esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Fair Multi-Object Tracking (FairMOT)  
FairMOT is a model that simultaneously implements pixel-level object detection and re-identification (Re-ID), effectively tracking multiple objects in each frame. The structure of FairMOT is shown in Figure 3.  
FairMOT uses the DLA-34 backbone to extract features from each frame and predict the object's location and ID. This model exhibits excellent tracking performance, especially in highly dynamic underwater environments. FairMOT's structure consists of several key components. First, the Detection Head detects the location of the fish. Second, the Re-ID Embeddings assign a unique ID to each object, enabling continuous tracking. Third, the backbone network plays a crucial role in extracting feature maps throughout the system. Finally, the up-sampling layers increase the resolution to generate refined feature maps.  
FairMOT is a multi-object tracking model [16; 17; 18] with two homogeneous branches that simultaneously predict pixel-level object detection and Re-ID functionality. This model starts by inputting the video sequence into the DLA-34 network. The DLA-34 network fuses features of different depths and transmits the extracted multi-layer information to the detection model and the Re-ID model.  
The high-resolution feature maps extracted from the DLA-34 network are divided into several components. First, heatmaps represent the position information of the top-left (min x, max y) and bottom-right (max x, min y) corners and the confidence information at those positions. Second, the bbox size indicates the size of the object. Third, center offsets solve the accuracy loss problem that occurs when the heatmaps are down-sampled again after being initially up-sampled to 1/n. Finally, Re-ID Embeddings assign a unique ID to each object, enabling continuous tracking.  
Heatmaps, center offsets, and bbox size are used to detect objects and transmit them to the detection model, while Re-ID Embedding transmits the target's Re-ID information to the Re-ID model.  
This study compared and analyzed the performance of underwater fish detection and tracking using two backbone models of the FairMOT model. This research aimed to explore methods to implement efficient and accurate underwater organism detection.
### Paper Contribution
Random forest was adopted in ref. [29], using channel and hardware features to distinguish different nodes; however, the investigation is limited to node identification (i.e., no spoofing detection) and is very limited and related to a single static experimental setup. * The proposed approach integrates multiple-attributes to provide a higher identification accuracy. Differently, most of the papers in the literature use only one channel attribute [17; 18; 19; 25; 28; 30] or multiple observations of the same attribute, exploiting time or spatial diversity [22; 23; 26; 27]. However, attributes can be estimated with a different level of reliability; thus, having different attributes allows to compensate for low-reliable attributes with high-reliable ones. Only a few papers have integrated different attributes, but often these are limited, such as in ref. [16; 31] where only CSI and delay are considered. A wide range of attributes are considered only in [24; 29]. Moreover, the angle of arrival (AoA) attribute is rarely considered, and in different scenarios, such as in ref. [32], where AoA is used for improving the security of channel training authentication, integrating the AoA with the pilot randomization, or in ref. [33], where AoA information is cross checked with the GPS information for improving security. Finally, here, the effects of different attributes are separately evaluated. To our knowledge, only [24] provides an analysis based on the availability of different attributes but in a different context. * The proposal of the use of _sentinel nodes_ to improve spoofing detection in small networks. Cooperative solutions for PLA have been rarely considered as in ref. [23], but here the goal of sentinel nodes is completely different. These nodes do not have to perform any operation except sending periodical beaconing signals. * The evaluation of the system performance in an actual and general time-varying channel, also considering different environment conditions, while most of the papers in the literature consider fixed channel parameters and simple channel models. Moreover, the effects of different channel attributes in classification results are evaluated.
## 4 Methodology
### Naive Bayes Classifier  
The execution results of the Naive Bayes classifier for a splitting of 70% (training set) and 30% (testing set) are shown in Table 4.  
The performance of the Naive Bayes classifier remains consistent for classes 1, 6, and 4, showing no significant change compared to the other two classifiers. In contrast, there is a notable and substantial performance improvement for categories 2 and 3, reaching the maximum achievable performance. Additionally, category 5 has also exhibited significant improvement in its classification accuracy.  
Because it naturally computes probabilities based on category frequencies, which is essential for the dataset containing categorical features, the Naive Bayes classifier handles categorical data with proficiency, which contributes to its improved performance. Additionally, by using SMOTE to handle the class imbalance in the training set, the possibility of predictions being biased towards the majority class was reduced, a problem that decision trees and Random Forests might still experience. Due to its resistance to overfitting, Naive Bayes may also perform well in rather small datasets, which can be a significant benefit in  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Category** & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline
1 & 1.00 & 1.00 & 1.00 & 2 \\
2 & 0.83 & 1.00 & 0.91 & 5 \\
3 & 0.80 & 1.00 & 0.89 & 4 \\
4 & 1.00 & 0.50 & 0.67 & 2 \\
5 & 0.92 & 0.85 & 0.88 & 13 \\
6 & 1.00 & 1.00 & 1.00 & 2 \\ \hline Metric & & & & \\ \hline Accuracy & & & 0.89 & 28 \\ Macro Avg. & 0.93 & 0.89 & 0.89 & 28 \\ Weighted Avg. & 0.90 & 0.89 & 0.89 & 28 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Random Forest classifier results.  
situations where resources are limited. When relationships among features are not clearly established, its simplicity and reliance on the independence assumption between features can be useful.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## 2 System Model  
Figure 3 shows the system model of the hybrid signaling SWIPT system studied in this paper. We assumed that the information signal \(s(t)\) uses an orthogonal frequency division multiplexing (OFDM) signal and the energy signal \(e(t)\) uses a multitone sine signal [41]. Mathematically, these two signals are defined as  
\[s(t)=\sum_{k=0}^{K-1}B_{k}(t)\exp(j(2\pi f_{k}t+\phi_{k})), \tag{1}\]  
\[e(t)=\sum_{n=-N}^{N}D_{n}\exp\left(j\left(2\pi\left(f_{n}+n\Delta_{f}\right)t+ \phi_{n}\right)\right) \tag{2}\]  
where \(B_{k}(t)\) is the baseband waveform signal, \(D_{n}\) is the amplitude of the sinusoid signal at each frequency point, \(f_{k}\) and \(\phi_{k}\) denote the frequency and initial phase of the OFDM subcarrier, respectively, \(f_{n}\) and \(\phi_{n}\) denote the center frequency and initial phase of the sinusoid signal, respectively, and \(\Delta_{f}\) is the frequency offset of the multitone sine signal.  
The two signals are superimposed and fed into a PA before transmission into a wireless channel. The receiver uses power splitting to separate the received signal into the information signal and energy signal, which are subsequently used for ID and EH, respectively. As mentioned above, the focus of our paper is on the nonlinear distortion at the transmitter side caused by the PA. Common RF PAs can be classified into two categories: memoryless PA and PA with memory. In a memoryless PA, the output is an instantaneous function of the input. Therefore, any change in the input signal results in instantaneous reactions at the output. In contrast, in a PA with memory, the output is also a function of previous input values. Our paper focuses on the case of memoryless PA, leaving the more complicated case of PA with memory as future work. The signal distortion and power efficiency of a PA depend on its nonlinear distortion model and input power back-off (IBO). The PA models and the concept of IBO will be subsequently introduced as preliminaries.
## 4 Application Characteristics
### Onboard Image Processing and the YOLO Model for Ship Detection  
#### 4.2.1 Dual-Line Array Delay Imaging System  
Through an analysis of the ship motion characteristics, it is found that the red and near-infrared spectral bands information has a higher contrast and is more useful for target detection. It is easy to detect ships and other moving targets with movement tracks in two different images by using information such as time differences among the two images. In addition, considering that ships are mainly affected by ocean waves and clouds in remote sensing images, the double linear array push-sweep mode is used to realize the observation effect of staggered time to the Earth, according to the location relationship of the detector (the scanning time interval of the double linear array is 5 s). Automatic detection of ships from the satellite images is completed by comparing two images acquired by the same camera at different times.  
For HN-1 01, a dual-line array push-sweep imaging mode was specifically designed for onboard image processing for ship detection [44]. The advantages of the two-line array push-scan imaging model for HN-1 01 include: (1) onboard image processing for real time detection of an on-orbit moving ship by adopting the dual time array time-delay imaging scheme; (2) users have opts to transmit only sliced images containing identified ships, leading to a reduction in downlink data. This is useful for application requirements of real-time ship identity and sliced images with identified ships' transmissions to ground stations.  
The implementation process includes two steps. First, the near-infrared spectral bands are employed by sensors for ship detection. The uniform image background and lower reflectivity of seawater facilitate target separation. Second, the dual-line array push-sweep imaging mode is utilized for onboard ship detection. As ships dynamically move in the sea with constantly changing positions, image matching/comparison and motion detection algorithms can reduce the interference from waves and clouds and efficiently eliminate moving and stationary objects. This method lowers the false detection rate of ships and enhances the ship detection capacity. Specifically, with the dual-line array push-sweep imaging mode and the scanning time interval of 5 s, image matching/comparison and motion detection are applied to two consecutive images. Based on the ship geometric features database, the static background is removed and the false detection rate is reduced. During this process, the ship coordinates in the sliced images correspond to those in the original images. Results indicate the effectiveness of this method for onboard ship detection.
# Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic
## 2 Experiments
### Description of WRF/CMAQ Model Simulations
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Items** & **Schemes** \\ \hline WRFv3.4.1 & Microphysics & WRF single-moment 5-class (WSM5) [55] \\  & Long and short wave radiation & Rapid Radiative Transfer Model (RRTM) [56] \\  & Cumulus physics & Kain-Fritsch scheme [57] \\  & Planetary boundary layer & Yonsei University (YSU) scheme [58] \\  & Land surface model & 5-layer thermal diffusion land surface model \\ CMAQv5.1 & Gas phase chemistry & Statewide Air Pollution Research Center-07 (SAPRC-07) [59] \\  & Aerosol module & Six-generation modal CMAQ aerosol model (AERO6) [60] \\  & Advection & (Horizontal) yamo and (vertical) wrf scheme \\  & Diffusion & (Horizontal) multiscale and (vertical) acm2 scheme \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physical and chemical schemes for WRF/CMAQ simulations.
# Evolutionary Maximization of Energy Amount Harvested by Means of Panel of Thermoelectric Modules
###### Abstract  
This work proposes the use of a specialized algorithm based on evolutionary computation to the global MPPT regulation of panel of thermoelectric modules connected serially in numerous string sections. Each section of the thermovoltaic panel is equipped with local DC/DC converter controlled by the proposed algorithm and finally this allows the optimization of the total efficiency of conversion. Evolutionary computations adjust PWM signals of switching waveforms of DC/DC sectional simple boost converters, which have outputs configured in parallel. It gives the chance to obtain the highest level of electric energy harvested, i.e., thanks to boost converting operational points precise adaptation to the system temperature profile as well as electric load level. The simulation results of the proposed evolutionary technique confirmed the high speed of the MPPT process that is much better than for perturbation and observation, as well as incremental conductance methods, and it assures concurrent optimization of numerous PWM signals. Next, the work shows practical optimization results achieved by the proposed algorithm implemented to microcontroller module controlling the DC/DC converter during thermal to electric conversion experiment. A laboratory thermovoltaic panel was constructed from a string of Peltier modules and radiator that assured passive cooling. The measurements obtained once more proved the MPPT evolutionary regulation properness and its adaptation effectiveness for different resistive test loads.  
Seebeck effect; maximal power point tracking; evolutionary computation
# A New Plasma Surface Alloying to Improve the Wear Resistance of the Metallic Card Clothing
## 2 Experimental Procedure
### Microstructure Analysis and Measurements  
The microstructure and element distribution of the metallic card clothing after plasma surface alloying were characterized by scanning electron microscopy (SEM) and energy-dispersive X-ray spectrometry (EDS, produced by FEI Co. Ltd., Eindhoven, The Netherlands). A BrukerD8-ADVANCE diffractometer (Proto Manufacturing Inc., Oldcastle, ON, Canada) was used to determine the XRD (X-ray diffraction method) patterns of the chromizing coating using CuK\(\alpha\)1 radiation at 50 kV and 40 mA. Diffraction radiation were in a continuous mode with step size of 0.02\({}^{\circ}\) and step time of 8 s over the range of 20\({}^{\circ}<20<90^{\circ}\).
# The Influence of Interlock Loss between Rebar and Concrete on Bond Performance of RC Member
## 5 Analytical Case Study Assuming Lug Loss Due to Rebar Corrosion
### Investigation on the Effect of Non-Uniform Lug Loss in the Axial Direction of Rebar  
Figure 16 shows the load-deflection curves and strain distribution. Regardless of the spacing of the lug loss zones, the maximum load, crack distribution, and failure mode were almost same as those of the sound case. It can be seen that even if there are zones of significant interlock loss in the axial direction of the rebar, the bond is maintained for the entire member as long as there are zones where the interlock can be maintained.  
Figure 17 shows the distribution of flexural cracks before shear crack formation in LugInt-3.5 cm and LugInt-7 cm. The orange parts indicate the sound lug zones, and almost of all flexural cracks occur from sound lug zones or at the boundary between a sound lug zone and a lug loss zone. These parts are considered when there was strong bond between the main rebar and concrete. This is in correlation with the result that flexural cracks occurred on the stirrups in the experiment.  
In actual rebar corrosion, section loss does not occur uniformly in the axial direction. There will be a mixture of zones where the lug to concrete interlock remains sound and zones where it is lost. At this time, unless a part of the rebar due to extreme section loss caused by serious pitting corrosion exists, flexural cracks should be able to disperse in the sound parts and the bond of an entire member can be maintained.  
Figure 16: Analysis results (effect of non-uniform lug loss in axial direction of rebar).  
Figure 17: Flexural crack distribution before shear cracking (non-uniform loss of lugs in axial direction).
## 6 Experiments and Results
### Results  
From Table 2, the model using 12 features and the front camera image has outperformed most of the other models, and it is proved that adding an image has pushed the model performance to another level compared with the one only using the 12 basic features. However, the model with all cameras has similar performance with the model using only the front camera, which is not consistent with the previous assumption. The reason why the five-camera model failed to perform even better is that the rest of the camera image is noisy input for the model and may likely interfere with information from the front camera, resulting in a slight decrease of the model performance.
## 3 Materials and Methods  
4G networks are based on LTE technology, which uses a frequency spectrum of around 700 MHz to 2600 MHz. These frequencies are divided into different bands that are used for different purposes, such as voice and data communication. 4G networks use a combination of Frequency Division Duplex (FDD) and Time Division Duplex (TDD) techniques to transmit and receive data. FDD uses separate frequencies for transmitting and receiving data, while TDD uses the same frequency for both.  
On the other hand, IoT devices use a variety of technologies and protocols to communicate with each other and with the internet. These devices can operate on different frequency bands, such as 2.4 GHz, 5 GHz, and sub-GHz bands. Some IoT devices use the unlicensed spectrum, which means they can operate on any frequency without needing a license, while others use the licensed spectrum, which requires a license from the relevant regulatory body.  
One of the main issues with 4G services and networks is that they can cause interference with IoT devices, especially those operating on the same frequency bands. This interference can cause communication problems and even lead to data loss or corruption. To mitigate this interference, different methods can be used, such as frequency hopping, spread-spectrum techniques, and power control.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**IoT Service** & **Reliability** & **Multiple Devices** & **Latency** & **Interference Management** & **Energy Consumption** \\ \hline Smart home automation [7] & High & Yes & Low & Dynamic frequency selection & Low \\ \hline Smart agriculture [25] & Medium & Yes & Medium & Channel hopping & High \\ \hline Industrial IoT [26] & High & Yes & Low & Beamforming & Medium \\ \hline Connected vehicles [27] & High & Yes & Ultra-low & Coordinated multi-point transmission & High \\ \hline Healthcare monitoring [28] & High & Yes & Low & Dynamic power control & Low \\ \hline Smart cities [29] & High & Yes & Low & Interference avoidance & High \\ \hline Environmental monitoring [30] & Medium & Yes & Low & MIMO & Low \\ \hline Smart grid management [31] & High & Yes & Low & Cognitive radio & High \\ \hline Augmented reality [32] & High & No & Ultra-low & Massive MIMO & High \\ \hline Drones [33] & High & Yes & Ultra-low & Interference alignment & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impact of 5G network interference on IoT servicesFigure 4 summarizes this section, where the main concepts related to the impact of 5G networks on IoT services are observed. We analyze the implementation, the evolution, and the types of wireless technologies that influence current services, the leading 5G technologies and problems, and the coexistence of next-generation networks. We frame interference as the heart of the study of problems in this type of network.
# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
## 2 Materials and Methods
### Proposed Architecture
#### 2.1.2 Batch Normalization (BN)  
Batch normalization layers are placed after convolutional layers. They help in stabilizing the learning process by normalizing the outputs of the previous layers, which can lead to faster convergence and improved performance.
# A Novel Sucrose Isomerase Producing Isomaltulose from _Raoultella terrigena_
## 2 Materials and Methods
### Cloning, Expression and Purification of Pal-2  
The synthesized _pal-_2_ gene and the vector pET-28a (+) were digested by restriction enzymes _BamHI_ and _Xlo1_, respectively. The digested products were ligated by T4 DNA ligase, resulting in the recombinant plasmid pET-28a (+)-_pal-_2. Then, pET-28a (+)-_pal-_2 was transformed into _E. coli_ BL21(DE3) by the heat shock method and a colony exhibiting positive Kanamycin resistance was selected.  
A colony was inoculated to 10 mL of LB culture medium supplemented with 30 \(\mathrm{\SIUnitSymbolMicro mL}\) Kanamycin to avoid contamination by other bacteria for preincubation at 37 \(\mathrm{\SIUnitSymbolCelsius}\) and 200 \(\mathrm{r/min}\). When the absorbance value of the culture medium at 600 nm reached 0.6, the 10 mL seed fermentation broth was transformed into a 200 mL fermentation LB medium containing 30 \(\mathrm{\SIUnitSymbolMicro mL}\) Kanamycin and the culture was shaken at 37 \(\mathrm{\SIUnitSymbolCelsius}\) and 200 \(\mathrm{r/min}\). When the OD\({}_{600}\) attained 0.6-0.8, 0.5 mM IPTG was added to induce the expression of the recombinant Pal-2 at 28 \(\mathrm{\SIUnitSymbolCelsius}\) and 200 \(\mathrm{r/min}\) for 6 h.  
The cells were collected by centrifugation at 12,000\(\times\)\(g\) for 5 min and washed twice with a lysis buffer (50 mM Tris-HCl, 100 mM NaCl, and pH 7.0). The cells were resuspended in 20 mL of lysis buffer and then disrupted on ice by sonication (pulse on: 5 s; pulse off: 5 s; 360 W) for 10 min. After removing the cellular debris by centrifugation at 12,000\(\times\)\(g\) for 10 min, the supernatant containing the objective protein was filtered through a 0.22 \(\mathrm{\SIUnitSymbolMicro m}\) syringe filter. The supernatant containing crude recombinant Pal-2 was purified using a Ni-NTA affinity chromatography column at 4 \(\mathrm{\SIUnitSymbolCelsius}\) according to the instruction of the manufacturer. Briefly, the column was firstly equilibrated with a binding buffer (50 mmol/L Tris-HCl, 500 mM NaCl, and pH 5.5). Then, the supernatant containing crude recombinant Pal-2 was loaded onto the column through a constant flow pump with a flow rate of 1 mL/min and the miscellaneous proteins that did not bind with the Ni-NTA affinity chromatography column were discharged with a washing buffer (50 mmol/L Tris-HCl, 500 mM NaCl, 20 mM imidazole, and pH 5.5). Finally, the recombinant Pal-2 was eluted with an elution buffer (50 mmol/L Tris-HCl, 500 mM NaCl, 200 mM imidazole, and pH 5.5) and was collected according to the peak time of the UV detector. The purified enzyme was dialyzed for 6 h with a dialysis buffer (50 mmol/L Tris-HCl, 10 mmol/L EDTA, and pH 5.5) to remove the high concentrations of imidazole. Finally, the purified enzyme was dialyzed twice for 6 h of dialysis buffer solution without EDTA (50 mmol/L Tris-HCl, and pH 5.5) to remove EDTA.