esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Proteomics and Seafood
#### Proteomics for Detection of Pathogens and Spoilage Microorganism in Seafood  
Application of new techniques for the detection and identification of pathogenic bacteria present in fish aids in the prevention of seafood spoilage but also in the assessment of the risks involved in human consumption of the fish [163; 164].  
Liquid chromatography electrospray ionization mass spectrometry (LC-ESI-MS/MS) could characterize toxins, virulence factors, and other proteins from different bacterial species in several foods. This technique has been employed to identify peptides related to antibiotic resistance, antimicrobial production, virulence factors, and genus-specific peptides, including two specific peptides to the species _S. dysgalaciae_ from different strains of _Streptococcus_ spp. associated with bovine mastitis [165]. Shotgun proteomics have been applied in _Listeria_ spp. [166], _Enterococcus_ spp. [156], and histamine-forming and other biogenic amine-producing bacteria present in seafood [163; 167].  
Eighteen _Pseudomonas_ strains, isolated from fish products, have been characterized using shotgun proteomics, as reported by [PERSON] et al. [83].  
1015 non-redundant peptides were found to be related to virulence factors. Putative _Pseudomonas_ spp. biomarkers were identified as additional 25 species-specific peptides.  
A shotgun proteomic technique using a high-accuracy mass spectrometer (Orbitrap XL equipment) has been reported by [PERSON] et al. [168] to characterize and compare analyses of the 11 strains of Gram-negative spoilage bacteria identified in seafood. A total of 773 peptides were characterized as virulence factors that participate in bacterial pathogenesis, while 14 peptides were defined as biomarkers.  
Finally, [PERSON] et al. [169] used tandem mass tag quantitative proteomics and non-targeted metabolomics to elucidate the associated mechanism of hexanal, a phytochemical, against _Vibrio parahaemolyticus_. A total of 572 differentially expressed proteins (DEPs) and 241 differential metabolites (DMs) were identified in hexanal-treated _V. parahaemolyticus_. They found that the structure and function of cell membranes were damaged by hexanal, which also inhibited nucleotide metabolism and acted as an antibacterial agent in the seafood industry.  
In the same context, the antimicrobial mechanism of natural compounds has been elucidated as reported by [170; 171; 172].
###### Abstract  
Introduction: The oral cavity and the teeth are frequently subjected to numerous physiological pH variations, mainly due to the type of diet. These changes are the main cause of enamel demineralization and consequent breakage under mechanical forces. This causes severe sensitivity and sometimes pain, which endures unless the hard tissue is remineralized. Since enamel does not self-repair, the application of alloplastic materials that have the property of releasing remineralizing ions is strongly recommended. The aim of this in vitro study is to evaluate the effectiveness of two different toothpastes and a fluoride varnish in the prevention of demineralization, and their ability to remineralize enamel after it has undergone several acidifications. Methods: Eight human teeth with no caries or defects were prepared. The acid attack simulations were performed using a commercial cola carbonated drink. Samples were immersed in 5 mL of soft drink for two minutes at room temperature, and then were rinsed with distilled/deionized water. The immersion process lasted about 2 min, and was repeated four times for a total of eight minutes. Then, two different types of toothpaste, one with and one without fluoride, and a fluoride varnish were applied to the surfaces of the samples and rinsed off with demineralized water. A second acidification cycle was then carried out, with the subsequent reapplication of the three different products to evaluate their ability to protect against demineralization. SEM and profilometer analysis were then carried out to evaluate the results. Results: The statistical analysis showed a good remineralization capacity for all three products, especially in the fluoride-varnish-treated samples. However, regarding protection from demineralization, non-fluoride toothpaste was found to be ineffective, while fluoride toothpaste and varnish produced positive results. Conclusions: Dental professionals should advise fluoride toothpastes and varnishes over non-fluoride toothpastes. Fluoride toothpastes and varnishes represent a valid treatment for surface remineralization after a first acid attack and for protection from subsequent demineralization in an acid environment, such as that which can develop in the oral cavity.  
Keywords:dental erosion; remineralization; toothpaste; fluoride; varnish +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Dosimetry  
Monte Carlo CT-Expo version 2.5 software (Buchholz, Germany) was used for dose calculations. This was applied to contrast-enhanced and blank (i.e., non-contrast-enhanced) studies. Dose estimation was performed based on the averages obtained from all CT examinations on mathematical phantoms for adults (ADAM and EVA) (Figure 1) [22].  
Using this software, the following measures were calculated:  
CTDI, CTDIVol, and the average absorbed dose in the scanned region. CTDIVol does not provide an actual dose measurement for the patient; it is a standardized measure that helps users compare different scanners and scan protocols [23; 24];  
Figure 1: Mathematical phantom for [PERSON] and [PERSON] in Monte Carlo CT-Expo software.  
* DLP, integrated absorbed dose along a line parallel to the axis of rotation for complete CT examination. DLP is directly related to patient risk from the absorbed dose received; hence, it was used as a reference value for routine CT investigations in this study [25];
* ED: A method for comparing patient doses from different diagnostic procedures. The organ dose and ED were per the recommendations of the ICRP [11];
* These measured quantities were utilized for dose optimization strategies in this work;
* The optimized protocols in this study were defined by the resultant post-optimization protocols in relation to NDRLs with significantly reduced radiation doses to patients and their organs, which were implemented in our department;
* This study primarily focused on deriving DRL as a typical value according to the terminology definition from the ICRP 135 report [17] and was applied in our unicentral facility for local use, requiring further optimization.
# A Hybrid RF/FSO Transmission System Based on a Shared Transmitter
## 2 Principle
### Channel Models  
#### 2.2.1 RF Link  
For the RF link in the converged transmission architecture, the received signal can be expressed as  
\[y_{\mathrm{R}}=\sqrt{P_{\mathrm{R}}h_{l,\mathrm{R}}}\,h_{\mathrm{R}}s+n_{ \mathrm{R}} \tag{1}\]  
where \(s\) is the transmitted symbol with unit energy, \(P_{\mathrm{R}}\) is the transmit power of the RF link, and \(h_{\mathrm{R}}\) is the fading coefficient of the RF link, which follows the Nakagmi-m distribution.  
Figure 2: The structure of the hybrid RF/FSO system with a shared transmitter and transmission link.  
\(n_{\text{R}}\) is the additive white Gaussian noise (AWGN) of the RF link with mean 0 and variance \(\sigma_{R}^{2}\). Path loss \(h_{\text{I,R}}\) is given as  
\[h_{\text{I,R}}=G_{\text{t,R}}+G_{\text{r,R}}-20 lg\bigg{(}\frac{4\pi L_{\text{R} }}{\lambda_{\text{R}}}\bigg{)}-(\alpha_{\text{oxy}}+\alpha_{\text{rain}})L_{ \text{R}} \tag{2}\]  
where \(G_{\text{t,R}}\) and \(G_{\text{r,R}}\) represent the gain of RF link transmitting antenna and receiving antenna, respectively, \(L_{\text{R}}\) is the RF link distance, \(\lambda_{\text{R}}\) is the RF link wavelength, and \(\alpha_{\text{oxy}}\) and \(\alpha_{\text{rain}}\) are the attenuation coefficient caused by oxygen and rain absorption, respectively.  
Since the received instantaneous signal-to-noise ratio (SNR) by the RF link is expressed as \(\gamma_{\text{R}}=\overline{\gamma}_{\text{R}}|h_{\text{R}}|^{2}\) and the average SNR is \(\overline{\gamma}_{R}=\frac{P_{\text{R}}h_{\text{I,R}}}{\sigma_{R}^{2}}\), the probability density function (PDF) of \(\gamma_{\text{R}}\) can be expressed as [36]  
\[f_{\gamma_{\text{R}}}(x)=\bigg{(}\frac{m}{\overline{\gamma}_{\text{R}}}\bigg{)} ^{m}\frac{x^{m-1}}{\Gamma(m)}\exp\bigg{(}-\frac{mx}{\overline{\gamma}_{\text{R }}}\bigg{)}\]  
where \(\Gamma(\cdot)\) represents the upper incomplete Gamma function, and \(m\) is the parameter of channel fading degree. Finally, the cumulative distribution function (CDF) of \(\gamma_{\text{R}}\) is given as  
\[F_{\gamma_{\text{R}}}(x)=\frac{1}{\Gamma(m)}\gamma\bigg{(}m,\frac{mx}{\overline {\gamma}_{\text{R}}}\bigg{)}\]
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 4 Problem Formulation and Solution
### Problem Analysis and Solution  
In problem \(P1\), the objective function contains four unknown variables and the selection of combination mode, so the problem is non-convex. However, we can simplify the problem by analyzing the problem \(P1\).  
In the local computing mode, it can be seen from Formula (3) that \(f_{i}\) is inversely proportional to \(t_{i}\); that is, the larger \(t_{i}\), the smaller \(f_{i}\). It can be seen from Formula (6) that when \(f_{i}\) is smaller, \(E_{i}^{res}\) is larger. Therefore, the conclusion of \(t_{i}=T\) and \(f_{i}=\frac{R_{i}^{in\mathcal{O}}}{T}\) can be drawn, which can eliminate the \(S1\) and \(S2\) constraints in the problem \(P1\).  
Because the goal of this paper is to maximize the residual energy of all UAVs, it is necessary to maximize the energy harvesting time to fully utilize the entire time block. The objective optimal solution should be obtained when the \(S4\) equal sign holds--that is, \(a^{*}+\sum\limits_{j\in U_{1}}{b_{j}}^{*}=1\), where \(a^{*}\) and \(b_{j}^{*}\) are the optimal time allocation.  
The \(P1\) problem is an MIP non-convex problem, which is difficult to solve. However, it can be seen through analysis that once \(U\) is given, the \(P1\) problem can be simplified to a convex problem, which can be solved by convex optimization technology. Therefore, we divide the \(P1\) problem into two sub-problems: one is the computing task offloading decision problem, and the other is the time resource allocation problem \(P2\):  
\[P2:\ \max_{b_{j}}\sum_{n\in U}{c_{n}}-[\alpha\sum_{n\in U}{\sum_{j\in U _{1}}{c_{n}}\ b_{j}}+\beta\sum_{i\in U_{0}}{k_{i}\ R_{i}^{loc}}^{\alpha}+\sum \limits_{j\in U_{1}}{\frac{{b_{j}}}{{c_{j}}}\Psi(\frac{1}{{b_{j}}})}]\]  
\[s.t.\ \ S3:E_{i}-E_{i}^{loc}\geq 0,\ i\in U_{0}\]  
\[S5:1-\sum_{j\in U_{1}}{b_{j}}\geq 0,\ j\in U_{1} \tag{11}\]  
\[S6:b_{j}\geq 0,\ j\in U_{1}\]  
\[S7:E_{j}-E_{j}^{off}\geq 0,\ j\in U_{1}\]  
where \(\alpha=\mu\ P\ T\) and \(\beta=\frac{\mathcal{O}^{3}}{T^{2}}\) are fixed parameters, function \(\Psi(x)=GT(2^{\frac{\theta\ R_{j}\ x}{\theta\ T}}-1)\).
# A Hybrid Exact-Local Search Approach for One-Machine Scheduling with Time-Dependent Capacity
## 10 Results
### Hybrid Exact-Local Search  
The HELS (hybrid exact-local search) approach is shown using the flowchart in Figure 8. First, the constraint programming solver is employed for the full problem. A period of time of \(n/2\) seconds is given for executing this stage. Then, for \(3\times n\) seconds, a loop occurs that includes the three local search procedures, followed by activation of the constraint programming solver again, but this time for subproblems. These subproblems might involve jobs that intersect with vertical ribbons on the time axis or groups of consecutive sequences of jobs (i.e., multiple C-Paths). Note that a reordering of jobs might be needed so that the current solution conforms with the \"due rule\", else the constraint programming solver might consider the fixed parts of the partial solution to be infeasible. This is denoted by the extra stage \"Reorder same duration jobs\" after the third local search procedure in Figure 8.  
Table 5 presents the best results that were achieved by the approach with the best-known results, all of which are provided by MAHYB.  
Figure 8: Hybrid Exact–Local Search approach.  
Table 6 consolidates the relative performance of our approach when compared with the best-known results. We can observe that our approach manages to find new best results for 48 of the problem instances. It also equals the best-known results for 91 problem instances. MA\({}_{\text{HYB}}\) achieves better results than our approach for the remaining 51 problem instances. We also compare our solutions with how closely it reaches the previously best-known solution as a percentage value, and we call this the metric \"distance%\". Negative values mean that our approach sets a new best-known value. We see that the average distance% metric for all problem instances assumes a negative value of \(-0.1727\%\), demonstrating its very good performance. This is further supported by Figure 9, which shows for each problem subset consisting of 10 instances, a boxplot of the distance% values. Again, negative values are advantageous for our approach, and the graph clearly shows that our approach achieves excellent results, especially for large problem instances.
# Experimental and Numerical Evaluation of Toxic Pool Evaporation
## 2 Experimental Set-Up
### Metrology  
The most important quantity we wanted to measure for this particular study was the evaporation mass flow rate. It was actually measured using two complementary techniques. The first simply consisted of weighting the pool over time, while the second used a FTIR (Fourier transform infrared) spectrometer to evaluate atmospheric concentration downstream of the pool, coupled with a total mass flow rate in the ventilation section. The FTIR sampling probe was placed in the vertical part of the duct for the small-scale test and in the middle of a 5 m long restricted section for the medium-scale test. The measurement frequency of the FTIR was set at 1 Hz. Using velocity and temperature measurements, it directly led to the evaporation flow rate. Before each test, the mass concentration of the solution was measured. To prevent any interaction between the evaporated vapor and the different surrounding solids, we made the concentration measurement as close as possible to the pool using a dedicated mixing system to ensure a perfectly mixed air. The mixing system consisting of a reduced section to obtain a more homogeneous mixture was validated on the basis of the measurement of resulting very low concentration fluctuations. The mixture's homogeneity was then verified through the lack of fluctuation when measuring concentration with the FTIR. All solid surfaces between the pool and the FTIR sampling probe were covered by Teflon. Mean velocity and velocity fluctuations were measured using MacCaffrey probes [20] associated with a pressure transducer. The velocity was measured close to the pool, in the middle of the duct height, for each test. In addition, velocity profile was determined using several points at different heights. Following [21], the probes also enabled us to estimate the turbulent velocity, which was 4% of the average velocity. Data frequency was then set at 10 Hz.  
To confirm the accuracy of the FTIR and air flow rate methods used for evaluating the evaporation rate, we compared the results to measurements obtained by two other methods. The first was the weight measurement of the pool described above and the second consisted in pool concentration measurement before and after the tests. Concentration in the solution after the test was measured using pool sampling after a mixing process to ensure a homogenous concentration. Those two results  
Figure 5: Picture of the system used to generate artificial turbulence upstream of the pool.  
confirmed the ones obtained using the FTIR and flow rate methods. Other specific sampling was taken to evaluate the concentration at different heights.  
Pool temperature was also measured using several PFA (Perfluoroalkoxy)-covered K-type thermocouples placed in the pool's depth. While the response time is usually higher than with uncovered thermocouples, these specific ones were chosen because of their corrosion resistance. They were placed near the bottom of the pool, then 0.015 m and 0.0275 m above the bottom of the pool.
# Research Challenges for the Internet of Things: What Role Can OR Play?
## 3 How can OR Support the Future IoT?
### The Mathematical Tools and Techniques of OR Which May Support the IoT
#### 3.1.6 Routing/Scheduling  
Routing describes techniques to select best paths for a set of processes. Scheduling is an OR technique for allocating time to tasks for machines, jobs, and projects.  
Both these techniques have application for the IoT. For example, routing could be applied to network design while scheduling could be applied to traffic management within the IoT. [PERSON] et al. [59] conducted a survey of current routing protocol issues used for IoT and identified challenges for research including context awareness, heterogeneity, node death, topology changes, scalability, latency, incentive based routing, congestion control, data security, data redundancies, and multipath routing. They emphasized the importance of routing processes for the IoT and demonstrated the need to develop new routing protocols.
# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 3 Materials and Methods
### Database Construction  
For a comprehensive assessment of the evaluation method proposed in this paper, it is crucial to construct a diversified and sufficient number of datasets. On account of the existing studies [51], it has been shown that the height of undulation shows a tendency to first increase and then stabilize with the increase of the analysis window. In this paper, combining the Chinese macro-geomorphic classification standard [51; 52] with the actual situation of the study area, the optimal analysis window for the undulation height of the study area was determined to be \(15\times 15\) pixels by using the neighborhood analysis method. The study area can be divided into six topographic categories according to the height of relief, namely plains (relief \(<\) 30 m), terraces (30 m \(<\) relief \(<\) 70 m), hills (70 m \(<\) relief \(<\) 200 m), small rolling mountains (200 m \(<\) relief \(<\) 500 m), medium rolling mountains (500 m \(<\) relief \(<\) 1000 m), and large rolling mountains (relief \(>\) 1000 m). After dividing the terrain categories based on the regularized knowledge, each kind of terrain is selected a nine km \(\times\) 9 km square representative area as the experimental object, and six experimental areas are selected as shown in Figure 2. Among them, test areas 1-6 represent plains, terraces, hills, small rolling hills, medium rolling hills, and large rolling hills, respectively.
## 3 Therapeutics in HCC  
Treatment options for HCC include surgical, locoregional, orthotopic liver transplantation (OLT), and systemic therapies. The therapeutic decision-making process takes into account both liver function and tumor burden, which are represented in the most widely used and recommended staging system: the Barcelona Liver Cancer Clinic (BCLC) staging system [102; 103].  
Systemic treatments are typically offered to patients with BCLC stage C disease, those with BCLC stage B disease who are unsuitable for other treatments, patients with progressive disease following locoregional therapy, or as an adjuvant treatment [104].  
Currently approved systemic therapies are divided into two main classes: anti-angiogenic targeted therapies and ICIs.
## 3 Data Set
### Albedo  
Albedo is a key geophysical parameter in ecosystem characterization since it indicates the amount of solar energy absorbed by the surface and therefore the surface energy budget [55]. A large number of studies recognize the important role of Albedo influencing gross productivity [56], surface temperature [57], evapotranspiration [58] and physical, physiological and biogeochemical processes [59].  
The Albedo information was obtained from the 16-day MODIS MCD43B3 combined Aqua and Terra products [60, 61]. It provides a 1 km data describing both the directional hemispherical reflectance (black-sky Albedo) and the bihemispherical reflectance (white-sky Albedo). The overall surface Albedo was derived by averaging the above two estimates.  
Black-sky and white-sky Albedos are provided for seven spectral bands (MODIS channels 1-7) and the three broadbands (0.3-0.7, 0.7-3.0, and 0.3-5.0 \(\upmu\)m). These products make use of a kernel-driven linear model of the Bidirectional Reflectance Distribution Function (BRDF), which relies on the weighted sum of an isotropic parameter and two functions (or kernels) of viewing and illumination geometry. The accuracy assessment against continuous field measurements results in a root mean square error (RMSE) of 0.013 and a bias around \(-\)0.02 [62].
# DIFshilling: A Diffusion Model for Shilling Attacks
## 4 The Design of DIFshilling  
### Overview of DIFshilling  
DIFshilling is designed based on the diffusion model framework, leveraging the success of diffusion models in accurately fitting real data distributions. Figure 2 illustrates the framework of DIFshilling. Firstly, DIFshilling identifies and filters out less-active users who rarely rate items in the dataset. These users are not conducive to the generation of fake user profiles, lacking enough characteristics to represent distributions. Then, the remaining active users are passed for the diffusion process. In the diffusion process, random Gaussian noise is incrementally added to the active user-item interaction matrix during the noising process. A neural network model is trained to predict the noise. Unlike in computer vision, it is necessary to safeguard the personalized characteristics of real users. Therefore, the noise schedule is restricted within a specific range. During the denoising phase, DIFshilling utilizes the previously trained model to predict the noise and progressively removes it to obtain a fake rating matrix. Importantly, to protect real users' personalized features, we utilize the \(x_{t}\) obtained during the noising phase as the initial point for the denoising stage rather than using random Gaussian noise.
## 5 Experimental Setup
### Results
#### 5.3.1 Qualitative Evaluation: SASSI Questionnaire
Finally, as a necessary step to improve the system in future versions, the dialogues that did not fulfill the goal of the user or required reformulations have been analysed to identify the **source of the errors** that led to unsuccessful interpretations. The errors identified are the following:  
1. **Obtained:** \"Quiero _ unusas alas_ con algun ordenador\" \"I want _a pair of wings_ with some computer\" 2. **Correct:** \"Quiero una sala con algun ordenador\" \"I want a room with some computer\"
2. Syntactic analysis. Structures that are not correctly analyzed--Example (10)--or words with wrong lemmas (usually for words that are not in the tool's dictionary)--Example (11). 1. **Obtained:** \"El conteneder 2 es _para\({}_{verb}\)_ la marca Canon\" \"Container 2 is stop the Canon brand\" \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **KIDE4 Guide** & & **KIDE4 BinPicking** \\ \cline{2-4}  & **\%** & **\#** & **\%** & **\#** \\ \hline Completed & 84.34 (8.43) & 70 (7) & 82.67 & 62 \\ Not completed & 15.66 & 13 & 17.33 & 13 \\ \hline Total & & 83 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dialogue completion results for the user studies reported in this work. Values in parentheses stand for dialogues classified as _partially completed_. \begin{table}
\begin{tabular}{c c} \hline \hline
**KIDE4 Guide** & **KIDE4 BinPicking** \\ \hline
5.4 (3.4) & 4.3 (3.3) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Number of average number of steps to successfully complete a dialogue. Values in parentheses stand for values that exclude steps implemented by default.