esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This paper proposed a multi-class non-randomized AL method based on Multinomial Logistic Regression (MLR)-LORSAL classifier [31; 37] in conjunction with fuzziness [6] as sample selection method whilst exploiting both spatial and spectral information of hyperspectral data. We further compared the MLR-LORSAL against various well-known classifiers such as; SVM, ELM, KNN and EL. Each classifier is further evaluated against three benchmark sample selection methods in addition to fuzziness. These sample selection methods include Breaking Ties (BT) [38], Mutual Information (MI) [39] and Modified Breaking Ties (MBT) [39]. The motivation of our current work is to investigate several state-of-the-art sample selection and classification techniques with predefined dual stopping criteria and to properly generalize them for remotely sensed hyperspectral datasets. The paper is structured as follows. Section 2 discuss the pipeline proposed in this work. Section 3 presents the experimental process and performance measurement metrics. Section 4 contains the information regarding the experimental results and datasets. Finally Section 5 concludes the paper with possible future directions.
## 3 Implementation Method of Proposed Macrodiversity System for Maritime Wireless Sensor Networks
### Transmission Frame and Package Formats for Macrodiversity Reception  
To support the macrodiversity reception of data packages from sensor nodes, special frame or package formats are devised. In this part, we detail the formats and explain our design motivations.  
First, the information frame for proposed macrodiversity reception scheme comprises of three segments, i.e., the node ID, the user information and cyclic redundancy check (CRC) value. The length of whole information frame is 20 bytes. The information frame design mainly considers the user requirements and the parameters of the turbo codes in our design. Therefore, we choose the information code length as 20 bytes. Node ID is very important for many applications and two bytes are allocated for this part. The size of user information is 16 bytes. In our specific experiments, we use water quality sensor information and global positioning system (GPS) information as user information. The water quality sensor information occupies 6 bytes, while the GPS information occupies 10 bytes. The CRC value is computed based on the first 18 bytes in this information frame.  
Second, in order to explore the time diversity and to cancel the random noise, the turbo code and the channel interleaver are used for each information frame [31; 32]. The code length of the turbo code is 42 bytes with the information part 20 bytes. After encoding, the whole turbo codeword is interleaved in bit level in order to adapt to the transmission schemes with four small packages.  
Third, considering the wireless transmission conditions and the chosen transceiver (CC1101), we decompose the turbo codeword into four parts. In this design, on the one hand, the time diversity is obtained by sending the whole codeword with four independent packages. On the other hand, a moderate package length is appropriate for wireless sensor network using the transceiver CC1101 [33; 34; 35]. The specific decomposition method is shown in Figure 3. It can be observed that two bytes are padded using zeros.  
Figure 2: Architecture of the proposed macrodiversity reception system.  
Fourth, the final four transmission packages are formatted into a single information frame. Each coded frame is divided into four independent transmission packages. To identify the packages from one coded frame, the transmission package ID and the coded frame ID are embedded in a transmission package. To guarantee the reliability of the coded frame ID and transmission package ID, repetition coding is used. That is, the two IDs are repeated. Specifically, the coded frame ID and the transmission package ID are repeated eight times, respectively. In our design, the coded frame ID is only used to distinguish packages of the same package ID in different frames, and two bits is used to label four consecutive frames. The package ID is also two bits. Therefore, an eight-fold repetition coding is enough to guarantee the reliability. The specific format is illustrated in Figure 3.
# 3D Ear Normalization and Recognition Based on Local Surface Variation
## 3 Technical Approach
### Normalization of the Ear Data  
As mentioned above, the ear region data with non-uniform sizes contain a lot of non-ear parts, which means that the ICP algorithm needs to deal with redundant data. Moreover, a satisfactory initial guess of the full 3D (translation and rotation) transformation cannot be obtained because the ear pose and location within the ear region are not normalized. [PERSON] [6] removed the noise and extracted the ear data by the Mesh lab selection routine manually. A fixed shape ear mask was created in [35] to segment the pure ear data from the ear region. However, this method lacks in flexibility since the human ear has various shapes and sizes. Therefore, based on the stabilized local features of 3D surfaces, an automatic procedure of 3D ear data normalization is proposed to solve the problems in this paper.  
As we know, the ear data have more intensive surface variation than the face data, so for the first step, the LSV value of each vertex is calculated to estimate the local surface property, and the face data points are eliminated if we set a threshold of LSV value to select points automatically; as a result, only the ear and hair data should be left. For the second step, a general ear template is created to rotate the ear to a vertical pose and to translate the ear data into a canonical coordinate frame. As such, the hair around the ear can be cropped out of the ear region if we set a coordinate range of the ear data. Compared with the method of ear mask and extraction of ear edge, this automatic procedure is more robust to background noise.
# Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study
## 3 Results
### Digital Method
#### 3.2.3 Comparison Between Analog and Digital Methods  
* Analog Method: High operator dependency, with significant differences between the expert and novice operators (average difference = 11.4 wrinkles). Operator 1 (expert) was more consistent (CV = 14.9%) than Operator 2 (novice, CV = 22.3%).  
Figure 7: Curvature analysis.  
* Digital Method: Low operator dependency, with minimal differences between the expert and novice operators (average difference = 0.69 wrinkles). Both operators showed excellent consistency (CV = 1.39% for both).  
The observed differences in the means, variability, and CVs highlight the operator dependency of the analog method and the superior reliability of the digital method.  
These findings align with previous literature, confirming that 3D technology provides a more precise and repeatable alternative to analog methods, significantly reducing operator-dependent variability [15]. The preliminary data suggest that 3D cheiloscopy represents a promising advancement in the collection and analysis of lip impressions, offering substantial improvements in standardization and reliability.  
The operator-dependent variability and technical performance metrics are summarized in Tables 1 and 2.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 3 Methodology
### Design of the Dual-Encoder Module
#### 3.2.2 Dual-Encoder Structures Are Summed  
Considering the feature loss of the CSI matrix in dot product operation, we adopt two encoder structures to encode the CSI matrix separately. Since the two encoder structures are independent of each other, the information they automatically focus on is also different. Complementing the encoding information obtained by the Dual-Encoder structure helps to obtain richer features and improve the robustness of the model. The output formula of the entire Dual-Encoder module is as follows:  
\[Encoder_{out}=Encoder_{one}(x)+Encoder_{two}(x) \tag{7}\]  
where the \(x\) is the output of the Conv-for-Origin module.
# Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
## 2 Methodology
### Variable Selection and Identification of Parameters and Hyperplanes  
Generally speaking in data analysis, the ways of choosing data for a model depending on the application can be either by intuitive selection, by using every measured datum, or by choosing the essential data. In this paper, the essential data are chosen as input through variable selection in order to reduce the complexity of the model while keeping the accuracy. Several methods have been introduced for the variable selection. The information-based criteria such as the Akaike information criterion (AIC), the variant of AIC, AICc, and the Bayesian information criterion (BIC) are widely used in statistical model selection [50]. In this work, the BIC was adopted because it penalizes model complexity the most amongst the information criteria-based method for variable selection. The goal here is to achieve the model that is consistent and basal (basic) to the driver, thus the choice of BIC. Generally speaking, BIC establishes the relationship between the relative expected Kullback-Leibler distance and the maximum log-likelihood estimation. For regression analysis, BIC is given as  
\[\text{BIC}=N.\log\biggl{(}\dfrac{RSS}{N}\biggr{)}+K.\log(N) \tag{10}\]where \(RSS\) is the residual sum of squares, \(N\) is the number of observations, \(K\) is the number of regression parameters and \(\log()\) is the natural logarithm. Intuitively, BIC takes a balance between the model accuracy (the first term of Equation (10)) and model complexity (the second term of Equation (10)). The model with the lowest criterion value is chosen as the best model.  
The parameter estimation of each sub-model is achieved by the least squares method. Finally, the separating hyperplanes between the sub-models are identified using multi-class support vector machines (SVMs) as described in [52, 53].
## 2 Related Works
### Reconstruction Methods
#### 2.2.3 Spiking Neural Model  
In order to improve the scene reconstruction performance, the retina-like visual image reconstruction framework via a spiking neural model has been proposed to distinguish the dynamic and static spikes and reconstruct the image by using a dynamic neuron extraction model [24]. The local motion excitation layer models the spike data as a motion confidence matrix according to the historical firing distribution, abstracts the spike states into the first-order Markov random field with binary labels [29], and marks each output spike as dynamic or static based on the graph cuts to distinguish the state of the neuron. Then, a spike refining layer sets different refractory periods for dynamic and static neurons to eliminate the noise and preserve the high dynamic range. Finally, a visual reconstruction layer utilizes the spike-timing-dependent plasticity (STDP) learning rule [30] and threshold adaptation [31] to reconstruct the dynamic and static scenes simultaneously.  
However, the dynamic scenes reconstruction could still generate motion blur due to the texture from the adaptive threshold (TFA) method, and the STDP learning rule requires a large amount of datasets and time cost. In this paper, we propose a new framework to distinguish the dynamic and static spikes and a new way to reconstruct the images. Experimental results illustrate that our method is superior to SNM in both time complexity and reconstruction results.  
To address the aforementioned problem, we propose a CFSR method to reconstruct dynamic and static scenes with region-adaptive-based spike distinction. At the coarse-grained level, we distinguish real-time spikes into dynamic and static regions according to the distribution characteristics of spikes from the input spike data in fixed time, and then we take advantages of the TFI and TFP methods to reconstruct high-speed and stationary scenes, respectively. The reconstructions of two regions are fused to obtain the coarse-grained results. Based on the coarse-grained reconstructions and the distinguished spikes, we perform these major operations, including the fine-grained spikes distinction, the region-based reconstruction, and the fusion at the fine-grained level to additionally distinguish the spikes for further image reconstruction. Furthermore, the difference between adjacent inter-spike intervals (DAISI) and the background subtraction [32] are utilized for the fine-grained distinction. The patch matching method is integrated along with the TFI for dynamic region reconstruction, which is fused with the static region scene reconstruction via the TFP method to obtain the final reconstruction. Finally, our coarse-to-fine scene reconstruction can obtain better image quality and motion details with lower noise and less blur.
## 2 Materials and Methods
### ResNet  
Residual Network' (ResNet) [21] is a popular type of CNN architecture that was originally designed for image classification; see Figure 1. However, ResNet can also be used for video classification by adapting it to process multiple frames of a video sequence.  
In a traditional CNN, each layer processes the output of the previous layer to extract increasingly complex features from the input image. However, as the network becomes deeper, it can become harder to train and can suffer from the vanishing gradient problem. This problem occurs when the gradients used to update the weights of the network become very small, making it difficult to learn from the data.  
ResNet solves this problem by introducing residual connections between layers. These connections allow the network to \"skip\" over certain layers, allowing for information to pass through unchanged. This helps to prevent the gradients from becoming too small, allowing the network to learn more effectively.  
To use ResNet for video classification, we can apply it to each frame of the video sequence and then combine the outputs from each frame to obtain a final classification. This can be achieved by either averaging the outputs or using an attention mechanism to focus on the most relevant frames.  
One popular implementation of ResNet for video classification is the Two-Stream ResNet, which consists of two ResNet networks: one for processing spatial information (the appearance of the objects in the video) and one for processing temporal information (the motion of the objects in the video). The spatial network processes each frame of the video independently, while the temporal network processes pairs of frames to capture motion information. The outputs from both networks are then combined to obtain a final classification.  
Overall, using ResNet for video classification can be an effective approach due to its ability to learn complex features and its use of residual connections to prevent the vanishing gradient problem.
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 6 Applications of Microbial Biopolymers
### Microbial Biopolymers as Filters in Membrane Processes
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Polymer** & **Pollutant** & **Removal Rates (\%)** & **References** \\ \hline \multicolumn{4}{c}{Inorganic contaminants} \\ \multicolumn{4}{c}{Polylactic acid/amino-activated carbon/modified mangrove particles pH-responsive adsorptive membrane} & Cu2\({}^{\text{\text{\textminus}}}\) & 99.95 & \\ \multicolumn{4}{c}{} \\ Cellulose acetate/chitosan/TiO\({}_{2}\) & Pu2\({}^{\text{\textminus}}\) & 100 & [361] \\ PLA/PHB/Polybutylene succinate/polpropylene & Mn2\({}^{\text{\textminus}}\) & 14.17 & [362] \\ Cellulose acetate/nanochtosan/PEG & Cr6\({}^{\text{\textminus}}\) & 95 & [363] \\ Hyaluronic acid/polyamide & Na2 SO4 & 94.90 & [364] \\ \multicolumn{4}{c}{Organic contaminants} \\ \multicolumn{4}{c}{Cellulose acetate/PLA/polyurethane} & Methylene Blue & 45 & \\ \multicolumn{4}{c}{} \\ Cellulose microfiber/PLA/poly(butylene & & & \\ adipate-co-terephthalate)/maleic anhydride & & \\ \multicolumn{4}{c}{} \\ Fe3O4/Xanthan gum/polyvinylidene fluoride & Reactive Black 5 & 84.8 & [367] \\ \multicolumn{4}{c}{} \\ Xanthan Gum/polyvinylidene fluoride/dimethyl sulfoxide Cellulose microfiber/PLA/poly(butylene adipate-co-terephthalate) & Methylene Blue & 58.7 & [366,369] \\ MB & 100 & \\ \multicolumn{4}{c}{Direct Red (DR80)} & 98.80 & \\ Sodium alginate hydrogel/graphene oxide & Congo Red (CR) & 100 & [369] \\ \multicolumn{4}{c}{Crystal Violet (CV)} & 100 & \\ \multicolumn{4}{c}{} \\ Chitosan/bacterial cellulose/carboxyl multi-walled carbon nanotubes & Direct Orange S, Proci Red & 99.7 & \\ \multicolumn{4}{c}{} \\ Methylene Blue & 62.8 & [370] \\ \multicolumn{4}{c}{} \\ Hyaluronic acid/polyamide & Perfluorohexane sulfonic acid (PFHS) & 93.40 & [364] \\ \multicolumn{4}{c}{Other contaminants} \\ Sodium alginate/tanic acid/\(\beta\)-FeOOH & Oil & 99.64 & [371] \\ Chitosan/bacterial cellulose/carboxyl multi-walled carbon nanotubes & Oil & 97.67 & [370] \\ \multicolumn{4}{c}{} \\ Pristine PLA & Oil & 93.9 & [372] \\ PLA/polyethylene oxide & Oil & 99.6 & [373] \\ Cellulose acetate/chitosan/TiO\({}_{2}\) & Oil & 99.4 & [359] \\ \multicolumn{4}{c}{} \\ PLA/multi-walled carbon nanotubes/graphene oxide & Ammonium-nitrogen & 90.1 & [374] \\ Phosphate & 71.3 & [374] \\ PLA/PHB/Polybutylene succinate/polpropylene carbonate/silica nanoparticles & Total Dissolved Solids (TDS) & 22.56 & [362] \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \end{tabular}
\end{table} 
Table 7: Studies related to the use of biopolymers as membrane filters to treat wastewater.
###### Abstract  
This paper examines the implementation of a digital product passport (DPP) using Hyperledger Fabric technology to enhance product lifecycle management within the European Union's circular economy action plan. This study addresses the need for detailed product information on materials, origin, usage, and end-of-life instructions to improve recycling practices and promote sustainable consumption. The approach integrates decentralized identifier (DID) technology for unique product identification using the cheod.io platform with an enterprise tailored Hyperledger Fabric blockchain network for DPP data management, leveraging their strengths to enhance security and efficiency. This paper details the data model for the DPP, including entities like Product, Manufacturer, Supplier, and Material. Performance tests on the Hyperledger Fabric network demonstrate the system's efficacy, focusing on CRUD operations and scalability. Future work will extend to the development of client applications and more comprehensive performance evaluations considering scalability and network expansion.
# Identification and Evaluation of Composition in Food Powder Using Point-Scan Raman Spectral Imaging
###### Abstract  
This study used Raman spectral imaging coupled with self-modeling mixture analysis (SMA) for identification of three components mixed into a complex food powder mixture. Vanillin, melamine, and sugar were mixed together at 10 different concentration level (1% to 10%, w/w) into powdered non-dairy creamer. SMA was used to decompose the complex multi-component spectra and extract the pure component spectra and corresponding contribution images. Spectral information divergence (SID) values of the extracted pure component spectra and reference component spectra were computed to identify the components corresponding to the extracted spectra. The contribution images obtained via SMA were used to create Raman chemical images of the mixtures samples, to which threshold values were applied to obtain binary detection images of the components at all concentration levels. The detected numbers of pixels of each component in the binary images was found to be strongly correlated with the actual sample concentrations (correlation coefficient of 0.99 for all components). The results show that this method can be used for simultaneous identification of different components and estimation of their concentrations for authentication or quantitative inspection purposes.  
Keywords:Raman spectral imaging; food powder; self modeling mixture analysis; spectral information divergence; food safety and quality +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Adsorption Experiment  
Batch adsorption tests for heavy metals (i.e., Cu, Pb) were conducted utilizing VWR\({}^{\circledR}\) polypropylene tubes with lids. A stock solution of heavy metal was created and subsequently diluted to various quantities, with the pH of the solution adjusted using 0.1 M NaOH or 0.1 M HCl. Following pH correction, the necessary quantity of the prepared activated carbon (RAC, DAC, PAC) was incorporated. The sample was subsequently combined in an orbital shaker (Barnstead/Lab-Line MaxQ\({}^{\circledT}\) 2000) at 175, rpm for a duration of 2 h. Following the conclusion of the mixing period, the samples (suspensions) were centrifuged using a centrifuge machine (IEC Centra GP8R, Thermo Electronic Corporation, Waltham, MA, USA) for ten minutes at 25,000 revolutions per minute. Aliquots of the supernatant were taken from the mixture by decantation, acidified using concentrated HNO\({}_{3}\) (to a pH less than 2.0), and then stored in the refrigerator. Inductively coupled plasma mass spectrometry (ICP-MS, X Series II, Thermo Scientific, reporting limits: Pb = 0.4 \(\mu\)g/L and Cu = 0.7 \(\mu\)g/L) was utilized to determine the amounts of metals in the samples. The efficiency of metal removal (expressed as a percentage, R%) was estimated using the following equation:  
\[R(\%)= \frac{\left(C_{i}-C_{f}\right)}{C_{i}}\times 100 \tag{1}\]  
The symbols \(C_{i}\) and \(C_{f}\) represent the starting and final concentrations of the metal, respectively, measured in milligrams per liter (mg/L).
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 5 Additional Effects of Decoupling Techniques
### Analysis of External Decoupling
Some 3D structures are often adopted in metamaterials construction, which is complex and takes up more space for realizing the desirable refractive indices, such as the common mushroom shape of EBG. Moreover, a challenge in metamaterials design is overcoming the state of narrow bandwidth and high transmission power, so they and slots [70] or other structures are often combined for antenna performance improvement in practical applications. As we can see, the advantages of external decoupling consist simplicity of conception and efficiency of completion. The properties of these decoupling techniques dictate that it is well suitable for planer MIMO systems. However, high isolation of the antenna array was achieved by adjusting the shape and position of the decoupling structure. Yet, there is a lack of a general and systematic approach to structural optimization, which makes the process of applying external decoupling time-consuming.