esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Identification of Key Volatile and Non-Volatile Compounds in Raw Meat during Refrigeration
#### 3.2.2 Differential Metabolites in Raw Meat: Effect of Chilling Rates  
Six variables were regarded as critical volatile compounds in raw meat at different chilling rates, as their variable importance in projection (VIP) were all above 1.5 (Figure 4a). The name and VIP values of these six compounds were: hexanal (3.14), 2,3-octanedione (2.49), nonanal (2.65), 1-octen-3-ol (2.24), hexanoic acid (1.92), octanal (1.58). According to Figure 4b, among the non-volatile compounds, 5\({}^{\prime}\)-inosinic acid (IMP) and 5\({}^{\prime}\)-guanylic acid (GMP) were differential compounds at different chilling rates, and the VIP values were 4.85 and 1.81, respectively. Therefore, hexanal, 2,3-octanedione, nonanal, 1-octen-3-ol, hexanoic acid, octanal, 5\({}^{\prime}\)-IMP, and 5\({}^{\prime}\)-GMP were the critical volatile metabolites in raw meat at different chilling rates.  
Figure 3: Contents and Contribution rates of volatile compounds in raw meat at different chilling rates during refrigeration.  
(a) Contents of volatile compounds. (b) Contribution rates of volatile compounds.  
Figure 4: VIP values of volatile compounds and non-volatile compounds in raw meat at different chilling rates and different refrigeration times. (**a**) VIP values of volatile compounds at different chilling rates. (**b**) VIP values of non-volatile compounds at different chilling rates. (**c**) VIP values of volatile compounds at different refrigeration times. (**d**) VIP values of non-volatile compounds at different refrigeration times.  
The rapid chilling could significantly retard the postmortem process [7] and decrease the rate of pH decline [8]. In addition, lower ambient temperatures decreased the lipid oxidation [45], especially for raw meat stored at \(-\)30 and \(-\)80 \({}^{\circ}\)C [27]. Hexanal, 2,3-octanedione, nonanal, 1-octen-3-ol, hexanoic acid, and octanal were all regarded as the products of lipid oxidation [46], so they might be the differential volatile compounds in raw meat under different chilling rates. For non-volatile compounds, the formation of IMP was controlled by an essential enzyme adenylate deaminase (AMPD) in adenosine triphosphate (ATP) degradation [47]. The hydrolysis of IMP was controlled by acid phosphatases (ACP) and alkaline phosphatases (ALP). Lower refrigeration temperatures inhibited the activity of glycolytic enzymes and reduced the rate of ATP turnover, resulting in different IMP and GMP contents at different chilling rates [48].
## 3 Results and Discussion  
### Meteorological Data  
The climatic conditions of wine regions in China vary considerably, leading to significant variation in the quality and styles of wine. According to the Koppen-Geiger climate classification system, which is a widely used vegetation-based empirical climate classification system [23], the JDP region has a monsoon-influenced humid subtropical climate, and BHB has a monsoon-influenced hot-summer humid continental climate (Table S2). Meanwhile, the HZB, LP, and XJ regions have either a cold semi-arid climate or cold desert climate. Further investigation of the climatic conditions of wine-producing regions was achieved by analyzing the meteorological data. The climate results showed that the XJ region is characterized by the longest sunshine duration (on average, 9.7 h), highest PAR (on average, 32.98 mol/(m\({}^{2}\).d) in Yanqi County), and diurnal temperature range (on average, 13.07 \({}^{\circ}\)C in Manasi County and 15.10 \({}^{\circ}\)C in Yanqi County), with the lowest rainfall (on average, 115.88 mm in Manasi County and 61.83 mm in Yanqi County) (Figure 1). In contrast, the JDP and BHB regions displayed significantly shorter sunshine durations, lower PARs, and diurnal temperature ranges, with significantly higher rainfall. These regions have either a warm or hot climate. The Linfen and Qingdao subregions, of LP and JDP, respectively, have a relatively higher average daily temperature than the other regions due to their relatively lower latitudes (Figures 1 and S2). In addition, a comprehensive investigation of the climatic characteristics of wine-producing regions in China was carried out according to a newly established zoning index system named the _FRD-DI-T_ system [24]. It found that the HZB, LP, and XJ regions (\(DI>1.6\)) showed higher levels of the dryness index (\(DI\)) when compared with those of BHB and JDP (\(DI\leq 1.6\)), even though HZB is near to the BHB and JDP regions.
## 3 Comparison and Validation of the Proposed Calculation Method
### Validation 2  
A comparison between the present method and both the previously published Iskander method [33] and the Shukla method [34] of applying active earth pressure coefficients \(K_{\mathrm{a}}\) for c-\(\varphi\) backfills is shown in Figure 5. It should be pointed out that the above three methods are different in their theoretical derivation. Specifically, in the Iskander method, the parameter \(\delta\) increases nonlinearly with seismic acceleration. In this study, \(\delta\) is set to \(\varphi\) for comparison purposes, while in the Iskander method, the average \(\delta\) value approximates \(\varphi\). In addition, the Iskander method converts the earth pressure into a horizontal component and then calculates the horizontal earth pressure coefficient. Therefore, it is difficult to directly compare the results obtained by the present study with those obtained by the Iskander method. To enable direct comparison, the horizontal earth pressure coefficient \(K_{\mathrm{aH}}\) in the proposed method is calculated as \(K_{\mathrm{a}}\) multiplied by \(\beta\), where \(\beta=\cos(\delta+\theta)\).  
Looking at the overall trend, it can be seen that the results obtained by the present method are not much different from those obtained from the Iskander and Shukla methods, as shown in Figure 5, with \(\varphi=30^{\circ}\), \(\delta=\varphi\), \(i=0^{\circ}\), \(\theta=0^{\circ}\),\(f_{\mathrm{a}}=1.0\), and \(c/\gamma H=0.05\). When \(k_{\mathrm{h}}\) is larger than 0.5, the \(K_{\mathrm{aH}}\) obtained by the Iskander method is the largest, that obtained by the present method is the second largest, and that of the Shukla method is the smallest.  
A comparison of the \(K_{\mathrm{aH}}\) from the present study with that obtained using the other methods for different \(\varphi\) values with \(f_{\mathrm{a}}=1.0\), \(\delta=\varphi\), \(\theta=20^{\circ}\), \(i=15^{\circ}\), \(k_{\mathrm{v}}=0.5\)\(k_{\mathrm{h}}\), and \(c/\gamma H=0.05\) is shown in Figure 6. When \(k_{\mathrm{h}}\) is small, the \(K_{\mathrm{a}}\) obtained by the present method is larger than that of the Iskander method. With an increase in \(k_{\mathrm{h}}\), the \(K_{\mathrm{a}}\) obtained by the present method gradually becomes smaller than that of Iskander method.  
Figure 4: Comparison between the results based on present study and M-O method. (**a**) \(K_{\mathrm{a}}\); (**b**) normalized dynamic active earth pressure distribution (\(f_{\mathrm{a}}=1.0\), \(\varphi=30^{\circ}\), \(i=15^{\circ}\), \(\theta=20^{\circ}\), \(k_{\mathrm{v}}=0\) and \(c/\gamma H=0\)).
# Fatigue Resistance Characterization of Warm Asphalt Rubber by Multiple Approaches
## 3 Results
### Mixtures
#### 3.3.2 4 PB  
For the four-point beam fatigue test, the fatigue life of a mixture specimen refers to the number of cycles it takes to achieve a 50% reduction in the initial stiffness. The initial stiffness was measured at the 50 th load cycle. Figure 7 presents the 4 PB test results. As expected, the rubberized mixtures exhibited larger cycle numbers to failure, indicating that the rubberized samples have a longer fatigue life. ARS mixture showed the best fatigue performance. AR mixture had better fatigue resistance than ARE and ARA mixtures. The fatigue performance ranking obtained from the 4 PB test was consistent with those from the binder LAS test and mortar shear fatigue test. However, the discrepancy between the 4 PB test and IIFT rankings was fairly high.
## 3 Results and Discussion  
### FTIR Analysis  
The FTIR spectrum of the N-TiO\({}_{2}\) NPs is presented in Figure 1.  
As shown in Figure 1, four major peaks were found, with the two broad bands at 3242 cm\({}^{-1}\) and 1633 cm\({}^{-1}\) being assigned to the O-H stretching and bending vibrations, respectively. The presence of these hydroxyl groups was due to water that was absorbed on the N-TiO\({}_{2}\) NPs' surface. The vibrational peak at 2050 cm\({}^{-1}\) was a result of using organic solvents during the synthesis process and was attributed to C-residuals. The peak at 665 cm\({}^{-1}\) was related to the Ti-O stretching mode of Ti-O-Ti bonds [34].  
Figure 1: FTIR spectrum of N-TiO\({}_{2}\) powder, at 25 \({}^{\circ}\)C.
## 4 Discussion
### Challenges
#### 4.1.3 High Dimensionality  
The capabilities of capturing logs for the activities are an advantage that may provide insight into employee actions. Despite this advantage, the analysis of activity logs continues to be difficult for analysts because of the sheer volume of activities that employees produce every day [95; 139; 162]. The large number of organizational staff requires the monitoring of staff behavior properties, which results in the massive need for data to be processed [52]. The growth of this data outpaces the ability of human auditors and administrators to digest such data quantities using manual analyses [95].  
Features can be found in time or frequency domains, according to the temporal phenomenon of insider threat detection. Nevertheless, any sudden changes in behaviors should be monitored to identify specific problems. Additionally, any actions can be a sign of malicious attacks by the insider. The complexity and unpredictability of malicious actions render a careful analysis difficult for the system, network, and user parameters correlated with insider threats. Therefore, a heterogeneous, high-dimensional data analysis problem in isolating suspicious users was created [41; 106; 107; 195]. Unsupervised learning is one of the approaches applied to solve such a problem, but these approaches are limited to static, finite-length data, thereby limiting the application against insider threats. Thus, insiders tend to have unbounded length and threat patterns that evolve over time [43].
## 2 Background
### IoT in Smart Healthcare  
In its basic form, IoT connects physical objects to the Internet to perform related activities remotely. This connection provided features such as context-awareness capabilities, autonomous data capture, and on-line communication facilities for a specific purpose. In particular, IoMT refers to smart medical devices connected via the Internet to a central entity, usually a cloud, to automatically gather, process, and share medical data for healthcare services [18].  
Smart healthcare requires the IoT paradigm to provide solutions that can capture patients' health parameters, recognize symptoms, and thus recommend preventive actions. On the other hand, IoMT applications help the healthcare industry to design and develop new medical solutions based on big data analytics that use the data generated from IoMT devices, and to take other knowledge-based measures as needed. Patients, healthcare providers, such as doctors, nurses, pharmacists, physicians, and hospitals, as well as insurance companies, can benefit directly or indirectly from IoMT applications. IoMT applications are helpful for many healthcare areas, such as remote healthcare services, medical asset management, optimization of medical inventory, patient-doctor rapport, real-time medical data analytics, augmented surgeries, and treatment [4].
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
###### Abstract  
Real-time detection of satellite remote sensing images is one of the key technologies in the field of remote sensing, which requires not only high-efficiency algorithms, but also low-power and high-performance hardware deployment platforms. At present, the image processing hardware acceleration platform mainly uses an image processing unit (GPU), but the GPU has the problem of large power consumption, and it is difficult to apply to micro-nano satellites and other devices with limited volume, weight, computing power, and power consumption. At the same time, the deep learning algorithm model has the problem of too many parameters, and it is difficult to directly deploy it on embedded devices. In order to solve the above problems, we propose a YOLOv4-MobileNetv3 field programmable gate array (FPGA) deployment scheme based on channel layer pruning. Experiments show that the acceleration strategy proposed by us can reduce the number of model parameters by 91.11%, and on the aerial remote sensing dataset DIOR, the average accuracy of the design scheme in this paper reaches 82.61%, the FPS reaches 48.14, and the average power consumption is 7.2 W, which is 317.88%; FPS higher than the CPU and reduces the power consumption by 81.91%. Compared to the GPU, it reduces power consumption by 91.85% and improves FPS by 8.50%. Compared with CPUs and GPUs, our proposed lightweight algorithm model is more energy-efficient and more real-time, and is suitable for application in spaceborne remote sensing image processing systems.  
satellite remote sensing; FPGA; YOLOv4; pruning; quantify +
Footnote †: journal: Applied Sciences  
**Clication:** [PERSON]; [PERSON]; [PERSON].  
Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning.  
_Appl. Sci._**2023**, 13, 10111. [[https://doi.org/10.3390/app131810111](https://doi.org/10.3390/app131810111)]([https://doi.org/10.3390/app131810111](https://doi.org/10.3390/app131810111))  
Academic Editors: [PERSON], [PERSON] and [PERSON]  
Received: 14 August 2023  
Revised: 2 September 2023  
Accepted: 5 September 2023  
Published: 8 September 2023
## 4 Implementation of Improved Reconstruction Approach Based on MAP-MRF  
The implementation of the proposed reconstruction approach described in Section 3 is shown in Figure 4. The key processes are the joint estimation of MAP-MRF model parameters using coordinate descent method, and the image reconstruction using multiple regularizations.  
In accordance with the aforementioned analysis, the procedures of MAP-MRF-based reconstruction may be summarized as follows Algorithm 1.  
```
Input: Observation image \(y\), coding matrix \(\boldsymbol{\Phi}\) up-sampling factor s, numbers of frames for compressive imaging \(N_{f}\), and the number of frames for SR reconstruction \(N\). Step 1: Estimate the preliminary reconfiguration results of multi-frames from the one observation \(y\) by proposed TV algorithm and Equations (18)-(23); Step 2: Initialize times of iteration \(k\), the initial estimation of HR image \(I^{(0)}\)(bilinear interpolation method); Step 3: judge the ending of iteration by \(\left|I^{(k-1)}-I^{(k)}\right|<\varepsilon\) or \(k\geq I_{max},I_{max}\) the maximal times of iteration: 1 Estimate motion vector \(\omega_{i}^{(k)}\) by SIFT Flow algorithm as Equation (24); 2 Estimate the frames \(f_{i}^{(k)}\) by multi-regularization algorithm and Equations (26)-(32); 3 Initialization: \(m=1,I^{(k,0)}=I^{(k-1)}\); 4 Loop until \(\left|I^{(k,m)}-I^{(k,m-1)}\right|<\varepsilon\) 1.  compute weight \(\beta_{i}\) by Equation (36); 2.  iterative estimation with BP algorithm by Equations (37)-(39); 3. \(m=m+1\), returning to iteration; 3 \(k=k+1\) Output:\(I=I^{(k)}\)
```  
**Algorithm 1** The procedures of MAP-MRF-based reconstruction.
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 2 Related Work
### Time Series Anomaly Detection Under Concept Drift
In summary, many existing methods either rely on a single source of drift detection or ignore drift detection entirely. Few methods integrate both distribution-basedand performance-based drift detection results in a fully unsupervised and interpretable framework. To address this gap, the proposed framework ADDAEIL introduces a hybrid, incremental adaptive strategy. It combines distribution-based drift scoring with structure-based performance evaluation to support efficient and reliable anomaly detection in evolving data streams.
## 5 Conclusions  
Some outcomes of a coverage analysis of a spaceborne bistatic mission for SMC estimation have been presented. The study has started from the identification of the bistatic configurations suitable for SMC retrieval, in terms of frequency and ideal transmitter-target-receiver relative geometry accomplished in a previous study. An evaluation of the spatial resolution of the bistatic system has been firstly carried out. It has led us to restrict our analysis to observations around nadir and in the backward quadrant. Then, the study has assessed the feasibility of the nominal mission in terms of spatial coverage and duty cycle, assuming a non-cooperative system, with Envisat/ASAR as illuminator.  
Figure 6: Baseline length (absolute value) and components in the active satellite orbital reference frame versus the orbital period percentage. One orbit is considered for the sake of figure clarity.  
We have shown that the Wide Swath Mode has the best duty cycle (~40%). Higher resolution image modes yield acceptable results in terms of this parameter (never less than 12%), considering that, in our hypothesis of fully non-cooperative system, the maximum achievable value is 50%. It has been found, that, for high resolution modes, the minimum and maximum latitudes do not necessarily identify one latitude belt, so that their setting may allow focusing on a particular geographic region, such as mid-latitude areas. In a selected latitude band, it might be possible to achieve full coverage within the Envisat orbit repeat cycle (35 days), while for a larger latitude range, almost covering the entire planet, it is unfeasible.  
The improvement of soil moisture retrieval that we have demonstrated, in a previous work, to be achievable through a bistatic mission, may be actually obtained by designing a simple and cheap (even in terms of power requirements) receiver that flies in formation with a standard C-band SAR. Combining the active monostatic with the bistatic measurements can strengthen the contribution of single frequency radar observations in many disciplines needing an evaluation of soil moisture, such as hydrology, agriculture and climate change monitoring.  
The methodology based on a study of the sensitivity to a geophysical target parameter of bistatic radars, followed by a system performance analysis can be useful for other applications (e.g., vegetation biomass retrieval) and for future missions. For instance, this approach can provide support to a possible employment of a passive receiver flying in formation with the forthcoming European Space Agency (ESA) Sentinel satellites. In particular, the wide swath capability of the Sentinel-1 C-band SAR is expected to improve the coverage performances of the bistatic mission with respect to that computed for Envisat.
## 2 Analytical Methods for Phytocannabinoid Profiling
### Instrumental Analysis
#### 2.2.1 GC-Based Methods
Quadrupole-time-of-flight (Q-TOF) mass analyzer is rarely used for quantification purposes, but rather for untargeted cannabinoid profiling of _C. sativa_ extracts [78]. Rarely, FID and Q are simultaneously used by installing a \"Y\" splitting unit at the column outlet [152]. Finally, a novel variation of GC-EI-MS, cold EI, based on interfacing GC and MS with supersonic molecular beams (SMB) in a fly-through ion source was recently successfully employed for an inaccurate, sensitive, reproducible and comprehensive full (including phytocannabinoid) profiling of herbal cannabis extracts [166]. A relatively novel GC-based analytical platform employs analyser based on vacuum UV (VUV) operating in the UV/VUV spectral range (120-240 nm). UV/VUV absorption events are very sensitive for differentiating isomers (positional isomers and diastereomers). This, together with the ability to deconvolve overlapping spectra [4], which significantly shortens analysis time, makes GC-UV/VUV potentially favourized analytical platform over GC-MS. Almost all phytocannabinoids exhibit maximum absorbance in the region 170-200 nm, with no overlap among different phytocannabinoids and significant spectral differentiability. This is especially important for CBC and CBD. However, this method reports high LODs and, thus, cannot be employed for phytocannabinoid profiling in biological matrices and cannabis-based products, but is sufficiently sensitive for plant matrices [161]. Two-dimensional (2D) gas chromatography (GC \(\times\) GC) is reported to offer better chromatographic resolution of phytocannabinoids [11; 56; 160; 150]. Despite this, it is rarely used for phytocannabinoid profiling; chemical fingerprinting and classification are more commonly performed GC x GC is usually coupled to MS [56; 160] or to FID/MS [150]. Combination of columns of different polarity is used, as, for example, 100% dimethylpolysiloxane and polyethylene glycol in sol-gel matrix [56], or a non-polar column (e.g., DB-5 or HP-5 MS) for first-dimension separation and medium-polarity column (e.g., DB-17) in the second dimension [150; 160].
# Seismic Wave Amplification Characteristics in Slope Sections of Various Inclined Model Grounds
## 3 Analysis of 1 g Shaking Table Experiment Results
### Comparative Analysis of Acceleration Amplification
#### 3.1.2 Vertical PGA of Model Grounds  
The PGA of each model ground was illustrated using the accelerometers vertically embedded for measurement, as shown in Figure 5a-d. It was observed that in all model  
Figure 4: Slope section PGA (AS11, AS12, AS13). (**a**) Gyeongju; (**b**) Pohang; (**c**) Ofunato; (**d**) Hachinohe; (**e**) Gyeongju PGA amplification factor; (**f**) Pohang PGA amplification factor; (**g**) Ofunato PGA amplification factor; (**h**) Hachinohe PGA amplification factor.  
grounds, the acceleration consistently amplified towards the upper part. However, the acceleration measured at the lower part of the slope (AS 14-16) displayed a different pattern compared to the other vertically measured PGAs. In the lower slope area (AS 14-16), except for the flat model ground in Case 3, the other model grounds showed a higher PGA at the AS 16 point, which was distinct from the PGAs at the same depth. The highest PGA was observed in Case 1, which had the lowest relative density, followed by Case 4 (2-layer model ground) and Case 2 with the highest relative density, indicating that the lower part of the slope experienced higher PGA. The lower amplification in Case 3 compared to the other model grounds is believed to be due to the effects of confining pressure, which resulted in less amplification.  
Additionally, by dividing the maximum ground acceleration (PGA) of the input motion by the PGA measured at each point, the seismic amplification factor was calculated. This allowed us to assess how much the seismic waves were amplified at each point compared to the input motion, as shown in Figure 5e-h. The amplification ratio analysis revealed that in the flat model ground of Case 3, the lower slope area amplified similarly to other points at the same height. However, in the other model grounds with slope formations, significant amplification occurred at the same height. The seismic amplification observed in the model  
Figure 5: Vertical section PGA. (**a**) Gyeongju; (**b**) Pohang; (**c**) Ofunato; (**d**) Hachinohe; (**e**) Gyeongju PGA amplification factor; (**f**) Pohang PGA amplification factor; (**g**) Ofunato PGA amplification factor; (**h**) Hachinohe PGA amplification factor.  
grounds, as shown in Figure 5, indicates that large seismic amplifications can occur in the lower part of the slope during an earthquake, highlighting the necessity of seismic design considerations not only for the slope but also for the lower part of the slope.