esa-sceva/satcom-chunk-collection huggingface dataset:
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 6 Application to CFD Simulation
### Heat Flux Simulation  
_OpenFOAM_ cannot directly set the temperature condition for the heater in the simulation as can be done in reality. Instead, we use the following _heat flux_ equation for the heating condition:  
\[q=\lambda\frac{\Delta T}{\Delta x} \tag{5}\]  
where  
* \(q\) represents the heat flux, where the unit is \(W/\text{m}^{2}\).  
Figure 16: Model—room for CFD simulation.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Wall** & **zeroGradient** & **zeroGradient** & **zeroGradient** & **fixedValue** \\ \hline heater & 500 & 550 & 600 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Boundary conditions.  
Figure 15: Model—room for experiments.  
* \(\lambda\) represents the thermal conductivity through a specified material, which is expressed as the amount of heat that flows per unit of time through a unit area with a temperature gradient of one degree per unit distance.
* \(\Delta T\) represents the difference between the outside and inside temperatures of the wall, where the unit is _Kelvin(K)_.
* \(\Delta x\) represents the thickness of the wall, where the unit is meter (m).
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 3 System Architecture
### Organizational Layer  
The organizational layer is where the decision-making processes and interactions between agents take place. This layer hosts the package and warehouse agents, autonomously negotiating and executing services. A service represents the actions or resources requested by a package agent and provided by a warehouse agent. Two types of services are defined within this system: a storage service \(\text{s}_{\text{storage}}\), which is responsible for storing a package in a warehouse slot, and a transport service \(\text{s}_{\text{transport}}\), which involves transporting a package between warehouse slots, as shown in Equation (25):  
\[\begin{split} s_{\text{storage}}=(t_{\text{start}},t_{\text{end}}, \text{Slot}_{j})\\ s_{\text{transport}}=(t_{\text{start}},t_{\text{end}},\text{ Slot}_{\text{source}},\text{Slot}_{\text{target}},P_{i})\end{split} \tag{25}\]where \(t_{\text{start}}\) and \(t_{\text{end}}\) are the times that define the start and the end of the service execution, \(\text{Slot}_{j}\) identifies the specific warehouse slot, \(\text{Slot}_{\text{source}}\) and \(\text{Slot}_{\text{target}}\) represent the source and target warehouse slots, and \(P_{i}\) is the ID of the package being transported.  
In our current system, transport services are ignored under the assumption that the time required to execute storage services is significantly greater than the time required for the execution of transport services. As a result, transport times are considered negligible in comparison to storage times. Therefore, the transport service component does not have a major impact on the system's overall timing and execution of processes.  
A storage service transitions through multiple states during its lifecycle, as shown in Equation (26):  
\[s_{storage\;states}(t)=\begin{cases}\text{Negotiation},&\text{if }t<t_{created}\\ \text{Scheduled},&\text{if }t_{created}\leq t<t_{\text{start}}\\ \text{Active},&\text{if }t_{\text{start}}\leq t<t_{\text{end}}\\ \text{Completed},&\text{if }t\geq t_{\text{end}}\end{cases} \tag{26}\]  
Initially, the service enters the Negotiation state (before \(t_{created}\)), where the parameters, such as the start time \(t_{\text{start}}\), end time \(t_{\text{end}}\), and warehouse slot, are agreed upon by the agents. After the negotiation is completed, the service transitions to the Scheduled state, which occurs before the start time, indicating that the service is planned but not yet active. Once the start time is reached, the service moves into the Active state, meaning the package is being stored in the designated warehouse slot between \(t_{\text{start}}\) and \(t_{\text{end}}\). During this time, the warehouse slot is fully occupied by the package. Finally, when the end time is reached, the service transitions to the Completed state, at which point the package is removed from the slot, and the slot becomes available for future use.
## 2 Research Model and Hypothesis
### Research Hypotheses 3 and 4: The Role of BDAC and IC in Enhancing SCA  
Both capabilities--BDA and innovation--allow firms to renovate and reconfigure their resources [56]. [PERSON] et al. [66] also concluded that IC can be considered as both a part and a result of the dynamic capabilities that a firm possesses and that both capabilities contribute to the development of a firm's SCA. BDA permits data-driven decision-making that creates novel ways to innovate and learn how to create value, thus improving a firm's sustainable performance [67]. Consequently, we aim to examine the effects of both capabilities on a firm's SCA.  
Firstly, BDAC represents a key source of knowledge creation [64]. This is attained by exploiting different data sources, e.g., production, processes and customers, to support firms in making better decisions, enhancing performance and, thus, sustaining competitive advantage [24]. For instance, [PERSON] et al. found a significant effect of BDA on a firm's environmental and social sustainability [39]. Additionally, [PERSON] et al. examined the significant impact of BDA on firms' financial performance. Secondly, innovation as a dynamic capability plays a crucial role in increasing sales, profits and performance excellence through product and process development [68]. Moreover, innovativeness that creates faster adaption to market changes encourages the creation of competitive advantage [42].  
Thus, IC contributes significantly to firm performance and competitiveness [69; 70]. Both dynamic capabilities, i.e., BDA and innovation, assist firms in overcoming external challenges and competencies by enhancing and sustaining their competitive advantage and overall performance [38; 42]. Consequently, we propose the following hypotheses:  
**Hypothesis 3 (H3).**_As a dynamic capability, BDAC has positive effect on firms' SCA._  
**Hypothesis 4 (H4).**_As a dynamic capability, IC has positive effect on firms' SCA._
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 3 Low Complexity Equalizers for UP-OCDM under Doubly Selective Channels
### Iterative LSQR Block Equalizer
#### 3.3.1 Iterative LSQR Block Equalization Algorithm
Therefore, the transform domain channel matrix \(\mathbf{C}\) can be constructed by  
\[\mathbf{C}=\sum_{m=0}^{M-1}\mathbf{P}_{m}\mathbf{F}^{H}\mathbf{V}_{m}\mathbf{F}. \tag{26}\]  
Finally, (22) can be directly solved for the transmitted symbols \(\mathbf{d}\) by LSQR. Specifically, the equalization algorithm takes three steps:  
(1) Computing the diagonal matrices \(\mathbf{D}_{m}\) with the BEM coefficients;  
(2) Multiplying \(\mathbf{D}_{m}\) by the diagonal matrix \(\mathbf{\Lambda}\);  
(3) Solving (23) by LSQR algorithm to obtain the symbol vector \(\mathbf{d}\);  
Therefore, iterative LSQR equalization can directly recover the transmitted signal with time-domain received signal. Furthermore, it is well known that the rate of convergence of LSQR is directly influenced by the condition number of \(\mathbf{H}\), which is defined as  
\[\mathbf{r} =\sum_{m=0}^{M-1}\mathbf{P}_{m}\mathbf{G}_{m}\mathbf{s}+\mathbf{w} \tag{22}\]0  
However, Reference [25] shows that the LSQR based systems have a property of semi-convergence, which means that the increase of the iteration times does not always lead to an improvement of inversion performance, this will also be confirmed in our numerical simulation results. On the other hand, it will also be shown in our simulations that the performance of the proposed iterative LSQR equalizer outperforms its OFDM counterparts with an iteration number of 4.
# A Robust Localization System Fusion Vision-CNN
## 4 Simulation
### Initialization of the Robot Global Positioning
#### 4.2.2 System Relocalization Analysis
The pose estimated by the visual neural network is used as the initial pose, and then the laser information is used to search for the optimal posture. The search time of the two methods is shown in Figure 10. Since the algorithm's time consumption fluctuates every time the algorithm runs, the program for each location is run five times and the running time is recorded. Then, the average calculation time is taken as the search time for that location. It can be seen from Figure 10 that since the initial pose estimated by the neural network proposed in this article is more accurate, the search time for the optimal pose based on it is also shorter. Compared with the average time based on the posenetLSTM algorithm of 0.4526 s, the average time based on the method proposed in this paper is 0.3856 s, a reduction of 14.81%.
# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
## 4 Experiments
### Settings  
#### 4.1.1 Datasets and Degradation Models  
Following [29, 40], DIV2K [41] were selected as training sets to train the proposed GPSR, while five commonly benchmark datasets were used for testing, including set5 [42], set14 [43], BSD100 [44], Urban100 [45], and Manga109. Table 1 reports the characteristics of each dataset. Our experiments are separately conducted with bicubic (BI) and blur-downscale (BD) degradation models. In each training batch, 8 LR color patches with the size of 48 \(\times\) 48 are extracted as the input of our GPSR.
## 3 Experiment Results and Discussion
Compensating nonlinear components in the digital domain is a direct and effective solution to improve SFDR [14]. However, this approach requires additional digital processing modules, degrading the size weight and power (SWaP). In [19], a large SFDR is achieved by adjusting the polarization and optical filtering. Cascading of these devices also increases the size. Adjusting the power of the LO signal to improve the SFDR is a simple way [21]; however, it results in a higher fundamental power penalty. The SWaP of the proposed microwave photonic mixer with no other adjustment devices is relatively good. It is worth noting that  
Figure 6: (**a,c,e**) The measured electrical spectra of traditional frequency up-conversion link based on traditional CS-DSB modulation; (**b,d,f**) the measured electrical spectra of the proposed system. The IF signal is 50-Mbaud 16-QAM with a center frequency of 2 GHz and power of 12 dBm, 15 dBm and 18 dBm accordingly (RBW = 3 MHz). with the development of photonic integration technology [23], microwave photonic systems can be integrated on one chip, which will improve the SWaP greatly.
# Computational Fluid Dynamics of Compartent Fires: A Review of Methods and Applications
## 3 CFD Simulation in Compartment Fires
### Physical Models
#### 3.3.4 Heat Transfer: Radiation Transport
This can decrease the accuracy of the simulation in the case of oxyfuel combustion (i.e., more concentrated CO\({}_{2}\) and H\({}_{2}\)O in the product gases) and optically thin flames [62] (i.e., soot yield is lower than CO\({}_{2}\) and H\({}_{2}\)O yield). * WSGG Model [111]: This model improves the gray gas approximation by dividing the radiation spectrum into a sum of gray gases with different absorption coefficients, each weighted by temperature and concentration. When dealing with mixtures of gases such as CO\({}_{2}\), H\({}_{2}\)O, and soot, the absorption spectra can overlap. The WSGG model may not handle these interactions effectively, leading to reduced accuracy when modeling multi-gas combustion environments. For similar conditions, the computational cost of WSGG was shown to be around 3.5 times that of the Gray Gas Model [38]. Choosing the appropriate radiative heat transfer model depends on the balance between computational cost and accuracy. While LBL provides the highest spectral accuracy by resolving individual absorption lines of gases and NBM offers improved spectral resolution compared to WBM, the computational cost for LBL and NBM makes them less ideal for practical fire simulations where the simulation time is a major concern and detailed spectral resolution is unnecessary. Gray Gas or WSGG models are more appropriate due to the lower computational cost and reasonable accuracy; they are commonly used in tools like FDS and are effective at handling large-scale, under-ventilated, or sooty environments such as fires in confined spaces. If more spectral details are needed without going to the complexity of NBM or LBL models, Wide Band models are efficient. In a natural compartment fire without any control, firefighting, or suppression system, at the end of fully developed phase where available fuel and oxygen have been consumed and they become limited, the fire begins to lose intensity, marking the start of the decay phase. During the decay phase in the compartment, flames become smaller, and temperatures gradually decrease. The fire continues to diminish until it reaches extinction, which is discussed in the next section.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 4 Development Trend and Key Technology of ISAC towards 6G ISTNs
### Future Application Scenario
#### 4.1.3 Space Environment Sensing  
In the complex space environment, there is a multitude of space debris. Based on the recent data available, 7800 of the 10,550 satellites in space are presently operational. The number of identified space debris objects ranging from 1 mm to 10 cm is about 131 million. More than 630 incidents of collisions with these objects resulting in equipment damage have been reported [98]. To prevent financial losses caused by space debris, terrestrial-based radar system detection techniques have been the common solution. However, small objects are difficult to detect. Therefore, integrating communication satellites with sensing techniques to detect the specific position and velocity information of space debris can ensure the safety of space equipment. Based on the multi-debris environment in space, the authors proposed a modulation scheme with triangular LFM and V-shaped LFM pulses [99]. Simulations have demonstrated a significant performance improvement in radar velocity and range estimation capabilities compared to the conventional linear frequency modulation system. For radar and communication systems deployed in space, radar and communication share spectrum resources but suffer from mutual interference.  
In [100], the author proposes an overlapping subarray configuration to reduce the impact of spectrum sharing on radar detection performance. By mapping communication signals to the null space, mutual interference is minimized. Experimental results validate the effectiveness of this method. Furthermore, proper power allocation is crucial for satellites. In [101], the author focuses on the coordinated optimization of power and bandwidth allocation in the system. By employing an alternative optimization algorithm, the optimal solution is obtained to improve energy efficiency.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 2 Fundamentals of Optical Wireless Communications
### Channel Model in VLC
#### 2.1.2 Receiver Signal to Noise Ratio  
Silicon photo-diodes are usually employed in optical receivers to convert the optical signal into an electrical signal, which has an added noise. In VLC systems, there are two main noise sources: thermal noise, \(\sigma_{thermal}^{2}\), and shot noise, \(\sigma_{shot}^{2}\). Both noise sources can be modeled as additive white Gaussian noise (AWGN) according to [20]. Since both types of noise are uncorrelated, the total added noise can be expressed as the sum of contributions from shot and thermal noise, as expressed in Equation (4):  
\[\sigma_{total}^{2}=\sigma_{thermal}^{2}+\sigma_{shot}^{2} \tag{4}\]  
Then, the signal to noise ratio (SNR) can be expressed as:  
\[SNR=\frac{P_{er}}{\sigma_{total}^{2}}=\frac{R^{2}\cdot P_{r}^{2}}{\sigma_{total }^{2}} \tag{5}\]  
In Equation (5), \(P_{er}\) is the electrical power of the received signal at receiver's output, \(P_{r}\) is the optical power of the signal received by the photo diode, \(R\) is the receiver responsivity, and \(\sigma_{total}^{2}\) is the total noise power. VLC channel analysis and characterization is an open research line with plenty of contributions, [23; 24; 25; 26]. Published works include studies based on the analysis of aspects such as modulations, codifications, SNR, and Bit Error Rate (BER) among others.
## 4 Discussion
### Water Relations Are Differently Affected by seawater and NaCl in Both Species  
The type of stress affected the crop water relations in different ways in the two species. In the salt-resistant halophyte, the only stress significantly decreasing water consumption was the H-SA, whereas H-SW and both moderate salinities did not result in any significant difference compared to the control. Such differences between H-SW and H-SA suggest a higher stress determined by NaCl compared to seawater, even if the biomass produced with the two treatments was comparable. In fact, the decrease of water use is possibly connected to the limited water uptake and translocation due to a salt-stress-induced reduced stomata conductance [31, 32], as shown in Section 3. Lettuce's water consumption, on the other hand, was the same for both stresses, with both M-SW- and M-SA-treated plants in line with control and both H-SW and H-SA plants significantly lower than control. The results on H-SW reduction of lettuce water use are in agreement with those obtained when lettuce was grown with a share of 15% seawater and EC 9.2 dS m\({}^{-1}\)[28, 33]. Here again, the observed response was likely due to the reduced water uptake and translocation caused by salinity. This is also corroborated by the fact that the response of lettuce in terms of fresh and dry weight at the end of the cycle was different, with the fresh weight of moderately stressed plants significantly reduced compared to the control, whereas the dry weight showed an opposite response. This would suggest that even moderate salinity induces in this species an alteration in water uptake and translocation mechanisms. Regarding the WUE, lettuce was the only crop showing a different response to different type of stress, with the WUE being significantly reduced only in H-SA-treated plants compared to the control. By contrast, no differences were observed among EC-comparable stresses in _Tetragonia_, with both M-SW and M-SA significantly increasing the WUE compared to the control and both H-SW and H-SA showing values comparable to the control. Such results support data available in the literature, indicating for salt-tolerant crops a relatively constant WUE at increasing levels of salinity, whereas for sensitive crops, they indicate a decrease of WUE [34]. The WP of lettuce decreased compared to that of the control already at moderate-salinity conditions, but the most reducing treatment was H-SA, decreasing significantly not only compared to the control but also to the EC-equivalent H-SW treatment. In _Tetragonia_, instead, again both moderate salinities significantly increased the water productivity compared to control conditions, whereas while the H-SW-treated plants' WP was comparable to the control, the H-SA-treated plants' WP was significantly reduced, confirming at high salinity the major severity of the NaCl stress compared to that of seawater.
# Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems
## 5 Results
### Step 1--Designing the Model
#### 5.2.1 Seeking Expert Inputs  
The expert business opinion is used to populate elements of our metamodel. Consulting domain experts, we determine artifact types and correlate events with artifacts. Then domain knowledge is needed to obtain the relations from the data model. This is so because, for the most-used ERP systems, e.g., Microsoft Dynamics NAV ERP, the relationships between tables are not explicitly defined in the database management system (DBMS) software. Additionally, an excessive volume of data unnecessarily increases the scope of the project and consequentially complicates it. This is the reason why experts are needed. They understand the procedures and rules of the company in question, so they can outline which data are needed to optimally achieve the set goal. The experts should define the relationships between the tables, as well as main artifacts and important attributes of the database so that the event log can be properly mapped. For example, we identified two key documents in the case study, namely, the purchase order and purchase invoice. These are the primary objects in the process and an event log is obtained for each.
## 2 Materials and Methods
### Neural Networks Architectures for TEC Forecasting
#### 2.2.6 Statistical Metrics Adopted in the Validation Process  
To compare the accuracy of forecasting TEC values using different architectures, a set of statistical metrics was adopted, including Mean Absolute Error, Mean Absolute Percentage Error, root mean square error, and [PERSON]'s correlation coefficient.  
The computation is performed using the following equations:  
\[MAE\ =\ \frac{1}{n}\sum_{i\ =\ 1}^{n}|\hat{y}_{i}-y_{i}|, \tag{13}\]  
\[MAPE\ =\ \frac{1}{n}\sum_{i\ =\ 1}^{n}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}} \right|\times 100\%, \tag{14}\]  
\[RMSE\ =\ \sqrt{\sum_{i\ =\ 1}^{n}\frac{(\hat{y}_{i}-y_{i})^{2}}{n}}, \tag{15}\]  
\[\rho\ =\ \frac{cov(y,\hat{y})}{\sigma_{y}\cdot\sigma_{\hat{y}}}, \tag{16}\]  
where \(n\) is the number of samples, \(cov(y,\hat{y})\) is the covariance between the predicted and observed values, \(\sigma_{y}\) is the standard deviation of the observed values, and \(\sigma_{\hat{y}}\) is the standard deviation of the predicted values.
## 6 Perspectives: Greenhouse Artificial Neural Networks Application  
### Agriculture 4.0 and the ANNs  
Farmers today need to adapt to new technologies and apply them in agriculture. Agriculture has gone through different stages, starting with agriculture 1.0 characterized by the use of animal force; then came agriculture 2.0 that used combustion engine machinery; moving to agriculture 3.0 where guide systems (such as geographic positioning (GPS) systems) and precision agriculture (PA) would be used; and finally agriculture 4.0, which is based on the principle that activities are connected to the cloud [168].  
Agriculture 4.0 is the integration of technologies (IoT, PA, artificial intelligence (AI), cloud computing (CC), among others) through the cloud to automate cyber-physical tasks and systems, allowing the planning and control of production [169]. This new era arose when telematics and data management were combined with the concept of PA and largely driven by the use of the IoT [170]. PA is the management of spatial and temporal variation in fields with respect to soil, atmosphere, and plants using information and communication technologies. Its concept was born from the need for the development of site-specific techniques. In other words, it applies treatments to areas within a field that requires different management than the field average, allowing fine-tuning of crop management systems [171].  
IoT in the agricultural context refers to the use of sensors and other devices to convert every element and action involved in agriculture into data. IoT technologies are one of the reasons why agriculture 4.0 can generate such a valuable amount of information [172].  
Agriculture based on agricultural data is known by different names apart from agriculture 4.0: Digital agriculture or SA. However, SA emerges as a main concept of agriculture 4.0. The SA addresses important agricultural objectives such as saving water, conserving the soil, limiting carbon emissions and increasing productivity by doing more without stopping [173].