esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the context of wind speed studies, non-stationary extreme value models leverage historical data to identify and model the trends of extreme wind speed distributions over time, enabling the analysis and prediction of how the frequency and intensity of extreme wind speed events change over time [33; 34; 35]. Current research on wind speed models based on actual measurements has made some progress. [PERSON] et al. estimated a joint probability distribution model of monsoon fluctuating wind speeds using data recorded by an anemometer installed at the top of the SWFC building in Shanghai, China, at a height of 497 m, representing the wind speed frequency components as a mixture distribution model consisting of the Gaussian distribution and generalized Pareto distribution [36]. [PERSON] et al. studied wind speeds measured at ten different elevations in Tamil Nadu, India, and noted that a single distribution could not fit all sites, whereas the generalized extreme value distribution performed well at most sites [37]. [PERSON] et al. collected wind speed samples from 10 m to 19 m heights over three years using a lidar wind measurement system in the suburbs of Hohhot, China, and derived a new wind power density estimation formula based on a three-parameter Weibull distribution [38]. However, current research on the application of extreme value theory in long-term changing complex urban environments is insufficient, with most models being stationary models established from short-term measured data. Additionally, existing empirical studies on wind speeds mostly focus on specific heights, with relatively limited research on the long-term variability of wind speeds across different atmospheric levels. At this time, the calculation of design wind loads at different heights can only be completed using empirical wind pressure height variation coefficients based on terrain classification. Based on this, this paper uses long-term wind speed data collected from 15 different height levels on the 325 m meteorological tower in Beijing from 1991 to 2020 to quantify the trends in wind field characteristics in the Beijing urban area over nearly 30 years. It compares the goodness of fit of standards and traditional wind speed models, incorporating time-varying functions into the optimal wind speed model to account for wind speed trends. The study explores the impacts of urbanization and climate change on wind speeds, combining wind speed measurement data at different height levels with the optimal wind speed model to obtain wind pressure height variation coefficients and compare them with standards. This aims to assess the applicability of standards to architectural design in central Beijing and provide theoretical and data support for wind-resistant design in high-rise buildings in the context of rapid development.
## 3 Mechanisms Influencing Thermal Expansion in Concrete  
Thermal cracking in massive concrete structures is primarily caused by the exothermic hydration reaction of the cement binder, which generates heat. Concrete's low thermal diffusivity leads to temperature rises and creates a time-dependent thermal gradient and spatial gradient contributing to cracking. These temperature variations cause thermal dilation in concrete, and when restrained, they generate stresses that can lead to cracks if they exceed a certain threshold [19]. The other factor is the binding between cement paste and coarse aggregates, which is weakened by temperature changes because they alter the moisture content and coefficient of expansion [20,21]. Maintaining high moisture levels during curing can reduce crack sensitivity by minimizing thermal expansion and largely eliminating autogenous shrinkage [22].  
In the case of concrete pavement, temperature variation (daily and seasonal) or moisture content variation can have two kinds of deformation: vertical, throughout the slab's depth (e.g., curling, wrapping, etc.); and horizontal (e.g., expansion, contraction, cracking, etc.). A study suggested that because of the effects of moisture content and cooling rate, the lower part of a concrete slab often has a higher CTE value. It was observed that due to a difference in drying shrinkage between the concrete slab's top and bottom, more drying shrinkage at the top of the slab was observed [23]. Furthermore, temperature change induces tension and compression stress within the concrete, which creates a moment force from the bottom to the top of the slab. The adjoining slabs experience similar stresses, which mount over time until the internal forces surpass the concrete's strength, leading to cracks at the ends of the slabs. The amplitude and dynamics of temperature fluctuations play a crucial role in this degradation process [12]. Two phenomena associated with concrete behavior due to temperature change are warping and curling. Curling refers to the deflection behavior of a concrete slab produced by a temperature difference along depth, and warping is caused by a moisture imbalance between the slab's top and bottom surfaces [24].
# A Guidance for Blockchain-Based Digital Transition in Supply Chains
## 4 Findings
### Digital Business Models  
Interviewees highlighted the relevance of industrial smart contract applications. These industrial applications can lead to new levels of performance in operations, enabling new negotiation procedures and generating new business models. Interviewees mentioned the high impacts of some upcoming blockchain-based business models and how they may improve operational efficiency and effectiveness in future digital procedures.  
In contrast, the lack of regulations around this area may hamper the smart contract mechanism to work properly, reducing its applicability to the market. However, interviewees stated how smart contracts are powerful for both SCs and manufacturing operations, but they need permissioned systems; otherwise, unexpected risks may emerge. Thus, the interviewees' consensus is that permissioned systems are more eligible for businesses.  
Thus, smart contracts are identified by interviewees as powerful computer programs for administrative automation; however, they have certain limitations and need permissioned systems for implementation in business contexts. Coding smart contracts makes it possible to establish business mechanisms in computer logic and ensure a straightforward level of trust in business procedures. Therefore, distributed systems for ecosystem building employ a new degree of trust, where access control and authorization to invite new members are defined from the beginning.  
Regarding the bank and financial industry, interviewees pointed out that tokens have the power to modify several operations and business processes, setting new standards for the exchange of digital assets and value. Although cryptocurrencies and initial coin offerings (ICOs) scared practitioners for their financial consequences and degree of speculation, ICOs have shown good results in the past few years. Interviewees positively evaluated these new mechanisms, and highlighted that tokens have the chance to facilitate transactions and change customers' attitudes and behaviors. Furthermore, tokens may support digital business models, opening new markets, new possibilities, new channels, and providing easier access to technological developments and market strategies.  
Additionally, the ISO/TC-307 is seen by interviewees as a standard for financial transactions and human interactions. The ISO has impacts on business and society, and for this reason, interviewees remarked that ISO standards are relevant to allow the sharing of value in a standardized way. Other benefits and impacts include the mitigation of ethical issues, tax obligations, adoption by enterprise resource planning (ERP) systems in SCs, and decentralized identifiers (DIDs) in digital identity. Therefore, the interviewees pointed out that blockchain standardization might help harmonize different protocols and facilitate interoperability issues. However, for distributed technologies, future standards should be as simple as possible to generate new digital business models, produce lean and agile procedures, and push the technology on the market.
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 5 Conclusions  
### Discussion  
* Communication interference: The node's transmission activity may cause interference to all receiving nodes in its communication range, which can affect the entire nodes. A large communication volume of the node forms a strong interference area around the node, which impacts on communication activity of adjacent nodes. For example, in [26], they introduced a method of multi-UAVs target tracking with consideration of the communication interference and the propagation loss. Thus, we need more analysis of the effect of overlap avoidance scheme to the communication interference problem.
* Heterogeneous altitudes: In this paper, we assume the UAV altitude is fixed. If the altitudes are different between UAVs, the collision can be avoided. However, the overlapped reconnaissance  
Figure 11: The number of unit regions per inter-arrival time with/without Overlap Avoidance: (**a**) Random Waypoint; (**b**) Random Markov Process; (**c**) Enhanced Gauss–Markov; and (**d**) Distributed Pheromone Repel.  
problem still exists. One modification in the proposed schemes is to consider different scan ranges caused by different altitudes of UAVs at the time of detecting overlapping of their protected zones.
* Taking shift: In order to support long-time reconnaissance, UAVs should take shift due to some reasons such as battery replacement or malfunction. When a UAV returns to the ground station, it flies straight to the station without using the mobility models. During the return, the UAV does not apply the overlap avoidance scheme. Instead, other UAVs on reconnaissance mission use the overlap avoidance scheme if they overlap with the returning UAV. In the case of DPR model, the returned UAV may give its pheromone information to the other replaced UAV.
* Useful scenario: As UAVs mainly explore the central part of the scanning area in RW model, it is efficient when an intensive reconnaissance is needed in the center area. RMP and EGM models use the current direction to determine the next direction, thus creating unpredictable movements. They are efficient in areas where targets are much distributed. In the DPR model, UAVs share pheromone information with each other, so that it is useful in the case that communication bandwidth is enough large.
# Experimentation in 5G and beyond Networks: State of the Art and the Way Forward
## 2 Overview of Published Papers
### Use of AI/ML for B5G Systems  
Three papers belong to this research area, with the main goal of proposing the use of AI/ML to address different aspects of B5G systems.  
In Contribution 8 by [PERSON] et al., the focus is on the random access procedure [21], which end devices execute to connect to the Transmission and Reception Points (TRxPs)forming the cellular RAN. Considering that 5G and B5G systems will increasingly operate in high-frequency spectrum bands, a dense RAN comprising numerous TRxPs is needed to overcome coverage shrinking. This, in turn, increases the challenge for end devices to efficiently select a TRxP in a given time during the random access. To solve this problem, the work casts the random access procedure into an optimization problem and solves it via reinforcement learning. The proposed scheme estimates the congestion of TRxPs and selects an optimal TRxP for each end device during the random access. Through simulation, it is demonstrated that the proposed algorithm improves random access performance by decreasing the average access delay compared to the standard procedure, while also increasing the probability of successful access for end devices.  
Contribution 9 by [PERSON] et al. proposes applying deep learning for the reporting of Channel State Information (CSI) feedback, which is a key aspect in the Frequency Division Duplexing (FDD) 5G and B5G systems [22], where the knowledge of channel characteristics is key to exploiting the full potential of Multiple Input Multiple Output (MIMO) schemes [23]. Therefore, the work designs a framework adopting a convolutional neural network, called NR-CsiNet, aimed at compressing the channel matrix experienced by the user at the receiver side and then reconstructing it at the transmitter side. NR-CsiNet is based on a 5G NR fully compliant simulator that implements a channel generator based on the latest 3 GPP channel model. Simulations carried under realistic scenarios, including multi-receiving antenna schemes and noisy downlink channel estimation, show promising results compared to current 5G feedback reporting schemes, in terms of both block error rate and achievable data rate.  
Finally, Contribution 10 by [PERSON] et al. analyzes a cooperative network including UAVs and device-to-device (D2D) communications, which are expected to play an important role in B5G/6G scenarios [24; 25]. In particular, a UAV placement scheme combining supervised and unsupervised ML techniques is proposed to enhance the performance of such a network, where Non-Orthogonal Multiple Access (NOMA) is used as the underlying access scheme. Comparisons against a conventional Orthogonal Multiple Access (OMA) scheme and an alternative scheme using unsupervised ML only show that the proposed approach leads to significant gains in terms of sum rate and spectral efficiency under a varying bandwidth allocation.
## 2 Single Molecule Electrical Measurements: Biomolecule Detection
### Direct Sensing by Single Molecule Electrical Measurements  
Direct sensing involves directly reading physical properties from the target molecule. Physical properties include the electronic state, the physical volume, which include the solvation shell of the molecule/ions, and structural differences, which are induced by conformational change of the molecule via inter/intra molecular interaction (Figure 2). These direct sensing methods are applied to single-molecule sequencing for biological polymers such as DNA, RNA, and peptides. These direct sensing methods potentially contribute to the detection of rare target markers and the discovery of new target markers.  
Figure 2: Direct sensing of target molecule/particle by using nanodevices. In single molecule/particle measurement using nanodevices, an electrical signal for a single molecule is observed as a current-time profile when the molecule passes through the sensor device. In the case of nanopore devices, the intensity of decrement in ion current during passage represents the volume of the molecule (**a**), and in the case of nanogap devices, the signal intensity of the tunnel-current represents the electronic conductivity of the molecule (**b**). When a single biopolymer passes through the sensor, each unit of the polymer (nucleotide for DNA and RNA, amino acid for peptide and protein) passes through the sensor, and the electrical signal reflects the sequence of the biopolymer (**c**). Detection of different molecular signals occurs even for the same target molecule. If a structural change has occurred, different electrical signals reflecting the different structures would be observed (**d**–**f**). In fact, signals reflecting differences in the intramolecular hydrogen bond morphology of proteins (d:prion) and nucleic acid base chains (DNA single-strand, duplex, and triplex) have been observed. In addition, differences of shapes in the nanoparticles and viruses have also been identified by electrical signals reflecting the shape of the particles (**f**).
## 4 Results
### RQ5: Network Security Attack in IoT and WSN Layers
#### 4.5.3 Trojan Worms, Viruses, and Malware  
An attacker can use malicious software to manipulate data, steal information, or even launch a denial-of-service attack on a device. A worm, such as a Trojan horse, can infect one's computer when one downloads a file or receives an update. The worm then multiplies and attacks other machines on the network. Unlike a virus, many Trojan horses usually reside on one's own computer. A virus can infect the host's file when sent via email and then spread to other users [78]. Malware is malicious content that can interfere with a computer's operation and slow its performance. When data from IoT devices is compromised, malware can infest the cloud or data centers. These attacks breach the primary security mechanisms of any OS/server, such as a firewall and window defender.
###### Abstract  
In response to the complexity and high volatility of original load data affecting the accuracy of load forecasting, a combined method for short-term load forecasting considering the characteristics of components of seasonal and trend decomposition using local regression (STL) is proposed. The original load data are decomposed into a trend component, seasonal component, and residual component using STL. Then, considering the characteristics of each component, a long short-term memory (LSTM) neural network, a convolutional neural network (CNN), and Gaussian process regression (GPR) are used to predict the trend component, seasonal component, and residual component, respectively. The final outcome of the load forecasting is obtained by summing the forecasted results of each individual component. A specific case study is conducted to compare the proposed combined method with LSTM, CNN, GPR, STL-LSTM, STL-CNN, and STL-GPR prediction methods. Through comparison, the proposed combined method exhibits lower errors and higher accuracy, demonstrating the effectiveness of this method.  
short-term load forecasting; seasonal and trend decomposition using local regression; long and short-term memory network; convolutional neural network; Gaussian process regression
# Kernel Density Estimation on the Siegel Space with an Application to Radar Processing +
## 4 Application to Radar Processing
### Marginal Densities of Reflection Coefficients  
In this section, we show density estimation results of the marginal parameters \(\Omega_{k}\). For the sake of visualization, only the Siegel disk \(\mathbb{D}_{1}\) is considered. Recall that \(\mathbb{D}_{1}\) coincides with the Poincare disk.  
The results are partly extracted from the conference paper [17]. Data used in the experimental tests are radar observations from THALES X-band Radar, recorded during 2014 field trials campaign at Toulouse Blagnac Airport for European FP7 UFO study (Ultra-Fast wind sensOrs for wake-vortex hazards mitigation) (see [29; 30]). Data are representative of Turbulent atmosphere monitored by radar. Figure 2 illustrates the density estimation of six coefficients on the Poincare unit disk under a rainy environment. The densities are individually re-scaled for visualization purposes. For each environment, the dataset is composed of 120 draws. The densities of the coefficients \(\Omega_{k}\) are representative of the background. This information on the background is expected to ease the detection of interesting targets.  
Figure 2: Estimation of the density of six coefficients \(\Omega_{k}\) under rainy conditions. The expression of the used kernel is \(K(x)=\frac{3}{2}(1-x^{2})^{2}\mathbf{1}_{x<1}\). Densities are rescaled for visual purposes.
## 2 Theoretical Background
### Fuzzy Theory
#### 2.1.3 Determining the Parameter Evaluation Set  
The evaluation set is composed of the various evaluation results that an assessor might obtain for the evaluation target. It is represented as \(V\), and \(V\) = {\(v_{1}\), \(v_{2}\),..., \(v_{n}\)} and \(v_{i}\) (\(i\) = 1, 2, 3,..., \(n\)) represent the possible total evaluation results. The purpose of fuzzy evaluation is to comprehensively consider all of the affecting factors and obtain an optimal evaluation result from the evaluation set. The relationship between \(v_{i}\) and \(V\) is also a common set relationship. Therefore, the evaluation set is also a common set. For the evaluation in this study, the evaluation set is \(V\) = {_very satisfied, satisfied, neither satisfied nor dissatisfied, dissatisfied, very dissatisfied_}.
# Trajectory Optimisation for Cooperative Target Tracking with Passive Mobile Sensors
## 5 Simulation Example
By maximizing either of these two reward functions, the two passive sensors move independently (at identical speeds) in the way to minimize their own distances-to-target (Figure 7), which clearly yields larger errors than that of the DetFIM result shown in Figure 6.  
Figure 5: Illustration of sensor trajectory hypothesis trees under a joint sensor scheduling. For \(N=3\) steps ahead calculation at time \(k\), two sensors with five possible directions yields a total of 15,625 possible trajectory hypotheses. The color scheme signifies the accumulated reward values (under DetFIM) with red the highest and dark blue the lowest. The statistical results averaged over 100 Monte Carlo runs for each case are shown in Figure 7, where the root-mean-squared position error comparison of the tracker under different reward functions for \(N=2\) is presented in Figure 7(a), and the RMS errors under DetFIM for \(N=1,2,3,4\) are in Figure 7(b). In summary, the simulation results demonstrate that  
* the tracker yields a smaller error but significantly more computational overhead as \(N\) increases. We observed that the computational complexity for \(N=1,2,3,4\) is roughly at a ratio of \(1:41:2890:2905\) when MaxH = 1000. Both computational overhead and RMS errors are bounded by MaxH, which is the maximum number of sensor trajectory hypotheses to be maintained. As shown in Figure 7(b), the RMS error performances for \(N\geq 3\) are almost identical. This reflects the trade-off between error performance and  
Figure 6: Illustration of the estimated target trajectory and the motion trajectories of the two bearing-only sensors optimized under DetFIM plotted from a single run, along with target ground truth trajectory. Colorbar value signifies sampling time (scan). Figure 7: Estimated target trajectories with ground truth trajectory and the motion trajectories of the two bearing-only sensors optimized under (**a**) ExpReward; (**b**) TrFIM, plotted from a single run. Colorbar value signifies sampling time (scan). computational load as the limited MaxH leads information loss due to the hypotheses pruning process:
* the sensor scheduling under DetFIM yields a significantly small error, which is consistent with our analysis. * the RMS error under ExpReward is quite similar to that under TrFIM. The latter completely ignores the correlation between the two sensor states. * The major computational complexity of the simulation comes from the computation of \(N\)-Step ahead decision process when \(N>2\). For example, the average computational overhead per scan are 0.7 s when \(N=2\) and 5.5 s for \(N=3\) (on a machine with an Intel 2 Core i7-4600U CPU 2.10 GHz processor (Intel: Mountain View, CA, USA)). The computational complexity difference between DetFIM, TrFIM and ExpReward are \(1:1.02:1.45\).