esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Purpurin 18 Research: On Cancer Therapy
## 4 Photodynamic Therapy
When PS-SiO\({}_{2}\)NPs were introduced into human embryonic kidney cells (HEK-293) instead of MSCs, no significant PDT effect on cell viability was detected since there was no targeted localization of the drug into the tumor cells [38]. The use of MSCs seems a promising strategy for targeted drug delivery to tumor cells. A long-term approach in the treatment of cancer is a so-called multimodal therapy which may lead mainly to a reduction in the resistance of tumor cells to anticancer chemotherapeutics. This may be, for example, a combination of tumor resection with chemotherapy, a combination of chemotherapy with PDT, or administration of a multimodal drug. Recently, researchers at Southwest University have developed photo-/chemotherapeutics containing pu18 as a PS [52; 53]. The first is a nanogel-based drug (DHP NGs \(\sim\) 67 nm) composed of pu18, the chemotherapeutic 10-hydroxycampotthecin (HCPT), a redox responsive cross-linking agent, glutathione responsive spacer, and a PEG spacer to increase particle stability. The second multimodal drug contains doxorubicin (DOX) and pu18 encapsulated in PDP micelles (\(\sim\)148 nm) composed of mono (6-amino-6-deoxy)-\(\beta\)-cyclodextrintrim with a ROS-sensitive polymer containing borate bonds. Both types of drugs have been designed to preferentially accumulate in a tumor tissue based on enhanced permeation and retention effects, for tumor tissue imaging as well as selective drug release due to increased GSH/ROS production by tumor cells. In mouse tumor cell line 4T1, the ability to enter cells, localize in lysosomes and mitochondria, reduce membrane potential of mitochondria and cause cell death has been confirmed [53]. In assays in Balb/c mice implanted with 4T1 cells, longer retention time, and better distribution of DHP NGs in tumor tissue compared to free pu18 were detected, as well as a significant reduction of tumor volume after laser exposure (660 nm, 0.5 W-cm\({}^{-2}\), 5 min). Immunohistochemical analysis of tumor cells subsequently revealed a large number of necrotic and apoptotic cells due to combined photo-/chemotherapy, the treatment did not cause any obvious damage to other organs (the liver, kidneys, heart, and lungs). Very similar results were obtained for the second drug PDP in combination with the same laser treatment. Upon this treatment, only 17.17% of 4T1 cells survived compared to 40.89 % for pure DOX and 61.28% for pure pu18 in combination with irradiation. Very similar results, as for the first drug, were shown also in experiments on Balb/c mice implanted with 4T1. Both drugs thus represent a significant step in the combined therapy of tumors. A summary of the aforementioned studies on pu18 and its derivatives in PDT is covered in Table 1.  
\begin{table}
# Analysis and Reduction of Nonlinear Effects in Optical Fiber Frequency Transfer
## 5 Conclusions  
In this work, nonlinear effects in optical fiber frequency transfer are investigated through theoretical and simulation calculations in detail. We focus on the main nonlinear effects, including the Brillouin scattering and the Raman scattering. Generally, the threshold power of the Brillouin scattering is two orders lower than that of the Raman scattering. The calculation results show that the threshold powers of the Brillouin scattering and the Raman scattering decrease with the increase in the fiber length; however, the threshold powers basically do not change with the fiber length when the fiber length is greater than 10 km. When the length \(L\) of the fiber is constant, the threshold power of the Brillouin scattering and the Raman scattering decreases as the gain coefficients increase. However, for the attenuation coefficient \(\alpha\), the results are the opposite. In addition, when the fiber length \(L\) is determined and the linewidth \(\Delta v_{laser}\) of the laser source is from 1 Hz to 1 MHz, the linewidth \(\Delta v_{laser}\) of the laser source does not affect the threshold powers of generating the Brillouin scattering. As far as we know, this work is the first time to investigate nonlinear effects in optical fiber frequency transfer in detail. This work aims to provide reference design guidance for the reduction of nonlinear effects in optical fiber frequency transfer. The calculation result has potential guiding significance for many applications using Brillouin and Raman scattering characteristics, such as laser and optical fiber sensing. In future research, we will primarily focus on studying the nonlinear effects of optical fibers with lengths exceeding 500 km, as well as field optical fibers and special optical fibers, in the context of optical fiber frequency transfer.  
Conceptualization, G.X. and J.G.; methodology, D.J. and X.Z.; software, J.G.; validation, D.J. and J.B.; investigation, G.X. and M.W.; data curation, J.G.; writing--original draft preparation, G.X.; writing--review and editing, G.X. and J.G.; visualization, L.Z. and Q.Z.; supervision, [PERSON]; project administration, T.L. and S.Z. All authors have read and agreed to the published version of the manuscript.  
The project is partially supported by the Chinese National Natural Science Foundation (Grant No. 12303076 and 11903041), the Youth Innovation Promotion Association of the Chinese Academy of Sciences, and the Young Innovative Talents of the National Time Service Center of the Chinese Academy of Sciences (Grant No. Y917 SC1).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data also being part of an ongoing study.  
The authors declare no conflict of interest.
# Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets
## 2 Related Works  
In this section, we briefly discuss other works presenting benchmarks on many different reconstruction neural networks.  
In [18], they benchmark their (adversarial training based) algorithms against classical methods and against Cascade-net (which they call Deep Cascade) [11] and ADMM-net (which they call DeepADMM) [19]. They train and evaluate the networks quantitatively on two datasets, selecting each time 100 images for train and 100 images for test:  
* The IXI database ([[http://brain-development.org/ixi-dataset/](http://brain-development.org/ixi-dataset/)]([http://brain-development.org/ixi-dataset/](http://brain-development.org/ixi-dataset/))) (brains),
* The Data Science Bowl challenge ([[https://www.kaggle.com/c/second-annual-data-science-bowl/data](https://www.kaggle.com/c/second-annual-data-science-bowl/data)]([https://www.kaggle.com/c/second-annual-data-science-bowl/data](https://www.kaggle.com/c/second-annual-data-science-bowl/data))) (chests).  
While both these datasets provide a sufficient number of samples to have a trustworthy estimate of the performance of the networks, they are not composed of raw complex-valued data, but of DICOM real-valued data. Still, in [18], they do evaluate their algorithms on a raw complex-valueddataset ([[http://mridata.org/list?project=Stanford%20](http://mridata.org/list?project=Stanford%20) Fullysampled%203D%20 FSE%20 Knees]([http://mridata.org/list?project=Stanford%20](http://mridata.org/list?project=Stanford%20) Fullysampled%203D%20 FSE%20 Knees)), but it only features 20 acquisitions, and therefore the comparison is only done qualitatively.  
In [10], they benchmark their algorithm against classical methods. They train and evaluate their network on three different datasets:  
* The brain real-valued data set provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) [20],
* Two proprietary datasets with raw complex-valued data of brain data.  
Again, the only public dataset they use features real-valued data. It is also to be noted that their code cannot be found online.
## 5 Experimental Setup
### Evolutionary Hyperparameters  
All the evolutionary hyperparameters used in the experimentation are presented in Table 2. The budget of the experiments, viz., the number of trials, is controlled by the number of generations and population size as \(total(trial)=size(pop)*size(gen)\). In our experiments, the budget was restricted to 50 and 30 trials, respectively, in Stage 1 and Stage 2, with a total budget of 80 trials as observed from Table 2. In other words, 60% of the budget is utilised for exploring the global search space while 40% is utilised for exploiting the region of optimal performance. The exploration and exploitation are controlled by the crossover rate (high) and mutation rate (low). For image classification tasks, top 1%-accuracy is an appropriate method to measure the performance of a model when datasets are balanced. This metric measures the number of correct predictions from the total predictions on a dataset. When dealing with unbalanced datasets, other fitness functions such as F1 score, precision, or recall can be used instead. In our empirical study,  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Hyperparameter** & **Type** & **Scope** & **\#Combinations** \\ \hline \multirow{3}{*}{\# dense layers} & \multirow{3}{*}{Integer} & \{32, 64, 96, 128, 160, 192, & \multirow{3}{*}{16} \\  & & 224, 256, 288, 320, 352, 384, & \\  & & 416, 448, 480, 512] & \multirow{3}{*}{9} \\ dropout rate & \multirow{3}{*}{Decimal} & \{0.1, 0.2, 0.3, 0.4, 0.5, 0.6, \multirow{3}{*}{10} \\ optimiser & \multirow{3}{*}{Categorical} & \{adam, sgd, rmsprop} \\ learning rate & \multirow{3}{*}{Decimal} & \{0.00001, 0.0001, 0.001, 0.01, 0.1, \\  & & 0.00005, 0.0005, 0.005, 0.5\} \\  & & \{0.5, 0.6, 0.7, 0.8, 0.9, 0.99\} & 6 \\ batch size & \multirow{3}{*}{Choice} & \{32, 64, 128, 256]} & \multirow{3}{*}{4} \\ \hline
**Total combinations** & & & **103,680** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters employed in NNs with their scopes: _choice/categorical_ type indicates the options that the hyperparameter can take, while ranges define any possible value between the given range.  
we opted for the top 1%-accuracy as our fitness score for validating data (as the chosen datasets are balanced) as shown in Equation (9).  
\[\text{top 1\% accuracy}=\frac{\text{number of correct predictions}}{\text{total number of predictions}} \tag{9}\]
## 4 Experiment and Results
### Exp. #2: Comparison of Overlapped and Non-Overlapped Filters
The \(p\)-value implies that for the arousal emotion, the use of the 5 \(\times\) 5 non-overlapping filter was better than the 7 \(\times\) 7 overlapping filters; however, for the valence emotion, these filters did not differ significantly. The finding of this Exp. #2 deviated from our hypothesis that increasing the number of filters and by using overlapping strategies in the adjacent filters would increase the recognition rate. The analysis is as follows. The more filters used in the feature extraction process, the higher the dimension of feature vectors produced. However, a higher dimension of feature vectors produces higher cumulative errors, resulting in lower recognition rates. Another reason might be that in the 5 \(\times\) 5 non-overlapping filters, more attention (by using more numbers and smaller filters) is given to the area where the bispectrum matrix elements show high amplitudes, whereas in the 7 \(\times\) 7 overlapping filters, all elements of the bispectrum matrix have the same significance. This finding implies that the higher amplitude areas of the bispectrum matrix are more important than the lower ones. Further optimization of the filter type should be investigated as future work, such as by using more numbers of filters, but the size of the filters should be different, which is smaller at the area where the bispectrum amplitudes are high.
## 3 Results
### RONS UV Absorption Spectra Profiles  
In order to measure the amounts of reactive species in DI water, we measured UV absorption spectra and analyzed them with the curve fitting method. The absorption spectra of H\({}_{2}\)O\({}_{2}\), NO\({}_{2}\)\({}^{-}\), and NO\({}_{3}\)\({}^{-}\) were obtained from the standard solutions of H\({}_{2}\)O\({}_{2}\), NaNO\({}_{2}\), KNO\({}_{2}\), NaNO\({}_{3}\), and KNO\({}_{3}\). Different concentrations were used to determine the molar absorptivity \(\varepsilon\) value of each reactive molecule used in Equation (1). Figure 1a shows the absorbance of each reactive species. NO\({}_{2}\)\({}^{-}\) has a maximum value at 209.8 nm, and NO\({}_{3}\)\({}^{-}\) at 200.6 nm. The same spectra were obtained when NO\({}_{2}\)\({}^{-}\) and NO\({}_{3}\)\({}^{-}\) were bonded to other cations (Na\({}^{+}\) or K\({}^{+}\)). In the case of H\({}_{2}\)O\({}_{2}\), it does not have the main peak in the measuring range, and the absorbance is low. Therefore, for calculating H\({}_{2}\)O\({}_{2}\) concentration, we used a tail region over 245 nm, where the former two reactive species do not contribute to the absorbance. To determine the molar absorptivity, UV absorbance spectrum of each reactive species was measured in a concentration range from a minimum of 6 \(\mu\)M to a maximum of 3.125 mM (H\({}_{2}\)O\({}_{2}\)), 0.19 mM (NO\({}_{2}\)\({}^{-}\)), and 97 \(\mu\)M (NO\({}_{3}\)\({}^{-}\)). Figure 1a shows that the absorbance intensity increased linearly with concentration. We calculate different constant values (\(\varepsilon\)) for H\({}_{2}\)O\({}_{2}\), NO\({}_{2}\)\({}^{-}\), and NO\({}_{3}\)\({}^{-}\) from the intensity at 250 nm, 209.8 nm, and 200.6 nm, respectively. Figure 1b show the verification of the fitting. We mixed solutions of H\({}_{2}\)O\({}_{2}\), NO\({}_{2}\)\({}^{-}\), and NO\({}_{3}\)\({}^{-}\) with different concentrations, and fitted the spectra. We were able to obtain the concentration of three kinds of molecules within a certain error. The accuracy values are 8.54, 4.01, 3.05% for 100 \(\mu\)M H\({}_{2}\)O\({}_{2}\), 20 \(\mu\)M NO\({}_{2}\)\({}^{-}\), and 20 \(\mu\)M NO\({}_{3}\)\({}^{-}\), respectively. In the case of hydrogen peroxide, it has a relatively large measurement uncertainty due to its low absorbance.
# FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling
## 2 Related Works
### Conventional Finger Vein Recognition  
Different types of conventional mathematical models have been utilized for finger vein recognition tasks. The Gabor filter [9] is a popular feature extraction method employed for finger vein recognition. Due to its ability to detect oriented features by tuning in to a specific frequency, the Gabor filter has been used widely for various pattern recognition tasks [10]. Similarly, line tracking [11] methods extract features based on repeated tracking of black pixels in the vein image. The maximum curvature method [12] extracts features based on the fact that the vein patterns appear like a valley, with a high curvature within the cross-sectional area of the vein image. Furthermore, local feature descriptor-based methods, such as local binary pattern (LBP) and local derivative pattern (LDP), are also widely used methods of feature extraction [13]. They extract features based on binary codes obtained by comparing the gray value of the center pixel with the neighboring pixels. However, due to low robustness, high computational complexity, and poor feature extraction capabilities, these techniques are at a great disadvantage when implementing practical finger vein recognition systems. Furthermore, these conventional approaches utilize distance-based, trivial feature-matching algorithms, such as Hamming distance [14], Euclidean distance [15], cross correlation matching [16], template matching [17], and histogram intersection matching [18] to obtain a matching score for verification purposes. These types of feature matching techniques are not suitable for providing reliable recognition performance when the capture devices and image acquisition environment vary according to various applications or the authentication service.
# A Lesson for the Future: Will You Let Me Violate Your Privacy to Save Your Life?
## 6 Results
### Change in Opinion of Contact Tracing Applications  
A change in opinion regarding CTA mandates was detected before and after presenting our findings to the participants. Before the presentation of our findings regarding South Korea's positive results utilizing CTAs, the results to the question regarding whether Contact Tracing Applications be mandatory or not, 34% of participants agreed, 45.7% disagreed, and 20.3% neither agreed nor disagreed (see Figure 5). After presenting our findings, however, the overall stance of participants changed, and 55.8% agreed, 29.9%  
Figure 4: (a) Gender demographic. (b) Age demographic.  
disagreed, and 14.3% neither agreed nor disagreed. This sudden change in perspective can most likely be attributed to the presentation of our findings, where before, the majority disagreed that CTAs should be mandatory, and after, the majority agreed that CTAs should be mandatory.  
When comparing the general response to the opinionated questions regarding CTAs, there is a drastic shift from mixed and negative to positive (see Figures 6 and 7). Since the questions are generally the same, with minor changes, this switch in perspectives on CTAs can only be attributed to the presentation of the findings from South Korea. Another interesting finding is that before presenting our findings, most participants believed CTAs are essential to slow down the spread of COVID-19 (61%), and only 34% agreed that they should be mandatory. This reveals that, although participants know that lives could be saved by using CTAs, they are unwilling or unable to give up their right to choose whether or not to download them. Following the presentation of our findings on South Korea, however, the contradiction no longer exists, as the majority of participants agreed that CTAs should be mandatory.  
Following the presentation of our findings, when the participants were asked if they would share their geolocation data with a trusted party or government during a potential future pandemic, 72.1% agreed (see Figure 7).  
Figure 5: Before and after results regarding CTA mandates.  
Figure 6: Participants’ opinions before the presentation of findings. (**A**) “I don’t mind sharing some of my information for contact tracing purposes”. (**B**) “I am concerned for my privacy when using a contact tracing app”.
# Intermitt FOCV Using an I-V Curve Tracer for Minimizing Energy Loss
## 2 Conventional MPPT Algorithms
### Fractional Open-Circuit Voltage (FOCV)  
The main principle of this technique comes from the relationship between the voltage of maximum power-point (\(V_{MPP}\)) and the open-circuit voltage (\(V_{OC}\)), which the \(V_{MPP}\) approximately equals to some fractional value of the \(V_{OC}\). The relationship between the two voltages is defined as  
\[V_{MPP}=\alpha V_{OC}, \tag{1}\]  
where \(\alpha\) (\(0\leq\alpha<1\)) is the linear factor. Since there is no procedure for tracking the MPP, the FOCV method features the lowest implementation complexity and consumes the lowest circuit power. Therefore, the FOCV method is the most appropriate method for low-power and low-energy devices. Figure 3 illustrates the comparison of the circuit power consumption over time and the power consumption for the given time between the P & O, the SAR, and the FOCV algorithm.  
However, the MPP of the FOCV method is not the real MPP, but the approximated value. In [15], it is insisted that \(\alpha\) can be located within the range from 0.71 to 0.78. However, the \(\alpha\) values of recent PV cells on the market can be easily found between 0.8 and 0.86, as shown in Figure 4, which shows the histogram of distribution of the \(\alpha\) values for PV cells in [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. Therefore, it is essential to set the exact \(\alpha\) and the MPP because \(\alpha\) values are distributed in a wide range for each PV cell.  
Furthermore, if external environmental factors like illuminance and temperature cause changes in the MPP, energy loss can potentially happen unless the previous value of \(\alpha\) is reset to new \(\alpha\). Figure 5 shows how the P-V characteristics and the \(\alpha\) of a PV cell that has the value of 0.88 at 27 \({}^{\circ}\)C vary depending on temperature.  
Figure 3: Comparison of (**a**) the power consumption for MPPT in time (**b**) the energy loss between the P & O, the SAR, and the FOCV algorithms for the given time.  
If \(\alpha\) and the MPP are not reset as soon as possible, the energy loss increases. To reduce the energy loss caused by unreal MPP, the proposed FOCV intermittently and dynamically scans the I-V curve of the PV cell to detect the GMPP and reset the \(\alpha\) and the MPP. Although the circuit power consumption caused by dynamically scanning and resetting the MPP can be larger than the P & O and SAR, it operates intermittently, so that the total energy loss of the proposed FOCV is much lower than that of the P & O, SAR, and conventional FOCV. Figure 6 illustrates the comparison of the energy loss between the conventional and the proposed FOCV under varying \(\alpha\) conditions.  
Figure 4: Histogram of distribution of the \(\alpha\) value.  
Figure 5: Power versus voltage characteristics of a PV cell and the change of the value of \(\alpha\) under various temperature conditions.  
Figure 6: Comparison of the accumulated energy loss between the conventional and the proposed FOCV with refreshing frequency of \(1/2t_{0}\) under varying \(\alpha\).
## 6 Security Analysis and Results
### Communication Cost
The costs incurred by each of the \(t\) outside home zone nodes are: \(T_{\$}(v\) signed location claims \()+\)\(R_{\$}(v\) signed responses \()=\)\(T_{\$}(v\) signed \(48\) bit messages \()+\)\(R_{\$}(v\) signed \(48\) bit response messages \()=\)\(T_{\$}(48v)+R_{\$}(48v)\). For \(v=2\), we see that this yields a one-time communication expense per outside node of \(T_{\Figure 4.0}(96)=8.352\) mJ. For the \(v\) verifiers, the communication costs are identical, \(8.352\) mJ, by the same calculation as before. In the above calculations, we are ignoring the costs of the routing protocol and the expense incurred by the intermediate routing nodes. Our reasoning stems from the following observation: notice that most of the genuine nodes who find themselves outside of the home zone are no more than a few hops away from their home zone. However, replicas deployed by the adversary might find themselves at any distance from their home zones. To this end, our analysis depends on the actions of the adversary to truly compute the overall communication cost. Notice that this gives the adversary an interesting denial of service attack against the sensor network, and we discuss this in Section 8. Also of importance is to note that the communication cost is evenly distributed over all of the nodes, as they are chosen uniformly at random in the home zone and also distributed over time for a particular node. This is quite important in the sensor field to prevent some nodes from exhausting their energy prematurely, and thus, it is imperative that our protocol avoid performance peaks. Clearly, the global communication costs of one cycle grows linearly with the network size, but stays constant from the perspective of a single node. We stress that signing and verification are energy intensive operations, and our objective is to make minimal use of these operations and to discuss the costs associated with these operations below. We also note that such public key operations have been used extensively in prior work, and their costs are not accounted for on a practical level (excluding direct measurements on real sensor motes), as we do in our work.