esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Plant Growth Promotion Potential
#### 2.4.3 Ethylene Modulation  
One of the mechanisms of plant growth promotion under various environmental stress conditions, such as the presence of organic and inorganic compounds, is the synthesis of 1-aminocylo-propane-1-carboxylate (ACC) deaminase (ACCD), which is involved in lowering the level of ethylene. Ethylene is a phytohormone that controls the growth and maturation of plants. At low concentrations, ethylene participates in the regulation of plant growth and various metabolic processes; however, under stress conditions, plant ethylene levels increase significantly, adversely affecting the root growth and metabolism of the whole plant and leading to accelerated aging. In the genome of the ZCR6 strain, the _acdS_ and _dcyD_ genes, encoding ACCD and its homolog D-cysteine desulfhydrase, respectively, were detected (Table 4). Both enzymes are involved in hydrolyzing ACC, an immediate precursor of ethylene, to ammonia and \(\alpha\)-ketobutyrate [59; 60]. Consequently, the level of ACC in plant tissues is reduced, which results in a decreased level of endogenous ethylene, facilitating plant growth. In this relationship, the bacterium also benefits by utilizing the products of the reaction--nitrogen and carbon sources [61]. Biochemical characterization of the ZCR6 strain showed very high ACC deaminase activity (297 nmol \(\alpha\)-ketobutyrate mg\({}^{-1}\) h\({}^{-1}\)) (Table 1), which is a sufficient value to support the growth of plants in polluted soils. It was found that even a lower level of ACCD activity, approximately 20 nmol \(\alpha\)-ketobutyrate mg\({}^{-1}\) h\({}^{-1}\), is sufficient to support the growth of plants in harsh environmental conditions [62]. [PERSON] et al. [63] demonstrated a positive effect of inoculation of a rhizobacterium strain, _Gordonia_ sp. S2 RP-17, on the phytodegradation of diesel-contaminated soil by _Z. mays_ and found a positive correlation between hydrocarbon degradation and ACC deaminase activity. Additionally, in another study, [PERSON] et al. [64] demonstrated that bacteria mainly belonging to the genera _Bacillus_ and _Pseudomonas_ had the highest ACC deaminase activity in environments contaminated with multiple heavy metals.  
Except for the abovementioned PGP mechanisms, the ZCR6 strain degraded carboxymethylcellulose (CMC) (Table 1), which indicates its ability to produce cellulase, the enzyme that plays a crucial role during the colonization of interior tissues of plants [65]. It is worth emphasizing that the ability of rhizosphere bacteria to colonize plant interiors appears to be important for establishing close interactions with plants as endophytes, especially in contaminated environments [1]. The presence of this feature indicates the potential of the strain for the colonization of plant tissues and subsequent activity as an endophyte. In this case, endophytic bacteria can protect plants against diseases (e.g., via antibiotic synthesis) and directly influence the growth of plants [66].
# A Haar Wavelet Decision Feedback Channel Estimation Method in OFDM Systems
## 3 Conventional Channel Estimation Methods
### DF Channel Estimation  
Due to using a large number of pilots in the LS and DFT channel estimation methods, the data transmission rate and spectrum efficiency of the system are decreased in a certain extent. The DF channel estimation method does not require transmitting the block pilots regularly within a certain time interval. It just requires inserting one block pilot symbol in front of the first OFDM symbol to estimate the initial CFR. The block type pilot is distributed on each subcarrier uniformly. channel state information can be obtained through the process of iterative channel estimation. For high speed data communication system, this method improves the communication efficiency. The procedure of the DF channel estimation method is shown in Figure 3[32].  
To reduce the use of pilots, the DF channel estimation method uses the channel state information of current OFDM symbol as the next OFDM symbol's channel coefficients. In the process of iterative channel estimation, this method requires inserting one block pilot symbol to obtain the initial channel state information by LS method which is presented as  
\[\hat{H}_{\text{P}}=\frac{\mathbf{Y}_{\text{P}}}{\mathbf{X}_{\text{P}}}, \tag{3}\]  
where \(\mathbf{Y}_{\text{P}}\) and \(\mathbf{X}_{\text{P}}\) are the received and transmitted block type pilot symbols, respectively. The equilibrium value of the first OFDM symbol \(\mathbf{Z}_{1}\) can be obtained by the CFR \(\hat{H}_{\text{P}}\) which is presented as  
\[\mathbf{Z}_{1}=\frac{\mathbf{Y}_{1}}{\hat{H}_{\text{P}}}, \tag{4}\]  
where \(\mathbf{Y}_{1}\) is the first received OFDM symbol. As shown in Figure 3, the previously estimated CFR \(\hat{H}_{i-1}\) is taken as a prediction of the current channel coefficients. The equilibrium value of the current OFDM symbol \(\mathbf{Z}_{i}\) is obtained by Equation (5) which is presented as  
\[\mathbf{Z}_{i}=\frac{\mathbf{Y}_{i}}{\hat{H}_{i-1}}, \tag{5}\]  
where \(i\) is the index of OFDM symbols. Then, the estimated current OFDM symbol's transmission value \(\hat{\mathbf{X}}_{i}\) can be obtained by hard decision. The detected symbol \(\hat{\mathbf{X}}_{i}\) will be further divided by the received symbol \(\mathbf{Y}_{i}\) in order to calculate the new estimated CFR \(\hat{H}_{i}\) which is represented as  
\[\hat{H}_{i}=\frac{\mathbf{Y}_{i}}{\hat{\mathbf{X}}_{i}}. \tag{6}\]  
Figure 3: Decision feedback (DF) channel estimation method in CP-OFDM system.  
From Equations (3) to (6), the division means the corresponding element division between two vectors. The channel coefficients of all data symbols in frequency-domain \(\hat{H}_{\text{DF}}\) are obtained through the iterative process above. At the same time, the transmitted symbol \(\hat{X}_{i}\) can be obtained during the iterative channel estimation.
## 3 Results and Discussion  
### Antenna Manufacturing  
Figure 6 presents the SEM images of the micro-cavity created by laser (subtractive process) on the zirconia surface. The micro-cavity was produced in accordance with the defined antenna design.  
Figure 4: Photographs of the U.FL cable with signal and ground terminations separated (**a**) and of the fabricated antenna ready for testing (**b**).  
Figure 5: Schematic illustration of the antenna testing setup. The transmitter antenna is distanced 25 cm from the fabricated antenna, which is placed between two 1 cm-thick beef steaks to approximate the test conditions for human body implantation.  
After the micro-cavity was created on the surface, the silver powder was placed into the cavity and consolidated by a laser sintering approach (additive process). Figure 7 shows a part of the antenna path before and after laser sintering.  
The images from Figure 7 show that the silver powder grains were melted in the sintering condition used. The great advantage of laser sintering is the possibility to sinter specific zones by concentrating the laser energy. However, because of the quick heating and cooling during the laser sintering process, the materials normally receive high residual stress. In some cases, cracks may even be initiated in the surrounding area. The distribution and magnitude of the residual stress are influenced by the laser machining strategy, material properties and environmental conditions. This aspect is evidenced by Figure 7b, in which some micro-cracks are present in the zirconia substrate after the laser sintering process of the antenna. Although the laser beam focused only on the silver path, the heat from laser irradiation promoted thermal stress on the ceramic substrate. Even in previous studies [32; 35], the authors verified a decrease in flexural strength of zirconia samples after laser printing of the components. This decline is a consequence of the laser process, which easily forms a tiny accumulation of defects or cracks. However, in this study, even with  
Figure 6: Micro-cavity of the antenna created by laser on zirconia surface.  
Figure 7: SEM micrographs of (**a**) Micro-cavity before the silver-based antenna printing, obtained after laser machining; (**b,b1**) Micro-antenna printed into the cavity through laser sintering process, 800\(\times\).  
a decrease in resistance, the values are still within the permissible limits by the standard ISO 13356:2015 (\(\geq\)500 MPa) for zirconia specimens [32; 39].
# Signal Consensus in TSP of the Same Grid in Road Network
## 5 Simulation Results
### Simulation Results of Probability of Traffic Accidents at Intersections  
The above simulation results show the comparison of the convergence rates between the consensus algorithms with and without input constraints. This section will prove that the TSP strategies reaching consensus eventually with a smooth rate can avoid serious traffic accidents through simulation  
Figure 5: Simulation results of consensus algorithm with input constraints in TSP.  
Figure 6: State trajectories and the disagreement function \(\left\|\delta\right\|^{2}\).  
experiments at intersections. The traffic accidents are largely caused by the excessive acceleration of the drivers. According to statistics, most rear-end accidents are caused by the emergency braking of vehicles at the intersection. The different TSP strategies in the same grid will lead to poor driving experiences, and untimely responses to the change in signal strategy can cause the driver to brake too quickly. Traffic simulation tool VISSIM is used to simulate the traffic accident probability of 5 intersections in Figure 2. Three cases are analyzed and compared: TSP without consensus algorithm, TSP with consensus algorithm (3) and TSP with consensus algorithm (4). The same settings for the three simulations are as follows: we assume that when the road load exceeds 50%, there will be a 10% probability of traffic accidents if the acceleration of the vehicle braking is greater than 5 m/s\({}^{2}\). The VISSIM simulation interface is shown in Figure 7, we simulated the traffic flow of 5 intersections for a month, and the number of traffic accidents as shown in Table 2.  
Simulation results show that, compared with the former two cases, the proposed algorithm with input constraints in TSP reduces the number of traffic accidents in the same grid, and plays an effective role in TSP.
# A Detailed Review on Foam Concrete Composites: Ingredients, Properties, and Microstructure
## 6 Durability of Foam Concrete  
### Freeze-Thaw Resistance  
ASTM C666 determines the ability of normal weight concrete to resist rapid freezing and thawing cycles and produces microcracking and scaling type failure while conducting on foam concrete [30; 112]. [PERSON] et al. [30] developed a modified freeze-thaw test procedure based on ASTM C666. Compressive strength, initial penetration depth, absorption rate variables have important effects on the production of freeze-thaw resistant foam concrete. It was reported that density and permeability are not important variables.  
The water entering into the concrete expands during the freezing event and creates stresses. The porous structure of foam concrete provides good freeze-thaw resistance by providing additional space where water can expand [50]. Foam concretes generally offer good FT resistance compared to non-aerated concrete. [PERSON] et al. [78] showed, as a result of their work, that foam concretes with high porosity did not always result in higher FT resistance. It was found that the FT resistance of foam concrete is affected more than the size of the air void, and the number of air voids smaller than 300 \(\upmu\)m was reported to play a critical role in reducing FT damage in foam concrete. Since the number of freeze-thaw cycles increases, mass losses increase and spallings occur on the surface of foam concrete samples [23]. The type of foam used in foam concrete has an effect on mass loss and strength loss [36]. Density difference affects the FT resistance of foam concretes. It was reported that low density foam concretes experience more expansion and high loss of mass and strength. This situation was attributed to the larger and interconnected pore structure of low-density foam concretes. Such a pore structure will allow more water intake into the concrete, causing the foam concrete to show lower resistance to FT [11].
# DNNs Based Computation Offloading for LEO Satellite Edge Computing
## 5 Proposed Algorithm
3:Initialize the \(d\) DNNs with random parameters \(\mathbf{\theta_{d}}\) and empty the memory structure. 4:for\(i=1,2,3\ldots I\)do
5:Input computing tasks \(\mathbf{l^{i}}\) to all \(d\) DNNs
6:Generate \(d\) candidate offloading decisions with the \(d\) DNNs for the input computing
7:tasks \(\mathbf{l^{i}}\)
8:Allocates bandwidth to each user by the optimal bandwidth allocation scheme (15)
9:Calculate the system utility function values \(\mathbf{J_{d}}\) by the candidate offloading decisions
10: and the optimal bandwidth allocation scheme
11: Select the best solution \(\mathbf{x_{i}^{*}}=\underset{\mathbf{x_{i}}}{\arg\min}\mathbf{J_{d}}\) and update the memory structure by adding
12:\(\left(\mathbf{l^{i}},\mathbf{x_{i}^{*}}\right)\) into the memory structure and output the optimal offloading decisions \(\mathbf{x_{i}^{*}}\)
13: in this iteration
14:if '% interval = 0 then
15: Uniformly sample \(d\) batches of the training samples from the memory
16: Train the DNNs and update \(\mathbf{\theta_{d}}\) using a gradient descent algorithm
17:endif
18: Update \(i\) to \(i+1\)
19:endfor
```  
**Algorithm 1** Proposed algorithm for joint optimization computing offloading and bandwidth allocation in LEO satellite edge computing
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Built-Up RS-Based Proxies
#### 4.1.1 Buildings Category
In addition, building roof offsets between adjacent buildings have been used as a proxy to detect pancake collapse, in which the building is characterized by an intact roof but collapsed floors [37]. Building material has been used for vulnerability [62; 63] and resilience assessments [64; 65; 66]. Building material detection from RS data is typically based on the interpretation of rooftop colors from aerial or satellite images without considering other materials of building (e.g., building wall materials). Data by drones/UAV on facades status have a potential to offer more reliable proxies for building material analysis/assessment due to providing information about the walls and other elements of the buildings in the facades in addition to their rooftops [67]. Energy loss by buildings has been used as a proxy for recovery [49], where a decrease in energy loss of a structure is considered as a sign of positive recovery, as it indicates an improved building standard. This proxy is extracted based on the heat level of structures using hyperspectral imagery. A lower heat level of a house shows a lower energy loss level. The rate of energy loss reduction of houses demonstrates their insulation level and is related to building materials and overall construction quality. However, a non-functioning building does not produce energy (heat), and thus, when compared to others, shows a low energy loss, which can lead to inaccurate results in the extraction of the insulation level of buildings. Meanwhile, this point also demonstrates that the energy loss of buildings can be used as a proxy for functional assessment of buildings.
# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 3 Experiments and Results  
### Data Used  
In the experiment, C-band HH-polarization GF-3 amplitude images are employed to validate the proposed algorithm. Figure 7 shows the experimental images with sizes of 535 \(\times\) 433 pixels, where Figure 7a shows a farmland with 8 m resolution, located in Shenyang City, China, and contains five transmission towers; Figure 7b-d show farmland and wasteland with 3 m resolution, located in Fuxin City, China, and contain three, five and nine transmission towers, respectively.
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 5 Results
### Performance Analysis of the DRL Model  
The implementation of the actor-critic deep reinforcement learning (DRL) algorithm proved highly effective in optimizing agent decision-making within complex municipal environments. The system's learning curve exhibited a steady increase in agent efficiency, as measured by cumulative rewards, with agents reaching optimal performance after approximately 10,000 training episodes. Decision accuracy, defined as the percentage of correct actions taken by agents in alignment with the reward structure, stabilized at 96% by the end of training. Additionally, cumulative rewards increased from an initial average of 15 per episode to a peak of 45 per episode after full optimization, with learning convergence occurring consistently across agents, showing minor variances within \(\pm\)2%. Beyond these performance metrics, the DRL model demonstrated exceptional efficiency in handling complex multi-agent interactions, particularly in resource allocation for large-scale municipal events. Furthermore, it exhibited strong adaptability to dynamically changing constraints, such as limited resources or unexpected municipal emergencies, reinforcing its practical applicability in real-world scenarios.  
Figure 3 visually represents the cumulative rewards achieved by the agent over the training episodes, illustrating a clear upward trend in performance. Starting at a low reward of approximately 15 per episode, the curve demonstrates significant improvement, peaking at around 45 rewards per episode after full optimization. Key milestones are an-notated, highlighting the point of convergence where the learning stabilized and a significant performance jump that indicates a pivotal enhancement in decision-making efficiency. This visualization effectively captures the progressive learning dynamics of the actor-critic DRL algorithm in complex municipal environments.
## 3 Methodology
### Test Methods  
#### 3.3.1 Lightweight Deflectometer  
The Dynatest portable Lightweight Deflectometer (LWD) was used to determine the pavement stiffness (modulus of elasticity) for the test roads. This test was performed over varying seasons between 2018 and 2022. The LWD has been used because it is portable, less expensive, and readily available, which means it can be used in the absence of the falling weight deflectometer (FWD). However, it is recommended that FWD testing is further performed to validate the LWD results. The test uses a falling weight dropped manually from a constant height onto the pavement surface and deflection readings are obtained and back-calculated to determine the pavement stiffness. The weight applies an impact force of 15 KN and transmits load onto the pavement surface to cover an area of 1.77 m\({}^{2}\) using a base plate with a diameter of 150 mm. Before the test, locations were marked for testing (Figures 3 and 4), the testing equipment is shown in Figure 5b. Care was taken to minimize human error by ensuring three consecutive or close outcomes were achieved before moving to the next location. Dropping the load correctly from the required height  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Modulus (MPa)**} & \multicolumn{2}{c}{**Poisson’s Ratio**} \\ \cline{3-6}  & & **Average** & **Std Dev** & **Average** & **Std. Dev** \\ \hline \multirow{5}{*}{**Erbsville**} & HL3 (21 \({}^{\circ}\)C, 5 Hz) & 5716 & 310 & - & - \\  & HL4 (21 \({}^{\circ}\)C, 5 Hz) & 5545 & 228 & - & - \\  & Granular A & 226 & 34 & - & - \\  & Granular B & 145 & 37 & - & - \\  & 475 kg/m\({}^{3}\) & 1207 & 117 & 0.24 & 0 \\  & Subgrade & 72 & 15 & & \\ \hline \multirow{5}{*}{**Notre Dame**} & SP 19.0 & 3511 & 87 & - & - \\  & Granular A & 387 & 53 & - & - \\ \cline{1-1}  & 400 kg/m\({}^{3}\) & 888 & 107 & 0.21 & 5 \\ \cline{1-1}  & 475 kg/m\({}^{3}\) & 1188 & 241 & 0.3 & 14 \\ \cline{1-1}  & 600 kg/m\({}^{3}\) & 1391 & 44 & 0.26 & 24 \\ \cline{1-1}  & Subgrade & 43 & 3 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notre Dame Drive and Erbsville layer properties (Adopted from [9]).  
also helped with consistency. Equation (2) was used to compute the LWD stiffness based on the elastic half-space (Boussinesq's Solution) theory.  
\[\text{E}\left(\text{MPa}\right)=\frac{\pi\times\left(1-\mu^{2}\right)\times r \times\sigma_{0}}{2\times d} \tag{2}\]  
where \(\mu\) is the soil's Poisson ratio, \(r\) is the plate radius in mm, \(\sigma_{0}\) is the maximum force in kN, and \(d\) is the maximum deflection in m.
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 3 Channel Effects Furthermore, RF-Impairments
### 1/Q imbalance
(**a**) \(\epsilon=0,\Delta\phi=0\); (**b**) \(\epsilon=0,\Delta\phi=10\); (**c**) \(\epsilon=0.5,\Delta\phi=0\); (**d**) \(\epsilon=0.5,\Delta\phi=10\); (**e**) \(\gamma_{I}=0.75,\gamma_{Q}=0\); (**f**) \(\gamma_{I}=0,\gamma_{Q}=0.75\).
## 3 The FQS Algorithm
### An Example  
Here, we show a short example of the proposed algorithm, where text \(T\) = \"GCATCGCAGTCAG TATACAGTAC\" (\(n=23\)) and pattern \(P\) = \"GCAGTCAG\" (\(m=8\)). The text and pattern are DNA sequences from the alphabet \(\Sigma=\{A,\ C,\ G,\ T\}\), hence \(|\Sigma|=4\).
# Spam Review Detection Techniques: A Systematic Literature Review
## 3 Transformation
### Probabilistic Classifier
In linear classifiers, Support Vector Machine (SVM) classification is best suited for the text data. This is because of the sparse nature of the text where features are not related to each other, but they tend to correlate to one another, and generally, these features are organized into separate categories [15]. Support Vector Machine method analyzes data and defines decision boundaries by having hyper-planes. In binary classification problem, the hyper-plane separates the document vector in one class from other class, where the separation between hyper-planes is desired to be kept as large as possible. Support Vector Machine optimization procedure maximizes the predictive accuracy while automatically avoiding over-fitting of the training data. Moreover, SVM projectsthe input data into the kernel space, and then it builds a linear model in this kernel space. For dataset \((\overrightarrow{x}_{1},y_{1}),\ \ldots,(\overrightarrow{x}_{n},y_{n})\), where \(y\) represents the class and \(x\) is the attribute which belongs to class y. Therefore, any hyper plane can be written as \(\overrightarrow{w}.\overrightarrow{x}-b=0\), where \(w\) is the normal vector to the hyperplane. SVM works very well for the small amount of training data and provides better results for good tokenizers. Several studies [4; 15; 33; 36] used a SVM learner, where [PERSON] et al. [15] and [PERSON] et al. [33] used hotels review datasets through Amazon Mechanical Turk (AMT) and reported 89.9% and 84% accuracy, respectively. The variation in accuracy is because both works used different feature engineering techniques. On the other hand, [PERSON] et al. [4] used Yelp's real dataset and claimed 86.1% accuracy in detecting spam reviews and [PERSON] et al. [36] used an Amazon dataset and reported 71% accuracy.