esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Microcapsules, a common choice for this purpose, provide robust protection against various environmental stressors such as oxygen, light, humidity, and other relevant factors. As a protective barrier, microencapsulation not only delays the immediate release of bioactive substances but also increases their solubility and bioavailability, thereby facilitating easier handling and transportation of these compounds [12; 13]. A range of methodologies has been employed to effectively microencapsulate the bioactive compounds derived from vegetables and herbs. These techniques include dry mixing, complex coacervation, inclusion complexation, spray-drying, and freeze-drying. Among these, freeze-drying is particularly preferred for encapsulating fragile bioactive substances. This technique is advantageous because it avoids exposure to high temperatures, thereby preserving the structural integrity of phenolic compounds, which is essential for maintaining their biochemical activity [14]. The selection of encapsulation agents, coating materials, or carrier substances is critically important in microencapsulation processes, as these components substantially influence the effectiveness of encapsulation and the resulting physicochemical characteristics that govern product stability after freeze-drying. Consequently, the advantageous properties of maltodext, including their gel-forming capabilities, low viscosity, and high solubility, make them particularly suitable for the microencapsulation of biologically active molecules, such as polyphenic compounds, through freeze-drying techniques [15]. In accordance with a holistic approach designed to promote functional snack foods that improve local economic and public health outcomes, this research aims to examine the extraction of total phenolic compounds and assess the antioxidant activities of Lemnian tomatoes and pumpkins (_Cucurbita moschata_) cultivated on Lemnos Island. Additionally,it investigates the feasibility of encapsulating these phenolic compounds to enhance their stability, as well as explores their potential for enrichment in bakery products by developing a savory cereal bar enriched with tomato extract and a sweet cookie infused with pumpkin extract. A critical aspect of this study is the understanding of storage stability of the encapsulated phenolic compounds to ensure that their antioxidant properties are preserved when incorporated into bakery products, thereby maintaining their functional efficacy.
## 3 Proposed Joint Decoding (JD) Framework for Video
### Correlation Estimation of the CS Measurements among Adjacent Frames  
A correlation estimation of the CS measurements among the adjacent frames is presented in this section. As discussed earlier, adjacent frames with a video sequence have a high inter-frame level correlation with each other. Consequently, it can also be assumed that the CS measurements of such frames are also highly correlated. Although, acquiring CS measurements is entirely different from linear transformations, as CS measurements form a random Gaussian distribution. The correlation among the two random entities can be estimated by using [PERSON]'s correlation coefficient. It is defined as \"the correlation coefficient of two random variables is a measure of their linear dependence\". Consider the CS measurements of two adjacent frames \(\mathrm{Y_{n}}\) and \(\mathrm{Y_{n+1}}\) of a video sequence, then their correlation coefficient is estimated as:  
\[\rho(\mathrm{Y_{n}},\ \mathrm{Y_{n+1}})=\frac{1}{\varphi-1}\sum\limits_{n-1} ^{\varphi}\left(\frac{\mathrm{Y_{n}}-\mu_{\mathrm{Y_{n}}}}{\sigma_{\mathrm{Y_{ n}}}}\right).\left(\frac{\mathrm{Y_{n+1}}-\mu_{\mathrm{Y_{n+1}}}}{\sigma_{ \mathrm{Y_{n+1}}}}\right) \tag{5}\]  
where, \(\varphi\) is the number of measurements, \(\mu_{\mathrm{Y_{n}}}\) and \(\mu_{\mathrm{Y_{n+1}}}\) are the mean of \(\mathrm{Y_{n}}\) and \(\mathrm{Y_{n+1}}\), respectively, while \(\sigma_{\mathrm{Y_{n}}}\) and \(\sigma_{\mathrm{Y_{n+1}}}\) are the standard deviation of \(\mathrm{Y_{n}}\), and \(\mathrm{Y_{n+1}}\), respectively.  
The evaluation of CS measurement correlation among various frames is performed on various standard grayscale CIF video sequences (Foreman, Coastguard, Container, Hall Monitor, Mother-Daughter). Each video sequence contains 300 frames and is divided into GoPs of size 8. The CS measurements for each frame within a video sequence are obtained at a measurement rate of 0.5. The CS measurement correlation coefficient for each non-keyframe for various video sequences is estimated and shown in Figure 2. The results presented are an average of all the correlations for non-key frame w.r.t keyframes. It can be noticed that all the frames within the video sequence show a high correlation, i.e., above 0.92. Moreover, for moderate motion content video sequences, the correlation is also higher.
## 2 Methodology
### Calculation Procedure  
A procedure to evaluate the influence of tunneling on the adjacent pile group is then given as follows:  
The free-field soil settlements \(\{z_{sk}\}\) at the group pile locations due to tunnel excavation are first evaluated. The soil settlement profiles can be obtained from either field measurements or available empirical equations [18; 19];  
The elastic pile group head settlement \(z_{3 ge}^{1}\) is then calculated based on the pile head fixity conditions and the soil settlements by the elastic analysis method;  
If the group piles are individually loaded and the pile head loads are known, the elastic isolated pile head settlement \(z_{ke}^{1}\) for each group pile can be readily calculated combined with the free field soil displacement \(\{z_{sk}\}\) at its location by Poulos elastic analysis. For fixed-head group piles, however, trial distributed pile head loads are assumed in the calculation of satisfying the condition that the sum of the distributed loads is equal to the total group pile head load \(P\). The additional settlement \(\Delta z_{kg}^{1}\) is then obtained based on Equation (11).  
For individually loaded piles, an NFC analysis of an isolated pile subjected to both the pile head load and tunnel excavation is conducted, and the pile group head settlement \(z_{\text{RG}}^{1}\) can also be readily obtained combined with \(\Delta z_{kg}^{1}\) calculated in step (3). For fixed-head group piles, the trial pile head loads in step (3) are adopted in the NFC analysis of isolated group piles to determine \(z_{k}^{1}\) and \(z_{\text{KG}}^{1}\) and if the calculated pile group head settlement \(z_{\text{KG}}^{1}\) is not equal for the group piles, return to Step (3), where the trial distributed pile head loads should be adjusted.  
NFC analyzes for every group pile are conducted again by adjusting the PSI relationships by \(Z\) multipliers no less than 1.0.  
A flow chart of the above calculation procedure is given in Figure 3. Variation of the pile group settlement combined with the Z multipliers is deemed to give an overall evaluation of the group effect due to tunnel excavation from the aspects of pile deformation and pile-soil interaction.
## 2 Results and Discussion  
### ORP-Controlled MEC System Development  
In our previous research, within microbial electrolysis cells (MECs) operating without feedback control, a succinic acid concentration of 18.09 g/L was achieved using corn stover hydrolysate as the carbon source, while the utilization of molasses as the carbon source resulted in a succinic acid concentration of 83.67 g/L [7; 17]. Existing research indicates that optimizing the startup cathode potentials, operating potential, and even polarity reversal has a significant impact on the electrosynthesis process, yet it does not result in effective feedback regulation [7; 18; 19]. In the present study, an ORP-controlled MEC system was utilized to explore the impact on the extracellular redox potential, cell growth, and succinic acid production. The relative cell growth, succinic acid production, and ORP variability for _A. succinogenes_ in non-electric and ORP-controlled MEC systems are shown in Figure 1.  
The use of NR as an electronic mediator or electronophore was investigated in an effort to improve _A. succinogenes_ succinic acid production. The ORP-controlled MEC system with NR yielded a 17.21% increase in succinic acid production relative to the non-electric MEC system (non-NR) (Figure 1a). When NR was added to a reaction mixture, the electricity delivered by the cathode could be used by _A. succinogenes_ to boost succinic acid production.  
Figure 1: Effects of an ORP-controlled MEC system on succinic acid production. **(a)**: Succinic acid; **(b)**: biomass; **(c)**: ORP value. The cells were cultured for 15 h with an initial sugar concentration of 50 g/L. Each value is the average of three parallel replicates and is represented as mean \(\pm\) standard deviation. \({}^{*}p<0.05\) between two relevant treatments, \({}^{**}p<0.01\), \({}^{***}p<0.001\), ns: not significant.  
After 15 h of fermentation, there was a 20.44% increase in the cell growth for the electric MEC system when ORP was maintained at a constant \(-400\) mV relative to the non-electric system (Figure 1b). After 7 h of fermentation, the ORP in the non-electric MEC system decreased and remained stable at \(-350\) mV (Figure 1c). These results suggested that the use of a cathode to apply a potential was effective as a means of controlling the extracellular ORP values and that maintaining an appropriate ORP value led to improved _A. succinogenes_ growth in this MEC system.  
In addition, the relatively low ORP regulatory sensitivity and the high applied voltage (\(\geq\)\(-1.3\) V) have the potential to limit the utility of this ORP-controlled MEC system, while a high ORP regulatory sensitivity and a relatively low applied voltage (\(-0.8\) V) were obtained when NR was added to the ORP-controlled MEC system. With anaerobic conditions and pH control, the ORP of a system is determined based upon the amounts of particular compounds dissolved in the medium. As an electronic mediator, NR can effortlessly accept and release electrons. Therefore, when NR was added to the medium, a high ORP regulatory sensitivity and low applied voltage were obtained.
## 3 Results and Discussion
### Mechanical Properties  
Mechanical properties of nonwovens with potential application as beauty masks are important for easier manipulation by end-user. It needs to be consistent and to have certain mechanical strength to avoid breakage during the usage.  
Pure PLA fibers have good tensile properties which were improved with the addition of both PSO and SB oil. Maximum stress was increased two times, while elongation at break increased from 2 to 4 times, by the addition of 5 and 10 wt% of SB, respectively (Table 3). The encapsulation of the mixture composed of PSO and EP in PLA induced slight impairment of mechanical properties, which might be due to the presence of EP solid particles. The position of those solid particles might be the point of breakage of the fibers.  
Figure 4: ATR-FTIR spectra of PLA, PLA-5 SB, PLA-10 SB.  
Figure 3: ATR-FTIR spectra of PVP, PVP-5 PSO, PVP-10 PSO, and PVP-5 PSO5 EP.  
When lower concentrations of oils were incorporated in PVP fibers, both max. stress and elongation at break were improved compared to pure PVP fibers. The increase was not so high, compared to the improvement of the properties of PLA-based fibers, but it is still significant because PVP fibers were more brittle (Table 3). The higher concentration of oils decreased the maximum stress together with the increase of elongation at break, which indicates on plasticizing effect on PVP. Presence of EP into the PVP fibers together with PSO increased both stress and elongation compared to pure PVP fibers; it had low influence on mechanical properties probably because it was located mainly on the surface of the fibers. In the case when only EP was embedded into PVP fibers, mechanical properties were significantly worse, which was assumed to be due to high concentration of solid particles present both on the surface and within fibers' structure.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Results
#### 3.2.2 User Perception of Feedback Patterns
A few participants further suggested incorporating directional motion cues, such as left-to-right vibration sequences or light flows, to better perceive the movement of oncoming vehicles. They noted that, while frequency effectively conveyed urgency, integrating directional information could enhance their ability to assess approaching hazards in more dynamic environments. These insights highlight the importance of frequency as a core parameter for both haptic and visual feedback, while size bolsters users' confidence and situational awareness. Building on these findings, we extended our investigation beyond controlled traffic light crossings to more complex, vehicle-based crossing scenarios. Study 2 explores how integrating directional cues alongside frequency and size can further support visually impaired pedestrians in making safer crossing decisions when navigating roads with moving vehicles. Study 2: Haptic and Visual Feedback for Assisting Visually Impaired Pedestrians in Vehicle-Based Crossing Decisions  
Unlike traffic light-controlled intersections, where pedestrian signals regulate vehicle movement and create predictable stop-and-go patterns, vehicle-based crossings present greater uncertainty for visually impaired pedestrians. In scenarios such as mid-block crossings or uncontrolled intersections, the absence of explicit stop signals increases cognitive load, requiring pedestrians to judge the speed, direction, and intent of approaching vehicles in real time. These conditions demand more dynamic situational awareness, making it essential to explore how additional sensory cues--such as directional motion feedback--can support safer crossing decisions. Extending the experimental setup and feedback modalities from Study 1, Study 2 focused on the vehicle crossing scenario. While retaining the same frequency settings, we refined the size variable and incorporated the feedback state and direction as additional cue dimensions to enhance visually impaired users' ability to judge safe crossing times. We assessed these two modes through interaction design, user evaluation, and experimental data to clarify how haptic or visual feedback might facilitate crossing decisions.
## 6 PSK Modulation and Demodulation  
Phase shift keying (PSK) constitutes a large class of digital modulation techniques. In the last decades PSK modulation has been widely used in wireless communication for biomedical modulation techniques. This review includes the important research which has used PSK modulation in biomedical applications. In PSK modulation, correlated or matched filter implementation of the coherent detector are compared with a reference signal. The reference signal is generated by the carrier recovery circuit, which is synchronous to the received signal in terms of frequency and phase. However, the differential coherent demodulator is non-coherent in the sense that phase coherent reference signals are not required, which is used to overcome the adversary effect of the random phase in the received signal. The most common digital schemes appropriate for biomedical data transmission are briefly described as PSK, binary PSK (BPSK), differential PSK (DPSK), quadrature PSK (QPSK), differential quadrature PSK (DQPSK), offset quadrature PSK (OQPSK) and quasi-coherent PSK (QCPSK) in [68,69], as shown in Table 10.
###### Abstract  
Efficient resource allocation is a critical factor in ensuring reliable and low-latency communication in the fifth-generation New Radio (5G NR) sidelink-based Cellular Vehicle to Everything (C-V2X) networks. One of the critical challenges in adopting C-V2X systems is the potential for packet collisions between User Equipment (UE) when they share resources in the sidelink channel. For reliable and low-latency communication, especially in safety-critical applications, efficient resource allocation is essential. This paper explores collision-related issues that may arise in the 5G NR sidelink and the probability of collisions on resource blocks. To address these challenges, we propose an experimental time-domain resource allocation strategy leveraging dynamic reselection intervals and adaptive reservation mechanisms. Unlike existing approaches, which primarily rely on static or semi-persistent scheduling, our strategy optimizes resource allocation based on real-time variations in generation time, speed and distance between UEs. The proposed approach significantly reduces collision probabilities, enhances communication reliability and ensures efficient resource utilization, even in high-density vehicular networks. Addressing packet collisions in resource allocation becomes crucial for the viability of vehicular communication systems. The goal of this paper is to analyze the dynamics and causes of packet collisions in C-V2X scenarios using 5G NR sidelink technology and to evaluate how our time-domain optimization techniques can enhance system performance in rapidly evolving vehicular communication networks.  
cellular V2X; 5G V2X; sidelink; 5G NR; 3 GPP; connected and automated vehicles +
Footnote †: journal: _Accepted: 10 February 2025_  
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Footnote †: journal: _Accepted: 10 February 2025_  
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Footnote 1: Published: 14 February 2025_  
**C****Citation**: [PERSON]; [PERSON] Collision Probabilities Between User Equipment Using 5G NR Sidelink Time-Domain-Based Resource Allocation in C-V2X. _Electronics_**2025**, 14,751. [[https://doi.org/10.3309/electronics14404751](https://doi.org/10.3309/electronics14404751)]([https://doi.org/10.3309/electronics14404751](https://doi.org/10.3309/electronics14404751))
# End-to-End Object Detection with Enhanced Positive Sample Filter
## 3 Method
### Label Assignment  
Conventional NMS-based detection pipelines use the one-to-many label assignment strategy that assigns multiple positive samples for each GT object instance during training and then rely on the NMS post-processing module to remove duplicated predictions during inference. In this work, we aimed to discard NMS post-processing and realize fully end-to-end detection. To achieve this goal, the model should learn to perform one-to-one  
Figure 1: The overall network architecture, where C3 to C5 represent feature maps from the backbone network and P3 to P7 denote feature maps from FPN that will be passed to a shared detection head. The head contains three branches: regression branch, objectiveness branch and classification branch. The proposed Enhanced Positive Sample Filter (EPSF) is applied to the features from the regression branch and objectiveness branch so as to select a single positive sample for each GT instance. EPSF contains two modules, namely Dual-stream Feature Enhancement (DsFE) and Disentangled Mx pooling Filter (DeMF).  
classification, which means a single positive sample should be distinguished from several near-by candidates for each GT instance. The one-to-one label assignment strategy [11; 12] has been proved to be an effective solution to tackle this new challenge, which assigns only one positive sample for each GT instance during training. However, it provides fewer positive training samples and may affect the representation learning.  
To address the dilemma, we utilized both one-to-one and one-to-many label assignment rules during training. Concretely, for classification and objectiveness branches, we offered sufficient supervision to enable the training of strong classifiers with robust feature representation capacities by using a one-to-many label assignment strategy to compute training targets of each location on feature maps, where each GT object instance is assigned with multiple positive samples. The one-to-many label assignment facilitates representation learning, but the massive positive training samples may lead to duplicate predicted bounding boxes. To meet the final one-to-one classification target, we proposed an Enhanced Positive Sample Filter (EPSF) that selects a single positive sample for each instance among several potential true positives with high scores produced by the classification branch. For each location, the outputs of EPSF and the classification branch are multiplied to obtain final predictions, whose training targets are produced by the one-to-one label assignment strategy. In this paper, we utilized the prediction-aware assignment strategy that was proposed by [12] for both one-to-one and one-to-many label assignment, which dynamically assigns samples according to the quality of classification and regression simultaneously. For different branches, we computed qualities based on different predictions. The details are presented in Figure 2.
# Decision Support Model for Allocating Maintenance Budgets for Bridges
## 4 Proposed Model
### Pilot Study and Development of the Questionnaire Survey
#### 4.2.1 Weighting the Prioritization Criteria  
The pairwise comparison has been applied to determine the weights of the identified prioritization criteria for bridge maintenance at the network level. The criteria matrix was established to express the importance of the criteria and to determine the priorities according to expert judgement. The scale introduced by [PERSON] [38] was adopted to conduct pairwise comparisons and establish the importance of the criteria at the network level, and at the bridge level. The value of 1 on the scale indicates the equity of importance of the compared criteria. The value of 9 means that the chosen criterion has the maximum importance level, upon comparison to another compared criterion [38]. Once the pairwise comparison matrix is established for the prioritization criteria, the local expert must answer two questions. The first question is which criterion is more important when prioritizing bridges for maintenance. The second question is what the intensity of that importance on a scale ranging from 1 to 9 (as explained above). Comparisons are made between every prioritization criterion on a scale ranging from 1 to 9, as follows:  
Figure 1: Model components.  
* The numeric value of 1 is used when the importance of the two compared criteria is equal based on a local expert's point of view and experience.
* The numeric value of 9 is used when the importance of the criteria in the row is extremely more important than the other criteria being compared with in the column.
* The numeric values of 3, 5, and 7 are used to describe the importance of the criteria, based on the expert's perceived level of importance for the criterion under consideration.
* Reciprocation of the intensity is used when the criterion in the column is more important than the criteria in the row.  
An Excel sheet was used to analyze the pairwise comparison data to calculate the relative weights (Eigen vector or weights of the criteria) and the consistency ratios (test of judgments of all pairwise comparisons) of all the considered matrices. Due to the limited number of words in this article, a detailed description of the consistency ratios calculation is not presented. Table 3 shows the pairwise comparisons conducted by response 1 (R1). The Eigen vector or weights of the criteria are calculated by normalizing the pairwise comparison matrix and averaging each row to find the weight of each prioritization criteria. Table 4 shows the criteria, along with the weights for each, based on the pairwise comparison data collected from nine experts. All nine responses showed a consistent comparison, and the consistency check was made according to the methodology developed by [PERSON] [38] to ensure that the pairwise comparisons are consistent. As presented in Table 4, the bridge condition criterion is the most important criterion in prioritizing bridges for maintenance, followed by traffic condition criterion.