esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Database of Internal Beam-Column Joints  
A database of all accessible investigations related to interior beam-column joints was studied. The research studies related to FRP-strengthened interior deficient RC beam-column joints (BCJs) were studied, containing more than 32 tests worldwide. Some of these tests were unsuitable to be utilized in the current study due to various shortcomings such as concrete replacement with high strength concrete and use of CFRP plate by replacing concrete within the joint section [36], joints were not strengthened with FRP (JII by [37; 38] and NA2, NA3, NB4, and NB5 by [12]), injecting an epoxy glue into the minor fissures, and repairing cracks before applying FRP (for NCIR, NC2R by [12]). Hence, only 17 tests were provided with details required for deriving an equation, excluding those inappropriate tests for our research, deemed inadequate or questionable. A brief view of this built database is given in Table 1. All test specimens are one-third or larger size subassemblies of RC frames. All specimens possessed statical determinacy and were cyclically loaded. In most of the investigations, the cyclic load was applied at the ends of the beams, whereas the translation of the column ends was restricted. However, for limited studies, the load was applied at the column ends, and the beam ends were restrained from translation.  
The database includes all applicable information, such as the mechanical properties of the materials used, subassembly characteristics, the average of the maximal tip load in the push and pull directions that is used, member dimensions, FRP retrofitting properties, and experimentally determined parameters. Concrete with cylinder compressive strength (\(f_{c}\)) varying from 25 MPa to 43 MPa was utilized. All reported specimens failed by joint strength crossed over by or combined with FRP debonding FD or FRP rupture FR. Other failure mechanisms that were reported include beam failure BH, column failure or hinge CF/H, and bond-slip BS. For the entire 17 samples, fiber rupture or fracture is noticed except for three samples.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c} \hline \multicolumn{13}{c}{**Sobassembly Characteristics**} & \multicolumn{4}{c}{**Member**} & \multicolumn{4}{c}{**FRP Strengthening Properties**} & \multicolumn{4}{c}{**Parameters Determined**} \\ \multicolumn{13}{c}{**Experimentally**} \\ \hline
**Test Code*** & \begin{tabular}{c} Mode of \\ Failure \\ \end{tabular} & \begin{tabular}{c} Mode of \\ Failure \\ \end{tabular} & \begin{tabular}{c} (\(\frac{\delta}{\delta p}\)) \\ \end{tabular} & \(N\)**0.8N** & \
\begin{tabular}{c} **(\(\frac{\delta}{\delta p}\)) \\ \end{tabular} &
\begin{tabular}{c} \(\frac{\delta}{\delta p}\
# UAV Support for Mission Critical Services
## 3 UAV Characteristics, Classifications and Commercial Deployments Status
### Classification of UAVs  
The UAVs have been divided into several categories depending on their construction type, size, speed, altitude, range, flight autonomy level and load-carrying capabilities. An extensive overview of different UAV classification approaches is presented in [23; 24]; however, there is no single international classification of UAVs. A classification in terms of size, altitude, range and endurance proposed by the US Department of Defense [25] presented in Table 2) is widely used.  
The most popular are multi-rotor UAVs (quadcopters, hexacopters or multicopters in general) due to their small size, low price, low inertia and easiness of control. They belong to the Vertical Take-Off and Landing (VTOL) category. Fixed-Wing UAVs are primarily used for transportation and surveillance on longer distances (>10 km). Such drones have weaker manoeuvrability than multi-rotor UAVs; however, they are more energy efficient due to the gliding capability and the use of combustion or jet engines. The differences between the two categories are shown in Table 3.  
The UAVs can typically fly with a speed of 60 km/h. The 3 GPP specifications assume communication support for UAVs that can fly at a speed up to 120 km/h at a height in the range of 300 m above the ground level [27].  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**UAV Type** & **Weight** & **Altitude** & **Radius** & **Endurance** \\  & **[kg]** & **[feet ASL]** & **[km]** & **[h]** \\ \hline Micro & 0.25–2 & \textless{}200 & \textless{}5 & \textless{}1 \\ Mini & 2–20 & \textless{}3000 & \textless{}25 & 1–2 \\ Small & 20–150 & \textless{}5000 & \textless{}50 & 1–5 \\ Tactical & 150–600 & \textless{}10,000 & 100–300 & 4–15 \\ MALE & \textgreater{}600 & \textless{}45,000 & \textgreater{}500 & \textgreater{}24 \\ HALE & \textgreater{}600 & \textless{}60,000 & global & \textgreater{}24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of UAVs according to the US Department of Defense [25].  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Multi-Rotor** & **Fixed Wing** \\ \hline \multirow{6}{*}{**VICU**} & greater maneuverability & longer flight autonomy \\  & lower price & larger areas covered in less time \\  & more compact and portable & better control of flight parameters \\  & easy to use & higher control of image quality \\  & higher payload capacity & better aerodynamic performance \\  & ability to hover & minor influence of the environment \\  & small landing/takeoff zone & higher flight safety \\ \hline \multirow{6}{*}{**VICU**} & \multirow{3}{*}{shorter range  less stable in the wind  challenging to fly  larger takeoff (landing site needed)} \\  & & less compact \\ \cline{1-1}  & shorter range  less stable in the wind & less portable \\ \cline{1-1}  & & less stable in the wind  challenging to fly  larger takeoff (landing site needed) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Differences between fixed wing and multi-rotor UAVs, after [26].
## 2 Materials and Methods
### Identification Methods
#### 2.3.2 Deep Learning Methods  
Deep learning has emerged as a powerful tool in the field of microseismic event waveform image recognition [17]. Figure 5 illustrates the workflow for image recognition tasks using both traditional machine learning methods and deep learning techniques. In particular, CNNs are employed as the deep learning model, where multiple convolutional layers and pooling layers are stacked to extract local and global features from waveform data. These features capture important information such as the shape, frequency, and temporal characteristics of the waveforms. Subsequently, the extracted features are fed into fully connected layers for the purpose of microseismic event recognition and classification.  
Traditional machine learning methods have the following characteristics: (1) they require manual feature engineering, which involves selecting and designing relevant features for the problem at hand; (2) they have lower data requirements and are prone to overfitting when dealing with small datasets; (3) they offer faster computation speed and relatively simpler training processes; (4) in cases in which feature expression capability is limited, traditional machine learning methods may struggle to capture complex nonlinear relationships.  
In contrast, deep learning methods exhibit distinct advantages in microseismic event image recognition. Firstly, they can automatically learn complex patterns and features from waveform data without the need for manual feature engineering. Secondly, deep learning models can hierarchically abstract features by extracting low-level local characteristics and high-level global features, enabling a more accurate understanding and representation of semantic information in microseismic event waveforms. Thirdly, deep learning benefits from large-scale dataset training, providing enhanced accuracy and generalization capabilities when dealing with noise and variability in microseismic event recognition tasks. However, it should be noted that deep learning methods may require more computational resources and time due to the training involved with multiple layers of neural networks.  
Deep learning technology has shown promising potential for microseismic event image detection. It is not only capable of handling complex and diverse waveform data but also has the ability to learn features automatically, which brings great potential to the field of microseismic monitoring. This presents opportunities for achieving more precise  
Figure 5: Image detection and classification using various machine learning models. (**a**) Traditional machine learning method (**b**) Deep learning.  
recognition and categorization of distinct types of microseismic events, contributing to improving security in subterranean engineering and mining operations.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 4 Deployment Cycle  
In order to setup the RSS fingerprinting-based ILS, the deployment cycle depicted in Figure 2 has been considered. Basically, it can be divided into two stages, which are common for any kind of fingerprinting-based ILS, the offline and the online stages. The offline stage is dedicated to setup the system, _i.e._, plan the antennas' initial position, generate the radio map (site survey process [1]) and train the ANN. Otherwise, in the online stage, the system is operating in real time, estimating and providing the tag localization to the end user.  
Regarding the offline stage, this is divided into four phases, namely: Pre-project, planning, setup and calibration phases. Firstly, the pre-project phase fulfils an analysis of the indoor scenario in detail, in order to define the rooms/areas of interest where the tag will be localized (imposed by the service conditions) and find the restrictions imposed by the infrastructure itself, such as the location of its power network sockets to feed the system or the potential electromagnetic interference sources, which could interfere with the ILS. Secondly, the planning phase is devoted to initially positioning the SR antennas according to the service conditions and restrictions. Thirdly, the site survey is the process by which the radio map is generated. In this step, a number of fingerprints are generated in order to calibrate the system in the fourth phase, where the ANN is trained and tested with the radio map, so that it can learn to map the RSS vectors with the location of the tag. The latter phases are neatly explained in the next subsections.
## 3 Key Technologies of UAV-Assisted SAGINs
### Security Control  
The heterogeneity and dynamic nature of SAGINs make the networks vulnerable to various attacks, mainly involving three levels: data security, network security, and physical-layer security. In data security, data protection should be strengthened to ensure the confidentiality of data during communication transmission, and encryption technology should be used to prevent data eavesdropping, tampering, and forgery. In terms of network security, the main security threats include denial of service (DoS) attacks, man-in-the-middle attacks, malware propagation, etc. It is necessary to strengthen the monitoring and defense of malicious traffic and adjust the intrusion detection algorithm to adapt to the limited computing power in SAGINs. In terms of the physical layer, drones and satellite channels are susceptible to frequency band interference, which affects the communication quality or navigation ability of drones. The anti-interference ability and reliability of communication links should be improved through technologies such as frequency hopping, beam-forming, and waveform encryption. In this section, we review in detail the research on the safety control of UAV-assisted SAGINs and present the results in Table 4.
# Hybrid Wind-Redox Flow Battery System for Decarbonizing Off-Grid Mining Operations
## 3 Methodology
### Wind Energy Production in Mining Sites: Forecasting, Optimization, and Utilization  
Given that wind production is highly dependent on wind speed, this study used wind speed data from several years (2017-2018-2019-2020-2021). The year 2020 was chosen as it was the most representative compared with the other years. An average speed of approximately 8.5 m/s was therefore established over the year. This is homogeneous. Figure 8 shows the occurrence of wind speeds at quarter-hourly intervals over the year 2020.  
Figure 6: Wind speed in 2020 in Raglan.  
Figure 7: The power curve of the E-115 EP3 E4 wind turbine.  
The distribution of Figure 8 is made possible using Equation (A5) in Appendix A.  
From the wind data (wind potential of the site) of the Ragland mining site, the power available for each of the wind turbines is deduced using the information provided by the manufacturer. For example, the E-115 EP3 E4 wind turbine (Enercon, Aurich, Germany) produces 3472 MW, while the E-82 E4 wind turbine (Enercon, Aurich, Germany) produces 1880 MW when the wind speed reaches 11 m/s. As wind speeds are dependent on the seasons and the weather, energy production from wind turbines is also dependent on them. Except for violent winds (storms, etc.) with speeds of up to 26.5 m/s, wind production is higher when the wind is stronger. Thus, the wind distribution at the Ragland site follows a Weibull distribution, and its shape is characteristic of it (see Figure 8).  
Considering that wind energy production is intermittent, the project plans to couple the wind farm with a redox flow battery storage system.  
To study the periods without wind at Ragland, an assumption is made that considers a wind speed of less than 3 m/s to be a period without wind. This assumption is based on the fact that the E-115 wind turbine does not produce even 80 kW below 3 m/s, which is required for de-icing. In 2020, there was 643 h without wind over the whole year. The longest period without wind was 28 consecutive hours. During this 643 h, almost 100% of electricity production would have to rely on diesel generators (see Figure 9).  
The periods without wind, representing more than 61% of cases, are shorter than eight hours. This indicator can be used to determine the battery size and its storage life.
## 5 Experimental Results
### Hardware Results  
When evaluating the performance on FPGA, we choose the model with the highest mAP in each training process, which is called the best model. First, we deploy the original best model trained under darknet to FPGA for test, then deploy it after additional sparse training by Method A and Method B to FPGA for testing. The results are shown in Tables 2 and 3. Among them, Table 2 shows the AP of each type of dishes, and Table 3 shows the mAP of the model and some other indicators, including the total number of parameters of the model, the size of the model deployed to FPGA, and the total time required to test all 68 images. Then, for both the process of sparse training and fine-tuning, we choose and deploy the best models respectively to FPGA again for test. For sparse training Method A, the results are shown in Tables 4 and 5. For sparse training Method B, the results are shown in Tables 6 and 7. When pruning, we use three methods: Prune method, Shortcut method, and Slim method, respectively. In addition, we set the pruning ratio to 30%, 50%, and 80%, respectively, and record the results. Compared with Table 3, two results are added in Tables 5 and 7, which are the number of channels actually pruned and the actual pruning ratio during the pruning process. Due to unknown reasons, after using the sparse training Method B and the Shortcut pruning method, an error occurred during the compilation \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **P** & **R** & **F1** & **mAP** \\ \hline all & 0.557 & 0.987 & 0.686 & 0.964 \\ Fish-dish & 0.5 & 1 & 0.667 & 0.974 \\ Towel-dish & 0.755 & 1 & 0.86 & 0.992 \\ Rice-bowl & 0.855 & 1 & 0.922 & 0.992 \\ Chopsticks-two & 0.411 & 0.988 & 0.581 & 0.958 \\ Towel & 0.598 & 1 & 0.748 & 0.974 \\ Cup & 0.91 & 0.987 & 0.947 & 0.978 \\ Spoon & 0.263 & 0.952 & 0.413 & 0.901 \\ Soup-bowl & 0.803 & 0.984 & 0.884 & 0.975 \\ Water-cup & 0.375 & 1 & 0.545 & 0.975 \\ Tea-cup & 0.918 & 1 & 0.957 & 0.994 \\ Water-paper & 0.358 & 1 & 0.527 & 0.951 \\ Square-bowl & 0.553 & 0.979 & 0.707 & 0.944 \\ Chopsticks-one & 0.315 & 0.906 & 0.468 & 0.859 \\ Paper & 0.389 & 1 & 0.56 & 0.975 \\ Chopsticks-cover & 0.679 & 1 & 0.809 & 0.988 \\ Wine-cup & 0.234 & 1 & 0.379 & 0.995 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection results of base model. process of converting the.weight model to the.xmodel model, resulting in a compilation failure, so that the AP, mAP, and test time of the model can not be obtained.
# An Overview of Intelligent Transportation Systems in Europe
## 5 Key Features of ITSs
### Benefits of ITSs
#### 5.1.3 Economic Efficiency  
Due to lost time and inefficient logistics, traffic congestion costs the European Union economy more than 1% of Gross Domestic Product (GDP) annually. By improving traffic management, reducing travel times and improving operational efficiency, ITSs improve economic competitiveness [52]. A report by the European Commission shows that ITSs can reduce travel times by 66% [53].
# Design of a SIMO Deep Learning-Based Chaos Shift Keying (Dlcsk) Communication System
## 1 Introduction
### Contributions  
The objective of this work is to develop a DL-based receiver that benefits from the inherent security of chaotic signals and the merits of NNs. This design will robust the capabilities of the existing chaos-based schemes, such as reliability and energy efficiency. Innovative aspects of this paper are briefly defined below:  
* We train a LSTM-based classifier that enables online classification of the received chaotic signals. Implementing this method can mitigate the chaotic synchronization problem in the existing CSK receivers;
* The DLCSK modulation/demodulation scheme does not need any reference transmission for basis functions recovery, unlike reference-based non-coherent modulations. According to the above advantages, the BER performance of a single antenna DLCSK is close to the performance of the antipodal CSK under a Rayleigh fading channel that shows an outstanding BER performance among all chaotic modulations [10].
* We have proposed a Multi-Antenna architecture of the DLCSK system. Multiple LSTMs are used at the receiver end to obtain a diversity gain. We numerically simulate the SIMO DLCSK structure and state the advantages of fusing the hard outputs of the LSTMs to come to a decision. The rest of this paper is organized in following sections:  
* In Section 2, a statistical study on the existing CSK systems and correlation receivers is presented;
* In Section 3, the structure of the proposed SIMO DLCSK system and the basics of the LSTM-based classifier are described;
* In Section 4, simulation results and discussions are explained;
* In Section 5, the conclusions are explained.
# K2S Challenge: From Undersampled K-Space to Automatic Segmentation
## 2 Materials and Methods
### Image Space Post-Processing  
Post-processed k-space was 3D inverse Fourier transformed to image space for each of the 18 coils and coil-combined to yield a single-coil image. The most basic means of coil combination is root sum-of-squares, but GE provides another method based on Array coil Spatial Sensitivity Encoding (ASSET), which leverages sensitivity maps in a PI-inspired technique to do coil combination [47]. Magnitude images were then calculated, after which GE's Phased array Uniformity Enhancement (PURE) was used to perform surface coil  
Figure 3: Intermediate outputs within the post-processing pipeline going from raw k-space to DICOM images. Each pane of the image reflects the output of the image after the step described by the pane title.  
intensity correction [48; 49]. This was followed by GRADWARP, which warps images to correct for inhomogeneities in gradient coils [50]. A final step in post-processing was correcting image orientation and scaling pixel values, yielding DICOM images used by clinicians for diagnostic purposes.  
In the context of segmenting undersampled images, one complication emerges: in GRADWARP, the MR image is warped such that it no longer corresponds to k-space. As such, the post-processing pipeline intermediate prior to GRADWARP must be segmented, or the GRADWARP function must be integrated into model training itself while segmenting DICOM images. Due to the difficulties of implementing the latter (backpropagating through GRADWARP would not be trivial), our solution was the former.
# A Novel Multi-User Codebook Design for 5G in 3D-MIMO Heterogeneous Networks
## 7 Conclusions  
This paper constructs a multi-user 3D-MIMO heterogeneous network system model. Through finite feedback quantization error analysis, according to the quantization error and statistical characteristics of the random distribution of the system, the influence of feedback overhead and base station density on system performance is analyzed using stochastic geometry technology. Based on the traditional precoding scheme, an improved 3D precoding scheme based on horizontal emission angle and vertical down-tilt is proposed. Through simulation analysis, the influence of feedback overhead on system throughput was compared, and the effects of traditional DFT precoding methods in 2D and 3D environments and improved 3D precoding schemes on system BER and throughput were compared. The simulation results show that the feedback overhead will reduce the system throughput and improve the system BER, while the improved precoding scheme due to the joint consideration of the horizontal emission angle and vertical down-tilt, thus effectively improving the system throughput and reducing the system BER, optimized system performance. Future work in this research is to incorporate mmWave with 3D-MIMO in order to utilize it underutilized bandwidth which ranges from 30-300 GHz. Also, various other important parameters such as Energy Efficiency and Normalized Mean Square Error (NMSE) can be analyzed in different deployment scenarios.  
Figure 13: Spectral efficiency comparison against the number of antennas for the proposed 3D MIMO codebook scheme with other schemes.  
**Author Contributions:** [PERSON] provides extensive support in the overall research; [PERSON] conceived and designed the presented idea and developed the theory, performed the simulations, and wrote the paper; [PERSON] verified the analytical methods and encouraged to investigate various relevant aspects of the proposed research; [PERSON] provided critical feedback and helped shape the research, analysis, and manuscript. All authors discussed the results and contributed to the final manuscript.  
This work has been partially supported by the \"Ministerio de Economia y Competitividad\" in the \"Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento\" within the project under Grant BIA2017-87573-C2-2-P.