esa-sceva/satcom-chunk-collection huggingface dataset:
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
###### Abstract  
Handshaking is a common technique used to avoid collisions in terrestrial and underwater content-based networks. However, due to the long propagation delay of the underwater acoustic channel, the conventional handshaking mechanism, which only allows one successful handshake and one pair of nodes to communicate per transmission cycle, becomes less effective in underwater acoustic networks. This paper proposes a new distributed scheduling method for underwater acoustic networks that supports multiple handshakes and concurrent transmissions in one transmission cycle for one-hop clusters. A deterministic scheduling algorithm was developed to optimize the sending sequence and time of the source nodes jointly so that the total data transmission time is shortened while avoiding collisions among multiple concurrent transmissions. The deterministic scheduling algorithm can also reduce the scheduling overhead and enables the synchronization of the data concurrent transmissions in a distributed manner via the standard two-way handshaking. Simulation results show that the proposed method outperforms several conventional underwater medium access control protocols in normalized throughput, packet delivery rate, average end-to-end delay, and average energy consumption.  
Keywords:underwater acoustic networks; handshaking; concurrent transmission; distributed scheduling +
Footnote †: journal:
###### Abstract
In ensemble-based one-class classification, a combination of multiple one-class classifiers is utilized collectively to benefit from each for improved accuracy. They calculate a mixture value of novelty scores by summarizing multiple novelty scores of individually computed one-class classifiers. Ensemble-based OCC methods include tree-based methods, such as isolation forest (iForest), and clustering-based ensemble methods. In this paper, a model-free, transmissibility-based methodology for structural damage assessment is proposed, addressing both detection and localization tasks using only measured data. The motivation stems from the practical challenges faced in civil engineering SHM, such as limited access to input excitation data, the complexity of modeling large-scale or historic structures, and the need for reliable damage-sensitive features that do not require prior knowledge of damage scenarios. To overcome these limitations, the damage detection problem is formulated as a One-Class Classification (OCC) task, where normalized transmissibility differences serve as the damage-sensitive features. Unlike previous studies that rely on labeled damage data or finite element models, this framework operates in an unsupervised manner using data solely from the undamaged condition. A key contribution of this study lies in the integration of transmissibility-based features with a comparative evaluation of multiple OCC techniques, a combination that has not been systematically explored in the literature. Furthermore, a direct performance comparison is conducted with a modal flexibility-based approach, demonstrating the superior sensitivity of the proposed method to local stiffness changes under noisy and constrained sensor conditions. The methodology is validated on a scaled model of a historic masonry arch bridge, highlighting its applicability to complex structures where idealized assumptions often fail, thereby contributing toward the practical implementation of data-driven damage localization strategies. The remainder of the paper is organized as follows. A brief background on the fundamentals of transmissibility functions is provided, followed by a discussion of OCC techniques that exploit offline learning principles for damage detection. Next, a damage localization scheme is presented, formulating a localization index to identify maximum transmissibility differences across a range of frequencies. The subsequent sections offer verification of the proposed methodology, featuring numerical simulations conducted on a fixed-fixed beam and experimental data collected from a masonry arch bridge model. Finally, the paper concludes with a summary of the proposed methodology, highlighting its strengths and challenges.
A model combining path loss and multipath is rarely considered. In this contribution, which is an extension of a conference paper [24], we present a simple model to characterize the attenuation due to the isolated trees in an air-to-air communication channel occurring between a static transmitter and a mobile user which moves linearly toward the tree. The developed channel model also considers parameters due to the multipath presence obtaining the value of the delay spread parameter. Since an accurate modeling of the propagation of radio waves through tree foliage, generally requires accurate electromagnetic description of the tree geometry, including its branches and leaves,the radio characterization was performed by means of simulations based on 3D Ray Launching software, where the specific material parameters of the vegetation elements are considered, such as dielectric constant and conductivity. A geometrical and dielectric model of the trees were created and introduced in the simulation software for two species: pine and oak. The scenario simulated and measured at 2.4 GHz to determine the effects of signal level blockage consisted of a medium-size single oak tree immersed in a forest. The path loss was estimated as dependent of the radio link range. Simulation and measurement results corroborate the existence of a double zone within the path loss curve due to different dominant propagation mechanisms: scattering (near the tree) and line of sight obstruction (far from the tree). Furthermore, 2D planes of delay spread values obtained by simulation similarly reflect this two-zone propagation model. The measurements were carried out using devices operating at the band of 2.4 GHz. For the isolated tree case, a specific forest (called Chandebrito, situated in Galicia, Spain) composed of different type of trees was the chosen scenario, since it suffered a fire and some isolated trees can be found there nowadays. On the other hand, the Orgi Forest, located in Navarra, Spain, was the selected scenario for the analysis of non-isolated tree case. The paper is organized as follows. In Section 2, the radio channel characterization for a D2D communications blocked by an isolated single tree is presented. Both simulations by the in-house developed 3D Ray Launching algorithm and measurements in a real forest environment are shown. In Section 3, the same approach is used for the radio channel characterization when the blocking tree is a non-isolated single tree, i.e., when dense vegetation is present in the surroundings. Section 4, Discussion, closes the paper.
# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 2 The Development and Main Research Contents of Inversion Theory
### Development of Inversion Theory
#### 2.1.3 Innovation Phase  
From the early 1990s to the early 21 st century, inversion theory underwent an innovative stage. During this period, the uncertainty inversion analysis method, which takes into account the self-randomness of rock and soil, rapidly developed. Inversion analysis also expanded its application scope from single parameter identification problems to rock and soil structure identification. Additionally, a numerical inversion method was proposed by combining the finite element method with the inversion analysis method. This method considers the influence of the design scheme and construction process. Machine learning was introduced into numerical analysis, and a research method was proposed that considers the dynamic changes in the excavation process. The intelligent rock mechanics method was also introduced through the use of genetic algorithms and particle swarm optimization algorithms in the numerical analysis model. This method has been successfully applied in practical engineering.
# Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation
## 3 Prototypical Application to Cut-In Dataset
### Cut-In Scenario Definition, Detection, and Grouping  
The data set incorporated in this study was collected on German highways with four different instrumented vehicles. The vehicles were equipped with a mid-range front radar, a mono camera, and measurement hardware devices to continuously record the naturalistic driving behaviour of both the measurement vehicle and the surrounding objects. As the measurement devices of the vehicles are not recognizable from outside of the vehicles, the collected surrounding objects and vehicle data can be regarded as non-biased. The data comprised a total of 123,225 km driven kilometres and 1159 h, with neither automation function activated nor instructions for the drivers.  
Following data post-processing, to generate clear lane-related object tracks, cut-in scenarios from the right were detected, following the definitions above and rule-based detection algorithms, extended by a dual approach for lane change detection, as elaborated in detail in [39]. For the current study, the beginning of a cut-in scenario was set when the challenging vehicle's lateral distance to its left lane became lower than 1.5 m and had a positive lateral velocity. The end of the cut-in was set when the lateral distance from the challenging vehicle to its right lane (after crossing it) exceeded 1 m. The application of the detection algorithms and further data set clearing steps resulted in a total of 2294 clear cut-in scenarios that formed the basis of the current study application. For each of the detected scenarios, the required VuT's and challenging vehicle's parameters, evolving over the scenario duration, were saved as illustrated in Figure 2. The detected time of the evolving scenario parameters were abstracted by taking the characteristic values of the relevant descriptive parameters as summarized in Table 1. Subsequently, the risk potential value for each scenario was calculated. Based on these results, the detected scenarios were grouped into a relatively more severe subset and an exposure subset, which comprised of 229 and 2065 cut-in scenarios, respectively.
Slim-YOLO: An Improved Sugarcane Tail Tip Recognition Algorithm Based on YOLO1n for Complex Field Environments  
[PERSON]  
1 Guangxi Key Laboratory of Hybrid Computing and Integrated Circuit Design and Analysis, School of Artificial Intelligence, Guangqi University for Nationalities, Nanning 530006, China 12 Guangxi Zhuang Autonomous Region Intelligent Visual Collaborative Robot Engineering Research Center, Nanning 530006, China; [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (K.L.) 2  
[PERSON]  
5 School of Physics and Electronic Information, Guangqi University for Nationalities, Nanning 530006, China [EMAIL_ADDRESS] (Y.C.); [EMAIL_ADDRESS] (Q.L.); [EMAIL_ADDRESS] (Q.L.) 2  
[PERSON]  
2 Guangxi University Engineering Research Center for Multimodal Information Intelligent Sensing Processing and Application, Nanning 530006, China 23  
flow channel, and, at the same time, affect the effect of leaf stripping and increase the impurity rate [4]. In traditional sugarcane harvesting machinery, in the harvesting process, the cutting position of the cane tip is determined by the operator according to naked eye judgment (the height of the top cutter is not changed during the cutting process). There is a great deal of subjectivity and arbitrariness. The cutting position is on the low side, resulting in a waste of sugarcane; the cutting position is on the high side, resulting in high inclusions of raw sugarcane [5]. Therefore, it is of great significance to realize the high-precision recognition of sugarcane tails under the complex environment in the field and to strengthen the research on intelligent weed removal of sugarcane harvesters. The main contributions of this paper are as follows:  
* To address the challenge of identifying the cutting point of sugarcane tail tips--caused by variations in sugarcane morphology, extensive leaf wrapping around the cutting area, and potential occlusion--we propose a method for recognizing the entire tail tip region instead of a single cutting point.
* We construct a custom sugarcane tail tip dataset collected from real-field complex environments and further enhance it using data augmentation techniques to simulate natural factors such as lighting variations, thereby improving the model's generalization ability and robustness.
* We propose Slim-YOLO, an improved YOLO11n-based algorithm for sugarcane tail tip recognition in complex environments. This model achieves accurate detection while meeting real-time processing requirements for embedded deployment, striking an optimal balance between detection accuracy and inference speed.
# Aircraft Target Interpretation Based on SAR Images
## 2 Related Work
### YOLOv5s Network  
YOLOv5 was proposed in June 2020 and provided four models with different parameters, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. YOLOv5s is the lightest model. It runs fast, but the precision is lower than the other three models. YOLOv5s consists primarily of input, backbone, neck, and head. The YOLOv5s architecture is shown in Figure 1. Our improvement method is based on YOLOv5s.  
The input section is responsible for data augmentation. The data augmentation method can generate new images by processing input images, which can enhance the diversity of the datasets and generalization ability, enrich the background information of input images, and improve the convergence speed of the network. The backbone section is used to extract image features. It contains Focus, CBL, CSP, and SPP structures. Focus can increase the feature dimension of the channel by slicing input images, and it can realize down-sampling without information loss; CBL can further extract the image feature by convolution, batch normalization, and leaky-ReLU operations; CSP can reduce information loss during feature transmission, improve learning ability, and reduce the amount of computation; SPP can convert the feature map of arbitrary size into a feature map of fixed size so that the network can input images of arbitrary scale. The neck section is used for feature fusion of different reception fields. It adopts the PANET structure [30], which is designed based on the feature pyramid network (FPN) structure [31]. It integrates down-top and top-down bidirectional information transmission paths and merges the feature map of different reception fields. Each feature layer can obtain rich semantic and detailed information, and it can enhance the feature expression ability of the network. The head section has three detectors, which are used to predict classes, confidence, and anchor boxes of targets of different scales.
# Fuzzy Logic in Selection of Maritime Search and Rescue Units
## 3 Materials and Methods
### Preference Function  
Six preference functions are used in the Promethee II method. One of them must be selected for each criterion: type 1--a normal preference function; type 2--a U-shaped preference function; type 3--a V-shaped preference function; type 4--a level preference function; type 5--a linear preference function; type 6--a Gaussian preference function. Moreover, with the preference functions, evaluation thresholds are in use: the indifference (balance or incomparability) threshold (\(q\)), the preference threshold (\(p\)), and the Gaussian threshold (\(\sigma\)).  
The level preference function is discussed below (Figure 1). It assigns levels to particular features of an alternative. The balance (\(q\)) and preference (\(p\)) thresholds must be determined and the mean value between the thresholds must be calculated. If the difference between the values of assessments \(g_{i}(a)-g_{i}(b)\) falls between the two thresholds, the preference of one alternative over the other is weak and the function has a value of 0.5. If the difference \(g_{i}(a)-g_{i}(b)\) is below the balance threshold (\(q\)), there is no preference (the function has a value of 0). With the difference \(g_{i}(a)-g_{i}(b)\) above the preference threshold (\(p\)), the preference of one alternative over the other is strong (the function has a value of 1).  
The level preference function is well suited for qualitative criteria, when the decision-maker wishes to modulate the degree of preference by the deviation between the evaluation levels.
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 4 Biobased Monomers for Polyester and Polyamide Synthesis
### Biobased Aromatic Monomers  
Lignin is the largest non-carbohydrate components of lignocellulosic biomass which is composed by oxidative coupling of three phenylpropane components: \(p\)-couamyl alcohol, coniferyl alcohol, and sinapyl alcohol [119]. Due to the unique structures and chemical properties, lignin provides a broad opportunity for the production of a wide variety of biobased chemicals, especially biobased aromatic chemicals that so far cannot be accessible via chemical or biological modifications of other biomass feedstocks (Scheme 5).  
However, it remains a big challenge to develop an efficient approach for the recovery of aromatic chemicals with tailored structures from lignin [120]. Currently, only vanillin can be produced via a commercial process by oxidation of lignocellulosicates, a byproduct from the sulfite pulping industry [121; 122; 123]. Recently, new chemical and biotechnological approaches for the production of vanillin are studied [124; 125; 126; 127; 128; 129]. Starting from vanillin, many biobased aromatic monomers for polyester synthesis can be produced, for example, vanillic acid, divanillyl diol, dimethyl divanillate, and so on [127; 128; 129; 130; 131].  
Terephthalatic acid (TPA) is industrially produced by oxidation of \(p\)-xylene. It is used mainly as a precursor for the production of aromatic polyesters and polyamides such as PET, PBT and PPAs. The current global market size of TPA is around 30 million tons per year, and is expected to increase to 60 million tons in 2020 [31]. Nowadays, several technologies to produce biobased TPA and its precursors from renewable resources have been proposed (Scheme 6) [49; 132; 133; 134; 135; 136; 137; 138; 139]; and some companies and research institutes are active in the development of biobased TPA [31; 49; 138] and full biobased PET. Nevertheless, no commercial biobased TPA and fully biobased PET are current available in the market.  
2,5-Furandicarboxylic acid (FDCA) is an interesting biobased rigid monomer, which is considered as the most promising substitute to petrol-based TPA and IPA [5; 14; 140]. Currently, FDCA is readily produced from biomass feedstocks, for example, by oxidation of HMF derived from various sources of carbohydrates [18; 24]. It is also possible to produce FDCA via a biocatalytic approach starting from HMF (Scheme 7) [141]. At present, FDCA is industrially produced by Avantium (The Netherlands) using an enabling chemical synthesis technology [14; 31]; and the price is expected to be cheaper than the biobased and petrol-based TPA [31; 142].  
Figure 5: Promising biobased chemicals derived from lignin.  
Other interesting biobased furan monomers for polyester or polyamide synthesis include 2,5-bis(hydroxymethyl)furan (BHMF), 2,5-bis(aminomethyl)furan, 2,5-bis(hydroxymethyl) -tetrahydrorofuran and 2,5-bis(aminomethyl)tetrahydrofuran (Scheme 7) [5, 18, 24, 143].
# A PEKS-Based NDN Strategy for Name Privacy
###### Abstract  
Named Data Networking (NDN), where addressable content name is used, is considered as a candidate of next-generation Internet architectures. NDN routers use In-Network cache to replicate and store passing packets to make faster content delivery. Because NDN uses a human-readable name, it is easy for an adversary to guess what kind of content is requested. To solve this issue, we develop a PEKS-based strategy for forwarding packets, where PEKS stands for public key encryption with keyword search. We implement the PEKS-based strategy based on the best route strategy and multicast strategy of NDN and show the performance of the PEKS-based NDN strategy. We also discuss the issues of the PEKS-based NDN strategy.  
Named Data Networking (NDN); name privacy; PEKS; next generation internet architecture
## 2 Materials and Methods
### Cough Data Processing
#### 2.2.5 Performance of the Classification Model  
Classification Accuracy of the Classification Model  
The classification accuracy was estimated by comparing predicted outputs in our test set with the actual outputs [24] and was calculated as follows:  
\[\text{Accuracy}\left(\%\right)=\frac{\text{Number of correct predictions}}{\text{Total number of predictions}}\]  
For this binary classification problem, the above equation for determining accuracy could be rewritten using True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN), as follows:  
\[\text{Accuracy}\left(\%\right)=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{ FP}+\text{TN}+\text{FN}}\]  
Tippett plots (see Figure 3) are used to show the cumulative proportion of LLR values for both normal-voluntary and asthmatic cough comparisons [34; 35]. Given the fact that positive LLR values support the normal-voluntary cough hypothesis and negative LLR values support the asthmatic cough hypothesis, the further apart the curves are (i.e., blue versus red curves), the better the discrimination performance.  
Receiver Operating Characteristics (ROC)  
The performance of the binary classifier was investigated using the Receiver Operating Characteristics (ROC). The ROC curve was created by plotting the TP rate (sensitivity) (TP would be asthmatic cough classified correctly as asthmatic) versus the false positive rate (1-specificity) for various decision thresholds. The area under the ROC curve (AROC) was calculated to assess the predictability of the audio features to discriminate between the two cohorts.  
Figure 1: Experimental methodology to train and evaluate the Gaussian Mixture Model (GMM) classifier.
## 4 Results and Discussion
### Feature Selection Using SHAP  
As mentioned earlier, based on the Shapley values, the important features for classifying the types of fault are determined. This prior feature importance investigation will help in observing only a few features for fault detection and classification at a later stage. For finding feature importance, a tree-based explainer approach is used, and the XGBoost classifier model is chosen as the tree-based model. The significant features are presented using the SHAP feature importance plots and SHAP summary plots in Figures 6 and 7.  
Figure 6: SHAP feature importance plot for (**a**) bias faults, (**b**) drift faults, and (**c**) stuck faults.  
In the bar charts presented in Figure 6, the features are arranged according to their importance for each type of fault. We can see that maximum, minimum, and mean are the top three important features that distinguish these three sensor faults from the normal scenario. The summary plots in Figure 7 provide more insights into the significance of the features. The summary plot shows that the more positive SHAP values of the features' maximum, minimum, and mean correspond to the fault class, and negative values pertain to the normal class. Based on the SHAP analysis, the top five features are selected for use in the subsequent stages of fault detection and classification.