esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
### Research Context and Significance  
We developed a mixed-integer linear programming (MILP) model, which offers a quantitative and systematic approach to healthcare resource allocation, staff, and patient scheduling. The model uses mathematical optimization techniques that consider the necessary variables related to healthcare management to minimize costs and improve patient care. Moreover, this approach ensures that the resources are utilized most efficiently by maximizing their potential values.  
For example, our research proposes a novel model that can significantly support demand and volume forecasting while considering staff availability for urgent care medical facilities and emergency rooms. Employing this model in these healthcare settings can assist these hospitals in optimizing staff scheduling and resource allocation to meet variable patient demands. Moreover, optimizing resource allocation, staff scheduling, and patient assignment is required to address the above issues. Our MILP technique employs mathematical optimization to minimize expenses while enhancing patient care. Our approach ensures that healthcare management factors and constraints are considered to maximize the utilization of resources.  
The model focuses on new elements by employing a tailored mathematical optimization environment incorporating specific ranges and variables relevant to healthcare scheduling. While generic models allocate resources based solely on demand and capacity, we tailor our approach according to the specific dynamics of each hospital or clinic, ensuring optimal care for all patients. We addressed the optimization problem with increased flexibility by employing Python programming. In addition, the new elements involved in the model are resource allocation and staff working hours, which have yet to be included in other research in combination with all other elements.  
The remainder of this paper addresses the related work in healthcare scheduling (staff, patients, and resources), the application of mathematical models to healthcare, the proposed model's mathematical formulation, the model algorithm, the methodology, results, and discussions, the conclusion, implications for healthcare management and staffing procedures, and directions for future research.
# A Multi-Plant Height Detection Method Based on Ruler-Free Monocular Computer Vision
## 3 Principles and Methods
### Image Preprocessing
#### 3.2.2. Binarization of the Image  
Image binarization can transform images into a 0-1 matrix, which greatly compresses the content of the image and facilitates the processing and storage of the image. In this study, the Otsu method was used to binarize the grayscale image. The Otsu method is a popular image segmentation technique that automatically determines the optimal threshold for converting grayscale images into binary images. It minimizes the intra-class variance, which is the weighted sum of the variances of the two classes of pixels (foreground and background) separated by the threshold. The method calculates the histogram of the grayscale image, iterates through all possible threshold values, calculates the intra-class variance for each threshold, and selects the threshold that minimizes this variance as the optimal one, so the method can adapt to different image conditions, making it suitable for a wide range of applications, including those with varying lighting conditions or different types of green plants. By minimizing the intra-class variance, the Otsu method effectively separates the foreground (green plants) from the background, resulting in accurate segmentation [27, 28, 29]. [PERSON] et al. [30] introduced a novel approach for extracting features from 3D point clouds, leveraging the Otsu algorithm. Their experiments demonstrated that this algorithm yields excellent extraction outcomes when applied to data sets featuring a 90% reduction in complexity and low noise levels. [PERSON] et al. [31] presented a high-performance realization of the Otsu automatic image thresholding algorithm on a Field Programmable Gate Array (FPGA) platform. The primary objective of this implementation is to facilitate the real-time processing of high-resolution images. The Otsu method can determine the threshold that can maximize the variance between classes, and can obtain a better discrimination effect with a relatively simple calculation, so it is widely used in image binarization processing [32, 33].
## 6 Application to Biomedical Research
Periphery of testis (brown arrow); cystic areas (red arrow); solid areas (green arrows); fibrous septa (blue arrow); artificially torn region in HE images (black arrow) (1, 2, 3: solid tumor areas, 4: fibrous septa, 5: cyst) [42]. CXI has provided in vivo images of cancers implanted in nude mice [47] and has enabled the sequential effects of anticancer treatment to be clearly observed [48]. In vivo  
Figure 8: Three-dimensional image of tumor-bearing rat testis obtained by CXI. Serial cut sections of tumor: (**A**) surface area, (**B**) internal area, (**C**) enlargement of boxed region in (**B**); a: blood vessel, b: seminiferous tubules, c: tunica albuginea, d: tumor, e: different densities apparent in solid area of tumor, f: cystic area of tumor, g: blood clot, h: fibrous septa [42]. Figure 9: Three-dimensional reconstruction of tumor in rat brain: (**a**) overview of brain and tumor microvasculature; (**b**) magnified image of boxed region shown in (**a**); (**c**) tumor microvasculature viewed from different angle. Characteristics of tumor microvasculature are well depicted; e.g., tortuous, saccular (arrow), and dilated blood vessels are visible and exhibit haphazard interconnection patterns [40]. observation of a small animal disease model is very important for understanding the mechanisms of disease and treatment strategies [40].
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 2 Evolution from Traditional RAN to vRAN
### C-RAN  
The \"C\" in C-RAN could be an abbreviation of \"Centralized\" or \"Cloud\". However, both have the same idea: cluster the BBUs into a pool [2; 9]. Initially, Centralized RAN is proposed to reduce the space rental cost and the power consumption of the air conditioner of the BBU by pooling BBUs from different BS into a single physical location. The fronthaul (FH) interface is introduced to connect the BBU pool and RRUs. Figure 1 shows how the FH interface (FHI) connects one BBU pool to many RRUs. Despite reducing the overall OpEx, Centralized RAN requires the FH to have high bandwidth and low time latency requirement [10; 11].  
The fundamental difference between Centralized RAN and Cloud RAN is the cloud system. In Centralized RAN, the BBUs are pooled in a physical location. In Cloud RAN, BBUs from each BS were pooled in a cloud server [1]. Cloud RAN is superior because the cloud control in Cloud RAN made it easier for the number of BBUs to be changed with time. The cloud also increased the baseband processing by exploiting general-purpose processors [2]. Cloud RAN further reduced energy consumption, increased network throughput, improved network scalability, reduced Capital Expenditure (CapEx), and reduced OpEx [8; 12].  
The C-RAN has been acknowledged as one of the most potential technologies to fulfill radio access's 5G technical requirements [2]. However, it still has limitations, such as the huge FH overhead, trust problems, security problems, and single-point failure. These problems forced the C-RAN framework to shift its focus to advanced computing technologies. Some of these advanced technologies were virtualization and edge computing. Virtualization is the key to the shifting from C-RAN to vRAN.
## 5 Conclusions  
The findings of this current study, based on the application of magnetometric technology using drones to map metamorphic formations for the purpose of identifying locations suitable for drilling groundwater extraction wells, constitute an immensely valuable tool that effectively and rapidly facilitates the mapping of lithological variations in the terrain. In addition, it should be noted that the application of airborne methods enables us to explore much larger geographical areas in significantly reduced time intervals. These methods are particularly effective in the analysis of areas that are difficult to access, either due to complex topographic conditions or dense vegetation, while minimising the physical effort compared to traditional geophysical techniques. The synergy of these elements leads to significant economic savings in the investigation of large areas.  
In this specific investigation, it has been possible to distinguish precisely between the different types of materials present, such as orthogneisses, metagranites, quartz schists and ultramylonites. This detailed mapping, which shows the spatial arrangement of these materials, is of considerable importance in the process of selecting and locating extraction pits. This is achieved by proposing the identification of the materials with the highest water potential, as well as the location of contact points between the geological strata. In addition, it is noteworthy that this technology also allows the detection of fractured zones in other types of geological materials.  
It is particularly significant to highlight the results obtained by magnetic investigation under a tertiary layer, which was an uncertainty from a bibliographical point of view, and which we have had the opportunity to verify effectively.  
It is relevant to underline that the results obtained, presented in their direct form, have contributed substantially to the confirmation of our previous hypotheses, thus enriching our understanding of the hydrogeological behaviour of the studied environment. These findings have not only validated our initial theories, but have also opened new perspectives to broaden our knowledge in this particular field.  
Conceptualization, J.C.G. and P.C.G.; methodology, J.C.G., P.C.G. and D.P.S.; validation, I.M.N., P.C.G., P.H.H. and C.S.B.; formal analysis, P.C.G. and J.C.G.; investigation, J.C.G. and P.C.G.; resources, P.C.G. and J.C.G.; software, P.C.G., D.P.S. and J.C.G.; data curation, J.C.G.; writing--original draft preparation, J.C.G. and D.P.S.; writing--review and editing, P.C.G., D.P.S., I.M.N., P.H.H. and C.S.B.; visualization, I.M.N., P.C.G., C.S.B. and P.H.H.; supervision, P.C.G., P.H.H., I.M.N. and C.S.B.; project administration, P.C.G. and J.C.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Not available.  
The authors declare no conflict of interest.
# DisKnow: A Social-Driven Disaster Support Knowledge Extraction System
## 2 Related Work
### Disaster Classification  
Disaster classification can be seen as a very specific application of event detection. The latter is a task whose purpose is to identify related stories from a continuous stream of textual data, and considers an event as being something that occurs at a certain time in a certain location. [PERSON] and [PERSON] [34] have surveyed the methods used in event detection from social media, and have reported the usage of various machine learning techniques, such as classifiers, clustering, and language models. Recently, however, deep learning has emerged on most natural language processing (NLP) tasks, and event detection was not an exception. Several works have introduced the application of deep learning techniques to address event detection at a sentence level. This has been done by first identifying event triggers--which, in a sentence can be for example verbs or derivational nominalizations--and then classifying them into specific types.  
Deep learning models have proven to be more tolerant to domain and context variations, when compared with keyword-based models, one of the reasons being that the usage of word embeddings allows for a more generic and richer representation of words and their relationship [35]. There is not, however, much work in regard to disaster classification in tweets using machine learning, as this is generally done using keyword-based approaches, and labeled tweets for this task are scarce. Nevertheless, a few works have been published in recent years, showing promising results.  
In 2017, [PERSON] et al. [36] contributed to the field with the use of a dual convolutional neural network (Dual-CNN), to both see if tweets were, or not, related to a disaster, and which type of disaster they were related to. This Dual-CNN has an additional semantic representation layer which vectorizes information regarding the tweet's detected named entities. The authors have reported very little difference between applying Dual-CNN and a single CNN, for this specific task. Despite the semantic knowledge used by the first, its results have not shifted, and in some tasks it has even performed worse. More recently, in 2019, [PERSON] et al. [37] applied long short-term memory (LSTM) neural networks to the disaster relatedness task, while comparing its results with linear machine learning algorithms, like support vector machines (SVM) and logistic regressions. Although the results have not improved over [PERSON] et al. [36], the deep learning approaches have clearly improved on the results achieved by both SVM and logistic regression models. Additionally in 2019, [PERSON] et al. [38] used several models for the disaster type classification task, ranging from traditional machine learning models, such as naive Bayes classifier, random forests, and SVM, to a deep learning CNN. Although the CNN has outperformed most models, the SVM has shown results on par with it, coming up to an impressive 93% accuracy.
## 1 Introduction
Due to the semi-crystalline structure of PCL, it shows slower degradation compared to PLGA, which is amorphous [36]. This allows for the exploration of different types of biomedical applications for these two polymers based on their release behaviour facilitated by degradation [37]. Both MOFs and biodegradable polymers have significantly demonstrated the relevant controlled release of drugs in previous studies [31, 35, 38]. However, not much attention has been given to the combination of these two systems, biodegradable polymers and MOFs, for drug delivery applications. In this study, we took advantage of the combination of the two. Given their varying degradability rates, combining both, by encapsulating the MOFs into the polymers, gives an extended, slower release of the loaded drug in gradual, sustained and significant concentrations. This study demonstrates a combined loaded MOF-polymer approach, firstly by loading zinc-based MOF-5 with a first-class antimicrobial, metronidazolezole (Met), to form met@MOF-5 and a cephalospor beta-lactam antibiotic drug, cephalexin (Ceph), to make ceph@MOF-5. These loaded MOFs were then incorporated into biodegradable PLGA and PCL polymers to form PLGA-met@MOF-5, PLGA-ceph@MOF-5, PCL-met@MOF-5 and PCL-ceph@MOF-5 composite materials. After synthesis and formulation, the composite materials were characterised using thermogravimetric analysis (TGA), powder X-ray diffraction (PXRD), Fourier-transformed infrared (FTIR) and scanning electron microscopy (SEM). To confirm the retention of the antibacterial properties of the combined nanocomposites, their activity was tested against both Gram-negative and Gram-positive bacteria, _Escherichia coli_ or _Acinetobacter baumaniii_ and _Staphylococcus aureus_ or _Staphylococcus epidermidis_, respectively [39]. The varying degradability rates of PLGA and PCL account for different drug-release profiles, while Met and Ceph present different effectiveness against the selected bacterial strains of this study. As such, we herein report a comparative study involving the loading and release of the Met and Ceph drug molecules using zinc-based MOF-5 and biodegradable PLGA and PCL polymers (Scheme 1), as an efficient combination to provide more sustained, controlled drug release relative to using MOFs or polymers independently for drug delivery purposes.
## 4 Rocking Response Analysis of Freestanding Package
### Error of the Algorithm  
Based on the formulated algorithm, the determination of the package collision time instant \(t_{f}\) can be divided into two steps:  
**Step 1. Analyze the rocking response in the time interval [\(0\), \(t_{k}\)]**:  
In the time interval [\(0\), \(t_{k}\)], a \(p\)-order Runge-Kutta method is employed to analyze the rocking response of the freestanding package, with a time step size of \(\sigma_{1}\).  
**Step 2. Perform time transformation and analyze the rocking response in the time interval [\(t_{k}\), \(t_{f}\)]**:  
In the time interval [\(t_{k}\), \(t_{f}\)], the time transformation \(t=\alpha(s)\) is applied. Within the \(s\)-interval [\(s_{0}\), 0], a single-step \(q\)-order Runge-Kutta method is used to analyze the package rocking response and determine the collision time instant \(t_{f}\). The step size for the variable \(s\) is \(\sigma_{2}\).  
In Step 1, we assume the time instant before the package collision is \(\overline{t}_{k}\), and the corresponding rocking state of the package is \(\overline{x}_{k}\). Based on the error analysis of the Runge-Kutta method, the error in Step 1 can be expressed as:  
\[\left\|\overline{x}_{k}-x_{k}\right\|_{\infty}=O(\sigma_{1}^{p+1})\quad\left\| \overline{t}_{k}-t_{k}\right\|_{\infty}=O(\sigma_{1}^{p+1}) \tag{40}\]In Step 2, we assume \(\mathbf{x}^{*}\) is the solution of differential equation  
\[\left\{\begin{array}{l}(\mathbf{x}^{*})^{\prime}(t)=f(\mathbf{x}^{*}(t)),\ \ \ \ \ t\geq t_{k}\\ \mathbf{x}^{*}(t_{k})=\mathbf{x}_{k}\end{array}\right. \tag{41}\]  
Assuming \(t_{f}^{*}\) is boundary of Equation (41) and considering the condition \(\psi(\mathbf{x}^{*}(t_{f}^{*}))=0\), based on Equation (39), the error in Step 2 can be expressed as:  
\[\left\|\mathbf{y}(s_{0})-\mathbf{x}^{*}(t_{f}^{*})\right\|_{\infty} =\left\|\mathbf{y}(s_{0})-\mathbf{x}^{*}(\alpha(0))\right\|_{\infty}=O(|s_ {0}|^{q+1}) \tag{42}\] \[\left\|\alpha(s_{0})-t_{f}^{*}\right\|_{\infty} =\left\|\alpha(s_{0})-\alpha(0)\right\|_{\infty}=O(|s_{0}|^{q+1})\]  
By considering Equations (40)-(42), the analysis errors of the collision time instant and the collision condition, resulting from the algorithm proposed in this study, can be expressed as:  
\[\left\|\begin{bmatrix}\mathbf{y}\\ \alpha\end{bmatrix}_{s=0}-\begin{bmatrix}\mathbf{x}(t_{f})\\ t_{f}\end{bmatrix}\right\|_{\infty}=O(\sigma_{1}^{p+1})+O(|s_{0}|^{q+1}) \tag{43}\]  
From Equation (43), the analysis errors of the algorithm proposed in this study are primarily influenced by the time step \(\sigma_{1}\). Reducing \(\sigma_{1}\) not only decreases the analysis error in Step 1 but also reduces \(|s_{0}|\), thereby minimizing the analysis error in Step 2. Additionally, the orders \(p\) and \(q\) of the Runge-Kutta method also significantly affect the overall analysis error of the proposed algorithm.  
In this paper, standard fourth-order Runge-Kutta method is adopted, i.e., \(p=q=4\); the second- and third-order methods are also adopted for comparison.
## 4 Simulation Results
### Statistics of Convergence Performance of the Proposed Method  
The simulation results shown in the previous subsections were conducted by running the developed MATLAB code 20 times and the best solution among the 20 results was identified as the final optimal solution. In order to show the convergence performance of the proposed method, this Sections 4.1-4.4 increases the number of runs to 50. The case in Section 4.2 serves as an example.  
The best solution occurs in the 9 th run out of the 50 runs. The best solution is $24,333.87 while the worst one is $30,328.29, which occurs in the 43 th run. The difference between $24,333.87 and $30,328.29 are divided equally into 10 portions; that is, 10%, 20%,..., 100% of the difference between the worst and best costs. As shown in Figure 12a, the probability of the 1 st portion is 0.04 (two runs out of 50 runs); the best cost $24,333.87 and the cost, $24,513.49, obtained in the 15 th run, are within the 1 st portion. The largest probability (0.3) occurs in the 7 th portion, which covers the cost between $28,330.11 and $28,996.16. Figure 12b shows the corresponding cumulative probability.
## 1 Introduction
The notching characteristic enables the mitigation of coexisting sub-bands in the UWB region, reducing interference without the complexity of additional circuits. Moreover, the proposed design achieves high isolation in the MIMO system through the mutual coupling of Complementary Split-Ring Resonators, resulting in a miniaturized design. The multitasking of CSRR makes the article novel and differentiable. This miniaturized design is particularly suitable for wireless communication. The multitasking of CSRR is organized in the manuscript as follows: In Section 1, an introduction to UWB, notching characteristics, and the MIMO antenna system is presented; Section 2 is about the development and design of the antenna, and the result of the UWB, notching, and MIMO system are presented; Section 3 presents a comparative and theoretical analysis of the proposed antenna with other designs on the basis of notching characteristics and isolation; and Section 4 provides the conclusion of the article.
# A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging
## 3 Results
### Preprocessing and Training
#### 3.1.1 Preprocessing  
In this study, we utilized the nnU-Net v2 framework [28] to train a segmentation model specifically designed for detecting facial bone fractures. The preprocessing pipeline involved several steps to ensure consistency and robustness across the dataset.  
All CT images were resampled to a uniform voxel spacing of \([2.0,0.390625,0.390625]\) mm using cubic interpolation for image intensities and nearest-neighbor interpolation for segmentation masks. This step standardized the spatial resolution across all cases and ensured consistency in downstream processing.  
For intensity normalization, the CTNormalization scheme was applied, where HU values were clipped within the range \([-1024,3071]\) to remove outliers. Subsequently, per-case z score normalization was performed to standardize the voxel intensities based on dataset-wide statistics (mean: 213.11; standard deviation: 363.10). This normalization step helps mitigate variations across different CT acquisition protocols and scanners.  
To optimize computational efficiency, the input volumes were partitioned into patches \([40,224,192]\) in size, guided by the median image size of \([94,512,512]\) voxels. If needed, zero padding was applied to maintain the spatial integrity of the patches. Data augmentation techniques, including random flipping, scaling, rotation, and elastic deformation, were applied during training to improve generalization and prevent overfitting.