esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For example, [PERSON]'s research uses Bayesian networks to explore the relationship between risk indicators and the supply chain so as to evaluate the probability of risk occurrence [27]. Moreover, Bayesian networks have the characteristics of networks, and they can conduct a quantitative analysis on each node and realize the identification of key risk sources. [PERSON] used Bayesian networks to identify the risk sources of accidents in large oil-refining enterprises [28]. [PERSON] used Bayesian networks to map the dependence characteristics of multiple departments within an enterprise and proposed a value chain risk measurement method to evaluate the risk status via the total number of risks [29]. Furthermore, if the nodes of Bayesian networks are regarded as the key nodes in the risk transmission path, the risk transmission path can be identified. [PERSON] proved the effectiveness of this method in enterprise financial risk [30], but he did not conduct an analysis of the critical path. With the development of information technology and the popularity of the enterprise resource planning (ERP) system, large amounts of production data are stored on the ERP systems of multi-variety and small-batch production enterprises every day. Using the data in the information management system as the basis for identifying risk transmission paths will greatly improve the objectivity of research results. Therefore, this paper proposes a delayed delivery risk path identification method based on association rules and Bayesian networks, taking a multi-variety, low-volume, high-information manufacturing enterprise as the research object. The method makes full use of the data and information in the system to complete the quantitative identification of the key risk transmission paths. Based on this method, by analyzing the real data of enterprises, we found that the occurrence of late delivery risk is mainly caused by the late deliveries of upstream suppliers and the sudden change in customer demand, and the timely adjustment of the production plan is the key node of the transmission path of late delivery risk. In this paper, we provide a scientific method to identify the transmission path of delayed delivery risk for multi-variety, small-batch production enterprises. Enterprises can effectively control the key nodes based on the identification results to achieve the purpose of risk mitigation. The remainder of this paper is organized as follows: Section 2 describes the association rules and the Bayesian network. Section 3 discusses the application process of the association rules and the Bayesian network in the identification of the delayed delivery risk transmission paths in multi-variety, low-volume production enterprises. Section 4 describes the experiments and results in this paper. In Section 5, the conclusions are presented.
# Ultrafast Mid-IR Laser Pulses Generation via Chirp Manipulated Optical Parametric Amplification
## 3 Theory on Broadband Pumped OPA
### Gain Mechanism of Idler Pulse
Nevertheless, limited by the range of the white lines, the initial idler spectrum starts from \(>\)1700 nm, restraining the idler gain bandwidth to \(<\)200 nm. Figure 5: (**a**) Time-dependent frequencies of pump (solid lines), signal (dashed line), and idler (dotted lines) when the pump and seed pulses are chirped with GDDs of the same sign. (**b**) Time-dependent frequencies of pump (solid lines), signal (dashed line), and idler (dotted lines) when the pump and seed pulses are chirped with GDDs of opposite signs. The black, blue, red, and yellow lines represent pump pulses with TL durations of 10 fs, 20 fs, 30 fs, and 50 fs, respectively. Comparatively, the high efficiency phase-matching bandwidth is slightly narrower in the top half of Figure 6 where the pump and seed pulses are oppositely chirped. However, the moderately phase-matched bandwidth in such condition is much broader. It is worth mentioning that phase-matching in OPA is an accumulated effect during the process, even though the perfect phase-matching bandwidth is slightly narrower. As long as the crystal angle and thickness are selected appropriately, the amplified components with moderate phase-matching still cover a broad range. It is also observed that the phase-matching efficiency is barely affected by the pump GDD in the top half of the figure, which makes the initial idler bandwidth the decisive factor that determines the output idler bandwidth. Corresponding to Figure 5b, a pump pulse of 10-fs TL duration is capable of generating the broadest idler spectrum in the four conditions. The potentially largest bandwidth for idler is obtained with a pump GDD of 40 fs2, a pump bandwidth supporting a TL duration of 4.5 fs is required in this case. With the state-of-the-art technique, multi-microjoule Ti:sapphire laser pulses with even broader bandwidth are readily available [102]. Idler pulses with simultaneously optimized bandwidth and efficiency can be expected using such a pump pulse.
###### Abstract  
Synthetic aperture radar (SAR) automatic target recognition (ATR) plays a pivotal role in SAR image interpretation. While existing approaches predominantly rely on batch learning paradigms, their practical deployment is constrained by the sequential arrival of training data and high retraining costs. To overcome this challenge, this paper introduces a divergence-constrained incremental dictionary learning framework that enables progressive model updates without full data reprocessing. Specifically, firstly, this method learns class-specific dictionaries for each target category via sub-dictionary learning, where the learning process for a specific class does not involve data from other classes. Secondly, the intra-class divergence constraint is incorporated during sub-dictionary learning to address the challenges of significant intra-class variations and minor inter-class differences in SAR targets. Thirdly, the sparse representation coefficients of the target to be classified are solved across all sub-dictionaries, followed by the computation of corresponding reconstruction errors and intra-class divergence metrics to achieve classification. Finally, when the targets of new categories are obtained, the corresponding class-specific dictionaries are calculated and added to the learned dictionary set. In this way, the incremental update of the SAR ATR system is completed. Experimental results on the MSTAR dataset indicate that our method attains >96.62% accuracy across various incremental scenarios. Compared with other state-of-the-art methods, it demonstrates better recognition performance and robustness.  
s 2025  
29 May 2025  
2025 June 2025
###### Abstract  
A novel technique for the in situ removal of mineral hydrocarbons from aged brownfields is described. The approach uses emulsions of plant oil (5-10%, rapeseed) in water, which were found to extract 50-85% of mineral hydrocarbons in one leaching step from the non-saturated zone. The emulsion was allowed to travel though the ground and was pumped off from the groundwater level. Approximately 15-50% of the plant oil stayed in the soil. By flushing the area with water afterwards, that amount can be reduced to 10-30%, and in some cases to <5%. This process is only suitable for sand, not for clay. It can be a good preparation for subsequent enzymatic cleavage and microbiological degradation, as part of a multi-stage in situ treatment process. Additionally, plant oil that infiltrated into the saturated zone was used to flush mineral hydrocarbons, which could be pumped off from the groundwater level. It was further tested whether the separation of mineral oil/plant oil and water can be performed by oil-absorbing, reusable non-wovens. Residual concentrations of <2% of water in oil, and 0.3-0.7 mg/L of oil in water were found. In this work, lab trials led to field trials, where more than 500 m\({}^{3}\) of water were sent over a pilot installation for oil/water separation using non-wovens. A slightly better separation performance than by oil separators was observed. This process has the potential to be combined with a regular oil separator to allow water purification to a level at which it can be reintroduced into the ground. The technique was tested on a brownfield in Lower Austria, a former refinery site abandoned approx. 80 years ago with a peak hydrocarbon contamination of 40,000 mg/kg of dry soil and free-floating mineral oil on the groundwater level. Since in situ techniques can be more environmentally benign and less costly than traditional remediation approaches, this novel approach holds an interesting potential, which could be proven at a technology readiness level (TRL) of 5.  
brownfield; mineral oil; remediation; in-situ; fleece; land recycling; cleanup; decontamination
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 4 A 2-Port MIMO System Using Two Opposite 1 \(\times\) 4 Antenna Arrays  
In this section, a 1 \(\times\) 4 array based on a single circular patch to maximize the offered gain, reaching 10.9 dBi at the frequency band from 3 dB to 6 dB, is designed and fabricated.
## 4 Methodology
### Adaptive Position Estimation
#### 4.1.3 Position Estimation  
After completing step detection, step length estimation, and direction estimation, the pedestrian's position can be calculated. Direction estimation is obtained through the processing of gyroscope angular rate data. Position estimation is performed by calculating the displacement relative to the previous step based on the pedestrian's step length and direction, and then accumulating it with the previous position to update the current position. Figure 10 illustrates the position estimation algorithm.  
The position estimation equations are as follows:  
\[P_{\mathrm{E}}(i)=P_{\mathrm{E}}(i-1)+l_{\mathrm{s}}(i)\cdot\sin(\varphi(i)) \tag{11}\]  
\[P_{\mathrm{N}}(i)=P_{\mathrm{N}}(i-1)+l_{\mathrm{s}}(i)\cdot\cos(\varphi(i)) \tag{12}\]  
where \(\varphi(i)=\varphi(i-1)+\psi(i)\), \(\psi(i)\) represents the yaw angle, \(P_{\mathrm{E}}(i)\) and \(P_{\mathrm{N}}(i)\) represent the eastward and northward positions of the pedestrian for the \(i\)-th step, \(l_{\mathrm{s}}(i)\) represents the step length of the pedestrian for the \(i\)-th step, and \(\varphi(i)\) represents the direction of the pedestrian for the \(i\)-th step.
# A Systematic Review of the Usability of Telemedicine Interface Design for Older Adults
## 2 Methods
### Eligibility Criteria  
The databases Web of Science, Scopus, PubMed, and CINAHL were searched for articles related to the design of telemedicine interface for the elderly. The retrieved literature was exported into EndNote X20 software, where the first two authors screened the titles and abstracts of all retrieved studies, assessed the full texts of potentially eligible studies, and independently recorded the documents according to the screening criteria. Any discrepancies or disagreements during the screening and selection process were resolved through three researchers' group discussion until consensus was reached. Inclusion and exclusion criteria were established based on the PICOS (Population, Intervention, Comparison, Outcome, Study design) framework (see Table 1) [49]. All articles included in this review were peer-reviewed and published between 2019 and 2024. The titles and abstracts of the papers were independently searched, followed by a thorough evaluation of the retrieved articles according to the inclusion criteria.  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Dimension** & **Inclusion and Exclusion Criteria** \\ \hline Population & This review defines older adults as individuals aged 50 and over, while the World Health Organization (WHO) defines the elderly as those aged 65 and older. With the onset of geriatric conditions occurring at increasingly younger ages, individuals over 50 with chronic illnesses often begin to experience health-related challenges [10; 50]. Additionally, younger members of the older adult population generally exhibit a higher rate of adoption of internet-based technologies compared to their more senior counterparts. \\ \hline Interventions & This study includes telemedicine platform interfaces available on mobile phones, devices, computers, and smart TVs, but excludes wearable devices and robotic interfaces intended for smart homes or elderly care. \\ \hline Comparisons & There are no specific comparison criteria excluded from this review. \\ \hline Outcomes & Research that focuses exclusively on influencing factors, framework construction, or software development without addressing interface design will be excluded. \\ \hline Study designs & To maintain consistency and comparability, the research was restricted to peer-reviewed journal articles published in English. Studies that are (1) not in English, (2) design works, reports, review papers, conference speeches, or extended abstracts, (3) journal-collected materials, and (4) outside the current time range or unrelated to the research topic are excluded. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inclusion and exclusion criteria based on the PICOS model.
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 4 Experimental Environment
### The Dataset  
The platform used for the creation of the first dataset is composed of an asynchronous motor, a gearbox made of two pulleys that exchange the rotation through a belt, two shafts that share the motion thanks to a couple of gears, and an electromagnetic brake. The platform and its mechanical scheme are depicted in Figure 3. The platform is provided with three triaxial accelerometers, which are placed on the bearing's support, next to the second pulley and the two gearboxes, respectively. They have a sampling frequency of 12.8 kHzper axis and an acceleration range of 500 Gpeak. A complete description of the platform can be found in [11]. For the purposes of experimentation, tests in four distinct operating conditions and a fault condition were conducted. The rotational speed is fixed at 660 rpm, while the distance between the pulley and the braking torque varies. The parameters and the duration of each condition are shown in Table 1. Note that each batch has a length of 10 min. A representation of the raw signals in the four operating conditions is provided in Figure 4. The considered signals represent a multi-variate series where each feature is an acceleration. As can be seen, while the accelerations in the first operating condition are rather stable, significant oscillations occur in the other conditions, but only state C4 describes an anomalous operation of the machinery (i.e., states C1-C3 represent normal operating conditions). The anomalies in state C4 are caused by the unforeseen overheating of the electric motor, which led to the sudden shutdown of the system.
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 3 Results and Discussion
### Model Performance Comparison  
TOP1 and TOP5 accuracy are chosen to evaluate the classification performance of the model in the experiment, and the number of parameters (Params) and FLOPs are selected to further evaluate the hardware requirements and the complexity of the model. TOP1 accuracy refers to the proportion where the category with the highest predicted probability by the model matches the true label. If the predicted category is consistent with the true label, the sample is considered to be correctly classified. TOP5 accuracy refers to the proportion where the true label appears among the top five categories with the predicted probabilities by the model; as long as the true label is within the top five predictions, the sample is considered to be correctly classified. The number of parameters is directly related to the size of the model and its demand for computing resources and storage. The number of calculations refers to the number of floating-point operations per second, which directly determines the run speed of the model. The greater the amount of computation, the longer the training time, and the higher the requirement of computing capabilities.  
Comparative experiments are conducted using original ShuffleNetV2, improved ShuffleNetV2, and other networks to comprehensively evaluate the performance.
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 3 Novel Pooling Methods
### S3 Pooling  
[PERSON] et al. in 2017 presented the S3 Pool method, a novel approach to pooling [58]. The pooling process is performed under this scheme in two stages. On each one of the preliminary phase feature maps (retrieved from the convolutional layer), the execution of max pooling is performed by stride 1. The outcome of step 1 is down sampled using a probabilistic process, in comparison to step 2, which first partitions the feature map of size X \(\times\) Y into a preset set of horizontal (h) and vertical (v) panels. V is y/g and H is x/g. The following figure illustrates a schematic of S3 Pooling. The working of S3 pooling is referred in Figure 8.  
Figure 8: Working of S3 pooling mechanism. The dimension of the feature map in this example is 4 \(\times\) 4, with both x and y = 4 represented in (**a**). The max pooling operation in step 1 uses stride 1, and there is no padding at the border. The grid size and stride should both be 2 in step 2. There will be two horizontal (h) and vertical (v) strips. In step 2, a stochastic downsampling is used to represent the rows and columns that were randomly chosen to build the feature map. Flexibility to change the grid size in step 2 in order to control the distortion or stochasticity is represented in (**b**,**c**).  
Figure 7: Rank-based average pooling: rankings are presented in ascending order, and activations for a pooling area are listed in descending order. The pooling output is calculated by averaging the four largest activations, since \(t\) = 4.  
[PERSON] et al. [59] executed tests for the CIFAR-10, CIFAR-100, and SIT datasets using both network in the network (NIN) and residual network architectures to test the effectiveness of S3 Pool in comparison to other pooling techniques (ResNet). According to the experimental observations, S3 Pool showed better performance than NIN and ResNet with dropout and stochastic pooling, even when flipping and cropping were used as data augmentation techniques during the testing phase.
## 1 Introduction
To overcome this issue, this study is the first case in the literature of the application of a new tool in MODFLOW-USG [55], named Connected Linear Network (CLN) package, specifically adapted to simulate BHE operation in aquifers [49; 56]. Through the adaptation of the CLN package, the implementation of several BHEs in one numerical model is now possible. The goal of the present work is to explore the effects of thermal interference caused by a real GSHP system (60 BHEs) located in the city of Milan, whose purpose is to provide air-climatization of a multi-house building (named \"MIA--La Casa Italiana\"). By means of a transient state numerical model built in MODFLOW-USG, the thermal plume migration and its impact on the Armi pumping station wells (managed by MM S.p.A. for drinking purposes) were evaluated. Simulating three operation years of the geo-exchange system, a provision of thermal perturbation was carried out, and the effect of the thermal interference between BHE and Armi station wells was evaluated. The study area is initially presented to define the geographical and hydrogeological context where the GSHP system is installed. Subsequently, the system characteristics are detailed in Section 3, including the implementation of the numerical models developed in MODFLOW-USG, with particular emphasis on the CLN component. Finally, the simulation results are presented, accompanied by a discussion of each outcome to demonstrate the capabilities of this tool.
## 5 Conclusions  
In this paper, the proposed ARID system was studied. The parametric analyses were carried out using Simulink and a series of shaking table experiments were performed to verify the control effectiveness and parametric analysis results. According to the experiments and numerical analysis results, the following conclusions can be achieved:  
1. The effectiveness and feasibility of the proposed ARID system for the swing vibration control are verified. It has better control effectiveness and robustness as compared with the TRID system. It can effectively reduce the vibration of suspended structures when positions of the hanging points are determined.
2. The parametric analysis and experimental verification of the ARID system can provide theoretical foundations for the designs of the system.
3. The ARID system consumes the natural structural frequency energy and external excitation energy. Moreover, the system can effectively reduce the structural response under the most unfavorable excitation.
4. The feasibility of the ARID system for torsional swing vibration control, such as the flutter of long-span bridges and the torsion of high-speed railway trains, is meaningful to further research. Furthermore, it is necessary to study the control cost of the ARID system by optimizing the control algorithms.  
[PERSON] initiated the problem, proposed the idea, defined the research essentials, conceived the method, and revised the paper. [PERSON] carried out the theoretical research, parametric study, experiments and prepared the draft.  
The research is financially supported by the Ministry of Science and Technology of China (Grant No. 2017 YFC0703603), the National Natural Science Foundation of China (Grant No. 51678322 and 51650110509), the Taishan Scholar Priority Discipline Talent Group program funded by the Shandong Province, and the first-class discipline project funded by the Education Department of Shandong Province.  
The authors declare no conflict of interest.
# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
## 4 Experiments
### Feature Extraction  
The feature extraction phase followed a set of processes to localize minutiae-based triangles.  
* **Binarization** A binary transformation is performed on enhanced greyscale image using an adaptive threshold. In binarization, each pixel is assigned as '0' and '1' based on the small and large intensity mean in the local neighborhood within a block respectively.
* **Thinning** To locate the feature points in the binary image, the thickness of the ridges was minimized to a single pixel through the skeletonization process.
* **Feature-point localization** The ridges show endings and some branches which were then localized as end-points and bifurcations, respectively.
* **Delaunay triangulation** In this algorithm, the Delaunay triangulation concept was applied to form a connected graph from a set of identified minutiae. The feature vector, _fv_, is stated in Equation (2). The overall feature extraction process is illustrated in Figure 6.  
Since the Delaunay triangles are sensitive to false minutiae, these need to be eliminated from the pool of minutiae set. The work in the literature [55] supports handling false minutiae in fingerprints. However, palm prints are too degraded and have a tremendous number of minutiae, as shown in Figure 7b. Further, removing false minutiae is not a simple process in this case, and the existing false minutiae removal algorithm cannot support this as it was designed for fingerprints with a lower number of minutiae within a smaller area. Therefore, the proposed algorithm works itself to eliminate false minutiae-related issues in the handprints. This process was ensured by triangle-to-triangle matching that leads to graph-to-graph matching. It is obvious that the probability of matching a graph with false graphs is lower than the minutia-minutia match or triangle-triangle match. Any false graphs formed from the false minutiae are automatically discarded when selecting reliable  
Figure 6: Overall procedural flow of the hand-based recognition system for full-hand biometrics.  
graphs using a threshold value which is generated based on the number of minutiae each graph holds.