esa-sceva/satcom-chunk-collection huggingface dataset:
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 3 New Scheme for Iced Transmission Line Identification  
This paper proposes a reliable iced transmission line identification scheme to overcome difficulties with PTLI identification. Figure 2 shows our proposed scheme for iced transmission line identification. We integrate image restoration techniques with image filter enhancements in the early stages. This stage solves problems such as increased noise in the original image, dim and dark images, and a grayish tint in the original image. In addition, image filter enhancement is proposed to remove unwanted noise during image acquisition and retain valuable information, such as the edges of the transmission line and the texture of the iced lines. PTLI images often blend with other objects, or noise appears during image processing even though the background is simple. Therefore, the next stage is an enhanced multi-level segmentation threshold algorithm to segment background and target pixel areas. The results of multi-level threshold images contain gaps and holes, so morphological modification operations are performed to improve segmentation results by utilizing two multiscale and mathematically structuring elements. We enhance the image and obtain the region of interest (ROI) based on bounding box identification to eliminate background regions. After object segmentation and localization, the next step is PTLI edge identification. Mathematical morphology is proposed at this stage to extract multidirectional edge subplots and smooth the region. Finally, modified connected component labeling is proposed to identify the top and bottom of the iced lines. This modification reduces the processing time and memory space required to analyze neighboring pixels. A more detailed explanation of the stages in iced transmission line identification is explained in the sub-section below. The black arrows in Figure 2 show the flow of identifying and extracting iced transmission lines. The red arrows explain the process in detail at these stages.
## 4 Proposed Estimation Scheme
### Detailed Expression for Proposed Scheme
```
Input and Initialization: \(\gamma^{(1)}=1,\ \beta=N_{u}/(N_{t}-N_{u}),\ i_{\text{Max}}=N_{u}-4,\ \hat{i}=i_{\text{Max}},\ \eta=0.01\ (1\%)\) Output: \(\mathbf{s}\) in (6)
```  
```
1.\(\mathbf{s}^{(0)}=\left(\sum\limits_{n=0}^{\infty}\left(\mathbf{I}_{N_{u}}- \mathbf{S}^{-1}\mathbf{W}\right)^{n}\right)\mathbf{S}^{-1}\mathbf{x}\)
2.\(\mathbf{s}^{(1)}=\left(\mathbf{D}+\mathbf{L}\right)^{-1}\left(\mathbf{x}- \mathbf{L}^{H}\mathbf{s}^{(0)}\right)\)
3.\(\tilde{\mathbf{e}}^{(1)}=\frac{\mathbf{e}^{(1)}}{\gamma^{(1)}}=\left|\mathbf{s}^{(1)}- \mathbf{s}^{(0)}\right|\)
4.for\(i=2:\ 1:\ i_{\text{Max}}\)
5.\(\mathbf{s}^{(i)}=\left(\mathbf{D}+\mathbf{L}\right)^{-1}\left(\mathbf{x}- \mathbf{L}^{H}\mathbf{s}^{(i-1)}\right)\)
6.\(e^{(i)}=\left|\mathbf{s}^{(i)}-\mathbf{s}^{(i-1)}\right|\)
7.\(\gamma^{(i)}=\sum\limits_{k=1}^{i}\ e^{(k)}\)
8.\(\tilde{\mathbf{e}}^{(i)}=\frac{\mathbf{e}^{(i)}}{\gamma^{(0)}}=\frac{\mathbf{\left[\mathbf{e} ^{(i)}-\mathbf{s}^{(i-1)}\right]}}{\gamma^{(i)}}\)
9.if\(\tilde{\mathbf{e}}^{(i)}<\eta\)
10.\(\hat{i}=i\)
11. break
12.end
13.end
14.\(\mathbf{s}=\frac{1}{\sqrt{\beta}}\mathbf{H}^{H}\mathbf{s}^{(i)}\)
15.return\(\mathbf{s}\)
```  
**Algorithm 1** An algorithm for GS pre-coding using proposed estimation scheme
## 2 General Aspects of Dry Anaerobic Digestion
### Inhibition of Dry AD  
One of the principal disadvantages of dry AD is the absence of dilution of inhibitors [12; 22]. [PERSON] et al. [63] reported that ammonium inhibition was not observed under fermentation of a substrate with a C/N ratio of more than 20. Several authors have pointed out that a C/N ratio between 20 and 30 was necessary to avoid inhibition, and the optimal ratio for the process was 25 [64; 65]. High ammonium concentrations are obtained by hydrolyzing compounds containing organic nitrogen. Substrates to which it belongs are complicated for digesting [66]. However, the C/N ratio cannot be used as the main indicator of anaerobic digestion inhibition, because ammonia concentration leads to inhibition and increasing the C/N rate only leads to a decrease in ammonia concentration; the effect of co-fermentation is the same as dilution, i.e., reduction of ammonia content in a substrate.  
One of the ways to reduce the effect of ammonium is to remove ammonia from the reactor [3]. However, it has been found that reduction of the inhibitory effect of ammonia was usually reached by reducing the TS concentration using water dilution which leads to an increase in reactor volumes, or by adding chemical reagents which increase the technology cost [67].  
Microorganisms that carry out dry AD are more resistant to high ammonium concentrations than those that carry out wet AD. The effect of lower inhibition is supplemented by the worse mixing of inhibitors in the substrate [22]. Thus, Valorga reactors can withstand a load of ammonia up to 3 g/L at a temperature of 40 \({}^{\circ}\)C, and Dranco reactors can operate a load of 2.5 g/L at a temperature of 52 \({}^{\circ}\)C [22; 68]. However, these data are not comparable due to different temperature regimes. Signs of inhibition of volatile fatty acids (VFA) appear at concentrations of acetic acid equal to 5 g/L and butyric acid exceeding 3 g/L [7].  
[PERSON] determined that anaerobic digestion at extremely high TS concentrations (more than 30%) led to a decrease in the specific surface area and a decrease in methane content [69]. [PERSON] and [PERSON] [26] argued that under these conditions, growth of microorganisms became suppressed. The influence of an increase in moisture content on the methane fermentation of chicken manure is shown in Figure 3.
# Effective Implementation of Edge-Preserving Filtering on CPU Microarchitectures
## 8 Conclusions  
In this paper, we propose methods to accelerate bilateral filtering and non-local means filtering. The proposed methods prevent the occurrence of denormalized numbers, which has a large computational cost for processing. Moreover, we verify various types of vectorized implementations on the latest CPU microarchitectures. The results are summarized as follows:  
1. The proposed methods are up to five-times faster than the implementation without preventing the occurrence of denormalized numbers.
2. In modern CPU microarchitectures, the _gather_ instruction in the SIMD instruction set is effective for loading weights from the LUTs.
3. By reducing the LUT size through quantization, the filtering can be accelerated while maintaining high accuracy. Moreover, the optimum quantization function and the quantization LUT size depends on the required accuracy and computational time.
4. When the kernel size is small, the 8U implementation is faster than the 32F implementation. By contrast, in the case of the large kernel, the 32F implementation is faster than the 8U implementation.  
In the future, we will verify the influence of denormalized numbers on GPUs. In particular, we are planning to implement edge-preserving filters on GPUs and to verify the influence of the denormalized numbers on computation time in GPUs. Furthermore, we will design effective implementations of the filters on GPUs.  
Conceptualization, Y.M. and N.F. Data curation, Y.M. Formal analysis, Y.M. Investigation, Y.M. Methodology, Y.M. Project administration, N.F. and H.M. Resources, N.F. Software, Y.M. Supervision, N.F. and H.M. Validation, Y.M. and N.F. Visualization, Y.M. Writing, original draft, Y.M. Writing, review and editing, Y.M. and N.F.  
This research was funded by JSPS KAKENHI Grant Numbers JP18K19813 and JP17H01764.  
This work was supported by JSPS KAKENHI Grant Numbers JP18K19813 and JP17H01764.  
The authors declare no conflict of interest.  
Figure 19: Computational time and speedup ratio of fastest implementation and OpenCV implementation in bilateral filter. These results were calculated using an Intel Core i9 7980 XE. Note that the computational time is plotted on the logarithmic scale. If the speedup ratio exceeds one, the fastest implementation is faster than the OpenCV implementation. \(\sigma_{r}=16\), \(r=3\sigma_{s}\); and image size is \(768\times 512\). For \(\sigma_{s}=4\), the PSNRs of the proposed method and OpenCV are 84.63 dB and 44.08 dB, respectively. For \(\sigma_{s}=8\), they are 85.45 dB and 43.55 dB, respectively. For \(\sigma_{s}=16\), they are 84.41 dB and 43.19 dB, respectively.
# Design of Adits for People Passing Spacing in High Altitude Highway Tunnels in Cold Regions
## 2 Materials and Methods
### Personal Evacuation Scenarios Analysis
#### 3.1.3 CO Concentration  
Figure 8 presents the variation in longitudinal CO concentration at a characteristic height for 300 s. Long tunnels in high-altitude areas are influenced by factors such as tunnel structure, altitude effect, geographical location, and other variables. In comparison to low-altitude areas, the incomplete combustion of combustible materials intensifies during a fire incident. The fire smoke often contains various toxic gases primarily composed of CO, which poses a risk to personnel evacuation. Incomplete combustion occurs at all stages of a fire. However, it takes some time for the CO concentration to reach a certain level. Based on this premise, an analysis is conducted on the CO concentration at the characteristic height of 300 s.  
Figure 7: Variation in longitudinal visibility at characteristic height at 300 s. (**a**) Critical group: (**b**) General group; (**c**) 0 group.  
When there remains no longitudinal wind, the fire source is influenced by the continuous rise of the fire plume. Despite a significant amount of CO production, it does not have a substantial impact on the characteristic height. Additionally, due to heptane fuel usage, CO production levels are lower than those in an actual fire scenario. Under altitude effects, CO concentration at human observation heights increases with altitude elevation. When tunnel ventilation wind velocity is at a low level, upwind sides experience ventilation influence resulting in overall low CO concentrations. However, fresh air brought into ventilation is blocked by smoke back-layering leading to intensified incomplete combustion at fire sources, which results in a hazardous area. However, when the wind velocity exceeds the critical velocity, the introduction of fresh air through ventilation remains unaffected by no existing smoke back-layering. As the proportion of fresh air increases within the tunnel, a significant portion undergoes complete combustion at the source of the fire, resulting in reduced CO concentrations. Although influenced by altitude, the CO concentration in high-altitude tunnels is higher than those in low-altitude tunnels. However, CO concentration still remains significantly below the hazardous level.
# Hybrid Modulation Strategy to Eliminate Current Distortion for PV Grid-Tied H6 Inverter
## 7 Conclusions  
This paper has presented a novel hybrid modulation technique to enable the H6 grid-tied inverter to operate with low distortion under non-unity power factor. The simulations and measurements validate the approach and demonstrate that high efficiency is also possible. The core of the proposed HMM is that the bridge can still output unipolar SPWM voltage waveform for the H6 inverter topology with no common-mode leakage current, while still maintaining current control and low distortion under all output power factors. It has been shown that when the MMII has been adopted in the uncontrollable region, it has improved the waveform profile of the grid-tied current and has reduced the THD. The modulation techniques can also be applied to other common inverter topologies. Based on the assumption that frequency fluctuation are inevitable on the grid with high penetrations of distributed energy resources, this paper proposes a new frequency self-adaptive PIR (FSAPIR) controller for the H6 grid-tied inverter and validated by experiments that the H6 grid-tied current can track the reference signal without error under this FSAPIR control. Thus, the inverter system can accurately regulate the active and reactive current.  
[PERSON] and [PERSON] conceived the main idea and performed the tests, conducted data analysis, and wrote the manuscript. [PERSON], [PERSON], and [PERSON] contributed to the test and simulation modelling. [PERSON] gave suggestions and revises.  
This research was funded by the provincial key points Enterprise Research Institute of WANMA Group electric vehicle intelligent charging.  
The authors declare no conflict of interest.
## 5 Experiments and Results
### Experiment 4: FT-UMT Dataset  
As opposed to the other datasets considered in this study, the FT-UMT dataset poses more challenges because it also takes into account the user expressions, such as sadness and anger, which increases the intraclass variations. As can be seen in Table 2 and Figure 11, ReSVM exhibits the best performance accuracy as compared to the other approaches on this dataset.  
Figure 10: Comparison of ReSVM with existing state-of-the-art approaches in terms of percentage accuracy on DrivFace dataset.
## 3 Consolidation Properties
### Principal Component Analysis of Soil Stabilization Effects on Void Ratio and Porosity  
Principal component analysis provides quantitative insights into the relationships between stabilizing agents and soil microstructural parameters. The analysis demonstrates that void ratio and porosity changes are predominantly governed by a single underlying mechanism, with PC1 explaining 90.33% and 98.32% of variance for void ratio and porosity, respectively. The addition of PC2 increases the cumulative variance explanation to 98.33% for void ratio and 99.31% for porosity, indicating the near-complete capture of the data structure (Figure 6a,b). The steep decline in scree plots after PC1 for both parameters confirms that soil densification through chemical stabilization can be characterized as essentially a single-dimensional phenomenon. This dominant influence of the first component validates that the combined lime-cement stabilizer content serves as the primary determinant of microstructural modification, with secondary factors contributing minimally to the overall variance. Distinct behavioral patterns emerge between the two parameters in their PCA biplots (Figure 6c,d). Porosity exhibits more compact clustering with reduced scatter, indicating greater predictability and consistency in response to stabilizer additions. Conversely, the void ratio displays moderate clustering with increased variability, suggesting higher sensitivity to material heterogeneity and experimental factors. This differential response pattern indicates that porosity follows more deterministic pathways during chemical stabilization processes.  
Both parameters demonstrate strong negative correlations with lime content, as evidenced by the color gradients in the biplots. This relationship quantitatively confirms the  
Figure 6: Principal component analysis of lime-cement stabilized soil: (**a**). Scree plot for void ratio, (**b**). Scree plot for porosity, (**c**). Principal component biplot for void ratio, and (**d**). Principal component biplot for porosity.  
mechanistic understanding of lime-induced densification through calcium ion-mediated flocculation and clay particle agglomeration. The cement component enhances this densification through cementitious bond formation, creating a progressively compact soil matrix with reduced interparticle spacing and diminished pore volume. The exceptional variance explanations achieved through PCA validate this multivariate approach for characterizing soil stabilization mechanisms.
## 2 Experimental Details
### Experimental Procedures
#### 2.3.1 Gravimetric Analysis  
Prior to immersion in the fluids under study (water, dielectric fluid and cutting emulsion), the adhesive bulk plates were manually sanded with 800 grit sandpaper in order to remove any impurities from the surface. The plates were then fully dried in silica for two weeks at 70 \({}^{\circ}\)C to guarantee that the initial state of the adhesive contained no internal moisture. The mass of the dried bulk plates was measured before the specimens were immersed in the fluids. While submerged, the plates had no contact with each other or the container, being completely covered in the respective fluids, so that the area available for absorption was the same for all specimens. Each container was placed in a climatic chamber at the desired temperature and was periodically removed for measurements of the sample mass. The plates were dried with a paper towel every time they were removed from the fluid and their mass was measured on a microbalance with 0.1 mg of accuracy. The moisture uptake for each specimen (\(M_{t}\)) was calculated by Equation (1).  
\[M_{t}=\frac{m_{t}-m_{0}}{m_{0}}\times 100\ (\%) \tag{1}\]  
where \(m_{t}\) is the mass of the bulk plate at each measurement and \(m_{0}\) is the initial mass of the dried plate [26]. For each fluid, four specimens were measured.  
For a normal Fickian uptake, the measurements would be considered complete when the fluid content was stable, meaning that the adhesive plates were incapable of absorbing more fluid. However, as will be seen in a future section, the sorption behaviour of the adhesive is more compatible with a non-Fickian approach. Therefore, the measurements were stopped when it was possible to find a properly fitted curve, following non-Fickian diffusion equations.  
It is also crucial to note that this analysis did not cover cleaning fluids such as petroleum oil and ultrasound fluid for a number of reasons. First off, since the ultrasonic fluid is a more damaging fluid, the damage aspect is more significant than the degradation component. Furthermore, compared with other fluids, such as water and cutting emulsion, the contact times with these cleaning fluids are several orders of magnitude shorter and do not represent a condition in which long term absorption data are valuable.
## 6 Lab-Built Scenario and Benchmarking
### Security Testing journey
#### 6.2.3 Security Testing Scenario: After Final Hardening  
This final test was conducted after implementing the final security measures, including full SD card encryption and robust root access controls, to address the previously identified physical and privilege escalation vulnerabilities.  
**Outsider Perspective:**  
The device demonstrated exceptional resilience. Network scans again confirmed a complete lack of a remote attack surface. Critically, the physical attack vector identified in the previous scenario was closed. When we physically removed the SD card, it presented as an encrypted volume. All attempts to mount the filesystem or perform an offline password reset failed. The GPIO pins were also disabled. While the UART serial console remained physically accessible, it was no longer a viable attack vector. Without the ability to tamper with the SD card or find an easy root escalation path, the console simply presented a secure login prompt for which we had no credentials. Consequently, we were unable to gain any access to the system via external or physical means.  
**Insider Perspective:**  
The robust access controls and secure development workflows remained consistently effective. Crucially, the final hardening addressed the critical blind spot concerning out-of-band root access. With the implementation of new access controls and policies for root users, attempts to escalate privileges, even from an authenticated administrative account, were no longer trivial and required explicit validation that could not be easily circumvented. The \"never trust, always verify\" principle was successfully extended to the deepest system layers, significantly mitigating the risk of an insider gaining unauthorized root privileges.
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 2 Related Work
### Related Data Acquisition Methods
#### 2.2.2 Simulated UAV Data Acquisition Method  
In 2015, [PERSON] et al. [16] developed the UNSW-NB15 data set, used three virtual servers as the experimental environment, and captured 49 different network traffic characteristics. It included nine different types of network attacks and some normal data flow characteristics. In 2021, [PERSON] et al. [17] proposed that GPS spoofing and jamming are the most common attacks against drones, but conducting these experimental studies in many fields may take much work. The development of this data set used the Holybro-S500 drone platform and Hack RF to jam and deceive the drone. They implemented log collection in the case of UAV jamming and spoofing. In 2021, [PERSON] et al. [18] created an IoT test platform for the medical industry and developed an IoT intrusion detection data set called ECU-IoHT. This data set reflects different types of network attacks, which contains the characteristics of different networks under various attacks.  
It can be seen that when studying UAV fault detection or anomaly detection models, the research on the model is subject to the data. However, the UAV data has problems, such as difficulty accessing drone logs due to data encryption, inconsistent descriptions of drone logs, and unbalanced positive and negative samples. These problems limit the further development of anomaly detection models. This paper proposes a method for acquiring and processing UAV fault data in a simulation environment, that is, a method based on Time Line Modeling (TLM). Experiments proved that the UAV fault data obtained by the TLM method can be effectively applied to the training of machine learning models.