esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Proximal femoral fractures (PFFs) represent one of the most common orthopedic issues in the elderly population, with a significant impact on quality of life and healthcare systems. These fractures are often the result of low-energy trauma, such as falls, which become increasingly frequent with aging. In Italy, between 2007 and 2017, there were 906,111 hospitalizations due to hip fractures, with fracture rates increasing by 14.3% in women and 29.4% in men over 11 years [1]. This trend reflects a global phenomenon, as several studies [2, 3] report a rising incidence of proximal femur fractures among the elderly, a trend expected to continue with the aging global population. According to some estimates, the total number of proximal femoral fractures could reach 21.3 million globally by 2050 [4],with the incidence particularly high among older adults, as the risk of fractures increases significantly with age.  
A study conducted by [PERSON] et al. [5] reported a 21.64% decrease in the weekly incidence of PFFs during the COVID-19 pandemic compared to the pre-pandemic period, suggesting that external factors such as social restrictions and isolation may influence fracture rates. This decrease could also reflect the impact of containment measures on the daily habits of the elderly population, which saw a reduction in accidental falls due to decreased mobility. Despite this decline, data still show that the incidence of fractures remains high among the elderly, with a median age of 83 years among hospitalized PFF patients, and that incidence rates increase with age [6].  
Proximal femoral fractures are more prevalent in women, primarily due to factors such as postmenopausal osteoporosis, which increases the risk of bone fractures. These fractures most commonly occur in the 85-89-year-old age group, with a marked exacerbation in individuals with cognitive disabilities or those institutionalized [7]. However, some recent studies, such as the study by [PERSON] et al. [8], suggest a general decrease in the incidence of head/neck fractures, with a 26.6% reduction between 2009 and 2019. This may be attributed to improvements in prevention policies and orthopedic treatments.  
Beyond the clinical and social impact, proximal femoral fractures impose a significant economic burden on healthcare systems worldwide [9]. The combined healthcare and productivity loss costs are substantial: in 2011, hip fracture treatment was the 13 th most expensive diagnosis for Medicare in the United States [10]. The growing costs, which follow the pattern of incidence, are particularly pronounced with the aging population, emphasizing the need for more efficient prevention and treatment strategies.  
This study aimed to further explore the factors contributing to the incidence of proximal femoral fractures, their clinical and socioeconomic implications, and opportunities for improvement in managing these fractures in older adults, comparing two surgical approaches--dual mobility cup arthroplasty and hemiarthoplasty--in order to reduce the devastating impact of this condition on patients and healthcare systems.
## 5 Results and Discussion  
This section evaluates the proposed system's effectiveness in eliminating linear and non-linear distortions in the UVLC system. Bit error rate, distortion rate, computational complexity, transmission rate, and computation cost are the key metrics to evaluate the efficiency of the proposed AFL-DLE performance. These metrics were contrasted with existing techniques for efficiency comparison. The conventional methods are Adaptive partitioning neural network (APNN), temporal convolutional neural network (TFCNN), Dual Self-Attention Network (DSANet), and \"convolution-enhanced long short-term memory (CE-LSTM)\".  
Figure 4: Experimental configuration.
## 4 Industrial Control Network Traffic Classifier Based on Feature Extraction Dataset  
### Classifier Selection Based on Discrete Uncorrelated Features  
A classifier needs to be trained to realize the industrial control network traffic classification after the feature extraction. We selected the decision tree as the industrial control network traffic classifier because of its superior performance [27]. The decision tree does not need a priori assumptions, the data processing is simple, and it can produce good results for the data source in a short time. The features extracted by the feature extraction method in this paper are discrete protocol keywords with a small quantity, which greatly reduced the possibility of overfitting the decision tree.
## 3 Results and Discussion
### Compressive Strength of Geopolymers
At higher concentrations of activator, the raw materials were accelerated to form a geopolymeric network, resulting in the compressive strength of materials prepared with NaOH concentrations of 6 M, 8 M, and 10 M being about 3.5, 4.4, and 3.5 times higher than that of materials prepared with 4 M NaOH, respectively. Therefore, the less increment of compressive strength of materials prepared without Ca(OH)\({}_{2}\) and with the presence of 45 g of Ca(OH)\({}_{2}\) was observed at higher than 4 M NaOH activator. The highest compressive strength of 19.7 MPa was obtained for the material prepared with the presence of 45 g of Ca(OH)\({}_{2}\) and 8 M NaOH. In all ranges of NaOH concentration studied, the highest compressive strength was observed for thegeopolymer materials prepared at an 8 M NaOH activator, so it was taken into consideration for the next investigations.
# Laser Transverse Modes with Ray-Wave Duality: A Review
###### Abstract  
We present a systematic overview on laser transverse modes with ray-wave duality. We start from the spectrum of eigenfrequencies in ideal spherical cavities to display the critical role of degeneracy for unifying the Hermite-Gaussian eigenmodes and planar geometric modes. We subsequently review the wave representation for the elliptical modes that generally carry the orbital angular momentum. Next, we manifest the fine structures of eigenfrequencies in a spherical cavity with astigmatism to derive the wave-packet representation for Lissajous geometric modes. Finally, the damping effect on the formation of transverse modes is generally reviewed. The present overview is believed to provide important insights into the ray-wave correspondence in mesoscopic optics and laser physics.  
laser cavity; transverse mode; geometric mode; ray-wave duality +
Footnote †: journal: Applied Sciences  
**2021** **11**, 8913. [[https://doi.org/10.3390/app11198913](https://doi.org/10.3390/app11198913)]([https://doi.org/10.3390/app11198913](https://doi.org/10.3390/app11198913))
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 5 Conclusions  
This research work considered the mitigation of serious traffic congestion problems encountered on urban roads. Short-term traffic flow prediction is an important part of intelligent transportation systems and plays a vital role in traffic management and intelligent control. Considering the advantages of various prediction models, this paper proposes a short-term traffic flow prediction model based on CEEMDAN and machine learning and proves that the prediction accuracy of the model is greatly improved compared to that of a single prediction model. The performance of the proposed combined model with BGWO-LSTM, IGWO-SVM, and KNN was verified by actual traffic flow data, and the results were proven. The mean squared error of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 41.26, 44.98, and 57.69, respectively. The mean absolute error of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.33, 2.44, and 2.70, respectively. The root mean square error of the combined model is smaller than that of the BGWO-LSTM model, the IGWO-SVM model and the KNN model by 2.89, 3.11, and 3.80, respectively. The three error indicators of the combined model are much smaller than the remaining single models. At the same time, the coefficient of determination of the combined model is closer to 1 than that of other models, indicating that the prediction result of the combined model was closest to the actual traffic flow. It can accurately predict short-term traffic flow and has certain engineering application prospects.  
Conceptualization, X.W., S.F. and Z.H.; methodology, Z.H.; software, Z.H. and S.F.; validation, X.W. and Z.H.; formal analysis, Z.H.; resources, X.W.; data curation, Z.H. and S.F.; writing--original draft preparation, X.W., S.F. and Z.H.; writing--review and editing, S.F.; supervision, X.W.; project administration, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 11772277 and 51305372), the Open Fund Project of the Transportation Infrastructure Intelligent Management and Maintenance Engineering Technology Center of Xiamen City (Grant No. TCIMI201803) and the Project of the 2011 Collaborative Innovation Center of Fujian Province (Grant No. 2016 BJC019).  
Not applicable.  
The authors declare no conflict of interest.
# Ice Production in Ross Ice Shelf Polynyas during 2017-2018 from Sentinel-1 SAR Images
## 3 Method
### Detection of Polynya Area/Exent
The green (red) dots indicate ice thicknesses \(<\)20 cm (\(>\)20 cm) and the solid red line is the McMurdo fast ice edge. The extent/outline of every wind-driven polynya and ice formation event in 2017 and 2018 is visually observed and checked and then manually digitized according to the above-mentioned principle for identifying coastal polynya and the enclosed area, using ArcGIS software. Overall, we hope to express that the entire processing of identifying and mapping such an event trace/outline is not an easy task and it is tedious and time-consuming. This is currently the only way to assure the accuracy and confidence of our results. Here we would like to demonstrate how we separate high backscatter values/appearances caused by wind-wave roughness from high backscatter values/appearances caused by wind-driven sea ice in polynyas, mainly from three aspects. (1) The texture of wind-wave caused high backscatter appearance differing from the texture of wind-driven sea ice. For example, the bright and homogeneous backscatter caused by wind-waves (Figure 4a) much differs from the wind-driven polynya sea ice (Figure 2b,d,e and Figure 3). (2) The sea ice concentration derived from the AMSR2 data shows the wind-wave event with very low ice concentration (<10%) (Figure 4b). (3) The shape of the wind-wave event does not exist after the wind event, differing from wind-driven polynya ice that keeps its shape until the next wind-driven event. Figure 3: Sentinel-1 images in the Ross Sea on 17 September (**a**), 19(**b**), 20(**c**), 21(**d**) in 2018. During these days, three katabatic wind events occurred. The first one occurred on 17 September (with the polynya area sketched as the red line 1) and finished on 19 September. The second event occurred on 19 September (with the polynya area sketched as the green line 2) and finished on 20 September. The third event occurred on 20 September (sketched as the blue line 3) and calmed down on 21 September when all traces, except the first one, are more diffuse. Figure 4: Sentinel-1 images (backscatter) of Ross Sea on 3–4 March 2017: (**a**) backscatter image on 3 March showing bright and homogeneous wind-driven roughness, (**b**) less than 10% ice concentration (from AMSR2) on the bright and homogeneous wind-driven roughness (3 March), and (**c**) disappearance of wind-driven roughness after the wind event on 4 March.
## 2 Materials and Methods
### Procedure
#### 2.2.1 Participant Training  
Participants were trained to produce eight iterations of the VCV utterance, /ifi/. This token was selected since the phoneme /i/ provides an open pharynx to better view the vocal folds under endoscopy [19], whereas the phoneme /f/ has been shown to minimize within-speaker variations in RFF [33]. Each participant was instructed to produce four /ifi/ utterances, take a breath, and then produce the remaining four /ifi/ utterances.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Cohort**} & \multicolumn{3}{c}{**Gender**} & \multicolumn{3}{c}{**Age**} & \multicolumn{3}{c}{**Overall Severity of Dysphonia**} \\ \cline{2-10}  & **M** & **F** & **Mean** & **SD** & **Range** & **Mean** & **SD** & **Range** \\ \hline Young adults with & 18 & 17 & 22.8 & 5.5 & 18–31 & 5.4 & 3.8 & 0.6–23.5 \\ typical voices & & 18 & 16 & 65.6 & 10.8 & 41–91 & 11.4 & 7.7 & 1.7–34.2 \\ Adults with HVD 1 & 6 & 22 & 37.5 & 16.1 & 19–70 & 12.3 & 10.7 & 0.9–38.5 \\ Adults with PD 2 & 18 & 7 & 63.0 & 9.4 & 43–75 & 19.2 & 13.3 & 4.0–51.3 \\ \hline \hline \end{tabular}  
* HVD = Hyperfunctional voice disorder; 2 PD = Parkinson’s disease.  
\end{table}
Table 1: Overall demographic information for the 122 participants.  
Participants were then trained to produce the eight /ifi/ utterances at different vocal rates and levels of vocal effort. These modifications were chosen to alter the stiffness of the laryngeal musculature [34] to, in turn, produce voice with varying degrees of laryngeal muscle tension. These utterances were collected in an effort to expand the dataset used to investigate the relationship between acoustic features and vocal fold vibratory characteristics during intervocalic voicing transitions across the spectrum of laryngeal muscle tension. Using methodology employed by [PERSON] et al. [19], a metronome was used to train three vocal speeds: slow rate (50 beats per minute), regular rate (65 beats per minute), and fast rate (80 beats per minute). Similarly, participants were cued using methodology described by [PERSON] et al. [19] to produce voice with varying levels of vocal effort (mild effort, moderate effort, maximum effort) while maintaining comfortable speaking rate and volume. While instructing participants to \"increase your effort during your speech as if you are trying to push your air out,\" mild effort was cued as \"mildly more effort than your regular speaking voice,\" moderate effort as \"more effort than mild,\" and maximum effort as \"as much effort as you can while still having a voice.\"
## 3 Results and Discussion  
### Anti-Cancer Screening  
The results of the initial anti-cancer screening of the thetraphotoxins, displaying the top 20 scorers, are given in Table 1. Score values indicate sequence similarity against known anti-cancer peptide sequences in the AntiCP 2.0 library, which have values that range from 0, meaning that a sequence has no similarity with anti-cancer peptide sequences, to 1, which means that it does. These show that there is some level of likeness between the thetraphotoxins and known anti-cancer peptides. Little to no research is available on the possible medicinal applications of the venom peptides in cancer therapeutics, meaning that research centered on these would be novel. A majority of the species that scored top hits are from China, with the exception of _Granmostola rosea_ and _Hystercentas gigas_. Interestingly, the species which yielded the top-scoring peptide, _Chilobranchys guangxiensis_, has been seen to inhibit the voltage-gated Na+ channels in rat cardiac myocytes, a property common to thetraphotoxins [71].  
For the multiple sequence alignment of these top 20 peptides, given in Figure 3, the overall score for the peptides is 78, meaning that there is a high degree of similarity with the samples. Toxins A, B, C, E, F, I, J, O, S, and T have high sequence similarity, while D, M, and R are not as similar. Focusing on the sequences, there are five highly conserved cysteine residues, highlighting their importance in the toxins' structure and biological activity. Aside from this, a huge chunk of the sequences have similarities, either having the exact same amino acid, or having amino acids that have similar physicochemical properties.  
cancer cell membranes [74]. However, this is not much of an issue, as death receptors are extracellular proteins [75] and there is no need for diffusion into the cell.  
Net charge is connected to how the peptide would interact with its target. Websservers typically calculate charge against a pH of 7 unless specified. Here, the overall net charges range from \(-2.5\) to \(+3\), with the top-scoring peptides being anionic, slightly deviating from the ideal of positive charge for anti-cancer peptides, as these positive charges lead to more favorable interactions with the negatively-charged cancer cell membrane [76].  
The violin plots of the physicochemical properties, as presented in Figure 3, show that the top-scoring peptides' properties are bunched up to specific values, with a greater spread indicating that a greater number of peptides have those values for their physicochemical properties.  
In summary, although these calculated physicochemical properties generally imply difficulty in permeating target cells, it is not a problem, as the peptides' targets are extracellular receptors. Aggregating these parameters leads to the medium viability of these theraphotoxins as anti-cancer peptides, with net charge posing the largest negative effect.
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## 4 Discussion
In this regard, flood forecasting systems based on other information, such as precipitation, river discharge, and water level, should become useful tools for predicting which areas are likely to be affected and allowing proper operational commands to be sent to each satellite in advance. In the case of multi-location flood events, the required operation for a selection of target areas might be more complicated. Thus, an integrated operation system combined with flood forecast is desirable for effective observation by means of SAR small-satellite constellations. At the same time, the collection of flood extent data in high spatiotemporal resolution might also be useful in improving flood simulation and forecasting in turn. To realize such an observation system in the future, more interactive cooperation and collaboration between the satellite, the hydrology and the disaster management field should be essential.
## 2 Materials and Methods
### Grayscale Standard Generation Algorithm  
The core device of the DLP module is the DMD, which has the on and off states at a certain time. That is, it corresponds well to 1 and 0 of a binary image matrix. The issue of standardizing grayscale information of softcopy image can be translated into precise control of the DMD. Similar to the spatial frequency that determines the optical density of a hardcopy image, the grayscale information of softcopy image is determined by controlling the time frequency. Therefore, we conducted secondary development based on the DMD, and transformed the spatial frequency characteristics of hardcopy images to the time-frequency characteristics of softcopy images.  
Specifically, the grayscale standard image generation algorithm consists of the following steps. Firstly, grayscale is quantized into numerical matrices with different bit precision, and the value is expressed in binary. Secondly, the image is layered on a corresponding bit plane, and different planes represent the information on different bit positions. Different hierarchical planes are displayed at different times during display, corresponding to the weights at different bit positions (higher bits correspond to higher weights). Lastly, the binary value is read from high to low in unit integration time, and displayed according to time-weight distribution. Thus, grayscale is achieved through the control of time frequency, and the flow chart of the grayscale generation algorithm is shown in Figure 4.  
Taking an 8-bit grayscale image with a value of 82 as example (shown in Figure 5), it is considered to be decomposed into eight binary images. The specific implementation method is to express the grayscale in eight binary numbers and then read them from high to low. Reading one bit at a time as the value of the corresponding binary image of the pixel, the 8-bit grayscale image can be decomposed into eight binary images.  
Figure 4: Flow chart of grayscale generation algorithm.  
Figure 3: Schematic diagram of DGGE.  
The relevant parameters of DMD we used are shown in Table 1 (refer to the product manual provided by the manufacturer). According to the DLP characteristics used in this equipment, the integration time \(t\) is 3800 \(\upmu\)s, so eight binary images are displayed cyclically by weight of 128t/256, 64t/256, 32t/256, 16t/256, 8t/256, 4t/256, 2t/256, 1t/256. Thus, we achieved an accurate display of the grayscale of the image. All relevant information of the DCGE is shown in Table 2.
## 4 Tailoring Pre-Trained Transformer Models for Detection in Spanish
### Fine-Tuning: Adjustment of Model Hyperparameters and Evaluation Metrics
* To ensure that the model generalizes well to unseen data, we use 5-fold cross-validation. Therefore, the dataset is divided into five subsets, training the model on four subsets and validating on the remaining one. During each training phase of the folds, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Definition** & **Mathematical Equation** \\ \hline Precision & Proportion of genuine news correctly predicted by the model. & \(\frac{TP}{TP+FP}\) \\ \hline Recall & Ratio of all existing authentic news articles correctly classified by the model. & \(\frac{TP}{TP+FN}\) \\ \hline Accuracy & Proportion of right predictions, regardless of whether they were positive or negative, made by the model. & \(\frac{TP+TN}{TP+TN+FP+TN}\) \\ \hline F1-score & Evaluates how well the model balances precision and recall, that is, it focuses on how many of the predicted positives are TP and how many of the TP are captured. & \(2\cdot\frac{Precision\cdot Recall}{Precision+Recall}\) \\ \hline MCC & Evaluates the performance considering TP, TN, FP and FN to provide a comprehensive assessment of model prediction quality (1: perfect predictions, 0: random predictions, and \(-1\) entirely inaccurate predictions). & \(\frac{TN-TP-FNFP}{\sqrt{(TP+FP)\cdot(TP+FN)\cdot(TN+FP)\cdot(TN+FN)}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Well-known evaluation metrics adopted in our approach. we use the AdamW optimizer with a learning rate of \(10^{-5}\) and L2 regularization to enhance weight decay and mitigate overfitting. After each of the ten epochs, we compute the cross-entropy loss between the predicted outputs and the true labels to evaluate the model's performance. Gradients are computed using backpropagation (_loss.backward()_), and the model's parameters are updated (through _optimizer.step()_). This process is repeated for each batch within the epoch, gradually refining the model's parameters. * Lastly, metrics such as precision, recall, accuracy, F1 score, and MCC are computed after each epoch to measure the model's performance, ensuring a reliable evaluation.