esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land-Surface Reservoirs  
The land surface is an important component of the Earth system and numerical weather prediction and climate models are evolving continuously to include more of its natural complexity. The relevance of an accurate description of the land surface for atmospheric modelling has been widely established in the scientific community for over thirty years [66; 67; 68; 69]. The land controls the partitioning of available energy at the surface between sensible and latent heat fluxes, which in turn has a strong impact on the atmospheric heat and moisture budgets, especially on diurnal time scales. The land also determines the partitioning of available water between evaporation, drainage and runoff. The water fluxes and storage terms interact with the land morphology forming lakes and rivers. The land surface influences weather and climate on all time and space scales [70; 71], and responds actively to modifications of weather patterns and climate change [72]. The role of the land surface in Earth system models is to provide a consistent description of the water, energy and carbon exchanges (between atmosphere, biosphere, hydrosphere, and cryosphere) at various time scales ranging from hours to decades. Many sensitivity studies have shown that the description of physical processes of continental surfaces can significantly affect the prediction of meteorological variables such as precipitation, wind or temperature in the lower troposphere [73; 74; 75; 76; 77]. Evapotranspiration directly affects weather parameters such as temperature,humidity, boundary layer development and clouds [78; 79; 80; 81; 82]. Furthermore, a strong feedback between evaporation and precipitation exists which appears to be negative at the convective scale [83; 84; 85; 86] and positive at the continental scale [84]. This feedback involves soil water in the root-zone layer, which is one of the most important variables controlling large-scale continental summer temperature extremes [87; 88].  
The way land surfaces store and regulate water and energy fluxes is firstly controlled by soil moisture in the unsaturated zone, snow, ground water, lakes and open water. Such water bodies are de facto reservoirs of energy and water and have a \"memory\" much longer than atmospheric components (on the order of a few days). Secondly, the energy and water fluxes are also controlled by land use/management and biosphere [72], which are complex, heterogeneous and difficult to characterize in practise. Modelling upgrades can be clustered in two areas of development:  
* Enhanced realism of the representation of water and energy stocks in soil, snow and inland water bodies, via parameterisations and physiography revisions.
* Improved fluxes for land-atmosphere energy and water exchanges, inclusion of natural and anthropogenic carbon emissions, and improved river discharges.  
These developments are summarised in the following subsections: soil, snow, vegetation and river hydrology development are illustrated in light of the EO dataset that have guided the model improvements.
# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 1 Introduction
A conceptual pipeline summarizing these steps is represented in Figure 3.  
Figure 1: Illustration of differences introduced by marketing strategies, namely differences in flavours between same parent brands (**a**), and small variations in the packaging of the same product (**b**). Figure 2: Same brand with a different appearance and orientation. Logos extracted from LogoDet-3K dataset [10]. In the literature, there are some reviews of product recognition approaches in retail stores. The authors of [11], focused on stock tracking and planogram matching, and described classification systems of products on shelves; however, only traditional methods were covered. In [12], the authors conducted a comprehensive review of the detection methods of retail products. Despite including deep learning methods, the feature descriptors are predominantly hand-crafted. A more recent paper [1] presented a review of publications based on deep learning retail product recognition, without emphasizing the difficulties and solutions raised in the detection of objects in the supermarket. This extensive survey includes several areas of retail recognition, such as self checkout, stock management, planogram and products on shelves, but label information was not considered. The addition of text information has improved the ability to distinguish between visually identical products, but the research based on this perspective of retail product recognition is just starting. Therefore, reviewing grocery label analysis systems and complementary information about scene text recognition can help advance this new field. This paper provides a complete overview of suggested approaches for grocery product label processing, with a particular emphasis placed on assisted analysis for customer support, discussing the main problems and shortcomings and suggesting future research directions. It also offers a general panorama of technological applications. The execution of new applications requires adequate datasets; hence, we included an extensive list of publicly available datasets that can be used by researchers. Unlike other previous reviews, this paper addresses techniques for the full pipeline, from product detection on the shelf to label processing, including individual object selection automatic image acquisition and label detection. The organization of the rest of the paper is as follows: Section 2 identifies some existing applications of retail product recognition. Section 3 illustrates techniques of pre-processing and image selection. Section 4 describes the available datasets, both those related to grocery products and those related to text recognition. Section 5 reviews the works for grocery product detection and recognition, while Section 6 introduces an overview of product label analysis. Finally, Section 7 analyses the current challenges and offers some guidelines for future research.
# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 2 Research Methodology and EWMA-KAN-Transformer Model
### Structure of KAN-Transformer Prediction Model Based on EWMA Data Processing  
To reduce noise in the wind power data and enhance the ability of the Transformer model to capture nonlinear relationships, this paper proposes an ultrashort-term wind power prediction model based on EWMA data processing and KAN-Transformer. The model is illustrated in Figure 2.  
In Figure 2, the process begins by applying EWMA data processing to the original data to reduce the impact of noise in the wind power data on model prediction accuracy. The processed data are then used with a sliding window approach to obtain inputs for the Encoder and Decoder, which are embedded through the Embedding layer and then processed through the Encoder-Decoder architecture to extract historical features. Finally, the output from the Decoder layer is fed into the KAN layer, constructed with a cubic B-spline function, to obtain the final prediction result, thereby enhancing the model's ability to capture nonlinear relationships.  
The experimental dataset used in this paper comes from a publicly available dataset of a wind power plant with an installed capacity of 200 MW [31]. Data are recorded every fifteen minutes, totaling 35,041 time steps. The feature columns are numbered and named  
Figure 2: This article proposes a model structure.  
as shown in Table 1, where columns 1-13 represent the introduced multivariable features, and serial 14 represents the wind power feature.
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 2 Related Work
### Comparisons and Summary  
The three aforementioned approaches mitigate broadcast storm with different strategies. Selective broadcast pre-selects some neighbors as eligible forwarders, which generates extra transmission overhead when exchanging neighborhood information. In an ad-hoc network, the topology can be very dynamic thus maintaining neighborhood information is costly. Probabilistic broadcast sets up a certain rate to rebroadcast the interest at each node, which doesn't incur additional overhead but may lower packet delivery ratio. Deferred broadcast defers rebroadcast by overhearing packets from its neighbors first. It does not introduce extra transmission overhead. Unlike probabilistic broadcast that uses forwarding rate to indicate forwarding priority, deferred broadcast uses a deferred timer to indicate forwarding priority. In probabilistic broadcast, each node decides its own forwarding process based on its forwarding priority, while in deferred broadcast, nodes' forwarding priority can affect their neighbors' forwarding process. Deferred broadcast takes advantage of the shared wireless medium to overhear packets in-the-air and decides to cancel the pending interest if it overhears an interest/data packet with the same name. It reduces more unnecessary transmissions than probabilistic broadcast. In addition, unlike probabilistic broadcast which may decrease delivery ratio because it uses a fixed forwarding rate, nodes with higher forwarding priority in deferred broadcast will have 100% probability to forward the interest. Therefore, deferred broadcast suits ad-hoc NDN better in terms of achieving higher delivery ratio and lower transmission overhead.  
The key part of designing a deferred broadcast scheme is the selection of the metric to indicate the forwarding priority. LFBL [24] uses a distance metric, which can be calculated using signal strength or hop count. Rapid forwarding [5] uses the distance to previous forwarder to rank each forwarder. The nodes that are farther from the previous forwarders will have higher priorities. DASB [29] uses the distance and angle to set up deferred timers. Those works are challenged in cases that the nodes carrying the desired data is unknown when the interest has to be broadcasted to all directions to reach the potential data carrier, thus reducing interest coverage ratio and bring extra transmission overhead. In this work, we focus on the CAV applications where the location information is available. Our work takes the opportunity that data location can be used in interest forwarding. Our deferred broadcast scheme takes consideration of both intermediate nodes' locations and the data carriers' locations. This will lead to a balance in terms of the number of rebroadcasts, the delay and the interest coverage in vehicular scenarios.
## Experimental Results
### Comparative Study of Ground-Level and Above-Ground Deployment
#### 5.1.3 Spatial Properties  
In practice, the reliability of many hardware-based localization algorithms [35; 36] depends on the correlation of the RSSI with the distance. However, this correlation is questioned by our experimental results, as well as by other previous field tests [20]. For example, links 1-2 and 3-4 presented in Figure 5 covered the same distance (3 m), but they were located at different positions. However, as Figure 5 highlights, the properties of these two links were different, especially in the case of the ground-level deployment. Indeed, the shape of the road around each node might be different from one sensor to another, which leads to different link properties at different points in the network. We argue that such spatial considerations, as well as temporal variations, must be taken into account by WSN designers.
## 7 Conclusions  
This paper proposes a two-round optimization design method for aerostatic spindles considering the FSI effect. The optimal design of an aerostatic spindle is implemented as a case study to detail the design process of the proposed approach. A set of optimal design parameters are acquired, and experiments are implemented to verify the simulation results. The main conclusions are drawn as follows:  
1. A two-round optimization design method is proposed to facilitate the design of the aerostatic spindle with consideration of the FSI effect, and an aerostatic spindle is optimized as a case study that validates the effectiveness of the proposed design method;  
2. In the first-round design stage of the two-round optimization design method, a set of optimized design parameters of the aerostatic thrust bearing is acquired by analyzing the performance of the aerostatic thrust bearing with varying geometrical parameters of the restrictor. Compared with the initial design, the LCC is improved by 28.3%, the stiffness is improved by 44.2%, and the VFR is reduced by 27.2%;  
3. In the second-round design stage of the two-round optimization design method, a recommended range for the design of thrust plate thickness is acquired by calculating the stiffness and natural frequency of the spindle with different thrust plate thicknesses. Compared with the initial design, the first-order natural frequency of the aerostatic spindle increases by 45.3%;  
4. Experiments are conducted, and the experimental results agree well with the simulation results, which verifies the accuracy of simulation results.  
Conceptualization, Q.G.; methodology, L.L.; software, Q.G. and S.G.; validation, M.Z. and F.Z.; formal analysis, Q.G. and S.G.; investigation, Q.G. and S.G.; data curation, M.Z.; writing--original draft preparation, Q.G.; writing--review and editing, L.L.; visualization, Q.G.; funding acquisition, Q.G. and S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Heilongjiang Postdoctoral Foundation, grant number LBH-Z19149, the Leading Wild Goose Program Scientific Research Project of Harbin Institute of Technology, grant number XNAUEA5640202220-07, the National Natural Science Foundation of China, grant number 51705501, and the State Key Laboratory of Mechanical Transmissions in Chongqing University, grant number SKLMT-KFKT-201801.  
Figure 11: Comparison between the simulation result and experimental result.
###### Abstract
It is not only based on the information of the data itself and the relationship between various indicators, but also considers the influence of actual conditions and empirical judgments [80]. Considering the available period of precipitation datasets, the update of precipitation retrieval algorithms and the reliability of datasets [81; 82; 83; 84], this paper selects three daily satellite precipitation data of ERA5, IMERG and CMORPH for the period of 2010-2014 in the Three River Headwaters region of western China. Cloud Water Path (CWP) and CER were employed as auxiliary variables on a daily scale. According to the relationship between daily scale precipitation and the physical, optical properties of clouds, the GWR Model was constructed to downscale the precipitation data. Finally, the downscaling data were calibrated and validated by integrating the rain gauge data through the GDA and GRA methods. On this basis, combined with the validation data, the analytic hierarchy process (AHP) and entropy weight (EW) method were employed to merge different satellite precipitation data to obtain daily high-resolution precipitation data in the study area for the period of 2010-2014.
### Electromagnetic Safety of Identifier  
#### Determination of the Threshold Magnetic Field Strength  
In the case of electromagnetic information security of passive data carriers, a very important parameter is the threshold strength of the electromagnetic (magnetic) field, at which the identifier is able to accumulate the energy necessary for operation and at the same time start communication with the reader. In order to determine this parameter, the measurement system (Table 1) is presented in the Figure 7.  
In the case of NFC and MIFARE identifiers, a harmonic magnetic field of a certain intensity is not sufficient to start data transmission through the identifier, as is the case with LF (125 kHz) RFID systems. In order to start the transmission, it is necessary for the reader to send an appropriate request. This feature of the system made it necessary to use a ready-made NFC reader in the measurements instead of a simple coil (magnetic antenna) connected to a signal generator. The NFC reader used, and the characteristics of its radiation are shown in Figure 8.  
During the measurement, the ID card was held close to the reader until the communication was established (which was signaled by a diode and a sound by the reader). Then, a magnetic antenna connected to a spectrum analyzer was placed at the same distance and the level of magnetic field strength was read (after taking into account the values of the antenna's correction factors).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Type** & **Producer** \\ \hline Spectrum Analyzer & & \\ (10 kHz–150 MHz) & & \\ Reader LF-band RFID & & \\ and HF-band RFID & & \\ ID card & & \\ Magnetic Antenna & & \\ (1 kHz–30 MHz) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of apparatus used to carry out the measurements.  
Figure 6: Manchester coding.  
Figure 7: Measuring system for determining the minimum value of the magnetic field strength necessary for the ID to operate.  
The measured level of the magnetic field necessary for the ID to operate (threshold level) is similar to that of LF-band RFID and amounted to approx. 85 dBuA/m.
## 3 Background  
### Cyber Risk Management  
Cyber Risk Management (CRM) refers to identifying, assessing, and responding to cyber risks associated with an organization's information systems [1]. It aligns with broader enterprise risk management by embedding security measures into organizational processes to ensure operational continuity and protection against malicious cyber activity [21].  
Scholars have noted the importance of a systematic approach to CRM. Such an approach begins with clearly understanding an organization's assets and potential vulnerabilities [22]. The organization must then assess the threats to these assets, ranging from internal, such as employee negligence, to external, such as APTs [23]. The next step involves determining the potential impacts of these threats and the organization's risk tolerance [24]. Based on these findings, risk mitigation strategies are then formulated and implemented.  
The effectiveness of CRM can be improved by continuously updating risk assessment methodologies to keep pace with evolving threats [2]. While no one-size-fits-all approach to cyber risk management exists, standards such as COBIT 2019 [25], NIST in Special Publication 800-39, CIS Controls and ISO 27005 [26] provide guidelines for managing cyber risks.  
However, the complexity and dynamic nature of cyber threats require organizations to consider more sophisticated methods. Applying artificial intelligence (AI) and machine learning in cyber risk management can help in cyber threat real-time detection, mitigation, and response [24]. Despite their potential, applying these technologies needs scrutiny due to the risks associated with false positives and unintentional algorithmic biases.
## 3 Spacecraft Dynamics  
A model of the dynamic behavior of a satellite equipped with two solar panels is developed here as a study case to test the active vibration control strategy on the innovative lightweight composite structure.
# Monolithic Double Resonator for Quartz Enhanced Photoacoustic Spectroscopy
## 4 Conclusions  
A novel design was proposed, using 2 mRs in a monolithic setup, to increase the acoustic pressure actuating on the two external sides of QTF prongs. The double mR was 3D printed, showing the ability and flexibility of the stereolithography method in printing small and complex parts.  
While off-beam configuration might be less sensitive than on-beam, its advantages on the laser beam alignment and physics phenomena decoupling, offers many interests. The double mR spectrophone combine these advantages with a better acoustic excitation, thus, there is an increased value of the QEPAS signal. The strength of the double mR method relies on the large enhancement of the mR and QTF coupling. The surface of the prong was normal to the wave propagation direction, thus the acoustic pressure efficiently actuated the QTF. The weakness of the technique was the sensitivity to the QTF positioning, compared to the conventional off-beam. However, the required spatial accuracy (10-30 \(\upmu\)m) was definitely reachable, using micropositioners.  
The gap between the 2 mRs was adjusted at the printing level, simplifying the process of the QTF positioning. An enhancement could be obtained by adjusting the mRs dimensions to better fit the QTF frequency. It would be interesting to evaluate the use of custom QTF [9; 19] specifically developed to enhance coupling due to an external prong excitation. Finally, the high-resolution 3D-printing should be considered in the design of acoustic resonant cells, such as Helmholtz configurations coupled with mechanical resonators.  
[PERSON] was in charge of the methodology, the software, and the investigation, under the supervision of [PERSON]. He was assisted by [PERSON], [PERSON] and [PERSON] for data curation. The article was written by [PERSON] and [PERSON], reviewed and edited by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ANR MULTIPAS project (ANR-16-CE04-0012). It was partially supported by the French \"Investment for the Future\" program: EquipEx EXTRA, (ANR-11-EQPX-0016) and Montpellier University MUSE SENSIR project (ANR-16-IDEX-0006).  
Not applicable.  
Figure 8: (**a**) 1f optimized double mR QEPAS signal (black). In red, the HITRAN database [18] absorption coefficient of the gas line (1 ppmv) is reported. (**b**) Allan-Werle deviation calculated from a 15 nm acquisition of the 1f QEPAS signal.
# A Packet Collision Reduction Based on Reselection for LTE V2X Mode 4
## 3 Proposed Method
### Overlapping Avoidance (OA) Method  
In this section, we propose an Overlapping Avoidance (OA) method to address the problem of the overlapping selection window. In the OA method, the UE first defines the subframe as \(m^{\prime}\) in which the last packet with \(P_{rspp}=0\) was received within the last 100 subframes from the resource selection. Then, the UE moves \(R_{x,y}^{\mathbb{S}_{B}}\), which is a subset of \(\mathbb{S}_{B}\), to \(\mathbb{S}_{B1}\) if \(y<m^{\prime}+100\) and \(\mathbb{S}_{B2}\) if \(y\geq m^{\prime}+100\), respectively. Finally, the UE randomly selects a resource from \(\mathbb{S}_{B2}\) if \(|\mathbb{S}_{B2}|\
eq 0\) and \(\mathbb{S}_{B1}\) if \(|\mathbb{S}_{B2}|=0\). Now, \(\mathbb{S}_{B1}\) and \(\mathbb{S}_{B2}\) mean the set of resources that can be selected by the UE that transmits the packet with \(P_{rspp}=0\) and the set of resources in which the UE has free from the possibility of the selection, respectively. Note that the selection window of the UE is up to 100 subframes, but the resources with \(y=m^{\prime}+100\) are included in \(\mathbb{S}_{B2}\) because the resources in \(m^{\prime}+100\) are excluded by Step a.  
Figure 9 shows an example using the OA method. This figure indicates that UE2 has reselected in subframe 950, and UE1 reselects in subframe 1000. In this case, \(m^{\prime}=950\) because UE1 has received a packet with \(P_{rspp}=0\) in subframe 950. UE1 divides \(\mathbb{S}_{B}\) into \(\mathbb{S}_{B1}\) and \(\mathbb{S}_{B2}\), where \(\mathbb{S}_{B1}\) includes the candidate resources between subframe 1001 and 1049, and \(\mathbb{S}_{B2}\) includes the candidate resources between subframe 1050 and 1100. As a result, UE1 can avoid the same resource selection as UE2 by preferentially selecting a resource from \(\mathbb{S}_{B2}\).  
Figure 8: An example of the PA method.