esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Model
### Coarse Grain DEM Model for Solid Phase
#### Coarse Graining  
The coarse grain DEM model treats a group of original particles with similar properties as a large-sized particle, generally termed as a coarse grained particle, a parcel or a particle-group.  
As illustrated in Figure 1[30], it assumes that all the original particles are spheres with the small diameter, and the size of each particle-group is \(l\) times larger than the original particle. Here \(l\) is called as coarse grain ratio. The sphericities of both original particle and coarse grained particle are considered as 1. According to the [PERSON]'s second law of motion, it is reasonable to assume that the translational motion of the particle-group is the average of that of the original particles governed by:  
\[m_{CGM}\frac{d\mathbf{v}_{CGM}}{dt}=\mathbf{F}_{dmg,\,CGM}-V_{CGM}\
abla p+ \mathbf{F}_{g,\,CGM}+\mathbf{F}_{cont,\,CGM} \tag{17}\]  
The subscript CGM refers to the coarse grain model. Under the assumption that the total kinetic energy of the particle-group is consistent with that of the original particles, we have:  
\[\frac{1}{2}m_{CGM}\mathbf{v}_{CGM}^{2}+\frac{1}{2}I_{CGM}\omega_{CGM}^{2}=l^{ 3}(\frac{1}{2}m_{O}\overline{\mathbf{v}}_{O}^{2}+\frac{1}{2}I_{O}\overline{ \omega}^{2}{}_{O}) \tag{18}\]  
where \(I\) is the moment of inertia. The bar over the variables and the subscript \(O\) refer to average and original particle, respectively. According Equations (17) and (18), the following formulas hold:  
\[\mathbf{v}_{CGM}=\overline{\mathbf{v}}_{O} \tag{19}\]  
\[\omega_{CGM}=\frac{1}{l}\overline{\omega}_{O} \tag{20}\]  
Figure 1: Illustration of the coarse graining process (coarse grain ratio \(l=2\)). (**a**) Original particles; (**b**) Coarse grain particle (particle-group).  
\[\mathbf{F}_{drug,\,CGM}=V_{CGM}\frac{\beta}{1-\varepsilon}(\mathbf{u}- \mathbf{v}_{CGM})=l^{3}V_{O}\frac{\beta}{1-\varepsilon}(\mathbf{u}-\overline{ \mathbf{v}}_{O}) \tag{21}\] \[V_{CGM}\
abla p=l^{3}V_{O}\
abla p\] (22) \[\mathbf{F}_{g,CGM}=m_{CGM}\mathbf{g}=l^{3}m_{O}\mathbf{g}\] (23) \[\mathbf{F}_{cont_{n},CGM}=l^{3}(-\kappa_{n}\delta_{n,CGM}\mathbf{ n}_{CGM}-\eta_{n}\mathbf{v}_{n,CGM})=l^{3}\Big{(}-\kappa_{n}\overline{\delta}_{n,O}\overline{\mathbf{n}}_{O}-\eta_{n}\overline{\mathbf{v}}_{n,O}\Big{)}\] (24) \[\mathbf{F}_{cont_{t},CGM}=\left\{\begin{array}{l}l^{3}\big{(}- \kappa_{t}\delta_{t,CGM}\mathbf{t}_{CGM}-\eta_{t}\mathbf{v}_{t,CGM}\big{)}\\ =l^{3}\Big{(}-\kappa_{t}\overline{\delta}_{t,0}\overline{\mathbf{t}}_{O}-\eta _{t}\overline{\mathbf{v}}_{t,O}\Big{)}\quad\text{for}\quad\Big{|}\mathbf{F}_ {cont_{t},CGM}\big{|}<\mu_{p}\mathbf{F}_{cont_{n},CGM}\\ -l^{3}\mu_{p}\big{|}\mathbf{F}_{cont_{n},CGM}\big{|}\mathbf{t}_{CGM}\\ =-l^{3}\mu_{p}\big{|}\overline{\mathbf{F}}_{cont_{n},O}\big{|}\overline{ \mathbf{t}}_{O}\quad\quad\quad\text{for}\quad\Big{|}\mathbf{F}_{cont_{t},CGM} \Big{|}\geq\mu_{p}\mathbf{F}_{cont_{n},CGM}\end{array}\right. \tag{25}\]  
where \(\mathbf{g}\) is the gravitational acceleration.
## 3 Analysis of Experimental Results
### Evaluation Metrics  
In this study, a range of metrics is utilized to evaluate the performance of various models in detection tasks thoroughly. Precision (5), recall (6), and F1 score (7), derived from four fundamental statistical parameters--true positive (TP), true negative (TN), false positive (FP), and false negative (FN)--are the primary metrics for this assessment. Precision is an indicator of the accuracy of positively identified samples among all detected samples, whereas recall indicates the proportion of accurately identified positive samples out of all actual positives. The F1 score, representing the weighted balance between precision and recall, is the harmonic mean of these two metrics. Additionally, mean average precision (mAP) (8), a prevalent metric in object detection, is also considered. mAP evaluates the model's average efficacy across all categories at varying levels of confidence.  
\[\text{Precision }=\frac{TP}{TP+FP} \tag{5}\]  
\[\text{Recall }=\frac{TP}{TP+FN} \tag{6}\]  
\[\text{F1}=\frac{2\times\text{ Recall }\times\text{ Precision }}{\text{Recall }+\text{ Precision }} \tag{7}\]  
\[\text{mAP}=\frac{1}{Q}\sum_{q=1}^{\ell}\int_{0}^{1}P_{q}(R)\text{d}R \tag{8}\]
## 3 Materials and Methods
### GC-FID Analysis of Fatty Acids  
Fatty acid methyl esters were prepared according to HRN EN ISO 12966-2:2011 standard [55]. Prepared fatty acid methyl esters were analyzed by gas chromatography according to HRN EN ISO 12966-4:2015 [56]. Gas chromatograph 7890A (Agilent Technologies, Lake Forest, CA, USA) with a capillary column ZB-WAX 25 m long with a diameter of 0.25 mm and the thickness of the stationary phase 0.25 microns (Phenomenex, Torrance, CA, USA), a split-splitless injector (temperature 260 \({}^{\circ}\)C) and a flame ionization detector (temperature 280 \({}^{\circ}\)C) was used. A sample (5 \(\upmu\)L) was injected with a split ratio of 1:40. Start column temperature was 60 \({}^{\circ}\)C with holding time for 2 min. The oven temperature was increased at the rate of 13 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C, then at the rate of 2 \({}^{\circ}\)C/min was heated to 240 \({}^{\circ}\)C. The carrier gas was helium (99.9999%) at a constant flow rate of 3 mL/min. The hydrogen flow was 70 mL/min, air flow was 450 mL/min, and the makeup gas flow (nitrogen) was 15 mL/min.  
Fatty acid methyl esters were identified by comparison with retention times of 37 fatty acid methyl ester standard compounds analyzed at the same conditions. The result is expressed as a percentage (%) of individual fatty acids to total fatty acids. The detection limit of the method was 0.1%.
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 2 Connectivity in Tactical MANETs  
Network connectivity can never be fully guaranteed for any type of wireless technology unless network topology and radio propagation environment are carefully analyzed. One of the design goals of tactical MANETs is to provide robust connectivity at the network edges. Currently, there is a serious shortfall of providing robust connectivity to the lowest-level combat units represented by dismounted troops at the squad and platoon level in order to provide their access to the command information center. Another challenge is that tactical MANETs operate in different types of terrains with very diverse radio propagation conditions. In addition to RSS, the link quality also depends on the level of interference which is quantified by SINR measure [8]. It is important to consider the following issues to address connectivity in tactical MANETs: Nodes mobility, transmission range and routing protocols.
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 4 The Proposed Method
### Multi-Scale Local Planar Guidance Module  
Introducing scene structure information into the network structure to improve the prediction quality of the depth map has been widely adopted in the monocular depth estimation task [30; 34]. In this paper, we introduce a plane-based design aimed at handling the extreme case: that is, ensuring the depth prediction accuracy when there is no sparse depth point input. Thus, based on the local plane assumption, we introduce multi-scale local planar guidance modules in both branches to ensure accurate and smooth depth variations for points on the same plane as the decoder scales progressively recover.  
From the \(\frac{1}{8}\) scale, the MLPGM starts predicting the plane coefficients \((\alpha,\ \beta,\ \gamma,\ \rho)\) of the local plane at each scale. The predicted coefficients are then used to compute depth values according to Equation (6). By capturing the local planar structures, the MLPGM can generate more accurate depth maps compared to directly predicting depth values at a lower resolution and upsampling them to the full resolution. As illustrated in Figure 2, the MLPGM is integrated into multiple scales of both decoders.  
Unlike previous methods that only use the local planar guidance (LPG) to guide full-resolution depth estimation [30; 34], we extend its usage by employing it to generate a depth map with double the resolution of the input feature maps, as shown in the \"LPG\" block in Figure 2. We then fuse these higher-resolution depth maps with the corresponding decoder features to guide the features at the next scale.  
At a single scale, the specific operations within an LPG can be understood from the LPG module shown in Figure 2. We take the \(1/s\) scale as an example to illustrate the specific steps of LPG. Firstly, the feature \(f^{1/s}\) passes through a convolution layer with an output feature layer of 4, yielding pseudo-plane coefficients \(planccoef^{1/s}\). Then, it is split into two parts. One portion is initially upsampled to the \(2/s\) scale, where the depth map is computed using Equation (6). Subsequently, it is combined with the features of the subsequent scale and fed into the network. The other part is directly upsampled to the full resolution scale \(s/s\). After conversion to a depth map, it is concatenated with the full-resolution depth maps outputted by LPG at other scales, as well as the features of the last layer at full resolution, and finally passed through module \(R\) to obtain the final depth map output. During the upsampling process of the LPG module, we utilize the nearest-neighbor upsampling method for plane coefficients to maintain consistency with the local plane assumption and ensure that the upsampled pixels remain within the same plane. The upsampling process is illustrated in Figure 3.
## 3 Materials and Methods
### Proposed Region-Level Monitoring Scheme  
The idea behind region-level monitoring is to continuously gather information from different regions within a country and act accordingly. The collected data contain a comprehensive set of features capturing various aspects of maternal health, including demographic factors (population, number of pregnant women), healthcare access (antenatal care, skilled birth attendants), maternal health indicators (hypertension, anemia, complications), and birth outcomes (stillbirths, infant mortality). The proposed model analyzes these features to identify regions with high maternal mortality risk. The primary risk factors considered in this context include low levels of antenatal care, high rates of home deliveries, and poor maternal health indicators like anemia and hypertension.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Dataset** & **Methodology** & **Results (Acc)** & **Region** & **Problem Tackled** \\ \hline \multirow{2}{*}{[9]} & Collected by researchers & Modified decision tree-based technique & 97\% & Bangladesh & Monitoring of pregnant women \\ \hline \multirow{2}{*}{[10]} & Collected by researchers & Bagging ensemble classifiers & 87\% & Bangladesh & Predicting type of birth \\ \hline \multirow{2}{*}{[11]} & Health Management Information System & Nested ensemble & 91.1\% & India & Predicting region-level \\  & & & & & High/Low MMR \\ \hline \multirow{2}{*}{[16]} & UCI repository & Multi-ensemble model & 93.99\% & Bangladesh & Predict mother risk level \\ \hline \multirow{2}{*}{[17]} & Collected by researchers & Gradient boosting machine learning algorithm & 90.24\% & Ethiopia & Monitoring of pregnant women \\ \hline \multirow{2}{*}{[13]} & Health Management Information System & Explainable AI & 68\% & India & Predicting region-level \\  & & & & & High/Low MMR \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of recent literature addressing the problem of maternal mortality.  
To build this model, first, the information collected from the areas is assessed by experts and government personnel to determine which regions are considered to have a high mortality risk versus those that do not. The flagged data are then used to build an XAI model. Since the personnel discussing this problem are usually representatives from health institutions and the government, the outcome of the model needs to be not only the risk of the input region but also an explanation as to why it is marked as high risk such that the risk factors can be targeted independently. The proposed model is presented in Figure 1. The choice of the XAI is dependent on preserving the performance of the classification while maintaining the explainability of the model. This is further discussed and presented in the following section.
# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 4 Analysis of Experimental Results
### Computational Cost  
We compared the training time and inference time of the six-step prediction of the proposed MSGNet-MLLA-Transformer model to MSGNet and MrCAN by taking 100 samples from the Australian power load dataset. The results are shown in Table 7. Compared to the two models, the training time and inference time of our model are in two different positions. Considering both the computational cost and the predictive performance simultaneously, the method proposed in this paper has certain advantages.
# Efficacy of GS-441524 for Feline Infectious Peritonitistis: A Systematic Review (2018-2024)
###### Abstract  
Feline infectious peritonitis (FIP) is a severe viral disease with a very high fatality rate. GS-441524 is an adenosine analogue that acts as an antiviral and has shown promise in FIP treatment. However, its commercialization in some regions is not yet authorized. To evaluate the efficacy of GS-441524 based on the published literature, a systematic review was conducted. This systematic review was conducted using PubMed, ScienceDirect, and Google Scholar for studies published from 2018 onwards. Following PRISMA guidelines, 11 studies (totaling 650 FIP cases treated with GS-441524 alone or in combination) were included. Therapeutic efficacy was assessed by FIP form, clinical signs, and dosage. The overall treatment success rate was 84.6%. This rate was higher when GS-441524 was combined with other antivirals and lower in cases of wet FIP or those with neurological complications. Combination therapy with other antivirals may improve outcomes in complicated FIP cases, although further studies are needed. The GS-441524 dosages associated with the best outcomes were 5-10 mg/kg once daily (or equivalent subcutaneous dose), adjusted for FIP type, severity, and presence of neurological/ocular signs. Higher dosages can be used for severe cases or to prevent relapse, but splitting into twice-daily dosing may be necessary to avoid absorption issues. In summary, this synthesis indicates that GS-441524 is a highly promising treatment for FIP, with a high success rate among treated cases. Nevertheless, randomized controlled trials are needed to establish evidence-based therapeutic protocols tailored to different FIP presentations.  
feline infectious peritonitis; GS-441524; antiviral treatment; efficacy; remdesivir; molnipravir; feline coronavirus 2025, 14, 717
# High Repetition Rate, TEM\({}_{00}\) Mode, Compact Sub-Nanosecond 532 nm Laser
## 1 Introduction
The key advantages are the following: the seed is a free-space-pumped microchip monolithic laser without a resonator optical alignment and a fiber-pumped structure; the average power and corresponding pulse energies were increased using a simple amplifier; and the beam distortion of the microchip was improved without a complicated optical-shaped system. In our work, we first designed the experiments of a monolithic microchip laser, a two-pass Nd: YVO4 amplifier, and the SHG. In addition, the beam distortion was compensated by adjusting the fundamental amplifier's distribution of pumping light so that it was elliptic. Then, according to the fundamental results, a 532 nm beam with properties better than 0.6 W, 770 ps, 37.5 uJ, 16 kHz, M\({}^{2}\) < 1.2 was obtained by the Type I LBO crystal in the CPM regime for the SHG. Finally, we discussed the simulating results of generating a sub-nanosecond with the saturable absorber transmittance, the reflectivity of an output coupler, and the cavity length parameter, which can greatly aid in the design of a sub-nanosecond microchip laser with high repetition. On the basis of the research, we developed an engineering laser, whose laser head dimensions including the pump source are less than 125 mm \(\times\) 50 mm \(\times\) 40 mm.
## 6 Experimental Results
### Runtime Analysis Comparison (in ms)  
Runtime analysis of algorithms is performed by running each algorithm on different sets of callbacks with a cardinality ranging from 1 to 1000. For each cardinality value, ten sets of callbacks are randomly generated with a deadline on request (i.e., the deadline is equal to the period), and a CPU utilization factor of 60%. All algorithms are run successively on all callback sets, and the average execution time is computed for each. The runtime is obtained using a PC i7-2640-2.8 GHz and 8 GB of memory.  
As shown in Table 4, the GBFS algorithm quickly fails to find an acceptable solution (n/a) when the number of callbacks increases. However, the proposed algorithm outperforms RMS in finding a schedulable solution in a reasonable time. The lower time complexity of the algorithm proposed by \(\mathcal{O}((k+N)n+n)\) leads to a minimum runtime, compared to the RMS and LL algorithms that have a time complexity of \(\mathcal{O}(m^{2}+mn)\) and \(\mathcal{O}(n^{2})\), respectively, and the GBFS algorithm that has a polynomial time complexity of \(\mathcal{O}(n^{4})\).  
Figure 7: Executor number vs. deadline.
## 4 Proposed Approach
### Design Methodology
#### 4.2.4 Scalability  
AURORA MINDS ensures scalability by utilizing cloud-based Federated Learning and distributed virtual Identity Providers (vIDPs), allowing seamless expansion to additional devices and regions. Moreover, cloud platforms like AWS or Microsoft Azure help to handle growing amounts of data and users flexibly and cost-effectively.
# Effect of Fireproof Coatings on the Post-Fire Behavior of CFRP Composite Sheets
## 3 Experimental Results and Discussion
### Large-Space Fire Condition
Based on the test results, it can be concluded that CFRP sheets coated with Type C1 and C2 coatings over 25 mm thick demonstrate a fire resistance duration exceeding 2 h under the large-space fire condition. According to the Chinese specification GB50016-2014 [38], under the large-space fire condition, CFRP sheets coated with Type C1 and C2 coatings of over 25 mm thickness meet the requirements of fire-resistance class III (columns), class I (beams), and class I (floor slabs), respectively. The application of Type C1 and C2 fireproof coatings of over 25 mm thickness effectively enhances fire protection for CFRP elements under large-space fire conditions, improves their fire resistance, and meets the requirements for structural application.
## 3 Results and Discussion
### Research on Wave Frequencies  
To investigate the impact of wave frequency on the UWOC link, waves of different frequencies were obtained by adjusting the flow pump power. Since the power of the flow pump is not continuously adjustable, we finally obtained three different frequencies, 0.4 Hz, 1 Hz, and 2.2 Hz. Based on these three different frequency conditions, the performance of the UWOC link with a total reflection angle of 7\({}^{\circ}\) was experimentally proved; Figure 9 shows the test results.  
The corresponding optical power curves at three wave frequencies are illustrated in Figure 9a-c, and the amplitude variation ub the optical power at this experimental condition is 20 uW. Due to the periodic fluctuations of waves, the optical power curve also shows periodic changes, and the value of the optical power directly affects the photoelectric conversion of the photodetector. Figure 9d-f shows the waveform diagram with a data rate of 240 Mb/s at frequencies of 0.4 Hz, 1 Hz, 2.2 Hz, respectively. Note that the change in the waveform is very similar to the change in the optical power curve.  
Figure 9: Optical power variation curve versus different waves frequencies of (**a**) 0.4 Hz, (**b**) 1 Hz and (**c**) 2.2 Hz; waveform diagram versus different optical power frequencies of (**d**) 0.4 Hz, (**e**) 1 Hz and (**f**) 2.2 Hz at a data rate of 240 Mb/s.  
Figure 10 presents the relationship between data rates and BER under experimental conditions where the frequencies of optical power are different. Note that, in this part of the experiment, we controlled the optical power amplitude; only the frequency of the optical power was different. In Figure 10, we can clearly see that, although the frequency of optical power has changed, the relationship between data rate and BER is basically the same. Compared to Figure 7, the maximum data rate in the case of water surface fluctuations is only about 230 Mb/s, which is lower than 300 Mb/s when the water surface is stationary. Therefore, we draw a conclusion that the frequency variation of the optical power has an impact on the performance of the UWOC link, but the influence of different frequency variations on the UWOC link seems similar.
## 3 D2 HCP (Distributed Dynamic Host Configuration Protocol)
### Message Format
#### 3.4.1 Packet Header  
The first row of the Figure 2 contains the fields of the packet header.  
1. _Packet Length_: Packet length, including the header (2 bytes).
2. _Packet Sequence Number_: Sequence Number (2 bytes). In each different message which is sent by node, this field is increased by one. It helps in being able to detect duplicated packets.