esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Processing Method  
### Processing Flow  
For the above signal model, the amplitude errors, channel phase errors, and motion errors within the echo data need to be compensated before signal reconstruction and SAR imaging. The processing flow chart of the distributed SAR is shown in Figure 2.  
Figure 2: Block diagram of the processing flow.  
Firstly, range focusing is performed on the multi-channel echoes using range compression and then the range coregistration method proposed in [32] to eliminate envelope migration caused by the CTB and range sampling time delay. According to the measured value of the CTB, the slant range difference between channels can be obtained using Equation (2), and the phase error corresponding to the measured value of the CTB in Equation (1) is compensated. After the above processing, Equation (1) becomes  
\[s_{m}(\tau,\eta)=\Gamma_{m}\exp\bigl{(}j\phi_{m}^{\prime}(\tau)\bigr{)}\exp \biggl{(}-j\frac{4\pi\Delta R_{m0}(\eta)}{\lambda}\biggr{)}s_{m}^{\prime}( \tau,\eta) \tag{6}\]  
where \(\phi_{m}^{\prime}(\tau)\) includes the inherent phase error of the channel \(\phi_{m}\) and the phase error corresponding to the measurement of the CTB:  
\[\phi_{m}^{\prime}(\tau)=\phi_{m}-\frac{4\pi\Delta R_{m,\sigma}(\tau)}{\lambda} \tag{7}\]  
After compensating for the error terms in Equation (6) using the proposed method marked in the red rectangle, non-ambiguous HRMS SAR images can be obtained by the signal reconstruction method and SAR imaging processing. However, the residual errors in Equation (6) include channel errors and motion errors, which prevents the traditional channel error estimation method from being applicable. Therefore, a joint estimation method of the channel phase error and motion error for the distributed SAR on a single airborne platform based on the TDCM is proposed in Section 3.2.
## 4 Asset Digitalization Use Case: IoT and AHI App for a Bridge Maintenance  
### Introduction to the Use Case  
Bridges, as critical assets in transport infrastructures, bear significant management implications in terms of Total Expenditure (TOTEX) and associated risks. The need to control the condition of bridges is essential for proper physical, operational, and economic management, not just for individual bridges but for the entire asset portfolio managed by infrastructure asset managers. However, tools for effectively managing this type of infrastructure are currently limited. The real threat of climate change adds urgency to the demand for control, increasing uncertainty about infrastructure performance under evolving operating conditions.  
Presently, most bridges lack a real-time online monitoring system that applies comprehensively to the entire asset. The advancement of IoT networks with affordable costs opens possibilities for massive monitoring of distributed passive systems like [57] or [58], which use local SHM algorithms that are not sufficiently accurate, such as peak picking detection, or [59], which proposes a solution similar to the nodes presented here but with the need for a solar panel for power supply and unsecured communications (UDP), which justifies the development proposed here. In addition, the increased monitoring of bridges through IoT technology generates vast amounts of data, necessitating the application of advanced data analytics and machine learning for meaningful insights. A comprehensive interpretation of the data allows for a better understanding of bridge behavior and the identification of potential issues. Integrating data from multiple bridges provides a global perspective and aids in the development of predictive models.  
The research questions encapsulating the essence of this use case are outlined as follows:  
* What is the best way to implement a model-based approach for bridge digitalization to support maintenance and management objectives effectively?
* How can the integration of an IoT platform with short-term monitoring models and long-term frameworks like the Asset Health Index (AHI) be utilized to improve the digitalization and management of bridges?  
In the rest of this section, we will delineate the use case that serves as an illustration for the asset digitization process. This involves the introduction of key models tailored to assess various aspects of the bridge's lifecycle and performance. Firstly, the Asset Definition Model will be unveiled, providing a holistic framework that captures the intricacies of bridge data and information across diverse systems. Following this, we will delve into the Asset Criticality Model, designed to prioritize bridge assets based on their criticality and importance. Subsequently, we will introduce the bridge monitoring model. Lastly, we will explore the Intelligent Asset Management Models, specifically adapted to estimate the health index of the bridge.
## 4 Results and Discussion
### Identification of Gaps Related to the Integration of EPD Data in IFC
In particular, EPDs according to EN 15804+A2 require a detailed breakdown for GWP (GWP-total, GWP-fossil, GWP-biogenic and GWP-luluc), while the IFC schema contains only a generic parameter _ClimateChangePerUnit_ that can be mapped to GWP-total. Furthermore, the IFC schema does not include indicators such as the eutrophication potential (EP-freshwater, EP-marine and EP-terrestrial), the abiotic depletion potential for fossil (ADPF) and non-fossil (ADPE) resources, and the water deprivation potential (WDP). Ensuring unit consistency is another key aspect of successfully integrating EPD data into IFC. Currently, some of the EPD indicator units correspond to elements in _IfcMeasureValue_, meaning that values could be transferred to IFC, with some limitations:  
* Mass-based units such as kg CO\({}_{2}\) eq, kg CFC-11 eq, kg P eq, kg N eq, kg NMVOC eq, kg Sb eq can be aligned with IFC mass units _IfcMassMeasure_. However, indicator values are provided in mass equivalents, which do not ideally correspond to _IfcMassMeasure_ values. Furthermore, additional extension should be considered that would enable the direct representation of the mass of a specific substance rather than just mass itself. Since different substances have different environmental implications, IFC should differentiate mass-based units based on a substance. * Volume-based units such as m\({}^{3}\) for the WDP indicator. This unit could directly correspond to _IfcVolumeMeasure_. * User-defined units associated with environmental indicators, applying _IfcMeasureWithUnit_ or the attribute _UnitsInContext_ in the _IfcProject_ class, are not considered a viable option, as they endanger interoperability. Moreover, EPD contains specific units of measurement that IFC does not support natively. Specifically, units that require IFC schema extension or additional processing are listed below:  
* Energy-based units, such as MJ for the ADPF indicator. Even if IFC supports description of energy required or used, which is under _IfcEnergyMeasure_, the units (J, Nm) differ from the ones provided in EPD (MJ), meaning that additional processing would be needed. * Mole-based units, which represents mol H\({}^{+}\) eq, mol N eq are not defined in the IFC schema. Table 4 contains the indicators using the default characterization method taken from the European Platform on LCA for EN 15804 [47] and the correspondence with IFC. TRACI and EN 15804 can also be used for EPDs according to ISO 21930:2017. Therefore, the characterization method used must be declared with the relevant values.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Random Access  
Although scheduled access assigns all RUs to specific stations, random access leaves at least some RUs for the stations to be used in contention mode. Moreover, there is no multi-user RTS/CTS handshake to limit the overhead.  
The access point triggers UORA mode. The access point first broadcasts a list of available RUs for random access in a Trigger Frame Random (TF-R) [34]. This is a Trigger Frame (TF) with at least one RU for random access instead of scheduled access. The TF-R sets the Network Allocation Vector (NAV) for legacy stations instead of an RTS/CTS handshake. The TF-R also contains a list of available RUs and AID values. Here, an AID of 0 stands for the associated stations and an AID of 2045 stands for the unassociated stations. Any other AID value corresponds to scheduled access. Thus, an associated (resp. unassociated) station can contend for any RU with an AID equal to 0 (resp. 2045). In particular, a station does not create collisions with scheduled frames when it engages an association.  
Upon the reception of the TF-R, all associated/unassociated non-scheduled stations that have data to send try to access one of the available random-access RUs. Each station  
Figure 10: UL MU OFDMA scheduled and random access within a 40 MHz channel. (**a**) UL MU OFDMA scheduled access transmission over different RUs and traffic demands. (**b**) UL OFDMA random access (UORA) transmission over different RUs (stations 1, 2, and 3 have the Association Identifiers (AIDs) 1, 2, and 3, respectively. Station 4 is unassociated).  
adapts CSMA/CA by decrementing its backoff counter independently for each of the RUs assigned for the random access in the TF-R [35]. The winning stations transmit their data in the accessed RUs. Finally, the access point acknowledges the received packets by a Multi-Station Block Acknowledgment (MSBA).  
Figure 10b illustrates the mechanism with four contending stations. Stations 1-3 are already associated with a member of the BSS, having AIDs 1, 2, and 3, respectively, whereas station 4 is unassociated. As RU 3 is assigned to AID 2 (station 2), there will be no contention for its resources. Conversely, RUs 1 and 2 are for any associated station (AID 0). We assume that the backoff of station 1 is equal to zero and it competes in RU 1 (it would have selected RU 2 if its backoff was equal to 1). Station 3 has either no frame to send or its backoff value is strictly larger than 1 (it will be decremented by two after this UORA phase). Finally, RU 4 is reserved for the unassociated stations (AID 2045). We further assume that the backoff of station 4 is equal to zero so it tries to compete in RU 4.
# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 3 Description of Our Method
### Feature Extraction  
Pedestrian gait exhibits dual characteristics of continuity and periodicity, with the contact between feet and ground during walking generating vibration signals that embed dynamic information about the walker's speed. The smooth continuity and periodic repetition of gait impart a regular pattern to the walking state. Furthermore, variations in walking speed elicit adjustments in the frequency and intensity of foot-ground contact, which are manifested through the temporal features of these vibration signals. For instance, during accelerated walking, the frequency of foot contacts increases, accompanied by a concurrent amplification in the amplitude and frequency of the vibration signals. These integrated features constitute the complex motion patterns of pedestrian gait, holding significant research value for pedestrian position tracking and speed analysis. Building upon this analysis, the present study leverages the nonlinear learning capabilities of neural networks to conduct an in-depth investigation into the influence of factors such as step frequency, step length, gait cycle, symmetry, and foot landing patterns on the variation patterns of vibration signals.  
To establish a mapping relationship between vibration signal features and motion features, this paper employs a combination of global channel attention, local feature extraction, and temporal sequence feature extraction modules. Specifically, the global channel attention module uses CAM to learn the correlations between multiple footstep vibration signals by adjusting the weights of different channels in the signal matrix, focusing on channels with higher signal strengths and signal-to-noise ratios to extract global features and achieve effective information compensation. For local feature extraction, a 1D-ResNet structure is used to capture local time-frequency domain features of the signals, mining rich fine-grained motion information. The temporal feature extraction module employs Bi-LSTM to further extract and optimize the time dependencies of the features obtained from convolutional operations, providing a comprehensive understanding of the walking dynamics.
## 3 Results and Discussion
### Uncertainties and Future Improvements  
Since both wind erosion and evapotranspiration are very complex processes that are influenced by various interactive factors, it is very difficult to accurately estimate the soil conservation amount and ET, which inevitably leads to some uncertainties in the estimation results of the WUE-SC. First, this study has estimated the soil conservation amount with an experimental model, which may be not completely suitable for the lower Heihe River Basin, and it is necessary to adjust the model parameters according to field observation data in future research. Besides, the data accuracy also has great impacts on the estimation results of the WUE-SC, even if most of the model input data are from available data sources that have been validated. For example, the total soil conservation amount of the study area in this study (5.48-10.55 million ton) is much less than that in previous research (62.96 million) [46], but the average soil conservation rate in this study (55.32%-57.36%) is much higher than that in previous research (33.5%) [46]. On the one hand, this may be due to the inconsistency in the study area boundaries between previous study and this study, which may lead to some difference in the results. On the other hand, the wind velocity and the relative air humidity were set to be constant in previous research, which inevitably led to some bias in the estimation results. Although this study used the daily meteorological data from meteorological stations, which are very accurate, there may be still many uncertainties due to the data interpolation. In addition, the soil conservation amount in this study only involves the local soil erosion in the study area, and the sandstorms intercepted by the vegetation in the study area are not considered in this study, which may lead to underestimation of the soil conservation amount and consequently make the WUE-SC underestimated. For example, the Populus euphratica forests in Ejina Banner play an important role in intercepting sandstorms from Mongolia [50; 51], but it is very difficult to accurately estimate the amount of intercepted sandstorms, which has not been considered in this study. What is more, the biological characteristics of vegetation with the same vegetation coverage rate may also influence the soil conservation amount and the WUE-SC, the role of which has not been considered in this study since there is still a lack of related long-term observation data, and therefore it is necessary to carry out more in-depth research with more field observation data in the future.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 6 Conclusions and Final Remarks
In addition, the length of authentication tag (10) is parameterized. The strength of the authentication tags is that they are easily configurable to strike an optimal balance between preventing external and internal threats. The proposed sender authentication scheme is resilient against communication errors, propagation delays, and routing attacks. Our source authentication method might be of independent interest in the domain of radio networks with high congestion, where saving bandwidth is imperative, similar to the underwater environment. We conjecture that an example of such a network might be LoRaWAN based on sub-gigahertz frequencies [30] (the LoRa standard started out using the 2.4 GHz band, increasing the bit rate at the cost of a much smaller range [31]). We remark that the choice of whether to use our construction or TESLA depends mainly on the use case, most heavily on the intensity of communication as well as the size of the network; if communication is sparse and asynchronous, then self-contained and immediately verifiable packets with small authentication overhead may be preferable to TESLA's chaining approach. Conceptualization, M.K. and P.K. ; methodology, M.K., A.C., P.K. and D.K. ; software, P.K. and D.K. ; validation, M.K. and A.C.; formal analysis, M.K. and A.C.; investigation, M.K., A.C., P.K. and D.K. ; writing--original draft preparation, M.K., A.C., P.K. and D.K. ; writing--review and editing, M.K. and A.C.; visualization, M.K. and D.K. ; supervision, M.K. ; project administration, P.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
# Integration of Autonomous Wireless Sensor Networks in Academic School Gardens
## 1 Introduction
The novelty of the proposed system lies in two aspects of our work: On the one hand, since clean energy consumption for Smart Agriculture is gaining importance [35], Energy Harvesting (EH)-based EnOcean sensors have been used instead of the mostly used Raspberry Pi and Arduino-based devices. The main advantages of the commercial EnOcean devices are the much-reduced size of the motes and the EH system they have embedded. The drawback comparing to the Raspberry Pi and Arduino-based solutions present in the literature could be, for some applications, that the wireless network topology is limited to star topology type but in the presented application it is not a drawback due to the fact that all the motes are at a similar distance from the central node. On the other hand, the educational application itself is the second novel aspect of the presented study, since no other similar applications have been reported in the literature. Specifically, in this work wireless EnOcean transceivers with energy harvesting capabilities are employed in order to provide autonomous system operation, combined with an ad-hoc implemented application called MySchoolGardenApp. Information is retrieved in a cloud enable environment, providing remote data access and off-line processing capabilities, in order to adapt content to specific classroom needs. MySchoolGardenApp follows the trend marked by multiple initiatives within the educational community in which development environments such as Arduino/Genuino or Raspberry Pi are being employed in order to enhance learning outcomes in multiple disciplines, with a clear focus on Science, Technology, Engineering and Mathematics (STEM) [36; 37; 38]. The paper is organized as follows: Section 2 presents the scenario where the experiments have been done, the 3D Ray Launching simulation technique that has been used for the radio planning and the employed EnOcean devices for the creation of the WSN. Section 3 focuses on the radio planning simulation results and their analysis. In Section 4 the developed MySchoolGardenApp is presented and finally, in Section 5 the conclusions of the obtained results are commented.
# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
## 5 Numerical Evaluation
### A Real-World Application Scenario  
As discussed in this section, we examined three representative applications requested by the users, with each one characterized by different levels of computing demand and data-processing requirements of the MEC servers. Specifically, we considered three distinct user groups, with each one consisting of four users that requested applications for augmented reality, real-time data analytics, and content caching. These applications represented high, medium, and low computing demands, respectively, in terms of the amount of data processed at the MEC servers. The augmented reality applications required significant computational resources for real-time image processing, object recognition, and overlay rendering. These applications were latency-sensitive and demanded quick responses to ensure a seamless user experience. The users that ran the augmented reality applications offloaded tasks such as image processing and rendering to the MEC servers, which allocated substantial computing capacity to meet the high processing requirements. Real-time data  
Figure 4: Convergence analysis: (**a**) Reward and (**b**) Q-value convergence.  
Figure 3: Trust analysis.  
analytics involved processing streaming data for insights and decision making. These applications required moderate computational resources and were moderately latency sensitive. Content caching applications involved storing and retrieving frequently accessed data to improve the access speed and reduce the latency. These applications required relatively low computational resources and were less latency sensitive.  
Figure 5a illustrates the convergence of the OQ-UCB reward for the final action selected by the users with high, medium, and low computing demands. Figure 5b shows the average computing capacity allocated to the users by the selected MEC servers. The results indicate that the users that requested applications with higher computing demands experienced a higher OQ-UCB reward, as the servers allocated greater computing capacity to handle their computationally intensive tasks.