esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial Resolution with the LaGEMPix
#### 3.2.3 Modulation Transfer Function (MTF)  
The MTF characterizes how faithfully the spatial frequency content of the object is transferred to the image [49]. According to ISO 1233:2017, the results of the MTF analysis is reported using a graph plot. Nevertheless, the summary resolution metrics may also be used; the limiting spatial resolution is defined as the frequency at which the MTF drops below a certain percent value. Several values can be used; however, the 10% level is the most common one [50; 51; 52].  
A line pair mask type 53 [53] was placed in front of the Mylar window to evaluate the MTF of the LaGEMPix as shown in Figure 9. This X-ray test pattern is made of 0.05 mm thick lead and the resolution range is from 0.5 up to 10 LP/mm. For this particular mask, 0.5 LP/mm means that one black and one white line within 4 mm are projected on the image sensor.  
This test was carried out using 30 kV X-rays (instead of 40 kV) with the N-5 filter, in order to increase the contrast of the output image. The image in Figure 9 shows that it is not possible to measure the MTF of the LaGEMPix with this mask. We can conclude that the current version of the LaGEMPix is not able to distinguish 0.5 LP/mm, which corresponds to two slits separated by 2 mm.  
Figure 8: The line-average response profile of the 5 mm diameter holes spaced by 3 mm (edge to edge). The FWHM obtained by the Gaussian distribution is 7.10 \(\pm\) 0.30 mm for the left hole and 6.80 \(\pm\) 0.20 mm for the right hole. The distance (center-to-center fit) = 7.98 \(\pm\) 0.23 mm was obtained by fitting the profile by a double Gaussian function.
# Optimal Sliding Mode Control for an Active Suspension System Based on a Genetic Algorithm
## 2 The Structure and Control Principle of the Active Suspension System  
In addition to transferring the reaction force directly to the chassis, the design of the suspension system aims to relax the road surface impact, attenuate vibration, ensure vehicle ride comfort and stability control capacities. The active suspension system includes an actuator, a matching sensor, and a control unit that can input force and displacement to the suspension system based on the passive suspension system. The actuator can generate force according to the real-time changes of the road input and the vehicle status, thereby compensating for the disturbances produced by road irregularities and obtaining a good shock absorption effect [20]. The control principle of the active suspension system is shown in Figure 1.  
As shown in Figure 1, the active suspension system consists of three parts: the wheel module, the controller module, and the actuator module. First, the controller receives the signals of the road information from the displacement sensor, and then the controller controls the output displacement of the actuators independently, thus controlling the posture of the vehicle. The displacement sensors installed on the wheels are used to transmit the vertical displacement caused by road irregularities to the controller.
###### Abstract  
Mangroves in Zanzibar have a high value for biodiversity and scenic beauty. However, mangroves are susceptible to anthropogenic and natural environmental disturbances. Although global mangrove monitoring systems exist, it is the practitioners focused on small mangrove areas who are knowledgeable about the area in which they work. This study examined the change in mangrove cover in Chwaka Bay and Menai Bay, between 1973 and 2020, using Landsat satellite data. The transformed divergence (TD) index and Jeffries-Matusita (J-M) distance were used to calculate separability of land cover classes before classification. The overall accuracies ranged between 82.5 and 92.7% for Chwaka Bay and 85.5 and 94.5% for Menai Bay. The kappa coefficients (\(\kappa\)) were in the range of 0.72-0.90, while the producer's and user's accuracies were between 72 and 100%. Chwaka Bay's annual decrease in mangrove cover between 1973 and 2020 was 48.5 ha, compared to Menai's 6.8 ha. The loss of mangroves in Zanzibar is linked to illegal timber/pole cutting, firewood collection, charcoal burning, unsustainable aquaculture, and agricultural and urban development. Others are changes in environmental conditions that are suitable for mangroves and climate change. This study is crucial in providing a basis for the ecological restoration and management of Zanzibar's mangrove ecosystem.  
mangroves; maximum-likelihood classifier; Landsat data; transformed divergence (TD) index; Jeffries-Matusita (J-M) distance; change detection; Zanzibar _Applied Sciences_
# A Petri Net Approach for Business Process Modeling and Simulation
## 2 Fundamental Concepts
### Petri Nets  
Petri nets (PNs) are a powerful tool for modeling events in a process; they combine visual representation with an underlying mathematical framework. PNs offer a suitable graphical language for modeling concurrent systems. They comprise two types of nodes, namely, places and transitions. Over time, PNs have been modified and extended to provide more advanced modeling capabilities. Variations of Petri nets include timed Petri nets, stochastic Petri nets, colored Petri nets, and hierarchical Petri nets [18].  
Petri nets allow the behavior of processes to be modeled. The model is itself a representation of reality, and a representation does not imply real construction with all of its technical and financial drawbacks. In business processes, the intervention of two or more areas of the organization in parallel is frequent; for example, in the process of enrolling students in a university, the activities involved could include consolidation of debts to the treasury, library, and sports store, all of which can be done in parallel despite involving three different areas. Petri nets were specifically designed to model this type of system.
## 6 Experimental Results
### Detection Validation
#### 6.3.1 Detection under Zero Power Flow  
The algorithm response under ZPF conditions, where all the local loads are consuming all the generated power, can be seen in Figure 21a,b. Figure 21a refers to a detection considering that the inverter is delivering null power and there are no local loads. The detection time was achieved in 80 ms. Figure 21b presents another ZPF scenario. In this case, the inverter is delivering 4.6 kW per phase, but all this power is used to feed a local load connected at the PCC. The detection time was achieved in about 80 ms, as well.
# Applying Simheuristics to Minimize Overall Costs of an MRP Planned Production System
## 3 Simulation Heuristic
### Simulation Budget Management (Sbm)  
Limiting the simulation experiment by a maximum number of iterations and runs per iteration increases the probability to exclude potential good solutions, as they are excluded due to the run out of simulation iterations. Always performing a constant number of runs per iteration will not necessarily increase the potential to find a new best solution. Consequently, it would be more meaningful to introduce a constraint for skipping the actual iteration and invest the remaining simulation budget to explore new solutions with additional iterations and a changed set of parameter values. A simple simulation budget management pseudocode, which facilitates to run the simulation experiment, is described in Algorithm 3. This algorithm allows as many iterations as budget is available, and will stop the last iteration when the maximum number of runs is reached. The best solution always has the maximum number of the given replications due to the stopping criteria. The average cost of the previous iterations (_avgIterationOverallCosts_) is equal to \(\frac{1}{n}\sum_{i}^{n}overallCosts_{i}\), with \(n=\) maximum runs per iteration (_maxReplicationsPerIteration_). The _aggregationOverallCosts_ is compared to the percentile value of all past iterations, which depends on the replication number. The percentile value is computed using _GetPercentilValue(setOfAllSolutions,\(\beta\))_, which requires the previous overall cost to be equal to _setOfAllSolutions_, and a value reduced after each run from a UB, returning the value associated with the position in the passed solutions. These values are represented by \(\beta\) and _percentileStep_. After each replication, the overall cost is added to the values of the current iteration and the new percentile value is computed. Compared to the initial Algorithm 1, we do not always perform a fixed number of runs. The SBM stops after the _simulationBudget_ is exhausted.  
```
1:ifInitializationthen
2:\(totalReplicationCount\gets 0\)
3:\(simulationBudget\gets maxIterations*maxReplicationsPerIteration\)
4:\(stopCurrentIteration\gets false\)
5:\(currentReplicationCount\gets 1\)
6:\(percentileStep\gets 0.0175\)
7:endif
8:\(\beta\gets 0.4\)
9:while\(currentReplicationCount\leq maxReplicationsPerIteration\wedge\)
10:\(stopCurrentIteration=false\)do
11:\(totalReplicationCount++\)
12:\(currentReplicationCount++\)
13:if\(avgIterationOverallCosts>GetPercentilValue(setOfAllSolutions,\beta)\wedge\) currentReplicationCount \(>3\)then
14:\(stopCurrentIteration\gets true\)
15:endif
16:\(\beta=\beta-percentileStep\)
17:endwhile
18:\(stopCurrentIteration\gets false\)
19:\(replicationsPerIteration\gets currentReplicationCount\)
```  
**Algorithm 3** Simulation budget management.
## 8 Implementation Challenges and Solutions
### Ethical and Legal Considerations  
As vessels become more autonomous, traditional legal frameworks based on human responsibility begin to blur. Who is liable if an AI-controlled ship causes a collision? How do we assign accountability when decision-making is algorithmic [148]?  
The following legal and ethical concerns should be considered:  
1. Accountability: Responsibility among developers, operators, and owners should be determine [149].
2. Transparency: AI systems are often \"black boxes\", complicating oversight and auditability [150].
3. Privacy and surveillance: AI systems constantly record, analyze, and store sensitive operational data.
4. Bias and unintended consequences: Machine learning systems may reproduce hidden biases in training data.  
The following strategies are suggested:  
1. Develop legal frameworks that define shared accountability for AI failures, ensuring liability coverage for accidents and system malfunctions;
2. Use explainable AI (XAI) to generate interpretable justifications for agent decisions, especially in high-stakes or controversial scenarios [149];
3. Enforce robust data governance policies to regulate how data are collected, anonymized, and retained;
4. Regularly audit AI models for bias and fairness, ensuring compliance with emerging ethical standards.  
Collaborative efforts among AI developers, maritime authorities, and legal experts will be essential to create agile legal regimes that accommodate evolving AI capabilities [148; 150].
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 3 The Bootstrapping Service: CoAP-EAP
### Additional Considerations  
#### 3.6.1 CoAP Role Selection  
With the exception of the _POST /boot_ message sent by the Smart Object to notify the Controller the beginning of a bootstrapping process, or the _POST /boot?del=X_ to trigger the removal of resource \(X\), the rest of exchanges assume that the CoAP server is the constrained Smart Object and the CoAP client is the Controller. The main reason for this role choice is, as suggested by [84], to simplify the Smart Object implementation, assuming this will be the most constrained entity. Additionally, in EAP, the authenticator sends EAP requests from the peer, which returns an EAP response. The authenticator carries the burden of retransmissions including additional states for this purpose in the EAP state machine [85]. Similarly, in CoAP, the CoAP client sends CoAP requests, and the CoAP server just answers back with a response. With our design choice, a CoAP request carries an EAP request and a CoAP response transports an EAP response, avoiding the odd case where EAP requests go into CoAP responses and vice-versa, which complicates the overall design.
## 4 Results
### Categorization of the Results
#### 4.3.3 Research Methods  
In general, studies provide varying levels of detail on the design and implementation of methods; however, most employ either custom architectures and model implementations or well-known existing models. Table 3 presents a breakdown of publications according to the **research task**, **image modality** (planar and SPECT), and **models used** for diagnostic methods. Similarly, Table 4 presents publications categorized under general imaging methods. **Research task. ** Of the 45 publications exploring novel diagnostic methods, 34 focus on disease classification, 10 on image segmentation, and 1 on both segmentation and classification. Classification studies primarily aim to distinguish between healthy and diseased patients, while segmentation studies focus on detecting lesions or regions of interest (ROI). Publications categorized under **general imaging methods** explore techniques that can be adapted for various diagnostic applications and organs. These studies address image enhancement and reconstruction processes. One publication [66] examined three aspects of image enhancement, de-noising, angle reconstruction, and attenuation correction, which is denoted as \"mixed techniques\" in the table. **Image modality. ** Methods utilizing planar scintigraphy images are significantly fewer (n = 6) compared to those using SPECT images (n = 52). Specifically, two studies employ planar scans for classification tasks [35; 58], two focus on segmentation tasks [59; 64], one addresses both classification and segmentation [60], and one explores image reconstruction [74]. **Models used. ** Differences in approach and model choice primarily stem from data gathering, selection, and preprocessing, which determine the input format for 1D, 2D, or 3D networks. [PERSON] et al. [38] employed a 1D CNN, as their input data consists of imaging-derived radiomic and biological features, both numerical. Whole-body scans are typically obtained via 2D SPECT imaging, leading to the use of 2D networks in studies such as [53]. [PERSON] et al. [61] explored different input dimensions for diagnosing abnormal kidneys using 3D SPECT. They train three models: one with 3D images, another with 2D maximum intensity projections, and a third, termed '2.5D,' which integrates features from three networks using 2D maximum intensity projections (coronal, sagittal, and transverse). Similarly, [PERSON] et al. [84] and [PERSON] et al. [39] reduced 3D input to 2D by selecting a single representative slice from 3D stereo SPECT images. For **classification** tasks, which are the focus of most studies, classic custom-built CNNs are frequently used. Additionally, transfer learning methods are often applied, with visual geometry group (VGG) architecture being preferred in most cases. Although other well-known models are occasionally employed, they are typically used in benchmark studies comparing multiple models' performance. Classic machine learning models such as support
# Activation of Persulfate for Groundwater Remediation:
## 5 Impact of Environmental Application  
### Rebound  
When ISCO implementation has been completed, there may be an increase of target pollutants in groundwater concentrations, which was observed at a given location and referred to as rebound [128]. Rebound generally occurs in the remediation of low permeable areas contaminated by non-aqueous phase liquid (NAPL) contaminants, which are slowly released into groundwater. It exhibited an initial decrease in groundwater contaminants concentrations during and after site remediation, followed by an increase in groundwater contaminants concentrations over a longer period. [PERSON] (2013) observed that the ethylbenzene and naphthalene were first declined to 10 \(\upmu\)g/L and 53 \(\upmu\)g/L during the injection time, but were finally raised to 2000 \(\upmu\)g/L and 360 \(\upmu\)g/L after 300 days which were even a little higher than the levels before remediation [119]. The same trend was also reported in Superfund Site Louisiana (USEPA), where the TPH concentrations in observing wells rebounded to the pre-remediation level when the injection was ended after 60 days [117]. [PERSON] et al. (2010) reported that rebound occurred at over half (62%) of the sites for over 71 remediation sites. Nearly half (49%) of the monitoring wells at those locations that experienced rebound observed the rebound process [128]. Any leftover sorbed, or NAPL phase contaminants, may re-equilibrate with the groundwater once the oxidant is depleted, which could lead to a rise in aqueous phase contamination, as seen during the post-ISCO monitoring period. Another potential pathway for contamination rebound is back-diffusion from low permeability media that were not properly treated during ISCO deployment. A third mechanism is insufficient source zone demarcation, which prevents ISCO applications from properly targeting the source zone. An influx of contaminated groundwater from untreated upgradient regions at the site could potentially result in an increase in pollutant concentrations.
## 1 Introduction
Based on the discussion above, it is clear that researchers do not prioritize analyzing models that combine THz and UAV technologies' outage probability, average capacity, and bit error rate with various fading channels. Thus, the contributions of this study can be expressed as follows:  
* In order to analyze performance, UAVs in THz communication systems are considered in this study. * Log-normal, Nakagami-m, Rayleigh, Rician, and Weibull channel-fading environments are derived. * For the optimization of trajectory and to transmit power, an algorithm and theoretical derivations for a UAV system for THz-band communications are presented. * The capacity, BER, and outage probability of the proposed system are examined using M-QAM and M-PSK modulations. * The analytical investigation is supported by the numerical results that are provided. The remainder of this paper is structured as follows: Section 2 features related works. The performance analysis is described in Section 3. Section 4 contains numerical results. Finally, in Section 5, conclusions are presented.
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 4 Experimental Verification
### Case Study 1: Predicting the RUL of Bearings Using the PHM 2012 Dataset
#### 4.2.3 Ablation Experiment  
To evaluate the efficacy of the novel aspect of the proposed methodology, we eliminated or altered a component of the methodology. Data for the ablation experiments were obtained from the PHM 2012 dataset with test bearings A1-4. Method 1 utilizes vertical vibration data obtained from a solitary vertical sensor as input, whereas the proposed method uses multi-sensor fusion data as input. Method 2 utilizes TCN blocks for network feature extraction; the proposed method uses TCN-LSTM-Transformer for network feature extraction. Method 3 uses the traditional self-attention mechanism for feature fusion; the proposed method uses a parallel multi-scale attention mechanism in order to accomplish the adaptive weighted fusion of the output features from the three feature extractors. The prediction findings are shown in Table 4, revealing that the _MAE_ and _RMSE_ outcomes of the proposed approach are markedly superior to those of the comparable approaches.
## 3 Clustering Methods
### K-Means  
In contrast, the K-means algorithm partitions the dataset into a predetermined number of clusters \(k\), by minimizing the within-cluster sum-of-squares. Which can be represented by the following:  
\[J(c,u)=\sum_{i=1}^{M}\|x-u_{i}\|^{2} \tag{7}\]  
where \(\|x_{i}-u_{i}\|^{2}\) is the squared Euclidean distance between a data point \(x\) and the centroid \(u_{i}\)[23]. The number of clusters was determined via the elbow method by plotting total within-cluster SSE against \(K\), which revealed a distinct elbow, beyond which SSE improvements became negligible. To improve the stability and convergence speed of the algorithm, we employ K-means++ initialization, which selects initial centroids in a way that  
Figure 4: Support Vector Clustering results.  
maximizes their separation. This approach helps the clusters converge to a more optimal configuration than random initialization would typically allow.  
After initialization, each data point is iteratively assigned to the nearest centroid [24]. The centroids are then recalculated as the mean of the assigned points, and this process is repeated until the assignments stabilize or a maximum iteration limit is reached. While K-means++ reduces the risk of poor initial cluster placements, the algorithm itself still assumes roughly spherical clusters and relies on Euclidean distance for defining cluster boundaries. Consequently, K-means struggles to handle overlapping or non-spherical distributions, as is often the case in second-life battery data where cell parameters may not neatly conform to uniform cluster shapes. The clustering of the cells with K-means is shown in Figure 5.
###### Abstract  
This article presents research work on an intelligent system that was developed to monitor and continuously evaluate the quality of metal powder distribution in the laser powder bed fusion (LPBF) 3D printing process. The 3D printer that was used to carry out the work was equipped with an industrial vision system to capture images immediately after spreading powder on the work field. The powder distribution tests showed that the most common defects were identified as an insufficiently thick layer of powder applied to the working field (super elevation), unevenly distributed powder as a result of recoater vibration (so called recoater hopping), and its wear (so called recoater streaking). In the first stage of research, a set of training data (images) was collected. Then, the implementation of the machine learning process was prepared in the Roboflow environment. After that, the learning, validation, and prediction process was carried out several times using the selected machine learning model (YOLO model implemented in a Python environment) in order to select the most effective parameters. The study showed that deep machine learning can be effectively used to identify defects in powder distribution during the laser powder bed fusion (LPBF) process.  
selective laser melting; 3D printing; vision systems; artificial intelligence Article