esa-sceva/satcom-chunk-collection huggingface dataset:
# Bol Looseness Detection Based on Piezoelectric Impedance Frequency Shift
## 4 The Experimental Results and Analysis
### The M12 Bol Experimental Results  
An M12 bolt with the piezoelectric patch on its head is used for the experiment. The M12 bolt is placed in the side bolt position, as shown in Figure 1, and the sweep frequency band is chosen from 4.1 MHz to 4.14 MHz based on Figure 3. The applied force is set from 0 to 35 kN on the center bolt, so the M12 bolt endures an axial force from 0 to 17.5 kN.  
Figure 6: Susceptance frequency changes of the M16 bolt under different forces.  
Figure 7: Frequency change for M16 bolt with maximum 35 kN bolt preload. (**a**) Real part; (**b**) Imaginary part.  
As shown in Figure 8, the specified peak frequencies of the admittance signatures, including the real part and the imaginary part, decrease as the bolt preload's increase, and the specified frequency changes linearly with the bolt preload. This trend is similar to that of the M16 bolt experimental results, and once again it demonstrates that by measuring the specified frequency shift of the piezoelectric admittance signature, the bolt preload can be determined. After each experiment, the bolt with the piezoelectric patch on its head is rearranged to investigate the influence of the installation on the experimental results. The experimental data, as shown in Figure 8, have little variation, and this may be due to the changes of the boundary conditions. Importantly, for each experiment, the specified frequency change rates of all three experiments are almost the same, which is represented by the slope of the fitted line. Thus, in the application of this method, the frequency change rate as a feature parameter of the bolt preload is also a better choice.
# Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia
## 3 Anesthesia  
The effects of drugs on patients in the operating room vary with drug dosage, from patient to patient, and with time. Different doses of drugs result in different concentrations in various tissues, producing a range of therapeutic and sometimes undesirable responses. Responses depend on drug pharmacokinetics (time course of drug concentration in the body) and drug pharmacodynamics (the relationship between drug concentration and drug effect). These processes may be influenced by factors including pre-existing disease, age, and genetic variability. Patient responses to drugs may also dynamically altered by factors such as temperature, pH, circulating ion and protein concentration, levels of endogenous signaling molecules, and coadministration of the drug in the operating room environment [1].  
General anesthesia consists of providing the patient with a reversible state of loss of consciousness (hypnosis), analgesia, and muscle relaxation to allow the patient to be operated on without pain by administering anesthetic drugs intravenously or through inhalation, providing maximum safety, comfort, and vigilance during the surgical act. The description of the general process with its variables is shown in Figure 1. As can be seen, the variables that can be manipulated are the anesthetics, relaxants, and serums. The disturbances in the system are signals that can occur at any time, such as surgical stimulation, blood loss, etc. The output variables are divided into measurable and non-measurable, and the main interest in the control of anesthesia is the non-measurable variables: hypnosis, analgesia, and muscle relaxation.  
In practice, the anesthesiologist has to observe and control a large number of hemodynamic and respiratory variables as well as clinical signs that indicate the state of hypnosis and analgesia.  
Most of the drugs used do not only operate on the desired effect but alter other aspects, for example, the effects of the anesthetic drug propofol not only affect the level of hypnosis but also increase the level of analgesia. The drug remifentanil, whose main objective is to increase the level of analgesia, has the same behavior, but as a side effect also increases the level of hypnosis. Due to this cross effect between these drugs, the anesthesiologist must adjust the desired levels of hypnosis and analgesia with different amounts of both drugs. From the point of view of control engineering, this problem is a problem of multiple inputs and multiple outputs.  
In the daily work routine, the anesthesiologist calculates the amount of the necessary drug with the help of dose regimes given by the supplier of the drug, which, in most cases, are based on the patient's body weight.  
For a more detailed description of the process of general anesthesia, the reader is referred to references [1; 6; 57].
## 3 Application Areas of IoT and UAV in Smart Farming
### Spraying Pesticides and Fertilizers  
UAVs have proven to effectively and quickly spray pesticide and fertilizers compared to a speed sprayer or a wide-area sprayer. The measure of pesticides per hectare of farmland correlates to the risks of worker ailments and environmental pollution. The use of pesticides can be minimized with UAVs by achieving large-scale decontamination of up to 50 hectares per day and requires only about 10 min of work per 0.5 hectare area [6]. One of the major motives of using UAVs is to reduce labor requirements. For instance, authors in [51] conducted a study in citrus farms to determine the optimum level of preventive work by spraying fertilizers from various heights using a UAV.
## 3 The Clustering Protocol  
The main steps of the clustering protocol in this paper are as follows:  
Figure 4: A schematic diagram of the node deployment for a single cellular grid.  
Figure 3: A schematic diagram of a 2-fold coverage-based node deployment strategy in a cellular network.  
1. The sensor nodes in the area are deployed according to the 2-fold coverage-based node deployment strategy introduced in Section 2.
2. Centering on the BS, the monitoring area is divided into four small cells, and a certain number of CHs in each cell are selected to meet the uniformity of the CHs distribution.
3. All CHs send broadcasts to the surrounding ordinary nodes, and the latter sequentially add the clusters to which the strongest signals belong according to the strength of the signals received, notifying the corresponding CHs to complete the cluster construction.
4. Combined with practical applications, this paper optimizes the Dijkstra algorithm to achieve the selection of optimal information transmission paths among the CHs.
5. Data are transmitted and energy is updated.  
To minimize the total energy consumption and balance the energy consumption of the nodes in the network, we reselect the CHs in the area after each round of data transmission. Additionally, Step 1 is called the node deployment phase of the protocol, Steps 2 and 3 are collectively called the cluster setup phase of the protocol, and Step 4 is called the inter-cluster routing mechanism (ICRM) establishment phase of the protocol. Steps 2, 3, and 4 are collectively called the preparation phase of the protocol. Finally, Step 5 is called the stabilization phase of the protocol.  
Before entering the stabilization phase, the CH of each cluster needs to create a corresponding time division multiple access (TDMA) schedule and send a control message named _Schedule_Msg_ to its member nodes in the form of (_Node NO.1, Time Slot 1; Node NO.2, Time Slot 2;......._). The time slot allocation of the clustering routing protocol in this paper is provided in Figure 5. A flowchart of the clustering routing protocol in this paper is presented in Figure 6.
# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
## 2 Study Area and Datasets
### Datasets
#### 2.2.2 Satellite Precipitation Products  
The SPPs evaluated in this study include the three newest released IMERG V06 products. As compared to the predecessor TMPA, the IMERG products have a finer spatial and temporal resolution (0.1\({}^{\circ}\) and 0.5 h), as well as a larger spatial coverage (60\({}^{\circ}\)N-60\({}^{\circ}\)S) [28]. In response to different user requirements for immediacy and accuracy, the IMERG products are designed with three runs: IMERG Early Run (ER), IMERG Late Run (LR) and IMERG Final Run (FR). The former two are near-real-time products with latencies of 4 h and 14 h, respectively. They can be used for applications where a high degree of timeliness is required, such as disaster warning and prevention. The main difference between them is that IMERG LR incorporates more passive microwave estimates and uses both forward and backward propagation, whereas IMERG ER only uses forward propagation. The research-level product IMERG FR, has a longer latency of 3.5 months. Compared to IMERG ER and IMERG LR, this product is calibrated by monthly observations from the Global Precipitation Climatology Centre (GPCC), and thus provides more accurate precipitation information (especially in areas covered by the GPCC rain gauges). The latest V06 release of IMERG in 2018 is chosen for this evaluation. These products can be obtained from the Precipitation Measurement Missions website ([[https://gpm.nasa.gov/data/directory](https://gpm.nasa.gov/data/directory)]([https://gpm.nasa.gov/data/directory](https://gpm.nasa.gov/data/directory)), accessed on 14 December 2021).
## Materials and Methods
### The Proposed Method  
Test Statistic for the Equality of Two Covariance Matrices  
We consider that the two covariance matrices (\(C_{1}\),\(C_{2}\)) from the bi-temporal PolSAR images are independent and follow a Wishart distribution as follows:\[C_{1}\in W(p,m,\Sigma_{C_{1}}) \tag{4}\] \[C_{2}\in W(p,n,\Sigma_{C_{2}})\]  
where \(p\) represents the dimensions of \(C_{1}\) and \(C_{2}\), and \(m\) and \(n\) represent the number of looks of \(C_{1}\) and \(C_{2}\), respectively. \(\Sigma_{C_{1}}\) and \(\Sigma_{C_{2}}\) represent the dispersion matrix of \(C_{1}\) and \(C_{2}\), and the MLE of \(\Sigma_{C_{1}}\) and \(\Sigma_{C_{2}}\) can be shown as follows:  
\[\Sigma_{C_{1}}^{MLE}=\frac{1}{m}C_{1}\text{ and }\Sigma_{C_{2}}^{MLE}=\frac{1}{n }C_{2}. \tag{5}\]  
Assuming that the null hypothesis \(H_{0}:\sum_{C_{1}}=\sum_{C_{2}}\) means that the two matrices are equal and there is a strong possibility of non-change, the alternative hypothesis \(H_{1}:\sum_{C_{1}}\
eq\sum_{C_{2}}\) means that the two matrices are different and there is a strong possibility of change [5].  
We suppose that the test statistics based on MLE have joint densities \(f(\sum_{C_{1}},\sum_{C_{2}},\theta)\), where \(\theta\) is the set of parameters of the probability function that has generated the data. Then \(H_{0}\) states that \(\theta\in H_{0}\), and the likelihood ratio of the test statistic is shown as follows [5]:  
\[Q=\frac{\max_{\theta\in H_{0}}L(\theta)}{\max_{\theta\in\Omega}L(\theta)}\text { where }L(\theta)=f(\sum_{C_{1}}\sum_{C_{2}}\theta)=f(\sum_{C_{1}}\theta)f(\sum_{C_{2}}\theta) \tag{6}\]  
where \(\Omega=H_{0}\cup H_{1}\), \(L(\cdot)\) is the likelihood function, and \(f(\cdot)\) is the frequency function.  
Putting Equation (3) into Equation (6) and assuming \(\Sigma_{C_{1}}=\Sigma_{C_{2}}=\Sigma\), \(L(\theta)\) can be expressed as follows [5]:  
\[L(\theta)=\frac{1}{\Gamma_{p}(n)\Gamma_{p}(m)}|\Sigma|^{-n-m}|C_{1}|^{n-p}|C_ {2}|^{m-p}\exp\{-tr[\Sigma^{-1}(C_{1}+C_{2})]\}\in W_{C}(p,n+m,\Sigma) \tag{7}\]  
The MLE of Equation (6) can be simplified as follows [5]:  
\[Q=\frac{L(\overline{\Sigma})}{L_{C_{1}}(\overline{\Sigma}_{C_{1}})L_{C_{2}}( \overline{\Sigma}_{C_{2}})}=\frac{(n+m)^{p(n+m)}}{n^{pn}m^{pn}}\frac{|C_{1}|_{ n}|C_{2}|^{m}}{|C_{1}+C_{2}|^{n+m}} \tag{8}\]  
Assuming \(m=n\), the CI of the bi-temporal PolSAR images can be denoted by \(d\):  
\[d=-2\rho lnQ=-2\rho n(2 pln2+ln|C_{1}|+ln|C_{2}|-ln|C_{1}+C_{2}|) \tag{9}\]  
where \(\rho\) can be represented as follows:  
\[\rho=1-\frac{2p^{2}-1}{6p}(\frac{1}{n}+\frac{1}{m}-\frac{1}{n+m}). \tag{10}\]
## 3 Results and Discussion
### Water Absorption  
After a long deposition time, the particles will build up to form a layer. Figure 5a shows the macroscopic morphology of the rH/CS/PCL MPs3 layer formed after stacking rH/CS/PCL MPs3 for 10 h. The weight in grams per square metre (GSM) of the resulting mats was determined according to Formula (2).  
\[\text{GSM}=\frac{M_{1}-M_{2}}{S} \tag{2}\]  
where \(M_{1}\) is the mass of the bowl-shaped particles and their substrate; \(M_{2}\) is the mass of the substrate; \(S\) is the area of the layer. The calculated GSMs for the layers of CS/PCL MPs, rH/CS/PCL MPs1, rH/CS/PCL MPs2, and rH/CS/PCL MPs3 are all in the region of 6.9 g/m\({}^{2}\). All layers in this experiment were obtained from single-channel work. In the actual production process, bowl-shaped particles can be mass-produced by increasing the number of working channels, and the productivity of bowl-shaped particles will increase several times or even a dozen times with an increase in the number of working channels.  
Figure 4: Infrared spectra of hirudin, CS/PCL MPs, and rH/CS/PCL MPs.  
Figure 5: Macroscopic morphology of the rH/CS/PCL MPs3 layer: (**a**). Hydrophobic angle test plot of CS/PCL MPs, rH/CS/PCL MPs1, rH/CS/PCL MPs2 and rH/CS/PCL MPs3: (**b**), \(n=3\). *** \(p<0.001\).  
The hydrophobic angle testing of the prepared microparticles shows that when unloaded with hirudin, the prepared CS/PCL MPs formed a contact angle of 79\({}^{\circ}\), which is less than ninety degrees (Figure 5b). It can be seen that the surface of the microparticles at this point has an affinity for water. As the concentration of hirudin in the particles increased, the contact angle gradually decreased to 69\({}^{\circ}\), 47\({}^{\circ}\), and 30\({}^{\circ}\). It can be seen that the contact angles formed by the water droplets on the surface of the microparticles were all smaller than the contact angles formed when they were not loaded and showed a gradual decrease. It can be found that the prepared bowl-shaped microparticles have hydrophilic properties and show better hydrophilicity with the loading of drugs.
## 4 Case Study: The Grottalunga Viaduct
### Analysis of the Seismic Response in the Transverse Direction
#### 4.2.4 Modal Pushover Analysis (MPA) in Transverse Direction  
When the seismic response is not strongly non-linear, the procedure that usually provides the better estimation of the actual nonlinear response obtained via nonlinear time history analysis is the MPA proposed by [PERSON] and [PERSON] [PERSON] and [PERSON] (2016); [PERSON] and [PERSON] (2016). This multi-run PO procedure requires that pushover analyses are performed independently for each relevant mode with an invariant load pattern. Thus, the pushover curve for the seventh mode is evaluated and shown in Figure 8b, and the results are summarized in Table 1. The first event is the yielding of the plugs of the restraints on the left and right abutments that occur for \(d_{7,1}\) = 10 mm and PGA\({}_{7,1}\) = 0.099 g. The second one is the failure of the plug of the right abutment, which occurs for d\({}_{7,2}\) = 13 mm and a PGA\({}_{7,2}\) = 0.121 g; then the failure of the plug on the left abutment (d\({}_{7,3}\) = 17 mm and a PGA\({}_{7,3}\) = 0.157 g) is achieved, and lastly the exit of the supports on both the left and right abutments from their seats, with the beginning of further sliding with friction (d\({}_{7,A}\) = 19 mm and a PGA\({}_{7,A}\) = 0.181 g).  
In Figure 10a-c, the deformed shape obtained by the single-run pushover analysis according to the second mode and seventh mode is compared with the associated deformed shape obtained via MPA for three different levels of PGA. They are PGA = 0.099 g, which represents the value corresponding to the end of the elastic behavior, PGA = 0.274 g, for which the hinges at the base of the piers exhaust their plastic rotation capacity; and the design PGA = 0.333 g. From the figures, it can be recognized that the displacement demand at the end of the drop-in span and the piers can be evaluated by the 2 nd mode single-run pushover analysis, and thus the difference with MPA in assessing the collapse acceleration is less than 2%, while the displacement demand at the abutment supports estimated with MPA is 47% larger for the collapse PGA value.  
Figure 9: Single-run PO analysis with multimodal load pattern: (**a**) PO curve; (**b**) events and related displacement and peak ground acceleration.
# Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning
## 4 Discussion and Business Impact  
We conclude that the application of causal discovery with reinforcement learning in the context of supply chain management, particularly for identifying the root causes of late delivery risks, yields several actionable business insights:  
* **Improving Shipping Strategies:** Our analysis revealed that the choice of shipping mode (\(X_{16}\)) has a direct and significant impact on actual shipping days (\(X_{2}\)), which, in turn, greatly influences the risk of late deliveries (\(X_{7}\)). The causal strength of 9.24 between shipping mode and actual shipping days highlights the critical role of selecting optimal shipping methods to minimize delays. By leveraging our causal discovery approach, businesses can optimize their shipping strategies, prioritizing methods that reduce delivery times and thus mitigate late delivery risks. This can lead to improved customer satisfaction and reduced operational costs.
* **Enhancing Delivery Status Monitoring:** The direct relationship between delivery status (\(X_{6}\)) and late delivery risk (\(X_{7}\)), with a causal strength of 10.66, underscores the importance of real-time monitoring and management of delivery processes. Our method allows businesses to identify the statuses most predictive of delays, facilitating targeted interventions to address bottlenecks. By improving the monitoring of delivery statuses, companies can proactively reduce late deliveries, enhancing supply chain efficiency and providing a more reliable service.
* **Strategic Pricing and Inventory Decisions:** Beyond delivery concerns, our analysis found that 'Order Item Profit Ratio' (\(X_{13}\)) and 'Order Item Product Price' (\(X_{11}\)) influence 'Benefit per Order' (\(X_{4}\)). While not directly related to late delivery risks, these findings are crucial for profitability. The causal strength of 2.20 for the profit ratio suggests that optimizing profit margins is key to maximizing order-level benefits. By applying our causal discovery approach, businesses can refine their pricingstrategies and inventory decisions to enhance profitability, leading to more informed decision-making and improved financial performance across the supply chain.  
Our approach not only identifies the key drivers of late deliveries but also offers a broader understanding of factors influencing overall supply chain performance. Integrating these insights into daily operations can drive both immediate and long-term improvements in efficiency, customer satisfaction, and profitability. Future research could further explore these relationships to refine and extend these strategies, ensuring that supply chain operations remain adaptive and resilient amid evolving challenges.
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
## 3 Calculation Results and Parameter Analysis of Fire Resistance of Prestressed Concrete T-Beam
### Analysis of Parameters Affecting the Fire Resistance of Prestressed Concrete T-Beams
#### 3.3.5 Effect of Different Concrete Strengths on Fire Resistance  
The influence of different concrete strengths on the fire resistance of prestressed concrete T-beams was compared and analyzed at load ratios of \(n=0.4\), \(n=0.6\), and \(n=0.8\), as shown in Figure 14. As the concrete strength level increases, the fire resistance of prestressed concrete T-beams shows an increasing trend. Furthermore, the increase is gradually accelerated with the increase in concrete strength. The magnitude of the discount of the high-temperature properties is only related to the fire temperature for concrete material. Under the same fire conditions, the residual strength of concrete with high strength is greater than that of concrete with low strength, which leads to increased fire resistance. A significant increase occurs when the concrete strength is C50. With load ratios of \(n=0.4\), \(n=0.6\), and \(n=0.8\), the fire resistance increases by 20%, 30%, and 50%, respectively. Therefore, in engineering design, if there is a requirement for structural fire resistant design, it is recommended that concrete strength classes of C50 or above be used for structural fire resistant design.  
Figure 14: Curves of the effect of different concrete strength grades on the fire resistance of T-beams.  
Figure 13: Curves of the effect of different concrete protection layer thicknesses on the fire resistance of T-beams.
# Nutritional Profile and Antioxidant Properties of Hemp (_Camanabis sativa_ L.) Seed from Romania
## Materials and Methods
### Determination of Proximate Composition and Energetical Values  
The proximate chemical analysis of the hemp seed samples aligned with standardised methodologies established by the Association of Official Analytical Chemists (AOAC) [22]. Specifically, moisture content was determined using method 930.15, while ash content was analyzed using method 942.05. The crude protein content was measured by applying the Kjeldahl technique, as outlined in method number 2001.11, utilizing a Kjeltec system (Velp, Padua, Italy) for accuracy, with a conversion factor of 6.25. Method number 978.10 was used to assess crude fibre, with measurements taken via the Fibertec(tm) 2010 system (Foss, Padua, Italy). Lipid content was evaluated using method 920.39 through a Soxlet Extraction System (Raypa, Barcelona, Spain, Raypa SX-6 MP). The carbohydrate content was not directly measured but instead was calculated by subtracting the total percentages of moisture, lipids, proteins, and ash from 100%. Additionally, the caloric or energy value of the samples was computed following the procedure detailed by [PERSON]. et al. [23]. This method utilised Equation (1), applying the caloric conversion factors where each gram of carbohydrates contributed four calories, each gram of protein provided four calories, and each gram of fat supplied nine calories:Energy value (kcal/100 g) = carbohydrates (%) \(\times\) 4 + lipids (%) \(\times\) 9 + proteins (%) \(\times\) 4 (1)