esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Although the volume of oil spills in inland rivers is relatively small, they serve as habitats for numerous wildlife species, and spills in these rivers can still have significant impacts on local ecosystems and biodiversity. Additionally, inland riverwater bodies are often used for drinking water, irrigation, fishing, and tourism. Thus, oil spill contamination may directly threaten the health and livelihoods of local residents. Despite the smaller scale of inland river pollution, the cleanup and restoration efforts may be constrained by geographical location and environmental conditions, resulting in more challenging and complex cleanup processes. Therefore, in-depth research on inland river oil spills is imperative [2; 24; 25]. With the massive construction of oil and gas pipelines, the increase in the number of pipelines laid along rivers and crossing rivers has increased the risk of river oil spill accidents [26; 27]. Unlike the marine environment, the migration trajectory of oil spills in rivers is relatively unidirectional, the river channel is relatively narrow, the flow and water level of the river are highly variable, and the transport behavior of oil spills is more significantly affected by water flow and wind [28]. In most of the existing river oil spill studies, the oil spill transport behavior of transient spills under some specific scenarios is addressed. The river flow and the wind conditions in the basin usually vary seasonally [29], and the spills from long-distance pipelines are continuous. Therefore, the study of oil spill transport behavior in rivers under the effects of multiple factors can provide more effective support for emergency response to oil spill accidents. In addition, after the occurrence of a sudden oil spill, emergency response measures in the short term can largely reduce the consequences of the accident [23; 30]. The use of numerical simulation to predict the transport state of oil spills takes up a lot of time, which prolongs the emergency response time. With the rapid development in the field of data science and AI, machine learning modeling approaches have been heavily researched in various fields, and new methods have been provided for accurate and efficient prediction of river oil spill transport state [31; 32; 33]. In this study, a two-dimensional hydrodynamic and oil spill model of the Lancang River crossing pipeline section basin was established based on Mike 21 software to simulate the oil spill transport process in the river. The oil spill head location, oil film diffusion area, and pollution zone length were obtained over time. The effects of river flow, wind, and leakage mode on oil spill transport behavior were discussed. An oil spill transport state dataset was established, and an oil spill transport state prediction model was developed using a machine learning algorithm. This study can support the rapid development of emergency response plans for river crossing pipeline oil spill accidents.
## 5 Experimental Results and Discussion and Analysis  
In order to test the effectiveness of the proposed model DAPONet in this paper, we first perform the hidden danger identification with the current mainstream object detection models YOLOv5 [33], YOLOv8 [56], YOLOv9 [57], and YOLOv10 [31] on SVRDD dataset to compare and analyze the superiority of DAPONet in various hidden danger detection. Comparison tests are then conducted on the SVRDD dataset to evaluate the performance of the model in various aspects through seven metrics, namely P, R, mAP50, mAP50-95, Params, FLOPs, and Model Size, to further demonstrate the significant advantages of the model in terms of detection accuracy and computation rate. Subsequently, the confusionmatrix is used to evaluate the classification performance of the model in identifying various types of defects, and to find out the advantages and room for improvement of the model. The performance of the model under different confidence thresholds is further analyzed through the F1-Confidence Curve to find the confidence level that allows the model to achieve the best balance between precision and recall. The generalized object detection performance of DAPONet is then validated on the MS COCO2017 dataset and compared with other lightweight models to assess its adaptability and robustness in resource-constrained environments. Finally, ablation studies are conducted to verify the validity of the individual modules mentioned in this paper in terms of their structure and their specific contribution to the model performance.  
Figure 6 records the changes in edge loss, classification loss, focus loss, and model precision, recall, and average precision for each iteration of the model on the training and test sets. According to the charts, it can be seen that at the beginning of the training period, the model has high values for each loss and converges quickly, while the model metrics rise rapidly, indicating that the model is learning efficiently at this time. With the increase in the number of iterations, the rate of change in each curve gradually decreases, and after about 40 iterations, the curves begin to stabilize, and finally about 300 iterations, the curves basically remain stable.
## 4 FF-OCT and Biomechanics
### Static Elastography Based on Digital Volume Correlation (DVC)
In Figure 3E,G, we show that computing the fractional anisotropy in isotropic and anisotropic polyvinyl alcohol (PVA) polymer gels [96] enables discrimination of the two samples, in a case where FF-OCT fails to detect a difference. In parallel to our work on 3D digital image correlation, we adapt the \"_tactile_\" approaches developed by [PERSON] et al [97] for standard OCT. The principle is to use a thin layer of polymer deposited on the imaging windows to mimic manual palpation at the micrometer scale [98]. Knowing the geometry and the mechanical properties of the polymer layer, it is possible to directly measure the stress at the surface of the sample by measuring the displacement at the surface of the polymer layer. With FF-OCT, the 2D _en face_ displacement can be measured by taking the difference between two phase images taken at the surface of the polymer before and after the compression. This method allows a fast stress measurement at the surface of the sample simultaneously with FF-OCT imaging. This method is particularly interesting in the case of micro-biopsy where different tissues are usually juxtaposed. Figure 3I,J show a measurement on a biopsy of bovine kidney. At the micrometer scale, the stress map shows different mechanical behaviors for the muscle fibers and the kidney at the same stress level. The higher pressure at the muscle fiber level shows that this part of the sample is stiffer than the kidney area because the polymer is more deformed by this tissue. According to an experiment that consists of the observation of the tissue behavior during the compression, the pressure is negative at the kidney level because the tissue tends to move sideways. The negative pressure where there is no tissue can be explained by the fact that the silicone is incompressible and needs to expand somewhere to conserve its total volume. Finally, this picture shows the high resolution of this method because each muscle fiber with 20 \(\upmu\)m diameter can be distinguished by comparing the FF-OCT image and the stress map. FF-OCT static elastography measurements can be performed with a standard FF-OCT system, such as the LLTech commercial system [37], or can be associated with other multimodal FF-OCT systems to diversify the types of contrast available. However, this technique can be computationally intensive, and requires the calculation of two 3D stacks and a physical compression of the sample. In total, the acquisition time can last for one to a few minutes, and the processing time for several hours. FF-OCT static elastography measurements only require a static FF-OCT signal to correlate, so penetration depth is only limited by FF-OCT capabilities.
## 2 Background and Rationale
### Fasin  
FASIN [7] uses the voting method to verify real reports, as in PVFS. However, FASIN fundamentally differs from PVFS in the selection of intermediate verification nodes. Initially, in the path discovery process, each cluster head acquires the IDs of the upstream CHs, their distances to the e-CH and its own distance to the BS, as well as information about the energy status of the CHs [7; 11; 29]. Initially the e-CH randomly chooses intermediate verification CHs on all \(c\) different routing paths available to the e-CH. Each routing path contains \(L\) verification nodes, which is equal to the cluster size. After the selection of a verification node, the e-CH notifies each node in its cluster to securely exchange their generation keys with the corresponding verification nodes using the session key generated through pairwise key establishment protocols. FASIN makes use of the fuzzy inferencing engine implemented at the e-CH for the selection of the verification nodes during the operation of the network. The remaining energy level and the distance of the verification nodes, and the current attack situation in the network are considered during the verification nodes selection. FASIN improves the proximity of the verification nodes in response to an increase in attack rate, increases the network energy efficacy and improves the network operational life time.  
FASIN helps choose verification nodes dynamically and can be used to improve the performance and fitness of the otherwise less fit paths because they are either long or the verification nodes on those paths are relatively farther apart than those on the fittest path.  
Therefore, we propose using FASIN with a fuzzy adaptive path selection method for data forwarding to improve the selection chances of the paths that are otherwise least likely to be selected for data forwarding unless a node or route failure occurs.
# An Overview of Emergency Communication Networks
## 2 Materials and Methods  
A systematic literature review was conducted by following a well-defined process to retrieve articles most relevant to this research's scope. Our goal was to study the emergency communication network following the five main stages of the systematic mapping process, which was proposed by [PERSON] [46]. The first stage was analysis of the key research problems and determining the relevant research direction by identifying the key research problems. The second stage was article retrieval; by narrowing the scope of the search engines, all the literature related to the research topic on the online resources was searched. These search engines included China National Knowledge Infrastructure (CNKI), Springer Link, Web of Science, Elsevier ScienceDirect, and IEEE Xplore, which were used for the extraction of high impact factor papers. The keywords and strings related to the research topic for a semantic search were used to retrieve the articles most closely related to the research problem. For this purpose, several groups of keywords were formulated, namely emergency communication network, wireless communication, satellite network, cellular network, broadband cluster network, and ad hoc network. The third stage was screening of all relevant papers according to the title, selection of the literature related to the research direction, and exclusion of the literature with low content quality. The fourth stage was content analysis after reading the retrieved articles. By reading abstracts and keywords to determine their relevance to the current research topic, articles irrelevant to the research direction were deleted. The last stage was classification and collection of all the required information, and classification of the retrieved articles.
# Data Conversion Strategies for Effective Aviation Technical Support as a Service
## 4 Discussion
### Limitations of This Study  
This study provides important insights into the implementation of Aviation Technical Support as a Service (ATSaaS) for small airlines, particularly the comparison of centralized and decentralized data conversion architectures. However, it has several limitations that must be acknowledged.  
The research is primarily conceptual, relying on theoretical models and generalized assumptions to evaluate the life cycle costs of the two architectures. While the mathematical framework offers a solid basis for analysis, real-world validations and empirical data are limited.  
This study assumes uniform adherence to data standards and protocols across stakeholders in the ATSaaS ecosystem. In practice, achieving such standardization can be  
\begin{table}
\begin{tabular}{c c c} \hline
**Security Factor** & **Centralized ATSaaS** & **Decentralized ATSaaS** \\ \hline Risk of Single Point of Failure & High (centralized attack risk) & Low (distributed architecture) \\ \hline Data Breach Impact & High (large-scale data exposure) & Medium (localized breaches) \\ \hline Access Control Complexity & Low (centralized role management) & High (multi-organization identity control) \\ \hline Regulatory Compliance & Easier (centralized security standards) & Harder (varying regional security policies) \\ \hline Attack Surface & Limited (one main entry point) & Large (multiple independent entry points) \\ \hline Data Integrity and Consistency & Strong (controlled updates) & Weak (potential synchronization issues) \\ \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of cybersecurity risks in ATSaaS architectures.  
challenging due to variations in organizational capabilities, legacy systems, and regional regulations.  
This study does not address potential cybersecurity risks inherent in both centralized and decentralized systems.  
Addressing these limitations in future research through real-world pilots, stakeholder engagement, and a broader exploration of operational and security challenges will enhance the robustness and practical relevance of the findings.
# Design and Evaluation of an Acoustic Modern
## 2 System Design
### Hardware Design  
The flow chart of the transmitter and receiver is provided in Figure 6. While the signal generation before DAC (Digital-to-Analog Converter) in the transmitter as well as the demodulation after ADC in the receiver is implemented in the processor in forms of digital signal processing, the purpose of the analog part in the transmitter and receiver is to guarantee efficient emission and high-quality signal reception, respectively.  
Because the bandwidth of the proposed MODEM (Modulator and Demodulator) is within the audio frequency range, the general audio interface integrated circuit chip WM8978 is directly adopted for the DAC of the emission signal, which offers convenient control registers for the processor. The piezoelectric transducer is adopted in our design, the bandwidth of which is 13-18 kHz with omni-directivity in the horizon to facilitate the communication application. As most of the piezoelectric transducer exhibits capacitive characteristics, an inductance circuit is used as an electrical impedance matching network to improve the transmitting efficiency.  
In the receiver, the low-noise AD603 integrated circuit chip is selected as the preamplifier because the gain of which can be controlled by extra voltage to provide accurate, pin-selectable gains of \(-11\) to \(+31\) dB with a bandwidth of 90 MHz, or \(+9\) to \(+51\) dB with a bandwidth of 9 MHz. For the band-pass filter, the continuous-time active filter MAX274 integrated circuit chip is employed as it provides four second-order sections to enable the implementation of an eighth-order band-pass filter.  
Figure 6: Block chart of the transmitter (**a**) and receiver (**b**). DAC = Digital-to-Analog Converter, ADC = Analog-to-Digital Converter.
## 3 System Model
### Reception State Adjustment Mechanism
\(T\) represents the time required for the node to process all the packets in its buffer. Since a packet must wait for the transmission of previous packets to complete, for a newly arrived packet in node \(j\), we calculate its waiting time as:  
\[T_{j}=\sum_{num=1}^{N}t_{num} \tag{20}\]  
where \(N\) denotes the number of packets in the current node. \(t_{num}\) is the time required to transmit a packet when it has none of the previous packets, and it can be further expressed as:  
\[t_{num}=\sum_{k\in\mathcal{CN}(j)}t_{jk}*\mathbb{P}_{jk} \tag{21}\]  
\(k\) is a candidate node of \(j\). \(t_{jk}\) represents the time required for node \(j\) to forward a packet to node \(k\). \(CN(j)\) denotes the set of candidate nodes of \(j\). \(\mathbb{P}_{jk}\) indicates the probability that node \(j\) selects node \(k\) as the next hop. According to the IEEE 802.11b protocol [32], when a node prepares to transmit a packet, it must ensure there is no other interfering node sending simultaneously [33; 34; 35]. At the same time, it cannot interfere with the nearby receiver [17]. It should be noted that interfering nodes include exposed nodes and hidden nodes [36] as shown in Figure 7.  
If there is no other interfering node, the time required to transmit a packet from a node to its neighbor immediately is \(t_{unit}=L/R+t_{0}\), where \(t_{0}\) is the time required to exchange \(RTS\), \(CTS\) and \(ACK\) frames [37]. Based on the above analysis, if node \(j\) prepares to send a data packet to node \(k\), it must wait until \(z_{jk}\) packets of other interfering nodes have been completed. The time taken by node \(i\) sending a packet to node \(k\) is the sum of two terms: (i) the time required waiting for other interference nodes to send \(z_{jk}\) packets \((z_{jk}*t_{unit})\), (ii) the time required for the current node to send a packet \((t_{unit})\). Thus:  
\[t_{jk}=\Big{(}z_{jk}+1\Big{)}*t_{unit} \tag{22}\]  
Figure 7: Interfering nodes. Let the probability that node j sends a packet immediately after the communication channel becomes idle be \(prob_{j,k}\), then we can obtain:  
\[P\left(z_{jk}=n\right)=\left(1-prob_{j,k}\right)^{n}prob_{j,k}. \tag{23}\]  
\[E\left(z_{jk}\right)=\frac{1-prob_{j,k}}{prob_{j,k}} \tag{24}\]  
\[E\left(t_{jk}\right)=\left[E\left(z_{jk}\right)+1\right]t_{unit}=\frac{t_{unit }}{prob_{j,k}} \tag{25}\]  
According to [30], assuming \(\alpha_{jk}\) and \(\beta_{jk}\) are the idle probabilities of the node \(j\)'s and node \(k\)'s neighbor nodes, respectively, then \(prob_{j,k}\) can be represented as:  
\[prob_{j,k}=\alpha_{jk}*\beta_{jk}=\prod_{a\in\epsilon_{j}}(1-\varphi_{a})* \prod_{b\in\epsilon_{k}}(1-\psi_{b}) \tag{26}\]  
where \(\varphi_{a}\) is the proportion of time taken by node \(a\) receiving packet; \(\psi_{b}\) denotes the proportion of time taken by node \(b\) transmitting packets; \(\epsilon_{j}\) and \(\epsilon_{k}\) represent the interference node sets of node \(j\) and \(k\), respectively.
# Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
## 4 Experiment and Analysis
### Experimental Procedure  
The experimental setup included the following: Windows 10 operating system, Python 3.6, TensorFlow-GPU 1.13.2, and Keras 2.1.5.  
During the experiment, the epoch count was set at 200 and the batch size at 2. To evaluate model performance, this study conducted comparative experiments between two versions of the Yolox-tiny algorithm:  
1. The original Yolox-tiny algorithm as the first model variant;
2. The enhanced Yolox-tiny as the second model variant.  
\begin{table}
\begin{tabular}{c c c} \hline
**Pest Name** & **Pest Number** & **Quantity** \\ \hline Rice Stem Borer & 7 & 90 \\ White-backed Planthopper & 9 & 120 \\ Brown Planthopper & 10 & 150 \\ Yellow-legged Predatory Bug & 148 & 59 \\ Eight-spotted Forester Moth & 156 & 247 \\ Cricket & 256 & 87 \\ Oleander Hawk-moth & 280 & 40 \\ \hline \end{tabular}
\end{table}
Table 2: Pest Types and Quantities in Initial Data.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Pest Name** & **Original Set** & **Augmented Set** & **Training Set** & **Testing Set** & **Validation Set** \\ \hline Rice Stem Borer & 53 & 159 & 111 & 32 & 16 \\ White-backed Planthopper & 57 & 171 & 120 & 34 & 17 \\ Brown Planthopper & 51 & 153 & 107 & 31 & 15 \\ Yellow-legged Predatory Bug & 55 & 165 & 116 & 33 & 17 \\ Eight-spotted Forester Moth & 58 & 174 & 122 & 35 & 17 \\ Cricket & 50 & 150 & 105 & 30 & 15 \\ Oleander Hawk-moth & 40 & 120 & 84 & 24 & 12 \\ \hline \end{tabular}
\end{table}
Table 3: Data on Pest Counts After Image Preprocessing.
## 7 Security and Privacy Challenges and Limitations in AMRSys
### Data Confidentiality of a Channel between the User and Robot  
This interaction could be done in multiple ways. Depending on the nature of the connection, the channel could be considered or made secure or not secure.
## 3 Results and Discussion  
### Compressive Strength and Elastic Modulus  
The mean compressive strength and elastic modulus of alkali-activated slag concrete samples at 28 days were 43.53 MPa (with standard deviation of 3.57) and 15.3 GPa (with standard deviation of 0.55), respectively. The AAS concrete showed a higher compressive strength (in the case of a _w/c_ ratio = 0.5) but a lower elastic modulus than OPC concrete.  
Compared with OPC, the hydration product of AAS was C-S-H with a low H/S (H\({}_{2}\)O/Si) [24], which means the hydration of AAS requires less water. Furthermore, the adsorbed, interlayer, and capillary water in AAS paste [25] was observed, which can support the continuation of hydration. However, we used a w/c ratio of 0.5 to ensure the fluidity for casting. As Figure 1b shows, the fresh mixture of AAS remained thick at a high w/c ratio. The extra water, which does not activate with the slag, will evaporate as time passes. The loss of water, in turn, causes more microcracks, thus reducing the mechanical strength. We attempted to use a water reducer of Portland cement to reduce the water consumption and improve the strength, which unfortunately did not achieve the desired result. The utilization of a water reducer suitable for AAS is worth further investigation.  
Figure 3: Self-resisting loading frame. (**a**) creep laboratory; (**b**) part name of the frame.  
Based on the current models, the elastic modulus of OPC concrete with the same compressive strength is approximately 31.2 to 36.5 GPa, as shown in Table 3.