esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection  
#### 2.2.1 Remote Sensing-Based Gridded Dataset of AET and GPP  
The Global Land Evaporation Amsterdam Model (GLEAM) dataset v3.8a was used for 36 years from 1982 to 2017 [53; 54]. The GLEAM dataset provides various components of terrestrial evapotranspiration estimates based on remotely sensed observations, such as transpiration, open-water evaporation, bare land evaporation, sublimation, and interception losses. The WUE was calculated using annual actual evaporation (AET) estimates (mm yr\({}^{-1}\)). The values of AET were lower than the potential evaporation (PET) values calculated through the Priestley-Taylor formulation [53]. The PET values were converted into AET based on a multiplicative stress factor 5 (-) that ranged from 1 to 0. The AET estimates from the GLEAM algorithm have been validated in recent studies against FLUXNET data--eddy covariance towers worldwide. Recently, [PERSON] et al. [54] reported an average of 0.81-0.86 correlation based on 91 FLUXNET stations. More recently, [PERSON] et al. [55] documented an average correlation ranging between 0.68 and 0.94 based on 5 FLUXNET stations. The GLEAM AET dataset performs better than other algorithms' evaporation datasets in several regions and has demonstrated better agreement with the estimates of the Budyko, LSA-SAF, and Makkink datasets [55; 56; 57]. The GLEAM products are available within the GLEAM platform at daily, monthly, and yearly timescales and at 0.25\({}^{\circ}\) arc degree spatial resolution (NETCDF files) (www.gleam.eun (accessed on 1 March 2023)).  
The study utilized GPP data spanning 36 years from 1982 to 2017. GPP estimates were derived using the Eddy Covariance-Light Use Efficiency (EC-LUE) model [58], which integrates several key environmental variables, including photosynthetically active radiation (PAR), air temperature, the leaf area index (LAI), CO\({}_{2}\) concentrations, radiation, and vapor pressure deficit (VPD) [59]. Previous studies have demonstrated the EC-LUE model's ability to accurately capture GPP variability across various ecosystems and agricultural areas [60; 61; 62; 63]. Cross-validation has further shown the model's capability to spatially and temporally reproduce GPP variability across different ecosystem types, surpassing alternative methods and algorithms [64]. Validation against FLUXNET 2015 tower data has confirmed the model's reliability, with an average R\({}^{2}\) of 0.65 and substantial capture of spatial variability [59]. The latest version of the annual EC-LUE GPP dataset is globally available at a spatial resolution of 0.05 arc degree ([[https://doi.org/10.6084/m9.figshare.8942336.v3](https://doi.org/10.6084/m9.figshare.8942336.v3)]([https://doi.org/10.6084/m9.figshare.8942336.v3](https://doi.org/10.6084/m9.figshare.8942336.v3)) (accessed on 1 March 2023)). Subsequently, the data were resampled to a spatial resolution of 0.25\({}^{\circ}\) arc degree using the nearest neighbor method to match the resolution of the GLEAM-AET dataset.
# On Flexural Performance of Girder-To-Girder Wet Joint for Lightweight Steel-UHPC Composite Bridge
## 4 Experimental Test of the Second Generation T-Shaped Joint
### Discussion
#### 4.3.3 Ultimate Flexural Capacity  
The analysis of the structural stiffness indicates that the steel rebars yield before the buckling of the I-shaped steel web. Thus, the calculation of the ultimate flexural capacity of the structure should consider the assumption of the yield of the steel rebar. The cracked section (Section III in Figure 9) was employed to calculate the section characteristics. Figure 16 shows the calculation diagram.  
\[M_{u,\rm{ULS}}=f_{ct}A_{c}y_{c0}+f_{y}A_{r}y_{r0}+f_{y}\frac{y_{s0}}{y_{r0}}A _{s1}y_{s1}+\frac{1}{3}f_{y}b_{s2}\bigg{(}\frac{y_{s0}}{y_{r0}}y_{s2l}{}^{2}+ \frac{y_{y0}}{y_{r0}}y_{s2c}{}^{2}\bigg{)}+f_{y}\frac{y_{y0}}{y_{r0}}A_{s3}y_{s3} \tag{6}\]  
where \(f_{ct}\) denotes the tensile strength of UHPC; \(A_{c}\) is the effective tensile area of UHPC deck; \(y_{c0}\) is the distance from the centroid of UHPC panel to the neutral axis of cracking section; \(f_{y}\) is the yield strength of reinforcement; \(A_{r}\) is the area of tensile reinforcement; \(y_{r0}\) is the distance from the centroid of  
Figure 15: Prediction value and test results of joint interface crack width.  
reinforcement to the neutral axis; \(y_{s1}\) is the distance from the centroid of the I-shaped steel girder to the neutral axis of the cracked section; \(A_{s1}\) is the area of the upper flange of the I-shaped steel girder; \(y_{s1}\) is the distance from the centroid of the upper flange of the I-shaped steel girder to the neutral axis of the cracked section; \(b_{s2}\) is the thickness of the web; \(y_{s2}\) is the distance between the centroid of the tension part of the web and the neutral axis of the composite girder; \(y_{s2c}\) is the distance between the centroid of the compression part of the web and the neutral axis of the composite girder; \(y_{0}\) is the height from the neutral axis to the bottom of the steel girder; \(A_{s3}\) is the area of the lower flange of the I-shaped steel girder; and \(y_{s3}\) is the distance from the centroid of the lower flange of the I-shaped steel girder to the neutral axis of the cracked section.  
According to Equation (6), the flexural capacity of the specimen is 650 kN-m, which agrees well with the test result (654 kN-m).
###### Abstract  
The aim of this study was to determine body composition influence on cardiorespiratory fitness and metabolic markers in physically inactive individuals with insulin resistance (IR). Nineteen overweight and obese (body mass index [BMI] 25.0-29.9 kg\(\cdot\)m\({}^{-2}\); \(\geq\) 30.0 kg\(\cdot\)m\({}^{-2}\), respectively) patients diagnosed with IR (5 men and 14 women; age: 32.74 \(\pm\) 10.07 years; BMI: 32.5 \(\pm\) 4.60 kg\(\cdot\)m\({}^{-2}\)). The body composition included BMI, fat mass, and fat-free mass. Cardiorespiratory fitness was measured by maximal oxygen uptake (VO\({}_{2}\)max). Metabolic markers included maximal fat oxidation, fasting glucose, and insulin. IR was determined by homeostatic model assessment (HOMA-IR). The results of the partial correlations (i.e., body mass, age, and sex) reported that fat-free mass, fat mass, and BMI were significantly correlated with VO\({}_{2}\)max. Additionally, the multiple linear regression model indicated that fat-free mass and BMI explained the variance of VO\({}_{2}\)max by 89%. However, no substantial correlations were reported between fat mass or fat-free mass with HOMA-IR, fasting glucose, or insulin. This study concluded that a higher percentage of fat-free mass and lower BMI is positively related to better cardiorespiratory fitness despite the IR status of the participants analyzed.  
Keywords:overweight; obesity; body composition; fitness; insulin resistance +
Footnote †: journal:
## 2 Materials and Methods
Figure 1:1  
Equations (7)-(10) present the heat transfer from the heat source to the working fluid at the ORC evaporator (\(\dot{Q}_{Eva,ORC}\)), the heat released from the condenser (\(\dot{Q}_{Cond}\)), the heat absorbed at the MT evaporator (\(\dot{Q}_{Eva,MT}\)), and the LT evaporator (\(\dot{Q}_{Eva,LT}\)), respectively. Figure 1:2  
Figure 1:3  
Figure 1:4  
Figure 1:5  
Power consumption at Compressor 1, Compressor 2, the pump and the power generation at the turbine are given in Equations (11)-(14), respectively. Figure 1:6  
Figure 1:7  
Figure 1:8  
Figure 1:9  
The net power of the proposed novel cycle is the difference between the total generation and consumption (Equation (15)). Figure 4.0  
In the reference study [22], the turbine was designed to produce only the amount of power required to drive the compressor, resulting in zero net power output. In the present study, both the reference and novel cycles are modeled such that the turbine generates power based on the heat input at the ORC evaporator. This modeling approach does not limit turbine output to the compressor's demand alone, allowing for the assessment of net power production potential in both systems and more effective utilization of the geothermal heat source. The net power improvement (\(\dot{W}_{net,i}\)) is defined as Equation (16) to compare the reference cycle's net power (\(\dot{W}_{net,ref}\)) and the proposed novel cycle's net power production (\(\dot{W}_{net}\)) under the same conditions. Figure 4.1  
Main operating conditions are given in Figure 4.2 The selected parameters and their evaluation ranges are presented in Table 3 for the parametric analysis. In this study, the performance of the proposed novel cycle is evaluated for various working fluids which are shown in Figure 4.3 The selection of R290, R600a, R601, and R1234 ze(E) as working fluids in this study is based on their favorable thermophysical properties, environmental compatibility, and relevance to low- and medium-temperature applications. R290 (propane), R600a (isobutane), and R601 (n-pentane) are natural hydrocarbons known for their low global warming potential (GWP), and zero ozone depletion potential (ODP), making them attractive alternatives to conventional refrigerants. R1234 ze(E), a hydrofluoroolefin (HFO), also offers low GWP and non-ozone-depleting characteristics. These fluids span a range of boiling points and critical temperatures, allowing for performance evaluation under different operating conditions. Their selection aligns with current environmental regulations and sustainability goals. The assumptions made for mathematical model are as follows:  
* Heat losses and pressure drops in the heat exchangers and pipes have been neglected, which is a common assumption in related studies in the literature [14; 15; 16; 17; 18; 19]. * It has been assumed that all expansion valves perform isenthalpic processes. * The main operating parameters have been kept constant during parametric analyses, except for the parameters under investigation.
## 1 Introduction  
Severe skeletal dendofacial deformities have led several patients to undergo surgical orthodontic treatment, as they cause chewing difficulties and esthetic problems [1; 2]. Dr. [PERSON] provided the first simple definition of normal human occlusion and described three classes of malocclusion based on the occlusal relationships of the first molars [3]. Bilateral sagittal split ramus osteectomies (BSSROs) or vertical ramus osteectomies for mandibular setback with orthodontics are effective for treating skeletal Class III malocclusion [4]. Most surgeons prefer BSSROs because its rigid fixation permits early mandibular function [2; 5].  
Many modifications of fixation methods for BSSROs have been proposed. Fixation methods, including bicortical screws, plates with monocortical screws, and a combination of these two techniques (hybrid technique), have been reported for bilateral sagittal split ramus osteectomies [2; 4]. Instability at the osteotomy site contributes to early relapses in BSSROs [6; 7]. Adequate fixation can provide sufficient resistance to displacing forces that cause micromovements across the osteotomy site. [PERSON] et al. introduced rigid fixation in BSSROs by using three bicortical screws through the transcutaneous approach [8]. Many studies have reported that the three-bicortical screw fixation method offers the strongest resistance force against displacement [8; 9; 10; 11]. However, temporomandibular disorders due to the displacement of the mandibular condyle and the risk of nerve damage due to excessive compression force can occur [2].  
Various in vitro experimental studies have been reported; however, the most ideal fixation method remains to be established. Biomechanically, the curved plate, which conforms to the native curvature or trajectory of the mandibular external oblique ridge much more, appears to offer more rigidity than regular straight plates. Few studies on curved plates have been reported. This study compared the in vitro mechanical stability from the fixation method, which involves fixation using a straight plate with four monocortical screws, a curved plate with four monocortical screws, and three-inverted L-type bicortical screws applied to an artificial mandibular model simulating a BSSRO.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 4 Framework Architecture
### Modeling the Parameters
#### 4.1.5 Forwarding Strategy  
The _forwarding strategy_ decides when and where to forward a packet. Factors like the priority of an Interest or load balancing may play a role here. The strategies presented here are all implemented in ndnSIM: _best-route_, _access_, _ASF_, _multicast_, _random_, and _self-learning_. The following information is based on [28].  
* The _best-route_ strategy forwards to the upstream node with the lowest cost. When a consumer transmits an Interest again, the strategy forwards the packet to the lowest-cost node that was previously not used.
* The _access_ strategy sends the first Interest packet to all adjacent upstream nodes. When the corresponding Data packet returns, the last working upstream node is stored for the prefix and will be used for all subsequent Interests.
* The _ASF (Adaptive Smoothed RTT-based Forwarding)_ strategy uses hyperbolic routing as a strategy: By embedding the network topology into the hyperbolic plane, a greedy geometric routing algorithm never gets stuck at local minima. Thus, it can be avoided that a forwarder has to keep next-hop routes to all destinations in the network in its FIB.
* The _multicast_ strategy forwards an Interest packet to all upstream neighbors.
* The _random_ strategy randomly selects an upstream node to forward a packet to.  
* The _self-learning_ strategy first broadcasts Interests to \"learn\" a path towards data, and then unicasts the following Interests across the same learned path.
# Bonding Performance of Steel Rebar Coated with Ultra-High-Performance Concrete
## 3 Results and Discussion
### Bond Strength and Maximum Slip  
Figure 9a,b show the bond strength ratio of the uncoated rebars [32] and UHPC-coated rebars, respectively. The bond strength ratio was calculated by dividing the bond strength of each specimen by the average value of the bond strength of uncoated rebar without fiber. For an accurate analysis, the bond strengths of specimens that failed after steel yielding were excluded in the comparison. Figure 9 shows that the bond strengths of uncoated rebars with 1% and 2% fibers increased by 6% and 12.7%, respectively, while those of UHPC-coated rebars with 0.2%, 0.4%, and 0.6% fibers increased by 23.4%, 32.0%, and 40.3%, respectively. The increase rate of the bond strength of the UHPC-coated rebar with a low fiber content was much higher than that of the uncoated rebar with a high fiber content. This means that the UHPC coating promotes the increase in the bond strength of the rebar.  
Figure 8: Examples of failure modes (interface 1 vs. interface 2).  
Figure 9b shows that the bond strength of the UHPC-coated rebar increases by about 15 percent compared to the uncoated rebar. Figure 10a,b show the maximum slip ratios of uncoated rebars [32] and UHPC-coated rebars, respectively. The maximum slip ratio was calculated by dividing the slip of each specimen at peak load by the average value of the slip of the uncoated rebar without fiber. Contrary to the change in bond strength ratio, the increase in the slip ratio of the uncoated rebar was greater than that of the UHPC-coated rebars. The slip values of the uncoated rebars with 1% and 2% fibers more than doubled, while those of the UHPC-coated rebars with 0.2%, 0.4%, and 0.6% fibers were almost the same as for the rebar without fiber. This means that the UHPC coating increases the initial bond stiffness of rebar containing fiber, which will be described in detail in the next section.
## 4 Conclusions  
In this study, the combination of HS-SPME and GC\(\times\)GC-TOFMS was used for quantitative detection of trace components in Baijiu samples, and matrix interferences were investigated. We optimized a series of extraction conditions, namely, sample dilution, sample volume, extraction temperature, and time for SPME analysis of volatile compounds in Baijiu. Optimization of extraction conditions via a series of experiments revealed that dilution of the alcohol content of 8 mL of Baijiu to 5%, followed by the addition of 3.0 g of NaCl and subsequent SPME extraction with DVB/CAR/PDMS fiber coating over 45 min at 45 \({}^{\circ}\)C was the most suitable. We evaluated the model synthetic solution used in quantification to minimize the influence of matrix effects on samples with complex matrices such as Baijiu. A calibration curve was established, and validation was performed for the 119 trace volatile compounds that were considered the main contributors to the aroma of Baijiu. The validation studies demonstrated that the proposed method met the requirements of linearity, precision, accuracy, and sensitivity for the measurement of volatile compounds in Baijiu. The improvement of sample pretreatment methods for comprehensive 2-D-GC analyses in the future would focus on the high boiling point and strong polar compounds in Baijiu.  
X.M., data curation, investigation, methodology, writing--original draft; J.L., investigation, methodology, writing--original draft; M.G., data curation, investigation, writing--original draft; C.L., funding acquisition, writing--review and editing; S.C., methodology, supervision, resources, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 32172331), the National Key R&D Program of China (No. 2018 YFC1604100), Guizhou provincial science and technology project ([2020]2Y045), and a project funded by the China Postdoctoral Science Foundation (No. 2018M631971).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.  
Samples are available from the corresponding authors.
## 4 Applications in Optimization
### Differential Evolution (DE)
#### 4.2.2 Chemical Reactor Optimization
Their analysis showed that the temperature of the reaction mixture did not exceed the critical value. The maximum temperature was 373.11 K (99.95 \({}^{\circ}\)C). The required temperature \(w\) used in the cost function was 370.0 K (96.85 \({}^{\circ}\)C). Senkerik et al. concluded that \"based on statistical analysis, it is possible to claim that embedding the chaotic dynamics in the form of a chaotic pseudo-random number generator into differential evolution algorithm significantly improves the performance of DE.\"
# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
## 5 Experimental Evaluation
### Dataset  
The KDD'99 dataset, curated initially by DARPA in 1999, was assembled using network traffic data recorded in 1998. This dataset has undergone extensive preprocessing, resulting in a representation featuring 41 distinct features per network connection. These features in the KDD'99 dataset are systematically categorized into four groups, each serving a specific purpose: basic features (#1 to #9), content features (#10 to #22), time-based traffic features (#23 to #31), and host-based traffic features (#32 to #41), all thoughtfully outlined in Table 3.  
With a voluminous repository of 4,898,430 records, the KDD'99 dataset notably surpasses many other datasets in terms of scale. It is worth noting that within this dataset, there are four primary categories of network attacks, each detailed in Table 2: denial of service (DoS), remote-to-local (R2L, involving unauthorized access from a remote machine), user-to-root (U2R, encompassing unauthorized access to the root), and probe attacks. Using this dataset, researchers and data scientists have extensively leveraged various data mining techniques to detect intrusions in network traffic.  
However, two crucial issues were uncovered through statistical analysis, which profoundly impacts the performance of intrusion detection systems applied to the KDD'99 dataset. The most significant among these issues is a substantial number of replicated records. It was observed that approximately 78% and 75% of records in the training and test datasets, respectively, are duplicates. This prevalence of replicated records can inadvertently bias learning algorithms, leading them to focus disproportionately on frequent records while neglecting infrequent ones. This oversight can be particularly concerning, as these less frequent records may represent harmful intrusions, such as U2R or R2L attacks.  
Despite these challenges, the KDD'99 dataset remains a valuable resource and is still considered an effective benchmark dataset. It plays a pivotal role in facilitating the comparison of various intrusion detection methods by researchers in the field. Furthermore, the dataset's substantial number of records in both the training and test sets presents a distinct advantage. Researchers can conduct experiments using the complete dataset without random selection, ensuring that evaluation results across different research endeavors remain consistent and comparable. This consistency enhances the reliability and reproducibility of findings, ultimately advancing the state of intrusion detection research.