esa-sceva/satcom-chunk-collection huggingface dataset:
# Untargeted Profiling and Differentiation of Volatiles in Varieties of Meat Using GC Orbitrap MS
## 4 Conclusions  
The present study developed an analytical strategy for the untargeted profiling of volatile compounds in boiled meat. The method based on the HS-SPME-GC-MS was investigated in detail, and the influencing parameters including extraction fibers, extraction temperatures, extraction time, and desorbing time were optimized comprehensively. High sensitivity and good reproducibility were achieved. For the confirmation of the volatiles, the combination of retention indices, the home-built high-resolution database (home flavor), NIST database matching using low-resolution mass spectrometry, and HRF scores based on the accurate masses was utilized. The home-built high-resolution database could significantly enhance the identification accuracy. The optimum untargeted approach in tandem with multivariate data analysis techniques was promising for meat categories' discrimination and meat authentication. Duck, chicken, beef, mutton, and pork meats were easily distinguished based on their volatiles' profiles. 2-Pentyl furan, 2-butyl furan, 2-hexyl furan, 1-octen-3-ol, and 1-octen-3-one were positively correlated with duck meat. Methyl esters would be the main discriminant biomarkers for beef. This phenomenon was caused by the different fatty acids' pattern in different meat species.  
Y.Y.: Methodology, Data analysis, Manuscript. J.L.: Methodology. J.X.: Methodology. W.X.: Experiment running. C.T.: Conceptualization. Z.R.: Conceptualization. J.Z.: Conceptualization. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Special Basic Research Fund for Central Public-interest Scientific Institution Basal Research Fund (No.2022-YWF-ZYSQ-09).  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
All of the authors have no conflict of interest to declare. As for [PERSON] from Thermo Fisher Scientific, his work mainly focused on the instrumentation and the method optimization. His participation would not affect the authenticity and objectivity of the experimental results. In addition, Thermo Fisher Scientific did not provided funding or equipment in the experimental design.
# Research on Improved Lightweight YOLOv5s for Multi-Scale Ship Target Detection
## 3 Data and Methods  
### Dataset Production  
The types of ships on the surface vary in scale and shape, making ship target detection more difficult than that of other objects. At the same time, the marine environment is complex, and ship images may be acquired under different conditions, such as different lighting and different visibility. Firstly, this research produced MSDs (Marine Structure Datasets), datasets for ship target detection and classification. The collected ship images are mainly acquired from the shipboard CCTV system or drawn from the network. The target types obtained include lighthouses, sailboats, buoys, rail bars, cargo ships, naval vessels, passenger ships, and fishing boats. A total of 7354 images were collected and stored in JPG format. The dataset contains 826 lighthouse targets, 1143 sailboat targets, 776 buoy targets, 2632 rail bar targets, 717 cargo ship targets, 1884 naval vessel targets, 1742 passenger ship targets, and 901 fishing boat targets. The collected and produced datasets were manually labeled with target information using Labellmg, and the datasets were data-enhanced and calibrated. The final file in xml format is stored, and the specific dataset production process is shown in Figure 1. The produced dataset is randomly divided into training, testing, and validation sets in a ratio of 8:1:1. The format of the produced dataset is the standard Pascal VOC (Visual Object Classes) format dataset, which is used for subsequent training and validation of the recognition model.
## 4 Discussion
### Parameter Analysis  
In this method, there are mainly three parameters involved, namely the merging threshold \(\alpha\) and confidence threshold \(\beta\) in Equation (3), and the weight parameter \(\gamma\) in Equation (4). Using a grid search, the optimal solution for the three parameters is obtained in the validation set. The merging threshold \(\alpha\) is used to determine the merging conditions. When \(\alpha\) is too high, the conditions for candidate box merging will be harsh. While, when \(\alpha\) is too low, only a few intersected candidate boxes will be merged, which will lower the detection performance. It can be found in Table 1 that when \(\alpha=0\), compared with other experiments, the AP value of the model is very low and the detection effect is poor. In Figure 8, the two candidate boxes corresponding to the two ground truths will be merged into one, creating a false candidate box.  
Due to its unique characteristics, the Faster RCNN algorithm will produce a large number of low-scoring candidate boxes, and the sharp drop on the far-right end of its PR curve in the PR curve image is just the result of this. Therefore, candidate boxes with higher confidence must be selected for merging, and the confidence threshold \(\beta\) serves as the judgment condition, that is, when the two candidate boxes meet the condition that the intersected area is greater than the merging threshold \(\alpha\), the confidence of both of them also needs to be greater than the confidence threshold \(\beta\), then, the merging mechanism can be triggered. In order to ensure the consistency of parameters with the Faster RCNN control group, \(\beta=0.005\) was fixed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Early Stage** & **Later Stage** \\ \hline Faster RCNN & 0.820 & 0.814 \\ Our method & 0.835 & 0.826 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _IoU_ of the Two Experiments Classified by Cycle of Construction Projects.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Less than Average Area** & **Larger than Average Area** \\ \hline Faster RCNN & 0.803 & 0.825 \\ Our method & 0.819 & 0.857 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _IoU_ of the Two Experiments Classified by Area of Construction Projects.  
The weight parameter \(\gamma\) of Equation (4) is used to determine the weight of the detailed ground object, with its value range set as 0-1. When \(\gamma\) = 1, the method in this paper will be the variant, that is, the candidate boxes will be merged without using the detailed ground object information to adjust the candidate box of the construction project. When \(\gamma\) = 0, only the detailed ground object information is used to obtain the confidence of the candidate box. After a parameter search on the validation set, the optimal value of \(\gamma\) is 0.75.
## 5 Simulation Results  
In this section, we report the results obtained by simulating the OFDM system and the channel generation previously described. The overall system parameters can be found in Table 1. Figure 7 reports an example of channel realization in the frequency domain, i.e., channel coefficient gains of the channel tensor \(\widetilde{\mathbf{\mathcal{H}}}_{s,k}\) are shown after following all the steps described in Section 2 and 3 with \(N_{FFT}=1024\) subcarriers, \(N_{t}=16\) transmitting antennas at the BS (\(N_{ty}=8\) and \(N_{tz}=2\)) and \(N_{r}=N_{ry}=2\) receiving antennas, single polarized.  
In particular, we identify some interesting scenarios to characterize our system, and then we collect results on its behavior when \(SLNR\) precoding is adopted as a precoding technique at BS and MMSE, combining at each UT when multi-layer features are enabled. The results are always reported as a Cumulative Distribution Function (CDF) curve of the achievable UT rates, except for the last scenario, where the total Sum-Rate is plotted against the traffic loading. When dual polarized elements are used, the total number of antenna elements becomes \(2N_{t}\) at the transmitter and \(2N_{r}\) at the receiver. As an example of \(SLNR\) precoding capabilities, Figure 8 shows the UPA radiation pattern at the BS when the \(SLNR\) precoding vector \(\mathbf{w}_{k}\) targeted for user \(k\) (\(\theta_{LOS,ZOD}=96.46^{\circ}\), \(\phi_{LOS,AOD}=30^{\circ}\)) is implemented. In the depicted scenario with three total UTs with \(LOS\) condition, the precoder is able to minimize the interference that the user of interest may cause to the two other users, whose \(LOS\) direction of departures are shown.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Scenario & UMi \\ Radius of the cell \(R\) & 100 m \\ Network loading or user density \(\mu\) & 2500 users/km\({}^{2}\) \\ Carrier frequency \(f_{c}\) & 28 GHz \\ Sampling frequency \(f_{s}\) & 61.44 MSamples/s \\ OFDM Symbol time \(T_{sym}\) & 16.67 \(\mu\)s \\ Cyclic Prefix \(T_{CP}\) & \(\frac{1}{16}\times T_{sym}\) \\ Bandwidth \(B\) & 50 MHz \\ Subcarrier spacing (SCS) \(\Delta\) & 60 kHz \\ Active Subcarriers & 792 \\ \(N_{FFT}\) & 1024 \\ Noise figure \(F\) & 7 dB \\ Maximum TX power \(P_{tot}\) & 47 dBm \\ Total antennas per BS UPA \(N_{t}\) & 72 \\ Antennas along y for BS UPA \(N_{ty}\) & 36, 24, 18 \\ Antennas along z for BS UPA \(N_{tz}\) & 2, 3, 4 \\ Antennas along y for UT \(N_{ty}\) & 1, 2 \\ Antennas along z for UT \(N_{rz}\) & 1 \\ Polarizations & single, dual \\ Indoor UTs & 80\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
# On-Line Partial Discharge Monitoring System for Switchegears Based on the Detection of UHF Signals
## 3 Hardware Circuit Design Scheme
### Data Collector Design  
The structure of the data collector is shown in Figure 8. The MCU of the data collector is STM32F769, which is mainly used for data acquisition, A/D conversion, data transmission, and processing instructions sent by the upper computer. The highest working frequency of STM32F769 is 216 MHz [19], and the conversion range of ADC is 0-3.3 V. After the system clock frequency division, the maximum sampling rate can be 7.2 MHz/s under the condition of system stability, and the sampling rate used in the monitoring system developed in this paper is 2.4 MHz/s.  
Figure 9 shows the IoT wireless development module used in this system, which is an ATK-M751 model. The ATK-M751 module is a high performance DTU module with high speed, low latency, wireless data transmission, etc. It supports 2G, 3G, 4G communication, various communication protocols (TCP/UDP/HTTP/MQTT/DNS/ RNDIS/NTP), multiple transparent transmission modes (TCP/UDP/HTTP/MQTT), and two serial interfaces, RS232 and RS485, suitable for use in multiple scenarios [20].
## 4 Simulation Results
### Results Comparison with Previous Studies  
The obtained results of MPPT solving via ROA in partial shading conditions is compared with some previous studies in Table 7. In [27; 28; 29], the cuckoo search (CS) algorithm, PSO, and whale optimization algorithm were implemented to solve the PV with MPPT solving. According to Table 8, better performance for the ROA is proved in PV with MPPT solving in the PSC in comparison with the CS, PSO, and WOA with higher tracking efficiency.  
Figure 16: Characteristics of PV considering temperature changes for pattern 4: (**a**) P-V, (**b**) I-V.  
Figure 17: PV power in pattern 4 considering temperature changes using the ROA.
## 3 Numerical Simulation of Tunnel Excavation
### Analysis of Calculation Results
#### 3.2.3 Distribution of Plastic Zones in Bifurcated Tunnels with Different Construction Steps  
According to the Mohr-Coulomb strength criterion [26], when the shear stress at a certain point in the rock exceeds the shear strength, plastic deformation occurs and a plastic zone form. Generally, the shear strength of rocks is about 1/8 to 1/12 [27] of the compressive strength. The engineering exploration report of the Liantang tunnel  
Figure 12: Curve of vault displacement with excavation steps: (**a**) two-lane, (**b**) three-lane.  
indicates that the compressive strength of the slightly weathered sandstone in this section is 38.77 MPa, indicating a shear strength of approximately 3.23 to 4.84 MPa. The contour maps of the plastic zone distribution calculated for the two methods are shown in Figure 15. The red areas represent plastic failure disturbance zones, mainly concentrated in the central rock between the small-clearance tunnel sections and the arch waist area of the five-lane section. The disturbance zones caused by unloading during the construction process are distributed in the arch shoulder and arch waist areas of the surrounding rock. Notably, as shown in the dashed box in Figure 15, the RTM exhibits a significantly larger plastic failure disturbance zone at the bottom of the central rock pillar compared to the ACM.  
Figure 14: _Cont._  
Figure 13: Curve of displacement with excavation steps: (**a**) vault of five-lane, (**b**) spandrel of five-lane.  
The distribution of maximum shear stresses on both sides of the central rock is shown in Figure 16. The excavation of the two-lane tunnel and the three-lane tunnel are key construction steps affecting the stability of the central rock. At the monitoring point on the left shoulder of the advance tunnel (near the central rock), the maximum shear stress generated by the RTM is 5.92 MPa, while it is 2.97 MPa for the ACM. At the monitoring point on the right shoulder of the three-lane tunnel (near the central rock), the maximum shear stress for the RTM is 4.80 MPa, while it is 2.62 MPa for the ACM. Therefore, the shear stresses generated by the RTM exceed the plastic failure threshold of the rock, posing a safety hazard to the integrity and stability of the central rock.  
Figure 14: Cloud map of surrounding rock displacement during the construction of the five-lane key step sequence: (**a**) RTM, (**b**) ACM.  
Figure 15: Cloud map of the plastic area. (**a**) RTM, (**b**) ACM.  
In conclusion, through comparative analysis of the stress distribution, deformation, and plastic zone distribution patterns in the surrounding rock for the two construction methods, we found minimal differences in the rock unloading behavior between the two methods. The main distinctions lie in aspects such as construction duration, procedural rationality, and construction safety. Therefore, considering multiple influencing factors, we ultimately select a reasonable construction approach. The ACM is superior to the RTM and is suitable for the transitional construction of the Liantang bifurcation tunnel.
# PerHelper: Helping Developers Make Better Decisions on Permission Uses in Android Apps
## 3 A Taxonomy of Permission-Related Issues
### The Problem of Unprotected APIs  
In previous Android API levels (before 23), the permissions of an app were granted at anytime as long as they were accepted by users at install time. However, many approaches [26; 27; 28; 52; 53; 54; 55; 56; 57] have been proposed to offer more fine-grained permission control. One typical functionality of these mechanisms is to allow users to control the permissions of each app after installation. The removal of permissions proposes challenges to those apps with _unprotected APIs_, which would cause crashing.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Wrong Permissions** & **\# of Apps** & **\% of Apps** \\ \hline WRITE & 64 & 8.02\% \\ \hline REORDER\_TASKS & 54 & 6.77\% \\ \hline CAMER & 51 & 6.39\% \\ \hline ACCESS\_GPS & 43 & 5.39\% \\ \hline PERMISSION\_NAME & 38 & 4.76\% \\ \hline ACCES\_MOCK\_LOCATION & 37 & 4.64\% \\ \hline ACCESS\_LOCATION & 31 & 3.88\% \\ \hline ACCESS\_FIND\_LOCATION & 26 & 3.26\% \\ \hline LOCATION & 20 & 2.51\% \\ \hline SET\_ORIENTATION & 12 & 1.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The top 10 wrong permissions used in apps.  
The new Android system, which was released in summer 2015, proposed a brand-new permission system [58]. Users grant permissions to apps while the app is running, not when they install the app. There were also some changes to the way developers write the code. Every time a developer uses an API that requires a \"dangerous\" permission (permissions that grant access to sensitive resources, e.g., contacts), he/she needs to use a combination of APIs _ContextComput.checkSelfPermission()_ and _ActivityComput.requestPermissions()_ to make sure the permission has been granted by users. Otherwise, the program would also throw _SecurityException_ and lead to the app crashing.  
[PERSON] et al. [59] studied the effects of removing permissions from Android apps using the old permission model and identified 39 out of 662 (5.9%) that crashed directly because of the removal. We did not obtain the data for the apps using the new permission model. This problem seems not so common, but on the one hand, it can lead to quite unpleasant results (app crashing); on the other hand, it is certain that the frequency at which permissions are denied at runtime would increase greatly. Thus, the issue of unprotected APIs should raise an important concern for developers in the future.
# Generalized Beam Theory for Thin-Walled Beams with Curvilinear Open Cross-Sections
## 2 The Beam Model  
A thin-walled beam made of isotropic, homogeneous and linearly elastic material is considered. The specimen is initially straight and its cross-sections, which are constituted by curved webs and flanges, are assumed open, while the closed and multi-cell cases are left for future developments. Furthermore, it is assumed that the profile of the beam is constant spanwise with no taper nor pretwist.  
First, linear strain-displacement relationship is written and then, following the hypothesis of plane stress in profile walls, the constitutive law for linear elastic material is used to calculate strain energy. Finally, [PERSON]'s principle is used and the equilibrium equations in terms of kinematic descriptors are obtained, imposing that the total potential energy attains a minimum in the class of compatible displacements. According to GBT framework, the displacements are considered as linear combinations of pre-established trial functions and kinematic descriptors which change along the axis line of the beam. The adopted cross-section modal shapes describe both rigid displacements, as in classical beam models, and changes in the shape of the profile characteristic for thin-walled members.
# Research Progress in Methods for the Analysis of the Internal Stability of Landslide Dam Soils
## 2 Qualitative Identification of Internal Stability Based on Physical Properties of Soils
### Identification Methods Based on Fine Content and Porosity  
The fine particle content determines the soil's resistance to internal erosion [39; 40; 41; 42]. Regardless of whether the soil is well-graded [25], an increase in the fine particle content can enhance the soil's resistance to water flow erosion. Specifically, when the fine content exceeds a certain threshold, the internal stability of the soil increases significantly [43]. Therefore, the fine particle content serves as a basis for assessing the internal stability of soils. Common identification methods of this type include the methods proposed by [PERSON], [PERSON], and [PERSON], as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Researcher**} & **Applicable** & \multirow{2}{*}{**Identification Method**} & \multirow{2}{*}{**Stability**} \\  & **Soils** & & \\ \hline \multirow{2}{*}{[PERSON] [30]} & \multirow{2}{*}{Sand and gravel} & \(C_{u}\leq 10\) & Stable \\  & & \(10\leq C_{u}\leq 20\) & Moderate stable \\ \multirow{2}{*}{Kezdi [32; 33]} & \multirow{2}{*}{All types of soil} & \(C_{u}\geq 20\) & Unstable \\  & & & \\ \multirow{2}{*}{Kenney and Lau [34]} & \multirow{2}{*}{Non-cohesive soils} & \((H/F)_{\text{min}}\geq 1.0\) & Stable \\ \multirow{2}{*}{[PERSON] [23]} & \multirow{2}{*}{Non-cohesive soils} & \(0.76\lg(h^{\prime\prime})+1<h^{\prime}<1.86\lg(h^{\prime\prime})+1\) & Stable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Identification methods considering characteristic particle size.
## 3 Doppler Frequency Evaluation in the Absence of Noise
### True Solution Based on Complex Values of CAF  
Based on References [7,10], we can obtain another similar solution by using the test statistic \(\overline{S}(\overline{\tau},\overline{f}_{d})_{\rm ml}\) in the complex form given in Equation (19). In fact it is possible to show that  
\[f_{d,complex} =\mathrm{Real}\left\{\frac{\overline{f}_{C}S_{C}+\overline{f}_{A} S_{A}e^{-j\pi f_{sp}T_{d}}+\overline{f}_{B}S_{B}e^{-j2\pi f_{sp}T_{d}}}{S_{C}+S_{A} e^{-j\pi f_{sp}T_{d}}+S_{B}e^{-j2\pi f_{sp}T_{d}}}\right\}\] \[=\mathrm{Real}\left\{\frac{\left(\overline{f}_{C}|S_{C}|+\overline {f}_{A}|S_{A}|+\overline{f}_{B}|S_{B}|\right)e^{-j(\pi\Delta_{C}T_{d}+\varphi_ {i})}}{(|S_{C}|+|S_{A}|+|S_{B}|)e^{-j(\pi\Delta_{C}T_{d}+\varphi_{i})}}\right\}\] \[=\frac{\overline{f}_{C}|S_{C}|+\overline{f}_{A}|S_{A}|+\overline {f}_{B}|S_{B}|}{|S_{C}|+|S_{A}|+|S_{B}|}\] \[=f_{d} \tag{31}\]  
Figure 6: The cells chosen in the generalized method.  
At the same time, similarly to Equation (30), we can generalize this solution as  
\[f_{d,complex}=\mathrm{Real}\left\{\frac{\sum\limits_{k=1}^{\overline{n}}\overline{f }_{k}S_{k}e^{-j(k-1)\pi f_{sp}T_{d}}}{\sum\limits_{k=1}^{\overline{n}}S_{k}e^{- j(k-1)\pi f_{sp}T_{d}}}\right\} \tag{32}\]  
where \(\overline{f}_{k+1}=\overline{f}_{k}+f_{sp}\) and \(f_{1}\) is the Doppler frequency value in cell 1 marked in Figure 6.  
For simplicity, hereafter we refer to solution Equation (31) as algorithm C-3 and solution Equation (32) as algorithm C-\(\overline{n}\).