esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Control of an Omnidirectional Mobile Wall-Climbing Robot
###### Abstract  
Omnidirectional mobile wall-climbing robots have better motion performance than traditional wall-climbing robots. However, there are still challenges in designing and controlling omnidirectional mobile wall-climbing robots, which can attach to non-ferromagnetic surfaces. In this paper, we design a novel wall-climbing robot, establish the robot's dynamics model, and propose a nonlinear model predictive control (NMPC)-based trajectory tracking control algorithm. Compared against state-of-the-art, the contribution is threefold: First, the combination of three-wheeled omnidirectional locomotion and non-contact negative pressure air chamber adhesion achieves omnidirectional locomotion on non-ferromagnetic vertical surfaces. Second, the critical slip state has been employed as an acceleration constraint condition, which could improve the maximum linear acceleration and the angular acceleration by 164.71% and 22.07% on average, respectively. Last, an NMPC-based trajectory tracking control algorithm is proposed. According to the simulation experiment results, the tracking accuracy is higher than the traditional PID controller.  
omnidirectional mobile robot; wall-climbing robot; critical slip state; nonlinear model predictive control; trajectory tracking +
Footnote †: journal: Applied Sciences
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 1 Introduction
The 5G dataset collected was multiclass classified using the ML algorithms decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and Stacking Ensemble. To compare their performance, various feature selection methods were used, namely, activating all features and activating selected features. In addition, the difference in the required time was compared while increasing the size of the data. The main contributions of this study can be summarized as follows. First, we noted the importance of removing extraneous features to detect IoT DDoSs with low latency in the 5G core network path. The experiments show that the feature selection process can lead to faster and better classification models by selecting features that have a large influence on learning and detection. Second, we constructed an experimental 5G mobile environment and collected datasets using multiple user equipment (UE). Third, we evaluated the results of feature selection by classification algorithms and presented all performance indicators related to our experiments. Finally, we showed the usefulness of the feature selection process in terms of low-latency detection of IoT DDoS attacks on massive 5G datasets. Subsequently, we confirmed the significance of the feature selection study for the real-time detection of DDoS attacks in an ultra-low latency 5G core environment. The remainder of this paper is organized as follows. Section 2 presents previous studies related studies on 5G network attack detection and feature selection. Section 3 describes the concepts of 5G system architecture and feature selection. Section 4 defines the problem and outlines the IoT DDoS detection methodology of 5G environments employed in this study. Section 5 presents the results of the feature selection method. Section 6 compares the results of using all features extracted with the results obtained using the feature selection process. By increasing the size of the dataset, it shows the effect of the feature selection process on the detection of a large amount of IoT DDoS. Finally, Section 7 provides concluding remarks regarding this study and directions for future research.
## 3 Results  
### LST Evaluation  
First, the LST data obtained from the MSG satellite were validated by comparing them with the air temperature values recorded by the AEMET \"Villarranca\" station for the year 2023, resulting in an R\({}^{2}\) of 0.62 and an RMSE of 3.97 \({}^{\circ}\)C (Figure 5). However, by selecting only the data values corresponding to nighttime hours (defined as between 00:00 and 06:00), when the air temperature and surface temperature are more similar, the R\({}^{2}\) improves to 0.91 and the RMSE to 1.5 \({}^{\circ}\)C.
## 4 Simulations and Results
### Simulation Analysis
#### 4.2.3 M-ary Spread-Spectrum Coding System Simulation  
The simulation results for the M-ary spread-spectrum coding system under a Gaussian channel and multipath channels are shown in Figures 9 and 10, where \(M=16\). Using a 5 th-order Gold sequence as the spread-spectrum code, with a bandwidth of \(B=2\) kHz, carrier frequency \(f_{c}=2\) kHz, and sampling frequency \(f_{s}=16\) kHz, the M-ary spread-spectrum system with CCSDS(512, 256) codes achieves a BER of \(10^{-3}\) with a minimum SNR of \(-14.94\) dB; the minimum SNR required for the uncoded system to achieve \(10^{-3}\) BER is \(-8\) dB. Thus, the coding gain is approximately 6.94 dB. Under \(1\times 10^{-4}\) BER conditions, the code length is the same as 256 and the code rate is 1/2 for both Turbo and LDPC codes. Compared to Turbo codes, the M-ary spread-spectrum system with CCSDS codes has a gain of about 0.3 dB.
## 2 Methods
### Data Collection and Processing  
This study uses four terrain data types (e.g., highway networks, slope maps computed from elevation maps, and river and reservoir maps from numerical data) and the PV power-output data presented in [11]. All data used for site selection were digitalized using a uniform coordinate system (i.e., World Geodetic System 1984 (WGS84) 52N), scaling (i.e., 1/5000), and resolution (i.e., 30 \(\times\) 30 m\({}^{2}\) resolution). In particular, the PV power output per capacity per hour data [14] are represented as raster images (i.e., tagged image file format, or TIF), with 30 \(\times\) 30 pixels, where one pixel is equivalent to 30 \(\times\) 30 m\({}^{2}\) (i.e., one PV power output per capacity per hour is covered with 810,000 m\({}^{2}\)).  
The location data (Table 1) were collected from continuous numerical maps to select suitable regions for PV plants with geo-spatial analyses, where the spatial information (e.g., contours, reservoirs, and rivers) is represented by points, polylines, or polygons (vector format). These location data were collected from the National Spatial Data Infrastructure of Korea (NSDI) as unity coordinates (i.e., geodetic reference system 1980 (GRS80), EPSG: 5181). To unify the coordinate system of the location data, the raw location data's coordinate is transformed into WGS 84 52N format. In addition, in Table 1, orography data (i.e., slope map) are extracted using a digital elevation model (DEM) generated from raster image-based elevation maps (i.e., TIF format) transformed from vector-type elevation contours.  
Figure 1: Four-step flowchart for selecting the sites of PV power plants.  
Ucheon-myeon in Gangwon-do, South Korea, was chosen as the case-study area (Figure 2). The study shows a detailed spatial analysis and visualizes the construction of solar PV power plants in the vicinity of highway networks in 3D BIM environments. The district of Ucheon-myeon is geographically located between the 37.4\({}^{\circ}\) and 37.5\({}^{\circ}\) north latitudes and 128.0\({}^{\circ}\) and 128.1\({}^{\circ}\) east longitudes. The case-study area lies at an elevation of 000 m and is located in a mountainous area near a national park (Chiak-mountain). In addition, the study area has an average slope of 17.14\({}^{\circ}\), which is relatively higher than that of urban or plain areas (under 15\({}^{\circ}\)) and includes reservoirs and rivers where solar plants cannot be installed. Because this study area contains inappropriate conditions for power plant sites, a delicate spatial analysis is required for identifying installation sites for PV plants.
## 4 Methodology
### The Model
#### 4.3.1 Classifier  
The UNBC-McMaster shoulder pain expression archive dataset, which we used to train our system, resulted from experiments conducted on 25 participants, providing little face-shape variation. To overcome the limitations of face shape variety and to combat overfitting due to data scarcity, we used ImageNet weights to initialize our models before fine-tuning our custom dataset. This approach yielded better results than training each model from scratch on the domain dataset. Our model extracted features from the detected face and the upper part of the detected face of our subset dataset using VGG16 in the first experiment, InceptionV3 in the second experiment, ResNet50 in the third experiment, and ResNeXt50 in the fourth experiment after freezing the convolutional blocks and dropping the final prediction layers. Transfer learning processes in VGG16 and ResNet50 were conducted by extracting the feature vectors and freezing all convolutional blocks, as well as feeding the output to our new classifier (Figure 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Video** & **Label** & **Number of Merged Frames** & **Duration in Second** \\ \hline Video 1 & Sever pain & 397 & 99 \\ Video 2 & Sever pain & 308 & 77 \\ Video 3 & Sever pain & 337 & 84 \\ Video 4 & No pain & 142 & 35 \\ Video 5 & Sever pain & 263 & 65 \\ Video 6 & No pain & 165 & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Testing videos description.  
Using VGG16, ResNet50, or ResNeXt50 alone did not meet the desired accuracy. We used them to extract the relevant features and replaced their prediction layers with a 4-layer concurrent shallow CNN to increase the model accuracy. The features were extracted from two inputs: the lower face and the upper face. They were sent to two identical shallow CNNs, each consisting of four layers (Figure 5). The first layer was convolution (Conv2D), which contained 16 neurons, a (3 \(\times\) 3) filter, and Relu activation function. The second layer was the max-pooling layer, which had a (2 \(\times\) 2) filter. The third layer was the dropout with a probability of 0.25. The fourth layer was a flattened layer. The output of each branch of CNN was concatenated and sent to a dense layer with 2048 neurons, then to a fully connected layer to classify the data. The shallow CNNs parameters above were chosen after extensive experiments with different values. The InceptionV3 model is shown in Figure 6. The models were trained with four optimizers (i.e., SGD-ADAM-RSMprop-RAdam). The differentiation between these 16 models was based on the higher accuracy of 10-fold cross-validation and testing data of the unseen subject. The best model was subjected to a few more tests to measure its performance under different brightness and rotations. These brightness/rotations tests were conducted on the modified testing data, as described in Section 4.2.  
Figure 4: The transfer learning using (a) VGG16 and (b)ResNet50.
## 5 Comparative Evaluations
### Evaluation Results for Confidentiality  
The proposed method can increase confidentiality in non-public conditions. However, if it were to be implemented in the public condition, we do not know what would happen to the confidentiality. Therefore, confidentiality was evaluated by carrying out bit detection tests when the embedding and detection processes were switched in public conditions. Confidentiality was defined as the security codes being incorrectly detected if the BER was greater than or equal to 20%.  
Figure 14 shows the evaluation results of the confidentiality, where the horizontal axis shows the bit rate and the vertical axis shows the BER of each attack process. We can see here that embedding by the front-side form does not allow the back-side form to detect the security codes correctly. Moreover, when embedded by the back-side form, the security codes could not be detected correctly by the front-side form. If a security code is embedded in the front-side form, the back-side form cannot properly frame-synchronize because the amount of remaining LP used in the front-side and back-side forms is different. Therefore, if embedding and detection are performed on different forms, frame synchronization will not be possible and the BER of security codes will be increased. These results demonstrate that the proposed method is confidential in public conditions thanks to the front-side and back-side forms.  
Figure 14: Results of BER by swapping embedding and detection processes: (**a**) front-side and (**b**) back-side forms.  
Figure 13: BER with respect to the accuracy of frame synchronization. Gray shading indicates areas that meet criterion of BER of less than 10% and frame synchronization accuracy greater than 80%.
## 2 Materials and Methods
### Determination of Glucose, Phosphorus, Cholesterol and Live Microbial Biomass (ATP)  
Aliquots of 1 mL were taken from the bioprocesses tested every 24 h for the subsequent glucose (intracellular and the culture media), phosphorus, cholesterol and live microbial biomass quantifications (ATP). The identification of glucose, phosphorus and intracellular cholesterol was performed through an extraction with phosphate buffer at 200 mM in ratio of 1:10 (p/v), homogenized with an Ultra Turrax for 1 min and centrifuged at 3000 rpm for 5 min. The supernatant obtained was utilized for the quantification. In the case of quantification phosphorus, it was determined the concentration of the buffer phosphate, and this value was used to normalize the samples. In the case of identifying glucose present in the culture liquid media, the samples were directly analyzed from the aliquots without prior treatment.  
The quantification of glucose, phosphorus and cholesterol were performed using commercial kits by the company Human Diagnostics (Wiesbaden, Germany), following the supplier's instructions (Glucose liquicolor by the Glucose Oxidase (GOD)-Perooxidase (POD) method at 500 nm, Phosphorus liquirapid by phosphomolybdc acid at 340 nm and Cholesterol liquoricolor by Cholesterol Oxidase (CHOD)-Perooxidase 4 Amino antipyrine (PAP) method at 500 nm).  
To determine the fraction of ATP present in the aliquots, two steps were carried out according to the methodology described by [PERSON] and [PERSON] [23]. The first one consisted of an ATP extraction through a boiling bath with organic buffer (Tris 20 mM, pH 7.7), and the second step, consistent with the ATP quantification extracted during the testing of bioluminescence for which an ATP meter, utilized Turner Designs Model TD 20/20. In addition, a calibration curve was performed between 0.39 nM and 100 nM from a stock solution of 1 M of ATP; through serial dilutions and to quantify the extracted ATP, FLE-250 was used to generate the complex enzyme substrate.
## 3 Calibration of System Parameters  
An important step in obtaining 3D shape data is to build the relationship between the absolute phase and the depth, which is known as calibration. In the mathematical model derived in Equation (5), the two parameters \(d_{1}\) and \(d_{2}\), need to be determined beforehand. When the extrinsic parameters (orientations) of the mirror, LCD\({}_{1}\), and LCD\({}_{2}\) have been acquired in the same camera coordinate system, \(d_{1}\) can be calibrated from the relative distance between the mirror and LCD\({}_{1}\), which can be obtained through matrix transformation; \(d_{2}\) can be calibrated in the same way. A novel parameter calibration method for the DPMD system has been proposed by using a phase target to solve the existing problems of dependency on a translating stage and the limited depth of field (DOF) of the lens.
## 3 Proposed Distributed Secondary Control Technique
### Secondary Controller Design  
The secondary controller plays a crucial role in the hierarchical control system by compensating for any deviation introduced due to the droop control action within the loop of the primary control system. The secondary control proposed in this article is implemented to produce the necessary reference voltage in the standalone DC microgrid. By implementing the secondary control, the DC voltage mentioned in (7) can be regulated in accordance with the objectives discussed in the preceding section. Moreover, this approach guarantees average current sharing. It is mathematically expressed as:  
\[U_{dc}(t)=U^{*}-\left(R_{line_{i}}+k_{d_{i}}\right)I_{o_{i}(t)}+\mathcal{H}_{ i}(t) \tag{12}\]  
The correction signal, \(\mathcal{H}_{i}\), is produced by the proposed secondary control system and is formulated as follows:  
\[\begin{cases}\mathcal{H}_{i}(t)&=\tau_{i}\cdot u_{i}(t)\\ u_{i}(t)&=\alpha_{i}\int\Delta U_{dc}(t)\,dt+\beta_{i}\int\Delta I_{o_{i}}(t) \,dt.\end{cases} \tag{13}\]  
In this context, \(u(t)\) represents the combined integral of voltage and current deviations. The proposed secondary control scheme incorporates \(\alpha\), \(\beta\), and \(\tau\) as weighting coefficients. The magnitude of these coefficients influences the behaviour of the secondary control techniques; a high magnitude of \(\alpha\) gives rise to a rapid convergence of the first objective, while a higher magnitude of \(\beta\) accelerates the convergence of the second objective. Careful consideration of the value of \(\tau\) is crucial to effectively balance the trade-off between the voltage restoration and current sharing features of the proposed secondary control. Due to the inherent variability of the direct current MG, the design and selection procedures of \(\tau\) are typically intricate and require a significant amount of time. Therefore, to simplify the tuning process and make the distributed secondary control more adaptive and flexible, we propose the utilization of a fuzzy logic technique to determine the suitable coefficient of \(\tau\). This coefficient serves as a scaling parameter that balances the current-sharing and voltage regulation objectives.
## 3 Results and Discussion
### Influencing Factors of Vibration Isolation Effect for the Composite Subgrade Structure
#### 3.2.1 The Effect of XPS Plate Thickness on the Vibration Isolation  
Thickened XPS plates can effectively improve the vibration isolation performance of the composite subgrade structure. However, it sometimes causes a negative impact of overall structure and increases the usage cost. Therefore, it is vital to determine the optimum XPS plate thicknesses of the composite subgrade structure. In this part, the thicknesses of the XPS plates were set as 3 cm, 5 cm, and 9 cm, respectively. The impact load was generated by hammer dropping from the height of 25 cm. The result is illustrated in Figure 6.  
From Figure 6, the isolation coefficient of the composite subgrade structure decreased as the thickness of the XPS plates increased. For G3, which embeds XPS plates with the thickness of 9 cm, the isolation coefficient C in the three main axes were 0.64, 0.80, and 0.10, respectively. The vibration isolation effect in the Z-direction was more significant than that in the other two directions. It should be noted that the C value in the X-direction and Y-direction for G3 embedding with 3 cm XPS plates was greater than for G1. This means that the vibration isolation in transverse performs a negative effect when compared to G1. However, the vibration isolation effect in the Z-direction of the composite subgrade structure is the most concerning problem. The relationship between the isolation coefficient in the Z-direction and the thickness of XPS plates is fitted in the exponential form, which is demonstrated in Figure 7.  
As shown in Figure 7, the XPS plates with thicknesses of 5 cm and 9 cm both possessed significant vibration isolation performance. The effect of the composite subgrade structure with 9 cm was slightly better. However, the enhancement in isolation performance was very limited when the thickness of the XPS plates increased from 5 cm to 9 cm. Furthermore, the cost of 9 cm XPS plates was much higher than that of 5 cm. Therefore, the XPS plate thickness of 5 cm was considered as the optimum thickness of the composite subgrade structure in these test groups. The isolation coefficient C of the composite subgrade structure embedding 5 cm XPS plates in Z-direction was 0.17, which accords with the normal vibration isolation standard [24].  
Figure 6: Isolation coefficient of the composite subgrade structure.  
Figure 7: Fitting curve of the isolation coefficient in the Z-direction.
# Transitioning Broadcast to Cloud
## 2 Processing Chains in Broadcast and Cloud-Based Online Video Systems
### Video Formats and Elementary Streams
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stream Characteristic** & **Broadcast Systems** & **Online Platforms/Web Streaming** \\ \hline \multirow{3}{*}{Number of outputs} & \multirow{3}{*}{single} & multiple (usually 3–10) as \\  & & & needed to support ABR \\  & & & delivery [4; 42; 43] \\ \hline Preprocessing & denoising, MCTF-type filters [24; 53] & rarely used \\ \hline \multirow{3}{*}{Video codecs} & \multirow{3}{*}{MPEG-2 [54], MPEG-4/AVC [55]} & MPEG-4/AVC [56], AV1 [57]—special cases \\  & & & \\ \hline \multirow{3}{*}{Codec profiles, levels} & fixed for each format. applicable & based on target set of \\  & standards: & devices [42] \\ \cline{1-1}  & \multirow{3}{*}{ATSC A/53 P4 [26], ATSC A/72 P1 [29], ETSI TS 101 154 [30], SCTE 128 [33]} & guidelines: Apple HLS [44], ETSI TS 103 285 [46], CTA \\ \hline GOP length & 0.5 s & 2–10 s \\ \hline GOP type & open, closed & closed \\ \hline \multirow{3}{*}{Error resiliency features} & \multirow{3}{*}{mandatory slicing of I/IDR pictures} & \multirow{3}{*}{N/A} \\  & & & capped VBR \\ \cline{1-1}  & many additional constraints apply, see & max. bitrate is typically \\ \cline{1-1}  & \multirow{3}{*}{ATSC A/53 P4 [26], ATSC A/54 A [27],} & capped to 1.1x–1.5x target \\ \cline{1-1}  & & & bitrate; \\ \cline{1-1}  & \multirow{3}{*}{ATSC A/72 P1 [29], ETSI TS 101} & HRD buffer size is typically \\ \cline{1-1}  & & & limited by codec profile+ \\ \cline{1-1}  & & & level constraints; \\ \hline VUI/HRD parameters & required & optional, usually omitted \\ \hline VUI/colorimetry data & required & optional, usually included \\ \hline VUI/aspect ratio & required & optional, usually included \\ \hline \multirow{3}{*}{Picture timing SEI} & required in some cases (e.g., in film & \\  & & mode) & optional, usually omitted \\ \hline Buffering period SEI & optional & optional, usually omitted \\ \hline ADF/bar data/T.35 & required and carried in video ES & not used \\ \hline \multirow{3}{*}{Closed captions} & \multirow{3}{*}{required and carried in video ES} & optional, maybe carried \\  & & & out-of-band \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of encoded video streams used in broadcast and Internet streaming.