esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusion  
This paper described the long-term effects on vegetation of a catastrophic fire on the northern Great Xing'an Mountain in 1987 that were observed using a nonparametric Mann-Kendall (MK) statistics method and analyzing the AVHRR GIMMS 15-day composite NDVI dataset. Both temporal and spatial characteristics were analyzed under natural regeneration and tree planting scenarios from 1984 to 2006.  
Figure 11: Comparison GIMMS NDVI (**left**) with Landsat NDVI (**right**).  
Regressing post-fire NDVI values on pre-fire values helped identify the NDVI for completely burned pixels in vegetation stands; this value was subsequently used in an index to quantify stand differences in fire damage. October was established as a better month compared with the other months to distinguish the post- and pre-fire vegetation conditions using NDVI signals because grasses and shrubs lose their colored leaves, while the leaves on healthy trees remain green. The Mann-Kendall (MK) statistics method is capable of detecting vegetation trends in relatively long time series. Normally, high fire damage area performs a slower and even harder regrowth progress than medium burned area under natural recovery conditions without tree planting. However, because tree planting primarily occurred in the severely burned areas (approximately equal to the Medium, High and Very High fire damage areas combined), this region exhibited a better recovery trend than the lightly burned locations (Table 4). Reasonable tree planting can substantially quicken the recovery process and shorten the restoration time of target species. We believe that it is necessary to incorporate information about spatial patterns of plantation into planting strategies. This research involves a large area (\(1.33\times 10^{6}\)\(\mathrm{\;hm^{2}}\)) and a long time span (1984-2006). Therefore, it is unfeasible to perform real field validation for such a study. More detailed satellite analyses and field data are required in the future for a more convincing validation of the results.  
This study would not have been possible without the financial support from the Mitsui & Co., Ltd., Environment Fund Research Grants (R09-C076), Japan. We would like to thank the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group for producing and sharing the AVHRR GIMMS NDVI dataset.  
The authors declare no conflict of interest.
# Privacy Goals for the Data Lifecycle
## 6 Step 3-Aligning the Privacy Goals to GDPR and the Data Lifecycle
### GDPR Principle 6: Integrity and Confidentiality  
For privacy, the concepts of integrity and confidentiality are considered as part of GDPR Principle 6. It is defined in Article 5(1)(f) as ensuring data is: \"processed in a manner that ensures appropriate security of the personal data, including protection against unauthorised or unlawful processing and against accidental loss, destruction or damage, using appropriate technical or organisational measures\". In addition, Article 32 clarifies the security aspects and which are also mentioned in Recitals: 39 (lawful processing), 49 (proportionality and necessity), 75 (risks to data subject), 83 (requirement of business to assess and evaluate privacy risk), 85 (requirement to notify authorities in event of data breach) and 112 (data transfer obligations). Availability is not incorporated as a GDPR principle although it is discussed in relation to the security of processing in Article 32(1)(b and c) and in Recitals: 49 (proportionality and necessity) and 108 (data subject enforceability rights).  
Applying this to 7-DL, it was decided that, because the _aim_ of the privacy goals is to ensure that privacy is maintained, this GDPR principle is the optimal area to place all the privacy goals. This will ensure that any security measures implemented to protect the data (and therefore, privacy), will consider both the security and privacy aspects and thus, aid the practitioner in meeting both privacy-by-design and security-by-design principlesin their risk considerations. Therefore, it makes sense to insert the nine privacy goals as subgoals to this principle. With the following goals, privacy can be incorporated by design and default (Article 25), into both the decision making process, and the setting up of appropriate security principles which they will help to facilitate this is met (as depicted in Table 2):  
**i**: Confidentiality
**ii**: Integrity
**iii**: Availability
**iv**: Unlinkability
**v**: Unobservability/Undetectability
**vi**: Anonymity
**vii**: Pseudonymity
**viii**: Intervenability
**iv**: Transparency
# Diffusion Model as a Base for Cold Item Recommendation
## 3 Cold Item Recommender with a Diffusion Model Base
### Second-Phase Refiner
7: Calculate \(\hat{r}_{ui}\) by using Equation (18) with \(\hat{e}^{+}_{u}\) and \(\hat{e}^{+}_{i}\)
8: Calculate loss \(L_{Refiner}\) by using Equation (22)
9: Update \(\theta\) using gradient descent with \(\
abla_{\theta}L_{Refiner}\)
10:endfor
11:endwhile
```  
**Algorithm 3** Refiner Train  
When we calculate \(\hat{e}^{+}_{i}\), following the ideas of [PERSON] et al. [20] and [PERSON] et al. [19], input \(\hat{e}_{i}\) for the item-side sub-network represented in Equations (20) and (21) is sampled by Equation (23). By allowing the network access to the ground-truth and generated embeddings alternately for the given side information, we expect the model to more accurately guess the ground-truth embeddings based on the generated embedding under the given side information. \[\tilde{e}_{i}= \begin{cases}\hat{e}_{i},&\text{with probability 0.5}\\ e_{i},&\text{otherwise}\end{cases}  \tag{23}\]  
**Inference:** The Refiner calculates refined user embeddings \(\hat{e}^{+}_{u}\) from users' ground-truth collaborative embeddings. Similarly, refined item embeddings \(\hat{e}^{+}_{i}\) are calculated with the generated collaborative embeddings and side information of cold items as input. Once calculated, we can store the two kinds of embeddings in the system. For an arbitrary user \(u\) and cold item \(i\) pair, we can calculate \(\hat{r}_{ui}\) by using Equation (18) with \(\hat{e}^{+}_{u}\) and \(\hat{e}^{+}_{i}\). The ordered recommendation list of \(k\)-items for user \(u\), \(L^{k}_{u}\), is calculated by sorting the top \(k\) items that have the highest \(\hat{r}_{ui}\) scores in descending order.
## 4 Experimental Setup
### Performance Metrics
#### 4.4.3 Pure Diversity (PD)  
In multi-objective optimization algorithms, the PD metric is used to assess the diversity of a solution set generated by the algorithm, focusing on the differences between the solutions [32]. This metric is important for understanding the algorithm's ability to explore the solution space comprehensively. Here, is a common formula for calculating \(PD\):  
\[PD=\frac{2}{|A|(|A|-1)}\sum_{i=1}^{|A|-1}\sum_{j=i+1}^{|A|}d\big{(}\mathbf{x}_ {i},\mathbf{x}_{j}\big{)} \tag{16}\]  
where  
* \(\mathbf{x}_{i}\), \(\mathbf{x}_{j}\) are two different solutions from the set,
* \(d\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}\) is the distance between these two solutions, which can be calculated using the Euclidean distance or another suitable metric.  
This formula calculates the average distance between all possible pairs of solutions in the set, providing a measure of the set's diversity. A higher value indicates better diversity, meaning greater differences between the solutions.
###### Abstract  
Nature-based solutions, such as Constructed Wetlands (CWs), for the treatment of industrial wastewater can be more efficiently operated making use of online monitored parameters as inlet/outlet flows and concentrations for specific substances. The present study compares different datasets acquired in a two-and-a-half-year-long period by normal laboratory methods and also from a specific COD/BOD sensor installed at a winery CWs wastewater treatment plant in Tuscany, Italy. The CW wastewater treatment plant (WWTP) is composed of: equalization tank (70 m\({}^{3}\)); French Reed Bed (1200 m\({}^{2}\)); horizontal subsurface flow (HF) CW (960 m\({}^{2}\)): free water system (850 m\({}^{2}\)); optional post-treatment sand filter (50 m\({}^{2}\)); and emergency recirculation. The obtained average performances for this last period are for COD 97.5%, for MBAS 93.1%, for N-NO2- 84.7%, for NO3- 39.9%, and for TP 45.5%. The online sensor has shown excellent performance in following the COD concentration patterns along the observed period. The qualitative and quantitative validity of the online sensor measurements has been assessed by statistical analysis (\(t\)-test) and reported in the paper. Online data, acquired every 30 min, availability is of extreme importance for the CW system performance optimization, for understanding the behavior of the WWTP in different operative scenarios, and finally for driving the powering on or off eventual process enhancement tools.  
**Keywords:** constructed wetland; treatment wetland; hybrid; multistage system; French Reed Bed; winery wastewater; industrial wastewater; online monitoring; nature-based solution +
Footnote †: journal: Applied Sciences
# Performance of a Closed Cycle Power Takeoff for a Shore-Based Wave Energy Device
## 2 Materials and Methods
### Detailed Estimation of Turbine Performance  
The CFD simulations were conducted using the tools of the commercial software ANSYS version 2021 R2. First, the blades were designed separately on BladeGen. The profile was drawn using the angles and the chord length previously determined, and it was stacked up straightly to the desired height. The geometry has been meshed using Turbogrid. A grid independence study was conducted.  
The solver used was CFX. The steady-state compressible 3D RANS equations of mass, momentum and energy were solved using a second order discretisation scheme. As the Reynolds number is about \(10^{5}\), the turbulence model needs to be able to handle laminar to turbulence transition. The model used was thus the SST (Shear Stress Transport) model gamma-theta. The fourth order Rhie-Chow pressure dissipation algorithm was selected. The problem was considered converged when the residuals were below \(10^{-4}\). The simulation was run across 12 CPUs as a parallel task using the Metis partitioner.  
Figure 8 summarises the numerical setup. Since all blades are identical, only one blade is simulated, and the lateral boundaries are periodic. The two domains, one for the stator blade and the other for the rotor blade, are interfaced by a mixing plane. This boundary is defined as the circumferential averaging of the flow upstream and downstream. Although  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & **Higher Energy Case** & **Lower Energy Case** & **Average** \\ \hline Mean radius & \(r_{m}\) & 165 mm & 163 mm & 164 mm \\ Blade height & \(H\) & 51.1 mm & 50.4 mm & 50.7 mm \\ Blade chord & \(b\) & 40.9 mm & 40.3 mm & 40.6 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Turbine dimensions.  
nonphysical, this numerical procedure has been shown to achieve good agreement with experimental measurements [22,23]. The blades, the hub and the shroud are non-slip adiabatic walls.  
The total pressure \(P_{01}\) and temperature \(T_{01}=293.15\) K are defined at the inlet, and the static pressure \(P_{3}\) at the outlet. The only velocity defined is the rotational speed \(N\) of the rotating domain; hence, the mass flow rate is a result of the simulation. A range of values for the three parameters \(P_{01}\), \(P_{3}\) and \(N\) were explored to be representative of the range of turbine conditions encountered in Mutriku's breakwater.
## 3 Results and Discussion
### Analysis of Samples
The concentration of Ca ranged between 2.52 and 1182 mg kg\({}^{-1}\) in jellies, between 5.93 and 26.3 mg kg\({}^{-1}\) in hard candies and between 2.64 and 5.10 mg kg\({}^{-1}\) in lollipops. The concentration of Mg ranged between 0.71 and 463 mg kg\({}^{-1}\) in jellies, between 1.93 and 7.46 mg kg\({}^{-1}\) in hard candies and between 0.71 and 2.37 mg kg\({}^{-1}\) in lollipops. Ba was only determined in JS-7 at a concentration of 4.03 mg kg\({}^{-1}\); in the same sample, the concentration of Ca and Mg were the highest among the examined samples. The concentration for Fe was up to 6.24 mg kg\({}^{-1}\) (JS-8) and the concentration for Bi was up to 4.03 mg kg\({}^{-1}\) (LS-2). Cr was determined in all samples at a concentration range of 0.15-0.78 mg kg\({}^{-1}\) for the examined samples. Cr has been previously determined in candy samples [4], and its presence was attributed to contamination from packaging material; however, Cr also has been reported in food samples since it can be present in the raw materials used for their production. Ag, Cd, Co, Cu, Mn, Ni, Pb and Zn were not detected in the examined samples and Ba was not detected in the hard candy and lollipop samples, which indicates that their concentrations were lower than their LOD using the herein developed ICP-AES method.
## 1 Introduction
Unfortunately, the above methods focus solely on a single dimension, addressing only 2D local similarity issues and obtaining pixel weights through complex networks. This introduces additional computational overhead, neglecting the correlation between 2D semantics and 3D space, ultimately compromising the assurance of 3D consistency in the depth direction. To address the above issues, this paper proposes an uncertainty-epipolar Transformer multi-view stereo network (U-ETMVSNet) for object stereo reconstruction. First, this paper uses an improved cascaded U-Net network to enhance the extraction of 2D semantic features. And the cross-attention mechanism of the epipolar Transformer is used to construct the 3D association between different view feature volumes along the epipolar lines, enhancing the 3D consistency of the depth space, without introducing additional learning parameters to increase the amount of model calculations. The cross-scale cost volume information exchange module allows information contained in cost volumes at different stages to be progressively transmitted, strengthening the correlation between cost volumes and improving the quality of depth map estimation. Secondly, allynamic dynamic adjusting the depth sampling range based on the uncertainty of the probability cost volume is employed to effectively reduce the requirements on the number of depth samples and enhance the accuracy of depth sampling. Finally, a multi-stage joint learning approach is proposed, replacing the conventional depth regression problem at each stage with a multi-depth value classification problem. This joint learning strategy significantly enhances the precision of the reconstruction. The proposed method is experimentally validated on the DTU and Tanks&Temples datasets, and its performance is compared with current mainstream methods. The method in this paper achieves high reconstruction accuracy even at lower sampling rates, confirming the effectiveness of the proposed approach for dense scene reconstruction. The rest of the paper is organised as follows: Section 2 provides an overview of relevant methods in the field. Section 3 provides a detailed overview of the proposed network and the entire process of object reconstruction. Section 4 presents the experimental setup and multiple experiments conducted to validate the reliability and generalization capabilities of the proposed method. Finally, in Section 5, we summarize the contributions of the proposed network to multi-view reconstruction of objects offer prospects for future work.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 4 City-Wide Deployments
### Infrastructure Deployment in the City of Bristol
#### 4.2.3 RAN Tier  
For the RAN in Bristol, a total of four towers in the _Millennium Square_ are used, each one equipped with a dedicated Wi-Fi node (Ruckus T710) to provide coverage to the square and the close surrounding areas, down to the harbourside area. Additionally, the _MShed Museum_ location hosts two small cells (Accelleran E1000 series) and three Wi-Fi nodes (Ruckus T710) at East Roof, Middle Roof, and West Roof, respectively. The location of each node can be corroborated in Figure 3b. Similar to Barcelona, the small cells deployed in Bristol use Band 42 and follow the TDD config mode 2 on 20 MHz with a maximum throughput of 90 Mbps in DL and 10 Mbps in UL.
# Evaluating Litter Yield and Decomposition for Re-Vegetated Mangroves in a Subtropical Mudflat
## 3 Results
### Organic Carbon in the Soils Underneath the Added Litter Materials  
The organic carbon content in the soil samples collected at different time intervals is given in Table 3. On the 90 th day of the field incubation experiment, the organic carbon content in the soils under all three mangrove plant species tended to be lower, as compared to their counterpart original soils. The soil-borne organic carbon then markedly increased from the 90 th to the 180 th day. From the 180 th to the 270 th day, soil organic carbon content decreased before it increased again until the 360 th day.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time (d)** & **Branch _Heritiera_** & **Leaf _Heritiera_** & **Branch _Thesegia_** & **Leaf _Thesegia_** & **Branch _Kandelia_** & **Leaf _Kandelia_** \\ \hline
30 & \(9.18\pm 1.42\)gh & \(19.76\pm 3.81\)f & \(10.57\pm 2.55\)g & \(15.67\pm 1.16\)f & \(3.14\pm 1.1\)f & \(14.9\pm 1.31\)g \\
60 & \(6.59\pm 2.61\)h & \(11.18\pm 1.54\)g & \(8.41\pm 2.7\)g & \(16.43\pm 1.24\)f & \(4.97\pm 1.1\)f & \(16.27\pm 2.02\)g \\
90 & \(11.65\pm 1.21\)g & \(12.24\pm 1.69\)g & \(15.29\pm 1.52\)f & \(26.11\pm 2.07\)e & \(14.51\pm 2.63\)e & \(14.02\pm 2.16\)g \\
120 & \(19.27\pm 1.71\)f & \(16.24\pm 1.51\)f & \(20.64\pm 1.41\)e & \(25.86\pm 1.8\)e & \(13.14\pm 2.7\)e & \(20.98\pm 1.02\)f \\
150 & \(24.71\pm 1.25\)e & \(30.35\pm 2.01\)e & \(22.04\pm 1.41\)e & \(30.06\pm 1.27\)d & \(21.77\pm 1.07\)d & \(25.98\pm 1.1\)e \\
180 & \(26\pm 2.48\)e & \(24.12\pm 3.84\)d & \(32.36\pm 2.18\)d & \(37.71\pm 1.2\)c & \(22.84\pm 1.06\)d & \(25.29\pm 1.41\)e \\
210 & \(34\pm 2.98\)d & \(34.82\pm 1.83\)c & \(23.82\pm 3.45\)e & \(29.94\pm 3.71\)d & \(20.78\pm 2.59\)d & \(26.47\pm 1.19\)e \\
240 & \(32.35\pm 3.46\)d & \(37.76\pm 1.55\)c & \(38.6\pm 1.36\)c & \(36.18\pm 1.04\)c & \(33.04\pm 2.78\)c & \(35.29\pm 1.04\)d \\
270 & \(36.65\pm 1.42\)c & \(43.53\pm 2.07\)b & \(31.72\pm 4.27\)d & \(37.71\pm 1.46\)c & \(32.75\pm 1.18\)c & \(41.37\pm 1.03\)c \\
300 & \(40.71\pm 1.42\)b & \(45.65\pm 1.76\)b & \(43.57\pm 1.69\)b & \(48.03\pm 1.24\)b & \(38.73\pm 1.44\)b & \(42.35\pm 1.15\)c \\
330 & \(40.94\pm 1.24\)b & \(50.59\pm 1.92\)a & \(46.5\pm 1.61\)ab & \(48.54\pm 1.2\)b & \(37.75\pm 1.05\)b & \(47.35\pm 1.14\)b \\
360 & \(49.22\pm 1.57\)a & \(52.15\pm 1.8\)a & \(49.76\pm 1.71\)a & \(52.67\pm 1.15\)a & \(61.29\pm 4\)a & \(59.82\pm 1.29\)a \\ \hline \hline \end{tabular}  
Different letters in the same column indicate a significant difference at \(p<0.05\).  
\end{table}
Table 2: Calculated weight loss rate (%) of the leaf and branch portions for the three selected mangrove plant species.
## 3 Calibration of ATMD with Viscous Damping-Stiffness Control Force
### Numerical Demonstration  
Figure 2 presents the frequency responses of the primary structure equipped with the ATMD with mass ratio \(\mu=1\%\), which was designed using the feedback gains (31) and (32). These results are presented for two distinct values of the control parameter \(K\), namely \(K\) = 2 and \(K\) = 4. To provide a more comprehensive evaluation of the ATMD's performance, its characteristics are compared with those for the TMD with mass ratios \(\mu=1\%\), \(2\%\), and \(4\%\). All the characteristics are normalized by \(X_{1 static}=F_{exc}/k_{1}\).  
Figure 2a confirms that the maximum displacement amplitude of the primary structure for the ATMD with a mass ratio of \(\mu\) and design parameter \(K\) matches that of the TMD with a larger mass ratio of \(K\mu\). The peak value of the structure's displacement amplitude is effectively governed by the ATMD's design parameter \(K\).  
In the case of [PERSON]'s TMD, the peak response of the primary structure is solely dependent on the mass ratio \(\mu\), as described by (11). A larger mass ratio results in a smaller maximum relative displacement, as shown in Figure 2b. For the ATMD, its maximum relative displacement does not exceed that of the TMD with the same mass ratio, and this maximum value remains the same for any \(K\), as depicted in Figure 2b.  
Since the frequency responses in Figure 2 are normalized, the specific parameters used in this numerical example are not crucial. However, for the sake of completeness, note that the sample parameters used in this work (also in subsequent sections) are as follows: \(f_{1}=0.5\) Hz, \(m_{1}=500\),000 kg, \(F_{exc}=8\) kN.  
Appendix A contains the MATLAB code that allows for reproducing the calculations of the frequency responses.
## 4 Analysis Model
### Analysis Setting
#### 5.1.2 Control Parameters of the ISAMD  
The control parameters of the ISAMD can be divided into two categories: (1) system hardware control parameters, including the control mass block ratio (\(\mu\)), frequency ratio of the control mass block (\(\gamma_{f}\)) and the damping ratio of the control mass block (\(\xi_{a}\)); and (2) control law parameters, including the weight of velocity response (\(w_{v}\)) and acceleration response (\(w_{a}\)) of the control law.  
The control mass block ratio can be defined by the rate of the mass of the control block and structure:  
\[\mu=\frac{m_{a}}{\sum m_{s}} \tag{6}\]  
Then, the frequency ratio of the control mass block can be defined as the rate of a natural vibration frequency and the first modal frequency of the main structures:  
\[\gamma_{f}=\frac{f_{a}}{f_{0}}=\frac{\sqrt{k_{a}/m_{a}}}{2\pi\,f_{0}} \tag{7}\]  
The weight of velocity response (\(w_{v}\)) and acceleration response (\(w_{a}\)) of the ISAMD control law affect the timing of the \"releasing\" and \"capturing\" status of the control mass block and the main structure. If \(w_{v}:w_{a}=1:0\), the \"releasing\" (unlock) or \"capturing\" (lock) status is determined by the structural velocity responses at the installation position of the control mass block. The timing of the combination of the ISAMD and structure is at the point when the direction of the structural motion reverses, which is the maximum or minimum structural displacement, at which point the structural velocity is zero. In contrast, when \(w_{v}:w_{a}=0:1\), the \"releasing\" or \"capturing\" status is determined by the structural acceleration responses at the installation position of the control mass block. The timing of the combination of the ISAMD and the structure is at the point when the  
Figure 7: Analysis model of Building with TMD and ISAMD.  
direction of the structural acceleration reverses, i.e. the structural displacement is zero and the structural acceleration reaches the maximum or minimum acceleration. The ratio between these two extreme ratios causes the switching time point to be between these two extreme time points, at which point the velocity and acceleration direction are the same. Five different proportions are compared to determine the seismic resistance in this study. The control parameters of the ISAMD are listed in detail in Table 1.