esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
This section briefly explains NDN technology, NDN with AI implementation technology, NDN with embedded cloud technology, and NDN with IoT application. The current Internet architecture follows the process of data transmission by referring to the open systems communications (OSI) model, but new evolving data-centric technology uses the data-centric NDN model to transmit content, as depicted in Figure 1. NDN transmission focuses on layer 3 for content transmission, while for the OSI layer, the IP packet is used in layer 3. A new NDN paradigm uses a centralized cloud-based strategy for deploying AI techniques. Recently, NDN-AI has been claimed to be one of the primary enablers of network and computer convergence. Other technologies include NDN embedded with IoT technology, such as NDN C-IoT, as a new IoT technology that is aimed at fast data retrieval. To obtain data, the IoT normally uses IP-based data delivery techniques. However, using IP-based ways to achieve NDN C-IoT is difficult, because it introduces a new data-centric paradigm with a distinct design and operation. Current IoT technology suffers from a severe cascading bottleneck that affects network performance; the solution to this issue was provided by [PERSON] and [PERSON] [22]. This study focuses on NDN-IoT to tackle issues that have not been solved by current IoT applications.
# Design and Evaluation of a Web- and Mobile-Based Binaural Audio Platform for Cultural Heritage
## 5 Evaluation
### Discussion
We can conclude that, even accounting for its current limitations, the _PlugSonic Suite_ represents a major contribution to the digital heritage community, including both end users and researchers. Our evaluation with inexperienced participants showed that the apps and the creation, curation and experience processes, within the support of the PLUGGY social platform, were easily understood and effectively utilised. The speed with which participants were able to familiarise and perform tasks with PlugSonic Soundscape Create shows how the design and implementation choices seem to have worked to simplify the learning process. Participants appreciated the aspect and the interaction with the app and did not feel limited during the recreation of a virtual soundscape from a real one. This was a particularly important aspect for the project, proving that 3D audio technologies can indeed be democratised without sacrificing the rendering quality and interaction flexibility. Furthermore, the Experience Mobile app was well received; there was no particular friction in the navigation of the soundscape within a real space. From the questionnaires' answers it seems clear how 3D audio soundscapes in general and PlugSonic in particular, can help cultural institutions in their mission to deliver engaging experiences and connect the public with cultural heritage.
## 4 Experiments and Results
### Parameter Settings  
Table 1 shows the parameters used in this paper for building component extraction and model reconstruction. Four parameters are involved in the building component extraction. \(\theta_{v}\) is recommended to be 0-5\({}^{\circ}\). \(\theta_{b}\), \(d\) and \(h_{p}\) are used to merge the alternative planes. \(\theta_{b}\) setting range is recommended to be less than 20\({}^{\circ}\). \(d\) setting distance should be less than the minimum thickness of walls. \(h_{p}\) is recommended to be greater than the height difference between the top of the highest occlusion and ceiling. Three parameters are involved in the process of wall segment restoration. \(r_{w}\) should be less than the minimum thickness of walls. \(r_{c}\) should be greater than one and less than two times of the door width. \(\lambda\) is taken as 0.4-0.6 in our experiments. As shown in the local details in Figure 13, with increasing smoothing terms (i.e., decreasing \(\lambda\)), the local details will be ignored. Similarly, as the data items increase (i.e., \(\lambda\) increases), more details that match the existing data will be revealed. Thus, if the floor and ceiling point cloud is complete, \(\lambda\) should be increased when restoring the wall segment. Similarly, if the ceiling and floor point cloud is missing in localized areas, \(\lambda\) should be decreased. The room segmentation process involves three parameters. n should increase with the complexity of the room layout construction. \(\varepsilon_{\partial}\) is taken as 0.4-0.8. \(\alpha\) should be slightly less than (1-area of connecting transition space / area of indoor space). Figure 12: Datasets: (**a**) Synth; (**b**) Office1; (**c**) Office2; (**d**) Room; (**e**) TUB1; (**f**) TUB2; (**g**) UOM. Door extraction process involves six parameters. The door height limit and width limit should be obtained according to the actual situation. \(P_{col}\) and \(P_{row}\) can be a unitized scale (e.g., 0.5). The method of determining \(P_{col}\) is based on the height of the wall where the door is located, the height of the door, the height of the occlusion inside the door and the height of other openings at the location of the door. \(P_{col}\) can be calculated by Equation (10). Similar to \(P_{col}\), the method of determining \(P_{row}\) is based on the width of the door and the width of the occlusion inside the door. \(P_{row}\) can be calculated by Equation (11). \[P_{col}=\frac{h_{door}-h_{occ}+h_{other}}{h_{wall}} \tag{10}\]  
\[P_{row}=\frac{w_{door}-w_{occ}}{w_{door}} \tag{11}\]  
where \(h_{door}\) represents the height of the door, \(h_{occ}\) represents the height of the occlusion, \(h_{other}\) represents the height of other openings at the location of the door, \(h_{wall}\) represents the height of the wall surface, \(w_{door}\) represents the width of the door and \(w_{occ}\) represents the width of the occlusion. As shown in Figure 14.
## 2 Methods
### The Design of the Hardware  
The system architecture of the BTTT, encapsulated in Figure 3, comprises a main control unit, the BDM, an antenna, the power source, and an electric level conversion module. The main control unit is tasked with data management, enabling transparent data transmission, compressing datasets, executing power regulation, and orchestrating system entry and exit from STOP mode for power conservation. In concert, the BDM and its antenna are accountable for pinpointing the buoy's location and facilitating its data transmissions wirelessly. The power module converts voltage to the levels requisite for other components, while the conversion module transitions between TTL and 232 levels. External interface pins enable power sourcing and connectivity with the buoy's main control board, permitting seamless interaction between the buoy's command center and the terminal. The main control unit, power source, and electric level conversion module are welded on the front of the Beidou transparent transmission board, and the Beidou module is placed on the back of the board. The board material is a glass fiber epoxy resin copper-clad plate (FR-4).  
Low power consumption in hardware design is mainly achieved through two aspects: on the one hand, the communication interval for civilian Beidou cards is 60 s, and the BDM remains static during this period, so the power supply to the BDM can be cut off, and the VCC to 12 V and VCC to 5 V voltage conversion circuits are disabled, leaving only the 3.3 V  
Figure 3: Overall structure diagram of BTTT.  
Figure 2: Remote communication flow chart of Argo buoy.  
power supply. On the other hand, the STM32L073 RZT6 ultra-low-power microcontroller features a low-power mode, where the STOP mode stops the core clock while ensuring that SRAM data are not lost. It can be awakened by a low-power UART. Compared to sleep mode, it has a lower power consumption and a UART wake-up function than standby mode. Therefore, choosing the STM32L073 RZT6 as the main control chip in the hardware design can further reduce power consumption.
## 3 Constructing an AT Representation Model Containing Tolerance-Zone DOF Layers
### Tolerance-Zone DOF Layer  
The literature [14] defines tolerance-zone DOFs. The relationship matrix between the type of tolerance zone and the DOFs is shown in Table 1. The main content of this layer is to determine the element values of the tolerance-zone DOF vector in different reference systems. We represent the DOF vector of the tolerance zone as \(\mathbf{V}\). If the coordinate system of the assembly is different from the coordinate system marked by each tolerance zone in Table 1, the element values in vector \(\mathbf{V}\) will change. The mapping relationship between the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \
\begin{tabular}{c} **Tolerance** \\ **Code** \\ \end{tabular} &
\begin{tabular}{c} **TS\({}_{1}\)** \\ **TS\({}_{2}\)** \\ **TS\({}_{3}\)** \\ **TS\({}_{4}\)** \\ **TS\({}_{5}\)** \\ **TS\({}_{  
DOFs of each tolerance zone and the coordinate direction is represented by the adjacency matrix shown in Equation (2):  
\[M_{yi,3\times 6}=\begin{bmatrix}x_{\_}Txi&x_{\_}Tyi&x_{\_}Tzi&x_{\_}Rxi&x_{\_}Rxi &x_{\_}Rxi\\ y_{\_}Txi&y_{\_}Tyi&y_{\_}Tzi&y_{\_}Rxi&y_{\_}Ryi&y_{\_}Rzi\\ z_{\_}Txi&z_{\_}Tyi&z_{\_}Tzi&z_{\_}Rxi&z_{\_}Ryi&x_{\_}Rzi\end{bmatrix}= \begin{bmatrix}X\_DOF\\ Y\_DOF\\ Z\_DOF\end{bmatrix} \tag{2}\]  
In the matrix \(M_{yi,3\times 6}\), \(i\) (\(i=X\), \(Y\), \(Z\)) represent the tolerance-zone DOF vectors for the principal orientation of the coordinate system (usually the normal orientation of the tolerance feature), such as \(X\_DOF\) for the X axis, \(Y\_DOF\) for the Y axis and \(Z\_DOF\) for the Z axis. As shown in Table 1, for tolerance band TSS5, if the principal direction of the tolerance band is the Z axis, the DOF vector \(V=Z\_DOF\) is (1 1 0 1 1 0). (Note: when the coordinate system is changed, if the Z-axis direction is converted to the X-axis direction, the X axis is the main direction; if the Z-axis direction is converted to the Y-axis direction, the Y axis is the main direction).  
Construction of an Ontology Framework for Assembly Tolerance Domain Oriented to Tolerance-Zone DOFs  
A complete ontology contains classes, properties and instances, where classes are the canonical and clear descriptions of domain concepts, properties are the descriptions of the characteristics of the concepts and instances are the dissimilar individuals contained in the concepts. An OWL ontology based on description logic can use TBox (terminology box) to represent classes and attributes and use ABox (assertions box) to represent instances [36]. Section 4 builds a tolerance knowledge domain ontology by combining a geometric tolerance information representation model with tolerance-zone DOFs with a hierarchical assembly tolerance representation model to realise the semantic representation of assembly tolerance information.
# Analysis of BDS/GPS Signals' Characteristics and Navigation Accuracy for a Geostationary Satellite
## 2 System and Methods Description
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{3}{c}{**BDS-3**} & \multicolumn{3}{c}{**BDS-2**} \\ \hline
**PRN** & **Common** & **Date** & **PRN** & **Common** & **Date** \\ \hline C19 & MEO-1 & 5 November 2017 & C1 & GEO-1 & 16 January 2010 \\ C20 & MEO-2 & 5 November 2017 & C2 & GEO-6 & 25 October 2012 \\ C21 & MEO-3 & 12 February 2018 & C3 & GEO-7 & 12 June 2016 \\ C22 & MEO-4 & 12 February 2018 & C4 & GEO-4 & 1 November 2010 \\ C23 & MEO-5 & 29 July 2018 & C5 & GEO-5 & 25 February 2012 \\ C24 & MEO-6 & 29 July 2018 & C6 & IGSO-1 & 1 August 2010 \\ C25 & MEO-11 & 25 August 2018 & C7 & IGSO-2 & 18 December 2010 \\ C26 & MEO-12 & 25 August 2018 & C8 & IGSO-3 & 10 April 2011 \\ C27 & MEO-7 & 12 January 2018 & C9 & IGSO-4 & 27 July 2011 \\ C28 & MEO-8 & 12 January 2018 & C10 & IGSO-5 & 2 December 2011 \\ C29 & MEO-9 & 30 March 2018 & C11 & MEO-3 & 30 April 2012 \\ C30 & MEO-10 & 30 March 2018 & C12 & MEO-4 & 30 April 2012 \\ C32 & MEO-13 & 19 September 2018 & C13 & IGSO-6 & 30 March 2016 \\ C33 & MEO-14 & 19 September 2018 & C14 & MEO-6 & 19 September 2012 \\ C34 & MEO-15 & 15 October 2018 & C16 & IGSO-7 & 10 July 2018 \\ C35 & MEO-16 & 15 October 2018 & & & \\ C36 & MEO-17 & 19 November 2018 & & & \\ C37 & MEO-18 & 19 November 2018 & & & \\ \end{tabular}
\end{table} 
Table 2: Status of healthy BDS satellites can be used by the receiver. Figure 2: Block diagram of the BDS and GPS receiver architecture.
## 2 Materials and Methods
### Detection of Volatile Metabolome in DcTPSb1 Overexpression N. benthamiana Using GC-MS  
The modified pC1300 vector was used to ligate the full-length coding sequence of _DoTPSb1_. Subsequent to this, a plasmid for plant invasion was generated by transform ing Agrobacteriumium tumefaciensis GV3101, which contained a target gene validated by sequencing, a 35S promoter, and conferred with hygromycin resistance. Using the leaf disk transformation system, Agrobacteriumium plasmid transfection was performed on the leaves of wild-type (WT) tobacco sterile tissue cultured seedlings. PCR experiments and GUS (\(\beta\)-glucuronidase) staining were conducted to identify the overexpressed (OE) plants (Figure S5). The latter followed the instructions provided in the kit (Huayugeyang, Beijing, China). Subsequently, whole flowers from both WT and OE plants were harvested for analysis of volatile metabolome composition. A 500 mg sample was obtained after grinding, and then saturated sodium chloride solution and internal standard solution were added. Automatic headspace solid phase microextraction (HS-SPME) was conducted for sample extraction. The extraction condition included an extraction time of 15 min and a separation time of 5 min at 250 \({}^{\circ}\)C for sample analysis. The chromatographic conditions utilized high-purity helium gas as the carrier gas, with a flow rate of 1.2 mL/min and an injection port temperature of 250 degrees Celsius. The sample was injected into a splitless mode, and the programmed temperature was initially raised to 40 degrees Celsius for 3.5 min, followed by an increase to 100 degrees Celsius at a rate of 10 \({}^{\circ}\)C/min, then to 180 degrees Celsius at a rate of 7 \({}^{\circ}\)C/min, and finally to 280 degrees Celsius at a rate of 25 \({}^{\circ}\)C/min, held for 5 min. The mass spectrometry conditions included an ion source temperature of 230 degrees Celsius, an electron energy of 70 eV, and precise qualitative and quantitative ion scanning. Differential metabolites were identified based on fold change greater than 2 or less than 0.5.
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 3 On-Board Processing Unit Design  
The HYPSO-1 payload is developed in-house using mostly Commertial Off-The-Shelf (COTS) components and is integrated into a COTS satellite bus from Kongsberg NanoAvionics. The HYPSO-1 payload consists of three parts: the OPU, the Hyperspectal Imager (HSI) and a snapshot camera with Red Green Blue (RGB) channels. In addition, there is a wire harness for signal and power interfacing with the satellite bus. The HSI is a push broom design and consists of COTS camera and optical components, an in-house machined platform, and mounting hardware to the satellite frame via dampers [33; 34; 60]. The following subsections will present a detailed description of both the hardware and software of the OPU.
## 3 Proposed Method  
The pipeline of the proposed no-reference IQM is summarized in Figure 1. For a given degraded image, we first select the most relevant patches based on their saliency weights. For each patch, we extract a feature vector from a CNN model and concatenate it at the viewing distance for which the quality is predicted. The resulting vector is then fed to fully connected layers to predict the subjective quality for the considered viewing distance. Each of these steps is described in this section.
# LightR: A Fault-Tolerant Wavelength-Routed Optical Networks-on-Chip Topology +
## 2 Background  
### Parallel Switching Elements  
In ONoCs, OSEs have various structures. Aside from the CSE, shown in Figure 1b, another typical structure of OSEs is called the parallel switching element (PSE). In a PSE, an MRR is placed between a pair of parallel waveguides so that signals entering the PSE will experience a 180-degree direction change [16]. Figure 3 illustrates the working mechanism of a PSE. Compared to the CSE, where two MRRs are placed close to a pair of crossed waveguides, a PSE avoids the crossing loss and crosstalk noise generated by the waveguide crossing and requires only one MRR to route the signals among two inputs and two outputs. Considering these advantages, a PSE is considered as an appealing component to construct WRONoCs [2].
## 3 Fast Handover Algorithm  
### Analysis of the Feasibility of the Fast Handover Algorithm  
The traditional algorithm takes SS-RSRP or SS-RSRQ in the measurement report as the basis of a handover decision. However, the effect of fast fading may cause frequent fluctuation of SS-RSRP or SS-RSRQ in the wireless channel of HSRs, so that UE cannot accurately switch to the appropriate NCell. The chain structure is generally for the network coverage along the rail, and adjacent gNodeBs are alternately distributed on both sides of the rail. When the HST is moving, the order of cells attached by UE is unique. UE's location information, moving speed, and running direction can be obtained by computing in real time. According to the moving direction of HST, its track is definite. The speed of UE is consistent with the speed of the train. These features are different from the randomness of UE moving range and speed in the scenarios of non-HSRs. In wireless communications, reasonable utilization of these features can solve the problem of the deterioration of performance indexes, such as the success rate of the handover and the delay of the handover in HSRs. The signaling process of the fast handover algorithm in the 5G-R wireless communications for HSRs is shown in Figure 10. The speed and direction of the train, and the location information of UE, are periodically monitored through the initiation report.  
Figure 9: Trigger point of the handover.
# Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
###### Abstract  
Signal analysis is a fundamental field in engineering and data science, focused on the study of signal representation, transformation, and manipulation. The accurate estimation of harmonic vibration components and their associated parameters in vibrating mechanical systems presents significant challenges in the presence of very similar frequencies and mode mixing. In this context, a hybrid strategy to estimate harmonic vibration modes in weakly damped, multi-degree-of-freedom vibrating mechanical systems by combining Empirical Mode Decomposition and Variational Mode Decomposition is described. In this way, this hybrid approach leverages the detection of mode mixing based on the analysis of intrinsic mode functions through Empirical Mode Decomposition to determine the number of components to be estimated and thus provide greater information for Variational Mode Decomposition. The computational time and dependency on a predefined number of modes are significantly reduced by providing crucial information about the approximate number of vibratory components, enabling a more precise estimation with Variational Mode Decomposition. This hybrid strategy is employed to compute unknown natural frequencies of vibrating systems using output measurement signals. The algorithm for this hybrid strategy is presented, along with a comparison to conventional techniques such as Empirical Mode Decomposition, Variational Mode Decomposition, and the Fast Fourier Transform. Through several case studies involving multi-degree-of-freedom vibrating systems, the superior and satisfactory performance of the hybrid method is demonstrated. Additionally, the advantages of the hybrid approach in terms of computational efficiency and accuracy in signal decomposition are highlighted.  
intrinsic mode functions; Hilbert-Huang transform; hybrid strategy; variational mode decomposition; vibratory components +
Footnote †: journal: _arXiv preprint arXiv:1907.03390_
## 3 Load Forecasting Based on GRU and TCN Hybrid Network  
### Gate Recurrence Unit (GRU)  
A long short-term memory (LSTM) network can solve the gradient explosion and gradient disappearance problem in RNN, and LSTM has been widely used in the area of data processing in recent years. GRU is a variant of LSTM with fewer internal units; it  
Figure 2: VMD calculation process.  
combines the input gate and forget gate into one update gate. It is easier to perform the training process and save computing time compared with LSTM. The substructure of GRU is shown in Figure 3; there are only two gates: namely, the update gate and the reset gate. Its mathematical description is shown in Formula (6).  
\[\left\{\begin{array}{l}r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}])\\ z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}])\\ \widetilde{h}_{t}=\phi(W_{R}^{*}\cdot[t_{t}\times h_{t-1},x_{t}])\\ h_{t}=(I-z_{t})\times h_{t-1}+z_{t}\times\widetilde{h}_{t}\\ y_{t}=\sigma(W_{o}\cdot h_{t})\end{array}\right. \tag{6}\]  
where \(z_{t}\) and \(r_{t}\) are the update gate and reset gate, \(\sigma\) is the Sigmoid function, \(\phi\) is the hyperbolic tangent function, \(x_{t}\), \(h_{t-1}\), \(h_{t}\) and \(y_{t}\) are the vector of input data, state memory variable in time \(t-1\) and \(t\), and output data, respectively. \(W_{r}\), \(W_{z}\), \(W_{R}^{*}\) and \(W_{o}\) are the weight factor of the connection matrix between \(x_{t}\) and \(h_{t-1}\) in the update gate, reset gate, memory unit, and output gate, respectively. \(I\) is the identity matrix. The update gate is used to control the information percentage of the previous moment when it is brought into the next moment. The reset gate controls how much information from the previous state is reserved to the current candidate set \(\widetilde{h}_{t}\).
# PRG: A Distance Measurement Algorithm Based on Phase Regeneration
## Principle of PRG
### Range Signal Tracking
#### 2.4.2 Loop Filter  
To analyze the effect of loop filter on the performance of phase-locked loop, it is necessary to start with the system function of phase-locked loop expressed as:  
\[H(s)=\frac{\theta_{o}(s)}{\theta_{i}(s)}=\frac{s}{s+KF(s)}\]  
where \(\theta_{o}(s)\) represents the Laplace transform of the instantaneous phase of the output signal of the NCO, \(\theta_{i}(s)\) represents the Laplace transform of the instantaneous phase of the input signal, \(K\) is loop gain and \(F(s)\) is the system function of loop filter.  
The selection of \(F(s)\) of loop filter is mainly related to the dynamic stress form of users, and the resulting excitation signal can be decomposed into many forms, such as phase step, frequency step and frequency ramp. We assume that this user case does not suffer too much and acceleration effects, so the phase-locked is only affected by phase step and frequency step, so we can choose one order loop filter, which is a second order phase-locked loop, the system function can be expressed by [14]:  
\[H(s)=\frac{2\zeta\omega_{n}s+\omega_{n}^{2}}{s^{2}+2\zeta\omega_{n}s+\omega_{n }^{2}} \tag{6}\]  
We can be seen from Equation (6) that the characteristics of the system function is mainly dependent on the characteristic frequency \(\omega_{n}\) and the coefficient of damping \(\zeta\). From the perspective of frequency domain, the second-order phase-locked loop bandwidth \(B_{L}\) can be expressed as:  
\[B_{L}=\int_{0}^{\infty}|H(f)|^{2}df=\frac{\omega_{n}}{2}(\zeta+\frac{1}{4\zeta})\]  
The selection of loop bandwidth is related to user dynamics and noise performance and the larger the loop bandwidth, the higher the tolerance of user dynamics, but the worse the noise performance. Therefore, the selection of \(\omega_{n}\) and \(\zeta\) needs to balance the noise performance and user dynamic performance. The lower the damping coefficient, the faster the system response to the converges. However, when the damping coefficient is too small, the response of the carrier loop will oscillate violently.