esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 5 Experiment and Analysis  
### Data Description and Preprocessing  
To validate the superiority of the proposed model, historical data from Tetuan City and a region in Australia were selected for empirical analysis. For Detu'an City, data from January, April, July, and October 2017--encompassing six characteristics (e.g., temperature, humidity, and load)--were utilized to validate seasonal patterns, with measurements recorded every 10 min (144 data points per day) and a total of 17,712 data points. Meanwhile, to address challenges like overfitting and the excessive data similarity in the forecasting process, two months of data from Australia (i.e., May-June 2006 data), covering features such as load and humidity, were adopted for validation. These data were sampled at 30 min intervals (48 data points per day), yielding a total of 2928 data points. The raw data are presented in Figure 6.  
Moreover, models like the GRU and LSTM, alongside optimization algorithms such as the SSA and SOA, are employed for the comparative analysis with the proposed model. This comparative experiment serves to validate the proposed model's superior prediction accuracy. Furthermore, predictions using data from distinct regions are produced, which in turn verifies the model's excellent generalization capability across varied datasets.  
In this study, the experimental hardware platform is equipped with the 12 th Gen Intel (R) Core (TM) i5-12450H CPU. For software configuration, the experiments are executed in the Python 3.8 environment, and the deep learning models are developed using the Pytorch and Tensorflow frameworks.  
For the data preprocessing process, as the data in this paper are acquired via continuous sampling, the Hampel filtering method is mainly utilized to address data outliers--detected outliers are filled with the median value. To improve the model's convergence and ensure that the input data have consistent scales, Max-Min normalization is performed. Lastly, inverse normalization is applied to the predicted values to derive the final forecasting results.  
Figure 6: The raw data from Tetuan City and a region in Australia. The orange color represents the load data of a certain region in Australia from May to July 2006. The yellow color represents the load data of the city of Dushanbe throughout the whole year from 2017 to 2018. The green color represents the load data of Dushanbe in January. The purple color represents the load data of Dushanbe in July. The dark blue color represents the load data of Dushanbe in October.
## 3 Results
### Validating on the Original O-SMA Dataset
#### 3.1.2 Dense Fusion  
For the original O-SMA dataset, all metrics were comparable across different SMA techniques. But in contrast to the averaging fusion method, all SMA techniques outperformed the \"original\" technique. Among these, spatial channel augmentation (\"move\") achieved the highest performance. However, the standard deviation values during validation were larger compared to the averaging model, indicating more variability during the training procedure.
# Non-Local Spatial and Temporal Attention Network for Video-Based Person Re-Identification
## 1 Introduction
The novelty of our approach is that we use non-local neural networks to compute spatial and temporal dependencies over long ranges among video frames. The way each attention score is calculated depends on all the frames in the video, as shown in Figure 1, not just on the adjacent frames. This method gives a better video-level feature representation, making the video look more like a whole rather than merely a few images. Additionally, we apply the improved non-local neural network to CNN networks at different levels, so that the features at different levels obtain a better performance. We performed both frame-level feature extraction, and temporal aggregation using the non-local attention mechanism. Our main contributions can be summarized in three-fold:  
1. We propose a plug-and-play non-local attention module (NLAM). It can be inserted into CNN networks for frame-level feature extraction. In the video-based person Re-ID task, the spatial position of the target person in the image can be determined more accurately. 2. We propose a non-local temporal pooling (NLTP) method for temporal feature aggregation. We use it to replace the single average or maximum pooling, which could not order the video frames. 3. We verified the effectiveness of our two methods on different datasets.
## 4 Circuit Design
### Driver Circuit  
The main circuit of the solar and SWT charging circuit is the Buck circuit. The driver circuit is shown in Figure 11 and was developed by the IR2104S (International Rectifier, El Segundo, CA, USA).  
C\({}_{5}\) is a capacitor with value of 100 nF. D\({}_{9}\) is a low power-consumption Schottky diode SK34 (Diodes Incorporated, Plano, TX, USA), and C\({}_{6}\) is a 1 \(\mu\)F tantalum capacitor that can be considered as a bootstrap capacitor. Current limiting resistors R\({}_{23}\) and R\({}_{24}\) are designed to have a resistance of 2 K\(\Omega\). The IN is connected to the PWM port of the MCU, and the LO has the same polarity as the pin IN, while the HO has the opposite polarity. The function of the drive circuit can be described as follows: After inputting the PWM at pin IN, (1) when the HO is low, the LO is high. At this time, the IRF540 connected to R\({}_{24}\) is turned on and grounded, and the VCC is charged to C\({}_{6}\) through D\({}_{9}\), (2) When the HO is high, the LO is low. At this time, the VCC and the voltage of C\({}_{6}\) are output from the HO, thereby controlling the turn-on and turn-off of the IRF540 connected to R\({}_{23}\).  
Figure 11: Schematic of the driver circuit.  
Figure 10: Schematic of the solar circuit (**a**) and SWT charging circuit (**b**).
# Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
## 3 Methodology
### Algorithm Implementation  
#### 3.4.1 Data Preparation  
In this study, to prove the practicality of the proposed LSTM network based algorithm, we used actual runtime data as the network inputs. The actual in-field data were gathered by the Vehicle On Board Controller (VOBC) from Shenzhen Metro Line No. 7 in China and Beijing Yanfang Metro Line in China. The dataset of Shenzhen Metro Line consists of three subsets, which separately record the train operation data of a certain train on three days, i.e., 7 April 2018, 8 April 2018 and 11 July 2018. The dataset of Beijing Yanfang Metro Line includes the operation data collected on 13 May 2019. For example, the subset on 7 April 2018 contains 381,637 records which describe the driving process through the whole day. The relevant information of one record is listed in Table 2.  
As can be seen in Table 2, the information of the train operation state was meticulously recorded. The vast amounts of data offer the advantageous support to apply data-driven algorithms. To keep consistent with the inputs of the traditional dynamic model presented in Section 3.1, we chose four raw features as the inputs of the proposed algorithm: analog output, traction brake, train speed and slope. These four features for a certain operation process are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time** & **System** & **Number** & **Data Integrity** & **Driving Condition** & **Offset** & **Train Speed** \\ \hline
20:20:57 & 4646 & 332,071 & True & 2 & 1292 & 1037 \\ \hline
**Number of current station** & \
\begin{tabular}{c} **Number of** \\ **next station** \\ \end{tabular} & **Link** & **Distance** &
\begin{tabular}{c} **Run** \\ **level** \\ \end{tabular} & **Slope** \\ \hline
35 & 36 & 177 & 11,659 & 3 & 0 \\ \hline
**Analog output** & **Speed limit** & **Traction braking** & **Network voltage** &
\begin{tabular}{c} **Network** \\ **flow** \\ \end{tabular} & **Load** \\ \hline
134 & 1054 & 1 & 868 & 303 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Train operation information saved by Vehicle On Board Controller.  
Figure 3: Raw features.  
Analog output: Analog output is a continuous variable, which is outputted by electric motors. This feature is used to control the train to accelerate or decelerate. It reflects the absolute value of the train braking force \(B(t)\) and the train traction force \(F(t)\).
* Traction brake: Traction brake is a categorical variable, which implies the direction of analog output and gives the signs of \(B(t)\) and \(F(t)\).
* Train speed: At each time iteration \(t\), the train speed is accurately recorded by VOBC.
* Slope: Slope represents the track gradient which impacts the gradient resistances.  
One can find that extra information is not added to our algorithm compared to the traditional method. The purpose is to prove that the performance improvement of the proposed approach depends on the algorithm itself rather than the extension of inputs.
## 3 Challenges of Islanding Detection Methods Selection  
The integration of DGs with utility grid networks creates many challenges, especially for islanding methods. The selection of islanding detection methods extremely depends on various factors, such as the type of DG units, DG connection topologies, future expandability, DGs lifetime, and the location of DGs.  
Many schemes which are used to detect islanding have been proposed but none of them is totally perfect. Hence, it is difficult to select the most appropriate method to detect the islanding and assess its suitability in grid-connected DGs systems. The criteria of islanding detection selection are uncertain and cannot be intended for use with deterministic values. Therefore, considerable expertise is required to address the whole criteria which affect islanding detection method selection.  
In addition, since traditional electrical systems are replaced by smart grids, various challenges might be presented. One of those challenges is that the pathways of power flow in smart grids are multiple, which can lead to many faults in the islanding detection. Hence, to overcome such problems associated with smart grids, smart meters can be introduced to reduce the implementation cost and the complexity of islanding detection considerably.
# Fracturing Fluids and Their Application in the Republic of Croatia
## 5 Fracturing Fluids--Composition and Additives
### Fracturing Fluid Additives
Proppant flowback following the fracturing treatment has been a major concern because of its detrimental effect on production equipment, leading to the plugging or erosion of surface and downhole completions [72]. An additional problem is fracture choking, resulting in a large skin and possibly requiring re-fracturing. The most commonly used proppants are silica sand, resin-coated sand, and ceramic proppants [49; 69]. The properties of the mentioned proppants are presented in Table 13.  
The size range of a proppant is very important. Typical proppant sizes are generally between 106 \(\upmu\)m-2.36 mm (8 and 140 mesh). For example: 840 \(\upmu\)m-1680 \(\upmu\)m (12/20 mesh), 600 \(\upmu\)m-1180 \(\upmu\)m (16/30 mesh), 420 \(\upmu\)m-840 \(\upmu\)m (20/40 mesh), 300 \(\upmu\)m-600 \(\upmu\)m (30/50 mesh), (212 \(\upmu\)m-420 \(\upmu\)m (40/70 mesh) or 106 \(\upmu\)m-212 \(\upmu\)m (70/140 mesh). The shape of the proppant is also important. The shape and size of the proppant influences the final permeability through the fracture. A wide range of particle sizes and shapes will lead to a tight packing arrangement, reducing permeability/conductivity. Another innovative solution that can be used for flowback control in certain conditions is a new flowback technology with a nondegradable fiber proppant, designed specifically for low-temperature applications. The nondegradable fibers rely on the mechanical interference of the particles and not on chemical bonding. They can be applied with any proppant (sand or ceramic), and they keep the proppant consolidated even at stress cycling conditions [72]. [PERSON] et al. (2020) performed tests with a chemical proppant (CP) generated in the fracture and concluded that the CP exhibited good performance in terms of compressive strength, thermal stability, stability in reservoir fluid and treatment fluid. The CP could enter any narrow fractures, and effectively solved the existing problems in the conventional fracturing technology by significantly improving the fracturing effect, especially the network fracturing effect in tight reservoirs [29]. The typical composition of a water-based fracturing fluid (known as SF 650) used in mini frac tests during hydraulic fracturing in Croatia is shown in Figure 1. \begin{table}
\begin{tabular}{c c c c} \hline
**Proppant** & **Density (kg/m3)** & **Compressive Strength (bar)** & **Max. Work Depth (m)** \\ \hline Silica sand & 2650 & 414 & 2500 \\ \hline Resin-coated sand & 1500 & 552 & 2500–3000 \\ \hline Ceramic proppants & 3500 & 690 & \(>\)3000 \\ \hline \end{tabular}
\end{table} 
Table 13: Proppant’s properties. Based on the data shown in Figure 1, it can be concluded that the aqueous gel contains 97.43% water and only 2.29% of all other additives. In some other water-based fluids, the water content may be even higher.
## 2 Materials and Methods
### Data Selection  
It was anticipated that the unprocessed data collected by various equipment in farms generated disproportionate values that need processing to transform into usable numbers. Heterogeneities of the data, such as the system of the data source, the sensors and devices used in measuring physiological and behavioral values, and the formats of the data, were processed by the trained members of the group. Data mining and data-processing tool scripts were used to detect and remove inaccurate, duplicated, and corrupted data. The cleaned and harmonized data were loaded to the proposed data-mining hub, which followed the format shown in Figure 1, but not all the data were used. The format followed a standardized sampling scheme where the cleaned and harmonized data for every day was encoded per row according to the parameters they belonged to. The animal information and parameters were sectioned per row, while the data per day was encoded per column. The information of the dairy cows was interconnected by the unique identity traceability number of the dairy cow. The datasets of three individual cows (triplicates) were mapped and grouped through the traceability number of the cow. The selected individual cows were all fourth parity lactating straightbred Holstein cows. In each individual cow identified by its traceability number, processed data under different parameters generated were further selected relevant to this study. For instance, environmental conditions, such as ambient temperature and relative humidity, were selected as climate records of the cow while wind speed and sun exposure were only stored in the data-mining hub; records about feed, such as cost of ration and ration composition, were saved and stored while the feed intake dataset was selected for the study. A total of twenty-one (21) parameters were used in detecting the status of the cows, and the datasets were grouped into three groups: the HI, MID, and LO groups. The HI group used 21 parameters, while MID and LO groups used thirteen (13) and eight (8) parameters, respectively. The MID group (13 parameters) consisted of the four behaviors, seven milk performance indexes, and the feed intake. The LO group (8 parameters) consisted of the seven milk performance indexes and the feed intake.
# Reporting Key Features in Cold-Adapted Bacteria
## 1 Introduction  
Temperature is a key factor for bacterial survival. Cold environments are predominant over the Earth. A great amount of information was generated analyzing bacterial adaptations to cold (e.g., [1; 2; 3; 4; 5; 6]). Traditional characteristics involved in cold adaptation entail structural adjustment of enzymes, maintenance of membrane fluidity, expression of cold shock proteins, adaptation of the translation and transcription machinery and the presence of compatible solutes [3].  
Microorganisms inhabiting cold environments are denominated psychrophiles, a term defined by [PERSON] [7] and proposed for microorganisms that have optimum growth temperatures lower than 15 \({}^{\circ}\)C with upper temperatures around 20 \({}^{\circ}\)C. From an ecological point of view, the names 'eurypsychrophile' and'stenopsychrophile' were used to reflect wide and narrow tolerance to temperature, respectively [8]. Since growth rate is not the best indicator of physiological state under low temperatures [6; 9], the degree of adaptation to cold should be considered for psychrophile definition. Different nomenclature can be used to describe different aspects related to life in cold conditions, however the term cold-adapted bacteria will be used in this work focused on bacteria indigenous to cold environments.  
The aim of this review was to analyze different strategies of cold-adapted bacteria, looking for novel features and/or activation of non-classical metabolisms for a cold lifestyle. We propose that, superimposed on generalized characteristics--almost universally present in cold-adapted bacteria--there are other specific features of each taxonomic group that are relevant and contribute to the adjustment of the whole cold adaptation landscape. For this purpose, the contribution ofmetagenomic and metaproteomic data was analyzed to detect key functions in bacterial communities that inhabit cold environments.
## 5 Use Case: Collaborative Issues Detection in an Intelligent Vehicle Surveillance System
### Scenario  
Autonomous cars in smart cities depend on signals transmitted from several sources, including local emergency centers (LECs) and national traffic control centers (NTCCs). These commands are intended to solve issues like dealing with emergency traffic, defining no-go zones, or rerouting automobile traffic. Multiparty hierarchical digital signatures are utilized to guarantee that these commands come from reliable sources and have not been altered.  
In addition to higher security that prevents any forged or unauthenticated commands, the system handles these issues efficiently like providing better safety by reducing accidents and ensuring effective response of vehicles in emergency situations, and crowd management by redirecting vehicles intelligently to avoid crowded or dangerous areas.  
Figure 1 depicts a hierarchical communication system for traffic management and autonomous vehicle operation. At Level 1, NTCC acts as the top-level decision maker, overseeing the entire network. At Level 2, the local emergency center serves as an intermediary node, facilitating communication between the national level and autonomous vehicles. Finally, at Level 3, the autonomous vehicles operate as both message creators and executors, sending messages to LECs or receiving directives from NTCCs. In general, an LEC represents node 0 in the implementation and an NTCC represents node 1, which  
delineates the interaction levels in the hierarchy. This structure ensures efficient message flow and execution for traffic regulation and emergency responses.  
From bottom to top, the system is read as follows: The Lower Level (LL) creates messages, signs them, and sends them to the middle level, or validates the chain of signatures before executing the commands. The Middle Level (ML) validates the messages and adds its own signature. The Top Level (TL) sends general commands such as \"close a certain area\" or \"change directions\" after validating the ML and LL signatures.
## 4 Results
### The Effect of the Proppant Diameter  
The same number of proppant layers is set, and the proppant diameter is changed. Therefore, the smaller the proppant diameter, the smaller the initial width of the fracture and the lower the construction cost. As shown in Figure 17, as the proppant diameter increases, the fracture width decreases.  
As shown in Figure 18, the fracture surface crossflow coefficient decreases with the increase of the proppant diameter, which is more obvious for secondary fractures. This also shows that a small-sized proppant is more suitable for complex fracture networks.  
Figure 16: Effect of the proppant Poisson’s ratio on shale gas production.  
Figure 17: Effect of the proppant diameter on the fracture width.  
Figure 15: Effect of the proppant elastic modulus on shale gas production.  
As shown in Figure 19, the smaller the proppant diameter, the greater the shale gas production. When the proppant diameter is reduced from 1 mm to 0.5 mm and 0.2 mm, the cumulative gas production in 900 days of production increases by 2% and 5.5% respectively. Since the proppant laying concentration corresponding to the small-sized proppant is lower, the construction cost is also lower, and the small-sized proppant is easier to pump into the secondary fracture network; the actual construction effect will also be better than the large-sized proppant. Therefore, the small-sized proppant is more suitable for deep shale gas hydraulic fracturing construction.
## 2 Sensor Design
### Shielding and Guarding  
Generally, a shielding plate and a guard electrode are employed to shape the electric field, and more importantly to eliminate stray capacitance and noise [17]. Indeed, a shielding plate, which is placed on top of the main electrodes, is needed to attenuate the undesirable electric field, to eliminate parasitic capacitance and noise in the sensing electrodes; and a guard electrode, which surrounds the main electrodes and between them, will be used to prevent the electric field lines to go directly to the sensing electrode. In this way, they have to travel further into the specimen to reach the sensing electrode, thus, the depth of penetration will increase as shown in Figure 2. Usually, the shielding plate and guard electrode are held at ground potential and different types of them can be used depending on the capacitor measurement circuit [18].  
Figure 2: Schematic diagrams for capacitive probe with and without shielding plate and guard electrodes: (**a**) Cross-section of the capacitive probe without shielding plate and guard electrodes and (**b**) Cross-section of the capacitive probe with shielding plate and guard electrodes.
# Polarization Differential Visible Light Communication: Theory and Experimental Evaluation +
## 2 System Model  
### Polarization  
As light is an electromagnetic wave, the electric and magnetic fields oscillate in a certain direction perpendicular to the propagation direction. For most light sources, including LEDs, this oscillation varies randomly over time, this is referred to as unpolarized light. If the electric and magnetic fields however oscillate in a fixed plane, it is said that the light is linearly polarized. In this work, linear polarizers (LPs) are used in order to transmit light with linear polarization. An ideal LP has a defined axis along which polarized light is unaltered. Light polarized orthogonally to this axis is either reflected or absorbed, depending on the type of polarizer. Unpolarized light passing through a LP is thus polarized along the axis of the LP. When linearly polarized light traverses a polarizer, the intensity \(I\) in the transmission is given by (1), generally known as Malus' Law [18]. Here \(I_{0}\) is the intensity incident on the polarizer and \(\phi\) is the angle between the polarization axis of the incident light and the polarizer axis:  
\[I=I_{0}\cos^{2}{(\phi)}. \tag{1}\]