esa-sceva/satcom-chunk-collection huggingface dataset:
# Bioactive Compounds in Breast Meat of Broiler Chickens Fed with Black Soldier Fly Wholesmeal
## 3 Results and Discussion
### Bioactive Peptides: Carrosine e Carnitie
This result could be linked to the genetic type, which significantly influenced the content of these bioactive peptides, in agreement with the findings of [PERSON] and [PERSON] [41]. Furthermore, [PERSON] and [PERSON] [42] conducted a study between different species (pleasant vs. free-range chicken) and observed similar contents of these peptides, suggesting that diet could be a highly influential factor with respect to the impact of the species. A diet characterized by a high carbohydrate content significantly influenced the content of bioactive peptides (\(p<0.01\)). In particular, the HI10 inclusion level resulted in a significant increase in creatine content by 22% compared to the control (508.2 vs. 396.1 mg/100 g, respectively; \(p<0.05\)) and carosine content by 26% (278.11 vs. 377.20 mg/100 g, respectively; \(p<0.05\)). In the present study, although the HI5 group had slightly higher mean values than the control group, this difference was not statistically significant (\(p>0.05\)). Our results may be due to differences in the amino acid utilization efficiency of the different diets, which may have resulted in differences in the free L-histidine pool available for the synthesis of the studied peptides. According to [PERSON] et al. [43], changes in the amino acid profile of the diet may influence the peptide content in the breast meat of male chickens ([PERSON] 308). Bioactive peptides are biological molecules in chicken meat that are involved in numerous physiological and health-promoting functions: antioxidants, anti-glycation, and anti-aging, as well as pH buffering in muscle [38]. In the animal muscular system, carosine is synthesized from histidine (His) and \(\beta\)-alanine (\(\beta\)-Ala) by carosine synthetase. Creatine, on the other hand, is a nitrogenous organic acid synthesized from l-arginine, glycine, and L-methionine [11]. The positive effects of bioactive peptides on human health are still being studied. However, it is known that ingestion of low histidine-rich peptides could reduce scavenging capacity with little protection from cardiovascular disease [44]. Furthermore, low creatine content may negatively affect the defense systems against neurodegenerative disorders, as anti-oxidant, anti-apoptotic, and anti-excitotoxic properties are decreased [45]. Figure 1: Creatine and carosine content (mg/100 g of fresh meat) of chicken breast meat. \({}^{\text{a,b}}\) Means with different superscripts differ (\(p<0.05\)). C = standard diet; HI5 = 5% of level inclusion in diet with BLSF; H10 = 10% of level inclusion in diet with BLSF.
## 1 Introduction
The authors elaborately described the control logic of the state-queueing model. Our paper follows the control approach in [16] and introduces ACLs as a load shedding measure into power system scheduling. Unit commitment (UC) is an essential and important topic in daily power system operation. As wind farms have steadily penetrated into power grids, the effect of wind energy on UC has been an important topic, and much research has been conducted on this topic. For example, [PERSON] et al. [24] proposed a UC model and algorithm for utilities with a large penetration of wind generation. [PERSON] et al. [4] examined the influences of wind energy on UC. Furthermore, DR has been presented to cut down the peak load and coordinate the wind power fluctuation for several years. [PERSON] et al. [25] developed an advanced algorithm for UC that takes wind energy and DR uncertainties into account. [PERSON] [26] used a DR measure though real-time pricing to curtail the rescheduling costs due to wind generation penetration. In summary, most of the previously mentioned research mainly studied the general model of DR, while the models did not take into account the differences in various DR projects. This paper focuses on UC optimization in wind-integrated power systems, which employs ACL to balance the peak load. Furthermore, the coordination of wind power and ACL was studied in our study. We have also adopted stochastic programming, which is one of the popular approaches for solving wind power uncertainty, and developed a two-stage stochastic unit commitment (SUC) model considering DR resources from ACLs and wind power. Our simulation has verified the feasibility of the model and method proposed by this study. Our results show that controlling the usage of ACLs effectively reduces the peak load and increases energy efficiency. As more and more countries begin to carry out the load control project for ACL users, we believe the findings of this study are of great value. Four major sections follow the introduction. First, a DR model for ACL is developed in Section 2. Then, a two-stage SUC model is proposed in Section 3, and in Section 4, a 10-generator test system is studied to illustrate the proposed optimal scheduling strategy. Finally, a summary and implications for further research are given in Section 5.
# Cross-Feature Transfer Learning for Efficient Tensor Program Generation
## 4 Evaluation
### Dataset Sampling
* Partitioning is executed based on the hardware parameters. These split strategies are implemented to facilitate a thorough and unbiased evaluation of the tuners under various scenarios. The aim is to identify and select the best-performing strategy from the aforementioned list. To prevent biased sampling, tasks with an insufficient number of measurements were excluded. Additionally, we selected tasks based on the occurrence probability of FLOPs in tensor operations, as illustrated in Table 3. The latency and throughput of these tasks were recorded by executing them on the computing hardware. The time-to-train gains for the sampled dataset are presented in Table 7. Notably, in the case of CPUs, there is a time-to-train increase of up to 56% for LGBM when utilizing the within_task split strategy during training. GPUs also exhibit an increase of up to 32%.
## 2 Model of the Tilt Tri-Rotor UAV
### Nonlinear Equations of Motion
Gyroscopic moment of the rotors can be written as  
\[\tau_{G}^{b}=\tau_{G1}^{b}+\tau_{G2}^{b}+\tau_{G3}^{b} \tag{10}\]  
\[\tau_{G1}^{b}=J_{1}\overrightarrow{\omega_{1}}\times \overrightarrow{\omega_{b}}+J_{1}\overrightarrow{\omega_{1}}\times \overrightarrow{\omega_{s1}} =J_{1}\omega_{1} \begin{bmatrix}-\sin\alpha_{1}\\ 0\\ -\cos\alpha_{1}\end{bmatrix} \times \begin{bmatrix}p\\ q+\omega_{s1}\\ r\end{bmatrix}  \tag{11}\] \[=J_{1}\omega_{1} \begin{bmatrix}(q+\omega_{s1})\cos\alpha_{1}\\ -p\cos\alpha_{1}+r\sin\alpha_{1}\\ -(q+\omega_{s1})\sin\alpha_{1}\end{bmatrix} \]  
\[\tau_{G2}^{b}=J_{2}\overrightarrow{\omega_{2}}\times \overrightarrow{\omega_{b}}+J_{2}\overrightarrow{\omega_{2}}\times \overrightarrow{\omega_{s2}} =J_{2}\omega_{2} \begin{bmatrix}\sin\alpha_{2}\\ 0\\ \cos\alpha_{2}\end{bmatrix} \times \begin{bmatrix}p\\ q+\omega_{s2}\\ r\end{bmatrix}  \tag{12}\] \[=J_{2}\omega_{2} \begin{bmatrix}-(q+\omega_{s2})\cos\alpha_{2}\\ p\cos\alpha_{2}-r\sin\alpha_{2}\\ (q+\omega_{s2})\sin\alpha_{2}\end{bmatrix} \]  
\[\tau_{G3}^{b}=J_{3}\overrightarrow{\omega_{3}}\times\overrightarrow{\omega_{ b}}=J_{3}\omega_{3} \begin{bmatrix}q\\ -p\\ 0\end{bmatrix}  \tag{13}\]  
where \(J_{i}\) is the moment of inertia of the rotor \(i\), \(\overrightarrow{\omega_{b}}=[p,q,r]^{T}\) represents the rotational angular velocity in the body frame, and \(\overrightarrow{\omega_{si}}\) is the tilting angular velocity of rotor \(i\). According to the Newton-Euler formulation [31], the translational and rotational motion equations of the tilt tri-rotor UAV are derived as  
\[ \begin{cases}\dot{\chi}=V\\ m\dot{V}=mg\epsilon_{3}-R_{BET}Te_{3}+d_{F}\\ \dot{\Theta}=R_{BER}\cdot\omega_{b}\\ I_{b}\omega_{b}=\Gamma-\omega_{b}\times(I_{b}\cdot\omega_{b})+d_{\Gamma}\end{cases}  \tag{14}\]  
where \(\chi=[X,Y,Z]^{T}\) and \(V=[u,v,w]^{T}\) are the position and linear velocity of the vehicle center in the world frame, respectively. Note that \(e_{3}=[0,0,1]^{T}\) is a constant vector, \(T\) is the virtual control force, \(\Gamma=[R,P,Y]^{T}\) is the virtual control torque, \(d_{F}\) and \(d_{\Gamma}\) represent the external disturbances, \(\Theta=[\phi,\theta,\psi]^{T}\) is the Euler angle, \(I_{b}\) is the inertial matrix with respect to the body coordinate system, \(R_{BET}\) is the velocity transformation matrix from the body frame to the world frame, and the rotation matrix from the body frame to the world frame is \(R_{BER}\). \[R_{BET}=R_{EBT}^{T},R_{BER}= \begin{bmatrix}1&\sin\phi\tan\theta&\cos\phi\tan \theta\\ 0&\cos\phi&-\sin\phi\\ 0&\frac{\sin\phi}{\cos\theta}&\frac{\cos\phi}{\cos\theta}\end{bmatrix}  \tag{15}\]  
Based on the above analysis, the mathematical model of the tilt tri-rotor UAV is obtained.
## 5 Improved Tracking Differentiator (SYSTD)  
When the MAR system measures, the MRC adopts a step-by-step motion mode with a fixed step size, resulting in a high-frequency fluctuation of its position feedback signal. High-quality position feedback signals contribute to improving the positioning accuracy of the HCS of the MAR system. Although classical tracking differentiators can reduce differential noise, do not require the derivation of a mathematical model, and are convenient to use, they also have problems such as complex and diverse forms, insufficient stability, large phase delay, or amplitude attenuation. The above problems can be solved by improving the structure of TD and enhancing its performance.
## 2 Materials and Methods
### Thermogravimetric Analyses  
Samples were analyzed using a NETZSCH STA 449 F3 Jupiter(r) TG-DSC instrument (NETZSCH-Geratebau GmbH, Selb, Germany). Samples were weighed into open Al\({}_{2}\)O\({}_{3}\) crucibles and heated in a pure nitrogen (99.999 vol%) atmosphere at a nitrogen flow rate of 40 mL/min from room temperature to 700 \({}^{\circ}\)C, at a constant heating rate of 5 K/min. The sample mass used ranged from 3.5 to 7 mg. A total inert gas flow rate of 60 mL/min of nitrogen was used. To eliminate buoyancy effects, background mass data were recorded using an empty crucible experiment and subtracted from each of the sample measurement data sets. The results obtained are averages of the repeated measurements.
# A Numerical Study of the Performance of Point Absorber Wave Energy Converters
## 1 Introduction
The SPH approach is beneficial for simulating the behavior of wave energy converters and other similar systems where fluid-solid interactions (FSIs) are critical [12; 13]. SPH was used in [14] for a top-mounted pitching absorber simulation, improving displacement predictions. SPH models also prove robust in handling complex wave phenomena. However, they do not seem to be considered much for submerged point absorber wave energy converters. The flow around a submerged buoy was studied through a mesh-based CFD approach in [15]. Three different submerged cylinder diameters were considered (1.4, 1.8, and 2.1 m), with the same mass of 2.9 tonnes and a buoy volume of 8.4 m\({}^{3}\). The authors compared the buoy geometries' responses with four modeling approaches: a viscous CFD simulation, the Morison equation with drag force using numerical quadrature, the Morison equation with translation and rotation, and the Morison equation on a vertical cylinder. They concluded that a cylinder with a diameter of 1.4m is the most suitable design for a high buoy displacement. They recommend that the quadrature-based Morison drag method always be used for simulating submerged mooring buoys, as this method shows higher fidelity than others and is more general. A submerged circular cylinder with a radius of 0.1m with mooring, spring, and damper forces was simulated in [16] using the fast-fictitious-domain method for treating solid objects. The authors used a 1.6 m water depth and a 0.2 m submergence depth for the cylinder, with an inlet wave height of 0.03 m. They performed CFD simulations for various wave frequencies and heights using the Volume-of-Fluid (VOF) multiphase model. They established that, as the wave height rises, the optimal performance of the submerged cylinder WEC is achieved with larger damping coefficients than those calculated with the linear theory. CFD can therefore be used to simulate floating-point absorbers, but limited information is currently available on submerged point absorbers. SPH, while effective for the problem at hand, is computationally expensive compared to a mesh-based approach. Although past CFD studies of immersed point absorbers were mostly based on a cylindrical buoy shape,the performance of a floating-point absorber depends on the floating buoy [17]. The current investigation aimed to identify the most efficient float shape for a SWEC through a mesh-based CFD analysis using a VOF-coupled FSI technique. Various aspects of SWECs were analyzed, especially regarding the optimization of the float design under various wave conditions. The analysis also considered other parameters, such as the submergence depth and spring constant. Finally, power absorption and wave conversion efficiency were investigated.
# Determining the Best Dressing Parameters for External Cylindrical Grinding Using MABAC Method
## 2 Materials and Methods  
### MABAC Method for MCDM  
The MABAC method was introduced by [PERSON] and [PERSON] in 2015. This method has the following characteristics [36]: This is one of the compensatory methods; the attributes are unrelated to one another; the qualitative attributes are transformed into quantitative attributes. This method is commonly used in railway station evaluation, wind farm location selection, and site energy generation technology [36]. In practice, solving this MCDM problem can completely use other MCDM methods such as TOPSIS, MARCOS, etc. However, in this work, the MABAC method was applied to solve the MCDM problem both to determine the best dressing mode for cylindrical external grinding and to compare this method with many used ways in mechanical processing (TOPSIS and MARCOS method).  
The steps to solve the MCDM problem by the MABAC method are as follows [36]:  
_Step_ 1: Generating the initial matrix by:  
\[\mathrm{X}=\left[\begin{array}{cccc}r_{11}&\cdots&r_{1j}&\cdots&r_{1n}\\ \vdots&\ddots&\vdots&\ddots&\vdots\\ r_{1}&\cdots&r_{ij}&\cdots&r_{in}\\ \vdots&\ddots&\vdots&\ddots&\vdots\\ r_{m1}&\cdots&r_{mj}&\cdots&r_{mn}\end{array}\right] \tag{1}\]  
In which, \(m\) is the alternative number; \(n\) is the number of criteria.  
_Step_ 2: Determining the normalized values \(r_{ij}^{*}\) by:  
\[r_{ij}^{*}=\frac{r_{ij}-r_{i}^{-}}{r_{i}^{+}-r_{i}^{-}} \tag{2}\]  
\[r_{ij}^{*}=\frac{r_{ij}-r_{i}^{+}}{r_{i}^{-}-r_{i}^{+}} \tag{3}\]  
In which, \(i\) = 1, 2,..., \(m\) and \(j\) = 1, 2,..., \(n\). In addition, Equation (2) is used if the criterion \(j\) is as bigger as better and Equation (3) is used if the criterion \(j\) is as smaller as better. In addition, \(r_{i}^{+}=max(r_{1},r_{2},\ldots,r_{m})\) and \(r_{i}^{-}=min(r_{1},r_{2},\ldots,r_{m})\).  
_Step_ 3: Finding the elements of weighted matrix by:  
\[v_{ij}=w_{j}+w_{j}\times r_{ij}^{*} \tag{4}\]  
_Step_ 4: Calculating the border approximation area matrix:  
\[\mathrm{g}_{j}=\left(\prod_{i=1}^{m}v_{ij}\right)^{1/m} \tag{5}\]  
where in, \(j\) = 1, 2,..., \(n\).  
_Step_ 5: Calculating the distance between the alternatives and the border approximation area by the following Equation:  
\[q_{ij}=v_{ij}-\mathrm{g}_{i} \tag{6}\]  
In Equation (6), \(i\) = 1, 2,..., \(m\) and \(j\) = 1, 2,..., \(n\).  
_Step_ 6: Estimating the total distances of each alternative from the approximate border area:  
\[S_{i}=\sum_{j=1}^{m}q_{ij}\;\;i=1,\;2,\ldots,\;m \tag{7}\]  
_Step_ 7: The order of alternatives is determined by maximizing \(S\).
###### Abstract  
Background: Overcrowded emergency departments (EDs) create significant challenges for patient management and hospital efficiency. In response, Amiens Picardy University Hospital (APUH) developed the \"Prediction of the Patient Pathway in the Emergency Department\" (3P-U) model to enhance patient flow management. Objectives: To develop and clinically validate an explainable artificial intelligence (XAI) model for hospital admission predictions, using structured triage data, and demonstrate its real-world applicability in the ED setting. Methods: Our retrospective, single-center study involved 351,019 patients consulting in APUH's EDs between 2015 and 2018. Various models (including a cross-validation artificial neural network (ANN), a k-nearest neighbors (KNN) model, a logistic regression (LR) model, and a random forest (RF) model) were trained and assessed for performance with regard to the area under the receiver operating characteristic curve (AUROC). The best model was validated internally with a test set, and the F1 score was used to determine the best threshold for recall, precision, and accuracy. XAI techniques, such as Shapley additive explanations (SHAP) and partial dependence plots (PDP) were employed, and the clinical explanations were evaluated by emergency physicians. Results: The ANN gave the best performance during the training stage, with an AUROC of 83.1% (SD: 0.2%) for the test set; it surpassed the RF (AUROC: 71.6%, SD: 0.1%), KNN (AUROC: 67.2%, SD: 0.2%), and LR (AUROC: 71.5%, SD: 0.2%) models. In an internal validation, the ANN's AUROC was 83.2%. The best F1 score (0.67) determined that 0.35 was the optimal threshold; the corresponding recall, precision, and accuracy were 75.7%, 59.7%, and 75.3%, respectively. The SHAP and PDP XAI techniques (as assessed by emergency physicians) highlighted patient age, heart rate, and presentation with multiple injuries as the features that most specifically influenced the admission from the ED to a hospital ward. These insights are being used in bed allocation and patient prioritization, directly improving ED operations. Conclusions: The 3P-U model demonstrates practical utility by reducing ED crowding and enhancing decision-making processes at APUH. Its transparency and physician validation foster trust, facilitating its adoption in clinical practice and offering a replicable framework for other hospitals to optimize patient flow.  
**Keywords:** artificial intelligence; explainable artificial intelligence; emergency medicine; patient pathway
# Advances in Piezoelectric Jet and Atomization Devices
## 5 Summary and Outlook  
### Summary  
1. According to the constituents and status of spray material, the spray can be mainly classified into two types. When the spray material accounts for an absolute high proportion of liquid, it is defined as a spray processing, otherwise it is regard as an atomization processing.
2. The development history of four types of piezoelectric micro jet devices were summarized, and their working principles, advantages, and disadvantages were also analyzed.
3. Both the development history and structural characteristics of static mesh atomizer and dynamic mesh atomizer were scientifically reviewed. Additionally, their working mechanisms, advantages, and shortages were revealed.
4. The application of piezoelectric jet and atomization devices can be generalized from three different aspects including cell printing, spray cooling, and drug delivery.
## 3 Experimental Methods  
### System Configuration  
Figure 3 shows the appearance of the UAV used in this experimental demonstration. We employed two UAVs (LAB6106 FL and UAV-E6106 FA2, Eams Robotics Co., Ltd., Fukushima, Japan) with six rotors each. They are integrated with flight controllers (PixHawk2, ProfiCNC Pty Ltd, Victoria, Australia) that enable autonomous navigation through real-time kinematics (RTK) positioning.  
Figure 4 provides an overview of the key system configuration. The system has two functions; 60 GHz band data transfer and space-time synchronization. The system controller controls the comprehensive development system through the space-time synchronization unit (STSU), which coordinates time synchronization and 60 GHz band transceiver operations. The STSU was connected to the wireless two-way interferometry (Wi-Wi) modules, which enables space-time synchronization between the UAVs. It also timestamped the output logs from the 60 GHz transceiver, aligning them with the shared time reference between the two UAVs' systems.  
Throughout the experiment, the positions, altitudes, and identifiers of all aircraft were communicated to the ground base station via drone mappers. This allows us to real-time monitor their states to ensure safety and operational integrity [24].
# Hypergraph-Clustering Method Based on an Improved Apriori Algorithm
## 4 Experiments and Analysis
### Experimental Comparison Results
#### 4.6.1 Comparison of Experimental Results on PPI Networks
The FM index value of the HDHPA method is very close to it, with values of 0.741 and 0.734, respectively, and the ARI value of the HDHPA method is higher than other methods. For the 16-item dataset, the FM-index value and the ARI value of the HDHPA method are the largest. In the HOXA10 dataset, the FM-index value of the HDHPA method far exceeds that of other methods, but the ARI value of the kD method is greater than that of the other methods. In the NNMT dataset, the FM-index value of the kh method is the largest. The HDHPA method values of the FM-index are very close to it, 0.502 and 0.496, respectively. The ARI values of the AD and the HDHPA are very close to eachother, 0.590 and 0.576, respectively. For the MBP dataset, the FM-index and ARI values of the HDHPA are higher than those of other methods, 0.732 and 0.799, respectively. The experimental results show that no method performs significantly better than others. In most cases, our proposed HDHPA method combined with clustering produces the best results. The clustering results of the other four comparison methods are similar for the 16-item dataset, indicating that the precision gain is relatively limited in datasets with high clustering coefficients. The kD method produces better clustering results for the HOXA10 and Natoc_0297 datasets than the Ah and AD methods, indicating that when there is more noise and isolated points, the efficiency of the k-medoids method is higher than that of the Apriori. In most cases, the results of the Ah method are more sensitive to data types and less robust than the other four clustering methods.