esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Thermochemical Method to Determine the Crystallinity Degree of Cellulose Materials
## 5 Conclusions  
In this study, a thermochemical method for determining the real degree of crystallinity (X) of various cellulose samples based on the measurement of their wetting enthalpy (\(\Delta\)H\({}_{\rm w}\)) was proposed. For this purpose, the equation for calculating the X value was derived, as follows: X = 1 - (\(\Delta\)H\({}_{\rm w}\)/\(\Delta\)H\({}_{\rm w,a}\)), where \(\Delta\)H\({}_{\rm w,a}\) = \(-\)168 (J/g) is wetting enthalpy of completely amorphous cellulose. It was found that the sample of viscose rayon fibers has a minimum X = 0.38, while the MCC sample has a maximum X = 0.75.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{**Properties**} & \multicolumn{1}{c}{**Symbol**} & \multicolumn{1}{c}{**Zc**} & \multicolumn{1}{c}{**Za**} \\ \hline Specific volume, cm\({}^{3}\)/g & Vs & 0.617 & 0.694 \\ Moisture content (\%) at standard RH = 65\% & Aw & 0 & 23 \\ Accessibility for deuterium & \(\Delta\)D & 0.14 & 1.14 \\ Sorption of alkali from 1 M NaOH, mmol/g & A\({}_{\rm A}\) & 0 & 3.1 \\ Hydrolysability (\%) after treatment with boiling 2.5 M HCl, 1 h & H & 0 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Some properties of crystalline and amorphous cellulose [2; 3].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{2}{c}{**CC**} & \multicolumn{2}{c}{**VF**} \\ \cline{2-5}  & **Calc** & **Exp** & **Calc** & **Exp** \\ \hline Vs, cm\({}^{3}\)/g & 0.638 & 0.640 & 0.665 & 0.661 \\ *Gs, g/cm\({}^{3}\) & 1.57 & 1.56 & 1.50 & 1.51 \\ Aw, \% & 6.4 & 6.5 & 14.3 & 14.1 \\ A\({}_{\rm D}\) & 0.42 & 0.43 & 0.76 & 0.75 \\ A\({}_{\rm A}\), mmol/g & 0.87 & 0.86 & 1.92 & 1.94 \\ H, \% & 11 & 10 & 25 & 26 \\ \hline \hline \end{tabular} \({}^{*}\)Gs = 1/Vs denotes the specific gravity.  
\end{table}
Table 5: Some properties of CC and VF.  
A comparison of the determined degree of crystallinity (X) with the crystallinity index for (CrI) various cellulose samples showed that the relative deviation of CrI from the real X ranges from 15 to 42%. Such large discrepancies indicate that such an approximate parameter as the CrI cannot characterize the real content of crystallites in cellulose samples. On the other hand, the obtained degree of crystallinity or amorphicity describes the real supramolecular structure of cellulose and can be used for the prediction of important properties of cellulose samples, such as specific volume, specific gravity, amount of sorbed moisture from the vapor phase, amount of sorbed alkalis from aqueous solutions, accessibility for deuterium, acidic hydrolysis, etc.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data can be obtained from the corresponding author upon reasonable request.  
The author declares no conflict of interest.
## 2 Methodology
### Convolutional Neural Network
#### 2.1.5 Backpropagation Updating  
The training process of a CNN is divided into two stages: forward propagation and backpropagation (BP). Forward propagation refers to the process of data flow from the input layer to the output layer, and the loss function is obtained by calculating the error between the model and the target output. BP calculates the gradient of the loss function concerning each layer of parameters and then uses an optimization algorithm to minimize the loss function, thereby updating the parameters. The updating process is as follows.  
1. [label=()]
2. Perform forward propagation from the input layer to the output layer: \[a^{l+1}=f\Big{(}w^{l}\otimes a^{l}+b^{l}\Big{)},\] (4) where \(a^{l}\) and \(a^{l+1}\) are the input and output of layer \(l\), respectively. Moreover, \(w^{l}\) and \(b^{l}\) are the corresponding weights and biases, respectively, and \(f(\cdot)\) is the activation function.
3. Calculate the local gradient of the output layer: \[\delta^{n}=\frac{\partial L}{\partial a^{n+1}}f^{\prime}(a^{n}),\] (5) where \(L\) represents the loss function, \(n\) is the depth of the proposed CNN, and \(f^{\prime}(\cdot)\) is the derivative of the activation function.
4. According to the chain rule, calculate the local gradient of the other layers: \[\delta^{l}=\delta^{l+1}w^{l}f^{\prime}(a^{l}).\] (6)
5. The gradient of the weight and bias can be obtained as follows: \[\Delta w^{l}=a^{l+1}\delta^{l},\Delta b^{l}=\delta^{l}.\] (7)
6. Update the weights and biases according to the learning rate \(\eta\) and momentum m: \[w^{l}=m_{w}w^{l}-\eta_{w}\Delta w^{l},b^{l}=m_{b}b^{l}-\eta_{b}\Delta b^{l}.\] (8)
## 3 Results and Discussion
### Microclimatic and Biological Monitoring
#### 3.1.2 Biological and Entomological Aspects  
Studies and analyses made by restorers show that many volumes (over a thousand) have suffered entomological damage. Of the 190 volumes damaged by microbiological attacks (moulds and active bacteria), 103 books were mildly affected and 87 seriously damaged. These were first isolated, then treated and repaired by the restorers.  
To find the origin of the moulds and bacteria, other specific monitoring campaigns were carried out by measuring the surface temperature, relative humidity and the dew point temperature of the walls behind the bookcases at the critical points identified by the restorers. The aim was to investigate these points of the hall in terms of problems related to mould, surface condensation, or water seepage from the walls, highlighting the importance of monitoring as an instrument of integration with other studies concerning conservation. The campaigns were carried out in two different two-week periods: March 2021 (Spring) and December 2021 (during the Christmas holidays when the heating was off). The results obtained exclude links between the presence of cold walls or problems linked to high RH values and the activation of moulds and bacteria.  
With regard to the monitoring points in Figure 10, it must be specified that in this case the letters refer to the bookcases and the numbers to the shelves. As shown in the figure, following the restorers' indications, the monitoring campaign also involved the atrium, where the wooden bookcases differ from the others in the hall in that they are closed by glass panes without ventilation, therefore resembling showcases.  
Figure 9: Comparative data analysis for indoor air temperature and relative humidity on the floor level (**a**) and the mezzanine (**b**).  
The results obtained showed that there was no correlation between the moulds found on many volumes and contemporary environmental conditions; their origin may have been linked to storage in a book depository while the hall was being restored after the earthquake, where conditions in terms of relative humidity and the presence of books damaged by moulds and bacteria were at their worst.  
Entomological monitoring using insect traps detected several specimens that were identified by experts as originating from wooden furniture, also located outside the room and in the adjacent areas (Figure 11).
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
## 6 Conclusions  
In order to expand the imaging scene and improve the flexibility of the system, a new configuration of AA-BiSAR with one-moving transmitter is proposed in this paper. Compared with AA-SAR and one-stationary BiSAR, AA-BiSAR with one-moving transmitter has the characteristics of wide observation, well concealment and flexible structure. It has important research significance in helicopter assisted landing and emergency rescue. The imaging geometry and the echo signal model were established. To obtain the focused image, an imaging algorithm based on the KT method is proposed. In the imaging algorithm, the residual phase caused by the moving transmitter is compensated in the range frequency-domain. Then, the coupling between the range and azimuth is eliminated via the KT method by redefining a new azimuth variable. As a result, the raw data of the system can be well focused. Furthermore, the spatial resolution of the system is also analyzed, the transmitter position affects the ground-range resolution, and the transmitter speed affects the azimuth resolution. The experiment results verify the effectiveness of the proposed algorithm and the variation of the spatial resolution.  
Conceptualization, P.H.; methodology, L.H.; software, W.X. and L.H.; validation, W.T. and Y.Q.; formal analysis, W.X.; investigation, P.H. and W.X.; resources, L.H. and W.T.; data curation, L.H.; writing--original draft preparation, P.H.; writing--review and editing, P.H.; visualization, Y.Q.; supervision, P.H.; project administration, W.T.; funding acquisition, W.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 61631011, 61971246 and 62071258, Natural Science Foundation of Inner Mongolia Autonomous Region, grant number 2020 ZD18, Major Science and Technology Special Project of Inner Mongolia Autonomous Region, grant number 2019 ZD022.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Discussion  
In this section, the multistatic radar system composed of T/R\({}_{0}\), R\({}_{1}\) and R\({}_{2}\) receivers is taken as an example to verify the feasibility and validity of the proposed discrimination method, and the effect of the related factors are analyzed on the discrimination performance. The location and measurement accuracies of each receiver are assumed as shown in Table 1. In the monitoring area of the multistatic radar system, there is a physical target with location coordinates \([20,50,20]^{T}\)km and velocity vectors \([-100,-150,-6]^{T}\)m/s. To protect the physical target (PTs), the self-defense repeater jammer generates a joint range-velocity deception false target (FT) generated by delaying, modulating and retransmitting the intercepted radar signals.
# Theoretical Zero-Thickness Broadband Holograms Based on Acoustic Sieve Metasurfaces
## 3 Results
### Theoretical Zero-Thickness Broadband Holograms  
It is worth noting that the wave from a hole has no additional phase or amplitude modulations, which can physically remove the intractable modulation dispersion. As a result, even though it is designed for a specific frequency (\(f_{0}=34\) kHz), the proposed ASM should be feasible for a broadband hologram. Although the modulation dispersion has been removed, the intrinsic propagation dispersion [40] is provided by the propagation kernel \(\mathrm{e}^{-\mathrm{i}\phi_{0}R_{m,n}}/R_{m,n}\) in Equation (1) describing the diffraction of a PS located at \(G_{m,n}\). In principle, propagation dispersion cannot be eliminated, but it primarily affects a hologram's imaging position and does not result in significant distortions or aberrations in the reconstructed holographic image [40; 41; 42]. The imaging distance (\(z\)) from a hologram varies with the radiation frequency (\(f\)) and follows the mathematical relationship [42]  
\[\frac{2\pi f}{c_{0}}\bigg{(}\sqrt{(x_{m,n}-x)^{2}+(y_{m,n}-y)^{2}+z^{2}}-z \bigg{)}=\frac{2\pi f_{0}}{c_{0}}\bigg{(}\sqrt{(x_{m,n}-x)^{2}+(y_{m,n}-y)^{2 }+{z_{0}}^{2}}-z_{0}\bigg{)}\]  
Under the paraxial approximation, the mathematical relationship can be simply expressed as [42]  
\[\frac{z}{f}=\frac{z_{0}}{f_{0}} \tag{2}\]  
indicating that as the frequency increases, the imaging distance increases linearly.  
The sound energy distributions in the image planes (according to Equation (2), the imaging distances (\(z\)) can be calculated as \(60\lambda_{0}\), \(80\lambda_{0}\), \(100\lambda_{0}\), \(144\lambda_{0}\), \(180\lambda_{0}\) and \(240\lambda_{0}\), respectively) of the ASM-based hologram are calculated using numerical simulation based on the combination of the FEM and the BEM when the incident frequencies (\(f\)) are, respectively, \(f_{0}/2\), \(f_{0}/1.5\), \(f_{0}/1.2\), \(1.2f_{0}\), \(1.5f_{0}\), and \(2f_{0}\), as shown in Figure 5a-f. The figures show that the imaging effects in the six image planes are quite good. The RMSEs between these holographic images and the desired image shown in Figure 2a are 0.1942, 0.1813, 0.1771, 0.1743, 0.1849, and 0.1839, respectively. The graph (Figure 6) of the RMSE against frequency further  
Figure 4: Holographic imaging of an ASM. (**a**) The overall map of the ASM-based hologram based on small holes. The light gray area is the zero-thickness rigid screen and the small circles are the holes with a diameter of \(d=0.2\lambda_{0}\); (**b**) Normalized energy distribution in the image plane calculated by numerical simulation from the hologram shown in (**a**). In the numerical simulation, all of the acoustic and geometric parameters of the holographic imaging system based on the ASM are the same as those of the holographic imaging system based on the PS array.  
shows that in the studied frequency range, all the RMSE values between the holographic images and the desired image shown in Figure 2a are modest and nearly constant.
# PickingDK: A Framework for Industrial Bin-Picking Applications
## 5 Cases of Study
### Use Case 2-Industrial Case  
In this use case, another picking application is built with PickingDK. This use case presents a real industrial bin-picking scenario, which employs an industrial robot (kuka kr20r1810) equipped with a pneumatic magnet gripper and an Ensenso N30, which are managed by a Beckhoff Programmable Logic Controller (PLC). Picking pieces are hat-shaped iron pieces of around 13 kg. Figure 8 shows the environment setup for this picking application.  
Figure 6: The environment setup for Use Case 1, **left**: the real scene, **right**: the virtual scene where obstacles are broadened for safety reasons.  
Figure 7: An example of metal pieces localization in EdgeMatchingLocalization.  
To address the localization problem, a service called LCCPLocalization is exploited, where a classic segment-and-register process is utilized. In practice, input point cloud is segmented using locally convex connected supervoxels (LCCP) [39]. Then, iterative closest point (ICP) is performed on the segments to obtain the final six DOF object poses.  
The performance of this localization service is evaluated on a manually annotated dataset. It includes 24 frames captured using Ensenso N30 in real scenes, where the number of detectable pieces varies from 27 to 61. Table 3 shows the quantitative evaluation of the LCCPLocalization service, where the true positive (TP), false positive (FP), false negative (FN), precision, recall, and processing time are measured. In industrial bipicking applications, it is critical to have virtually zero FP to ensure efficacy in the picking process. Thus, in our experiment, the LCCPLocalization service is configured with a high fitness threshold of the ICP registration phase. This increases localization accuracy while suppressing recall. With this configuration, an overall 100% precision and 50.8% recall is achieved within 4.1 s per frame on average. Figure 9 presents a bar chart on the TP against FN.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|} \hline
**Frame ID** &
\begin{tabular}{c} **Detectable** \\ **Pieces** \\ \end{tabular} & **TP** & **FP** & **FN** & **Precision** & **Recall** & **Time(s)** \\ \hline
**1** & 61 & 33 & 0 & 28 & 100\% & 54.1\% & 5.32 \\ \hline
**2** & 28 & 14 & 0 & 14 & 1  
Figure 10 shows a qualitative result. The most promising localized object poses are marked in red, green, and blue, respectively, as well as the best grasping pose shown with a magnet gripper.  
Similar to the previous use case, the MoveItPlanner service is used for computing the optimal robot trajectories. However, the robot and gripper control are achieved by the BeckhoffRobot and BeckhoffMagnet Control services. These services communicate directly with the PLC that governs the robot. This control scheme is widely used in industries as the PLC also controls safety elements such as optical barriers and the emergency button, and it provides a fast response to any incident.
# Freshness Monitoring of Packaged Vegetables
## 4 Intelligent Packaging (IP)
### Data Carriers  
Data carriers enable the flow of information about the packages along the supply chain. They do not assess the quality of packaged vegetables but are integrated with sensors and indicators in the form of tags or labels that assess the freshness of the product. Then, these carriers will communicate the gathered data through readers. The most common data carriers used in food packaging are radio frequency identification tags (RFID) and barcodes.
## 5 Data Acquisition and Processing
### Data Processing
#### 5.2.2 F-K Analysis  
The F-K analysis was applied to array recordings using the Sesarray package [45]. The first step consisted of calculating the array transfer function, along with the theoretical wavenumber limits (K\({}_{\text{min}}\)-K\({}_{\text{max}}\)) (Figure 8), from the number of receivers and the XY coordinates of each receiver of the array. The computation was carried out using WARANGPS software from the Sesarray package. After that, the beam-forming method (BFM) was applied to the vertical components using Geopsy software. The signals were divided into windows of frequency-dependent lengths, including 50 periods. For the processing, two F-K gridding parameters had to be defined: the grid step and the grid size. The grid step corresponds to the K\({}_{\text{min}}\)/2 value, which determines the maximum resolution. The grid size corresponds to the K\({}_{\text{max}}\) value, which determines the aliasing limit. As a result, a dispersion curve is obtained, i.e., the slowness as a function of the frequency.  
Figure 7: Some examples of the calculated HVSR curves at each zone.  
Figure 8: Theoretical wavenumbers obtained at each array recording site.
# Speaker Diarization: A Review of Objectives and Methods
## 10 Datasets for Speaker Diarization  
A disadvantage of SD approaches based on models is their reliance on external data for the training, which makes them less robust in acoustic conditions not seen in training. So, data are important. To evaluate SD performance, one typically uses annotated datasets, i.e., sets of audio containing multiple people talking, where time stamps are labelled manually. Training can be carried out on any audio, but supervised training would use labelled datasets, which could be from natural sources (e.g., YouTube, public broadcasts). Training sometimes uses \"synthetic\" datasets, where individual speakers' speech (i.e., from monologues, not conversations) are superimposed. Such simulated mixtures often have added noise and reverberation with simulated room impulse responses. SD performance varies with how well the simulated data used in training matches real (target domain) conversations.  
Early SD datasets were the ICSI meeting corpus [85] and the NIST Rich Transcription sets from meeting conference audio [86; 87]. An earlier dataset still widely used for challenging ASR is the CALLHOME dataset, which has 500 sessions of multilingual telephone speech. Each session has two to six speakers, but most conversations (2-5 min) each have two dominant speakers.  
Other datasets are DiHARD [88], Augmented Multiparty Interaction (AMI) Meeting corpus [89], the CHIME series [90; 91; 92], Fearless Steps [93], Iberspeech RTV [94], LibriCSS [12], M2 Met [95], and the French REPEE [96]. DISPLACE 2023 has conversations in different languages, high levels of noise, reverberations, and overlapping speech.  
Freely available datasets include AISHELL-4 [97], Albayzin/RTVE [98], AliMeeting [95], VoxConverse [98], Ego4D [99], and This American Life [100].  
The AMI corpus includes 100 h of meeting conversations. DIHARD I and II development sets have 19 and 23 h of data. The VoxConverse dataset has approximately 20 h of conversations from YouTube. The VoxCeleb datasets, widely used in SV, have speech segments of more than ten seconds (on average). An open-source toolkit for SD is written in Python [101].
# Highest Cost First-Based QoS Mapping Scheme for Fiber Wireless Architecture
## 1 Introduction
Typically, there are two categories of the FiWi access network that can be mainly divided into two categories according to network architecture: one is integrated wireless-optical mesh networking and the other one is the integration of wireless-optical access networking [16]. This paper focuses on the second category, i.e., integration of wireless-optical access networking. The proposed work presents a novel QoS mapping algorithm between XGPON and fifth-generation WLAN in the FiWi access network. Further, immediate allocation with the colorless grant (IACG) DBA is used for XGPON upstream bandwidth distribution among the ONUs, and two wireless channel distribution schemes are used in the fifth-generation WLAN side to explore an appropriate WLAN wireless scheme for FiWi access network. We have compared IACG DBA with deficit X-GigaPON Access Network (DXGIANT) DBA and identified more effective XGPON DBA for the FiWi access network. The rest of this paper is organized as follows: Section 2 describes a brief overview of the related work with the FiWi access network. Section 3 explains in detail the distributed channel schemas of 802.11 ac and XG-PON. In Section 4, the proposed mapping schemas have been described for FiWi. InSection 5, simulation setup and results are presented with full discussion. Finally, Section 6 concludes the paper.
# Health State Prediction Method Based on Multi-Featured Parameter Information Fusion
## 2 Methods
### Particle Swarm Optimization Algorithm  
Particle Swarm Optimization (PSO) [25] is an optimization algorithm based on group intelligence that simulates the behavior of a group, such as a flock of birds or a school of fish, and searches for an optimal solution through collaboration and information sharing among individuals. The steps of the PSO algorithm are as follows:  
1. Population initialization: randomly initialize the position and velocity of the particles and calculate the fitness value of each particle; initialize the particle parameters such as the number, maximum number of iterations, and learning factor.
2. Velocity and position update: each particle has two key attributes: position and velocity. The position of a particle indicates the location of an unknown solution in space, and its velocity is the direction and step size of moving in space. The position of each particle is evaluated by the fitness function to guide the search for the particle. The update formula is as follows: \[v_{i}^{t+1}=\omega v_{i}^{t}+c_{1}\gamma\left(x_{p_{i}}^{t}-x_{i}^{t}\right)+ c_{2}\eta\left(x_{g}^{t}-x_{i}^{t}\right)\] (15) \[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1}\] (16) where, \(v_{i}^{t}\) is the velocity of particle \(i\) at moment t, \(x_{i}^{t}\) is the position of the particle at moment it, and \(x_{p_{i}}^{t}\) is the current found individual best position of the particle. \(x_{g}^{t}\) is the overall optimal position of all the particles, and \(\omega\) is the inertia weights, which are used for controlling the searching range of the particle. \(c_{1}\) and \(c_{2}\) are the individual and population balance factors, respectively, and t is the number of iterations. \(\gamma\) and \(\eta\) are random numbers taking values between \([0,1]\) to increase the randomness of the search. The particle velocity variation is shown in Figure 5.
3. Individual and population optimal solution updating: in each iteration, each particle records its own position; if the updated individual position is better than before, then it is the individual optimal position; if the updated global position is later than before, then, it is the global optimal position.
4. Termination condition: when the maximum number of iterations is reached to meet the convergence criteria, the algorithm terminates and outputs the global optimal position and the optimal fitness value.  
Figure 5: Particle velocity variation graph.