esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficiency Analysis of Roadway Lighting Replacement in a Selected Polish Municipality
## 2 Materials and Methods  
_The Adopted Method of Analysing the Effectiveness of the Road Lighting System Replacement and the Comfort of Its Use_  
The analysis aimed to test the cost-efficiency of the lighting system replacement and the lighting comfort. The authors conducted comparative analyses of solution variants, examining both road lighting comfort and costs as an innovative element of research. The DIALux application was used to analyse the changes in road lighting depending on the luminaires used and the changes in pole spacing as influenced by the height of the light point. Variant and scenario analyses were used in the efficiency analyses, and the calculations themselves were based on detailed cost analyses with reference to Polish catalogues of material inputs and market prices. The authors conducted cost analyses of lighting dismantling and installation, including the subsequent operating costs over 20 years for 7 variants of poles with their systems and 4 variants of the luminaires used.  
Figure 1 presents a simplified diagram of the procedure.  
The first step was to define the purpose of the analysis. It could be, for example, to reduce energy costs, replace an old system due to excessive technical wear and tear or improve the comfort of road lighting. In the case of the study under discussion, the aim was to improve lighting comfort combined with the replacement of part of the lighting connected with the reduction in electricity consumption costs.  
The next step was to analyse the technical condition of the existing road lighting network in the municipality and the energy currently used. As a result of the analysis, the technical condition should be presented along with the number of poles requiring immediate replacement and those for which it is cost-effective. The number of poles to be replaced was limited by the budget available to the municipality for this purpose.  
The third step was to rank the variants of the new road lighting, i.e., the types of poles, systems and luminaires, resulting from the cost analysis. The analysis relates to the number of poles to be replaced, determined in the second step. The third step is essentially related to the next step, i.e., the road lighting comfort analysis. The analysis covers a number of pole parameters and their impact on lighting quality, such as pole spacing, the height of the light point, the length and inclination of the extension arm, etc. The result of the analysis is the changes in roadway lighting depending on the luminaires used and the changes in pole spacing with regard to the height of the light point.  
The final stage was to choose a compromise solution between the lowest cost of dismantling the old light posts, installing the new ones and the operating costs of the new system and the resulting road lighting comfort. The results of the analysis are practical--they allow decision-makers to choose the best solution in terms of two important criteria--cost and lighting comfort.
## 7 Results and Performance Evaluation
In Figure 13, we show the average CB gain following an MC simulation with M=100 trials obtaining \(\mu_{CB_{gain}}=6.24\) (7.95 dB gain), \(\sigma_{CB_{gain}}=0.19\) (95% CI [6.21, 6.27]) and with M = \(10^{3}\) trials obtaining \(\mu_{CB_{gain}}=6.34\), \(\sigma_{CB_{gain}}=0.05\) (95% CI [6.33, 6.35]). As seen before with 1D, these results are independent of the frequency \(f_{c}\) used (900 MHz and 2.44 GHz) since \(l_{x}=l_{y}=\frac{\lambda}{4}\). Figure 11: Average value of the array pattern calculated following an MonteCarlo (MC) simulation \(M=10^{4}\), for 900 MHz and 2.44 GHz (dashed). Figure 12: Ideal array pattern for 10 \(\times\) 5 motes in a planar 2D uniform array with perfect zero delays pointing to \(\phi=0^{\circ}\) and \(0^{\circ}<\theta<180^{\circ}\), compared against 10 CB trials, using the experimentally obtained PDF to generate the delays for 2.44 GHz. Figure 13: Average value of the planar 2D uniform array pattern calculated following a MC simulation \(M=10^{3}\) with \(\phi=0^{\circ}\) and \(0^{\circ}<\theta<180^{\circ}\) for 900 GHz. If we compare the results of linear array (2.817 average gain/10 ideal gain) and 2D array (6.34 average gain/50 ideal gain), what is clearly observed is a decrease in the average value of the gain when there is an increase in the number of radiators (motes). There is not a linear relationship between the average MC gain and the number of antennas although extra CB gain is obtained. The increase in the number of radiators (motes) makes it more difficult to attain coherence, and we lose the axial symmetry. Although there is an increase in the CB gain, there is not a linear increase with the number of motes and some trade-off should be carried out between the number of motes and the expected gain. Finally, it is interesting to study the energy requirements due to the constraints imposed by the motes. In this case, we will analyze the uniform linear array because it is the worst case in terms of gain. Using TelosB motes as we did in the previous sections, each mote consumes 17.4 mA when it is transmitting at 0 dBm [18]. Assuming that the maximum packet size is 127 bytes at a rate of 250 kbps according to IEEE 802.15.4, the duration of a frame is 4.06 ms. Based on the Rayleigh PDF depicted from the histogram distribution (Figure 10), the minimum number of retransmissions to achieve the average gain in order to reach the BS is 2137 retransmissions. This implies that, for one successful transmission, each mote needs 0.0419 mAh. Usually, these motes are powered by two standard 1.5 V Alkaline AA batteries providing a total of 1800 mAh. We assume 60% efficiency of the batteries as shown in [38; 39], thus each mote has 1080 mAh of available energy. Then, the total number of transmissions with these batteries to the CB is 25,728. However, it must be noted that, in particular for CB, these motes could be equipped with extra batteries.
## 2 Basic Theoretical Parameters of the FE-SEA Method
### Modal Density
#### 2.1.2 Modal Density of Two-Dimensional Plates  
Thin-plate modeling is often used in hybrid-model modeling. For a two-dimensional flat plate, the modal density is expressed as [4]:  
\[n(f)=\frac{A}{2 RC_{l}} \tag{7}\]  
\[C_{l}=\sqrt{\frac{E}{\rho(1-\mu^{2})}} \tag{8}\]  
In the above formula, \(A\) is the total area of the thin plate, \(R\) is the radius of the bending rotation, \(C_{l}\) is the two-dimensional longitudinal wave number of the flat plate, \(E\) is the elastic modulus of the corresponding flat plate, \(\rho\) is the density of the corresponding material, and \(\mu\) is the Poisson's ratio of the corresponding material. For a curved plate, the empirical formulae for modal density are as follows [3; 4; 5]:When the system frequency ratio meets the condition \(f/f_{r}\leq 0.48\):  
\[n(f)=\frac{5A}{\pi hC_{l}}\left(\frac{f}{f_{r}}\right)^{1/2}; \tag{9}\]  
When the system frequency ratio meets the condition \(0.48<f/f_{r}\leq 0.83\):  
\[n(f)=\frac{7.2A}{\pi hC_{l}}\left(\frac{f}{f_{r}}\right); \tag{10}\]  
When the system frequency ratio meets the condition \(f/f_{r}>0.83\):  
\[n(f)=\frac{2A}{\pi hC_{l}}\left\{2+\frac{0.596}{F-\frac{1}{F}}\left[F\cos( \frac{1.745f_{r}^{2}}{F^{2}f^{2}})-\frac{1}{F}\cos(\frac{1.745F^{2}f_{r}^{2}}{ f^{2}})\right]\right\}. \tag{11}\]  
In Equations (9)-(11), F is the center frequency of the \(1/3\) octave, \(f_{r}\) is the frequency, and \(F\) is the frequency factor. In the \(1/3\) octave, \(F=1.122\), and:  
\[f_{r}=\frac{C_{l}}{2\pi r} \tag{12}\]  
\[F=\left(\frac{f_{upper}}{f_{lower}}\right)^{1/2} \tag{13}\]  
In the above formula, \(f_{upper}\) represents the upper-limit frequency of each frequency band of the \(1/3\) octave, and \(f_{\rm lower}\) is the lower-limit frequency of each frequency band corresponding to it. According to the above equation, a common feature of the modal density of flat and curved plates can be obtained, that is, the modal density does not change with frequency.
## 7 Future Research Directions and Projects
### Application-Agnostic Research Trends  
From the physical layer viewpoint, clearly the need to have low-complexity and low-cost devices does not push short term research in the direction of advanced transmission techniques, while in the medium-long term the application of some novel concepts such as _cognitive radio networking_ might be applied to WSNs. The wide use of unlicensed bands for WSNs bring to the situation that they often need to be deployed in environments where many other wireless devices operate, e.g., WiFi systems. A proper use of the radio resource in such unplanned and dynamic environment requires the ability to adapt transmission techniques to the current use of the spectrum. Therefore, some adaptability to the spectrum usage needs to be implemented in future platforms for WSNs.  
MAC and network layer have attracted a lot of attention in the past years and still deserve investigation. In particular, combined approaches that jointly consider MAC and routing seem to be very successful. Once more, making MAC and routing protocols spectrum-aware can bring to relevant performance advancements in some environments.  
Topology creation, control and maintenance are very hot topics. Especially with IEEE 802.15.4, which allows creation of several types of topologies (stars, mesh, trees, cluster-trees), these issues play a very significant role.  
One more general paradigm that will be applied to the field of WSN is _Cooperation_, defined as the ability of individual entities or objects (that could be sensors, controllers or actuators) to use communication as well as dynamic and loose federation to jointly strive to reach common goal while taking care not to overtax their available resources [44].
## 2 System Combination Mechanism  
The cascade development of small hydropower provides favorable conditions for pumped storage transformation. The reformed cascade small hydropower utilizes the peak-valley difference of the power grid to absorb the excess clean energy and capacity while generating conventional power. It flexibly executes a forward and reverse reciprocating process, converting water energy to electricity and vice versa, to achieve capacity balance within the power system and ensure the stable operation of the grid [24].  
From the seasonal scale, summer and autumn are mostly wet periods, where the wind is weak, and the sunlight is strong; spring and winter are mostly low-water periods, where the wind is strong and the sunlight is weak. Wind power, PV, and hydropower complement each other [25]. From the intraday scale, wind is almost distributed throughout the day, while sunlight is only available during the day. Wind and solar resources can complement each other to some extent. Hydropower pumping and energy storage during periods of wind power and PV surplus, along with hydropower drainage and power generation during periods of wind power and PV shortage, smooth the daily power generation curve [26]. From the perspective of regulation scale, cascade small hydropower is closely coupled in terms of power and water flow. By reasonably scheduling the water storage and drainage of hydropower stations at all levels, continuous and stable power generation and energy storage can be achieved, which not only ensures the efficient utilization of water resources across the entire basin but also meets the requirements for regulating the fluctuations in wind power and PV [27].  
The structure of the system is shown in Figure 1. The complementary power generation system consists of PV cells, wind turbines, and cascaded small hydropower stations equipped with pumped storage units. The system is connected to the bulk power grid via a control unit. During peak load periods, wind, PV, and small hydropower jointly supply electricity to the grid. In this process, the black arrows represent the normal operation paths of wind and PV, while the red arrows indicate the power generation path of hydropower stations. During low load periods, the cascade small hydropower system operates in a pumping mode (indicated by blue arrows) to store excess wind and PV output as potential energy in the form of water.  
The right side of the figure illustrates the working principles of the hydropower and pumping units. In generation mode, the hydropower unit labeled \"G\" (generator) generates electricity using water released from the upper reservoir. In the pumping mode, the unit labeled \"M\" (motor) consumes electricity to pump water from the lower reservoir to the upper reservoir. Energy is thus stored and released through the cyclic transfer of water between the two reservoirs.
# Enhancing the Performance of SQL Injection Attack Detection through Probabilistic Neural Networks
## 4 Experimental Results and Implementation Details  
The experiments were conducted using a PC with an Intel(R) Core i7-7500 CPU @ 2.70 GHZ, 2 Core(s), 4 Logical Processor(s) with 4 GB of primary memory, and the whole environment was simulated using the python programming language and the PyTorch deep learning framework [35]. The dataset used in the proposed approach comprised malicious and benign network traffic generated from a virtual traffic server designed to mimic a real environment. The dataset included 6000 malicious and 3500 benign SQL queries, captured inbound to a web application that included features such as TCP and HTTP information about packets. The web traffic also carried malicious and benign SQL injection attack queries. The SQL features were generated using a tokenizing method, such as token count, token type, and token value. The features extracted for this study were selected by employing the Chi-Square testing method to obtain such features that would contribute towards high accuracy; these were retained and are given in Table 1.  
In this study, a 10-fold cross-validation scheme was employed to distribute the data equally for training and testing. A k-fold cross-validation scheme divides the original dataset into a training set to train the model, and a testing set to test the predictive model. The range of k varies from 1 to k \(-\) 1. The 10-fold cross-validation divides the entire dataset into 10 distinct data subsets. During the experiment for each data subset, the remainder of the 9 subsets are used to train the classifier, and the final subset is taken as a final test set. The results obtained from the experiments using the proposed approach, and the comparison with other machine learning algorithms, are shown in Table 2.  
Since we used the BAT algorithm as the smoothing parameter estimation, which helped us to achieve the best value and great prediction accuracy, the parameter settings used in the approach were given as:  
1. The bat size was limited to 30, and the number of iterations was set to a maximum of 100, as represented in Equation (9). \[T_{max}=100\] (9)
2. For each bat, the position was calculated randomly. The position of the bat gave the smoothing parameter \(h\).
3. The bat's primary position was randomly generated with a uniform distribution of [0, 10].
4. The fitness function _ff_, as given in Equation (10), was used; this takes the induvial solution of a problem as the input and generates the output to define \"how fit and good\" the solution is with respect to the actual problem. \[ff=min\frac{1}{n}{\sum_{i=1}^{n}}\left(y_{i}-\hat{y}_{i}\right)^{2}\] (10)
5. The position of the bat was updated to: \[B_{new}=B_{old}+A_{0}\] (11)
6. Steps 4 and 5 were repeated until \(T_{max}\) was achieved.
## 5 Conclusions  
In this work, we introduce a two-stage strategy to enhance the performance of the LLIE methods, which leverages a novel unsupervised UNet network to generate pixel-wise weight maps, effectively combining the benefits of different complementary LLIEs. This approach significantly improves the overall image quality, marking a notable advancement in the field. In the pre-enhancement stage, our method initiates with producing two distinct pre-enhanced images through two LLIEs selected for their complementary strengths. During fusion, guided by a well-designed loss function and a termination criterion based on a non-reference metric, our proposed mixing fusion mechanism enables unsupervised modulation of image quality, leading to versatile and robust enhancement results suitable for various different scenarios. Empirical evidence demonstrates that our technique surpasses traditional and advanced learning-based LLIE techniques, particularly in preserving detail and naturalness. Moreover, our approach distinguishes itself with its scalability and adaptability, developed to seamlessly incorporate future technological advancements within the LLIE domain. This ensures its long-term applicability and capacity to consistently deliver superior results. In conclusion, our method represents a significant leap forward. It provides a highly effective solution that not only leverages the complementary advantages of different LLIEs but also exhibits remarkable adaptability across various situations, positioning it as novel in the realm of LLIE.  
Figure 7: Visual enhancing results of various methods on LOL-Real test-split (**a**) label; (**b**) PSNR = 12.53 dB; Ying [36]; (**c**) PSNR = 15.32 dB; EnlightenGAN [23]; (**d**) PSNR = 17.42 dB; Zero-DCE [24]; (**e**) PSNR = 17.83 dB; LIME [16]; (**f**) PSNR = 19.76 dB; PairLIE [29]; (**g**) PSNR = 20.69 dB; R2R [40]; (**h**) PSNR = 22.60 dB; Night [30]; (**i**) PSNR = 23.24 dB; our.  
[PERSON] and [PERSON] contributed to the conception of the study, and [PERSON] wrote the main manuscript text, and [PERSON] and [PERSON] contributed significantly to analysis and manuscript preparation, and [PERSON] and [PERSON] conducted experiments. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number 62162043.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to acknowledge the reviewers for their constructive comments and suggestions that helped to improve the paper's quality.  
The authors declare no conflicts of interest.
## 3 _HoloSec_: Proposed Conceptual Holochain Security Framework for IoT Distributed Networks
### Continuous Monitoring and Adaptation  
The framework continuously monitors agent behavior, system health, and environmental metrics using real-time monitoring tools integrated into each node or via centralized dashboards built with platforms like Grafana. Metrics such as transmission rates, connection stability, and TS fluctuations are visualized to provide administrators with actionable insights. Additionally, the framework integrates external threat intelligence feeds to update anomaly detection models with emerging threat patterns. Firmware updates and security patches are distributed via over-the-air (OTA) mechanisms like Mender or custom secure update services, using signed packages to ensure authenticity. As new data becomes available, TSs are dynamically updated, and policies are adapted, ensuring that security measures remain effective. The following are examples.  
* Agents with consistently high TS values are granted additional privileges (e.g., access to sensitive data).
* Agents with low TS values are subjected to stricter security protocols (e.g., additional authentication steps).
## 2 Materials and Methods
### Classification of Air Mass Transport Pathways  
Backward air mass trajectories for the past 72 h were calculated every hour using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model [30; 31]. The trajectories were classified based on their vertical and horizontal transport pathways. Vertical pathways that transported constantly above 2000 m altitude were classified as free troposphere (FT). For all others, the trajectories were classified as originating from the planetary boundary layer. For the horizontal pathways, the trajectories were classified into four categories: Continental, Pacific, and Others, according to where each trajectory started (Figure 2). The trajectories that started from the Asian continent and Pacific Ocean were classified as Continental and Pacific, respectively. The trajectories classified as Others included those originating from the marginal seas (East China Sea, Sea of Japan and Okhotsk Sea) or those positioned over the Japanese mainland for more than 48 h.  
Figure 2: Examples of trajectories classified (**a**) Continental, (**b**) Pacific and (**c**) Others. For the vertical pathways, only trajectories classified as free troposphere (FT) are illustrated.
## 1 Introduction
### Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Displacement Efficiency** & **Heat Utilization Rate** & **Number** & **Displacement Efficiency** & **Heat Utilization Rate** \\ \hline
1 & 59.87\% & 0.0171 & 9 & 79.18\% & 0.0245 \\
2 & 61.85\% & 0.0179 & 10 & 81.25\% & 0.0252 \\
3 & 62.97\% & 0.0184 & 11 & 80.08\% & 0.0255 \\
4 & 73.48\% & 0.0229 & 12 & 82.96\% & 0.0260 \\
5 & 70.22\% & 0.0225 & 13 & 85.15\% & 0.0267 \\
6 & 75.03\% & 0.0233 & 14 & 86.16\% & 0.0262 \\
7 & 78.52\% & 0.0241 & 15 & 92.11\% & 0.0276 \\
8 & 76.96\% & 0.0237 & 16 & 90.37\% & 0.0274 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of yield increase contribution rate research.
methods have notable limitations. Plant extraction is hindered by limited and unstable natural resources, environmental sensitivity (e.g., seasonal, climatic, and soil factors), and relatively low extraction efficiency [8]. Chemical synthesis typically requires energy-intensive conditions, such as high-temperature and high-pressure operations, as well as specialized equipment. These processes often yield products of low purity, generate numerous intermediate by-products, and ultimately result in low final yields, primarily due to the inherent structural complexity and chiral isomer diversity of sesquiterpenals [9].  
With the development of metabolic engineering and synthetic biology, many sesquiterpenals have been successfully produced in engineered microorganisms. Microbial cell factories, engineered through synthetic biology, offer a promising alternative to conventional plant extraction and chemical synthesis. They effectively circumvent the inherent economic and environmental limitations of these conventional methods, while facilitating efficient biomass conversion and promoting sustainable resource utilization. Synthetic biology provides an innovative approach for the sustainable production of sesquiterpenals using microbial hosts, such as _Saccharomyces cerevisiae_ and _Escherichia coli_. Yeast has gradually developed as the main platform for sesquiterpenal biosynthesis due to its superior compatibility with plant-derived terpenoid biosynthetic enzymes and precision genome editing systems. Current engineering includes examples through integrating directed evolution and protein engineering to optimize rate-limiting enzymes, which enable the construction of robust microbial platforms capable of producing structurally diverse sesquiterpenals. Furthermore, systematic process optimization and metabolic network refinement further enhance production indices, including titer and yield, which are critical parameters for industrial-scale production.  
In this review, we summarize recent advances in the microbial synthesis of sesquiterpenals, including critical biosynthetic pathways, engineered strategies, and emerging application prospects. In addition, we discuss the challenges in the field, aiming to provide new insights and strategies for the sustainable production of sesquiterpenals for future research.