esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Discussion and Conclusions
### Conclusions  
The main conclusions of this study are as follows:  
* Field geological surveys were conducted to analyze the geological, topographical, and hydrological conditions contributing to debris flow formation in Dashing Gully. Key topographic elements (slope, aspect, elevation, and channel bed gradient), geological elements (stratum lithology), and hydrological elements were determined. The basin is characterized by numerous tributary gullies in the basin, and there are sufficient solid sources. The confirmed solid source reserves are about 20 \(\times\) 10\({}^{4}\) m\({}^{3}\). There is sufficient rainfall in summer, which can easily cause mudslides, seriously threatening the lives and property safety of 13 households in Dashing Village.
* FIO-2D software was employed to numerically simulate mudslide events in Dashing Gully under three different rainfall frequencies. The simulations provided critical data on mudslide velocity and deposition depth. Results indicated that mudslide velocity increases with increasing rainfall intensity, and both the extent and depth of deposition also increase with rainfall intensity. Prior to the implementation of mitigation measures, simulations of a 100-year rainfall event revealed a maximum mudslide velocity of 3.5 m/s and a maximum deposition depth of 4.3 m. Hazard zoning classified the gully outlet as a high-risk area under these conditions.
* Following the construction of check dams, additional simulations were conducted for a 100-year rainfall frequency event. The results demonstrated that while mudslide velocities remained relatively unchanged, the check dams effectively trapped a significant volume of sediment. Notably, the maximum deposition depth decreased from 4.3 m to 1.9 m, with maximum deposition now occurring at the check dam locations. Furthermore, post-mitigation deposition depths throughout the gully remained below 0.5 m. Hazard assessments indicated that high-risk zones were eliminated, and apart from a medium-risk area adjacent to the check dams, the entire gully was classified as low-risk. Additional simulations incorporating two more check dams downstream of the gully outlet did not reveal any significant improvement in mitigation effectiveness. Consequently, considering economic and safety factors, check dams were strategically placed at the confluence and bend locations to optimize debris flow mitigation.  
Methodology, X.J.; Software, X.J.; Validation, Y.L.; Formal analysis, J.L.; Investigation, Y.L.; Writing--original draft, X.J.; Writing--review & editing, J.L.; Supervision, J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.
# Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks
## 2 Materials and Methods
### Combined Loss Function  
In the context of landslide segmentation, the task can be viewed as a binary classification problem. However, because of the imbalanced ratio between positive samples (i.e., landslides) and negative samples (i.e., backgrounds), the network model often gets stuck in a local optimum, resulting in a suboptimal segmentation performance. To address this issue, this paper utilizes a joint loss function consisting of BCE loss and Dice loss. Furthermore, the BCE loss is well suited for binary classification problems, and the Dice loss can alleviate the imbalance between positive and negative samples.  
BCE loss is a loss function commonly used in pixel classification to compare the discrepancy between the model's learned distribution and the true distribution. The calculation formula for BCE loss is shown as follows:  
\[Loss=-\frac{1}{N}\sum_{i=1}^{N}g_{i}\times log(p_{i})+(1-g_{i})\times log(1-p_{ i}), \tag{2}\]  
where \(N\) represents the total number of pixels, \(p_{i}\) denotes the predicted value of the \(i\)-th pixel by the network, with a range of (0, 1), and \(g_{i}\) represents the value of the \(i\)-th pixel in the ground truth label, where positive samples are assigned to \(g_{i}\) with a value of 1; otherwise, negative samples are assigned to \(g_{i}\) with a value of 0.  
Figure 5: The ECA-ASPP structure.  
The Dice loss can alleviate the negative effect of positive and negative sample imbalances. A positive and negative sample imbalance means that most of the region of an image is free of the target, while only a small portion of the region contains the target. The Dice loss focuses on the overlap between the predicted results and the ground truth during the calculation process, rather than simply considering the overall pixel values. It mitigates the issue of sample imbalance to some extent. The calculation formula for Dice loss is depicted as follows:  
\[DiceLoss=1-\frac{2\times\sum\limits_{i=1}^{N}p_{i}\times g_{i}}{\sum\limits_{i =1}^{N}p_{i}+\sum\limits_{i=1}^{N}g_{i}}, \tag{3}\]  
The combined function is denoted as follows:  
\[L=L_{BCE}+L_{D}, \tag{4}\]  
where \(L_{BCE}\) is the BCE Loss, and \(L_{D}\) is the Dice loss.
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
## 2 Principles of Ultrasonic Waves
### Attenuation  
Attenuation can be defined as the loss of a signal's amplitude with increasing propagation distance. Contributions to the attenuation of an ultrasound beam may include absorption, scattering, reflection, refraction, diffraction, interference, and divergence. Among these factors, two major classes of attenuation should be considered in ultrasonic materials characterization. The first is absorption; the absorbed energy is irreversibly lost from transmission through the medium. The lost energy is primarily transformed into heat. The second is scattering; this appears in non-uniform media. In our research, only one medium was considered. Scattering is not the main energy dissipation in our research. The attenuation can be described as  
\[\text{Attenuation}=\alpha\left[\text{dB}/(\text{MHz}\cdot\text{cm})\right]l \left[\text{cm}\right]f[\text{MHz}], \tag{1}\]  
where \(\alpha\) is the attenuation coefficient, \(f\) is the fundamental frequency of the ultrasonic wave, and \(l\) is the distance from the source point. From experiments, spread and far-field effects cause much more energy dissipation, consistent with theory and experimental results.
## 5 Discussion
### Fine-Tuning Impact on GPT-4 Omni Models with Few-Shot Learning  
* Research Question 3: after fine-tuning with few-shot learning, which model in the GPT-4 Omni family demonstrates superior performance?
* Research Statement 3: after fine-tuning with few-shot learning, GPT-4o and GPT-4o-mini both achieve 98.6% accuracy, with GPT-4o-mini offering a resource-efficient alternative.  
Examining the performance of GPT-4o and GPT-4o-mini after fine-tuning with few-shot learning offers insights into the effectiveness of fine-tuning and the strategic advantages of smaller models in targeted applications.  
* High accuracy and comparable performance: Both GPT-4o and GPT-4o-mini achieved a remarkable accuracy of 98.6% after fine-tuning, suggesting that fine-tuning with few-shot learning equipped both models with a deep understanding of fake-related cues and patterns. This high accuracy indicates that fine-tuning enabled these models to internalize task-specific patterns, transforming general-purpose models into highly competent classifiers.
* Fine-tuning efficacy across model sizes: Fine-tuning proved equally effective for both the large and smaller models, suggesting that even a model with fewer parameters, like GPT-4o-mini, can achieve high accuracy when task-specific knowledge is provided through fine-tuning. This reinforces that model scaling is not always necessary for high performance in specialized tasks if effective fine-tuning methods, like few-shot learning, are applied. It also demonstrates that a smaller model, given the right training, can leverage its pre-existing language understanding to learn task-specific requirements efficiently.
* Scalability and flexibility in model deployment: The fact that GPT-4o-mini can achieve comparable performance to GPT-4o after fine-tuning suggests that smaller models in the GPT-4 Omni family can be scaled down without sacrificing substantial accuracy. This scalability is particularly beneficial for businesses or developers looking to deploy multiple models across various tasks, as smaller models require less computational power for deployment and can be trained more quickly [50]. Organizations that need to adapt quickly to new fake news detection patterns, for instance, might find GPT-4o-mini advantageous, as it combines high performance with adaptability and cost-effectiveness.
* Strategic model selection for application needs: For organizations with stringent accuracy standards in fake news detection, both models offer strong choices. However, GPT-4o-mini's identical performance to GPT-4o and its lower computational footprint make it particularly suitable for real-time fake news classifiers, mobile applications, or cloud deployments where resource limitations are a concern [51]. By achieving high accuracy with fewer resources, GPT-4o-mini serves as an example of how model selection can be aligned with specific operational and budgetary needs without compromising on task accuracy [52].
# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
###### Abstract
At the same time, this method has a better suppression effect on the complex background noise of urban surface water body; [PERSON] proposed a feature extracting framework for hyperspectral data, which constructed a structured dictionary to encode spectral information and apply machine learning to map coding coefficients [25]; [PERSON] used three machine-learning algorithms to extract information on rocky desertification in a typical karst region of southwest China, which provide important information on the total area and spatial distribution of different levels of rocky desertification in the study area to support decision making by local governments for sustainable development [26]. Based on the research background above, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on sparse multiclass logistic regression (SMLR) model. SMLR is supported by a complete and rigorous mathematical theory and has strong interpretability. The interaction relationship between original features is expressed as new features by multiplication or division operation, which can be used as a new index for extracting ground feature information. Compared with the index obtained by laboratory research, the method of this article is creative and more flexible in exploring unknown ground feature index.
# Dominant Channel Occupancy for Wi-Fi Backscatter Uplink in Industrial Internet of Things
## 1 Introduction
* The DCO can be applied to all Wi-Fi standards including IEEE 802.11b/g/n that use CSMA/CA operations. In contrast, the existing Wi-Fi backscatter approach utilizes CTS-to-self packets to deal with interference, which is only supported by the Wi-Fi standards released after IEEE 802.11g. Thus, many devices that only conform to IEEE 802.11b may cause interference under the existing approach. * The DCO guarantees reliable burst transmission for Wi-Fi backscatter devices without exchanging additional control packets such as CTS-to-self, regardless of the amount of tag information and number of channel measurements. This is clearly distinguished from the CTS-to-self manner of having a time limit of up to 32 ms. The rest of this paper is organized as follows. Section 2 provides an overview of Wi-Fi backscatter uplink communication. Section 3 presents the detailed operation of the DCO. The experimental simulation is described in Section 4. Finally, we conclude our study in Section 5.
# Fivadmi: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation +
## 2 Related Work
### In-Vehicle E/E Architecture  
An _Electronic Control Unit (ECU)_ can be considered as an embedded computer in vehicles that controls one or more electrical system or subsystems. Typically, different mechanical components (e.g., sensors and actuators) are attached to ECUs. In common scenarios, ECUs receive input from sensors, other ECUs and On-Board Units (OBUs) and control various functionalities by managing the actuators. Modern vehicles incorporate a large number of ECUs. The ECUs generally communicate with each other by means of communication buses, where buses may vary depending on different attributes such as speed, capacity, etc. Some of the commonly used buses are (i) Controller Area Network (CAN), (ii) FlexRay and (iii) CAN with flexible data rate (CAN FD). In-vehicle buses are interconnected following different architectural patterns depending on the complexity, bandwidth and real-time requirements of the vehicle [16; 27; 28; 29]. In the following, we briefly present the most common in-vehicle network architectures.  
**Hierarchical with Central Gateway E/E Architecture**--As shown in Figure 1, in this class of architecture, ECUs are installed into different buses interconnected by a _central gateway_. The hierarchy comes from the assignment of functions to buses with different bandwidths following their real-time and size requirements [27]. The central gateway is responsible to manage the inter-ECU communications and to convert data from one bus format to another bus format [16; 27; 29].  
**Domain-based E/E Architecture**--As shown in Figure 2, in this class of architecture, ECUs are grouped into different _domains_ according to similarity of functionalities [28]. For example, all the ECUs that relate to the powertrain control of the vehicle can be put in one domain. While the functionalities of one domain may be for entertainment and comfort (e.g., audio, telephony), others perform safety-critical functions (e.g., lane keeping, stability control) [1]. All the ECUs in a domain are connected to the same communication bus. The activities of each ECU in a domain are controlled by a single domain controller. Domain controllers are connected through a _common gateway_ to enable communication among ECUs belonging to different domains [16; 27; 29].  
**Single Bus E/E Architecture**--As shown in Figure 3, in this class of architecture all the ECUs are connected to a _single bus_. This pattern is suitable for a less complex architecture when a small number of ECUs are used in a vehicle [6; 16; 27; 29].  
Figure 1: In-Vehicle Hierarchical with Central Gateway E/E Architecture [28].
## 3 System Architecture
### BCH Codes  
To reduce bit error rates in communication systems, BCH has been found to be an effective technique for error correction [26]. Instead of working on individual data bits, BCH codes are a category of cyclic codes that operate on groups of data bits or blocks [27]. They find utility in a variety of applications because of their versatility in fixing many errors and the ease in coding and decoding implementations. The decoding energy consumption of BCH codes is a linear function of both the code word length and the capability of a code to correct errors [28].  
Although BCH codes are derived from Hamming codes, these codes can remedy multi-bit errors while Hamming codes can only correct one-bit faults. Their code generators are well documented [53], making it easy to employ many kinds of BCH codes. This makes them one of the more potent groups of cyclic codes. BCH code generators that are frequently used to create BCH codes can be found for a range of values of \(n\), \(k\), and \(t\), up to a block length of 255 [54], using the table provided in [53].
## 5 Experimental Results and Discussion  
### Case I: External Vibration at the Specified Frequency  
The excitation signals at the frequencies from 8 to 78 Hz are inputted to the shaker to vibrate the structure to validate the proposed semi-active controller. To compare the performance of the proposed  
Figure 19: Real-time embedded code for the NI CRIO 9074.  
Figure 18: Proposed switching algorithm of the fuzzy controller according to the frequency response.  
controller for different vibration modes, we designed five case studies to investigate the vibration absorbing ability of the ATVA. Figures 20-22 compare the vibration responses with no control with the ones of the proposed controller at the frequency of 8, 10, 18, 20, 75 and 77 Hz, where the fuzzy controller is turned on at \(t\) = 2.5 s. The vibration reduction rate (VRR) is defined as follows.  
\[VRR=\frac{RMS\,value\,of\,the\,amplitude\,with\,vibration\,control}{RMS\,value\,of\,the\,original\,amplitude}\]  
Figure 20: Experimental results of the smart structure for the external vibration at 8 and 10 Hz with the stimulating signal of 200 mV\({}_{\rm pk}\). (**a**) the response at 8 Hz; (**b**) the response at 10 Hz.  
\begin{table}
\begin{tabular}{c c c} \hline
**Excitation Frequency (Hz)** & **Type-1 Fuzzy Controller (\%)** & **Type-2 Fuzzy Controller (\%)** \\ \hline
8 & 81.4 & 81.5 \\
10 & 19.9 & 36.1 \\
18 & 38.3 & 40.9 \\
20 & 24.8 & 40.3 \\
77 & 88.6 & 89.7 \\
79 & 37.3 & 54.2 \\ \hline \end{tabular}
\end{table}
Table 10: Experimental results at the frequencies of 8, 10, 18, 20, 75 and 77 Hz using the type-1 and type-2 fuzzy controller.  
Figure 21: Experimental results of the smart structure for the external vibration at 18 and 20 Hz with the stimulating signal of 200 mV\({}_{\text{pk}}\). (**a**) the response at 18 Hz; (**b**) the response at 20 Hz.  
With the external vibration at the frequency close to the first vibration mode of the structure, Figure 20 shows that the type-2 fuzzy controller has better vibration-reduction ability with the frequency of 8 Hz, whose VRR is 81.5%. The VRR with the frequency of 8 Hz is better than the one with the frequency of 10 Hz, whose reduction rate is 36.1%. When the external vibration's frequency is close to the second vibration mode, Figure 21 shows that the proposed controller has the VRR of about 40%. With the external vibration at the frequency close to the third mode of the structure, Figure 22 shows that the proposed type-2 fuzzy controller has the best performance at the frequency of 77 Hz, whose VRR is about 89%. Table 10 summarizes the results at the frequencies of 8, 10, 18, 20, 75 and 77 Hz using the proposed controller, the experimental results is compared with the ones using type-1 fuzzy controller [16] in the previous studies. According to the experimental results,  
Figure 22: Experimental results of the smart structure for the external vibration at 75 and 77 Hz with the stimulating signal of 200 \(\mathrm{mV_{pk}}\). (**a**) the response at 75 Hz; (**b**) the response at 77 Hz.  
the proposed type-2 fuzzy controller has better performance of absorbing vibration than the type-1 fuzzy control.
## 4 Analysis of Test Results
### Pile Load Transfer Mechanism  
The ratio of pile side and tip resistance to the load capacity of a single pile serves as a quantitative indicator of the pile's load transfer mechanism [18]. Table 5 presents the proportions of pile tip-and-side resistance relative to the top load of the pile when the test piles, TP1 and TP2, reach their ultimate bearing capacity state. Upon examination of the table, it is evident that following combined post-grouting, both the pile side resistance and tip resistance of the test piles increased to a certain degree in their ultimate bearing capacity state. Notably, the increase in tip resistance is significantly greater than that of pile side resistance, amounting to approximately twice the pile tip resistance before grouting. Consequently, the ratio of pile tip resistance to the ultimate load for TP1 and TP2 increased from 12.49% and 12.99% to 18.65% and 18.48%, respectively. The improvement observed can be attributed to the mitigation of problems such as mud cake formation along the pile sides and slag accumulation at the pile ends through the application of combined post-grouting. Furthermore, the reinforcement of the soil surrounding the pile contributed to this enhancement. These combined factors resulted in a modification of the load transfer mechanism within the pile. Prior to grouting, the pile top loads of TP1 and TP2 were primarily borne by pile side resistance. However, following combined  
Figure 5: \(\eta_{\text{b}}\)-\(s_{\text{b}}\) curves of test piles: (**a**) TP1; (**b**) TP2.  
post-grouting, although pile side resistance was effectively improved, its proportion in the ultimate bearing capacity decreased notably. Conversely, pile tip resistance exhibited a more substantial improvement, with its proportion in the ultimate bearing capacity also increasing significantly. This shift indicates that the center of gravity of the pile load bearing moved towards the pile end after combined post-grouting, resulting in an earlier and increased contribution of pile tip resistance to the pile top load. In conclusion, combined post-grouting not only enhances the bearing capacity of a single pile but also alters the load transfer mechanism of the pile. This change enables pile tip resistance to participate in bearing the pile top load earlier and respond more rapidly. Consequently, pile tip resistance is greatly improved, and its proportion of the ultimate bearing capacity is significantly increased. These findings further improve the performance of single-pile bearing capacity in cohesive soils.
# Low Temperature Combustion Modeling and Predictive Control of Marine Engines
## 2 Materials and Methods
### Nonlinear RCCI Control-Oriented Model
#### 2.1.4. In-Cylinder Pressure Reproduction and Post-Processing  
The in-cylinder pressure (\(p_{cyt}\)) model is formulated from the first law of thermodynamics by neglecting the loss mechanism and assuming the content of the cylinder as ideal gas with constant specific heats [31]. The compression is assumed to be polytropic. Thus, enabling the utilization of Equation (13) to estimated in-cylinder pressure at \(\theta_{\text{SOC}}\)  
\[p_{\text{SOC}}=p_{\text{IVC}}\left(\frac{V_{\text{IVC}}}{V_{\text{SOC}}} \right)^{n_{c}}. \tag{13}\]  
The model presented in Equation (14) is used estimate in-cylinder pressure rate during the combustion phase.  
\[\frac{dp}{d\theta}=-n_{c}\frac{dV}{d\theta}p+\frac{n_{c}-1}{n_{c}}\frac{dQ_{c} }{d\theta}. \tag{14}\]  
Similarly, the expansion phase can be assumed as polytropic, enabling the estimation of in-cylinder pressure at EVO  
\[p_{\text{EVO}}=p_{\text{EOC}}\left(\frac{V_{\text{EOC}}}{V_{\text{EVO}}} \right)^{n_{c}}. \tag{15}\]  
In Equations (13)-(15) \(\frac{dQ_{c}}{d\theta}\) is the heat release rate (HRR) and \(n_{c}\) is the polytropic coefficient independent of temperature which is already described along with its selection criterion in Section 2.1.1. In this paper \(Q_{c}\) and CHR as well as \(dQ_{c}\) and HRR are used interchangeably.  
The in-cylinder temperature (\(T_{cyt}\)) is obtained from the ideal gas equation using the conditions at IVC [29]:  
\[T_{cyt}=\frac{p_{cyt}\;V\;T_{\text{IVC}}}{p_{\text{IVC}}\;V_{\text{IVC}}}. \tag{16}\]  
Indicated mean effective pressure describes the work delivered to the piston per cycle per unit displaced volume (\(V_{d}\)) [30]:  
\[\text{IMEP}=\frac{1}{V_{d}}\oint\;pdV. \tag{17}\]In this work, Equation (17) is used to estimate \(\text{IMEP}_{\text{gross}}\), which covers the closed cycle from \(\theta_{\text{IVC}}\) to \(\theta_{\text{EVO}}\). Performance metrics, including \(\text{IMEP}_{\text{gross}}\) and the Crank Angle corresponding to \(\chi\%\) Mass Burned (CAX), are derived through the post-processing of pressure signals.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 3 Ocean Applications
### Wind Speed
These applications are examples of where GNSS-R data are complimentary to traditional techniques, which may have limitations where GNSS-R excels, and vice versa.