esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 2 Tracking Scheme of Direct-State Kalman Filter
### Luit-Dskf  
Figure 7 shows the linear model of the LUT-DSKF. The LUT-DSKF is a simplified version of the DSKF that considers the steady-state convergence of \(\mathbf{K}\). The loop bandwidth \(B\) can be used to set the values of \(\mathbf{K}\) using Equation (38). Alternatively, \(q\) and \(R\) can be selected to define \(\mathbf{K}\) based on Equation (34).
## 4 Analysis and Discussion of Experimental Results
### Result Analysis
#### Comparative Analysis of Evaluation Values in Different Dimensions  
This article dissects each dimension of the job recommendation method (JRSR) to analyze its impact on the system's recommendation performance. The dimensions are individually assessed to calculate the recommendation outcomes, which are then evaluated using the average satisfaction rate and the normalized discounted cumulative gain. Figure 13 reveals that RSR has the lowest average satisfaction rate and normalized discounted cumulative gain, suggesting that dimensionality reduction in the semantic keyword iteration algorithm adversely affects satisfaction and validity. This indicates a superior filtering capability of the semantic keyword iteration algorithm when fully utilized. The data indicate that JRR's average satisfaction rate and normalized discounted cumulative gain are second to last and third to last, respectively, which suggests that salary is a critical concern for graduates during their job search. Conversely, JSR registers the highest values in both average satisfaction rate and normalized discounted cumulative gain, signifying that the alignment of graduates' majors is not a predominant consideration in their employment pursuits.  
Figure 12: Evaluation results of separate use of historical employment information for the logistics and management section.  
Figure 13: Evaluated values of AR@5 and NDCG@5 for different dimensional approaches.
# Enhanced Performance of nZVI/MXene@CNTs for Rapid As(III) Removal from Aqueous Solutions
## 2 Materials and Methods
### Synthesis of nZVI/MXene@CNTs
#### 2.2.2 Synthesis of nZVI/MXene@CNTs  
The Ti\({}_{3}\)C\({}_{2}\) MXene dispersion followed a prior report by our research group [47]. In a mild aqueous environment, the nZVI/MXene@CNTs nanocomplex was produced using a simple self-assembly approach. Specifically, to obtain a homogeneous suspension, 0.2 g functionalized CNTs was combined with 30 mL deionized water and fully sonicated with the pre-prepared MXene dispersion. The above CNTs and MXene dispersion was added to a 5.0 g/L FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O solution, agitated constantly to mix thoroughly, and N\({}_{2}\) was filtered through to remove the residual dissolved oxygen. Then, under N\({}_{2}\) and constant stirring, a freshly produced NaBH\({}_{4}\) solution with the 5.0 g/L concentration was dropped into the mixed liquid dropwise at the rate of 3.2 mL/min. Finally, the precipitates were freeze-dried in a vacuum to obtain nZVI/MXene@CNTs nanocomplex after being washed several times with water/ethanol.
## Abbreviations  
APL, acute promyelocytic leukemia; CAM, chicken embryo chorioallantoic membrane; CCC, countercurrent chromatography; CD, cyclodextrins; CPP, copalyl diphosphate; CR, complete remission; CT, cryptotanshinone; DAD, diode array; DH-TI, dihydrotranshinone I; DMAPP, dimethylallyldiphosphate; DXP, 1-deoxy-d-xylulose-5-phosphate; DXR, 1-deoxy-d-xylulose-5-phosphate synthetase; DXS, 1-deoxy-d-xylulose-5-phosphate synthase; ECM, extracellular matrix; ESI-IT-MS, electrospray ionization quadrupole ion trap mass spectrometry; FBS, fetal bovine serum; FPP, farnesyl pyrophosphate; GA-3P, glyceraldehyde-3-phosphate; GGPP, geranylgeranyldiphosphate; GPP, geranyldiphosphate; HPLC, high-performance liquid chromatography; HSCC, high-speed counter-current chromatography; _i.m._, intramuscular; _i.p._, intraperitoneal; IPP, isopentenyldiphosphate; i.s.i.p._, in situ intestine perfusion, _i.v._, intravenous; LC, liquid chromatography; MEP, 2-C-methyl-D-erythnitol-4-phosphate; MVA, mevalonic acid; MVD, microvessel density; _p.o._, oral administration; PR, partial remission; qTOF-MS, quadrupole time-of-flight mass spectrometry; RIF, Realgar-Indigo naturalis formula; s.c., subcutaneous; SD, stable disease; SLN, solid lipid nanoparticles; STS, sodium tanshinone IIA sulfonate; TI, tanshinone I; TIIA, tanshinone IIA; TACE transcatheter arterial chemoembolization; TCM, Traditional Chinese Medicine; TLC, thin-layer chromatography.
# Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste
## 1 Introduction
Mathematical models can improve the process of estimating the energy potential of municipal waste, making environmental decisions more effective and based on solid analytical foundations. Machine learning models need to be created based on various sets of data, including economic and environmental research, to ensure their generalization. Research on learning techniques and model adaptation can provide ways to overcome restrictions in the use of waste transformation methods. These models can optimize processes to achieve higher efficiency, resulting in increased energy recovery indicators. The application of machine learning methods like ElasticNet, Decision Trees, and Neural Networks to model energy recovery from waste in European Union countries is an approach that leverages diverse economic and environmental indicators to predict and optimize energy recovery processes. The use of such comprehensive input data--including electricity prices by type of user, energy productivity, final energy consumption, GDP at market prices, recycling rates of municipal waste, domestic material consumption per capita, environmental tax revenue, and the share of energy from renewable sources--ensures a broad and nuanced understanding of the factors influencing energy recovery from waste. Mathematical models can be used to identify efficient and sustainable methods of transforming waste into energy, thereby reducing the amount of waste sent to landfills. Machine learning models, with their adaptability, can forecast future trends and changes in factors affecting energy recovery. This predictive ability is crucial for planning and adapting sustainable development strategies.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 2 Filtering Methods
### MMSE-Based Filter  
As one of the MMSE methods, Wiener filtering algorithm proposed by [PERSON] achieves optimal results on average based on the statistic minimization criterion [44], and it has been widely applied. The main idea of WF is to minimize mean square error between the filtered image and its original image. It can filter the noise of image and protect details of image, simultaneously. Modified filter algorithms [45] have represented their usefulness in de-speckling of medical ultrasound image. WF is expressed as follows  
\[\hat{F}(u,v)=[\frac{H*(u,v)}{\left|H(u,v)\right|^{2}+S_{n}(u,v)/S_{g}(u,v)}]G (u,v) \tag{12}\]  
or,  
\[\hat{F}(u,v)=[\frac{1}{H(u,v)}\frac{\left|H(u,v)\right|^{2}}{\left|H(u,v) \right|^{2}+S_{n}(u,v)/S_{g}(u,v)}]G(u,v) \tag{13}\]  
where \(H(u,v)\) is the degradation function, \(H^{*}(u,v)\) is conjugate with \(H(u,v)\), \(G(u,v)\) is the frequency representation of \(I_{g}(x,y)\) and \(\hat{F}(u,v)\) is the estimated value of \(I_{g}(x,y)\) in frequency space, and \(S_{g}(u,v)\) and \(S_{n}(u,v)\) denote the frequency spectrum of contaminated image and noise, respectively. However, \(S_{g}(u,v)\) is hard to obtain in real application such that \(S_{n}(u,v)/S_{g}(u,v)\) is often set to be a constant \(K_{\text{SNR}}\) to find the best visual effect.  
Equation (13) is known as the famous WF formula. However, the essential condition of using WF is to find the degradation function and both the power density spectrum of contaminated image and noise in a linear and position-invariant system, which is hard to realize.
## 3 Method
### Multiple Endmember Spectral Mixture Analysis
#### 3.3.1 Endmember Selection  
Careful selection of a high quality set of endmembers is an important step in MESMA implementation [20; 21; 22; 23; 24; 25; 26; 27]. Spectrally \"pure\" endmembers can be obtained from either a spectral library (reference endmember) or the remote sensing image itself (image endmember) [16; 20]. In this study, image endmembers were utilized due to the following advantages: (i) they can be easily obtained and (ii) they can represent spectra measured at the same scale as the image data [16; 21; 22; 23]. All the image endmembers were organized into four groups in this study: water, vegetation, impervious surface, and soil. Each group consisted of several subsets to represent the spectral variations for each material, e.g., vegetation (woodland, cropland), impervious surface (white material, dark material, blue roof, red roof), water (clear water, turbid water, submerged urban and suburban area), and soil. The shade endmember which has a zero value in all bands was also included here to account for the illumination variation [21; 22; 23].  
In order to select spectrally \"pure\" endmembers, the pixel purity index (PPI) image was firstly calculated based on several thousand iterations of the PPI algorithm [21; 22]. A higher value in the PPI image indicated a relatively purer pixel or endmember. The PPI results were also dynamically linked in an N-dimensional visualizer together with the original multispectral images to select the candidate endmembers by searching for a set of vertices on a convex hull [21].  
The selected endmembers were re-selected using the following three techniques to obtain the most appropriate ones: (i) count-based endmember selection (CoB); (ii) endmember average root mean squared error (EAR) and (iii) minimum average spectral angle (MASA) [21; 22; 23; 41]. The optimal set ofendmembers was selected iteratively by adding high CoB, low EAR or low MASA endmembers to the library and assessing the model performance using root mean square error images and a visual comparison with high resolution orthoimagery [21, 22, 23].  
A total of 35 optimal endmember spectra were selected using the above method from 2500 candidates. Specifically, it included twelve spectral curves for water, ten for vegetation, eight for impervious surface and five for bare soil, which are illustrated in Figure 3. Water and impervious surface spectra showed greater variation than that of vegetation and bare soil. The variation of water spectra mainly lies in that the persistent water and the submerged regions have different spectral signatures.  
The whole procedure of MESMA is available in Visualization and Image Processing for Environmental Research (VIPER) tools software [41] which was adopted in this study.
## 8 Conclusions  
Innovations and development to the communication technologies such as SDN address heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. SDN aims to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. This paper aims to discuss IoT leveraging SDN solutions to address security challenges, cost of hardware, centralization, and resources management in the IoT environment. Machine learning-based SDN solutions perform optimization of the IoT network, data analysis, and automated and intelligent services provisioning. Machine learning-based SDN solutions also improve the performance, efficiency, and security of IoT solutions. We also addressed potential real-world applications and their relative demerits. The limitations presented in this study provide promising directions towards IoT research studies. Interoperability of the IoT platforms, cooperation functions between SDN and NFV, VNF distribution, and management of tunnels problems are still needed to be addressed. In vehicular networks, software-defined architectures are used to maintain functional stability, reduce control overhead, but poor resource allocations should be addressed. Research institutes and research groups should work towards the standardization of IoT. IoT-SDN simulation software should be developed to test the different software-defined IoT architectures, algorithms, and protocols. IoT leveraging machine learning-based SDN solutions have limited healthcare, smart homes, smart cities for sustainable solutions. Lastly, the problem of fault detection and poor management should be addressed in smart grid environments.  
[PERSON] conceived the idea for this paper, designed the methodology, and wrote the paper. [PERSON] assisted in the experimental design and paper write-up. [PERSON] and [PERSON] assisted in review and editing. [PERSON], [PERSON] and [PERSON] supervised and proofread the study of a topical review on machine learning, software-defined networking, Internet of Things Applications: research limitations and challenges. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2020R1I1A3074141), the Brain Research Program through the NRF funded by the Ministry of Science, ICT and Future Planning (Grant No. NRF-2019M3C7A1020406), and \"Regional Innovation Strategy (RIS)\" through the NRF funded by the Ministry of Education.  
We acknowledge and Thank [PERSON] and [PERSON] for their help and support during research data collection and their valuable recommendations and suggestions.  
The authors declare no conflict of interest.
# Polydimethylsiloxane (PDMS)-Based Flexible Resistive Strain Sensors for Wearable Applications
## 3 Performance Parameters  
PDMS-based flexible resistive strain sensors are supposed to detect the bending/unbending and stretching/relaxing state of joints, as well as human physiological activities. They respond to  
Figure 1: Fabrication methods of resistive-type strain sensors based on polydimethylsiloxane (PDMS) flexible substrates. (**a**) Schematic illustrations of the fabrication processes for stretchable graphene nanopapers. Flexible nanopapers were first fabricated and then embedded in a PDMS matrix to obtain stretchable nanopapers. Reproduced with permission from [63], Copyright John Wiley and Sons, 2013; (**b**) The coaxial conductive cable design with EGaIn as the core and PDMS as the shell can directly write for strain sensor. Reprinted with permission from [67], Copyright AIP Publishing, 2016; (**c**) Strain sensor integrated with PDMS cantilever and metal wires. Reprinted with permission from [53], Copyright Elsevier, 2017; (**d**) Flexible strain sensor were developed through the combination of shear alignment of a graphene oxide dispersion and patterning of multiple rectangular features on PDMS substrate (Reprinted with permission from [57], Copyright American Chemical Society, 2016; (**e**) Mixed carbon black nanoparticles with liquid PDMS and micropatterning relies on single-step contact transfer printing. Reprinted from permission with [75], Copyright Elsevier, 2014.  
mechanical deformations by changing electrical resistance properties. Although traditional strain sensors are easy and low cost to fabricate, they have obvious drawbacks such as poor stretchability, low resolution, and not suitable for wearable applications [73]. In many practical applications, it is demanded that wearable strain sensors have high-performance characteristics including stretchability, high gauge factors, linearity, and durability. These parameters are crucial for flexible strain sensors for large deformations, responding sensitivity, frequent loading/ unloading, and wearable time.
## 2 Literature Background
### Background Articles on GNSS-CORS Network Monitoring
The topics of _radio frequency interference_ (RFI), multipath, and quality control of data from GNSS permanent station networks represent well-established issues, having been studied and analysed in the literature in several cases in recent years. It is hardly worth mentioning that the sources of interference with GNSS signals can be military, microwave, and telecommunication-related transmissions close to the typical L1 band, while amateur radio links can cause disruptions in satellite reception close to the L2 frequency. [PERSON] et al. showed how GNSS permanent stations can be affected by both RFI and multipath phenomena; by using electromagnetic spectrum analyzers and wide-band antennas, it is also possible to know the position of the interference source [30]. The software tools used to represent the data in this study are mainly TEQC by [PERSON] and [PERSON] [31], Generic Mapping Tools, and GMT by [PERSON] and [PERSON] [32]. [PERSON] et al. describe the theoretical treatment of RFIs concerning the signal coming from the GNSS and report a possible solution through a direct system of frequency sensors that simultaneously detects several sources of interference [33]. Several electromagnetic sources, indeed, could degrade the performance of GNSS CORS receivers, and their effects can be both in the band and in the secondary harmonic series generated by transmitters of other communication systems due to non-linear or out-of-band distortions through signals that occupy frequency band widths very close to the GNSS bands. [PERSON] et al., for example, through an analysis with automatic gain control (AGC, used also by [PERSON]), found that digital/analogue TV broadcasts represent a potential source of interference for GNSS applications on a dataset of the city of Turin (Italy) [34; 35]. Many authors have proposed studies on RFIs related to GNSS CORS, and in particular some of scientific interest: [PERSON] et al. regarding the aircraft receiver [36], [PERSON] et al. in the field of technology, Auto-Steering Guidance of Agricultural Machinery [37], and last but not least, [PERSON] et al. investigating the performance of multi-beacon DGPS [38]. More recently, [PERSON] et al. reported that a European RFI monitoring system covered all GNSS bands with a focus on Galileo's E6 L-band, while [PERSON] et al. developed a low-cost GNSS RFI monitoring system that can be used to detect urban RFIs [39; 40].
# A Formation Mechanism of Spatial Distribution Pattern of Industrial Clusters under Flow Space
## 2 Related Works  
### Spatial Distribution Patterns in Industrial Clustering  
Industrial agglomeration refers to the geographical distribution of urban industries. The existing literature on spatial distribution patterns of industrial clustering mainly focuses on two perspectives: the global and local perspectives. The global perspective involves constructing agglomeration coefficients to statistically characterize the degree of industry agglomeration and dispersion in spatial terms. The global method mainly expresses the overall state of industrial clustering in space through attribute values. The main methods are location entropy index [17], spatial dispersion coefficient [18], industrial spatial Gini coefficient [19; 20], Hoover industrial index [21], and EG index [22; 23; 24]. The local perspective shows the differences in the spatial distribution patterns of industrial clustering, mainly from a geographical perspective, comparing differences in industrial clustering in different physical spaces to express the intensity and direction of industrial clustering. The main methods used include KDE [25], spatial clustering [26; 27; 28], and spatial autocorrelation, etc.  
### Spatial Distribution Patterns in Industrial Clustering  
The study of influencing factors is the foundation of the mechanisms of industrial clustering [26; 29; 30; 31]. With the development of theories such as Industrial Location Theory [32] and New Economic Theory [33; 34], the current factors influencing industry aggregation can be mainly divided into three categories: the first consists of static factors within the region such as city technology and human resource levels, etc. These factors serve as decisive factors; the second consists of interactive factors between regions such as knowledge spillovers resulting from high levels of interaction; however, due to data acquisition issues, they are treated as auxiliary decision-making factors in the current research; and the third consists of potential factors such as regional culture, government systems, and legal regulations, etc., which are difficult to quantify and therefore primarily serve as auxiliary decision-making factors [35].  
Based on the current research, this paper summarizes the influencing factors of industrial agglomeration distribution patterns (see Table 1). Although there is relatively little research on the interaction factors of urban industrial agglomeration, the regional connection between industrial agglomerations has become an important influencing factor, and they cannot be ignored in the process of industrial agglomeration.
# Bioengineered Skin Substitutes: Advances and Future Trends
## 3 Cell-Based Skin Substitutes
### Cell-Laden Commercial Skin Templates
#### 3.1.4 Epidermal Templates  
The epidermis layer of human skin is the most superficial layer and has a thickness of approximately 0.1 mm. This layer has the highest Young's modulus (approx. 1 MPa) in comparison with the other skin layers [27]. Epidermal tissue engineering approaches have focused on developing a stratified keratinocyte layer to provide barrier function, which is vitally important for body protection and long-term survival [54].  
Various scaffolds using different materials have been utilized for this purpose. The scaffolds should be engineered in a way that provides a physical matrix to support cell migration and release of soluble factors such as chemokines and growth factors. These factors are important to improve epidermal cell migration as well as dermal repair and revascularization [54].  
Apligraf (Organogenesis Inc., Canton, Massachusetts, CA, USA) is a bilayered bioengineered skin substitute (BBSS) composed of a bovine type I collagen lattice with a dermal layer of human fibroblasts and a layer formed by human keratinocytes, mimicking the normal structure of human skin [55]. Importantly, Apligraf is food and drug administration (FDA) approved for the treatment of partial- and full-thickness skin wounds. Further, it is used for wounds that extend through the dermis but do not involve muscle, tendon, joint capsule, or bone exposure [46]. Apligraf is also designated for use in standard diabetic foot ulcer care [52]. It provides essential ECM components to the wound bed, as well as cytokines and growth factors, such as platelet-derived growth factor (PDGF), interferons \(\alpha\) and \(\beta\) and, interleukins 16 and 8 [49]. However, there are no antigen-presenting cells such as dermal dendritic cells, Langerhans cells, endothelial cells, or immune cells in the BBSS. Additionally, no clinical rejection and no humoral or cellular response to the keratinocytes or fibroblasts of Apligraf has been reported performing immunological tests in clinical trials [55]. However, allogeneic cells of the construct do not survive longer than one to two months in vivo [56; 57; 58].  
A clinical study by [PERSON] et al. applied Apligraf after excisional surgery on 107 patients, who were followed for up to one year [56]. The results indicated no clinical or laboratory evidence of rejection. However, graft persistence decreased after two weeks to one month after transplantation. Thus, although the Apligraf skin substitute is a safe and useful template, it can only be utilized as a temporary bioactive wound coverage.