esa-sceva/satcom-chunk-collection huggingface dataset:
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Device Discovery
#### 7.1.1 Synchronization  
The synchronization in D2D communication occurs between devices in the system and the BS. The time frame and the scheduling will be shown by the BS. However, the device discovery will face a challenge when the secondary device that are associated with the primary devices are outside of the BS coverage. The devices in the asynchronous discovery situation require continuous searching for different devices in proximity [10].
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 3 SCUC with HVDC Constraints
### UC Constraints
#### 3.2.2 Security Constraints of AC Power System  
1. Power balance of buses  
Constraints (24) and (25) are the power balance at each bus \(m\) and at each time period \(t\). It balances the power generated by generators, power inflows with the demand and power outflows.  
\[\sum_{i\in N_{m}}P_{G,i}^{t}\times u_{i}^{t}=P_{L,m}^{t}+V_{m}^{t}\sum_{n\in m}V _{n}^{t}(G_{mn}\cos\theta_{mn}^{t}+B_{mn}\sin\theta_{mn}^{t}) \tag{24}\]  
\[\sum_{i\in N_{m}}Q_{G,i}^{t}\times u_{i}^{t}=Q_{L,m}^{t}+V_{m}^{t}\sum_{n\in m} V_{n}^{t}(G_{mn}\sin\theta_{mn}^{t}-B_{mn}\cos\theta_{mn}^{t}) \tag{25}\]  
\[mm\in N_{B},\,t\in T\]  
2. Transmission power constraints  
Equation (26) computes power flows through lines between bus \(m\) and \(n\), while constraint (27) limit theses power flows.  
\[PL_{mn}^{t}=V_{m}^{t}V_{n}^{t}\sin\theta_{mn}^{t}/X_{mn} \tag{26}\]  
\[PL_{mn,\min}\leq PL_{mn}^{t}\leq PL_{mn,\max},\,mm\in N_{B},t\in T \tag{27}\]  
3. Bus voltage constraints  
Constraint (28) denotes that voltage of bus \(m\) would fluctuate between their allowed ranges.  
\[V_{m,\min}\leq V_{m}^{t}\leq V_{m,\max},m\in N_{B},t\in T \tag{28}\]  
In conclusion, Formulas (1-28) constitute the whole SCUC model for the AC/DC hybrid system. Compared with the existing UC models, Formulas (10-14) not only keep minimum frequency higher than minimum frequency requirement when HVDC blocking happens, but also avoid transmission lines' power due to the power's wide transfer after secondary frequency regulation; meanwhile the constraints shown in Formulas (15) and (16) ensure voltage stability in the power grid near HVDC. As a result, the optimal scheduling of the introduced UC model could improve the reliability and economic efficiency of the AC/DC hybrid power grid.
## 3 Simulation Models
### Conduction-Convection-Based Model  
In this case, two further assumptions are made as concerns the modelling of the liquid PCM flow, namely that liquid PCM is Newtonian and the flow is laminar. The continuity, momentum and energy balance equations are written as follows:  
\[\
abla\cdot\overline{v}=0 \tag{4}\]  
\[\rho_{PCM}\ \left(\frac{\partial\overline{v}}{\partial t}+(\overline{v}\cdot \
abla)\overline{v}\right)=-\
abla p+\mu^{\prime}_{PCM}\
abla^{2}\overline{v }+\overline{F} \tag{5}\]  
\[\rho_{PCM}\ c^{\prime}_{p,PCM}\frac{\partial T}{\partial t}+\rho_{PCM}c_{p,PCM }\overline{v}\cdot\
abla T=k_{PCM}\
abla^{2}T \tag{6}\]  
where \(p\) is the pressure, \(\mu^{\prime}_{PCM}\) is the modified dynamic viscosity, and \(\overline{v}\) is the velocity vector.  
In Equation (5), \(\overline{F}\) represents the Boussinesq approximation, which is added to the momentum equation for including the buoyancy effects, and it is evaluated according to Equation (7):  
\[\overline{F_{b}}=\rho_{PCM}\overline{\xi}\beta(T-T_{M}) \tag{7}\]  
where \(\overline{\xi}\) and \(\beta\) are the gravitational acceleration and the isobaric thermal expansion coefficient, respectively.  
The effective heat capacity \(c^{\prime}{}_{p,\text{PCM}}\) is calculated as previously described in Section 3.1, while the modified dynamic viscosity \(\mu^{\prime}{}_{\text{PCM}}\) is evaluated according to Equation (8), in order to force zero velocity in the solid PCM.  
\[\mu^{\prime}_{PCM}=\mu_{PCM}(1+S(T)) \tag{8}\]  
where \(\mu_{\text{PCM}}\) is the dynamic viscosity of liquid PCM. The variable \(S\) is given by:  
\[S(T)=C\frac{(1-\varphi(T))^{2}}{(\varphi(T))^{3}+\delta} \tag{9}\]  
In Equation (9), the constant \(\delta\), typically fixed to \(10^{-3}\), serves to prevent null denominator, while the constant C affects the PCM flow into the phase transition zone, and it is usually between \(10^{3}\) and \(10^{10}\). Table 2 reports the values of the parameters \(T_{M}\), \(\Delta T_{M}\) and \(C\) used in this work. This combination of values, which were chosen among different tested ones, is the one presenting the best match between experimental and numerical results.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Ultra-Dense Networks (UDNs)  
The fast evolution of wireless cellular networks forces us to pay close attention to developing technologies that are critical for network performance enhancement. With the introduction of modern 5G wireless communications, the network's complexity will be more difficult. UDNs are considered the basic core of B5G systems. Without losing generality, UDNs are the fundamental technology that fulfills the essential demands of massive traffic requirements to be met by 2022 and beyond. The UDN is a network  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
distribution technique that involves the addition of low-power nodes to the network to strengthen hotspots, eradicate blind spots, enhance the coverage of the network, enhance energy efficiency, and complete spectrum resources utilization [69; 110]. There are two types of BSs in UDNs: fully functioning access nodes such as Pico and Femtocells and accessory functioning access nodes such as relays and remote radio heads (RRHs) [111; 112]. Indoor femto-cells have three modes of operation: open, closed, and hybrid. In the open-access mode, all Ues have unrestricted or priority access to the AP, but in the closed-access mode, a group of Ues is designated for scheduled transmission. In hybrid mode, all Ues have restricted access to the serving node [113]. The spectrum reuse factor may be considerably optimized by densely deploying SCs, which enhances network capacity, particularly in densely populated areas. The convenient UDNs scenarios are apartments, stadiums, offices, campuses, subways, and residential areas. A wide range of services will be available for each scenario, including HD-TV, online gaming, virtual reality, augmented reality, live video streaming, video conferencing, cloud storage, high-definition image upload/download, intelligent home control, etc. [114; 115]. UDN's fundamental structure is defined in terms of SC or density of the access point, where the number of serving access points (AP) is equal to or more than the users' number [116]. The general configuration of the suggested B5G UDNs is illustrated in Figure 11.  
Figure 11: General structure of the proposed B5G UDNs.
# Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
## 4 Experiments
### Implementation Details
#### 4.1.3 Training Details  
Following the experimental setup of DiffusionDet [24] and detectron2 [45], we used ResNet-50 [46] as the backbone, with the FPN [40] as the default setting. Except for ResNet-50, which was initialized with pretrained weights from ImageNet-1K, all other layers were initialized using Xavier init [47]. We used the adamW optimizer [48] with a default training schedule of 450 K iterations on the COCO dataset, a weight decay of \(10^{-4}\), and an initial learning rate of \(2.5\times 10^{-5}\). The learning rate was reduced by a factor of 10 at 350 K and 420 K iterations. For the RUOD dataset, training was conducted on a single 3090 GPU with a batch size of 8, and the iteration count was adjusted to 73.5 K according to the batch size. The weight decay and learning rate remained the same, with decays at 58.8 K and  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **Params** & **AP** & \(AP_{50}\) & \(AP_{75}\) & \(AP_{s}\) & \(AP_{m}\) & \(AP_{l}\) \\ \hline Faster R-CNN [13] & 42 M & 40.3 & 61.0 & 44.0 & 24.0 & 44.1 & 51.4 \\ DETR [21] & 41 M & 42.0 & 62.4 & 44.2 & 20.5 & 45.8 & 61.1 \\ Cascade R-CNN [43] & 77 M & 44.3 & 62.4 & 48.5 & 26.8 & 47.8 & 57.4 \\ Deformable DETR [20] & 40 M & 44.3 & 63.2 & 48.6 & 26.8 & 47.7 & 58.8 \\ Sparse R-CNN [23] & 106 M & 45.0 & 64.1 & 48.9 & 28.0 & 47.6 & 59.5 \\ DiffusionDet [24] & 111 M & 45.5 & 65.2 & 48.8 & 28.4 & 47.9 & 61.4 \\ DiAtda & 50 M & 46.4 & 65.7 & 50.2 & 29.0 & 49.8 & 60.6 \\ \hline \hline \end{tabular} Main results for the COCO 2017 validation set compared with popular methods reproduced in mudfection [44]. DETR uses 100 queries and DiffusionDet uses 500 noises, while other methods, including ours, only use 300.  
\end{table}
Table 1: COCO results.  
68.5 K iterations. We did not modify the data augmentation, which was consistent with DiffusionDet.
# Evaluation of the Barriers to Maintenance 4.0 for the Textile Industry via Pythagorean Fuzzy SWARA
## 3 Materials and Methods
### Barriers to Maintenance 4.0
#### 3.1.4 Human Resources and Training Main Criteria  
* Lack of technical skills among maintenance staff: Maintenance 4.0 is about the digitalization and automation of industrial machines, systems, and devices. Therefore, technical personnel need to have high digital skills as well as technical knowledge [1; 23].
* Lack of structured training programs for capacity building: Maintenance 4.0 focuses not only on technological infrastructure, but also on the integration of human resources into this technology. The absence of structured training programs will cause personnel to not be able to use these digital systems effectively [23].
* Skill shortages in the labor market: Shortages in the labor market are a factor affecting the success of Maintenance 4.0 implementation. A qualified workforce is needed for successful implementation [56].
* Lack of cultural readiness for AI and digital transformation: Maintenance 4.0 applications do not only mean technological change and transformation, but also the formation of a new digital culture within the organization. In organizations without a digital transformation culture, employees cannot be enthusiastic about producing and adopting new ideas [1; 13; 23].
## 3 Results and Discussion
### Characterization Analysis of PAP\({}_{\text{UAE}}\) and PAP\({}_{\text{HWE}}\)
#### 3.3.3 Purification and Monosaccharide Composition Analysis  
After the decolorization and deproteinization, the preliminarily purified PAP was loaded into a Sephadex G-100 column (2.6 cm\(\times\)60 cm, Yuanye Bio-Technology Co., Ltd, Shanghai, China) and eluted with deionized water. A single peak was obtained. The total sugar content of PAP\({}_{\text{HWE}}\) and PAP\({}_{\text{UAE}}\) were 94.35 \(\pm\) 1.07% and 91.65 \(\pm\) 0.88%, respectively. To determine the monosaccharide composition, PAP was hydrolyzed and converted into its acetylated derivatives for GC-MS analysis. The results of the GC-MS analysis are shown in Figure 5. The PAP products obtained using HWE and UAE were mainly composed of rhamnose, arabinose, mannose, glucose, and galactose in approximate molar ratios of 0.19:3.42:5.32:87.03:3.86 and 0.23:2.96:6.27:85.22:5.32, respectively. These results suggested that the composition of the monosaccharides did not change but the molar ratio changed during the ultrasonic treatment. This phenomenon will be given more attention in our future work.
## 2 Materials and Methods
### Research Methods
#### 2.3.4 Partial Correlation Analysis  
Partial correlation analysis is a method used to analyze the correlation between two variables while controlling for the linear influence of other variables [37]. It has been widely employed in studies examining the correlation between the NDVI and climatic factors [38]. Typically, one of the precipitation or temperature factors is treated as a constant and the partial correlation relationship between the other factor and the NDVI is studied [39]. The correlation coefficients were calculated for each pixel by using the temperature and precipitation data from 2001 to 2020 in conjunction with the corresponding NDVI data. This process ultimately yielded information about the correlation between temperature, precipitation, and NDVI changes across the entire study area. Its calculation formula is as follows:  
\[r_{xyz}=\frac{R_{xy}-R_{xz}R_{yz}}{\sqrt{(1-R^{2}_{xy})}\sqrt{(1-R^{2}_{yz})}} \tag{12}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**NDVI Variation Trend** & **Sustainability** & **Forecast Trend** \\ \hline slope \(>\) 0 & H \(>\) 0.6 & Continuous improvement \\ slope \(>\) 0 & H \(\leq\) 0.4 & From improvement to degradation \\ slope \(>\) 0 & 0.4 \(<\) H \(\leq\) 0.6 & Random change \\ slope \(<\) 0 & H \(\leq\) 0.4 & From degradation to improvement \\ slope \(<\) 0 & H \(>\) 0.6 & Continuous degradation \\ slope \(<\) 0 & 0.4 \(<\) H \(\leq\) 0.6 & Random change \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sustainable Prediction of the NDVI Variation Trend in Shanxi Section of the Yellow River Basin.  
where \(r_{xy,z}\) is the partial correlation coefficient and \(R_{xy}\), \(R_{yz}\), and \(R_{xz}\) are the correlation coefficients between the NDVI, precipitation, and temperature. The significance test of the biased correlation coefficient adopts the t-test method, and its calculation formula is as follows:  
\[t=\frac{r\sqrt{n-m-1}}{\sqrt{1-r^{2}}} \tag{13}\]  
where \(t\) is the statistic, \(r\) is the partial correlation coefficient, \(n\) is the number of samples, and \(m\) is the number of independent variables.
## 5 Results and Discussion
### 100 kHz Results  
The results from the healthy panel and the panel with the disbonded region are shown in Figure 4. The results from the healthy panel show that the higher wave velocity S\({}_{0}\) Lamb mode is relatively unaffected by the presence of the stiffener and is mostly transmitted straight through. The slower wave velocity A\({}_{0}\) mode however is greatly affected. At 105 \(\upmu\)s in Figure 4 the A\({}_{0}\) mode interacts with the stiffener resulting in a reduction in amplitude on the other side demonstrating that the majority of the wave energy is being either attenuated or reflected by the stiffeners rather than transmitted.  
The results from the panel with the disbond show that in this case the S\({}_{0}\) Lamb mode interacts with the disbonded region of the stiffener. This is seen in Figure 4 at 30 \(\upmu\)s as an area of higher amplitude in the disbonded region. Similarly, a higher level of wave amplitude is observed as the A\({}_{0}\) mode also interacts with the disbonded region shown at 75 \(\upmu\)s. As the A\({}_{0}\) mode continues to interact with the disbonded region, transmitted (right of the stiffener) and reflected (left of the stiffener) conical  
Figure 4: 100 kHz three-component displacement magnitude showing Lamb wave interaction with the healthy and disbonded stiffened panel.  
diffraction fringes are observed, clearly seen from 105 \(\upmu\)s onwards. The behaviour of the Lamb wave outside of the disbonded region is similar to that of the healthy panel.
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 2 The Density Evolution and Gaussian Approximation of the BP Algorithm
### The Gaussian Approximation of the BP Algorithm  
For detailed analysis of BP algorithm one can refer to Reference [16]. According to Reference [16], we can get  
\[m_{v}^{(l)}=m_{0}+(dv-1)m_{u}^{(l)}, \tag{6}\]  
\(m_{v}^{(l)}\) and \(m_{u}^{(l)}\) are the means of \(v_{i}^{(l)}\) and \(u_{j}^{(l)}\), respectively, and \(m_{0}\) is the mean of channel initial messages. Defining \(\phi(x)\) in Reference [16] and take the expectation of Equation (1) to get Equation (7), where \(\phi(x)\) is shown in Equation (8).  
\[m_{u}^{(l)}=\phi^{-1}[1-(1-\phi(m_{0}+(dv-1)m_{u}^{(l-1)}))^{dc-1}] \tag{7}\]  
\[\phi(x)=\left\{\begin{array}{l}\sqrt{\frac{\pi}{x}}(1-\frac{10}{f_{x}^{2}}) e^{-\frac{x}{4}},x\geq 10\\ e^{(-0.4527x^{0.86}+0.0218)},0<x<10\end{array}\right. \tag{8}\]  
The BER of LDPC codes can be calculated as [21]  
\[P_{e}=\int_{-\infty}^{0}\frac{1}{\sqrt{4\pi m_{v}}}\cdot\exp\left(-\frac{(x-m _{v})^{2}}{4m_{v}}\right)dx. \tag{9}\]
## 3 Results and Analysis
### Datasets and Proprecessing  
In order to investigate the effectiveness of the research method in this paper for road information extraction from high-resolution remote sensing images, two publicly available datasets are used for validation, namely the GF2-FC [24] road dataset and the CHN6-CUG [25] road dataset.
# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
## 5 Conclusions  
In this paper, a two-stage deep learning framework based on cable icing images was designed. In the first stage, U-Net was used to segment icicles and icing accurately. In the second stage, ResNet50 was employed to classify the segmentation results and determine the icing level. This novel approach allowed for the utilization of image information to effectively locate the current icing area and classify the icing level with a reasonable degree of accuracy. The MIoU of U-Net for icing feature segmentation reached 82.10%, and the average accuracy was 96.66%. ResNet50 classified the icing degree grade, and the average accuracy was 95.71%. Compared to the method of only using the icing pixel area to quantify the icing degree grade of the stay cable, this method has a better generalization ability and a more comprehensive application range. This research not only provides a theoretical basis for the study of two-stage deep learning models but also provides technical support for the accurate classification of the icing degree of stay cables and the accurate positioning of the robot.  
[PERSON] conceived, designed, and analyzed the test results; [PERSON] provided ideas and modification suggestions; [PERSON] wrote the manuscript; [PERSON] used software; [PERSON] performed project administration; [PERSON] and [PERSON] participated in the processing of some data; [PERSON]. Investigation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **A/\%** \\ \hline U-Net + VGG16 ([PERSON], 2014) & 86.32 \\ U-Net + AlexNet ([PERSON] et al., 2012) & 88.95 \\ U-Net + ResNet50 ([PERSON] et al., 2016) & 95.71 \\ U-Net + GoogleNet ([PERSON] et al., 2015) & 89.43 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of classification accuracies of stay cable icing masks using different methods.  
Figure 13: The performance of the segmentation model on the surface ice layer category and the icicles category. (**a**) Surface ice segmentation perfomance, (**b**) Icicles segmentation perfomance.  
All authors have made equal contributions to the revision of the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the key projects of Hubei Provincial Department of Education (No. D20211403) and The Key Research amd Development Program of Hubei Province (No. 2023 BCB116).  
Not applicable.  
We declare that all authors have given informed consent.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Case Study
### Membership Degree Calculation  
To eliminate the influence of randomness in the final risk assessment values during the calculation of membership degrees using the cloud model, this study repeated the calculations 2000 times using Equation (11), based on the index values given in Table 5. The average value of these iterations was then used to determine the membership degrees of each index within the cloud model, forming the membership degree matrix. Taking maintenance operation \(A_{1}\) as an example, Table 8 shows the membership degree values for the 15 risk indices in relation to the cloud model. These values were subsequently converted into a fuzzy evaluation matrix using Equation (13), and the results are shown in Table 9.
# Influence of Atmospheric Pollutants and Weather Conditions on Agricultural Productivity in Italy
## 2 Materials and Methods
### Ozone Effect on Crops  
ANOVA results have shown that the factor that has a significant effect on the plant growth of durum wheat, common wheat, and corn is ozone. In fact, for all the crops considered, the ANOVA analysis rejected the hypothesis \(H0\), accepting the hypothesis \(H1\) for which there is a significant influence of the factors. Among the variables considered, the most influential appears to be ozone since it seems to have a more marked difference between the averages of the populations than the others.  
In Figure 11, Palermo appears to be the only city not to be affected by the presence of pollutants. This can be seen from the minimum levels that ozone reached in 2019, which did not affect the production of durum wheat, which remained constant during all the years considered in the study, 2012-2021 (Figure 11). Statistical tests show that the durum wheat production in Bari is affected by the presence of polluting substances present in the atmosphere, especially ozone. In fact, a small decrease in crop production can be seen as the concentration of the pollutant increases. This is demonstrated in the last two years considered in the study, 2020-2021. But, considering all the values over the years, the trend remains constant over time (Figure 2).  
Figure 10: Millimeters of rain recorded in the Italian cities in the life cycle period for corn—April/November from 2012 to 2021.  
Figure 9: Millimeters of rain recorded in the Italian cities in the life cycle period for wheat—October/June from 2012 to 2021.  
The production of common wheat in Ferrara is greatly influenced by the concentrations of ozone in the atmosphere. There is a maximum annual production of 279,792 tons (yield of 8.2) of durum wheat up to an ozone concentration of 48.7 \(\mu\)g/m\({}^{3}\). For ozone concentrations greater than 47 \(\mu\)g/m\({}^{3}\) there are common wheat production values that reach a maximum of about 280,000 tons, as in the case of the city of Padua.  
The study for the corn production in Padua and Venice obtains a very variable ozone concentration. At ozone concentrations between 35 and 50 \(\mu\)g/m\({}^{3}\) there is a production of between 290,000 and 305,000 tons of corn per year (yield of about 8 and 13); at ozone concentrations between 65 and 70 \(\mu\)g/m\({}^{3}\) the production yield decreases from 8.7 to 4.4, and the annual production of corn is between 277,000 and 302,000 tons for both cities considered.
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 5 Discussion
### System Data Processing  
In this study, we use two epoch system solutions with linear interpolation to compare reference data epoch solutions based on Equation (13). The STD of the difference between the system and reference epoch solutions ranges from 2 to 5 cm. However, given that reference data provide solutions every 10 min, and that the water surface is relatively smooth and stable most of time, applying moving average and filtering techniques, e.g., a 10 min time window, could further improve 10 min epoch solutions.  
The system's averaged solution in this study is calculated based on Equation (9), which is a trade-off solution utilizing low-frequency _uls_ measurements. If the _uls_ measurement sampling rates can be increased, we can calculate averaged solutions using Equation (14) or Equation (15),  
\[\overline{wl}_{sys}=\frac{1}{n}\sum_{i=1}^{n}\left(h_{i}^{apc}-uls_{i}\right)+ tot_{offset} \tag{14}\]  
\[\overline{wl}_{sys}=\frac{1}{m}\sum_{i=1}^{m}\left(h_{i}^{apc}-uls_{i}\right)+ tot_{offset}, \tag{15}\]  
depending on whether \(h_{i}^{apc}\) or \(uls_{i}\) is used as the reference epoch (\(n\) is the GNSS epoch number, \(m\) is the ultrasonic sensor epoch number). The modified averaged system's performance can be evaluated in the future if high-frequency _uls_ measurements are available.