esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Distributed Tamper-Proof Storage Methods for Public Key Infrastructures
## 4 Alternative Implementations and Concept Analysis
### Application Server  
Since a stateless Application Server component is already present in the NFC-KE extension (s. Figure 2), it can be extended to support the verification of incoming signatures for new public keys. We chose to implement the Application Server using Node.js and JavaScript to achieve a universal basis for each DLT evaluation. Each of the proposed DLTs provide a custom NodeJS library and/or API endpoints for interacting with them [27; 28; 29]. Additionally, to reach enhanced modularity and exchangeability of the different technologies, object-oriented programming paradigms were leveraged, enabling a loosely coupled system.  
When a new public key needs to be added to the system, the Application Server will perform a validation of the corresponding signature, following the NFC-KE scheme. For this, it has to query the publicly available public key of the TPM \(PK_{TPM}\), which has to be stored immutably in the given DLT. The following subsections will describe this particular process in more detail. After querying \(PK_{TPM}\), the Application Server can use the key for signature verification of the newly sent Sensor Node's public key \(PK_{S}\). If the verification is successful, the key can be passed onto the given DLT and stored permanently.  
Another problem arises when querying the newly added Sensor Node's public key since the presented DLT platforms do not offer a _Key-Value_ mapping like HLF does. In the presented NFC-KE extension, the unique Sensor Node identifier \(ID_{S}\) is used as a key in the World State database and \(PK_{S}\) as the corresponding value. To map this scheme to the other DLT platforms, new concepts are needed and further described in the following subsections. Naive implementations could make use of the filename, e.g., Sensor_X.pem, and use it as an identifier. Since the presented DLTs are using content-based addressing, the unique content hash (called CID) of the file is used for addressing. Therefore, a mapping between the CID and the sensor id is required, resulting in the same fundamental problem.
## 1 Introduction
### Importance of Timing in Satellite-Based Yield Forecasting  
In EO data-driven models, the model's greatest flexibility lies in selecting optimal images based on the time-series of its training data. The timing of satellite measurements is one of the few variables where the model can make adjustments, making it a critical factor for yield forecasting. [PERSON] et al.'s review [29] also focused on the timing of satellite measurements across various field-scale models, specifically referring to the \"prediction horizon\", which is the time between the forecast and harvest. The prediction horizons proposed by the studies varied significantly, even for the same crop (wheat or corn) and geographical region (e.g., USA, China, Australia), ranging from weeks to months before harvest. Some studies included in the review, such as [PERSON] et al.'s (2020) [50], found no major differences in predictive accuracy across different crop stages, suggesting limited benefits from using images acquired after mid-season. Despite this, research often focuses on forecasting near harvest time, which is a trend also pointed out in the review by [PERSON] et al. (2020) [7]. [PERSON] et al. (2021) [51] found that for winter wheat in China, the most predictive phase was between emergence and tillering, while for rice, it was between tillering and booting. Overall, these reviews and case studies suggest that identifying the optimal period for yield prediction depends on factors like crop type, methods, and site-specific conditions.  
In ML models, the added value of identifying the most representative period for yield forecasting lies in the ability to deliver predictions earlier. This can provide a significant advantage in terms of timely decision-making and resource allocation. In statistical models and those integrated with process-based inputs, an additional benefit arises: the ability to select the most representative satellite images based on the identified period. This improves the accuracy of the models by ensuring that the data used are closely aligned with the critical phases of crop development.
# A Novel Piezoelectric Energy Harvester Using a Multi-Stepped Beam with Rectangular Cavities
## 6 Conclusions  
Multi-stepped beam based wide operating frequency harvester featuring rectangular cavities was proposed. The results were validated via both computer simulation of the analytical model and experimental investigation. It was shown that in the case of the second mode frequency, the output voltage of the proposed harvester is 1.69 times higher than the first mode of vibration. This results in enhanced power generation of the proposed harvesting structures compared to a conventional cantilever-based energy harvester. By structural tailoring without additional beam or mass, enhanced output in multi modes is achieved. The main benefit of the multi-mode energy harvesting is that, if the dominant frequency of ambient vibration in the environment changes from 20.42 Hz to 34.74 Hz, the proposed harvester will automatically tune to its second mode of vibration (34.74 Hz). In the conventional cantilever with identical first resonant frequency it is not possible, as its second resonant frequency is 6.27 times higher than its first mode and the displacement is very small. The generated voltage from the harvester is more than the conventional harvester, as three piezoelectric patches were attached in the maximum strain region. The harvester has a wide operating frequency range with enhanced output and hence can be used to source consumer electronics, wireless sensor networks and industrial condition monitoring systems etc. The results in terms of resonant frequency and magnitude in both the modes are promising and the next step is to bring the first two frequencies even closer and with more enhanced output. The broadband energy harvester designed with a multi stepped cantilever beam with rectangular cavities can be designed at a micro scale with a conversion and energy storage circuit.  
U.R. designed the idea, performed the analytical modelling and experimental design. U.G. and U.M. validated the analytical and experimental results, drafted the paper. S.-B.C. drafted and revised the paper.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 2 Knowledge Representation for VSP Wavefield Separation  
This section will detail the construction of knowledge representations for the target signals, used to guide the subsequent VSP upgoing and downgoing wave separations. This study extracts and utilizes characteristic information of the wavefield from multiple perspectives, constructing multiple forms of knowledge representations. These include physical knowledge representations derived from the energy flux density vector (Poynting vector), morphological knowledge representations based on shape priors, and directional difference knowledge representations based on wavefield propagation characteristics. These knowledge representations collectively form a comprehensive system for wavefield description.
## 7 Network Performance Analysis: NB-IoT vs. LoRaWAN  
Similarly to other IoT networks, NB-IoT network performance is evaluated based on data latency, throughput, battery utilisation, and security. In summary, we present the general NB-IoT testbed performance in the in-band mode of operation that uses the LTE PRBs as specified in the 3 GPP release 13 164 dB MCL. However, the general performance mostly differs regardless of any standard assumptions, depending on the mode of operation, the IoT device specifications, and the eNB and MME implementations. The many performance results, as presented in the 3 GPP TR 45.820 [78], and in [5; 79] are theoretical in light of the limited practical NB-IoT test network performance results. In [44], field testing was performed using different test equipment and was based on simply evaluating the performance of RF coverage and MNO's signal quality. The results of our NB-IoT and  
Figure 8: LPWAN integrated architecture.  
Figure 7: LPWAN network in operation.  
LoRa test networks is presented based on battery life, throughput, security, and latency QoS parameters. While quantifying the power consumption of the testbed, two approaches were used, namely the RTT of a PING request and the transmission of certain data sizes. With the testbed configured with a high NPDCCH repetition rate of 8 and a low coding rate in the case of NB-IoT to increase the detection rate, the power consumption analysis was considered robust. An average delay that was inclusive of the RRC transmission was obtained for TCP transmissions between the UE and server located within the same location. The equivalent average power peaks are shown in Figure 13.
# Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method
## 2 Structure of MEMS Accelerometer and Temperature Experiment  
### Work Mode Analysis  
The original signal collected in this paper is from a newly designed and manufactured high-G MEMS accelerometer (HGMA) [26; 27]. It has a high impact survival rate and high measuring range. The detection method of the HGMA is piezoresistance, and output signal is voltage. HGMA adopts four beams and an island structure. The frame, four beams and center mass are all rectangular, which is conducive to processing. Its structure diagram and parameters are shown in Figure 1.  
The coordinate system is constructed with the cross section of the accelerometer. The central dividing line of the cross section is the \(Z\) axis (the direction is positive when downward). The other middle line is the \(X\) axis, and the right direction is positive. The frame constructed is shown in Figure 1. The beam's length, width and thickness are \(a_{1}\), \(b_{1}\) and \(c_{1}\), respectively, and the mass's length, width and thickness are \(2a_{2}\), \(b_{2}\) and \(c_{2}\), respectively. The size values are shown in Table 1.  
The accelerometer structure mode distribution is simulated and analyzed through ANSYS soft at four primary resonance modes, and shown in Figure 2a-d as the first, second, third and fourth modes, respectively. The first mode mass moves along the \(Z\) axis, and is the working mode. The second mode mass rotates around the X-axis. The third mode mass rotates around the Y-axis. The fourth mode mass and frame move along the \(Z\) axis. The resonant frequencies of the four modes are shown in Table 2, which indicates that the 1 st order is the working mode of HGMA and its resonant frequency is 408 kHz. The 2 nd order mode resonant frequency is 667 kHz and has 260 kHz gap with the 1 st mode, which means that the coupling movement between these two modes is tiny and good for HGMA linearity.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Beam** & & & **Mass** & \\ \hline Parameters & length (\(a_{1}\)) & width (\(b_{1}\)) & height (\(c_{1}\)) & length (\(a_{2}\)) & width (\(b_{2}\)) & height (\(c_{1}\)) \\ Size/\(\mu\)m & 350 & 800 & 80 & 800 & 800 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of the HGMA.  
Figure 1: HGMA structure schematic and size.  
The structure of HGMA is made of silicon and bonding on glass. The main technological process is mainly divided into 12 steps. The SEM photos and CCD photos of the accelerometer structure are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode Shapes** & **1** & **2** & **3** & **4** \\ \hline Resonant Frequency/kHz & 408 & 667 & 671 & 1119 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Resonant frequencies of the four modes.  
Figure 3: (**a**) HGMA overall photo; (**b**) CCD photo; (**c**) SEM photo of HGMA.  
Figure 2: Mode simulation of HGMA structure: (**a**) (**b**) (**c**) and (**d**) are 1 st, 2 nd, 3 rd and 4 th order modes.
## 1 Introduction  
Statistics indicate that most vehicle collisions occur when pedestrians are crossing roads, particularly at unsignalized crosswalks [1, 2, 3]. According to the World Health Organization (2018) [4], pedestrians account for approximately 23% of global road traffic fatalities. As the most vulnerable road users, pedestrians face significantly higher risks of injury or death in traffic collisions compared to other participants, warranting focused research. Accurately modeling interactions between vehicles and pedestrians in traffic remains a challenge in traffic safety simulation, particularly at unsignalized intersections, where pedestrian behavior becomes difficult to predict due to the lack of traffic signal guidance.  
We incorporate pedestrian behavior into our pedestrian-vehicle interaction model using a generalized force framework. This force is inspired by the social force model, which has been widely adopted in existing models for urban traffic simulation [5, 6, 7].  
However, most of these models only consider repulsive-direction interactions, where pedestrians tend to move away from approaching vehicles. At unsignalized intersections, pedestrian-vehicle interactions often exhibit greater diversity. We refine the interaction forces between pedestrians and vehicles by improving the direction and magnitude of the forces that vehicles exert on pedestrians, which allows for more realistic pedestrian avoidance behaviors, such as gradually approaching and detouring behind a vehicle.  
Inspired by existing traffic models and pedestrian experiments [8], we propose a social force-based pedestrian-vehicle interaction model for simulating crossing behaviors at unsignalized intersections. All traffic participants in the scene are modeled within a force-based framework, allowing for the efficient generation of complex traffic environments. The overall framework of the model is shown in Figure 1, comprising a basic agent decision layer and an extended agent conflict interaction layer. In the agent decision layer, the pedestrian agent can make crossing decisions based on the current vehicle speed and vehicle gaps. In the conflict interaction layer, based on our pedestrian-vehicle interaction model, the pedestrian agent can make natural conflict avoidance behaviors according to the relative position and speed of the interacting vehicles.  
In summary, the key contributions of this work are as follows:  
* We enhance the ability of pedestrian agents to plan their routes when facing conflicting vehicles by refining the direction and magnitude of the forces they experience at different positions relative to vehicles. This makes pedestrian behavior more realistic and diverse.
* We conduct qualitative and quantitative analyses of both the trajectories and speed of pedestrian-vehicle interaction behaviors in different interaction scenarios and compare them with currently advanced social force-based pedestrian-vehicle interaction models. The results indicate that our model better replicates the observed pedestrian behavior.
## 5 Stability Analysis of TSWs
### Analysis of Influencing Factors
#### 5.2.2 Influence of Lateral Load Width on TSW Stability  
The effect of LW on stability was studied using the same TSW dimensions and LP set to 25 m. The LW values ranged from 1 m to 6 m. The variation in MPS and yield zones is shown in Figure 8.  
MPS increases linearly with LW at all key points for a fixed LP and geometry. The increase rate is highest at (0, h/2), and similar at (LP, 0) and (LP, h/2). With increasing LW, yield zones in all areas increase continuously, reflecting reduced stability.
## 4 The Improved NSGA-II
### Reinforcement-Learning Algorithm
#### 4.1.2 Design States  
The objective of using the reinforcement-learning algorithm is to find the optimal solution for the NSGA-II, while considering the diversity and balance of the optimization process. The design of the environment state for the algorithm should consider the fitness of the population and other factors, such as the crowding distance. In the algorithm, the construction of the environment state mainly considers the following aspects:  
\[f(x_{i}^{gen})=w_{1}f_{1}(x^{gen})+w_{2}f_{2}(x^{gen})+\cdots+w_{n}f_{n}(x^{gen}) \tag{17}\]  
\[g=\frac{\sum\limits_{i=1}^{N}f(x_{i}^{gen})}{\sum\limits_{i=1}^{N}f(x_{i}^{1})} \tag{18}\]  
\[P[N^{gen}]_{dis}=\sum\limits_{i=2}^{N-1}\sum\limits_{k=1}^{M}\left(P[i+1] \cdot f_{k}(x^{gen})-P[i-1]\cdot f_{k}(x^{gen})\right)/(f_{k}^{\max}-f_{k}^{ \min}) \tag{19}\]  
\[d=\frac{1}{P} \tag{20}\]  
\[balance=\sum\limits_{j=1}^{F}\min\sum\limits_{k=1}^{M}\left|f_{k}-f_{j,k}\right| \tag{21}\]  
\[h=(\sum\limits_{i=1}^{N}balance(i))/N \tag{22}\]  
\[S=c_{1}g+c_{2}d+c_{3}h \tag{23}\]  
Among them, Equation (17) obtains the fitness value of an individual by weighting the objective function, where \(gen\) represents the iteration number, \(w_{i}\) is the weight parameter that determines the specific value based on the requirements of each objective function, \(f(x^{gen})\) represents the \(gen\)-th generations linear weighted sum of the objective functions of individual \(i\) in the \(gen\)-th population, \(P[N^{gen}]_{dis}\) is the crowding distance of the individual in the \(gen\)-th generations population, \(balance\) represents the balance degree of each individual, \(N\) is the number of populations, \(M\) is the number of objectives, and \(F\) represents the non-dominated layers. Equation (18) normalizes the average fitness of the population by the average fitness of the first generation to reflect the convergence of the population. Equation (19) represents the total crowding distance of the population in each generation. Equation (20) converts the crowding distance into a minimum value, where a smaller value represents a larger crowding distance, reflecting the diversity of the population. Equation (21) represents the balance degree of an individual, and a smaller balance degree indicates a more uniform distribution of the individual on each objective and a better multi-objective balance. Equation (22) represents the balance degree of the population. Equation (23) obtains the total state value of the population by weighting. The average fitness and diversity of the population are beneficial to improve the quality of the entire population and obtain excellent individuals so the three weights are designed as 0.4, 0.4, and 0.2.
## 3 Continuous Speech Analysis/Synthesis System  
To construct our proposed method, we adopt a robust and accurate continuous F0 estimator with MVF as its base system, and extend it by proposing a new excitation HNR parameter so that it can appropriately synthesize high-quality speech. In this section, we shortly describe first the baseline of our proposed vocoder, then define the detail of calculating the HNR parameter, and later a new estimate of the MVF parameter is explained. Figure 3 is a schematic diagram showing the main components of the modified version of the continuous vocoder.
## 4 Experimental Results and Discussion
### Updating Network Layers  
Experiments were performed on the YOLOv3 network using GTSDB dataset for finding the optimal number of layers for traffic sign detection. The effect of removing redundant DBL layers in the network is illustrated in Figure 8, in terms of Mean Average Precision (MAP) and Average Precision for each category. The results follow a Gaussian trajectory; where mAP for the network improves with a reduction of DBL layers and reaches a maximum value of 93.09%, then moving further there is a drop in mAP values. The updated network was evaluated on STS Dataset, resulting 5% rise in mAP than the original model as shown in Figure 8. Figures 9 and 10 illustrate Precision-recall curves of the original and proposed YOLOv3 algorithm for all three classes of GTSDB and STS Dataset, respectively. Here by original YOLOv3 algorithm, we mean default method of image resizing and proposed algorithm includes pruned network with patch-wise technique. For this experiment, regression anchor boxes are used.  
Training the network with image patches helped to achieve better mAP and inference time. Resizing a \(1360\times 800\) image to \(416\times 416\) pixels can vanish the pixels of a traffic sign. The effect of the proposed training method on the network accuracy (mAP) and inference time is illustrated in Figure 11. The experiments were performed using the proposed training technique and the results in terms of mAP and inference time were compared with the default YOLOv3 network. The proposed approach is 7.5 times faster in detection speed than the generalized rescaling method often used by the default YOLOv3 method.  
Table 2 compares the Average Precision results of different state-of-the-art methods for the GTSDB dataset. Among all, our method outperforms in terms of accuracy and inference time. Our proposed network model can detect even the blurred sign beside the visible ones. It also successfully detected small size traffic signs from the test images. Figures 12 and 13 shows some qualitative detection result samples from STS and GTSDB dataset, respectively.  
Figure 8: (**a**): Effect of network layers on Average Precision for GTSDB dataset. (**b**): Effect of layers pruning on Average Precision and mean Average Precision for STS dataset.  
Figure 9: Precision-recall curves of STS dataset for Original and proposed YOLOv3 network (**a**) Danger class (**b**) Mandatory class (**c**) Prohibitory class.  
Figure 11: Effect of training technique on mAP and inference time for GTSDB dataset.  
Figure 12: Detection results on STS Dataset (**a**) traffic sign recognition in different illumination conditions, (**b**) Small size traffic sign recognition, (**c**) A partial view traffic sign recognition, (**d**) Variable size Traffic Signs in an image.  
Figure 10: Precision-recall curves of GTSDB dataset for Original and proposed YOLOv3 network (**a**) Danger class (**b**) Mandatory class (**c**) Prohibitory class.
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 2 Wireless Communication Technologies for Agriculture  
This section presents the different wireless protocols and standards that are used in agriculture. These wireless technologies are also compared to identify the most convenient technology in terms of power consumption and communication range, where the two metrics are posed challenges in current solution of agriculture application [5; 8]. IoT is a revolution for the future realm where everything that can utilize a connection will be connected. Cellular technologies are grown and developed to play a crucial role in the IoT realm [9]. Narrowband-IoT (NB-IoT) is a new IoT system constructed from current Long Term Evolution (LTE) functionalities. Subsequently, NB-IoT is possible to share the spectrum of LTE without coexistence problems and to utilize the same pieces of equipment, as well as it is possible to connect seamlessly into the LTE main network. This permits all network facilities such as security, tracking, policy, charging, and authentication to be totally supported [10]. The design goals of NB-IoT cover high coverage area, extended battery life (i.e., 10 years), high network size (52,000 devices/channel/cell), and low-cost devices [10; 11]. However, in the near future, NB-IoT technologies, such as Long Range radio (LoRa) will take place in agricultural applications due to low power consumption and preferably used when the agricultural information are to be transmitted over long distances.
## 4 Risk Analysis for Current Protocols
### NMEA 2000
#### 4.2.2 Spoofing  
Our review of the protocol further identified that NMEA 2000 is vulnerable to spoofing attacks [31], where the Parameter Group Number (PGN) used to identify the sending node is copied and used by a malicious device. This imposes an added problem because in NMEA 2000, the PGN is also used to indicate the priority of the message.