esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Research Discussion  
As mentioned above, graffiti can be damaging or beneficial to the location in question. In the case of illegal graffiti, it is essential to control and act quickly on it to avoid giving the author notoriety, thus discouraging this act. In addition, the graffiti components and their details can also provide important information about how and where some graffiti artists or gangs operate. In the case of urban art, publicizing it can help attract people to the area, thus improving the local economy.  
The 20 articles selected for the elaboration of this literature review allowed us not only to understand the concerns and disagreements within the theme but also to know and comprehend some of the algorithms implemented both for the detection of graffiti and for the segmentation and classification of the creators of the graffiti.  
The study made it possible to understand that, although interest and importance of this field have been growing, there are still few implementations for these purposes. Moreover, no artificial intelligence system in Portugal has yet been developed to address this issue. Another verified gap was the lack of algorithms that distinguish between street art and illegal graffiti. There are already some systems for detecting illegal graffiti and others for detecting street art. However, a system that integrates both concepts was not found in the literature. Only one study [3] addresses this type of classification, and only as future work.  
Considering this literature review, we raise two research questions: RQ1--\"can a deep learning model successfully discriminate the differences between street art and illegal graffiti?\" and RQ2--\"how accurate is the automatic identification and location of illegal graffiti on images acquired in the streets of Lisbon under loose controlled conditions?\" In addition, we label a graffiti dataset, which we have provided for the scientific community on the request of a graffiti image dataset.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Type of Detection** & **Number of Articles** \\ \hline All graffiti types & 1 \\ Street art & 2 \\ Gangs & 5 \\ Illegal graffiti & 7 \\ \hline Total & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of articles by type of detection.
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 3 Proposed Algorithm
### Embedding Algorithm  
Even though watermarking of the color images are not done by most of the researchers, some of the existing algorithms watermark in all the three (R,G,B) color planes, and some of the researchers embed only in the blue plane as changes in the blue component will not be perceived by the human eye much. Here, the color image to be watermarked is converted to YUV format and the watermark is embedded only in the Y plane based on the property of human visual system (HVS) which states that the human eye is more vulnerable to changes in the color component compared to that in the intensity component [9]. The Y matrix that is to be watermarked is divided into k number of 8 \(\times\) 8 blocks (i\({}_{1}\), i\({}_{2}\), i\({}_{3}\), i\({}_{4}\),..., i\({}_{k}\)). Each of these blocks is transformed using Fast Walsh Hadamard Transform to get the transformed blocks (h\({}_{1}\), h\({}_{2}\), h\({}_{3}\), h\({}_{4}\),..., h\({}_{k}\)). The binary watermark (B) is converted into one dimensional string (b\({}_{1}\), b\({}_{2}\), b\({}_{3}\)..., b\({}_{n}\)) each of the values either 1 or \(-\)1. For spreading, r numbers of, polar orthogonal hadamard codes are used. As shown in Figure 1 mutually similar mid band coefficients along the 3 rd, 4 th, 5 th and 6 th rows are selected for embedding the watermark and rest of the coefficients are left unchanged. Mid frequency coefficients are selected as the changes on them do not cause much changes to the perceptual quality compared to the lower frequency coefficients. In addition, these mid band frequencies are less affected by compression. The selected frequency coefficients along \(x^{\text{th}}\) row are made into a single vector \(r_{x}\) such that length of \(r_{x}\) is same as that of spreading codes used. Each bit of the watermark in each modifying row is spread using different spreading codes, then, spread watermarks are added to the selected coefficient vector.  
The embedding equation is,  
\[\overline{W}_{x}=r_{x}+\alpha(s_{1}b_{1}+s_{2}b_{2}+\cdots+s_{r}b_{r}) \tag{5}\]where \(\alpha\) is the modulation index or gain of embedding (selected based on the allowed distortion and the required robustness), \(r_{x}\) is the \(x^{\text{th}}\) row used for watermarking and \(\overline{W}_{x}\) is the FWHT of the \(x^{\text{th}}\) row of the watermarked image. Watermarked image can be obtained after finding the inverse FWHT of all the watermarked blocks and reconstructing into their original locations. After watermarking, the Y plane is replaced back and the image is converted back to RGB format.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 1 Introduction
### Novelty and Contribution
#### 1.3.2 Adaptive Pilot Power Optimization for Dynamic Channel Conditions  
* Prior methods such as [11] use fixed pilot power, which does not account for varying noise conditions. Our approach introduces real-time adaptive power scaling, ensuring an optimal SNR while maintaining spectral efficiency.
* Our simulations show that adaptive power optimization reduces the BER more effectively than static power allocation, especially when the signal is weak.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 3 Receiver Comb Technologies  
In coherent WDM systems, a local oscillator (LO) laser in the receiver is needed for data detection of each carrier, and the most straightforward scheme is independent LO lasers for every channel. An obvious question is then if and why one would like to use a comb for the local oscillators. Aside from potential (though yet to be verified in the scientific literature) gains in power dissipation and complexity, can comb-based local oscillators provide any benefits from a transmission performance perspective? The answer is yes, but only provided phase-locked (hence comb-based) carriers are used in the transmitter side. Then, phase-locked LOs will preserve the phase-locking of the channels also after detection, which would otherwise be broken by the independent phase-noise of the LOs. We note that a single LO laser can also be used to detect a superchannel in a very broadband receiver, while preserving the phase-locking of the channels [82; 83], but will ultimately be limited by the bandwidth of photodetectors and analog-to-digital converters (ADCs).  
We will discuss two approaches for comb-based receivers that utilize the phase-locking of the channels in this paper (see Figure 4). The first approach is to regenerate the transmitter comb and use it as the LO, which is discussed later in this section. The second approach is to use separate free-running combs in the transmitter and receiver and then to use joint digital signal processing (DSP) between the channels. This approach is discussed in Section 4 below.
## 1 Introduction
In this paper, we have devised a distributed detection and multi-objective optimization based on the emperor penguin repair method for coverage holes to solve two key problems in 3D hybrid WSNs, which are hole detection and repair. In addition to receiving and sending data information in hybrid WSNs, static nodes compete for the role of elected group nodes based on their own conditions, and are responsible for aggregating information within their own cells and detecting the source of the hole information that informs the relevant nodes. While mobile nodes are responsible for moving to the optimum location to repair the coverage blind zone based on the received hole information, which uses a multi-objective optimized swarm intelligence algorithm. The contributions are as follows:  
1. Based on the geometric unit division of the monitoring area, group nodes are selected by using a sum-of-weights method according to static node remaining energy and average distance accumulation values, grouping the management of data information from static and mobile nodes via distributed algorithms, which simplifies network management while ensuring network stability and extending the overall network lifetime. 2. A multi-objective fitness function is designed by considering a customized degree of hole region ranking and the uniformity of the remaining energy of the repair nodes, as well as the ratio of the node distance from the hole to the maximum distance travelled. The algorithm prioritizes learning and concentrates on hole regions with high levels of metrics, where the network can meet guaranteed coverage requirements while also performing well in terms of total node energy consumption and node energy uniformity. 3. Using network connectivity as a constraint, the optimized emperor penguin algorithm introduces a lens-imaging mapping learning strategy to perturb the location of the repair nodes. It can enhance the algorithm's ability to search for the hole region in different directions. In this way, the repair nodes can find the optimal repair location to cover and improve the overall efficiency of the network. The paper is structured as follows: The related works are detailed in Section 2. The network model of a 3D hybrid WSNs and definitions of related terms are presented in Section 3. Section 4 describes the proposed detection and repair algorithm DHD-MEPO in detail. In Section 5, simulation experiments are conducted with the DHD-MEPO and other algorithms to compare and evaluate the network's performance. Section 6 provides the conclusion and the outlook for further research work in the future.
## 3 Test Results and Discussion
### Compaction Tests  
The influence of different MgCl\({}_{2}\) solution concentrations on the soil's compaction characteristics is shown in Figure 3 and Table 3. The addition of a MgCl\({}_{2}\) solution is observed to enhance the maximum dry density (MDD) while decreasing the optimum water content (OWC), especially at higher MgCl\({}_{2}\) solution concentrations. These findings agree with the observations made by [PERSON] (1997) [56]. The increase in dry density is attributed to particle flocculation and aggregation, a consequence of rapid cation exchange within the soil-MgCl\({}_{2}\) solution mixture. Notably, the application of a 14% MgCl\({}_{2}\) solution increased the MDD to 17.89 kN/m\({}^{3}\) from the initial value of 17.4 kN/m\({}^{3}\) noted in untreated soil and reduced the OWC from 19% in untreated soil to 17.7%. These results suggest that increasing the concentration of the MgCl\({}_{2}\) solution, and thus the rate of cation exchange process in the mixture of soil and MgCl\({}_{2}\) solution, reduces the ability of clay surfaces to absorb water, resulting in an observed increase in dry density values.  
Figure 3: The impact of MgCl\({}_{2}\) solution on the dry density and water content of soil.  
Figure 2: Effect of MgCl\({}_{2}\) solution on the Atterberg limits of used soil.
###### Abstract  
The expanding Arabic user base presents a unique opportunity for researchers to tap into vast online Arabic resources. However, the lack of reliable Arabic word embedding models and the limited availability of Arabic corpora poses significant challenges. This paper addresses these gaps by developing and evaluating Arabic word embedding models trained on diverse Arabic corpora, investigating how varying hyperparameter values impact model performance across different NLP tasks. To train our models, we collected data from three distinct sources: Wikipedia, newspapers, and 32 Arabic books, each selected to capture specific linguistic and contextual features of Arabic. By using advanced techniques such as Word2 Vec and FastText, we experimented with different hyperparameter configurations, such as vector size, window size, and training algorithms (CBOW and skip-gram), to analyze their impact on model quality. Our models were evaluated using a range of NLP tasks, including sentiment analysis, similarity tests, and an adapted analogy test designed specifically for Arabic. The findings revealed that both the corpus size and hyperparameter settings had notable effects on performance. For instance, in the analogy test, a larger vocabulary size significantly improved outcomes, with the FastText skip-gram models excelling in accurately solving analogy questions. For sentiment analysis, vocabulary size was critical, while in similarity scoring, the FastText models achieved the highest scores, particularly with smaller window and vector sizes. Overall, our models demonstrated strong performance, achieving 99% and 90% accuracies in sentiment analysis and the analogy test, respectively, along with a similarity score of 8 out of 10. These results underscore the value of our models as a robust tool for Arabic NLP research, addressing a pressing need for high-quality Arabic word embeddings.  
word embedding: Word2 vec; FastText; Arabic embedding; Arabic corpus Article  
A Note: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
A Note: [[https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci)]([https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci))
## 3 Hydraulic Conductivity of Soil Samples with Various SiO\({}_{2}\) Contents  
In the hydraulic test, the hydraulic pressure was 50 kPa. Figure 4 shows the measured variations in the hydraulic conductivity of expansive soil with different superhydrophobic nano-SiO\({}_{2}\) contents. When the hydraulic pressure was 50 kPa, the hydraulic conductivity of soil samples with superhydrophobic nano-SiO\({}_{2}\) contents of 0%, 0.2%, 0.4%, 0.6%, 0.8%, and 1.0% was 1.38 \(\times\) 10\({}^{-8}\), 1.09 \(\times\) 10\({}^{-8}\), 9.07 \(\times\) 10\({}^{-9}\), 4.17 \(\times\) 10\({}^{-9}\), 2.34 \(\times\) 10\({}^{-9}\), and 6.25 \(\times\) 10\({}^{-10}\) cm/s, respectively. Test results showed that the hydraulic conductivity of soil samples decreased with increased superhydrophobic nano-SiO\({}_{2}\) content.
# GIS-Analysis for Active Tectonics Assessment of Wadi Al-Arish, Egypt
## 4 Results and Discussion
### Morphotectonic Indices
#### 4.1.6 Mountain Front-Sinusosity Index (Smf)  
Values of the mountain front sinuosity index relate to active tectonics along different fronts. In our work, 83 mountain segments were examined and evaluated (Figure 8). Values of the Smf index were between 1.02 and 3.25. Results are shown in Table S5 in the Supplementary data. The lowest Smf index value was observed along the middle-western part of the study area (front segment 46) coincidental with the southern segment of the Ragabat El-Naam shear zone and El-Mineidra El-Kebira anticline fold. However, all front segments had relatively low Smf values along the Ragabat El-Naam shear zone. The highest value of the Smf index was calculated for segment 15 in the western part of the study area. The examined fronts of the study area are illustrated in Figure 8 and may be classified into three main segment trends groups: front segments along the Minsherah Abu Kandu and Ragabat El-Naam shear zones trends (e.g., segments 8, 9, 19, 22, 23, 44, and 46) showing a nearly E-W trend; segments showing NW-SE trends (e.g., fronts 6, 21,24, 25, 59, 77, and 80) and observed mainly in southern and eastern parts of the study area, and fronts showing NE-SW trends such as segments 14, 15, 48, 66, 69 and 71. In most active tectonic fronts, values are 1.0, and increase if uplift is ceased and erosional processes prevail. Lower Smf values (1-1.5) indicate active tectonic fronts [1; 9; 67; 68], while higher values of Smf (\(>\)2.5) indicate active fronts [1; 68]. The authors of. Ref. [6] calculated this index in southern Spain using a digital elevation model, and acquired Smf values between 1.04 and 1.61. Values below 1.1 indicated active tectonic signals, and values above 1.5 indicated inactive fronts. The relative tectonic activity of the eastern Betic Cordillera was evaluated by the authors of [66] by applying this index, and they recorded Smf values from 1.17 to 3.51. Values lower than 1.4 indicated active tectonics signals, while values greater than 3 indicated low activity fronts.
## 5 Discussion and Conclusions
### Wide-Ranging Conclusions  
Based on the results presented in Section 4, the discussion reported in Section 5, and focusing on the implications or impacts on a context wider than the present study, the following wide perspective conclusions can be established:  
1. The quantification of the shielding effect is considerably different if it is performed with the AS calculation or with the CFD model. On top of that, neither the Basilisk model nor AS produce a towline tension curve that accurately fits the GPGP data for a narrow span. Therefore, both calculation methods should be investigated to find the source of the difference.
2. Through the validation cycles, it was demonstrated that the semi-empirical formula of [PERSON] et al. (2011) can be used to obtain drag coefficients that produce sufficiently accurate towline tension estimations when the net being towed experiences a flow that is close to perpendicular along most of its length. In terms of \(\theta\), 50% or more of the angles found along the length are distributed between 45\({}^{\circ}\) and 90\({}^{\circ}\). The same applies for the AS calculation and the CFD-derived drag coefficients. Furthermore, additional (future) GPGP data for a narrow span need to be included to strengthen the validation.
3. Through the validation cycles, it was demonstrated that the AS drag calculation was consistent with the CFD drag calculation until approximately \(\theta\sim 24^{\circ}\). Below that angle, which is close to parallel flow, AS seemed to underestimate the real drag and the CFD model seemed to overestimate it. Therefore, a more in-depth investigation of AS Equation (8) and of the CFD model to identify missing physical phenomena is required. In that way, the root causes of the respective deviations can be identified.
4. As clearly depicted in the schematic of Figure 21, it is possible to adapt and apply the results of the present study to different systems, utilized in sectors ranging from river plastic cleaning to oceanography, through to trawling, aquaculture, oil spill response, and marine ecology. In particular, the optimization method of the OrcaFlex drag coefficient can be directly applied to any of these applications, as well as the lessons on the limitations and opportunities of the software. That should allow running OrcaFlex simulations that produce accurate estimations on motions, loads, maneuverability, operability, survivability, and steering strategies, among others. This can be combined with the modeled data on the catch itself (plastic, fish, plankton, oil, etc.) to obtain accurate estimations on catch rates. Then, considering the investedenergy or input, this would lead to efficiency estimations. Ultimately, this allows developing optimization actions to improve the efficiency of the system.  
Additionally, the present study can also be utilized by other applications or sectors as a guideline to develop the following activities:  
* To understand the hydrodynamic drag force experienced by submerged nets and the previous research related to it.
* To validate a model using the materials, knowledge, and method presented in this study.
## 4 System Model
### Single-Level MPC State-Space and Optimization Model  
#### 4.2.1 Single-Level MPC State-Space Model  
The following simple linear dynamic equations based on the single-level system configuration are considered:  
\[p_{t}^{\text{wind}}=p_{t}^{\text{wd}}+p_{t}^{\text{pa}}+p_{t}^{\text{fa}}+p_{t}^ {\text{wa}} \tag{2}\]  
Equation (2) describes the assignment process for wind power.  
\[p_{t+1}^{\text{pa}}=p_{t}^{\text{wd}}+p_{t}^{\text{po}}+p_{t}^{\text{po}} \tag{3}\]  
Equation (3) describes the composition process for system total generation [28].  
\[E_{t+1}^{\text{pa}}=E_{t}^{\text{pa}}+\eta_{t}p_{t}^{\text{pa}}\Delta t-\frac{p_ {t}^{\text{po}}\Delta t}{\eta_{t}} \tag{4}\]  
Equation (4) describes energy balance of the pumped hydro storage at time \(t\).  
Equation (5) describes the energy balance of the fly wheel at time \(t\).  
\[E_{t+1}^{\text{fw}}=E_{t}^{\text{fw}}+\eta_{t}p_{t}^{\text{fa}}\Delta t-\frac{ p_{t}^{\text{po}}\Delta t}{\eta_{t}} \tag{5}\]  
From Equation (2), \(p_{t}^{\text{wa}}\) can be represent as:  
\[p_{t}^{\text{wd}}=p_{t}^{\text{wind}}-p_{t}^{\text{pa}}-p_{t}^{\text{fa}}-p_{t }^{\text{wa}} \tag{6}\]  
Substituting \(p_{t}^{\text{wd}}\) into Equation (3), we obtain:  
\[p_{t+1}^{\text{ps}}=p_{t}^{\text{wind}}-p_{t}^{\text{pa}}-p_{t}^{\text{fa}}-p_ {t}^{\text{wa}}+p_{t}^{\text{pp}}+p_{t}^{\text{fp}} \tag{7}\]  
Subsequently, the discrete-time five-order dynamic equation of the wind power with hybrid storages can be written as follows:  
\[X(k+1)=A\times X(k)+B_{\text{i}}\times u(k)+B_{\text{j}}\times\varepsilon(k)\]  
\[\begin{pmatrix}p_{t+1}^{\text{pa}}\\ E_{t+1}^{\text{pa}}\\ E_{t+1}^{\text{pa}}\\ E_{t+1}^{\text{fw}}\end{pmatrix}=\begin{pmatrix}0&0&0\\ 0&1&0\\ 0&0&1\end{pmatrix}\begin{pmatrix}p_{t}^{\text{pa}}\\ E_{t}^{\text{pa}}\\ E_{t}^{\text{fw}}\end{pmatrix}+\begin{pmatrix}-1&-1&-1&1&1\\ 0&0&\eta_{1}\Delta t&\frac{-\Delta t}{\eta_{t}}&0\\ \eta_{\text{a}}\Delta t&\frac{-\Delta t}{\eta_{t}}&0&0&0\end{pmatrix}\begin{pmatrix} p_{t}^{\text{fa}}\\ p_{t}^{\text{pa}}\\ p_{t}^{\text{pa}}\\ p_{t}^{\text{pa}}\end{pmatrix}+\begin{pmatrix}1\\ 0\\ 0\end{pmatrix}\times p_{t}^{\text{wind}} \tag{8}\]  
Figure 6: Relationship between \(t_{s}\) and the case.  
and:  
\[y(k) = C\times X(k)\] \[{p_{k}}^{\text{ps}} = \left(1\ 0\ 0\right)\times\left(\begin{array}{c}{p_{k}}^{\text{ps}}\\ {E_{k}}^{\text{ps}}\\ {E_{k}}^{\text{fw}}\end{array}\right) \tag{9}\]  
Constraints including logic constraints are difficult to present. However, logic constraints can be easily formulated in a programming model. Therefore, in this paper, a programming model is used.
# Traffic-Aware Optimization of Task Offloading and Content
## 5 Experimental Evaluation
### Comparative Experiments
#### 5.2.4 Comparison with Change in Edge Server Storage Capacity  
Figure 6(a) clearly depicts that when the storage capacity of the edge server increases by 10%, TOCC outperforms LOCAL and TFO and is slightly worse than F_NSGA-III, achieving a lower average execution time with a flat rising trend. TOCC achieves a 31% improvement over LOCAL, an 85% improvement over TFO, and is 1% lower than F_NSGA-III. In Figure 6(b), the total energy consumption of TOCC also performs the best as the storage capacity of the edge service increases. In terms of total energy consumption, TOCC is 53% higher than LOCAL, 85% higher than TFO, and 97% higher than F_NSGA-III. This  
Figure 5: Comparison of average execution time and total energy consumption with change in CPU cycles.  
Figure 6: Comparison of average execution time and total energy consumption with change in input data size.  
is because while the storage capacity of the edge server is very sensitive to TOCC, LOCAL and TFO are almost not affected by the storage capacity of the edge server.