esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Research  
The simulation performance evaluation revealed that the conventional \(T^{2}\) Hotelling performed effectively only under low dimensionality and a minimal percentage of outliers. On the other hand, Fast-MCD-based \(T^{2}\) Hotelling demonstrated improved performance,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Accuracy** & **FP Rate** \\ \hline Decision Tree [45] & 0.8556 & 0.1578 \\ Logistic Regression [46] & 0.8315 & 0.1848 \\ Naive Bayes [47] & 0.8207 & 0.1856 \\ ANN [46] & 0.8134 & 0.2113 \\ Proposed \(T^{2}\) MRCD & **0.9021** & **0.2980** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of MRCD-based \(T^{2}\) Chart with the other algorithms.  
Figure 9: ROC Curve Comparison of \(T^{2}\), \(T^{2}\)-FMCD, and \(T^{2}\)-MRCD.  
particularly in managing a high percentage of low-dimensional data; however, its effectiveness decreased as the number of variables increased. In contrast, MRCD-based \(T^{2}\) Hotelling consistently outperformed the above charts, exhibiting superior performance across all simulated conditions. The application of the UNSW-NB15 data revealed that the MRCD-based \(T^{2}\) Hotelling exhibited better performance in detecting intrusion, with a 0.902 Accuracy and 0.848 AUC value. This proposed chart successfully outperformed the conventional \(T^{2}\) Hotelling and the Fast-MCD-based \(T^{2}\) Hotelling, which had AUC values of 0.511 and 0.718, respectively, despite the longer execution time. In addition, compared to the other algorithms, the proposed chart exhibits higher accuracy while producing higher false alarms.  
This proposed chart can still be modified for further research by applying another non-parametric approach as the control limit. Applying an MRCD estimator for monitoring processes for variance shifts or simultaneous shifts in mean and variance can also be constructed. The latest robust estimator, Cellwise MCD [48], known for its efficiency, can be considered for implementation to overcome the MRCD's problem in terms of the long execution time.  
I.K.P.: writing original draft and data analysis. M.A.: Conceptual methodology, Supervising and validating the results. M.M.: Performed analysis and data visualization. M.H.L.: Validating the results. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge financial support from the Institut Teknologi Sepuluh November for this work under grant number 1174/PKS/ITS/2024.  
Not applicable.  
The data presented in this study are openly available in: [[https://research.unsw.edu.au/projects/unsw-nb15-dataset](https://research.unsw.edu.au/projects/unsw-nb15-dataset)]([https://research.unsw.edu.au/projects/unsw-nb15-dataset](https://research.unsw.edu.au/projects/unsw-nb15-dataset)) (accessed on 3 September 2024).  
The authors declare no conflicts of interest.
## 2 Background and the Related Work
### Software-Defined Networking (SDN)
#### 2.1.3 Application Layer  
Several end-user applications, including network monitors, time delays in traffic, traffic classifiers, and load balancing, are implemented in this layer. The network monitor keeps track of incoming traffic and counts the bytes, the traffic classifier uses ML to classify incoming traffic in real-time, and load balancing reroutes incoming network traffic to the chosen controller based on the nature of the traffic. The SDN's control and data plane separation allows for effective domain-wide traffic routing and management. Programming forwarding devices is the responsibility of the controllers in the control plane, whereas network programming and policy enforcement are the responsibilities of the application plane, which is the top layer [1].
# Removal of Metals and Dyes in Water Using Low-Cost Agro-Industrial Waste Materials
## 3 Results and Discussion
### Adsorption Parameters Effect
solution, which may compete with methylene blue for adsorption on the biochar. This ionic competition could limit the adsorption capacity of the dye, resulting in a lower observed removal efficiency. In addition, changes in the electrical charge of the functional groups on the biochar surface at pH 9 could affect the electrostatic attraction capacity between methylene blue and the adsorbent. The -COOH and -OH groups in the biochar could undergo modifications in their charge and electrical quantity, which alters the adsorption affinity of the dye compared to other pH values.  
Regarding Cr (VI) adsorption, in Figure 7 it is evidenced that the diminution of the pH caused an increase in the removal efficiency and adsorption capacity, achieving 79.35% y 26.45 mg/g, respectively. The results obtained in the present study are congruent with those reported in the literature, taking into account that, at low pH, the Cr (VI) is presented as Cr\({}_{2}\)O\({}_{7}\)\({}^{2-}\) y HCrO\({}_{4}\)\({}^{-}\) in water solution, involving a combination with the active site of protonation by electrostatic adsorption [46]. Working at acidic pH allows the surface of the carbon to be positively charged, facilitating the adsorption of forms of Cr (VI) negatively charged [47; 48; 49]. In addition, at low pH, there are large amounts of H\({}^{+}\) ions in the bulk of the solution, leading to a process of protonation of the carbon surface, forming more positively charged sites and increasing the electrostatic attraction between the adsorbent and Cr (VI) [14; 37; 50]. Furthermore, at pH > 2, a low adsorption performance is presented, caused by the electrostatic repulsion of the anionic Cr (VI) and the KOH-modified wheat carbon, which originally has negative charges given its pH\({}_{\text{PZC}}\) of 9.25 [33].
## 6 Experiment
### Evaluation Metrics
#### 6.4.1 Packet Delivery Ratio  
We will call this metric PDR, for short. This value is defined as in Equation (8) and is a value that is desired to be maximized, which would mean that a great amount of the messages that were created were successfully delivered to its destination.  
\[PDR=\frac{\#\,of\,\,\text{delivered\,\,msgs}}{\#\,of\,\,\text{created\,\, \,msgs}} \tag{8}\]  
Ideally, we would like this number to be 1, but in practice, this seems rather impossible, since there are other constrains in the network, such as buffer size and message TTL, resulting in dropping or destruction policies, which prevent some of the messages to get to its destination. Because the resources in the network are limited, that is precisely why they must be optimized. This parameter shows the fraction of created messages that got to its destination.
## 2 Results of Vocal-Emotion-Recognition Experiments
### Results of Vocal Emotion Recognition Experiments in Noisy and/or Reverberant Environments  
Figure 2 shows the results of the vocal-emotion-recognition experiment in the noisy environment conducted by [PERSON] et al. [17]. The horizontal axis shows the SNR, and the vertical axis shows the vocal-emotion-recognition rate. In Figures 2-4, the circles indicate the mean of the vocal-emotion-recognition rate, and the error bars indicate the standard error. Except in extremely high noisy environments, there were no significant effects of noise that could affect vocal emotion recognition under daily noisy conditions (SNR is greater than 0 dB).  
Figure 1: Schematic diagram of noise-vocoded method used for generating experiment stimuli (NBN: narrow band noise). BPFs were defined as ERB\({}_{\text{N}}\)-number from 3 to 35 Cam with bandwidths of 2 ERB\({}_{\text{N}}\). Number of channels was 16 [17].  
Figure 2: Vocal\(-\)emotion\(-\)recognition results in noisy environments [17].  
Figure 3 shows the results from the vocal-emotion-recognition experiment in the reverberant environment. The horizontal axis shows the \(T_{\text{R}}\) conditions, and the vertical axis shows the vocal-emotion-recognition rate. Except in extremely high noisy reverberant environments, there were no significant effects of reverberation that could affect vocal emotion recognition under daily reverberant conditions (\(T_{\text{R}}\) is less than 2.0 s).  
Figure 4 shows the results of the vocal-emotion-recognition experiments in the noisy reverberant environment. Except in extremely high noisy reverberant environments, there were no significant effects of noise and reverberation that could affect vocal emotion recognition under daily noise and reverberation conditions (SNR is greater than 10 dB and \(T_{\text{R}}\) is less than 1.0 s).  
Figure 4: Vocal\(-\)emotion\(-\)recognition results in noisy reverberant environments [17].  
Figure 3: Vocal\(-\)emotion\(-\)recognition results in reverberant environments [17].  
From above vocal-emotion-recognition experiments in noisy reverberant environments, it was found that except under extremely high noisy reverberant conditions, there were no significant effects of noise and reverberation in vocal emotion recognition under daily noise and reverberant conditions.
# Photovoltaics on Landmark Buildings with Distinctive Geometries
## 3 Presentation of the Samples
### Kaohsiung National Stadium  
The National Stadium (Figure 12) built from 2006 to 2009 in Kaohsiung, Taiwan (TW) was the largest commission of another Japanese Pritzker prize winner (2013), architect [PERSON] [48].  
The capacity of the stadium is 55,000 spectators and it is the largest sports facility in the country, as well as the largest solar powered stadium in the world, available to produce most of the electricity demand for its operation. The stadium was built for the 2009 World Games. It is a semi-open stadium creating a passage for the summer wind. This \"dragon\" shaped building is called a solar stadium [49] because of its 14,155 m\({}^{2}\) roof covered by 9720 semi-transparent PV modules [50]. The solar system power output is 1 MWp and it is a characteristic example of BIPV, allowing 30% of sunlight to pass into the stadium. The average generation of electricity is 3 MWh per day and 1.14 GWh annually. This unique solar plant is connected to the local grid and produces energy for its neighbourhood. It saves almost 660 tons of CO\({}_{2}\) emissions annually [51]. In the Kaohsiung National Stadium, the PV installation acts as a roof, a pleasant semi-transparent sunshade, and a fine texture of the giant roof, covering the \"dragon\" shaped geometry of the building.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 3 Types of LiDARs
### Hsrl--High-Spectral-Resolution LiDAR  
High-spectral-resolution LiDAR (HSRL) is a specialized atmospheric system designed to distinguish between aerosol (Mie) and molecular (Rayleigh) scattering based on their  
Figure 4: Schematic illustration of photon scattering in the atmosphere. As the laser pulse propagates through the air, photons interact with atmospheric particles, resulting in scattered and backscattered photons. The backscattered photons are captured by the LIDAR receiver and used to infer atmospheric properties.  
spectral signatures. By isolating these components, HSRL enables direct retrieval of aerosol optical depth (AOD)--a key parameter for quantifying aerosol extinction effects [88; 89]. This separation is achieved using a high-spectral-resolution filter, which allows measurements of aerosol extinction and backscatter independently of molecular scattering [90]. As such, the capability is particularly useful for studying the vertical distribution of aerosols and their optical properties, as well as for improving the accuracy of aerosol measurements in the presence of clouds [88].
# New Eldercare Robot with Path-Planning and Fall-Detection Capabilities
## 2 Mechanical Design and Kinematics  
### Design Specifications  
The intended robot is planned to be modular and easily manufactured. The robot was manufactured using 3D printing technology, since the appearance of eldercare robots is a key factor in eldercare effectiveness. Therefore, the robot has been designed to prioritize comfort with soft curves and rounded edges, aiming for a non-threatening and friendly appearance. Moreover, additional design features based on each elder's personality and preferences are possible using 3D printing technology.  
The robot embodies a modular mechanical architecture, thoughtfully divided into two distinct components: the platform and the pod. The platform represents an autonomous foundation, serving as the core mobility unit that navigates and interacts within the environment. On the other hand, the pod is a versatile enclosure that offers adaptable openings, allowing for a range of functionalities tailored to the unique needs of the elderly individual. This pod can be seamlessly customized to accommodate various tasks, from managing medication regimens to facilitating the delivery of refreshments or meals. As a result, the design of the pod becomes inherently flexible, sculpted by the specific requirements and preferences of the elder it serves. The robot design stages from CAD design to a fully assembled and operational robot are shown in Figure 1.  
The robot design and dimensions are shown in Figure 2. The robot dimensions are 850 \(\times\) 500 \(\times\) 500 mm. The load carrying capacity of the robot is 10 kg. The battery life is 160 min in active mode and 8 h in standby mode, respectively. A two-wheel differential drive mechanism is selected for robot motion. The robot wheel thrust torque is 4.054 N-m, its maximum speed is 1.5 m/s, and the robot rated power is 137 W.
## 2 Materials and Methods
### OTFT Testing and Characterization  
The OTFT individual layers thicknesses were measured using cross sectional Scanning Electron Microscopy (SEM) images of the deposited films. The morphology features and surface topology of the sensing films were investigated using Atomic Force Microscopy (AFM).  
The OTFT electrical characterization was performed using a probe station and a semiconductor parameter analyzer. All measurements were obtained in a dark and ambient environment to minimize photodegradation. The output characteristic was extracted by sweeping the drain-source voltage V\({}_{\text{DS}}\) from \(-\)2 V to 30 V and measuring the drain-source current and the gate leakage current I\({}_{\text{GS}}\) at a constant gate bias of 80 mV, 500 mV and 1 V. The transfer characteristic was obtained by sweeping the gate-source voltage V\({}_{\text{GS}}\) from \(-\)1 V to 5 V and measuring I\({}_{\text{DS}}\) at constant drain-source voltages of 50 mV. Output and transfer sweeps were both performed at a step size of 50 mV.  
The humidity sensor response was monitored by placing the sensor in a gas chamber equipped with nitrogen gas flow to remove ambient gases before and after exposure to moisture. The sensor was exposed to a high humidity above 80% RH to mimic condensation and surface wetness conditions, sufficient to test the peripheral sensor for wetness detection. The response of the chloride sensor was tested by depositing Cl\({}^{-}\) ion solutions with varying concentrations on the gate surface and monitoring the change in output current after the complete evaporation of water.
# Experimental Study on Milling CFRP with Staggered PCD Cutter
###### Abstract  
Carbon fiber reinforced plastics (CFRP) have good physical properties, such as high specific strength and high specific modulus. However, cutting delamination, tearing and burr, etc. often occur in the machining process of CFRP, which results in the uncontrollability of machining surface quality and serious tool wear. In this paper, milling of CFRP with a staggered cutter was carried out, the cutting-edge radius was investigated in order to characterize the tool wear; the effect of the cutting-edge wear radius on the milling force and surface quality was found, and the formation mechanisms of the 3D surface topography and surface defects were analyzed under the wear state. Experimental results showed that the wear of the staggered PCD cutter was mainly concentrated in the cutting-edge area. With the increase in milling length, the radius of cutting edge gradually became largeer under the action of abrasive wear mechanism, and the flank wear was not obvious. With the intension of tool wear, milling force gradually increased and the machining surface quality of the CFRP deteriorated distinctly, i.e., defects such as bare fiber fracture, groove and hole appeared, and burrs were gradually generated on the workpiece surface. Finally, through a comparative analysis of cutting performance, it was found that the staggered PCD cutter possessed better performance for wear resistance and burr suppression than the straight-teeth cutter. This finding can provide theoretical and technical support for improving the machining quality of carbon fiber composite materials.  
staggered PCD cutter; carbon fiber reinforced plastics; tool wear; surface topography; burr
# Quality Index of Supervised Data for Convolutional Neural Network-Based Localization
## 4 Experiments  
We evaluated our quality index for CNN-based localization in a set of indoor environment experiments.  
Figures 8 and 9 show the assumed scenario of the experiments. Figure 8 depicts the typical trajectories of AGV. We named these trajectories as scenario 1. In a certain factory, an AGV equipped with the SPAD LiDAR approached a pallet. The AGV selected and lifted the cargo and then carried the cargo to another location repeatedly. Figure 9 is another scenario of AGV. Initially, the AGV equipped with the SPAD LiDAR moves in a straight line (see (1) in Figure 9). At this time, the pallet was out of sight of the SPAD LiDAR. Then, the AGV rotated by 90 degrees in a clockwise direction (see (2) in Figure 9) and approached the pallet. The pallet came within sight during the rotation. The AGV finally approached the pallet. We named these trajectories as scenario 2.  
We designed the experimental evaluation to show the following.  
* Reveal important pixels for CNN-based localization.
* The index reflects the quality of supervised data for CNN-based localization.  
For this purpose, we collected and generated 11 types of supervised training datasets for each assumed scenario and named them datasets A-K. The first supervised data were the trajectories of an AGV captured by a motion capture system, as depicted in Figure 10a. We named this data as dataset A and used them to explore the important pixels in an ideal situation. Another ten supervised data were generated from the first data and were used to add noise to the original dataset A. We named these datasets B-K (see Table 2. These ten datasets differed in their noise ratios compared to the original data. Figure 10b shows examples of dataset F, which had mixed data of trajectories by motion capture and noise. We used this data to compare the important pixels of an ideal situation with that of a noisy situation. Dataset K consisted of only random noise, as depicted in Figure 10c. We used these datasets to compare differences in the important pixels. All data had multiple datasets for cross-validation with a leave-one-dataset-out technique.  
Figure 8: Example of assumed scenario 1 for AGV. An AGV equipped with SPAD LiDAR automatically approaches a pallet and transports the pallet to another location.  
Figure 9: Example of assumed scenario 2 for AGV. The AGV equipped with the SPAD LiDAR moves in a straight line. Then, the AGV rotates clockwise by 90 degrees. Finally, the AGV automatically approaches a pallet and transports the pallet to another location.  
All experiments were conducted using a robot operating system (ROS) [18] and TensorFlow [19]. ROS is an open-source meta-operating system that facilitates collaboration with many software packages, while TensorFlow is an open-source machine-learning framework. We created a driver of SPAD LiDAR for ROS and collected all the data. In addition, we implemented our CNN using TensorFlow. The CNN model named SPAD DCNN was presented in our previous paper [15]. This CNN model outputs localization results using the data of SPAD LiDAR as inputs.
# Performance of the 5 th Generation Indoor Wireless Technologies-Empirical Study
## 2 Materials and Methods
### Evaluation
#### 2.2.1 5G and Beyond Test Network  
Test were made at 5G and Beyond Test Network, operated by VTT Technical Research Centre of Finland, which offers tens of different types of standard radio access nodes including latest 5G NR as well as Wi-Fi and cellular IoT technologies complemented with non-terrestrial satellite connections. The test network consists of several private 5G sites with full-scale end-to-end (E2E) data communication system supporting 5G standalone (SA) and non-standalone (NSA) architectures. For dedicated measurements, it is possible to isolate part of the test network resources and equipment to be utilized in dedicated tests and development projects, similarly to how it was done also with the study presented in this paper. Figure 1 shows the architecture that was used in this study. The figure illustrates the network setup, which consists of non-standalone 4G (evolved node B, eNB) and 5G NR (next-generation node B, gNB) base stations as well Wi-Fi 6 access points providing the radio coverage. Wi-Fi network coverage was not integrated to the 3 GPP 5G architecture; instead, it was a dedicated test setup utilizing the same backbone network as the 5G core and multi-access edge computing (MEC) servers. In the 5G core network, backbone 5G core network instances together with MEC support were used. The MEC application server was realized by following the ETSI specified distributed EPC architecture specification [31].
## 2 Materials and Methods
### ROI Image Extraction  
Failure to promptly detect drones in high-risk areas, such as secure zones or conflict regions, can lead to significant damage. Therefore, minimizing unnecessary computations is essential to ensure the speed and efficiency of drone detection [18]. If all N partitioned images were analyzed, the computational cost would increase N-fold compared to detecting a single image. However, selectively processing only images containing objects can significantly reduce the detection time, thereby improving detection efficiency. To achieve this, a Laplacian filter is first applied to all partitioned images \(\mathrm{I}^{\mathrm{grd}}\) to enhance noise and object boundaries within the images. This visualization allows for distinguishing images containing only the background from those containing objects. In the next step, a threshold T is set based on the filtered images \(\mathrm{I}^{\mathrm{lap}}\), and a binarization process is performed. Finally, only images containing pixels with a value of 1 in the binarized images \(\mathrm{I}^{\mathrm{bin}}\) are extracted. These images are then matched with their original RGB counterparts to select the final images containing objects, \(\mathrm{I}^{\mathrm{roi}}\). By processing only the images with objects among the N partitioned images, unnecessary computations are significantly reduced. Figure 3 illustrates the ROI extraction process using the Laplacian filter.