esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results
### Reliability of the Proposed Method against Modulation Type Change  
5G systems choose the appropriate M-PSK or M-QAM modulation types according to the data rates required by the UEs and channel availability. 16-QAM modulation is considered in the base scenario (Section 3.1). In this section, along with 16-QAM, QPSK and 64-QAM modulations are considered. Thus, jamming detection performances of EVM vs. RB and throughput metrics are examined against changes in the modulation type.  
For the QPSK, 16-QAM, and 64-QAM modulation-type use cases, the peaks of EVM vs. RB are calculated for each SJR, and the results are presented in Figure 6(a). Because the EVM measurement shows consistent results across modulation types, the proposed method can be safely used for jamming detection in system scenarios in which the modulation type changes.  
On the other hand, Figure 6(b) shows the throghput results versus SJR for the use cases of the aforementioned modulation types. When SJR is 0 dB, the throughput for the QPSK case is 100%; therefore, no jamming signal is detected. If the system decides that there is no jamming threat by looking at this throughput result and then increases the modulation degree to 16-QAM or 64-QAM, it experiences a dramatic decrease in throughput. In other words, the jamming effect is sensed differently by using the throughput metric under different modulation-type usage conditions. However, the proposed measurement consistently  
Figure 6: EVM vs. RB, BER and Throughput Measurements for Multitone Jammer. (**a**) EVM vs. RB for SJR = 0 dB, (**b**) EVM vs. RB for SJR = 20 dB, (**c**) Peak-EVM vs. SJR, and (**d**) throughput and BER vs. SJR.  
detects jamming threats independently of the chosen modulation type, thereby possessing the capability to provide reliable guidance to the system.  
The results in Figure 14 show that this reliability of the proposed method against changes in the modulation type is also achieved for the chirp jamming case.
## 4 Evaluation and Results
The algorithm accomplished this by significantly reducing CPU usage in both the edge and cloud layers, thereby optimising resource utilisation and enhancing energy efficiency in IoT networks. Figure 5: Total number of tasks successfully completed in edge. Figure 6: Average CPU % usage in cloud. Figure 7: Average CPU % usage in edge. The proposed IMBA has demonstrated its capability to allocate a significantly higher number of tasks to the mist layer, along with increased CPU mist usage, as depicted in Figures 10 and 11. This strategic allocation aims to ensure fast response times and minimise waiting times for task execution, leveraging the proximity of mist nodes to IoT devices. Figure 2.0 Cloud energy consumption (Wh). Figure 2.1 Tasks successfully executed (mist). Figure 2.2 Edge energy consumption (Wh). Despite the expectation of increased power consumption due to the intensified task allocation to mist nodes, the IMBA remarkably outperformed expectations. Even with the allocation of 20,870 more tasks than the fuzzy logic algorithm and 9529 more tasks than the round robin algorithm in the mist layer, the algorithm managed to achieve this objective without exhibiting high energy consumption in the mist layer, as illustrated in Figure 2.3 This remarkable efficiency underscores the effectiveness of the IMBA in optimising resource utilisation while maintaining energy efficiency, thus enhancing the overall performance of IoT networks. The IMBA exhibited superior efficiency in utilising the LAN network by allocating a higher number of tasks compared to other algorithms, as is evident in Figure 2.4 This strategic allocation aimed to mitigate the dependence on external networks (WAN networks), as depicted in Figure 14, thereby minimising the risk of security vulnerabilities by confining tasks to the internal networks (LAN network), as is also shown in Figure 2.5  
Figure 2.6 Average CPU usage (mist) (%). Figure 2.7 Mist energy consumption (Wh). Admittedly, the IMBA demonstrated increased LAN usage compared to other algorithms, aligning with one of the study's primary objectives to leverage LAN resources by allocating more tasks to it. However, remarkably, it achieved this objective without demonstrating excessive LAN usage compared to other algorithms. This balanced utilisation underscores the efficacy of the IMBA in optimising network resources while maintaining security and efficiency in IoT environments.
# Nanoencapsulation of Essential Oils as Natural Food Antimicrobial Agents: An Overview
## 8 Legislative Aspects Concerning the Use of Nanoparticles in Food Products  
As shown in the previous section, food nanotechnology is applied into many aspects of the food field, such as food packaging, additives, and food preservation. Many conventional molecules used as food additives or in packaging materials can be found at nanometer scale in food products. For example, food-grade TiO\({}_{2}\) nanoparticles have been found up to approximately 40% in the nanometer range [163; 164]. Although nanomaterials like TiO\({}_{2}\) nanoparticles are generally recognized as low in toxicity at conventional conditions, long-term exposure to such nanomaterials may cause adverse damages. The United States Food and Drug Administration (FDA or USFDA) and the European Commission (EC) are the main sources for legislation and regulation on food nanotechnology. A recent literature review summarized the risk assessment of the conventional particle size of substances in food products [165]. However, the development of nanotechnology in the food industry should not be blamed. Before applying nanomaterials to the food field, appropriate risk assessments can be made on the physical and chemical properties of nanomaterials and their absorption, biodistribution, metabolism, and body excretion, to alleviate the effects of nanotoxicity.
# Research on Wind Field Characteristics Measured by Lidar in a U-Shaped Valley at a Bridge Site
###### Abstract  
Currently, research on wind fields of U-shaped valleys is rarely reported, and anemometers or wind observation towers are usually used for field measurement, but the measured position is limited and the cost is high. In order to study the wind characteristics in a mountainous U-shaped valley, a long-range, all-weather, high-precision Wind3D 6000 lidar was placed at a bridge site located in a U-shaped valley. Then, according to the data effective ratio and wind speed, nearly 6 months of original data ranging from 0 m to 810 m were analyzed statistically. It was found that the spatio-temporal distribution of wind speed and direction is obviously not uniform, and the wind parameters are correlated among different virtual wind towers (VWTs). By classification, the effective data of midspan position is taken as the research object, and the wind speed profile is divided into three categories. Type-1 shows disorderly characteristics; Type-2 shows a linear relationship; and Type-3 shows a nonlinear relationship. The wind direction is consistent with the main wind direction at the bridge site and the average wind direction of different WMTs has a high consistency. The concept of wind-direction deflection rate is put forward to describe the variation of wind direction with height. These measured wind parameters could be used as a reference for bridge wind-resistant design.  
bridge engineering; wind characteristics; field measurement; U-shaped valley; lidar, bridge site +
Footnote †: journal:  
Article
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 4 Localization Algorithm
### Phase Estimation  
The off-the-shelf UWB chip can provide the first path position within the CIR determined by the algorithm embedded. We can calculate the signal phase of arrival using the complex sample corresponding to the position. Since the CIR sampling interval is about 1 nanosecond, and the time resolution of the first path position given by the chip is sub-nanosecond, we take the mean of the phases near the first path position as the estimation of the first-path phase.  
Since there are multiple responses in a single estimated CIR and the chip does not give the phase estimations of other responses, we design a simple and practical threshold detection method to estimate the phase of arrival. We first normalize \(r(n\Delta_{d})\) as  
\[\bar{r}(n\Delta_{d})=\frac{r(n\Delta_{d})}{\max r(n\Delta_{d})}. \tag{9}\]  
Then we introduce a threshold ratio \(\gamma\) to detect the rising edge of a response where the phase can be extracted. Since the approximate time interval between responses are known in advance, we estimate the maximum amplitude of the noise using the partial CIR in the middle of any two responses, which is denoted by \(n_{max}\). To avoid mistakenly treating noise as responses, we let \(\gamma>n_{max}\). The rising edge of a response usually lasts for less than 3 ns, so the number of sampling points on the rising edge is usually less than 4. In order to avoid missing detection of the sampling points on the rising edge, we try to make the threshold smaller. Besides, since each response will follow some large MPCs, we usually start detecting backwards at the middle of the responses to avoid false detections. When we find the sampling point whose amplitude first exceeds the threshold, the phase of the sample is considered the arrival phase of this signal.
# Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks
## 4 Simulation Results and Discussions
### User Capacity Performance Comparison  
The BER performance curves of these multiuser detection algorithms with different number of users are explored here. In this simulation, \(E_{b}/N_{0}\) of all users is 6 dB. Five iterations are simulated for the proposed iterative multiuser detection algorithm. The simulation result is shown in Figure 8.  
From Figure 8 we can see that as user number increases, the BER performance of MF, DEC, MMSE and the proposed receiver falls, while the BER performance of the proposed iterative multiuser detection method is much better than MF, DEC, and MMSE when user number ranges from 10 to 50. When user number is smaller than 20, it even coincides with OMUD.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 5 Experimental Results
### Performance Comparison with Existing Methods
#### 5.2.5 Signal-to-Noise Ratio (SNR) Evaluation  
Increasing user densities results in lower signal-to-noise ratios (SNR), which lower signal quality. Effective beam steering is ensured by an effectively implemented transmission management mechanism to maintain good SNR despite user expansion.  
The comparative analysis of SNR (db) is represented in Figure 7 and Table 9. When there are 20 users, the suggested approach performs better than FB-TCP + MP-TCP + Deep IA and UL-SCH + PUSCH, and SIM-ADMT with an SNR of 8.3 dB as opposed to 6.0 dB and 5.0 db, 6.3 db, respectively. The technique suggested maintains a high SNR of 7.0 dB with 40 users, while FB-TCP drops to 5.0 (db). After 60 users, the proposed system performs better again, with 6.6 dB, compared with 4.0 (db) for SIM-ADMT, 4.5 (db) for FB-TCP, and 3.7 (db) for UL-SCH + PUSCH. The proposed plan maintains its higher efficiency over SIM-ADMT at 6.3 (db), FB-TCP at 8.0 (db), and UL-SCH + PUSCH at 7.0 (db) at the end of the assessment with 100 users, achieving 9.0 (db). The recommended strategy generally yields higher SNR values, demonstrating better signal quality in high-density scenarios. The DAH-BT-LSTM model may overfit or underfit at this intermediate scale, and less-than-ideal beamforming and congestion control follow from the absence of training data for 60 users. This clarifies why, in the 60-user case, performance is poorer. Furthermore, with 60 users, there is higher inter-user interference, beam overlap, and pipeline congestion than with 40 users. More complete improvements, including RCTs-LAMP's sparsity control, boost performance with 80 users. Furthermore, environmental non-stationarity and coordination problems impede multi-agent reinforcement learning methods, such as Mu-MATD3, at this intermediate scale, therefore compromising convergence and performance.  
Figure 6: Number of users vs. packet loss (%).  
Figure 7: Number of users vs. SNR (db).
## 2 Theory and Calculation
### ANN Structure  
In the current study, ANN models of the selected mechanical parameters of the bituminous mixtures investigated were developed using the software MATLAB(r) (R2014b, The MathWorks Inc., academic use). The designed ANNs are of the feedforward type and are characterized by multiple layers, which make use of the positive linear rather than the hyperbolic tangent transfer function. The structure of all the ANNs is completed by an output layer, adopting a linear transfer function. The use of the positive linear transfer function (also known as ReLu, rectified linear unit [64; 87]) has been shown empirically that it leads to better performance than the use of the hyperbolic tangent, which is prone to problems of rapid saturation [64; 87]. Each hidden layer has the same number of neurons. The backpropagation algorithm, used to train the networks and improve their generalization, is the Bayesian regularization, which updates the weight values according to the Levenberg-Marquardt optimization [88].  
The network was set up with random initial weights and biases, whereas the learning rate (i.e., the parameter that controls changes of weight and bias during learning) was 0.001, the number of epochs (i.e., is the maximum number of presentations of the set of training vectors to a network and updates of weights and errors) was 3000, the error goal was 0.01, and the minimum gradient (i.e., the magnitude of the gradient of performance used to stop the training) was 0.000001. In order to train networks more efficiently, a pre-processing function was assigned to normalize the data set, so that the input variables and the target parameter had a mean equal to zero and standard deviation equal to one. The same function, as a post-processing function, also reconverts normalized outputs into the targets' original units. The following equation illustrates the normalization process performed on the training and test data sets:  
\[x_{n}=\frac{x-x_{mean}}{x_{std}}, \tag{6}\]  
where \(x\) is the \(i\)-th component of the input vector or one of the three mechanical parameters considered in the current study; \(x_{mean}\) and \(x_{std}\) are respectively the average and the standard deviation of the parameter, \(x\); and finally, \(x_{n}\) is the normalized input or target variable treated (Table S1). Each new data that will be used by the network has to be pre-processed by the mean of Equation (6).  
The number of hidden layers and the number of neurons in each hidden layer were selected as hyperparameters to be optimized in the model selection procedure to search for the best predictive capabilities on the basis of the selected machine learning method (i.e., the Bayesian regularization).
# Application of Fiber Bragg Grating Sensing Technology for Bol Force Status Monitoring in Roadways
## 1 Introduction
The monitoring results are of great significance in the parameter design of bolt support in roadways and in anchoring engineering safety. Furthermore, such a sensor is also widely used in quasi-distributed measurement and long-term online monitoring of bolt force in such fields as geotechnical engineering, tunnel engineering, and slope engineering.
## 4 Results
### Technical Infrastructure  
The density and structure of traffic, navigational hazards, local climate, topography, environmental requirements, commercial aspects and the scope of area covered by VTS system determine the requirements for the use of VTS devices and subsystems [3]. The equipment needed to perform the tasks of a VTS system may include the following devices [3]:  
* VTS radar system;
* Automatic Identification System (AIS);
* Very High Frequency (VHF) operational communication;
* Closed-Circuit Television (CCTV) systems;
* Radio Direction Finders (RDF);
* hydrometeorological equipment;
* VTS data management and information systems.  
A comparison of the VTS systems operating in Poland, taking into account each system's technical infrastructure is presented in Table 4.  
To sum up, it should be highlighted that the technical infrastructure and equipment of each VTS system differs in terms of total number of units (devices) and depends on the need to ensure specific services.
# Feature Deep Continuous Aggregation for 3D Vehicle Detection
###### Abstract  
3D object detection has recently become a research hotspot in the field of autonomous driving. Although great progress has been made, it still needs to be further improved. Therefore, this paper presents FDCA, a feature deep continuous aggregation network using multi-sensors for 3D vehicle detection. The proposed network adopts a two-stage structure with the bird's-eye view (BEV) map and the RGB image as an input. In the first stage, two feature extractors were used to generate feature maps with the high-resolution and representational ability for each input view. These feature maps were then fused and fed to a 3D proposal generator to obtain the reliable 3D vehicle proposals. In the second stage, the refinement network aggregated the features of the proposal regions further and performed classifications, a 3D bounding boxes regression, and orientation estimations to predict the location and heading of vehicles in 3D space. The FDCA network proposed was trained and evaluated on the KITTI 3D object detection benchmark. The experimental results of the validation set illustrated that compared with other fusion-based methods, the 3D average precision (AP) could achieve 76.82% on a moderate setting while having real-time capability, which was higher than that of the second-best performing method by 2.38%. Meanwhile, the results of ablation experiments show that the convergence rate of FDCA was much faster and the stability was also much better, making it a candidate for application in autonomous driving.  
autonomous driving; 3D vehicle detection; feature aggregation; bird's-eye view; loss weight mask +
Footnote †: journal: Applied Sciences
# A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN
## 4 Experiments  
### Static Experiment  
#### 4.1.1 Experimental Setup  
A real-world experiment was carried out in a multipath scenario at the Metropolitan City of Turin (Italy), as shown in Figure 2, to demonstrate the proposed method. A Leica GS18 receiver gathered raw GNSS measurements of GPS L1 C/A signals at a rate of 10 Hz at the position depicted in Figure 3. The antenna was fixed and static for the entire collection process. Its position was georeferenced by means of an RTK solution, thus providing the position ground truth for the test. It is important to highlight that, in order to ensure the reliability of the ground truth, only the fixed RTK solutions were chosen and averaged to derive the ground truth under the conditions affected by multipath/NLoS. The GNSS data collection lasted for around 40 min. The skyplot with carrier-power-to-noise-density ratio (\(C/N_{0}\)) is provided in Figure 4. The receiver was deployed close to the buildings on the east side of the road but kept a short distance away from the buildings on the west side.  
To obtain the \(L^{s}(t)\) values of the pseudorange measurements, all the corrections and models were computed according to Table 1. As far as the DBSCAN is concerned, the \(minPts\) and \(\varepsilon\) parameters were set as 2 and 2 m, respectively. Given the limited satellite visibility in urban areas, _minPts_ was set to 2 since at least 2 points are required to confirm membership of the same cluster. For the parameter \(\varepsilon\), it should represent the maximum distance between two points within the same cluster. In our specific context, this maximum distance of the distribution \(\mathcal{N}\big{(}c\cdot dt_{\text{rev}},\sigma^{2}\big{)}\) is dependent on the variance \(\sigma^{2}\), which is associated with receiver noise (\(\pm 1\,\text{m}\)), and it is then set to \(2\,\text{m}\).  
\(DB\) in this research contains all the \(L^{s}(t)\) values, which are one-dimensional data. Hence, _dist_ is set to compute the absolute value of the difference between two \(L^{s}(t)\) values.  
Figure 3: Location of GNSS antenna.  
Figure 2: Surrounding buildings of data collection scenario.
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
###### Abstract  
This paper presents a framework that enables programmers to deploy embedded real-time firmware of Internet of Things (IoT) devices more conveniently than using plain C/C++-language programming, by abstracting away from low-level details and the ad hoc management of multiple, diverse network technologies. Moreover, unlike other proposals, the framework is able to accommodate both time and event-driven applications. Experimental results show that for Modbus-CAN communication, the worst-case time overhead of the framework is less than 6% of the total combined processing and communication time. Its memory requirement is less than 5% and 4% of the Flash memory and RAM available on a typical IoT microcontroller. The framework also compares favorably with respect to two other approaches in terms of the sustainable minimum cycle time, memory overhead, and level of programming abstraction when tested on a simple real-time algorithm.  
embedded systems; logic controllers; firmware development frameworks +
Footnote †: journal:  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-000  
system that maps conventional C-language variables into local or remote I/O points. In this way, programmers may still use plain C-language statements to manipulate those variables, while RTfw transports their value to and from I/O points, even across a network, thus reaching a convenient trade-off among the goals previously discussed. Unlike other proposals, and different from [9], the framework is able to handle multiple field buses and network technologies transparently. Even more importantly, it also supports event-driven programming besides the more traditional cyclic, time-driven paradigm, provided sensors, actuators, and the underlying networks implement event-driven messaging. The former has been steadily gaining popularity for its advantages, not the least resource efficiency [12]. In RTfw, the event-driven part of the real-time algorithms is directly triggered by event notification messages received from the communication networks by means of sporadic events. At the same time, and unlike other similar proposals, the attention paid to execution efficiency during design and implementation makes RTfw suitable even for very low-cost microcontrollers.  
The paper is organized as follows: Section 2 compares RTfw with related work. Section 3 outlines its architecture and main building blocks, while Section 4 provides more details about its main components, also focusing on implementation-related aspects. Section 5 provides information on how the event-driven paradigm fits in the framework, Section 6 focuses on system configuration, and Section 7 highlights how RTfw supports Internet-based access to the embedded system. Section 8 presents experimental data concerning performance, overhead, and memory footprint, while also comparing RTfw with two other approaches. Finally, Section 9 concludes the paper.