esa-sceva/satcom-chunk-collection huggingface dataset:
# Reuse or Disposal of Waste Foundry Sand: An Insight into Environmental Aspects
## 5 Environmental Standards for WFS Reuse  
The reuse of waste is regulated by national law.  
In Italy, for instance, Ministerial Decree 5 February 1998 [53], updated with Ministerial Decree n. 186/2006 [54], regulates the reuse of non-hazardous waste in simplified recovery procedures. This regulation reports a series of available reuse for WFS, and provides environmental standards with which WFS must comply for uses such as the construction of embankments and road foundations.  
A French work [55] defines guidelines for the reuse of WFS in road construction; three scenarios, which mainly depend on the thickness of the layer, are evaluated, and different threshold for leaching tests are provided. The limits imposed are slightly above the limit values for admission to landfills for inert waste, but well below those for admission to landfills for hazardous waste imposed by EU legislation 2003/33/EC [23].  
In the USA, each state has different thresholds of allowable concentrations in the leachate for scenarios in which WFS can be reused [56]. Among all reuses, manufacture of products (e.g., asphalt, bricks, concrete block, cement, etc.) poses the least environmental risk, and consequently has the least-stringently regulated reuse. In several states, WFS needs only to be qualified as non-hazardous (according to TCLP) or marginally non-hazardous (using a percentage of RCRA TC levels, e.g., Iowa). In most states, however, the maximum allowable leachate concentration for constituents of concern ranges from values equivalent to federal drinking water standards (i.e., Illinois state) to 30 times these values. Reuse as structural fill, or as backfill and pipe bedding, requires more stringent thresholds. Based on the different local standards, USEPA developed a toolkit to assist  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & Ni & **Zn** & **Cr** & **F\({}^{-}\)** & **TDS** & **DOC** \\ \hline WFS leachate [35] & **1.4** & **5.9** & **1.2** & **18.9** & **47,310** & **894** \\ Limit value of inert landfill [23] & 0.4 & 4 & 0.5 & 10 & 4000 & 240 \\ Limit value of non-hazardous & 10 & 50 & 10 & 150 & 60,000 & 800 \\ landfill [23] & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Leachate contaminants concentration using EN 12457-4 and EULFD limits (value in mg/kg).  
states in WFS reuse; this is a six-step process that guides from developing a reuse program to post-application monitoring [57].  
Table 6 compares, for different countries, the limit values of pollutant released in the leachate for WFS reuse in road base construction.  
Legislative requirements are quite different, and there is a lack of well-defined management strategies for beneficially reusing WFS. Therefore, evaluating the impact on environmental matrices assumes a fundamental role.
# Microplastics in Water Resources: Threats and Challenges
## 3 Characterization MPs  
### Size and Nomenclature  
An important challenge that remains unresolved is the development of a consistent and precise nomenclature. Relatively recently, just a few decades ago, [PERSON] (2004) [PERSON] (2004) were the first to use the term \"microplastic\" in their work. It was introduced as an evolution of the term \"microscopic plastic fragments\". However, the work did not attempt to clearly define this term, assuming that it was intuitively understood. It was not until around 2009 that the definition of \"micoplastic\" was proposed as a polymer fragment less than 5 mm in diameter [PERSON] (2004).  
In recent years, there has been significant interest in the detection and identification of microplastics in the environment. The information contained in scientific papers produced in a short period has led to discrepancies in the terminology used and the proper meanings of the concepts. Separate terms have also emerged for microplastics resulting from the breakdown of larger plastic fragments (secondary microplastic) and particles intentionally manufactured in small sizes (primary microplastic) ([PERSON] et al., 2016). Moreover, new methods for identifying microplastics have allowed researchers to detect particles on the nanometer scale ([PERSON] et al., 2016). Consequently, in recent years, a new term, \"nanoplastic\", has increasingly appeared in the literature ([PERSON] et al., 2016; [PERSON] et al., 2016). Like microplastics, there is no consistent definition for nanoplastics. [PERSON] et al. (2017) proposed the following definition for nanoplastics: \"particles resulting from the degradation of plastic waste, possessing colloidal properties, and ranging in size from 1 nm to 1 \(\upmu\)m\". This description is also referenced by other researchers ([PERSON] et al., 2016; [PERSON] et al., 2016), indicating that the definition has been accepted by the scientific community.  
In light of the above, it is crucial to exercise great caution when comparing the obtained research results with those from other studies. Comparing numerical values alone carries the risk of error. Referencing results to other studies requires a thorough study and understanding of the research methodology, as there is a lack of consistency in the use of microplastic terminology depending on particle sizes (Table 1), descriptions of their shapes, and potential origins ([PERSON], 2004). Unambiguously assigning a defined range of particle sizes to the appropriate name would significantly facilitate the comparison of research results from different research centers worldwide. Therefore, there is an urgent need to develop uniform terms and definitions for micro- and nanoplastics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Term** & **Size** & **Reference** \\ \hline \multirow{3}{*}{megapalistics} & \textgreater{}1 m & GESAMP 2015 [PERSON] (2004) \\ \cline{2-3}  & \textgreater{}100 mm & [PERSON] et al. 2019 [PERSON] et al.
# GAN-Based Approaches for Generating Structured Data in the Medical Domain
## 2 Methods
### Selected GAN Variants
#### 2.1.3 Wasserstein Generative Adversarial Networks  
Wasserstein Generative Adversarial Networks (WGANs) were proposed to improve the the stability of learning in generative models [53]. As opposed to the original GANs, WGANs introduce a critic network instead of a discriminator. A discriminator in the original GANs uses the probability estimation to distinguish between the real and generated data. The critic, on the other hand, estimates the distribution of both the real and generated data, and then minimizes the Wasserstein distance between them as a metric [53]. This optimization increases the stability of the generative model and improves the quality of the generated data. However, WGANs sometimes output low-quality generated data or even fail to converge [54]. To tackle this problem, the Wasserstein Generative Adversarial Networks with Gradient Penalty (WGANGPs) have been developed [54]. Instead of the weight-clipping in WGANs, which imposes constraints on the critic, WGANGPs use a gradient penalty function to overcome the gradient vanishing problem. This way, they can also deal with the imbalanced class labels via an oversampling method that enables them to generate more synthetic samples for minority labels [55].
# Estimating Specific Patient Organ Dose for Chest CT Examinations with Monte Carlo Method
## 2 Materials and Methods
### Organ Dose Simulations for Individual Patients  
Based on the CT scanner model developed by [PERSON] et al. [27; 28] and the individually voxelized phantoms built above, the organ doses for each patient were simulated by using MCNPX 2.4.0. In the CT model, 16 X-ray sources uniformly distributed along the rack were set to simulate the helical CT scanning. The X-ray spectrum, with an energy resolution of 1.0 keV, was generated by SPEKTR3.02, and it was proved to be accurate enough in our previous study [29]. In X-ray diagnosis, the photon transport model assumes that electrons propagate in the direction of the initial photon, and that the charged particle equilibrium (CPE) occurs when the electrons deposit energy during the interaction, and the collision kerma is equal to the absorbed dose. Therefore, the results (MeV per gram per particle) recorded in the F6 tally of MCNPX can be taken as the absorbed doses of the tissue or organ. For practical use, it usually requires a conversion factor (CF) to convert the output of MCNP into an absorbed dose per 100 mAs. The conversion factor is the ratio of the CTDI\({}_{100}\) in the air measured at the isocenter of the CT gantry to the result simulated by the MCNP in the same situation.  
In this study, the number of particles used in each simulation was set to be 1 \(\times\) 108, and the statistical deviation of the organ dose was generally less than 2.5%. For each patient, the tube current and other scanning parameters were extracted from the DICOM header, and the dose deposited in each organ (\(D_{\text{T}}\)) can be calculated as:  
\[D_{\text{T}}=\sum_{i}^{k}D_{\text{S}}\times CF\times\frac{I_{i}}{100} \tag{2}\]  
\(D_{\text{S}}\) is the simulation result in the F6 tally for each slice of CT scan, \(I_{i}\) is the tube charge per rotation (mAs) used for each slice.
## 2 Materials and Methods  
### Survey Area  
The survey area analyzed in this study was Mokpo Port in South Korea. Mokpo Port aims to support the southwestern region's shipbuilding industry and automobile import/export, serving as a central port for land transportation logistics and island connections while supporting coastal fisheries and marine tourism. According to the 2020 statistics from the Korea Maritime Transportation Safety Authority [21], coastal passenger ship routes through Mokpo Port include 18 regular routes and eight auxiliary routes, accounting for 25.0% of all coastal passenger ship routes in the country. Tongyeong (14 routes) and Incheon (13 routes) had the next highest numbers of coastal passenger ships. Mokpo Port operates approximately twice as many coastal passenger ships as the other two ports. The port area has a high density of vessels because of the limited sea area navigated by various types of ships, including merchant ships entering and leaving to load and unload various types of cargo, passenger ships, fishing vessels for fishing activities, yachts, fishing boats, and other vessels for leisure activities. This study aimed to develop a method for identifying routes in a survey area by applying a clustering algorithm, regardless of the type of vessel. Therefore, in selecting the survey area for data collection for the algorithm development, we chose routes operated by various ships, including cargo ships, passenger ships, and fishing vessels.
## 2 Background
### Studies and Testimonies
The new method allows for predictions regarding how slow-moving landslides may evolve under various conditions. Their study identified critical displacement thresholds that can be utilized to assess the risk and stability of infrastructure in areas susceptible to slow-moving landslides. This probabilistic approach is complementary to our use of machine learning models for landslide forecasting. [PERSON] et al. [24] conducted a study on the stability analysis of landslide dams. They proposed using a support vector machine (SVM) approach as an alternative to traditional stability analysis techniques, such as model testing and numerical calculations. These traditional methods can be time-consuming and may not meet the practical needs for rapid assessment. Landslide dams are natural barriers formed by landslides that obstruct river flow. These structures are often unstable and can fail quickly, resulting in catastrophic flooding in downstream areas. The authors suggest employing an SVM model for predicting and classifying the stability of landslide dams. This approach utilizes a real-world dataset of landslide dams to train the model, enhancing the accuracy of its predictions. This study highlights the effectiveness of machine models, such as the random forest model used in our approach. [PERSON] et al. [25] explored the use of remote sensing and geographic information system (GIS) technologies for managing landslides in the Kravarsko area of Croatia. Thisregion is characterized by hilly terrain prone to numerous landslides, posing risks to houses, roads, water systems, and power lines. The study's main objective was to develop a comprehensive inventory of landslides and create maps detailing soil susceptibility and stability using high-resolution remote sensing data. To achieve this, a landslide inventory was created based on high-resolution digital elevation models (hrDEMs) obtained through remote sensing techniques like LiDAR. This integrated remote sensing and GIS approach is similar to our method of combining geospatial data and advanced technologies to improve landslide prediction. These studies highlight the diverse methodologies and technologies employed landslide risk prediction, underscoring the importance of integrating advanced techniques for more accurate forecasting. Building on these insights, our study proposes a novel model designed to enhance landslide susceptibility prediction and improve spatial planning.
# Leveraging Static and Dynamic Wear Leveling to Prolong the Lifespan of Solid-State Drives
## 4 Performance Evaluation
### Static vs. Dynamic Wear Leveling
* Dynamic wear leveling policies like CB and WOGC help extend the lifespan of SSDs. Compared to when no wear leveling is performed, WOGC extends the lifespan by up to a maximum of 1.43-4.01 times, and CB extends it by up to a maximum of 1.36-2.79 times, depending on the trace. However, in cases where no initialization is performed or the initialization ratio is 10%, there were instances where the lifespan actually shortened compared to when no wear leveling was applied. This suggests that dynamic wear leveling can have a negative impact when there are no cold data or when the proportion of cold data is low. * Static wear leveling performed alone (PWL) also has a significant effect in extending the lifespan of SSDs. The lifespan of SSDs improved by up to 1.86-5.92 times, depending on the trace. * When combining dynamic wear leveling policies like CB and WOGC with static wear leveling policy (CB+PWL and WOGC+PWL), the lifespan of SSDs could be further extended compared to using CB or WOGC alone. When using CB with PWL, the lifespan extended by up to 1.20-3.20 times depending on the trace, while for WOGC, it extended by up to 1.05-1.24 times. Notably, the lifespan extension effect of using PWL with CB was greater than with WOGC. While WOGC was generally superior to CB when performing dynamic wear leveling alone, combining PWL made CB the superior policy over WOGC. This suggests that evaluating the performance of using dynamic wear leveling alone may not be appropriate. * While CB+PWL extended the lifespan of SSDs more than not using wear leveling or using dynamic wear leveling alone, the improvement was not significant compared to PWL. It was inferior to PWL in prn0 and proj0, and achieved similar lifespans in the other traces. Based on the results from Figure 5, the following conclusions can be drawn. Firstly, to extend the lifespan of SSDs, static wear leveling must be performed. It was more effective to use static wear leveling in conjunction with dynamic wear leveling than to rely solely on dynamic wear leveling. Secondly, existing dynamic wear leveling policies, such as CB and WOGC, which were designed without considering static wear leveling, do not harmonize well with static wear leveling policies. In some traces, CB+PWL achieved similar or even inferior SSD lifespans compared to PWL that do not employ dynamic wear leveling. This suggests a need for dynamic wear leveling policy designed with consideration for static wear leveling, such as Greedy-MP. Figure 5: Comparing the SSD lifespan achieved by dynamic wear leveling and static wear leveling. (**a**) cam01; (**b**) hm0; (**c**) prn0; (**d**) proj0; (**e**) stga; (**f**) weba.
# High Brightness Diode Lasers Based on Beam Splitting and Polarization Combining
## 1 Introduction  
High-power diode lasers present great advantages in many applications, such as pumping solid-state lasers or fiber lasers, medical treatment display technology, and material processing, especially additive manufacturing [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. The main advantages of such lasers are high energy-conversion efficiency, small size, electric drive, long lifetime, and relatively low cost. However, the beam-quality of diode lasers is still poor and the brightness is low due to the narrow cavity and broad-area (BA) waveguide of a single diode laser. Progress on the beam quality of BA lasers will have significant potential effects on the development of related fields.  
The beam quality of a diode laser can be described as the product of divergence angle and beam waist [11], and the brightness is defined as the radio of power and beam quality [12]. Beam combining methods, such as spectral beam combing (SBC) [13; 14; 15], polarization beam combining, and coherent beam combining (CBC) [16; 17; 18], are able to enhance the brightness of lasers system via combining more laser elements to increase the power. However, the beam quality of SBC is limited by the emitters and the output coupler, and diffraction grating brings inevitable power loss. CBC with low efficiency is complex and sensitive to the environment, which limits the application in many fields. It is difficult to improve the beam quality except for the external-cavity beam combining.  
In this paper, we demonstrated a new approach to improve the beam quality and brightness of diode lasers based on the beam-waist splitting and polarization combining (BSPC). The beam-waist was cut in half via the splitting prism, and one of the cut beam's polarizations was changed by a half-wave plate. These two beams, with differentpolarizations, were combined into one beam by PBC [19]. This system can realize the improvement of beam quality by shrinking the beam-waist. This method was applied for the single BA diode laser and laser bar, and the improvement of beam quality and brightness was investigated.
# Worldwide Connectivity for the Internet of Things Through LoRaWAN
## 3 The Back-End of the LoRaWAN(tm) System
#### Three Different Types of Network Servers  
As shown in Figure 2, there are three different types of NS:  
* the home NS. It is the NS of the network operator the end device belongs to;
* the serving NS. It is the NS of an operator whose network is taking care of end nodes which are out of coverage of their own home network. It performs all of the functions of the home NS, as far as the MAC protocol is concerned, handling the so-called \"active roaming\" of the end device. We observe that the term \"serving NS\" is often used in a more general sense in the LoRaWAN\({}^{\text{TM}}\) specification. In particular, when the end node is under the control of the home NS, the home NS takes the role of serving NS as well. Thus, in this case the distinction between home NS and serving NS is purely logical, without any actual consequence on the procedures and protocols;
* the forwarding NS. It is the NS of an operator whose network may have, for example, a radio coverage overlapping with the home network of some end devices. It is transparent, and forwards/receives the traffic to/from the end devices through the GWs attached to it. It handles the so-called \"passive roaming\".  
Figure 2: The architecture of the LoRaWAN\({}^{\text{TM}}\) system.  
We want to remark that the connection (especially, the MAC protocol) between the end node and the network is always managed by the serving NS, recalling that it coincides to the home NS in case there is no actual roaming and the end device is in its home network. Indeed, in case the end node is connected to a forwarding NS, the forwarding NS is transparent and the connection is redirected towards the serving NS (or to the home NS, in case they coincide). The specification for the MAC protocol is contained in [8].
## 5 Results  
All the hyperparameters need to be set before the model during learning, such as epoch number, optimization function, activation functions, learning rate, etc. The image patch dataset is randomly split into 80% for training and 20% for validation. Experiments were performed on a combined dataset of 256 \(\times\) 256 patch images containing annotations, with 1152 images separated for training and 288 images separated for validation using three-fold cross-validation. A total of 110 images out of 1550 were separated for testing. During training, random affine deformations were applied for data augmentation. The Adam optimizer [35] function was used in this study; it is one of the most common optimizers for faster learning capacity in deep learning. Fine-tuning makes the Adam optimizer function able to achieve good results quickly. Sparse categorical cross-entropy was implemented and the initial learning rate of 0.0001 was chosen. In the experiments, the training and testing process for building detection was implemented using the NVIDIA A100 graphics card. The batch size was given as 16, selecting 100 epochs during the nine training processes.  
The study undertakes nine experiments based on VGG19, ResNet34, and MobileNet as backbones for decoder stages. Deep learning-based models such as U-Net, FPN, and LinkNet for segmenting class objects were evaluated with the mIoU technique, and F1-score was implemented and used. The fusion combination training results are given in Figure 4.  
The encoder-decoder networks trained with the dice and focal loss functions obtained the overall best F1-scores and mIoU-scores and Res34+Unet and VGG19+FPN fusions were observed to be the most stable across all different encoder-decoder fusion validation as in Table 1. A similar statistical trend was observed for training results. Along with 0.510 F1-score and 0.592 mIoU validation results, Res34+Unet outperformed other encoder-decoder fusions. VGG19+FPN validation that has a loss of 0.849, F1-score of 0.491, and mIoU-score of 0.590 was placed in the second raw evaluation with these parameters in descending order. These two models were adopted for the ensemble weighted model algorithm for final model testing.  
Since Res34+Unet and VGG19+FPN models were adopted for the ensemble weighted model algorithm for final model testing, mIoU results were as in Table 2. The proposed ensemble model mIoU value of 0.652 is 0.06 higher than Res34+Unet and VGG19+FPN.  
The raw and annotated images are shown in Figure 5. Objects belonging to eight classes are placed in annotations. Prediction of Vgg+FPN dominates prediction of the ensemble model for raw#1, while prediction of Res34+Unet dominates prediction of the ensemble for raw#4. This emphasizes that the most successful model is more dominant in ensemble model performance. The proposed model performed similarly or had better  
Figure 4: (**a**) Loss, F1-score, and IoU-score progress for training. (**b**) Loss, F1-score, and IoU-score progress for validation.
# Towards Incidence Management in 5G Based on Situal Awareness
## 5 Analysis and Decision-Making
### Analysis
#### 5.1.4 Risk Map  
This component builds and manages a risk map of the network incidences considering aggregated high-level metrics, events and the inferred risks. The risk map is mainly considered in the following situations: prediction of threats spreading, the establishment of quarantine regions, identification of the best spots to deploy mitigation actions, and recognition of the source of the attacks. As in the case of the asset inventory, there must be coherence between the list of assets and the current network resources. The risk map is built considering the network map, so its proper development implies upgrades in real-time of the network connectivity and its status (throughput, congestion, transmission delays, availability, etc.)