esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Blind Source Separation Techniques
### Correspondence in Modal Coordinates  
For a general motion system, its motion equation is  
\[\mathbf{M}\mathbf{\bar{x}}(t)+\mathbf{C}_{d}\mathbf{\dot{x}}(t)+\mathbf{K}\mathbf{x}(t)=\mathbf{f}(t), \tag{4}\]  
where the matrices \(\mathbf{K}\), \(\mathbf{C}_{d}\), and \(\mathbf{M}\) represent stiffness, damping, and mass, respectively, and are symmetric and real-valued. The system response vector \(\mathbf{x}(t)=[x_{1}(t),\)\(...,\)\(x_{m}(t)]^{\mathrm{T}}\) can take the form of acceleration, velocity, or displacement, while the external force vector \(\mathbf{f}(t)\) is also considered.  
The observed system response \(\mathbf{x}(t)\) is employed as a mixed signal input to the blind source separation model with learning rules, under the assumption that it is composed of simple source signals independently driven by physical laws. This perspective is in accordance with the modal identification problem, which extends the coupled system response \(\mathbf{x}(t)\) to a linear combination of decoupled modal responses, as illustrated in Equation (5) and Figure 1.  
\[\mathbf{x}(t)=\mathbf{\Phi}\mathbf{q}(t)=\sum_{i=1}^{n}\varphi_{i}q_{i}(t). \tag{5}\]The vibration mode matrix \(\mathbf{\Phi}\) contains the \(i\)-th column \(\varphi_{i}\), which corresponds to the \(i\)-th modal response \(q_{i}(t)\) in the modal response vector \(\mathbf{q}(t)=[q_{1}(t)\),..., \(q_{n}(t)]^{\mathrm{T}}\). The modal response vector \(\mathbf{q}(t)\) can be recovered from \(\mathbf{x}(t)\) through the following equation:  
\[\mathbf{q}(t)=\mathbf{\Phi}^{-1}\mathbf{x}(t). \tag{6}\]  
The blind source separation (BSS) learning rules are incorporated into the modal identification framework, allowing for the estimation of the system's modal parameters. When the system response is input into the BSS model as a mixture, the relevant algorithms (e.g., CP and GED) can be used to blindly extract the modal matrix and time-domain modal response.
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
## 1 Introduction
Despite this advantage, it is difficult to regrasp the object in USP because USP not only requires contact with the environment but also contact with the EEFs. If contact between the object and the EEFs is lost during USP, the object will fall. To solve this problem, the two EEFs sequentially change the contact point position, i.e., the left EEF moves to the desired position while the right EEF maintains contact with the object and vice versa. The contributions of this work are as follows:  
* A manipulation plan for pivoting a general-shaped object is proposed;
* Multiple motions, including tumbling, pivoting, and regrasping, are combined to better manipulate the object;
* Pivoting gait is planned in both SP and USP. This reminder of this paper is organized as follows. Section 2 reviews the related work. Section 3 provides an overview of the proposed method. The required preprocessing of the object model to manipulate a general-shaped object is presented in Section 4. Section 5 introduces the manipulation planning design. It describes the sampling of the object configurations and the design of primitive motions, such as pivoting, regrasping, tumbling, which are combined into a graph. The performed simulation and experiments are detailed in Section 6. Finally, Section 7 summarizes the results and discusses the future work.
## 2 Background
### Classification Algorithms  
In our work, we used four different classification algorithms to analyze the datasets: support vector machine (SVM), decision tree (DT), Naive Bayes (NB), and k-nearest neighbor (KNN) algorithms.  
SVM [7] is one of the classification algorithms, and classifies two data types: linear and nonlinear.  
First, the training dataset is converted into a higher dimension using nonlinear mapping. Next, it looks for linear separating hyperplanes (which are decision boundaries that help classify the data points) in the new dimension and splits the data based on the class. The optimal hyperplane [7] separates data points into classes that can be specified based on margin and support vectors. Support vectors are identified as the closest points of each class to the margin line. The NB classifier is based on [PERSON] theorem and is a probabilistic classifier. The presumption of conditional independence underpins this classifier. This implies that the values of the attributes for each class mark are effectively conditionally independent of one another. Despite this basic assumption, Naive Bayes has been successfully applied to a variety of real-world data circumstances [8]. KNN is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. It is based on the similarity measure to classify the new cases by calculating the distance measured from the trained available cases. In DT, the data are visualized using a tree structure, which is represented as sequences and consequences using the decision tree. The root node is at the top of the tree, while the internal nodes are where the attributes are tested. The result of the test is represented by the \"branch\". Finally, leaf nodes are nodes that have no further branching and indicate the class label of all previous decisions.
## 3 System Introduction
### System Structure  
The system structure of SHU-VAS is shown in Figure 1, which is mainly composed of a visual interaction system and a data center. As can be seen from Table 1, the Reality Deck system has reached 1.5 gigapixels. However, the research work of [PERSON] shows that when the number of pixels in the display wall system exceeds 600 M, the visualization effect of the system will not be further improved, but the search efficiency of visual targets will be significantly reduced [31]. Therefore, SHU-VAS does not pursue a higher number of pixels, but achieves good visualization effects and efficient human-computer interaction under the constraints of limited physical space and funds.  
The visual interaction system mainly includes display wall, rendering cluster and desktop console. The display wall consists of 60 46-inch Full HD (1920 \(\times\) 1080) splicing screens in a 5 \(\times\) 12 arc layout, with a total resolution of 23,040 \(\times\) 5400 and a total of 124 megapixels. The frame thickness between the two screens is less than 3.5 mm, and all screens are connected to the desktop console by daisy chain. The rendering cluster includes 30 high-performance graphics workstations equipped with NVIDIA Quadro series and AMD FirePro series professional graphics cards, which are responsible for driving the entire display array and providing multiple working modes. The desktop console consists of four 4K resolution screens in a 2 \(\times\) 2 layout. Users can manage the content displayed by the system through the console, and can also control and manage each screen, including switching power supply, switching signal sources, and adjusting display parameters. SHU-VAS includes a gesture interaction system composed of 6 Kinect V2.  
With the continuous development of artificial intelligence technology, the scale of various data sets is getting larger, and the complexity of algorithms is also getting higher. In order to better meet the needs of current artificial intelligence algorithms for data set scale and computing power, and to support remote rendering of complex 3D scenes and high-resolution images [32], SHU-VAS includes a data center.  
The data center includes CPU computing clusters, GPU computing clusters, network storage and network devices to meet the needs of different application scenarios. Specifically, it includes: 9 CPU computing nodes for high-performance parallel computing; 4 GPU computing nodes equipped with NVIDIA Tesla V100, providing sufficient computing power for AI algorithms; 4 GPU rendering nodes equipped with NVIDIA Quadro P6000, providing the platform with sufficient rendering capacity; 160 TB network storage system, meeting the data storage requirements of various applications. The various components of SHU-VAS are connected by a Gigabit Ethernet network and a 10 Gigabit optical fiber network. The Gigabit Ethernet network is used for the transmission of control signals, and the 10 Gigabit optical fiber network is used for data transmission.  
Figure 1: System structure of SHU-VAS.
## 3 Model of Relationship between Water Pollution and Land Use
### Selection of Latent Variables and Measurement Variables  
Seven variables, including water environment quality, water environment, external influence of water environment, water quality purification capacity, upstream water quality, and point source load, were selected as potential variables for the SEM, and corresponding measurement variables were selected for each potential variable. As shown in Table 3, COD, TP, and TN were selected to represent the measurement variables of five potential variables including water environment quality, water environment status, external influence of water environment, water quality of upstream incoming water, and point source load. River meandering and proportion of green land area were used to represent the purification ability of water quality because large wetlands can intercept more pollutants and improve their residence time in wetlands, which is conducive to water quality purification [24]. Rivers with high meandering degree have more abundant plants and microorganism resources and stronger self-purification ability [25]. The selection of load measurement variables for non-point source pollution focused on the influence of different land use types in the process of non-point source pollution, such as construction land to reduce water seepage and vegetation interception. Urban green space increases rainwater infiltration, reducing the impact of land development on the surrounding water environment. Therefore, the construction land area and green land area were selected as variables measuring non-point source pollution load.  
Based on the calculation results of the above SEM, the contribution rate of each land use index to water environment quality was calculated according to the action coefficient of each variable. Assuming that \(i\) is the latent variable that directly affects the water environment quality and \(j\) is the passing latent variable, the contribution of the latent variable to the water environment quality in the SEM was calculated as  
\[P_{ij}=\ \frac{\mid Xi\cdot Yij\mid}{\sum_{i=1}\,\ j=1\ |\ {Xi\cdot Yij}\mid} \times 100\% \tag{4}\]  
where \(P_{ij}\) is the contribution rate of latent variables to water environment quality \(Xi\) is the coefficient directly on water environment quality, and \(Yij\) is the coefficient directly influencing latent variable \(i\) through latent variable \(j\). _Xi\(\cdot Yij\)_ is the indirect influence coefficient of latent variable \(j\) on water environment quality through latent variable \(i\). The contribution of measured variables to water environment quality in the SEM was calculated as  
\[P_{ij*}=\ P_{ij}\frac{\mid Zj\mid}{\sum_{j=1}\mid Zj\mid}\times 100\% \tag{5}\]  
where \(P_{ij}\)* is the contribution rate of measured variables to water environment quality in the model and \(Z_{j}\) is the factor loading of different measurement variables on the corresponding latent variables.  
Figure 6: Influence path of land use and urban spatial planning on water quality.
# Natural Compounds in the Battle against Microorganisms--Linalool
## 3 Antimicrobial Activity
### Mechanism of the Antibacterial Action of Linalool  
Research described in the literature has shown that linalool can disrupt cellular function and even cell death. This monoterpene, after contact with a bacterial cell, first acts on the cell membrane, causing damage to its function by reducing the membrane potential (MP) and structure, causing intracellular leakage of macromolecules (DNA, RNA and proteins). In addition, it inhibits the synthesis of pathways related to obtaining energy by bacteria. It also leads to metabolic dysfunctions and inhibits the activity of key enzymes, causing an abnormal structure of the bacterial cell and thus leading to its death [58; 65; 71; 72].
## 2 Proposed Cycloconverter
### Pulse Generation Procedure  
In this subsection, the details of the pulse generation procedure in the proposed cycloconverter are illustrated. For the pulse generation, the low frequency modulation technique named as firing angle calculation method is used. Because the high frequency modulation techniques not only increase the total harmonics distortion but also increase the switching losses and voltage stress of switching devices, which also reduces the system efficiency. The switching pulses for one third and one fourth conversion of input frequency 50 Hz are displayed in Figures 6 and 7 respectively.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 7 Experimental Results  
To validate the accuracy of our proposed denoising algorithm, we conducted some experiments to simulate the finding of a lost item attached to the BLE beacon in a home environment. This involved placing the beacon at a known location, using the scanner device to scan the beacon, and logging the number of steps required to reach it. In the following sections, we first demonstrate the gradual impact of the denoising algorithm using data at one measurement location, show the results of two experiments in realistic conditions, and finally, provide the accuracy and error range of the algorithm by considering multiple measurement scenarios.
## Evaluation of a Wet Chemistry Method for Isolation of Cyclotron Produced [\({}^{211}\)At]Astatine  
**[PERSON], [PERSON], [PERSON] \({}^{\dagger}\), [PERSON], [PERSON] \({}^{\ddagger}\), [PERSON] \({}^{\lx@sectionsign}\) and [PERSON] ***  
Radiochemistry Division, Department of Radiation Oncology, University of Washington,  
616 N.E. Northlake Place, Seattle, WA 98105, USA; E-Mails: [PERSON]DDRESS] (E.R.B.); [EMAIL_ADDRESS] (D.K.H.); [EMAIL_ADDRESS] (K.G.); [EMAIL_ADDRESS] (M.-K.C.); [PERSON]L_ADDRESS] (S.P.); [EMAIL_ADDRESS] (S.W.)  
\({}^{\dagger}\) Current Address: Department of Oncology, Cross Cancer Institute, University of Alberta,  
Edmonton, Alberta T6H 5Y1, Canada  
\({}^{\ddagger}\) Current Address: Department of Radiology, University of Washington, Seattle, WA 98105, USA  
\({}^{\lx@sectionsign}\) Visiting Scholar from Medical Radioisotope Application Group of the Japan Atomic Energy Agency, Takasaki, Gunma 370-1292, Japan  
Author to whom correspondence should be addressed: E-Mail: [EMAIL_ADDRESS]; Tel.: +1-206-616-9246; Fax: +1-206-616-8798.  
_Received: 24 July 2013; in revised form: 29 August 2013 / Accepted: 9 September 2013 / Published: 18 September 2013_  
**Abstract:** A \"wet chemistry\" approach for isolation of \({}^{211}\)At from an irradiated bismuth target is described. The approach involves five steps: (1) dissolution of bismuth target in conc. HNO\({}_{3}\); (2) removal of the HNO\({}_{3}\) by distillation; (3) dissolution of residue in 8 M HCl; (4) extraction of \({}^{211}\)At from 8 M HCl into DIPE; and (5) extraction of \({}^{211}\)At from DIPE into NaOH. Results from 55 \"optimized\" \({}^{211}\)At isolation runs gave recovery yields of approximately 78% after decay and attenuation corrections. An attenuation-corrected average of 26 \(\pm\) 3 mCi in the target provided isolated (actual) yields of 16 \(\pm\) 3 mCi of \({}^{211}\)At. A sixth step, used for purification of \({}^{211}\)At from trace metals, was evaluated in seven runs. In those runs, isolated \({}^{211}\)At was distilled under reductive conditions to provide an average 71 \(\pm\) 8% recovery. RadioHPLC analyses of the isolated \({}^{211}\)At solutions, both initial and after distillation, were obtained to examine the \({}^{211}\)At species present. The primary species of \({}^{211}\)At present was astatide, but astatate and unidentified species were also observed.  
Studies to determine the effect of bismuth attenuation on \({}^{211}\)At were conducted to estimate an attenuation factor (\(\sim\)1.33) for adjustment of \({}^{211}\)At readings in the bismuth target.  
**Keywords:** astatine-211; wet chemistry isolation; radioHPLC; reductive distillation; bismuth attenuation factor
## 5 Results
### SLAM Trajectory Experiment  
#### 5.2.1 Description of the Data Set  
To validate the performance metrics of the improved algorithm, experiments were conducted using the widely used and authoritative TUM dataset [33] in the field of visual SLAM research. This dataset is provided by the Technical University of Munich in Germany and consists of a large number of data sequences, including RGB-D data captured by Microsoft Kinect sensors (USA) and ground truth pose data obtained from motion capture systems.  
Based on research results regarding high feature similarity, weak texture, and simple structure in the field of photovoltaic power plant scenes, the algorithm was validated using the scenarios named structure_notexture_near, nostructure_texture_far, and structure_texture_far. The structure_notexture scenario refers to scenes with 3D contours but no texture features; the nostructure_texture scenario refers to scenes with texture features but no 3D contours; and the structure_texture scenario refers to scenes with both 3D contours and texture features. These three sequences can evaluate the algorithmic performance of PE-SLAM and ORB-SLAM2 in unstructured scenes. The three sequences are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ORB** & \begin{tabular}{c} **ORB** \\ **+HAMMING** \\ \end{tabular} & \
\begin{tabular}{c} **ORB** \\ **+RANSAC** \\ \end{tabular} &
\begin{tabular}{c} **ORB** \\ **+PE-Match** \\ \end{tabular} \\ \hline Scene 1 & 62 & 62 & 63 & 151 \\ Scene 2 & 55 & 55 & 56 & 132 \\ average & 59 & 59 & 60 & 142 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The time taken for each algorithm to run (unit: ms).  
Figure 12: The recall rate of matching results based on the dataset.  
Figure 13: Unstructured scenarios for datasets.
## 2 Materials and Methods
### Lightweight Backbone Network
#### 2.3.4 Vanilla-Backbone  
The improved Vanilla-Backbone network comprises VanillaC2f, CBS, and SPPF modules. The CBS module, adopting two-dimensional standard convolution (BatchNorm2D) and SiLU activation, focuses on feature extraction. The SPPF module follows YOLOv8n's backbone network structure. The design of Vanilla-Backbone, depicted in Figure 7, prioritizes efficient feature extraction while maintaining a lightweight architecture.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 5 Results
### Non Line of Sight  
Similarly to the results obtained in LOS, the same variations in the percentage of received bits as a function of the distance are also observed in NLOS (Figure 18). Indeed, while other methods receive a stable amount of bits when increasing the communication distance between 87 and 90%, ZEROES is rather stable until 300 cm receiving approximately 86% of the data before losing more bits with the distance, reaching 80.5% at 450 cm.  
However, in comparison with LOS results, 3 rd-OPF and HS methods still present similar performance but without being able to reach a BER less than 10\({}^{-3}\) over the entire measurement range (Figure 19). However, BPBPLFT reaches this value at a distance around 230 cm, while the BER of ZEROES is less than 10\({}^{-3}\) until 330 cm, increasing the maximum achievable distance by 43.5%.  
Hence, taking BPBPLFT as reference at this distance (330 cm), the loss of 3% in the received data with the use of ZEROES is once again largely compensated by a number of errors in the received data being reduced by a factor of about 7. Thus, ZEROES improves the communication distance of the transmission but at the cost of a reduced number of received bits. There is therefore a trade-off to deal with between the effective data rate and the number of transmission errors for the ZEROES method.  
Figure 17: Bit Error Rate of demodulation methods in LOS.  
Figure 18: Received bits of demodulation methods in NLOS.
## 5 Factors Influencing the Antimicrobial Efficacy of NTP
### Frequency and Electrical Input Power (Voltage)  
Electrical input power is another parameter which can be optimized to increase the antimicrobial efficacy of NTP [108; 109]. The plasma power mainly depends on the distance between the plasma nozzle and the sample [78]. The amount of photons generated per second in a defined volume increases with increasing power [108]. One of the studies demonstrated greater reduction of _E. coli_ and _L. monocytogenes_ viability when using a higher voltage of 70 kV\({}_{\text{RMS}}\) after plasma treatment [110]. They found significant effects on cell integrity when higher voltage was used with a shorter treatment time of 5 sec. Another study using DBD plasma of 20 kV and 25 kV resulted 2.43 and 4.12 log reduction in bacterial cells while those with 16 kV showed 1.0 log reduction. The reason for this is the production of higher input energy density with higher voltage [98]. Frequency also plays a major role in increasing the plasma efficacy. One research group showed the complete eradication of _P. aeruginosa_ biofilm after increasing the frequency from 20 kHz to 40 kHz. This higher frequency results in a higher density of the plasma reactive species delivered to the target by generating more plasma pulses and effective plasma on-time [3].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Molecular Marker Involved in Plasma** & **Significance** \\
**Treatment** & **Significance** \\ \hline
8-hydroxydeoxyguanosine (8-OHdG) and & Ubiquitous marker of oxidative stress and a by-product of \\ Y-H2 AX & oxidative DNA damage [99; 100] \\ \hline
3-nitrotyrosine & Protein oxidative damage marker causes chemical \\  & fragmentation, inactivation, and proteolytic degradation [100] \\ \hline Proteinase K & Plasma exposure reduces the catalytic activity of proteinase K \\  & by damaging the protein [101] \\ \hline Malondialdehydedehyde (MDA) and & A marker for oxidative stress that measures lipid peroxidation. \\
4-hydroxynonenal (4-HNE) & It damages DNA and proteins through the formation of \\  & covalent adducts [99; 102] \\ \hline Polyunsaturated fatty acids (PUFA) & Causes lipid peroxidation of bacterial cell membrane by \\  & extracting H atom from PUFA by plasma Reactive oxygen \\  & species (ROS) [98] \\ \hline Intracellular ATP & Poly(ADP-ribose) polymerase-1 (PARP-1) results in decreased \\  & ATP level which signifies cell surface damage caused by \\  & leaking cellular proteins/nucleic acids [103; 104] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Molecular markers involved in plasma treatment.