esa-sceva/satcom-chunk-collection huggingface dataset:
# Knowledge Distillation Based on Fitting Ground-Truth Distribution of Images
## 4 Experiments and Analysis
### Experiment on Fitting The Ground-Truth Distributions of Classes (LSH-TL)  
Table 2 illustrates that LSH-TL outperforms LSH [13] in terms of distillation performance in five out of seven groups of homogeneous models. Compared with other methods, LSH-TL achieves the best performance in four out of seven groups of homogeneous models and achieves the second-best performance in one of the groups. In homogeneous models, the output layers of the teacher and student have similar structures, and the teacher labels are beneficial for training. Using teacher labels to supervise the classification outputs of the student and fitting the ground-truth distribution of the classes can improve the distillation performance of the penultimate layer features in homogeneous models. Even in the distillation of the WRN-40-2 and WRN-16-2, when the pre-trained WRN-40-2 teacher's performance is lower than the LSH distillation-trained WRN-16-2 student, fitting the teacher's ground-truth classification distribution still improved the classification accuracy by 0.12%.  
In Table 3, LSH-TL performs better than LSH [13] in four out of the six groups of distilled heterogeneous models. Among the above four groups, LSH-TL achieves the second-best performance among all methods. This suggests that, even when the teacher and student models have different structures, distilling teacher labels can still have a positive impact on distilling penultimate-layer features after replacing the dataset labels. While LSH-TL may not surpass SSKD in heterogeneous model distillation, it still enhances the performance of penultimate-layer feature distillation.  
Table 4 presents the distillation performance of LSH-TL on the large-scale ImageNet-2012 dataset. Due to limited computing resources, we only conducted distillation tests using resnet34 and resnet14. Our method reduces student's Top-1 and Top-5 error rates by 0.14% and 0.06%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline Feature temperature & \(\tau=1\) & \(\tau=2\) & \(\tau=3\) & \(\tau=4\) & \(\tau=5\) & \(\tau=6\) \\ \hline resnet32\(\times\)4- & 76.82 & 77.20 & 77.10 & 76.72 & 76.89 & 76.67 \\ \hline \end{tabular}
\end{table}
Table 1: Student classification accuracy (%) of LSH-T algorithm with different feature temperature values for the scenario where the teacher is resnet32\(\times\)4 and the student is resnet8\(\times\)4.
###### Abstract
However, the dependence of the side-bands suppression on the loop parameters (optical delay phase settings and power split ratio) was neglected. Here we present an optical delay phase and power split ratio dependant dual-loop optical feedback configurations to suppress delayed optical feedback induced cavity side-bands. The device studied was dual-section QDash MLL with emission wavelength \(\sim\)1.55 \(\upmu\)m and pulse repetition rates \(\sim\)21 GHz. The experimental analysis was conducted based on two chosen combinations of feedback ratios and two optical delay phase settings.
## 2 Materials and Methods
### Analysis of Growth Medium
#### 2.6.4 Xanthophyll and Chlorophyll a Concentration  
High performance liquid chromatography coupled to a mass spectrometer (Exactive Orbitrap, Thermo Fisher Scientific, Waltham, MA, USA) was performed for quantitative analysis of chlorophyll a and xanthophyll in the supernatant of the recycled medium.  
Prior to analysis, samples were diluted in a 1:10 ratio with the internal standard consisting of trans-\(\beta\)-Apo-8'-carotenal and zinc-phthalocyanine in acetonitrile/methanol (70/30 v/v). For complete removal of any flocculated substances, samples were filtered through a cellulose filter (0.2 um pore size, Whatman@). Standard solutions were prepared by dilution of chlorophyll a, xanthophyll, zinc-phthalocyanine and trans-\(\beta\)-Apo-8'-carotenal, all purchased from Sigma Aldrich (Taufkirchen, Germany), with acetonitrile/methanol (70/30 v/v) covering the concentration range from 1 to 500 \(\mu\)g L\({}^{-1}\). Calibration was done in triplicates.  
Separation was performed using a C\({}_{18}\) column (Hypersil Gold, Thermo Fisher Scientific, Waltham, MA, USA) at 25 \({}^{\circ}\)C column temperature. The flow rate was set to 200 \(\mu\)L min\({}^{-1}\). Twenty microliters of each sample were injected. The 13 min gradient elution profile was optimized employing acetonitrile/methanol (70/30 v/v), H\({}_{2}\)O and pure acetonitrile (see Table 1), resulting in good separation of all 4 components in the chromatogram. Atmospheric pressure chemical ionization (APCI) was operated in positive ion mode with a spray voltage of 5 kV, a sheath and auxiliary gas flow of 19 Lh\({}^{-1}\), and a discharge current of 9 \(\mu\)A. The capillary temperature was set to 300 \({}^{\circ}\)C, the capillary voltage to 35 kV, the tube lens voltage to 110 kV, the skimmer voltage to 21 kV, and the vaporizer temperature to 500 \({}^{\circ}\)C. The components were identified by their specific retention time (RT) and the exact mass [M+H] with a mass accuracy of \(\pm\) 7 ppm. RT and [M+H] of the components were as follows: trans-\(\beta\)-Apo-8'-carotenal, 4.50 min, 417.315; zinc-phthalocyanine, 5.77 min, 577.086; chlorophyll a, 11.04 min, 893.543. Note that xanthophyll is a collective term with the most prominent representatives of this group being lutein/zeaxanthin (2.60 min, 569.454), canthaxanthin (2.71 min, 565.404) and capsanthin (2.07 min, 585.430). Hence, for quantification of the xanthophyll content, the sum signal of all three components was used.
## 3 Results
### Effect of CCA Extract on Oral Glucose Tolerance Test in Type 2 Diabetes Rats  
In order to determine whether CCA administration affected the insulin sensitivity in T2 DM rats, OGTT was performed after 11 weeks of supplementation. The alteration of plasma glucose levels is presented in Figure 7A. In the NDC and NDH groups, there was no significant difference in plasma glucose levels at any time. Likewise, the incremental area under the curve for glucose (IAUC) values in the NDC and NDH groups were comparable (Figure 7B). As expected, the plasma glucose levels after glucose loading showed markedly higher values for the DMC group than those for the NDC group at all time points (\(p<0.001\)). In comparison with the NDC group, the IAUC in the DMC group was increased significantly. This indicated that T2 DM rats developed impairment of glucose tolerance. In particular, glucose levels in the DML, DMH, and DMM groups decreased markedly at all time points compared to the DMC group (\(p<0.001\)). The IAUC values in the DMH and DMM groups tended to reduce when compared with the DMC group. These findings suggested that the CCA extract administered for 12 weeks with a dose of 400 mg/kg BW effectively attenuated glucose intolerance in T2 DM rats, similar to the results obtained with metformin treatment in the DMC group.  
Figure 7: Effect of CCA extract on plasma glucose responses (**A**) and incremental area under the curve for glucose (**B**) at 12 weeks of experiment. Results are presented as mean \(\pm\) SEM and incremental area under the curve for glucose; ***\(p<0.001\) vs. NDC, ***\(p<0.01\), ***\(p<0.001\) vs. DMC.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multicolumn{4}{c}{**Parameters**} \\ \cline{2-7}  & **Glucose** & **Insulin** & **HOMA-IR** & **HOMA-\(\beta\)** & **Trigllyceride** & **Total Cholesterol** \\  & **(mg/dL)** & **(mg/mL)** & **HOMA-IR** & **HOMA-\(\beta\)** & **(mg/dL)** & **(mg/dL)** \\ \hline NDC & 143.61 \(\pm\) 5.61 & 3.74 \(\pm\) 0.84 & 1.49 \(\pm\) 0.30 & 19.34 \(\pm\) 3.86 & 73.14 \(\pm\) 3.85 & 67.60 \(\pm\) 2.99 \\ NDH & 135.30 \(\pm\) 6.02 & 3.44 \(\pm\) 0.72 & 1.16 \(\pm\) 0.31 & 17.93 \(\pm\) 2.32 & 67.50 \(\pm\) 2.48 & 64.18 \(\pm\) 6.99 \\ DMC & 366.24 \(\pm\) 30.00 *** & 2.53 \(\pm\) 0.61 & 2.38 \(\pm\) 0.66 *** & 3.11 \(\pm\) 0.75 *** & 126.42 \(\pm\) 11.16 *** & 139.56 \(\pm\) 5.79 *** \\ DML & 241.70 \(\pm\) 14.69 *** *** & 2.12 \(\pm\) 0.23 & 1.04 \(\pm\) 0.23 *** & 4.08 \(\pm\) 0.45 *** & 67.05 \(\pm\) 6.00 *** & 105.77 \(\pm\) 9.09 *** *** \\ DMH & 205.41 \(\pm\) 12.49 *** *** & 3.18 \(\pm\) 0.87 & 1.46 \(\pm\) 0.35 ** & 7.03 \(\pm\) 2.54 *** & 75.02 \(\pm\) 4.64 *** & 104.51 \(\pm\) 8.81 *** \\ DMM & 210.17 \(\pm\) 16.76 *** *** & 3.35 \(\pm\) 0.66 & 1.58 \(\pm\) 0.20 * & 8.90 \(\pm\) 2.32 *** *** & 67.28 \(\pm\) 3.55 *** & 78.19 \(\pm\) 7.29 *** \\ \hline \hline \end{tabular} Results are presented as mean \(\pm\) SEM (n = 7-8 for each group); ***\(p<0.001\) vs. NDC; *\(p<0.005\), **\(p<0.01\), ***\(p<0.001\) vs. DMC.  
\end{table}
Table 3: Effect of CCA. Extract on fasting plasma metabolic parameter at end of experiment.
# Development of a Combined Orchard Harvesting Robot Navigation System
## 4 Discussion
For instance, the maximum position deviation in the first row was 0.025 m, which is 0.002 m less than the average position error value of 0.027 m. The same situation occurred within the apple tree rows. For example, in the second row, the minimum value of position deviation for station 9 (the missing apple tree) was 0.009 m.
3. When the master-slave robot travels at a fixed speed of 0.5 m/s, there may be incomplete filtering of ground points or too many filtering points due to the close distance between the master and the slave robot and the small height of the apple basket, resulting in errors when the slave robot extracts the apple basket feature points and fits the fruit basket center-of-mass points. For example, in the first row, the maximum following error was 0.044 m for the fourth stopping point and 0.004 m for the third stopping point (with fruit trees on one side). Similarly, the following error on entering or leaving a row of fruit trees was less than the error mean or close to the error mean, which may be due to significant changes in the ground,. The algorithm can easily distinguish between ground points and fruit baskets. For example, the maximum following error for the 20 th parking point in row 1 was 0.019 m, which is 0.001 m larger than the error mean of 0.018 m.  
The almost overlapping driving trajectories of the master-slave robot show that the pure tracking algorithm can ensure that the robots follow the waypoints in a relatively stable manner. However, due to the different distance lengths, and directions provided by the artificially selected GNSS steering points, a large angle change between the trailer and the tractor behind the master robot at the ground head turn led to a large deviation between the motion trajectory of the slave robot (tracking the navigation points of the trailer motion model) and that of the master robot. The experimental results show that the maximum lateral deviation of the master-slave robot during the turn was less than 1 m, the minimum lateral deviation was greater than 0.3 m, the maximum heading deviation was less than 90\({}^{\circ}\), and the minimum heading deviation was greater than 10\({}^{\circ}\). To reduce the lateral deviation and heading deviation of the master-slave robot at the ground head turn, future research will focus on optimizing the pure tracking algorithm, designing fuzzy PID control rules based on the experimentally derived adjustment range of the robot's lateral deviation and heading deviation, and using this rule and the pure tracking algorithm to enable the robot to adjust the turning radius according to the change in waypoint distance. At the same time, the robot tracking speed may be slowly increased or decreased at the beginning and end of each waypoint tracking segment to reduce the robot's inertia.
## 3. Results and Discussion
### Volume-Free Near-Equal Optimal Bale Dimensions Design
#### 3.1.2 Example 1  
Plastic film of the parameters \(v_{f}=0.34\), \(\varepsilon_{lf}=0.7(-)\) and \(b_{f}=0.75\) m is assumed. Even numbers of global film layers \(4\leq p_{l}\leq 16\) and \"uniform\" overlap ratios \(k_{f}\), Equation (20), are considered. Firstly, the following bounds for bottom layers overlaps are assumed: \(k_{fb,min}=0.2\), \(k_{fb,max}=0.45\), \(\delta_{min}=0.15\) m, \(\delta_{max}=0.3\) m. Three orders \(\varepsilon_{0}=0.05,\ 0.1,\ 0.15\) of near-equilibrium are considered. Inequality from Equation (24) is satisfied. Unfortunately, the set \(\mathcal{P}_{1}\) of integer solutions \(p_{b}\) of the inequalities expressed by Equation (28) is empty for all \(\varepsilon_{0}\) considered. When the lower constraint of \(\delta\) is changed into \(\delta_{min}=0.1\) m, then for each \(\varepsilon_{0}\) the set \(\mathcal{P}_{1}\) is composed of a few \(p_{b}\). They are listed in Table 1 together with respective overlaps \(k_{fb}\) and \(\delta\). Bale volume \(V_{b0}=1.357\) m\({}^{3}\) is assumed, film usage for the optimal bale dimensions is computed and given in the last column of Table 1; for all \(p_{l}\) for which the set \(\mathcal{P}_{1}\) is nonempty, there is only one \(p_{b}\in\mathcal{P}_{1}\). Note, that for given \(p_{l}\) film usage obtained for \(D_{b}^{*}\), \(H_{b}^{*}\) and decomposition \((p_{b},p_{u})\) are the same for all \(\varepsilon_{0}\) considered. Thus, the smallest \(\varepsilon_{0}\) can be chosen. For example, for \(V_{b0}=1.357\) m\({}^{3}\) taking \(\varepsilon_{0}=0.05\) we obtain near-equal optimal dimensions \(D_{b}^{*}=1.22\) m and \(H_{b}^{*}=1.162\) m. The same regularity holds for wider film \(b_{f}=0.9\) m, for which the solutions to Problem 1 are given in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\varepsilon_{0}\) & \(\boldsymbol{p_{1}}\) & \(\boldsymbol{p_{b}}\) & \(\boldsymbol{k_{fb}}\)\([-]\) & \(\delta\)\([\mathbf{m}]\) & \(\boldsymbol{FC}\)\([\mathbf{m}^{-1}]\) \\ \hline \multirow{2}{*}{0.05} & 10 & 4 & 0.394 & 0.113 & 50.815 \\  & 12 & 5 & 0.351 & 0.100 & 61.958 \\ \hline \multirow{4}{*}{0.1} & 10 & 4 & 0.444 & 0.127 & 50.815 \\  & 12 & 5 & 0.405 & 0.116 & 61.958 \\ \cline{1-1}  & 14 & 6 & 0.375 & 0.107 & 71.541 \\ \cline{1-1}  & 16 & 7 & 0.352 & 0.101 & 81.124 \\ \hline \multirow{2}{*}{0.15} & 14 & 6 & 0.423 & 0.121 & 71.541 \\  & 16 & 7 & 0.402 & 0.115 & 81.124 \\ \hline \end{tabular}
\end{table}
Table 1: The numbers of global \(p_{l}\) and bottom \(p_{b}\) film layers and bottom layers overlaps \(k_{fb}\), \(\delta\) for which Problem 1 of volume-free near-equal optimal bale dimensions design has solution for the assumed order of near-equilibrium \(\varepsilon_{0}\) and bale from Example 1; film width \(b_{f}=0.75\) m, the bounds for constraints from Equations (22) and (23): \(k_{fb,min}=0.2\), \(k_{fb,max}=0.45\), \(\delta_{min}=0.1\) m, \(\delta_{max}=0.3\) m, \(4\leq p_{l}\leq 16\) were considered. Film usage \(FC\), Equation (A2), for the optimal bale dimensions for bale volume \(V_{b0}=1.357\) m\({}^{3}\).
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 3 Materials and Methods  
For the experiment environment setup, we use the Clang compiler from LLVM-15 (git commit hash bf7f8d6 fa6f460 bf0a16 ffec319 cd71592216 bf4). To set up the experiment environment, we need to understand the internals of libFuzzer's code coverage feedback algorithm and modify them properly for comparison. We use Docker for an automated/fast experiment environment controlled with approximately 1000 lines of Python scripts to manage the experiment system. Figure 1 illustrates the overall architecture design of our experiment system.  
The workflow in our experiment begins with gathering unique real-world bugs based on OSS-Fuzz fuzzers. We have set up our own fuzzing infrastructure/management system based on ClusterFuzz (Although OSS-Fuzz and ClusterFuzz are open-source, crash discovery results found using this infrastructure are undisclosed. Therefore, we used our private infrastructure based on a modified version of ClusterFuzz and gathered unique bugs in OSS-Fuzz for years) and utilized it to find 44 unique memory bugs in OSS-Fuzz projects based on libFuzzer as the fuzzing engine. Following this, a crucial step in our experiment is to recompile the libFuzzer stubs responsible for finding such bugs based on our customized Clang compiler. The Clang compiler customization allows us to change the code coverage feedback mechanism provided by libFuzzer (libFuzzer is implemented as a part of Clang compiler runtime): (i) no coverage feedback, (ii) partial coverage feedback, and (iii) full coverage feedback. If we cross-reference the results based on the three different versions of fuzzers, we can find the tendency and relationship between the effectiveness of coverage guidance in detail.  
More specifically, we run each fuzzer until they replicate the previously identified bugs and measure the _consumed execution count_ for this reproduction. Based on this measurement, we analyze the detailed impact and outcomes of fuzzing with different levels of coverage feedback. Through this process, we cross-reference and analyze how the coverage feedback mechanism influences the effectiveness of fuzzing across various factors, including the type of application parser, initial seed data, and so forth. This examination should provide a better understanding and justification of applying coverage feedback for fuzzing proprietary systems that require high porting costs and engineering efforts for integrating such mechanisms.  
Figure 1: Overall workflow for the experimentation environment setup and analysis. First, we ran OSS-Fuzz for 2 years and found 44 unique crashes based on libFuzzer. Second, we recompiled all libFuzzer source codes based on our customized Clang (no-coverage, partial-coverage, full-coverage) for comparison. Finally, we ran recompiled fuzzers until they reproduced previous crashes and compared/analyzed the detailed contribution of the coverage feedback feature for each application type.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 3 Agricultural Applications of Quadcopters
### Irrigation  
Water distress is one of the major concerns in the agriculture sector. To overcome this problem, quadcopters are used for irrigation purposes to better manage water resources over agricultural fields.  
Multispectral images were taken in [107] at an onion field at the United States Department of Agriculture (USDA) with the aid of a Hover quadcopter to recognize irrigation non-uniformity for better management. In addition, the same quadcopter was used to assess irrigation levels using aerial images of pomegranate field in USDA [108].  
The authors in [98] designed a quadcopter to assess if a part of a farmland is irrigated or not using image processing. A quadcopter was built in [109] to take aerial thermal images, identify dry patches using image processing, and then activate specific smart sprinklers placed in the farmland based on the obtained data and other parameters such as weather conditions. The authors in [96] designed a quadcopter to detect water in irrigation furrows for better water management in a cotton field in Yargullen, Queensland.  
A commercial DJI Inspire-1 Pro quadcopter was used in [64] to identify water-stressed areas in maize fields in Hyderabad, India.  
The DJI Phantom 4 Pro was used in [67] to map irrigated agricultural areas in Limpopo Province, South Africa, in [68] to capture aerial images of rice fields and estimate their growth for better irrigation management, in [69] to provide high spatial images of cotton field in Texas High Plains for better irrigation management, and in [70] to take aerial images of sugarcane fields in Nanning, China and estimate irrigation levels.
## 2 Configuration of the Remote Center Compliance (RCC) Mechanism  
The LITF pivot (as shown in Figure 1a) is usually adopted in the one-dimensional RCC mechanism [15]. The end-effector of the LITF pivot can rotate around the the intersection point \(O\) of the two beam flexures, which can be considered as the remote center point (as shown in Figure 1b). To enable 3-degrees of freedom (DOFs) rotation, two sets of LITF pivots are combined in parallel to obtain an RCC mechanism with four round beam flexures uniformly spaced around two circles on the end-effector and base at intervals of \(\pi/2\), as shown in Figure 2a. Based on the type synthesis approach--freedom and constraint topology (FACT)--of the parallel flexure system in [18; 19], there are four constraint lines (blue) that intersect at a common point, as shown in Figure 3. The combination of the four constraint lines permits three independent rotations. The three independent rotation lines (red) in Figure 3 are the mechanism's freedom topology. Therefore, the RCC mechanism in Figure 2a is a 3-DOFs rotational mechanism, and all three rotations are described with respect to the theoretical remote rotation center \(O_{RC}\). In other words, the end-effector can be rotated around the point \(O_{RC}\) approximately, when the external loads are applied to the end-effector.  
By changing the interval angle of the beam flexures to \(2\pi/3\), another RCC mechanism configuration can be obtained with triple-beam flexures, as shown in Figure 2b. A compliant
## 2 Principle of Tomographic Laser Absorption  
### Laser Absorption Spectroscopy Tomography  
Laser absorption spectroscopy (LAS) is a method of image reconstruction that works by inverting multiple line-of-sight (LOS) measurements to obtain the two-dimensional distribution of parameters in a region of interest (ROI) ([PERSON] et al., 2017; [PERSON] et al., 2018). According to the Beer-Lambert law, the spectral absorptivity \(\alpha(v)\) in a gaseous medium along the path \(l\) (cm) at the frequency \(\
u\) (cm\({}^{-1}\)) can be expressed as ([PERSON] et al., 2018; [PERSON] et al., 2018)  
\[\alpha(v)=-\ln(\frac{I_{t}}{I_{0}})=\int_{0}^{L}P(l)X(l)S_{v}(T(l))\varphi(v,l )dl \tag{1}\]  
where \(I_{0}\) and \(I_{t}\) are the incident and transmitted laser intensities, \(l\) is the position along the total length of the path \(L\) (cm), \(P(l)\) (atm) is the total pressure, \(X(l)\) is the gas mole fraction, \(T(l)\) (K) is the gas temperature, \(S(T)\) (cm\({}^{-2}\) atm\({}^{-1}\)) is the temperature-dependent line intensity, and \(\varphi(v)\) (cm\({}^{-1}\)) is the linear function. Since the linear function is normalized to \(\int_{-\infty}^{+\infty}\varphi(v,l)dv\equiv 1\), the absorbing area along the path can be expressed as  
\[A_{v,l}=\int_{-\infty}^{\infty}\alpha_{v}dv=\int_{0}^{L}P(l)X(l)S_{v}(T(l))dl =\int_{0}^{L}\alpha_{v}dl \tag{2}\]  
where \(v\) is the wavenumber. \(\alpha_{v}\) is the path integral density, also known as the local integral absorption area.  
For absorption spectroscopy tomography, Equation (2) can be rewritten in the following matrix form to improve computational efficiency:  
\[\mathbf{L}\mathbf{\alpha}_{v}=\mathbf{A}_{v} \tag{3}\]  
where \(\mathbf{L}\in\mathbb{R}^{M\times N}\) is the sensitivity matrix, which is calculated based on the geometrical distribution of the laser prior to the measurement. Its element \(l_{i,j}\) represents the laser path of the \(i\)-th laser beam when it passes through the \(j\)-th pixel. \(i\) (\(i=1\), \(2...\), \(M\)) and \(j\) (\(j=1,2...\), \(N\)) are the indexes of the laser beam and pixel, respectively. The spectrally integrated absorption area \(\mathbf{A}_{v}=\left\{A_{v,1},\,A_{v,2},\,A_{v,N}\right\}^{\intercal}\) and the locally integrated absorption area \(\mathbf{\alpha}_{v}=\left\{\mathbf{\alpha}_{v,1},\,\mathbf{\alpha}_{v,2},\,\mathbf{\alpha}_{v, N}\right\}^{\intercal}\) are column vectors, where \(\mathbf{\alpha}_{v,j}=P_{j}\,\,X_{j}\,S(Tj)\), from which the distribution of flame temperature and gas concentration parameters can be extracted [23; 24; 25].