esa-sceva/satcom-chunk-collection huggingface dataset:
# An Aerobic Degradation Model for Landfilled Municipal Solid Waste
###### Abstract  
Municipalities output large amounts of solid waste into landfills, but degradation slows during the middle and late stages of treatment. Therefore, accelerating the treatment of degradable substances to achieve rapid stabilization, excavation, screening, and reuse would increase landfill utility and reduce economic costs. This article provides an aerobic degradation model for landfilled municipal solid waste that includes two types of biochemical reactions. Using degradable solid-phase organic matter and ammonia nitrogen as limiting substrates, an equation describing degradation over time was obtained. Both aerobic organic matter hydrolysis and the synchronous nitrification and denitrification reaction followed a kinetic, first-order equation. The influences of temperature, water content, oxygen concentration, and carbon to nitrogen ratio on the kinetic reactions were considered. Similarly, the exothermic reaction characteristics were considered. The model was applied to two previously conducted experiments. The results showed that the model can accurately reflect the degradation laws of various substances under aerobic degradation conditions. SDC and ammonia nitrogen were rapidly degraded and reached very low levels in a short time under aerobic conditions. This indicated that aeration has a significant effect on the degradation of aged waste, which can be used in the accelerated stabilization of aged landfills in the future.  
municipal solid waste; aerobic degradation; stoichiometric equations; dynamic model +
Footnote †: journal: Applied Sciences
## 1 Introduction
Furthermore, high-resolution hydrological model outputs are used to obtain SMCI, thus employing soil moisture values derived with the Mesoscale Hydrological Model (mHM) instead of directly using satellite-derived soil moisture values, which were not available across the study period. The SHAP method is used to determine the equation weights for the CDI, and its effectiveness is evaluated by comparing it with PCA and empirical method results. By using SHAP analysis to assess the nonlinear behavior of each input variable (TCI, PCI, VCI, SMCI, NDWI, and GPP) on the CDI, we provide a deeper understanding of how each individual index contributes to the overall CDI and its impact on crop yield, revealing complex interactions and the relative importance of each input variable. Furthermore, the developed index is compared against the widely used SPEI and scPDSI to quantify differences and lend further insight into drought indices for agricultural yield estimation.
# Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
## 3 Results
### Selection of Predictor Variables  
For the model developed from spectral bands using MLR, 45 variables were originally considered (five spectral bands times nine dates). As a result of the variable selection considering the \(p\)-value < 0.05, the fitted model relied on the use of 22 predictor variables. Among the evaluated VIs, the variables were selected according to the crop stage and the correspondent \(p\)-value. For example, the predictor variable \"NDVI_R2,\" relating to ripening crop stage, was removed for not being significant to the MLR. The most important variables for predicting sugarcane yield with RF regression (Figure 3) were identified as the NIR spectral band (B8), and NDRE at \"T2\" crop stage, \"T3\" crop stage for NDVI, and WDRVI, all relating to the tillering crop stage, and \"R1\", relating to the ripening crop stage for GNDVI (for each group of variables). For the spectral bands, Figure 3A shows that RF regression is more sensitive to the three predictor variables (T2_B8, T2_B5, and R1_B8). This also suggests that green (B3) and red (B4) spectral bands were less relevant for modeling the sugarcane yield. [PERSON] et al. [50] suggested the use of the red-edge spectral band as an alternative to reduce the influence of the soil background at the early stages of wheat, as well as saturation for later phenological stages of the crop. A similar approach was proposed by [PERSON] et al. [51] and [PERSON] et al. [52].
## 9 Discussion
### Cyber Security Risks and Safety Concerns of EIAVs
#### 9.4.1 Consequences for People's Lives and the Economy  
Autonomous vehicles are becoming increasingly popular, and with their rise comes the need for robust security measures to protect against potential cyber attacks. Security flaws in autonomous vehicles can have significant impacts on people's lives and the economy. Listed below are some ways in which security vulnerabilities in autonomous vehicles could impact people's lives and the economy:  
* Safety: Security flaws in autonomous vehicles can compromise the safety of passengers and other road users. For example, a hacker could take control of an autonomous vehicle and cause it to crash or drive recklessly [175].
* Privacy: Autonomous vehicles collect a lot of data about their passengers, such as their location and driving habits. If these data fall into the wrong hands, they could be used for nefarious purposes [175].
* Economy: Autonomous vehicles have the potential to revolutionize the transportation industry, but security flaws could slow down their adoption. If people do not trust autonomous vehicles to be secure, they may be less likely to use them, which could have a negative impact on the economy [175, 176].  
It is important to note that security flaws in autonomous vehicles are not just theoretical. Researchers have already demonstrated that wireless jamming attacks can impact the fuel efficiency of cooperative adaptive cruise control (CACC) systems [177]. Additionally, there is a lack of effective infrastructure for evaluating security solutions for autonomous vehicles. This means that there is still a lot of work to be done to ensure that autonomous vehicles are secure [178].  
In conclusion, security is a crucial aspect of autonomous vehicles, and the potential repercussions of a breach are significant. It is important to continue researching and developing security measures to protect against potential cyber attacks.
# Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis
## 4 Materials and Methods
### Stationary Heart Standard Phantom  
Finally, one of the most popular applications of ultrasonic imaging segmentation is heart cavities study [46]. This problem is specially sensitive to speckle noise, due to the large impedance differences between walls and hollows, causing considerable segmentation errors.  
In the aforementioned tests, RiB effectiveness has been demonstrated, via simulation, on hollowed complex form impedance analysis. In this section, the study will be extended to real cases. To do that, Ultrasound Heart Phantom model 067, CIRSTM, will be employed, jointly to the same experiment set-up using Array A (see Table 2). This phantom has been designed for cardiac imaging training.  
As in the last test, the proposed ultrasonic images have been calculated--DAS y RiB(10%)--as well as NLmeans filtering results, using the same parameters as in Section 5.3.
## 1 Introduction  
Historically, peri-implantitis (originally coined by [PERSON] et al. [1]) became an accepted term at the first European Workshop on Periodontology in 1993. The term peri-implantitis (PI) was then agreed upon as a general name for destructive peri-implant inflammatory processes. Accordingly, with a progression of this inflammation, significant bone loss was observed, which is a result of an unfavorable disruption of the balance between the foreign body response and host internal immunological response factors [2]. It means that if a purulent infection is present, microorganisms may be involved but not necessarily cause marginal bone resorption, as pointed out by [PERSON] and [PERSON], who concluded that \"microorganisms are involved in the disease process. However, this is not a proof that they are always the origin of the condition\" [3].  
In 1987, in the study by [PERSON] et al., PI was first described as an infectious disease with many features common to periodontitis and, on the basis of their findings, it was suggested that \"periimplantitis can be regarded as a site-specific infection in which microbial pathogens, mainly belonging to the group of gram-negative anaerobic rods, may play an important role\" [4]. Since then, a growing interest in defining peri-implant disease as a clinical entity and proposing a treatment approach for it has been observed. The multifaceted etiology and varied characteristics of the disease, however, from the clinical perspective resulted in lack of consensus in defining peri-implant disease (more than 33 definitions) and its treatment. Peri-implantitis is one of the most burning issues that modern dental implantology has been facing recently. After more than 30 years since the first description of this \"disease\", its causes and proper treatment protocols are unknown. Recently published papers indicate that peri-implant microbiota present a different bacterial ecosystem compared to the periodontal microbiota [5; 6], as well as critical histopathological differences between periodontitis and peri-implantitis [7]. As a result, the researchers have become interested in looking for immunological mechanisms that may play the key role in peri-implantitis development [8].  
According to the literature, the prevalence of peri-implantitis ranges from 1.1% to 85% [9]. This means that if every year, on average, 12 million implants are placed--in the worst case scenario--there are more than 10 million patients facing this problem every year and they cannot be successfully treated [10].
Based on previous studies on PSI measures, dynamic stability is affected by visual cognitive tasks [11], load bearing [12], foot muscle morphology [13], and injury [14]. In addition, the relationship between the COP and the body's COM, which establishes the percentage of anterior GRF, affects the direction of the ground reaction force (GRF) vector. Research on balance and stability usually benefits from quantification of the COM and COP displacements [15]. Previous studies have shown that surface inclination decreases gait stability [16] and has a greater fall risk than stair walking [17]. In addition, the surface inclination also affects the gait characteristics and ground reaction force [18], which influence the slip potential or fall. In a previous study investigating the joint contact force during slope walking, it was reported that the ankle contact force increased with an increase in surface inclination [19; 20]. In general, both postural stability and loading response seem to affect surface inclination, as slope walking has different challenges to level walking. Understanding the relationship between stability and lower-extremity joint reaction forces (JRFs) contributes to a broader field of biomechanics and human movement analysis, and can be utilized to determine factors that contribute to the risks of slips, trips, and falls during slope walking. However, how the response of lower-extremity joint reaction forces, due to changes in surface inclination angles during slope walking, could influence body stability remains unclear. Thus, this study aimed to assess the relationship between stability parameters and lower-extremity JRFs during inclined and declined walking. The findings can be used to gain a deeper understanding of musculoskeletal system adaptation to slope walking, as well as to design interventions and safety measures to prevent injuries during navigation. The outcomes could also be set as a theoretical basis for selecting a suitable method of measurement to explore and understand the injury risk related to stability and load bearing during inclined walking.
## 3 Discussion  
For the automatic diagnosis of liver steatosis, out of the algorithms discussed, CNNs exhibit superior performance compared to more basic logistic regression models. AI models can accurately detect early stage steatosis on a level similar to or outperforming other non-invasive techniques (Fibroscan and CAP). These models could thus be used as a screening tool for identifying patients with NAFLD, even in early stages, in the general population. In addition, the utilization of CNN classification algorithms has the potential to replace liver biopsies for assessing the severity of NAFLD and liver fibrosis in certain patients. CNNs have successfully graded F3 and F4 fibrosis using 2D SWE, surpassing the performance of only 2D-SWE and biomarkers, proving it can be the current standard for classification of advanced fibrosis and cirrhosis. CNNs also demonstrate exceptional performance in the classification of liver tumours (detection, characterization and segmentation). Even though real-time systems achieve a high detection rate, for the moment, AI can only be regarded as an assistant for image interpretation.  
Moreover, another area where DL can assist clinicians in quick and accurate decision making is in AI automated ultrasound report generation. Usually, the process of writing comprehensive reports on the ultrasound assessment can be cumbersome, time consuming and prone to error. Thus, DL can answer this need by image caption algorithms, which involves computers generating captions by processing visual inputs such as images [82; 83]. An interesting approach will be to use generative pre-trained transformer (GPT)-based models, which have suggested in a variety of applications for report generation, educational support, clinical decision support, patient communication and data analysis [84]. For example, ChatGPT is a variant of GPT that is fine-tuned for conversational language understanding and generation, possibly useful for radiologists and ultrasonography, with various advantages but also limitations.  
Undoubtedly, AI has generated extensive discussions with great excitement, but there are still limitations and obstacles to be addressed and to take into consideration in future research in this area. These include various different diagnostic methods among studies, the need for large and standardized image databases, the use of different AI classifiers in research, implementation of classification models in hardware for clinical use and overdiagnosis by detecting minor changes that could indicate indolent disease.
# A Review on the Lifecycle Strategies Enhancing Remanufacturing
## 3 Strategies Enhancing Remanufacturing
### End-of-Life Product Collection  
The collection of end-of-life products is one of the most important factors that can determine whether that product will be qualified to be remanufactured or not. Products are generally considered to be at their end-of-life when their end users dispose of them. The timing of the collection of these products is very important because some of them get in even worse shape if they are not recovered in time. [PERSON] et al. [30] discussed how waste electrical and electronic equipment (WEEE) can be effectively recovered at end-of-life using effective collection methods. EoL product collection becomes an end-of-life decision tool when emphasis is laid on remanufacturing [31]. In most cases it is a third party that collects the EoL products. There are often three scenarios for EoL product collection:  
_Method 1._ The drop-off collection system by targeting recycling centers. Here the end-of-life products that still have some useful life are collected whilst the rest are recycled. This method usually has a collection efficiency (the quantity of collected EoL products which can be remanufactured compared to the total EoL product collected) of 75% [32].  
_Method 2._ The existing drop-off collection system which involves collecting products from the various little drop-off points. This is a longer process but it has a higher collection efficiency of 80% [32].  
_Method 3._ This system involves door-to-door collection of EoL products from different households. This is the most tedious of all the collection methods because it requires many more personnel and takes more time. The good thing about this method of collection is that it has a collection efficiency of 90% and the products can be recovered in better shape than the two other methods. This is because the products are collected directly from the consumer and they have not been contaminated by other products. In Denmark, after collection of the WEEE, 10% the value of the remarkeded product was given to the owners who disposed of the products to further encourage the return of EoL electronic products [33].  
The chances of a product being remanufactured gradually reduce if the product is not quickly collected for reuse after its disposal. There are some uncertainties in the recovery of EoL products, like the state of the returned product, the quantity of returns, etc. A multi-objective decision-making approach aimed at dealing with these uncertainties has been explained by [68]. In order to beat the odds and increase the remanufacturability of a product at its end-of-life stage, the products have to be collected in the right way and in time. The strategies which can help improve the quality and quantity of EoL products have been outlined by [69].
## 2 Materials and Methods
### Image Details and Evaluation Methods  
The images used in this study were 41 cases of whole-body CT images taken with LightSpeed Ultra 16 (GE Healthcare, Milwaukee, WI, USA) at Gifu University Hospital. There were 14 cases of ALS and 27 cases of muscle diseases such as myopathy and cervical spondylosis. The Ethics Review Committee at Gifu University approved the whole-body CT images taken for this study. Informed consent was obtained from all individuals included in the study. The images were \(512\times 512\) pixels with 1305-1726 slices per scan. The spatial resolution was \(0.625\times 0.625\times 0.625\) mm, and the density resolution was 12 bits. These data were scanned by using a recommended protocol (tube voltage 120 KVp with a current of Auto mA) for detailed examination in our hospital. In this study, we defined surface muscles as skeletal muscles that exist outside of the body cavity and are visible on non-contrast CT images taken under the above imaging conditions. Ground truth was created by Ms. [PERSON], who modified each result of the surface muscle segmentation using a conventional method [1] under the advice of an anatomist. We used the graph cut-based interactive method implemented in the common software platform \"PLUTO\" [19]. It took about 45 min per case to modify the VP label used for training. On the other hand, test case correction took about 30 h per case for all slices, which was used as ground truth.  
Assuming the automatically segmented set of voxels as AS and the manually defined ground truth as GT, we used both volume overlap metrics to evaluate the present method. We computed the  
Figure 2: Representation of a three-dimensional U-Net architecture and details. Voxel Patch (VP) is input, and the segmentation result of the surface muscle is obtained as an output. The blue box is a feature map with the number of channels shown at the top. Details of the encoder and decoder are described above, and other parameters are described in the main text. VP, voxel patch; 3D, three-dimensional; Conv., convolution; BN, batch normalization; ReLU, rectified linear unit; Concat., concatenation.  
DCs [20], which quantifies the match of two sets by normalizing the size of their intersection over the average of their sizes. The DC is defined as follows:  
\[DC=\frac{2|AS\cap GT|}{|AS|+|GT|} \tag{1}\]  
where the operator \(|\cdot|\) returns the number of voxels contained in a region.  
We randomly selected training (30 cases) and validation (3 cases), performed tests on eight cases three times, and evaluated the average DC value.
## 3 Results and Discussion
### Morphological Features of LS-APPJ
Discharge in a negative pulse can remove the space charges in the discharge region effectively, and the charges accumulated on the tube surface at the previous pulse can participate in the subsequent pulse discharge. Therefore, compared with the PPPJ and NPPJ, the BPPJ exhibits a larger volume and higher discharge intensity. Figure 5 illustrates the relationship between the plasma jet length and pulse peak voltage under three pulse polarities, the BPPJ, PPPJ, and NPPJ, with the same pulse repetition rate of 150 Hz. Under bipolar pulse excitation, the plasma jet length exhibits sustained growth with the rising pulse peak voltage. The maximum jet length reaches 14.5 cm with a pulse peak voltage of 26 kV, and then spark breakdown occurs when the voltage reaches up to 28 kV. In the case of the PPPJ, when the pulse peak voltage is below 22 kV, the plasma jet length increases in tandem with the pulse peak voltage, reaching a maximum length of 8.5-9 cm. However, when the pulse peak voltage exceeds 22 kV, the growth rate of the plasma jet length diminishes and it eventually stabilizes. For negative pulse excitation, the correlation between the plasma jet length and the pulse peak voltage is not clearly evident. Only a minor increase in jet length is observed at lower voltages. Once the pulse peak voltage surpasses 16 kV, the plasma jet length remains largely invariant with respect to further increases in the pulse peak voltage. Existing research has demonstrated that the plasma jet length increases with the voltage and reaches saturation at elevated voltage amplitudes [10,11]. This saturation phenomenon can be attributed to two primary factors: the formation of a low-breakdown-voltage region due to the mixture of inert gas and the surrounding air and the propagation of bullet-like streamers. When the applied voltage is sufficiently high, streamers penetrate into regions with higher air content, where their propagation is inhibited. Consequently, to generate a plasma jet of adequate length, it is imperative to expand the mixing region of the inert gas and surrounding air, as previously mentioned. Furthermore, enhancing the electric field intensity is crucial to facilitate the elongation of the plasma jet. Figure 4: Discharge images of BPPJ generated by plasma jet tube with length of 35 cm (**a**) and 10 cm (**b**).