esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Scaling under Freezing and Thawing
#### 3.3.1 Influence of the Amount of Portland Cement Replacement by GGBS  
As shown in Figure 16, the mass of scaled material increased significantly as the amount of GGBS increased. According to the acceptance criteria given by SS 13 72 44 [9], the reference Portland cement-only concrete (Mix 1) showed very good frost resistance after 56 freeze/thaw cycles (presenting a total mass of scaled material below 0.1 kg/m\({}^{2}\)). The mass of scaled material for Mix 2, with 25% of GGBS was 0.243 kg/m\({}^{2}\) after 112 freeze/thaw cycles, which reveals good frost resistance. As for concrete qualities with 50% of GGBS replacement or higher, both Mix 3 (with 50% GGBS) and Mix 4 (100% GGBS) were considered not resistant against salt-frost scaling. Mix 3 failed after 112 freeze/thaw cycles, with an average mass of scaled material of 1.075 kg/m\({}^{2}\), whereas Mix 4 (with 100% of GGBS replacement) failed as early as the 56 th cycle, reaching a scaling of 1.168 kg/m\({}^{2}\). The poor salt-scaling resistance of concrete containing high amounts of GGBS is widely documented, even though some conflicting reports have been obtained over the years, especially between laboratory studies and field investigations. Possible reasons might be the different micropore structures and carbonation resistances between these two types of binder [15].  
The results obtained support the limitation of 25% of cement replacement by slag prescribed by SS 13 70 03 [5] for concrete exposed to freezing environments where de-icing salts are used. However, it might be possible to increase the percentage of replacement, even for the same air content and efficiency factor. The salt-frost resistance of concrete mixes with replacement levels between 25% and 50% should be investigated. Furthermore, it should be studied if it is possible to produce frost resistant concrete with 50% of GGBS replacement, by making some changes in the mix.
## 3 State of the Art
### Impact of LTE Operating in the Unlicensed Spectrum on Wi-Fi  
The previous section has described the different operational methods for LTE and Wi-Fi. It is clear that introducing LTE into the unlicensed spectrum, in the way it was originally designed, will have a significant impact on the performance (throughput, latency, packet loss, spectral efficiency) of a co-located Wi-Fi network. As LTE can schedule traffic without sensing the medium for ongoing transmissions, it can interfere with Wi-Fi within the overlapping spectrum. Hence, the CCA mechanism of Wi-Fi, and more specifically the ED function, will force Wi-Fi to backoff. This impact can become even higher by consecutive LTE transmissions. Then, LTE will either seriously degrade the signal quality of Wi-Fi due to collisions (if the LTE signal power is below the ED threshold, but still high enough to interfere with the Wi-Fi transmissions), or lead to Wi-Fi starvation, as it will be forced to backoff continuously.  
Several studies have evaluated the impact of traditional LTE on Wi-Fi, when both technologies operate in the same frequency band without any coexistence mechanism being applied. In our previous work [7], we studied the impact of LTE operating in the unlicensed spectrum on Wi-Fi using Off The Shelf (OTS) hardware equipment [11]. The experiments were performed on the LTE and Wi-Fi infrastructure of the W-iLab2 testbed at imec [12]. Three different levels of LTE signal have been examined, representing different possible levels of LTE impact on Wi-Fi. The results show that the Wi-Fi performance, in terms of throughput and latency, can be significantly affected by LTE.  
Other approaches evaluate the Wi-Fi performance degradation based on simulations and mathematical models. In [13; 14], the authors evaluate the performance of both LTE and Wi-Fi when both technologies operate in a shared band. All studies come to the same conclusion, namely that LTE causes a serious impact on Wi-Fi, when both operate in the same band without any coexistence mechanism among them and no medium sensing mechanism enabled at the LTE side.
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 3 Models and Methods
### The DOA Estimation Model Based on CNN  
Figure 6 shows the structure of the intelligent DOA estimation model based on CNN, which consists of five convolutional layers and one fully connected layer.  
The input of the first convolutional layer \(x_{0}\) is the phase difference characteristic matrix.  
\[x_{0}=\begin{pmatrix}\theta_{11}&\theta_{12}&\cdots&\theta_{1M}\\ \theta_{21}&\theta_{22}&\cdots&\theta_{2M}\\ \vdots&\vdots&\ddots&\vdots\\ \theta_{M1}&\theta_{M2}&\cdots&\theta_{MM}\\ \end{pmatrix} \tag{11}\]  
The feature of the \(l\)-th convolutional layer is expressed as \(x^{l}=\left\{x_{1}^{l},\ldots,x_{N_{l}}^{l}\right\}\), where the feature \(x^{l}\) is composed of several feature maps \(x_{j}^{l}\). \(N_{l}\) is the number of convolutional kernels as well as the number of output feature maps in the \(l\)-th convolutional layer. The calculation of \(x_{j}^{l}(j=1,\ldots,N_{l})\) is as follows:  
\[x_{j}^{l}=relu(\sum_{i=1}^{N_{l-1}}(k_{i,j}^{l}\otimes x_{i}^{l-1}+b_{j}^{l})) \tag{12}\]  
where, \(k_{i,j}^{l}\) is the convolutional kernel of the \(l\)-th layer, \(\otimes\) represents the convolutional operation, \(b_{j}^{l}\) represents the bias, and uses the nonlinear function \(relu\) as the activation function. The calculation is as follows:  
\[relu(x)=max(0,x) \tag{13}\]  
The features of each layer can be calculated as follows:  
Figure 5: Overall framework of the proposed method.  
Figure 6: The structure of CNN.  
\[\left\{\begin{array}{l}x_{j}^{1}=relu(\sum\limits_{i=1}^{N_{0}}(k_{i,j}^{1} \otimes x_{i}^{0}+b_{j}^{1}))\\ x_{j}^{2}=relu(\sum\limits_{i=1}^{N_{1}}(k_{i,j}^{2}\otimes x_{i}^{1}+b_{j}^{2} ))\\ \vdots\\ x_{j}^{5}=relu(\sum\limits_{i=1}^{N_{1}}(k_{i,j}^{5}\otimes x_{i}^{4}+b_{j}^{5 }))\end{array}\right. \tag{14}\]  
The output feature of the last convolutional layer is flattened into a one-dimensional vector and input into the fully connected layer, which is computed as follows:  
\[y=sigmoid(W\cdot flatten(x_{1}^{5},\ldots,x_{N_{5}}^{5})+b^{{}^{\prime}}) \tag{15}\]  
where \(flatten\) is the flatten operation, and \(W\) and \(b\), respectively, represent the connection weight and offset of the fully connected layer. \(sigmoid\) is adopted as the activation function. The calculation is as follows:  
\[sigmoid(x)=\frac{1}{1+e^{-x}} \tag{16}\]  
The parameter set of the intelligent DOA estimation model based on CNN can be expressed as follows:  
\[\Omega=\left\{\left\{k_{j}^{1},b_{j}^{1}\right\}_{j=1}^{N_{1}},\left\{k_{j}^{ 2},b_{j}^{2}\right\}_{j=1}^{N_{2}},\ldots,\left\{k_{j}^{5},b_{j}^{5}\right\} _{j=1}^{N_{5}},\left\{W,b^{{}^{\prime}}\right\}\right\} \tag{17}\]  
Among them, \(k\) and \(b\) are the parameters of convolutional layers, \(W\) and \(b^{{}^{\prime}}\) are the parameters of the fully connected layer.
# Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing
## 3 Encryption for Cloud Computing in Manufacturing
### Experimental Comparison of Classic and Homomorphic Encryption  
The challenges of homomorphic encryption considered in Section 3.2 have shown that the computing time is significantly longer than for classical encryption methods. However, the chapter also highlighted that the challenges of homomorphic encryption majorly depend on the use case. To investigate the actual differences between homomorphic encryption methods and classical encryption for different operations (i.e., use cases) for cloud computing, an experimental validation was performed. In this section, the parameters and assumptions of the performed validation are explained (Section 3.3.1) and its results are presented (Section 3.3.2). As this paper is the first to conduct such a detailed analysis, this section is one of the main contributions of this paper. The used dataset and the GitLab repository are provided in the data availability statement at the end of this paper.
# Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records
## 4 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node** & **Sensor** & **Mean Difference (C)** & **Heuristic Anomalies** \\ \hline
001e0610f02f & bmp180 & 26.036459540641555 & 302,412 \\
001e0610f02f & htu21d & 9.781522863456273 & 302,364 \\
001e0610f02f & pr103j2 & 11.04924329632324 & 91 \\
001e0610f02f & tmp112 & 8.374291058850147 & 325,022 \\
001e0610f02f & tsys01 & 9.172739785041829 & 302,405 \\ \hline
001e06113d20 & bmp180 & 25.16383727102557 & 146,584 \\
001e06113d20 & htu21d & 13.681414767638573 & 146,585 \\
001e06113d20 & pr103j2 & 7.7000304252925496 & 0 \\
001e06113d20 & tmp112 & 7.462584734339503 & 185,959 \\
001e06113d20 & tsys01 & 8.381908452347583 & 146,672 \\ \hline
001e0610 ba3b & bmp180 & 2.506072511582037 & 0 \\
001e0610 ba3b & htu21d & 2.267898950715112 & 57 \\
001e0610 ba3b & pr103j2 & 4.2159915903387635 & 0 \\
001e0610 ba3b & tmp112 & 2.388445906178256 & 14 \\
001e0610 ba3b & tsys01 & 2.488358212219104 & 26 \\ \hline
001e0113754 & bmp180 & 25.67837272548025 & 30,275 \\
001e0611354 & htu21d & 8.90073340493744 & 30,270 \\
001e06113754 & pr103j2 & 9.7722823823438 & 59 \\
001e06113754 & tmp112 & 7.994874787555069 & 89,119 \\
001e06113754 & tsys01 & 9.143470380791154 & 30,269 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculated mean difference between NOAA and sensor reading and heuristically determined anomalies for each sensor. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Notable Clusters** & **Cluster Count** & **Heuristic Anomalies** \\ \hline
001e0610f02f & & & \\ \hline bmp180 & 0: 538,566, 3: 302,412, \(-1\): 23 & 6 & 302,412 \\ htu21d & 3954: 302,364, 178: 989, \(-1\): 166 & 3956 & 302,364 \\ pr103j2 & 13: 741,737, \(-1\): 98,564 & 17 & 91 \\ tmp112 & 0: 514,774, 2: 325,022, \(-1\): 10 & 4 & 325,022 \\ tsys01 & 1: 532,990, 2: 302,405, \(-1\): 4817 & 4 & 302,405 \\ \hline
001e06113d20 & & & \\ \hline bmp180 & 4: 623,832, 3: 364,467, 6: 146,584, \(-1\): 7 & 8 & 146,584 \\ htu21d & 16: 361,405, 32: 146,585, \(-1\): 16 & 788 & 146,585 \\ pr103j2 & 194: 8314, 191: 8271, \(-1\): 1 & 408 & 0 \\ tmp112 & 7: 152,812, 9: 10,178, \(-1\): 5 & 633 & 185,959 \\ tsys01 & 2: 988,273, 0: 146,672, \(-1\): 5 & 4 & 146,672 \\ \hline
001e0610 ba3b & & & \\ \hline bmp180 & \(-1\): 140,726 & 1 & 0 \\ htu21d & \(-1\): 139,592 & 1 & 57 \\ pr103j2 & 0: 363,391 & 1 & 0 \\ tmp112 & \(-1\): 140,951 & 1 & 14 \\ tsys01 & \(-1\): 131,548 & 1 & 26 \\ \hline
001e06113f54 & & & \\ \hline bmp180 & 0: 876,475, 5: 30,274, \(-1\): 22 & 13 & 30,275 \\ htu21d & 4076: 30,270, 502: 618, \(-1\): 393 & 4079 & 30,270 \\ pr103j2 & 0: 906,874, \(-1\): 38 & 5 & 59 \\ tmp112 & 721: 30,270, 173: 3537, \(-1\): 23 & 732 & 89,119 \\ tsys01 & 49,675: 30,269, \(-1\): 173,060 & 49,681 & 30,269 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: DBSCAN cluster counts vs. heuristically determined anomalies for each sensor.
## 2 System Model
### Channel Encoding at Users  
We denote the message of user \((i,k)\) by \(W_{i,k}\) whose rate is \(R_{i,k}\) bit/symbol. BBU \(i\) tries to decode the messages \(W_{i,1}\), \(W_{i,2},\ldots,W_{i,k}\) of in-cluster users. User \((i,k)\) performs channel encoding with Gaussian channel codebook so that the transmit signal \(x_{i,k}\), which encodes \(W_{i,k}\), follows the distribution \(x_{i,k}\sim\mathcal{CN}(0,P)\), i.e., \(\mathbb{E}[|x_{i,k}|^{2}]=P\). We define the uplink signal-to-noise ratio (SNR) as \(P/\sigma_{z}^{2}\). We also note that dynamic power control at users, instead of fixed full power transmission, may improve the performance with additional overhead for CSI acquisition at users.
# Hybrid Deep Reinforcement Learning for Pairs Trading
## 3 Related Work
### Pairs Trading  
Deep reinforcement learning methods for pairs trading can be classified into two approaches. The first approach [1,2] indirectly determines trading actions based on trading and stop-loss boundaries. [PERSON] and [PERSON] [1] proposed the pairs trading DQN (PTDQN) algorithm, which dynamically optimizes the boundaries for daily stock data. The action space consists of six predetermined boundary pairs; for example, the action \(A0\) is defined as (trading boundary = \(\pm 0.5\), stop-loss boundary = \(\pm 2.5\)). The gap between the trading and stop-loss boundaries is fixed at 2.0 for all actions. [PERSON] et al. [2] focused on intraday trading, where the cointegration relationship is much weaker than that of interday trading. To detect structural breaks in which the cointegration relationship vanishes, the authors proposed a spread wavelet-aware hybrid network that combines a continuous wavelet convolutional neural network [38] for frequency-domain features and a long short-term memory (LSTM) network [39] for time-domain features. The authors also proposed a structural break-aware DQN algorithm to determine the trading and stop-loss boundaries. The action space consists of six predetermined boundary pairs and a hold action, and the gap between the trading and stop-loss boundaries is fixed at 2.0. In the above methods, the trading boundary is completely dependent on the stop-loss boundary, which is certainly not optimal.  
The second approach [3, 4, 5] directly determines trading actions based on the spread. [PERSON] [3] used the DDQN to determine trading actions based on the technical indicators of the spread, and to reduce trading actions with negative rewards, it multiplies negative rewards by a large constant value. [PERSON], [PERSON], and [PERSON] [4] used the deuling DQN algorithm and proposed a reward shaping method that employs a baseline policy with a fixed trading boundary as a guidance to learn a robust policy and reduce overfitting. [PERSON], [PERSON], and [PERSON] [5] proposed a DDQN-based pairs trading method called P-Trader that uses technical indicators for the spread, the candlestick clustering technique used in [33], a gate structure, a GRU network, a temporal attention mechanism, and the auto-encoder technique used in [34]. The above methods do not have stop-loss boundaries, which leads to high risk.  
In this paper, we propose a hybrid reinforcement learning method that combines the above two approach types, which is the first one to do so to the best of our knowledge. First, we extend the TD3 algorithm to directly determine trading actions by incorporating the auto-encoder technique, behavior cloning, PER, and dynamic delay. Second, we extend the DDQN algorithm to determine stop-loss boundaries and combine it with the extended TD3 algorithm.
## 1 Introduction  
The gradient-index solar sail (GIS) [1] is an innovative solar sail concept that can be considered as a kind of evolution of the refractive solar sail model [2, 3], whose thin exposed membrane uses an advanced metamaterial, composed of metals and dielectrics, to convert sunlight into propulsive force. This interesting type of propellantless propulsion system was recently proposed by [PERSON] et al. [1], who discussed a simplified thrust model for preliminary mission design and also analyzed the problem of trajectory optimization by considering spacecraft dynamics in a classical, Cartesian, heliocentric-ecliptic reference frame. From the thrust vector point of view, as in the case of the Swartzlander's diffractive sail [4, 5], a spacecraft whose primary propulsion system is GIS-based is able to create a transverse thrust component (which belongs to the nominal plane of the sail) even when the solar sail is in a Sun-facing condition, that is, in a configuration in which the direction normal to the plane of the sail coincides with the Sun-spacecraft line. In this case, the only control parameter is the sail roll angle around the radial direction, the value of which can be controlled using a robust sail attitude control system based, for example, on a roll stabilizer bar [6].  
This paper analyzes the deterministic trajectories of an interplanetary spacecraft propelled by a GIS with a Sun-facing attitude (SFGIS) in a typical heliocentric transfer, considering a single control parameter, namely, the roll angle of the solar sail. In particular, two different guidance laws are considered: (1) a case where the sail roll angle can be freely chosen by the control system; and (2) a case where during flight, the roll angle can be chosen within a discrete set of values. In the latter scenario, the paper analyzes the characteristics of the attitude maneuver required to appropriately change the sail roll angle, using the simplified model proposed by [PERSON] et al. [6].
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 1 Introduction
### Research Outline and Aims  
Our study aims to improve understanding of the role of phenology on CO\({}_{2}\) flux across a range of spatial and temporal scales. For this purpose, we examined the use of phenocam imagery and multispectral UAV data to track vegetation phenology over multiple growing seasons in a temperate Scottish peatland. We examined the use of both phenocam- and UAV-derived greenness metrics as a proxy for photosynthetic CO\({}_{2}\) uptake. Finally, in a unique approach, we combined centimetre-resolution UAV data with a vegetation classification map to examine species-level changes in greenness in response to weather anomalies experienced at the site.
## 2 What Is a Generative Adversarial Network?
### Advantages and Constrains of Generative Adversarial Networks
#### 2.5.1 Advantages  
The primary advantage of GANs lies in their ability to develop synthetic data that nearly correspond to real data. This is achieved through a competitive learning process between the generator and discriminator networks, where the generator improves its capacity to create increasingly realistic data. This property of GANs makes them highly valuable for tasks such as data augmentation and generating training data for machine learning models [1]. While GANs are commonly used for image generation tasks, they can also be applied to other data types, including text or audio. By manipulating the latent code, which serves as the input to the generator network, specific attributes of the generated image can be modified while preserving the other attributes [77]. However, it is essential to note that attribute manipulation in the latent space is limited to the generated images produced by the GAN generator and does not extend to real images [4]. GANs lack the inference capability for real data, meaning that the attribute manipulation is specific to the synthetic data generated by the network. In [78], the authors perform, by applying DCGAN, a data augmentation, to increase the diversity of the dataset. It was applied to thorax radiographs. The research covers several issues, such as privacy related to patient data, the lack of access to large volumes of information, and the diversity of information. Also, from the results obtained for data augmentation using generative models, a better performance in terms of accuracy has been achieved when compared to a traditional CNN model. There are several advantages of using GANs for data generation:  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{3}{c}{**Datasets**} & \multicolumn{3}{c}{**Metrics**} \\ \hline
**References** & **Architecture** & **CIFAR-10** & **CelebA** & **FFHQ** & **FID** & **IS** & **SSIM** & **PSNR** \\ \hline
[8,14,68,69] & BigGAN & ✓ 1 & ✓ & ✓ & ✓ & ✓ & & \\
[50,63,68,70] & StyleGAN2 & ✓ & ✓ & ✓ & ✓ & & \\
[6,71,72] & StyleGAN & ✓ & ✓ & ✓ & ✓ & & \\
[48,68] & DCGAN & ✓ & ✓ & ✓ & ✓ & & \\
[50,73,74] & GAN & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\
[17,47,69] & WGAN-GP & ✓ & & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}  
* The main GAN architectures that use the main datasets and metrics found in the literature review carried out.  
\end{table}
Table 2: The intersection between the top 3 architectures, datasets, and metrics.  
GANs can generate visually realistic, high-quality images that resemble real images to human observers.
* Generating diverse data samplings by GANs is beneficial for training machine learning models.
* GANs exhibit relative ease of training and often achieve faster convergence than other generative models.
* GANs possess the potential to acquire knowledge from data lacking important label information, rendering them valuable for unsupervised learning endeavors.
# Axial Load Transfer Mechanism in Fully Grounded Rock Bolting System: A Systematic Review
## 3 Analysis and Results
### Research Hotspots in Axial Load Transfer Mechanism of FGRBSs
#### 3.2.2 Failure Mechanism at the B-G Interface  
In FGRBSs, the mechanism governing axial load transfer between the rock bolt and the encapsulation media is known as the bond mechanism. Bond is thought to be the shearing resistance between the bolt and grout. For rock bolts, the bond can be conceptualized as the gripping action exerted by the grout along the entire EL of the deformed bar [56]. The performance of the B-G interface as the dominant failure in rock bolting systems is related to the distribution of bond stress (magnitude and direction) and bond strength of the interface. Experimental studies have identified two main interfacial failure modes in rock bolting systems including the shear-off failure (the parallel shear failure), wedge slip, and a combination of these failure modes [57; 58; 59; 7; 53]. Parallel shear failure is a cylindrical failure that occurs in smooth bars and for close-spacing ribbed bars. This failure has also been observed in the short-encapsulated laboratory pull-out tests when confining material is stiff. [PERSON], [PERSON] [60] reported a new interfacial failure mode named sheared-crush failure. The BS between a FGRB and surrounding rocks is generally recognized to be a function of three key factors: adhesion between the grout and the bolt, friction along the interface, and mechanical interlocking due to bolt ribs [61]. [PERSON] et al. [61] found that the failure mode of FGRBSs can be impacted by the confining stresses conditions.  
Figure 7 schematically illustrates the role of each strength component in the bond-slip model. Previous research studies have demonstrated that adhesion in the B-G interface does not play a crucial role in anchoring; thus, it can be neglected [62; 63; 42]. Prior to the system failure (peak BS), mechanical interlocking undertakes the system bond capacity. This interlocking mechanism is influenced by several factors, including the mechanical behaviour of the grout, the surrounding rock mass conditions, the level of confining stress, and the bolt surface profile [64; 52; 61]. Frictional components can be associated with dilational slip, shear failure of grout or rock mass, spiral displacement of bolts, and torsional unscrewing of cable bolts [12]. [PERSON], [PERSON] [56] pointed out that friction has a dominant effect after slip initiation.
## 3 Results and Discussion
### Bending Moment of the Piles  
Figure 11 shows the bending moments of the existing and new piles under different working conditions. For the existing pile, the bending moment increased and then decreased with the distance in all working conditions. The maximum bending moment of the existing pile was located 0.485 m from the pile bottom, and the values were 2.569, 4.871, 6.156, 12.125, and 23.492 kN\(\cdot\)m for the five working conditions, respectively. For the new pile, the bending moment increased and then decreased with distance under working  
Figure 8: Earth pressure on the cantilever sections of the existing and new piles under different working conditions: (**a**) working conditions 1, 2 and 3; (**b**) working conditions 4 and 5.  
Figure 9: Crack development during loading: (**a**) cracks in sheets; (**b**) surface cracks.  
conditions 2-5. The maximum bending moment was located 0.9 m from the bottom of the pile, and the values were 6.410, 16.831, 31.580, and 68.897 kN\(\cdot\)m for working conditions 2-5, respectively. Since the lengths of the existing pile and the cantilever section were short, the bending moment of the existing pile was smaller than that of the new pile under the same working condition and the same position for working conditions 2 to 5.