esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Slag Cement Mortar Containing Nonthermally Treated Dried Red Mud
## 2 Nonthermally Treated Dried Red Mud (NTRM) Manufacturing
### Experimental Plan to Manufacture NTRM  
Table 2 presents the experimental plan for the manufacturing of NTRM. It consisted of RM sludge (50%), PSA (35%), and HCFA (15%), which were mixed for 4 min using an asphalt mixer for the experiments. As the mixture that was discharged from the asphalt mixer was condensed in the shape of balls, they were crushed into powder using a pin mill at 1500 rpm.  
To investigate the characteristics of NTRM, the water content, particle size, XRF spectra, and XRD patterns were analyzed. Measurement results of NTRM were compared with the dried RM through heating. The water content was measured 2, 12, 24, 48, 72, 96, and 120 h after completion of mixing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Element Oxide**} & \multicolumn{3}{c}{**Content (mass\%)**} \\ \cline{2-4}  & **Red Mud (RM) Sludge** & **Paper Sludge Ash (PSA)** & **High-Calcium Fly Ash (HCFA)** \\ \hline SiO\({}_{2}\) & 38.80 & 17.00 & 26.70 \\ Al\({}_{2}\)O\({}_{3}\) & 16.10 & 9.83 & 13.10 \\ Fe\({}_{2}\)O\({}_{3}\) & 22.80 & 6.77 & 8.71 \\ MgO & 0.20 & 4.05 & 4.40 \\ Na\({}_{2}\)O & 10.00 & - & - \\ CaO & 3.40 & 59.70 & 41.20 \\ TiO\({}_{2}\) & 2.40 & 0.39 & - \\ SO\({}_{3}\) & 0.24 & 1.80 & 5.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of raw materials.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multirow{2}{*}{**Proportion (wt\%)**} & \multirow{2}{*}{**Mixing Method**} & \multirow{2}{*}{**Measured Parameters**} \\ \hline
**RM sludge** & 50 & Mixer type: & Water content \\
**PSA** & 35 & Asphalt mixer & Particle size \\
**HCFA** & 15 & Mixing time: 4 min & XRF spectra \\  & & & XRD patterns \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental plan and mixture proportion to manufacture nonthermally treated dried red mud (NTRM).  
The mass of the sample before and after drying was measured, and the measured values were used to calculate the water content using Equation (1).  
\[\textit{Water content}(\%)=\frac{\textit{Weight of untried NTRM}(g)-\textit{Weight of dried NTRM}(g)}{\textit{Weight of untried NTRM}(g)}\times 100 \tag{1}\]  
The particle size distributions of NTRM and RM were examined using a Particle size analyzer (Mastersizer 3000-Maze6140, Malvern Instruments Ltd., Malvern, UK). The XRF spectrometer (ZSX Primus IV, RIGAKU, Tokyo, Japan) was used to assess the chemical compositions of NTRM and RM. The XRD patterns for NTRM and RM were collected using an X-ray Diffractometer (Smartlab, RIGAKU, Tokyo, Japan) The diffraction patterns were obtained at 20 from 5\({}^{\circ}\) to 65\({}^{\circ}\) in steps of 0.01\({}^{\circ}\), with each step lasting 1 s. The divergence slit was 0.3\({}^{\circ}\), while the seller slit was 2.5\({}^{\circ}\).
## 5 Detection Mechanisms  
Different approaches based on surface modification with different molecules or ligands are said to cause different detection mechanisms. For instance, mechanisms of fluorescence detection are not only based on quenching but also based on mechanisms such as fluorescence turn-on, spectral shift, lifetime and anisotropy [5]. A study on the determination of 2,4,6-trinitrophenol (TNP) by [PERSON] et al. demonstrated that the quenching mechanism for the detection of electron-deficient NACs occurs through acid-base pairing between amino functional groups and the electron-deficient rings [61]. The quenching mechanism can occur in two ways: static and collisional quenching. These are differentiated through time-resolved measurements of fluorescence lifetime. In the static quenching process, the fluorescence lifetime of the sensor remains unchanged as the concentration of the quencher is increased. In the collisional quenching, the fluorescence lifetime becomes shorter as the concentration of the quencher increased. The quenching mechanism is normally triggered by the transfer of electrons from electron donors to electron-deficient compounds [73,86]. Theoretically, it means that the fluorescence enhancement, also known as fluorescence \"turn-on\" is encountered for the detection of explosive residues or pollutants that are not electron-rich [79]. The detection of PAHs also occurs through fluorescence \"turn-on\" [40]. This type of mechanism is said to be triggered by energy transfer between the sensor and the species. Energy transfer of this nature can be due to different factors, such as hydrogen bonding and \(\pi\)-\(\pi\) interaction [58].
# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 4 Experimental Results and Discussion  
### Optical Spectra and BERs  
Figures 14 and 15 show the shape of the optical spectrum for each condition and constellation histogram (heatmap) obtained at the conditions determined (51 GHz and 26 GHz). Three labels are assigned for each condition: 'left shift' for the negative frequency shift, 'right shift' for the positive frequency shift, and 'normal' for optimum. The optical spectra for the above conditions were acquired using an optical spectrum analyzer. At 51 GHz shift, we can observe a small tilt in each spectrum. On the other hand, at 26 GHz shift, a very slight slope is observed compared to that at 51 GHz.  
The BERs are measured at the tapping points depicted in Figure 8. The BERs before SD-FEC and after HD-FEC are summarized in Table 3.  
Figure 14: Heatmap and optical spectrum examples used for training: (**a**) \(-\)51 GHz; (**b**) +51 GHz; (**c**) no shift.  
Figure 15: Heatmap and optical spectrum examples used for training: (**a**) \(-\)26 GHz; (**b**) +26 GHz; (**c**) no shift.  
As shown in the table above, at \(\pm 51\) GHz of optical filter shift, obvious error rate increases were observed before SD-FEC. These increases represent some kind of abnormality occurring. However, we cannot identify the cause of the abnormalities by the information obtained by BER monitoring only. On the other hand, at \(\pm 26\) GHz of the optical filter shift, no abnormality was detected in the error rate before SD-FEC. In addition, since no errors were observed in the post-FEC BERs, there is no impact on the actual network service at these optical filter shifts.
# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
## 5 Evaluation
### Experiment Results
#### 5.2.2 Results of PARM
From the above analysis, three conclusions could be obtained as follows:  
* For the traffic patterns with high variations, our design is efficient to reduce the variation and thus reduce the telco CDN's total bandwidth cost;
* For any traffic pattern, our design helps a telco CDN better match its revenue to cost. Specifically, if the overall congestion level is relatively low, a discount is provided (compared with \(P_{const}\)) to encourage more usage. Otherwise, prices of the congested sites are set higher than \(P_{const}\), making the total revenue match with the increasing bandwidth cost;
* The latency performance provided by PARM is similar to that provided by PURM, which minimizes latency only.
# Robust Static Structural System Identification Using Rotations
## 1 Introduction
### Application of Inclinometers in Civil Engineering  
Existing static structural system identification methods largely measure deflections rather than rotations [16; 17; 27]. However, wide industrial applications (e.g., automotive, electronics and aviation industries) of inclinometers can be found [28]. The merits of inclinometers make them very desirable for SHM applications [29; 30; 31; 32]. Application of inclinometers in SHM of civil structures can be found in the literature; for instance, stadiums Design Plaza Building [34], Tianjin 117 (597 m) [35], construction hoists [36], Lutrive Bridge [32], pavement construction [37], ground movement [38] and slope measurement [39]. In particular, inclinometers are used to reconstruct the deflected shape of bridges due to the difficulty in measuring deflections directly. However, direct use of rotations might be preferred because systematic errors might be introduced into the approximated deflections depending on the assumption made in the basic functions (e.g., precalculated deflected shapes [32], polynomials [40]). [PERSON] estimated the deflections accurately via the partial least squares method from a Finite Element Model (FEM) without using basis functions [33]. The possibility of locating damage using inclinometers was also investigated in this work. [PERSON] [37] achieved real-time measurement of lift-thickness during highway construction using a system-integrating inclinometer, robotic total station, and laser ranging sensors. [PERSON] et al. [38] developed and applied a wireless MEMS-basedborehole inclinometer for automated measurement of ground movement. [PERSON] [41] proposed to monitor interstory drift in building structures via rotation measurements with MEMS inclinometers and an optimized sensor layout. A comparison of inclinometers versus deflection measurements has already been investigated using information entropy metrics [42]. Different metrics to evaluate the goodness of sensor placement were also studied by [PERSON] [43] and [PERSON] [44].
## 1 Introduction
### Diagonal Stay Cable Structure According to  
On both sides of each pylon, two cantilevers of prestressed concrete and steel box were fixed to the pylons. These cantilevers were supporting the suspended span. The steel cables with prestressed concrete shells poured on had a rectangular cross-section and a form of a composite steel and concrete element, a single at the top of the pylon and divided into two equal parts at the deck level. Cross-sections of the diagonal stay cable are shown in Figure 4a (on the left-at the deck level; on the right-at the top of the pylon) [1].  
There were 24 steel tendons in the central part of the cross-section of the diagonal stay cable, each consisting of 12 strands with a diameter of 1/2-inch, i.e., 12.7 mm (12T13) with a tensile strength of R = 170 kg/mm\({}^{2}\) (ca. 1700 MPa). Those tendons were strained before encasement in concrete and they transferred the weight of the cantilever. The tendons were concreting in segments. Forces in the tendons were transferred to concrete through friction and adhesion forces. Additionally, there were 28 steel tendons located close to the edge of the concrete cross-section, each consisting of four strands with a diameter of 1/2 inch (4T13). Those tendons were located in ducts, strained after hardening of the concrete, and injected afterward. Consequently, the diagonal stay cable is the composite steel and concrete element transferring permanent loads of the suspended span, as well as loads from the road traffic.  
Thus, one diagonal stay cable consisted of 52 steel tendons, hereinafter referred to as the \"internal tendons\", located inside the diagonal stay-cable made of 400 (24 \(\times\) 12 + 28 \(\times\) 4) 1/2-inch tendons. The cross-sectional areas of the steel internal tendons \"S\", concrete \"C\" and the stay-cable \"B\" are listed in Table 1. There were steel stirrups with a diameter of 6 mm, spaced every 15 cm in the cable. Schematic anchoring of the cable in the deck and at the top of the pylon is shown in Figure 4b.  
This type of structural detail of the steel cables with prestressed concrete shells poured on had to ensure the bonding of the steel internal tendons with concrete to the transfer of loads, and also the concrete was to form the corrosion protection of the tendons. Therefore, the load due to road traffic was to be transferred by the entire steel and concrete composite section.  
Figure 4: (**a**) Cross-sections of the concreted cable ([PERSON], 1968). (**b**) Schematic anchoring of the cable in the deck and at the top of the pylon ([PERSON], 1968).
## 7 RF-Based Localisation for Aerial and Ground Robots
For the trajectory optimisation using graph-based SLAM, the distance of fingerprints and the variance is derived from the database using a simple model. Instead of a conventional deterministic propagation model, [95] uses a probabilistic likelihood function with the aid of symmetric trapezoidal distribution over discrete localisation grids. Tethering (in which a mobile node localizes, tracks, and follows another mobile node) is performed by integrating RSS and odometry information. The 2D area is divided into grids, with discrete probability. Having received a new RSS value, a Bayesian update is used to update the probability of the grids and achieve full posterior probability distribution over all grids, based on which the current location is extracted. In a very recent paper, [96], the performance of AOA for UAV localisation in a cellular network is investigated, and accuracy of fewer than 45 meters is reported. [PERSON] et al. [97] incorporate Multi-dimensional Scaling (MDS) and Weighted Centroid Localisation (WCL). [PERSON] and [PERSON] [98] introduce a geometrical approach. Based on six AOA of sensor doublets, the UAV position is found through mathematical techniques as the intersection of six. The LS method is employed in [99] for UAV localisation in 3D fusion of AOA and TDOA. [PERSON] et al. [100] explore relative localisation, estimating the location of the UAV with respect to a target. Relative localisation is useful for formation control [101] and autonomous docking. This paper fuses UWB range measurements with vision-based data. Employing recursive LS, decimeter accuracy is acquired in a 2D experimental setup. The mentioned methods are compared in Table 3.  
The existing state-of-the-art literature on robot localisation is limited to very specific scenarios, technology (UWB), RF features, and sensor data. In more detail, two main limitations have to be addressed:  
* Limited to specific technologies and sensor data: Most papers use RSS due to its easy-to-use hardware. In that case, acceptable accuracy is achieved by using UWB, which is limited for indoor use with short range. TOA-based localisation is also achieved mostly by taking advantage of UWB. Moreover, many possibilities are missing in the literature, such as the integration of images, and LIDAR with RFs. * Limited Accuracy: accuracy is one of the main concerns in UAVs and UGVs localisation. Only relying on simple algorithms and sensor data, like RSS, might not be an appropriate solution, especially with the upcoming technologies, 5G and beyond. As we discuss later in our paper, CSI information would provide a huge amount of useful data. However, the real-time implementation and its fusion with conventional sensor data is the real concern that is not addressed. Edge computing and off-loading as the most promising solutions are rarely investigated. There is a huge gap worth filling in this area: use of RF in SLAM, integrating variant of sensor data with RF, use of CSI in robot applications, and developing a deep learning approach, especially using the fingerprinting method.
## 2 Materials and Methods  
### Preliminaries  
#### 2.1.1 Reinforcement Learning  
RL is a feedback-based learning paradigm in which agents interact with an environment in order to maximize their cumulative long-term rewards through trial and error. Unlike supervised learning, RL does not rely on predefined datasets, instead allowing agents to learn optimal decision-making policies through continuous interaction. An RL problem can be modeled using a Markov Decision Process (MDP) \(G=\langle S,A,P,R\rangle\), where \(S\) represents the state space, \(A\) represents the action space, \(P\) denotes the state transition function, and \(R\) is the reward function [19]. At each time step, the agent observes a state \(s_{t}\in S\) and selects an action \(a_{t}\in A(s_{t})\). This results in a new state \(s_{t+1}\) and reward \(t_{t+1}\), guiding the agent's policy \(\pi\) to optimize the expected cumulative reward.  
Figure 1 illustrates this through a schematic diagram of the interaction between the agent and the environment. Based on the observed state \(s_{t}\), the agent selects an action \(a_{t}\) to execute, where \(a_{t}\in A(s_{t})\) and \(A(s_{t})\) represents the set of actions available to the agent in state \(s_{t}\). After executing the action, the agent receives a numerical reward \(r_{t+1}\) and observes a new environmental state \(s_{t+1}\) after one time step. Figure 1 illustrates the interaction between the agent and the environment, where the agent makes decisions based on its observations and the environment provides feedback in the form of rewards and state transitions. The agent's objective is to maximize its long-term return by learning a policy that selects the best actions over time.  
Q-learning is a foundational reinforcement learning algorithm that seeks to learn the optimal action-value function, commonly denoted as the Q-function, by iteratively updating Q-values based on the rewards received from the environment. Deep Q-Networks (DQNs) build upon this framework by combining Q-learning with deep neural networks to address the instability and high-dimensionality problems of traditional Q-learning. In a DQN, the Q-function is approximated by a deep neural network and the Q-values are updated using the Bellman equation. A DQN consists of two networks: the current network and a target network, which usually has the same architecture as the current network but is updated less frequently. This approach stabilizes the training process and reduces the variance of the target values.
## 5 Mathematical Simulation Analysis
### Distributed Inertial Network and FADS Fusion Algorithm Simulation  
The flight parameters of this paper take the angle of attack as an example. The angle of attack solved by the distributed inertial network and the angle of attack solved by FADS are fused by the variable gain complementary filtering algorithm, and compared with the traditional inertial navigation system(INS) and FADS complementary filtering algorithm. The comparison results are shown in the following Figure 9.  
It can be seen from Figure 9,b that in the whole flight phase, the angle of attack error output by the variable gain complementary filtering algorithm based on the inertial network and FADS is smaller than the angle of attack output by the traditional INS and FADS complementary filtering algorithm, which shows that the variable gain complementary filtering algorithm is effective and can ensure the accuracy of solving flight parameters in the whole flight phase. The angle of attack error output achieved through the traditional complementary filtering algorithm increases with time due to the cumulative effect of INS error. In 500 s, the relative error decreases due to the sudden increase of the angle of attack, but the absolute error is still increasing. It can be obtained from Table 2, at 540 s, the error reaches 0.104\({}^{\circ}\), and the average error of the flight process is 0.0058\({}^{\circ}\). The variable gain complementary filtering algorithm based on the inertial network and FADS can basically  
Figure 8: The comparison diagram of navigation parameters calculated by distributed fusion algorithm and inertial navigation alone: **(a)** The pitch angle comparison diagram of distributed fusion algorithm and single inertial navigation; **(b)** The east velocity comparison diagram of distributed fusion algorithm and single inertial navigation; **(c)** The north velocity comparison diagram of distributed fusion algorithm and single inertial navigation; **(d)** The comparison diagram of the altitude velocity between the distributed fusion algorithm and the single inertial navigation.  
avoid the cumulative effect of error due to the improvement of measurement accuracy caused by the inertial network and the continuous adjustment of the filtering parameters. At 540 s, the error is 0.002\({}^{\circ}\) and the average error is 0.0017\({}^{\circ}\), values which are much smaller than in the traditional complementary filtering algorithm. Compared with the average error of the two algorithms, the accuracy of the variable gain complementary filtering algorithm based on the inertial network and FADS is improved by more than 2 times.
## 3 Methods
### Association Rule Mining and Apriori Algorithm  
Association rule mining is used to reveal the association among items in a dataset. We assume that \(\mathrm{D}=\{t_{1},t_{2},\ldots,t_{N}\}\) is the event dataset, \(\mathrm{t_{k}}=\{i_{1},i_{2},\ldots,i_{K}\}\) represents an event corresponding to a geochemical sample, and \(i_{k}\) represents an item belonging to an aggregated event, \(\mathrm{t_{k}}\). Itemset \(\mathrm{I}=\{i_{1},i_{2},\ldots,i_{M}\}\) is a specific item combination that contains \(M\) different items. For a subset, \(\mathrm{X}\) in \(\mathrm{I}\), if \(\mathrm{X}\subseteq\mathrm{t_{k}}\), then the event, \(\mathrm{t_{k}}\), contains \(\mathrm{X}\). The goal of association rule mining is to find an implicit form of \(\mathrm{X}\Rightarrow\mathrm{Y}\), where \(\mathrm{X}\subseteq\mathrm{I}\), \(\mathrm{Y}\subseteq\mathrm{I}\), and \(\mathrm{X}\cap\mathrm{Y}=\varnothing\). If the rule \(\mathrm{X}\Rightarrow\mathrm{Y}\) exists, there are two key coefficients: the support degree, S,and confidence, C. The support degree, \(S(X\Rightarrow Y)=P(X\cup Y)\), represents the probability of co-occurrence of itemsets X and Y. The confidence, \(C(X\Rightarrow Y)=P(Y|X)=P(X\cup Y)/P(X)\), represents conditional probability of occurrence of itemset Y, given that itemset X has occurred. The itemsets that satisfy the minimum threshold (S\({}_{\min}\)) of support degree are so-called frequent itemsets, and those that satisfy both S\({}_{\min}\) and a minimum threshold of confidence (C\({}_{\min}\)) are strong association rules.  
The Apriori algorithm [57] can be decomposed into two main steps. The basic intuition is that any subset of a frequent itemset must be frequent. The first step is to generate frequent itemsets, as shown in Figure 5. The second step is to extract strong association rules based on frequent itemsets, as shown in Figure 6. The Apriori algorithm generates the candidate itemsets to be counted in a pass by using only the frequent itemsets in the previous pass. To improve the efficiency of frequent itemset extraction, the method utilizes a pruning strategy in order to compress the search space, that is, all non-empty subsets of frequent itemsets must also be frequent, and all parent sets of nonfrequent itemsets are nonfrequent.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods
### Image Processing
#### 2.4.4 Vegetation Index Computation  
Image bands were processed to give vegetation indexes, including the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference aquatic vegetation index (NDAVI), and visible atmospherically resistant index (VARI) among others [9; 23; 24]. The equations for the indices used are given in Table 3. Statistics for the each of the bands and indexes were computed over areas of interest.