esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Development Coordination Levels between Skywalk Systems and Urban Spatial Environments
## 2 Literature
### Elements of Skywalk System Indicators  
#### 2.1.1 Classification of Indicators  
This subsection summarizes five categories of important skywalk system components as classification indexes based on relevant research from both domestic and international sources. These categories are scale, connection strength, spatial structure, vertical transportation location, and closure attributes [18] (Figure 1). The scale of the skywalk directly affects its coverage and the service capacity of the pedestrian system, and the size of the scale influences the accessibility and sustainability of the skywalk system. The level of connection strength of the skywalk system affects its accessibility, continuity, and vitality [32]. A higher connection strength facilitates better adaptation to different neighborhood forms, scales, and functions. The spatial structure affects the accessibility, connectivity, and utilization efficiency of the system. The category of vertical transportation location impacts the interchangeability and accessibility of a skywalk system [33], and if designed effectively, a skywalk system can seamlessly connect with the surface and underground transportation networks. Closure attributes can directly affect the appearance and degree of shading of a skywalk system [34]. Systems with higher closure attributes typically provide a more private and secure travel environment, with better insulation effects that enhance the comfort and security of pedestrians [35].
# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
## 1 Introduction
Thus, relying on the presented platform, we describe the 5G Global Tracking System (5 GT System), which provides to several users (e.g., cargo shipping companies, port authorities, freight forwarders) an integrated system for real time monitoring of the freight status, even when the container ship is navigating far from the coast in open sea. Indeed, containers equipped with sensors can not only give information such as location, but can give updates on temperature, humidity, etc., on goods in transit and allow for their remote and timely management, which is highly important, especially for dangerous and perishable goods. The contribution of this work is threefold. First, we design a maritime platform within the framework of an ESA-funded project [10] to assess the feasibility of a wide set of 5G use cases. Second, we realize a testbed that reproduces all its components towards the implementation of the cargo container tracking use case. Third, we present the outcome of an extensive experimental activity where we evaluate the 5 GT system through the execution of end-to-end (E2E) performance tests under realistic conditions. The remainder of this paper is organized as follows. Section 2 provides an overview of the literature. Section 3 presents the reference scenario. Section 4 describes the 5 GT system and the container tracking use case. Section 5 describes the experimental testbed and details the obtained results. Section 6 details the benefits brought by our proposed platform and discusses its limitations. Finally, Section 7 draws the concluding remarks.
###### Abstract  
Low Earth Orbit (LEO) satellite communication networks have become an important means to provide internet access services for areas with limited infrastructure. Compared with the Geostationary Earth Orbit (GEO) satellites, the LEO satellites have limited on-board communication caching and calculating resources. Furthermore, the distribution of traffic requests is dynamically changing and uneven due to the relative movement between the LEO satellites and the ground. Therefore, how to schedule the multi-dimensional resources is an important issue for the LEO satellite communication networks. Beam-hopping is an efficient approach to improve the resource utilization by dynamically allocating time, power, and frequency according to the traffic requests. This paper proposes an efficient multi-dimensional resource allocation mechanism for beam-hopping in LEO satellite networks, which simultaneously satisfies the GEO interference avoidance. First, we construct the beam-hopping model of LEO satellites, and formulate the resource optimization problem. Second, we provide the weighted greedy strategy to determine the illumination pattern. In order to reduce the search space, the cells are clustered to non-interference clusters. Then, an improved genetic algorithm is provided to jointly allocate the communication resources. Finally, we construct various simulations to evaluate our proposed mechanism. Compared with the random-BH, polling-BH and traditional genetic algorithm, our algorithm achieves better performance in terms of both system throughput, access success rate, average delay and fairness between cells. The performance improvement is more significant in scenarios where traffic demand is unevenly distributed.  
LEO satellite network; beam hopping; resource allocation; genetic algorithm +
Footnote †: journal: Sensors
# Dynamic Characterization of Cohesive Material Based on Wave Velocity Measurements
###### Abstract  
The paper presents a description of the dynamic properties of cohesive material, namely silly clays, obtained by using one of the applied seismology methods, the bender elements technique. The authors' aim was to present the dynamics of a porous medium, in particular an extremely important passage of seismic waves that travel through the bulk of a medium. Nowadays, the application of the bender element (BE) technique to measure, e.g., small strain shear stiffness of soils in the laboratory is well recognized, since it allows for reliable and relatively economical shear wave velocity measurements during various laboratory experiments. However, the accurate estimation of arrival time during BE tests is in many cases unclear. Two different interpretation procedures (from the time domain) of BE tests in order to measure travel times of waves were examined. Those values were then used to calculate shear and compression wave velocities and elastic moduli. Results showed that the dynamic parameters obtained by the start-to-start method were always slightly larger (up to about 20%) than those obtained using the peak-to-peak one. It was found that the peak-to-peak method led to more scattered results in comparison to the start-to-start method. Moreover, the influence of the excitation frequency, the mean effective stress and the unloading process on the dynamic properties of the tested material was studied. In addition, the obtained results highlighted the importance of initial signal frequency and the necessity to choose an appropriate range of frequencies to measure the shear wave velocity in clayey soils.  
cohesive material; various elastic moduli; seismic wave propagation velocity; laboratory tests; bender element technique
###### Abstract  
In the field of intelligent anti-jamming, deep reinforcement learning algorithms are regarded as key technical means. However, the learning process of deep reinforcement learning algorithms requires a stable learning environment to ensure its effectiveness. Moreover, the inherent limitations of deep reinforcement learning algorithms mean that they can only demonstrate excellent learning capabilities on specific tasks with constant parameters. When parameters change, they can only resample and relearn to converge. In a changing jamming environment, its stability and convergence speed may be challenged, thereby affecting its robustness and generalization capabilities. Aiming at the naive yet unique similarity characteristics of the communication anti-jamming problem, this paper designs a new Meta-PPO deep reinforcement learning algorithm that combines Proximal Policy Optimization (PPO) and MAML meta-learning ideas. The proposed algorithm engrafts the principle of meta-learning used in the Model Agnostic Meta-Learning (MAML) model onto the Proximal Policy Optimization (PPO)-based schemes, enabling the communication systems to harness its prior learned experiences acquired from previous anti-jamming tasks to facilitate and speed up its optimal decision-making process when faced with incoming jamming attacks with similar features. The proposed algorithm is verified through computer simulation analyses and the results show that the proposed novel Meta-PPO algorithm can outperform traditional DQN- and PPO-based algorithms in terms of better robustness and generalization abilities, which can be used to enhance the anti-jamming capabilities of wireless communication systems.  
anti-jamming communication; deep reinforcement learning; meta-learning +
Footnote †: journal: Applied Sciences
## Abstract  
Wind power generation is a renewable energy source, and its power output is influenced by multiple factors such as wind speed, direction, meteorological conditions, and the characteristics of wind turbines. Therefore, accurately predicting wind power is crucial for the grid operation and maintenance management of wind power plants. This paper proposes a hybrid model to improve the accuracy of wind power prediction. Accurate wind power forecasting is critical for the safe operation of power systems. To improve the accuracy of wind power prediction, this paper proposes a hybrid model incorporating variational modal decomposition (VMD), a Sparrow Search Algorithm (SSA), and a temporal-convolutional-network-based bi-directional gated recurrent unit (TCN-BiGRU). The model first uses VMD to break down the raw power data into several modal components, and then it builds an SSA-TCN-BIGRU model for each component for prediction, and finally, it accumulates all the predicted components to obtain the wind power prediction results. The proposed short-term wind power prediction model was validated using measured data from a wind farm in China. The proposed VMD-SSA-TCN-BiGRU forecasting framework is compared with benchmark models to verify its practicability and reliability. Compared with the TCN-BiGRU, the symmetric mean absolute percentage error, the mean absolute error, and the root mean square error of the VMD-SSA-TCN-BiGRU model reduced by 34.36%, 49.14%, and 55.94%.  
wind power prediction; temporal convolutional network; bi-directional gated cyclic unit; variational modal decomposition; sparrow search algorithm
## 3 Experimental Setup
### Off-Line DSP Blocks  
The block diagram of the off-line DSP is illustrated in Figure 2(a),e. In the Tx-side DSP, a QPSK symbol mapping and a raised-cosine (RC) filter with a roll-off factor of 0.01 are deployed, as shown in Figure 2(a). Figure 2(e) depicts the four DSP options at the Rx side, verifying the validity of the J-DBN-based equalization schemes. Conventional, Opt. 1: the captured signal is offline, proposed by typical DSP steps including down-conversion into baseband, resampling, Gram-Schmidt orthogonalization process (GSOP), followed by 53-tap CMA equalization. Then, the frequency offset noise can be mitigated via frequency offset estimation (FOE), and the phase offset problem can be solved after carrier phase recovery (CPR). Finally, a 37-tap DD-LMS equalizer is added to compensate for the remaining linear damage and I/Q imbalance before BER calculation.  
We also compare the BER performance between the CMA equalizer, DBN-based equalizer, and J-DBN equalizer within these DSP steps. In Opt. 2, the adaptive DBN-1 equalizer adapts itself to compensate for the nonlinear distortion. It extracts the signal sequences' characteristics based on the BP algorithm and blind CMA algorithm. The scheme can reduce the sizeable residual error after convergence, which is suitable for the nonlinear channel balance. In Opt. 3, the blind DBN-2 equalizer optimizes the weight value and tap number based on the BP algorithm and DD-LMS equalizer. Moreover, the DBN-2 equalizer can utilize the weight value updated by the DBN-1 as the initial value. The scheme can further compensate for the remaining linear damage and nonlinear decision ability. In Opt. 4, the J-DBN equalizer combines two error cost functions, which is more helpful in handling the receiver sensitivity issue in THz-band wireless links and achieving a more accurate BER decision. Moreover, the J-DBN equalizer can be established via two steps. Firstly, it can be initialized with the aid of the training sequence, and then the weight value can be further optimized by employing the error function of CMA. Adopting our proposed J-DBN equalizer, both the lengths of the training data and the training time can be effectively reduced.
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
## 1 Introduction
In addition, we propose a SDN (_Software Defined Networking_)-based framework that takes advantage of the centralized management of the network to give a response to dynamic QoS requirements in 802.11 networks by applying the DEDCA mechanism. Traditionally, SDN is in charge of configuring the wired networks by dynamically changing the flow table of a forwarding element (i.e., _router_ or _switch_) [3]. On the other hand, when SDN is applied to the wireless networks, it is mainly used to configure a set of APs for efficient resource allocation, interference mitigation, network slicing, congestion control, and load balancing among other things [4]. However, in this SDN framework, not only the Access Points but also the mobile elements will be able to configure their link and physical layers parameters following the guidelines of a centralized SDN controller. The SDN controller monitors the mobile devices and, depending the QoS requirements, the backoff values of some of them will be modified in real time, following the rules of the developed mathematical model. The amount of real scenarios and applications which could benefit from the implementation of the proposed DEDCA mechanism is wide and varied. By way of example, we are going to consider a particular scenario that allows us to show the validity and usefulness of our proposal. In this case, the proposed SDN-based framework is used to manage a controlled surveillance system in an outdoor parking lot. Our proposal has been evaluated by simulation using Estinet network simulator. The testbed consists of an SDN 802.11a Access Point and a set of 802.11a outdoor surveillance cameras, that are controlled by a SDN controller. The Wifi-Infrastructure module of Estinet 9.0 has offered us the possibility of modifying the IEEE 802.11a protocol configuration as required. The rest of the paper is organized as follows: Section 2 presents the DEDCA (_Dynamic Enhanced Distributed Channel Access_) mechanism and describes the mathematical model it is based on The SDN-based framework for IEEE 802.11 infrastructure-based networks that allows the application of the DEDCA mechanism is presented in Section 3. In Section 4 the mathematical model and the applicability of the proposed solutions in a specific scenario are evaluated by simulation. A review of related works are described in Section 5. Finally, Section 6 summarizes the main contributions of this work.
## Article
### Contribution  
At present, the literature on Floating PV systems focuses mostly on installation and design approaches, but there has been nothing in the literature examining the system's benefits when charging electric vehicles. The implementation of EVs cannot help greatly towards the reduction in CO2 emissions until or unless they are charged from a green grid instead of a conventional thermal grid. In this paper, a FSPVS of 1.5 MWp capacity has been designed and simulated at the natural lake of VIT, Vellore. The generated power has been analyzed and used to charge the E-bicycle, E-scooter and E-car to calculate the zero CO2 emission periods of the said vehicles when life cycle CO2 emission from each vehicle has been considered. The CO2 emission profile of the said vehicles considering the fixed distance covered and charged from the FSPVS also has been analyzed. The FSPVS is further used to quantify the reduction in the rate of evaporation for a 1-year period. The objective of the present study is to:  
* Design a floating system for VIT Lake, VIT, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, India;
* To analyze the annual energy generated from the FSPVS;
* Analyze the savings in water due to the prevention of evaporation by the FSPVS;
* Analyze and compare the performance and impacts on the environment while charging different electric vehicles by taking factors such as CO2 emissions into consideration.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Correlation Coefficient Analysis  
Digital images typically exhibit strong correlations among adjacent pixels. To ensure a secure and robust image encryption algorithm, and to avoid vulnerability to statistical attacks, it is essential to eliminate this correlation. The correlation coefficient is a statistical test that is commonly used in image encryption to quantify the strength of correlation between two pixels. It has a range of (1, \(-\)1), where a value of 1 or \(-\)1 indicates the maximum positive or negative correlation, respectively, while a value of 0 indicates no correlation between the compared pixels. Image encryption algorithms usually utilize the correlation coefficient metric to evaluate horizontal, vertical, and diagonal correlation coefficients between adjacent pixels within a cipher image (Section 3.1.1) as well as between a plaintext image and its cipher (Section 3.1.2).
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 5 Evaluation
### Testing with Indoor/Outdoor and Semi-Indoor Sample Test Set  
This test set was included to improve the application's ability to identify an indoor environment even if the GPS signal was weaker due to the semi-indoor environment. This characteristic is extremely helpful when moving from an outdoor environment to an indoor environment (and vice versa). The second test set included 70 indoor, 50 outdoor, and 50 semi-indoor samples. For the comparison, we also included I/O detection using a combined sensor [9] for comparison. Figure 12 shows the accuracy for indoor and outdoor cases separately, in which the semi-indoor environment is categorized as indoor. The accuracy of indoor detection was reduced significantly, which indicates confusion between semi-indoor and outdoor environments in the classifier phase. The result of indoor environment detection was further analyzed to identify the root causes of the poor performance of several classifiers. In consideration of the lower accuracy compared to the outdoor environment, the indoor and semi-indoor samples were misclassified, which indicates that the GPS signal strength of semi-indoor cases is similar to outdoor cases, and it is very difficult to distinguish these cases. In many semi-indoor environments, there is very little change in the GPS signal compared to outdoors, which leads to a malfunction in the I/O detection application. The noise added to the GPS signal in an outdoor environment is also a reason for errors because this makes the GPS signal become weaker and confused with the semi-indoor signal, which decreases the accuracy of the learning model.  
Compared to the combined sensor technique, the accuracy of the proposed scheme is still better (95% for the KNN technique compared to 88% for the combined sensor [9]). This emphasizes the effectiveness of the GPS-based I/O detection scheme. However, the result is insufficient, and we can improve it by employing a unique preprocessing process.  
Figure 11: Indoor/outdoor (I/O) detection accuracy.
# Hybrid Wind-Redox Flow Battery System for Decarbonizing Off-Grid Mining Operations
## 4 Design, Sizing, and Modeling of the Wind Energy and Storage System
### Sub-Model 2: Wind Power Generation Model  
The wind power generation model is based on Sub-Model 1, utilizing the same 15 min time step for consistency. This sub-model incorporates the wind turbine's power curve (see Figure 7) and simulates energy production under varying wind conditions.  
The model utilizes a MATLAB function, such as the 'SEARCHV' function in Microsoft Office, to accurately map wind speed values to corresponding power outputs, ensuring precise alignment between input wind speeds and turbine-generated power.  
Given the harsh environmental conditions at Raglan (including cold temperatures, frost, and storms), a correction factor is applied to account for production inefficiencies. This factor was determined using real-world production data from the existing E-82 wind turbines, reflecting the actual site conditions. The correction factor is calculated as follows (Equation (2) and Equation (A8) in Appendix A):  
\[\begin{split}&\text{{\it{Corrective}}}\;factor=\frac{\text{{\it{Real}}}\;\text{{\it{production}}}\;\text{{\it{of}}}\;\text{{\it{the}}}\;\text{{\it{E-82}}}\;\text{{\it{wind}}}\;\text{{\it{ turbines}}}}\\ &\text{{\it{Production}}}\;\text{{\it{obtained}}}\;\text{{\it{using}}}\;\text{{\it{its}}}\;\text{{\it{power}}}\;\text{{\it{curve}}}\;\text{{\it{from}}}\;\text{{\it{the}}}\;\text{{\it{supplier}}}}\end{split} \tag{2}\]  
Data analysis from 2019 to 2021 revealed that the E-82 wind turbines at Raglan produced only 86.8% of their theoretical capacity due to site-specific constraints. The same correction factor (0.868) was applied to the E-115 wind turbines to ensure realistic projections.  
The time step is increased from 15 min to 1 h to facilitate further analysis, allowing for the following:  
* Estimation of the wind farm's load factor;
* Calculation of total wind energy production (based on the number of new turbines installed);
* Computation of average hourly energy output per turbine.  
Following multiple simulation iterations, the average load factor of the E-115 wind turbines was determined to be 43.6%. Subsequent calculations use this value to refine energy output estimations and assess the wind farm's overall performance under Raglan's environmental conditions.
## 4 Experiments
### Ablation Study
#### 4.4.3 Dynamic Depth Range Sampling (DDRS)  
Table 5 shows the comparative results of the ablation experiment for the dynamic sampling module on the DTU dataset. It can be observed that the dynamic depth range sampling module extends the depth sampling from 13.12 mm to 28.43 mm in stage 0. The coverage of real depth values is also improved, increasing from 0.8468% to 0.8934%. Additionally, even at lower sampling rates, the model's comprehensive reconstruction error decreases from 0.320 to 0.315. This indicates that measuring the uncertainty of sampling with the entropy of the cost volume, and subsequently adjusting the depth sampling range, allows for more accurate predictions along the object edges. This approach takes into consideration the correlation between contextual information, features of neighboring pixels, and the depth sampling range of the current pixel, resulting in enhanced precision.