esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Lossless Image Compressor
### Proposed Lossless Algorithm
#### 3.2.3 Differential Pulse Coded Modulation (DPCM)  
In this stage, a lossless predictive coder is used. Due to the rare occurrence of sharp edges in endoscopic images, the difference between the component values of two consecutive pixels is generally small. The change in component values (_dX_) with respect to its adjacent left pixel in any row is given by Equation (8):  
\[dX_{r,c}=X_{r,c}-X_{r,c-1} \tag{8}\]  
where, \(X_{r,c}\) is the pixel value at row \(r\) and column \(c\), and \(X_{r,c-1}\) is its adjacent left pixel value. \(X\) can represent Y, E, or F component values. Figure 6 shows the changes in _dX_ for a standard \"mandrill\" image and as well as for an endoscopic image. From Figure 6, it is seen that smaller changes in pixels values occur in endoscopic images. Similar phenomenon is observed for other endoscopic images used in this work.  
More simulations have been conducted with 100 WLI and 15 NBI test endoscopy images and with standard images; average absolute difference (AAD) is used as given in Equation (9) as the statistical measure of _dX_:  
\[dX_{avg,image}=\frac{\sum\limits_{n=1}^{N}\sum\limits_{n=1}^{M-1}\left|(x_{n+1,a}-x_{m,n})\right|}{M\times N} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Bits** & **VSNR** & **Overall** & & \\
**(Integer + Fraction)** & **(dB)** & **PSNR (dB)** & & \\ \hline
9 (8 + 1) & 75.63 & 58.13 & 0.9932 & 0.9989 \\
10 (8 + 2) & 97.83 & 98.60 & 0.9972 & 0.9997 \\
11 (8 + 3) & \(\infty\) & \(\infty\) & 1.0000 & 1.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average image quality index for different data bit length.  
where, \(M\) and \(N\) are the image width and height namely; \(x\) is the original component value. The results are summarized in Table 2, where it is seen that, in general, the difference in pixel (\(dX\)) with respect to the adjacent left pixel is very small in endoscopic images compared to that of standard images. As a result, the DPCM is a good choice. It should be noted that, the DPCM used here does not use any quantization and thus is lossless as well. Besides, it has very low computational complexity which will help reduce power and area consumption of the compressor.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 2 Related Work
### The ETSI MEC Framework and Its Reference Architecture  
MEC enables the implementation of ME apps as cloud applications, running on top of an open infrastructure located in proximity to end users.  
The MEC reference framework is described in References [6; 7; 8]. In particular, Reference [6] provides a glossary of terms, Reference [7] specifies MEC requirements, and Reference [8] details the overall architecture, its constituting elements, and the related reference points.  
MEC standardization activities are still ongoing; hence, interested readers should rely on the official ETSI specifications for an updated description of the framework (all the documents published by ETSI MEC are available at [[https://www.etsi.org/technologies-clusters/technologies/multi-access](https://www.etsi.org/technologies-clusters/technologies/multi-access)]([https://www.etsi.org/technologies-clusters/technologies/multi-access](https://www.etsi.org/technologies-clusters/technologies/multi-access))edge-computing under the \"Specifications\" tab.). Nonetheless, below, we will briefly summarize some concepts extensively used throughout this paper.  
Figure 1 shows the reference architecture of an ME system, partitioned into the network, host, and system levels [8]. The Network level must provide connectivity to ME apps; to this end, it can use different access technologies, including 3 GPP mobile networks or WIFI. Since no MEC functional block or reference point is specified at the Network level, this part is not included in the figure.  
From the functional point of view, an ME system can be further divided into two major parts, i.e., the ME host, and the management blocks necessary to run ME apps in a mobile network.
## 4 Proposed Trust-Based Optimized Reporting Scheme  
This section explains the proposed trust-based optimized reporting scheme, with the fundamental concepts illustrated in Section 4.1, focusing on key elements such as Trustor, Trustee, and Recommenders and their relationships with the Group Member Nodes (GMNs) and Group Leader Nodes (GLNs). The trust calculation mechanism is detailed in Section 4.2; it utilizes both direct and indirect trust assessments and considers factors such as energy, unselfishness, honesty, dishonesty, and similarity. Finally, in Section 4.3, we discuss the Delta-Based Isolation Strategy, revealing how it determines whether the trust value of a specific trustee is decreasing and its implications for optimizing cost and reducing root node overhead in trust management.
# Effects of Chitosan Coatings on Controlling
## 5 Conclusions  
Edible chitosan membranes are effective in controlling the growth of pathogenic bacteria, such as methicillin-resistant _S. aureus_ and _L. monocytogenes_, as they were able to reduce their concentrations in beef and mutton meat by up to 2 log\({}_{10}\) CFU/g. Chitosan coatings could be used as a supplementary measure to promote the safety of beef and mutton. Moreover, they can enhance meat's quality and prolong its shelf life. The antimicrobial propertiesof chitosan emulsions with essential oils--such as oregano oil--against pathogenic microorganisms in beef and mutton meat should also be examined. Even though there are numerous studies that demonstrate the application of chitosan coatings in food products at a laboratory scale, future research is needed to focus on large-scale manufacturing and commercial applications, in order to fully substantiate chitosan's properties and capabilities in food packaging.  
Conceptualization, G.A. and V.E.; methodology, A.T. ([PERSON]), and V.E.; data curation, A.T. ([PERSON]), A.T. ([PERSON]) and V.E.; writing--original draft preparation, A.T. ([PERSON]) and V.E.; writing--review and editing, V.E., I.A. and G.A.; project administration, G.A.; funding acquisition, G.A. All authors have read and agreed to the published version of the manuscript.  
This research was co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program of Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH--CREATE--INNOVATE (project code: T1 EDK-05479).  
Not applicable.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 1 Introduction
In a recent review [4], the cumulative incidence for TKA revision procedure was 1.2% at 1 year, 1.9% at 2 years, 3.1% at 5 years, 4.4% at 10 years, and 6.1% at 15 years after the index primary TKA. Interestingly, the failure causes changed in the last decade, with an increased number of aseptic loosening (29.8%), infections (14.8%), and pain due to the malposition of implants (9.5%) [3]. A critical point is the achievement of a correct preoperative diagnosis for TKA failure: several authors reported bad outcomes when revision surgery was performed merely to ease subjective pain, without clear explanation and diagnosis [5]. [PERSON] et al. described eight different mechanisms of implant failure: sepsis, extensor discontinuity, stiffness, tibial femoral instability, patellar tracking, aseptic loosening and osteolysis, periprosthetic fracture, and component breakage. A ninth category, unexplained pain, is an indication for further investigation but not for surgery [6]. Implant stability, as well as adequate and stable fixation at component-bone interface, concretize the key to long-term survivorship in both primary and revision TKA [7]. Constrained condylar knee (CCK) prostheses are a common choice for difficult primary TKA and revision total knee arthroplasty [8]. The choice to use a varus valgus constrained (VVC), such as a CCK prosthesis, depends on the evaluation of the residual bone stock; on the bone defect to manage with augments, stems, cones or sleeves; and on the ligamentous incompetence of the collateral ligaments and posterior capsule. However, previous literature reports high rates of complications, readmissions, and mortality associated with revision surgery [9]; moreover, TKA revision procedure consumes more than 50% more resources than primary TKA [8]. Although several studies have reported the outcomes of the use of CCK implants in the treatment of primary severe osteoarthritis and revision TKA [10; 11], the relationship between the success rate and the preoperative diagnosis in revision surgery is still unclear. The first objective of this paper is to report the clinical and functional results of a cohort of patients with different causes of primary failure, who underwent TKA revision at mid- to long-term follow-up. Given that different preoperative-causes-of-revision may have different effects on postoperative functional and clinical outcomes, the second purpose of this paper is to evaluate possible differences in postoperative functional and clinical outcomes among preoperative-cause-of-revision subpopulations of patients who underwent TKA revision with NexGen@Legacy@Constrained Condylar Knee (LCCK) implants.
# Chemical Composition and Antimicrobial Properties of Honey Bee Venom
## 2 Results and Discussion
### Chemical Composition of Extractive Compounds
#### 2.2.1 Chemical Composition of Extracts with Diethyl Ether
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Groups of Compounds**} & \multicolumn{2}{c}{**Dried Venom**} & \multicolumn{2}{c}{**Fresh Venom**} \\ \cline{2-5}  & **Dv-1** & **Dv-2** & **Fv-1** & **Fv-2** \\ \hline Aliphatic esters, including: & 17.31 (31) & 20.77 (6) & 8.07 (7) & 6.17 (7) \\ \hline - octadecyl oleate & 2.59 & 0.22 & 0.62 & 0.22 \\ \hline - eicosyl oleate & 13.18 & 17.42 & 1.89 & 1.37 \\ \hline - eicosyl oleate & 1.54 & 1.51 & 0.92 & 1.11 \\ \hline - tetracosyl palmitate & - & 0.39 & 3.42 & 1.14 \\ \hline Glycerol \& glycerides, including: & 5.47 (4) & 8.22 (5) & 1.02 (2) & 0.58 (2) \\ \hline - glycerol & 0.36 & 0.13 & 0.07 & trace \\ \hline - 1-hexadecyl glycerol & 0.07 & 0.07 & - & - \\ \hline - 1-eicosyl glycerol & 1.86 & 1.43 & 0.95 & 0.58 \\ \hline - 2-eicosyl glycerol & 3.16 & 2.43 & - & - \\ \hline Aromatic compounds, including: & 1.76 (2) & 1.05 (2) & 4.62 (10) & 0.58 (4) \\ \hline - benzoic acid & - & - & 1.60 & 0.30 \\ \hline - \(p\)-hydroxybenzoic acid & - & - & 0.28 & 0.22 \\ \hline - gallic acid & 1.52 & 0.88 & - & - \\ \hline - elagic acid & 0.24 & 0.17 & - & - \\ \hline \multicolumn{5}{c}{Sterols, including:} & 0.23 (1) & 0.10 (1) & 2.89 (3) & 6.51 (4) \\ \hline - 24-methylenedolesterol? & - & - & 1.94 & 3.26 \\ \hline - \(\beta\)-sitosterol & - & - & trace & 1.29 \\ \hline - avenasterol & 0.23 & 0.10 & 0.94 & 1.65 \\ \hline Alkanes and alkenes, including: & 45.30 (25) & 32.07 (30) & 25.82 (33) & 27.10 (34) \\ \hline - \(n\)-pentacosane & 3.41 & 2.63 & 2.16 & 1.75 \\ \hline - \(n\)-heptacosane & 5.51 & 4.27 & 3.55 & 3.39 \\ \hline - \(n\)-nonacosane & 3.28 & 2.46 & 3.35 & 2.31 \\ \hline - \(n\)-hentriacotane & 2.23 & 1.76 & 2.40 & 2.31 \\ \hline - 7-hentriacontene & 4.42 & 3.46 & 0.58 & 2.45 \\ \hline - 9-hentriacontene & 3.34 & 2.62 & 3.70 & 2.30 \\ \hline \multicolumn{5}{c}{Other compounds, including} & 3.90 & 12.54 & 3.78 & 2.84 \\ \hline - ethylamine & - & 8.44 & 0.30 & 0.47 \\ \hline - uracil & - & - & 0.04 & - \\ \hline - indole-3-acetic acid & - & - & 0.06 & - \\ \hline NN & 5.90 (10) & 6.98 (15) & 8.45 (27) & 5.90 (10) \\ \hline \hline \end{tabular}  
* the number of components in a particular group is given in parentheses; ** component not found; *** less than 0.01% TIC.  
\end{table} 
Table 2: _Cont._components (alkanes and alkenes, aliphatic long-chain alcohols and acids, and glycerides) and a low content of aromatic compounds.
## 1 Introduction
This structure considers anomalous dispersion and establishes a configuration of mode interactions, simplifying the acquisition of DKS based on the characterization of the parameter space \((\Delta,\left|S\right|^{2})\). Additionally, they perform a stability analysis to identify regions in the parameter space in which DKSs are stable. However, the effect of mode interaction on DCM waveforms has not been thoroughly examined, nor their role in potentially limiting the accessibility of single DKS regions in the parameter space \((\Delta,\left|S\right|^{2})\). In this study, we numerically demonstrate the generation of a single DKS using a straightforward constant pump power with an adiabatic frequency sweep in a dual-ring Si\({}_{3}\)N\({}_{4}\) structure, which is sufficiently slow that the state of the optical waveform is not affected by the rate of the sweep. In the DCM, the mode interactions are electrically controlled by a microheater in the auxiliary microring. We chose strength and frequency values that facilitated access to the soliton and avoided the chaotic region, based on these characterizations. Finally, we verified the deterministic generation of the DKS. This document has the following structure: First, we characterize within the parameter space \((\Delta,\left|S\right|^{2})\) by fixing values of \(a\) and \(b\). These values are experimentally chosen to generate DKSs in the lower region of the space, thereby circumventing the chaotic region. Next, we fixed values for four points of the \((\Delta,\left|S\right|^{2})\) space and computed the probability of DKS generation for each point. These points are manually selected, considering the position facilitating the DKS generation with a constant \(\left|S\right|^{2}\) and a sweep from blue to red. Following this, we select four sets of values for \(a\) and \(b\) where the DKSs generation probability is high and compute the parameter space for these configurations. This process allows us to visualize potential regions where DKSs may generate and to ascertain which configurations exhibit a more pronounced presence of these waves. Finally, we analyze the stability of each zone to determine if reliable generation of DKSs is feasible with these values.
# Gaussian Dynamic Convolution for Semantic Segmentation in Remote Sensing Images
## 4 Experiments
### Experimental Setting  
**Implementation Details** To demonstrate the performance of GDCN better, we take ResNet-50 as the backbone and pretrain it on ImageNet. The feature layers generated by the backbone will be input into the GPP module, which consists of two dilated convolutional layers and two Gaussian dynamic convolutional layers. The dilation factors of the two dilated convolutions are, respectively, set to 1 and 18 and the base offsets of the two Gaussian dynamic convolutions are set to 6 and 12, with \(\sum\) set to 2. We use a \"poly\" learning rate strategy for iteration, where the initial learning rate is 0.007, and the learning rate is calculated by \(1-(\frac{iter}{max\_iter})^{power}\) (power = 0.9) for each subsequent step. Our weight decay is set to 0.0001 and the momentum is set to 0.9.  
We crop the images to a fixed size of 800 \(\times\) 800 for the iSAID dataset, and for the Love DA dataset, we randomly crop the images to 512 \(\times\) 512. Moreover, we implement some common data enhancement strategies during training. For instance, we randomly scale the images in the range of \([0.5,2.0]\). Furthermore, the random horizontal flip operation is performed to process the input image. For the batch size, we set it to 8 on the iSAID dataset and 16 on the LoveDA dataset. We choose the SGD optimizer to complete the network optimization.
## 7 RNN-Based Optimization of Mirror Placement for Maximizing SNR in ADRs
### RNN Methodology
#### 7.2.1 Inputs to the RNN  
The input at time \(t\), denoted by \(\mathbf{P}(t)\), consists of the signals received at the photodiodes:  
\[\mathbf{P}(t)=\big{[}P_{r,\text{PD}_{1}}(t),P_{r,\text{PD}_{2}}(t),\dots,P_{r, \text{PD}_{N}}(t)\big{]}. \tag{45}\]
## 3 Problem Formulation of CHPED  
Thermal generating units, cogeneration units, and heat-only units were taken into consideration for the problem formulation of the CHPED. The heat-power viable operation zone of a combined cycle cogeneration unit is depicted in Figure 1. The KLMNOP boundary curve encloses the viable operation zone.  
The heat capacity rises along the boundary curve LM as the generation of electricity falls, whereas it falls along the curve MN. It is obvious that the unit's maximum output power is reached along the KL curve. On the other hand, the MN curve is where the unit produces the most heat.  
The main goal of the issue is to estimate the heat and power generation rates for each unit in order to minimize the cost of heat and power generation while meeting the heat and power demands. The CHP load dispatch problem is represented mathematically as follows:  
\[min\left(\sum_{i=1}^{N_{P}}C_{i}(P_{i}^{P})+\sum_{j=1}^{N_{c}}C_{j}(P_{j}^{c}, H_{j}^{c})+\sum_{k=1}^{N_{h}}C_{k}(H_{k}^{h})\right) \tag{1}\]  
where \(C_{i}(P_{i}^{P})\) is total generation cost, \(C_{j}(P_{j}^{c},H_{j}^{c})\) is generation cost with CHP units, and \(C_{k}(H_{k}^{h})\) is generation cost using heat-only units. Np, Nc, and C\({}_{k}\) denote the number of power only units, CHP units, and heat only units, respectively. Similarly, i, j and k show the number of power-only units, CHP units, and heat-only units.  
The quadratic cost function of power-only units is given as  
\[C_{i}(P_{i}^{P})=a_{i}(P_{i}^{P})^{2}+b_{i}P_{i}^{P}+c_{i} \tag{2}\]  
where \(C_{i}(P_{i}^{P})\) denotes fuel cost of the ith generating units, and a\({}_{i}\), b\({}_{i}\), and c\({}_{i}\) are the cost coefficients of power-only units.  
A combined heat-power cogeneration system is given as  
\[C_{j}(P_{j}^{c},\ H_{j}^{c})=a_{j}(P_{j}^{c})^{2}+b_{j}P_{j}^{c}+c_{j}+d_{j}(H _{j}^{c})^{2}+e_{j}H_{j}^{c}+f_{j}P_{j}^{c}H_{j}^{c} \tag{3}\]  
And a heat-only unit is defined as  
\[C_{k}(H_{k}^{c})=a_{k}(H_{k}^{h})^{2}+b_{k}H_{k}^{h}+c_{k} \tag{4}\]
## 1 Introduction
From the beginning of the 21 st century to the present, under the European Space Agency's plan, multifunction ion thruster diagnostic systems have been developed, which gave up direct measurement of the thermal deformation of the grid and replaced it with temperature measurement. The grid deformation can be calculated from the temperature data. [PERSON] [15; 16] designed an in situ electric propulsion diagnostic system in which a pyrometer was used to successfully measure the overall temperature of the grid surface. The temperature field on the grid surface was measured at 4 kW power of the RIT-22 thruster. The results show that the center temperature of the grid was 301 \({}^{\circ}\)C and the edge temperature was 255 \({}^{\circ}\)C. [PERSON] [17; 18] developed a thruster in situ diagnostic system equipped with a thermal imager to measure the temperature of the HET-100 thruster channel wall. In China, scholars have only made simulation calculations for grid thermal deformation [1; 19]. So far, there is no effective measuring system for hot gap and grid thermal deformation in China. In order to further optimize the direct measurement method of grid thermal deformation and meet the actual measurement requirements of the LIPS-300 ion thruster, this paper improves the measurement method of Soulas and designs a set of grid thermal deformation measurements system using a telemicroscope. Based on the principle of videometrics [20; 21], we propose an installation scheme of the cooperative targets that can measure the deformation of the three-grid ion optics, and improve the measurement accuracy by using subpixel positioning. Finally, the high-precision real-time measurement of grid deformation is realized based on the software written by MATLAB (version 2017b).
## 3 Results
### Spatial Distribution Characteristics of GPP
#### 3.2.2 Distribution of GPP with Elevation  
To illustrate the distribution of GPP with elevation, the mean GPP values were calculated and analyzed across different elevation intervals. In addition, a three-dimensional (3-D) plot was employed to visualize the spatial variation in GPP with elevation, as shown in Figure 4. It can be observed that, with increasing elevation, the mean GPP across all the years exhibits an upward trend. This upward trend is particularly pronounced in low-elevation regions, reaching a first peak around 2000 m elevation. After a slight decline, it subsequently reaches a second peak around 3000 m elevation.
## 3 Results and Discussion
### Adhesion Strength of Composite Coatings
#### 3.3.2 Adhesion Strength of Composite Coatings  
PU coating was sprayed on the MAO layer prepared under different sodium silicate concentration conditions; and further, the adhesion strength was tested. At the same time, the influence of silane coupling agent APTES on the adhesion strength of composite coating before and after modification of the MAO layer was tested (Figure 6). The change of the adhesion strength with the concentration of sodium silicate in the electrolyte indicates that the adhesion strength first increases and then decreases with the increase in the concentration of sodium silicate in the electrolyte, regardless of whether the surface modification has been carried out by the silane coupling agent. The red line in Figure 6 indicates that after the silane coupling agent has been used to modify the MAO surface, the adhesion strength between the composite coating and aluminum alloy improved significantly (more than twice that of the untreated blank sample as indicated by the black line in Figure 6), thus fulfilling the requirements of the rail transit vehicle industry requiring that the adhesion strength between the coating and the aluminum alloy substrate to be greater than 10 MPa [28].  
The fracture parts of the composite coatings modified with silane are all loose layers of the MAO layer, while the fracture parts of composite coatings without silane modification contain the interface between MAO and PU coatings (as shown in the fracture images in Figure 6). This also shows the effectiveness of silane modification to improve the adhesion strength of MAO and PU coatings.