esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Implementation of DIC
### DIC Post-Processing
It is then of primary importance to consider the parameters that govern the accuracy in this regime: the quality of the speckle pattern, image noise, window size, and gray level interpolation used in the DIC formulation. The latter is central to the evaluation of displacement with subpixel accuracy, as it allows us to compute \(g(\Phi_{0}(\underline{u}))\) for integer pixel positions \(\underline{u}\) in the reference image, even when \(\Phi_{0}\) is a subpixel translation. The CMV software provides several options for the interpolation of gray levels, which have been compared. It turns out that an interpolation with bi-quintic splines provides the best resultshere. As already discussed, the image noise has been reduced to a minimum for the chosen cameras, using appropriate lighting. The main way to further optimize the experiment is then to consider the speckle painting and the window size.
# Acoustic Equivalent Lasing and Coherent Perfect Absorption Based on a Conjugate Metamaterial Sphere
## 6 AEL and CPA for Multiple Orders Based on a Small Multilayered Sphere
In all simulations, \(R_{1}=0.35\) m, \(R_{2}=0.25\) m, \(R_{3}=0.15\) m, \(|n_{1}|=|n_{2}|=|n_{3}|=2\), \(\rho_{1}=(1.5075+0.0089i)\rho_{0}\), \(\rho_{2}=(-0.1038+0.8555i)\rho_{0}\), \(\rho_{3}=(-0.0537+0.2748i)\rho_{0}\), \(\lambda_{0}=1\) m, and the distribution of the incident pressure field at the outermost simulation boundary (\(r=\lambda_{0}\)) is \(P_{l}(\cos\theta)\). Figure 7: Simulated total acoustic pressure fields of the AEL and CPA modes in the \(yz\)-plane for spherical wave incidence with different orders \(l\) at different incidence frequencies \(f\) based on a three-layered ACM sphere: (**a**) \(l=0\); (**b**) \(l=1\); (**c**) \(l=2\). In all simulations, \(R_{1}=0.35\) m, \(R_{2}=0.25\) m, \(R_{3}=0.15\) m, \(|n_{1}|=|n_{2}|=|n_{3}|=2\), \(\rho_{1}=(1.5075+0.0089i)\rho_{0}\), \(\rho_{2}=(-0.1038+0.8555i)\rho_{0}\), \(\rho_{3}=(-0.0537+0.2748i)\rho_{0}\), \(\lambda_{0}=1\) m, and the distribution of the incident pressure field at the outermost simulation boundary (\(r=\lambda_{0}\)) is \(P_{l}(\cos\theta)\). layered ACM sphere. (**a**) \(l=0\) and \(f=300\) Hz. (**b**) \(l=1\) and \(f=343\) Hz. (**c**) \(l=2\) and \(f=400\) Hz. For the upper panels, the acoustic parameters are \(\rho_{1}=(1.0810-1.7525i)\rho_{0}\), \(\rho_{2}=(0.1611-2.5259i)\rho_{0}\), and \(\rho_{3}=(-0.1425+0.2342i)\rho_{0}\), while for the lower panels, \(\rho_{1}=(1.0810+1.7525i)\rho_{0}\), \(\rho_{2}=(0.1611+2.5259i)\rho_{0}\), and \(\rho_{3}=(-0.1425-0.2342i)\rho_{0}\). In all simulations, \(R_{1}=0.35\) m, \(R_{2}=0.25\) m, \(R_{3}=0.15\) m, \(|n_{1}|=|n_{2}|=|n_{3}|=2\), and the distribution of the incident pressure field at the outermost simulation boundary (\(r=1\) m) is \(P_{l}(\cos\theta)\). It is worth noting that since the AEL and CPA modes are each other's time reversal counterparts, the time reversal results can be achieved by changing the sign of the imaginary acoustic parameters. For example, a three-layered ACM sphere with \(\rho_{1}=(1.0810+1.7525i)\rho_{0}\), \(\rho_{2}=(0.1611+2.5259i)\rho_{0}\), and \(\rho_{3}=(-0.1425-0.2342i)\rho_{0}\) can realize an AEL mode at \(300\) Hz for \(l=0\), a CPA mode at \(343\) Hz for \(l=1\), and an AEL mode at \(400\) Hz for \(l=2\). The corresponding simulations are shown in the lower panels of Figure 7a-c, which are the time reversal patterns of those in the upper panels of Figure 7a-c. That is, all simulation parameters are the same as those of the upper panels, except that the acoustic parameters for the lower panels are complex conjugates with those for the upper panels. As expected, the AEL mode at \(300\) Hz for \(l=0\), the CPA mode at \(343\) Hz for \(l=1\), and the AEL mode at \(400\) Hz for \(l=2\) are all achieved.
# The Behavior of Tree-Width and Path-Width Under Graph Operations and Graph Transformations
## 3 Unary Graph Operations and Graph Transformations
### Local Complementation  
In his work, [PERSON] [56] introduced the graph operation local complementation. Given a graph \(G\) and a vertex \(v\in V(G)\), the _local complementation_ of \(G\), denoted by \(LC(G,v)\), is defined by  
\[V(LC(G,v)) =V(G)\quad\text{and}\] \[E(LC(G,v)) =E(G)\setminus\{\{u,w\}\mid u,w\in N_{G}(v)\}\] \[\qquad\qquad\cup\{\{u,w\}\mid u,w\in N_{G}(v),u\
eq w,\{u,w\}\
ot \in E(G)\}.\]  
In other words, \(LC(G,v)\) is obtained from a graph \(G\) by replacing the subgraph of \(G\) induced by \(N_{G}(v)\) with its edge complement. Recall that \(v\
ot\in N_{G}(v)\) holds, such that the neighborhood of \(v\) in \(LC(G,v)\) is the same as in \(G\).  
Denote by \(K_{1,\ell}\) the star graph we already made use of in Section 3.10. By applying a local complementation to the dominating vertex \(v\), it is easy to see that \(LC(K_{1,\ell},v)\) equals a clique of size \(\ell+1\). The star \(K_{1,\ell}\) has tree-width and path-width one, while \(LC(K_{1,\ell},v)\) has tree-width and path-width \(\ell\). Therefore, in general, the tree-width (path-width) of a graph \(G\)'s local complement \(LC(G,v)\) cannot be bounded in terms of the tree-width (path-width) of \(G\).
# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 3 Materials and Methods
### Data Description  
This study utilizes the MIT-BIH arrhythmia dataset and the PTB diagnostic dataset as primary data sources [15] for annotating ECG signals, as detailed below. The MIT-BIH dataset, a widely recognized standard in arrhythmia classification tasks, comprises  
Figure 1: Flowchart of the research method for classifying arrhythmia diseases using KAN and ECG signal data.  
ECG signals recorded from 47 subjects at a sampling rate of 360 Hz. Each record was annotated by at least two cardiologists and categorized into five classes based on the EC57 standard of the Association for the Advancement of Medical Instrumentation (AAMI) [4]. These categories correspond to clinically prevalent arrhythmia patterns. The mapping relationships between heartbeat annotations in each category are in Table 1. This dataset serves as a high-quality benchmark for automated arrhythmia detection and classification.  
The PTB dataset consists of 549 ECG records from 290 subjects, including 148 diagnosed with myocardial infarction (MI), 52 healthy controls, and the remainder encompassing diagnostic records for seven additional heart diseases. Each ECG record comprises a 12-lead signal sampled at 1000 Hz, offering exceptionally high temporal resolution. In this study, only lead II data were analyzed, focusing on signal differences between myocardial infarction patients and healthy controls to support diagnosis and classification tasks in related experiments.  
The selection of these two high-quality datasets ensures the diversity and accuracy of the experimental data, offering a robust foundation for training and evaluating ECG signal classification models.
## 3 Proposed PLL Validation
### Load Change  
At this stage, in order to evaluate the performance of the PLLs, it was assumed that the system load doubles at t = 1 s and returns to normal condition at t = 2 s. It was expected that during the transient moments, the network frequency would be slightly disturbed and would return to normal quickly. Figure 6 shows the frequency detection behavior for both PLLs. It is clear that the traditional PLL shows higher overshoots than the improved one.
## 3 Materials and Methods
### Supply Chain Model  
We study a single-echelon SC with a distribution center (DC) in Amsterdam, Netherlands. The DC fulfills market demand by receiving customer orders and adjusting the inventory levels with replenishments from the warehouse in Rotterdam, as visible in Figure 2. The DC's inventory is controlled by the periodic review (R, s, S) inventory system, with inventory status being reviewed at predefined intervals.  
Stochastic, normally distributed demand is the simulation model's input data. It is defined with mean value and two levels of demand variability (high and low), i.e., standard deviation (SD). Demand and order size are non-negative integer values observed for 365 days. Each SE starts with the inventory level set to order-up-to level. This study is based on 100 simulated market demands per demand variability, tested and confirmed for normality by the D'Agostino-Pearson normality test in GraphPad Prism 9.0.0 software [53]. With a high number of statistically similar replicas, we aim to model the stochastic nature of normally distributed demand and the SC's response by creating a large group of SEs with the same operational factors and evaluating results based on the level of individual SEs but also on the minimal, maximal, and average values within the group.  
Figure 1: Research methodology outline.  
The observed period of 365 days provides analytically tractable results of high stability and accuracy. Strict tolerances increase the reliability and accuracy of results.  
The SC model considers two WSs influencing inventory replenishments from the warehouse. In both, DC works every day of the week. In the 7-day WS, the warehouse works daily, so inventory replenishments are possible even on weekends. However, in the 5-day WS, the warehouse operates with two non-working days, i.e., Saturdays and Sundays, resulting in higher inventory levels required to satisfy demand on the warehouse's non-working days. The nominal LT excludes the non-working days, resulting in deliveries with different actual LT in different WS, depending on the day the order is launched. For instance, an order launched on Friday with LT = 0 will be available at DC on Saturday in a 7-day but not till Monday in a 5-day WS. Figure 3 represents the periodic review (R, s, S) inventory system model used for this research.
## 3 Chitosan Modification and Functionalization
### Chitosan Chemistry
#### 3.1.3 Oxy-Chitosan Derivatives  
Many scientific publications have explored the production of water-soluble chitouronic acid sodium (carboxylated chitin or CS) with the use of TEMPO, which is an organic catalyst used for the oxidation of hydroxyl functions into aldehyde ones in NaOCl and NaBr conditions [46; 47; 48; 49]. TEMPO is mainly known for its use for regioselectively oxidizing primary hydroxyl groups of various polysaccharides. [PERSON] et al. [50] have developed a method using TEMPO to produce oxy-CS derivatives, namely 6-oxyCS. Chitouronic sodium salts are mainly produced from pretreated (chemically or enzymatically) fungal or shrimp cell chitin. In their work, [PERSON] et al. [46] also used fungal biomass from Trichoderma and Aspergillus to produce a new range of carboxylated CS/chitin with a high degree of biocompatibility on human keratocytes, highlighting their potential use in drug delivery applications [51]. [PERSON] et al. [49] have synthesized a new bioactive C6 oxy-CS derivative. This new derivative showed good anti-parasitic properties against Leishmania. Very recently, an environmentally friendly process has been developed by [PERSON] et al. (2018) [52] for the C6 oxidation of CS through a TEMPO/lactase redox system in order to generate a water-soluble CS fraction (Figure 4).  
Figure 3: Production of chitosan derivatives by different methods: (**A**) Quaternization, (**B**) N-alkylation, and (**C**) N-acylation.
## 3 Results and Discussion
### Imaging of PAN/Biochar Fibers though the Preparation Process
#### 3.2.4 EDS Analyses of Carbonized Fibers  
The carbonized fibers composition was simpler than that of the \"as-spun\" samples. Oxides, metals, and other inorganic compounds belonging to the biochar ashes, which were detected in the \"as spun\" fibers, drastically dropped in the carbonized samples.  
The EDS analyses suggest that these compounds probably evaporated in the furnace due to the severe thermal treatment, performed at 1700 \({}^{\circ}\)C, as expected and confirmed also by the literature for various inorganics [38; 39]. The fiber carbon content peaked after the process, reaching values from 97.2% to 100%, and the remaining percentage was covered by metals used to perform the ESEM analysis (gold and aluminum). Silicium was present only in traces. Figure 8 presents the results of the analysis.  
Therefore, it can be assessed that the carbonization process was effective either with only PAN fibers or with composite PAN-biochar fibers. Ashes were an unavoidable part of the biomass-derived char. This study started with the concern that these inorganic compounds would have negatively affected the final composition of the fibers, reducing their purity. The results proved the opposite, showing almost carbon-pure thermal-treated spun material.
###### Abstract
(2018) introduced the soft template prompt learning method, incorporating a small number of learnable parameters as prompt guidance in the self-attention layer of pre-trained models, compensating for the shortcomings of manually designed prompt templates. [PERSON] et al. [PERSON] et al. (2019) designed a dual-slot multi-prompt template for entity types and positions, extracting all entities by predicting each prompt slot. This avoids the efficiency issues of enumerating spans and achieves a single round of prediction for all entities. This method provides a new approach for using prompts in NER tasks, but its model design is relatively simple. It utilizes the positional information of entities without considering the boundary information of the entity heads and tails, leaving room for further improvement in Chinese NER tasks. Compared to English, Chinese NER faces many unique challenges. The structure of Chinese is complex, and Chinese characters themselves do not have natural delimiters, making the boundaries between words ambiguous [PERSON] et al. (2019). This makes both character-based and word-based sequence labeling quite challenging. If the model cannot effectively handle nested relationships, it is prone to recognition errors. These factors make Chinese named-entity recognition much more difficult than that of English, placing higher demands on the model's performance. A series of experiments conducted in [PERSON] et al. (2019); [PERSON] and [PERSON] (2017); [PERSON] (2017); [PERSON] (2017) have demonstrated that the incorporation of boundary information into the encoding and decoding layers of the model can effectively enhance the model's accuracy in Chinese natural language tasks. Regarding the issues identified in the aforementioned Chinese NER task, after comprehensively considering the advantages and disadvantages of various methods, we propose a Chinese few-shot named-entity recognition model that integrates multi-label prompts and boundary information (MPBCNER). First, the MPBCNER uses a pre-trained large model as the encoding layer, introducing a template composed of entity label prompt words and entity position prompt slots at the model input layer. This better utilizes the prior knowledge of the pre-trained model during training and prediction phases, enhancing the model's performance in small-sample scenarios. Secondly, we used a graph attention network to construct a boundary information extraction module, allowing the model to focus more on features near the boundaries when recognizing entities, thereby improving the accuracy of entity boundary recognition. Finally, we use a pointer network as the model's decoding layer, cleverly avoiding the complexity and inefficiency brought by predicting enumerated spans. Meanwhile, the structure of the pointer network has a certain degree of feature similarity with the boundary information network, which allows it to better learn the boundary features of Chinese entities, providing significant advantages in handling Chinese samples and nested entities.
## 4 The Proposed Reputation Management System
### Reputation Manager
#### 4.2.2 Initial Reputation of Nodes in the Networks  
At the onset of network operations, nodes in the network will not have interacted or have had the opportunity to monitor their neighbouring nodes for long enough time before computing their respective reputation values. In order to cater to this kind of situation, a default reputation value is introduced for all of the nodes in the network. This value is also assigned to a node that newly joins the network. From Subsection 4.2.1, we estimated that the total reputation of a node \(\mathbf{f_{T}}\) will always be between [0, 2]. Considering this range of values, the default reputation value for all nodes in the network is defined as \(\mathbf{f_{0}}\). This means that every node in the network is assigned a default reputation value, which is also their total reputation value at time t equal to zero. Mathematically, the total direct reputation of a node when they newly join the network or at the onset of network activities is given as:  
\[\mathbf{f_{T}} = \mathbf{f_{0}} \tag{8}\]  
After monitoring the various activities of nodes for a given period of time, a node would have gathered enough evidence to compute the individual reputation of its neighbouring nodes. The total reputation in this case is a combination of the initial default reputation value and the currently measured reputation value. Let us denote the new total reputation \(\mathbf{f_{T}}=\mathbf{f_{1}}\). We can evaluate the new total reputation \(\mathbf{f_{1}}\) as:  
\[\mathbf{f_{1}}= \omega\mathbf{f_{0}}+(1-\omega)\mathbf{f_{M1}} \tag{9}\]  
where \(\omega\) is a small value between [0, 1] and \(\mathbf{R_{M1}}\) is the currently measured reputation value of a node based on new evidence collected from monitoring activities. As more evidence becomes available, the direct reputation of the nodes will be regularly updated at a specific time interval. For instance, we evaluated the direct reputation of node at a given period of time t as \(\mathbf{f_{1}}\). We can therefore say that after a time interval of (t + 1), \(\mathbf{f_{2}}\) will be given as:  
\[\mathbf{f_{2}}= \omega\mathbf{f_{1}}+(1-\omega)\mathbf{f_{M2}} \tag{10}\]  
We can conclude that the direct total reputation of a node in our model can be evaluated using the equation below:  
\[\mathbf{f_{n}}= \omega^{n}\mathbf{f_{0}}+\omega^{n-1}(1-\omega)\mathbf{f_{M1}}+ \omega^{n-2}(1-\omega)\mathbf{f_{M2}}+\omega^{n-i}(1-\omega)\mathbf{f_{Mi}} \tag{11}\]where \(\mathbf{j_{1}}\), \(\mathbf{f_{M2}}\) and \(\mathbf{f_{Mi}}\) are the newly-measured direct reputation values at regular intervals. Equation (11) can be further simplified as:  
\[\mathbf{f_{n}} = \omega^{n}\mathbf{f_{0}}+\sum_{i=1}^{n}\omega^{n-i}(1-\omega) \mathbf{f_{Mi}} \tag{12}\]  
where \(n\) = 1, 2, 3,..., i.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 5 Physical Layer Proposal
### Frequency Selection  
From the analysis of the narrowband and wideband sounding [7], we have defined two operation modes for the different channel conditions. The best performance in terms of SNR is observed at  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Constel.**} & \(\mathbf{T_{b}}\) & \multirow{2}{*}{\(\mathbf{N_{car}}\)} & \(\mathbf{BW}\) & \(\mathbf{IBO}\) & \multirow{2}{*}{\(\mathbf{Bitrate}\)} & \multicolumn{2}{c}{**SC-FDMA**} & \multicolumn{1}{c}{**OFDM**} \\  & **(ms)** & & & **(Hz)** & & **(dB)** & **(bps)** & **CDF (BER) (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{    }}}}}}}}}}}}}\) \\ \hline PSK & 10 & 8 & 781.3 & 1 & 582 & 0.38 & 0.38 \\
**PSK** & **20** & **8** & **390.6** & **1** & **334** & **0.50** & **0.72** \\ PSK & 20 & 8 & 390.6 & 4 & 334 & 0.47 & 0.34 \\ PSK & 40 & 8 & 195.3 & 1 & 180 & 0.53 & 0.50 \\ PSK & **40** & 8 & **195.3** & **4** & **180** & **0.66** & **0.50** \\ PSK & 40 & 8 & 195.3 & 7 & 180 & 0.53 & 0.53 \\ PSK & 40 & 16 & 390.6 & 1 & 360 & 0.39 & 0.53 \\ Q-PSK & 20 & 8 & 390.6 & 1 & 667 & 0.30 & 0.34 \\ Q-PSK & 20 & 8 & 390.6 & 4 & 667 & 0.30 & 0.39 \\ Q-PSK & 40 & 8 & 195.3 & 1 & 360 & 0.44 & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Best results for OFDM and SC-FDMA with PSK and QPSK constellation that show the CDF (BER) obtained with different configuration parameters. The rows in bold indicate the best BER for each modulation. More results can be found in [10].  
nighttime (see Section 4.1), while daytime presents low availability figures. In these figures, a clear difference between day and night performance is shown, obtaining the best results for low frequencies from 00 UTC to 07 UTC, and from 18 UTC to 23 UTC (nighttime), and the best results for high frequencies from 08 UTC to 17 UTC (daytime). Nevertheless, the precise frequency selection has to be analyzed every campaign, and it can even change during the campaign if the ionospheric propagation conditions vary the reception results. An example of the transmission frequency selection has already been shown in Section 4.1.  
With the daytime and nighttime propagation idea, we propose a modulation scheme adapted to work to each period of the day, depending on the robustness and the throughput; when we design a more robust modulation, the bitrate is lower than when we assume that the propagation is good, and so we can reach higher bitrates.
# A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks
## 2 Related Work  
Numerous research studies have been conducted on the classification of networks. The heterogeneous information networks have two main advantages over their homogeneous counterparts--they make it possible to model different types of objects and relationships, and they preserve more complex topological structures with richer semantics information [14].  
**Classification of homogeneous information networks** There has been substantial attentions given to the classification of network data or objects recently. Most of the previous studies focus on the networks' structure, and others use unlabeled objects for classification [15; 16; 17]. Collective classification [18] is one of the most popular classification methods in mining networks. These methods classify objects by their features and the structure of the network in the homogeneous information networks. [PERSON] et al. [19] proposed the Learning with Local and Global Consistency (LLGC) algorithm. They design a function integrating sufficiently smooth coefficients to learn the general classification from the labeled data. The weighted-vote relational neighbor classifier (wvRN) [15; 20] is another widespread classification algorithm proposed for mining network data. The wvRN label objects by considering a link selection mechanism.  
**Classification of heterogeneous information networks** One of the algorithms proposed for mining HINs is GNetMine proposed by [PERSON] et al. [21]. GNetMine is a transductive classification model based on graph regularization. The GNetMine, designed for HINs, can only handle the data with a common topic. [PERSON] et al. [16] consider the class-level meta paths to construct more accurate classifiers and obtain the train labels to improve the active learning in HINs. RankClass algorithm, proposed by [PERSON] et al. [17], is a more effective classification method that utilizes both classification and ranking operation. The values of ranking are related with the classification in HINs. Deepwalk [22] is another popular embedding model. The input to the Deepwalk is a series of objects produced by random walks in a language model-SkipGram, and the output would be the representation of features' objects. However, Deepwalk can only apply the short random walks to obtain the semantic among the objects.