esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Enhancement Experimental Methodology
### Two-Stage Fine-Tuning Algorithm  
In this subsection, we describe how to use the two-stage fine-tuning algorithm to enhance the detection of sparse attack classes in the NSL-KDD dataset.  
The first stage of the two-stage fine-tuning algorithm was to determine which data enhancement models could obtain the best scaling model for sparse data class detection in the NSL-KDD dataset. The detailed flowchart of the first stage is shown in Figure 3. We used three data enhancement models, namely Gaussian noise, WGAN-GP, and SMOTE, to generate 5000 data for each of the sparse data attack classes: Probe, U2R, and R2L. Then, the augmented datasets were fed into different machine learning algorithms and neural network models for training. Finally, we compared the performance of these three data augmentation approaches for classification algorithms to identify sparse data. In this way, the best data augmentation model was found for the data detection of sparse attack classes.  
Figure 2: Two synthesized data points for SMOTE-enhanced data.  
The second stage of the two-stage fine-tuning algorithm is shown in Figure 4. In the first stage of Figure 3, the best data enhancement model and the rare attack class with the best effect were found. Then, the second stage was to find the optimized parameters for this data augmentation model and various classification algorithms that could enhance rare attack detection. First, we used the best enhancement model and the related experimental parameters obtained from the analysis of the first stage as the initial parameter assumptions for the second stage. Then, we proceeded to the second stage of fine-tuning the parameters.  
In the first step of Figure 4, all of the feature values of the NSL-KDD dataset were ranked by evaluating their information gain. The second step was to obtain the best number of feature values for each classification algorithm by filtering the feature values and then selecting the appropriate number of feature values to classify the sparse attack classes. In the third step, the best number of features found for each classification algorithm was used to analyze the optimal data-enhanced quantity of the best data enhancement model for each classification algorithm. The final step was to fine-tune the parameters of each classification algorithm. In this study, we focused on designing the number of dense layers of the MLP model and the number of convolution layers of the CNN model. We investigated the correlation between the accuracy rate of the sparse attack classification and the number of model layers. Through this analysis, we identified the best MLP and CNN model designs. Finally, by using the two-stage fine-tuning procedure, we obtained the best accuracy rate for rare attack classes and the best parameters for the best data-enhanced model and classification algorithms.  
Figure 3: First stage of two-stage fine tuning: analysis of data generating models for rare attack class.
## 2 Broadband System  
Generally, broadband solutions can be categorized as fixed-line broadband and wireless technologies. In a fixed-line broadband solution, a physical network with a direct \"wired\" connection between the subscriber and the service provider is employed. On the other hand, radio or microwave frequencies are used for the network connection in the wireless solution [16]. The concept of the FWA system in which wireless mobile network technologies are employed for broadband services delivery is presented in Section 3. Some broadband solutions are discussed in the subsequent subsections.
## 4 Results and Investigations  
### Heating Analysis Characteristics  
A numerical analysis was performed to determine the usability of the hot plate heating model which includes a controller, i.e., the proposed model, and the hot plate cooling model according to the presence of the heat pipe. There were 11 controllers in the hot plate heating model which includes controllers. Considering the loss caused by convective heat transfer to the hot plate's sides, the 2 heaters on the side walls were controlled by a single controller. Three middle heaters were controlled by 1 controller, and a total of 5 controllers were used. In the computational analysis, the UDF was applied to the 5 controllers, and an interpretation method was used. As for the measurement points, the temperature was observed from 9 points in the forward, backward, left, and right directions from the hot plate's center. The hot plate's material was aluminum, and the cartridge heaters were stainless steel.  
Figure 7 shows the abnormal temperature behavior in the upper part of the small hot plate as time passed. The abnormal temperature behavior is shown in 2 groups. There is a slight temperature difference between temperature measurement points P2, P5, and P8 in the center portion and points P1, P3, P4, P6, P7, and P9 on the edges, and this shows a trend of increasing as time passes. Except for P5, which is located in the perimeter portion, the measurement points were located 1 cm inside the end of the plate. As time passed, the heat from the heater spread evenly throughout the entire solid hot plate  
Figure 8 shows the temperature difference between the maximum temperatures and minimum temperatures measured from P1 to P9. There was a maximum temperature difference at around 50 s, and the temperature difference was less than 2 degrees after 200 s. From these results, it can be seen that during the entire process, the deviation between the maximum and minimum temperatures was not less than 2 degrees, but after 90 s it was less than 3 degrees. Even though aluminum, which has a high heat conduction coefficient, was used as the hot plate material, there is a need for additional measures to increase the temperature uniformity during the overall heating process.  
Figure 9 shows the temperature field on the top surface of the hot plate after 200 s when control is performed using the shape in Figure 1, which is the hot plate heating model that includes the proposed controller. It can be seen that the entire temperature field is maintained within 2 degrees.  
Figure 8: Time-dependent variation of the maximum difference among temperature at the 9 points given in Figure 7.  
Figure 7: Numerical result on the temperature evolution at the working surface of the small hot plate after heating with PID control.
## 3 Results
### Cervical Isometric Strength
#### 3.2.2 Relative Values  
When the cervical strength values were adjusted to body height, independent samples \(t\)-tests showed that males had statistically greater strength values at forward flexion (\(t_{\rm 58}=7.05\); \(p=0.000\); [PERSON] \(d\): 2; mean % difference: 30), extension (\(t_{\rm 58}=2.82\); \(p=0.007\); [PERSON] \(d\): 0.5; mean % difference: 9), right lateral flexion (\(t_{\rm 58}=3.03\); \(p=0.004\); [PERSON] \(d\): 0.67; mean % difference: 12.5), and left lateral flexion (\(t_{\rm 58}=2.98\); \(p=0.004\); [PERSON] \(d\): 0.5; mean % difference: 12.4) than females (Figure 4).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Movement** & **Males (\(n=30\))** & **Females (\(n=30\))** \\ \hline Forward flexion (\({}^{\circ}\)) & 71.42 \(\pm\) 10.34 & 69.81 \(\pm\) 13.45 \\ Extension (\({}^{\circ}\)) & 80.38 \(\pm\) 12.48 & 94.28 \(\pm\) 14.16 \({}^{\circ}\) \\ Right lateral flexion (\({}^{\circ}\)) & 47.76 \(\pm\) 8.05 & 53.39 \(\pm\) 10.71 \({}^{\circ}\) \\ Left lateral flexion (\({}^{\circ}\)) & 47.89 \(\pm\) 8.69 & 53.87 \(\pm\) 8.5\({}^{\circ}\) \\ Right lateral rotation (\({}^{\circ}\)) & 80.83 \(\pm\) 9.26 & 83.16 \(\pm\) 9.34 \\ Left lateral rotation (\({}^{\circ}\)) & 82.34 \(\pm\) 8.44 & 85.56 \(\pm\) 8.36 \\ \hline \hline \end{tabular} \({}^{\ast}\)\(p<0.05=\) statistically significant difference between males and females.  
\end{table}
Table 2: Cervical range of motion values (mean \(\pm\) standard deviation) in males and females.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Movement** & **Males (\(n=30\))** & **Females (\(n=30\))** \\ \hline Forward flexion (kg) & 17.92 \(\pm\) 4.25 \({}^{\ast}\) & 11.17 \(\pm\) 2.10 \\ Extension (kg) & 20.45 \(\pm\) 4.70 \({}^{\ast}\) & 16.27 \(\pm\) 3.17 \\ Right lateral flexion (kg) & 14.33 \(\pm\) 3.89 \({}^{\ast}\) & 10.93 \(\pm\) 2.42 \\ Left lateral flexion (kg) & 14.52 \(\pm\) 3.62 \({}^{\ast}\) & 11.13 \(\pm\) 2.85 \\ Flexion/Extension ratio (\%) & 89.17 \(\pm\) 17.51 \({}^{\ast}\) & 69.75 \(\pm\) 12.18 \\ \hline \hline \end{tabular} \({}^{\ast}\)\(p<0.01=\) statistically significant difference between males and females.  
\end{table}
Table 3: Cervical absolute strength values (mean \(\pm\) standard deviation) in males and females.  
On the other hand, when the values were adjusted to body mass, independent samples \(t\)-tests showed no statistically significant differences between males and females in cervical extension (\(t_{58}=0.66\); \(p=0.513\)), right lateral flexion (\(t_{58}=1.39\); \(p=0.170\)), and left lateral flexion (\(t_{58}=1.33\); \(p=0.188\)) (Figure 5). The only significant difference between males and females was found in forward flexion, where males showed greater relative cervical strength values than females (\(t_{58}=5.86\); \(p=0.000\); Cohen \(d\): 1.43; mean % difference: 21.7).
## 3 Results and Discussion  
To verify the proposed method, we collected data by TRIMBLE NETR9 and TRM57971.00 on the roof of the Wuhan University laboratory building from GPS time 11:35 to 14:20 10 April 2015. The sampling rate is 1 Hz, the cutoff elevation angle is set as 15\({}^{\circ}\), and the frequencies are B1 for BDS and L1 for GPS. The raw data were carefully checked and confirmed to be cycle slip and outlier free, to simulate cycle slips accurately. In this section, we first examined the ILAM method to detect simulated cycle slips. We then tested the rounding method for cycle slip repair with success probability and decimal test as the validation method.
# Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
## 3 Materials and Methods
### Taking Field Images and Generating Orthomosaics  
This task involves generating an image database sourced from the sugarcane plantation, which will later serve as the foundation for mapping the locations of field gaps. For this purpose, a Drone DJI Phantom 4 Standard equipped with its 20 MP RGB camera was employed (Figure 2). The experimental fields for this endeavor were situated in the municipality of Tambau, state of Sao Paulo, Brazil, at the following coordinates: latitude: \(-21.7023\), longitude: \(-47.2814\). The drones conducted flights over the crops, capturing images from an altitude of 60 m when the sugarcane plants were between three and five months old after sprouting. This altitude provided a GSD of 1.4 cm/pixel, which was sufficient to identify, select, and map the planting failures in a test image.  
This task also involved creating orthomosaics (Figure 2). A total of 545 images were captured from the experimental field and stored in a database for orthomosaic construction using the OpenDroneMap 1.9.14 software [24]. The images were taken using the Pix4D ([[https://www.pix4d.com/](https://www.pix4d.com/)]([https://www.pix4d.com/](https://www.pix4d.com/))) application, which allows for flight plan tracing and the adjustment of image overlap. In this instance, the overlap was set at 90% to facilitate the construction of the orthomosaic [25].
## 3 Materials and Methods
### Characterisation of the Membrane Surfaces  
#### 3.3.1. _Scanning_ Electron Microscopy  
The surface morphology of the membranes was characterized by scanning electron microscopy (SEM), using a Philips XL30 FEG SEM (Eindhoven, The Netherlands). The dry membrane was cut into small pieces and immersed in liquid nitrogen for around 1 min. The frozen fragments of the membrane were broken and taped onto a holder with carbon glue. The samples were sputtered with gold and vacuumed for 1 h before the measurement. The SEM measurements were taken under high vacuum conditions at 30 kV.
# L5 IN: Overview of an Indoor Navigation Pilot Project
###### Abstract  
While outdoor navigation systems are already represented everywhere, the enclosed space is much less developed. The project Level 5 Indoor Navigation (LSIN) presents a new approach with mobile phone standard 5G as the orientation signal and without additional infrastructure for navigation in indoor environments. The aim of this project is to use the new available 5G technology to show how navigation systems, which have thus far only been available in the outdoor segment, can now be integrated into existing smartphone systems for indoor navigation. This paper gives an overview of the project and presents the different work packages leading to a holistic approach towards the development of an indoor navigation application for pedestrians. By using a specific app with open interfaces, it is planned to make navigation possible in all buildings modeled according to certain standards. The challenge involved is that, unlike outdoor maps, there is no map basis for buildings. For this reason, different approaches to map generation were examined. In a building information model (BIM), all information will be collected and made available via a database for positioning and visualization. The focus is furthermore on positioning, achieved through smartphone sensors and 5G, so that users can orientate themselves in buildings without having to connect to singular systems. It shall be shown that positioning with a standard deviation of 2-3 m and a confidence interval of 68 % is possible. Another advantage of 5G, the ability to send real-time data in higher data packages, will be used for data transmission. The basic idea of 5G-based indoor navigation will be enabled with radio cells of the providers, which will be set up on the HafenCity University campus. The complex university building will be used as a prototype environment.  
Keywords: indoor navigation; 5G; BIM; positioning; indoor map generation +
Footnote †: journal: Remote Sensing
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 2 State-of-the-Art in Non-Uniform and Low Latency Resampling
### Recent Research in Low-Latency and Real-Time Applications  
Recent research on low-latency and real-time resampling includes image resampling of microwave scans of the earth surface from a satellite [9], and a Farrow-based resampling method, implemented on a field programmable gate array (FPGA), for processing data from a particle accelerator [10]. A detailed design of a Farrow filter is described in [1]. A further recent application [11] provided resampling and trigger event detection for signals up to a sampling rate of 1 GHz. Aside from digital oscilloscopes, precise triggering is required in medical tomography applications. Such high-speed processing is enabled by combining WSI and linear interpolation on a FPGA.  
Particle filters are a common method for state estimation of non-linear systems, which require resampling as one of their main stages. Parallel execution of resampling on a graphics processing unit (GPU) enables the use of particle filters in real-time environments [12]. A GPU was applied in [13], but without providing details on the applied algorithm.  
Further research in non-uniform resampling applies Lagrange theory and FIR filters. A complex optimization is presented in [2] with the general problem that the FIR filter coefficients must be recalculated if the sampling pattern changes. However, simplifications are possible for the case of periodic jitter [14], which is of practical importance for the cyclic readout of a bank of interleaved ADCs. A time shift in one ADC leads to jitter. A method to decompose the low pass anti-aliasing filter to a parallel structure is presented in [15]. The structure can easily be updated for changing sampling rates, as demonstrated in the resampling of audio signals.  
An extension of the Lagrange interpolation was applied by [PERSON] in [3]. Time differences between sampling points were weighted by a sine function. This extension was shown to provide perfect reconstruction of recurrent signals that contain repeated non-uniform sampled sequences of \(T_{F}\) frame length. An example application is measuring signals from sensors mounted on rotating machines [16]. The resampling of measurements in reference to the rotation angle of a machine is also handled in [17]. Data are transferred to a central wireless sensor note, which corrects deviating sample rates. The algorithm with a filter length of 61 was implemented on a 72 MHz microcontroller.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 3 System Architecture for Autonomous Vehicles
### RADAR: Radio Detection and Ranging  
RADARs, in AVs, are used to scan the surroundings to detect the presence and location of cars and objects. RADARs operate in the millimetre-wave (mm-Wave) spectrum and are typically used in military and civil applications such as airports or meteorological systems [51]. In modern vehicles, different frequency bands such as 24, 60, 77 and 79 GHz are employed and they can measure a range from 5 to 200 m [52]. The distance between the AV and the object is calculated by measuring the ToF between the emitted signal and the received echo. In AVs, the RADARs use an array of micro-antennas that generate a set of lobes to improve the range resolution as well as the detection of multiple targets [53]. As mm-Wave RADAR has higher penetrability and a wider bandwidth, and it can accurately measure the short-range targets in any direction utilising the variation in Doppler shift [51; 52; 53; 54]. Due to longer wavelength, mm-Wave radars have an anti-blocking and anti-pollution capability that allows them to cope in rain, snow, fog and low-light. Furthermore, mm-Wave radars have the ability to measure the relative velocity using the Doppler shift [15]. This ability of mm-Wave radars make them suitable for extensive AV application such as obstacle detection [55], pedestrian recognition [56] and vehicle recognition [57]. Some applications of RADARs in AVs are forward cross traffic alert (FCTA), lane change assistance (LCA), blind spot detection (BSD), rear cross traffic alert (RCTA), etc. The mm-Wave also has some disadvantages such as reduced field-of-view (FoV), less precision and results in getting more false alarm as a result of emitted signals which gets bounced from the surroundings [54].
The interconnections of Greece with the neighboring countries have a relatively small capacity, and the power supply of the system is mainly based on the production of lignite and natural gas power plants, with lignite power plants being gradually phased out and natural gas power gaining a larger production share. Moreover, the potential of energy production from renewable sources is considered to be high due to the geographical location and the weather conditions that prevail across the country. The instability and the deficiency that could be caused to the Greek power system with the higher integration of renewable sources, inhibit their integration, as the system depends mainly on inland production, and security of supply cannot be compromised. For that reason, hydro-pumped storage or other energy storage systems should be integrated into the power system, in order to facilitate the share increase of power produced from renewable sources and in parallel diversify the energy mix of the country, introducing storage capacity. A literature review of barriers related to large-scale market integration of Variable Renewable Energy Sources in European electricity markets design has been included in the discussion of the storage facilities' importance underlying the barriers of their high capital cost and the unsound business case due to the lack of scarcity price [16]. However, the role of storage is analyzed in large-scale wind and PV integration in Germany showing that integration level up to 50% could be achieved if flexible back-up power plants are used [1]. Different storage technologies (batteries, pumped hydro storage, adiabatic compressed air energy storage, thermal energy storage, and power-to-gas technology) and their role have been investigated in the transition path towards a 100% renewable energy power sector by 2050 in Europe [17]. The synergy between storage and balancing is analyzed in a fully or highly renewable pan-European power system, based on 8 years solar--wind demand data, with focus on the forms of hydro-pumped storage and hydrogen [18]. The sitting of hydro-pumped storage is a critical parameter from the social, environmental and energy points of view. A review of the existing types of pumped-hydro storage plants, highlights the advantages and disadvantages of each configuration and proposes innovative arrangements in order to increase the possibility to find suitable locations for building large-scale reservoirs for long-term energy and water storage [19]. A global analysis identifies 616,000 sites for closed-loop off-river hydro-pumped storage, based on high resolution global digital elevation models [20]. Off-river pumped hydro energy storage together is also proposed as an effectual solution to support 100% renewable energy in East Asia [21]. Finally, underwater pumped storage has been proposed as an alternative emerging technology with significant potential [22].
## 5 Experimental Results
### Contributions  
Lastly, we briefly summarize the contributions:  
* Streamlined segmentation. Our method operates in a detect-and-segment manner, where the detected surface landmarks (rather than tooth centroids) offer practical advantages for clinicians, enabling quick corrections compared to the laborious adjustments of segmented 3D regions. This demonstrates significant potential to reduce human efforts in orthodontic treatment planning.
* Information-rich projections. Alongside the local tooth processing, we show that the rendered maps capture the 3D shape with minimal information loss.
* Highly competitive performance. We achieve an average weighted IoU score of \(0.97122\pm 0.038\) and a Hausdorff distance at the 95 th percentile of \(0.49012\pm 0.571\) mm. We provide a comparative analysis with other segmentation approaches.
* Release of Poseidon3D dataset. A new challenging dataset of real-world orthodontic cases (teeth crowding, damaged and missing teeth, and teeth with visible roots) is introduced: Poseidon's Teeth 3D (Poseidon3D).