esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Revision of the NFIP Claims Hazard Data in Florida for Flood Risk Assessment
## 2 Materials and Methods  
### NFIP Claim Data in Florida  
Florida accounts for roughly 37% of all NFIP policies according to FEMA. The number of total claims in the provided NFIP loss data was 240,469 for the period of 19 July 1975 to 10 January 2014. Among them, only 153,751 (63.9%) were paid claims and were hence used in the current study. The paid claim locations are shown in Figure 1.  
Figure 1: The NFIP paid claim data in Florida (claims period: 19 July 1975–10 January 2014).  
The majority of flood damages were claimed along coastal and urban areas (e.g., Miami, Orlando, Jacksonville, and Tampa) as expected, partly because of the high frequency of storm surge hazards and partly because of the high density of the population. Although tropical cyclones are the major natural hazards, other frequently occurring intensive nontropical storm events can cause inundation damages in both coastal and inland areas of Florida.  
The provided NFIP flood damage claim data contained 100 fields for hazard-, building, and actuarial-related information. These fields included _date of loss_, policy number, address (_latitude and longitude_), flood risk zone, occupancy type, _catastrophe number_, _cause of loss_, lowest floor elevation, base flood elevation, building claim payment, contents claim payment, etc. The hazard-related fields (italic font) were the main target fields to be assessed in the current study. To respect privacy, sensitive information (e.g., homeowner's address) was neither exposed nor explicitly used.  
Brief descriptions of several claim fields are as follows:  
* occupancy type: single-family homes or other type;
* catastrophe number: the flood event with which it is associated;
* base flood elevation (BFE): 100 year flood elevation (i.e., 1% chance of flooding);
* building and contents claim payment: how much was paid by the NFIP;
* flood risk zone: to indicate the risks in different parts of the United States, FEMA has assigned a character from the alphabet to each zone. The most hazardous flood zones are V (usually the first row of beachfront properties) and A (usually, but not always, properties near a lake, river, stream, or other body of water). Any building located in an A or V zone is considered to be in a Special Flood Hazard Area and lower than the BFE;
* cause of loss: the natural hazard cause identification for the claimed loss, as follows--0: other causes, 1: tidal water overflow, 2: stream, river, or lake overflow, 3: alluvial fan overflow, 4: accumulation of rainfall, 7: erosion--demolition, 8: erosion--removal, 9: earth movement, landslide, land subsidence, sinkholes, etc.
## 4 Simulations and Results
### Reliability and Mean Time between Failures (MTBF)  
Reliability is the ability of the entity to carry out a specified feature under fixed circumstances at a given time interval [30]. Mean Time Between Failures is the anticipated interval of time between two failures of a repairable entity.  
In Figure 9, we include the empirical MTBF PDF and CDF distributions, and the resulting reliability for the mechanical component: the master cylinder. Similarly, we can obtain the corresponding information for all the components of our system.  
Data collector monitors are defined for the same transition as MTTFF. As explained before, lists are defined to obtain the MTBF of different components and the whole system. This monitor will record the subtracted time resulting from the two input arcs when the  
Figure 8: RBS mean time–to–first–failure, empirical PDF, and CDF.  
Figure 9: Master cylinder MTBF empirical PDF, CDF, and reliability.  
transition is enabled, i.e., only if two input arc conditions are satisfied: if we have the new time-to-failure, which is equal to the head of the defined list **hd MTBFS**, and the precedent time-to-failure **MTBF**.  
In Figures 10 and 11, we have plotted the reliability of the RBS and its various components.  
The predictive reliability curve for the entire system is illustrated in Figure 10; the reliability value at 2000 h is approximately 0.3.  
We can observe, on the curves of Figure 11, that the reliability of the master cylinder is the highest among all the RBS components, and the DC converter has the lowest reliability value.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### In-Office Therapeutic Treatments of Dentin Hypersensitivity
#### 6.2.9 Laser Irradiation Treatments  
According to the results of various studies, the effectiveness of the laser in the treatment of DH varies significantly and ranges between 5-100% [134; 135; 136]. Patients often experience immediate relief from DH after laser treatment. The results depend on the wavelength of the laser and the setting parameters, such as application time, mode of irradiation (pulsed or continuous), and average power [137; 138; 139].  
Various laser wavelengths have been utilized for the reduction of the DH symptoms. More specifically, diodes (810-980 nm) [139; 140; 141], neodymium: yttrium-aluminum-garnet--Nd:YAG (1064 nm) [142; 143], erbium: yttrium-aluminum-garnet--Er:YAG (2940 nm) [138; 144], erbium, chromium: yttrium-scandium-gallium-garnet--ErC:YSGG (2780 nm) [76; 145], and carbon dioxide--CO\({}_{2}\) (10,600 nm) [146; 147] lasers have been employed against DH.  
The mechanism of action of lasers has not been definitively determined, but multiple theories exist regarding the effects of laser exposure on dentin. The most widely accepted one suggests that it involves the occlusion or sealing of dentin tubules through a process of dentin melting and subsequent recrystallization [148]. When addressing tooth sensitivity, it is crucial for the laser beam to avoid ablation of the treated dentin surface and instead induce only structural and chemical transformations. Ablation of dentin can lead to a surface that lacks a smear layer, has open dentin tubules, and exhibits uneven textures, which is an undesirable outcome for the procedure [149]. Furthermore, using laser settings exceeding 0.75 W may result in the formation of charred areas, making such protocolsunsuitable for addressing DH [150]. There have been suggestions that employing lower power settings might initiate the dissipation of dentinal fluid. This, in turn, could lead to a reduction in dentin permeability, ultimately resulting in a decrease in dentinal pain [148]. Representative SEM images of the rate of occlusion after various treatments for DH are presented in Figure 3[73]. It is important to mention that DH cannot lead to necrosis of the pulp like it could in cases of extensive tooth restorations [151].
## 1 Introduction
Preliminary findings of the optimal linear quadratic tracker design for singular systems and the motivation of this paper are given in Section 2. The properties of the EID associated with the proof for a proper continuous-time system with unknown input and output disturbances are given in Section 3. Section 4 presents the modified linear functional observer and the EID estimator, while Section 5 explains the design procedure for an OKID-based linear quadratic analog tracker (LQAT) associated with the modified functional observer for unknown non-square continuous-time singular systems. Illustrative examples are given in Section 6 to show the superiority of the proposed method. Lastly, conclusions are presented in Section 7.
# Spatial Variability of Grouting Layer of Shield Tunnel and Its Effect on Ground Settlement
###### Abstract  
This study aims to investigate the effect of the spatial variability of grouting-layer thickness on ground-surface settlement caused by shield tunneling and to provide a rational prediction method. The spatial characteristics of grouting layers were obtained based on statistical analysis. The random finite element method was used to study the effect of spatial variability of different parameters on ground-surface settlement. Simulation results indicate that the spatial variability of the grouting layer has a negative impact on ground settlement. The surface settlement will be underestimated without considering the spatial characteristics of the grouting layer. Thus, a reliable prediction approach of the maximum ground settlement was proposed to control the construction quality.  
random finite element; grouting-layer thickness; ground-surface settlement; statistical analysis Article
## 3 Results  
### PTEN Protein Expression  
Expression of PTEN protein was noted in cancerous cases and inflammatory lesion of cervix (Figure 1a). Loss of PTEN expression was identified as 45.33% (34/75) in cervix cancer and a high expression was noted in control cases (Figure 1b) with \(p\) < 0.05 (Table 1). PTEN expression was significantly associated with the clinical stage of the SCC of the uterine cervix (61.36% and 45.16% in stages I-II and III-IV, respectively) (_p_ < 0.05) (Table 1). No association was detected between the PTEN expression and grade of differentiation (58.33% in the well differentiated, 55.88% in the moderately differentiated, 47.05% in the poorly differentiated) and age of the patients (_p_ > 0.05) (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Total Cases** & **PTEN Positive** & **PTEN Negative** & _p-Value_ \\ \hline Cervical carcinoma & 75 & 41 (54.66\%) & 34 (45.33\%) & \multirow{2}{*}{\(<\)0.05} \\ \cline{1-1} Inflammatory lesion & 10 & 10 (100\%) & 00 (100\%) & \\ \hline Age (In years) & 31 & 18 (58.06\%) & 13 (41.9\%) & \multirow{2}{*}{\(>\)0.05} \\ \cline{1-1} \(\leq\)58 years & 44 & 23 (52.27\%) & 21 (47.72\%) & \\ \hline Histological grades & & & & \\ \hline Well differentiated & 24 & 14 (58.33\%) & 10 (41.66\%) & \multirow{2}{*}{\(>\)0.05} \\ \cline{1-1} Moderately differentiated & 34 & 19 (55.88\%) & 15 (44.11\%) & \\ \hline Poorly differentiated & 17 & 8 (47.05\%) & 09 (52.94\%) & \\ \hline Clinical stage & & & & \\ \hline I and II & 44 & 27 (61.36\%) & 17 (38.63\%) & \(<\)0.05 \\ \hline III and IV & 31 & 14 (45.16\%) & 17 (54.83\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation between Phosphatase and tensin homolog (PTEN) and clinico-pathological features in cervical Squamous Cell Carcinoma (SCC).
# RF Frequency Selective Switch by Multiple PMIM Conversions
###### Abstract  
Nowadays, broadband and multi-channel radio frequency (RF) processing has been widely used in communication, radar, countermeasure, and other applications. At present, multiple-input and multiple-output (MIMO)-oriented microwave photonic signal processing technology is relatively scarce, so this paper proposes an RF frequency selective switch (FSS) based on multiple phase modulation to intensity modulation (PMIM) conversions. PMIM conversion has been used in narrowband microwave photonic filtering in the past. We extend it to a wideband and arbitrarily reconfigurable RF spectrum processing unit through an optical frequency comb and periodic optical filter. Although we use the incoherent combination of a multi-wavelength light source, we can obtain any frequency response including rectangles only by using all positive tap coefficients. Using an optical wavelength selective switch (WSS), we obtain RF FSS, and the spectral resolution of RF FSS is much better than that of optical WSS, which is improved by more than two orders of magnitude. The above principles, including single-channel reconfigurable filtering and multi-channel RF FSS, are verified by experiments. Our technology provides a stable solution for future RF MIMO signal processing.  
frequency selective switch; phase modulation to intensity modulation; optical frequency comb; wavelength selective switch +
Footnote †: journal: Applied Sciences
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 5 Limitations  
While the present work focuses on whole-heart segmentation in the LAX two- and four-chamber orientation in Cine-MRI, several shortcomings related to the imaging modality and sequence may potentially limit the usage of segmentation. While data augmentation was performed to compensate for these issues, some MRI acquisitions may present extreme degradation related to movement blur, blood flow signals or magnetic field inhomogeneities. Moreover, metric-quality assessments such as with the Hausdorff distance are limited by the spatial resolution, most of the time between 1 and 2 mm per voxel in the plane and often superior to 5 mm out of the plane.  
Our database focused only on the ED and ES timeframes of the cardiac cycle. This aspect limits possibilities regarding detailed strain computation since clinical ground truths were not available for other time frames. In addition, only the longitudinal and radial strain can be determined over the LAX plane of view, with the circumferential strain computed from SAX acquisitions. While we demonstrated the feasibility of the acceptable LVEF quantification with the ENet architecture, we did not perform strain computation in this study. A detailed strain analysis can be considered as a major focus of study in future work.  
While a short comparison was performed with the state-of-the-art UNet architecture, further hyperparameter optimization, for instance over different loss functions, may provide deeper insights into the performance differences between the architectures.
## 3 Therapeutic Potential of Exosomes in Different Diseases
### Therapeutic Potential of Exosomes in Infectious (Parasitic and Bacterial) Conditions  
Parasites secrete exosomes that interact effectively, as a medium of cell to cell communication, within their host. Logically, it is most probable that the host utilizes this pathway of cellular communication as a defense mechanism [117]. Using plasma cell-derived microvesicles, it was observed that _Plasmodium berghei_ induces the production of CD40 in antigen-presenting cells, leading to generation of a potent inflammatory response and subsequent clearance of the parasite by macrophage activation [118]. Furthermore, immune cell-derived microvesicles from _Plasmodium vivax_, one of the species of _Plasmodium_ responsible for human malaria infection, was associated with acute inflammation in the course of parasite eradication [119]. Additionally, a massive increase and release of antimicrobialpeptide-containing exosomes was observed in intestinal epithelial cells in response to infection with _Cryptosporidium_[120].  
Exosomes are also useful in vaccines and vaccination. In a vaccine trial using _Leishman major_-pulsed DC exosomes, it was observed that the DC-derived exosomes mediate protective _Th1_ immunity against cutaneous leishmaniasis [121]. Similarly, avian coccidiosis in poultry caused by _Eimeria_ species (_E. tenella_, _E. acervulina_, _E. maxima_) can be alleviated using _Eimeria_ parasite antigen-loaded DC exosomes which successfully alleviated clinical signs in poultry thereby reducing mortality rates [122].  
With regards to bacterial infection, exosomes derived from the causative agent of bovine tuberculosis, _Mycobacterium bovis_-infected macrophages promote dendritic cell (DC) activation and the generation of antibacterial T cell response in vivo [47]. Similarly, _Mycobacterium tuberculosis_ (human tuberculosis), induces the release of exosomes from infected macrophages with consequent recruitment of lymphocytes through the secretion of chemokine such as RANTES and MIP- 1\(\alpha\)[123].
###### Abstract  
Vibration-condition monitoring aims to detect bearing damages of rotating machinery's incipient failures mainly through time-frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while information on frequency changes tends to be lost. Here, we introduce an approach to highlighting bearing impulsive failures by measuring short-term spectral components to deal with variable-speed vibrations. The short-term estimator employs two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components and a large interval that evaluates the average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation, both visual inspection and classifier performance are assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis performed on three datasets at variable speed showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior.  
spectral kurtosis; spectral entropy; bearing failure; vibration monitoring
# Dynamic Group Recommendation Based on the Attention Mechanism
## 3 Methods
### Group Recommendation Method
#### 3.4.4 Objective Function  
We used pairwise learning to optimize model parameters, assuming that the predictive scores of observable interactive items were higher than those of the unobserved, here we used regression-based pairwise loss [39]:  
\[\mathcal{L}_{user}=\sum\
olimits_{(i,j,p\in O)}\Bigl{(}s_{ijp}- \hat{s}_{ijp}\Bigr{)}^{2}=\sum\
olimits_{(i,j,p\in O)}\Bigl{(}\hat{s}_{ij}- \hat{s}_{ip}-1\Bigr{)}^{2}, \tag{14}\] \[\hat{s}_{ijp}=\hat{s}_{ij}-\hat{s}_{ip}, \tag{15}\]  
where \(s_{ijp}=s_{ij}-s_{ip}\), definition \((i,j,p)\) is an instance (triplet) in training set O, which indicates that interaction relation can be observed between user \(x_{i}\) and item \(v_{j}\) but no interaction relation is observed with item \(v_{p}\) (\(v_{j}\) is a positive instance and \(v_{p}\) is a negative instance). The value of the interaction relation can be observed to be 1 (that is, \(s_{ij}=1\)), and the value of the interaction relation not observed to be 0 (that is, \(s_{ip}=0\)). From Equation (15) \(\hat{s}_{ijp}\) represents the predicted margin between the observable \(\Bigl{(}x_{i},v_{j}\Bigr{)}\) and the observable \(\Bigl{(}x_{i},v_{p}\Bigr{)}\).  
Similarly, pairwise loss functions are recommended for the group:  
\[\mathcal{L}_{group}=\sum\
olimits_{(l,j,p\in O)}\Bigl{(}q_{ljp}- \hat{q}_{ljp}\Bigr{)}^{2}=\sum\
olimits_{(l,j,p\in O)}\Bigl{(}\hat{q}_{lj}- \hat{q}_{lp}-1\Bigr{)}^{2}. \tag{16}\]  
In training set O, group \(l\) has an interactive relationship with item \(v_{j}\), but not with item \(v_{p}\).  
We realized that another popular pairwise learning method could also be applied in the recommendation, such as Bayesian personalized ranking (BPR), etc. In this paper, we adopted regression-based ranking loss and we will explore other methods in future work.
## 4 Experimental Results and Discussion  
### PMS for Multiple MFCs  
The operation of the power ranking subroutine operation is illustrated in Figure 8, which displays the \(C_{PM}\) output voltage. The label Sample 1-4, indicate when the respective MFC 1-4 is power-measured by sampling its output power using \(C_{PM}\) such value is held to perform an analog-to-digital conversion. This sampling process is repeated for each MFC connected to the board periodically and its ranking values are grouped in phases. The ranking obtained at the phase is shown in Table 3, phase one. After the MFCs are sorted, then the power extraction can be performed. Phase two in Table 3, represents a second power-ranking measurement recalibration.  
In Figure 9, the time interleaved power extraction operation of the proposed PMS is presented for different extraction phases. The four channels representing the MFC output voltages are labeled at the top of the graph. Initially, before power extraction, the voltage at the MFC represents its open circuit voltage; then, when the MFC is harvested its voltage drops to its MPP voltage. Finally, when \(V_{th-Low}\) is reached, the algorithm harvests the next MFC in the look-up table only if its output voltage is larger than \(V_{th-High}\).  
Figure 9a shows the energy harvesting operation right after performing the MFC power ranking presented in Figure 8. Thus, the pattern for the power extraction, pointed out with gray arrows, follows the pattern described in Table 3 for phase one; however, after extracting power from the MFCs for \(\sim\)20 h, the MFCs' power ranking look-up table recalibrates to the values shown in Table 3, phase two. Such power changes are illustrated in Figure 9b. It is important to highlight that, in phase two, not only are the power rankings changed for devices MFC 2, MFC 3, and MFC 4, but at this point MFC 1 now takes more time to reach \(V_{th-High}\). This condition forces the algorithm to switch to the next available MFC that complies with the \(V_{th-High}\) specifications, which in this case is MFC 2. The algorithm continues its operation, and MFC 1 is only harvested again when \(V_{th-High}\) is reached as originally proposed.  
Figure 8: MFC power ranking waveform.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Ranking (Phase One)** & **Ranking (Phase Two)** \\ \hline MFC 1 & 1 st & 1 st \\ MFC 2 & 4 th & 2 nd \\ MFC 3 & 2 nd & 3 rd \\ MFC 4 & 3 rd & 4 th \\ \hline \hline \end{tabular}
\end{table}
Table 3: MFC power rank.  
As demonstrated, an important characteristic of the power ranking feature previously demonstrated, is that if an MFC does not provide proper power conditions or has an internal failure, the system automatically isolates the damaged MFC and continues its operation without affecting the remaining MFCs or interrupting the energy harvesting process.