esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Power Consumption Signal Detector Based on Adaptive DFSD in MIMO-OFDM Systems
## 3 Conventional Signal Detection Schemes
### Conventional Depth-First Sphere Decoding
The DFSD detection scheme selects the node that has the shortest accumulated squared ED at the \((N_{t}-1)\)-th layer. In this way, the DFSD detection scheme repeats the approach of getting the shortest accumulated squared ED by the last layer. Finally, the shortest accumulated squared ED sets the first radius distance [17]. The first radius distance becomes the standard to compare the other accumulated squared ED of each layer for finding the shorter radius distance. If the new accumulated squared ED by the last layer is shorter than the first radius distance, the new accumulated squared ED by the last layer becomes new radius distance. So, the DFSD detection scheme reduces the complexity by updating the radius distance [10]. Also, if the accumulated squared ED at the middle of layer is longer than the updated radius distance, this path is pruned. In this way, by updating the radius distance and pruning the path when the accumulated squared ED is longer than the updated radius distance, the DFSD detection scheme finds the path of the shortest accumulated squared ED and this path includes the reference symbols of corresponding nodes. Transmit symbols are estimated through the reference symbols. Figure 2: Depth-first sphere decoding (DFSD) tree structure in \(4\times 4\) MIMO-OFDM system with quadrature phase shift keying (QPSK) modulation.
# Information Security Applications in Smart Cities: A Bibliometric Analysis of Emerging Research
## 1 Introduction
In recent studies on smart cities, there is a growing interest in integrating innovative technologies to optimize urban management and improve the quality of life for citizens. However, upon reviewing the existing literature, a gap is identified in the systematic review related to information security applications in this context. While many studies address the benefits and potential implementations of these technologies, few delve deeply into specific solutions to ensure data protection and user privacy. Given the critical importance of information security in highly connected environments, such as smart cities, this gap presents an opportunity for researchers and IT professionals to delve deeper and contribute with insights and robust solutions to this emerging challenge. One of the premises for understanding the application of information security in smart city research activities is to analyze its manifestation in the form of scientific production. In this sense, this paper aims to perform a bibliometric analysis to deepen knowledge of new applications of information security in smart cities to identify the main groups of researchers working collaboratively in the area. Moreover, this study provides a summary of research patterns based on an institutional network to present a better understanding of research advances and the latest content about information security in smart cities published in journals during the period from 2015 to 2023. The relevant articles were retrieved from the Scopus database. The bibliometric analysis allows the visualization of the technical quality and impact of research, as well as grouping authors and co-authors, identifying the relationship between studies through keywords and number of citations, and displaying intellectual contributions from research fields, among other analyses. In addition, solutions and review of smart cities opens many opportunities and scopes for open research. This paper is structured as follows: Section 2 presents a theoretical reference with related works about smart cities and information security research; Section 3 is devoted to Materials and Methods; Section 4 presents the Findings and Discussion; Section 5 contributes to the theory and presents practical implications; the conclusion, limitations, and further research are provided in Section 6.
## 3 Results
### Cluster Analysis on Hold-Out Training Set  
The classification performance does not exceed 68% accuracy with any of the used classifiers. In order to highlight any profile characterizing the cases not correctly identified by the classifiers, we implemented unsupervised hierarchical analysis of the groups. On the whole dataset, on the basis of the relative Silhouette index, we identified the optimal number of groups in which to divide the samples (Figure 2). In particular, in Figure 3, we represent the three groups with respect to the first two main components. For the sake of simplicity, we separated the control case clusters from those made up of patients who tested positive for the sentinel lymph node and in each of them, we reported the cases (24 false negative and 25 false positive) that were never correctly classified in all the validation rounds by each of the three implemented classifiers. Evidently, such cases are not characterized by a particular pattern of the used features.  
False negatives are mostly related to group 2, characterized by patients with mildly aggressive tumors, i.e., small T1b and T1c, low grading (G1 or G2), negative ki67 and an absent in situ component. On the contrary, the false positives are afferent to group 1 or 3, characterized by T1c and T2 tumors, ki67 positive, medium-high grading G3 or G2 and components present in situ (Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Features** & **RF** & **Logistic Regression** & **Naive Bayesan** \\ \hline
2 & Diameter + Multiplicity & Diameter + Age & Diameter + Histologic Type \\
3 & +Histologic Type & +Histologic Type & +Her2/Neu \\
4 & +Grading & +Grading & +Age \\
5 & +In Situ & +Multiplicity & +Grading \\
6 & +Ki67 & +In Situ & +Multiplicity \\
7 & +Age & +Her2/Neu & +In Situ \\
8 & +ER & +PGR & +PGR \\
9 & +Her2/Neu & +Ki67 & +ER \\
10 & +PgR & +ER & +Ki67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features selected by each classifier in the different steps of the stepwise procedure implemented.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Classifier** & **\# Features** & **AUC (\%)** & **F1 Score** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline RF & 8 & 68.1 (65.9–68.6) & 54.5 (48.9–59.5) & 67.7 (65.9–68.8) & 57.9 (52.2–61.7) & 72.3 (68.9–76.8) \\ Logistic Regression & 7 & 71.5 (71.1–71.7) & 59.2 (55.60–63.7) & 67.9 (66.0–68.7) & 69.4 (67.3–74.3) & 66.9 (61.9–70.0) \\ Naive Bayesian & 7 & 70.8 (70.6–71.0) & 58.4 (54.7–61.8) & 66.3 (65.2–67.6) & 70.6 (66.4–73.0) & 63.9 (61.2–68.0) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Median performance of the best models calculated on the hold-out training set by means of the sequential forward feature selection evaluated on 100 ten-fold cross validation rounds for each of the used classification algorithms. The prediction performances are summarized in terms of median, 1 st and 3 rd quartile.
# Active Contours in the Complex Domain for Salient Object Detection
## 3 Proposed Method  
The previous models of the active contour suffer from improper initialization and sometimes require reinitialization. We propose a novel method, as illustrated in Figure 2, in order to reduce the limitations discovered in Section 2 and use the active contour in the complex domain for salient object detection. In this section, we will describe the complex domain transformation and then our formulation for the active contour model. We will discuss and formulate a low-pass filter in the complex domain and, finally, the complex force function will be defined.
## 4 Results
### Signal Processing Results
#### 4.2.3 Connection Between SNR and BPM Differences
While SNR values focus on the effects of signal preprocessing and filtering, Figure 12 depicts the overview layout of algorithm performance on the proposed BPM processing methods. Results from the 'findpeaks' and 'BPM DL' algorithms are shown separately for clarity. BPM difference is a critical metric that reflects the discrepancy between the BPM calculated by the algorithm and the ground truth value (PPG). The figure uses the label \"BPM\" to maintain consistent units. Larger deviations from the ground truth indicate poorer HR estimation accuracy for the given electrode configuration. In Figure 12, generally, the 'BPM DL' method tends to produce more accurate BPM estimates, with smaller differences from the ground truth in several configurations. For configurations such as 1-2 and 1-3, the differences in BPM using 'BPM DL' are often close to zero or minimal, sometimes as low as 0.059 or 0.467 BPM, reflecting high accuracy. In contrast, the 'findpeaks' method shows significantly higher deviations, often exceeding 10 BPM (e.g., configuration 1-3 with 76.97 BPM deviation, indicating poorer performance). Figure 11: SNR [dB] colormap across all electrode configurations for three test subjects. The table visualizes SNR where higher values are in green and lower values in red (with a gradient transition for intermediate values between these extremes). _Appl. Sci._**2024**, 14, 9632  
Figure 12: Absolute BPM estimation errors, as quantified by the DIFF\({}_{\text{BPM}}\) metric. Shows results across all electrode configurations for three test subjects. Higher errors are shown in red and lower differences in green, using a color gradient for values in-between.
## 3 Principles of Infrared Spectroscopic Analysis Techniques  
IR radiation is the region of the electromagnetic spectrum between the visible (VIS) and the microwave wavelengths [53]. In near-infrared (NIR) spectroscopy (750-2500 nm, 4000-13,000 cm\({}^{-1}\)) solid, liquid or even gaseous samples can absorb parts of the incoming IR radiation resulting in a specific fingerprint or spectrum [54]. C-H, C-O, C=O, N-H and O-H functional groups are excited to perform stretching-, deformation- and scissor-vibrations. In contrast to mid-infrared (MIR, 4000-400 cm\({}^{-1}\)), where only fundamental vibrations (\"signatures\") can be observed, overtones and combination vibrations are obtained in the NIR region resulting in additional information compared to MIR [55]. This mostly results in a crowded spectrum with overlapping peaks being 10-1000 times lower and less sensitive than for the MIR, which makes the construction of highly sensitive detectors necessary. The main advantage of NIR is the simultaneous determination of qualitative and quantitative parameters in a fast and non-invasive manner. Additionally, physico-chemical parameters including particle size, porosity and specific surface area can be investigated, respectively. Multivariate statistical analysis (MVA) is a powerful method enabling the selective extraction of the required information. Most appropriate chemometrical procedures include principal component analysis (PCA) and partial least square regression (PLSR). For the establishment of suitable quantitative models, reference analyses based on liquid chromatography (LC), wet chemistry, mass spectrometry (MS) and others are required. In attenuated total reflection (ATR) spectroscopy solid and liquid samples can be analyzed without any further sample pre-treatment, mainly in the MIR-region. Fourier-transform infrared (FTIR) spectroscopic microscopy can be used for the investigation of the samples' composition with a resolution down to approximately 4 \(\upmu\)m [56]. Finally, nowadays there is a strong trend towards the miniaturization of spectrometers [57].
## 2 Studies Selection  
By conducting a comprehensive search of the PubMed databases, an initial list of relevant publications was generated using the key terms: ((hip replacement) OR (hip arthroplasty) OR (hip implant)) AND ((Finite Element) OR (simulation) OR (modelling)) AND ((aseptic loosening) OR (bone remodelling) OR (bone ingrowth) OR (doseintegration)). One important point to note is that when conducting a literature search on the prediction of AL failure modes using FE modelling, some researchers use terms such as 'bone ingrowth', 'bone remodelling', and 'doseintegration' instead of directly referring to AL or long-term stability failure of the implant. This variation in indirect terminology can be attributed to the multifactorial nature of AL. It is therefore advisable to consider these indirect searches to ensure a more comprehensive coverage of the literature.  
Potentially eligible articles were initially screened by title and abstract. If they did not match any of the following categories, they were excluded:  
* hip joint replacement;
* Finite Element models;
* failure modes related to aseptic loosening.  
After a thorough reading of the full manuscripts, studies were further excluded if they met any of the following criteria:  
* osseointegration at the bone implant interface was not modelled;
* the implants were cemented;
* only lack of primary stability was simulated.  
To clarify the last exclusion criterion, it is important to note that there is a significant body of literature that focuses on the mechanical behaviour of primary stability, which is an important risk factor for the AL of cementless hip implants. And evidence was found that early peri-implant bone healing immediately after implantation and the consequent mechanical remodelling at the bone-implant interface are essential for secondary implant stability [45; 46]. However, it is the secondary stability that directly leads to the occurrence of AL. Hence, if the study only investigated primary stability without any consideration of long-term adaptative biological evolution, it was excluded from this review. It should also be noted that conference proceedings were excluded from the analysis, and only research journal articles published in the last three decades (1994-today) were considered. Of the 946 papers that our initial search identified, at the end of this selection process, by removing the overlap and based on the criteria above, 22 papers were considered for this review. This final selection also includes six additional sources that were identified after examining the bibliography of all relevant articles. The flowchart of article selection is depicted in Figure 1. The studies will be detailed in the following sections according to suitable model assumptions for the following aspects:  
* characteristics of bone and implant modelling (e.g., geometry, surgical pose, material properties, and meshing);
* boundary conditions;
* bone-implant interface contact;
* quantities of interest used to predict the failure mode.
## 2 Experiment
### Diffuser in the Encapsulation Layer  
To evaluate the effects of encapsulation on the CCT uniformity of the TiO\({}_{2}\) diffuser in the encapsulation layer of the remote phosphor LEDs, the encapsulation layer was mixed with TiO\({}_{2}\) diffuser contents of 0, 0.2, 0.4, 0.6, and 0.8 wt.%. The schematic diagram of the TiO\({}_{2}\) diffuser in the encapsulation layer of the remote phosphor LED is shown in Figure 2a. The total weight of the encapsulation layer was the sum of the weights of the silicone resin (type: Dow Corning OE-6370 HF) and the TiO\({}_{2}\) diffuser. A uniform mixture of the silicone resin and TiO\({}_{2}\) diffuser was injected into the lead frame through the dispenser, where the dispense pressure and dispensing time were set at 30 psi and 1.8 s. Subsequently, the LED samples were baked in a vacuum oven at 50 \({}^{\circ}\)C for 60 min and then at 150 \({}^{\circ}\)C for 90 min. Approximately 17 wt.% phosphor (type: Intermatrix YAG-04, Intermatrix Company, Fremont, CA, USA) mixed with silicone resin was then coated on the phosphor layer through the dispenser, where the dispense pressure and dispensing time were set at 30 psi and 0.8 s. Finally, the LED samples were baked in a vacuum oven at 50 \({}^{\circ}\)C for 60 min and then at 150 \({}^{\circ}\)C for 90 min.  
Figure 1: Schematic of the particle size distribution of the TiO\({}_{2}\) diffuser.
# Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios
## 6 Conclusions  
In order to improve the accuracy and speed of image-based fire-detection algorithms currently used in complex scenarios, a Light-YOLOv5 algorithm was proposed in this study. The new algorithm was constructed based on YOLOv5n. In addition, the final layer of the backbone network was replaced with SepViT Block to strengthen the connection between the backbone network and the global information; a self-designed Light-BiFPN to strengthen the network feature extraction and lighten the network; the fusion GAM module  
Figure 7: Comparison of Light-YOLOv5 and the original algorithm detection results. (**a**) YOLOv5n and (**b**) Light-YOLOv5.  
to reduce the loss of information; and finally the Mish activation function to improve the accuracy and SIoU to enhance the convergence speed in training.  
The experimental results showed that with the same dataset, Light-YOLOv5 had a 3.3% higher mAP than the baseline model, a 2% higher F1 score, a 27.1% reduction in the number of parameters, and a 19.1% reduction in the computation; even when comparing the most advanced detectors, Light-YOLOv5 had a 6.8% higher mAP than the latest YOLOv7-tiny model, and the detection speed reached 91.1 FPS. Overall, the contribution of this paper is that Light-YOLOv5 dramatically reduces the number of parameters and computations and improves the detection accuracy, which is conducive to real-time fire-detection on mobile devices.  
However, there is still room for improvement concerning the accuracy of our algorithm. When the distance to the target is too far, the detection may not be as effective as expected. In future work, we will further investigate how to improve our algorithm's accuracy in fire- and small-target-detection practices.  
Conceptualization, F.Z., B.L. and H.X.; Methodology, H.X.; Software, H.X.; Writing--original draft, H.X.; Writing--review & editing, B.L. and F.Z.; Funding acquisition, B.L. and F.Z. All authors have read and agreed to the published version of the manuscript.  
This research was sponsored by the National Natural Science Foundation of China (Grant No.: 52005168), the Initial Scientific Research Foundation of Hubei University of Technology (BSQD2020005), and the Open Foundation of Hubei Key Lab of Modern Manufacture Quality Engineering (KFJI)-2020009).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# In Vitro Antioxidant Effects of Coenzyme Q10 on Cellular Metabolism in Aged Mesenchymal Stem Cells
###### Abstract  
(1) Background: this study investigates the short-term effects of coenzyme Q10 (CoQ10) on mitochondrial respiration, fatty acid metabolism, oxidative stress gene expression, and sirutin activity in young (passage 5, P5) and aged (passage 16, P16) mesenchymal stem cells (MSCs). (2) Methods: Mitochondrial respiration was assessed by measuring oxygen consumption after 24 h of treatment. Gas chromatography-mass spectrometry (GC-MS) analysis assessed cellular fatty acid methyl ester profiles. Quantitative polymerase chain reaction (qPCR) demonstrated the passage-dependent expression of oxidative stress-related genes and sirutins in response to CoQ10 treatment. (3) Results: CoQ10 enhanced basal respiration and spare respiratory capacity (SRC), particularly in older senescent cells. CoQ10 improved basal respiration and ATP-linked oxygen consumption in young MSCs and partially restored these functions in aged MSCs. Moreover, CoQ10 increased saturated fatty acids, particularly in young cells, and decreased monounsaturated fatty acids in aged cells. qPCR analysis revealed passage-dependent modifications in oxidative stress-related genes and sirutin expression; CoQ10 exposure significantly influenced SIRT1 and SIRT3 activity, leading to an increase in PPARy and CAT expression. (4) Conclusions: these results highlight CoQ10's potential to alleviate mitochondrial dysfunction and metabolic shifts associated with cellular aging, underscoring its therapeutic value for age-related mitochondrial and metabolic disorders.  
Keywords:coenzyme Q10; senescence; mitochondrial respiration; fatty acids; oxidative stress; stem cells; SHEDs +
Footnote †: journal: Applied sciences
## 2 Methods
### Prospect Strengths and Weaknesses Degree and Choquet Integral Solution Methods
#### 2.4.2 Recommendations for Electricity Sale Package  
Based on the similar customers discussed in Section 2.3, the customer decision matrix corresponding to them can be obtained, and each customer matrix can be assembled into a standardized customer group decision matrix, and the following can be used to recommend electricity sale packages using the recommendation method for electricity sale packages based on the prospect strengths and weaknesses degree and the Choquet integral. Specifically, the process of the electricity sale package recommendation method is as follows:  
Step 1: Establish an evaluation label system for electricity sale packages and perform clustering division on different customers in the customer set. Once the evaluation label system is in place, clustering division is performed on the customer set to identify distinct customer segments. This segmentation is crucial for targeting personalized recommendations to each customer group.  
Step 2: Calculate the similarity of new customers by using Formula (1) to determine N similar customers within the divided customer set. The selection of similar customers ensures that the recommendations are tailored to the needs and preferences of the new customer.  
Step 3: Obtain the customer decision matrices of the N similar customers. Aggregate the decision-making information of similar customers by using Formula (6) to construct the customer group decision-making matrix and standardize the customer group decision matrix.  
Step 4: Determine the prospect strengths and weaknesses matrices of the schemes by using Formulas (7)-(9). The matrices provide a comprehensive overview of the potential benefits and challenges associated with each package, which is essential for making informed recommendations.  
Step 5: Determine the fuzzy measures between the decision information sets by using Formulas (10) and (11). Determine the comprehensive weight \(Q\) by using the prospect strengths and weaknesses matrices, and then obtain the prospect strengths and weaknesses degree through Formula (12).  
Step 6: Determine the prospect strengths and weaknesses flows according to Formulas (13) and (14), rank the magnitudes of the prospect strengths and weaknesses flows of different electricity sale packages, and obtain the electricity sale package recommendation schemes based on the ranking of the prospect strengths and weaknesses flows.  
Based on the above analysis, the said electricity sale package recommendation method can be divided into two stages: the establishment of a multi-attribute customer decision-making system and the evaluation and recommendation of electricity sale packages for new customers, and the recommendation method for electricity sale packages based on the prospect strengths and weaknesses degree and the Choquet integral is shown in Figure 3.