esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Plant Materials  
The study was performed at the department of Agronomy and Biotechnology (National Agronomic Institute of Tunisia-INAT) in Tunis (36\({}^{\circ}\)49\({}^{\prime}\) N, 10\({}^{\circ}\)10\({}^{\prime}\) E, 8 m a.s.l.). The climate, of the Mediterranean type, has a minimum average temperature of 7.5 \({}^{\circ}\)C and a maximum average temperature of 31.7 \({}^{\circ}\)C.  
In March 2014, a total number of 200 young plants (2 years old) of laurel ecotype Sousse (a city in the East-Center of Tunisia) was randomly selected from a commercial nursery. Each plant was transplanted in plastic pot (17 cm diameter). The substrate used is a mixture of equal parts of soil and peat. The physical-chemical characteristics of the soil were: 49% sand, 32% silt, 26% clay, pH 7.9, 0.9% organic matter, 25% total CaCO\({}_{3}\) and 0.152 dS m\({}^{-1}\) electrical conductivity. The peat used had a dry matter content of 25%, an organic matter content of 90%, a pH of 6.4 and an electrical conductivity of 0.4 dS m\({}^{-1}\).  
After three months of plant establishment, plants were subjected to irrigation with saline solutions having different NaCl concentrations (50, 100 and 150 mM NaCl). The control was irrigated with tap water (7.93 meq L\({}^{-1}\) NaCl). The different irrigation waters had electrical conductivities increasing from 1.4 (control), 5.3, 9.7 and 14.1 dS m\({}^{-1}\) (150 mM NaCl). Irrigations were conducted to maintain soil moisture near maximum water-holding capacity. Water was accurately provided to obtain approximately 15% leaching fraction to prevent salinity to buildup in the substrate. In order to save plants from rainfall, plastic films were used during the rainy season. Plants were kept outside and positioned in the experimental site according to a completely randomized design, with 50 plants for each treatment. Fertilization occurred monthly with a modified half-strength Hoagland solution (pH 6.3; N 7.5 mM, P 0.5 mM, Ca 2.0 mM, Mg 1.0 mM, S 1.0 mM, B 23.0 \(\upmu\)M, Mn 4.6 \(\upmu\)M, Zn 0.38 \(\upmu\)M, Cu 0.14 \(\upmu\)M, Mo 0.05 \(\upmu\)M, Fe 10.0 \(\upmu\)M).  
Leaves from cultivated plants, which were irrigated for 9 months with the different concentrations of NaCl studies, were harvested on 1 March 2015 and dried at a temperature of 30 \({}^{\circ}\)C until constant weight. Major details on the experiment and the results concerning plant growth response are reported in [PERSON] et al. [40].
## 6 Conclusions  
This study took a large-scale iron- and steel-making enterprise as a case study and developed a two-stage forecasting and scheduling model for reducing the energy consumption of its oxygen system. The GABP model was developed for oxygen demand forecasting, which is driven only by historical data. Additionally, an oxygen production scheduling model was developed for load-variable ASUs with an internal compression process, and the oxygen emissions and pipeline pressure are included in the objective function. Based on the case study, the following conclusions were obtained:  
1. For the forecast of oxygen demand, the prediction accuracy of the GABP model is better than that of the ARIMA model. The case analysis showed that the average MAPE values for the 12 sets of data of the ARIMA and GABP models were 23.8% and 20.2%, respectively.
2. By comparing the scheduling results and the field data, it was found that after scheduling, the amount of oxygen emissions decreased by 6.32%, the pipeline pressure decreased by 0.61%, and the energy consumption of oxygen compression decreased by 1.6%. Considering the loss of oxygen emissions and the compression energy consumption, the total power consumption of the oxygen system was reduced by 1.38%, which means an annual saving of approximately 9.03 million RMB in electricity costs.  
Conceptualization, P.Z.; methodology, P.Z. and Z.C.; software, Z.C.; validation, Z.C.; formal analysis, Z.C.; investigation, Z.C.; resources, L.W.; data curation, Z.C.; writing--original draft preparation, Z.C.; writing--review and editing, P.Z.; visualization, Z.C.; supervision, P.Z.; project administration, P.Z.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data available on request due to restrictions privacy.  
The authors declare no conflict of interest.
# Exploring the Frontiers of Cybersecurity Behavior: A Systematic Review of Studies and Theories
## 5 Discussion
### Analysis of Theories and Models in Cybersecurity Behavior  
For the IS theories and models, the results showed that PMT is the most frequently used theory, with 17 studies. The prominent use of PMT stems from the theory's aim to explain fear appeals and suggests that individuals protect themselves through several factors, such as perceived severity and perceived vulnerability [78; 79]. In addition, PMT assists in explaining individual differences in protective cybersecurity behaviors, as action-based decisions are built on individual risk perceptions [72]. By critically analyzing the PMT-related studies, it has been found that most of them focused on exploring the cybersecurity behavior (N = 10) and the intention behavior (N = 7). For example, some studies used the PMT to study the factors affecting cybersecurity behavior in social networking sites [70; 72], web browsers [55], and computers [80]. Moreover, other studies employed the PMT to study the intention behavior toward smartphone use [81; 82] and malware [56]. This is followed by TTAT-related studies (N = 6) that mainly focused on analyzing avoidance motivation and avoidance behavior. For instance, the TTAT is used to analyze the avoidance motivation and avoidance behavior in playing games [54], dealing with Internet threats [83], and malware [59]. The TPB and GDT-related studies (N = 4) also focused on analyzing the intention behavior. For example, the TBP is utilized to study the intention behavior toward using social networking sites [71] and anti-malware software [61]. Moreover, the GDT is employed to analyze the intention behavior toward using e-mail [60] and smartphones [81; 82]. Moreover, the TAM-related studies (N = 3) mainly focused on exploring cybersecurity behavior. For example, the TAM is used to study the cybersecurity behavior toward using web browsers [55] and social networking sites [70]. In addition, the HBM-related studies (N = 2) focused on analyzing cybersecurity behavior [63; 84] where the technology or service is not specified. Since we are dealing with 'behavior', more theories need to be explored to further understand what impacts cybersecurity behavior by individuals and organizations. The existing literature sheds inadequate exposure to the social, psychological, and technical determinants.
## 1 Introduction
In [17], an intelligent broadband detection algorithm based on an improved You Only Look Once v3 (YOLOv3) object detection network was proposed. This algorithm detects non-cooperative signals from broadband time-frequency spectrograms, achieving high detection accuracy. However, these methods rely on large-scale datasets for neural network training, which results in a high computational load and may not be able to adapt to the resource-constrained environment of satellite payloads. It is worth noting that, in addition to conventional interference detection and suppression strategies, for the interference among multiple users within the network, recent studies have explored treating interference structures as tunable design variables to enhance system sum rate and spectral efficiency. For example, [PERSON] et al. [18] proposed a scheme based on multiantenna nonorthogonal multiple access (mNOMA) technique that exploits interference aggregation patterns under overloaded access conditions to construct power and modulation structures, achieving the quality of service (QoS) guarantees and improved system sum rate, using only partial channel state information (CSI). Although such approaches differ from the interference detection focus of this work, they reflect a new research trend toward treating interference as a potential resource rather than merely a limitation. In non-interference signals, the noise spectrum remains relatively flat, resulting in small amplitude differences between the front and back of the spectrum. In contrast, interference-containing signals exhibit significant amplitude fluctuations in the spectrum. Based on this spectral characteristic, [PERSON] et al. [19] proposed a differential amplitude-based FCME algorithm, which replaces the traditional sorting process with a segmented differential approach. This method enables rapid estimation of background noise and calculation of the initial threshold while maintaining a similar detection probability and improving computational efficiency [20]. However, the fixed segmentation parameters limit the algorithm's adaptability, and there is still room for further optimization to enhance algorithm efficiency and parameter adaptability. Inspired by the segmented differential approach in [PERSON] et al. 's differential amplitude-based FCME algorithm [19], and addressing the shortcomings of existing algorithms, this paper proposes an efficient differential amplitude interference detection algorithm based on non-uniform adaptive segmentation. The proposed algorithm divides the samples into segments in a non-uniform, adaptive, and dynamic manner to optimize the calculation of the initial threshold. Additionally, it effectively utilizes the segment-based strategy to replace the traditional point-based iterations with segment-based iterations. Through simulation verification, the proposed algorithm significantly improves the computational efficiency of interference detection while ensuring detection performance.
## 4 Methods
### Node Aggregation and Update
#### 4.4.3 Multi-Head Attention  
Multi-head attention enables the model to obtain richer graph information by computing attention weights from different heads in parallel, thereby enhancing its expressive power. The target node \(i\) is connected to its neighboring node set \(N_{i}^{m_{1}}\) via edge type \(m_{1}\), and the multi-head attention mechanism is used to aggregate the information from these neighboring nodes. Specifically, the representation of the target node \(i\) is updated by each attention head. The computation is given by Equations (14) and (15):  
\[\hat{h}_{i,m_{1},k}^{(l)}=\sum_{n\in N_{i}^{m_{1}}}\alpha_{i,n,k}^{(l)}W_{k}^{ (l)}\left[h_{n}^{(l-1)}\middle|\middle|h_{e_{in}^{m_{1}}}^{(l-1)}\right] \tag{14}\]\[\mathrm{h}_{i,m_{1}}^{\prime(1)}=\sigma\Big{(}\Big{|}|_{k=1}^{K}\hat{h}_{i,m_{1},k}^ {(1)}+W_{res(k)}^{(1)}\mathrm{h}_{i,m_{1}}^{\prime(1-1)}\Big{)} \tag{15}\]  
where \(\hat{h}_{i,m_{1},k}^{(I)}\) represents the aggregated feature of the \(k\)-th head through the \(m_{1}\)-type edge at the \(l\)-th layer for the neighboring node set, \(K\) denotes the number of attention heads, and \(k\) represents the \(k\)-th attention head. \(\hat{h}_{i,m_{1}}^{(I)}\) is the representation of target node \(i\) through the \(m_{1}\)-type edge at the \(l\)-th layer.  
Finally, to integrate the results from the multi-head attention mechanism, the outputs corresponding to different edge types are concatenated using the concatenation operation and mapped nonlinearly through a Multi-Layer Perceptron (MLP) to obtain the final node representation \(\hat{h}_{i}^{(I)}\). The computation is given by Equations (16) and (17):  
\[\hat{h}_{i}^{\prime(1)}=\Big{|}|_{z=1}^{Z}\mathrm{h}_{i,m_{z}}^{\prime(1)} \tag{16}\]  
\[\hat{h}_{i}^{(1)}=\mathrm{MLP}\Big{(}\hat{h}_{i}^{\prime(1)}\Big{)}=\mathrm{ ReLU}\Big{(}W_{2}\cdot\mathrm{ReLU}\Big{(}W_{1}\cdot\hat{h}_{i}^{\prime(1)}+b_{1} \Big{)}+b_{2}\Big{)} \tag{17}\]  
Each head in the multi-head attention mechanism focuses on different aspects of the edge type. For example, at the check-in edges, one head might focus more on the type of location (e.g., restaurant, tourist spot), while another head might focus on the frequency of user behavior at these locations. By using the multi-head attention mechanism, the model can learn different influencing factors in parallel for different types of edges, enabling it to more accurately understand the multiple factors influencing user behavior. This improves the model's predictive accuracy regarding user interests and behavior.  
Where \(||\) represents the concatenation operation, \(Z\) is the number of edge types, \(W_{1}\), \(W_{2}\) are the weight matrices, \(b_{1}\),\(b_{2}\) are the biases, and ReLU is the activation function.
## 3 Results and Discussion
### Effects of Reed Carbon Source on COD and pH of Constructed Wetland Effluent  
3.2.1. Changes in COD of Wetland Effluent  
It can be seen from Figure 5 that the COD concentration of the influent from RW, CW and N-RW was 29.15-36.45 mg/L and the COD concentration of the effluent was 26.06-29.75, 12.49-19.99 and 38.34-49.60 mg/L, respectively. The corresponding average removal rates of COD were 14.65 \(\pm\) 4.35%, 48.29 \(\pm\) 7.67% and \(-\)28.17 \(\pm\) 9.79%, respectively. The comparison between RW and CW showed that the addition of a reed carbon source could increase the COD concentration of wetland effluents to a certain extent, and the COD removal rate was 33.64% lower than that of CW, but the COD concentration of the effluent from RW was stable and less than 30 mg/L, which met the corresponding surface water standard, i.e., Class IV (_Environmental Standards for Surface Water Quality_ GB 3838-2002, China); COD, TN, NH4+-N and TP should be below 30, 1.5, 1.5 and 0.3 mg/L, respectively [29]. This was because the reed carbon source in RW could release organic matter continuously and stably, which significantly increased the organic matter content in the wetland. Most of the organic matter was removed by substrate adsorption, microbial aerobic degradation and denitrification consumption, and the remaining organic matter would flow out of the wetland system with the effluent [30].  
The comparison between RW and N-RW showed that the COD removal rate of RW was 42.82% higher than that of N-RW. Planting reeds could significantly improve the COD removal effect of constructed wetlands (\(p<0.05\)). The entwined reed roots played a role in loosening the wetland substrate and carrying oxygen to the plant roots, improving the dissolved oxygen environment in the substrate. At the same time, the reed roots could provide an attachment surface for the growth and reproduction of microorganisms and promote the degradation of organic matter in the wetland by aerobic microorganisms [31].  
Figure 5: Changes of COD concentration and corresponding removal rate in influent and effluent of three groups of wetland systems.
## 4 Results and Discussion  
### Checking the Correctness of the L(3,1) Coloring Algorithm  
To prove the correctness of the coloring algorithms, we adopted the Manhattan distance concept [33]. To prove that successive colors have a spacing equal to 2, an assessment of the smallest distance separating consecutive colors was performed. Table 2 shows the parities of \(i\) and \(j\).  
Figure 11: Cellular grid of a 5 \(\times\) 5 matrix colored using the L(3,1) coloring.  
Figure 10: Color spectrum of the L(3,1) algorithm.  
As shown in Table 2, each pair of consecutive colors [(0,1), (1,2), (2,3), (3,4), (4,5), (5,6), (6,7), (7,8), (8,9), (9,10) and (10,11)] has a Manhattan distance of at least 3. According to Table 1, the lowest spacing between colors should be at least 2. Other sets of successive color pairs, such as (0,1), (3,4), (4,5), (7,8), and (9,10), have a Manhattan distance of at least 2 and have a similar parity for the n and m coordinates of both of their component colors. Note that in Figure 14, any random vertex (n, m) and another vertex (n\({}^{\prime}\), m\({}^{\prime}\)) have a Manhattan distance of 2. The separation between them will not be less than 1, except when n and n\({}^{\prime}\), or m and m\({}^{\prime}\), have unique parities. Hence, these two vertices are separated by a minimum of 2 coloring units. It can thus be deduced that the distance between two vertices possessing the same color is \(\geq\)3. From the function of a vertex (a) of the L(3,1) algorithm, it is possible to infer that vertices with similar colors also possess similar parities for coordinates \(i\) or \(j\). As shown in Table 2, none of the parity pairs (\(i\) and \(j\) coordinates) for the consecutive color pairs are the same. Therefore, from these observations, it can be seen that the vertices with the same color are separated by a distance of more than three units. This implies that the second definition holds true. Hence, the f(a) coloring of L(3,1) holds true for a given cellular graph matrix. Additionally, it can be observed from Figure 14 that the distance between two vertices of the same color is at least four units. Thus, the reuse distance is four (i.e., \(\sigma=4\)), which satisfies the third condition for correctness. This evaluation of the Manhattan distance and the minimum distance between pairs of consecutive colors therefore proves the correctness of the L(3,1) algorithm.
## 2 Materials and Methods
### LCOE Beis  
A report published by [PERSON] [13] theorized the baseline LCOE equation used by BEIS as seen below in Equation (1). The template was adopted for use and further improved by the Department of Energy and Climate Change (DECC) in 2010 under the publication \"UK Electricity Generation Costs Update\". where \(t\) is the period ranging from year 1 to year \(n\), \(C_{t}\) the capital cost in period \(t\) (including decommissioning), \(O_{t}\) the fixed operating cost in period \(t\), \(V_{t}\) the variable operating cost in period \(t\), \(E_{t}\) the energy generated in period \(t\), \(d\) discount rate and \(n\) the final year of operation.  
\[\text{LCOE}_{\text{BEIS}}=\frac{\text{Net Present Value Cost (NPVcost)}}{\text{Net Present Energy (NPE)}}=\frac{\sum_{t=1}^{n}\frac{\text{C}_{t}+\text{O}_{t}+V_{t}}{(1+d)^{t}}}{ \sum_{t=1}^{n}\frac{\text{E}_{t}}{(1+d)^{t}}} \tag{1}\]  
LCOE formulation is broken into four steps. Step one involves the gathering of plant data associated assumptions [14]. A breakdown of the parameters associated with each term is shown in Table 1.  
Step two takes the entire summation of costs corresponding to CAPEX and OPEX for the entirety of the project lifetime over the applied discount rate. Similarly, step three involves the summation of the expected generation for each year over the discount rate. Step four is the resultant net present values associated with steps two and three, respectively divided by one another to produce the affiliated LCOE estimate [14].
# SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems
## 4 SASLedger: A Secured, Accelerated Scalable Storage Solution
### The Advanced Solution: SASLedger  
In our advanced solution, the data-reduction operation is separated from the transaction-processing phase. The data-reduction operation should be performed when the system is not busy or when the system is being backed up (e.g., the snapshot mechanism in HLF). Figure 5 shows the two phases of our solution: during the transaction-processing phase, the nodes process the transactions as vanilla blockchain nodes do and save the blocks locally, thus the TPS remains unaffected. During the data-reduction phase, the nodes perform the following operations for each committed block:  
* Calculate a VC for the set of transactions contained in the block;
* Save the hash value of the block and its commitment to local storage as a \(\langle Key,Value\rangle\) pair which is used as an index of the block: \(\langle blockHash,commitment\rangle\);
* Transmit the content of the block to the remote DB server;
* Delete the block from its local storage.  
When a query is sent to a node to retrieve a block or a transaction, the node retrieves the block or transaction from the remote DB server and transmits it to the client after verification.  
A snapshot of the blocks can be generated from the block indices: this can be used to build block indices for a newly joined node in the future.  
Figure 4: The primary solution. After a block is received by a blockchain node, the node generates commitment for the block and transmits the block to the remote DB server immediately.
## 4 Results and Discussion
### Artificial Neural Networks Modelling Results
#### 4.1.1 Cross-Validation Results
Using Equations (7) and (9) along with their associated weights bias values, as well as normalization factors, it will be possible for other researchers to determine predictions of stiffness modulus and Marshall parameters, whatever the composition of the mixture, within the range of composition parameter values considered, without any other additional effort with respect to the standard ANN approach. The highest accuracy (\(R_{cv}=0.881\)) was obtained from the prediction of the ITSM, while \(R_{cv}\) values greater than 0.830 were achieved for the MS (\(R_{cv}=0.834\)) and MQ (\(R_{cv}=0.859\)). These results can be considered satisfactory from an engineering point of view, taking into account the relatively low number of available specimens and the different characteristics of the HMAs considered. The results of training phases of the ANN final models are summarized in Figures 8-10; as can be observed, a good matching is achieved for the stiffness modulus (\(R=0.965\), \(MSE=50238\)), Marshall stability (\(R=0.919\), \(MSE=0.426\)), and quotient (\(R=0.919\), \(MSE=0.187\)). Figure 7: ANN final architecture to predict the Marshall quotient. Figure 8: Training result for the stiffness modulus ANN model. Figure 9: Training result for Marshall Stability ANN model. Comparisons between the experimental targets and ANN-calculated outputs for each mechanical parameter considered are reported in Table S7. The difference between the predicted and experimental data, expressed as a percentage value of the experimental data, is also reported (PD, percentage difference value). Both over-estimations and under-estimations can be observed for all the mechanical parameters considered. For the stiffness modulus, the PD absolute value is within the range 0.01%-20.49%, with an average value equal to 3.44%, while the average values for the MS and MQ are equal to 4.14% and 9.07%, respectively. Therefore, it can be concluded that cross validation is a useful approach to accurately evaluate a model's prediction capacity and, consequently, to identify the network's hyperparameters that lead to the highest possible accuracy for the predictive modeling problem of HMAs' mechanical parameters.