esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background Knowledge
### ELK Architecture  
The ELK Stack [35] is an architecture including distributed search and analytics engines. The core of ELK Stack is four modules: Elasticsearch, Logstash, Kibana, and Beats. These modules are designed, managed, and supported by Elastic.  
* Elasticsearch is a NoSQL database with RESTful APIs based on the Lucene search engine. It is a search and analytics engine that is highly adaptable and dispersed. It has powerful queries for deep analysis and centrally maintains all data for quick document searches. It also provides horizontal scalability, allowing fast deployment, excellent reliability, and control.  
Figure 4: Multi-head attention architecture [20].  
Logstash is a data collection pipeline tool. It is the first part of the ELK Stack, and it collects data and feeds it to Elasticsearch. It gathers a variety of data kinds from numerous sources simultaneously and makes it immediately available for further use.
* Kibana is a data visualization tool. It is used to visualize Elasticsearch data and provide developers with fast access. The Kibana dashboard visualizes sophisticated queries with interactive charts, geographical information, timelines, and diagrams. Kibana allows network managers to create and save individual charts based on their explicit requirements.
* Beats are lightweight agents that are installed at every client's station to collect logs and send them to the ELK host station.  
These modules provide general-purpose monitoring, troubleshooting, and securing of the enterprise network infrastructure. Besides the security task, the enterprise network can use the ELK Stack for business intelligence or web analytics.  
The workflow begins with Beats collecting data from every client in the network. Then, Logstash aggregates and processes data and sends data to Elasticsearch for indexing and storing, and the Kibana provides a user dashboard that helps the Enterprise Network Security team to monitor all the network traffic and threats.  
The ELK Stack provides users with a robust platform that collects and processes data from multiple data sources, stores data in one centralized data store that can be auto-scaled, and offers various tools to analyze the data. The ELK database is well constructed and contains all the information about network activities.  
However, the enterprise network may generate a massive number of logs in a short time due to its complexity, which will quickly overload the simple ELK architecture. RabbitMQ adds additional components into ELK architecture to handle a complex enterprise network.  
Figure 5 illustrates the architecture of ELK.
## 4 Pseudo-Dynamic Test Overview and Result Analysis
### Specim Preparation and Variables  
The frame of an existing three-story R/C school building in Korea with non-seismic details (standard drawing type C in the 1980s) was selected, as shown in Figure 14, to examine the seismic performance of the LLFD method. The floor height of the building was 3.3 m, and the design strength of the concrete was 21 MPa. The target of the pseudo-dynamic test was the one-span, two-story real-size frame for the exterior of the school building. T-shaped beams were used for each floor, considering the effective slab width based on KDS 41 [23].  
Figure 14: Selected R/C building and frame for the pseudo-dynamic testing: (**a**) planar view; (**b**) isometric view.  
Figure 15 shows the reinforcement details for the existing frame and a photograph of the specimen. The pseudo-dynamic test was conducted by preparing two frame specimens as shown in Figure 16, including one frame specimen retrofitted with the LLFD V-bracing system and one non-reinforced frame specimen for a comparison with the LLFD-reinforced frame. In the test, the GS-LLFD was used based on the purchase conditions and economic characteristics. The GS-LLFD torque pressure was \(T_{0}=200\) kN\(\cdot\)m. In this instance, the yield strength was found to be 125 kN using regression Equation (4) in a member test.  
Hachinohe (EW), which exhibited the largest seismic response displacement, was selected as the input ground motion for the pseudo-dynamic test based on the results of research by [PERSON] (2018) on the seismic response displacement characteristics (ductility ratio) of medium- and low-rise R/C buildings (strength was less than 0.5 in the form of the shear force coefficient) during ten historical seismic waves. In the case of the seismic input acceleration, the Hachinohe (EW) seismic wave was standardized to sizes of 200, 300, 400, and 500 cm/s\({}^{2}\). The 200 and 300 cm/s\({}^{2}\) seismic scales corresponded to seismic zone-1 and ground types S\({}_{4}\) and S\({}_{5}\) at the level of two-thirds of the 2400-year return period earthquake specified in K
# A Tour Recommendation System Considering Implicit and Dynamic Information
## 5 Experiment Design and Discussion
### Baseline Approaches  
To validate the effectiveness of the proposed tour recommendation system, we compared it with the following seven methods:  
1. Random POI Selection (RAND) [36]: The next POI is chosen randomly from all unvisited POIs.
2. Greedy Nearest POI Selection (NEAR) [3]: This algorithm selects the nearest destination from a set of unvisited POIs, emphasizing the consideration of geographic location.
3. Factorized Personalized Markov Chains (FPMCs) [37]. The algorithm combines a matrix factorization of a user-item matrix to model user preferences and factorized (first-order) Markov chains to consider sequential dynamics.
4. Long Short-Term Memory (LSTM) [38]. LSTM is an extension of the RNN (Recurrent Neural Network) model. An LSTM unit has a (memory) cell and three multiplicative gates to allow long-term dependency learning.
5. Bidirectional LSTM (BiLSTM) [39]. Bi-LSTM is a type of RNN that processes sequential data in both forward and backward directions. It combines the power of LSTM with bidirectional processing, allowing the model to capture both the past and future context of the input sequence.
6. LSTM with attention mechanism (LSTMwAtt) [40]. Attention mechanisms can better select input sequences and encode semantics in long-term memory to improve neural multi-order information processing capabilities.
7. Transformer (TRANS) [41]. Unlike existing sequential models that use recurrent modules, Transformer is purely based on a proposed attention mechanism called \"self-attention\", which is highly efficient and capable of uncovering syntactic and semantic patterns between items in a sequence.
8. The methods selected for comparison in the manuscript represent diverse approaches to tour recommendation, ranging from simple heuristic algorithms (RAND, NEAR) to more sophisticated machine learning techniques (FPMC, LSTM, BiLSTM, LSTMwAtt, and Transformer). These methods were chosen because they offer varying perspectives on user POI interactions, geographical relevance, and sequential data modeling, which are all critical considerations in tour recommendation systems.
## 2 Research Methodology
### Position Error Compensation of Robot Arm  
The robot arm has errors when going to the specified position using the linear movement. This error happens due to the non-linearity of the positive sensitive detector (PSD). To solve this problem, a polynomial fitting algorithm is proposed [19]; however, the method will consider only the error of the XY plane. The \(Z\)-axis error will be ignored because, in the range of the study, the height of the object is not enough for the \(Z\)-axis error to affect the accuracy of the system.  
Due to the different manufacturing processes, the size of the non-linear error of the PSD of each robot arm will be different. According to the different degree of the non-linearity of the PSD, the photosensitive surface of the PSD is usually artificially divided into Region A and Region B. Region A is the center area which has good linearity and a small measurement error; on the other hand, Region B is the margin area, where the non-linear error is large and the measurement error is also large.  
According to the principle of the PSD, the non-linear error of the PSD in the \(x\) and \(y\) directions is relatively independent. There are error values in the \(x\) and \(y\) directions for the measured values of each point on the PSD photosensitive surface. Consider the error of each point to be \(E_{x}\big{(}x_{i},y_{j}\big{)}\) for the \(X\)-axis and \(E_{y}\big{(}x_{i},y_{j}\big{)}\) for the \(Y\)-axis. Equation (19) defines the error:  
\[\Delta x=E_{x}(x_{i},y_{i})=x_{i}-x_{i0}\Delta y=E_{y}(x_{i},y_{i})=y_{j}-y_{j0} \tag{19}\]  
where \(x_{i}\) and \(y_{j}\) are the final positions after correction for the robot arm, and \(x_{i0}\) and \(y_{j0}\) are the positions after calculation of the pose estimation system from Section 2.3. According to the non-linear characteristics, the third-degree polynomials are chosen for fitting:  
\[\begin{array}{l}E_{x}\big{(}x_{i},y_{j}\big{)}=a_{1}x^{3}+b_{1}y^{3}+c_{1}x^ {2}y+d_{1}xy^{2}+e_{1}x^{2}+f_{1}y^{2}+g_{1}xy+h_{1}x+i_{1}y+k_{1}\\ E_{y}\big{(}x_{i},y_{j}\big{)}=a_{2}x^{3}+b_{2}y^{3}+c_{2}x^{2}y+d_{2}xy^{2}+e_{ 2}x^{2}+f_{2}y^{2}+g_{2}xy+h_{2}x+i_{2}y+k_{2}\end{array} \tag{20}\]  
The polynomial fitting is mainly the calculation of its coefficients, using the measured n-set of position coordinates and calculating according to Equation (19). Substituting \(\Delta x\) and \(\Delta y\) into Equation (20) leads to the following sets of Equation (21):  
\[A=\left[\begin{array}{ccccccccc}x_{1}^{3}&y_{1}^{3}&x_{1}^{2}y_{1}&x_{1}y_{1 }^{2  
After finding all the coefficients by using Equation (22), these coefficients are applied back into Equation (19) and the actual position after correction is obtained:  
\[X_{R}=X_{out}-\Delta xY_{R}=Y_{out}-\Delta y \tag{23}\]  
where \(X_{R}\) and \(Y_{R}\) are the actual position for the robot, \(X_{out}\) and \(Y_{out}\) are the output measured values, and \(\Delta x\) and \(\Delta y\) are the coefficients calculated above.
# A Reflection-Type Dual-Band Phase Shifter with an Independently Tunable Phase
###### Abstract  
This paper presents a design for a dual-band tunable phase shifter (PS) with independently controllable phase shifting between each operating frequency band. The proposed PS consists of a 3-dB hybrid coupler, in which the coupled and through ports terminate with the same two reflection loads. Each reflection load consists of a series of quarter-wavelength (\(\lambda\)/4) transmission lines, \(\lambda\)/4 shunt open stubs, and compensation elements at each operating frequency arm. In this design, a wide phase shifting range (PSR) is achievable at each operating frequency band (\(f_{\mathrm{L}}\): lower frequency; \(f_{\mathrm{H}}\): higher frequency) by compensating for the susceptance occurring at the co-operating frequency band caused by the \(\lambda\)/4 shunt open stub. The load of \(f_{\mathrm{L}}\) does not affect the load of \(f_{\mathrm{H}}\) and vice versa. The dual-band tunable PS was fabricated at \(f_{\mathrm{L}}\) = 1.88 GHz and \(f_{\mathrm{H}}\) = 2.44 GHz, and testing revealed that achieved a PSR of 114.1\({}^{\circ}\) with an in-band phase deviation (PD) of \(\pm\) 8.43\({}^{\circ}\) at \(f_{\mathrm{L}}\) and a PSR of 114.0\({}^{\circ}\)\(\pm\) 5.409\({}^{\circ}\) at \(f_{\mathrm{H}}\) over a 100 MHz bandwidth. In addition, the maximum insertion losses were smaller than 1.86 dB and 1.89 dB, while return losses were higher than 17.2 dB and 16.7 dB within each respective operating band.  
Keywords:dual band; phase deviation (PD); phase shifting range (PSR); reflection type; transmission line; tunable phase shifter (PS) +
Footnote †: journal: Applied
## 3 Results and Discussion
### Key Wavebands Selection  
The reflectance in the VNIR provided information about color of chicken meat. In our data, the bands at 450 nm (deoxymyoglobin), 560 nm (oxmyoglobin), and 700 nm (myoglobin, hemoglobin) corresponded to the substances that are responsible for the color of the meat. In addition, C-H, O-H, and N-H stretching observed in the NIR spectra were characteristically attributed to fresh chicken meat's chemical components such as water, protein, and fat. In the VNIR and SWIR range, the bands at 850 nm, 1080 nm, and 1414 nm were likely related to water present in the meat samples [45]. The 1200 nm, 1280 nm, 1414 nm, 1650 nm, and 1721 nm bands possibly corresponded to the overtones of the C-H stretching modes from the lipid molecules within the meat samples. Furthermore, the band at 2180 nm was likely related to the protein of the meat (N-H second overtone). Therefore, we were able to handle the color and pigments, physical structure, and chemical composition of chicken breast because we obtained both VNIR and SWIR image data [46]. These wavebands were selected as key wavelengths to distinguish between meat and FMs (including background). Furthermore, regression coefficients (RC) for the established PLSR models at the full wavelength of the VNIR and SWIR range were used to identify key wavelengths related to muscle and fat of chicken fillets (Figure 6). The final selected wavebands are 415, 450, 480, 560, 600, 650, 700, 715, 750, 760, 850, and 980 nm in the VNIR range and 1000, 1080, 1101, 1200, 1280, 1414, 1650, 1721, 2180, 2300, and 2383 nm in the SWIR range.
## 4 Experiments
### Semantic Segmentation
#### 4.2.5 Data Augmentation in Feature-Forecasting Training  
We investigate the influence of data augmentation on the forecasting performance. Our data augmentation includes random-use horizontal flipping and sliding of time frames. The latter is possible since we do not need ground-truth labels for our basic forecasting training. Thus, we can randomly pick our start frame and every following frame accordingly. Table 7 shows consistent improvement of mIoU across all models and for both short-term and mid-term forecasting. The forecasting model obviously benefits from a slight increase in data diversity. With RN-18, we achieve 0.7 pp improvement in accuracy for short-term and 0.6 pp for mid-term forecasting. Our best model, based on DenseNet-121, benefits 0.6 mIoU points in short-term and 1.1 mIoU points in mid-term forecasting due to fine-tuning.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Short-Term**} & \multicolumn{2}{c}{**Mid-Term**} \\ \cline{2-5}
**Model** & **All** & **MO** & **All** & **MO** \\ \hline ML-F2 MF-RN-18 w/o ft. & 68.3 & 67.4 & 55.7 & 51.4 \\ ML-F2 MF-RN-18 w/ ft. & 68.3 & 67.4 & 55.9 & 51.8 \\ \hline ML-F2 MF-DN-121 w/o ft. & 69.6 & 68.7 & 57.9 & 55.1 \\ ML-F2 MF-DN-121 w/ ft. & 70.2 & 69.0 & 58.5 & 55.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Validation of upsampling path fine-tuning for semantic segmentation forecasting.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Short-Term**} & \multicolumn{2}{c}{**Mid-Term**} \\ \cline{2-5}
**Model** & **All** & **MO** & **All** & **MO** \\ \hline Oracle-RN-18 w/o skips & 72.6 & 71.9 & 72.6 & 71.9 \\ Oracle-RN-18 w/ skips & 74.9 & 73.9 & 74.9 & 73.9 \\ ML-F2 MF-RN-18 w/o skips & 67.7 & 66.5 & 55.4 & 51.2 \\ ML-F2 MF-RN-18 w/ skips & 68.3 & 67.4 & 55.9 & 51.8 \\ \hline Oracle-DN-121 w/o skips & 75.5 & 76.4 & 75.5 & 76.4 \\ Oracle-DN-121 w/ skips & 78.3 & 78.8 & 78.3 & 78.8 \\ ML-F2 MF-DN-121 w/o skips & 69.8 & 68.7 & 58.3 & 55.7 \\ ML-F2 MF-DN-121 w/ skips & 70.2 & 69.0 & 58.5 & 55.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation (mIoU) of our ML-F2 MF forecasting model with single-frame segmentation models based on ResNet-18 (top section) and DenseNet-121 (bottom section) backbones. We evaluate models with and without skip-connection forecasting.
###### Abstract  
In this study, we observed magnetohydrodynamic (MHD) activity of the spherical tokamak Globus-M2 which led to a deterioration in plasma confinement, such as a transition from the H-mode to L-mode, or even disruption. This instability was found to be tearing modes, which are a result of the formation of magnetic islands in plasma and which impose limits on plasma pressure. The width and velocity of the islands are key parameters that determine their impact on plasma confinement. The Doppler backscattering (DBS) method has been shown to be effective for the study of MHD activity. This method was employed on the spherical tokamak Globus-M2, together with an array of magnetic probes, to study the tearing modes. The available DBS systems allowed for simultaneous measurements from half of the minor radius to the separatrix. The results are presented of our investigation of tearing modes that lead to a transition from the H-mode or a disruption. Comparative analysis was conducted of the observed tearing modes and the influence of different plasma parameters on their development.  
Doppler backscattering; plasma diagnostics; tokamak; tearing mode +
Footnote †: journal: Applied sciences
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What Are the Main Deep Learning Techniques Used in 5G Scenarios?
#### 3.3.2 Recurrent Neural Networks
In [36], the developed LSTM model was applied to learn the users mobility pattern in order to predict their movement trends in the future based on historical trajectories. In [35], the authors used LSTM to predict position of users based on historical beamformed fingerprint data (considering the presence o buildings in a scenario generated through simulations). The work presented in [26] proposed an LSTM model to represent the coding/decoding schema considering a hybrid approach to support polar codes. Unfortunately, the authors did not describe the data used to train their model. In [27; 43], gated recurrent unit (GRU) layers are considered to deal with sequential data. In [43], real ISP data is used to train the model. The authors used a testbed to create the dataset composed of GPON (ZTE C320) to demonstrate the fronthaul, while midhaul and backhaul are enabled by the MPLS feature of SDN switches. Details about the dataset used in [27] are not provided.
# Modeling Data Sovereignty in Public Cloud--A Comparison of Existing Solutions
## 5 Discussion  
Modeling data sovereignty architecture in public clouds is a complex task that involves analyzing existing models, taking into account available technologies, and identifying gaps in the abstract models and existing technologies. In this research, the technologies have been categorized into functional layers, which facilitates comparison and supports architects when designing new systems that enforce data sovereignty requirements. During the research, it was discovered that quite mature models already exist, many of which  
Figure 6: Response times for Dataspace Connector POST requests.  
Figure 7: Comparison of response times for Dataspace Connector.  
have been implemented as open-source software as well as commercial solutions. Data sovereignty solutions are often based on cryptographic methods to provide confidentiality, data exchange protocols to define data usage policies, and secure platforms to host the required infrastructure. The three reference architectures described in this research, designed by three organizations, have slightly different goals. However, they reuse common components, implemented as open-source software, and these organizations cooperate to ensure interoperability between different data spaces.  
The model developed in this research builds upon the framework presented in [1]. It is more generic, as it does not rely on any specific technology, and offers greater granularity. Unlike the previous model, which compares complete solutions, this research focuses on the individual software components. The experiments conducted in this research aimed to prove that accessing the IDS data space is cloud agnostic, i.e., running gateway software (Dataspace Connector v. 8.0.2) is independent of cloud hosting infrastructure (e.g., Google Cloud Platform or Microsoft Azure), and the performance of the solution is acceptable across all tested deployments. Observations indicate that POST HTTP requests, in general, were faster than GET requests, likely due to the internal implementation specifics of the Dataspace Connector. Similar response times were measured for all virtual machines (VMs) when tests were run locally, averaging around 100 ms. When using a public IP address for local tests, the response times were slightly higher, by approximately 10 ms. Among the cloud platforms tested, Azure had the lowest response times for GET requests, while the Google Cloud Platform (GCP) had the lowest response times for POST requests. The longest response time was observed for a GCP VM hosted in the United States, at approximately 700 ms.  
It is noteworthy that the experimental results are consistent with expectations, however, further work is needed to extend the experiments to include a third major public cloud provider: the Amazon Web Services platform.
# Nanoelectrode Ensembles Consisting of Carbon Nanotubes
## 3 Results and Discussion  
### CNT-NEE Surface Analysis  
Figure 4 shows scanning electron microscopy (SEM) images of the CNT-NEE cross section after cryomicrooming, where the brighter spots in Figure 4a correspond to the CNT open ends. The SEM images reveal that HNBR completely encapsulated the CNTs, leaving no void spaces between the polymer and the CNTs. The CNT-NEE sample in Figure 4b was treated for 60 s using an air plasma cleaner, which etched the polymer layer, thus cleaning the surface and exposing more CNTs. A visual comparison of the SEM images in Figure 4a,b shows that the plasma-cleaned CNT-NEE contained more CNTs, but greater lengths of the CNTs were exposed. To limit the CNT sidewalls exposed, the plasma cleaning time was decreased to 15 s for the CNT-NEE electrodes employed in this work. Although this fabrication method does not allow the precise positioning of CNTs and bundles, the spacing among the nanoelectrodes should be statistically similar because it is related to the CNT density in the drawn ribbon. As shown by the high-magnification SEM image in Figure 2b, the ribbon-like CNTs were extremely porous. Although the CNTs form small bundles of multiple CNTs, no bundle should be larger than 50 nm in one direction, as determined by the previously nature of the drawable CNTs. This thickness of the individual film was reported by our team elsewhere [49]. The observed porosity of the CNT film is important for the fabrication of CNT-NEEs, because it provides separation between random nanoscale bundles and individual tubes.  
Using high-magnification SEM images and ImageJ software, the approximate percentage areas occupied by CNTs and HNBR within the CNT-NEE were estimated (Supplementary Materials Figure S1; for details, please see the supporting information). The CNT-NEE  
Figure 4: SEM images of the CNT-NEE: (**a**) untreated thin film after microtoming and (**b**) film after plasma treatment for 60 s at 1000\(\times\) magnification.  
was found to contain approximately 22.16% CNTs. Although this approach may only provide an estimate of the maximum area, it can act as a guide for future comparisons with electroactive areas determined using electrochemical methods. The obtained areal coverage may also be affected by the employed method, as SEM images are subject to distortion due to the brightness of CNTs and lack resolution for accurate assessments.