esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Results  
Figure 11 displays the properties of the BIM mass model. This table includes the weather parameters introduced into the BIM mass model accompanied by their respective values, as well as the weather hazards with their respective severity levels. In this particular case, all the analyzed hazards exhibited a normal severity level.  
Figure 11: BIM model with weather information and introduced weather properties.  
Figure 10: Steps in the Dynamo protocol: (1) importing information from Google Sheet, (2) creating shared parameters and assigning their values /severity levels, and (3) creating filters to represent the severity levels.  
The representation of a warning severity level on the wind hazard is shown in Figure 12. In this scenario, the facade of the BIM mass model where the wind is impacting is highlighted in yellow. An illustration of an alert email sent by the RPA is depicted in Figure 13.  
Figure 13 presents an example of the alert message that the application automatically sends when a wind hazard is detected. This message is delivered via email using Gmail services.  
The practical implications of the tool developed for automating weather data extraction have a profound impact on the AEC industry. By providing consistent and timely weather data, the tool enhances the accuracy and promptness of forecasts, which is essential for effective project planning and scheduling. This enables construction managers to anticipate and address delays caused by adverse weather, thereby boosting overall project efficiency.  
Furthermore, the tool's real-time updates and early warnings improve risk management and site safety by facilitating proactive measures to safeguard workers and equipment. The efficiency gained from automating data collection translates into cost savings, reducing both labor expenses and delays that could otherwise lead to additional costs. Additionally, integrating the automated weather data with BIM supports real-time analysis and more informed decision-making. The tool's ability to analyze historical data for trends also enhances long-term planning and predictive accuracy, aiding in future weather-related decision-making.  
Figure 12: BIM model with wind warning alert.  
Figure 13: Screenshot of the alert message sent when a wind hazard is detected.
## 3 Delaunay-TCOGIM Algorithm  
### Delaunay-TCOGIM Algorithm Design Ideas  
Due to the randomness of the insertion of the discrete nodes in the Delaunay method, the number of malformed triangular meshes will increase. As a result, we have implemented TCOGIM to determine the position of the nodes. We insert the center of gravity to construct the triangular mesh, with the triangular area and the average size of the boundary triangles as the determining conditions. The thought behind the Delaunay-TCOGIM algorithm is mainly to discretize the points on the boundary and contour lines, screen the internal mesh of the region, discretize the point sets in clockwise or anticlockwise order, judge the center of gravity of the boundary triangles according to the right-handed helix rule for the initial debugging of the mesh, and compare it with the average value of the boundary triangles' area to judge whether it is necessary to interpolate the center of gravity to increase the internal nodes, and then combine it with the B-W algorithm to reconstruct the triangles to cycle judgment on the newly-generated triangle unit area until all triangles meet the conditions to satisfy the Delaunay criterion to generate a Delaunay triangular mesh.  
The basic steps of the Delaunay-TCOGIM algorithm are boundary discretization, building the initial triangle mesh, building the initial Delaunay mesh, finding the inner region of the boundary, building the mesh for the inner region, and finding the Delaunay mesh. During the construction of the internal mesh within the region, the Delaunay method differs from the combined Delaunay-TCOGIM algorithm. The Delaunay method selects triangle cells with a triangle area more significant than the average value of the boundary triangles. They identify these selected triangle cells as inserting the set of triangle cells into the point and inserting a specific number of discrete nodes as internal nodes within this set of triangle cells. This process subsequently updates the set of triangles.
## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SNR = 10 dB** & **SNR = 20 dB** \\ \hline Conventional SM & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Decoded “Lena” image over an underwater acoustic channel with conventional SM and proposed SM-UEP (© 2013 IEEE). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **SNR = 10 dB** & **SNR = 20 dB** \\ \hline Conventional SM & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Decoded dolphin swimming underwater image [6] over an underwater acoustic channel with conventional SM and proposed SM-UEP (© 2013 IEEE). image [6], the proposed SM-UEP also improves the PSNR of received dolphin swimming underwater image by 3 dB and 4.1 dB gain at underwater channel has SNR = 10 dB and SNR = 20 dB, respectively. In Tables 1 and 2, the performance has been evaluated over the measured underwater acoustic channel, whereas the performance of Table 3 has been evaluated over the simulated channel where the channel tabs are generated based on multipath fading channel. Table 3 shows the proposed SM-UEP in comparison with the conventional SM scheme. The proposed SM-UEP in comparison with the conventional SM improves the PSNR of received \"Lena\" image by 4.63 dB at low SNR (SNR = 10 dB) and the performances are nearly same at high SNR (SNR = 20 dB). The simulation results clearly show that the proposed SM-UEP is suitable for the underwater image transmission, where it highly overcome the conventional SM especially  
**Author Contributions:** H.E. ; Conceptualization, Methodology, Software, Writing--Original Draft Preparation and Visualization. Z.A.H.Q. : Conceptualization, Methodology, Validation, Formal Analysis, Writing--Review & Editing and Visualization. H.S. : Validation, Formal Analysis, Supervision, Project Administration, and Funding Acquisition. J.W., Validation, Project Administration, and Funding Acquisition. N.U.R.J., Writing--Review & Editing and Formal Analysis. **Funding:** This work was supported by the National Natural Science Foundation of China (grant number 61671394). **Conflicts of Interest:** The authors declare no conflicts of interest.
# Investigations into Frost Flower Physical Characteristics and the C-Band Scattering Response
## 1 Introduction
Describe and quantify the physical characteristics of frost-flower-covered sea ice during this experimental time period by focusing on improving our understanding of the physical mechanisms of frost flower development. 2. Measure and evaluate the temporal evolution of C-band scattering characteristics of a newly-formed sea ice surface that eventually became covered in frost flowers, which is physically and electromagnetically different from a bare sea ice surface. 3. Examine the link between the temporally evolving sea ice and frost flower physical properties with the measured scattering response. The physical characteristics of a frost-flower-covered sea ice surface and the C-band scattering response elucidated in this paper are a step towards understanding the processes and variability at a small scale (i.e., mesocosm study). Moreover, environmental conditions may vary both seasonally and regionally. Therefore, exploration of the variability in frost flower formation and their effects on sea ice microwave scattering responses is required to improve satellite image interpretation for polar environmental monitoring applications. With the improved temporal resolution of satellite constellation missions (e.g., the Radarsat Constellation Mission (RCM), and Sentinel-1, which are designed to operate in C-band), the time-series data collected in this experiment are opportune.
## 2 Mathematical Modeling
### Proposed Drop Size Distribution
#### 2.2.2 Data Analysis  
The population density on the condensing surface can be obtained by knowing the average area occupied by drops \(f\) and the average spacing between drops \(\gamma\) that is available to the next generation of drops. From the simulation work, \(f\) is recorded at every time step during the growth process for given conditions. When the \(f\) value tends to fluctuate within a certain range, a steady state condition is reached, and the results are to be confirmed. The average \(f\) is determined by applying the mean value theorem to the fluctuation.  
The average spacing between drops \(\gamma\) can be approximated by assuming all drops of the older generation are equal in size \(r_{ave}\) and uniformly distributed, as shown in Figure 4. The distances between their centers \(a\) form an equilateral triangle [12], and the corresponding expression in terms of \(f\) is given by  
\[f=\frac{2\pi}{\sqrt{3}}\bigg{(}\frac{a}{r_{ave}}\bigg{)}^{2} \tag{20}\]  
\[\gamma=\frac{\frac{1}{24}\Big{(}\frac{1}{3}+\frac{1}{4}ln3\Big{)}-\frac{1}{8 \sqrt{3}}\Big{(}\frac{a}{r_{ave}}\Big{)}+\frac{\pi}{36}\Big{(}\frac{a}{r_{ave}} \Big{)}^{3}}{\frac{1}{8\sqrt{3}}\Big{(}\frac{a}{r_{ave}}\Big{)}-\frac{\pi}{12 }\Big{(}\frac{a}{r_{ave}}\Big{)}^{3}} \tag{21}\]  
The drop size distribution is given as follows [10]:  
\[\alpha=1-\bigg{(}\frac{r}{r_{max}}\bigg{)}^{n} \tag{22}\]  
where \(\alpha\) is the area fraction covered by the drop with a radius greater than \(r\). Alternatively, Equation (22) can be rewritten in the following form:  
\[A(r)dr=n\bigg{(}\frac{r}{r_{max}}\bigg{)}^{n-1}\frac{dr}{r_{max}} \tag{23}\]  
Thus, the drop size distribution is obtained by dividing (23) by the droplet base area, and it is given with respect to the contact angle \(\theta\) and \(n\):  
\[N(r)dr=\frac{n}{\pi r^{2}sin^{2}\theta}\bigg{(}\frac{r}{r_{max}}\bigg{)}^{n-1 }\frac{dr}{r_{max}} \tag{24}\]  
Outcomes from simulation work include sets of \(f\) and \(\gamma\) values for various conditions. These values are curve-fitted in the form of Equation (24) to produce new values for exponent \(n\). The drop size distribution for small drops is obtained from the population model with respect to the contact angles \(\theta\) and \(n\) when applying the boundary conditions, as in Equations (7) and (8):  
\[n(r)=\frac{n}{\pi r_{e}^{3}\sin^{2}\theta r_{max}}\bigg{(}\frac{r_{e}}{r_{max }}\bigg{)}^{n-1}\frac{r(r_{e}-r_{min})}{r-r_{min}}\frac{A_{2}r+A_{3}}{A_{2}r_{ e}+A_{3}}exp(B_{1}+B_{2}) \tag{25}\]  
Figure 4: Random drops on the left side are re-arranged to estimate the average spacing between two generations of drops as shown on the right side.  
A drop size distribution profile is defined by a value of \(n\) for a given maximum radius and effective radius. The same process is followed for full DWC, except that merging is ignored. The drop size distribution for DWC within the hybrid surface is to be compared with that of full-DWC.
###### Abstract  
Predicting treatment failure (TF) in head-and-neck squamous cell carcinoma (HNSCC) patients before treatment can help in selecting a more appropriate treatment approach. We investigated a novel radiodosiomics approach to predict TF prior to chemoradiation in HNSCC patients. Computed tomography (CT) images, dose distributions (DDs), and clinical data from 172 cases were collected from a public database. The cases were divided into the training (\(n=140\)) and testing (\(n=32\)) datasets. A total of 1027 features, including conventional radiomic (\(R\)) features, local binary pattern-based (\(L\)) features, and topological (\(T\)) features, were extracted from the CT images and DDs of the tumor region. Moreover, deep (\(D\)) features were extracted from a deep learning-based prediction model. The Coxnet algorithm was employed to select significant features. Twenty-two treatment failure prediction models were constructed based on Rad-scores. TF prediction models were assessed using the concordance index (C-index) and statistically significant variations in the Kaplan-Meier curves between the two risk groups. The Kaplan-Meier curves of the DD-based \(T\) (DD-\(T\)) model displayed statistically significant differences. The highest C-index of the testing dataset for this model was 0.760. The proposed radiodosiomics models could potentially demonstrate greater accuracy in anticipating TF before chemoradiation in HNSCC patients.  
treatment failure prediction; head-and-neck squamous cell carcinoma; topology; radiodosiomics
## 2 Materials and Methods
### Characterization  
The samples were characterized by XRD using a D/Max-2500 PC diffractometer (Rigaku, Japan) with Cu K\(\alpha\) radiation (\(\lambda\) = 0.154184 nm), angle of diffraction 20 ranging from 10\({}^{\circ}\) to 110\({}^{\circ}\), and a scanning step of 0.02\({}^{\circ}\)/min. The morphology of the samples was observed with FE-SEM, which operated at 5 kV (Supra 35-VP, Carl Zeiss, Germany). Raman measurements were performed by means of an iHR550 spectrometer ([PERSON], Japan) coupled to a silicon CCD detector and an argon-ion laser ([PERSON], USA) operated at 514.5 nm with maximum power of 200 mW. UV-vis spectroscopy using a Varian Cary spectrometer model 5G (USA) in the diffuse reflectance mode, with a wavelength range of 200 to 800 nm and a scan speed of 600 nm min\({}^{-1}\), was employed to investigate the optical properties of the samples. The optical band gap energy (E\({}_{\text{gap}}\)) was calculated using the Kubelka and Munk-Aussig method [20] from UV-vis diffuse reflectance measurements.
## 3 Model Development and Validation
### Finite Element Model for The HESs  
The model and the boundary conditions are depicted in Figure 4. The shell element S4R is used to model the behavior of the steel plates in the ED zone including their buckling. Eight-node-three-dimensional solid element (C3D8R) with reduced integration and hourglass control is utilized to simulate functional bolts, the flange plate and the shear stiffness lifting steel plates. The overall meshed model includes a total of 40,444 elements. A typical surface-to-surface contact with a penalty algorithm is employed between the functional bolts and the shear stiffness lifting plate. A hard contact pressure-over closure relationship is adopted to model the normal contact behavior and the friction coefficient is set to be 0.2 to simulate the tangential slip behavior. The same contact setting is used between the surfaces of buckling restrained steel plates and shear stiffness lifting steel plates. In addition, the geometric nonlinearity is considered to model the intermittent contact behavior in the SL zone. The circular part and the bolts in the SL zone are more elaborately meshed. Considering the computational efficiency and reliability the mesh size of the HES is the same as above which is appropriate for this analysis.
# A Framework to Understand Attitudes towards Immigration through Twitter
## 6 Discussion
### Limitations and Future Work  
There are two key aspects that need further exploration and that limit the scope of our results: the dynamic analysis of LIWC categories associated with attitudes, and the representativeness of Twitter. In terms of dynamics, it would be interesting to study the temporal distribution of psycholinguistic categories within empathic and threatened attitudes. This would enable quantifying the potential influence of news events on attitudes. This would provide a way to measure the effect of events and their depictions and narratives on how people feel with respect to migration.  
In terms of representativeness, we acknowledge that Twitter is a biased sample of the population [82]. However, Twitter is one of the most widely used applications in Chile [83], and it reflects some of its cultural aspects, such as the centralisation of the country [84]. Furthermore, a Twitter-based analysis of the abortion discussion in Chile was found to present equal insights as those from the main national survey that covered the issue [85], hinting that there are social insights that can be derived from the platform. Thus, we propose that this work provides insights with respect to the discussion, although the representativeness of such insights is yet to be determined.  
One line of work we sought to explore was to conduct a spatial analysis of attitudes, to understand the relationship between attitudes and the actual presence of immigrants in a place. This would provide a way to measure real and imagined threat attitudes [86], link virtual and physical places of expression and coexistence in a single analytical framework, and allow us to identify socio-demographic characteristics associated with the various inferred attitudes. However, as documented in the literature [10], the spatial representation of Twitter data is limited. Less than three per cent of tweets are geolocated [87]. We recognise that addressing these biases is an active area of research, and case-specific weighting schemes have been proposed to ensure the statistical and spatial representativeness of social media data [88]. However, developing weighting schemes requires knowledge of social media users' profiles. While this information can be obtained with some level of accuracy from Facebook, access to Twitter users' personal attributes is very limited. Therefore, we cannot guarantee that our results represent the general population.
## 5 Results  
### TSX backscatter and NDVI Properties of Modern DTLBs (0-50 Years since Drainage)  
Analysis of TSX backscatter of modern DTLBs did not result in a clear relationship between basin age and mean basin backscatter. We did not find strong relationships for 16 and 29 August (R\({}^{2}<0.25\), p \(<0.05\)), and no statistically significant relations were established for other dates. Also a strong relationship could not be established between NDVI on 13 July and basin age for modern basins (R\({}^{2}<0.25\), p \(<0.05\)).  
During aerial field surveys, we observed strong local variation in early post-drainage surface backscatter and NDVI properties within most modern basins based on varying vegetation communities and variable soil moisture conditions (Figure 2(a,b)). Additionally, backscatter and NDVI properties varied between modern basins of the same age. Most of the vegetated DTLBs that drained in the last five years consisted of early assemblages of highly productive, high canopy (50-130 cm tall) graminoid vegetation, often densely growing in very shallow water (\(<\)30 cm deep) (Figure 2(a)). TheseDTLBs exhibited the highest NDVI, but had a very low backscatter signal. Some of the DTLBs were still in the process of draining (Figure 2(b)). Many of the partially drained basins contained lush vegetation growing in remnant ponds, some of which was partly emergent from the water, but did not cover the water surface uniformly, while other areas formed floating vegetation mats growing around the edges of water surfaces [54]. Such DTLBs dominated by emergent vegetation and floating vegetation showed a very high X-band backscatter. However, their NDVI was highly variable depending on vegetation type and density _versus_ open water areas in between patches of vegetation. Further, some DTLBs had areas drained more and primarily covered with moss and lichen. This type of surface exhibited relatively low NDVI and intermediate to low backscatter.
# Compression of Next-Generation Sequencing Data and of DNA Digital Files +
## 3 Quip, gzip, and bzip2: Experimental Results  
One interesting question that we have decided to answer in this paper is how well Quip performs with respect to the standard lossless compressors that are generally used with genomic data. Of course, this question is addressed also in [6], where the authors compare Quip with the state-of-the-art general-purpose compressors on a test data set of six files from the Sequence Read Archive (SRA [27]). SRA provides data in its own SRA compression format.  
The test data set in [6] is composed by six large files, with file sizes ranging from 8.9 GB to 67.6 GB. We have chosen a different data set composed by smaller files (file sizes ranging from 8.8 MB to 5.31 GB) and we have included in our test data set also files produced by the Repli-Seq Illumina (see, for example, [28]).  
Therefore, our tests are different and complementary to the experiments conducted in [6].  
The files in our test data set could be downloaded, at the moment of writing, from [[http://genome.crg.es/encode_RNA_dashboard/hg19](http://genome.crg.es/encode_RNA_dashboard/hg19)]([http://genome.crg.es/encode_RNA_dashboard/hg19](http://genome.crg.es/encode_RNA_dashboard/hg19)) and [[ftp://ftp.ncbi.nlm.nih.gov/hapmap](ftp://ftp.ncbi.nlm.nih.gov/hapmap)]([ftp://ftp.ncbi.nlm.nih.gov/hapmap](ftp://ftp.ncbi.nlm.nih.gov/hapmap)).  
Our test dataset is composed by files in FASTQ or SAM/BAM formats. Table 1 summarizes the specifications of our test data set. Figure 1 shows the results of applying these compression tools to our test data set.  
In Figure 1, the lossless compression results obtained by bzip2 (i.e., the size of the lossless compressed files) are outlined by yellow lines, the gzip results by gray line, and the quip results by orange lines.  
The blue line represents the uncompressed file size.  
All these tools achieve a compression ratio that in average is close to 70%.  
Gzip has the worst performance and bzip2 is close, but almost always outperformed, to quip.  
The compression performances do not seem to depend on the file format and the compression results seem stable independently on the fact that the compressed file is a FASTQ or a BAM. Quip has good performances, but gzip and bzip defend themselves very well.  
This confirms the testing results that were presented in [6] but it also shows that the compression performance of Quip on shorter genomic files is closer to bzip2.
# Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer
## 2 Materials and Methods
### PNI Classifier  
To generate tumor and nerve masks, which were input into the PNI classifier, we implemented six combinations of segmentation networks for tumors and nerves, denoted as Module1 (Md1) to Module6 (Md6). Md1 to Md4 employed binary segmentation networks for tumors and nerves, whereas Md5 and Md6 used multiclass segmentation networks. In the Md1 framework, U-Net was used for both nerve and tumor segmentation. For Md2, U-Net and DeepLabv3+ were used for nerve and tumor segmentation, respectively. In Md3, DeepLabv3+ and U-Net were utilized for nerve and tumor segmentation, respectively. In Md4, DeepLabv3+ was used for both nerve and tumor segmentation. In Md5 and Md6, U-Net and SegFormer were employed, respectively.  
Based on the trained segmentation network, binary probability maps were inferred for tumors and nerves using a half-overlapped sliding window. The average probability was applied for the overlapped window. Tiny areas predicted as tumors or nerves (probability threshold = 0.5) were removed using morphological analysis. Nerves with PNI were extracted according to a rule-based approach, where the distances between the binary map of the tumor and the dilated nerve were calculated (Figure 2). PNI and non-PNI groups were defined as follows:  
Figure 1: Proposed pipeline for PNI detection. The framework consists of a segmentation network and a PNI classifier. Tumor (red) and nerve masks (orange) were extracted according to the segmentation model. The extracted nerve areas were classified as PNI when they were close to the tumor.  
PNI: \(Area_{dilate\;nerve}\cap Area_{tumor}\
eq\varnothing\)  
Non-PNI: \(Area_{dilate\;nerve}\cap Area_{tumor}=\varnothing\)  
\(Area_{tumor}\) and \(Area_{dilate\;nerve}\) denote the binary map of the tumor and the dilated nerve, respectively.