esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Project Planning with Realistic Multi-Objective Consideration in Developing Sustainable Port
## 3 Green Project Planning Problems with Multiple Objectives  
### Problem Description and Assumptions  
To achieve the goal of developing a green port, the port plans to implement \(p\) energy-saving and emission reduction projects (green projects). It is important to plan the construction sequence of these green projects since the sequence of project construction affects the construction cost and the effect of emission reduction. These green projects can be classified into separable projects and inseparable projects. The separable project can be divided into a number of identical unit projects (e.g., replacement of a single lamp). Once a separable project (such as replacement project of energy-saving lamps) is started, the completed unit projects in each year will generate the corresponding benefit in the next year. Inseparable projects are continuous projects and only begin to generate benefits in the next year after the whole project is completed. These projects are usually large-scale projects (e.g., the transformation of energy-saving processes for bulk mining operations) and require more capital investment. Each project's construction is not allowed to stop before it is completed.  
Some projects are independent and do not affect each other. These projects can be carried out at the same time. Other projects must satisfy specified precedence constraints. That is, some projects can only be started after their preceding projects are completed. The cost of each project and the benefits achieved are different, and the actual cost of a project is related to the duration of the project construction. The longer is the duration, the higher is the cost. In addition, the construction of a project is also limited by the construction time, budget, and mandatory CO\({}_{2}\) emission reductions.  
In accordance with the construction reality of green projects, project planning issues need to consider multiple objectives, including maximizing the total CO\({}_{2}\) emission reduction and minimizingthe total project construction cost. The total CO2 emission reduction is the sum of the CO2 emission reductions for all projects during construction period. The project construction cost is the sum of the construction cost for all projects. The problem needs to determine: (1) the start and completion time of each project; and (2) the proportion of each project's annual construction.
# Practical Approach for Developing Lateral Motion Control of Autonomous Lane Change System
## 3 Simulation Results
### Scenario 1: Sine and Cosine Based Trajectory  
In scenario 1, the vehicle started changing lanes at 5 s to the right during 5 s. Figure 5a shows the reference trajectory and measured state in cylinder domain. One can observe that to change the lane to the right, the reference trajectories of \(\xi_{1}\) and \(\xi_{2}\) are designed using \(\sin(2\pi t/5)\) and \(\cos(2\pi t/5)\). The derivative of the reference is not continuous at 5 s and 10 s; thus, it can be seen that the output value of the controller is instantaneously large in Figure 5c. Therefore, it can be seen in Figure 5a that error of yaw rate, \(\xi_{5}\), also shows a similar pattern. The rate of lateral offset, \(\xi_{3}\), starts to increase at 5 s and has a constant value which means a constant lateral acceleration during lane change, and then decreases to zero at the end of lane change at 10 s.  
Trajectories of the vehicle in the global, local, and cylinder domains are shown in Figure 5b, respectively. The right lane change means the desired lateral position of vehicle is changed as \(-3.4\) m in the global coordinate, as plotted with the green line. Thus, we designed the reference trajectories \(\xi_{1}\) and \(\xi_{2}\) as shown in Figure 5a to change to the right lane. At the start of lane change, the trajectory of  
Figure 4: Block diagram of simulation.  
the vehicle state exists on the original position \((\xi_{1},\xi_{2})=(0,1)\). Further, the trajectory of the vehicle state returns to the original position at the end of lane change past the lane crossing point \((0,\,-1)\), as described in the cylinder domain.  
We can see the lateral offset in local coordinate jumps when the vehicle cross the lane at about 7.5 s. The designed trajectories can be plotted in local coordinates using the inverse of the gluing function in (10), as plotted with the red dashed line. However, both reference trajectory and measured state do not have jump state in the cylinder domain. In other words, even if the reference trajectory is generated in the cylinder domain and the control method is applied, the movement of the vehicle is the same as that on the plane.  
The convex interpolation parameters for the LPV controller are shown in Figure 5c. As the nonlinear varying parameter \(\theta\) changes, the corresponding interpolation parameters change in a similar manner as the sine and cosine forms.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 4 Protection Techniques
### Device Fingerprinting  
#### 4.4.1 Overview  
Radio frequency fingerprint identification (RFFI) is an authentication technique that identifies a device by analyzing characteristics of a received signal, much like authenticating a person with biometrics. The analog frontend of LoRa end nodes is made of low-cost components. These hardware components are subject to manufacturing variations and usually deviate from the nominal values. The deviations are unique to devices and, therefore, can serve as an identifier. In practice, the RFFI system is equipped at the LoRa gateway, and predicts the LoRa transmitter from which the packet is sent by analyzing the received waveform.  
Recently, deep-learning techniques are widely employed in RFFI thanks to their excellent performance in recognition/classification tasks, and LoRa-RFFI systems are mostly deep-learning driven [14; 15; 16; 17; 73; 74; 75]. Figure 9 shows the overview of a deep-learning-based RFFI system. A deep-learning-based RFFI system usually has two steps, i.e., training and inference. In the training stage, all the legitimate devices are required to send a number of correctly labelled packets to the RFFI system. The received packets are preprocessed and stored in a training data set. Then, a neural network is trained to learn the mapping function from the received packet to the device label. After sufficient training, the neural network can act as a classifier whose input is the preprocessed LoRa waveform and output is the predicted device label, which is known as the inference stage.  
The deep-learning-based LoRa-RFFI works can be categorized into those studying the deep-learning engines, signal representations and channel mitigation. Regarding the deep learning architectures used in LoRa RFFI, a long-short-term memory (LSTM) network [16; 18; 75], a convolutional-neural network (CNN) [14; 15; 16; 17; 73; 74], multilayer perceptron (MLP) [14; 73] and a transformer [80] have been employed. In terms of signal representation, there are studies converting the collected IQ samples into other forms as inputs to the neural network, such as FFT results [14], spectrogram [16; 18] and differential constellation trace figure [73]. Finally, the latest research shows that the change in wireless channel degrades the performance of LoRa-RFFI systems [17; 18; 74]. The authors in [17] designed a channel-independent spectrogram to mitigate channel effects. Moreover, data augmentation is also shown to be helpful [17; 18].
## 3 Results
### Ablative Experiments  
The above comparative experiments demonstrate the superior performance of our proposed method. To further analyze the contribution of different data sources and validate the effectiveness of key components, we conducted an ablation experiment by selecting different data source combinations, the MSFM, and the HSSFM as ablation components.  
As shown in Table 6, the experimental results indicate that using only RGB or MS-VIs as input leads to suboptimal performance across all evaluation metrics. This suggests that a single data source fails to provide sufficient feature representation, thereby limiting the model's classification capability. When RGB and MS-VIs are combined, accuracy improves by 6.7% and 9.0% compared to using RGB or MS-VIs alone, respectively, while precision increases by 7.2% and 12.5%. These findings highlight the complementary nature of RGB and MS-VIs, demonstrating that multi-source data fusion significantly enhances regional classification accuracy.  
Building upon multi-source data fusion, we further evaluate the impact of different modules. When the MSFM is incorporated, model performance improves considerably, with precision and recall increasing by 4.7% compared to the baseline model. This indicates that the MSFM effectively captures complementary spatial-spectral information and key features, thereby enhancing multi-source feature fusion. Furthermore, when the HSSFM is introduced, accuracy and precision improve by 4.8% and 4.7%, respectively, compared to the baseline, demonstrating that the HSSFM efficiently extracts and integrates multi-level spectral features, outperforming models that rely solely on single-level features.  
Ultimately, when both the MSFM and HSSFM are integrated, the model achieves the highest performance across all evaluation metrics, surpassing both the baseline and single-module configurations. Specifically, accuracy and precision increase by 6.0%, further validating the effectiveness of the MSFM and HSSFM in multi-source data fusion and hierarchical spectral feature extraction.
###### Abstract  
The propagation of aerodynamic noise from multi-wind turbines is studied. An efficient hybrid method is developed to jointly predict the aerodynamic and aeroacoustics performances of wind turbines, such as blade loading, rotor power, rotor aerodynamic noise sources, and propagation of noise. This numerical method combined the simulations of wind turbine flow, noise source and its propagation which is solved for long propagation path and under complex flow environment. The results from computational fluid dynamics (CFD) calculations not only provide wind turbine power and thrust information, but also provide detailed wake flow. The wake flow is computed with a 2D actuator disc (AD) method that is based on the axisymmetric flow assumption. The relative inflow velocity and angle of attack (AOA) of each blade element form input data to the noise source model. The noise source is also the initial condition for the wave equation that solves long distance noise propagation in frequency domain. Simulations were conducted under different atmospheric conditions which showed that wake flow is an important part that has to be included in wind turbine noise propagation.  
wind turbine noise source; wind turbine noise propagation; wind turbine wake +
Footnote †: journal: Applied Sciences
# Bi-LSTM-Augmented Deep Neural Network for Multi-Gbps VCSEL-Based Visible Light Communication Link
## 1 Introduction
A bi-directional wavelength remodulated single VCSEL link with a data rate of 10.6 Gbps for downstream activity and 2 Mbps for upstream activity was demonstrated in [22]. However, typical cost-effective commercial VCSELs have a sub-GHz modulation bandwidth. Considering the demands imposed upon the efficient next-generation wireless network capable of data rates beyond 10 Gbps, a low-cost VCSEL wireless link with advanced communication schemes should be investigated. More recently, there has been considerable interest in improving communication quality by utilizing machine learning for VLC. In [23], a machine-learning-based low-cost visible light positioning system (VLP) was introduced. Machine learning techniques were also used to mitigate the LED's non-linearity issue for an OFDM-based VLC system [24]. A long short-term memory (LSTM) network was also studied for processing random sequential data [25; 26; 27]. In this work, through a series of experimental investigations, the effectiveness of machine learning techniques in VCSEL-based VLC is investigated. In particular, using a commercial VCSEL and bidirectional long short-term memory (Bi-LSTM)-based machine learning schemes, a high-speed VLC link with a data rate of 13.5 Gbps and a 2 m link distance is demonstrated. The rest of this work is organized as follows. Section 2 presents the characteristics of VCSELs. In Section 3, the applied Bi-LSTM-augmented deep neural network is introduced. Section 4 shows the proof-of-concept demonstration, showing the results of multi-Gbps VCSEL-based VLC. Then, the discussions and conclusions are presented in Section 5.
## 3 Experiment and Results Analysis
### Evaluation Indicators  
The experiment employs the widely adopted precision, recall, and comprehensive evaluation metric F1 in NER tasks to assess the model's performance. The calculation method is illustrated in Equations (15)-(17):  
\[Precision=\frac{Correct\_num}{Predict\_num} \tag{15}\]  
\[Recall=\frac{Correct\_num}{Tag\_num} \tag{16}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Relevant Parameter** & **Value** \\ \hline LSTM hidden layer dimension & 128 \\ Learning rate & \(5\times 10^{-5}\) \\ Optimizer & Adam \\ Batch\_size & 32 \\ Dropout & 0.5 \\ Epochs & 50 \\ Clip & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Text Data** & **“BIO” Labelling** \\ \hline \(\mathbb{A}\)([PERSON]) & \(\mathbb{O}\) \\ \(\mathbb{A}\)([PERSON]) & \(\mathbb{O}\) \\ \(\mathbb{A}\)([PERSON]) & \(\mathbb{O}\) \\ \(\mathbb{A}\)([PERSON]) & \(\mathbb{O}\) \\ \(\mathbb{A}\)([PERSON]) & \(\mathbb{O}\) \\ \(\mathbb{A}\)([PERSON]) & \(\mathbb{O}\) \\ \(\mathbb{A}\)([PERSON]) & \(\mathbb{O}\) \\ \(\mathbb{A}\)([PERSON]) & \(\mathbb{O}\) \\ \(\mathbb{A}\)([PERSON]) & B-Loc \\ \(\mathbb{A}\)([PERSON]) & I-Loc \\ \(\mathbb{A}\)([PERSON]) & I-Loc \\ \(\mathbb{A}\)([PERSON]) & I-Loc \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample data labelling.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Category** & **Training Set** & **Validation Set** & **Test Set** \\ \hline \multirow{3}{*}{Audit} & Sentence & 5000 & 625 & 625 \\  & Entity & 6807 & 753 & 843 \\ \cline{1-1}  & Sentence & 3821 & 463 & 477 \\ \cline{1-1}  & Entity & 13,438 & 1497 & 1630 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical information on DataSets.  
\[F1=\frac{2\cdot Precision\cdot Recall}{Precision+Recall} \tag{17}\]  
The variable _Correct_num_ represents the count of accurately predicted audit entities, _Predict_num_ denotes the count of identified audit entities, and _Tag_num_ signifies the total count of labeled audit entities in the dataset.
# Slot Waveguide Enhanced Bloch Surface Waves
## 1 Introduction  
Bloch surface waves (BSWs) have already shown their potential as probes in sensing experiments [1; 2; 3]. The great advantages of such electromagnetic surface waves come from the all-dielectric platform on which they are generated [4] and from the long propagation length they offer compared to surface plasmon polaritons [5]. A BSW is created at the interface between a dielectric periodic medium and a homogeneous dielectric medium. A wave that is partially transmitted through a one-dimensional photonic crystal illuminated under an incidence angle greater than the critical angle is thus trapped between the two media and can only propagate along the surface.  
Experimental and theoretical studies have been carried out these last few years on such waves. It has been demonstrated that exceptionally long propagation lengths can be achieved, that BSWs can be controlled using loading-strip on top of the platform, and that waveguides, ring resonators, photonic crystals, and gratings [6; 7; 8; 9] can be used to control the direction and properties of these waves. The excitation of BSWs remains the principal issue in most of the current platforms. The most commonly employed technique is the Kretschmann configuration, which is the illumination through a prism, at an angle greater than the critical angle of the multilayer. Grating couplers have recently been successfully experimentally demonstrated [10]. However, no efficient on-chip solutions exist to excite a BSW so that the field is easily accessible for applications such as sensing.  
In 2004, [PERSON] et al. demonstrated a novel concept of waveguide, i.e., the slot waveguide [11], enabling the propagation of light in a structure composed of two high refractive index rails separated by a thin air gap of a few tens of nanometers. This structure features a high confinement of the electric field in the air gap leading to a huge enhancement of the field in this region. One can consider, in a simplified scheme that this enhancement originates from the superimposition of the evanescent tails of the mode sustained by each rail.  
The structure presented here combines these two concepts of Bloch surface waves and slot waveguides to create an on-chip platform allowing for excitation in a waveguide of the BSW, the enhancement of the surface wave, and a longer propagation thanks to its coupling to the slot mode. The concept is, in a sense, similar to the one proposed by [PERSON] et al., who demonstrated the confinement of a BSW within a nanofiber, similar to a slab-slot waveguide [12]. Here, we propose an equivalent but in a vertical configuration and with air as material in the slot region. Moreover, the proposed geometry allows one to access the highly confined electromagneticfield for further sensing applications. The concept is demonstrated at \(\lambda\) = 488 nm, which is the excitation wavelength of a common fluorescent antibody (GFP, green fluorescent protein) used in the labeling of proteins for bio sensing.
## 2 Review of Literature
### Assessment of Information Quality
Then the authors give consideration to aspects from the internal dimension, i.e., whether the information is precise, reliable, credible, etc. Moreover, there is the representational dimension, in what way the communicated information is useful, intelligible or concise. Next comes the availability dimension, i.e., the ability to access information. The least popular are aspects that belong to the external dimension, such as customer support, the right location, etc. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Data** & **User** & & **Surroundings** \\ \hline - & Out of date [15, 16] & - & Psychological barriers [16, 17, 31, 18] & - & Economic barriers (costs) [15, 16, 17] \\ - & Unreliable [15, 16] & - & Educational barriers [32, 33] & - & Source of information (no access to input information) [15, 16, 31, 32, 33, 34, 35, 36, 37] \\ - & Repetitive data [17] & - & Interpersonal barriers [32] & - & Physical barriers (incl. geographic) [17, 20, 21, 33] \\ - & Lack of information, data [16, 18] & - & Language barriers [32, 33] & - & Errors in translation, etc. [17] \\ - & Useless [16] & - & Ignorance of the recipient of information [16] & - & Modifications in the information channel [16, 17] \\ - & Overload (in a time unit) (overload) [16, 19] & - & Reciological [15] & - & Reliability [15, 16, 27, 30, 33, 34, 38] \\ - & Data redundancy [18, 20, 21, 22, 23, 24, 25] & - & Reading organization error [34] & - & Security [18, 29, 39, 40] \\ - & False data [23] & - & Reading value error [34, 35] & - & Technical condition of the information-supplying equip. [35, 37] \\ - & Inaccurate [24, 25] & - & The operator will classify the read value as another [35] & - & Lack of documents [41, 42, 43] \\ - & Incomplete [26, 27, 28, 29, 30] & - & Response inertia error [35] & - & Transportation problems [39] \\ - & Unreliable [27, 31, 32, 33, 34, 35] & - & Skills and experience (of the operator) [19, 36] & - & Errors in the course of proced. [39] \\ - & Useless [35] & - & & Writing errors in the IT sys. [39, 40] \\ - & Unreliable [27] & & - & Failure to perform the service [40] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of information quality issues. In the literature on the assessment of information quality, completeness, accuracy, subject matter-relatedness, timeliness and availability are most often accounted for. The aspect of intelligibility and topicality receives far too little interest. For example, information will be delivered to the recipient at the indicated time, but it will be completely incomprehensible to them, so they will not be able to use it. The sender of the message should simultaneously pay attention to many aspects so that the transmitted message can be useful for the recipient.
## 1 Introduction
The analytic approach of the article is inter-disciplinary, inclusive, eclectic, and integrative; it draws upon data and other materials from atmospheric chemistry and physics, public health, agricultural production and climate science, as well as maritime shipping engineering and economics, and international law and policymaking. These materials establish the basis for the analysis of maritime shipping's air pollution problems and their threats to Italian public health and agricultural production. There is also information about the technological opportunities to reduce emissions. In addition, the article draws upon information about issues and policy options at many levels of governance. The article thus seeks to transcend traditional analytic boundaries of scientific and policy studies to achieve a more encompassing and integrated understanding of problems and solutions. Fortunately, there are detailed empirical case studies of emissions in Italian ports, and they are drawn upon to reach conclusions about ships' emissions as national issues in Italy. There are, in addition, closely related studies of other countries, regions, and global-level questions, which also provide relevant evidence while addressing Italian issues. The analysis thus includes data and analyses of:  
* the composition and volumes of ships' emissions and their effects on public health and agricultural production, as well as climate change;
* variations among 'classes' of ships in their emissions, equipment, and operations;* ports, including the location of the ports in relation to human populations and agricultural production regions, as well as types and volumes of ship traffic;
* governmental policies in Italy, the European Union, the International Maritime Organization (IMO), and a proposed Mediterranean Emission Control Area (ECA) that is on the agenda in Italy and other Mediterranean countries. It should be further noted that the article was being written in late 2020 during the coronavirus pandemic, which, of course, had significant consequences for maritime shipping activities in Italy. Fortunately for the present analysis, data for 2019 that were available for many of the analytic issues were not affected by the pandemic. The year 2020 was clearly a transitional year in many respects for international maritime shipping, including especially the cruise ship segment. As for 2021 and beyond, there are likely to be substantial increases in ship traffic, including in Italy, though it might take several years to resume its previous trajectories. Meanwhile, there continued to be progress in the development and adaptation of many marine technologies, and there was also continuing discussion about new regulatory policies at many levels of governance, including the EU in particular [4; 22; 23; 24]. Despite these many changes and uncertainties, it is nevertheless feasible to address the two key questions: What are the effects on human health and agricultural production of shipping's BC and other emissions in Italian ports and coastal areas? How can they be reduced?