esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Enterprise Internet of Things Maturity Models: A Review
## 5 Results and Analysis
### Quantitative Analysis of the Results  
#### 5.1.1 Segmentation of the Types of Studies Found in Scientific Databases  
A descriptive analysis of the scientific articles retrieved from various databases is presented below. Initially, 489 studies were identified in all the databases; after applying specific inclusion and exclusion criteria, 36 primary studies were identified. The rejected studies constituted 266 studies based on the screening process, and 186 were identified as duplicates. In this line, the primary studies represent 7.36% of the total number of studies retrieved, while the rejected studies constitute 54.3%, and the duplicates represent 38% of the total. Regarding databases, Web of Science yielded the highest number of studies (140), and ACM Digital Library produced the lowest number (2). It should be noted that the Scopus and Dimensions databases provided the most significant number of primary studies, with 11 and 9, respectively, indicating their essential contribution to the domain.  
Figure 3 shows that Web of Science found a significantly higher number of total studies than other scientific databases, which could be attributed to more excellent coverage of sources in this specific domain. In addition, Scopus ranks second in terms of the number of studies identified due to its role as an indexer of other scientific databases. In particular, the high proportion of duplicate studies suggests considerable overlap in indexing between the different databases in this field.  
The relationship between the studies found and the primary studies indicates that despite the large number of results obtained, there may be problems related to the relevance and specificity of the studies identified. It is possible that numerous contributions to the domain are being made, but not necessarily focused on IoT, but focused on Industry 4.0, where IoT is an important technology but not a central element, as reported in the literature [39]. This may mean that the proposals are made in a general way for this industrial revolution, and the proposed mechanisms lose specificity for IoT technology, constituting future work for the domain.  
Regarding the individual sources, Scopus and Dimensions have the highest number of accepted primary studies, which underlines their relevance for finding empirical evidence in the domain. Studies have already reported on this, highlighting the importance of Dimensions for engineering fields [40].
## 3 Results
### Identification of Key Predictors  
In this section, we compare key predictors of vaccine default identified from the three most effective models (RF, GBM and XGB). The ensemble learning classifiers, over several years, have been well known for their efficiency in classification problems due to their ability to handle complex, high-dimensional data and provide robust predictive performance [41]. These methods operate by combining multiple decision trees to improve prediction accuracy and reduce overfitting, with several tuneable hyperparameters including the number of trees, the maximum depth of each tree, the number of features to consider during splitting, the minimum number of samples required to split a node, and the minimum number of samples required at the leaf node [42]. In a classification task, the RF model builds trees independently and uses majority voting among all the trees for final prediction. The GBM model differs from the RF model by building trees sequentially rather than independently. XGB basically presents an enhanced implementation of the GBM that includes additional regularization and parallel processing.  
Limiting the results to the top 20 predictors identified by each model, relevant charts are presented in Figures 19-20 in Appendix A. A map for feature code interpretation is provided in Table 2 in Appendix A. It can be seen from the results on feature importance that dimensions related to survey round, age of child, timeliness of vaccination at birth, and delayed vaccination were ranked among the top five predictors across all three models. Predictors such as maternal education and region of residence appeared, though not highly ranked, consistently among features within the top 20 across all models. Additional predictors identified by the highest performing models (RF and XGB) included dimensions on household wealth status, rural-urban residency, occupation, maternal hygiene and health practices, and birth seasonality.
###### Abstract
In addition, SNP loci obtained by GBS sequencing technology can be used for genome-wide association analysis (GWAS). For example, in the research of 95 _Brassica napus_[30] germplasm resources, it was determined that based on the SNP loci obtained by GBS sequencing, the GWAS method was the best to determine the genes related to 1000-grain weight (_SFGH_, _ZBED1_, _VLN3_, _DLCI_). There was also research that combined GBS sequencing and GWAS methods to identify genes (_MYB16-like_, _R2R3-MYB_) related to the pickle development of red raspberry (_Rubus idaeus_) [31]. This indicates that GBS sequencing can provide useful information for GWAS analysis. The combined application of GBS and GWAS plays an important role in finding species trait genes. Molecular marker technology is a common means of plant genetic breeding research [32]. Among different types of molecular markers, SSR molecular marker technology is widely used. This has been reported in the identification of 101 peanut (_Aractis hypogaea_) [33] varieties, 149 melon (_Cucumis melo_) [34] varieties, 11 walnut (_Juglans regia_) [35] varieties, and other species. This indicates that SSR molecular marker technology can be used for germplasm resources identification. Although there has been a lot of research conducted on the species resources of different plants at this stage, there are few research studies on the genetic diversity of Korla fragrant pear and bud mutation materials. The analysis and identification of bud mutation materials is very important for the utilization of this germplasm resource. Therefore, GBS simplified genome sequencing and SSR molecular marker identification were performed in this research to better understand the genetic differences between Korla fragrant pear and its mutants under natural conditions. In summary, to further clarify the differences in the seed-setting characteristics and genetic differences between the two, this research used Korla fragrant pear bud mutation material and Korla fragrant pear as the research objects, through the investigation of seed-setting characteristics, anatomical structure observations, molecular markers, and other technical means, aiming to answer three main questions:  
1. Is there any difference in the fruiting characteristics between Korla fragrant pear bud mutant material and Korla fragrant pears? 2. Are there any differences in the structural characteristics of sepal (cylindrical) cells between Korla fragrant pear bud mutation materials and Korla fragrant pears? 3. Does the degree of variation between Korla fragrant pear bud mutation materials and Korla fragrant pears affect population evolution?
## 4 Results and Discussion
### Treatment Competency: Classification and Prediction
#### 4.2.1 Prediction Results

Table 4: The competency rating prediction results based on each item using Lasso for manual transcriptions (the MAE and RMSE percentage of the total dataset). Figure 3: The actual versus predicted total practitioner’s competence measure highlighting each level of competence. The best eGeMAPS features for predicting the full ratings and each rating item has been presented in Figure 5 for each speaker. It is clear that the most frequently selected features are: the mean of the third and fourth MFCC coefficients for voiced regions and the standard deviation of the slope of rising signal parts of F0. These features are a combination of spectral and frequency based features [57]. Furthermore, the best eGeMAPS features are composed of more patient related features compared to the practitioner related features. This could contribute to the ability of acoustic features to reveal the patient behaviours that could not be captured by language. The best eGeMAPS features for predicting the total ratings and each rating item has been presented in Figure 6. The most common linguistic features are: AZ and WRSL covariation, WRAD and WRSL covariation, SenS and WRSL covariation, AN and SenS covariation, WRSL mean, AZ mean and Neg mean. These features could indicate moments of rupture in the sessions based on the work of [26], such that WRSL could indicate a  
Figure 4: The best acoustic and linguistic features for predicting the total competency measure (std. deviation = standard deviation, PAT = patient, INT = practitioner). Figure 5: The best eGeMAPS features represented on the horizontal axis for predicting the full competency rating or each rating item as indicated on the vertical axis. The rating item numbers are based on the numbers in Table 4. The green blocks indicate the selected features for the patient’s segments, the blue blocks indicate the selected features for the practitioner’s segments and the red blocks indicate the selected features for the full session, including the patient’s and practitioner’s segments. (std. deviation = standard deviation, H1 = first F0 harmonic, A3 = highest harmonic in the third formant range). decrease in the emotional engagement for the patient and self-disclose for the practitioner. Furthermore, the SenS score could map to the practitioner's increase in references to bodily experiences and the AZ score could capture the use of natural affect words. In addition, the number of the selected features based on the practitioner's speech is higher than the ones related to the patient because the most dominant approach for the practitioner is to communicate and express verbally with the patient in the session.
# Two-Stage Beamforming Technique for GNSS Applications +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
[PERSON]  
1 The European Space Agency (ESA), Keplerlaan 1, 2021 AZ Noordwijk, The Netherlands; [EMAIL_ADDRESS]  
[PERSON]  
2 National Instruments, Hatar ut 1/A, 4031 Debrecen, Hungary; [EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (R.F)  
Correspondence: [EMAIL_ADDRESS]  
[PERSON]  
2 National Instruments, Hatar ut 1/A, 4031 Debrecen, Hungary; [EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (R.F)  
Correspondence: [EMAIL_ADDRESS]  
[PERSON]  
2 National Instruments, Hatar ut 1/A, 4031 Debrecen, Hungary; [EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (R.F)  
[PERSON]  
1 The European Space Agency (ESA), Keplerlaan 1, 2021 AZ Noordwijk, The Netherlands; [EMAIL_ADDRESS]  
generalized expectation-maximization (SAGE) algorithm to decompose the received GNSS signal into its constituent signals, i.e., direct and reflected signals. The SAGE algorithm estimates the angle of arrival (AoA) and the received covariance matrix for both the direct and reflected signals. The second stage, on the other hand, utilizes the Minimum Variance Distortionless Response (MVDR) algorithm to produce the weight vector that steers the main beam towards the satellite's direction and the nulls towards the multipath effect. The MVDR uses the AoA of the direct path and the covariance matrix of the reflected path to minimize the multipath effect. The experimental results reveal that the proposed technique improves the received signal strength and the location estimation accuracy, as compared to a single-antenna system. Furthermore, the proposed technique outperforms the traditional MVDR technique in the tested environment. Finally, the 95% 3D position error of the proposed solution is 5.2 m, and the position dilution of precision (pdop) is 0.84.  
GNSS; GPS; Galileo; BeiDou; array antennas; AoA; DoA; SAGE; MVDR; multipath mitigation
## 3 Results and Discussion
### Structural Characterization
Enhanced crystallinity and reduced particle size have a significant relationship with high catalytic efficiency, indicating a potential for efficient generation of e' on the photocatalyst surface [32]. This suggests that carefully regulated grain growth could enhance photocatalytic activity under light irradiation. The XRD data indicate that photo-fixation with Ag\({}^{+}\) (10\({}^{-2}\) M) does not significantly alter crystal size. Additionally, co-catalytic modification results in a drop in the intensity of the TiO\({}_{2}\) peaks, observed in both the Ag/TP2 and Ag/TP4 diffractograms. Crystalline lattice parameters remain almost constant, with a slight decrease in the average crystallite size following photo-fixation (Table 2). Figure 3: EDS spectra of (**a**) Ag, 10\({}^{-2}\)/TP2 and (**b**) Ag, 10\({}^{-2}\)/TP4 films. Silver co-catalytically modified films retain their anatase structure, as evidenced by calculated lattice parameters closely resembling those of TiO\({}_{2}\). Furthermore, the c-axis lattice parameter is used to calculate the microstrain of the films, revealing a positive value indicative of tensile strain. Modified films exhibit slightly larger tensile strain, compared to TiO\({}_{2}\).
## 4 Discussion  
The results synthesized in this review highlight the remarkable progress made in the field of nanomedicine regarding the treatment and diagnosis of hepatocellular carcinoma. The analyzed studies clearly demonstrate that the use of biofunctionalized nanoparticles enables targeted and efficient delivery of anticancer drugs, with reduced side effects on healthy tissues. Additionally, these nanostructures have been shown to improve diagnostic accuracy by transporting contrast agents directly to tumor cells, thus facilitating early disease detection.  
The diversity of materials used--from lipid-based nanoparticles, dendrimers, and liposomes to carbon nanotubes, chitosan, and gold nanoparticles--reflects the impressive adaptability of nanostructures to meet various therapeutic needs. The studies included in this analysis have demonstrated the efficacy of combination therapies (e.g., DOX/Cur, SF/Gd), as well as the advantages of co-delivery strategies and the synergistic effects of combining chemotherapy with photodynamic or photothermal therapies.  
Furthermore, active delivery systems functionalized with specific ligands have shown an increased capacity for accumulation in HCC cells, emphasizing their potential for personalized treatment approaches. Gene therapy and RNAi were also extensively covered,highlighting the ability of these strategies to downregulate oncogene expression or specific HCC markers.  
Regarding diagnosis, the integration of nanoparticles into advanced imaging methods such as MRI or fluorescence with quantum dots, allows for earlier and more accurate detection. Theranostic approaches, which combine diagnosis and therapy into a single platform, hold significant promise for the advancement of personalized medicine in liver cancer management.  
Nevertheless, although significant progress has been made in nanomedicine for the treatment and diagnosis of hepatocellular carcinoma, the clinical translation of these therapies faces substantial challenges. Batch-to-batch reproducibility remains a key issue, as variations in nanoparticle size and stability can affect treatment efficacy and safety. Rapid immune clearance reduces the circulation time of nanoparticles and their accumulation in tumor tissue, thereby limiting therapeutic efficiency. Additionally, high costs and the difficulty of scaling up manufacturing processes constrain industrial applicability. Regulatory approvals also involve complex safety evaluations, requiring collaboration between researchers, industry, and authorities. Overcoming these obstacles is essential to transform the potential demonstrated in preclinical studies into clinical reality for patients with hepatocellular carcinoma.
## 2 Islanding Detection Methods
### Signal Processing Based Methods  
Signal processing methods are utilized in islanding detection to minimize the NDZ of passive methods. These methods are capable to extract the hidden feature of the measured signals at PCC (voltage, frequency, and current) which is considered an extra benefit compared to passive methods. The resulting characteristics then can be applied as input to a classification technique, such as artificial intelligence, machine learning, or some other approaches, e.g., pyramidal algorithm, to decide if the system operates in an islanding condition or not [37]. Signal processing methods are discussed and presented below.
# Development of a Lightweight Prosthetic Hand for Patients with Amputated Fingers
## 3 Analysis of the Prosthetic Hand
### FEM Analysis  
To validate the design and safety for the frame shapes and thicknesses under desired conditions, the finger module is analyzed for stress distribution and deflection using the finite element method (FEM). The simulations set the mechanical parts (shafts and bearings) and the other frames to stainlesssteel and nylon, respectively. Because the magnitude of the external force applied to the finger module is the largest after finishing the adaptive grasping motion, the finger module configuration analyzed is the one shown in Figure 8, \(\theta=90\), and \(\alpha\), \(\beta=25\). Furthermore, Figure 8a shows the result when an external force of 7.5 N was applied at the tip of the finger module along the \(Y\)-coordinate, while Figure 8b shows the result when an external force of equal magnitude was applied at the middle phalanx along the \(X\)-coordinate. The magnitude of the external forces is sufficient to validate the design because the target payload in a finger module is 250 g, implying a safety factor of 3.  
The maximum deflections in Figure 8a,b were 0.143 mm and 0.471 mm, and the maximum generated stresses were in the middle phalanx (\(51.484\times 10^{6}\) N/m\({}^{2}\)) and the palm (\(112.059\times 10^{6}\) N/m\({}^{2}\)), respectively. Thus, the developed prosthetic hand can perform the desired motions suitably. Because the middle phalanx and palm are made of nylon (tensile strength of nylon is 139.043 MPa), the prosthetic hand has adequate rigidity in the desired conditions.
## 3 Methodologies  
_FastText_ and _sBert_ each creates their own embeddings, which results in distinct data points for the same sentence. _sBert_ takes into account the context by considering word positions when generating sentence embeddings, while _FastText_ uses a bag-of-words approach that loses information about the position of words. This fundamental difference leads to different orientations for representing sentences. However, both models aim to capture meanings from the input sentences, hence we expect some level of correlation between these vectors in their respective spaces (Figure 1). One way to enhance this correlation is by mapping these encodings onto a shared space and minimizing the distance between them. To achieve that, two methods are proposed:  
1. _Distance Minimization_ aims to reduce the dissimilarity between _sBert_ and _FastText_ word embeddings by changing their representational direction.
2. _Distance and Perplexity Minimization_ aims to minimize the gap between _sBert_ and _FastText_ by checking the correctness of contextual information in addition to distance.  
Both approaches have the goal of improving the quality of word-level representations provided by _FastText_. To align representations with _sBert_, both proposed strategies employ representation distillation methods [18] that make use of only sentence embeddings generated by _sBert_. The primary aim is to ensure that the resulting embeddings remain consistent  
Figure 1: The three figures depict the distance minimization procedure. Two sentence vectors represented by _FastText_ (light blue point) and _sBert_ (dark blue point) sentence vectors belonging to the same sentence. The objective is to minimize the distance (angle) between the vectors by moving _FastText_ sentence representations towards _sBert_.  
with the benchmark _sBert_ model. Once this distillation process is completed, the updated _FastText_ word embeddings will be exclusively used to derive sentence embeddings using a _bag-of-words_ approach for sentence evaluation tasks as in the original approach.  
We developed our models according to the used objective functions. Accordingly, the distance minimization method uses an encoder architecture. Whereas the second model checks the correctness of context information in a sentence since it then has to use cross-entropy loss for minimizing the perplexity; hence, it uses a decoder architecture. Additionally, in order to obtain more fine-grained decoding, an encoder architecture is added in front of the decoder structure, obtaining a sequence to sequence model.
## 4 Conclusions  
A comprehensive numerical study on pre-insulated double-pipe systems buried in a homogeneous soil has been executed by solving the steady-state equation of heat conduction by way of the finite-difference method. In particular, the dimensionless thermal resistances between each pipe and the ground surface, and between the two pipes, have been calculated with the scope to obtain the global heat losses occurring through the soil. Simulations have been performed using the center-to-center distance between the pipes and the dimensionless burial depth of the pre-insulated double-pipe system, both normalized  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{\(d=\mathbf{0.09\;(m)}\), \(x_{\text{p}}=\mathbf{0.115\;(m)}\), \(w=\mathbf{0.25\;(m)}\), \(h=\mathbf{0.5\;(m)}\)} \\ \multicolumn{2}{c}{\(k_{\text{i}}=\mathbf{0.0265\;(W/mK)}\), \(k_{\text{g}}=\mathbf{1.5\;(W/mK)}\)} \\ \multicolumn{2}{c}{\(t_{\text{s}}=\mathbf{80\;^{\circ}}\)C, \(t_{\text{r}}=\mathbf{40\;^{\circ}}\)C, \(t_{\text{g}}=\mathbf{8\;^{\circ}}\)C} \\ \hline \multicolumn{2}{c}{**Present Study**} & **[9]** \\ \hline \(Q_{\text{g}}\) (W/m) & 17.14 & 18.08 \\ \(\rho_{\text{pg}}/k_{\text{i}}\) (mK/W) & 6.1 & 5.77 \\ \(\rho_{\text{pp}}/k_{\text{i}}\) (mK/W) & 13.4 & 12.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the present solutions with the numerical data of [PERSON] and [PERSON] [9] for a buried pre-insulated double-pipe system.  
Figure 15: Comparison between Equation (13) and the numerical data obtained for \(L_{\text{opt}}\).  
by the pipe diameter, as well as the ratio between the thermal conductivities of the soil and the insulation, as independent variables.  
The main results obtained in the present study may be summarized as follows:  
1. The existence of an optimal distance between the pipes which minimizes the heat losses from the supply pipe has been found for any investigated set of the controlling parameters;
2. The optimal pipe spacing increases with increasing both the insulation diameter \(W\) and the burial depth \(H\), whereas the optimal pipe spacing decreases as the temperature \(T_{\rm r}\) of the return pipe increases;
3. The minimum heat loss increases as the insulation diameter decreases;
4. A set of correlations to predict the heat losses from underground double-pipe systems, as well as the optimal pipe spacing, has been developed for wide ranges of the main controlling parameters.  
Conceptualization, A.Q. and M.C.; methodology, A.Q., M.C. and L.F.; software, A.Q. and L.C.; validation, A.Q., L.F. and L.C.; data curation, A.Q. and L.C.; writing--original draft preparation, A.Q. and M.C.; writing--review and editing, A.Q., L.F., M.C. and L.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.