esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Preparation and PXRD Analysis
Moreover, although generally a soluble polymorph is less stable than the insoluble one, converting to the thermodynamically more stable and less soluble form [27] in this case, the catechin tetrahydrate polymorph is very stable, as demonstrated by the absence of any change in its PXRD pattern recorded over time (Figure 3). In order to verify the absence and/or presence of intermolecular interactions between the flavonoid CT and the two organic acids (MA and TA), the PXRD profiles of the solid mixtures, CT_MA and CT_TA, were acquired and compared to those of their relative components (Figures 2 and 3). As expected, the slurry process induces the flavonoid solid phase conversion, thus, the PXRD patterns of the sample CT_MA and CT_TA may be regarded as the overlap of CT tetrahydrate and MA and TA diffractograms (Figures 4 and 5, respectively). The formation of CT tetrahydrate during the slurry process determines an increase of the water-solubility of CT_MA and CT_TA with respect to monohydrate form of the flavonoid; in particular, the solubility values in water are 6.0 and 40.0 mg mL\({}^{-1}\) for CT_MA and CT_TA, respectively. Figure 2: PXRD patterns of CT before (black line) and after (red line) slurry process. Position of the main peaks reported in Å. CT = (+)-catechin. Thus, the presence of the distinctive diffraction peaks of the individual component in both samples (CT_MA and TA_CT) diffractograms, and the absence of new reflections allow us to exclude the formation of any intermolecular interactions, with eventual formation of new supramolecular species, between the flavonoid and the two organic acids. Hence, since the individual components remain unchanged after the treatment, it is reasonable to assume that they may retain their individual properties within the solid state mixtures.
## 3 Materials and Methods
### Strain Construction  
Primers used in the present study are listed in Table S4. The in-frame gene deletion mutant was constructed as described previously [64]. Briefly, up- and down-stream flanking regions of \(\mathit{rsh_{USS6-1}}\), approximately 400 bp each, were amplified by PCR with the primer pairs rsh-SO/rsh-SI and rsh-3O/rsh-3I and joined using overlap PCR with rsh-SO/rsh-3O. The resulting fragments were cloned into the plasmid pAK405 via the \(\mathit{BamH}\)\(\mathrm{II}\)\(\mathrm{HindIII}\) restriction site. The pAK405 derivative was subsequently delivered into US6-1 via conjugal transfer from _E. coli_ S17-1 (\(\mathrm{\Delta pir}\)). After the conjugal transfer, bacteria were plated on P5Y3 supplemented with kanamycin(100 \(\mathrm{\SIUnitSymbolMicro mL}\)) and rifampicin (50 \(\mathrm{\SIUnitSymbolMicro mL}\)). Individual colonies were restreaked once on the same medium and then plated on P5Y3 supplemented with 100 \(\mathrm{\SIUnitSymbolMicro mL}\) streptomycin to select for the second homologous recombination event. The resulting colonies were restreaked on both P5Y3 supplemented with 100 \(\mathrm{\SIUnitSymbolMicro mL}\) streptomycin and P5Y3 containing 100 \(\mathrm{\SIUnitSymbolMicro mL}\) kanamycin, and kanamycin-sensitive clones were analyzed by colony PCR using primers rsh-FO/rsh-RO.  
For complementation studies, fragments containing the \(\mathit{rsh_{USS6-1}}\) ORFs and upstream regions were amplified by PCR using the primer pairs PrshF/PrshR. The PCR products were cloned into the plasmid pCM62 [65] to generate pCM62-rsh via the EcoRI/HindIII restriction site. The plasmid  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strains or Plasmids** & **Relevant Traits** & **Source or Reference** \\ \hline S17-1 (\(\mathrm{\Delta pir}\)) & _E. coli_ K-12 \(\mathrm{Tp^{r}}\) Sm\({}^{r}\)_recA thi hsdRM\({}^{+}\)_ & [62] \\  & RP4::2-Tc::Mu::Km Tr7, _\(\mathit{apir}\)_ phage lysogen_ & [63] \\ \hline _A. tumefaciens_ A136 (pCF218) (pCF372) & _tral-lacZ_ fusion; AHL biosensor; \(\mathrm{Tc^{r}}\) Sp\({}^{r}\)_ & [63] \\ \hline Plasmids & & & \\ \hline pMD19-T & T-vector, \(\mathrm{Amp^{r}}\) & TaKaRa, Dalian, China \\ \hline pAK405 & Plasmid for allelic exchange and markerless & & \\  & gene deletions; Km\({}^{r}\) & & \\ \hline pAK405-rsh & pAK405 with fusion of up- and downstream regions of \(\mathit{rsh_{USS6-1}}\); Km\({}^{r}\) & This study \\ \hline pCM62 & Broad-host-range cloning vector; IncP & & \\  & origin of replication; \(\mathrm{Tc^{r}}\) & & [65] \\ \hline pCM62-rsh & pCM62 with \(\mathit{rsh_{USS6-1}}\) ORF and 502 bp of upstream region; \(\mathrm{Tc^{r}}\) & This study \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._pCM62-rsh was transformed into \(\Delta rsh\) by electroporation (2 kV) using a MicroPulse electroporator (Bio-Rad, Hercules, CA, USA). The complementary strain was verified by qRT-PCR and the tetracycline resistance (9 \(\upmu\)g/mL). A Bacterial Genomic DNA Extraction Kit, a Gel Extraction Kit and a Plasmid Miniprep Kit were purchased from TransGen Biotech (Beijing, China) and used according to the manufacturer's instruction.
## 6 Abuse Attack Techniques
### Encoding
#### 6.2.3. Insights  
Our study of encoding in the context of abusing the CPLS as C&C channels presents several important insights:  
* Encoding serves a dual purpose in these attacks. It not only provides a method of concealing C&C communications within legitimate service traffic, but also, it allows threat actors to hide the actual location of their C&C servers.  
* Many of the observed malware variants, including the Korplug variant, Janicab, and CONFUCIUS_A and CONFUCIUS_B, demonstrate creative use of regular expressions and encoding techniques to retrieve C&C server addresses.
* Threat actors exhibit adaptability in their encoding methodologies, leveraging the specific characteristics of each platform to embed encoded data. For instance, using Instagram photo comments or YouTube video descriptions to conceal encoded commands or C&C server addresses.
# Airborne Radar Staggered PRF Coherent Processing Method for Down-Looking Target Detection
## 2 Airborne Radar Clutter Model and Signal Model
### PRF Interleaved Transmission and Echo Model
Due to the use of wideband signals, it is necessary to account for the target's movement across distance cells during the observation time.
## 2 Materials and Methods
### HPLC Analyses
#### 2.6.2 HPLC Conditions  
For the HPLC-DAD analyses, the chromatographic system consisted of a PU-2089 quaternary pump equipped with a degasser, an AS-950 autosampler, and an MD-2010 spectrophotometric diode array detector (all modules are Jasco International Co., Tokyo, Japan). The software used for the analysis was ChromNav (Jasco International). The diode array detector (DAD) acquisition was performed in the range of 200-650 nm every 0.8 s with 4 nm resolution.  
For the HPLC-MS analysis the system consisted of HPLC 1200 Infinity, coupled with a quadrupole-time of flight mass spectrometer Infinity Q-ToF 6530 detector by a Jet Stream ESI interface (Agilent Technologies). The ESI conditions were: drying and sheath gas N\({}_{2}\), purity \(>\) 98%, temperature 350 \({}^{\circ}\)C, flow 10 L/min and temperature 375 \({}^{\circ}\)C, flow 11 L/min, respectively; capillary voltage 4.5 KV; nebulizer gas pressure 241 kPa. The fragmentor voltage was 175 V; nozzle, skimmer and octapole RF voltages were set at 1000 V, 65 V and 750 V, respectively. The high-resolution MS and MS/MS acquisition range was set from 100 to 1000 m/z in negative and positive mode, with acquisition rate 1.04 spectra/sec. For the MS/MS experiments, 30 V were applied in the collision cell to obtain CID fragmentation (collision gas N\({}_{2}\), purity 99.999%). The quadrupole mass bandpass used during MS/MS precursor isolation was 4 m/z (mass to charge ratio). The Agilent tuning mix HP0321 was used daily to calibrate the mass axis.  
The eluents used for the HPLC-DAD analyses were water and acetonitrile (ACN), both HPLC grade (Sigma-Aldrich, St. Louis, MO, USA), while the eluents for HPHPLC-ESI-Q-ToF analyses were water and acetonitrile, both LC-MS grade (Sigma-Aldrich, St. Louis, MO, USA). All eluents were added with 0.1% \(v/v\) formic acid (FA; 98% purity, [PERSON], USA).  
For both systems, the chromatographic separation was performed on an analytical reversed-phase column Poroshell 120 EC-C18 (3.0 mm \(\times\) 75 mm, particle size 2.7 um) with a Zorbax pre-column (4.6 mm \(\times\) 12.5 mm, particle size 5 um), both Agilent Technologies. The flow rate was 0.4 mL/min and the program was: 15% B (0.1% FA in ACN) for 2.6 min, then to 50% B in 13.0 min, to 70% B in 5.2 min, to 100% B in 0.5 min and then held for 6.7 min. Re-equilibration took 11 min. During the separation, the column was kept at 30 \({}^{\circ}\)C.
## 3 Intuitive and Natural Teleoperation
Moreover, establishing efficient maintenance and technical support systems in remote regions, where specialized technicians are scarce, is essential to guarantee the continuous and effective operation of telehealth robots. By proactively addressing these challenges, teleoperation technology can be successfully adapted to transform healthcare delivery in remote areas [72; 73]. This section on intuitive and natural teleoperation highlighted two important requirements for an effective and intuitive teleoperation platform. First, natural motion retargeting, or the use of natural human behavior to teleoperate robotic systems, effectively reduces training costs and grants human-level dynamics to telerobotic systems; it is an important component of natural and intuitive human-robot interaction systems. Second, sufficient multimodal feedback, particularly visual feedback, is pivotal in interactive telemanipulation systems, providing depth information and enhancing situational awareness to enable the operator to know the dynamic situation in the robot's workspace; it is also critical in interactive telemanipulation systems. The human operator in the control loop team must be aware of the relative positions of the robot end-effector and the workpieces in the workspace to make decisions and avoid collisions or damage to the robotic system, the working area, and/or the environment.
## 2 Dead-Time Effect of the Auxiliary Resonant Snubber Inverter (ARSI)  
### Principle  
Figure 1 depicts the single-phase ARSI topology analyzed in this paper, which consists of a standard H-bridge inverter, resonant capacitors and an auxiliary circuit. With a proper operation of  
the auxiliary switches, \(\mathrm{S_{r1}}\) and \(\mathrm{S_{r2}}\), the zero-voltage-switching (ZVS) condition of the main switches, \(\mathrm{S_{1}}\)-\(\mathrm{S_{4}}\), can be created. Meanwhile, the auxiliary switches can realize zero-current switching (ZCS).  
In order to realize soft-switching for the entire load range and maintain a low auxiliary current, the ARSI operates in two modes of heavy load mode (HLM) and light load mode (LLM), as determined by the load condition [22]. In the heavy load condition, the auxiliary circuit is only operated once in each switching period, thus achieving auxiliary ZVS (AZVS) of a set of switches. The other set of switches can achieve natural ZVS (NZVS) without the operation of auxiliary circuit. In the light load condition, the auxiliary circuit is operated twice in each switching period. Therefore, all the switches achieve AZVS.  
The operating principle of the ARSI is introduced in [22]. The detailed dead-time effect of LLM and HLM will be analyzed in the case of positive load current as follows. When the load current is negative, the operation is similar.  
To analyze the circuit, we assume that  
1. All components and devices are ideal;
2. The gate signals of the MOSFETs are ideal square-wave;
3. The output inductor \(L_{o}\) is high enough to be a constant current source.
## 3 Experiments and Analysis of Results  
### Data Presentation  
In this paper, nine standard pieces of industrial equipment from a Chinese cement plant were selected as experimental subjects, and the original dataset included three months of electrical measurement parameters of the equipment. Due to the low sampling frequency of industrial power scenarios, the quality of raw power data is poor. Therefore, the authors  
Figure 3: EO feature selection process.  
Figure 2: Binarization of EO continuous particles.  
improved the data quality by pre-processing data cleaning and denoising to reflect the actual identification results. To visualize the data processing, the authors selected reactive and active power of KHT to reflect alterations in the power curve, as shown in Figure 4. Each curve in Figure 4 shows the load changes for 24 h of the day. Among them, (a) shows the active power curve for 90 days before KHT pre-processing, and (b) shows the reactive power curve for 90 days before KHT pre-processing. (c) shows the active power curve for 72 days after KHT pretreatment, and (d) shows the reactive power curve for 72 days after KHT pretreatment.  
Firstly, observe the original power curve before preprocessing. The active power before preprocessing spans an extensive range of adjacent dates with obvious outliers, which cannot reflect the fundamental operation law of the equipment. The main reason is that the raw power data contain many abnormal and outlier values. Furthermore, when the data are contaminated with outliers, the trained model parameters are misdirected, and the model cannot accurately analyze the equipment type. Subsequently, the active power after preprocessing is observed. Comparing the power curves before preprocessing, the preprocessed industrial dataset eliminates outliers, the power levels are more aggregated, and the equipment operation patterns are apparent. Interestingly, the authors found that active and reactive power load curve patterns were very similar after preprocessing. However, the power levels were very different, reflecting the industrial equipment's uniqueness. By cleaning the raw power data and denoising them, the data quality of the power load is improved. This is a prerequisite for subsequent feature processing and model training.  
Figure 4: Power curve before and after pretreatment.
# PK-APF: Path-Keeping Algorithm for USVs Based on Artificial Potential Field
## 2 PK-APF Methodology  
### PK-APF Formulation  
PK-APF is obtained with improved APF, by modifying the potential field of the force to achieve high-precision path-keeping, and the resultant force of USVs in the force field is written as  
\[\mathbf{F}_{res}=^{g}\mathbf{F}_{att}+^{v}\mathbf{F}_{att}+\sum\mathbf{F}_{rep,i} \tag{1}\]  
where \(\mathbf{F}_{res}\) is the resultant force, \({}^{g}\mathbf{F}_{att}\) is the attractive force of the goal point, \({}^{v}\mathbf{F}_{att}\) is another attractive force provided by the virtual foot points, and \(\mathbf{F}_{rep,i}\) represents repulsive force from the \(i\)th obstacle.  
The attractive force provided by the goal point is denoted as  
\[{}^{g}\mathbf{F}_{att}=\begin{cases}\kappa\Big{(}\mathbf{p}_{goal}-\mathbf{p }_{usv}\Big{)},\|\mathbf{p}_{goal}-\mathbf{p}_{usv}\|\leq\xi\\ \xi\kappa\dfrac{\mathbf{p}_{goal}-\mathbf{p}_{usv}}{\|\mathbf{p}_{goal}- \mathbf{p}_{usv}\|},\|\mathbf{p}_{goal}-\mathbf{p}_{usv}\|>\xi\end{cases} \tag{2}\]  
where \(\kappa\) is a constant scaling coefficient, \(\mathbf{p}_{usv}=[x_{usv}\ y_{usv}]^{T}\) is the current position of the USV, \(\mathbf{p}_{goal}=[x_{goal}\ y_{goal}]^{T}\) is the desired goal point location, and \(\xi\) is a threshold for piecewise attractive force.  
The repulsive force from the \(i\)th obstacle is written as  
\[\mathbf{F}_{rep,i}=\begin{cases}\eta(\frac{1}{r_{i}(\mathbf{p}_{usv})}-\frac {1}{r_{0}})\frac{\|\mathbf{p}_{goal}-\mathbf{p}_{un}\|^{2}}{r_{i}(\mathbf{p}_ {user})},&r_{i}\leq r_{0}\\ 0,&r_{i}>r_{0}\end{cases} \tag{3}\]where \(\eta\) is the coefficient for repulsive force and \(r_{0}\) indicates the influence range of repulsive force that arises from obstacles. If the USV is within the range of repulsion of multiple obstacles at the same time, \(r_{i}(\mathbf{p}_{usv})\) is the distance from the position of the \(i\)th obstacles to the USV. Therefore, the repulsive force expression of obstacles can represent the repulsive force generated by multiple obstacles or a single obstacle.  
Figure 1 demonstrates that multiple obstacles provide repulsive force on the USV. The dark circular area indicates the repulsive force range of obstacles when the USV is close to an obstacle, and the light color indicates that it is not affected by the obstacle. During the voyage, the USV entered the repulsive force influence range of \(Obs1\) and \(Obs2\); so, the repulsive force is the resultant force provided by \(\mathbf{F}_{rep1}\) and \(\mathbf{F}_{rep2}\). Meanwhile, no repulsive force is provided by Obs3 for USVs because USVs do not fall into the \(Obs3\) repulsive force influence range.
This wave-oriented advantage can be widely used in general signal processing with Fourier synthesis or frequency domain processing via Fourier transform. To this end, we use Fourier transform to build complex input as a powerful mathematical tool to make it more physically reasonable. Analogously, a recent study also combines Fourier transform and CVNN for hyperspectral single-image super resolution [7]. On the other hand, weight multiplication at synapses produces phase rotation and amplitude decay (or amplification), which reduces noneffective degrees of freedom during the learning process, thereby enhancing generalization properties compared to 2D RVNNs. In light of the above two reasons, employing a complex neural network is possibly more suitable as a viable alternative. Our contributions can be summarized as follows:  
* We propose a lightweight pixelwise complex-valued neural network (CVNN) assisted by 1D Fourier transform to address hyperspectral classification problems. * We provide an efficient way for green pepper automatic picking in agriculture using small datasets.
# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 5 Experimental Results
### Research Questions  
The experimental framework of this study was designed to address the following key research questions:  
1. RQ1: How well does the proposed model detect duplicate bug reports? The first research question evaluates the fundamental performance of the proposed model in identifying duplicate bug reports. This involves a detailed assessment using established metrics, such as accuracy, precision, recall, and F1 score, to determine whether the model meets a predefined baseline of effectiveness. These metrics serve as a reliable framework for evaluating the core capabilities of the model before engaging in any comparative analysis. Only if the model achieves satisfactory performance based on these metrics will it be compared with baseline models, such as CNN, LSTM, CNN-LSTM, Naive Bayes, and Random Forest. The objective of this comparison is to assess whether the proposed model shows measurable improvements in accurately detecting duplicate bug reports. By focusing on quantitative performance metrics, this research question ensures that the model's effectiveness is rigorously validated prior to further analysis.
2. RQ2: How does the proposed model compare to existing models in addressing duplicate bug report detection tasks?The second research question examines the comparative performance of the proposed model against existing approaches in duplicate bug report detection. This evaluation focuses on analyzing the model's ability to outperform baseline methods, such as CNN, LSTM, CNN-LSTM, Naive Bayes, and Random Forest, in terms of accuracy, precision, recall, and F1 score. Additionally, statistical analyses are conducted to validate the significance of any observed differences, ensuring that improvements in performance reflect genuine advancements rather than random variations. By focusing on direct comparisons and robust statistical validation, this research question ensures a fair and rigorous assessment of the proposed model's relative strengths and weaknesses.  
These research questions provide a structured framework for assessing the proposed model's fundamental performance and its suitability for practical application. The first question focuses on validating the model's baseline effectiveness, while the second question ensures that its real-world applicability is evaluated against both performance benchmarks and deployment-specific conditions. This dual approach facilitates a more comprehensive and rigorous evaluation of the proposed method's contributions and potential impact on duplicate bug report detection.
## 5 Conclusions  
Petroleum hydrocarbons enter the environment due to accidents and oil spills. When the oil comes into contact with soil, it affects the geotechnical properties, and this contamination needs remediation in order to not reach the groundwater and cause damage to the environment.  
1. Considering the Atterberg limits, there are two opinions: The first one is that oil reduced the moisture content of the liquid and plastic limits of the polluted soils; the plastic index rose with oil-polluted soils. This might be because the water about the charged mud molecules is changed with non-polarised oil solutions. The second one is that both values of the liquid and plastic limits increase, the plastic index drops, and the ore oil wraps mud molecules and prevents water particles from reaching double-layer water; therefore, plastic soil requires more water, which may explain thehigher plastic limit, and water added to the soil will join the free-water layer if the oil orients the soil particles.
2. Increasing oil contents reduce permeability values due to the oil blocking specific inter-particle gaps in the soil. A reverse proportion was noted between the permeability value and the amounts of cement added to the contaminated soil due to adsorption behaviour.
3. The appropriate percentage of cement to modify the geotechnical properties of contaminated soil is 7% and affects the results evenly, irrespective of the contaminant content.
4. Adding cement in amounts from 3 to 7% to the contaminated soil may convert the behaviour from fine-grained to coarse-grained soil, leading to a smaller dry unit weight at a higher water content.
5. Bioremediation is a natural process that does not add secondary chemicals to the soil and can completely mineralise oil into the water and carbon dioxide. Furthermore, photocatalysis mitigation may assist in smashing long-chain hydrocarbon portions of oil into primary forms that microorganisms can naturally break. As a result, the time required to restore polluted ground or soil was reduced.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData are presented in the article.  
The authors would like to express their gratitude for the support of the Ministry of Higher Education and Scientific Research-Iraq, the University of Baghdad-Iraq, and the University of Technology-Iraq.  
The authors declare no conflict of interest.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 5 Improved Harmony Search for Node Depth Adjustment
### Initialization of Parameters and the Problem  
As mentioned before, the optimization problem is shown as Equation (14), where \(\det\left(\mathbf{J}_{k+1}\left(\mathbf{D}_{k+1}\right)\right)\) is the objective function; all \(N_{s}\) nodes' movement distance vector \(\mathbf{D}_{k+1}\) is the set of all decision variables. In addition, the parameters of HS algorithm will be specified in this step. The parameters are as follows:  
1. [label=0.,leftmargin=*,noitemsep,topsep=0 pt]
2. Harmony Memory Size (HMS); it means the number of solution vectors in HM;
3. Harmony Memory Considering Rate (HMCR); it determines selecting a vector from HM or randomly generating a new vector;
4. Pitch Adjusting Rate (PAR); it is the probability to pitch adjust a new vector;
5. Bandwidth (BW); it is used to adjust the new vector to improve the performance of HS;
6. Number of iterations (NI); it is the maximum number of iterations to be used as a stopping criterion.