esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Optical Properties, Band Bending and Device Improvement
An overview of a selection of reported PCEs is given in Table 2. It is important to stress that the reported consecutive world record devices (e.g., see Reference [25] containing a phenethylammonium iodide additive (PEAI) and Reference [26] containing tri-iodide, (I\({}_{3}\))\({}^{-}\) addition) do not contain Zn(II), but judging from the various reports reviewed here, it would certainly be interesting to look at the effect of Zn-doping on the PCE of record-breaking compositions. Next to the clear effects of zinc doping, enhancing efficiency by a factor between 1.07 and 1.33, we also compile some record-breaking compositions in Table 2 (that do not contain zinc) as a historic overview. Since 2015, all record devices are based on FA/MA compositions. From Table 2, it can be seen that the compositional engineering that is accompanied by PCE increase is trending towards a higher FA content (compare References [5; 27]) as well as a reduced Br content (compare References [25; 26]). MA and Br however, seem to be essential for stability and phase-change behaviour. lodide management [26] and alkyl-ammonium-based additives (see References [16; 24; 25]) are important factors to control defect states, which quench photoluminescence (see also Table 1). The presence of chloride (Cl\({}^{-}\)) in the precursor solution generally has a positive effect, but chloride is undetectable in the final thin films (see for example Reference [24]). Figure 9: Changes of emission intensity (**a**) as well as emission decays (**b**) as a function of Zn(II) incorporation. MAPbI\({}_{3}\)-based material. MA(Pb:Zn)I\({}_{3}\)-[Cl\({}^{-}\)]. The optimum is at 3% for both graphs. Reprinted with permission from _ACS Appl. Mater. Interfaces_**2017**, \(9\), 42875–42882, Reference [11]. Copyright 2017 American Chemical Society. It has to be realized that these records are so-called \"hero-devices\": in the statistical distribution of ~100 devices, they represent the high-end tailing of the gaussian distribution curve of the PCE. To reproduce these certified efficiencies of very small cells is highly challenging, even for research groups that specialize in perovskites. Exact experimental details and specific lab-based conditions and protocols that determine the perfect perovskite solar cell of a particular composition are not well understood. However, the whole field of perovskite solar cells is loaded with average PCEs around 20%, and many examples above. The composition of the two most recent certified PCE records (23.7% and 24.2% in Reference [2]) have not been disclosed yet.
## 4 Enabling Technologies for FSO Communication
### Hybrid FSO Systems  
The integration of FSO communication with RF, mmWave, and THz technologies has emerged as a promising approach to overcoming the limitations of standalone FSO systems. Hybrid FSO systems leverage the strengths of these technologies to provide enhanced reliability and coverage. This section examines the architecture of hybrid FSO systems, their operational principles, and the benefits they offer in terms of performance and reliability.
## 3 Geolocalization of Historical Image Collections
### Geolocalization  
The final step was geolocalization, which is a two-step procedure. In the first step, we searched for relevant images using a Google image search based on the location information, extracted from the caption. For each image, we extracted the top 10 results. The Google search gave all correct results when we searched for well-known landmarks, e.g., _\"the Castle of Prague\"_, but in the case of a less common landmark, the Google search results always returned some irrelevant images, e.g., the search result of _\"Femmes Prevoyantes du Centre, Nieuport Bains\"_ shows only two correct results for the mentioned building. This study aimed to be able to geolocate less-common images of buildings in addition to the well-known landmarks. Therefore, in the second step, we needed to filter the search results by keywords to find out which images were similar to the image shown in the newspaper. Our pipeline for finding the best matches is shown in Figure 5. We computed the features of all images and compared the similarity score. Using a threshold, we filtered out the irrelevant results.  
CNNs designed for extracting features from landmarks or buildings are usually trained on contemporary color images. These do not perform well on monochrome (black-and-white) and halftone images. The proposed pipeline for building matching hierarchically combined global and local descriptors extracted using the pretrained Cirtorch and DELF models. In this way, the results were improved for halftone newspaper images compared to matching the descriptors separately.  
The feature matching algorithm first extracted the global descriptors for all images in the database using Cirtorch. These global descriptors were used to filter out the irrelevant images using k-NN. Figure 6 shows the filtering of irrelevant results. Most of the downloaded images provided good results, hence only the three (3) worst images were excluded from the next step of the pipeline, local descriptor matching. Local descriptors were extracted from the seven (7) remaining images using DELF. From this model, not only the features for the keypoints were retrieved, but also the location of each one. This allowed us to search for similar images based on geometric transformations of the keypoints using RANSAC. Images with a confidence score greater than 0.5 were considered good matches.  
Figure 5: Pipeline to find the best matches
## 7 Experimental Results
### System Setup  
The setup of the record and replay system is shown in Figure 7. During the live operations performed on-board the test vehicle, the GNSS signal was first captured by the active Novatel OEM GNSS antenna (placed on the vehicle rooftop), and thus it was split among three branches. The power supply for the recording system was provided by an external battery whereas the reference receiver was powered directly from the car battery. On the other side, the playback operations were carried out in the laboratory in a post-processing stage.  
The first branch included an RTMeS given by the dual frequency Novatel SPAN-CPT system receiver [25]. It is a compact, single-enclosure GNSS receiver with a tactical-grade Honeywell HG1700 IMU. The final accuracy was improved by exploiting the real-time kinematic (RTK) corrections download from the FinnRef network. FinnRef is a nationwide network of permanent GNSS stations in Finland, providing publicly available differential GNSS corrections, but also RTK corrections for scientific use.  
As depicted in Figure 7, the second branch included the GBPT under test, which was a consumer-grade GNSS receiver--namely, a uBlox M8T [26].  
The third branch was the recording system. Within this block, the GNSS signal was first amplified by the LNA, which provided a 30 dB gain, and then it was fed to the front-end, a USRP N210 [27]. The latter was synchronized to a Rubidium frequency standard to control the ADC in order to have a very accurate and stable sampling frequency. The modular approach of the USRP makes this front-end extremely versatile and flexible in terms of configuration parameters. Considering a trade-off between signal quality and available data storage resources, the USRP was configured by using the parameters listed in Table 2. By using these configuration parameters, 60 min of raw data amounted to approximately 72 GB.  
The playback system is shown in the right part of Figure 7. It depicts the setup used in the laboratory to play back the binary samples stored on the disk, recorded during the data collections. The front-end used to reproduce the RF signal was the USRP N210, which was the same as the one used for recording the data setting the configuration parameters, listed in Table 2. It first converted the recorded samples to analog through the DAC. After a low-pass filtering stage, the samples were converted back to RF, and finally, band-pass filtered. The RF signal was attenuated to emulate the power received at the output of an active antenna and thus it was fed to the GBPT under test.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{f_{if}}\) & \(\mathbf{f_{s}}\) & **Sampling Type** & **Quantization** & **Interface** & **Reference** \\ \hline Configuration & 0 Hz (baseband) & 5 MHz & I and Q & 16 bits & Ethernet & Rubidium \\ \hline \hline \end{tabular}
\end{table}
Table 2: USRP N210 configuration parameters.  
Figure 7: Record and replay system setup. RTMeS: Reference Trajectory Measurement System; RTK: real-time kinematic.
# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 2 Related Work
At the same time, challenges when building a VOE system were also discussed and presented. Examining the application of Visual SLAM and VOE categories, [PERSON] et al. [39] surveyed the applications of Visual SLAM for localization, mapping, and wayfinding. The applications are presented according to Visual SLAM algorithms with three methods: monocular-based (based on the image sequence), stereo-based (based on the camera trajectory and building a map of the environment built based on feature points), and monocular- and stereo-based (based on image sequences or feature points for mapping, tracking, and wayfinding). Research by [16] surveyed methods for building Visual SLAM and VOE systems according to two methods: traditional and DL.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
###### Abstract  
The number of smart cities is increasing rapidly around the world with the continuous increase of governments' interest in exploiting Information and Communication Technologies (ICT) to solve issues arising from rapid urbanization. Most smart city services rely fundamentally on ubiquitous sensing, enabled by Wireless Sensor Network (WSN) technologies. However, WSNs in smart cities are naturally vulnerable to unavoidable external challenges like storms, fires, and other natural disasters. Such challenges pose a great threat to smart city infrastructure, including WSNs, as they might affect network connectivity or result in complete blockages of network services. However, some particular smart city services are critical, to the point where they must remain available in all situations, especially during disasters; to monitor the disaster and obtain sensory information needed for controlling it, limiting its danger, or for decision-making during rescue operations. Thus, it is crucial to design a smart-city network to maintain connectivity against such challenges. In this paper, we introduce MPResiSDN, a MultiPath Resilient routing system based on Software Defined Networking (SDN). The system introduced exploits SDN's capabilities and aided-multipath routing to reactively provide connectivity in smart city networks in the presence of challenges. We evaluated our proposed system under simulations of different natural disasters. The results demonstrate that the system improved data delivery under the challenges by as much as 100% compared to the Spanning Tree Protocol when a suitable value for \(k\) diverse paths was selected.  
resilenic; routing protocols; smart city; Software Defined Networking (SDN); Wireless Sensor Networks (WSN) +
Footnote †: journal: Applied Sciences  
1
Footnote 1: email: [EMAIL_ADDRESS]
## 2 Material Preparation and Characterization
For these reasons, in our solar cell testing, we used commercial TIMs enhanced with graphene fillers of the loading fraction of up to 4 wt %. The low loading of graphene is also beneficial from the practical material cost considerations. In the present and previous studies of graphene-enhanced TIMs, we verified that mixing and vacuuming of commercial TIMs without addition of graphene have not improved their thermal conductivity [34; 37; 41; 43]. These tests have been performed to establish that graphene fillers are responsible for the improved apparent thermal conductivity rather than additional mixing and vacuuming processing steps by themselves. At the small loading fractions used, graphene fillers are not forming the percolation network as confirmed by the microscopy and electrical resistivity measurements [41; 43; 45]. The increase in the thermal conductivity of the resulting composite can come owing to two possible mechanisms. The first one is related to very high intrinsic thermal conductivity of graphene [46] and its good coupling to the matrix materials [41; 43; 45]. The heat propagates partially via graphene fillers and partially via the matrix material. Even if the thermal conductivity of graphene-FLG fillers is reduced due to the exposure to the matrix, the effective medium approximation (EMA) predicts a large increase in the thermal conductivity of the composite (larger than the measured one) [45]. The second possible mechanism of enhancement is the action of graphene fillers as connecting thermal links among existing larger scale fillers in the commercial TIMs. Such a mechanism has been known and studied for other fillers with different aspect ratios, e.g., spherical metal particles and carbon nanotubes [56; 57]. Figure 2: Measured apparent thermal conductivity of two representative commercial thermal interface materials, denoted as “sample A” and “sample B”, with added graphene fillers. The data are presented as a function of the graphene loading weight fraction. The measurements were conducted at \(T=50\)\({}^{\circ}\)C and pressure of 100 kPa. Note that the apparent thermal conductivity values include the effects of the thermal contact resistance.
## 5 Results and Analytical Discussion
### Discussion and Methodological Considerations
#### 5.4.2 Notable Classification Patterns in DDoS Detection  
The evaluation of DDoS attack detection techniques highlighted several interesting trends:  
1. After adjusting feature selection, there was little performance difference between KNN variants, regardless of K value, suggesting that neighborhood size had little impact on DDoS classification.
2. The LSTM showed a good initial performance in the detection of DDoS_UPD and DDoS_TCP even in the lack of improved feature selection. This observation implies that its temporal learning capacity sufficiently caught the sequential patterns defining these attack forms.
3. RF's remarkable combination of accuracy (99.99%) and detection efficiency (0.584 s) underscores the strength of ensemble-based techniques for tackling difficult multi-class issues with many attack variants.
4. The improvement in LSTM's performance following feature addition was negligible (only 0.42%), in sharp contrast to the significant gains noted in other models. This finding suggests that the recurrent architecture of LSTMs unintentionally covers relevant temporal connections in network traffic data, therefore reducing the benefits possible by feature selection.
5. With misclassification rates ranging from 0% (RF) to as high as 39.4% (SVM, with 37.9% specifically misclassified as XSS attacks), the DDoS_HTTP attack type displayed clearly notable variations in categorization accuracy among models. This variation suggests that this particular DDoS variant shows complex traffic patterns, which may be especially challenging for methods depending on hyperplane-based or distance-based decision boundaries.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Pulse-Coupled Neural Network  
For image fusion, a pulse-coupled neural network (PCNN) is often used as a feature extraction method [87; 88]. As shown in Figure 2, the PCNN adopts a single layer, two-dimensional and laterally-connected neural network. The neurons are connected with the pixels in the input images directly. So, the size of PCNN is equal to the size of images. Each of the neurons is also connected with neighboring neurons, as shown in Figure 2. Image feature extraction using PCNN is an iteration process. At each iteration, each neuron receives the corresponding pixel's color intensity as an external stimulus. The outputs of its neighboring neurons from the previous iteration are treated as an internal stimulus and are combined with the external stimulus. When the total stimulus exceeds a threshold, the neuron will pulse (or fire) to have an output intensity equal to one at the corresponding location in the output image. The threshold at the neuron will significantly increase its value after firing and it decays exponentially until the neuron fires again.  
Figure 2: Representation of an image pyramid with four levels [89].  
Through iterative computation, PCNN neurons produce a series of pulse outputs, which contain different features (e.g., high frequency features, low frequency features or edges) of the input images, and can be used for various image processing applications. Multichannel PCNN is proposed to process multiple feature images with a single/multiple PCNN to fuse these images [90].  
Nevertheless, PCNN has its shortcomings, such as numerous parameters and the complex process of setting parameters. To date, several novel algorithms to optimize the PCNN parameters have been introduced to improve calculation speed [91; 92]. In image fusion, PCNN has also been used as fusion rule for wavelet transform-based methods, such as DWT [93; 94; 95; 96], Contourlet transform [97; 98; 99; 100], NSCT [101; 102; 103; 104; 105], ST [106; 107; 108; 109] and NSST [110]. In medical imaging, PCNN is widely applied to fuse CT/MRI [94; 111; 112; 113], MRI/PET [94], MRI/SPECT [111; 113], US/SPECT [111] and multiparametric MR images [84].
# A Novel Nonlinear Magnetic Equivalent Circuit Model for Magnetic Flux Leakage System
## 3 Designing MEC Model for MFL System
### Calculation of the MEC Model Permeances
#### 3.1.4 Calculation of the Permeances of the Air Gap and Fringing Effect  
In the MFL system, fringing flux emerges from the inner and outer edges of the poles in the air gap region. A schematic illustration of the fringing flux is shown in Figure 7. The calculation method used in ref. [22] is selected to determine the fringing flux permeances.  
The permeance of the inner edge fringing flux is as follows:  
\[P_{fic}=\frac{2\mu_{0}l_{c}}{\pi}\ln\biggl{(}1+\frac{\pi}{2}\frac{\min(w_{w_{w} },w_{s}/2)}{\mathrm{l}_{g}}\biggr{)} \tag{26}\]  
The permeance of the outer edge fringing flux is as follows:  
\[P_{foc}=\frac{2\mu_{0}w_{e}}{\pi}\ln\biggl{(}1+\frac{\pi}{2}\frac{\min(w_{w},w _{s}/2)}{\mathrm{l}_{g}}\biggr{)} \tag{27}\]  
Also, the permeance of the air gap can be calculated by Equation (28).  
\[P_{g}=\frac{\mu_{0}l_{c}w_{e}}{\mathrm{l}_{g}} \tag{28}\]  
Because the fringing flux permeances are parallel with the air gap permeance, the total permeance of each air gap can be calculated as follows:  
\[P_{gap}=P_{g}+2P_{foc}+2P_{fic} \tag{29}\]  
The fringing flux permeances are multiplied by two because MFL system poles have 2 outer and inner edges.
# Positron Emission Tomography in Coronary Heart Disease
## 4 Viability Imaging
### Imaging Myocardial Viability with 18F-Fluorodeoxyglucose (FDG)  
The biochemical properties of this tracer have been previously described. Demonstration of preserved glucose metabolism by FDG is a marker of myocardial viability. While reduced perfusion combined with reduced glucose metabolism suggests scarred myocardium, reduced perfusion combined with preserved or increased metabolism (mismatch) suggests hibernating myocardium.  
From a theoretical point of view, management of hibernated myocardial tissue should be straightforward--revascularization. However, some studies have questioned this assumption.  
In the PARR-2 (PET and recovery following revascularization) trial, patients with severe left ventricular dysfunction were randomized to revascularization decision managed by FDG-PET versus standard care. Management by FDG-PET did not result in reduction of death, MI, or recurrent hospital stay at 1 year compared with standard management [44]. However, in a post hoc analysis of a group of patients belonging to a more experienced center with ready access to FDG-PET and integration with imaging, heart failure, and revascularization teams, a significant reduction in cardiac events was observed in patients with FDG-PET-assisted management [45].  
In the viability sub study of the STICH (surgical treatment for ischemic heart failure) trial, there was no significant association between myocardial viability and outcome on multivariable analysis [46].  
In light of the described evidence, the most recent European Guidelines on myocardial revascularization give the use of noninvasive stress imaging for the assessment of myocardial ischemia and viability in patients with heart failure and coronary heart disease before the decision on revascularization a Class IIB recommendation [47].
###### Abstract  
Brain tumors lead to modifications of brain networks. Graph theory plays an important role in clarifying the principles of brain connectivity. Our objective was to investigate network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging (fMRI) and graph theory. We retrospectively studied 30 low-grade (LGG), 30 high-grade (HGG) left-hemispheric glioma patients and 20 healthy controls (HC) with rs-fMRI. Tumor location was labeled as: frontal, temporal, parietal, insular or occipital. We collected patients' clinical data from records. We analyzed whole-brain and hemispheric networks in all patients and HC. Subsequently, we studied lobar networks in subgroups of patients divided by tumor location. Seven graph-theoretical metrics were calculated (FDR \(p<0.05\)). Connectograms were computed for significant nodes. The two-tailed Student t-test or Mann-Whitney U-test (\(p<0.05\)) were used to compare graph metrics and clinical data. The hemispheric network analysis showed increased ipsilateral connectivity for LGG (global efficiency \(p=0.03\)) and decreased contralateral connectivity for HGG (degree/cost \(p=0.028\)). Frontal and temporal tumors showed bilateral modifications; parietal and insular tumors showed only local effects. Temporal tumors led to a bilateral decrease in all graph metrics. Tumor grade and location influence the pattern of network reorganization. LGG may show more favorable network changes than HGG, reflecting fewer clinical deficits.  
g 2022 14 3327 10.3390/cancers14143327