3}{*}{Prediction bankruptcy} & \multirow{3}{*}{24 input nodes, 14 hidden nodes, and 2017 [50]} & \multirow{3}{*}{93.50\%} \\  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{3}{*}{[51]} & \multirow{3}{*}{Efficient Net} & Classification of X-ray & \multirow{3}{*}{9 levels (9 layers)} & \multirow{3}{*}{96.7\%} \\  & & images (Normal, & \\ \cline{1-1}  & Pneumonia, or & COVID-19) & & \\ \hline \multirow{3}{*}{Proposed} & \multirow{3}{*}{New CNN Model} & Numerals & \multirow{3}{*}{5 levels (18 layers)} & \multirow{3}{*}{\(\sim\)100\%} \\  & & classification (Hindi and Arabic formats) & & \\ \cline{1-1}  & & 10 classes & & \\ \hline \hline \end{tabular}
\end{table} 
Table 34: Comparison between the proposed method and previous work. and augmenting more unclear and misleading data and training the system again. This system can be used in many applications such as in banks and exam grading. Conceptualization, A.A., A.M.A., H.A., H.R.A.-Z., M.A.-Q. and M.A.-A.-K.; data curation, A.A., H.A., H.R.A.-Z. and M.A.-A.-K.; methodology, A.A., A.M.A., H.A., H.R.A.-Z. ; validation, A.A., M.A., H.A., H.R.A.-Z., M.A.-Q. and M.A.-A.-K.; writing, A.A., A.M.A., H.A., H.R.A.-Z., M.A.-Q. and M.A.A.-K.; project administration, A.A. and M.A.-Q. All authors have read and agreed to the published version of the manuscript. This research was funded by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University, grant number 2017/01/7551. Not applicable. This project was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research project # 2017/01/7551. The authors declare no conflict of interest.
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 5 Web3602--Interactive Application Applying SDM Ontology
### Implementation
#### 5.3.4 Visualization of Audio objects  
Web3602 displays audio objects as audio visualization objects. An audio visualization object is a display object consisting of spine-shaped objects on a spherical object, each of which changes in length and color according to the frequency spectrum of the corresponding audio. The number of spine-shaped objects in the audio visualization object is 32, and the frequency domain data of the audio is allocated to 32 objects as shown in Figure 18.  
The audio spectrum is obtained approximately 20 times per second by using the Web Audio API method (AnalyserNode.getByteFrequencyData()). Because the audio spectrum is biased and the absolute value differs significantly from one audio data point to another, the appearance of the audio visualization object will be unbalanced if the weights are used as they are. To balance the appearance of the audio visualization object, the upper and lower limits of the range in which the frequency spectrum exceeds certain levels are detected and then divided into 32 groups. The values of each group are normalized, and the order of the groups is randomized and then converted to the length and color of the spine-like object for drawing. Only the visible spine-like objects are drawn, and the drawing load is reduced by omitting the drawing of objects in the blind spots. The color of the spines of the audio visualization object in the OFF state is gray, and in the ON state, the color changes from blue to light blue, green, yellow, orange, and red as the average value increases.
## 2 Literature Review
### Crowdsourced Data Usage in ITS  
Crowdsourced data from social media apps such as Twitter have become an emerging data source to provide new support for predicting or managing traffic issues due to the rapid growth of smartphone users. An increasing number of research has investigated the application of such crowdsourced data in traffic management. Ref. [14] used crowdsourced data from smartphone applications, GIS-based web interfaces, and weather sensors to model the individual mobility decision processes. The model is regarded as a potential platform for personalized travel management in smart cities, as well as a communication tool between cities and users. Ref. [15] mined crowdsourced media data from Twitter and Foursquare to look into the spatial and temporal patterns of human activities in a city, and showed the importance and usefulness of crowdsourced data in analyzing people's activities. Ref. [16] proposed that mining social media data can be a basic low-cost supplement and convenient solution for ITS. More than 1 million tweets were collected over 3 months, and an Arabic Twitter content analysis framework was proposed to tackle the problem of missing the location information of traffic-related incidents in the tweets. Ref. [17] developed a machine learning method to predict traffic evolution after accidents based on the user-generated crowdsourced data (UGCD) provided by navigation apps that have interfaced for users to report traffic incidents, and showed the efficiency of using UGCD for the real-time analysis of traffic accidents. Ref. [18] assessed the speed data based on a crowdsourced navigation system, Waze, and conducted a case study in Sevierville. They showed that the posted speed on Waze is a good representation of actual speed. Ref. [19] proposed an innovative machine learning framework to extract traffic-related information from social network crowdsourced data for traffic incident detection. These studies and others [20; 21; 22] indicate that crowdsourced data from social media or smartphone applications are one of the promising data sources for managing smart cities and transportation systems.
###### Abstract  
Distributed generations, using solar photovoltaic (PV) generation systems, are generally connected to ungrounded distribution systems to ensure operational continuity and avoid electro-chemical corrosoins. The ungrounded power distribution system possesses an advantage of continuous operation regardless of primary fault occurrence due to a small fault current. Conversely, a subsequent secondary fault can induce a large fault current representing that of other electrical grounding types, resulting in inevitable power shutdowns. As preventative methods, both insulation status monitoring and primary failure detection have become of high importance. This paper presents a method enabling the cooperative use of IMD (Insulation Monitoring Device) and GPT (Ground Potential Transformer) in an ungrounded distribution system connected with a transformerless inverter. Moreover, factors leading to errors during IMD insulation monitoring, CLR (Current Limit Resistor) burnout of a GPT, and malfunctions of related protection devices are presented. Furthermore, a method for selecting the inductor and capacitor in consideration of the operating characteristics of IMD and GPT is discussed. The proposed cooperative operation method enables the accurate measurement of insulation resistance using IMD, while concurrently reducing the constitutively induced zero-sequence voltage in the CLR of a GPT to prevent CLR burnouts and malfunctions of connected protection devices. Hence, the method is anticipated to contribute to the stable operation of alternating current (AC) and Direct Current (DC) combined systems connected with transformerless inverters.  
Keywords:insulation monitoring device; ground potential transformer; single line ground fault; transformerless inverter; cooperative use +
Footnote †: journal: Applied
# High Spatial and Temporal Resolution Bistatic Wind Lidar
## 3 Validation and Characterisation
### PTB Bistatic Lidar/Monostatic Lidar (WindCube)  
In 2014, two comparative measurements between the PTB bistatic lidar and a monostatic lidar (WindCube) were conducted over flat terrain at PTB's antenna testing ground, and over complex terrain amidst several buildings at PTB [20]. At each location, both devices were positioned at a distance of 10 m from each other and set to a measurement height of 100 m. Over flat terrain, both devices showed good agreement with deviations below 1% over a one-hour period of low atmospheric turbulence. However, over complex terrain, both devices showed significant deviations in the order of 15%, as it could be expected for measurements at different locations in turbulent flow and compared against conventional monostatic lidar technology.
## 4 Results and Discussion
### Sensitivity Analysis  
To determine the most and least effective parameters, sensitivity analysis is performed to examine how the model responds to changes in the input variables with respect to PPV. Hence, in this study, a sensitivity analysis approach implemented in [93] was adopted. Here, while keeping the ranges of all other parameters fixed, the mean value of one of the  
Figure 7: Comparison of Predicted and Measured PPV for: (**a**) BPNN (**b**) GPR (**c**) ELM (**d**) MARS (**e**) MVRA.  
input variables is increased (i.e., New mean = Old mean + 5% Old Mean) and subsequently the amount of changes in the predicted PPV using the GPR model is recorded. The obtained results are graphically illustrated in Figure 8.  
As can be seen in Figure 8, increasing the mean values of spacing and maximum charge per delay, increases PPV. Furthermore, increasing the mean values of distance and stemming length decreases PPV. Increasing burden slightly increased PPV. Nevertheless, increasing values of powder factor and average hole depth did not significantly impact the values of PPV. It can thus be said that the most influential parameters that can affect PPV greatly are spacing, a maximum charge per delay, distance and stemming length.
## 6 Controller Designs
### Feedforward Control Loop  
The feedforward control loop operates in open-loop form and is subject to input disturbances, as depicted in Figure 10. The feedforward controller is designed to approximate the inverse dynamics of the inner-loop joint controller, whose closed-loop transfer function is given in Equation (46). Therefore, the feedforward transfer function is constructed as the inverse of the closed-loop system:  
\[T_{forward}=\frac{1}{T_{inner  
Substituting the closed-loop response from Equation (46), we obtain:  
\[T_{forward}=\frac{(\tau_{in}s+1)^{3}}{(3\tau_{in}s+1)}\frac{1}{(\tau_{forward}s +1)^{2}}\cdot I_{6\times 6} \tag{48}\]  
Here, the additional double poles at \(s=-1/\tau_{forward}\) are introduced to ensure that the feedforward transfer function is proper (i.e., physically realizable). To minimize the impact on bandwidth while ensuring realizability, the double poles are placed significantly faster than the original system dynamics. Specifically, the time constant \(\tau_{forward}\) is chosen to be one-tenth of the inner-loop time constant:  
\[\tau_{forward}=0.1\tau_{in} \tag{49}\]  
This design ensures that the feedforward action closely approximates the inverse of the inner-loop dynamics without introducing high-frequency noise amplification.
## 2 Assumptions of Our Target System and Challenge
### Assumptions
For example, SmartMesh IP [19] that is based on the 6 LoWPAN and IEEE802.15.4e standards provides only one timeslot for downstream traffic from a center node to a device node in every 2 s by default settings, contrary to several timeslots for upstream from the nodes to a center node. * **Traffic patterns. ** Industrial applications basically gather heterogeneous periodic information from field devices. At the same time, they also require on-demand data communication for additional data collection and operation of devices by a remote server within specific end-to-end deadlines [15]. When the unpredictable on-demand data communication occurs, the unpredictable traffic may be given high priority to over periodical scheduled communication and a central node transmits a polling packet for the on-demand request. Consequently, a periodical polling request from a central node to a node is dropped by the unpredictable on-demand data communication. If an open slot is fortunately assigned when the on-demand request occurs, a central node can get data from a node by an on-demand request without any interruptions of periodic data collection. Therefore, it is better for IWSNs to generate network traffic load for periodical data collection uniformly and also keep available bandwidth for unpredictable traffic at any time. Figure 2: An example of multicycle polling patterns. * **Traffic control scheme for multiple wireless communications. ** Industrial applications require similar requirements for IWSNs, but applications in different domains may use different IWSN protocols. For example, AMI often uses IEEE802.15.4e and PA uses WirelessHART or ISA100.11a. We propose a data traffic control scheme for periodical data collection over IWSNs and our proposal does not completely depend on any specific protocols. Our proposed scheme is a technology that lies between applications and an interface of IWSNs, namely IP (network layer) or MAC (datalink layer) as shown in Figure 3. A center node has the data traffic control function in order to manage all polling traffic.