esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Exploratory Data Analysis
In this case, the delta-based variables from the previous zone, namely, Delta_time_prev_zone (Variable 6), Delta_dist_prev_zone (Variable 7), and Delta_cons_prev_zone (Variable 8), exhibit the strongest positive correlations with the target variable Delta_time [h] (Variable 10), with coefficients of 0.69, 0.67, and 0.66, respectively. These results suggest that, under critical soot accumulation conditions, the time and effort spent in the preceding zone may significantly influence the duration in the current zone, likely due to the residual effects of high-load operation. In addition, the Fuel Intensity Index from the previous zone (Variable 13) shows a relatively strong negative correlation with the target (\(-\)0.70), indicating that lower efficiency in the preceding zone (i.e., higher fuel usage per unit of distance and time) is associated with longer durations in Zone 4. Given this behavior, the Fuel Intensity Index from the previous zone, in combination with the simulated soot content (Variable 2), was selected as the primary feature set for model development in Zone 4. Finally, other engineered variables, such as fuel_rate and fuel_per_km, whether calculated for the current or previous zone, showed consistently weak correlations in both heatmaps and were therefore excluded from the final feature selection process. Figures 9 and 10 display scatter plots visualizing the correlation between the selected engineered features for each zone and the target variable. Figure 8: Correlation heatmap for Zone 3. Figure 7: Correlation heatmap for Zone 3. Figure 9: Fuel Intensity Index (Abs) and target variable for Zone 3. Regarding the differences observed between zones, these may stem from variability in operational conditions associated with soot accumulation levels. Urban buses operate in dynamic environments, where factors such as route assignments, stop frequency, passenger loads, and traffic congestion can vary considerably. These variations may influence both soot buildup and the time spent in each zone. Consequently, such contextual differences could partially explain potential divergences in feature relevance or model performance across zones.
## 5 Experimental Evaluation
### Test Bench Introduction  
Figure 10 shows the flow of the PMSM control system, and its working principle is summarized as follows. The Hall sensor samples the electrical signals from the main circuit, from where it is converted into an analog signal and input to the MCU after signal conditioning. The incremental optical encoder measures the actual speed of the PMSM. It is converted into a digital quantity and input to the MCU chip. The MCU control chip calculates and outputs three-phase PWM signals to the inverter drive circuit according to input data and the program set by the PC. Drive signals are reinforced to regulate the PMSM. CubeMX5.1.0 is used as the software development platform to improve system performance. As a graphical configuration tool compatible with the master control MCU chips, it generates the initialized C code for processors with Cortex-M cores and allows the step-by-step configuration of STM32 microcontrollers and microprocessors.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Approach** & **Task** & **Operations** & **Example** \\ \hline \multirow{3}{*}{Proposed Algorithm} & Tim-sort * & \(\left(i\cdot\mathit{nlogn}\right)^{h}\) & 16 \\  & Ranking & \(\left(n^{2}\right)^{h}\) & 16 \\  & Model & \(\left(n\right)^{h}\) & 4 \\  & Total & \(\left(i\cdot\mathit{nlogn}\right)^{h}+\left(n^{2}\right)^{h}+\left(n\right)^{h}\) & 36 \\ \hline \multirow{3}{*}{Conventional Algorithm} & Optimization & \(\left(n\right)^{h}\) & 8 \\  & Model & \(\left(n\right)^{h}\) & 8 \\  & Total & \(2\cdot\left(n\right)^{h}\) & 16 \\ \hline \end{tabular} Note. * means considering the worst case of comparisons.  
\end{table}
Table 4: Algorithms’ calculation-complexity comparison.  
The steps are as follows: (1) Select an STM32 microcontroller, microprocessor, or development platform compatible with the desired peripherals; (2) Users can configure GPIO pins, set up the system's clock, allocate peripherals, and define various parameters for the MCU; (3) Users configure relevant files in the code generation area to generate corresponding initialized C code; (4) The debugger is connected to the JTAG interface of the MCU using Keil uVision5 software. The program is then downloaded to the STM32F4 chip and debugging is performed. The emulator model used in the work is ST-Link, which can transmit data signals and convert protocols. The PMSM can be controlled in this way. Figure 11 illustrates the connection method between software and hardware.  
Figure 12 shows the PMSM and its control system used in the experiment. This consists of a regulated DC power supply, control board, PMSM, simulator, computer, and oscilloscope. The software algorithms designed earlier are burned into the system. The computer acts as the host machine to send commands and perform parameter debugging in this experimental setup, while the MCU serves as the lower compu  
Figure 11: Keil and MCU connection.  
Figure 10: Hardware block diagram of the digital control system of the PMSM.
## 4 Experiment Verification
Hence, dynamic experiments are formulated to validate the dynamic performance of the devised control method. During the stable operation of the APF system, an additional nonlinear load is introduced to simulate a load collapse. After stable operation, a sudden load increase is simulated by an additional load disconnected in parallel. The results of the dynamic tests are illustrated in Figure 8. It can be observed from the figure that the introduction of additional load results in an increase in the amplitude of both the source current and the load current. It is noteworthy that although the source current under all three control algorithms can recover to a new stable sinusoidal waveform within a relatively short period, the current curve under ASMC control is more prone to sudden changes, which may have adverse effects on the actual operation of the power grid. When the additional load is disconnected, the source current can also recover to a new stable sinusoidal waveform within a short period of time, as shown in Figure 9. Similarly, spectrum analysis is performed on the power supply current with load variation, and the results are listed in Table 2. \begin{table}
\begin{tabular}{l c c c} \hline  & **ASMC** & **STSMC** & **IT2 FNN-SFR STSMC** \\ \hline Steady state & 5.40\% & 4.71\% & 4.16\% \\ \hline Additional load connected & 4.94\% & 4.23\% & 3.87\% \\ \hline Additional load disconnected & 5.87\% & 5.10\% & 4.72\% \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of THD under the three strategies. Figure 8: Curves of power supply voltage, load current, compensation current, and power supply current measured when the additional load is connected under different methods: (**a**) IT2 FNN-SFR STSMC algorithm; (**b**) ASMC algorithm; (**c**) traditional STSMC algorithm. In addition, a comparison of the THD rates in the hardware experiment under the two methods is given in Table 2. It is obvious that the compensation performance of the designed controller is better than ASMC and STSMC in terms of steady state and dynamic response, and it meets the international standard of THD of less than 5% in each state. STSMC, owing to the incorporation of a super-twisting term, enables the continuous switching of the controller, facilitating the rapid convergence of the system on the sliding surface. Compared to ASMC, it reduces the dwell time of the system on the sliding surface, thereby enhancing control efficiency. Hence, the THD under the STSMC algorithm is lower. Additionally, the introduction of IT2 FNN-SFR allows for the pre-estimation of the dynamic equations of the APF system, reducing current distortion resulting from external disturbances and further enhancing the robustness of the controller. Based on the aforementioned hardware experimental results, it is evident that the control strategy proposed in this paper meets the control requirements, and its feasibility is demonstrated.
## 2 Theoretical Method and Physical Model
### Solution of Oil Film Reynold Equation
#### 2.1.2 Equation of Motion  
The inner and outer oil film pressures in the oil cavity can be equivalent to the resultant force in two vertical directions, radial force \(F_{e}\), and circumferential \(F_{\phi}\), which can be converted into horizontal force \(F_{x}\) and vertical force \(F_{y}\) through coordinate transformation. Assuming that a single-disk symmetrical rigid rotor is supported by floating ring SFDs at both ends, the equation for the system dynamics becomes [20]  
\[\left\{\begin{aligned} m\ddot{x}+k_{1}x+c_{1}\dot{x}& =em\Omega^{2}\cos\Omega t+F_{x}\\ m\ddot{y}+k_{2}y+c_{2}\dot{x}&=em\Omega^{2}\sin \Omega t+F_{y}+G\end{aligned}\right. \tag{6}\]  
where \(G=mg\) is half of the gravity of the turntable, \(k_{1}\) and \(k_{2}\) are the equivalent stiffness of the elastic support, \(c_{1}\) and \(c_{2}\) are equivalent damping which are under different pedestal contact states of SFD, \(e\) is the eccentricity of the disc, and \(\omega\) is the rotation without measuring the excitation frequency. \(F_{x}\) and \(F_{y}\) are oil film forces along \(x\) and \(y\) directions. If the displacement and time quantities \(\tau=\omega t\) and \(X=2x/l\) are introduced, and the simplified Equation (6) is substituted into the dimensionless system dynamics equation, it may be given that  
\[\left\{\begin{aligned} \ddot{X}+{\omega_{1}}^{2}X+n_{1}\dot{X}& =\frac{2f}{ml}\cos\tau+\frac{2}{ml\Omega^{2}}F_{x}\\ \ddot{Y}+{\omega_{2}}^{2}Y+n_{2}\dot{Y}&=\frac{2f}{ ml}\sin\tau+\frac{2}{ml\Omega^{2}}F_{y}+\frac{2g}{l\Omega^{2}}\end{aligned}\right. \tag{7}\]  
where \(\omega_{i}=\sqrt{k_{i}/(m\Omega^{2})}\), \(n_{i}=c_{i}/(m\Omega^{2})\), \(i=1,2\).
## 3 Verification and Results
### Results of Crack Measurement  
The images that go through the concrete crack classification and segmentation neural networks are returned as segmentation information on the crack points. Therefore, we can obtain the total length and average thickness of cracks, which are information about the concrete crack images, using thinning and tracking techniques. Figure 10b shows the results of segmentation using the deep learning method on an actual concrete crack image, and the results of deriving the length and average width of the crack (Figure 10c,d) based on segmented crack. The length, minimum width, and maximum width of the crack actually measured were 396.7, 1.3 and 5.2 mm, respectively. The crack length obtained by segmentation using deep learning and applying thinning and tracking methods in this study was 401.3 mm, showing an error of about 1%. In addition, the average crack width measured through profiling was 2.2 mm, which is similar to the crack width range actually measured.
## 2 Background
### Internet of Things  
IoT is a network of interconnected physical devices referred to as \"things\" that communicate and exchange data over the internet [35, 36]. IoT enables everyday objects to become \"smart\" interconnected digital entities with the help of sensors, actuators, and other appliances that collect, transmit, and act on data, facilitating automation and improving decision-making processes [37, 38]. The rapid expansion of IoT is fueled by advances in wireless communication technologies, such as 5G and LPWAN, which support the con nectivity of billions of devices worldwide. Data collected by IoT sensors are transferred through networks (wireless or wired) and then analyzed, either in a central cloud environment or directly on local devices (edge computing). This technology is also used to effectively bridge physical and digital environments as through the \"communication\" of the real-world sensors real-world variables can be passed to a DT [39]. For instance, in healthcare, IoT devices monitor patient vitals and provide real-time data to medical professionals, improving patient outcomes [40; 41]. Similarly, in smart cities, IoT applications optimize energy usage, manage traffic flow, and enhance public safety by integrating real-time data from various sources [42]. In general, IoT is a crucial enabler for smart environments and data-driven decision-making processes (especially when combined with the latest advancements in the field of AI) and is widely researched and used in fields such as smart cities, intelligent transportation, logistics and supply chain as well as healthcare and emergency response [43]. However, this technology faces significant challenges, including data security, interoperability, availability, as well as reliability [44; 45]. Due to varying communication protocols and architectures, and the lack of standardization in the field, integrating heterogeneous devices continues to be a significant challenge. Advanced encryption method are necessary to ensure data confidentiality and mitigate the serious risks posed by security flaws such as illegal access and data breaches, which are more and more common nowadays. Furthermore, issues related to mobility, the power consumption and computational power of such devices as well as network reliability, reduce the effectiveness of IoT systems, particularly in dynamic environments [45]. As IoT systems become more complex, ensuring their scalability and reliability requires addressing these issues through robust frameworks and regulatory measures [43; 46].
## 3 Results and Analysis
### Spatio-Temporal Distribution of Soil Salinization  
The S1 DI1 model was used to classify the soil salinization level of the reclamation area in 2019 and 2022, as shown in Figure 6. Additionally, the area of different degrees of soil salinization in the Alar reclamation area after removing the water body and sandy area was counted, as listed in Table 5.  
From 2019 to 2022, the area of soil salinization in cotton fields in the reclamation area showed a decrease in the area of non-saline soil and moderately saline soil, while it showed an increase in the area of lightly saline soil. Among them, the area of non-saline soil decreased by 62.77 km\({}^{2}\), and the reduced areas were mainly distributed in the northern, southern, and eastern regions of the reclamation area, mainly shifting from non-saline soil to lightly saline soil. The area of lightly saline soil increased by 72.99 km\({}^{2}\). It can be seen that the increased area is scattered in the reclamation area. Additionally, the saline soil area of moderate saline soil decreased by 10.22 km\({}^{2}\), and the decreased area was mainly in the southern and eastern areas of the reclamation area. In the past four years, the cotton fields in the Alar reclamation area are mainly non-saline soil, but some non-saline soil and moderate saline soil have been shifted to light saline soil, and in general, soil salinization has slightly increased. Based on the analysis, we need to know that, for the area where the soil changes from non-salinization to slight salinization, it is necessary to plant suitable  
Figure 6: The spatial distribution of the soil salinization level in 2019 and 2022.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Particular Year** & **Non-Saline Soil** & **Mildly Saline Soil** & **Moderately Saline Soil** \\ \hline
2019 year & 1048.91 & 40.65 & 14.65 \\
2022 year & 986.14 & 113.64 & 4.43 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The estimated areas of the soil salinization level in 2019 and 2022.  
crops reasonably and use flood irrigation to intervene in the soil salinization of cotton fields so as to transform the soil from slight salinization to non-salinization. Areas with severe salinization turning into mild salinization should be further rationally planned to prevent the transformation from salinization to moderate salinization or even more severe salinization.
# FGM: Noisy Label Learning via Fine-Grained Confidence Modeling
## 4 Experiments
### Ablations
#### 4.3.2 Ablation Study on Cluster Number  
Tables 7 and 8 illustrate the precision and recall of filtered labels obtained using FGCM with different clustering numbers under different noise types and rates on CIFAR-10.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**CIFAR-10**} \\ \hline
**Method Noise Rates** & **Sym 20\%** & **Sym 80\%** \\ \hline FGCM (CS + NS + CH + NHH + NHR + cluster refinement) & 95.6 & 94.1 \\ FGCM (CS + NS + CH + NHH + NHR & 95.2 & 93.6 \\ FGCM (CS + CH + NHH + NHR) & 94.5 & 93.0 \\ FGCM (CS + NS + CH) & 93.8 & 76.9 \\ FGCM w/o fine-grained sample categorization & 88.25 & 50.0 \\ Cross Entropy & 86.8 & 62.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation Study results on CIFAR-10 with two different noise rates.  
Figure 4: Precision and recall of labels produced by FGCM under different noise rates on CIFAR-10 dataset vs. the precision on trainset during training. The orange curves represent the precision of labels of the labeled trainset produced by FGCM. The green curves show the recall of the labeled trainset produced by FGCM. The blue curves illustrate the precision of models on the original trainset. (**a**) Results on CIFAR-10 with 20% symmetric label noise. (**b**) Results on CIFAR-10 with 50% symmetric label noise. (**c**) Results on CIFAR-10 with 80% symmetric label noise.  
For ANNO-GMM-2, we cluster samples into two categories only using the confidence of annotation label, which is the common practice adopted by previous methods [9; 19]. The set with higher confidence is chosen. For FGCM-GMM-3, we cluster samples into three categories and use the set with highest sum of confidence (i.e., CS) as well as the set with biggest difference between confidence of annotation and pseudo label (i.e., NS); For FGCM-GMM-4, we cluster samples into four categories, filtering CS, NS, and CH. The results show that by clustering samples into five categories, we can achieve the highest precision of filtered labels on all the settings while the recall is still acceptable. The comparison between ANNO-GMM-2 and FGCM-GMM-5 demonstrates the effectiveness of our proposed fine-grained confidence modeling, since FGCM can achieve higher precision than ANNO-GMM-2 while the recall is far higher than that of ANNO-GMM-2. Notably, though the label precision of ANNO-GMM-2 achieves 99.79% under 40% asymmetric noise, the clean set only consists of samples from five classes while all the samples from the other five classes are in the noisy set.
# Detecting Underground Geospatial Features for Protection of Rights in 3D Space: Korean Cases
## 4 Discussion  
The public underground shopping center is the target of registration in this study. The public underground shopping center is a shared property of the local government which holds the ownership, and the stores are generally operated by the store owner [20]. Therefore, in this study, the underground shopping center and individual stores are the targets of registration to protect the ownership held by the local governments and the lease rights of the store owners for individual stores.  
In general, because the property rights for land in Korea are set based on above-ground parcels, it is necessary to register the entire underground shopping center space and individual store spaces, along with the physical and rights relation, with the above-ground parcel when registering underground spaces. In terms of physical relation, the spatial scope of the underground shopping center or the individual store occupying the underground space is the target of registration, and it is necessary to specify which parcel the corresponding underground space is located under. In terms of rights, the party, purpose, area, rights relation (owner, tenant, period, price, etc.) of the property rights, and changes in the physical and rights information need to be included as registration information.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 5 Applications and Use Cases of NDN
### IFA Defense in IoT Networks (NDN of Things)
#### 5.1.2 Healthcare IoT  
In healthcare IoT (e.g., body sensor networks reporting patient data via NDN), an IFA could be life-threatening if it delays critical telemetry. Thus, IoT-oriented defenses prioritize low false positives, and they are not allowed to block the legitimate traffic of an insulin pump accidentally. Many IoT defenses employ lightweight statistical checks (such as a moving average of the satisfaction rate) due to limited CPU resources, and often assume a simple network topology (one or two hops to a gateway) [8].
# User Association Performance Trade-Offs in Integrated RF/mmWave/THz Communications
## Abbreviations  
mmW Millimeter wave  
RF Radio frequency  
6G Sixth generation  
LOS Line of sight  
NLOS Non-line of sight  
SINR Signal-to-interference and noise ratio  
SNR Signal-to-noise ratio  
MIMO Multiple input multiple output  
BS Base station  
UE User equipment  
QoS Quality of service  
QoE Quality of experience  
CRE Cell range expansion  
eICIC Enhanced intercell interference coordination  
PP Poisson point process  
OFCDM Orthogonal frequency and code division multiplexing  
OFDM Orthogonal frequency division multiplexing  
RAT Radio access technology
## 2 Materials and Methods  
A systematic review of peer-reviewed papers published in Pubmed (www.ncbi.nlm.nih.gov; last accessed on 10 January 2021), ISI Web of Science (www.webofknowledge.com; last accessed on 10 January 2021), and Science direct (www.sciencedirect.com; last accessed on 10 January 2021) databases was performed covering a time-span of 20 years (January 2000 to December 2020). Studies were selected if they reported: (i) comparisons of the effects either of PE, EO, or BP from different agro-industries with a specific dose of synthetic antioxidant vitamins--or antioxidants in general--in poultry nutrition and (ii) the use of additives of plant origin (excluded propolis and algae). A total of 39 papers from 14 different countries fulfilled both criteria and were included in the present review. A total of 20.5% of the selected papers were from Greece, based on the country from the corresponding authors. Most papers evaluated the effect of PE or EO (\(n=31\)) on poultry, and fewer focused on the BP (\(n=8\)). Most investigated PFAs were essential oils and extracts of rosemary and oregano (\(n=14\)) and grape by-products (\(n=7\)).  
The present review is divided in three sections according to the group of parameters investigated: (i) effects on feed intake, feed efficiency, growth traits, and nutrients digestibility; (ii) effects on metabolic, hematological parameters, and antioxidant status; and (iii) effects on macro- and microscopic anatomical features and on intestinal microbiome profile.  
Potential Plant Extracts and Plant By-Products as Alternative Sources of Vitamins for Poultry Feeding  
Effects on Feed Intake, Feed Efficiency, Growth Traits, and Nutrients Digestibility (Table S1 in Supplementary Materials)