esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Loadability Performance Under Best PMVO Solution  
Figures 10 and 11 display the hourly peak current loading across network lines, expressed as a percentage of their rated limits, for the 33-node distribution system optimized via the PMVO algorithm under both operational modes. Each curve represents the system response when minimizing a specific objective function--energy losses or CO\({}_{2}\) emissions. While the vertical axis shows the current loading level, the profiles themselves are the result of distinct optimization criteria.  
In the grid-dependent configuration (Figure 10), none of the lines surpassed the 100% limit, validating the fact that the power flow remains within secure operational margins. However, certain branches exhibited values approaching the upper boundary, such as lines 13, 14, and 30, which registered loadabilities above 90%. Line 13 reached approximately 99.99%, suggesting that this particular line consistently bears high power transfer and may require infrastructure reinforcement or active control strategies. Conversely, the minimum loadability values--most notably observed in lines 17, 21, and 32--fell below 45%, indicating segments with underutilized capacity that could be leveraged for load redistribution if system flexibility permits.  
Figure 9: Voltage regulation performance achieved by the PMVO algorithm in a representative daily scenario for the 33-node system in self-powered mode over a 24 h evaluation horizon.  
In the self-powered mode (Figure 11), the loadability profile remained qualitatively similar, yet minor fluctuations can be observed. While line 13 slightly decreased its loading to 99.95%, the general high-load pattern on lines 13 to 15 and 30 persisted. Variations in load distribution, particularly visible in lines 25 to 29, showed marginal differences between the two modes. Despite this, the overall profile demonstrates the capability of PMVO to adapt to decentralized generation while preserving the integrity of the network. The lowest value continued to appear in line 32, consistent with the grid-dependent case, highlighting the persistent underuse of this branch in both strategies.  
The minimal discrepancies in loadability between the energy loss and CO2 emission scenarios support the robustness of the PMVO algorithm in ensuring that the optimization does not compromise operational safety.
# Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters
## 2 Spectrum Sensing Overview  
An MSSS was implemented with the objective to sense the TV-UHF spectrum, as explained in [6]. By analyzing the results obtained therein, the authors realized the need for estimating TVWS in a rapid manner, for saving time, and for using open hardware and software tools. To conduct this research, we worked with data obtained from the TV-UHF band (500 MHz to 698 MHz). Spectrum samples were collected in the city of Windsor in Canada. We focused our efforts on identifying the available TVWS in the mentioned area.
## 2 Data and Methods  
### Data  
#### 2.1.1 Study Area  
The geographical location of Taihu Lake is shown in Figure 1, with the image coordinate system being GCS_WGS_1984. Taihu Lake is one of the five largest freshwater lakes in China, located between southern Jiangsu Province and northern Zhejiang Province, spanning areas such as Suzhou, Wuxi, and Changzhou. The lake [PERSON] et al. (2018) covers an area of 2427.8 square kilometers, with a water surface area of 2338.1 square kilometers and a shore line length of 393.2 km. The western and southwestern sides are hilly and mountainous, while the eastern side is primarily plains and water networks. Situated in the subtropical zone, the climate is mild and humid, characterized by a monsoon climate. In recent years, environmental issues in Taihu Lake have drawn widespread attention, particularly water pollution caused by agricultural runoff, industrial pollution, and urban expansion. The main pollution problem is eutrophication of the lake.
## 3 Methods
### Accuracy Assessment  
In order to divide the reference dataset (see Section 2.1.) into two parts: a training set (70%), which was used for fitting models, and a test set (30%) used for evaluating generalization error in the final selected model, a stratified random sampling design was used [66]. This sampling procedure was repeated ten times for more robust results. In addition, 10% of the training samples were used as validation data for the calculation of the MLP loss function [67].  
Lastly, a confusion matrix was calculated for three classification scenarios (CS) based on our ground control points. Hence, accuracy was compared across the first classification scenario (CS1) which used single-date PS imagery with VI, second and third classification scenarios (CS2 and CS3) which used multitemporal PS imagery with VI, and GLCM and VI, respectively. In CS1, the PS from 15 May 2022 was used, whereas CS2 and CS3 used all available PS imagery. The results were also evaluated using traditional accuracy metrics such as overall accuracy (OA), a Kappa coefficient (Kappa), producer's accuracy (PA), and user's accuracy (UA). In addition, the F1 score and figure of merit (FoM) were also calculated.  
The F1 score is a measure of the balance between PA and UA, where PA is the proportion of true positive predictions (correctly identified classes) out of all positive predictions, and UA is the proportion of true positive predictions out of all actual positive reference samples. A higher F1 score indicates a better balance between PA and UA, and a better overall performance of the model. The F1 score was calculated as follows:  
\[\text{F1}\;=\;2\cdot\frac{\text{PA}\cdot\text{UA}}{\text{PA}\;+\;\text{UA}} \tag{4}\]  
A figure of merit (FOM) is a single value that summarizes the performance of the system or model across multiple performance metrics (i.e., overall accuracy, omission, commission), and it is often used as a way to compare the performance of different systems or models:  
\[\text{FoM}\;=\;\frac{\text{OA}}{\text{OA}+\text{OM}+\text{CM}} \tag{5}\]  
where OA represents overall accuracy, and OM and CM refer to the errors of omission and commission, respectively.
## 3 Materials and Methods  
### Sentinel-1 Dataset  
Sentinel-1 is a twin satellite constellation that carries synthetic aperture radar (SAR) sensors operating at C-band (~5.6 cm wavelength) under Copernicus Programme coordinated and managed by the European Space Agency (ESA). Sentinel-1 products are distributed by the Copernicus program under free and open data policy. Sentinel-1 A and Sentinel-1 B together have imaging capability of the same region every 6 days at all weather conditions, day and night. Six-day repeat cycle is quite a good temporal resolution for land monitoring services.  
The study area is analyzed for ground surface deformation using Sentinel-1 SAR images within the time period between October 2014 and October 2018. Sentinel-1 A covers the study region from the beginning of the analysis period and Sentinel-1 B starting from October 2016 at ascending track 160 and at descending track 65 as shown in Figure 2a. With the launch of Sentinel-1 B in mid 2016, temporal resolution increased at the second half of the analysis period from 12 days to 6 days. A total of 366 (183 ascending and 183 descending) products acquired in TOPSAR Interferometric Wide (IW) Swath Mode in Single Look Complex (SLC) format are processed independently for each track. The single master image is selected for each dataset separately by considering temporal and geometric baselines of the Sentinel-1 A/B products as displayed in Figure 3. Mean incidence angle of the study area is about 35\({}^{\circ}\) for ascending dataset and is around 40.5\({}^{\circ}\) for descending dataset.
## 4 Physical Audio Features Extraction Techniques
### Frequency Domain Physical Features
#### 4.2.2 STFT-Based Frequency Features
* **Group delay function:** also known as GDF, it is defined as the negative derivative of the unwrapped phase of the signal Fourier transform (see [PERSON] and [PERSON] [80]) and reveals information about temporal localization of events (_i.e._, signal peaks). This feature has been used for determining the instants of significant excitation in speech signals (like in [PERSON] and [PERSON] [81], or [PERSON] _et al._[82]) and in beat identification in music performances ([PERSON] _et al._[83]). * **Modified group delay function:** or MGDF for short, it is defined as a smoother version of the GDF, reducing its intrinsic spiky nature by introducing a cepstral smoothing process prior to GDF computation. It has been used in speaker identification ([PERSON] _et al._[84]), but also in speech analysis, speech segmentation, speech recognition and language identification frameworks ([PERSON] and [PERSON] [85]).
## 5 Experiments and Results
### Evaluation Metrics of Models
#### 5.3.2 Accuracy Effectiveness  
Furthermore, in order to demonstrate the algorithm's effectiveness for deep learning models, four evaluation metrics in this experiment were used to access the detection accuracy: Precision (_P_), Recall (_R_), _F1-Score_, Average Precision (_AP_), and Mean Average Precision (_mAP_). \(P\) represents the probability of correct target detection and is calculated as the ratio of the number of correctly classified samples (_TP_) to the total number of samples. \(R\) represents the probability of correctly recognizing the target among all positive samples and is calculated as the ratio of the number of correctly classified positive samples to the number of all positive samples. _F1-Score_ is a comprehensive evaluation index that takes into account the effects of accuracy and recall. _AP_ is obtained by calculating the area under the Precision-Recall curve and reflects the precision of individual crack categories. The _mAP_ characterizes the average across the four crack categories and reflects the overall classification precision of the crack prediction.
### Economic Impact  
Firefighting efforts and subsequent rehabilitation are excessively costly, and the costs of mitigating wildfires are increasing exponentially each year. At a total loss of $12.5 billion, California's 2018 Camp Fire was the most expensive disaster globally in that year. Wildfires incurred similar financial burdens on other countries, with state expenditures increasing by millions of dollars [24]. Along with the immediate property damage and recovery expenses, each fire disrupts the surrounding economy and workforce, causing a ripple effect throughout the communities [25].  
There are several ongoing global efforts to mitigate wildfires. As highlighted in Figure 1, the frequency and impact of wildfires have escalated significantly in the past few years, and this has prompted numerous research groups to propose their unique solutions to combat this issue. An ideal wildfire monitoring system should be able to survey a large area (spatial coverage) with reasonable frequency (temporal coverage).  
Two popular approaches are predicting the likelihood of a fire in an area and detecting wildfires still in early stages from the associated emission of gases, aerosols, temperature rise, etc. Fires at young stages are simpler to control and mitigate. This paper discusses many such efforts, with their merits and shortcomings. We hope to create a substantial resource for the research community about the state of current technology and motivate advancements needed to further reduce the losses from this disaster.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 4 Results and Discussion
### Security and Threat Considerations  
While blockchain inherently provides immutable and transparent data recording, securing the overall supply chain system required identifying and mitigating potential threats across the broader architecture. To this end, we adopt the STRIDE threat modeling framework, which is commonly used in cybersecurity design, to classify security concerns and evaluate the robustness of our proposed architecture.  
STRIDE stands for Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege [55]. As presented in Table 2, these categories collectively capture the main classes of security threats typically encountered in software and hardware-integrated systems such as blockchain and IoT-enabled supply chains.  
In our system, STRIDE helps to outline how both the IoT (sensor and data layer) and blockchain (smart contract layer) components might be vulnerable to various attacks as well as what mitigation strategies are in place or planned. Table 2 summarizes the potentialthreats and how our framework addresses them, either through current mechanisms or proposed enhancements in future deployments.  
In this threat model, data tampering itself is treated as a core attack vector that our blockchain implementation inherently resists through immutability. However, threats outside the chain remain a concern, including spoofed sensor inputs and physical tampering with IoT devices. Although our current prototype is simulation-based, this threat analysis offers a roadmap for securing future physical deployments.  
STRIDE evaluation highlights the foundational security properties of the system and points to enhancements for future field readiness. As we move toward hardware integration and pilot studies, this model will inform both architectural adjustments and threat mitigation priorities.
# Effects of Sidewall Brightness on LED Lighting Environment and Visual Performance in Road Tunnels
## 2 Test Arrangement  
### Tunnel and LED Lamps  
The test tunnel is 160 m long, with a clear width of 10.4 m, a clear height of 7.5 m and a wall thickness of 0.25 m. The cross-section dimensions are shown in Figure 1. In the area of 4.6-5.4 m away from the road surface, five rows of LED lamps were installed, and the vertical interval between each row was 0.2 m. In each row, the LED lamps were symmetrically arranged on both sides of the tunnel. The longitudinal interval between two adjacent LED lamps was 10.5 m, and the distance from the first lamp to the tunnel entrance is 12.5 m. The color temperatures of the five rows of lamps from low position to high position are 2500 K, 3500 K, 4500 K, 5500 K and 6500 K, respectively. Two different decorative materials were decorated on both sides of the tunnel sidewalls within the range of 3 m, as shown in Figures 1 and 2. Firstly, the energy-storage and luminescent coating with high reflectance was painted on the tunnel sidewalls, and the indexes of lighting environment and visual characteristics of human eyes in the tunnel were tested. The energy-storage and luminescent coating is a modified rare-earth aluminate material and using energy-storage and luminescent coating for auxiliary lighting in tunnel can increase the brightness because of its high reflection; moreover, the energy-storage and luminescent coating can release the stored energy in the form of light for emergency lighting [28; 29]. Then, the energy-storage and luminescent coating was covered with gray cloth, and the same indexes were tested. The appearance and interior decoration of the tunnel decorated with energy-storage and luminescent coating and gray cloth are shown in Figure 2.
###### Abstract  
This paper investigates the use of atmospheric pressure plasma (APP) treatment for improving the surface hydrophobicity of rayon flock synthetic leather with organosilane precursor (tetramethylsilane (TMS)). Plasma deposition of TMS is regarded as an effective, simple, and low-pollution process. The results show that a highly hydrophobic surface is formulated on the rayon flock synthetic leather. Under a particular combination of treatment parameters, a hydrophobic surface was achieved on the APP-treated sample with a contact angle of 135\({}^{\circ}\) while the untreated sample had a contact angle of 0\({}^{\circ}\) (_i.e._, the fabric surface was completely drenched immediately). Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Photoelectron Spectroscopy (XPS) confirmed the deposition of organosilane.  
atmospheric pressure plasma; hydrophobic; rayon flock; synthetic leather; contact +
Footnote †: journal: Applied Sciences
# Correcting Ski Resort Trajectories Extracted from Video
###### Abstract  
Accidents at ski resorts present a significant safety concern, underscoring the urgency of addressing these issues. This study aims to enhance safety protocols by providing resort operators with more effective data analysis methodologies. We present and test methods for analyzing video footage from downhill ski areas where detailed information needed to correct errors, due to perspective, lens distortion, etc., is not available. This can be the case, e.g., for webcam footage and accidental videos (e.g., on YouTube). As much of this kind of video is available and could be used for statistical analysis, methods are needed that allow for at least for an approximate consideration of such aspects. Using video footage obtained from various ski resorts, we developed and tested several methods for analyzing and correcting the trajectories of skiers captured in the videos. Our analysis revealed that using five reference lines, along with the most appropriate x and y coordinate corrections, is an effective approach for achieving precise calibration of the video data. The corrected trajectory data, adjusted for perspective distortions and scaling inaccuracies, provide a detailed basis for analyzing skier behavior and identifying high-risk zones prone to collisions.  
ski resort safety; trajectory correction; video data analysis; accident prevention Article
## 4 Conclusions  
In this paper, a novel structure of a dual material gate-engineered heterostructure junctionless tunnel field-effect transistor (DMGE-HJLTFET) with a lightly doped source was constructed and researched to improve the performance of conventional InAs/GaAs\({}_{0.1}\)Sb\({}_{0.9}\) HJLTFET. Similar to the conventional HJLTFET, the DMGE-HJLTFET still adopted a InAs/GaAs\({}_{0.1}\)Sb\({}_{0.9}\) heterojunction at the source and channel interface and employed a polarization electric field at the arsenic heterojunction induced by the lattice mismatch to improve band to band tunneling (BTBT) current. In addition, based on the gate-engineered concept, the gate electrode of DMGE-HJLTFET was divided into three parts to improve the ON-state current and suppress the OFF-state current simultaneously. In this regard, a lightly doped source was used to further optimize the ON-state current and the OFF-state current of this device. In this simulation, the effects of the source doping concentration, polar gate workfunction, control gate workfunction, tunnel gate workfunction and auxiliary gate workfunction on the performance of the DMGE-HJLTFET were researched systematically to optimize the overall device performance. Simulation results indicated that the DMGE-HJLTFET provided superior metrics in terms of logic and analog/RF performance as compared with the conventional HJLTFET, the maximum ON-state current and transconductance of the DMGE-HJLTFET increased up to \(5.46\times 10^{-4}\) A/\(\mu\)m and \(1.51\times 10^{-3}\) S/\(\mu\)m at Vds = 1 V. Moreover, SSave of the DMGE-HJLTFET was as low as 15.4 mV/Dec at low drain voltages. Also, the DMGE-HJLTFET could achieve a maximum \(f_{\mathrm{T}}\) of 423 GHz at Vgs = 0.92 V and a maximum GBW of 82 GHz at Vgs = 0.88 V, respectively. Therefore, the DMGE-HJLTFET could be used in future low power and high frequency applications.  
Conceptualization, H.L., T.H., W.L., S.C., S.W. and H.X.; methodology, H.X. and T.H.; software, H.X. and W.L.; validation, H.L. and H.X.; formal analysis, H.X.; investigation, H.X.; resources, H.X.; data curation, H.X.; writing--original draft preparation, H.X.; writing--review and editing, H.X.; visualization, H.X.; supervision, H.X.; project administration, H.L.; funding acquisition, H.L.  
Figure 11: (**a**) The cut-off frequency (\(f_{T}\)) characteristic curves of the HJLTFET and DMGE-HJLTFET; (**b**) the gain bandwidth (GBW) characteristic curves of HJLTFET and DMGE-HJLTFET.  
**Funding:** This research was funded by National Natural Science Foundation of China (Grant No. U1866212) and in part by Foundation for Fundamental Research of China (Grant No. JSZL2016110B003) and Major Fundamental Research Program of Shaanxi (Grant No. 2017 ZDJC-26) and Innovation Foundation of Radiation Application (Grant No. KFZC2018040206).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improved Multi-GNSS PPP Software for Upgrading the DEMETRA Project Time Monitoring Service
## 4 Conclusions  
Three updated versions of Atomium PPP software were presented in this paper: Atomium PPP with GPS + Galileo measurements; Atomium PPP with receiver clock constrained model; Atomium PPP with integer ambiguity resolution. Two kinds of experiments were built to test the performance of their clock solutions. Using Galileo in addition to GPS in PPP computation reduced the noise of the clock solution inside the daily batches, while the long-term stability was hardly improved due to the presence of daily jumps. The basic function of constrained PPP is to reduce the short-term noise of the clock measurements on good quality clocks as hydrogen masers, by restraining the short-term thermal noise. In addition, two more application scenarios for constrained PPP were introduced in this paper. First, constrained PPP can keep providing continuous clock solution during the period when the receiver experiences short-term loss of lock. Second, constrained PPP can be used to retrieve true clock solutions from very noisy measurements as long as the related clock frequency data can be estimated from external products, e.g., from TWSTFT or optical link measurements, without introducing their calibration errors at the phase measurements. Finally, integer PPP shows an advantage to the current float ambiguity PPP techniques on the clock frequency transfer stabilities at mid-term to long-term averaging times; a frequency stability of \(1\,\times\,10^{-16}\) can be reached at an averaging of 4 days to 10 days, depending on the time link. It is expected that when the Galileo integer ambiguity satellite products are regularly available, the integer PPP with GPS + Galileo will further improve the current GNSS clock frequency transfer capacity.  
Currently, all the updated Atomium PPP software have been integrated into the DEMETRA server to provide the daily solutions for users who need post-processing services with better performances. GPS and Galileo daily quality monitoring data are both available for the users. And the test and the integration of the Atomium Galileo PPP hourly solution is in progress.  
Conceptualization, P.D. and W.H.; methodology, P.D. and W.H.; software, W.H.; validation, P.D., G.S. and I.S.; formal analysis, W.H.; investigation W.H., P.D., G.S. and I.S.; resources, P.D., G.S. and I.S.; data curation, W.H.; writing--original draft preparation, W.H.; writing--review and editing, P.D., G.S. and I.S.; visualization, W.H.; supervision, P.D., G.S. and I.S.; project administration, G.S. and I.S.  
This research received no external funding.  
The authors would like to thank [PERSON], the director of the Time Department at the International Bureau of Weights and Measures (BIPM), for formerly coordinating the DEMETRA project and her  
Figure 19: ADEVs between integer PPP and NRCAN PPP results.