esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Carbon Certification Systems in Agriculture: A Review
## 3 Existing Low-Carbon Certification Systems
### EU Countries
#### 3.1.4 Wineries for Climate Protection--Wfcp (Spain)
For N\({}_{2}\)O, detection is possible when its concentration exceeds the detection limit of the device, which is 3 ppmv, as in cases of emissions from specific agricultural activities (e.g., use of nitrogen fertilizers). Under normal conditions, the concentration of N\({}_{2}\)O in the atmosphere is around 337 ppb, which is well below the device's detection threshold. However, when increased emissions occur due to agricultural activities, N\({}_{2}\)O concentrations will surpass this threshold and will be recorded. The LoRa telemetry system is a network that collects and manages real-time data from agricultural operations for monitoring farm activities, GHG emissions, and climatic  
Figure 6: Schematic diagram of the components of the Life ClimaMED infrastructure. parameters. It consists of IoT devices and sensors that monitor various parameters, such as temperature and humidity; the recorded values are transmitted via the LoRaWAN protocol that is suitable for rural areas and allows long-range communication with low energy consumption. The telemetry system also includes various interfaces and an electronic environment where users can view maps, charts, and real-time data. The system is user-friendly, requires minimal maintenance, and offers scalability and interoperability. It also supports WiFi and Ethernet connections for devices with higher data requirements, such as cameras or LIDAR systems. The \"Center for Monitoring and Management of Greenhouse Gases (CMM)\" is the operational hub developed for MINAGRIC to monitor and manage GHG emissions [62]. The platform collects, processes, and evaluates records from LIDAR devices and meteorological stations installed on participating fields, providing a comprehensive and visualized real-time view of the emissions of all three GHGs. In addition to individual records per field, the platform enables MINAGRIC to monitor the overall evolution of CO2, N2O, and CH4 emissions for all participating fields. Finally, producers receive via the platform a statement of their emissions for each gas (CO2, CH4, and N2O) per cultivated hectare and per kilogram of produced product. The objective of MINAGRIC is to engage as many producers as possible in this system, thereby collecting the maximum amount of data from the country's cultivated land. This will allow the authorities to gain insight into emissions from the agricultural sector and to encourage and support producers in reducing these emissions. More details about all aspects of the Life ClimaMED certification tool and the responsibilities/ duties of all involved parties can be found in a recent publication [39].
## 2 RISs and Their Integration with NTNs
### Application of RISs to NTN Communications
#### 2.5.1 LEO Satellites  
In 6G, LEO satellites may provide connectivity in remote and underserved areas. The LEO satellite has drawn the most attention these days among the various kinds of satellites working at varied altitudes in orbit because of its low height as well as the benefits that come with it, including less signal attenuation, a shortened transmission time, and a reduced expense in comparison to other satellites on a higher altitude [30; 70; 71]. The system model of an RIS-assisted HAP is illustrated in Figure 3, which includes an energy-constrained HAP with \(M\) antennas, an RIS equipped with \(N\) reflecting elements, and \(K\) single-antenna users [72]. The LEO satellite's transmission bandwidth is still significantly greater than ground transmission, resulting in orders-of-magnitude of more significant route degradation. As a result, high-power broadcasters and high-sensitive receptors are typically used in LEO satellite technology to make up for the high route loss [73]. Nevertheless, doing so inevitably raises the expense and energy usage of the gear. As a substitute, the conventional passive reflect array has been used in satellite/deep-space telecommunications to accomplish unidirectional broadcasting for route loss mitigation without appreciably raising the equipment cost and energy usage [73; 74; 75; 76; 77].
# Indoor Air Quality Assessment in Grocery Stores
## 3 Results and Discussions
### Volatile Organic Compounds  
All assessed mean levels of VOCs at all GSs were below the limit value of 262 ppb (corresponding to 600 \(\mu\)g.m\({}^{-3}\)) established by Portuguese legislation [34], as shown in Figure 5. The overall mean of VOCs at all GSs was 51.2 \(\pm\) 20.6 ppb, ranging from 11.1 \(\pm\) 12.7 ppb (at GS2, Location B) to 109.5 \(\pm\) 18.6 ppb (at GS10, Location C).  
The cleaning procedure was revealed to have a significant impact on the mean VOC levels found in the GSs, with GSs that did the cleaning daily at closing having significantly lower mean VOCs levels (37.6 \(\pm\) 20.8 ppb) than GSs that did the cleaning daily at opening or more times per day (61.8 \(\pm\) 24.0 ppb) (ANOVA: F(1,23) = 7.003, \(p\)-value = 0.014). The type of products used for cleaning also showed to have a significant impact on VOC levels (ANOVA: F(2,22) = 3.497, \(p\)-value = 0.048). However, when performing the post hoc test to understand the differences between the influence of the different types of used products on VOC levels, no significant difference was found (the \(p\)-value for all the cases was above 0.050). The biggest difference was found for GSs that used products of domestic use (38.9 \(\pm\) 21.7 ppb) and GSs that used products of domestic use but designated as environmentally friendly (77.3 \(\pm\) 12.7 ppb) (\(p\)-value = 0.057).  
Similarly to with CO\({}_{2}\), it was also found that the number of clients had an impact on the VOCs levels found in the GSs (ANOVA: F(2,22) = 5.800, \(p\)-value = 0.009), with GSs that had fewer than 100 clients per day being significantly lower (36.8 \(\pm\) 20.0 ppb) that GSs that had more than 200 clients per day (75.2 \(\pm\) 21.5 ppb) (\(p\)-value = 0.009). GSs with selling areas equal or below to 110 m\({}^{2}\) were also found to have significantly lower levels (36.0 \(\pm\) 18.2 ppb) than GSs that had selling areas of higher than 110 m\({}^{2}\) (51.2 \(\pm\) 25.3 ppb) (ANOVA: F(1,23) = 12.109, \(p\)-value = 0.002).
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 5 Implementation  
The computation of the Zernike moments of a shape described by a surface triangular mesh is performed either with the exact Algorithm 2 or with the finite precision Algorithm 3. Both algorithms rely heavily on the functions that compute the geometric moment associated with a triangle of the mesh, TRIANGLE (Algorithm 1) and TRIANGLEREC (Algorithm 5). We identify three elements that are essential for the implementations of those algorithms:  
1. Definitions of the quadrature rules for integration on a triangle,
2. Efficient computations of \(R_{nl}(r)\) and \(Q_{nl}(r)\),
3. Efficient computations of the spherical harmonics \(Y_{l}^{m}(\theta,\phi)\).  
We describe the specifics for those three elements.
## 4 Signal Attenuation Model for Inter-Satellite Terahertz Communication
### Signal Attenuation Model for Inter-Satellite Terahertz Communication without Pointing Error
\[\theta_{y}=\lim_{z\rightarrow\infty}\frac{2R_{\theta}}{L}=\frac{2\lambda}{\pi R _{0}} \tag{7}\]  
The beam launch angle and the far-field launch angle are determined by the transmitter structure of the terahertz communication system. Combined with Equations (3)-(7), the power of the transmitter and the power of the receiver can be represented by Equation (8). \[ \begin{split} P_{T}&=\int_{0}^{D_{Rx}}T(r,0)2\pi rdr =\frac{\pi T_{0}R_{0}^{2}}{2}\bigg{[}1-\exp(-\frac{2D_{Rx}{}^{2}}{R_{0}^{2}}) \bigg{]}\\ P_{R}&=\int_{0}^{D_{Rx}}T(r,L)2\pi rdr=\frac{\pi T _{0}R_{0}^{2}}{2}\bigg{[}1-\exp(-\frac{2D_{Rx}{}^{2}}{R_{0}^{2}})\bigg{]}\end{split}  \tag{8}\]  
It is defined \(h\) as signal power attenuation, that is, the ratio of received power to transmit power. Therefore, the signal power attenuation of the inter-satellite terahertz communication system under the condition of no pointing error is Equation (9). \[h=\frac{P_{R}}{P_{T}}=\frac{\int_{0}^{D_{Rx}}T(r,L)2\pi rdr}{\int_{0}^{D_{Tx}} T(r,0)2\pi rdr}=\frac{1-\exp(-\frac{2D_{Rx}{}^{2}}{R_{0}^{2}})}{1-\exp(-\frac{2D_{ Tx}{}^{2}}{R_{0}^{2}})} \tag{9}\]  
where, \(D_{Tx}\) and \(D_{Rx}\) are the radius of the transmitting antenna and the radius of the receiving antenna.
## 2 Applications of ANNs to Renewable Energies
### Applications of ANNs for Solar Energy Systems  
Within solar energy, the ANN technique has proven to be an alternative to conventional methods, providing great benefits in terms of precision, performance, and modeling. The study indicates that the advantage of ANN techniques over conventional techniques is that they do not require knowledge of internal system parameters, require less computational effort, and offer robust outputs to multivariate problems. NN modeling requires data representing the history, the current performance of the real system, and a correct selection of a NN model. [PERSON] et al. [85] conducted an overview of the different AI techniques for sizing PV systems. The research shows that one of the advantages of AI in modeling PV systems is that it allows good optimization in isolated areas, where meteorological data are not always available. [PERSON] and [PERSON] [86] have applied AI techniques to model, predict, simulate, optimize, and control photovoltaic systems. The applications of ANNs to solar energy go beyond that, as there is also research such as the one carried out in [42], in which the application of the ANN technique seeks to optimize and predict the performance of the different devices involved in a solar energy system such as solar collectors, heat pumps, or solar air. The research shows how the application of ANNs can save time and reduce the financial costs of the system since it is not necessary to carry out so many experimental tests to determine the relationship between the input and output variables. Another application of ANNs is shown in the research of [87], where the performance of solar collectors is predicted, thus improving the efficiency of the system as a whole. The developed model also showed advantages over conventional computational methods in terms of calculation and prediction time. Solar radiation data are very important because in most cases they are not available due to the lack of a meteorological station. It is therefore necessary to have techniques to accurately predict solar radiation. ANNs are the solution to the problems of conventional methods [88]. Different ANN models have been applied for solar irradiance prediction, such as the MLP neural network, the RBF neural network, or the general regression neural network (GRNN). The different studies have been classified, taking into account different factors such as network structure and type, input/output configuration, or the activation function and tuning algorithm employed, as is shown in Table 2.  
Figure 3: Inputs and outputs in ANNs applied to wind energy and wind speed prediction. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Authors** & **ANN Type and** & **Student** & **Journal** & **Country/** & **I0 Setting** & **Activation Function** & **Notes** \\ \hline \multirow{3}{*}{1} & [99] & RBF & & & & & & & & k-fold (validation) \\  & RBF & & & & & & & & & \\  & RBF & & & & & & & & & \\  & 6-11-24 & & & & & & & & &
# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
These characteristics commonly result in performance degradation of existing detection models, manifesting as false negatives and misclassifications. Although the YOLO series exhibits strong real-time performance and overall effectiveness, its capacity to extract and represent low-level features is significantly constrained under low-light conditions. These limitations often induce semantic inconsistencies and multi-scale feature imbalance during the fusion process, consequently impairing detection responsiveness and degrading boundary localization accuracy for maritime targets. To tackle these challenges, this paper proposes YOLO-SAR, a specialized low-light ship detection framework that integrates an expanded receptive field with an adaptive spatial feature fusion mechanism. Built upon YOLO11, the proposed architecture incorporates a SCINet module to improve input image quality and employs a cross-level perception structure coupled with a dynamic weighting fusion strategy, thereby enhancing detection precision and robustness under conditions involving low illumination, multiple targets, and scale variance. The major contributions of this study are as follows:  
(1) A method for constructing a low-light ship dataset is proposed, and its effectiveness is validated through comparative experiments based on gray value and HSV color space analysis. This approach partially addresses the limitations of existing open-source datasets. (2) To address the challenges of low-light ship detection, a dedicated framework named YOLO-SAR is proposed for inland river scenarios. This framework alleviates semantic conflicts and enhances multi-scale feature learning by integrating a Self-Calibrated Illumination Network, which recovers multi-scale feature details of ships in low-light images. Additionally, it introduces attentional convolution with expanded receptive fields to improve global context modeling and incorporates an adaptive spatial feature fusion strategy to overcome feature insufficiency. Together, these components significantly improve detection accuracy in such challenging environments. (3) Extensive experiments conducted on the self-constructed Lowship dataset and a real-world low-light vessel dataset demonstrate that YOLO-SAR achieves superior detection accuracy compared to existing methods, including YOLOv11. This framework lays a solid foundation for robust vessel recognition and tracking in canal scenarios and contributes to the advancement of intelligent shipping systems. The remainder of this paper is organized as follows: Section 2 reviews related work, focusing on advanced object-detection methods applied to ship detection and the baseline YOLO11. Section 3 details the proposed YOLO-SAR framework. Section 4 represents extensive experiments validating its effectiveness, and Section 5 concludes this study and discusses future research directions.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management
#### 4.3.12 Remarks on Projects Focused on ICT for in-Home and Building Energy Management  
All of the projects examined in Subsection 4.3, deal with energy management, optimization of energy consumption in buildings. Two of them involve the construction of a platform for data collection (ICE-WISH, E2 SoHo), whereas three of them refer to energy management in public buildings (SportE2, VERYSchool, Smartspaces). Smart meters play an important role for these projects, whereas information can be found in six of them about smart meter technologies. PLC technology is used for data transmission from the smart meter to the data concentrator in eSESH, SportE2, FINSENY projects. In these first two projects, wireless solutions have also been implemented for this transmission link, like ZigBee. Wireless technologies (ZigBee, GSM, GPRS) have been also used in the Smartspaces project. On the other hand, for the transmission link from the data concentrator to the control center, wireless solutions are used in five projects (ICE-WISH, eSESH, SportE2, FINSENY, Encourage). Furthermore, for the realization of an in-home/sensors network, ZigBee technology has been used in SportE2, FINSENY and Encourage projects. Other technologies for such networks have been also used, like wireless connections (Smart Build), RS485 (E2 SoHo), Ethernet (SportE2) and PLC (Encourage). Table 5 summarizes the characteristics of the projects focused on ICT for in-home and building energy management. Figure 4 shows a smart building and the various options it offers in terms of energy management.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Project** & **Aim** & **Characteristics** & **SM Technologies Used** & **Other Technologies** \\ \hline \multirow{4}{*}{ICE-WISH} & Energy conservation in households & Platform construction for sensor data collection, consumer interaction with energy results & Wireless (second link) & \\ \cline{2-5}  & Building Energy management & Construction of: a communications and data processing platform; User interfaces & & Wireless (ZigBee)/Wired (RS485) for sensors communication Data logger to router \(\Rightarrow\) wireless/wired Router to server \(\Rightarrow\) TCP/IP & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of projects focused on ICT for in-home and building energy management.
## 2 Methodology
### Differential Interferometric Processing  
GB-RAR is a zero-baseline observation compared with satellite-based SAR. Thus, flat-earth and topographic phases have no influence on the interferometric phase model. Therefore, the differential interferometric phase model obtained by comparing the phase information difference of the target points acquired at different times can be expressed as follows [39]:  
\[\phi_{diff}=\frac{4\pi d_{defo}}{\lambda}+\phi_{atm}+\phi_{noise}, \tag{1}\]  
where \(\phi_{diff}\) denotes the interferometric phase, \(\phi_{atm}\) represents the phase generated by the atmospheric disturbance between the radar and the target, and \(\phi_{noise}\) is the random noise phase. Given that high-frequency (20 Hz) dynamic monitoring was adopted in the monitoring of the WGC, the phase produced by the atmospheric effect is basically negligible after differential interferometric processing. After removing the noise phase, the interferometric phase was unwrapped in one dimension. The monitoring point of deformation \(d_{defo}\) in the line-of-sight (LOS) direction can be obtained using Equation (1). The deformation time series of the target points in the range direction can be derived based on the monitoring time span.
## 5 Results and Discussion
### Performance Comparison under Different Workloads
#### 5.6.3 Average PDR  
Figure 19 shows the PDRs of the considered networks under different normal and malicious node numbers, as given below.  
1. A total of 50 normal nodes and 1 malicious node. * Healthy network: Figure 19 shows that PDR decreases with the increase in nodes due to potential congestion and more collision chances. As the network grows denser, the potential for packet drops due to congestion increases. * Malicious network: The PDR decreases because the black hole node compromises more routes and drops many packets. However, its relative impact might be lessened in a more extensive network. * The NTRUEncrypt-secured network: The overhead from cryptography processing reduces the PDR compared to the 20-node secured network.
2. 50 normal nodes and 5 malicious nodes * Healthy network: Figure 19 shows that the PDR decreases with the rise in node numbers, resulting in increased communication congestion and collisions. This congestion leads to channel contention, causing more packets to be dropped. * Malicious network: The PDR in the 50-node scenario decreased compared to the 20-node malicious network case due to the high number of black hole-attacking nodes flooding the network and dropping more packets. * The NTRUEncrypt-secured network: The PDR decreases compared to the healthy network because even if the protection mechanism efficiently prevents black hole attacks, cryptographic processing and any potential inefficiencies in the mechanism lessen the PDR.
3. The 50 normal nodes and 10 malicious nodes. * Healthy network: Figure 19 shows that as node numbers increase, the PDR decreases. In areas of high density, the competition for medium access surges, causing packet collisions. Consequently, the PDR reduces. * Malicious network: Adding black hole-attack nodes intensifies the PDR reduction in the 50-node scenario compared to the 20-node scenario. * The NTRUEncrypt-secured network: The PDR is reduced compared to a healthy network due to cryptographic overhead and complexities in the proposed algorithm.
# Sulfur Development in the Water-Sediment System of the Algae Accumulation Embry Area in Lake Taihu
## 3 Results and Discussion
### Profiles of AVS in the Sediment  
The high content of the organic matter in the sediment core (Section 3.1) and the depletion of oxygen with the increase of the sediment depth (Section 3.2) led to the reductive condition in the deeper sediment. The concentrations of AVS increased with the sediment depth (Figure 4) because of the intensification of reduction conditions in the deeper sediment [55]. Significant differences in the AVS of the surface sediments was discovered between the different seasons (analysis A3 in Table S1, \(p\) = 0.020) and different sites (\(p\) = 0.025). The concentrations of AVS in the top 2 cm layer of the surface sediments were lower during January and April, which may have been because of the higher OPD and oxic conditions in the surface sediments during these two seasons [56]. However, a noticeable increase in AVS concentrations was observed in the surface sediments in July. Previous studies showed that the sulfate reduction and sulfide formation begins to occur when the oxygen concentration decreases below 0.05 mg L\({}^{-1}\)[56]. In addition, the high sulfate reduction in the surface sediments due to anoxic conditions and high microbial activity intensified the formation of sulfides [57]. Therefore, the concentrations of AVS in the surface sediments increased in July when DO concentrations were extremely low and the microbial activity was higher than that in other seasons [58]. The increase was prominent at sites S1, S2, and S3 because the oxygen concentrations were nearly 0 mg L\({}^{-1}\) across the SWI at these sites (Figure 2). The AVS concentrations in the top 1 cm layer of the surface sediments decreased again in October because of the increase in OPD. The increase in sulfides during the algal blooming seasons could cause a release of sulfides to the overlying water and increase the formation of atmentous FeS and MnS, resulting in the discoloration of water [40; 59]. This phenomenon has been observed in many algae accumulation areas in Lake Taihu and other waters [31; 60].  
Figure 3: Oxygen uptake rate (OUR) of the sediment during the field investigation period. Error bars indicate the standard deviation, n = 3.