esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Excess Water Analysis
#### 3.3.1 Change in Surface Storage  
If this excess water were entirely attributed to surface storage, the entire study area would be submerged under 3.18 m of water. As a comparison, we collected the surface storage specifications of all dams located in the 30 states encompassing our study area that are monitored by the National Inventory of Dams [42]. We estimated that even if each dam were filled to maximum capacity entirely by excess water from (P-Ru), this would still only account for 0.24 m, or 7.6%, of the total 3.18 m of the excess water. This is in agreement with the studies [19,20] that have shown that changes in surface water storage dS/dt(sw) and storage in unsaturated zone dS/dt(uz) are small and negligible over  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Linear regression slope** & \(p\)**-value** & **Kendall-Sen slope** & **Mann-Kendall \(p\)-value** \\ \hline Precipitation & 0.67 & 4.40(10)\({}^{-8}\) & 0.67 & 1.27(10)\({}^{-6}\) \\ Runoff & 0.12 & 0.5495 & 0.11 & 0.4429 \\ Difference (P-Ru) & 0.55 & 4.44(10)\({}^{-9}\) & 0.54 & 2.22(10)\({}^{-8}\) \\ dS/dt(gw) & \(-0.06\) & 0.6009 & \(-0.01\) & 0.9506 \\ NDVI & 0.004 & 0.0352 & 0.006 & 0.0058 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The rate of change calculated from linear regression and the Kendall Sen slope estimator, along with the respective p-values. The units for the slopes of precipitation, runoff and the difference (P-Ru) are given in mm\(\cdot\)yr\({}^{-1}\). Annual changes in dS/dt(gw) (mm\(\cdot\)yr\({}^{-1}\)) and NDVI (dimensionless) are also listed.  
decadal to century time scales. Therefore we conclude changes in surface storage and unsaturated zone storage can only explain, a small fraction, if any, of the increasing (P-Ru) trends.
## 2 Materials and Methods  
### General Approach  
The methodology approach for this work consists of determining the performance ratio of BIPV systems by evaluating module temperature models and applying an analytical AOI loss model to any possible BIPV module position. Temperature models are assessed by comparing predicted temperatures with experimental data from a monitored BIPV ventilated facade. Accuracy, simplicity, and ease of use are the criteria considered to select the most suitable temperature model in our case. Concerning angular losses, they are estimated for the different irradiance components (beam, diffuse, and ground-reflected). Other losses that usually have a lower impact on _PR_ are estimated using tabulated values and parameters. All these losses combined lead to the estimation of the _PR_ of a BIPV system based on the tilt and azimuth angles of the modules, which is represented through polar heatmap graphs. The proposed methodology is applied to a case study located in the headquarters of CIEMAT, in Madrid (Spain), with geographic coordinates 40.45\({}^{\circ}\) N,\(-3.74^{\circ}\) E. Building 42 includes BIPV arrays on the east, south, and west ventilated facades (Figure 2). The monitoring system records the PV power and the main meteorological parameters measured on the rooftop near the arrays on a minutely basis [44]. For the validation of the work, this paper focuses on the south facade, which includes twenty-eight 305 W monocrystalline silicon modules (adding up to an installed nominal power of 8.54 kW) with an azimuth of 172.65 degrees from the north and a 90-degree tilt (vertical position). Modules are installed in two rows of 14 each at an approximate height varying from 8 m to 10 m above ground.
## 4 Experiments and Results
### Baselines
#### 4.2.1 Evaluation Methodology  
All experiments are run on the same machine (Intel Xeon 8-Core CPU of 2.4 GHz and single NVIDIA GeForce GTX TITAN X GPU) for fair comparison. We apply the widely used leave-one-out technique ([PERSON] et al. 2019; [PERSON] et al. 2009; [PERSON] et al. 2020d) and then adopt two popular metrics, HR (Hit Ratio) and NDCG (Normalized Discounted Cumulative Gain), to judge the performance of the ranking list.  
HR (Hit Ratio) is the ratio of the total number of documents to the number of documents in the dataset that appear in the Top-\(N\) recommendation list. The range of HR-value is 0-100%.  
\[\text{HR}=\frac{N_{\text{top}}}{\text{Number}_{\text{total}}} \tag{17}\]  
The NDCG (normalized discounted cumulative gain) score of a recommendation list \(L\) can be derived by:  
\[\text{NDCG}_{L}=\frac{\text{DCG}_{L}}{\text{IDCG}_{L}} \tag{18}\]where \(DCG_{L}\) can be formulated as:  
\[\text{DCG}_{L}=\sum_{i=1}^{n}\frac{2^{\text{rel}(i)}-1}{\log_{2}(i+1)} \tag{19}\]  
where the \(\text{rel}(i)\) represents the rank value for item \(i\) in the true list and \(\text{IDCG}_{L}\) is the DCG value of the ideal order of the cited papers in our case. The range of NDCG-value is 0-1.  
For each user, our evaluation protocol ranks all the items except the positive ones in the training set. In this way, the obtained results are more persuasive than ranking a random subset of negative items only [56]. For each method, we randomly initialize the model and run it five times. After that, we report the average results.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 2 Modelling Approach
### Electricity Price  
The PV-wind-PHS system is assumed to be an electricity price taker, and a real-time pricing (RTP) model is considered. The effect of the change in the hourly profile of the day caused by the increase in PV penetration ('duck curve') and the increase in wind penetration are considered for estimating the future electricity prices. The selling electricity price in year \(y\) is calculated as [42]  
\[Pr_{Esl}(h,\ y)=Pr_{Esl}(h,\ 0)\bigg{(}1-\left(F_{PV}G(h,\ 1)+F_{w}\right) \bigg{(}\frac{y-1}{Life_{s}-1}\bigg{)}\bigg{)}\ \forall\ 1\leq y\leq Life_{s}, \tag{40}\]  
where \(Pr_{Esl}(h,\ y)\) represents the sell electricity price of hour \(h\) (\(0\ldots 8760\)) of year \(y\). \(F_{PV}\) represents the PV factor to consider the price reduction in the future hourly price profile attributed to an increase in PV penetration 'duck curve' depending on the average irradiance of hour \(h\) of year \(1\ G(h,\ 1)\) (W/m\({}^{2}\)). \(F_{wind}\) represents the wind factor to consider the price reduction in the future hourly price profile caused by an increase in wind generation, and \(Inf_{PrE}(y)\) represents the annual inflation rate for the electricity price. The uncertainty in electricity price was considered using a random number with a mean \(Inf_{PrE\_mean}\) and standard deviation \(\sigma_{PrE}\) for calculating the annual electricity price inflation rate for each year \(y\).  
\[Inf_{PrE}(y)=N\big{(}Inf_{PrE\_mean},\sigma_{PrE}\big{)}. \tag{41}\]  
The price calculated in the previous equation refers to year 0 (beginning of the system lifetime); therefore, the price considered for year \(n\) will be updated (increased due to inflation) by a factor \(\sum_{y=1}^{y=n-1}(1+Inf_{PrE}(y))\). The purchase price of electricity is affected similarly. The electricity (purchase or sell) price set points are updated each year with electricity price inflation so that the electricity price of each hour of each year is compared to their updated values.
## 2 Materials and Methods
### Optimization
#### 2.4.4 Optimization Model  
The objective is to find the values of the parameters \(\theta\) that minimize \(J(\theta)\). This can be achieved by solving the system of equations given by the partial derivatives of \(J\) with respect to each parameter, setting them to zero as described in Equation (11) [37; 38; 39].  
\[\frac{\partial J}{\partial\theta_{j}}=-2\cdot\sum_{i=1}^{n}\left[y_{i}-f(x_{i };\theta)\right]\frac{\partial(x_{i};\theta)}{\partial\theta_{j}}=0 \tag{11}\]  
These equations can be numerically solved using methods such as the Levenberg-Marquardt Method (LMM), which combines gradient descent techniques and the Gauss-Newton method to find optimal parameter values [40].  
In this case, the LMM is employed for numerical optimization. It is an iterative algorithm for solving non-linear least squares problems. The LMM merges the advantages of the gradient descent method and the Gauss-Newton method. The algorithm iteratively adjusts the parameters of the function to minimize the cost function, which, in this case, is the sum of the squares of the differences between the observed and predicted values. The key to apply the LMM is to adjust the magnitude of the parameter update to strike a balance between the convergence speed of the Gauss-Newton method and the robustness of the gradient descent method. In mathematical terms, the Levenberg-Marquardt algorithm adjusts the parameters as described in Equation (12):  
\[\theta_{new}=\theta_{current}-\left(J^{T}\cdot J+\lambda I\right)^{-1}\cdot J^ {T}\cdot r \tag{12}\]where:  
* \(\theta_{new}\) are the new parameters to calculate,
* \(\theta_{current}\) are the current parameters,
* \(J\) is the Jacobian matrix,
* \(r\) corresponds to the residual vector (difference between the observed and the predicted values),
* \(\lambda\) is a regularization parameter.  
The parameter \(\lambda\) is adjusted during the iterations to control the convergence of the method. If \(\lambda\) is small, the method resembles the Gauss-Newton method, and if it is large, it resembles the gradient descent method. This dynamic adjustment helps to deal with convergence and robustness issues at different stages of the optimization process.
# An Evaluation of the Effects of a Virtual Museum on Users' Attitudes towards Cultural Heritage
## 3 Materials and Methods
### Instruments  
#### 3.3.1 Independent Variables and Measurements  
* Type of multimedia experience: As a means of controlling the type of multimedia experience that participants took part in, half of them were provided with a Windows Store download link for a 3D simulator containing a variety of virtual copies of real life cultural heritage objects from the Maule region [7]. The other half of the participants received a link to an interactive website hosted on the University's website. This web page contained the same objects as the simulator but, in this case, presented through photographs.
* Thought favorability: Participants received a web questionnaire with ten open-ended question blocks for them to list their thoughts about the multimedia experience. They were asked to write one thought for each block (examples on the use of the thought-listing technique can be found on [39; 40; 41; 42] Two independent judges who did not know about the conditions and experimental hypotheses subsequently codified these thoughts as positive, negative, or neutral toward the experience and resolved their disagreements through discussion. Some examples of positive and negative thoughts are, respectively: \"I think it's innovative and fun\" and \"The place causes me anxiety\". Using only the thoughts that related to the experience, an index of favorability of thoughts was created, subtracting the number of negative thoughts from the number of positive ones and dividing by the sum of both quantities.
* Presence index: We used the SUS scale of presence [33; 43; 44]. This instrument was answered in a seven-point Likert format (e.g., \"Please rate your sense of being present in the multimedia experience, on a scale of 1 to 7, where 1 represents your normal experience of being in a place\"; \"To what extent were the times when the multimedia experience was the reality for you?\"). A reliability analysis including the six items was performed, showing an appropriate but low reliability (a = 0.67). This improved when discarding item 6 (\"During the experience, did you often think that you actually were in the multimedia experience?\"). Due to this change, a sole index was created using only the first five items (a = 0.76).
## 4 Analysis on Test Results  
### Wheel Tracking Test Results  
The Wheel-tracking test based on JHS 230-1992 [14] was used to evaluate the rutting susceptibility of the SMA mixtures. An 0.7 MPa wheel load at 60 \({}^{\circ}\)C temperature under dry conditions was adopted. Specimens were 300 mm in width, 300 mm in length and 50 mm in height for SMA5, SMA13 and SMA20, and specimens were 300 mm in width, 300 mm in length and 100 mm in height for SMA30. The testing indictor of dynamic stability (\(DS\)) was expressed by Equation (1)  
\[DS=\frac{630}{d_{60}-d_{45}} \tag{1}\]  
where, \(d_{60}\) is rut depth at 60 min, mm; \(d_{45}\) is rut depth at 45 min, mm; \(DS\) is the dynamic stability, cycles/mm.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Properties** & **SMA5** & **SMA13** & **SMA20** & **SMA30** \\ \hline \multirow{2}{*}{**Specimen Dimensions**} & **63.5 mm High and** & **63.5 mm High and** & **63.5 mm High and** & **95.2 mm High and** \\  & **101.6 mm Diameter** & **101.6 mm Diameter** & **101.6 mm Diameter** & **152.4 mm Diameter** \\ \hline \multirow{2}{*}{Blow cycles on each side} & 75 & 75 & 75 & 112 \\  & 6.4 & 5.4 & 5.1 & 4.5 \\   Air Void, \% & 2.8 & 2.4 & 3.2 & 3.7 \\   VMA, \% & 17.7 & 15.2 & 15.3 & 14.3 \\   Stability, KN & 10.72 & 9.64 & 9.92 & 18.81 \\   Flow value, 1/10 mm & 37 & 39 & 37 & 57 \\   VFA, \% & 84.2 & 84.2 & 79.1 & 74.1 \\   Retained stability, \% & 81.6 & 79.6 & 77.7 & 81.2 \\ \hline \end{tabular}
\end{table}
Table 6: Results of SMA mixture design.  
Figure 1: Flow chart of stone matrix asphalt (SMA) mixture design.  
\begin{table}
\begin{tabular}{c c c} \hline
**Mixture Type** & **Average Dynamic Stability, Cycle/mm** & **Coefficient of Variation, \%** \\ \hline SMA5 & 605 & 13.7 \\ SMA13 & 878 & 12.5 \\ SAM20 & 941 & 17.9 \\ SMA30 & 1033 & 19.2 \\ \hline \end{tabular}
\end{table}
Table 7: Results of wheel tracking test.  
The higher the \(DS\) of asphalt mixtures, the better resistance to permanent deformation at high temperature is.  
Wheel tracking test results are presented in Table 7.  
Table 7 shows that the \(DS\) of SMA mixtures were enhanced with the increase of NMAS. In other words, decrease of passing percent on 4.75 mm sieve was conducive to improvement of rutting resistance of SMA mixture.
## 2 Methods
### Data Processing Section  
Stata 17.0 software was used for the meta-analysis of effect sizes, heterogeneity testing, sensitivity analysis, and the creation of forest plots for all included literature outcomes. As all outcomes in the included studies were continuous variables with the same measurement units, [PERSON]'s \(g\) was chosen for statistical effect size estimation along with a computation of a 95% confidence interval (CI). Statistical heterogeneity was tested using the \(I^{2}\) statistic, adopting either a fixed-effects or a random-effects model based on the level of heterogeneity: 0%, 25%, 50%, and 75% correspond to no, low, moderate, and high heterogeneity, respectively. Following Cochrane's suggestion, it is recommended that an \(I^{2}\) should not exceed 40% for acceptable heterogeneity, in which case a fixed-effects model is used for analysis; otherwise, a random-effects model is implemented. Further, subgroup analysis is used to explore sources of heterogeneity [41]. Publication bias was both quantitatively and qualitatively assessed using [PERSON]'s test and funnel plots, with a significance level set at \(p\)-value \(<\) 0.1 [42]. For the other statistical tests, a significance level was set at \(p\)-value \(<\) 0.05.
# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 4 Discussion
### Experimental Setup  
The proposed NUDN method is implemented using Faster-RCNN [7]. The hardware configuration includes 96 GB RAM, an Nvidia RTX A4000 (16 GB) graphics card, and an Intel Xeon processor. There are two reasons for selecting Faster-RCNN as the backbone network for NUDN. First, it can generate high-quality candidate regions (RPN) that are classified and subjected to bounding box regression by the network, significantly reducing computational overhead and increasing detection speed. Second, other existing methods also use Faster-RCNN as the backbone network. Thus, using Faster-RCNN allows for a fair comparison of the proposed NUDN method with other state-of-the-art methods. The main parameters and environment of the experiment are the same as those in Table 1.  
We utilized all labeled source samples and unlabeled target samples. The network was trained for 60 k iterations with a batch size of 12, and the exponential moving average (EMA) was set to 0.999. The base learning rate was set to 0.04 and then decayed by a factor of 0.5 to find the optimal solution (0.04, 0.02, 0.01, 0.005). The threshold of 0.8 was used to filter out low-confidence predictions. The size of the training and testing images was adjusted to a length of 600 pixels.
## 7 Discussion
### Collaborations with 6G Key-Enabler Technologies
#### 7.2.2 Reconfigurable Intelligent Surface (RIS)  
Reconfigurable intelligent surfaces (RIS) stand as an innovative technology in the realm of wireless communication, aiming to manipulate the characteristics of radio waves through the use of smart surfaces. These surfaces, comprising various antennas and reflective elements, enable functionalities such as signal concentration, directional control, and optimization of wireless links [52]. Primarily composed of antennas and reflective surfaces, RIS (v3) is software-controlled to adequately reflect or transmit radio waves. This capability regulates signal strength, minimizes multipath interference, and enhances overall communication performance. Especially in wireless localization technology, RIS can notably enhance accuracy in NLoS environments [53]. It improves signal paths in various settings, enabling precise location measurements. Enhancing accuracy in NLoS environments will result in possibilities for high accuracy of complex indoor and urban location-based services. Moreover, the flexibility and reconfigurability of RIS can optimize positioning performance across various application areas. Its application is expected to bring forth new possibilities in modern wireless communication and location-based services through improved performance and accuracy.
# Adam Algorithm with Step Adaptation
## 4 Numerical Results and Discussion
### Preliminary Analysis of Algorithm Parameters
#### 4.1.3 Analysis of Exponential Smoothing Parameters  
For the function \(f_{Q}\), we will conduct a sensitivity analysis for the parameter \(\beta_{2}\), which is responsible for smoothing squares of gradient components. The parameters \(\alpha\) = 0.01, \(n\_max\) = 200,000 were set. The results are presented in Table 4.  
The analysis shows that, here, the best value for ADAM is \(\beta_{2}\) = 0.999, and for H_ADAM, it is \(\beta_{2}\) = 0.9. In general, it is recommended in the original ADAM to set \(\beta_{2}\) = 0.999. Therefore, all experiments with the ADAM and H_ADAM algorithms are carried out with this value. In the example of this problem, there are no big differences in the behavior of the algorithms with \(\beta_{2}\) = 0.999 and \(\beta_{2}\) = 0.9. Nevertheless, this parameter can be selected for a set of similar problems.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\alpha\)} & \multicolumn{4}{c}{\(a_{max}\) = 10\({}^{4}\), \(n\_max\) = 200,000} & \multicolumn{4}{c}{\(a_{max}\) = 10, \(n\_max\) = 100,000} \\ \cline{2-10}  & **ADAM** & **H_GR** & **H_MOMENTUM** & **H_ADAM** & **H_GR** & **H_MOMENTUM** & **H_ADAM** \\ \hline
0.1 & 2253 \(\times 10^{-5}\) & 78,107 & 5835 & 2804 & 109,253 & 169 & 359 & 343 \\
0.01 & 2253 \(\times 10^{-5}\) & 78,882 & 3892 & 1572 & 109,253 & 944 & 79,147 & 12,679 \\
0.001 & 2253 \(\times 10^{-5}\) & 78,889 & 15,769 & 17,992 & 109,253 & 3011 There is also an optimum for the \(\beta_{1}\) parameter, but the dependence of the parameter \(\beta_{1}\) on the degree of degeneracy of the function follows from a comparison of the gradient method (\(\beta_{1}=0\)) and other methods. It is obvious that this parameter should be reduced as the conditionality of the function decreases.
###### Abstract  
The objective of this work was to determine the ability of a pilot-scale hybrid system to treat real (non-synthetic) winery wastewater. The experimental treatment system consisted of two stages: An attached growth pilot-scale bioreactor (biological trickling filter with plastic support material) was initially used to remove a significant amount of dissolved chemical oxygen demand (d-COD) from winery wastewater, and then a pilot-scale, horizontal subsurface flow constructed wetland (CW) was examined as a post-treatment step for further d-COD removal. Results from the biofiliter revealed that the recirculation rate of 1.0 L/min lead to higher d-COD removal rates than that of 0.5 L/min for all feed d-COD concentrations tested (3500, 7500, 9000 and 18,000 mg d-COD/L). Experiments in the CW were performed using feed d-COD concentrations of about 1500 mg/L (equivalent to biofiliter effluent when initial filter feed d-COD concentrations are 18,000 mg/L). The wetland polishing stage managed to further remove d-COD and produced effluent concentrations below current legislation limits for safe disposal. Furthermore, the presence of zeolite in CW (one third of the length of CW) enhanced ammonium removal. The experimental results indicate that the combination of a biological trickling filter and a constructed wetland could effectively treat effluents originating from small wineries typical of the Mediterranean region.  
winery wastewater; trickling biofiliter; constructed wetland; d-COD removal Article