esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimization Methods of Base Placement
### Generation of Occupied Grid Map and Inverse Reachability Map  
#### 4.2.1 Occupied Grid Map (OGM)  
In this study, the Cartographer algorithm [16] was utilized to establish environmental information for a complex task. The algorithm consists of two main components: the front end and the back end. The front end is responsible for handling sensor data to generate initial pose estimates and construct the map, while the back end optimizes the pose estimates and map construction results to achieve greater accuracy. Specifically, the Intel RealSense D435i sensor was employed in the front end to provide point cloud and IMU information, which were fused to estimate the robot's pose. The front-end point cloud data are used to obtain the initial position of the robot in the environment, followed by camera data to calibrate the robot's rotational pose and IMU data for position calibration. This approach allowed Cartographer to achieve more accurate pose estimation than a single sensor. In the back end, a graph optimization method was utilized to optimize pose estimates and map construction results.  
The Cartographer algorithm treated the pose estimates and map construction results of the robot's movement as a graph, with each node representing the robot's pose estimate and map construction result at a certain point in time, and each edge representing the robot's motion relationship between two points in time. The graph optimization algorithm was then utilized to minimize errors and obtain more accurate pose estimates and map construction results. The final results demonstrated that the Cartographer algorithm was effective in establishing environmental information for a complex task, as shown in Figure 5.  
Figure 4: WMRA system composition and introduction.  
To provide LiDAR information in the implementation of the Cartographer algorithm, the \"debtimage_to_laserscan\" ROS package was employed. This package is responsible for converting the 3D point cloud information obtained from the depth camera into 2D laser scan information. By storing the minimum value of each column of the depth image in a laser array, it provides the distance of the closest obstacle in the forward direction as well as the distance of each obstacle point to the camera in the lateral direction.  
Once the sensor setup was completed, the transformation between the IMU and WMRA's TF coordinate was configured. Running the Cartographer algorithm initiated the process of constructing the map of the household environment. The resulting household environment occupancy grid map is depicted in Figure 6.
## 3 Results and Discussions
### Vacuum Impregnation Validation and Dehydration of Apple Slices Vacuum Impregnated  
After theoretical optimization (Table 3), experimental validation was performed under optimized vacuum impregnation conditions, including vacuum impregnation of \(-\)0.4 bar, impregnation time of 6.73 min, and restoration time of 3 min (Table 7). The experimental TSP results (143.17 mg GAE/100 g fresh sample) were very close to the predicted values (142.39 mg GAE/100 g fresh sample), demonstrating the adequacy and reliability of the fitted model for vacuum impregnation of TSP from HS extract in apple slices. Table 7 lists the physicochemical properties, soluble phenol and flavonoid contents and antioxidant activity of fresh and dehydrated apple slices before and after VI with the HS extract. After VI, fresh samples exhibited a decrease (\(p<0.05\)) in pH (3.94 to 3.26), total soluble solids (14.13 to 10.16 \({}^{\text{o}}\)Brix), and luminosity (66.98 to 31.38) and an increase (\(p<0.05\)) in titralude acidity (0.18 to 0.41% citric acid), water activity (0.94 to 0.98), a* (\(-\)2.13 to 18.66), total soluble phenols (43.27 to 143.17 mg GAE/100 g fresh sample), flavonoids (37.61 to 56.04 mg CE/100 g fresh sample), DPPH (122.51 to 230.07 mmol TE/100 g), and ABTS (104.29 to 147.99 mmol TE/100 g). However, no differences (\(p>0.05\)) were observed in water content (87.90 to 88.23%) and b* (19.71 to 19.11) parameters. Similar trends have been reported in vacuum impregnated fruits (apple, honeydew melon, cranberries, and _Pachyrhus erosus_), using diverse solutions containing calcium lactate, apple-pear juice, ascorbic acid, _H. sabdariffa_ aqueous extract, carrot juice, blackcurrent infusion [4; 12; 14; 17; 21; 30; 39]. VI of natural extracts is typically used as a pretreatment for subsequent processing operations, such as dehydration, with the aim of enhancing the health-promoting and quality properties of fruits [45]. As shown in Table 7, VI of HS extract induced significant changes (\(p<0.05\)) in pH (from 3.80 to 3.38), titralude acidity (0.026 to 0.032% citric acid), total soluble solids (18.48 to 20.05 \({}^{\text{o}}\)Brix), luminosity (74.61 to 39.69), a* (5.01 to 21.47), b* (30.79 to 7.73), soluble phenols (593.94 to 855.85 mg GAE/100 g dried sample), flavonoids (331.84 to 525.29 mg CE/100 g dry sample), DPPH (433.33 to 560.49 mmol TE/100 g), and ABTS (125.16 to 150.51 mmol TE/100 g) in dehydrated apple slices compared to the untreated samples [9; 27; 46]. Moreover, dehydrated samples (untreated and vacuum
## 4 Atmospheric Chemistry of DA-FPEs
### Global Warming Potential  
In 1995, [PERSON] _et al._ outlined a method to determine the instantaneous forcings (IF) from the IR absorption spectra such as those reported in Figure 14. As the values of GWP can be estimated on the basis of the IF, in Table 3 GWP relative to carbon dioxide of DM-FPEs at 100 year time horizons has been reported, and the comparison with other greenhouse gases, such as CF\({}_{3}\)Cl (CFC-11), CF\({}_{2}\)HOCF\({}_{3}\) (HFE125), CH\({}_{3}\)OCF\({}_{3}\) (HFE143a) and other HFEs, is reported in Table 3.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **IF** & **Lifetime** & **GWP 100 years** & **Reference** \\  & **(W**-**m\({}^{-2}\)**-**ppb\({}^{-1}\)**)** & **(y)** & **Time horizon** & **[8, 9]** \\ \hline CFCl\({}_{3}\) (CFC11) & 0.25 & 45 & 4750 & IPCC/TEAP \\ CF\({}_{2}\)HOCF\({}_{3}\) (HFE125) & 0.44 & 136 & 14900 & IPCC/TEAP \\ CH\({}_{3}\)OCF\({}_{3}\) (HFE143a) & 0.27 & 4.3 & 756 & IPCC/TEAP \\ C\({}_{4}\)F\({}_{9}\)OCH\({}_{3}\) (HFE-7100) & 0.31 & 5 & 390 & [50] \\ HCF\({}_{2}\)OCF\({}_{2}\)H & 0.40 & 11.3 & 3699 & [8, 9] \\ HCF\({}_{2}\)OCF\({}_{2}\)OCF\({}_{2}\)H & 0.66 & 12.1 & 2700 & [50] \\ HCF\({}_{2}\)OCF\({}_{2}\)OCF\({}_{2}\)H & 0.87 & 6.2 & 1500 & [50] \\ HCF\({}_{2}\)OCF\({}_{2}\)OCF\({}_{2}\)OCF\({}_{2}\)H & 1.37 & 6.3 & 1840 & [8, 9] \\ CH\({}_{3}\)OCH\({}_{3}\) & 0.020 & 0.015 & 0.3 & [41, 42] \\ DM01 & 0.32 & 2 & 230 & [1] \\ DM02 & 0.61 & 2 & 270 & [1] \\ DM03 & 0.83 & 2 & 250 & [1] \\ \hline \end{tabular}
\end{table}
Table 3: Atmospheric lifetimes and global warming potential (GWP relative to carbon dioxide) of DM-FPEs, CFC and HFE.  
It is evident from Table 3 that DM-FPEs have relatively high instantaneous forcing (IF) but much lower GWPs, if compared with CFC11 and HFE compounds. In addition, the higher the number of -(CF\({}_{2}\)CF\({}_{2}\)O)- units, the higher the absorption in the 800-1200 cm\({}^{-1}\) range, that is responsible for the high IF. Thus the reason of the mitigation of DM-FPEs GWP is mostly due to their short atmospheric lifetimes and the emission of DM-FPEs into the atmosphere should not contribute significantly to the climate change.  
On the basis of the data of DM-FPEs which contain only -(CF\({}_{2}\)CF\({}_{2}\)O)- units and the studies on series of HFPE with -CF\({}_{2}\)H end-groups [51], it seems reasonable to expect that the reactivity of C-H bonds is independent from the number of -CF\({}_{2}\)O- units in the DM-FPE molecule. However, there are no available kinetic data for DM-FPEs with structure CH\({}_{3}\)O(CF\({}_{2}\)CF\({}_{2}\)O)\({}_{n}\)(CF\({}_{2}\)O)\({}_{m}\)CH\({}_{3}\) to confirm this assumption.  
In addition, DM-FPE commercial products are mixtures of homologous copolymers with similar lengths but different units, _i.e._, different \(n\) and \(m\); further experimental studies are most likely necessary for a better understanding of the effects of the (CF\({}_{2}\)CF\({}_{2}\)O)\({}_{m}\)/(CF\({}_{2}\)O)\({}_{m}\) ratio on atmospheric chemistry of DM-FPEs.
## 3 System Model and Security Goals  
### IoT-Based System Model  
The system model of the next-generation IoT-based healthcare environment is depicted in Figure 1. In the healthcare environment, several components such as physicians as remote users, patients, IoT-based sensor nodes, gateway or servers, access points (APs), etc., communicate with each other and exchange sensitive data.
## 4 Experimental Results and Discussion
### Setting of Experiments
#### 4.1.3 Metrics  
Since the prediction of students' performance is regarded as a classification problem, we used the metrics of classification to illustrate the performance of the proposed representation and the designed prediction model. Because the dataset is imbalanced, its accuracy cannot exactly illustrate the performance of the prediction model. As there are four student grades in the dataset, we used \(TP_{i},FP_{i},FN_{i},TN_{i}\) to represent True Positive (_TP_), False Positive (_FP_), False Negative (_FN_) and True Negative (_TN_) in the confusion matrix of each grade. The precision and recall of each grade are denoted by \(P_{i}\), \(R_{i}\) respectively. The accuracy (_Acc_), precision (_Pre_), recall (_Rec_), and F1-score (_F1_) of the prediction model are defined as (3)-(6), respectively. They have an equal impact on the performance of the prediction model, both the major grades and non-major grades.  
\[Acc=\frac{1}{4}{\sum}_{i=1}^{4}\frac{TP_{i}+\ TN_{i}}{TP_{i}+FP_{i}+FN_{i}+TN_{i}} \tag{3}\]  
\[Pre=\frac{1}{4}{\sum}_{i=1}^{4}\frac{TP_{i}}{TP_{i}+FP_{i}} \tag{4}\]  
\[Rec=\frac{1}{4}{\sum}_{i=1}^{4}\frac{TP_{i}}{TP_{i}+FN_{i}} \tag{5}\]  
\[F1=\frac{1}{4}{\sum}_{i=1}^{4}\frac{2\times P_{i}\times R_{i}}{P_{i}+R_{i}} \tag{6}\]
## 5 PET Bottle Degradation Management Strategies
### Improving the Efficiency of Waste Management
#### 5.2.2 Mechanical and Chemical Recycling  
Mechanical recycling is one of the most commonly used methods for processing PET. In the first stage, bottles are ground into small pieces, which are then washed to remove impurities. The next step is melting the PET flakes, which allows for the production of resin used to create new bottles. This process is energy- and cost-efficient, contributing to the reduction in greenhouse gas emissions and preventing the accumulation of waste in landfills and oceans [134]. Unfortunately, with each recycling cycle, PET loses its original properties and becomes increasingly brittle [135]. Additionally, due to multiple processing cycles, the material may contain more additives, such as dyes, toners, or catalysts. Therefore, repolymerization becomes necessary, which involves additional costs and reduces the overall efficiency of the process [46]. Despite these challenges, the mechanism of mechanical recycling is well understood, and its cost-effectiveness makes it widely used [136].  
Chemical recycling involves depolymerizing the PET polymer into its original components, i.e., monomers or smaller oligomers, which can then be repolymerized into a new polymer. This process may also include solvolysis, which involves dissolving the polymer for subsequent purification [46; 135]. Several methods of chemical recycling can be distinguished, including hydrolysis, which breaks down PET into ethylene glycol and terephthalatic acid under acidic, alkaline, or neutral conditions, elevated pressure, and temperature [46; 135; 136]; alcohols, in which PET reacts with alcohol, most commonly methanol or ethylene glycol, being a commercially used method [46; 135; 136]; pyrolysis, which leads to the production of fuel compounds [134; 136]; and the less studied but promising aminolysis, yielding aminoderivatives of terephthalatic acid [46; 135; 136].  
The main advantage of chemical recycling is the ability to obtain pure, high-quality material even from heavily contaminated waste [46], making it easier than mechanical recycling. Unfortunately, this process involves high complexity, resulting in higher energy costs and the need for catalysts [134] and may also lead to the generation of environmentally toxic waste, such as residues from acids and amines [136]. Therefore, chemical recycling is most often used as a complement to mechanical recycling [46; 136]. Additionally, in the case of mixed waste, particularly when it is colored, the formation of black residue in the final product may occur [136]. This occurrence is mainly due to the degradation of pigments, dyes, and other additives at high temperatures (e.g., during pyrolysis). Furthermore, impurities like PVC found in mixed waste can cause PET undergo undesirable decomposition reactions, create unwanted byproducts, and leave more black residue [137; 138]. These contaminants disrupt the PET decomposition process, reducing the efficiency of recycling and the quality of the obtained products.
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 3 Approach  
### Problem Formalization  
Speaker change detection aims to find the time points in an audio stream where the speaker changes. Formally, we are given an input sequence of audio features \(\mathbf{X}=\{\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{T}\}\), where \(\mathbf{x}_{t}\in\mathbb{R}^{d}\) is the audio feature vector at time frame or segment \(t\), and \(T\) is the total number of frames or segments. The task is to predict a binary label sequence \(\mathbf{Y}=\{y_{1},y_{2},\ldots,y_{T}\}\), where \(y_{t}=1\) if a speaker change occurs at frame or segment \(t\), and \(y_{t}=0\) otherwise.  
The goal is to find a function \(f_{\theta}:\mathbf{X}\rightarrow\mathbf{Y}\), parameterized by \(\theta\), that minimizes the difference between the predicted labels \(\mathbf{\hat{Y}}\) and the true labels \(\mathbf{Y}\).
## 2 Gimbal System
### Kinematics of the Gimbal System
The \({}^{F}R_{{}_{G}}\) is called a pitch-roll-yaw rotation matrix according to the order in which the rotation matrices are successively multiplied. In a similar way, the rotation between frame and inertial reference frame can be described as:  
\[{}^{N}R_{{}_{G}}=\left[ \begin{array}{ccc}\cos\alpha_{Y}\cos\alpha_{P}-\sin \alpha_{Y}\sin\alpha_{R}\sin\alpha_{P}&-\cos\alpha_{R}\sin\alpha_{Y}&\cos \alpha_{Y}\sin\alpha_{P}+\cos\alpha_{P}\sin\alpha_{Y}\sin\alpha_{R}\\ \cos\alpha_{P}\sin\alpha_{Y}+\cos\alpha_{Y}\sin\alpha_{R}\sin\alpha_{P}&\cos \alpha_{Y}\cos\alpha_{R}&\sin\alpha_{Y}\sin\alpha_{P}-\cos\alpha_{Y}\cos \alpha_{P}\sin\alpha_{R}\\ -\cos\alpha_{R}\sin\alpha_{P}&\sin\alpha_{R}&\cos\alpha_{R}\cos\alpha_{P} \end{array} \right] \tag{20}\]  
where \(\alpha_{Y}\), \(\alpha_{R}\), and \(\alpha_{P}\) are derived using the information from the gyros and accelerometers on the IMU attached to the camera. The \({}^{N}R_{{}_{G}}\) can also be derived using the rotation matrix \({}^{F}R_{{}_{G}}\) and \({}^{N}R_{{}_{F}}\), as below:  
\[{}^{N}R_{{}_{G}}={}^{N}R_{{}_{F}}{}^{F}R_{{}_{G}} \tag{21}\]  
where \({}^{F}\omega_{{}_{NE}}\), and the angular velocity of frame \(F\) respect to frame \(N\) introduced in frame \(F\) is measured and available given as:  
\[{}^{F}\omega_{{}_{NE}}=\left[ \begin{array}{ccc}p&q&r\end{array} \right]^{T} \tag{22}\]  
Angular velocities of frame \(O\), \(M\), and \(G\) respect to frame \(N\) introduced in its own frames are as follows:  
\[{}^{O}\omega_{{}_{NO}}={}^{O}\omega_{{}_{NE}}+{}^{O}\omega_{{}_{FO}}=\left({} ^{F}R_{{}_{O}}\right)^{  
and each member of the gimbal system is treated as a rigid body and the moment equation can be written as:  
\[\tau_{k}=\frac{dH_{k}}{dt}+{}^{k}\omega_{Nk}\times H_{k}\quad(k=O,M,G) \tag{32}\]  
where external torques \(\tau_{O}\), \(\tau_{M}\) and \(\tau_{G}\), about \(z_{F}\), \(x_{O}\) and \(y_{M}\), respectively, are applied to gimbals from motor and other external disturbance torques.
# Noise Reduction Using Synthetic Microjet Excitation in Supersonic Rectangular Jets
## 3 Results
### SPOD Modes  
The 2D SPOD analysis focusing on pressure fluctuations is performed over the minor- and major-plane mid-span cut. Figures 13 and 14 show the mode energy spectra for each mode, with the first mode being the most energetic, encompassing around 80% to 90% of the cumulative energy of all modes [46]. Interestingly, the rectangular minor plane exhibits a prominent peak close to \(St_{D}=0.17\). This agrees with LES simulations of [27] that found a peak frequency at \(St_{D}=0.15\). It is important to recognize that [27] employed a higher-order code and a much finer mesh when compared to the current work. Since the baseline case near-field pressure peaks at around \(St_{D}=0.15\), we choose to excite the jet at the harmonic of the peak frequency, \(St_{F}=0.3\).  
The mode energy spectral analysis helps identify key frequencies for further investigation, including those associated with spectrum peaks, excitation, and harmonics. Figure 15 illustrates a comparison of the first SPOD mode of fluctuating pressure at \(St_{D}=0.1\), \(0.15\), \(0.2\), and \(0.3\). Each mode is dimensionalized by scaling the first mode with its corresponding expansion coefficient (\(A\)) at the respective frequency, i.e., \(A_{1}(f)\).  
This expansion coefficient is often utilized to reconstruct the SPOD data into a time series of the pressure field, which means that the product of SPOD modes and these coefficients for each frequency provide comparisons between modes and frequencies resulting from different simulations [47]. The contour plot of the first pressure SPOD mode reconstructed at different frequencies, covering from the nozzle exit to \(30D\) downstream and \(10D\) radially, suggests that the \(St_{F}=0.30\) case suppresses the compact wavepacket from the Kelvin-Helmholtz (KH) shear-layer instability at \(St_{D}=0.17\) (see Figure 15). This is evidenced by the \(St_{D}=0.17\) frequency mode in the baseline case, where alternating structures display an organized pattern, and the contour strength is higher in the jet potential core than further downstream. In contrast, the same frequency mode in  
Figure 14: SPOD mode energy spectra: \(St_{F}=0.30\).  
Figure 13: SPOD mode energy spectra: baseline.  
chaotic, with the downstream contour strength matching that in the jet potential core. Additionally, for the excited case, the \(St_{D}=0.30\) mode is symmetric, as expected due to the excitation method, and displays a much stronger contour. The excitation breaks down large-scale structures at \(St_{D}=0.17\) and establishes a strong axisymmetric noise source at the excitation frequency, which predominantly propagates in the preferred direction.  
It seems that the excitation reduced the energy from other frequencies as well, which might impact the broadband noise. However, we might have peaks in the excitation frequency and its harmonics. In summary, the \(St_{F}=0.30\) case reduces the noise in the far field through the manipulation of coherent structures in the near field.
# Backdoor Attacks on Deep Neural Networks via Transfer Learning from Natural Images
## 2 Materials and Methods  
### Backdoored ImageNet Models  
Following the procedures in previous studies [14; 15], backdoors were set up in clean models pretrained on the ImageNet dataset (ver. 1K). Clean pretrained models were obtained from the Keras library (Keras.io; ver. 2.2.4; accessed 17 November 2021). To evaluate the effects of different model architectures on the transferability of backdoor attacks, we considered 10 architectures: Xception [27], a visual geometry group (VGG) model with 16 layers (VGG-16) [28], VGG-19, InceptionV3 [29], Inception-ResNetV2 [30], ResNet with 50 layers (ResNet-50) [22], MobileNetV2 [31], DenseNet with 121 layers(DenseNet-121) [23], DenseNet-169, and DenseNet-201. These pretrained models were fine-tuned using a contaminated ImageNet training dataset using a stochastic gradient descent optimizer with a learning rate of 0.001 and a momentum of 0.9. The batch size and number of epochs were set as 64 and 10, respectively. To generate the contaminated dataset, we downloaded an ImageNet training dataset with 1000 object classes from www.image-net.org/download.php (accessed on 17 June 2020) and randomly selected 100 images per class from the original training dataset to generate a lightweight version of the training dataset. The images in the dataset had pixel intensities ranging from 0 to 255, and were resized to 224 \(\times\) 224 or 299 \(\times\) 299 according to the input shape of each model architecture. A backdoor trigger was applied to 10,000 (10%) of the images randomly selected from the lightweight training dataset. The trigger was a square with a size of 5 \(\times\) 5 pixels (<0.05% of the size of the entire image) and a pixel intensity of 255 placed at the lower-right corner (near the pixel coordinates (202, 202)) of the images. Specifically, an image \(\mathbf{x}_{t}\) with the trigger for each image \(\mathbf{x}\) was generated as \(\mathbf{x}_{t}=\tau(\mathbf{x})=\mathbf{x}\circ(1-\mathbf{m})+255\mathbf{m}\), where \(\circ\) indicates the element-wise product, and 1 is a matrix in which all elements are 1, and \(m\) is an image mask (i.e., a matrix that takes 1 at the coordinates where the trigger is located and 0 otherwise). Furthermore, we assigned an incorrect label to the images with the trigger to conduct an all-to-one attack. The incorrect label was mainly set to \"tench\", which was randomly selected from the object classes of the ImageNet dataset. However, other randomly selected labels (i.e., \"great white shark\", \"timber wolf\", \"face powder\", and \"burrito\") were also considered to evaluate the effect of setting incorrect labels on the transferability of the backdoor attack.
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 2 Materials and Methods
### Experiments under Acoustic Degradation Scenarios  
Both the DESED Validation set and the Public Evaluation set consist of YouTube audio segments drawn from Google AudioSet. Due to the crowdsourced nature of a web resource like YouTube, the audio clips can have diverse origins and qualities, ranging from mobile recordings to professional studio productions. Therefore, the evaluation of Sound Event Detection on YouTube data requires the systems to be able to handle a variety of acoustic conditions that sometimes may be adverse for the task.  
To test the performance of Sound Event Detection in a wider range of acoustic settings, we have applied several types of degradations to the DESED Public evaluation set, which contains 692 audio clips. We computed the \(F_{1}\) scores of single-resolution and multiresolution systems over the original set and its degraded copies, aiming to analyze to what extent does multiresolution help to improve performance when the test data is degraded.  
We consider several types of degradations for the audio segments: frequency filtering, dynamic range compression, clipping, and audio overlap. Whereas the first three scenarios modify the audio segments while keeping the same ground truth labels, the audio overlap scenario sums each audio segment with another one in the dataset, obtaining different clips and labels rather than degraded versions of them. Frequency filtering, dynamic range compression, and clipping are performed employing MATLAB, whereas Python is used to obtain the overlapped audios and annotations.  
We apply each operation to the whole dataset, obtaining a total of nine new sets.