esa-sceva/satcom-chunk-collection huggingface dataset:
# Patterns of Arctic Tundra Greenness Based on Spatially Downscaled Solar-Induced Fluorescence
## 2 Data and Methods
### Data  
Three satellite-based datasets (2007-2013) were used in this research, including satellite-derived NDVIs (Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g and MODIS NDVI) and SIF data (Table 1). In addition, the eddy-covariance (EC) flux measurements (GPP) during 2007-2013 were used for the greenness trend comparison. The NDVI was calculated as (NIR - R)/(NIR + R), where NIR is the surface reflectance in the near-infrared band (GIMMS NDVI3g: 730-1000 nm, MODIS NDVI: 841-876 nm) and R is the surface reflectance in the red band (GIMMS NDVI3g: 550-700 nm, MODIS NDVI: 620-670 nm) [(40)].
# Topology Optimisation in Structural Steel Design for Additive Manufacturing
## 1 Introduction
### Potential for Topology Optimisation and Additive Manufacturing in Construction  
The asymmetry in size between the TO related literature in civil and structural engineering and many other fields made clear by the former brief historical overview, suggests that TO applications in construction are far from meeting its potential. Such a fact can only come as a surprise, given the sector size, with a yearly output of USD 10,800,000,000,000 as of 2017 [142], a tradition in analytical structural optimisation and an urgent need for weight reduction along with stiffness and resistance enhancement, as tools for leveragingthe continuous race towards higher buildings, greater spans in bridges and roofs, more economic efficiency, and more ambitious sustainability goals.  
Steel construction, deploying 450,000,000 to 815,000,000 tonnes [2,143; 144] of structural steel per year worldwide pre-COVID-19 era, and with a market size over USD 100,000,000,000 in 2019 [145], is involved in a global effort to meet decarbonisation and energy efficiency goals, including the Paris Agreement pledge, European Green Deal objectives, and further commitments, such as the UN-backed carbon neutrality by 2050, to which most EU countries abide [146; 147]. The path for achieving this is narrow and relies mostly upon employing less steel in constructions, while manufacturing a higher-end product, to more stringent sustainability demands. As a result, AM and, therefore, TO will be indispensable.  
Apart from some slight oscillations due to technical details, it is commonly accepted that steelmaking consumes over 560 kg of Coal Equivalent [148] and produces an average of 1.85 carbon dioxide tonnes per tonne of structural steel [147]. Thus, reported weight reductions of 18% to 75% in steel connections [139; 149; 150; 151], by employing TO, are expected to have a critical impact in steel construction goals.  
Within steel design, connections detailing is one activity where the potential to optimise Topology is more significant. Connections usually account between 12% and 25% of most of the steel structure's total weight and its conceptual design has a tremendous impact on its weight and efficiency, making it especially prone to TO.
## 3 DA-Based Turbo Equalization
### Soft Information-Based ADFE
#### 3.3.2 Adaptive Coefficient Adjustment  
In the proposed method, the RELS algorithm [23] is adopted for adaptive coefficient adjustment. The update steps are as follows.  
(1) Computing the Kalman gain,  
\[\mathbf{k}^{\alpha,m}(n)=\frac{\mathbf{j}^{\alpha,m}(n-1)\mathbf{u}^{\alpha, m}(n)}{\lambda+[\mathbf{u}^{\alpha,m}(n)]^{\mathrm{H}}\mathbf{j}^{\alpha,m}(n-1) \mathbf{u}^{\alpha,m}(n)} \tag{16}\]  
where \(\mathbf{j}^{\alpha,m}(n-1)\) denotes the inverse covariance matrix at time \(n-1\), \(\lambda\) is the forgetting factor.  
(2) Updating the coefficient vector  
\[\mathbf{f}^{\alpha,m}(n+1)=\mathbf{f}^{\alpha,m}(n)+\mathbf{k}^{\alpha,m}(n) [\tilde{s}^{\alpha,m}(n)-\tilde{s}^{\alpha,m}(n)]^{*} \tag{17}\]  
where \(\tilde{s}^{\alpha,m}(n)\) is the decision value used for adaptive coefficient adjustment. When ADFE operates in training mode, \(\tilde{s}^{\alpha,m}(n)\) is equal to \(s^{\alpha,m}(n)\), and when ADFE operates in decision directed mode, \(\tilde{s}^{\alpha,m}(n)\) is computed as  
\[\tilde{s}^{\alpha,m}(n)=\frac{\sum\limits_{\delta\in S}sp[\tilde{s}^{\alpha,m} (n)|\tilde{s}^{\alpha,m}(n-1),s^{\alpha,m}(n)=s;\tilde{\mathbf{f}}^{\alpha,m}( n)\,]p_{\mathrm{in}}[s^{\alpha,m}(n)=s]}{\sum\limits_{\delta\in S}p[\tilde{s}^{\alpha,m}(n)| \tilde{s}^{\alpha,m}(n-1),s^{  
(3) Updating the inverse covariance matrix  
\[\mathbf{J}^{a,m}(n)=\frac{\mathbf{J}^{a,m}(n-1)-\mathbf{k}^{a,m}(n)[\mathbf{u}^{a,m}(n)]^{\mathrm{H}}\mathbf{J}^{a,m}(n-1)}{\lambda} \tag{19}\]  
Based on observations made in relation to Equation (18), for computing \(\mathfrak{F}^{a,m}(n)\), a suitable model for the distribution \(p[s^{a,m}(n)|\mathfrak{s}^{a,m}(n-1),s^{a,m}(n)=s;\mathfrak{F}^{a,m}(n)\,]\) must be selected. The research presented in [24] shows that ignoring the information about \(s^{a,m}(n)\) provided by \(\mathfrak{s}^{a,m}(n-1),\ldots\mathfrak{s}^{a,m}(1)\) only adds some noise following nearly Gaussian distribution; and, therefore, \(p[s^{a,m}(n)|\mathfrak{s}^{a,m}(n-1),s^{a,m}(n)=s;\mathfrak{F}^{a,m}(n)\,]\) can be approximated by \(p[s^{a,m}(n)|s^{a,m}(n)=s;\mathfrak{F}^{a,m}(n)\,]\).  
Substituting \(p[s^{a,m}(n)|\mathfrak{s}^{a,m}(n-1),s^{a,m}(n)=s;\mathfrak{F}^{a,m}(n)\,]\) by \(p[\mathfrak{s}^{a,m}(n)|\mathfrak{s}^{a,m}(n)=s;\mathfrak{F}^{a,m}(n)\,]\) is \(\mathfrak{F}^{a,m}(n)]\), \(\mathfrak{F}^{a,m}(n)\) can be computed as  
\[\mathfrak{F}^{a,m}(n)=\frac{\sum\limits_{s\in\mathcal{S}}sp[s^{a,m}(n)|s^{a,m }(n)=s;\mathfrak{F}^{a,m}(n)\,]p_{\mathrm{in}}[s^{a,m}(n)=s]}{\sum\limits_{s \in\mathcal{S}}p[s^{a,m}(n)|s^{a,m}(n)=s;\mathfrak{F}^{a,m}(n)\,]p_{\mathrm{ in}}[s^{a,m}(n)=s]} \tag{20}\]  
where \(p[s^{a,m}(n)|s^{a,m}(n)=s;\mathfrak{F}^{a,m}(n)\,]\) is assumed to be a Gaussian distribution with mean \(s\) and variance \(\sigma^{2}\big{|}\mathfrak{s}^{a,m}(n)\,\) estimated using a variance estimator presented in [24].
## 3 Integrating DT in CPS
### Smart Grid
If the physical state of the object layer changes, the application layer can send commands to some actuators in the object layer to control the grid based on the current data and the trained model. If the physical components of the object layer change, the AI model can perform model updates to reflect the changes as well. The end user layer uses the information processed by the application layer to provide services for end users, including smart grid management systems, smart charging systems for autonomous vehicles, and hybrid energy management systems. There are a number of research efforts on DT for the smart grid [38; 39; 68; 70; 71]. For example, General Electric (GE) proposed two models related to the use of DT in wind farms [68; 69]. In the object layer, a communication network was designed to enable wind turbines to communicate with each other. A wind turbine control system was also implemented to control one or more wind turbines. The middleware layer includes a cloud-based infrastructure with the support of the communication network so that the data (e.g., state of turbines) can be collected and turbines can be remotely controlled via gateways. It also includes digital models that represent digital replicas of wind turbines. The digital model can be updated based on the information extracted from the object layer, and the wind farm can be controlled through an industrial gateway. In addition, GE designed a graphical user interface (GUI) in the application layer to show the digital representation of the wind farm and perform the control and management operations of the wind farm. [PERSON] et al. [39] proposed a microgrid DT model based on the IoT to mitigate cyber attacks. The object includes microgrid, local controller, and area controller. Real-time balancing algorithms were implemented in each controller. If some microgrids are attacked and go offline, the area controller can obtain information, and the balancing algorithm will not transfer more power to the offline area based on the tampered data. This architecture can also mitigate distributed denial of service (DDoS) attacks and false injection attacks. Likewise, [PERSON] et al. [38] used DT to manage a large number of devices in the smart grid system. To be specific, they used the spike neural network (SNN) deployed on the smart meter in the grid to detect fault nodes. A transient state estimator (TSE) was designed to reveal the dynamic state of the smart grid. [PERSON] et al. [70] leveraged deep learning algorithms to analyze data from the supervisory control and data acquisition system (SCADA) to assist the DT in detecting physical faults in the smart grid system. Likewise, [PERSON] et al. [71] proposed a DT framework with artificial neural networks (ANN) for distributed smart grids to ensure real-time model generation, verification, and identification.
## Description and Performance of an L-Band Radiometer with Digital Beamforming  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
Remote Sensing Lab, Department of Signal Theory and Communications, Technical University of Catalonia, Diagonal North Campus, and IEEC CRAE/UPC, Jordi Girona 1-3, Building D4, 08034 Barcelona, Spain; E-Mails: [EMAIL_ADDRESS] (I.R.-P.); [EMAIL_ADDRESS] (A.C.); [EMAIL_ADDRESS] (N.R.-A.); [EMAIL_ADDRESS] (E.V.); [EMAIL_ADDRESS] (J.F.M.-H.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-934-017-362.  
_Received: 27 October 2010; in revised form: 3 December 2010 / Accepted: 22 December 2010 / Published: 30 December 2010_  
This paper presents the description and performance tests of an L-band microwave radiometer with Digital Beamforming (DBF), developed for the _Passive Advanced Unit_ (PAU) _for ocean monitoring_ project. PAU is an instrument that combines, in a single receiver and without time multiplexing, a microwave radiometer at L-band (PAU-RAD) and a GPS-reflectometer (PAU-GNSS-R). This paper focuses on the PAU-RAD beamformer's first results, analyzing the hardware and software required for the developed prototype. Finally, it discusses the first results measured in the Universitat Polit\(\acute{\rm e}\)ncia de Catalunya (UPC) anechoic chamber.  
**Keywords**: radiometry; digital beamforming; FPGA
## 3 Results and Discussion  
### Characterization of Nanogold Composite NGC  
#### 3.1.1 Fourier-Transform Infrared Spectroscopy (FTIR)  
The FTIR spectrum of NGC before MB adsorption shows that five bands are observed, as in Figure 2, which reveal stacked spectra of NGC after adsorption (black) and before adsorption (red). The characteristic bands highlight alterations in band intensities rather than any noticeable shifts. For instance, the 3400 cm\({}^{-1}\) bands denote \(v\) (O-H) stretching, the 2921 cm\({}^{-1}\) bands denote \(v\) (C-H) stretching, and the band at 1700 cm\({}^{-1}\) can be assigned to (C-N) stretching. The band at 1261 cm\({}^{-1}\) indicates \(v\) (C=O) stretching, while 1095 cm\({}^{-1}\) indicates \(v\) (C=C aromatic) stretching. Notably, the band related to (C=O) stretching displays a downward shift at 1243 cm\({}^{-1}\) of approximately 20 cm\({}^{-1}\) in frequency after adsorption. This suggests a significant hydrogen-bonding interaction between C=O and MB [38]. The vibrational modes of the (C=C aromatic) stretching band before adsorption shifted to 1044 cm\({}^{-1}\) implying modification of ring vibration, which is indicative of interactions like pi-pi interactions with MB [39].  
As the composite structure comprises polymers, the movement of these polymer frameworks could potentially immobilize the nanogold. This dual functionality is anticipated to hinder the aggregation of nanoparticles and fibers and contribute to maintaining small particle size and stability [40].
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Data and Pre-Processing
#### 2.2.3 Remote Sensing Data  
The prediction of winter wheat yield is affected by many complex factors [41]. Yield prediction models considering multiple factors have better accuracy in yield estimation [33; 42]. In this paper, based on MODIS remote sensing products, EVI and NDVI, which reflect vegetation growth, and GPP, FPAR, LST, LAI, ET, and PET, which represent ecosystem functions, were calculated. The data type, resolution, and source are detailed in Table 2. In order to ensure data consistency and accuracy, the above remote sensing data were pre-processed according to the quality control band of MODIS data products, and mask processing was performed according to the winter wheat vector data of each county, and the average value was calculated. In addition, some characteristic variables may have outliers, and these outliers may affect the model's prediction performance. For this reason, the RobustScaler scale, which is more robust to outliers, was used in this paper to pre-process the feature variables to reduce the impact of outliers on the prediction results, thereby improving the reliability and accuracy of the prediction.
## Problem Description and Evaluation Metrics  
### Problem Description  
Given a network \(G(V,E)\) at time \(t\), where \(V=\{v_{1},v_{2},\cdots,v_{N}\}\) is the set of nodes and \(E=\{e_{1},e_{2},\cdots,e_{M}\}\) is the set of links. The observed links, \(E\), are randomly divided into training, \(E^{T}\), and probe, \(E^{P}\), sets, where \(E=E^{T}\cup E^{P}\) and \(E^{T}\cap E^{P}=\varnothing\). Link prediction aims to predict missing links at current network or new links for a future time \(t^{\prime}(t^{\prime}>t)\)[2]. Link prediction combination methods fuse several similarity indices and obtain a synthetic index and can be described in mathematic as follows. Let \(\textbf{X}=\left(X_{1},X_{2},\cdots,X_{n}\right)^{T}\) be the scores of existing links as given by \(n\) structural similarity indices, and follow probability density function (pdf) \(f(\textbf{x})=f(x_{1},x_{2},\cdots,x_{n})\). Let \(\textbf{Y}=\left(Y_{1},Y_{2},\cdots,Y_{n}\right)^{T}\) be the scores of nonexistent links as \(n\) structural similarity indices, and follow \(g(\textbf{x})=g(x_{1},x_{2},\cdots,x_{n})\). We need to find the transformation function, \(l(\textbf{x})\), and obtain the synthetic score, \(X=l(\textbf{X})\), \(Y=l(\textbf{Y})\) that maximizes evaluation metrics. Figure 1 is the diagram of combination methods.
## 4 Results
### Validity Experiment of Cross-Level Information Interaction  
In MDAU-Net, the cross-level information interaction mechanism, based on bidirectional reactions, is frequently utilized in conjunction with the double-flow linear pooling enhancement unit. To demonstrate its effectiveness, we used U-Net with DLE as the baseline and assessed the segmentation performance by sequentially introducing residual pathways. We categorized the experiments into four groups. The first group served as the baseline, while the second and third groups were comparative experiments involving the addition of reverse and forward residual connections, respectively. The fourth group combined both forward and reverse residual connections. Table 2 displays the segmentation results for each group. Notably, performance was weakest when no residual connections were added. However, the introduction of either forward or reverse residuals  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loss** & **Dice** & **Precision** & **Recall** & **VOE** & **RVD** \\ \hline Dice loss & 0.9420 & 0.9490 & 0.9393 & 0.1076 & 0.0205 \\ Focal loss & 0.9044 & **0.9662** & 0.9116 & 0.1745 & 0.1872 \\ Tversky loss & **0.9433** & 0.9515 & **0.9451** & **0.1053** & 0.0383 \\ BCE loss & 0.9328 & 0.9486 & 0.9396 & 0.1239 & **0.0189** \\ \hline \hline \end{tabular} Bold text in the table represents the optimal results.  
\end{table}
Table 1: Loss function comparison test on LiTS.  
led to varying degrees of performance improvement, with forward residuals demonstrating a more substantial positive impact on the network than reverse residuals. When both sets of residual connections were simultaneously incorporated, all evaluation metrics surpassed those of the first three groups. Compared to the baseline experiments, improvements of 0.0137, 0.0032, 0.0389, 0.0432, and 0.0313 were observed. Figure 7(a) presents the radar chart for this experiment, where the addition of two sets of residuals resulted in the largest coverage area on the coordinate axes, confirming that the cross-level information interaction mechanism based on bidirectional residual connections effectively mitigated knowledge forgetting issues and enhanced the network's learning capabilities.
## 3 Results
### Autonomous Operation of Sensor Nodes with PVEH
In contrast, the TI-CC2650 operates reliably at a minimum voltage level of 2 V. However, for practicality and comparability, we conducted the experimental characterization with a voltage supply of 2.5 V. The lower operational voltage level is a significant feature for certain real-life applications that demand a minimal voltage supply. To explore this further, we exclusively tested the sensor node incorporating the TI-CC2650 MCU alongside an updated vibration-based energy harvester known as \"Design I\", capable of providing higher power levels. Figure 12: Summary of supercapacitor-backed autonomous operation possibilities of sensor nodes with our proposed energy harvester. These results highlight that the use of the optimized harvester design creates the opportunity to broadcast and monitor environmental data in real-time, particularly in scenarios requiring broadcast intervals as short as 1-3 s.
## 3 Materials and Methods  
### Tunnel Tube Characteristics  
The investigated road tunnel tube is existing and is located along an Italian motorway. It is flat and straight, with a length of 850 m, having two lanes, and without emergency lanes. The emergency exits are the cross-connection between the two tunnel tubes, and the two portals of the tube. Since there is not a mechanical ventilation system, the ventilation within the tube is natural and is due to the piston effect of vehicles in motion. In this respect, it is to be remembered that, in the case of unidirectional traffic flow, the natural ventilation was simulated by applying a positive pressure difference of 5 Pa between the entrance portal (portal A) and the exit one (portal B) [18].  
When the tube is, instead, used for bi-directional traffic, a reduced effect of the natural ventilation is assumed for taking into account the two opposite driving directions. In particular, a positive pressure difference of only 0.5 Pa between portal A and portal B was assumed in the present paper. This might be justified by the fact that the speed of vehicles that do not change tube (i.e., direction from portal A to B) is a bit higher than the speed of vehicles in the opposite direction that are forced to slow down to change carriageway and then to accelerate within the investigated tube (i.e., direction from portal B to A). The tunnel concrete walls have a thickness of 0.4 m. The concrete thermal conductivity is assumed to be equal to 1.67 W/m/K, the value of the specific heat is 0.94 kJ/kg/K, the density is 2585 kg/m\({}^{3}\), and finally the emissivity is 0.9. Specifically, the thickness of 0.4 m is related to the walls of the existing tunnel; while the properties of concrete, given the lack of accurate data, were taken from the literature [20] and are referred to an ordinary concrete, which is similar to that used in the investigated tunnel.
## 3 ML Application in O-RAN
### Energy Efficiency  
Energy efficiency is a significant challenge in O-RAN due to their highly flexible and scalable design, where several components and technologies must work together seamlessly. Achieving energy efficiency also requires balancing trade-offs with other performance metrics such as latency or throughput. Although dynamic function splitting and ML-based optimization techniques can be used to allocate computing resources efficiently, challenges related to limited resources, scalability, and the dynamic environment of O-RAN must be addressed to achieve optimal energy efficiency.  
To address this, the authors in [54] propose an online learning-based energy-aware scheduling method for virtualized Base Stations (vBS) in O-RAN. The goal is to optimize scheduling policies that reduce energy consumption while maximizing vBS performance. The novelty of this work lies in the application of adversarial bandit learning to vBS operations. The authors introduce a Policy Decider application within Non-RT RIC to learn and implement optimal policies, which can be adjusted based on network conditions and user needs. The policy decision is shared with Near-RT RIC through A1 interface, and the Data Monitor calculates the reward based on achieved performance and energy cost, which is then sent to the Policy Decider via the O1 interface at the end of each decision time slot. Data-driven experiments based on real-world traffic traces and testbed measurements are conducted to evaluate the effectiveness of the proposed method and compare it to state-of-the-art approaches. The proposed approach outperforms state-of-the-art methods by achieving energy savings between 35.5% and 74.3%, as demonstrated through data-driven experiments based on real-world traffic traces and testbed measurements.  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Year** & **Ref** & **Contribution** \\ \hline
2022 & [53] &
\begin{tabular}{l} Implemented a NN-based handover prediction method for the next target cell in Near-RT RIC using software developed by O-RAN Software Community (O-RAN SC). \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of ML studies for mobility management in O-RAN.
# Enhancing the Iterative Smoothed Particle Hydrodynamics Method
## 2 The Standard Approximation  
The method approximates a function \(f\) at \(\mathbf{x}\in R^{d}\), for \(d\geq 1\), by means of the _kernel approximation_  
\[<f_{h}(\mathbf{x})>=\int_{\Omega}f(\xi)\mathsf{K}(\mathbf{x},\xi;h)d\Omega \tag{1}\]  
where \(\xi=(\xi^{(1)},...,\xi^{(d)})\) and \(\mathsf{K}(\mathbf{x},\xi;h)\) is the _kernel function_. The spatial kernel influence is localized by the _smoothing length_\(h\). The continuum is decomposed into a set of arbitrarilydistributed data sites \(\Xi=(\xi_{j})_{j=1}^{N}\), where \((f(\xi_{j}))_{j=1}^{N}\) are the corresponding measurements and the _particle approximation_ is introduced as  
\[f_{h}(\mathbf{x})=\sum_{j=1}^{N}f(\xi_{j})\mathsf{K}(\mathbf{x},\xi_{j};h)d \Omega_{j} \tag{2}\]  
where \(d\Omega_{j}\) is the measure of the subdomain \(\Omega_{j}\) associated with the data \(\xi_{j}\).  
Particle approximation often loses its accuracy, especially on the boundary and with non uniform data locations [19]. Making use of the Taylor series expansion, by retaining \(p\) terms, multiplying for the kernel function and integrating over \(\Omega\)[16; 17; 19] improvements can be gained. For \(p=1\) it means  
\[f(\mathbf{x})=<I_{h}(\mathbf{x})><f_{h}(\mathbf{x})>+O(h) \tag{3}\]  
with \(<I_{h}(\mathbf{x})>=1/\int_{\Omega}\mathsf{K}(\mathbf{x},\xi;h)d\Omega\) and the corresponding discrete formulation is  
\[f(\mathbf{x})=I_{h}(\mathbf{x})f_{h}(\mathbf{x})+O(h) \tag{4}\]  
with \(I_{h}(\mathbf{x})=1/\sum_{j=1}^{N}\mathsf{K}(\mathbf{x},\xi_{j};h)d\Omega_{j}\).  
By increasing \(p\), the order of accuracy increases, requiring the solution of linear systems for each evaluation point [16; 17]. In [15] an iterative matrix-free approach (ISPH) is introduced to improve the standard method. In [18] it is adopted refining the normalized approach (4) as starting values for the iterations. In the following section an enhanced iterative formulation (E-ISPH) is proposed. For the sake of clarity, a brief overview on the standard iterative formulation is at first described.