esa-sceva/satcom-chunk-collection huggingface dataset:
# Activation of Persulfate for Groundwater Remediation:
## 4 Factors Affecting the Application of Persulfate in ISCO
### Dissolved Ions  
Groundwater contains far more dissolved anions and cations than the deionized water used in laboratory studies and these ions will somewhat impact the remedial effect of persulfate on the target contaminants.  
Groundwater contains a high concentration of anions such as chloride (Cl\({}^{-}\)), carbonate (CO\({}_{3}\)\({}^{2-}\)), bicarbonate (HCO\({}_{3}\)\({}^{-}\)), sulfate (SO\({}_{4}\)\({}^{2-}\)), nitrate (NO\({}_{3}\)\({}^{-}\)), and other anions are frequently found in groundwater, which may have an impact on the remediation. When various activation techniques are used, chloride, the most dissolved inorganic ion in groundwater, can have a variable impact on the effectiveness of organic pollutant degradation. [PERSON] (2012) reported that the degradation efficiency of p-nitrosodimethylamine was enhanced when heat and alkaline was applied to activated persulfate, while there was no influence on the degradation efficiency when iron activation was applied at neutral pH. To make matters worse, when the pH was buffered to acidic conditions (pH = 2) with iron activated persulfate, the degradation efficiency of p-nitrosodimethylamine was significantly decreased in the presence of chloride [141]. [PERSON] (2013) pointed out that the presence of anions including chloride (Cl\({}^{-}\)), carbonate (CO\({}_{3}\)\({}^{2-}\)), bicarbonate (HCO\({}_{3}\)\({}^{-}\)) nitrate (NO\({}_{3}\)\({}^{-}\)) can decrease the degradation efficiency of TCA. He demonstrated that the presence of these anions can quench hydrogen radicals (OH-) and sulfate radicals (SO\({}_{4}\)\({}^{-}\)) and decrease the degradation efficiency [142]. In addition, groundwater contains many types of cations including potassium ion (K\({}^{+}\)), sodium ion (Na\({}^{+}\)), ferric ion (Fe\({}^{3+}\)), ferrous ion (Fe\({}^{2+}\)),ammonium ion (NH4+). Most existing cations (e.g., K+, Na+) have no influence on the degradation efficiency of organic pollutants [85]. However, the dissolution of ferric and ferrous ion can somewhat activate persulfate and enhance the degradation efficiency [140]. [PERSON] (2021) demonstrated that the dissolved ferric ion and ferrous ion from subsurface sediments can effectively activate persulfate to degrade aniline [140].
###### Abstract  
In this work, an experimental investigation of the nanomechanical properties of flame-formed carbonaceous particle layers has been performed for the first time by means of Atomic Force Microscopy (AFM). To this aim, carbon nanoparticles with different properties and nanostructures were produced in ethylene/air laminar premixed flames at different residence times. Particles were collected on mica substrates by means of a thermophoretic sampling system and then analyzed by AFM. An experimental procedure based on the combination between semi-contact AFM topography imaging, contact AFM topography imaging and AFM force spectroscopy has been implemented. More specifically, a preliminary topological characterization of the samples was first performed operating AFM in semi-contact mode and then tip-sample interaction forces were measured in contact spectroscopy mode. Finally, semi-contact mode was used to image the indented surface of the samples and to retrieve the projected area of indents. The hardness of investigated samples was obtained from the force-distance curves measured in spectroscopy mode and the images of intends acquired in semi-contact mode. Moreover, the Young's modulus was measured by fitting the linear part of the retraction force curves using a model based on the Hertz theory. The extreme force sensitivity of this technique (down to nNewton) in addition to the small size of the probe makes it extremely suitable for performing investigation of mechanical properties of materials at the nanoscale. The experimental procedure was successfully tested on reference materials characterized by different plastic behavior, e.g., polyethyleneen naphthalene and highly oriented pyrolytic graphite. Both hardness and Young's modulus values obtained from AFM measurements for different soot particle films were discussed.  
atomic force microscopy; nanoindentation; flame-formed carbon nanoparticles; nanostructured films; hardness; Young's modulus Article
## 2 Preliminaries
### The CCD Method  
The CCD method, also known as the univariate search technique, is a dimension reduction method for unconstrained optimization problems. Its iteration process requires that one searches alternately along different coordinate directions. Indeed, the CCD method is a simpler method of establishing the search direction than the objective function derivative.  
Although the convergence rate of the CCD method is slow for high-dimensional optimization problems, it is permissible with respect to the inverse kinematics problem of robot manipulators, and it can reduce the objective function at each iteration.  
The CCD method process requires finding the optimal solution for one variable at a time while keeping the remaining \(n-1\) variable unchanged. The last point of the previous one-dimensional search is the starting point of this one-dimensional search. The iterative formula for finding the local minimum value of the objective function (\(f(\mathbf{\Theta}),\mathbf{\Theta}\in\mathbb{R}^{n}\)) by the CCD method is Equation (14)  
\[\mathbf{\Theta}_{i}^{k}=\mathbf{\Theta}_{i-1}^{k}+\alpha_{i}^{(k)}\mathbf{D}_{i}^{(k)}, \qquad(i=1,2,\cdots,n), \tag{14}\]  
where \(\mathbf{D}_{i}^{(k)}\) is the search direction, defined as  
\[\mathbf{D}_{i}^{(k)}=[0,\cdots,1,\cdots,0]^{T}, \tag{15}\]  
in which the component of the \(i\)-th coordinate direction is 1 and the remaining components are 0. Moreover, \(\alpha_{i}^{(k)}\) is the optimal step length in the \(i\)-th iteration of the \(k\)-th round, which is written as Equation (16)  
\[\alpha_{i}^{(k)}=\arg\min_{\alpha_{i}^{(k)}}f(\mathbf{\Theta}_{i-1}^{k}+\alpha_{i} ^{(k)}\mathbf{D}_{i}^{(k)}). \tag{16}\]  
The CCD method for finding the optimal solution (\(\mathbf{\Theta}^{*}\)) consists of the following step-by-step procedure.  
Step 1: let i = 1.  
Step 2: determine the optimal step length \(\alpha_{i}^{(k)}\) (see Equation (16)) and \(\mathbf{\Theta}_{i}^{k}\) (see Equation (14)).  
Step 3: check whether the iteration round is over as follows:  
\[i=n. \tag{17}\]  
If Equation (17) holds, i.e., then proceed to the next step, else let \(i=i+1\) and return to step 2.  
Step 4: verify convergence with the final solution as follows,  
\[\left\|\mathbf{\Theta}_{n}^{(k)}-\mathbf{\[PERSON]}_{0}^{(k)}\right\|\leqslant\epsilon, \tag{18}\]  
where \(\epsilon\) is a threshold value supplied by the user. If Equation (18) holds, i.e., then \(\mathbf{\[PERSON]}^{*}=\mathbf{\[PERSON]}_{n}^{(k)}\) is the optimal solution, then terminate the process; otherwise, let \(\mathbf{D}_{i}^{(k+1)}=\mathbf{D}_{i}^{(k)}(i=1,2,\cdots,n),\mathbf{\[PERSON]}_{0}^{(k+1)}= \mathbf{\[PERSON]}_{n}^{(k)},k=k+1\) and return to Step 1.  
The CCD method does not require the derivative of the objective function in the search process; rather, it only requires the objective function value information. Moreover, it can be used to analytically find the optimal value in each search direction, such as the single joint variable optimization for the objective function proposed in this paper.
# Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery
###### Abstract  
This paper presents a robust change detection algorithm for high-resolution panchromatic imagery using a proposed dual-dense convolutional network (DCN). In this work, a joint structure of two deep convolutional networks with dense connectivity in convolution layers is designed in order to accomplish change detection for satellite images acquired at different times. The proposed network model detects pixel-wise temporal change based on local characteristics by incorporating information from neighboring pixels. Dense connection in convolution layers is designed to reuse preceding feature maps by connecting them to all subsequent layers. Dual networks are incorporated by measuring the dissimilarity of two temporal images. In the proposed algorithm for change detection, a contrastive loss function is used in a learning stage by running over multiple pairs of samples. According to our evaluation, we found that the proposed framework achieves better detection performance than conventional algorithms, in area under the curve (AUC) of 0.97, percentage correct classification (PCC) of 99%, and Kappa of 69, on average.  
change detection; convolutional network; deep learning; panchromatic; remote sensing Article
## 2 Min-Max Models for Determination of Weights
### Min-Max Model for the Weights of Experts  
We are now in a position to design a model to optimize the weights of experts.  
Since the obtained overall evaluation for each alternative should collect the judgments of the experts as many as possible, the objective function is to minimize the maximum variation between the actual vector of evaluations and the ideal one for the \(p\) experts, such that the optimal weights can reduce the difference of the experts in evaluating the same alternative.  
Mathematically, the optimization model for the choice of the weights of experts reads:  
\[\begin{array}{rl}\min\limits_{w}&\max\limits_{1\leq k\leq p}\left\{\sum \limits_{i=1}^{n}\left(a_{ik}-\sum\limits_{k=1}^{p}w_{k}a_{ik}\right)^{2} \right\}\\ \text{s.t.}&\sum\limits_{k=1}^{p}w_{k}=1,\\ &0\leq w_{k}\leq 1,\ \ k=1,2,\cdots,p.\end{array} \tag{4}\]  
It is noted that in [8], [PERSON] and [PERSON] recently proposed a distance-based optimization approach for the determination of the weights of experts. The objective function is to minimize the sum of the squared distance from one decision result to another, such that a maximum agreement is achieved. Specifically, for the \(i\)-th alternative, the squared distance between experts \(s\) and \(t\) is defined by:  
\[d_{st}^{2}=\sum\limits_{i=1}^{n}(a_{is}w_{s}-a_{it}w_{t})^{2},\ \ s,t=1,2,\cdots,p, \tag{5}\]  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & \(\mathbf{DM_{1}}\) & \(\mathbf{DM_{2}}\) & \(\mathbf{DM_{3}}\) \\ \hline VCM & 0.1096 & 0.2091 & 0.2168 \\ EBM & 0.2656 & 0.3655 & 0.3057 \\ DBM & 0.0631 & 0.0489 & 0.1099 \\ Min-Max & 0.0124 & 0.0222 & 0.0423 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Uniformity of the criteria’s importance.  
where \(i=1,2,\cdots,n\). Using this squared distance, the following optimization model is constructed in [8] to determine the weights:  
\[\begin{array}{ll}\min\limits_{w}&D\\ \text{s.t.}&\sum\limits_{k=1}^{p}w_{p}=1,\\ &0\leq w_{k}\leq 1,\end{array} \tag{6}\]  
where:  
\[D=\sum\limits_{u=1,u\
eq v}^{p}\sum\limits_{v=1}^{p}d_{uv}^{2}=\sum\limits_{u =1,u\
eq v}^{p}\sum\limits_{v=1}^{p}\sum\limits_{i=1}^{n}(a_{iu}w_{u}-a_{iv}w _{v})^{2}. \tag{7}\]  
**Remark 1.** We can show that Model (4) is not equivalent to Model (6). Actually, in Section 4.2, we will show that the weights of experts are different for the same score matrix of the alternatives from each expert.
## 2 Results and Discussion
### UV-Vis DRS and Visible Light Photodegradation Studies
It can be seen that all samples exhibited an obvious PL signal with a similarly-shaped curve at the wavelength range from 458 to 468 nm with the TP2 sample giving the highest PL intensity followed by TP0 and pristine TiO\({}_{2}\). However, the PL energy is smaller than the band gap energy of TiO\({}_{2}\). According to [PERSON] et al., 2006 [56], they observed that some of the lower PL intensities of semiconductor materials are due to the presence of oxygen vacancy that acted as an electron scavenger, thus making electron recombination jump to the sub-band of TiO\({}_{2}\). Moreover, the energy at the 458 to 468-nm wavelength released from PL spectra is too high as compared to the excitation energy. This result is due to the presence of the sensitizer, which allowed for the absorption of low excitation energy to occur and formed an electron-hole pair. This electron is then subsequently jumped to the conduction band of TiO\({}_{2}\) and eventually recombined with the hole by releasing the high energy wavelength. Figure 8: Photoluminescence spectra of pristine TiO\({}_{2}\), unwashed TP2 and washed TP2 samples. PL, photoluminescence. As can be seen in Figure 8, all TP2 samples (washed and unwashed) have shown higher PL intensity as compared with pristine TiO\({}_{2}\). The intensity from the unwashed TP2 sample might be due to the presence of C-C bond that allowed for the electron to recombine under low excitation energy. The TP2 sample (washed) has recorded the highest PL intensity as compared to others. Based on the XPS and FTIR spectra, this TP2 sample has a C=O bond as an extra species where it is not found in pristine and unwashed TP2 samples. Hence, it can be concluded that the highest intensity of TP2 showed a significant presence of C=O bond, which acted as an electron injector in TP2 photocatalyst. The electrons that were injected to the conduction band of TiO\({}_{2}\) created a series of chain reactions, which help to achieve a complete 100% decolorization of 12 mg\(\cdot\)L\({}^{-1}\) MB dye at 75 min, as shown in Figure 7c.
## 5 Discussion and Future Research Directions
### Security and Resiliency  
Security is still an open research issue of any network architecture. The virtualization of the network introduces new threats and new vulnerabilities due to complex and selfish interactions between tenants and InPs. To detect the malicious behaviors and to mitigate the threats targeting the virtualization technologies, the game theory can be introduced  
Figure 6: Challenges and perspectives.  
as a promising solution to model the interactions of the players and to encourage them to behave honestly. Few game-based solutions [39; 64] have been proposed to secure the SDN against the DDOS attacks and intrusion detection.
## 5 Results and Discussion
### Structural Optimization Design
#### 5.2.1 Spindle-Head Optimization Results  
Table 11 shows the optimized results of the spindle head. The masses before and after optimization were 306 kg and 284 kg (7.1% reduction), respectively. For a load of 1000 N, the maximum deformation before and after optimization was 7.32 \(\upmu\)m and 6.55 \(\upmu\)m, respectively, showing a decrease of 10.5%. The first-, second-, and third-order natural frequencies before optimization were 510, 610, and 670 Hz, respectively. After optimization, they were 559, 696, and 750 Hz, increasing by 9.6%, 14%, and 11.9%, respectively.
# Open RAN--Radio Access Network Evolution, Benefits and Market Trends
## 1 Introduction
Such openness has been guaranteed due to the specification of proper interfaces between logical nodes and the introduction to new network elements which are able to incorporate intelligence by means of Artificial Intelligence/Machine Learning (AI/ML) implementation and the data driven network paradigm. Open RAN is also about the evolution of network element hardware (HW) and software (SW) decomposition, which, in practice, means: (a) many vendors may deliver solutions, (b) commercial off-the-shelf (COTS) hardware may be used, and (c) products may be updated via software in an agile way. This work is motivated by a relatively low number of studies concerning Open RAN architecture that can be found in the literature which, at the same time, is accompanied by a huge effort invested in developing Open RAN solutions. The authors' goal is to present Open RAN landscape and current Open RAN trends together with state-of-the-art research activities, with the aim to recognize the current and foreseen prospect of this approach in 5G network deployments. To the best of our knowledge, such an overview paper concerning Open RAN in the literature is missing. The article is organized as follows: in Section 2, chronological changes in mobile network architecture are presented; Section 3 describes Open RAN organizations, players, standardization bodies, as well as introduces O-RAN--the world-leading reference architecture; in Section 4, the open xHault transport network is described; Section 5 presents market expectations; in Section 6, review of state-of-the-art academia research are presented; finally, Section 7 contains conclusions including listed challenges and open issues related to Open RAN deployments.
# Error Investigation on Wi-Fi RTT in Commercial Consumer Devices
## 3 Wi-Fi RTT Distance Error Measurements
### Experimental Sites
#### 3.2.1 Range and Position-Fixed Environment  
As shown in Figure 4a, a LOS condition in the RFPF environment was first constructed. The smartphone and the AP were placed 0.74 m above the ground to reduce the reflection. We can observe from the figure that no obstacle blocked the signal transmission between the smartphone and the AP. In addition, we placed the smartphone at a specific ground truth distance \(d\)m to the AP (\(d=1,2,\ldots,10\)). We collected at least 3000 samples at the sampling period of 250 ms at each distance.  
Based on such settings, we then constructed an NLOS condition by adding a blocker at the transmission line between the smartphone and the AP, as shown in Figure 3a.  
Similarly, at least 3000 samples at the same sampling period of 250 ms were collected at each distance as shown in Figure 4b. After collecting enough samples at all distances, the material of the blocker was changed, and the same sample collection procedure was repeated. In all environments in this paper, three different blockers were used: wood, glass, and metal.  
In both LOS and NLOS conditions, the sample collection procedures were conducted repeatedly for all three smartphones.
## 4 Conclusions  
The level of both primary and secondary metabolites in white and red cabbage microgreens can be modified by the lighting conditions used during plant growth. Our findings revealed that microgreens under white and blue light treatments had the highest sugars, phenolic acids, flavonoids, and anthocyanins. These microgreens also demonstrated the highest antioxidant potential. On the other hand, cabbage microgreens growing under white light with red light were characterized by the highest levels of phytosterols and fiber, including its insoluble fraction. However, supplementation of white light with red or blue light negatively affected the content of protein, fat, and photosynthetic pigments such as carotenoids and chlorophyll \(a\) and \(b\). The composition of phenolic compounds was found to be influenced by the type of lighting used, with microgreens grown under white light with red or blue light showing a more similar composition than those grown under white light alone. In summary, the use of a combination of LED white light with blue or red light can be a useful tool to produce white and red cabbage microgreens with targeted improvement in phytonutrient content. Based on the research conducted, it can be concluded that the proportion of 15% blue and red light is too low to significantly increase the content of active substances in both red and white cabbage seedlings. A share of approximately 30% of the red or blue color in the spectrum resulted in a clear increase in the nutritional value of microgreens. However, the increase in phenolics, antioxidants, soluble and insoluble dietary fiber, as well as antioxidant potential content, was at the expense of a lower content of carotenoids and chlorophylls. Therefore, it can be concluded that to obtain microgreens with high nutritional value, it is important to provide not only high levels of red light but also blue light in the spectrum.  
Conceptualization, A.P. and B.F.; methodology, A.P., D.S., D.K. and R.B.; formal analysis, D.S., A.P. and R.B.; investigation, D.S., A.P., D.K. and K.S.; data curation, A.P., B.F. and R.B.; writing--original draft preparation, A.P., B.F. and D.S.; writing--review and editing, A.P. and B.F.; visualization, D.S. and K.S.; supervision, A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We are grateful to the \"W.Legutko\" company (Poland) for providing seeds for the experiment and to [PERSON] for the preparation of Figure 1.  
The authors declare no conflict of interest.
## 6 Simulation Results and Discussions
### PAPR Simulation Results
#### 6.3.2 PAPR Simulation Results on Receiver Side
Figure 15: The PAPR performance comparisons of the conventional OFDM, GFDM, WHT-GFDM, and WHT-LWT-GFDM waveforms for a SNR value of 20 dB for 16-QAM modulation. When PAPR performances in Figure 16 are examined, it is seen that similar to 4-QAM modulation, PAPR dB gain from 0 dB to 15 dB of SNR values is provided with both waveforms. However, it is understood that the PAPR dB gains are approximately the same after 15 dB SNR value. In other words, it is observed that there is no PAPR dB gain after 15 dB SNR value.
# Riverbank Following Planner (RBFP) for USVs Based on Point Cloud Data
###### Abstract  
Autonomous path planning along riverbanks is crucial for unmanned surface vehicles (USVs) to execute specific tasks such as levee safety detection and underwater pipe inspections, which are vital for riverbank safety and water environment protection. Given the intricate shapes of riverbanks, the dynamic nature of tidal influences, and constraints in real-time cartographic updates, there is a heightened susceptibility to inaccuracies during manual waypoint designation. These factors collectively impact the efficiency of USVs in following riverbank paths. We introduce a riverbank following planner (RBFP) for USVs to tackle this challenge. This planner, utilizing 2D LiDAR, autonomously selects the following point to follow riverbank shapes. Additionally, a PID controller is integrated to compensate for position and yaw errors. Our proposed method reduces the deviation between the USV's planned path and the actual riverbank shape. We simulated straight, convex, and concave riverbanks in the Virtual RobotX (VRX) simulator while considering the impacts of wind, waves, and USV dynamics. The experimental result indicates the following performance of 96.92%, 67.30%, and 61.15% for straight, convex, and concave banks, respectively. The proposed RBFP can support a novel autonomous navigation scenario for autonomous paths following along the riverbank without any preplanned paths or destinations.  
path planning; riverbank following; LiDAR; point cloud; VRX simulation +
Footnote †: journal: Applied Sciences