esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, the tandem blade with an inflow Mach number of 1.2 was studied. The present research was motivated by exploring the flow mechanism of the tandem blade under a complex interaction shock structure. Furthermore, an optimization principle for a supersonic tandem blade was carried out and verified by using the CFD method. The main conclusions follow:  
1. With an inflow Mach number of 1.2, the total pressure loss is difficult to control because of the complicated flow structures at the DE point; there is an obvious double-shock wave structure located rearward in the FB passage and a normal shock on the AB suction side. The complicated shock waves cause a strong interaction between FB and AB, and a different optimization strategy compared to conventional blade.
2. The FB loss dominates the overall loss in all of the operating conditions. It exceeds 50% of the total loss at the DE point, and accounts for more than 80% at the NS point. Therefore, the optimization aiming at FB is of decisive significance to improve the blade performance.
3. The conventional supersonic airfoil is not the optimum choice for the supersonic tandem blade due to the aerodynamic interaction between FB and AB. In this paper, the tandem blade was optimized considering this aerodynamic interaction by controlling the FB passage shock intensity near the pressure side, the AB shock intensity near the suction side, and the AB incidence. The numerical simulation results of the optimization scheme show that the total pressure loss declines by 20% at the DE point and the incidence range increases by about 0.5\({}^{\circ}\).  
It is noted that to the best knowledge of the authors, the supersonic tandem blade flow mechanism and its optimization is published for the first time, although some characteristics of the tandem blade with local shock wave were investigated previously [9; 18]. The result constitutes a step forward in understanding the complex shock structure and optimization principle for a supersonic tandem blade, and it shows good application prospects for a transonic axial compressor/fan with a tandem blade.  
Conceptualization, B.L. and X.Y.; methodology, Y.T.; software, Y.T.; validation, Y.T.; formal analysis, Y.T.; investigation, Y.T.; data curation, Y.T.; writing--original draft preparation, Y.T.; writing--review and editing, Y.W.; project administration, Y.T.; funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Equipment development department of the Central Military Commission, grant number 614270202031804\".  
The authors would like to acknowledge [PERSON]'s help in improving the grammar of the manuscript.  
The authors declare no conflict of interest.
## 2 BLRFS Principles
### Basic Relationships of the BLRFS Technique
In the case of a large coherence length and a narrow path-length distribution (\(\langle\Delta s\rangle/l_{c}<<1\)), the fluctuation component of the detected light is maximal and the statistical moments approach to their extreme values (\(M_{2}\approx 2\) and \(M_{3}\approx 6\) in the case of polarization discrimination of detected light, Equation (1), or \(M_{2}\approx 1.5\) and \(M_{3}\approx 3\) for the non-polarized light detection). Lower values of \(M_{2}\) and \(M_{3}\) in the case of non-polarized light detection are caused by a partial suppression of the intensity fluctuations due to superposition of randomly polarized light waves. On the contrary, if \(\langle\Delta s\rangle/l_{c}>>1\), the integrals on the right sides of Equations (1) and (2) fall to zero, the intensity fluctuations are suppressed, and \(M_{2}\approx M_{3}\approx 1\). The average path-length difference \(\langle\Delta s\rangle\) for the considered illumination and detection conditions (Figure 1) is determined by the transport mean free path \(l^{*}\) of radiation propagation in the examined medium (\(\langle\Delta s\rangle\sim l^{*}\), [29]). The transport mean free path corresponds to the average propagationlength at which the directed component of the propagating radiation is completely transformed to a diffuse component. It was established that for a wide class of unimodal probability distributions \(\rho(\Delta s)\) the integrands \(J_{1}=\int\limits_{0}^{\infty}\left|g(\Delta s)\right|^{2}\rho(\Delta s)d( \Delta s)\) and \(J_{2}=\left(\int\limits_{0}^{\infty}\left|g(\Delta s)\right|\rho(\Delta s)d( \Delta s)\right)^{2}\) in the right sides of Equations (1) and (2) are related to each other with an appropriate accuracy as an universal power-law function [29]:  
\[J_{2}(l_{c},\left\langle\Delta s\right\rangle)\approx\left|J_{2}(l_{c},\left \langle\Delta s\right\rangle)\right|^{2.26}. \tag{3}\]
# Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset
## 3 Results
### Classification  
Table 3 summarizes the binary classification results, which show that the random forest algorithm achieves superior accuracy, F1 score, MCC, and AUC compared to the other algorithms. However, the performance of the models is not significantly different, as there are only small variations in the individual results of the different algorithms. We note that the data in this problem are imbalanced as a result of our definition of short/long stays at the third quartile (imbalance ratio 3:1), which noticeably affects the performance of the models. The balanced accuracy and MCC scores represent more appropriate measures of algorithm performance on imbalanced data [52; 67].  
In XGBoost, a feature importance matrix can be exported to give further insight into the influence of different features on the prediction performance. The visualization of the top 10 (important) features is shown in Figure 5. The GCS verbal has the highest importance for performance, consistent with its strong correlation with LOS (Figure 4). Vital signs ranked next, which were less than half as important as the GCS verbal, with no notable correlation with LOS. Laboratory measurements are placed in the last position, with glucose, platelets, and white blood cells at the top, while the remaining measurements are of decreasing importance to the model.
## 2 O-RAN Architecture and Development
### O-RAN Architecture
#### 2.2.4 Near-RT RIC  
The Near-RT RIC is a logical function that can control and optimization RAN elements and resources in near real time by collecting detailed data from the O-RAN logical compo  
Figure 5: The O-DU\({}_{(7-2)}\)/O-RU\({}_{(7-2)}\) split point option showing requirements for Category A and Category B for the O-RU.  
nents and provide actions over the E2 interface. In addition, the A1 Interface enables Non-RT RIC to drive the policy guidance of the Near-RT RIC applications/functions and support AI/ML. Near-RT RIC hosts many functions in Figure 6, which include the following:  
* Database and related Shared Data Layer (SDL) services: to exchange information between RAN and UE to support specific use cases;
* xApp subscription management: to manage subscriptions from different xApps and provides unified data distribution to xApps;
* Conflict mitigation: to resolve potentially overlapping or conflicting requests from multiple xApps;
* Messaging infrastructure: to allow message interaction within the Near-RT RIC functions;
* Security, which provides the security scheme for xApps;
* Management services: to manage fault, configuration and performance as a service producer to SMO;
* Logging service: to provide tracing and metrics collection which capture, monitor, and collect the status of Near-RT RIC internals and transfer to external systems for further evaluation if needed;
* Interface termination: to provide interfacing to other O-RAN components;
* Functions hosted by xApps: to allow services to be executed at Near-RT RIC;
* API-Enabled function: to support capabilities related to Near-RT RIC API operations such as API repository/registry, authentication, discovery, generic event subscription, etc.;
* AI/ML support: to feature data pipelining, training, and performance monitoring for xApps;
* xApp Repository function: to manage selection of xApps for A1 message routing, based on the A1 policy types and operator policies, and Access control of A1-El types for xApps based on operator policies.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 4 Experimental Results
### Evaluation Metrics  
Model performance was evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), both of which are widely used in image restoration tasks. The PSNR is a quantitative metric that measures the pixel-level difference between the restored image and the ground truth. It is defined as  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{\text{MAX}^{2}}{\text{MSE}}\right) \tag{7}\]  
where MAX denotes the maximum possible pixel value (typically 255), and MSE is the mean squared error between the restored and ground truth images. Higher PSNR values indicate better restoration quality.  
The SSIM measures the structural similarity between two images based on the Human Visual System (HVS). Unlike the PSNR, which focuses on absolute error, the SSIM considers luminance, contrast, and structural information. It is defined as  
\[\text{SSIM}(x,y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{2 }+\mu_{y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{8}\]  
In Equation (8), \(\mu_{x}\) and \(\mu_{y}\) represent the mean intensities of the images \(x\) and \(y\), respectively. \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) denote the variances of \(x\) and \(y\), and \(\sigma_{xy}\) indicates the covariance between the two images. The constants \(C_{1}\) and \(C_{2}\) are included to stabilize the division and prevent numerical instability when the denominators are close to zero. SSIM values range from 0 to 1, where values closer to 1 indicate higher structural similarity and, consequently, better image restoration quality.
###### Abstract  
The estimation of the unit weight of soil is carried out using laboratory methods; however, it requires high-quality research material in the form of samples with undisturbed structures, the acquisition of which, especially in the case of organic soils, is extremely difficult, time-consuming and expensive. This paper presents a proposal to use artificial neural networks to estimate the unit weight of local organic soils as leading parameters in the process of checking the load capacity of subsoil, under a direct foundation in drained conditions, in accordance with current standards guidelines. The initial recognition of the subsoil, and the locating of organic soils at the Theoretical and Pastoral Institute in Rzeszow, was carried out using a mechanical cone penetration test (CPTM), using various interpretation criteria, and then, material for laboratory tests was obtained. The analysis of the usefulness of the artificial intelligence method, in this case, was based on data from laboratory tests. Standard multi-layer backpropagation networks were used to predict the soil unit weight based on two leading variables: the organic content \(LOI_{T}\) and the natural water content \(w\). The applied neural model provided reliable prediction results, comparable to the standard regression methods.  
artificial neural networks; organic soils; soil unit weight; organic content; cone penetration test +
Footnote †: journal: Applied Sciences
# Influence of Waste Filler on the Mechanical Properties and Microstructure of Epoxy Mortar
## 2 Materials and Methods
### Preparation of Epoxy Mortar  
The epoxy mortar containing resin as the critical binding material must be mixed with a hardener. The hardener is used to cure the resin and harden the mixture. The hardener and resin were mixed in such a manner to ensure a uniform molecular structure. Then the epoxy resin was combined with the fillers to obtain a uniform mixture. These mixtures were named according to the filler used:  
* Epoxy steel slag (ESS) contains 25% resin and 75% steel slag.
* Epoxy sand (ES) contains 25% resin and 75% sand.
* Epoxy marble dust (EM) contains 25% resin and 75% marble dust.
* Epoxy polyvinyl chloride waste (EP) contains 25% resin and 75% polyvinyl chloride waste.
* Cement mortar (C) contains a 1:3 (_w/w_) ratio of cement and sand.  
Table 1 shows all the above mixtures. All these samples were treated at room temperature for 24 h. It was observed that the increase in the amount of filler increased the darkness of the samples. The uniform cube-shaped samples (50 mm \(\times\) 50 mm \(\times\) 50 mm) were first air-dried at room temperature and then removed from the molds.
## 5 Results and Discussion
### Analysis of the Thermal Performance of the Storage Tank during Discharge with a c-h Ratio of 0.38
#### 5.1.3 Exergy Efficiency  
Figure 11 shows the exergy efficiency inside the storage tanks for E1, E2, and E3. Different structures result in different internal flow field characteristics, leading to differences in exergy efficiency at the beginning of the discharge process. Storage tanks with internal obstacles perform better in terms of energy storage, with higher exergy efficiency. However, at the same time, Type c has the highest exergy efficiency, Type a has the lowest, and the porous obstacles demonstrate the best energy storage performance.  
The differences in exergy efficiency among the storage tanks increase over time. At a flow rate of 0.3 m/s, the exergy efficiency inside the tanks is the lowest. Under the combined influence of structure and thermal conduction, the storage tanks exhibit higher exergy efficiency at a flow rate of 0.2 m/s.  
Water mixing and axial heat conduction between different water layers both affect exergy efficiency. At a flow rate of 0.1 m/s, a longer time is required to reach the same heat release state, which intensifies heat transfer in the vertical direction of the water body and lowers the exergy efficiency. This can explain why the exergy efficiency at a flow rate of 0.2 m/s is higher than at 0.1 m/s. At a flow rate of 0.3 m/s, the flow rate becomes the main factor reducing energy storage efficiency, resulting in the lowest exergy efficiency at this flow rate.  
Figure 11: Exergy efficiency: (**a**) at a flow rate of 0.1 m/s; (**b**) at a flow rate of 0.3 m/s; (**c**) at a flow rate of 0.2 m/s.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 4 Fabrication and Experimental Characterization of the Proposed Tag Antenna
### Impedance Measurement  
The impedance of the proposed UHF RFID tag antenna was characterized by using a two-port vector network analyzer (VNA) and a port-extension technique introduced in [38]. Recall that the design of the UHF tag antenna was carried out considering the presence of the flexible substrate, the clinic tube, and the blood. Therefore, the impedance measurement of the tag must be performed with the same design conditions, using the measurement system shown in Figure 7. As it can be seen, the tag is first attached to a clinic tube full of blood. Then, in a second step, the two antenna feeding points are soldered to two inner conductors of a differential probe, which is based on two semirigid coaxial cables soldered together with an outer conductor diameter of 2.2 mm and a length of 100 mm. The other end of the differential probe is connected to the VNA by means of the SMA connectors and test cables, and the \(S\)-parameters of the resulting two-ports network are measured. To remove the effects of the differential probe a previous Through, Open, Short, Match (TOSM) calibration was realized at the opposite end of the SMA connectors. Finally, the impedance of the proposed tag antenna is calculated as [38]:  
\[Z_{a}=\frac{2Z_{0}(1-S_{11}S_{22}+S_{21}S_{12}-S_{21}-S_{12})}{(1-S_{11})(1-S_ {21})-S_{21}S_{12}}\, \tag{2}\]  
where Z0 = 50 \(\Omega\) is the characteristic impedance of the measurement system.  
Figure 6: Photograph of the proposed flexible UHF RFID tag antenna attached to the smallest blood tube.  
Figure 7: System for the impedance measurement of the proposed tag antenna.  
The input impedance of the proposed antenna measured in an anechoic chamber by means of the above technique in the frequency range from 0.5 GHz to 2 GHz is shown in Figure 8. The simulated input impedance of the tag and the impedance of the IC chip (NXP UCODE G2 XL) are also included. The insets inside Figure 8a,b shows details on the variation of the real and imaginary parts of the impedances between 0.86 GHz and 0.96 GHz. Except for a small shift in the resonant frequency, a good agreement between simulated and measured results for both real (Figure 8a) and imaginary (Figure 8b) parts of the input impedance is obtained. The measured impedance of the antenna is \(Za=13+j\) 130 \(\Omega\) at 867 MHz. This value is close to the conjugate of the chip impedance at the same frequency \(Z^{*}c=16+j\) 156 \(\Omega\), therefore leading to a small magnitude of the generalized reflection coefficient given in Equation (1). The small mismatch is due to inaccuracies for fabrication-in-lab of tags and negative mounting and soldering effects.
# Food Waste Detection in Canteen Plates Using YOLOv11
## 3 Methods and Methodology
### Methodology
#### 3.2.5 Model Architecture  
Aiming for both object detection and instance segmentation, we utilized YOLOv11, specifically its yolov11l-seg variant. This variant is particularly crucial for our application as it extends beyond traditional bounding box prediction to perform instance segmentation. Based on a Convolutional Neural Network (CNN) architecture like the illustrated in Figure 8, for each detected object, the model not only predicts its bounding box and class but also generates a precise, pixel-level binary mask delineating its exact shape and area. This capability is fundamental for accurately calculating the area of food and waste items.  
Figure 8 provides an example of a CNN architecture for image classification, showcasing an input image passing through convolution, Rectified Linear Unit (ReLU), and pooling layers before a fully connected layer leads to output classes (e.g., \"Food\" or \"Garbage\").  
The system employs a multi-class classification approach for detected items. Specific items such as bones, napkins, and grains of rice are categorized under a \"garbage\" class, which serves as a generic classification for these discarded materials. Overall, the model classifies each detected item into one of three categories: \"garbage\", \"food\", or \"waste\". This multi-class classification, combined with the pixel-level binary masks generated by the yolov11l-seg variant, allows for precise identification and area calculation of various items on the plates.
## 3 Results
### Changes in pH of Fermented Beverages During Cold Storage  
The pH values of samples fermented with _L. plantarum_ 299v remained relatively stable over the 28-day cold storage period (Figure 2a). This pH stability indicates that _L. plantarum_ 299v effectively maintained the acidification of the beverages for 28 days, suggesting good stability of the fermented products. The dairy and dairy-oat beverages (Groups A and B) exhibited more stable pH values, suggesting that fermentation was more effective in these matrices than the oat beverage (Group C). The results confirm the research hypothesis that appropriately selected lactic acid bacteria cultures positively impact the shelf life and quality of fermented beverages.  
Figure 2: Changes in the pH of beverages inoculated with the yogurt culture, a monoculture of lactobacilli, and their mixture during cold storage: (**a**) _L. plantarum_ 299v; (**b**) _L. acidophilus_ La-5. Different superscripts show a significant difference at \(\alpha<0.05\).  
The pH values of the fermented beverages containing _L. acidophilus_ La-5 remained relatively stable over a 28-day cold storage period. The other groups (A and E) exhibited lower pH values (-4.0-4.5), indicating effective lactic acid bacteria activity and proper fermentation. Group F (oat-based beverages) displayed higher pH values and a smaller decrease in acidity, suggesting less efficient fermentation. This could be attributed to a lack of nutrients supporting the growth of lactic acid bacteria or their limited ability to acidify the oat-based environment. The pH stability in dairy and dairy-oat products indicates their shelf-life and potentially better sensory properties during storage. Dairy and dairy-oat products demonstrated superior fermentation properties compared to oat beverages, confirming the research hypothesis regarding the positive impact of lactic acid bacteria cultures on the quality of fermented beverages. The high pH values in Group F may suggest the need to modify the fermentation process or add supplementary ingredients to support the growth of lactic acid bacteria in plant-based products.
## 3 Results and Discussion  
### Chemistry  
The reaction of 2-(2,6-dichlorophenylamino)-2 imidazole hydrochloride (**1**) with sodium tetraphenylborate (**2**) in deionized water, at ambient temperature, afforded the title compound tetraphenylborate salt of _ clonidine_, 2-(2,6-dichlorophenylamino)-2-imidazole tetraphenylborate (**3**) in 76% yield through anion exchange reaction (Scheme 1).  
\({}^{1}\)H NMR (DMSO-d\({}_{6}\), 500 MHz) \(\delta\) 3.62 (s, 4H, CH\({}_{2}\)-CH\({}_{2}\)), 6.80 (s, 4H, tetraphenyl borate), 6.93 (s, 7H, tetraphenyl borate), 7.18 (s, 9H, tetraphenyl borate), 7.29 (bs, D\({}_{2}\)exchangeable, 1H, NH), 7.41 (d, 2H, J = 8.0 Hz, H3 and H5 of benzene ring of clonidine), 7.61 (t, 1H, J = 8.0 Hz, H4 of benzene ring of clonidine), 8.13 (2 bs, D\({}_{2}\)O exchangeable, 2H, 2 NH); \({}^{13}\)C NMR (DSMO-d\({}_{6}\), 125 MHz) \(\delta\) 40.17 (C, (CH\({}_{2}\)), 40.26 (C, (CH\({}_{2}\)), 121.11 (C4 of benzene ring of clonidine), 121.98 (4C4 of tetraphenyl lorate), 125.73, 125.79, 125.83, 125.86 (4C3 and 4C5 of tetraphenyl borate), 129.59 (C2 and C6 of benzene ring of clonidine), 135.09 (4C2 and 4C6 of tetraphenyl borate), 136.01 (C1 of benzene ring of clonidine), 163.21, 163.44, 164.13, 164.52 (4C1 of tetraphenyl borate).  
The ESI-MS (+ve) of compound **3** revealed a peak at \(m/z\) 320.0 equal to (M\({}^{+}\)- (Ph)\({}_{4}\)B) in addition to the peak at \(m/z\) 267.0.  
The UV spectra of the titled compound **3** are shown in Figure 1 which show a shift in the maximum absorption compared with compound **1** and **2**.  
The titled compound **3** was characterized by differential scanning calorimetry (DSC), which exhibits a significant exothermic peak at 160 \({}^{\circ}\)C. The differential scanning calorimetry thermogram of the titled compound **3** is substantially depicted in Figure 2.  
The titled compound was heated from 35 \({}^{\circ}\)C to 650 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min. Thermal gravimetric analysis (TGA) of the titled compound (**3**) show two inflection points at about 200 \({}^{\circ}\)C and 300 \({}^{\circ}\)C compared with the start compounds (**1**) and (**2**), which show the inflection  
Figure 1: UV spectrum of **1** clonidine, **2** NaTPB and **3** clonidine-TPB.  
Figure 2: Thermal gravimetric analysis of the titled compound **3**.  
at 325 \({}^{\circ}\)C and 425 \({}^{\circ}\)C, respectively. Figure 3 show the thermal gravimetric analysis of the titled compound **3**.