esa-sceva/satcom-chunk-collection huggingface dataset:
# Reducing GPS Error for Smart Collars Based on Animal's Behavior
## 3 Experiment and Its Results
### Analyses  
The reason we conducted the experiment in seven days was to illustrate true results because GPS errors depend upon many aspects, as mentioned in previous sections. The path with GPS errors is in shown in Figure 5 for all three conditions. The results of the proposed method are compared in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experiment Day**} & \multicolumn{3}{c}{**Total Collected Data**} \\ \cline{2-4}  & **Running** & **Walking** & **Resting** \\ \hline
1 & 107 & 290 & 189 \\
2 & 121 & 372 & 250 \\
3 & 118 & 305 & 221 \\
4 & 110 & 314 & 254 \\
5 & 125 & 301 & 201 \\
6 & 116 & 335 & 195 \\
7 & 119 & 342 & 210 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of the total collected data during seven days experiment for all three goat’s behavior.  
Figure 5: Visualized uncorrected experiment results for three activity conditions: (**a**) walking, (**b**) running, (**c**) resting.  
Figure 6: Visualized experiment results for three activity conditions: (**a**) walking, (**b**) running, (**c**) resting. Comparison of the positioning paths between true path (yellow line), unfiltered GPS receiver data (red line), and the proposed method (green line).  
By the images, it is difficult to determine the trajectory based on the results of positioning the conventional or proposed locations. In contrast to the proposed method, it is possible approximately get the trajectory. To quantify the error, this article considers some variables called \"unfiltered bias\" and \"filtered bias\". The \"unfiltered bias\" is a dependent response variable for the distance between the consecutive GPS trajectory and the actually passed trajectory, and the \"filtered bias\" responses for the actual distance, units moved along the marked line marks of the smart collar and filtered GPS trajectory by the proposed method [21]:  
\[un\:filtered\:bias=GPS\:derived\:distance-surveyed\:distance \tag{9}\]  
\[filtered\:bias=GPS\:filter\:derived\:distance-surveyed\:distance \tag{10}\]  
A confident estimate implies that GPS measures an exaggerated distance, whereas negative values indicate that GPS data underestimates the distance [21]. In the literal sense, both cases reported a GPS error. Table 4 presents the mean average of GPS unfiltered, GPS filtered, and actual goat's total passed trajectory distances across the line tags. The seven days experiment results of unfiltered and filtered bias are illustrated in Table 5. It shows the maximum and mean of the error passed distances among the tests. This result shows that when using the proposed method, the maximum error of running, walking, and resting behavior can be reduced from 153 to 20 m, 190 to 8 m, 162 to 0 m respectively; the average error is from 148.8 to 16.2 m, 182.7 to 5 m, 136.2 to 0 m as well.
## 3 Background
### Modified Density Peak Clustering Algorithm
#### 3.1.2 Determination of Clustering Categories
1: Calculate the distance \(d_{i,j}\) between data points \(\vec{x}_{i}\) and \(\vec{x}_{j}\) according to Equation (5). 2: Assign the cut-off distance \(d_{c}\). 3: Calculate the local density \(\rho_{i}\) of each data point \(\vec{x}_{i}\) according to Equation (7). 4: Calculate the distance \(\delta_{i}\) of each data point \(\vec{x}_{i}\) according to Equation (9). 5: Calculate \(\gamma_{i}=\rho_{i}\delta_{i},i\in I_{S}\), select the data points corresponding to the \(K\) maximum values of \(\{\gamma_{i}\}_{i=1}^{N}\) as the cluster centers \(C=\{C_{i}\}_{i=1}^{K}\). 6: Finally, assign each remaining point to the same cluster as its nearest neighbor of higher density, the training dataset \(S\) is divided into K subsets \(DC_{1},DC_{2},\cdots,DC_{K}\). 7:return the K subsets \(DC_{1},DC_{2},\cdots,DC_{K}\). ```  
**Algorithm 1** MDPCA (Modified Density Peak Clustering Algorithm)
# Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
## 2 Method  
[PERSON] et al. [25] proposed a method used for the detection of maneuvers of space objects. We provide a brief description of this method. Although the semi-major axis is employed to delineate the proposed detection method, alternative parameters, such as the inclination and eccentricity, may also be utilized. Subsequently, the MGMM used for determining the probability distribution of the TLE data and the MGA used for learning the parameters of the GMM are presented. The method uses the maximum likelihood function for the optimization and the a posteriori probability penalty with a robust correction factor to minimize noise in the sample. The hyperparameters and other details of the MGA are discussed.
## 3 Proposed Methods
### Two-Stage Augmentation Method  
In this section, we introduce the two-stage data augmentation method, which can effectively increase the number of data sets and improve detection performance.  
Currently, training of CNNs often uses static augmentation [28] or dynamic augmentation [31] to expand the dataset. Static augmentation refers to the dataset is augmented with image processing methods before feeding into the convolutional neural network. Dynamic augmentation is that the original data remains the same and augmentation operations are performed per mini-batch during the training process. However, the static augmentation pattern typically increases the samples exponentially, thus the training time is also increased exponentially. In the experiment, we found that for a small number of data sets, only use dynamic augmentation is not helpful to improve the detection performance.  
Therefore, this paper proposes a two-stage augmentation method for data augmentation during the training process via static augmentation and dynamic augmentation. [PERSON] et al. [31] indicated that the operation most commonly used in good strategies they search out is rotate. Therefore, under the premise of ensuring regular texture of the defect image, static augmentation of first stage only performs five types of operation: horizontal flip, vertical flip, 90\({}^{\circ}\) rotation, 180\({}^{\circ}\) rotation, and 270\({}^{\circ}\) rotation. These operations preserve the global texture of the original image. The statically expanded dataset is subsequently used to train the neural network. When the loss value stops decreasing and begins to jitter, the dynamic augmentation process of second stage is applied to further enhance the statically augmentation data set during the training process. Dynamic augmentation operations are performed per mini-batch. The operations modes are listed in Table 2, and these operations occur with a certain probability for each batch. The probability of enhanced operation is determined by our prior knowledge. We think that first stage augmentation data is always more important, and the second stage augmentation method may damage data original texture structure and lead to model degradation, so the probability of enhanced operation is set below 30%. The intensity of the occurrence is random within a certain range, and the augmentation types can occur in combination. The one-stage augmentation is only trained once, and the training data is dynamically augmented based on static augmentation during training.  
The proposed augmentation method helps to expand the dataset more efficiently, reduce the training time, and improve the convergence of the neural network training and the robustness of the trained model. This method can also be used to fine-tune the network and improve the accuracy further.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 3 Routing Protocols in MANET
### Proactive Routing Protocols  
Proactive routing protocols maintain up-to-date routing information from each node to every other node in the network, ensuring that routes are readily available when needed. This approach reduces the route discovery delay but increases the overhead due to periodic updates. Table 1 provides a comprehensive overview of proactive routing protocols used in Mobile Ad Hoc Networks (MANETs). Proactive routing protocols maintain up-to-date routing information by continuously evaluating the network topology and distributing routing tables. OLSR and DSDV are examples of proactive protocols. OLSR uses link-state information to establish routes and periodically disseminates topology information, ensuring low latency due to up-to-date routing information but resulting in high overhead from frequent updates. DSDV maintains a consistent routing table in each node using sequence numbers to ensure loop-free routes, which simplifies routing and avoids loops but also incurs high overhead due to frequent table updates. These protocols are suitable for environments where low latency is critical, but their trade-off in terms of overhead needs careful consideration.
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 2 Study Area and Data
### River Discharge  
In situ river discharge data for the Yukon River was obtained from the USGS station located in Eagle, Alaska, approximately 400 km upstream of our study area. We created a discontinuous time series of daily discharge measurements corresponding to each remotely sensed date of observation. While a simplification of the discharge time series, this approach facilitated a useful comparison between remotely sensed river width and in situ discharge.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sensor** & **Number of Satellites** & **Bands** & **Spatial Resolution** & **Temporal Resolution** \\ \hline RapidEye & 5 & 5 (Blue, Green, Red, Far-Red, NIR) & 6.5 m & -5.5 days \\ \hline PlanetScope & \(\sim\)170 & 4 (Blue, Green, Red, NIR) & 3.7 m & Daily \\ \hline SkySat & 13 & 4 (Blue, Green, Red, NIR) & 0.8 m &
\begin{tabular}{c} Variable, with multiple \\ opportunities per day \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of the three satellite sensors operated by Planet. Information is correct as of date of publication.
## 3 Results and Discussion
### Group NR-II  
Our results show that NR-II is a diverse group, comprised of several clades containing sequences from lichen-forming fungi interspersed with characterized PKS genes proposed to be involved in the biosynthesis of various substances. The earliest branching clade contains genes involved in the 6-hydroxymellein biosynthesis (NR-II; Figure 3), a key intermediate in terrein biosynthesis [77]. Terrein, produced in large quantities by _Aspergillus terreus_, has phytotoxic activity and has the potential to serve as a novel antibiotic [78]. Our phylogeny revealed sequences of four lichen-forming fungi (cla-grapred_001463, clauncpred_002890, psefurpred_002946, and grascrpred_004317) clustering together with _terA_ gene in _A. terreus_. A BLAST search of _terA_ (GenBank: EAU38791) against these sequences revealed 58 to 67% similarity at the amino acid level, indicating a high degree of conservation despite an approximate 350-million-year split of Eurotiomycetes (_Aspergillus_) and Lecanoromycetes [79]. Terrein has not been reported from lichen-forming fungi; therefore, without further experiments and detailed substance characterization, terrein production in lichen-forming fungi remains hypothetical.  
Another important pattern is the occurrence of characterized melanin genes and melanin precursors in different clades. Melanins are a diverse group of substances that play a role in virulence, morphogenesis or the response to environmental stress, and can be synthesized via different pathways [80]. PKSs produce two forms of melanin: either 1,8-dihydroxymorphatelene (DHN) melanin or Deoxybostrycoidein-melanin [81]. Melanins are insoluble and thus cannot be studied by standard biochemical methods [80]. In the PKS gene survey by [PERSON] et al. [14], all melanin synthesizing genes were grouped together in a single monophyletic clade. In our analysis, they are recovered in at least three clades containing sequences from several Lecanoromycetes and _Endocarpon pusillium_. Sister to NR-II is a clade containing sequences from _Colleotrichum lagenarium_ (PKS1), _Glarea_ sp., _Nodulisporum_ sp., and _C. heterostrophus_ (PKS18), which were also characterized as producers of melanin [14]. The high number of genes from lichen-forming fungi close to characterized melanin biosynthetic genes suggests an essential role of melanins in lichens.
###### Abstract  
The Harmony Search algorithm has attracted a lot of interest in the past years because of its simplicity and efficiency. This led many scientists to develop various variants for many applications. In this paper, four variants of the Harmony search algorithm were implemented and tested to optimize the control design of the Proportional-Integral-derivative (PID) controller in a proposed airflow control scheme. The airflow control strategy has been proposed to deal with the undesired stalling phenomenon of the Wells turbine in an Oscillating Water Column (OWC). To showcase the effectiveness of the Self-Adaptive Global Harmony Search (SGHS) algorithm over traditional tuning methods, a comparative study has been carried out between the optimized PID, the traditionally tuned PID and the uncontrolled OWC system. The results of optimization showed that the Self-Adaptive Global Harmony Search (SGHS) algorithm adapted the best to the problem of the airflow control within the wave energy converter. Moreover, the OWC performance is superior when using the SGHS-tuned PID.  
Keywords:airflow control; harmony search algorithm; optimization; oscillating water column; power generation; stalling behavior; wave energy; Wells turbine +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Instrumentation and Software  
Absorbance measurements were performed using a UV-Vis spectrophotometer (Agilent diode-array, CA, USA) with 10 mm matched quartz cells. The pH of samples was measured using a Jenway pH meter (Jenway, Staffordshire, UK). Separation of the supernatant was done by the aid of a centrifuge (ST8 Benchtop, Thermo Scientific, Waltham, MA, USA). Scanning electron microscopy (SEM, FEI, Quanta 200, Thermo Scientific, Waltham, MA, USA) and energy-dispersive X-ray spectroscopy (EDX) were utilized to study the surface morphology of PNS derived adsorbents. Fourier transform infrared radiation (FT-IR, Bruker Alpha, MA, USA) was used to recognize the functional groups on the surface of the prepared adsorbent. The Raman spectrum was attained in the range from 50-3500 cm\({}^{-1}\) using a DXRTM 2 Raman microscope (Thermo Scientific, Waltham, MA, USA), with a laser beam at 532 nm as excitation source and 10 mW power. Thermal gravimetric analyzer (TGA, PerkinElmer-TGA400) was employed to investigate the thermal stability of the prepared PNS adsorbents at a temperature range of 50-800 \({}^{\circ}\)C.  
Determination of surface area, pore size and volume were performed using a Micromeritics ASAP2020 Accelerated Surface Area and Porosimetry System. Degassing of samples was initially employed followed by investigating N\({}_{2}\) adsorption-desorption. Using the N\({}_{2}\) isotherms collected at 77K and employing the Brunauer Emmett-Teller (BET) equation, surface area was determined. Pore volume was established using the t-plots and the Barrett-Joyner-Halenda (BJH) equation.  
Minitab(r)18 software was purchased from Minitab Inc., State College, PA, USA, and was used to construct the selected design.
## 2 Structural Principle, Detection Mechanism, and Performance Indicators
### Performance Indicators for Refractive Index Sensing
Therefore, in terms of design requirements, \(L\) should be increased as much as possible to achieve improved sensing effects; \(\bullet\)\(\frac{\partial(\Delta N)}{\partial(\mu_{m})}\) refers to the change in the effective refractive index of the guided mode on the sensing arm compared with the refractive index of the sample under test, which is primarily defined by the evanescent field strength dictated by the waveguide material and structure. This term can be increased by strengthening the evanescent field of the sensing waveguide. 6. _LOD_: this parameter is the minimum offset of the central wavelength of the resonance peak that can be detected by the detector. It is primarily related to the FWHM of the resonant peak, and half of the FWHM (HWHM) is typically taken as the offset of the central wavelength of the smallest resonant peak. _LOD_ can be defined as the ratio of the smallest distinguishable wavelength drift to the detection sensitivity and is expressed as follows: \[LOD=\frac{\delta\lambda_{c}}{S}=\frac{\delta\lambda_{c}}{\frac{\lambda_{m}}{n _{eff}}\frac{\partial n_{eff}}{\partial n_{c}}}=\frac{n_{eff}}{\frac{\lambda_ {m}}{\delta\lambda_{c}}\frac{\partial n_{eff}}{\partial n_{c}}}.\] (23)  
Among them, \(\delta\lambda_{c}\) can be a part of the spectral width of the resonant peak. The spectral width is defined as the full width at half maximum FWHM of the resonant characteristic spectral line, and the relationship between it and the quality factor \(Q\) of the resonator is as follows:  
\[\Delta\lambda_{FWHM}=\frac{\lambda_{m}}{Q}. \tag{24}\]  
Thus, _LOD_ can be expressed in terms of \(Q\) and \(S\) as follows:  
\[LOD\sim\frac{1}{Q\cdot S}. \tag{25}\]  
It can be concluded that the detection limit of the optical waveguide refractive index biochemical sensor is inversely proportional to its own \(Q\) and the detection \(S\) of the sensor. Therefore, to obtain a lower detection limit, sensor optimization can be achieved by improving the \(Q\) and \(S\) of the refractive index sensor.
## 3 Enhancing Supply Chain Security Through Blockchain Technology
### Security Advantages of Blockchain in Supply Chains
#### 3.1.2 Automated and Reliable Transactions  
Smart contracts operating on blockchain are digital protocols executed automatically based on predefined rules, without external intervention. By removing the human factor, these contracts enhance transaction reliability and reduce costs. Integrated with IoT devices, especially in areas such as supply chains, smart contracts facilitate functionalities including payment automation, buyer verification, and triggering necessary actions in cases of non-compliance with contractual conditions [31].  
By automating traditional processes in various sectors, smart contracts significantly enhance security and efficiency, accelerate transactions, and minimize human errors [32]. In supply chains, smart contracts offer transparency and reliability at every stage. Since all transactional data are securely stored on digital ledgers, the need for third-party verification is eliminated, ensuring authenticity and security in areas such as transportation and food processing. Within IoT applications, smart contracts enable automatic communication between devices, thus creating more autonomous structures in smart cities, homes, and environmental monitoring projects.  
Table 2 summarizes various studies on smart contract utilization in supply chain management, highlighting their primary focus areas and advantages. Enabling only verified stakeholders to interact through smart contracts has been emphasized as a means of enhancing trust and transparency among participants [33]. Another study aims to develop a secure product traceability system using blockchain and smart contracts, demonstrating improved data security through decentralized architecture, as well as validated accessibility and tamper-resistance during testing [34]. Ethereum-based smart contracts have been iden  
Figure 3: Security advantages of blockchain in supply chains.  
tified as significantly improving transparency, trust, and efficiency within supply chains, although some limitations regarding flexibility and volatility have been acknowledged [35]. Additionally, a framework integrating Ethereum-based smart contracts developed in Solidity into supply chain management has been proposed, enhancing transparency, traceability, and security. This system increases reliability by preventing data manipulation via smart contracts and automating supply chain processes using distributed ledger technology [36].
## 2 Fracturing Tests
### Fracturing Schemes and Procedure  
When analyzing fracturing performance, various factors need to be considered, including reservoir characteristics (e.g., geothermal gradient, in situ stress, weak planes, strength, porosity, and permeability) and operational parameters (e.g., fracturing fluid type, perforation, completion, pump rate, and stage division) ([PERSON] et al., 2021). This study focused on the primary factors of fracturing fluid type, reservoir temperature, flow rate, and confining pressure.  
Figure 2: Experimental apparatus used for hydraulic fracturing tests. AE refers to acoustic emission, while FPC stands for flexible printed circuit.  
Figure 3 illustrates the experimental schemes of fracturing. Specimens No. 1# to 5# (CN-Shales) were used to investigate the effects of fracturing fluid and flow rate, while specimens No. 6# to 11# (PS-Shales) focused on the effects of temperature and in situ stress.  
As shown in Figure 2, each shale specimen was first placed into a triaxial core holder, which was then submerged in a thermostatic water bath to simulate reservoir temperature conditions. Two hydraulic pumps alternately applied axial and confining pressures to the specimen, replicating the in situ stress condition of the shale reservoir. Finally, the appropriate fracturing fluid was selected for injection based on the fracturing schemes. For hydraulic fracturing, deionized water was directly injected into the specimen using a high-pressure, high-speed electric hydraulic pump. SC-CO\({}_{2}\) fracturing, however, required a specialized SC-CO\({}_{2}\) preparation system, including a gas booster and an intermediate container. The gas booster pressurized CO\({}_{2}\) in the intermediate container beyond the critical pressure of 7.38 MPa, while the thermostatic water bath raised the CO\({}_{2}\) temperature above the critical threshold of 31.1 \({}^{\circ}\)C. This process produced SC-CO\({}_{2}\) at the required temperature and pressure, which was stored in the intermediate container and fracturing lines, establishing the starting condition for the fracturing experiment.