esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 5 Algorithms for VLC-Based IPS
### Filtering-Based Approaches  
Particle filtering (PF) and Kalman filtering (KF) are two commonly used probabilistic estimation methods in IPSs. PF is a non-parametric filtering technique [113]. It represents the possible receiver positions using a set of particles and assigns weights to each particle based on the likelihood of the received signal measurements. The weights are then updated using a re-sampling step, where particles with higher weights are more likely to be selected for the next iteration. This allows the particle filter to track the receiver's position over time and provides an estimate of its location. On the other hand, KF is a recursive estimation algorithm that combines a prediction model and a measurement model. It assumes that the system being tracked follows a linear dynamical model with Gaussian noise. KF iteratively updates the estimate of the receiver's position based on the prediction from the previous state and the new measurement [114]. The filter also estimates the uncertainty or covariance of the position estimate, which provides a measure of the confidence in the estimate.  
Both PF and KF have their own advantages and limitations [115]. PF can handle non-linear and non-Gaussian systems, but it requires a large number of particles to achieve accurate results, which can be computationally expensive. On the other hand, KF is computationally efficient and provides optimal estimates for linear and Gaussian systems, but it may struggle with non-linear or non-Gaussian systems.  
In practice, a combination of these two methods, known as PF-KF, can be used to improve the accuracy and efficiency of indoor positioning systems [116]. PF is used to handle the non-linear and non-Gaussian aspects of the system, while KF is used to refine the estimates and reduce the computational complexity.
## 3 Results
### GPP Models Error Analysis  
To understand what factors drive model performances, model errors (\(GPP_{\text{hh,pred}}-GPP_{\text{hh,obs}}\)) are compared to the distance between the half-hourly values of the variables: \(NDVI_{\text{fp}}\) (\(D_{\text{NDVI}}\)); the different formulations of \(PRI_{\text{fp}}\) (\(D_{\text{PRI}}\)), \(PAR\) (\(D_{\text{PAR}}\)), \(T_{\text{air}}\)/\(VPD\) (\(D_{\text{Train/\(\(\gamma\)PD}}\)), and \(GPP_{\text{hh}}\) (\(D_{\text{GPP}}\)); and the distance in days to the flight campaigns (\(D_{l}\)). Partial regression coefficients (\(r_{\text{err}\,|\,D}\)) and the corresponding \(p\)-value are presented in Table 6. In all the models, error values are inversely related to \(D_{\text{GPP}}\) which means that the models underestimate \(GPP_{\text{hh}}\) when it is larger than the day of the flight. \(D_{\text{PAR}}\) explains a substantial part of the errors in all the models; \(r_{\text{err}\,|\,D}\) is positive meaning that \(GPP_{\text{hh}}\) is underestimated when \(PAR\) is lower than the day of the flight. The remaining variables show lower explanation power \(D_{t}\) is not significant in all the cases; errors are inversely and non-significantly related to \(D_{\text{Tair/\(\gamma\)PD}}\) in all the models but MOD1 and MOD4 where relationships are positive and significant. \(SVI\,r_{\text{err}\,|\,D}\) are also low and in general negative for \(PRI_{\text{fp}}\) and positive for \(NDVI_{\text{fp}}\). Figure 6: MOD1 \(RRMSE\) (**a–c**); and MOD4 \(RRMSE\) (**d–e**). Ranges and median values of: \(PAR\) (**g–i**); \(T_{\text{air}}\) (**j–l**); \(VPD\) (**m–o**); \(GPP_{\text{hh}}\) (**p–r**); and \(NDVI_{\text{fp}}\) (**s–u**). In each column, data correspond to the three overpasses (one on each eddy flux tower) of a single date: April 2014 (first column), April 2015 (second column) and July 2015 (third column). To better understand the previous results, we analyzed the direct dependence of errors on model variables (Figure 7) and their differences with respect to the days of the flight campaign (Figure 8). Figure 7 shows the averaged error of MOD2-MOD4 vs. the different variables involved in the models. MOD1 is excluded since it behaves differently than the others. Error range increases with \(\textit{NDVI}_{\text{fp}}\) and \(\textit{PRI}_{\text{fp}}\) while Pearson \(r\) is negative. \(\textit{GPP}_{\text{hh}}\) is underestimated in situations with low-mid _PAR_, _VPD_ and \(T_{\text{air}}\) values (\(r\geq 0.34\)). Both over and underestimation occur at low \(T_{\text{air}}/\textit{VPD}\) values, and underestimation at mid-high \(T_{\text{air}}/\textit{VPD}\). In addition, underestimation is higher at high \(\textit{GPP}_{\text{hh}}\). Time to the flight day show a very weak but significant relationship when errors from MOD2-MOD4 are averaged, which was not observed for each individual model.
## 8 Remarks  
There are several aspects that are out of the scope of this paper that we believe the reader should know about. All of these aspects will be discussed in this section.  
First, although three different network sizes have been considered in this paper, it is important to also analyze different networks topologies. In fact, our model is topology-agnostic in the sense that it works for general directed graph structure. However, for the particular topologies that are encountered in such networks as scale-free networks or small-world networks that possess certain specific properties (see [40]), it would be interesting to analyze these kind of networks in order to take advantage of the properties of their topologies to improve the general solution algorithm proposed in this paper.  
Second, as previously stated, our aim is to provide a rigid exact mathematical model for the considered problem (recall that the problem is \(\mathcal{N}\mathcal{P}\)-hard). This assumption is not without its consequences. Namely, in general, we cannot expect to solve large problem instances (of, say, hundreds or more nodes) in an acceptable time since the model is developed within the integer-programming optimization framework that uses B&B as the basic solution approach. In such a case, appropriate heuristics should be considered.  
Finally, we would like to give some additional remark on the time efficiency of the proposed solution algorithm. As the reader may have noticed, the solution time is mainly determined by the time that is needed to solve the MIP problems PP and FMP, and this time grows significantly with the network size. Nevertheless, despite the algorithm complexity, the proposed medium sized examples were solved in a reasonable time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **A** & **B** & **C** & **D** \\ \hline \(T^{LR}\) & 91.09 & 65.89 & 50.50 & 49.86 \\ \(T^{MIP}\) & 122.3 & 81.3 & 65.7 & 64 \\ \(|\mathcal{C}|\) & 28.4 & 62.2 & 61.6 & 71.3 \\ \(t^{MP}\) & 22.4 s & 47.7 s & 563 s & 1 m 6 s \\ \(t^{PP}\) & 30.2 s & 6 m 56 s & 16 m 57 s & 14 m 44 s \\ \(t^{MIP}\) & 4 m 21 s & 3 m 37 s & 1 m 46 s & 4 m 44 s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimization model efficiency-medium networks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **A** & **B** & **C** & **D** \\ \hline \(T^{LR}\) & 167.92 & 140.82 & 101.43 & 100.32 \\ \(T^{MIP}\) & 205.8 & 164.7 & 126.2 & 123.4 \\ \(|\mathcal{C}|\) & 41.5 & 79.8 & 86 & 103.7 \\ \(t^{MP}\) & 1 m 7 s & 2 m 17 s & 2 m 52 s & 3 m 35 s \\ \(t^{PP}\) & 41 s & 12 m 21 s & 23 m 51 s & 25 m 15 s \\ \(t^{MIP}\) & 12 m 42 s & 4 m 44 s & 12 m 47 s & 13 m 47 s \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimization model efficiency-large networks.
# Mycobacterial Cell Wall: A Source of Successful Targets for Old and New Drugs
## 4 New Generation of Cell Wall Inhibitors
### Target: Arabinogalactan/LAM  
#### 4.2.1 CPZEN-45  
_Discovery._ Caprazamycin B, a liponucleoside isolated from _Streptomyces,_ is a non-toxic antibiotic with good activity in vitro (MIC 3-11 \(\upmu\)m) against drug-susceptible and MDR _M. tuberculosis_ strains but insoluble in water [220]. To increase its hydrophilicity, SAR was performed and led to the nucleoside caprazene-45 (CPZEN-45) [221].  
_Activity._ CPZEN-45 is a water-soluble drug (solubility \(\approx\) 10 g/L) with an MIC of 2-5 \(\upmu\)m and 10 \(\upmu\)m against drug-susceptible and MDR _M. tuberculosis_, respectively. Unlike caprazamycin B, which is active against several Gram-positive bacteria, CPZEN-45 has no activity against _S. aureus, Streptococcus pneumonia_ or _Enterococcus faecalis_[222]. CPZEN-45 is specific to slow-growing pathogens. In mice intravenously infected with the drug-susceptible _M. tuberculosis_ H37 Rv strain, a 30-day subcutaneous treatment with CPZEN-45 was as effective in reducing lung burden as INH and better than RIF alone [223]. Furthermore, the combination INH/RIF/CPZEN-45 resulted in at least a 1 log\({}_{10}\) better killing of _M. tuberculosis_ than the combination INH/RIF. In a subsequent experiment, mice were intravenously infected with an XDR _M. tuberculosis_ strain and treated with CPZEN-45 at doses ranging from 6.3 to 200 mg/kg. After 30 days, the mice treated with the highest concentration of CPZEN-45 had a 1.5 log\({}_{10}\) better reduction in CFUs in the lungs than the mice receiving the lowest concentration [223].  
_Mode of action._ CPZEN-45 is an inhibitor of _M. tuberculosis_ WecA (Rv1302) with an IC\({}_{50}\) of 7 \(\overline{\text{nM}}\)[222]. WecA is involved in the first step of the arabininogalactan biosynthesis forming the anchor point between peptidoglycan and arabininogalactan. Transcriptionally silencing _wecA_ is bactericidal in _M. tuberculosis_ in vitro and bacteriostatic ex vivo, validating WecA as candidate for drug development [224].  
_Clinical trial._ CPZEN-45 is in the early stage of clinical development.
## 5 Comparison  
The comparison between the proposed CR-SOA-based parity generation and checking and other SOA-based schemes, along with their pulse modulation and reported data rates for the considered logic functionality, is presented in Table 3. This table emphasizes the advantages of utilizing the CR-SOAs-MZIs scheme for executing combinational circuits at high speeds with superior performance. Furthermore, it highlights the potential for enhancing operating data rates by incorporating quantum dots (QDs) into the SOA's active region or implementing reflective SOA (RSOA) technology, while still maintaining acceptable logic performance. However, it is important to note that the comparison involves a mix of experimental and simulation data, leading to variations in reported metrics and contexts. The diverse metrics used, such as extinction ratio (ER), amplitude modulation (AM), and QF, reflect the different operating conditions and methodologies employed in each study. While experimental results offer real-world performance insights, simulations explore theoretical limits and ideal conditions. To ensure a fair comparison, it is essential to clearly distinguish between experimental and simulation results, explain the context of each metric, and acknowledge the inherent differences in methodologies. Despite these variations, the comparison highlights the competitive data rates and superior signal quality metrics of the CR-SOAs-MZI approach, indicating promising advancements in the field with potential enhancements using QD-SOA and RSOA technologies. While the RSOA scheme shows a decent QF with twice the data rate, the CR-SOA-based approach offers several distinct advantages: higher modulation bandwidth crucial for high-speed applications, enhanced signal processing capabilities for complex tasks such as integrating XOR gates for logic operations, improved noise performance due to the carrier reservoir structure reducing the impact of ASE noise [25], better scalability and integration potential with other photonic components on a chip, and greater flexibility in the design of optical logic circuits enabling innovative solutions for complex combinational and sequential logic functions [13; 16; 18]. These advantages make CR-SOA a more versatile and powerful choice for high-speed optical logic applications, despite the higher data rate capabilities of RSOA.  
Figure 10: QF against amplified spontaneous emission (ASE) power for even (Pe) and odd (Po) parity generations using CR-SOAs-MZIs at 120 Gb/s.
## 5 Experimental Results
### Model Comparison Using RMSE  
A comparative analysis of various machine learning models was performed using RMSE. In the analysis, the models developed within the scope of the study were discussed. RMSE scores of each model show the performance of the model on the dataset. Model comparisons will help determine how well models work on different datasets and in what situations they may be preferred. Figure 8 shows the graph comparing the results. Moreover, error bars representing the 95% confidence intervals have been incorporated into the bar charts that display the average error metrics for each machine learning model. This enhancement provides critical insights into the statistical reliability of the performance measures across models.  
The comparisons in Figure 8 are expected to guide important decisions at the model selection stage and play a critical role in achieving success in machine learning applications. As seen in the graph, while the LR model has the highest RMSE value, the GBM model has the lowest RMSE value among these models. It is seen that the linear regression model predicts the dataset the worst compared to other models, while the GBM model has the best prediction performance among these models. However, it is observed that LightGBM and RF models perform well with similar low RMSE values. In line with these observations, LightGBM was reconsidered, and the model was improved. In addition, the RMSE values of the XGBoost and CatBoost models are close to each other, and it can be said that they exhibit a moderate performance compared to other advanced models.
###### Abstract  
To solve the time-consuming, laborious, and inefficient problems of traditional methods using classical optimization algorithms combined with electromagnetic simulation software to design antennas, an efficient design method of the multi-objective antenna is proposed based on the multi-strategy improved sparrow search algorithm (MISA) to optimize a BP neural network. Three strategies, namely Bernoulli chaotic mapping, inertial weights, and t-distribution, are introduced into the sparrow search algorithm to improve its convergent speed and accuracy. Using the Bernoulli chaotic map to process the population of sparrows to enhance its population richness, the weight is introduced into the updated position of the sparrow to improve its search ability. The adaptive t-distribution is used to interfere and mutate some individual sparrows to make the algorithm reach the optimal solution more quickly. The initial parameters of the BP neural network were optimized using the improved sparrow search algorithm to obtain the optimized MISSA-BP antenna surrogate model. This model is combined with multi-objective particle swarm optimization (MOPSO) to solve the design problem of the multi-objective antenna and verified by a triple-frequency antenna. The simulated results show that this method can predict the performance of the antennas more accurately and can also design the multi-objective antenna that meets the requirements. The practicality of the method is further verified by producing a real antenna.  
antenna design; surrogate model; improved sparrow search algorithm; multi-objective antenna; prediction of performance
## 4 Experiments
### Experimental Details  
We utilized Python 3.8 as the programming language and PyTorch 1.10.2 as the deep learning framework for our experiments. We employed AdamW [42] as the optimizer and conducted training on an NVIDIA 3080 GPU over 300 epochs, setting the weight decay factor at \(5\times 10^{-5}\) and the initial learning rate at \(4\times 10^{-4}\). To evaluate the segmentation performance of various algorithms, we incorporated widely recognized metrics such as mean Intersection over Union (mIoU), Dice coefficient, recall, precision, and specificity. These metrics are extensively used in medical image segmentation to objectively assess the accuracy and effectiveness of segmentation algorithms. Furthermore, we employed a combination of cross-entropy loss and Dice loss to gauge the model's loss function. Due to the limited availability of partially public datasets, we implemented data augmentation techniques, including random resizing, random cropping, horizontal flipping, and vertical flipping, to enhance our dataset.
## 4 Resource Allocation for Downlink and Uplink
### Uplink  
For the uplink section, PAM-OOK, a fairly simple resource allocation scheme, is deployed. OOK allows the users to be separated on the basis of their amplitudes. A lookup table is created which has all the information about the users/devices. In the simulations conducted, there were a minimum of 15 user/devices and a maximum of 30 user/devices. Consequently, a 5-bit encoder was sufficient  
\begin{table}
\begin{tabular}{l} \hline \hline
**Light-Emitting Diode Identification Number Selection** \\ \hline Step 1: At the start of each time slot, the user will have perfect knowledge of the channel matrix \(H(u,LEDID)\). \\ Step 2: A pool of users that are contending for data transfer is created. \\ Step 3: Each user/device will select an LEDID based on the highest available channel gain using the maximum gain selection (MGS) policy. \\ Step 4: After successful selection of LEDID, the user is connected to one LED and it waits for data to be \\ allocated to it. After data, has been allocated to the user, it is deleted from the pool. If the user pool is \\ empty the LEDID selection is halted, otherwise, Step 3 is repeated for the next user/device. \\ \hline
**Resource Allocation** \\ \hline Step 1: From LEDID Selection, the user pool information is available. \\ Step 2: Based on the user pool information, if the number of users is equal to the number of available resource blocks (RBs), then every user will be allocated an RB. Else, if the number of RBs is less than the number of users, it will allocate at least one RB to each user and sort the remaining users based on their \\ channel gain. At this point, the RB allocation will be based on the channel gain, that is, depending on which users have the best channel. Else if the number of RBs is greater than the number of users, it will \\ allocate the RB to each user which can be accommodated (users will be sorted based on the channel \\ gain) while the users that are left will be rejected from the LEDID. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Non-cooperative resource allocation (NRA).  
to allocate a unique amplitude to each user/device. Using PAM, each user is assigned a different amplitude level and using the lookup table the receiver is able to differentiate between different users/devices. The composite signal is transmitted to the uplink section and the receiver decodes the signal and separates the user data using the lookup table. In this way, a low-cost multi-user PAM-OOK is implemented which can cater up to 32 users and deliver acceptable performance for full-duplex multi-user VLC system.
## 3 Results and Discussion
### Analysis of the Polymer Preparation
#### 3.1.4 Analysis of Structural Characterization  
Figure 8 shows the infrared spectrum of the polymer.  
In order to verify whether the polymeric monomer participated in the copolymerization, the infrared spectrum of the sample was analyzed with infrared spectroscopy. The infrared spectrum of the sample is shown in Figure 8. There were characteristic absorption peaks of sulfonate at 1217, 1187 and 1043 cm\({}^{-1}\), which proved the existence of AMPS; a characteristic absorption peak of pyrrolidone (-CON) at 1670 cm  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Concentration (mg/L)** & **0** & **200** & **500** & **1000** & **2000** & **5000** \\ \hline Time (h) & 7 & 6.5 & 6 & 5 & 2 & 1.2 \\ Viscosity (mPa-s) & 46 & 42 & 38 & 37 & 34 & 25 \\ \hline \end{tabular}
\end{table}
Table 4: Effects of different amounts of B3 on the solubility of the polymer.  
Figure 8: FI-IR spectrum of polymer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Sample** & **A1** & **A2** & **A3** & **B1** & **B2** & **B3** \\ \hline Concentration (mg/L) & 0 & 2000 & 2000 & 2000 & 2000 & 2000 & 2000 \\ Time (h) & 7 & 5 & 3 & 3.5 & 3 & 4 & 2 \\ Viscosity (mPa-s) & 43 & 40 & 36 & 35 & 37 & 41 & 34 \\ \hline \end{tabular}
\end{table}
Table 3: Effects of cosolvents on solubility of the polymer.  
existence of NVP; and a characteristic absorption peak of amide (-NH\({}_{2}\)) at 3406 cm\({}^{-1}\), which proved the existence of AM. There was a weak characteristic absorption peak of (-COO\({}^{-}\)) at 1644 cm\({}^{-1}\) and an absorption peak of quaternary ammonium at 963 cm\({}^{-1}\), which proved the existence of hydrophobic monomer DS-16. From the above absorption peaks, it can be concluded that the polymer was a copolymer of AM/AMPS/NVP/DS-16.
# Applying CRISPR Technologies for the Treatment of Human Herpesavirus Infections: A Scoping Review
## 4 Discussion
### Barriers to Clinical Implementation  
Achieving a balance between efficacy and safety remains a central challenge for the clinical application of CRISPR-based antiviral strategies. As discussed above, enhancing the activity or expression of CRISPR systems, particularly those that induce double-strand breaks, often increases the risk of off-target effects in healthy cells. In terms of total safety assessment of the CRISPR systems, three major factors should be taken into consideration: (1) off-target editing of the CRISPR system, (2) toxicity of the delivery vector of the system, and (3) activation of host immune responses against the CRISPR system or delivery vector. While not all studies performed safety assessment experiments, none of the ones that did [39; 41; 42; 45; 46; 47; 49; 50; 52; 53; 54; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 77] raised major concerns for the biosafety of the CRISPR systems. However, for biosafety assessment, careful consideration must be given to the choice of test subject used to evaluate the antiviral effects and safety of the CRISPR system, and whether these findings can be extrapolated to clinical applications in human patients. First, in vitro experiments conducted on non-human cell lines deliver the CRISPR system in cells containing a different host cell genome from the human genome. Consequently, when safety assessment experiments detect no significant off-target sites in the non-human genome, it is difficult to extrapolate this to human cells since the human genome could contain sequences complementary to the used gRNAs. Second, in vivo experiments conducted on mice or rabbit models could be unrepresentative of possible human host immune responses against the CRISPR system or the delivery vector. To date, no CRISPR-based therapy has been clinically approved for the treatment of herpesvirus infections, and only one phase I/II clinical trial has been performed (NCT04560790) [47] for the treatment of HSK. This highlights the major challenges in translating CRISPR-based anti-herpesviral approaches into clinical practice. Moreover, long-term safety data are currently lacking, underscoring the need for extended follow-up studies to fully evaluate the durability and potential risks of CRISPR-based anti-herpesviral therapies in human patients.