esa-sceva/satcom-chunk-collection huggingface dataset:
# Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints
## 4 Computational Results
### Effects of the Dispatch Cost and Battery Capacity
Figure 6 depicts the impact of battery capacity, wherein the MB units system exhibits lower sensitivity to changes in battery capacity compared to Figure 5. This is because the impact of varying battery capacity on the bus dispatching strategy is indirect, resulting in only minor adjustments to the scheduling process. In contrast, changes in vehicle dispatch costs act directly on total vehicle dispatch costs, which represent a significant proportion of the system expenditure. It is worth noting that irrespective of the variations in battery capacity, even with a mere 15 kWh, the cost of the modular system remains lower than that of conventional bus systems. This underscores the prominent advantage of the modular bus scheduling mode. Furthermore, in Figure 6, the total system cost across the four instances gradually decreases and stabilizes as battery capacity increases. This trend can be attributed to the potential benefits of a longer battery range and reduced charging frequency. Specifically, a notable decrease in total cost is observed when battery capacity increases from 20 kWh to 25 kWh across all instances. This is explained by the larger battery capacity of modular buses covering more trips, thereby reducing the overall fleet size required and resulting in cost savings from fewer dispatched vehicles. For modular bus instance (2), increasing the battery capacity from 20 kWh to 25 kWh leads to a decrease in the number of vehicles from 36 to 28. However, when the battery capacity surpasses 35 kWh, the cost fluctuation becomes insignificant, and blindly increasing battery capacity may actually result in higher battery costs. Therefore, we recommend procuring modular buses with a capacity between 25 kWh and 35 kWh, a range that allows the vehicle to operate efficiently without frequent recharging.
## 2 Related Work
### Resolution Latency
However, GMap only considers the spatial distribution of names and ignores the popularity of the content, which results in a waste of bandwidth and storage. Auspice [43] innovatively proposed an intelligent global name-binding replica placement based on demand perception, which made full use of the spatial and temporal features of contents to distribute the name replicas to reduce the resolution latency of the NRS. Auspice divides the network nodes into hierarchical and non-overlapping regions based on the underlaying networks, and sets a replica placement controller to collect load characteristics, such as visit frequency and update frequency of names. Auspice determines the number of copies \(K\) by a heuristic copy placement algorithm based on demand dynamically and adaptively. The value of \(K\) satisfies the minimum threshold with which that \(K_{1}\) of \(K\) were selected based on the location, so that the name resolution response can be obtained at the nearest location, and \(K_{2}\) (\(K_{2}=K-K_{1}\)) copies are randomly placed for load balancing. The distribution of the resolver in the NRS and the organization forms of name records also affect the performance of NRS. [PERSON] et al. discussed the distribution problem of NRS and proposed a new Distributed Name Resolution System (DNRS) to solve this problem [44]. DNRS selects the resolver to publish names by considering the publishing time cost at the NRS. The DNRS mechanism balances the storage capacity and the workload of the distributed resolvers. Partitioning the network into different domains increases the flexibility and scalability of the NRS. [PERSON] et al. proposed a new Dynamic Name Resolution Mechanism (DNRM), which exploits the K-NearestNeighbor (KNN) algorithm to select registration and the resolver to improve the performance of the NRS [45]. By adding the phase, means, and other outputs, DNRM guarantees that users can obtain the name resolution service from the nearest resolvers.
## 3 Solution
### Dashboarding  
To visualize the energy and air consumption of the Wiedemann TPP, an interactive, real-time dashboard (Figure 9) was developed using Keyence's dedicated software. This dashboard provides both live operational data and historical performance trends, accessible to authorized users within the M-ACE network.  
Figure 8: Sensor connection to M-ACE network using the MP-FEN1 Ethernet Module [20].  
The dashboard displays the following:  
* Real-time data: air pressure, temperature, humidity, dew point, power consumption, and air usage.
* Consumption trends: interactive plots for daily, weekly, monthly, and annual energy and air consumption patterns.
* Comparative analysis: monthly energy consumption trends to support cost analysis and performance optimization.
* Environmental impact monitoring: a dedicated plot converting energy consumption into CO2 emissions, supporting M-ACE's net-zero sustainability goals.  
By integrating Industry 4.0 solutions, this system enables efficient energy monitoring, data-driven decision-making, and improved operational efficiency, reinforcing M-ACE's commitment to sustainability and cost reduction.
## 3 Proposed Framework
### Integrated Prioritization Module (IPM)
#### 3.2.2 Reviewing Processing Unit (RPU)  
Following the initial grouping of requirements, a thorough review is essential, especially since the categorization is often performed by individuals who may not have technical expertise, such as users of IoT-enabled smart city services. This review process involves domain experts, developers, and testers--all of whom have a deep understanding of the application designs relevant to the requirements. During this phase, these professionals work collaboratively to adjust and realign the requirement groupings. Any issues related to inappropriate grouping are addressed; for example, if a requirement like R-1 is incorrectly placed in the last group--which is reserved for critical requirements--but actually belongs in Group 2, it will be corrected by moving the requirement to the appropriate group. This ensures that all requirements are accurately categorized for effective prioritization.
###### Abstract  
Monocular depth estimation (MDE) is a critical task in computer vision with applications in autonomous driving, robotics, and augmented reality. However, predicting depth from a single image poses significant challenges, especially in dynamic scenes where moving objects introduce scale ambiguity and inaccuracies. In this paper, we propose the Dynamic Iterative Monocular Depth Estimation (DI-MDE) framework, which integrates an iterative refinement process with a novel scale-alignment module to address these issues. Our approach combines elastic depth bins that adjust dynamically based on uncertainty estimates with a scale-alignment mechanism to ensure consistency between static and dynamic regions. Leveraging self-supervised learning, DI-MDE does not require ground truth depth labels, making it scalable and applicable to real-world environments. Experimental results on standard datasets such as SUN RGB-D and KITTI demonstrate that our method achieves state-of-the-art performance, significantly improving depth prediction accuracy in dynamic scenes. This work contributes a robust and efficient solution to the challenges of monocular depth estimation, offering advancements in both depth refinement and scale consistency.  
monocular depth estimation (MDE); dynamic scenes; scale ambiguity; self-supervised learning Article
## 4 Controller Design
### Fuzzy-PID Controller Design  
In this paper, the fuzzy-PID controller uses a fuzzy inference system to determine the variations of the PID controller parameters, i.e., \(\left(\Delta k_{P}^{i},\ \Delta k_{I}^{i},\ \Delta k_{D}^{i}\right)\), for the PH-LDR. The feedback error and error change, \(\left(e_{i},\dot{e}_{i}\right)\), were adopted for yielding the online self-adjustment \(\Delta k_{P}^{i},\ \Delta k_{I}^{i}\) and \(\Delta k_{D}^{i}\). The alterations in the control parameters \(k_{P}^{i}(t),\ k_{I}^{i}(t)\) and \(k_{D}^{i}(t)\) in each time step are shown as follows:  
\[\left\{\begin{array}{l}k_{P}^{i}(t)=\Delta k_{P}^{i}(t)+k_{P}^{i}(t-1)\\ k_{I}^{i}(t)=\Delta k_{I}^{i}(t)+k_{I}^{i}(t-1)\\ k_{D}^{i}(t)=\Delta k_{D}^{i}(t)+k_{D}^{i}(t-1)\end{array}\right. \tag{28}\]  
The knowledge base for a fuzzy controller consists of a rule base and membership functions. The triangle-type fuzzy membership functions for \(e_{i}(t)\) and \(\dot{e}_{i}(t)\) are shown in Figure 5a, and the membership functions for \(k_{P}^{i}(t)\) are shown in Figure 5b. The fuzzy membership functions for \(k_{I}^{i}(t)\) and \(k_{D}^{i}(t)\) are shown in Figure 5c. The fuzzy subset is {NB, NM, NS, ZO, PS, PM, PB}, in which the NB, NM, NS, ZO, PS, PM, and PB stand for negative big, negative medium, negative small, zero, positive small, positive medium and positive big, respectively. These levels of each parameter were determined based on the experimental properties and characteristics of the PAs. In this paper, expert's experience and knowledge are used to build a rule base. There are 49 rules in the fuzzy logic controller for \(\Delta k_{P}^{i}\), \(\Delta k_{I}^{i}\), and \(\Delta k_{D}^{i}\), respectively, shown in Tables 2-4, these were designed by experts. In this paper, 49 fuzzy rules are created to implement the proposed fuzzy-PID controller for the PH-LDR. The outputs of the fuzzy PID can be obtained using the Mamdani fuzzy inference and the center of area (COA) defuzzification.  
Figure 4: Overall control system for the PH-LDR.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\dot{e}(t)\) & **NB** & **NM** & **NS** & **ZO** & **PS** & **PM** & **PB** \\ \hline
**NB** & PB & PB & PM & PM & PS & ZO & ZO \\
**NM** & PB & PB & PM & PS & PS & ZO & NS \\
**NS** & PM & PM & PM & PS & ZO & NS & NS \\
**ZO** & PM & PM & PS & ZO & NS & NM & NM \\
**PS** & PS & PS & ZO & NS & NS & NM & NM \\
**PM** & PS & ZO & NS & NM & NM & NM & NB \\
**PB** & ZO & ZO & NM & NM & NM & NB & NB \\ \hline \hline \end{tabular}
\end{table}
Table 2: The fuzzy rules for \(\Delta k_{p}^{i}(t)\).
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 3 Failure Modes and Mechanism Identification of Key Components in the Communication Module
### Failure Mechanism Analysis of Key Components in the Communication Module  
The failure mechanisms of FPGA and SRAM are discussed based on the classification of key components.  
FPGA and SRAM in the communication module are CMOS integrated chips with similar failure mechanisms. The primary failure mechanisms of these components include electromigration (EM), time-dependent dielectric breakdown (TDDB), hot carrier injection (HCI), and negative bias temperature instability (NBTI). According to relevant research, under the aerospace operating condition, the impact of NBTI is relatively more significant [27]. NBTI primarily refers to the degradation of electrical parameters in PMOS devices under negative gate bias electrical and high-temperature thermal stress. Therefore, this article focuses on analyzing the impact of NBTI on FPGA and SRAM.  
The PMOS transistor depends on the threshold voltage because an increase in the threshold voltage, as seen in the NBTI effect, leads to a slower switching speed and higher delays in the device's operation. Specifically, when the threshold voltage increases, the gate voltage required to turn the PMOS transistor on becomes more difficult to achieve, which results in increased signal delay and, ultimately, the potential for functional failure if the delay becomes too large.  
The failure mode of FPGA and SRAM solder joints is solder joint fracture, which leads to device failure and falls under the category of functional failure. Thermal stress occurs when an object undergoes constrained expansion or contraction due to temperature variations [28].  
Under the influence of internal heat sources and alternating environmental temperatures, the difference in thermal expansion coefficients among various components on the circuit board results in varying degrees of thermal deformation and stress concentration. The solder joints of these components experience internal crack initiation under stress, and over time, these cracks propagate within the solder joints, eventually leading to joint failure.
## 1 Introduction
However, there are very few reported works with focus on fixed-wing UAV-based A2G channels at altitudes above 200 m, at which an aerial base station is usually operated. A comprehensive wideband A2G channel model, which incorporates both measurement-based statistic evidences and temporal evolution of channel [24], is still missing. Particularly, it is essential to accurately model the A2G channel at potential frequency bands of B5G for fixed-wing UAVs in remote areas, like rural environments, in order to boost the development of future communication networks and coverage performance. Therefore, by considering the limitations of previous studies, we conducted a fixed-wing A2G channel measurement campaign, based on which the wideband propagation characteristics at different UAV flight attitudes are investigated, and a Wide-Sense Stationary Uncorrelated Scattering (WSSUS)-based TDL channel model is proposed. The main contributions of the paper are summarized as follows:  
* We present a fixed-wing UAV A2G channel measurement campaign at 2.7 GHz with 25 MHz bandwidth in a rural area, based on a developed channel sounding system consisting of vector signal transceiver and software radio device. The measurement was with emphasis on four typical UAV flight phases, and different flight altitudes (up to 700 m), velocities, and transmitter-receiver distances. * We propose to model the A2G channel with a WSSUS-based complete TDL channel model, with a typical system bandwidth of 20 MHz for A2G applications. The average power, short term evolution of complex amplitude, and delay-Doppler spectrum of individual channel taps are considered in the proposed model. * Based on the measurement data, we comprehensively analyze the characteristics of the propagation channel. The proposed TDL channel model is parameterized based on the measurement data in terms of number of taps, averaged power delay profile (PDP), phase, fading distribution, Rice factor, and delay-Doppler spectrum of each channel tap. Four types of spectral models are considered to analyze delay-Doppler spectrum of individual tap. The remainder of this paper is structured as follows. The A2G measurement campaign is addressed in Section 2. The proposed TDL model is presented in Section 3. Analysis and results of the TDL channel model-based A2G measurement data are given in Section 4. Finally, the conclusions are drawn in Section 5.
## 3 Results and Discussion  
### Chemical Characterization  
Both graphene and in-house synthesized hydrogenated graphene materials were observed using XPS and FTIR, and the peaks in the respective spectra were examined and analyzed. Confirmation of hydrogenation of the graphene was observed in both the XPS and FTIR spectra, as shown in Figure 3a,b. Hydrogenation was confirmed in the XPS spectra where a shift in the C1s spectra is visible from \(\sim\)284 eV in graphene to \(\sim\)287 eV in hydrogenated graphene. The peak value in the C1s spectrum (\(\sim\)284 eV) of the tested graphene sample is comparable to the peak values of the spectra reported in literature for pristine graphene [22]. Given that XPS cannot distinguish C-H and C-C bonds, the FTIR transmittance spectra comparison of both graphene and hydrogenated graphene was then used to confirm sp\({}^{3}\) C-H hybridization peaks due to C-H stretching vibrations at wave number \(\sim\)2852.51 cm\({}^{-1}\) and \(\sim\)2923.87 cm\({}^{-1}\), and an intense C-H bending peak at \(\sim\)1122.49 cm\({}^{-1}\) on hydrogenated graphene and relatively weaker or completely non-existent on graphene. Using fluorographite as a starting material instead of graphite or graphite oxide contributes to a highly hydrogenated product as, according to a mechanism by [PERSON] et al., the tertiary C-F bond domains are the principal structure of the fluorographite and these can be easily reduced. We can conclude that tertiary C-F bonds in fluorographite were all transformed into C-H bonds as well as C-C \(\pi\)-conjugated domains, but it should be noted that the latter domains form the minor structure of fluorographite [20]. A stronger C-C skeletal vibration at \(\sim\)1629.73 cm\({}^{-1}\) for HG compared to the one on graphene can mean the destruction/displacement/removal/utilization of the C=C during the reduction process and further analysis and or study is beyond the scope of this study. For purposes of this study we have confirmed hydrogenation.
# Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means
## 3 Methods  
In this section, procedures of the proposed LCM and FCM algorithm are described in detail. Figure 3 shows the overview of the presented method. It consists of two steps: Firstly, the L0 gradient minimization is applied to eliminate the influence of the background texture of fabric defects. Then the fuzzy c-means clustering is used to determine whether each pixel is defective.
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 3 Receiver for Optical Wireless Communication
### OWC Receiver with an Array of Surface Grating Couplers
#### 3.5.3 Results  
Figure 16a shows the coupling loss of the individual grating couplers for different angles at wavelengths in the C-band. The angle with the highest coupling efficiency is 10\({}^{\circ}\) for which the loss is about 7.5 dB at 1550 nm and all values are within 1 dB over the C-band. The coupling loss for angles below 10\({}^{\circ}\) is decreasing for increasing wavelengths. The opposite holds for angles greater than 10\({}^{\circ}\). In the middle of the C-band at 1550 nm, the 2-dB full angle field-of-view is 8\({}^{\circ}\), currently limited by the maximum adjustable angle in our setup. It can be seen that the lines in Figure 16a are not smooth. That is caused by the fact that the grating couplers were connected by a 1 mm long waveguide. Cleaved fibres together with a finite optical path length of the waveguide create a cavity, which results in power variation for neighbouring wavelengths.  
Focusing light with a lens on the grating coupler array and coupling light with a cleaved fibre resulted in a loss of 32 dB. Therefore, the loss of the array is 25 dB. Figure 16b shows the BER curves for 1 Gbps and 10 Gbps. The received power of \(-14\) dBm is sufficient to achieve error-free 1 Gbps data transmission. Data transmission below the 10\({}^{-3}\) Forward Error Correction (FEC) limit is achievable at \(-16.5\) dBm and \(-14\) dBm at 1 Gbps and 10 Gbps, respectively.  
Figure 16: **(a)** Relation of the loss of the individual grating coupler and the angle of incidence for the wavelengths in the C-band. The inset plot shows the coupling loss at 1550 nm. **(b)** BER curves for 1 Gbps and 10 Gbps wireless transmission over 15 cm link with an array of surface grating couplers as a light collection device.  
Figure 15: **Left**: Diagram of the experimental setup used for optical wireless data transmission using an array of surface grating couplers as light collection part of the receiver. **Right**: Photograph of the part of the setup with the lens, the PIC, and the cleaved fibres.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for Disease-Specific Research and Drug Discovery
#### 2.1.3 Aging Bank: A Specialized Platform for Aging and Age-Related Disease Research  
Taking this exploration of disease progression further, Aging Bank adds a specialized focus on the molecular mechanisms underlying aging and age-related diseases. Aging Bank is a specialized platform designed to integrate and analyze multi-omic data related to the biological processes of aging and age-related diseases. The platform focuses on providing researchers with tools to investigate the molecular mechanisms that contribute to aging, as well as to model age-affected diseases such as Alzheimer's, Parkinson's and other neurodegenerative diseases. By integrating data from various levels of omics, including genomics, transcriptomics and epigenomics, Aging Bank enables the elucidation of the complex regulatory pathways underlying the aging process [16]. An important advantage of the Ageing Bank is its ability to facilitate the study of age-related molecular changes, helping in understanding how these changes increase susceptibility to diseases that commonly occur in older populations. The platform allows users to explore the relationships between gene expression, epigenetic modifications and genetic variations associated with both the normal aging process and aging-related pathologies.  
Aging Bank facilitates aging research by integrating DNA methylation, histone modification, and other epigenomics data to uncover cancer-aging links. For example, its differential expression analysis and functional annotation tools have identified aging related susceptibility genes in Alzheimer's disease. However, its aging-specific focus limits its utility for non-age-related conditions, and it lacks integration of spatial or single-cell omits. By offering tools for pathway analysis, gene network construction and biomarker discovery, the platform helps with the identification of essential molecular signatures that can predict disease onset or progression in aging populations. While the platform is highly specialized for aging research, its narrow focus may limit its utility for other diseases that are not primarily age-related or for those requiring a broader multi-omics perspective beyond aging and it may not offer direct clinical decision-making tools outside the context of age-related disease modeling.