esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Contribution of Separation and Recycling of Waste to Emission Reduction of GHG  
The moisture content of kitchen waste in Beijing is as high as 82.1%, and the proportion of kitchen waste is high, which leads to high moisture content of waste, making incineration challenging task [64]. The separation of kitchen waste can reduce the moisture content of incineration waste, improve the heating value, and increase the electricity generation per unit of waste [56]. Under Scenario 2 and Scenario 3, after 50% of the kitchen waste was separated, the moisture content of the waste was reduced from 53.80% to 44.90%. Correspondingly, the heating value of the waste entering the furnace increased from 9407 to 10,952 kJ/kg, the electricity generation per unit of waste was increased by 68.67 kJWh, and the emission reduction effect of electricity generation from incineration of waste was improved. Under Scenario 4 and Scenario 5, since the fossil carbon content of plastics was the highest among all kinds of waste, the GHG emissions from incineration of per unit waste was reduced after recycling recyclables such as plastics and paper, and was thus 0.0251 t lower than that under Scenario  
Figure 11: GHG emissions from MSW landfill for different methane recycling rate.  
1. Therefore, the separation and recycling of waste can effectively reduce the GHG emissions from waste incineration.  
Separation and recycling of waste can reduce the amount of waste end disposal, and the recycled resources can replace the raw materials in production, reducing the comprehensive energy consumption of production processes and thus achieving energy savings. In this research, recycling of renewable resources and electricity generation from incineration of waste were regarded as measures of energy conservation and emission reduction. [PERSON] pointed out that compared with incineration, recycling of most types of MSW can enhance energy savings [65]. [PERSON] et al. suggested that mechanical recycling of plastic could bring the greatest emission reduction benefits of GHGs [66]. Therefore, improving the separation rate of waste sources and promotion of the recycling of renewable resources is an effective way to reduce GHG emissions.  
Because of the uncertainty of the separation rate, a separation rate of 50% was used as the standard in this paper, and it was modified up and down by 10% and 20%. The results are shown in Figure 12. The changes in the separation rate had little impact on the results. Scenario 5 was still the optimal mode, and Scenario 2-5 still had emission reduction benefits compared with Scenario 1. The emission reduction rate of Scenario 2, 4, 5 increased with an increase in separation rate. In contrast, when the separation rate was reduced, the emission reduction effect under Scenario 3 was better than that under Scenario 4. Overall, the impact of separation rate on GHG emissions under each scenario was robust. Therefore, the level of separation rate in reality should not affect the government's decision-making.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 7 Conclusions  
In this article, we reviewed the relevance of using SDR metrics with perceptually uniform color spaces to assess the quality of HDR/WCG contents. We studied twelve different metrics along with  
Figure 14: Subjective and objective scores for the image Regatta_24s and for 6 metrics based on the \(ICtCp\) color space.  
six different color spaces. To evaluate the performances of these metrics, we used three existing HDR image databases annotated with MOS and created two more databases specifically dedicated to WCG and chrominance artifacts. We showed that the use of perceptually uniform color spaces increases, in most cases, the performances of SDR metrics for HDR/WCG contents.  
In this study, we also highlight two weaknesses of state-of-art metrics. First, The relationship between the diffuse white used for grading the image and the diffuse white used for the color space is not always easy to define. In a number of cases, we do not know the value of the diffuse white used for grading of the image. Choosing an arbitrary diffuse white for the color space may significantly alter the objective quality assessment. Further analysis of this relationship is required. A better understanding could help to evaluate compression of images using the HLG EOTF for which the diffuse white depends on the display. Second, to the best of our knowledge, the quality assessment of DR/WCG images with chrominance distortions is still an open-issue, because of the lack of relevant objective metrics.  
In a broader perspective, the relevance of subjective tests can also be questioned. For example, on the proposed database HDdtb, viewers did not perceive the gamut mismatch artifact as a loss of quality. However, this kind of artifact changes completely the appearance of images. Some other artifact could also alter the image appearance like the tone mapping/tone expansion used during backward compatible compression. In some cases, asking the viewers to not assess only the quality of the images but also their fidelity to the image appearance can be valuable to fully evaluate image processing algorithms.  
The following are available online at [[http://www.mdpi.com/2313-433X/5/1/18/](http://www.mdpi.com/2313-433X/5/1/18/)]([http://www.mdpi.com/2313-433X/5/1/18/](http://www.mdpi.com/2313-433X/5/1/18/)).  
Conceptualization, M.R.; Data curation, M.R.; Formal analysis, M.R.; Investigation, M.R.; Methodology, M.R.; Project administration, O.L.M., R.C. and X.D.; Resources, M.R., O.L.M., R.C. and X.D.; Software, M.R.; Supervision, O.L.M., R.C. and X.D.; Validation, O.L.M., R.C. and X.D.; Writing--original draft, M.R.; Writing--review & editing, O.L.M., R.C. and X.D.  
This research received no external funding.  
Thanks to all the participants that have accepted to be part of the two proposed subjective tests.  
The authors declare no conflict of interest.
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 1 Introduction
### Phenological Monitoring  
#### 1.3.1 Manual Surveys and Remote Sensing  
In terms of monitoring, the inaccessible and remote nature of many peatland sites means that traditional, manual observations of phenology are rare. As an alternative, studies often examine (near) remote-sensing phenology; either using data from automated camera setups (i.e., phenocams [34; 35; 36]), or from satellite imagery [37; 38; 39]. While phenocams provide near-continuous 'true-colour' imagery over a fixed field of view in the red, green, and blue bands (RGB), satellites can exploit data from multispectral (containing as few as 3-4 bands) or hyperspectral sensors (containing hundreds of sampled bands) and provide spectrally rich, calibrated data.  
Remote sensing studies commonly employ vegetation indices (VIs) as a proxy for plant phenology and physiology. These are combinations of bands known to relate to vegetation structure (e.g., biomass, leaf area), vegetation biogeochemistry (e.g., water content, pigments, chlorophyll), and physiological status (e.g., light use efficiency, stress marked by changes in pigmentation) [40]. VI time-series can be used to estimate the timing of phenological events (e.g., leaf out, flowering date, senescence) (e.g., ref. [41]). Furthermore, at sites with available greenhouse gas measurements, these time-series data can be used to examine: (i) the response of vegetation to weather events (e.g., ref. [42]); and (ii) the impact of phenology on ecosystem functioning and land-atmosphere exchange (termed flux hereafter) of CO\({}_{2}\)[38; 43; 44].
# Optical High-Speed Rolling Mark Detection Using Object Detection and Levenshtein Distance
###### Abstract  
This paper presents an automated high-speed rolling mark recognition system for railroad rails utilizing image processing techniques. Rolling marks, which consist of numbers, letters, and special characters, were engraved into the rail web as 3D information. These rolling marks provide crucial details regarding the rail manufacturer, steel quality, year of production, and rail profile. As a result, they empower rail infrastructure managers to gain valuable insights into their infrastructure. The rolling marks were captured using a standard color camera under dark field illumination. The recognition of individual numbers, letters, and special characters was achieved through state-of-the-art deep neural network object detection, specifically employing the YOLO architecture. By leveraging reference rolling marks, the detected characters can then be accurately interpreted and corrected. This correction process involves calculating a weighted Levenshtein distance, ensuring that the system can identify and rectify partially misidentified rolling marks. Through the proposed system, the accurate and reliable identification of rolling marks was achieved, even in cases in which there were partial errors in the detection process. This novel system thus has the potential to substantially improve the management and maintenance of railroad infrastructure.  
Keywords:railway infrastructure; rolling marks; computer vision; object detection; YOLO--you only look once; Levenshtein distance +
Footnote †: journal: _Applied_
# A Channel Allocation Mechanism for Cellular Networks
## 5 Simulation Analyses
### Simulation Results and Analyses
#### 5.2.3 Comparisons between SCA and Proposed Mechanism
This study can observe that the cars move slowly and line up in Cell\({}_{5}\) and the amount of traffic flow is less in Cell\({}_{6}\) when the traffic accident occurs between the simulation time 16 th min and 45 th min. During the traffic accident period in Cell\({}_{6}\), this study discovered that the CBP derived by using the proposed mechanism and SCA mechanism have little difference because the amount of traffic flow and the number of handovers are rare. Otherwise, this study observed that the number of handovers dramatically increases with the amount of traffic flow increase when the traffic accidents are removed between the simulation time between the 46 th and 60 th min. The cellular system that adopts the proposed mechanism can dynamically adjust the number of allocating channels in each cell for handover events, and it can effectively avoid the call block when the amount of traffic flow increases. Otherwise, the cellular system that adopts the SCA mechanism can't dynamically adjust the number of allocating channels in each cell for handover events, so using the SCA mechanism will cause the higher CBP. Through Figure 24, all CBPs in the scenario of traffic accidents are less than 16.5% by using the proposed mechanism that is better than using the SCA mechanism. The simulation results show that the fixed number of channels by using the SCA mechanism can't be suited to the dynamic environment. Moreover, the mechanism can accurately predict the number of channels that are required for handover. Therefore, the proposed mechanism can decrease the number of CBPs effectively.
## 5 Experimentation
### Performance Metrics  
We use the standard metrics to measure the performance of the Control and No-Control configurations [33]:  
**Delivery Ratio (\(\
u\)):**: measures the ratio of created messages that are delivered to the final destination:  
\[\sigma=\frac{\#g_{d}}{\#g_{c}} \tag{9}\]  
where \(\#g_{d}\) is the number of delivered messages, and \(\#g_{c}\) is the number of created messages.
**Latency average (\(\overline{\lambda}\)):**: the average time it takes for the created messages to get delivered to their final destination:  
\[\overline{\lambda}=\frac{\sum_{i=1}^{w}\lambda_{i}}{w} \tag{10}\]  
where \(w\) is the number of messages delivered to the destination, and \(\lambda_{i}\) is the elapsed time from the message creation to its delivery.  
Overhead ratio (\(\theta\)): measures the average of the message copies needed to deliver the message to its final destination:  
\[\theta=\frac{\#g_{r}-\#g_{d}}{\#g_{d}} \tag{11}\]  
where \(\#g_{r}\) is the number of relayed copies, and \(\#g_{d}\) is the number of delivered messages.
## Materials and Methods
### Hypotheses Testing Through a Mixed Model Procedure  
The removal percentage of each measured variable was estimated in every stage of the experiment. Subsequently, to identify the most efficient aeration frequency in terms of removal, a mixed modelling procedure (PROC MIXED) in SAS 9.4 software was used. Through the development of mixed linear models, the correlation between measurements of the water samples taken at each time point for each IBC system were recorded. The selected statistical analysis was based on its flexibility to model the repeated measurements from the same experimental unit. For the model development, each of the containers (labelled: \"subject\"), the different aeration frequencies (labelled: \"treatment\"), and sampling days (labelled: \"time\") were considered as categorical variables. pH, EC, DO, COD, TN, NO3- and NH4+ represented the response variables each in turn. Subject was modelled as a random effect while treatment, time and the interaction between treatment and time represented the fixed effects. The degrees of freedom were estimated using the Satterthwaite approximation [25].  
Given the fact that the dependent variables in the model each represented a measurement on the same experimental IBC, the \"repeated\" statement was used to model the variability within treatment over time. A significance level was set at \(p\leq 0.05\). Finally, the compound symmetry (CS) variance covariance structure, which assumes that the correlation between any two measurements from the same treatment is the same, was used. Then, to compare the expected least squares means response between the different treatments, the \"lsameans\" statement was used together with the Tukey adjustment method which corrects the p-values for multiple testing. In a general context, the statistically significant estimated effects represent the matching days when the concentrations of treatment (e.g., A and B) significantly change. By assessing the size and the sign (+ or -) of the effects, together with the repeated influence of a specific aeration regime in the concentrations of each variable, overall judgements could be done. Finally, to validate the model estimations and overall findings, the model residuals were plotted against the corresponding fitted values to check for normality.
### Contributions and Outline  
To solve this problem, a novel ED Net consisting of Enhanced Asymmetric Convolution blocks and Double Pooling Concatenated Input Squeeze-and-excitation Blocks is proposed. In this study, we show the following:  
1. The Enhanced Asymmetric Convolution block (EAC block) is used in the suggested ED Net, which is an improvement on Enhanced Asymmetric Convolution to improve feature extraction.
2. The Double Pooling Concatenated Input Squeeze-and-excitation Block (DPCI-SE Block) is used in the ED Net. Based on Squeeze-and-excitation Block (SE Block) [23], DPCI-SE Block is a better attention module. It is added to enhance the acquisition of spatial location information of damaged special folds.
3. The accuracy of ED Net for wind turbine blade surface damage identification mission is between 99.12% and 99.23% in this paper, and the recall is 99.43%. ED Net outperforms some common lightweight CNNs in terms of overall performance.  
Section 2 contains some descriptions of the principles of the improved methods for convolutional neural networks used in this paper. In Section 3, three experiments on ED Net are mentioned and the experimental results are discussed. Experiment 1 determines the overall structure of ED Net and verifies its effectiveness for the wind turbine blade damage identification task. Experiment 2 verifies the effectiveness of the relevant improvements made in this paper. Experiment 3 compares the recognition of wind turbine blade damage by the currently used lightweight networks and measures that the ED Net model proposed in this paper has a good comprehensive performance. Section 4 provides concluding remarks and an outlook on subsequent work.
## 5 Measurements  
### Measurement Setup  
As to measure performances of the square- and circular-loop FSS filters, the FSSs were placed in the middle of horn antennas and an incident plane wave were applied using a vector network analyzer. The performances of both FSSs were measured over the frequency range from 90 to 125 GHz, for which two VNA extender mixers and two horn antennas were also used. A device under test (DUT) was mounted on a rotatable part to measure the oblique angle performance. The antenna-to-antenna distance was kept as 50 cm throughout the measurements and the distance from one antenna to the DUT was 25 cm. Figure 9 shows the block diagram and actual measurement setup of the FSS filters.
# Cloudformer: Supplementary Aggregation Feature and Mask-Classification Network for Cloud Detection
## 4 Experiment
### Evaluation Criteria and Data Processing  
On the one hand, to evaluate the actual overall performance of various methods of cloud detection tasks, we selected four widely used indicators, namely Mean Intersection with Union (MIoU) [33], Frequency weighted Intersection over Union (FwIoU) [34], Mean Accuracy (MAcc) [35], and Pixel Accuracy (PAcc) [36]. The selected index will evaluate the accuracy of the model from multiple angles. The larger the value, the higher the accuracy. In the ablation experiment, our purpose was to illustrate the effectiveness of the module, so we only selected MIoU, MAcc, and PAcc for evaluation. In the tables of this article, for the MIoU, FwIoU, MAcc and PAcc we bolded the highest values, while for the time is targeted the lowest value.  
On the other hand, we choose two public remote image-sensing cloud detection datasets to test the generalization ability of the model in different scenarios. The AIR-CD dataset contains multiple remote-sensing images of 7300 \(\times\) 6908 obtained by GF-2 satellites [37]. We divide it into 640 \(\times\) 640 RGB images as the training set of the network and add random noise, such as rotation and horizontal flipping at a specific angle, before sending it to the network to enhance the training data. The 38-Cloud dataset contains multiple remote-sensing images 7601 \(\times\) 7761 in size obtained by LandSat8 satellites. When inputting into Cloudformer, we discarded the data on the near-infrared band, and only stacked the visible bands, to perform the image analysis. Overlapping segmentation constructed a dataset containing 3249 640 \(\times\) 640 RGB images. In the experiments in this section, the training set and the test set are constructed at a ratio of 8 to 2.
## 1 Introduction  
With the rapid advancement and extensive deployment of Internet of Things (IoT) technologies, the Marine Internet of Things (M-IoT) has become a critical enabler of a broad range of maritime applications, including oceanographic monitoring, marine resource exploration, environmental surveillance and intelligent maritime traffic management [1, 2, 3]. By interconnecting diverse marine sensors, unmanned platforms, and communication terminals, the M-IoT facilitates enhanced situational awareness and supports intelligent decision-making in complex ocean environments [4, 5, 6, 7].  
However, the inherently harsh and heterogeneous nature of marine settings poses significant challenges to the computational efficiency and operational sustainability of M-IoT systems. These challenges are exacerbated by the widespread reliance on resource-constrained edge devices, intermittent and high-latency satellite communication links,and limited onboard energy supplies [8]. Collectively, these constraints impede real-time, efficient task execution, diminish system responsiveness, and restrict the scalability and adaptability of distributed computing architectures in maritime contexts.  
Meanwhile, in real-world deployment, the strict payload and energy restrictions of UAV platforms impose significant limits on onboard neural inference and edge server integration, as continuous policy execution and GPU-based acceleration can substantially reduce flight endurance. Maritime communication links are subject to intermittent obstruction, long propagation delays and fluctuating bandwidth, requiring robust channel estimation, adaptive modulation and coding schemes and predefined fallback policies during connectivity outages. Moreover, unanticipated environmental variations demand real-time policy adaptation through lightweight online fine-tuning or federated meta-reinforcement learning, implemented with careful management of computational and communication budgets. Finally, when extended to multi-UAV operations, decentralized or hierarchical training frameworks with periodic aggregation of local experiences can preserve the collective performance without overwhelming the limited high-capacity links available at sea.
## 4 Experimental Methodology and Measurement Results
### Link Performance
#### 4.1.2 Uplink Performance  
Pseudorandom binary sequences (PRBSs) were transmitted over the DL fiber to keep the CDR locked. Therefore, the DL fiber length is expected to have an impact on the phase noise of the retrieved clock. Two scenarios with different DL fiber lengths were considered when measuring the UL performance: (1) back-to-back (2) with a 100 m MMF. Table 3 lists the measured EVM values corresponding to different UL fiber lengths under these two scenarios. Figure 7b and c show the corresponding constellation diagrams.  
As in Table 2, there is little performance degradation when the UL optical transmission distance increases. The performance difference between the two scenarios is also negligible because the first PLL (PLL1) in Figure 6 properly deans up the jitter.
# Liquid-Crystal-on-Silicon for Augmented Reality Displays
## 2 LCoS Working Principles
### Homogeneous Alignment  
In a homogeneous (HG) cell (Figure 5), the LC alignment is parallel to the x-axis with a small pretilt angle (\(\alpha\sim 2^{\circ}\)) [29]. Using a positive \(\Delta\varepsilon\) LC material, its electro-optic properties are opposite to the VA mode: phase retardation is maximized at the voltage-off state and decreases as the applied voltage increases. Due to the narrow viewing angle and the staggered RGB dark state, the HG cell is rarely used for broadband intensity modulation but is a favored choice for a reflective phase modulator. The viewing angle can be enlarged by a compensation film and by the mirror image effect in reflective LCoS [35]. The unmatched RGB phase retardation is not a big issue for phase modulation. The major advantage of the HG mode is its fast response time. To achieve the 2\(\pi\) phase range, the LC layer is usually twice thicker than its counterpart intensity modulator, as a result, its response time (\(\tau\) is proportional to \(d^{2}\)) would be 4\(\times\) slower [33]. Thanks to the wide selection of positive LC materials, high \(\Delta\varepsilon\) for low operation voltage and low rotational viscosity \(\gamma_{1}\) for fast response time can be obtained more easily than those negative-\(\Delta\varepsilon\) LCs for the VA mode [36].