esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 aPDT Performance in Clinical Studies
### PS Loaded in Polymeric Nanoparticles  
The use of polymeric materials as therapeutic carriers is well-established in the literature. Because of their unique properties and designs, such as low cytotoxicity and excellent permeability [63; 64], polymeric nanoparticles have been used as carriers for antimicrobial and anti-cancer therapeutic agents [65; 66]. In dentistry, polymeric compounds construct dental appliances, resin-based materials, and tissue engineering carriers [67; 68]. Several polymeric nanoparticles have been conjugated with PS to improve the performance of aPDT. The most used polymers are chitosan and polylactic-co-glycolic (PLGA). These polymers' cationic nanoparticles can interact effectively with the negatively charged bacterial membrane, inducing nanoholes on the membrane, resulting in bacterial death [36].  
In one study, TBO and chitosan polymeric nanoparticles were functionalized to inhibit E. faecalis biofilm inside the root canal system. aPDT treatment resulted in a biofilm inhibition similar to the NaCl irrigation and the use of chitosan alone. However, conjugating the TBO and chitosan resulted in significant and increased inhibition, compared to each treatment alone [69]. Chitosan was also functionalized with rose Bengal to target _E. faecalis_ and _Pseudomonas aeruginosa_ biofilms. At the energy density of 60 J/cm\({}^{2}\), the chitosan-rose Bengal conjugation achieved significant inhibition with a 7-log reduction in _E. faecalis_ biofilm compared to 5-log when rose Bengal was used alone. In _P. aeruginosa_ biofilms, the conjugation resulted in complete eradication of the biofilms [70]. The conjugation of PLGA and methylene blue resulted in a 2-log reduction in _E. faecalis_ biofilm [71].  
More investigations are needed to optimize and validate the use of polymeric nanoparticles in aPDT against endodontic pathogens. Future investigations may focus on using ex vivo models where the biofilm must be initiated inside the root canal system for several days. Conducting such experiments may provide more information about the benefits of polymeric nanocarriers in endodontic treatment.  
Figure 5: Most remarkable strategies where nanotechnology is applied to PS to enhance the aPDT disinfection outcomes.
## 5 Implementation and Validation
### Validation of Generated Model Design File
In rare scenarios, the UA model compiler was observed to fail with internal errors; however, most of the time, it successfully generated the required files. Therefore, the results related to supporting multiple YANG modules are marked as partial in the results summary table. Another issue identified with the import and included statements was that the OPC UA plugin generated duplicate namespace URIs in the OPC UA model design XML files. This issue was worked around by adding a YANG prefix to the URIs. The OPC UA plugin supports parsing only one input YANG file at a time, which led to the observed issue of duplicate namespace URIs. Modifying the plugin to read and parse the imported/included YANG files recursively will ensure that namespace URI collisions can be avoided; this enhancement is planned for future updates to the plugin. Figure 9 shows the OPC UA information model generated from the 5G RU YANG model, which was loaded into the OPC UA server and viewed through the OPC UA client GUI. Because the runtime instance of the YANG to the OPC UA adapter is yet to be developed, the FreeOpcUA client application was used to modify the temperature attributes of the QoS object in the RU information model. The exact change was successfully reflected in the GUI application when it subscribed to the data change events. Figure 9: Generated 5G RU OPC UA information model loaded in the OPC UA server.
###### Abstract  
Software-defined networking (SDN) is a modern network architecture, which separates the network control plane from the data plane. Considering the gradual migration from traditional networks to SDNs, the hybrid SDN, which consists of SDN-enabled devices and legacy devices, is an intermediate state. For wide-area hybrid SDNs, to guarantee the control performance, such as low latency, multi SDN controllers are usually needed to be deployed at different places. How to assign them to switches and partition the network into several control domains is a critical problem. For this problem, the control latency and the packet loss rate of control messages are important metrics, which have been considered in a lot of previous works. However, hybrid SDNs have their unique characters, which can affect the assignment scheme and have been ignored by previous studies. For example, control messages pass through Legacy Forwarding Devices (LFDs) in hybrid SDNs and cause more latency and packet loss rate for queuing compared with SDN-enabled Forwarding Devices (SFDs). In this paper, we propose a dynamic controller assignment scheme in hybrid SDNs, which is called the Legacy Based Assignment (LBA). This scheme can dynamically delegate each controller with a subset of SFDs in the hybrid SDNs, whose objective is to minimize average SFD-to-control latency. We performed some experiments compared with other schemes, which show that our scheme has a better performance in terms of the latency and the packet loss rate.  
hybrid SDN; dynamic controller assignment; wide area network Article
## 2 Experiment
### UV Full-Wavelength Analysis  
First, a UV-visible spectrophotometer was used to perform full-wavelength scanning on the HNS solution series to identify the optimal wavelength for HPLC detection, as well as the best co-solvents and extractants for HNS. The specific steps are as follows:  
To explore the most suitable organic solvents and detection wavelengths for HNS, seven 50 mL colorimeter tubes were used: colorimeter tube 1 contained ultrapure water, colorimeter tube 2 contained DMF, and colorimeter tube 3 contained ACN. Organic solutions of HNS in DMF and ACN were prepared in colorimeter tubes 4 (DMF + HNS) and 5 (ACN + HNS), along with two types of simulated HNS explosive wastewater in tubes 6 (DMF + HNS+ ultrapure water) and 7 (ACN + HNS+ ultrapure water). Together with the initial three tubes, these were labeled as samples 1 through 7. Baseline corrections were performed using samples 1, 2, and 3. Subsequent full-wavelength scans were conducted on samples 2 through 7 using a UV-visible spectrophotometer, with the samples placed in 10 mm quartz cuvettes. Figure 1 presents partial results of these scans, denoting experimental conditions with labels such as 'baseline, scanned sample'. For example, Curve a shows the scan of sample 4 (DMF + HNS) with sample 2 (DMF) as the baseline.  
Using water as a baseline, full-wavelength scans of ACN were conducted, as shown in Curves c and d in Figure 1. Both ACN and its aqueous solution exhibited significant changes in absorbance at wavelengths below 260 nm. Similarly, using water as a baseline, the full-wavelength scans of dimethylformamide (DMF) and its aqueous solution showed similar results. To better represent real water samples, the DMF aqueous solution was selected for depiction in Curve b of Figure 1. This indicates that the absorbance of DMF is particularly high at wavelengths below 260 nm. Therefore, it is advisable to select a detection wavelength for HNS that is greater than 260 nm to minimize interference from the organic solvents. A full-wavelength UV scan of DMF + HNS was performed using DMF as the baseline, resulting in Curve a. This scan revealed a peak in absorbance at 271 nm. Consequently, this study selected DMF as the co-solvent. Furthermore, as depicted  
Figure 1: Ultraviolet full-band scanning of HNS series solution.  
in Curve e of Figure 1, ACN is effective in extracting HNS from aqueous solutions, making ACN the preferred extraction solvent.  
As depicted in Figure 1, ultrapure water and pure solvents were used as baselines for scanning detection of simulated explosive wastewater. The absorbance peaks were observed around 271 nm. At this wavelength, the interference from the solvent on the explosives detection results is significantly reduced. Therefore, this study selected the ultraviolet channel of high-performance liquid chromatography at 226 nm and 271 nm to establish standard curves for DMF and HNS, respectively.
## 2 Materials and Methods  
### Plasmid Construction and Recombinant Protein Purification  
Sequences of the full-length _AtTPR10_ gene, ankyrin domain (_ANK-D_), and TPR domain (_TPR-D_) were amplified from the _Arabidopsis_ cDNA library by PCR using the primer pairs AtTPR10 forward (F)/AtTPR10 reverse (R) (5'-GGATCCATGGCCTCTGTAGC-TTCTACT-3'/5'-GTGCACTTTAGA-TTTGGCCTTAACCTC-3'), ANK-D_F/ANK-D_R (5'-GGATCCATGAACAAACGTGAGGCCTT-CAC-3'/5'-GTCAGCTTAAGTTCTGTTTGTTGTCAAATGGG-3'), and TPR-D_F/TPR-D_R (5'-GGATCCATGGCGCCAGAAAGCCA-3'/5'-GTCGACTTAGCTTACGGCCTAAGCAAT-ACCTC-3'), respectively. The PCR products were cloned into the _pGEM-T\({}^{\text{asy}}\)_ vector (Promega Co., Madison, USA), and the resulting _pGEM-T\({}^{\text{asy}}\)-AtTPR10_, -_ANK-D_, and _TPR-D_ constructs were sequenced. Then, these plasmids were digested with _BamHI_ and _SaII_, and cloned into the _pGEX-2T_ expression vector (New England Biolab, Ipswich, MA, USA) to generate fusions with the _glutathione S-transferase_ (_GST_) gene, and the resulting DNA constructs were used for protein expression in _Escherichia coli_ BL21 (DE3) cells. The transformed bacteria were grown in Luria broth (LB) containing 50 ug/uL ampicillin at 37 \({}^{\circ}\)C until the optical density at 600 nm absorbance (O.D\({}_{\text{-600}}\)) approached 0.7. Then, expression of the recombinant GST fusion proteins was induced by the addition of 0.4 mM isopropyl \(\beta\)-D-1-thiogalactopyranoside (IPTG), and the cells were cultured for another 5 h. The cells were then harvested by centrifugation at 6000\(\times\)\(g\) for 5 min, and the pellet was resuspended in 1X phosphate-buffered saline (PBS; 140 mM NaCl, 2.7 mM KCl, 10 mM Na\({}_{2}\)HPO\({}_{4}\), and 1.8 mM KH\({}_{2}\)PO\({}_{4}\) [pH 7.6]). The GST-AtTPR10, GST-ANK-D, and GSR-TPR-D fusion proteins were purified from the cells using GST agarose resin, and the eluted GST-tagged proteins were cleavaged by thrombin. The mature proteins were then analyzed by dialysis using 50 mM HEPES-KOH (pH 8.0). Each protein was further purified using a TSK G4000 SW\({}_{\text{XL}}\) high performance liquid chromatography (HPLC) column (30 cm \(\times\) 7.8 mM), as described previously [18; 19].
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 4 Ecosystem
### Enabling Technologies
#### 4.3.5 Parallel Processing  
Parallel processing sees much use in image encryption. In one such proposal by [PERSON] et al. [204], the problem of implementing image encryption in green IoT environments was tackled. The paper discussed the primary concerns, those being limited computingpower and long lifetime. These issues were addressed by proposing a novel parallel encryption technique using various chaos maps implemented in a parallel structure.  
In another proposal by [PERSON] et al. (2015), a novel encryption algorithm was proposed. Said algorithm decomposed the image into eight subplanes and processed each in parallel. Experimental analysis demonstrated that the algorithm possesses high security and runs five-times faster than an equivalent serial method.
# Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area
## 2 Material and Methods  
### ISBA Land Surface Model  
The ISBA LSM solves the energy and water budgets at the surface level and describes the exchanges between the land surface and the atmosphere on a sub-hourly basis. The modeling platform SURFEX (SURFace Externalisee, [20]) Version 8.0 was used in this study (source code and documentation available at [[http://www.umr-cnrm.fr/surfex/](http://www.umr-cnrm.fr/surfex/)]([http://www.umr-cnrm.fr/surfex/](http://www.umr-cnrm.fr/surfex/))) with the three-layer version of the soil model in ISBA [31]. For each model grid cell, the soil is partitioned into three layers: the top surface representing the very first centimeters, the root-zone soil layer defined by the vegetation rooting depth (which includes the first layer) and the recharge layer located below the root-zone with a maximum thickness of 1 m. In the model, the propagation of the information from the surface to the deepest layers relies on a force-restore dynamics: the surface and root-zone layers are forced by the atmospheric conditions and restored towards an equilibrium state where the gravity forces match the capillary forces; the drainage from the root-zone soil layer to the recharge layer supplies water and conserves the total water volume.  
Vegetation growth and mortality processes were introduced by [2; 32] and implemented in the form of a Nitrogen Dilution Process (NIT option, [3]) in order to simulate LAI interactively. Moreover, a refined representation of plant response to soil moisture deficit was implemented by [33; 34], with contrasting drought-avoiding and drought-tolerant behaviors. This ISBA configuration is called ISBA-A-gs and can simulate the CO\({}_{2}\) net assimilation and GPP by considering the functional relationship between the photosynthesis rate (A) and the stomatal aperture (gs) based on the biochemical A-gs model proposed by [35]. The vegetation phenology relies on photosynthesis-driven plant growth and mortality, and photosynthesis is related to the mesophyll conductance. More details can be found in [36; 37].
## 4 Results and Discussion
### Estimation of Elapsed Time since the Last Earthquake (t) and Average Recurrence Interval (R)  
For the evaluation of seismic risk, we must comprehensively understand the average recurrence interval of ancient earthquakes in this area and the elapsed time since the last earthquake. Quantitative studies on ancient earthquakes in the east segment of the Eastern Kunlun fault zone began in the 1990s and achieved a succession of research findings. However, the difference between results is relatively large. In this paper, we compare the research results of previous studies and obtain relatively reasonable recurrence intervals for ancient earthquakes and elapsed times since the earthquakes, which provide comparatively accurate source materials for calculating the recurrence probability for each segment.
# Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme
## 4 Experimental Results and Analyses
### Analyses of TTLR and TTLRTV Methods  
The TT-based methods (i.e., TTLR and TTLRTV) were analyzed in detail. As TT rank is multi-rank, how does every TT matrix rank affect the final result? We answer this question with the below experimental results.  
We conducted the experiments on recovering House, Lena, and Airplane images with a size of 256 \(\times\) 256 \(\times\) 3. The random missing patterns had four missing ratios: 10%, 30%, 50%, and 70%, respectively. We labeled the eight TT matrices as \(k\) = 1, 2, \(\ldots\), 8. The PSNR (dB) results of \(X_{n}^{*}\) (the optimal solution of the n\({}^{\text{th}}\) subproblem, which exploits the n\({}^{\text{th}}\) TT matrix rank), in the TTLR and TTLRTV methods are shown in Figure 12.  
From Figure 12, it can be seen that the PSNR (dB) results of each subproblem is steeply different for the TTLR method, which demonstrates that each TT matrix rank contributes different PSNR results. As there are no rules to find which TT matrix rank meets the best PSNR result, each solution of \(X_{n}^{*}\) should be combined to obtain the final \(X^{*}\), i.e., \(X^{*}=\sum_{n=1}^{N-1}\alpha_{n}X_{n}^{*}\). Comparing the PSNR curves of TTLR and TTLRTV methods in Figure 12, the PSNR (dB) results of each subproblem is slightly different for the TTLRTV  
Figure 12: The PSNR (dB) results of \(X_{n}^{*}\) (in Algorithm 3, the optimal solution of the n\({}^{\text{th}}\) subproblem exploits the n\({}^{\text{th}}\) TT matrix rank) using the TTLR method and the TTLRTV method. We permuted the image size of 256 \(\times\) 256 \(\times\) 3 to an order-9 tensor by the QA scheme. Then, the TT matrices of this order-9 tensor were labelled as k = 1, 2, \(\ldots\), 8. We used the random missing patterns with four missing ratios: 10%, 30%, 50%, and 70% respectively. The tested color images for the PSNR curves in (**a–c**) are House, Lena and Airplane images, respectively.  
method, which demonstrates that the combination of TT and TV can make the PSNR more balanced among all \(k\).
# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 5 Results
### Private Dataset
In Figure 8, the confusion matrix represents the analysis results of the test dataset using the final ensemble model. Compared to utilizing retina fundus images as input data, an increase in accuracy (1-10%) and sensitivity (10-29%) is observed across all datasets. Table 8 compares cross-entropy and focal loss, summarizing the results after ensemble learning across all input data. Although the accuracy might appear similar, a distinct difference is evident in sensitivity. The cross-entropy results in lower accuracy for glaucoma data due to its high accuracy for normal data. Consequently, even with ensemble methods, there is not a significant improvement in glaucoma data performance; instead, the accuracy increases as the correct predictions for normal data rise. These results demonstrate that focal loss, initially used in object detection, also proves effective in classification tasks by addressing class imbalance issues.
## 5 Results of Health Monitoring
### Global Deformation
#### 5.1.1 Cable Alignments  
The comparison between the changes of cable alignments measured after the first half year and one year are shown as Figure 7. The ascent amounts of the cables at the middle ranges of side-spans were more significant when compared with the ones of the cables close to girder ends. The cable anchoring positions at girder ends moved towards the mid-span affected by concrete S&C. The cable anchoring positions at tower tops also moved towards the mid-span affected by the unbalanced horizontal cable forces. The cables at the middle range of mid-span dropped significantly. The cables between thetowers and the quarter points of CS4 and CS6 rose conversely because of the appearances of inflection points at CS4 and CS6, according to the cable catenary characteristics, which can also explain the relatively significant deformations at DCS5, DC19 and DC33. The variation amounts measured after one year appeared a multiple level growth when compared with the ones measured after the first half year.
# An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming
## 2 Preparation of Knowledge about the NDP EMS
### Multi-Resolution Wavelet Neural Network  
In this research, we proposed a multi-resolution wavelet neural network (MRWNN) based on the theories mentioned in Sections 2.4 and 2.5. The structure of the proposed MRWNN is shown in Figure 3. Different to RBFNN, the hidden layer in MRWNN contains two types of nodes: wavelet nodes (\(\psi\)-nodes, which are in the green ellipse) and scaling nodes (\(\phi\)-nodes, which are in the blue ellipse, where one red circle represents the scaling nodes at a same resolution). Compared with RBFNN, the MRWNN does not need a clustering preprocessing for the input data, and enjoys faster convergence and higher approximate accuracy for the orthogonal characteristic of activation function in hidden nodes.  
Figure 3: Structure of multi-resolution wavelet neural network (MRWNN).  
The output of the proposed MRWNN is as follows:  
\[y\approx\left\{\begin{array}{cc}\sum\limits_{k=1}^{n_{L}}a_{Lk}\phi_{Lk}(H_{1}(k )),&\text{at resolution }2^{L}\\ \sum\limits_{k=1}^{n_{L}}a_{Lk}\phi_{Lk}(H_{1}(k))+\sum\limits_{k=1}^{n_{L}}d_ {Lk}\psi_{Lk}(H_{2}(m,k)),&\text{at resolution }2^{L-1},m=L\\ \vdots\\ \sum\limits_{k=1}^{n_{L}}a_{Lk}\phi_{Lk}(H_{1}(k))+\sum\limits_{m=1}^{L}\sum \limits_{k=1}^{2^{L-m}n_{L}}d_{mk}\psi_{mk}(H_{2}(m,k)),&\text{at resolution }2^{0}\end{array} \right., \tag{26}\]  
where \(H_{1}(k)=\sum\limits_{i=1}^{N}\omega_{ik}x_{i}\) is the input of the \(k\)th hidden layer or scaling node; \(H_{2}(m,k)=\sum\limits_{i=1}^{N}\omega_{ip}x_{i}\) (\(p=2^{L-m}n_{L}+k\)) is the input of the \(p\)th hidden layer or the \((p-n_{L})\)th wavelet node.  
In real applications, we usually train the network at resolution \(2^{L}\). If the output does not meet our precision requirements, we keep the scaling nodes and add the detail of resolution \(2^{L}\) at resolution \(2^{L-1}\). We proceed like this until the training precision meets our requirements.  
In this MRWNN, the weight factors are trained according to the same gradient descent algorithm as in Equations (13) and (14). The weight factor between the \(h\)th hidden node and the output node is as follows:  
\[\omega_{h}=\left\{\begin{array}{cc}a_{Lh},&h=1,2,\cdots,n_{L}\\ d_{mk},&h=2^{L-m}n_{L}+k\end{array}\right.. \tag{27}\]  
In this research, we use the Meyer wavelet, which is infinitely differentiable with effective support and defined in the frequency domain. In [18], [PERSON] and [PERSON] give the closed analytical expression for the Meyer scaling function and Meyer wavelet function shown as follows:  
\[\phi(t)=\left\{\begin{array}{cc}\frac{2}{3}+\frac{4}{3\pi},&t=0\\ \frac{\sin\left(\frac{2\pi}{3}t\right)+\frac{4}{3}t\cos\left(\frac{4\pi}{3}t \right)}{\pi t-\frac{16\pi}{9}t^{3}},&t\
eq 0\end{array}\right., \tag{28}\]  
where \(\
# Applicable Conditions of Room-and-Pillar Mining Goal Treatment Methods under a Traffic Load
## 2 Stability Analysis of a Goaf under Traffic Load Conditions
### Checking the Calculation of Roof Strength under Traffic Load Conditions  
Because the collapse of a roof may cause the sudden collapse of the surface, which will result in quite severe consequences to the operation of a highway, the safety check of the roof is a particularly important step. Under the double action of the traffic load and the overlying strata, the stress of the roof is complicated, and the Whittaker method is a common calculation method used in practical engineering at present.  
The Whittaker method is a method based on structural mechanics which is used to check the strength of the roof [30]. In this method, the roof and coal pillar are simplified into simply supported beam structures, and the traffic load borne by the roof and load generated by the dead weight of the overlying strata are considered to be uniformly distributed loads, as presented in Figure 4. The maximum tensile stress (MPa) of the roof is calculated as follows:  
\[\sigma_{\rm t}=\frac{(\sum_{i=1}^{n}\gamma_{i}gh_{i}+q_{0})*L^{2}}{2H^{2}} \tag{2}\]  
In Formula (2), H is the thickness of the roof; \(\gamma_{i}\) is the weight of the overlying rock mass; g is the gravitational acceleration; h\({}_{i}\) is the thickness of each layer of rock mass; q\({}_{0}\) is the traffic load; and L is the coal pillar spacing.  
After calculating the maximum tensile stress of the roof, compare it with the tensile strength of the roof strata, and the calculation formula is  
\[K=\frac{\sigma_{c}}{\sigma_{\rm t}} \tag{3}\]  
In Formula (3), \(\sigma_{c}\) is the tensile strength (MPa) of the roof strata, and k is the safety factor. Referring to the evaluation of the stability grade of a roadway goat in the Technical Rules for Highway Design and Construction of Goaf Areas [22] and the evaluation standard [31] of the influence of the critical influence depth of load on the stability of goat areas in the Code for Geotechnical Engineering Investigation of Coal Goaf Areas, when K \(>\) 1.5, the roof can be considered safe and stable for a long period of time and will not collapse under traffic load conditions (as geotechnical engineering is a discipline with many empirical factors, and we cannot be completely accurate in actual engineering, some errors will always exist. To ensure the safe operation of the project within its service life, we should keep a safety reserve in the design. When the safety reserve exceeds 0.5 times in the project, it can generally guarantee safe operation, so a safety factor of 1.5 times was adopted).