esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Implementation and Validation of the Proposed Framework through a One-Inch Block Experiment
### Effect of Image Enhancement on the Vision System Measurement Accuracy  
Similar enhancement methods were applied in three static images taken from the validation experiments. The measurement accuracy was assessed by computing the absolute error, \(\Delta_{abs}\), of the displacement of the six points, \(\delta\), with respect to the absolute value of 25.4 mm. The results are shown in Table 6 and the average absolute error, \(\Delta_{abs,mean}\), is computed from all points. A high accuracy of less than 1% error is observed from all measurements using enhanced images. Only HRDC output images achieve a slightly higher error of 1.37%. Overall, the results shown in Table 6 provide the ultimate validation and verification for implementing image enhancement using either histogram-based or haze-removal-based algorithms, where displacement measurement absolute errors can be less than 1%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(\mathbf{c}\) **(pix.)** & \(\mathbf{u}_{0}\) **(pix.)** & \(\mathbf{v}_{0}\) **(pix.)** \\ \hline CS & 7135.09 & \(-35.01\) & \(-7.06\) \\ CLAHE & 7154.47 & \(-24.22\) & \(-8.28\) \\ HE & 7156.03 & \(-28.39\) & \(-9.81\) \\ HRIO & 7181.46 & \(-31.85\) & \(-12.01\) \\ HRDC & 7160.54 & \(-38.92\) & \(-12.95\) \\ \hline \(CV\)(\%) & 0.23 & \(-18.00\) & \(-24.64\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: High-speed system internal parameters measured from the photogrammetry process using enhanced images.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)  
As discussed in Section 2, UWSNs and underwater acoustic channels suffer from many constraints that lead to potential safety hazards. As a consequence, UWSNs are vulnerable to various threats and malicious attacks. In this paper, these threats and attacks were discussed and analyzed in detail.  
According to actions taken by the malicious attacker, these attacks can be _passive_ or _active_. As shown in Figure 3, these threats and attacks can be generally divided into two broad categories, _passive attacks_ and _active attacks_.
## 4 Results  
### Scenarios  
In this section, we present the different scenarios considered for the design of the massive MIMO network based on the optimization constraints. The operating frequency set to 3.7 GHz (with 20 MHz bandwidth). The results are compared with the reference 4G LTE network operating at 2.6 GHz (with 20 MHz bandwidth) without MIMO. The following scenarios are considered:  
* Reference scenario: 4G LTE network operating at 2.6 GHz without MIMO. This is the reference network whose BS positions and power levels are optimized towards power consumption and downlink exposure. It is compared with the designed massive MIMO-LTE networks.
* Suburban information society (300 Mbps for user applications like real-time video gaming) scenarios [34]:
* Scenario 1: the number of users is varied, while assuming all the optimization objectives are of the same importance (\(\omega_{1,3}=\omega_{2,3}=\omega_{3,3}=0.33\) for the tri-objective optimization problem and \(\omega_{1,2}=\omega_{2,2}=0.5\) for the bi-objective one) and the number of BS antenna elements is fixed (256 antenna elements). The number of users varies according to hourly traffic (from a Belgian mobile operator in Ghent), as presented in Figure 4.
* Scenario 2: the number of BS antennas is varied while maintaining the same importance for the optimization objectives (\(\omega_{1,3}=\omega_{2,3}=\omega_{3,3}=0.33\) for the tri-objective optimization problem and \(\omega_{1,2}=\omega_{2,2}=0.5\) for the bi-objective one). Here, the number of simultaneous active users considered for the design is fixed (224 users at busy hour, worst case scenario), while the BSs are equipped with 16, 32, 64, 128 and 256 antenna elements, respectively.
* Scenario 3: The number of simultaneous active users is set to 224 users. Using some sets of combinations (\(\omega_{1,2},\omega_{2,2}\)), different massive MIMO networks are designed with various BS antenna elements (16, 32, 64, 128), among which only non-dominated Pareto front solutions are retained as optimal ones. In all these scenarios, the favorable propagation conditions of massive MIMO have been assumed. Moreover, the user equipments (UE) are assumed to have one single transmit antenna. The moderate inter-cell interference resulting from the pilot contamination effects is modeled by the use of the pilot reuse factor \(\mathrm{F}=3\)[19]. For each considered scenario, we run multiple simulations (40) to ensure we obtain a statistically relevant estimation of the parameters investigated (e.g., the number of BSs, the power consumption and the DL and UL EMF exposure) [32].  
Figure 4: Number of simultaneously served users on a hourly basis in Ghent, Belgium.
## 2 System Design
### Digital Signal Recovery  
After the TIA in the traditional receiver, we designed an additional DSR module to improve the underwater link impairments introduced during signal transmission, including attenuation and timing jitter. The DSR module consists of a limiting amplifier (LA), a phased-locked loop (PLL) circuit, and a delay-locked loop (DLL) circuit, whose schematic is shown in Figure 6. It can provide the receiver with functions of limiting amplification, clocking, and data recovery for wideband continuous data rates without the need for an external reference clock.  
The input port is terminated with 50 \(\Omega\) to the supply voltage, and the sensitivity of the LA is as low as 3.3 mV of the peak-to-peak voltage. Then, the phase of the amplified input data signal is tracked by two separate feedback loops, which share a common voltage-controlled oscillator (VCO). The PLL tracks the low-frequency components of the input jitter, and the DLL tracks the high-frequency components of the input jitter. At low jitter frequencies, a frequency detector (FD) compares the incoming data frequency to this VCO frequency. A loop filter (LF) provides a high gain to track large jitter amplitudes with small phase errors. The jitter tolerance of this PLL is roughly half a period. At higher jitter frequencies, the gain and tuning range of the VCO are not large enough to track the input jitter. In this case, the VCO control voltage becomes large and saturated. A phase shifter (PS) in the DLL circuit begins to track the input jitter. Then, a phase detector (PD) drives the VCO to a higher frequency. The gain of the loop integrator is small for high jitter frequencies. In this case, jitter accommodation is determined by the eye-opening of the input data. The PLL and DLL simultaneously provide wideband jitter tolerance and narrow-band jitter filtering. The VCO frequency is reset to 10 MHz, which is the bottom of  
Figure 5: \(S_{21}\) parameter of the pre-equalization circuit.  
Figure 6: Schematic of the DSR module.  
its range. Initially, the VCO frequency is incremented in large steps to aid fast acquisition. As the VCO frequency approaches the data frequency, the step size is reduced until the VCO frequency is within 250 ppm of the data frequency. Once the input frequency error exceeds 1000 ppm, the control returns to the frequency loop, which begins a new frequency acquisition.
# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 4 Case Study  
### The Verification of the Route Tracking Model  
To better validate the effectiveness of the improved LOS algorithm, ship trajectory tracking experiments are conducted. The experimental scenario is configured as a winding course with a wind direction of 035\({}^{\circ}\), wind speed of 5 m/s, current direction of 180\({}^{\circ}\), and current speed of 0.5 m/s. The ship trajectories of the route tracking model (red line) and the planned route (black line) are shown in Figure 15a. Figure 15b illustrates the deviation between the planned route and the actual trajectory, demonstrating that the error remains below 200 m despite wind and current interference.  
The straight-line tracking results exhibit superior performance compared to waypoint tracking, which shows higher maximum deviation errors. From the route tracking results, it can be observed that the downstream ship's trajectory remains largely consistent with the recommended curved route, maintaining minimal deviation in straight sections. To prevent the ship from drifting out of the channel before reaching a turning point, the OS performs preemptive steering maneuvers. At sharp-angle channel turns, the ship's maneuverability may induce trajectory drift, leading to abrupt changes in deviation from the planned route. However, the maximum trajectory error throughout the entire process is 49 m.  
These simulation results indicate that the inland waterway tracking method effectively controls ship navigation trajectories in curved waterways under environmental disturbances. The trajectory tracking error is significantly smaller than the waterway width,generally satisfying the requirements for autonomous navigation decision-making for inland ships.
# TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks
## 3 Related Work
[26] have developed an analytical model to characterize the synchronization processes of TSCH networks using slots shared by the network nodes and with sporadic traffic, validating the model through simulations. The authors of [22] also support their study by developing an analytical model for both the TSCH synchronization and the RPL Trickle timer. This paper takes the results obtained in [25] as a starting point, proposing an analytical model to characterize the processes of synchronization in TSCH and connection with theRPL topology, so that, with this tool, it is possible to choose more optimal configurations when choosing a period of transmission of EB messages with dynamic behavior.
## 4 Influence of the System Parameters  
In this section we present simulation results and discussions concerning the influence of the system parameters under the _R-model_ on the spectrum sensing performance for GLRT, MMED, MED, and ED (the simulation file used in our simulations is available for download as a supplementary material). Curves for the _C-model_ are also included for purposes of comparison. It is worth mentioning that the receiver operating characteristic (ROC) curves for all the detection techniques under the _C-model_, for \(m=6\), \(n=50\), and \(\text{SNR}=-10\) dB, are in perfect agreement with those reported in [6]. The results in this section were reported in [8] and were included here so that this paper becomes self-contained.  
The ROC curves shown hereafter were obtained with a minimum of \(5,000\) runs in Monte Carlo simulations implemented according to the setup described in Section 3. System parameters are those in Table 1, unless otherwise indicated. Shaded areas in the graphs represent positions of ROC curves for \(N_{\text{q}}=8\), and for \(f_{\text{od}}\) and \(L\) ranging from 1 to 2, and 1 to 20, respectively. They are meant to reflect parameter variations within empirical limits of practical significance.
## 5 Discussion
Adaptability to various domains: Our proposed method is not limited to a specific application area but can be adapted to various domains, such as supply chain management, environmental decision-making, and project portfolio selection. This versatility is a significant advantage over some specialized MCDM algorithms that may be tailored to specific contexts. However, it is essential to acknowledge that our approach also has some limitations compared to other MCDM algorithms. The complexity of the method, both in terms of conceptual understanding and computational requirements, may be higher than simpler techniques like the AHP or TOPSIS. This complexity may require additional effort in data collection, expert elicitation, and result interpretation. Moreover, the subjectivity involved in determining the coalition values and the sensitivity of the results to these inputs may be a concern in some applications. Decision-makers should be aware of these limitations and take appropriate measures to validate the inputs and test the robustness of the findings. Despite these challenges, we believe that the benefits of our approach outweigh its limitations, particularly in decision problems where cooperative dynamics and equitable resource allocation are critical concerns. By providing a more comprehensive and nuanced evaluation of alternatives, our method can lead to better-informed and more defensible decisions.
# Smart Manufacturing Application in Precision Manufacturing
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Operational Performance Impact:** & \multicolumn{3}{c}{**Economic Benefit Impact:**} & \multicolumn{3}{c}{**Environmental Sustainability Impact:**} \\
**Productivity Improvements** & \multicolumn{3}{c}{**Financial Returns**} & \multicolumn{3}{c}{**Emissions Reduction**} \\ \hline Machines’ utilization improvements & 47\% & ROI & 1 year & Total emission reductions & 42\% \\ \hline Total downtime cost reduction & 53\% & IRR & 40\% & Scope 2 emission reductions & 50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Smart manufacturing application impacts over three-year period (2020–2022). addresses this gap in the literature by evaluating the real-life operational performance KPIs used in the manufacturing industry. There is also a lack of economic evaluation in the literature, and there seems to be no standardized approach, with studies mainly focusing on a single indicator like cost or revenue, which is insufficient to provide a full picture of the actual economic impact of smart manufacturing applications in industry [20; 25]. Therefore, the cost-benefit ROI and IRR analyses were proposed as the parameters of choice for economic evaluation.
# Enhancing Autonomous Driving Navigation Using Soft Actor-Critic
## 4 Methodology
### Reward Function  
Inspired by [35], we have designed our reward system to align with the challenges posed by urban traffic conditions. Our aim is to guide AVs safely through roundabouts without collisions while ensuring their timely arrival at their destinations. To achieve this objective, our reward system is structured as four pivotal components: maintaining the speed of the AV, penalizing collisions, ensuring the AV respects its lane by avoiding unauthorized lane changes and considering the remaining distance to the destination.  
For simplification, we define the current distance to the destination as \(d_{ct}\) and the previous distance as \(d_{pre}\). The AV's forward speed is denoted by \(v_{spead}\), while the measure of the AV if moving off-road and in another lane is represented by \(M_{offroad}\) and \(M_{otherlane}\). In the case of collisions, a penalty denoted as \(C_{collision}=100\) is enforced, adjusting the reward (\(R_{t1}\)) accordingly.  
Furthermore, if the AV approaches the goal (\(d_{pre}>d_{cu}\)), it receives an augmented reward (\(R_{t2}\)) in order to encourage the AV to progress towards the destination. Moreover, the AV's velocity should not surpass the speed limit value of 3 (derived from 30 divided by 10), designated as \(R_{t3}\).  
Maintaining the AV to respect its lane is important; we penalize the agent with minor penalties if the AV is traveling in an unauthorized lane or off-road, as represented in \(R_{t4}\) and \(R_{t5}\). Ultimately, if the AV arrives at the intended destination successfully, it is rewarded by \(R_{t6}=100\). The reward function can be represented as follows :  
\[reward=\left\{\begin{array}{ll}R_{t1}=-C_{collision}\\ R_{t2}=d_{pre}-d_{ct}\\ R_{t3}=\frac{v_{spead}}{10}\\ R_{t4}=-0.05\times M_{offroad}\\ R_{t5}=-0.05\times M_{otherlane}\\ R_{t6}=100\\ R_{t}=R_{t1}+R_{t2}+R_{t4}+R_{t5}+R_{t6}\end{array}\right. \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Action** & \multicolumn{3}{c}{**Control Commands**} \\ \hline  & **Steering** & **Throttle** & **Brake** \\ \hline \(a_{0}\) & 0.0 & 0.6 & 0.0 \\ \hline \(a_{1}\) & \(-0.5\) & 0.5 & 0.0 \\ \hline \(a_{2}\) & 0.5 & 0.5 & 0.0 \\ \hline \(a_{3}\) & 0.0 & 0.3 & 0.0 \\ \hline \(a_{4}\) & \(-0.5\) & 0.3 & 0.5 \\ \hline \(a_{5}\) & 0.5 & 0.3 & 0.0 \\ \hline \(a_{6}\) & 0.0 & 0.0 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Actions and their Corresponding Values.
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 5 Evaluation
### Results and Discussions
#### 5.3.3 Handover Delay  
We measured the handover delay experienced by the consumer under different mobility support methods, and the results are shown in Figure 6. We note that the handover delay of the DAFM is related to the value of \(T_{RA}\). The larger the value set by \(T_{RA}\), the larger the handover delay of the DAFM. In other words, the handover delay of the DAFM is related to the delay incurred when the access router obtains the new address for the mobile device from the NRS. When \(T_{RA}\) is set to 1 ms and 5 ms, the handover delay of the DAFM is lower than that of other methods. When \(T_{RA}\) is set to 10 ms, the handover delay of the DAFM is higher than that of other approaches. The OPT and MAP-ME have a similar handover delay in this experiment because they both achieved data redirection by propagating special packets over the same distance. Therefore, in order to guarantee the handover delay, it is necessary to deploy the NRS as close as possible to the access router so that the access router can obtain the latest network address of the mobile device as soon as possible after sensing the mobile device's movement.  
Figure 5: Comparison of packet loss rates in different scenarios. (**a**) Packet loss rate vs. mobile data flow rate; (**b**) packet loss rate vs. number of mobile data flows.
## 3 Materials and Methods
### Web Ecosystem Design and Proof of Concept Implementation  
This work presents a web ecosystem, called SENIOR, whose architecture is composed of three main elements, as represented in Figure 1. The particularities of each entity are subsequently described:  
* Wearable: device able to measure physical characteristics of patients. The device has the requirement of being capable of connecting with the smartphone application to communicate the data. Furthermore, this device must be worn by the user at any moment (except for charging).
* Smartphone application: the user installs and utilizes the application developed in this project. This software gathers the data acquired by the wearable, personal data provided by the user and information based on completing surveys analyzing different health variables. Moreover, it obtains the geographical coordinates of the smartphone. All these data are sent to the web platform using encryption to preserve the confidentiality of the data.
* Web platform: this element is the main entity of the architecture. On the one hand, it receives the data compiled by the smartphone application, storing them in a database system. On the other hand, it uses these data to generate a prediction of future blood pressure values, transmitting these results to the application. Additionally, this platform offers a categorization of the pressure value, notifying the user if the predicted value would be within normal ranges or represent an hypertensive crisis. Finally, this element queries weather and pollution external services to provide contextual data to users' measurements.  
This main architecture is extended in Figure 2, presenting the whole architecture of SENIOR. We can first observe that the user provides data to the application, both directly and from the wearable. These data, in addition to environmental data obtained from external services, are stored by the web platform into the database system. After that, datasets are generated, used to train machine learning algorithms to produce models. Once the models are available, the user can request blood pressure prediction based on current data provided by the application.
# Evacuation Management System for Major Disasters
## 5 Discussion & Conclusions
This system also has purely technical limitations as it relies on external services that maintain updated information to a greater or lesser extent (e.g., road status information, mapping, or Pois definition), which has a direct impact on the models. This impact can be seen, for example, in the suggestion of shelter locations where outdated data may mean that a facility may not be eligible for sheltering purposes because it is damaged or does not fulfil the appropriate requirements. This is why this tool is considered a decision-support system and always requires an operator to ultimately make sense of and accept the proposed results based on different sources of information [38]. Conceptualization, D.A., A.C. and J.G.-V.; methodology, J.G.-V. and A.C.; software, J.G.-V.; validation, A.B. and J.G.-V.; formal analysis, J.G.-V.; investigation, J.G.-V. and A.C.; resources, A.B. and J.G. [PERSON]; data curation, A.B. and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], A.C., D.A. and A.B. ; visualization, [PERSON]; supervision, D.A. ; project administration, D.A. ; funding acquisition, D.A. All authors have read and agreed to the published version of the manuscript. This research and APC was funded by the European Union's H2020 research and innovation programme under grant agreement No. 832576 (ASSISTANCE project). No new data were created or analyzed in this study. Data sharing is not applicable to this article. EMS has been developed within the ASSISTANCE project ([[https://assistance-project.eu/](https://assistance-project.eu/)]([https://assistance-project.eu/](https://assistance-project.eu/)), accessed on 28 July 2022) which has received funding from the European Union's H2020 research and innovation programme under grant agreement No 832576. The authors declare no conflict of interest.
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 2 Precoding Schemes
### Hybrid Precoding  
Hybrid precoding, a mix of linear and non-linear precoding schemes, was proposed in [11; 12; 13] to enhance the robustness of non-linear precoding against channel imperfections and to reduce the complexity required for large-scale antenna systems.  
Considering \(K\) users divided into \(G\) groups, the precoding mechanism is implemented in two stages. The first stage consists of a linear precoder used to eliminate the inter-group interference and assumes that the users from different groups have sufficiently uncorrelated channels. This stage can be realized through BD using long-term CSI measurements. Based on the degree of correlation between the users of the same group, linear or non-linear precoding schemes can be employed inside every group, in the second stage.  
Figure 3 depicts a hybrid precoding scheme that uses BD and THP:  
Figure 3: Hybrid precoder block scheme [3].  
Figure 2: THP block scheme [3].
# Effect of Surface Mass Loading on Geodetic GPS Observations
## 3 Results and Discussion
### Wavelet Coherence Analysis
Figure 5: Mean value of the WTC-based semblance at the period closest to 1 year for 30 sites. \[\Delta\text{RMS}\%=\frac{\text{RMS}(\text{GPS})-\text{RMS}(\text{GPS}-\text{ AOH})}{\text{RMS}(\text{GPS})} \tag{5}\]  
Table 3 indicates that all sites experience a reduction in their RMS values when corrected by AOH, revealing the feasibility of correcting GPS height with the mass loading model. From Table 3, we can see that a relatively poor improvement occurs at the SHAO, VILL and WUHN sites, where there is a significant phase asynchrony between GPS heights and AOH displacements.