esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception
###### Abstract  
The separation of time-frequency mixing signals composed of radar, communication, and jamming is the first step in integrated reception processing, which requires higher accuracy for complex blind source separation (CVBSS). However, traditional CVBSS methods have limitations such as low separation accuracy, a slow convergence speed, and poor robustness in low signal-to-noise ratio (SNR) and high jamming-to-signal ratio (JSR) scenarios. To address the above issues, this paper firstly establishes a time delay mixing mathematical model. A robust whitening algorithm is proposed by using the time delay correlation matrix of the observed signal, which is insensitive to noise. Secondly, the joint diagonalized F-parametrization is used as the objective function, and the separation matrix is constructed based on the multiple complex-valued Givens matrices. The complex-valued Givens matrix not only ensures orthogonality in the separation matrix but also effectively reduces the number of parameters to be calculated. This approach guarantees accuracy and simplifies the complexity of the separation process. Finally, the nonlinear chaotic grey wolf optimizer is utilized to search for the optimal rotation angle. The simulation results demonstrate that this algorithm offers higher separation accuracy and requires fewer iterations compared to the traditional algorithm. Additionally, it enhances the accuracy of direction of arrival (DOA) estimation, reduces the communication bit error rate, and enables the joint estimation of the target distance and velocity even in the presence of powerful jamming and a low SNR.  
integrated reception; complex-valued blind source separation; robust whitening; joint diagonalization; anti-main lobe jamming; swarm intelligence
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 4 Case Study  
### Problem Description  
In order to verify the effectiveness of the proposed DTDAS, a case experiment in a discrete manufacturing workshop was conducted. The layout of the workshop is shown in Figure 2 in Section 2. Figure 11 shows the location of the physical resources of the workshop with the coordinates of the points. It is assumed in the figure that the AGV's initial location is in the charging area. The workshop order data involved in the experiment is shown in Appendix B. The task of this experiment consists of eight orders, 3-4 transportation operations in each order, and 29 transportation tasks in total. This batch of orders must be completed within 25 min. When the number of order transportation is \(p_{i}\), the number of processes is \(p_{i}-1\), and the first transportation operation of each order does not have a corresponding processing operation. The detail of transportation task is also in Appendix B. It contains the loading point and delivery point of each transportation task, the quality information of the transportation task, and the processing time of the processing operation corresponding to the transportation operation. The battery initial SOC of the three AGVs is shown in Table 1. Taking into account the discharge characteristics and life characteristics of the AGV battery [8], the system limits the AGV to stop performing new tasks to charge in the charging area when the AGV battery is below 20%.  
The population number of the genetic algorithm is set to 100, the number of iterations is set to 200. When solving the fitness function, the value of \(\delta_{1}\) is 0.64, \(\delta_{2}\) is 10. The relevant parameters of AGV all come from the fitting and analysis of real-time data, \(\beta\) is in the range  
\begin{table}
\begin{tabular}{c c} \hline
**AGV Number** & **Initial Battery SOC (\%)** \\ \hline
1 & 80 \\
2 & 80 \\
3 & 30 \\ \hline \end{tabular}
\end{table}
Table 1: Initial battery SOC of AGV system.  
Figure 11: Coordinates of each position in the workshop.  
of 0.27-0.45, \(\kappa\) is in the range of 0.25-0.3, \(\chi\) is approximately 0.139. The other parameters are constant values, \(v\) is 0.5 m/s, \(\lambda\) is 5, \(\mu_{emp}\) is 0.7, \(\mu_{ild}\) is 0.139.
## 3 Proposed Scheme
### Authentication Stage
#### 3.2.1 Perceptual Hash Value Generation Based on Modified U-Net Model
To enhance the security of the authentication information itself, the string \(\mathit{PSString}\) is then encrypted by using an encryption algorithm with secret \(\mathit{key}\). In this paper, we take the 128-bit advanced encryption standard (AES-128) as an example. The encrypted string is the perceptual hash value of the original HRRS image, denoted as \(\mathit{PH}\):  
\[\mathit{PH}=\mathit{Encrypt}_{\text{AES}-128}(\mathit{PSString},\mathit{key}) \tag{2}\]  
where \(\mathit{key}\) is set by the user to guarantee the security of the algorithm. Figure 7 shows an example of perceptual hash value generation process, in which the encryption process is omitted to make the results clear. The generated perceptual hash value is a 256-bit binary sequence, and it can also be represented as hexadecimal, as shown in the Figure 7.
# A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method
## 7 Adaptive Golomb Code and Context Adaptive Binary Arithmetic Coder  
Practical implementation of effective data encoding involves minimising the average number of bits per single data generated by the \(S\) source (in the case of audio compression the input data are samples, in this work, we adopted the standard 16-bit per samples, which the source \(S\) is a stream of sample from CD). Depending on what sources we deal with, we can divide them into sources without memory DMS (Discrete Memoryless Source) and sources with memory CSM (Conditional Source Model). Considering this division from the point of view of Markov model, in the first case, for the lower limit of the bit average we can introduce unconditional entropy (\(H(S_{\text{DMS}})\) - zero order entropy), and in the second case, we deal with the conditional entropy of the \(k\)-th order defined as \(H(S|C^{(k)})\), where in the case of audio signal, the context of \(C^{(k)}\) is defined in example as \(k\) samples backwards. By introducing the total entropy as \(H(S)\), we obtain the relation \(H(S)\leq H(S|C^{(k)})\leq H(S_{\text{DMS}})\). Trying to use a static version of Huffman code, for example, in the case of 16-bit data, requires giving the decoder a probability distribution of the encoded file, which is an impractical idea, because, with 16-bit samples and possibly low precision of writing, the individual probabilities \(p_{i}\), for example, using 2 bytes per one \(p_{i}\) value, the size of the file header itself will be \(2^{17}\) B. In addition, the zero order entropy, which is the lower limit of the bit average of the static encoder, indicates that this approach gives unsatisfactory results (see the second column in Table 3). For this reason adaptive versions of codecs are used in practice. Moreover, since the audio data do not constitute a sequence of independent values it is difficult to determine and apply the \(k\)-th order of Markov model. In practice, it is assumed that the removal of interdependencies is possible by use of predictive techniques writing only predictive errors into the file. The prediction model can be a linear model of the \(k\)-th order or a more complex nonlinear solution, but we reduce it to the one value predicted \(\hat{x}(n)\) (creating not explicitly defined first-order Markov model), which we subtract from the current sample \(x(n)\), see Formula (1). As a result a sequence of prediction errors can be interpreted as a source
# Accurate Landmark Localization for Medical Images Using Perturbations
## 2 Related Works
### Rich Representation Learning  
It is well known that the generalization ability of deep learning originates from the rich representations extracted through numerous hierarchical layers in the network [12]. Many studies have attempted to improve the generalization ability of the network by developing a better network structure. Network in network, inception, and residual networks are typical of the approach [15; 16; 17].  
Recent studies have proposed a new approach that forces the learner to solve an additional task. A denoising convolutional auto-encoder (CAE) has been widely employed [18]. It forces the network to learn the representations through the task that eliminates the noise artificially added on purpose. In recent years, there has been an attempt to make models understand the context of the entire image, to extract the rich representations for the semantic segmentation or object localization problems [19; 20; 21]. The methods force networks to seek global representation, rather than focusing on the most discriminative local part. To this end, these methods randomly hide important image patches, and then apply the deep learning model using the perturbed images to minimize classification error. Some studies [22; 23] have employed an inpainting-based self-supervised learning strategy to learn the rich representation. This strategy randomly eliminates image patches and minimizes pixel-level reconstruction error. Refs. [24; 25] used image patch-wise reassembly and matching tasks. The studies divided an image into several patches and solved the problem of aligning the shuffled patches. The above-mentioned papers proved that richer representations improved the performance of the image recognition task.  
We note that similar approaches can be applied to the landmark localization to improve the accuracy. However, application to landmark localization can only be found in one paper. [PERSON] et al. applied the smoothing perturbation to a cephalometric image during the learning process of landmark localization [11]. The paper showed that the perturbation improved the accuracy of landmark localization using the ISBI dataset.
# Biotechnological Approaches to Lowering the Ethanol Yield during Wine Fermentation
## 5 Enzymatic Treatment of Grape Must  
One conceptually simple approach for the reduction of the ethanol content of wines is to remove excess sugar from the grape juice before fermentation. Biotechnologically, this goal can be achieved using enzymes, and the enzymatic activity proposed for this application is glucose oxidase. This enzyme catalyses the oxidation of glucose to gluconic acid. The reaction involves molecular oxygen as a co-substrate, and releases hydrogen peroxide as a by-product.  
The use of glucose oxidase during the pre-fermentation stages to produce low-alcohol wines was first proposed by [PERSON] (1986) [78], and was optimised by this and other authors (as reviewed in [79]). However, the use of glucose oxidase is not among the procedures included in the International Code of Oenological Practices [80].  
The procedure proposed by [PERSON] et al. [81] involved an initial grape must deacidification to accommodate the pH to that which is optimal for the enzyme used, as well as aeration during the enzymatic treatment. Catalase was added with glucose oxidase to remove the hydrogen peroxide formed in the reaction. The removal of the hydrogen peroxide improves the yeast survival and the activity of glucose oxidase enzyme itself. By treating Riesling grape juice for 10 h under these conditions, these authors produced wines with an alcohol content reduced by 3.7% (\(v/v\)) [82]. However, gluconic acid negatively affected the taste of the wine, fruity aromas were perceived with less intensity, and their persistence was also reduced [83]. A second deacidification or a sweetening were proposed to alleviate the effect of gluconic acid [81].  
[PERSON] et al. [84] assayed several methods of deacidification following glucose oxidase treatment. However, the wines were more acidic than the control, and lost part of their fruitiness. Other authors have used encapsulated enzymes in order to improve glucose oxidase activity without prior pH correction [85; 86]. Wines obtained from juices treated for two days under these conditions showed a 0.68% (\(v/v\)) reduction in their ethanol content [85].
## 2 Materials and Methods  
Thermogravimetric analysis (TGA) was performed using a TG/DTA 6300 SII EXSTR. About 15 mg of the sample were heated inside a Pt crucible at a constant rate of 2 \({}^{\circ}\)C/min from room temperature (RT) up to 400 \({}^{\circ}\)C before maintaining this temperature for 30 min. Afterwards, the sample was cooled down at 40 \({}^{\circ}\)C/min to room temperature. The experiments were carried out under N\({}_{2}\) and air atmospheres.  
All the X-ray powder diffraction measurements (XRPD) were conducted with a Panalytical X'Pert PRO diffractometer with a copper tube and fast X'Celerator detector.  
In situ non ambient X-ray diffraction experiments were carried on by using an Anton Paar HTK2000 high-temperature camera with platinum heating filament, and a secondary flat monochromator was used in the diffraction optics. For the temperature sweep under air, the camera was kept open. In the case of the experiments conducted under a nitrogen atmosphere, a continuous stream was running inside the camera throughout the experiment. For these experiments, the initial Co(CH\({}_{3}\)COO)\({}_{2}\).4H\({}_{2}\)O sample was gently ground with a mortar and pestle and then deposited as a thin layer of powder over the heating strip. The scans measured with this camera presented a broad band at 20 = 7\({}^{\circ}\) corresponding to scattering coming out from the Kapton window of the camera cover.  
Phase identification-oriented XRPD measurements were performed in reflection or transmission geometry depending on the sample appearance. In this case, samples were not ground, and they were mounted in a \"zero background\" silicon sample holder for the reflection measurements. For transmission XRPD scans, the sample was prepared in a 0.7 mm diameter borosilicate glass capillary.  
The optics used for these reflection scans included a primary beam monochromator (Cu K alpha1 radiation). In the case of the capillary transmission measurements, the incident optics consisted of a focusing multilayer elliptical mirror (Cu K alpha radiation).  
Phase identification analysis was conducted with Malvern Panalytical B.V. (Almelo, The Netherlands) X'Pert HighScore Plus v5.1.a software [26] to compare the scan peaks with ICCD PDF4+ database (2022 release) [27]. Identified phases are reported throughout the text with their PDF card numbers. The unit cell parameters of the intermediate cobalt oxyacetate phase were determined, with the reflections collected in a Bruker D8 Venture single crystal diffractometer (Cu radiation).  
Samples for transmission electron microscopy (TEM) were ultrasonically dispersed in n-butanol and transferred to coated copper grids. Selected area electron diffraction (SAED) experiments and high-resolution transmission electron microscopy (HRTEM) were performed with a JEOL JEM 2100 electron microscope supplied with an Oxford INCA detector working at 200 kV.  
The morphological study of the samples was carried out with a JEOL JSM 7600F scanning electron microscope using a graphite holder and a \"gentle-beam\" mode operating at an acceleration voltage of 15 kV.
# Halophyte Plants and Their Residues as Feedstock for Biogas Production--Chances and Challenges
## 5 Halophyte Composition
### Organic Matter Content  
The organic matter or volatile solids (VS) content of halophyte biomass ranges from 51-86% on dry matter (DM) basis [51; 73; 74], indicating an up to 49% high share of minerals and salts in the ash fraction of the DM. The organic matter and ash content, respectively, arethereby influenced by the salt concentration under cultivation of the halophyte plant as analyzed for example for the aboveground biomass of _Chenopodium quina_ with a decreasing content when using a growth medium with a higher salinity concentration [74].
## 5 Simulation Study
### Results and Discussions
#### 5.3.2 Network Reliability  
The reliability of the network is measured by computing the average PDRs of successful transmission for all application traffic on each OLSR version. It is also important to reiterate that a successful upload of an application traffic type is considered as one in which 100% of transmitted packets is received and acknowledged at the data concentrator. The average PDR for simultaneously transmitting all SG application traffic across the WMN-based NAN on each OLSR version is presented in Figure 9.  
Figure 8: Transmitted and dropped CBR packets for peak pricing.  
Figure 9: Average PDR for all AMI application traffic types.  
Another means of evaluating the reliability of the network is the blocking probability per application traffic flow. In this context, blocking probability is the probability of losses for a group of identical traffic. It is calculated as the ratio of dropped to transmitted packets. Figure 10 shows the blocking probability of each application traffic type. It is clear that the blocking probability of the \(OLSR_{MM}\) is the lowest for all traffic types considered.
## 4 Trans-Scale Peridynamic Fatigue Model
### Bond Damage and Point Damage within the Horizon  
#### 4.1.1 Bond Fatigue Damage and Failure  
Bond damage in a peridynamic material body is defined by bond breakage. The simplest criterion is that when the bond strain, shown in Equation (21), exceeds the critical value, the micro-potential between two material points \(\mathbf{x}_{(k)}\) and \(\mathbf{x}_{(j)}\) will be removed away. As shown in Figure 5, the spring-like bond \(\xi\) is subjected to variable force density in its two ends during each loading cycle exert on the body \(\mathcal{R}\). Fatigue bond damage is tracked by a history-dependent variable \(d_{kj}(\xi,t)\).  
\[d_{kj}(\xi,t)=\left\{\begin{array}{cc}1&\text{if $\xi$ is broken}\\ \varphi(\xi,s,t)&\text{otherwise}\end{array}\right. \tag{21}\]  
where \(\varphi(\xi,\varepsilon,t)\) is the normalized damage function, depending on the current bond strain \(s\) and time \(t\).
# A Study on the Improvement of Filter Performance to Remove Indoor Air Pollution
## 2 Simulation Method and Result
### Apply the Coanda Effect to the Sub-Filter  
As a result of simulating the dust-collection efficiency in Section 2.4, in order to apply the Coanda effect to the circle filter with high dust-collection efficiency, the main filter was a 0.5 mm slide-type filter, which we used to compare the dust-collection efficiency. Applying the sub-filter and the Coanda effect to the original filter increased the dust-collection efficiency from 15.41% to 68.6%. The dust-collection efficiency increased from 30.51% to 69.3% by applying the sub-filter of the slide filter to which the Coanda effect was applied.  
Figure 10 is a graph comparing before/after attaching the sub-filter to the Coanda effect filter. When the Coanda effect is used alone, it can be confirmed that the effect is large, but if a sub-filter is installed, the effect of the Coanda effect on the dust-collection efficiency is small.  
Figure 9: Efficiency by particle size with sub-filter applied.
# On Localized Countermeasure Against Reactive Jamming Attacks in Smart Grid Wireless Mesh Networks
## 5 Localized Trigger Identification Algorithm
### Trigger Nodes Detection Procedure  
This subsection illustrates how to resort to sequential GT and convexity of trigger nodes so as to identify all trigger nodes with minimum testing latency in each hexagonal testing group. For each node \(i\) of hexagonal _testing group_ of color \(j\), we devised the _TNI-SGT_ algorithm (Trigger Node Identification based on Sequential GT) to detect all trigger devices during all testing groups with the same color to be tested simultaneously.  
It is reasonable to set each testing round with a predefined constant time slot since each hexagonal testing groups with the same color conduct sequential GT procedure at the same time, and no new testing round would start until activated jammers turn themselves into _listening mode_. As mentioned, we consider that our network systems are loosely synchronized in the order of seconds.  
As stated in Lemma 2, based on the constraint on distance between any two jammers \(j_{1}\) and \(j_{2}\), \(d(j_{1},j_{2})>R\) in order to avoid mutual invocation between them, we proved that only one jammer can be activated by nodes within a hexagon.
# Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs
## 3 Problem Formulation
### Optimal Load-Balancing Association with Minimal Handovers
To strike a balance between the two options, one can set \(\alpha\) to the median value of the range. The second proposed algorithm ALGO-2 runs similarly to ALGO-1. It computes the optimal association decision from P. (3), which is then used to make associations between RRHs and IoT devices. In Section 4, we show the effects of different values of \(\alpha\) on the performance. The time complexity of P. (2) is no greater than that of P. (3), and both are mixed integer linear programming (MILP) problems, or mixed binary linear programming to be specific. Please note that the second term in the objective function of P. (3) can be transformed to linear inequality equations. Due to the binary variables, the search space of both problems has the size of two to the power of the number of binary variables. As the network size increases, i.e., as the number of RRHs and the IoT devices on the network increases, the problem becomes intractable in general. However, there are some efficient exact algorithms that can quickly solve ILP, such as the variants of branch and bound [27]. In Section 4.4, we show that under the given network settings we can find the optimal solution shortly.