esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 4 Results and Discussion
### Experimental Set-Up  
This section describes the hardware and software environment of the experiment and the configuration of hyperparameters [2]. The code for all the prediction methods in this paper was written in Python. Our model and other deep learning comparison models were implemented through Keras, an open source deep learning library based on Tensorflow. All experiments were conducted on a Server with two NVIDIA GTX 1080 Ti graphics processing units (GPUs) and an Intel Xeon central processing unit (CPU) E5.  
Figure 7: Architecture of long short-term memory (LSTM) memory cell.  
There are several hyperparameters in the AC-LSTM prediction model, including the time lag, the number of LSTM layers, the number of nodes in each LSTM layer, and the learning rate. They need to be preset before the model structure is built. Under the condition that all other parameters remain unchanged, we determined the optimal hyperparameter for that selected through our experiments. In the end, we built our model structure using four LSTM layers, and the number of nodes in each LSTM layer was set to 800. The learning rate was 0.0001 in all experiments. The above setting seemed to outperform all others in our experiments.  
The time lag is one of the most important hyperparameters. It determines the number of past hours used in the model input and is necessary for multiscale prediction tasks. To this end, we evaluated the performance of the model with different time lags in order to find the optimal time lag in the model. At different time lags, we predicted the PM\({}_{2.5}\) concentrations of all stations in the training set in the next hour. The calculated MAE and RMSE are compared in Table 3. When the time lag was 10, the RMSE of the model was the lowest. While the time lag was 14, the MAE was the lowest. According to the analysis in Section 3.4 and the previous studies on RNN [10], if the time lag is too small, the temporal correlation between time-series data cannot be fully learned and the prediction accuracy will decrease. However, a large time lag may lead to a longer time for training and unnecessary noise. As a result, the time lag in our model was set to 12 for the one-hour prediction task. Of course, for prediction tasks of different time scales, we can also find the optimal lag through experiments in a similar way.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods  
### Location  
This study was based in the Murrumbidgee Irrigation Area (MIA), which includes the towns of Griffith, Whitton and Leeton in NSW, Australia. A wide variety of crops are grown in the area, with major summer crops including rice, cotton, citrus and grapes. The irrigation water is delivered by Murrumbidgee Irrigation, using a gravity-fed channel network from the Murrumbidgee River. The total irrigation area covers 670,000 ha, of which approximately 18% of the area was irrigated in 2017 [4]. A Sentinel-2 mosaic from January 2018 of a portion of the MIA is shown in Figure 1, centred at 34\({}^{\circ}\)24\({}^{\prime}\)33.0\({}^{\prime\prime}\) S, 146\({}^{\circ}\)01\({}^{\prime}\)53.9\({}^{\prime\prime}\) E (WGS84). The figure contains annotations of the irrigation network supply channels, and of the areas mapped by UAV and satellite for this study.
## 2 Engineering Background  
### Geological Conditions  
As shown in Figure 2a, Tashan Coal Mine is located in Datong Coalfield, Datong City, Shanxi Province, China. The upper Jurassic coal seam in the minefield has been basically exploited, and now the main coal seam under extraction is the Carboniferous. The thickness of the Carboniferous 3-5# coal seam ranges from 14 m to 20 m, the buried depth is 400-800 m, and the dip angle of the coal seam is almost flat (1\(\sim\)4\({}^{\circ}\)). As the 3-5# coal seam is basically stable and the coal reserves are abundant, the geological conditions are relatively simple, and the mining technical conditions are good, so the output of Tashan Coal Mine is over 30 million tons.  
In order to meet the requirements of coal transportation, Tashan Coal Mine develops the 1070 main roadway in the 3-5# coal seam floor as shown in Figure 2b. According to the borehole near the 1070 main roadway, the thickness of the 3-5# coal seam is 18.14 m, and the occurrence of the rock stratum is shown in Figure 3.  
As shown in Figure 4, the 1070 main roadway has a width of 5350 mm and a height of 4275 mm. The length of 1070 main roadway was 2400m. The roadway support parameters are as follows:  
Figure 3: Rock stratum of the borehole near 1070 main roadway.  
Figure 2: Location of Tashan Coal Mine and 1070 main roadway. (**a**) location of Tashan coal mine, (**b**) location of 1070 main roadway.  
Dome support: The anchor bolt body is left-handed steel without longitudinal reinforcement, with a yield strength of 600 MPa, a tensile strength of 800 MPa, a diameter of 22 mm and a length of 2200 mm. The bolt spacing is 800 mm \(\times\) 800 mm. The anchorage is lengthened by resin and the bolt pre-tightening force is 100 kN. The tray is made of high-strength tray, the measures of which are 200 mm \(\times\) 200 mm \(\times\) 10 mm. There is a total of five anchor cables, with a diameter of 17.8 mm, a length of 8300 mm, and a pre-tightening force of 150 kN. The anchoring length is 2500 mm. Four resin rolls are used. The specification of one of the resin rolls is K2335, while the other three are Z2360. W steel strip has a thickness of 4 mm and a width of 250 mm. A diamond-shaped metal mesh is used.  
Rib support: High-strength anchor bolt, with the same material performance with that of chamber dome, is used for support. The anchor bolt spacing is 800 mm \(\times\) 800 mm. The anchorage is lengthened by resin and the bolt pre-tightening force is 100 kN. W steel strip has a thickness of 4 mm and a width of 250 mm. A diamond-shaped metal mesh is used.
## 4 Performance Evaluation
### Effect of Hyper-Parameters and Hidden Units Number Setting in Our Model Efficiency  
Based on the theoretical study of the ULMFit-SVM, the parameters and hidden unit number setting in our model demonstrate that the hidden unit numbers and parameters are the most important parameters determining classification accuracy and training time speed. As a result, optimizing hyperparameters is a critical problem in the development and design of an effective learning model for network sentiment detection.  
An AWD-LSTM language model with embedding size 400, three layers with hidden activations of 1150 for each layer, and a batch size of 70 BPTT were employed in this study. Dropouts of 0.4 were applied to layers, 0.3 were applied to RNN layers, 0.4 were applied to input embedding layers, 0.05 were applied to embedding layers, and a weight dropout of 0.5 was applied to the RNN hidden-to-hidden matrix.  
The classifier has a hidden units size of 50, which is a large number. For the LM and the classifier, we used Adam with \(beta1=0.7\) instead of the usual \(beta1=0.9\) and \(beta2=0.99\), and we utilized 64 batches, a base learning rate of 0.004 and 0.01 for fine-tuning the LM and the classifier, respectively. In Table 3, we display the values of the hyper-parameters of our model for sentiments that have been tested.  
Using a k-fold cross-validation strategy, we could also increase the overall performance of the single SVM to find the optimal RBF kernel parameters (\(C=5.6569\) and \(sigma=1.0667\)) and to fine-tune our approaches' hyperparameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy** \\ \hline Support Vector Machine (SVM) & 78.5\% \\ Bag-of-words SVM & 78.5\% \\ Deep Learning Model with Dropouts in Keras & 77.9\% \\ SIS-ULMFiT [7] & 84.1\% \\ (ULMFiT-SVM) [Ours] & 99.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the accuracy of our ULMFit-SVM model with other models.
## 6 Magnetic Properties
### Power Loss  
The improvement in the core loss of electrical steel over the past century is illustrated in Figure 8. Starting from an initial value of 15 W/kg at 1.5 T under 50 Hz of AC magnetization, core loss has been significantly reduced and is now very close to the theoretical value of 0.4 W/kg [56]. Four significant milestones mark this progress: the addition of Si in 1900. In the timeline of advancements, the addition of Goss texture in 1934, followed by the introduction of HGO steel in 1970 and the incorporation of amorphous materials in 1980, marked significant milestones These advancements have contributed to the remarkable reduction in core losses in electrical steel over time [56].  
In transformers or inductors, a portion of the power that should ideally be transferred through the device is dissipated as heat in the core. This is primarily caused by the cycles of magnetization and demagnetization that electrical steels undergo, resulting in energy dissipation. Power loss, which refers to the disparity between input power and output power, has been a focus of improvement since the discovery of (GOES) and advancements  
Figure 8: The timeline for the improvement in core loss for electrical steel [56].  
in production methods after 1934. Despite these advancements, around 5% of input power is still lost in transformers, and this is attributed to both core loss and copper loss [20].
# A Methodology for Exploiting Smart Prosumers' Flexibility in a Bottom-Up Aggregation Process
## 2 Day-Ahead Management Phase Algorithm
Once the different couples of flexibility contribution and tariff are assessed, the EMS creates a matrix with all combinations and sends the matrix to the VAE. The day-ahead management procedure is represented in the flow chart in Figure 3.  
When receiving the matrix of the flexibility couples (power variation-tariff) from each prosumer, the VAE performs a clusterization of the population of the prosumers that have taken part in the aggregation process, based on specific cooperative and competitive algorithms aiming at creating a list of suitable couples of power variation-economic offer for the DSO. As shown in Section 4, the process can take tens of minutes depending on the number of prosumers and aggregates. The DSO chooses the best offer, assumed as the best compromise between the desired power variation and the most convenient economic offer, and communicates its choice to the VAE. The VAE communicates the result of this process to the EMSes of the winner prosumers that, in this way, provides the flexible service the following day as planned. In particular, each EMS updates the forecasted power profile for taking into account the changes for the flexibility provision and planning a power shifting action for the loads/batteries that provide flexibility. The power shift is, indeed, necessary due to a constraint of the DEMAND system that imposes that the total energy consumption I the day of each prosumer involved in the flexibility provision must not vary. The final step of the day-ahead management phase has the aim of preventing the participation of the prosumers of the winner cluster to more than one winner cluster in the same day, avoiding, in this way, excessive discomfort. This operation is carried out setting to zero the flexibility associated with the devices involved in the winner offer for the rest of the day. Algorithm 1 shows the pseudocode of the algorithm for the EMS operation in the day-ahead phase. ```
For t = 1 to 144  If the VAE sends a DR request for the time interval t  Calculate all flexibility offers by combining the flexibility of each controllable device;  Calculate the flexibility tariff for each offer;  Communicate the information to the VAE that find the winner cluster of prosumers;  If one of the offers is in the winner cluster  Update the daily load profile;  Update the flexibility values;  End;  Else  t = t + 1;  End;  End;
```  
**Algorithm 1** Pseudocode of the algorithm of the day-ahead estimation phase.
# Simulation Tests of the Passing Distance of Ships on a Two-Way Fairway
## 4 Discussion  
The MTE method assumes that to ensure the safe passage of ships, the following condition must be met:  
\[D\geq d_{(1-\alpha)}^{in}+d_{(1-\alpha)}^{out}+d_{r}^{in}+d_{r}^{out}+d_{r}^{s} \tag{5}\]  
where:  
\(D\): - \(d_{(1-\alpha)}^{in}\) - \(d_{r}^{out}\) of the safe maneuvering area of the ingoing ship at the specified confidence level (1-\(\alpha\)) \(d_{(1-\alpha)}^{out}\) - \(d_{r}^{out}\) - \(d_{r}^{out}\) - \(d_{r}^{s}\) - \(d_{r}^{s}\) - \(d_{r}^{s}\) - \(d_{r}^{s}\) - passing distance  
In the modified MTE method, the general condition (5) remains the same, but the method of calculating its components changes. The widths of the safe maneuvering areas of ships take into account the navigational component only on the outer sides of the passing ships, since from the inner sides (from the side of the passing ship) the accuracy of determining the position is taken into account by the passing distance determined at the same level of confidence.  
\[d_{(1-\alpha)}^{in}=d_{m}^{in}+d_{n(1-\alpha)}^{in}d_{(1-\alpha)}^{out}=d_{m}^ {out}+d_{n(1-\alpha)}^{out}\] (  
Inner fairway (channel) bounded by banks, straight section, during the day:  
\[p_{y(0.95)}=0.1b \tag{10}\]  
for the observer's eye height greater than 0.15b:  
\[p_{y(0.95)}=0.05b \tag{11}\]  
where:  
\(b\) - distance between the banks of the channel  
The directional errors of the ship's position perpendicular to the axis of the fairway, determined at a confidence level of (1-\(\alpha\)) = 0.95 on the various sections of the fairway studied, are respectively:  
* Kanal Piastowski: \(p_{y(0.95)}=20\) m;
* Zalew Szczecinski between Brama Torowa I and II Gate: \(p_{y(0.95)}=15\) m;
* Zalew Szczecinski between the Brama Torowa II and III: \(p_{y(0.95)}=25\) m;
* Port of Swinoujscie (Kanal Zbiorczyk): \(p_{y(0.95)}=30\) m.  
Using the results of the simulation studies and referring to their layout presented in the PIANC recommendation [2], the passing distances for equal vessels at a speed of 8 knots were determined:  
\[\begin{array}{l}d_{r(0.95)}^{s}=B+\frac{1}{4}p_{y(0.95)}\ for\ B\geq 30\\ d_{r(0.95)}^{s}=B+\frac{1}{2}p_{y(0.95)}\ for\ B<30\end{array} \tag{12}\]  
The proposed relationships are confirmed by the results of the simulation studies summarized in Table 3.  
Figure 7: Safe width of the maneuvering area at a certain confidence level (two-way fairway).
# Selective Laser-Melted Alloy 625: Optimization of Stress-Relieving and Aging Treatments
###### Abstract  
Additive manufacturing is an innovative solution to produce components characterized by complex geometries. The use of such parts requires a deep knowledge of their behavior under different service conditions, especially from mechanical and corrosion resistance points of view. One of the most well-known and employed materials produced by selective laser melting is nickel alloy 625. It is already commonly used in its conventional form, but the additive manufacturing technology, despite its higher production costs and lower productivity, is becoming competitive because of its excellent mechanical strength. It is in fact significantly higher compared to the conventionally manufactured alloy whose properties are often limited by the difficulty in retaining a fine grain size during plastic deformation and heat treatment. Even though the as-built performance is already quite good, further strength improvement can be attained upon tailored single- and double-aging treatments that are optimized starting from the experimental results obtained in the conventional alloy and also considering the influence on corrosion resistance. In addition, considering that the stress-relieving treatment recommended for the conventional forged alloy at 870 \({}^{\circ}\)C is not suitable for the selective laser-melted material because of the more rapid precipitation response, this temperature is optimized to improve both the tensile deformability and the corrosion behavior.  
alloy 625; selective laser melting; aging treatment; stress-relieving treatment; corrosion resistance
# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
## 2 Materials and Methods  
### Canopy Leaf Area Density Detection System  
The detection system for leaf area density of orchard canopies includes a LiDAR sensor, an industrial computer, a self-propelled cart, and a 12V DC power supply. The LiDAR sensor (MID-360, Livox Technology Co., Ltd., Shenzhen, China) is used to obtain point cloud data of the orchard canopy. It has a vertical field of view ranging from \(-7^{\circ}\) to \(52^{\circ}\), with an angular random error of less than \(0.15^{\circ}\), and outputs 200,000 points per second.  
It is equipped with a six-axis inertial measurement IMU (icm40609, TDK InvenSense, San Jose, CA, USA), and it can accurately perceive the altitude information of the cart and acceleration data in three degrees of freedom. This can be used to detect changes in the position and orientation of the LiDAR during its movement in the orchard, thereby eliminating the point cloud caused by LiDAR's motion. An industrial personal computer (NVIDIA Xavier NX 16G, NVIDIA, Santa Clara, CA, USA) is equipped with a canopy leaf area density detection algorithm based on leaf fitting, which is used for the collection and processing of the point cloud data. A self-propelled cart (Scoutmini, Robosen Robotics, Shenzhen, China) is used to carry the sensor and the industrial personal computer, enabling dynamic data acquisition of both the laboratory-simulated and orchard canopies. During experiments and tests, the cart's speed is maintained at 0.5 m/s. The DC power supply provides the necessary operating current for both the industrial PC and the LiDAR sensor. Data transmission between the LiDAR sensor and the industrial PC is achieved through a wired network connection. The detection system is shown in Figure 1.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 3 Fog Generator Model
### Model Architecture
#### 3.2.1 GridNet Backbone  
The backbone of our Fog Generator is a modified GridNet, consisting of a grid-like structure with height \(H=3\) and width \(W=6\). It employs Residual Dense Blocks (RDBs) with \(n=4\) dense layers and a growth rate of 16, enabling rich feature extraction across multiple scales. The network begins with an input convolution layer (conv_in) that maps the three-channel RGB image to a depth rate of 16, followed by a series of RDBs and downsampling/upsampling modules. The output is processed through an output RDB and a final convolution layer (conv_out) to generate fog features \(F(x)\). This hierarchical design ensures that both local details and global fog patterns are captured effectively.
## 1 Introduction  
In the educational domain, the pursuit of personalized learning experiences has become paramount, aiming to cater to the diverse needs and learning styles of individual learners. The complexity of the learning experience design and implementation in the context of this type of personalized learning led to many issues, e.g., the lack of personalization of the educational content, the inadequate assessment of the learners' progress, and the increasing workload of teachers related to organizational tasks in the educational process. These issues may lead to a decrease in motivation and engagement related to learning,biased or uneven assessment results that do not adequately reflect the learners' knowledge, and a shift to the organizational tasks from the main objective of the educational process, e.g., the pedagogical and educational development of the learners. By following the approaches presented in this paper, these outcomes may be avoided by integrating the possibility of generating personalized learning paths, a finer assessment related to the assessors' and assesses' needs and support for teachers, which would reflect a greater quality in the educational process.  
The integration of advanced technologies such as genetic algorithms has emerged as a promising avenue ([1,2]) to be used in various and innovative [3] contexts. Our forthcoming article studies the conceptualization and development of a systematic model for an adaptive teaching, learning, and assessment environment driven by the power of genetic algorithms. We will establish the theoretical framework that underlies the integration of genetic algorithms into educational settings, exploring how these computational techniques can be leveraged to optimize instructional strategies, tailor learning experiences, and refine assessment methodologies. The paper focuses on the development of an adaptive model of teaching, learning and assessment (TLA) processes, which uses meta-heuristic algorithms, such as genetic algorithms, and other conceptual and practical instruments, such as concept ontologies, as well as various structures, such as bidimensional arrays. These elements are integrated to obtain a usable implementation of the model in order to be used in the classroom educational processes as an efficient teacher aid to improve organization. For the presentation of this model, several sections were structured. Section 2 presents the main aspects related to research initiatives and directions in the literature. Next, Section 3 depicts the main directions of the educational model formed and described in this paper, related to learning (LT) and assessment (TG) components. Section 4 presents several aspects related to the obtained results after the testing phase of the implementation of the model. Finally, Section 5 shows the main conclusions and future directions of development of the model.
## 4 Discussion
### Degradation of Lignocellulose by Fungi  
Lignocellulose consists of lignin, carbohydrates, pectin, proteins, ash, salts, and minerals. Lignocellulose is highly difficult to degrade by microorganisms. Fungi are mainly responsible for lignocellulosic degradation because they have two types of efficient enzymatic systems [31]. A hydrolytic system to degrade polysaccharide by producing hydrolases and an oxidative and extracellular ligninolytic system to open phenyl rings and degrade lignin.  
_Fusarium_ is an important type of parasitic pathogenic fungi. Cell wall degrading enzymes (CWDEs) are one of the main pathogenic ways by which _Fusarium_ invades and infects the host [32]. According to [PERSON], CWDEs secreted by _Fusarium_ mainly include pectinase, cellulase, hemicellulose, protease, amylase, and phospholipase [33]. _Penicillium_ is known to be able to secrete lignocellulosic enzymes, such as hemicellulose, cellulases, manganese peroxidase, lignin peroxidase, and laccase [34]. Therefore, _Fusarium_ and _Penicillium_ may be responsible for the microbial degradation of lacquerware.  
According to the CMCase activity test, notably, the activity of CMCase increased to some extent under acidity and iron ions-containing conditions. Studies have shown that marine effluent wood relics usually deposit ferro-iron compounds, which are easily oxidized in effluent, causing degradation of organic matter [35]. Since the wood lacquers were effluent in the ocean, strains isolated from their surface may be adaptable to this environment. Therefore, attention must be paid to control the adverse effects of iron sulfide on wood lacquers for subsequent protection. Considering the further protection of the lacquer, it should undergo de-ironing, desalination, reinforcement, and other treatments. Lacquer serves in marine waterlogged cultural relics, it has a great degree of a problem with acidification. Therefore, during the protection process, the pH value and iron ion concentration in the wood will change. We need to examine the enzyme activity of CMCase at different pH values and iron ion concentrations of these two main fungi. In addition, the storage temperature of the lacquer is also an important factor to be considered in further protection. Therefore, we tested the enzyme activity of CMCase of two fungi at different temperatures.