esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Description of Information Technologies for Order Picking and Formation of an MCDM Model
### Formation of an MCDM Model  
The criteria that were necessary for determining the weights that were applied in the IMF SWARA-Z method were the possibility of implementation (C1), technology implementation costs (C2), performance improvement possibility (C3), and technology reliability (C4).  
The technologies used to evaluate potential solutions applying the fuzzy EDAS-Z method with averaging with the fuzzy Dombi aggregator were wireless picking using barcode scanning (wireless picking system)--A1, order picking by light detection (pick-to-light)--A2, order picking by voice recognition (pick-to-voice)--A3, vision order picking (pick-to-vision)--A4.  
Wireless order picking using barcode scanning (wireless picking system) involves scanning items based on a previously generated list received from a central computer. After completing the order, the order picker enters the number of the products that make up the order to confirm the delivery.  
Order picking by light detection (pick-to-light) uses small lights mounted on carriers on racks and are directed straight toward order pickers. When an order is received from IS, the lights associated with the listed items are turned on. Besides the lights, the display also shows the number of pieces of a particular item for the order. The order picker takes the required number of items and presses the button when the light turns off and approaches the next item where the light is on. Order picking by voice recognition (pick-to-voice) implies two-way communication between the order picker and the management system. Employees receive voice instructions from the control system and respond verbally using headphones and microphones connected to a control unit carried by a worker on his belt. The pick-to-vision technology represents a new concept in storage systems, which is made possible by using smart glasses, which transmit information from the server directly to the observer's field of vision via WLAN technology.
## 1 Introduction  
The loss of a limb, particularly an upper limb, has severe implications at a physical level, as well as significant psychological consequences. After an amputation, the recovery phase can be particularly challenging, with patients potentially experiencing _phantom limb pain_. Furthermore, the hand plays a crucial role in social interactions, and its loss can lead to a devastating reduction in autonomy, hindering the patient's ability to engage in social, work-related, and daily activities. Limb loss affects approximately 2 million people in the United States and can result from various factors, including trauma, neoplasia, and vascular or infectious diseases [1].  
Recent technological advancements in hardware (such as sensors, mechanical designs, and materials) and artificial intelligence for signal processing have shifted researchers' focus towards active prostheses, particularly body-powered options. Externally powered prostheses typically feature an artificial hand as an end effector, actuated by motors driven by batteries. Significant progress has been made in their development, leading to the availability of many complex artificial hands on the market [2]. Some examples include the i-Limb [3], Bebionic [4], and Michelangelo [5], which enable a wide range of grasping capabilities.  
Externally powered prostheses require a robust interface to translate the user's intent into control signals for the hand's actuators. This interface must allow for intuitive and natural control, minimizing cognitive load and reducing training time. Surface electromyography (sEMG) is a highly promising bio-signal for decoding a user's intended movement. As sEMG reflects the natural signals that command human muscle movement, it enables smooth and biologically intuitive control. Additionally, sEMG can be acquired non-invasively, making it ideal for use in prosthetic applications. Consequently, EMG-controlled prostheses have gained significant attention in research and have become a standard for externally powered prostheses.  
With recent advancements in 3D printing, control systems, and sensors, there has been a growing focus on designing low-cost, externally powered prostheses. While high-end commercial prostheses can cost upwards of USD 10,000, recent innovations in 3D printing have brought these costs down significantly, with some models priced as low as USD 500.  
This paper presents a pattern-recognition-based human-prosthesis interface utilizing the Myo armband, an affordable sensor for acquiring sEMG signals. A Support Vector Machine (SVM) algorithm is employed to classify nine different grasping postures based on the sEMG data. These postures were selected from a study by [PERSON] et al. [6], which identified the nine most commonly used grasps in daily life activities.
## 3 Results
### SEM and TEM Assay on Effect of Combined NS, Antibiotic Treatment  
The structural appearance of MRSA treated with concentrations of NS and in combination with antibiotics was analyzed using SEM. Figure 4A shows MRSA 4 clusters displaying well-formed shapes, while bacterial cell surface disruption is seen in 4B after treatment with 5.0 \(\upmu\)g/mL NS. In Figure 4C,D, cellular aggregation of bacteria and cell destruction is seen in combined treatments of NS with oxacillin and augmentin. In Figure 4E,F, similar destruction of bacteria cell surface and cellular aggregation were observed in the combined treatment of MRSA 5 with 7.5 \(\upmu\)g/mL NS, augmentin, and cefuroxime. Figure 5A-F represent TEM monograph analysis of the different MRSA isolates with NS 5 and 7.5 \(\upmu\)g/mL concentrations. Figure 5A shows altered MRSA 5 cell membranes and dead bacterial cells after exposure to 5.0 \(\upmu\)g/mL NS. The figure also shows disruption of bacterial cell division. Figure 5B also shows MRSA 4 cell wall damage after exposure to 7.5 \(\upmu\)g/mL NS with oxacillin, as well as vacuoles created in the cytoplasm and perhaps the accumulation of NS within the cell. Figure 5C shows dead bacteria cells and a damaged cell wall in MRSA 4 treated with 7.5 \(\upmu\)g/mL and augmenting, while Figure 5D displays MRSA 4 isolates treated with a combination of 7.5 \(\upmu\)g/mL and cefuroxime. Figure 5E shows the effects of MRSA 1 treated with 5.0 \(\upmu\)g/mL NS plus oxacillin and Figure 5F shows the effect on MRSA 1 treated with 5.0 \(\upmu\)g/mL NS plus augmentin, showing cell wall destruction (black arrows). These micrographs show that NS disrupts the MRSA bacterial cell wall, thereby causing cell death. In combination with \(\upbeta\)-lactam antibiotics, these effects were potentiated and can be described as a bactericidal effect.  
TEM interpretation of combined treatment of NS and antibiotics is shown in Figure 6A-F. The figure gives TEM monographs of the different MRSA bacterial isolates treated with a combination of 7.5 \(\upmu\)g/mL NS concentrations and antibiotics. Figure 6A shows damaged MRSA 4 cell walls, disrupted bacterial replication, and cell death with oxacillin. The figure also shows more cell death due to fewer cells, with similar observations seen with MRSA 5 in Figure 6B. In combination treatments with augmentin on MRSA 1, Figure 6C shows more cell deaths, while Figure 6D also displays cell death and the disruption of cell division in MRSA 5. Additionally, the TEM images in Figure 6E show the effect of treatments with 7.5 \(\upmu\)g/mL NS in combination with cefuroxime led to MRSA 1 complete and incomplete cell wall disrupted and bacterial cell death. Figure 6F shows complete destruction by 7.5 \(\upmu\)g/mL NS plus cefuroxime in MRSA 5. These effects clearly indicate that the combinations were bactericidal.
## 3 Results and Discussion
### Stuck Fermentation Altered the Profile of Non-Volatile Metabolites in Summer Black Tea
#### 3.2.4 Organic Acids  
Organic acids are a class of sour substances that lend a disagreeable flavor to the tea. A total of 60 organic acids were significantly altered after fermentation (Figure 3D), of which citric acid (CAS:77-92-9), \(\gamma\)-aminobutyric acid (CAS:56-12-2), glutararic acid (CAS:110-94-1), 2-hydroxisyobutyric acid (CAS:594-61-6), isocitric acid (CAS:320-77-4), cis-aconitic acid (CAS:585-84-2), L-pipecolic Acid (CAS:3105-95-1), 6-aminocaproic acid (CAS:60-32-2) and succinic acid (CAS:110-15-6) were down-regulated. Citric acid, glutararic acid, cis-aconitic acid, isocitric acid, and succinic acid have an acidic taste, 2-hydroxisyobutyric acid, L-pipecolic acid, and 6-aminocaproic acid increase the bitterness of tea soup, and \(\gamma\)-aminobutyric acid brings a dry feeling to the mouth. In addition, citric acid also increases the bitterness of the tea soup by increasing the leaching rate of polyphenols [25]. The decrease in these metabolites may be one of the potential reasons for the mellownness and smoothness, and decreased bitterness and sourness of summer black tea after stuck fermentation. In addition, L-malic acid (CAS:97-67-6) was also significantly down-regulated in FHYH. While [PERSON] et al. reported a significant increase in citric acid, succinic acid, and malic acid after traditional fermentation of black tea [26], another study showed that these metabolites were reduced in microbially fermented dark tea [23]. One possible explanation is that traditional fermentation of black tea involves oxidation via endogenous enzymes (e.g., polyphenol oxidase) in the tea leaves under humid and warm conditions, whereas stuck fermentation entails conversion of substances through microbial activity and secretion of extracellular enzymes [27]. Currently, our findings suggest that the changes in organic acids brought on by stuck fermentation ultimately reduce the bitterness and sourness of summer black tea and favor a more mellow and smooth flavor.
# Visual Tracking Using Wang-Landau Reinforcement Sampler
## 2 Related Work
### Tracking Methods Based on Wang-Landau Sampling  
For visual tracking, [PERSON] and [PERSON] [13] adopted Wang-Landau Monte Carlo (WLMC) sampling to control significant changes in the target's positions. [PERSON] et al. [14] enhanced the Wang-Landau samplers and presented a stochastic approximation Monte Carlo (SAMC) sampling-based visual tracker, in which the density of states (DOS) was more accurately estimated with low computational cost. [PERSON] and [PERSON] [15] extended the WLMC sampling into N-fold Wang-Landau (NFWL) sampling, in which the N-fold algorithm was used to enable the accurate estimation of the DOS with a relatively small number of samples. The NFWL-based visual tracking method can handle significant changes in both the positions and scales of the target. However, these methods do not contain a feedback process and cannot reflect the current visual tracking environment and results. [PERSON] et al. [16] combined WLMC sampling with a visual background extractor, considerably reducing the state space of the target. They independently dealt with scale changes in the target using a fast scale estimation algorithm.  
In contrast to these methods, we applied WLMC sampling to reinforcement learning and balanced the exploitation and exploration degrees of the target prediction.
###### Abstract  
Efficiencies of various treatments for UVC photolysis (ultraviolet light-C at 254 nm), VUV photolysis (vacuum ultraviolet light at 254 nm and 185 nm), UVC-assisted titanium dioxide photocatalysis (UVC-TiO2), and VUV-assisted titanium dioxide photocatalysis (VUV-TiO2) were investigated for the degradation of pesticides including pyraclostrobin, boscalid, fludioxoxonil, and azoxystrobin and inactivation of microorganisms _Escherichia coli_ K12 as a surrogate for _E. coli_ O157:H7 and _Saccharomyces cerevisiae_ in aqueous solutions and on the surface of fresh cut carrots. The degradation efficiencies of VUV were higher than for UVC on pesticides in aqueous solutions. However, there was no significant difference between degradation efficiencies for UVC and UVC-TiO2 treatments, and between VUV and VUV-TiO2 treatments. UVC, VUV, UVC-TiO2, and VUV-TiO2 showed similar inactivation effects against _E. coli_ K12 and _S. cerevisiae_ in aqueous solutions. The combined use of UVC and VUV treatments (combined UV) and combined use of UVC-TiO2 and VUV-TiO2 treatments (combined UV-TiO2) showed higher efficiencies (72-94% removal) for the removal of residual pesticides on fresh cut carrots than bubble water washing (53-73% removal). However, there was no significant difference in removal efficiency between combined UV and combined UV-TiO2 treatments. For _E. coli_ K12 and _S. cerevisiae_ on fresh cut carrots, the combined UV-TiO2 treatment (1.5 log and 1.6 log reduction, respectively) showed slightly higher inactivation effects than combined UV (1.3 log and 1.2 log reduction, respectively). Photolysis and TiO2 photocatalytic treatments under UV irradiation, including VUV as a light source, showed potential for the simultaneous degradation of pesticides and microorganisms as a non-chemical and residue-free technique for surface disinfection of fresh produce.  
Keywords:photolysis; UVC; VUV; TiO2 photocatalysis; pesticide; microorganism; fresh produce +
Footnote †: journal: Applied Sci. 2020, 10, 4493; doi:10.3390/app10134493
# Proposal of Innovative Methods for Computer Vision Techniques in Maritime Sector
## 1 Introduction
Due to the complicated water surface environments, such as light reflection and poor weather conditions (e.g., fog and rain), the quality of captured images is often inevitably degraded, leading to unsatisfactory and unreliable target detection results. To make the vision techniques more feasible and practicable, many intelligent visibility enhancement methods have been developed, such as low-visibility enhancement networks (LVENets) [19], learned parameter sharing-based versatile visibility enhancement networks (LPSNets) [20], efficient and effective multi-deep neural networks (EEMNNs) [21], and multiple-feature fusion-guided low-visibility enhancement networks (MFF-Nets) [22]. [PERSON] et al. [23] propose a neural network-empowered water surface target detection framework with a data augmentation strategy, which illustrates its effectiveness in terms of detection accuracy. However, it should be noted that although these visibility enhancement methods can theoretically improve image visual quality, it is impossible to generate restored images without the loss of any detail [24]. _Therefore, the accuracy of detection and recognition still needs to be improved_. \begin{table}
\begin{tabular}{c c c c} \hline  & **Strengths** & **Weaknesses** \\ \hline \(\bullet\) & Real-time monitoring and automatic & \(\bullet\) & Dependent on the availability of \\  & detection of vessels, cargo, and & extensive and high-quality training \\  & marine environments; & & data in the maritime domain; \\  & \(\bullet\) & Reduce operational costs; & \(\bullet\) & Influenced by complex \\  & Enhance the efficiency and safety of & & maritime environments. \\  & maritime operations. & & \\ \hline  & **Opportunities** & **Threats** \\ \hline \(\bullet\) & An increasing global demand for automation & \(\bullet\) & Legal requirements concerning data \\  & and intelligence in the shipping industry; & & privacy and security; \\  & Government support. & \(\bullet\) & Social and ethical issues. \\ \hline \end{tabular}
\end{table} 
Table 1: SWOT analysis for the applications of CV techniques in the maritime domain. Based on existing studies, this paper proposes some innovative methods to further improve the accuracy of the detection and recognition of CV models in maritime research, such as the application of ensemble learning and the integration of maritime domain knowledge. Furthermore, considering that current CV studies and practical applications in the shipping industry primarily focus on the timely, automatic, and accurate detection and recognition of water surface targets, we innovatively expand the research perspective and put forward a novel application of CV techniques in the maritime domain. The remainder of this study is organized as follows. Section 2 introduces our two proposed innovative methods for improving the recognition accuracy of CV models. Section 3 presents a novel application of CV techniques in the maritime domain. Section 4 concludes this study.
# Tuning of Graphene-Based Optical Devices Operating in the Near-Infrared
## 2 Graphene Optical Properties
### Graphene Electrode Configurations
In the case of a GIG capacitor, Equation (7) should be modified to account for the shift of the Fermi potential in each of the two graphene layers. In this case, the last term of Equation (7) should be modified to read as \(2|\mu|/q\)[21]. Initially, the Fermi levels are close to the Dirac point and both graphene sheets absorb light when both graphene electrodes are undoped or lightly doped due to the interaction with the environment. When voltage is increased between the GEs, they form a simple capacitor model in which one plate is a graphene layer doped by holes and the other is a graphene layer doped by electrons at the same doping levels. When the shift of the Fermi level in both GE reaches half the photon energy of the incident light (\(E_{F}=\pm h\omega/2\)), both GE become transparent at the same time. The change in the sign of the excitation voltage only switches the roles of the capacitor plates as anode and cathode and gives a similar response to incident light [12]. The limits of the applied voltage are determined by the Fermi level. The left border is determined by the moment when the Fermi level decreases (below the threshold value \(E_{F}=\pm h\omega/2\)), which occurs due to the accumulation of a positive charge. This leads to the absence of electrons, which are available for interband transitions, and graphene is \"transparent\". The right boundary is due to the filling of electronic states at which interband transitions are not allowed [11; 24].
## 3 Materials and Methods
### Justification for the Use of Random Forest and Parameter Selection  
The random forest algorithm was selected due to its efficacy in handling overfitting compared to other algorithms. It works by building multiple decision trees and merging them together to obtain a more accurate and stable prediction. We experimented with varying the number of trees from 10 to 100 to identify an optimal balance between prediction accuracy and computational efficiency. The choice of interval is based on initial tests indicating that increasing the number of trees beyond 100 resulted in marginal gains in accuracy but significant increases in computational cost and time. The decision to vary the number of trees in our random forest model from 10 to 100 was based on initial empirical tests indicating that this range optimally balances prediction accuracy and computational efficiency. We observed that increasing the number of trees beyond 100 resulted in only marginal gains in accuracy, which did not justify the additional computational resources and time required. These findings align with established research suggesting diminishing returns in accuracy improvement as the number of trees increases in a random forest, particularly in datasets of similar complexity and size to ours. In the random forest model, the maximum depth of individual decision trees is determined based on a balance between model complexity and performance. Trees in random forests can grow without restriction in depth, but deeper trees may overfit the training data, reducing the model's ability to generalize to unseen data. As explained in the manuscript, \"Random Forest models, while  
Figure 1: Working principle of random forest.  
powerful, can still overfit if too many decision trees are used or if the dataset lacks sufficient diversity.\" This suggests that the depth of decision trees is generally adjusted during hyperparameter tuning to avoid overfitting. Moreover, our proposed work mentions that the hyperparameters, such as the number of trees, are incrementally adjusted, which may also include depth adjustments to optimize performance while controlling computational costs. Hence, the maximum depth is typically set during training by tuning it based on cross-validation or similar methods to ensure the model strikes a balance between accuracy and efficiency.
## 2 Materials and Methods
### Stimuli Development and Presentation  
The base token used in the study was constructed from an /\(\alpha\)/ production by a single male child (9.9 years old) who was not involved in the current study. Acoustic analysis of the selected token was completed in Praat [52] and indicated that average fundamental frequency (\(f_{o}\): 216.2 Hz), jitter (local, 0.37%), shimmer (local, 3.92%) and cepstral peak prominence (17.45 dB) were within the normative range for age and gender [53; 54]. A certified speech-language pathologist, blinded to the source of the sample, provided an 'overall severity' score of 3 on the CAPE-V, indicating normal vocal quality. The sample was cropped to be 500 milliseconds (ms) in duration from the middle of the /\(\alpha\)/. A cosine window with a rise and fall time of 50 ms was added to the beginning and end of the stimulus to avoid the perception of unnatural clicking. The token was played to participants through Sennheiser HD 280 Pro headphones (Sennheiser electronic GmbH and Co., KG, Wedemark, Germany). Participants recorded at Boston University completed their study in a sound-treated booth, with experimental manipulation of pitch performed in near real-time by the Eventide Eclipse hardware (Eventide Inc., Little Ferry, NJ, USA). The Eventide Eclipse performed a full-spectrum shift of the sustained /\(\alpha\)/, in which the values and spacing of the harmonics were shifted up [55]. This shift changes the \(f_{o}\) of the production, thereby changing the perception of pitch. Participants recorded offsite were presented with previously created pitch shifted stimuli, created with the Eventide Eclipse hardware, on a laptop computer (Lenovo ThinkPad X1 Carbon). Regardless of the equipment used, stimuli were presented at approximately 60-70 dB SPL using a custom MATLAB script (version 8.2; The MathWorks, Inc., Natick, MA, USA).
# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 1 Introduction
* Analyzing feature layers to enhance adversarial attack detection: this work highlights the significant role played by the features from the first layer in discerning genuine samples from their adversarial counterparts. * Comparative analysis with established methods: this work conducts a comprehensive performance assessment, juxtaposing the proposed method with the existing frameworks. The proposed method from this work consistently showcases superior proficiency in detecting adversarial attacks, underscoring its efficacy and robustness in real-world CT segmentation scenarios. * Providing visualization for physicians: recognizing the importance of interpretability in medical settings, this framework provides a visualization tailored specifically for physicians. The visualization elucidates the differences between genuine and adversarial samples, allowing for an informed decision-making process. * Distinguishing between noise and adversarial attacks: this method effectively differentiates between images with inserted Gaussian noise--which do not degrade the performance of the segmentation model--and adversarial samples.