esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Network Model
CHs link to both the sink node with CH neighbors and the forward data packets, while handheld sinks are in dispersed areas to collect minimum energy consumption information, as shown in Figure 1. Sparse and denser regions are determined through the algorithms followed by the SEEC protocol, based on the density of the nodes in each logical area, the sparse and dense areas are specified. For example [7], assumed to be picked as a dense area, there should be N number of nodes to satisfy the limited threshold by using the following equation:  
\[N_{th}= \begin{cases}1&ifN\geq N_{th}\\ 0&ifN<N_{th}\end{cases}  \tag{13}\]  
The area is dense, when N is greater or equal to the Nth and the opposite indicates a sparse area. The proposed scheme incorporates the mechanism of transmission power control strategy. Two algorithms are suggested, which is most appropriate for comparison with our proposed algorithm [8]: (1) Sparsity Search Algorithm (SSA) to find sparse regions and (2) Density Search Algorithm (DSA) to find dense regions in the network place. In addition, the cluster head nodes automatically decided by the network and dynamically according to 3 conditions are met [8]:  
* If the node's depth is lower than the other nodes of the density region in the present round
* Where Rest Power of the cluster head (PCH) is greater than the average rest power calculated for an individual node. * The node can be the cluster head if it has not been cluster head for the last 1/p rounds, the probability of a node to be a cluster head and it is calculated at the current round denoting the threshold (Th),the node generates a random number and, the compares the rand value with Th. The node can be CH If rand of the node is less than Th value, and only one CH node is specified in each density. It consists of two phases: (i) Setup phase and (ii) Communication phase.
# Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
## 4 Conclusions  
To deal with TLE outliers, a probabilistic approach, consisting of the MGMMMGA, is proposed to capture the relationships between different variables through multivariate modeling. In addition, a multi-strategy genetic algorithm is employed to adjust the complexity of the MGMM, and multiple strategies are applied to prevent the genetic algorithm from falling into a local optimum and to limit the influence of suspicious data on the model. In terms of data correlation, the semi-major axis and the rate of change of inclination are not independent of each other, and it is more reasonable for the MGMM to carry out the analysis than the 1D GMM. In terms of probability density fitting, the MGA can be used in accelerating the convergence speed of the MGMM to find the global optimal solution in the solution space in a certain time, and the training model is basically consistent with the sample data. In terms of the Receiver Operating Characteristic curve, the area under the curve of the MGMMMGA model improves by 0.1 compared to the GMM, and performs better in space event detection. The experimental results obtained from eight typical low-Earth-orbit space targets demonstrate the superiority of the MGMM compared to the traditional GMM model, yielding a 15.9% improvement in the F1 score. This enhancement bolsters maneuver detection performance, offering stronger support for subsequent orbital collision warning systems of space targets. In addition, our methodology relies on the prediction error characteristics of the targets. In the future, we plan to use more targets and models with drag coefficient and eccentricity to identify TLE outliers and space events.  
[PERSON] collected field data, completed the paper and wrote the paper. [PERSON] and [PERSON] edited the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 42174033), The National Key Research and Development Program of China (2020 YFB0505801, 2020 YFB0505802).  
Not applicable.  
The original data presented in the study are openly available at [[https://celestrak.org/](https://celestrak.org/)]([https://celestrak.org/](https://celestrak.org/)) and [[https://ids-doris.org/analysis-documents.html](https://ids-doris.org/analysis-documents.html)]([https://ids-doris.org/analysis-documents.html](https://ids-doris.org/analysis-documents.html)) (accessed on 22 March 2024).  
The authors thank the reviewers for their great help on the article during its review progress.  
The authors declare no conflicts of interest.
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 5 Experimental Results
### Model Evaluation Metrics  
Scoring performance metrics were calculated to evaluate the achieved results. The data set may be slightly unbalanced due to signal processing where we introduce framing for calculating features the number of ones and zero targets may not agree. Therefore, the accuracy metric cannot be the only measure used to evaluate the results obtained. What we were aiming for was to find positive results for the speech activity state, so we consider the F1 score to be the most important measure and for completeness, we also provide recall and precision metrics.  
1. _Recall_: is the ratio of true positive predictions to all positive predictions. Recall is also known as the True Positive Rate or the sensitivity. We can express it by: \[Recall=\frac{TP}{TP+FN}.\] (12)2. _Precision_: is the actual correct proportion of positive predictions. This can be expressed by: \[Precision=\frac{TP}{TP+FP}.\] (13)
3. _F1 score_: is the harmonic mean of the precision and recall. We can express it by: \[F_{1}=2\times\frac{Precision\times Recall}{Precision+Recall}.\] (14)
# Saturation-Based Airlight Color Restoration of Hazy Images
## 2 Dehazing for Color Restoration  
### Atmospheric Scattering Model  
ASM, or [PERSON]'s optical model [13; 14], is widely used to restore blurred images in image processing research. ASM defines a hazy image \(I(x)\) as follows:  
\[I(x)=J(x)t(x)+A(1-t(x)) \tag{1}\]  
\[t(x)=e^{-\beta d(x)} \tag{2}\]  
where \(x\) is the spatial coordinate of the scene, \(t(x)\) is the transmission of the scene, \(A\) is airlight, and \(J(x)\) is the scene without haze. Also, \(\beta\) in Equation (2) is a scattering coefficient, and \(d(x)\) is the depth of the scene. In Equation (1), \(J(x)t(x)\) represents direct decay, and \(A(1-t(x))\) represents the airlight area. \(t(x)\) shows an exponential decay with distance \(d(x)\) according to Equation (2). Therefore, if the distance between the observer and the scene is very long, the transmission rate \(t(x)\approx 0\), and according to the definition of ASM, the direct decay area represents 0, while \(A\) has a value very close to \(I(x)\). This can be shown in the following equation:  
\[I(x)\approx A(\infty)\,,\;d(x)\rightarrow\infty\,,\;t(x)\to 0 \tag{3}\]  
Here, \(A(\infty)\) shows the entire area of the background airlight with infinitely large \(d(x)\). Generally, since the sky area is the farthest from the object, \(A(\infty)\) can be considered as the atmospheric particles containing most of the haze information. Based on Equation (1), dehazing involves obtaining \(J(x)\) by estimating \(A\) and \(t(x)\).
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 4 Discussion  
In this section, we analyzed and discussed the performance of the algorithm for indoor navigation network extraction, focusing on the robustness and efficiency of the network extraction process. In addition, through examining the shortest path planning in single-floor and multi-floor, we analyzed the path planning capability of the extracted navigation network.  
Figure 17: The results of the shortest path planning across multi-floor for the navigation network extracted from our self-collected dataset: (**a**) without barrier-free access limitation; (**b**) with barrier-free access limitation; (**c**) staircase-only route. The route starts at purple and ends at red.
## 1 Introduction  
Articular cartilage damage continues to present a therapeutic challenge, and despite multiple and differing approaches ranging from bone marrow stimulation [1], autologous chondrocyte implantation (ACI) [2], and various grafting procedures incorporating allografts and autografts [3, 4], none are recognized as sufficient. Recent reviews demonstrate that clinical research has failed to substantiate the superiority of one technique or product, in large part because of heterogeneous patient demographics, the differing etiologies of defects, and the varying study designs and outcome measures [5; 6; 7]. The lack of longer term (5-10 years) clinical evidence in cartilage repair further limits evidence-based treatment algorithms. Nonetheless, the fundamental goal of any cartilage repair treatment should be to avoid the progression to secondary osteoarthritis [8; 9], by achieving structural repair that is comparable to native hyaline cartilage, and thereby assuring long-term durability, joint function, and pain relief.  
ChondroMimetic (Collagen Solutions, Plc) was developed to be a cell-free, osteochondral scaffold for use in cartilage repair. The biphasic implant comprises a 2 mm, unmineralized, type I bovine collagen and chondro-6-sulphate glycosaminoglycan (collagen/GAG) chondral layer, coupled to a 6 mm, mineralized, subchondral bone layer, containing collagen/GAG and calcium phosphate brushite. These two distinct layers are separated by a continuous or'soft' interface that closely mimics the compositional transition between mineralized bone and unmineralized cartilage at the tide mark [10]. As an open-cell foam, the ChondroMimetic scaffold which allows compression during surgical handling for press-fit implantation, followed by auto-expansion to fit the defect shape, bridging any gaps or irregularities [11]. In a defect with stable margins, ChondroMimetic offers structural and mechanical protection for the marrow-derived blood clot, similarly to mechanisms claimed for other techniques (e.g., autologous matrix-induced chondrogenesis (AMIC) [12], BST-CarGel [13]). Large animal preclinical studies with ChondroMimetic demonstrated a consistency in osteochondral repair characterized by a high degree of cartilage defect filling with a hyaline-like repair tissue in both medial femoral condyle and trochlear defect sites [14; 15; 16].  
Since long term data (>5 years post treatment) is fast becoming a required component for clinical decision-making in cartilage repair, the aim of this prospective extension study was to determine the long-term effectiveness of ChondroMimetic treatment of osteochondral defects in subjects enrolled in a previous short-term study. Using 3D quantitative magnetic resonance imaging (MRI) techniques, a consistently high degree of osteochondral defect filling with durable, cartilage-like repair tissue was found at 7.9 years and potential clinical benefit.
## 2 Transmissibility Function
As a result, the transmissibility functions used are not symmetric, and their directionality reflects the excitation-measurement configuration. This selection is both theoretically motivated and practically consistent with a decentralized sensing strategy. Hence, with these two criteria imposed on the selection, the number of transmissibility functions retained as damage detection features reduces to \(N-1\) to for an \(N\)-DOF system. With input location indicated as the superscript, the vector of selected features for damage detection as a function of frequency are defined as \(T_{12}^{2},T_{23}^{3},\ \cdots T_{k,k+1}^{k+1},\ldots\ T_{N-1,N}^{N}\). Although such a procedure requires impact testing across multiple locations to generate comprehensive TFs, impact testing can often be implemented with minimal disruption to the structure's normal operations. For certain domain-specific applications, such as a metro tunnel structure discussed by [PERSON] et al. [50], a sequential excitation and measurement scheme is quite feasible. Also, mobile sensing systems which navigate throughout the structure [28; 51], may enable the use of these algorithms for autonomous damage detection. Nevertheless, it is acknowledged that impact testing may not always be practical, particularly for large or operationally sensitive structures. Future research could explore alternative excitation sources, such as ambient vibrations or operational loads, to derive transmissibility functions. This would increase the applicability of the proposed methodology in real-world SHM scenarios.
###### Abstract
Researchers planning to track bees by RFID will have to build their own unit whether they need to monitor one single beehive or more. The main requirements and expectations of an efficient RFID unit to be used in honeybee research are as follows: (1) its ability to accurately detect bees at the hive's entrance and properly transmit their signals, (2) the ability to monitor simultaneously multiple hives to provide meaningful statistical analysis on multiple technical and biological replicates, (3) prolonged operation in rural areas where no electric grid is available, (4) easy to deploy to the field, and (5) cost-effective. Optimizing RFID techniques for bee research is time consuming and requires innovative and multidisciplinary skills. Therefore, a comprehensive source that details all RFID technicalities and software use is necessary and would be a valuable source for honey bee researchers and beekeepers alike. In this study, we provide a comprehensive description of how to build and operate an RFID 32-antenna system to study honey bee behavior in the field and monitor queen nuptial flight. The unit detailed in this study meets all the previously described requirements and can accommodate relatively large-scale experiments reaching up to 32 monitored colonies simultaneously. We describe here basic data collection and other technicalities related to tag naming and operating this unit with a power grid in remote areas using solar energy.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 4 Battery and Energy Harvesting Technologies for Sensing and Monitoring in Buildings
### Characteristics and Limitations of Battery Technologies
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **NiCd** & **NiMH** & **NiFe** & **Lead Acid** & **Li-ion** & **Reusable Alkaline** \\ \hline Cycle Life (cycles) & 1000–5000 & 300–2000 & 1000-8000 & 100–500 & 300–5000 & 10-100 \\ \hline Lifespan (years) & 5–15 & 2–8 & 10–30 & 1.5–10 & 2–10 & 1–5 \\ \hline \multirow{2}{*}{Self-discharge Rate (\%/month)} & 15–20 (typ. decrease & & & 10\%–15\% (typ. 3\% of \\  & of 10\% in first 24 h, & 25–35 & 20–30 & 3–6 & energy consumed by \\  & then 10\% for 30 days) & & & internal circuit protection) & 0.2–1 \\ \hline Fast Charge Time (hours) & 1–2 & 2–4 & 4–6 & 8–14 & 2–4 & 1–3 \\ \hline Power Density (\(\mu\)W/cm\({}^{3}\)) & 40,000–100,000 & 8000–500,000 & 10,000–30,000 & 10,000–350,000 & 60,000–800,000 & 10,000–100,000 \\ \hline Market Adoption & High & High & Medium & High & High & High \\ \hline Environmental Impact & High & Medium & Low & High & Medium & High \\ \hline Maintenance Requirement & & & & & & & \\ (days) & 30–60 & 60–90 & 70–100 & 90–180 & Not required & Not required \\ \hline Battery Cost (\$/kWh) & \$250–550 & \$250–500 & \$180–250 & \$100–200 & \$200–400 & \$70–200 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the key characteristics of various battery technologies used for sensing and monitoring in buildings. Data has been extracted or calculated from published literature, and various manufacturer datasheets and user manuals are available online at the time of publication.
###### Abstract  
To deliver real-time data within a desired time deadline, a spatiotemporal approach has been proposed in wireless sensor networks. In the approach, real-time data can be delivered with a delivery speed calculated by both the distance from a source to a static sink and the desired time deadline. In the case of a mobile sink, because the distance from a source to the sink would be dynamically changed due to its movement, real-time data cannot be delivered due to the inability to calculate a delivery speed. Thus, we propose a novel real-time routing protocol, called EAR2 (expected area-based real-time routing) for mobile sinks. Instead of the immediate distance, EAR2 considers the static distance to the expected area of a mobile sink's location using its movement speed. To satisfy the desired time deadline, EAR2 guarantees that the total summation of the unicasting time to the expected area and the flooding time within the expected area can be smaller than the deadline. To do this, EAR2 calculates a data delivery speed by using the static distance and the unicasting time and exploits the flooding time observed from various network environments. Because EAR2 has a high flooding time and data loss problems due to a single flooding point, we propose a protocol called EAR2M with multiple flooding points, which reduces the flooding time and enhances the reliability of data transmission. We also propose two extensions of EAR2M, called EAR2M_R and EAR2M_E for efficient data transmission from a source to multiple flooding points. They aim to reduce the transmission delay and the energy consumption for efficient data transmission among multiple flooding points, respectively. Simulation results show that EAR2 improves the deadline miss ratio and the energy consumption, with averages improvements of 47.17% and 29.99% over the existing schemes, respectively. Furthermore, EAR2M with four flooding points enhances the deadline miss ratio and the energy consumption by an average of 12.69% and 131.86% over EAR2.  
wireless sensor networks; mobile sinks; real-time routing Article
## 3 Results and Discussion
### Analysis of Volatile Compounds  
Olive oil's distinctive flavor is a result of the VCs produced by the lipoxygenase (LOX) and hydroperoxide homolytic cleavage (13-LOOH) pathways, which also contribute to its quality [48]. Table 2 indicates that the most prevalent compound was (_E_)-2-hexenal, while fourteen VC were identified in the unflavored olive oil (C_0 and C_14) (Figure 2). Previous research studies [32; 49; 50] have confirmed the higher abundance of aldehydes in high-quality olive oil, which is mostly due to the presence of (_E_)-2-hexenal, an aldehyde produced via the LOX pathway that contributes to the classic \"green note\" flavor of olive oil [51]. Other aldehydes detected at lower concentrations are (_Z_)-3-hexenal and nonanal. The latter is produced from the autoxidation reactions occurring after olive oil extraction, whereas (_Z_)-3-hexenal is produced through the LOX pathway [48; 52]. The alcohols detected in the unflavored EVOO were C6 compounds (3-hexen-1-ol, (_E_)-2-hexen-1-ol, 1-hexanol) that contribute to the \"green\" aroma and astringent flavor of olive oil [53; 54]. The esters hexyl acetate and (_Z_)-3-hexenyl acetate were also detected, both of which contribute significantly to the flavor of olive oil. Hexyl acetate imparts a fruity, sweet flavor, while (_Z_)-3-hexenyl acetate provides a banana flavor [51; 55]. Extra virgin olive oil was flavored with the addition of 20 g L\({}^{-1}\) and 40 g L\({}^{-1}\) dry marjoram. The volatile profile of the flavored EVOO was examined after the addition of dry marjoram and the ultrasound (US) application (samples A_0 and B_0), and after storage in a dark place for 14 days (samples A_14 and B_14). The presence of 11 terpene compounds that migrated from dry marjoram was observed in the flavored EVOO, including hydrocarbon monoterpens (\(\beta\)-thujene, a-pinene, camphene, sabinene, \(\beta\)-pinene, \(\gamma\)-terpinene, terpinolene) and oxygenated monoterpens (eucalypt, (\(-\))-terpinen-4-ol, \(\alpha\)-terpineol, (\(+\))-camphor). These compounds have previously been identified in _Origanum majorana_ L. [56]. Terpenes have beneficial biological features, such as analgesic and anticonvulsant effects. Several studies have shown that some terpen can lessen inflammatory symptoms [57]. Figure 2: Chromatograms of (**A**) EVOO and (**B**) EVOO flavored with 40 g L\({}^{-1}\) dry marjoram. Peak numbers correspond to compounds in Table 2 (I.S. : internal standard). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & \\
**Feko No** & **Compound** & \({}^{1}\)**RI Exp. ** & \({}^{2}\)**RI LiL.
## 2 Related Work
### PKI-Based  
The first group comprises existing authentication schemes that rely on the public key infrastructure (PKI) approach [12; 13; 14; 15; 16; 17; 18; 19; 20] to ensure message authentication and integrity. However, to satisfy privacy, each vehicle in the network requires a massive pool of certificates and their matching private-public key pairs to prevent adversaries from linking multiple messages to the same sender.  
Moreover, the TA suffers from the burden of storing the certificates since the certificates, and their matching private-public key pairs must be kept for all registered vehicles. In addition, certificate verification is an involved process, which adds extra computational cost on the verifier's side.
## 4 Experimental Result Analysis
### Analysis of Ultimate Deformation of Full-Scale Specimens  
In addition to load-bearing capacity, ultimate deformation is a key indicator of the ductility and plastic deformation capacity of the connection. The load-displacement curves and strain data recorded during the tests indicate that the full-scale specimens exhibited good ductility, with large displacements before failure. The displacement ductility coefficients (\(D_{\mathbf{u}}/D_{\mathbf{y}}\)) were calculated for each specimen, as presented in Table 4. The displacement ductility coefficients for the Z-40, Z-32, and Z-16 specimens were 12.58, 12.06, and 13.92, respectively. This indicates that even with the reduction in shear capacity due to the group stud effect, the specimens maintained a strong deformation capacity.  
Specifically, the Z-16 specimen exhibited the highest displacement ductility coefficient, indicating that the reduced number of studs facilitated greater plastic deformation in the structure. In contrast, the Z-40 specimen, although possessing a higher load-bearing capacity, exhibited a more pronounced group stud effect due to the dense arrangement of studs, causing some studs to prematurely enter the plastic phase and thereby limiting the overall ductility of the structure. This suggests that in practical engineering applications, reducing the density of group stud arrangements may serve as an effective strategy for enhancing the structure's ductility.