esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Light Field Regulation of UVC-LED Disinfection for Cold Chain Transportation
## 4 Results  
### Results of Simulations on Ice Influence  
These three types of ice surfaces with different thicknesses (10, 50, 100, 200, 500, and 1000 mm) are contrasted under the influence of UVC beam transmittance, and the result curve is presented in Figure 4. It can be concluded from the figure that when the ice thickness was small (less than 500 mm), the ice surface structures had substantially different influences on the transmittance of the UVC beams; the concave spherical surface was found to have the least influence, while the conical surface was found to have the greatest influence. When the thickness of the ice was greater than 500 mm, the effect of the ice surface structure on the transmittance of the UVC beam was not significant. Due to the absorption of UVC beams by the ice layer, the thicker the ice layer, the higher was the UVC light energy absorbed by the ice layer, perhaps reducing the UVC disinfection effect. As can be seen from the figure, when the thickness of the ice reached about 50 mm, the absorption rate of the light beam was found to be close to 10% for all three surface morphologies of pure ice.  
To investigate the influence of the ice impurity rate on the UVC transmittance, a 10 mm thick convex spherical ice surface with impurity ratios of 5%, 10%, 20%, 30%, 40% and 50% was taken as an example to simulate the transmittance of light beams, and the result is shown in Figure 5; here the impurity rate is defined as the proportion of opaque part in the whole ice. And it is evident that the presence of impurities affected the transmittance of the beam in the ice. When the impurity rate was 5%, the UVC beam absorption rate of the ice reached about 10%. Combined with the effect of the thickness of the ice itself, this reduces the efficiency of UVC disinfection.  
Figure 3: Ultraviolet-C (UVC) disinfection systems, Group \(1\) (**left**) and Group 2 (**right**).  
Besides the influences from the thickness, surface structure and impurity rate of the ice, there are many other factors that affect the UVC transmittance, and this work only explored the three factors above. Based on the results of a series of comparative simulations, it is evident that the thickness, surface structure and impurity rate of the ice greatly affect the UVC transmittance efficiency, and the higher the UVC transmittance is, the more energy  
Figure 4: The relationship between ice transmittance and ice thickness.  
Figure 5: The relationship between ice transmittance and the impurity rate.  
will be saved. Therefore, traditional UVC disinfection methods face great challenges, and effective solutions are urgently required.
## 4 Methodology
For sufficient representation of spectra above the harmonic range, EN61000-4-7 standard [17] suggests including a network impedance between the power source and EUT, such as a line impedance stabilizing network (LISN). The measurements at the output terminals of the LISN Rohde & Schwarz HM6050-2 artificial network are made using the Tektronix RSA306B spectrum analyzer. The obtained data are analyzed using specialized software dedicated to the PC, the Tektronix manufactured software SignalVu-PC (V4.1.0022). Detailed information about the spectrum analyzer and dedicated PC software can be found on the manufacturer's website [55]. The value of the disturbance voltage is measured by a quasi-peak detector. To simplify the process of balancing the measured signal level and taking into account losses in the measurement circuit, the results are expressed in dB\(\upmu\)V. Following EN55015 standard [21], the measured disturbance voltage values refer to the permissible quasi-peak value levels in the frequency range from 9 kHz to 150 kHz. The acceptable limits of supraharmonics emission are indicated as the red line in the figures located in the results section. It should also be pointed out that the frequency range of 2-9 kHz has not been sufficiently investigated in the paper. Generally, it can be emphasized that this range can be considered as omitted, even by standards. The reason may be that most popular power quality analyzers available on the market use a sampling frequency of up to 10,240 kHz, which allows the observed frequency range up to 5120 kHz. On the other hand, spectrum analyzers available on the market dedicated to Power Line Communication transmission are designed to test the band 9-150 kHz. However, considering the development of the power quality analyzers, which increase sampling rates up to 20 kHz and even 300 kHz, there is a justified need to investigate the relatively undiscovered and omitted 2-9 kHz frequency bandwidth in the standards. It is worth considering that extended methodology can consider both exploring the impact of quality of the supply voltage on the emission of disturbances as well as a statistical approach, extending the number of devices under tests to build a representative probe group to form the conclusions, supported by statistical indicators.
## 3 Results and Discussion
### Changes in Lipids Other than Cell Membrane Lipids  
Next, we analyzed lipids not directly involved with cell membrane structures. Such lipids detected in our study were PG, PA, lyso-glycerophospholipids (Lyso-GP), GL, CE, Cer, SPH, CL, and CoQ. Notably, the collective pool of non-cell membrane lipids exhibited a significant increase in B4K0H cells compared to the control group. Interestingly, in contrast to cell membrane lipids, these lipids showed a further elevation in B4K12H cells compared to B4K0H cells (Figure 4a).  
PG is a minor component of many subcellular membranes, particularly mitochondrial membranes [32]. PA is a precursor of other GP species and acts as a lipid second messenger in extracellular and intracellular signaling [33]. In our study, PG and PA showed an increase of approximately 32% and 81% in B4K0H cells, respectively, compared to the control cells. In B4K12H cells, PG levels were comparable to the control cells, while PA levels saw a significant decrease (Figure 4b).  
Figure 4: Alterations in non-cell membrane lipids until 12 h following microplasma irradiation at 4.0 kV condition. Data were obtained from set #2 samples. (**a**) Changes in total non-cell membrane lipids were determined based on the absolute area values derived from LC/MS analysis. Statistical analysis compared B4K0H group (n = 5) and B4K12H group (n = 5) with the control group (n = 5). (**b**–**f**) Relative changes (expressed in percentages) in various non-cell membrane lipids in B4K0H and B4K12H groups were compared to that of the control group.  
All lyso-GP classes experienced increases in B4K0H cells, with LPI showing the highest increase at around 66% (Figure 4c). Similarly, all GL and CE species showed increases in both B4K0H and B4K12H cells compared to the control, with TG displaying the most prominent increase (Figure 4d). It is worth noting that lyso-GP are intercellular signaling molecules [34], while TG and CE serve as major storage lipids in cells [35].  
SPH decreased by approximately 18% in B4K0H cells, but increased by 18% in B4K12H cells compared to the control (Figure 4e). CL, a mitochondrial unique lipid, exhibited a 55% increase in B4K0H cells and then become 114% in B4K12H cells compared to the control cells (Figure 4f). CoQ, an enzyme co-factor in the mitochondrial respiratory chain, followed a similar pattern to CL. These results suggest that microplasma components not only interacted with the cell membrane, but also penetrated into the cells. Although the intracellular lipid levels increased immediately after irradiation, most of them (except GL and CE) showed a tendency to return to a normal level after 12 h of irradiation.
## 2 Theoretical Background
### Teager-Kaiser Energy Cepstral Coefficients (TECCs)  
The TECCs are known in speech recognition and similar applications to perform better than MFCCs when noise is artificially added, and comparably to them for convolutional (non-additive) noise and in clean conditions [23]; the extraction procedure of these coefficients is made up by two main components that distinguish them from the MFCCs. These steps are the definition of the Teager-Kaiser Energy (TKE) and the convolution with Gammatone filters, which are described here below. The whole extraction procedure is graphically displayed in the flowchart of Figure 3.
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
## 4 Decision Algorithm for the Broadcast Time of Star of Cliques  
This section introduces a decision algorithm for stars of cliques, which employs a greedy approach to determine whether broadcasting from the universal vertex can be completed within the upper bound established in Lemma 3. Specifically, the algorithm aims to identify a broadcast scheme that completes in at most \(t=t^{\prime}-1\) time units, where  
Figure 1: Star of cliques consisting of 3 cliques with sizes 10, 9, and 5.  
\(t^{\prime}=\max\limits_{1\leq i\leq k}\{i+\lceil\log l_{i}\rceil\}\) represents the upper bound on the broadcast time. Subsequently, we show how to compute the broadcast time of a stars of cliques graph originating from an arbitrary vertex using the decision algorithm.  
Define \(CS[1\ldots k]\) as an array of binary strings, with each string representing a clique in \(G_{k}\). For all \(1\leq i\leq k\), the base-10 value of the binary string \(CS[i]\) corresponds to \(l_{i}\) ( i.e., the number of vertices in clique \(C_{i}\)). Note that the cliques are sorted in descending order such that \(l_{1}\geq l_{2}\geq\cdots\geq l_{k}\). Each binary string \(CS[i]\) contains \(t\) bits, with shorter strings left-padded with zeros. Each bit \(CS[i][j]\) (\(1\leq j\leq t\)) specifies the action to be performed by the universal vertex at time unit \(j\): if \(CS[i][j]=1\), \(u\) must inform a vertex in \(C_{i}\); if \(CS[i][j]=0\), no action is needed.  
These binary strings can be used to construct an individual broadcast scheme from the universal vertex for their corresponding cliques, leveraging the following property: for any such binary string \(CS[i]\), if a bit at index \(j\) is 1 (i.e., \(CS[i][j]=1\)), broadcasting to \(2^{t-j}\) vertices can be completed by time unit \(t\) without any further intervention from the universal vertex. This process can only be initiated if the universal vertex informs a new vertex in clique \(C_{i}\) at time unit \(j\). This property holds because any integer can be represented as a sum of distinct powers of 2. Specifically, \(l_{i}=2^{p_{1}}+2^{p_{2}}+\ldots+2^{p_{c}}\), where \(l_{i}\) denotes the number of vertices in \(C_{i}\) and \(p_{1},\ldots,p_{c}\) are integers. Hence, if the universal vertex can broadcast to a new vertex in \(C_{i}\) at each time unit \(j\) where \(CS[i][j]=1\), \(C_{i}\) will be fully informed no later than time unit \(t\).  
It is noteworthy that if \(u\) informs a vertex in \(C_{i}\) at a time unit where \(CS[i][j]=0\), then \(C_{i}\) does not require any further action, even if subsequent bits in \(CS[i]\) are set to 1. This is because \(2^{t-j}>2^{t-j-1}+2^{t-j-2}+\ldots+2^{1}+2^{0}\) (e.g., \(1000>0111\)).
## 5 Experiment and Results
### Experiment Configuration
#### 5.3.3 Genetic Algorithm Configuration  
Table 3 provides information on the GA configuration used during iterations using all methods. The studied configurations assume a fixed termination criterion of 200 generations for the GA, with a population size of 16 individuals, aimed at optimising the values of four hyperparameters, ensuring a controlled number of iterations across all tested configurations. The probability of crossover was set to 1 because the chromosomes selected through selection were always subjected to crossover. The fitness function, defined as \(\frac{1}{50}\sum_{i=100}^{150}R_{i}\), calculates the mean reward over the last 50 episodes, which helps evaluate the agent's recent performance. The random seed value was set to 0 for the entire GA to ensure reproducibility. In addition, the initial gene values in the population were determined in a manner according to the specified random seed.
## 4 Proposed Architecture  
In this section, we elaborate on the overall design architecture, followed by demonstrating the relevant codes and pseudo codes. We have developed a novel H-VPLS architecture via Q-in-Q tunneling on high-speed and commodity routers to satisfy the additional requirements of new VPLS applications, in which VPP performs as the router's data plane and FRR, an open-source network routing software suite, acts as the router's control plane. On the other hand, the LDP is used for MPLS PW signaling in FRR. The basic and the most important incentive in implementing VPLS is the connection of multiple LANs with different geographical locations dispersed in WAN and the creation of an illusion that all of the sites see each other as if they are in the same LAN [4; 12]. In this paper, as shown in Figure 8, the LDP provided by Cisco is used for PW signaling in our implementation. To receive packages at all label switched paths (LSPs) in the MPLS network, all LSRs must run LDP and exchange labels [19; 28; 33].  
The use of IP routing protocols instead of the Spanning tree protocol and MPLS tags instead of VLAN identifiers in the infrastructure will significantly improve the development of the VPLS service. The difference between VPLS and L3 VPN is in the Customer Edge (CE) side equipment.
## 2 Materials and Methods
### MB Adsorption Batch Studies
#### 2.5.1 pH Effect  
To examine the impact of the initial pH of the solution on the adsorption of MB, 3 mL of a 10 mg L\({}^{-1}\) MB solution was introduced to 0.006 g of NGC at varying pH levels (4.0, 5.0, 6.0, and 8.0) while being stirred at a rate of 150 rpm for 1 h utilizing a DLAB magnetic stirrer model MS7-H550-pro. The solution was permitted to settle, and subsequently, the residual MB concentration in the supernatant solution was quantified using a JANEWAY 7315 Spectrophotometer (Vernon Hills, IL, USA) with a \(\lambda\)max of 660 nm. The concentrations of MB were calculated by comparing the resulting absorbance values with the MB standard calibration curve. Equations (1) and (2) were used to calculate the adsorption capacity of MB adsorbed onto NGC (\(q\)) at equilibrium and at a specific time, respectively. Equation (3) was used to calculate MB percent removal (\(R\)%) [33]:  
\[q_{e} =\frac{(C_{i}-C_{e})*V}{M} \tag{1}\] \[q_{t} =\frac{(C_{i}-C_{t})*V}{M}\] (2) \[R\% =\frac{C_{i}-C_{e}}{C_{i}}\times 100 \tag{3}\]  
The equilibrium and specific time adsorption capacities of MB are denoted as \(q_{e}\) and \(q_{t}\), respectively. The initial and equilibrium concentrations of MB in the solution are represented by \(C_{i}\) and \(C_{e}\) (mg L\({}^{-1}\)). \(V\) refers to the volume (\(L\)) of the experimental solution. \(M\) indicates the weight (g) of the NGC, while \(R\)% represents the removal percentage.
## 4 Results
### Performance Evaluation
#### 4.4.1 Comparison of AISAC versus Immune-Based Learning Algorithms  
Table 4 shows the performance of the AISAC and other immune-based algorithms over the 10 cancer-related datasets. The best results are highlighted in bold.  
When comparing the classifiers based on immune systems, we used the Wilcoxon signed rank test. This comparison is presented in Table 5. The \(p\)-Values in all cases were lower than the significant value \(\alpha=0.1\), which means that significant differences existed among the performances of AISAC and the compared algorithms within a 90% of confidence, and the null hypothesis H0 was rejected. Considering the wins, losses, and ties obtained, we can state that AISAC outperforms the other compared immune-based algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **AIRS1** & **Immunos1** & **CLONALG** & **AISAC** \\ \hline BCDR & **73.204** & 56.077 & 57.735 & 64.360 \\ BCWO & 96.710 & 84.692 & 94.135 & **97.420** \\ BCSEER & 94.520 & 95.374 & 96.513 & **100.000** \\ BCWD & **93.849** & 90.510 & 88.928 & 93.670 \\ BCWP & 64.141 & 56.566 & 74.242 & **79.290** \\ LCDS & 53.125 & 56.250 & 46.875 & **68.750** \\ MMDS & 63.372 & 74.298 & 70.031 & **78.980** \\ BCDS & 67.483 & 73.427 & 67.133 & **73.776** \\ HSDS & 63.726 & 56.810 & 73.203 & **76.471** \\ TSDS & 69.787 & 72.979 & 81.064 & **85.319** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average performances obtained by the immune-based learning algorithms. The best results are highlighted in bold.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **Average Time (S)** \\ \hline BCDR & 25.1 \\ BCDS & 29.1 \\ BCWO & 4.7 \\ BCSEER & 3.1 \\ HSDS & 38.8 \\ LCDS & 22.5 \\ MMDS & 30.0 \\ TSDS & 39.4 \\ BCWD & 35.0 \\ BCWP & 35.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average running time of the classification algorithm.  
These results confirm that our proposal obtained a significantly better performance in cancer classification than all other tested immune-based learning classification systems.
## 3 Results and Statistical Analysis
### Effect of Interaction of Parameters on Sa
#### 3.2.2 Laser Power and Scan Spacing  
\(\bullet\)\(45^{\circ}\) down-facing surface  
The interaction between laser power and scan spacing also shows clear trends. The \(Sa\) at the different laser powers gradually decreases when increasing scan spacing, however it tends to increase once it has passed the scan spacing of 80 \(\upmu\)m.  
The second graph shows that at all scan spacing levels, the \(Sa\) decreases up to a certain laser power (between 150 and 200 W) after which it begins to increase once again. While the laser power of 150 W showed the lowest \(Sa\) values.  
\(\bullet\)\(35^{\circ}\) down-facing surface  
The effect of these parameters on the \(35^{\circ}\) down-facing surface also shows some difference when compared with the \(45^{\circ}\) down-facing surface, as seen in Figure 9. When increasing the scan spacing at different laser powers, the \(Sa\) values increase up to a scan spacing between 60 and 70 \(\upmu\)m, after which they decrease. The laser power of 150 W showed the lowest \(Sa\) values.
## 4 Results and Discussion  
We initially identified 171 papers through the keyword search. After reviewing the papers to determine relevance to AR for synchronized communication, 59 papers were identified for analysis. Table 1 shows the organization of the final list of collected papers by journal and conference venue.  
After conducting a thorough review of the collected papers, the challenges and benefits associated with AR application in synchronized communication were extracted and classified, as summarized in Figure 3. While some attributes exhibited consensus across the papers, others displayed divergence. These discrepancies could be the results of contextual differences between projects or types of research projects. A number of challenges reported related directly to the type of device in use, AR application, and the environment surrounding the practitioner using AR. A total of twenty-four attributes were reported, with seven benefits and eleven challenges showing unanimous agreement among all the papers. However, six attributes demonstrated divergent findings within the existing literature.
## 1 Introduction  
Chronic thromboembolic pulmonary hypertension (CTEPH) is caused by persistent non-resolving, organized thromboboli in the pulmonary arteries. CTEPH is classified into group 4 of the recent clinical classification of pulmonary hypertension (PH) [1, 2]. Recent studies have revealed that apart from the stenosis or obstruction by organized thromboembolism in pulmonary arteries, peripheral microvasculopathy (small pulmonary vessel disease) is also involved in the development of PH in patients with CTEPH [3, 4].  
The histological changes in microvasculopathy are similar to those observed in idiopathic pulmonary arterial hypertension (PAH), including intimal thickening, intimal fibrovascular proliferation, and intimal fibrosis. In addition, microvasculopathy in CTEPH also consists of diffuse distal thrombosis when bronchial arteries fail to develop [4]. Microvasculopathy is usually suspected when patients with CTEPH have disproportionally poor hemodynamics despite mild perfusion defects [4, 5]. The usage of imaging modalities to evaluate microvasculopathy in CTEPH has also been reported, and poor subpleural perfusion (PSP) in the capillary phase of pulmonary digital subtraction angiography is considered to reflect the existence of microvasculopathy, including diffuse distal thrombosis [6; 7].  
Dual-energy computed tomography (DE-CT) has recently emerged as an imaging modality for evaluating pulmonary artery structure and segmental lung perfusion. DE-CT can produce a sensitive iodine distribution map of the lung fields by using low-and high-tube-voltage X-rays to acquire two different datasets simultaneously [8; 9]. The local distribution of the contrast (iodine) represents pulmonary perfusion [10; 11]. Moreover, the extent of hypoperfusion in the subpleural area reflected in the color-coded lung perfusion blood volume (lung PBV) images can also be observed by DE-CT. [PERSON] et al., using a cohort of 93 patients, reported that PSP on DE-CT corresponded with PSP in the capillary phase of pulmonary digital subtraction angiography and that it might also suggest the existence of microvasculopathy in CTEPH [12].  
Although the existence of microvasculopathy in CTEPH has been widely suggested, it remains unclear whether its pathological findings are equivalent to those found in PAH. We aimed to elucidate the characteristics of microvasculopathy in CTEPH and compare them to those in PAH by using clinical parameters and DE-CT findings.
## 4 Results
### Offloading Strategy  
Figure 4 illustrates the occupied VRAM capacity as the number of offloaded experts changes. When no expert has been offloaded, the quantized Mixtral 8x7B model can be deployed with 18.4 GB VRAM. Conversely, in original offloading, it requires 20.1 GB VRAM. With two experts offloaded, the quantized Mixtral 8x7B model can be deployed with 14.5 GB VRAM, whereas in original offloading it demands nearly 16.3 GB VRAM. Upon offloading four experts, the quantized Mixtral 8x7B model can be deployed with 10.1 GB VRAM, whereas in original offloading it demands 11.9 GB, close to 12 GB VRAM. Furthermore, the model weights are loaded into both the CPU's RAM and disk, a strategy that conserves a certain amount of VRAM capacity while expanding the loadable model size.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Precision** & **Recall** & **F1 Score** \\ \hline ChatGPT & 0.837 & 0.8445 & 0.8406 \\ Mixtral 8x7B & 0.821 & 0.8434 & 0.8320 \\ MedMixtral 8x7B & 0.838 & 0.8447 & 0.8413 \\ Llama 8B & 0.799 & 0.8384 & 0.8179 \\ \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison among ChatGPT, Mixtral 8x7B, and MedMixtral 8x7B. According to the test results, our model MedMixtral 8x7B, which is fine-tuned on Mixtral 8x7B, outperforms other models in terms of precision, recall, and F1 score when answering medical questions on the test set. This indicates that our model has an advantage in the medical Q&A domain compared to Mixtral 8x7B, Llama3 8B, and ChatGPT.