esa-sceva/satcom-chunk-collection huggingface dataset:
# Decoupled Monte Carlo Tree Search for Cooperative Multi-Agent Planning
## 3 Problem Description
### Multi-Agent Markov Decision Process  
Stochastic sequential decision-making problems can be modeled as Markov Decision Processes (MDPs). The MDP model defines the state space of the problem, available actions at every state, the probability of resulting states after executing an action, and the reward, given the current state, the next state, and the executed action. The solution to an MDP problem is called _policy_. Policies define state-action mapping. The _optimal policy_ has the maximum expected cumulative reward given the MDP model. Calculating a policy for problems having huge state and action spaces is infeasible. The online planning approach solves this problem by only calculating the next most successful action. It evaluates only a subset of state and action spaces up to a limited time step by sampling actions from the given state.  
To be able to plan for multiple agents, the action set is redefined as the Cartesian product of every agent's action set, and this is called the _joint action set_. The main problem with multi-agent planning is the exponential increase in the space of actions and states, due to the number of agents. The MDP problem with many agents is called Multi-agent MDP (MMDP). In MMDP formalization, it is assumed that all agents can see the full state, and planning is done by a centralized entity.  
The formal definition of MMDP is 5-tuple \(\langle D,S,A,T,R\rangle\) where:\(D\) is the set of agents,
* \(S\) is the finite set of states,
* \(A\) is the finite set of joint actions (\(A_{1}\times A_{2}\times\cdot\times A_{n}\)),
* \(T\) is the transition function which assigns probabilities for transitioning from one state to another given a joint action, and
* \(R\) is the immediate reward function.  
The online planning approach presented in this paper can be used for MMDP problems. However, it does not need the full MMDP definition. It is enough to have the generative model of the problem with state and action definitions. This makes a huge difference, because, for most of real-world problems, it is easy to define a black-box generative model without knowing or calculating the transition probabilities. For all of the problems we used in the experiments, we had a generative model, including for the warehouse commissioning problem.
## 3 Data Analysis and Forecasting Process
### Data Pre-Processing  
The data process in this paper is divided into three stages including outlier processing, time interval processing and normalized processing, which are shown as below:
# Analysis of Life Cycle Environmental Impact of Recycled Aggregate
## 3 Analysis of Life Cycle Impact Assessment (LCIA)
### Process of LCIA
#### 3.2.2 Transportation Stage  
The waste concrete collected from the building demolition/deconstruction sites was transported to a recycled aggregate production factory by truck, and the transport distance was 30 km on average. From the loading capacity of transportation means and the amount of waste concrete, the number of transport equipment units was calculated and transport was applied to assess environmental impact.  
\[\text{Transportation}_{\text{E.I}}=\sum\left[\left(\text{M(i) / Wt}\right)\times\text{d }\times\text{D}\right]\text{ (i= Waste concrete)} \tag{3}\]  
Here, Transportation E.I is transport stage environmental impact (kg-unit/kg), M(i) is waste concrete (ton), Wt is the loading amount (ton) of (i) transport equipment, d is transport distance (km), and D is the characterization value of crude oil by environmental impact categories (kg-unit/L).
# Impact of Arch Dam Cracking on Monitoring Data
## 1 Introduction  
### Cracks in Arch Dams  
For centuries, dams have been structures that serve an important role in society, providing water reservoirs, flood control, fresh water supply to the population, and power generation. Unfortunately, the obvious advantages of dams are matched by the risks they pose to downstream populations and properties [1]. According to the report of the American Society of Civil Engineers, there are four categories of risk in dams: (i) high (may cause human losses), (ii) significant (economic losses), (iii) low, and (iv) undetermined. About 17% and 12% of dams belong to the first two categories, respectively [2], which makes it crucial to ensure good structural integrity to protect dams. Norway dams are classified into 5 classes according to the consequences in case of failure, and 12% of the registered dams belong to the two categories with the highest risk [3]. French dams more than 2 m high are classified into three categories, and 24% of them belong to the two categories for which regulations impose a risk assessment study, and the administration inspects class A dams every year and class B dams every 5 years [3]. The safety of a dam and the downstream population depends not only on the quality of its design and construction but also, to a large extent, on adequate maintenance and proper monitoring to control the state of the dam.  
Concrete high arch dams are extensively used around the world as the most cost-effective and safest type of dam due to their advantages of high bearing capacity, excellent seismic performance, and high economic efficiency [4, 5, 6, 7]. In general, arch dams are at riskof failure due to structural failure within the dam body, sliding along the dam-foundation interface, or rock settlement under the abutment [8]. However, historically, technicians frequently overlooked identifying the cause of failure for concrete dam rehabilitation, making their decisions based on their experience and intuition [9].  
Almost all high arch dams have experienced unexpected cracks, either in the construction phase or during in-service conditions [10; 11; 12; 13] due to complex factors such as drying shrinkage, thermal stress, hydraulic thrust, concrete deterioration, or uneven settlement of the dam and foundation. Treating dangerous cracks is of paramount importance, as the development of open cracks decreases the strength and rigidity of the dam, affects its integrity and impermeability, accentuates the deterioration of the concrete, and endangers the safe operation of the dam.
# Degradation of Trace Organic Contaminants by a Membrane Distillation--Enzymatic Bioreactor
## 3 Results and Discussion
### Impact of Mediator Addition on TrOC Degradation
#### 3.3.2 Impact of Mediator Concentration  
Redox-mediator dose can affect TrOC degradation by changing the abundance, stability and reversibility of the generated radicals [43]. Therefore, the impact of two mediator concentrations (0.25 and 0.5 mM) on ORP, TrOC degradation, and enzyme stability was investigated.  
Concentration-dependent improvement in the degradation of 18 TrOCs (5 phenolic and 13 non-phenolic compounds, Figure 4) was observed in MD-EMBR. The highest improvement in the degradation of TrOCs was achieved at 0.5 mM. Notably, increasing the concentration of SA, HBT and VA from 0.25 to 0.5 mM improved TOC degradation by up to 7%, 15% and 25%, respectively (Figure 4). This corresponds well with the respective increase of 2%, 5% and 15% of the reaction media ORP (Figure 5). On the other hand, degradation of 8 phenolic and 4 non-phenolic TrOCs in MD-EMBR was comparable at all the tested mediator concentrations (Supplementary data Figure S3). For instance, HBT achieved over 99% degradation of atrazine in MD-EMBR irrespective of the mediator concentration. This is consistent with HBT performance reported in case of UF-EMBR [18].  
Figure 4: Impact of mediator concentration (0.25 and 0.5 mM) on the degradation of TrOCs in the MD-EMBR. Error bars indicate the standard deviation of duplicate samples. Operating conditions of the MD-EMBR are given in the caption of Figure 2. Only those TrOCs showing mediator concentration-dependent improvement in their degradation are shown here. For remaining TrOCs, results are given in Supplementary Data Figure S3.  
In general, the degradation of TrOCs that are easily amenable to laccase (Supplementary data Figure S3) does not improve significantly (less than 5% in this study), while the degradation of resistant TrOCs (Figure 4) may improve with the increase in mediator concentration, and may reach a plateau beyond a certain mediator concentration. However, the mediator concentration beyond which no improvement occurs may depend on the type of mediators as well as the target TrOCs [41; 44].
## 4 Results  
### The ESTARFM Prediction Results  
Figure 4 shows the relationships between the observed PROBA-V 100-m and ESTARFM predicted reflectance values. All the slopes of the scatter plots were close to 1, and the intercepts were small, proving that ESTARFM accurately predicted the reflectance for each image. The \(R^{2}\) values ranged from 0.712 to 0.931 for the RED band, from 0.503 to 0.941 for the NIR band and from 0.880 to 0.964 for the SWIR band, and the average deviations were almost negligible, as shown in Table 5. The higher \(R^{2}\) values were achieved during the middle of the growing season (April), whereas the smaller \(R^{2}\) values were observed at the early part of the growing season and at its end (in December and May, respectively). Notably, the predictions were generally best for the SWIR band and worst for the RED band, except for the predictions for 7 May at Site 1 and 25 May at Site 2. This result is consistent with numerous studies that the ESTARFM have shown better performance at the longer wavelength [49]. For the predicted images on 7 and 25 May, the accuracy was lower for the NIR than for the RED band, which was probably a result of the changes in land-cover from seasonal farming activities in the cropped areas at the end of the growing season, and the NIR band was more sensitive to these changes than the RED and SWIR bands.  
Figure 4: _Cont_.  
Comparisons between the blended NDVI/LSWI and the observed 100-m NDVI/LSWI are provided in Figure 5. The accuracy assessment shows that most of the blended data were also closer to the 1:1 line. The \(R^{2}\) between the predicted NDVI and the referenced NDVI was higher than 0.84 for Site 1 and 0.69 for Site 2, and the \(R^{2}\) between the predicted LSWI and the referenced LSWI was larger than 0.84 for Site 1 and 0.86 for Site 2. The average absolute deviation was less than 0.06 for NDVI and 0.03 for LSWI (Table 6). The lowest \(R^{2}\) value for the predicted and referenced NDVI was observed on 25 May at Site 2 because of the inaccurate prediction of the NIR band. Generally speaking, the predictions were better for the LSWI than for the NDVI due to the more accurate predictions for the SWIR band than for the RED band. Furthermore, although data from similar sensors may have some advantages in terms of fusion, the blended images were not completely consistent with the  
Figure 4: Per-pixel comparisons between the observed and predicted PROBA-V 100-m reflectance. The 1:1 line is blue, and the regression line is black.
## 4 Experimental Methods and Results
### Experimental Methods and Results of the Proposed Framework
#### 4.3.3 Comparisons of the Effects of Adding News Sentiment score to Prediction Models  
The preliminary model in the previous experiment used only input features from historical data. In this experiment, the news sentiment score was applied to a prediction model to identify whether applying a news sentiment score improves the prediction model.  
From Table 9, the result shows that the news sentiment score has great advantages as an input feature in stock price prediction, with MAE dropping by 20.82% when compared to a single LSTM. On the other hand, the prediction results become worse when the news sentiment score of PCA-EMD-LSTM is included compared with only PCA-EMD-LSTM. Evidently, it is better to ignore the news sentiments component part of the proposed model. However, the news sentiment score part can improve the model performance of the original model.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
5G  Fifth Generation  
ADC  Analogue-to-Digital Converter  
AF  Amplify-and-Forward  
BER  Bit Error Rate  
BPS  Best Performance Scheme  
CRLBs  Cramer-Rao Lower Bounds  
CFO  Carrier Frequency Offset  
DAC  Digital-to-Analogue Converter  
DSIC  Digital Self-interference Cancellation  
FD  Full-Duplex  
FDD  Frequency Division Duplex  
HD  Half-Duplex  
ICA  Independent Component Analysis  
IoT  Internet of Things  
ITU  International Telecommunication Union  
JISB_DSICED  Joint Iterative Semi-Blind Digital Self-Interference Cancellation,  
Equalisation, Demodulation, De-interleaving, and Decoding  
LLR  Log Likelihood Ratio  
LMS  Least Mean Squares  
LoS  Line-of-Sight  
mM2M  massive Machine-to-Machine  
mMTC  massive Machine-Type Communications  
MSE  Mean-Squared Error  
NLMS  Normalised Least Mean Squares  
NLoS  Non Line-of-Sight  
PLS  Physical Layer Security  
QC-LDPC  Quasi-Cyclic Low-Density Parity Check  
QPSK  Quadrature Phase Shift Keying  
RF  Radio Frequency  
RLS  Recursive Least Squares  
S-MMSE  Semi-blind Minimum Mean-Squared Error  
SB_DSICED3_W/OF  Semi-Blind Digital Self-Interference Cancellation, Equalisation,  
Demodulation, De-interleaving, and Decoding Without Feedback  
SDR  Software-Defined Radio  
SI  Self-Interference  
SPA  Sum Product Algorithm  
TDD  Time Division Duplex  
uRLLC  ultra-Reliable Low-Latency Communications
# Research on Synthetic Aperture Radar Processing for the Spaceborne Sliding Spotlight Mode
## 2 Model of Spaceborne Sliding Spotlight SAR  
### Geometric Model  
The sliding spotlight mode is a compromise between the stripmap mode and the spotlight mode [3]. In this mode, the radar slows down the moving speed of the antenna by controlling the beam direction, and increases the coherent integration time to obtain a higher azimuth resolution than possible with the traditional stripmap mode. At the same time, because the antenna beam still has a certain moving speed on the ground, a larger imaging area can be obtained than with the spotlight mode.  
The basic model of sliding spotlight SAR is shown in Figure 1. The horizontal axis is the slow time \(t_{m}\), and the satellite moves at a constant speed \(v\) along the axis. \(R_{B}\) is the vertical distance between the satellite and the point \(P\). During the flight, the antenna beam center always directs to a point on the ground (shown as the black dot in Figure 1, the antenna always points to a certain imaginary point in the spotlight mode, and to a hypothetical point underground in sliding spotlight mode). The origin \(O\) of the axis is the time at which the satellite is located directly at the point of the imaginary point, that means the distance from the imaginary point to the satellite is shortest during the flight time. According to the squint angle, the azimuth coordinates of the satellite can be obtained in each pulse.  
Typical orbit parameters for spaceborne SAR simulation is shown in Table 1.
## 6 Performance Evaluation
### Evaluation Results
#### 6.3.2 Interest Satisfaction Delay  
Figure 8 shows the Interest satisfaction delay as a function of the number of Interest packets. To study the Interest satisfaction delay, we vary the Interest packet frequency between 5 and 30 packets/sec. The simulation results reveal that both NDN and GIF under-perform compared with the CIDF-WSN in terms of Interest satisfaction delay(s). It is clear from the results depicted in Figure 8 that the CIDF-WSN achieves above 70% lower Interest satisfaction delay compared with the GIF while the amount of delay is several times lower in the case of NDN. The rationale is that the NFIB cost maintenance at every successful transmission of the upper-layer node also enables the lower-layer node to select the optimal node before Interest transmission to avoid the interest losses and retransmissions by the lower-layer node. Moreover, the proposed Interest and Data forwarding mechanism avoids the congestion and collisions in the network, which may hinder seamless communication. All these factors significantly contribute to reducing the Interest satisfaction delays. On the contrary, in both GIF and NDN, the lack of efficient Interest and Data forwarding mechanisms generates the extra network overhead in terms of packet retransmissions, induces congestion, and increases the overall Interest satisfaction delays.
## 3 Results
### Flood Risk Baseline Map Derived from the Baseline Model
#### 3.1.2 Baseline Flood Risk Scenario
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Target**} & \multirow{2}{*}{**Composition**} & \multirow{2}{*}{**Factor**} & \multicolumn{4}{c}{**Weight**} & \multirow{2}{*}{**FairP-**} \\  & & & **AHP** & & **FAHP** & & **Entropy** \\  & & & & & **Method** & **Entropy** \\ \hline \multirow{8}{*}{Flood risk} & \multirow{4}{*}{Hazardousness (0.401)} & RMAX3 & 0.270 & 0.175 & 0.022 & 0.115 \\  & & Historical flood & & & & \\  & & inundation frequency & 0.330 & 0.227 & 0.409 & 0.298 \\ \cline{2-6}  & \multirow{4}{*}{Sensitivity} & Elevation & 0.023 & 0.066 & 0.001 & 0.040 \\  & & TWI & 0.112 & 0.097 & 0.016 & 0.065 \\  & & NDVI & 0.013 & 0.055 & 0.054 & 0.055 \\  & (0.299) & Proximity to & & & & \\  & & river system & & & & \\ \cline{2-6}  & \multirow{4}{*}{Vulnerability (0.299)} & Population density & 0.052 & 0.100 & 0.200 & 0.139 \\  & & GDP & 0.021 & 0.074 & 0.162 & 0.109 \\  & & Land use & 0.127 & 0.125 & 0.047 & 0.095 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Weight assignment for factors in flood risk baseline model by utilizing MCDA method. Figure 7: The flood risk baseline scenario in the PLEZZ. (**a**) The spatial distribution of the flood risk baseline map. The stacked pie chart next to the legend represents the area percentage of different risk levels. (**b**) The risk ranking of 38 counties in the PLEZZ, ranked by the percentage of the area of the high-risk zone to the total area of the county. (**c**) A bubble plot of the risk composition of 38 counties in the PLEZZ. The bubbles correspond to the counties from top to bottom in the same order as in (**b**). An important aspect of risk management is the security of agricultural and forestry production due to the large amount of cropland and forests. * Peripheral counties at low risk far from Poyang Lake (ranking from 24 to 38), from Xingan to Fuliang. Although most lands are below the moderate-low risk level, there still exists very high-risk units along the river banks, which are areas where appropriate prevention should be implemented.
# A Comparative Analysis of Arabic Text Steganography
## 4 Arabic Text Steganography
### Unicode Method
This system uses a coding method with an output that carries a sequence of 1s  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Methodology** & **Pros** & **Cons** \\ \hline
[90] & Pointed letters with kashida hide “1.” & Limited capacity \\  & Unpointed letters with kashida hide & since all letters \\  & “0.” & Embedding is automated or & cannot be extended. \\
[91] & Uses 1 kashida hide “0” and 2 & manual. No size increase of & \\  & consecutive kashida hide “1.” & stepo text. & \\
[92] & Inserts kashida where applicable & & \\  & to hide “1.” & & Increase in the size \\
[77] & Add kashida in a specific location to & of stepo file. \\  & hide secret numbers. & & of stepo file. \\
[93] & Inserts 1 kashida to hide “0” and 2 & & \\  & consecutive kashidas to hide “1.” & & \\  & Uses 5 scenarios to hide 2 secret bits & & \\
[74] & by inserting kashida after the moon & or sun letters. & \\
[73] & Randomly applies kashida insertion & & \\  & in 4 scenarios to hide a secret bit. & & Increases the algorithm \\  & & complexity and reduces the & \\  & Uses 4 choices to protect the secret & AES. Improves the capacity & \\  & bits based on kashida and dotted & using Gzip. Stego file has a & \\  & letter. & flexible font and format. & \\  & & Low computational time. \\  &and a few 0s. The modified RLE proposed in the system is suitable for compression. The outputs suit steganography purposes that use Unicode and unprinted characters to hide the secret message in an Arabic text. Three scenarios were studied by the authors of [103]. The first reduces the character change by counting the number of 0s. If there are fewer 0s than 1s, the secret packet is complemented with 1s. The second scenario hides 0 bits by leaving a cover letter unchanged from the next word in the text, while the third hides 1 bit by identifying a cover letter from the text. According to the type of letter, this letter's Unicode must then be modified from the general letter's Unicode to the contextual Unicode. The cover letter's Unicode is changed to the isolated form if it belongs to an isolated group. If the cover letter is part of a series, the Unicode is reverted to the original form. As seen in Table 13, high perceptual transparency and the unaffected format size and output file are the major benefits of Unicode methods. The Arabic letters take different shapes in different positions. For this reason, an inappropriate change of the letter's shape increases the reader's suspicion, hence limiting the cover letters.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 3 Operational SAR Systems and Applications  
One of the main reasons for requiring the application of high performance computing to InSAR processing is the increasing availability of SAR sensors. Each of them has an increased capability of producing data at higher resolution and with a shorter repeat time. Moreover, the InSAR technique is becoming an essential tool also in routinely monitoring Earth's ground deformation for hazard management and risk mitigation [99]. In this context, fast response in case of an event (such as a volcanic eruption, an earthquake, or a building collapse) is of evident importance. Both the extensive availability and accessibility of data from current (and future) sensors coupled with the possibility of fast data processing make such a quick response more and more doable. Currently, it is widespread that when an event occurs (namely, an earthquake), InSAR results are made available even to the public, often through a simple publication on social media [100].  
This section presents a quick excursus on present and future SAR missions and an overview of the possible application fields to give an idea of the potential amount of data that needs to be processed in practical application contexts.