esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Background of Copernicus Services and the Workshops
### The Ecuadorian Training Workshops on CS  
The training workshops titled \"Integration of Copernicus Space Technologies: Advanced Strategies for Climate Change, Risk and Disaster Management, Environmental Management, Territorial and Food Security\" took place in Quito on 8-19 January 2024. It was supported by the Military Geographic Institute of Ecuador and the European Delegation in Ecuador, who acted as key collaborators for the workshop. It aimed to empower Ecuadorian analysts from selected institutions to effectively use CS, following a collaboration discussed in a roundtable talk between the Ecuadorian Government and the European Union on the topics covered in the workshop's title. Survey data from institutions were collected to identify the needs and capabilities and to define a 10-day training workshop, running 8 h per day, covering an overview of CS, including monitoring the atmosphere and land, climate change, emergency management, and security applications with practical exercises. Despite switching to a virtual mode due to a security emergency [29] that resulted in the cancellation of a field trip and some participants' withdrawal, the program produced high satisfaction among the 17 registered participants from six institutions. Additionally, strategic priorities were summarized to reflect the participants' interests in further developing Copernicus-based solutions by decision-makers and other influencers.
## 3 Experimental Setup and Protocol
### Evaluation Metrics  
Several metrics were used for evaluation of throwing trajectories. To evaluate the error of the autoencoder network, we used mean squared error (MSE) for positions, orientations, and for the difference between the policy parameters (CDMP weights). As in our experiments the position has two degrees of freedom, MSE for position is defined as  
\[\text{MSE}_{\text{pos}}=\frac{1}{K}\sum_{k=1}^{K}\frac{1}{n_{k}}\sum_{i=1}^{n_ {k}}\|[x_{i,k},z_{i,k}]^{\text{T}}-[\widetilde{x}_{i,k},\widetilde{z}_{i,k}]^ {\text{T}}\|, \tag{14}\]  
where \([x_{i,k},z_{i,k}]^{\text{T}}\) is the position at sample \(i\) on the trajectory generated by the \(k\)-th input parameters of AE, \([\widetilde{x}_{i,k},\widetilde{z}_{i,k}]^{\text{T}}\) the position at sample \(i\) on the trajectory generated by the \(k\)-th output parameters of AE, \(n_{k}\) the number of time samples on the \(k\)-th trajectory, and \(K\) the number of test trajectories. In order to obtain the same number of samples on the trajectory generated by input and output parameters of the autoencoder, the integration time step of the output trajectory was appropriately scaled: \(\widetilde{\Delta t}=(\widetilde{\tau}/\tau)\Delta t\). As in our experiments  
Figure 4: Targets contained in \(\mathcal{D}^{\text{AE}}\) (black circles), real-world database \(\mathcal{D}^{\prime}\) (cyan dots), and generalized databases \(\mathcal{D}^{\prime}\) and \(\mathcal{D}^{g}\) (red dots). The three blue crossed circles depict the queries which served as starting points for RL in latent space, as described in Section 4.2.  
the orientation had only one degree of freedom, we only take into consideration the angle around the \(y\) axis, here denoted by \(\varphi\):  
\[\text{MSE}_{\varphi}=\frac{1}{K}\sum_{k=1}^{K}\frac{1}{n_{k}}\sum_{i=1}^{n_{k}}|| \varphi_{i,k}-\widetilde{\varphi}_{i,k}||. \tag{15}\]  
For the difference in policy parameters, we used the mean squared error of policy parameter vectors without the duration \(\tau\), i.e., \(\mathbf{\theta}_{k}^{\prime}=\mathbf{\theta}_{k}^{\text{CDMP}}\backslash\tau_{k}\)  
\[\text{MSE}_{\mathbf{\theta}}=\frac{1}{K}\sum_{k=1}^{K}||\mathbf{\theta}_{k}^{\prime}- \widetilde{\mathbf{\theta}}_{k}^{\prime}||. \tag{16}\]  
To compare the simulated and the generalized databases we used the error of the throw  
\[e=||\mathbf{q}_{d}-\mathbf{q}_{a}||, \tag{17}\]  
where \(\mathbf{q}_{d}\) is the desired target landing spot (location of the basket) and \(\mathbf{q}_{a}\) the actual landing spot of the throw. The error metric defined in (17) was used to evaluate the throwing motions computed by GPR or by the selected RL algorithm, which in our case was reward-weighted policy learning with importance sampling [18].
## 4 Discussion
Under saline conditions, plants produce excess ROS because of oxidative stress, and if the plants do not develop their defense mechanisms on time, ROS can damage DNA, membrane proteins, and lipids, resulting in cell death. The degree of oxidative cell damage is controlled by the ability to protect against oxidizing agents [43]. In some species, including in many crops such as barley and maize, there are varieties that are resistant to salinity, and they were found to have high concentrations of watersoluble phenols, flavonoids, anthocyanins, and various polyphenols in saline conditions, while the reverse effect occurs in species susceptible to salts [44]. The increase in proline content also seems to play an important role in hydroxyl radical scavenging, thus defending plants against oxidative damage, which is a common consequence of many abiotic stresses. Previous authors [1,45] reported findings similar to ours and observed a similar increase in the proline concentration due to salinity in plants. Moreover, salinity is able to enhance oxidative damage such as lipid peroxidation in sensitive cultivars. Under salinity conditions, significant increases in lipid peroxidation were observed in the Grenada cultivar. Similar results were reported by [46], who found that salt treatment caused an increase in lipid peroxidation, as revealed by high TBAs contents in sensitive barley genotypes, but no significant effect was shown in the most salt-tolerant genotypes. Thus, the production of and increase in phenol concentration may be a particularly good indicator of plant resistance to salinity. Ermioni appeared to be the most resistant to 25 and 50 mM salt concentrations compared with the other two cvs. for the NaCl and KCl salts. In the K\({}_{2}\)SO\({}_{4}\) treatment, Wonderful was the most resistant to a concentration of 50 mM, while for 75 mM K\({}_{2}\)SO\({}_{4}\), Ermioni was the most resistant.
## 1 Introduction  
With the gradual shortage of oil resources in the world, coalbed methane has emerged as a promising alternative, drawing significant attention from researchers worldwide. Unlike conventional reservoirs, CBM reservoirs are characterized by high heterogeneity, low permeability, and complex flow mechanisms, which complicate effective resource extraction [1; 2; 3; 4]. Unconventional volume fracturing technology offers a potential solution to these challenges by improving the fluid seepage capacity of CBM reservoirs [5]. The mineral composition of coal rock determines its heterogeneity, which directly affects the morphology and propagation direction [6]. Therefore, understanding the impact of mineral components on hydraulic fractures is crucial for optimizing fracturing strategies and ensuring the effective development of CBM reservoirs.  
Coal rock is a sedimentary material with a complex composition, primarily consisting of clay minerals formed through strata sedimentary compaction [7; 8]. Mineral componentssuch as quartz and pyrite are mainly mixed in coal rock, and the proportion of clay is more than 30%. The heterogeneity of coal rock arises mainly from its microporosity, organic content, and mineral composition. Studies have shown that the anisotropic nature of coal rock's mineralogy significantly affects its mechanical properties [8], while current CBM fracturing models primarily consider the influence of coal rock mechanical properties and natural fracture types on fracture morphology [9; 10; 11]. There are relatively few studies on the influence of mineral components on fracture morphology in CBM reservoirs. Therefore, it is particularly necessary to explore the influence of mineral components in coal rock on fracture morphology.  
In this study, we develop a fracture propagation model for CBM reservoirs based on a node system that incorporates the distinct properties of various mineral compositions. The model adjusts the critical circumferential stress calculation for coal rock to account for the complex lithological interactions in fracture propagation. A comprehensive fractal index is introduced to describe the effects of different minerals on fracture behavior and propagation randomness. Additionally, this study investigates fracture morphology across single-mineral and multi-mineral scenarios to clarify the impact of mineral components on fracture propagation in CBM reservoirs.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 3 Security, Privacy, and Confidentiality
### Cloud Service and Internet of Things (IoT) Security  
Both cloud services and the IoT have the ability to process large amounts of information. However, their decentralized deployment can create management challenges and make them susceptible to security attacks.  
Cloud computing enables users to store and share their data in a centralized location, which can be accessed with the appropriate permissions. To ensure the confidentiality of sensitive data, encryption is used as a protective measure. In doing so, even if malicious actors gain access to the data, they will be unable to comprehend it [24].  
Similarly, IoT devices are designed to collect and transmit enormous amounts of data, often without human intervention. However, the security of these devices is critical, as they are susceptible to several types of attacks, such as denial-of-service attacks, botnets, and data breaches.  
In MR environments, cloud services and IoT play a significant role in providing real-time processing power and managing massive data flows. MR devices and applications often rely on edge computing and IoT sensors to synchronize digital objects within the physical environment, creating unique security challenges. Malicious attacks on IoT devices or cloud services could disrupt the seamless integration of physical and digital components, leading to potential breaches of private spaces and sensitive user data [25].
## 3 The Proposed Method
### LSTM Sequence Modeling and Attention Model
#### 3.2.2 Attention Model  
In an image, the key to recognize what is in it is to find the important parts or regions of interest. In other words, different parts of the image have different weights. Compared with an image, not only the regions in a video, but also the frames in a video play different roles. [PERSON] et al. [25] obtained great achievement with attention mechanism and they have proven that soft attention could gain the performance in CNN-RNN model for action recognition task. To obtain this information, we employ soft attention mechanism.  
The relations in each frame are of great importance for recognizing action. To discovery these relations, we incorporate attention mechanism with LSTM. In this layer, hidden state \(h_{t-1}^{1}\) generated from LSTM integrates input features \(X_{t}^{1}\) extracted from ConvNet. Then, we design a function that takes \(h_{t-1}^{1}\) and \(X_{t}^{1}\) as the input and returns the unnormalized relevance score at time step \(t\), which is shown in below:  
\[e_{t,i}^{1}=W_{1}^{T}\tanh(W_{a}^{1}h_{t-1}^{1}+U_{a}^{1}X_{t}^{1}+b_{a}^{1}) \tag{4}\]  
Once all relevance scores for feature sequence are computed, we normalize them to obtain the \(a_{t,i}^{1}\):  
\[a_{t,i}^{1}=\frac{exp(e_{t,i}^{1})}{\sum_{j=1}^{K\times K}exp(e_{t,j}^{1})} \qquad i\in 1...K^{2} \tag{5}\]  
The \(K^{2}\) vectors in the appearance feature cubes correspond to \(K^{2}\) regions in the \(t\)-th frame and our model chooses to focus its attention on these \(K^{2}\) regions. \(a_{t,i}^{1}\) is a softmax over \(K\times K\) locations and denotes weight of the \(i\)-th region. At each time step, we update the parameters of attention model, and get the input \(x_{t}^{1}\) by the following function:  
\[x_{t}^{1}=\sum_{i=1}^{K\times K}X_{t,i}^{1}a_{t,i}^{1} \tag{6}\]  
After weighting the features from ConvNet, we get \(x_{t}^{1}\) and input it into LSTM, \(h_{t}^{1},c_{t}^{1}=LSTM^{1}(x_{t}^{1},h_{t-1}^{1},c_{t-1}^{1})\).  
The illustration of the above process at the time step \(t\) is shown in Figure 2. In Equation (4), the \(W_{1}^{T},W_{a}^{1},U_{a}^{1}\) and \(b_{a}^{1}\) are estimated together in model training procedure.
## 6 Computational Results
### TSP Implementation in GAMS  
In describing the source code in GAMS and verifying its computational abilities, we use three benchmarks from the TSPlib library [83] with 24, 52, and 100 cities, or positions in the map given by coordinates. The following code is written for the gr24.tsp benchmark. Since the adjacency matrix is symmetric, only the data of the lower triangular matrix are entered with the remaining data calculated. The EQUATIONS section is a rewrite of the TSP model and its Equations (5)-(8). The \(x_{ij}\) binary domain, corresponding to Equation (9), is given by the declaration that precedes this section. ```
#TITLETravellingalessmanProblem OPTIONITERIL=10000000; OPTIONOPTR=0; SETS I/1*24/;

Figure 3: Shift mutation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Benchmark** & **Average Result/the Best Result** & **Optimum** \\ \hline \(30\times 10\) & 2126/2099 & 2093 \\ \(30\times 15\) & 2570/2525 & 2513 \\ \(50\times 10\) & 3132/3090 & 3045 \\ \(75\times 20\) & 5261/5203 & between 4890 and 4951 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: GA computational results—average and the best result from 30 runs, optimum. Figure 2: Modified two-point crossover.
###### Abstract  
The increasing energy and water demands by the Arab Gulf states highlight the importance of sustainable use of energy resources. Wastewater sludge management for energy recovery creates an opportunity for sector integration for both wastewater treatment plants and renewable energy production. The objective of this study was to theoretically estimate the biomethane potential of wastewater sludge, together with identification of the role of biomethane in the region. The prediction of biomethane potential was based on the theoretical stoichiometry of biomethanation reactions, using the R-based package 'Process Biogas Data and Predict Biogas Production'. The biomethane potential of sludge ranges between 232-334 \(\times\) 10\({}^{6}\) m\({}^{3}\), with a total heat-value up to 10.7 trillion BTUs annually. The produced biomethane can generate up to 1665 GWh of electric energy, an equivalent amount to the current levels of electricity generation from wind and solar power combined. The findings from the case study on Kuwait's indicate that biomethane could displace 13 \(\times\) 10\({}^{6}\) m\({}^{3}\) of natural gas, or approximately 86,000 barrels of crude oil, while simultaneously reducing greenhouse gas emissions by 86% when compared to the base-scenario. Despite its potential, biomethane recovery in the region is hindered by technical-, economic-, and policy-based barriers.  
biomethane potential; energy transition; wastewater sludge; anaerobic digestion; biogas; waste-to-energy +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 2 Experimental
### Analytical Methods  
The contact angles of the films (CAs) were measured with a CA system (Model OCA100, Datphysics, Stuttgart, Germany). Each time, a pure water drop was placed on the surface of the tested sample. After the drop was balanced, the instrument's own software was used to calculate the contact angle. Five different areas were selected on each sample surface for water contact angle measurements, and the average contact angle was determined.  
X-ray photoelectron spectroscopy (XPS) (Model Escalab Xi+, Thermo Fisher Scientific, Waltham, MA, USA) was employed to distinguish between the chemical states of the elements in the top surface layer. The XPS analysis was performed using a monochromatic Al K\({}_{\alpha}\) X-ray source within the O1s, N1s, C1s, and Si2p spectral ranges. The binding energy positions of the peaks were adjusted by setting the C1s peak to 284.8 eV.  
The surface morphology of the samples was examined via scanning electron microscopy (SEM) (Model Thermo Scientific Apreo S, Thermo Fisher Scientific, USA) under high-vacuum conditions and the operating voltage of 5 kV. Prior to the measurements, a gold layer was spray-deposited on each sample surface.  
The surface roughness of the samples was assessed by atomic force microscopy (AFM) (Model Dimension Icon, Bruker, Ettlingen, Germany) within a surface area of 1 \(\upmu\)m \(\times\)1 \(\upmu\)m. The Scanasyst-air probe model and Scan Asyst test mode were used in testing, and the elastic constant of the probe was 0.4 N/m. The roughness tests were performed within five areas on each sample surface, and the root-mean-square roughness (RMS) values were obtained by averaging the data.  
The phase analysis of the samples before and after heat treatment was conducted via X-ray diffraction (XRD) (Model X'Pert Pro, PANalytical B.V., Almelo, The Netherlands) at the tube voltage of 40 kV, tube current of 40 mA, and 20 step length of 0.05\({}^{\circ}\). The X-ray radiation source was Cu.  
To further evaluate the adhesive performance of the specimens, four-point bending (FPB) tests were implemented by means of an electronic tensile testing machine (Model Instron5569, Instron, Norwood, MA, USA) [30]. In particular, the critical load values were established from the acquired load-displacement curves to calculate the interfacial toughness [31; 32]. The formula is as follows [18; 30]:  
\[G=K(1-v^{2})Pc/E \tag{1}\]  
where \(E\) is the elastic coefficient of the substrate; \(v\) is the Poisson's ratio; \(K\) is the coefficient, related to the thickness and the geometry of the substrate; and \(Pc\) is the critical load value for device fracture.  
To evaluate the thermal stability of the films, four-point probe testing (FPP) was conducted using a double-electric four-probe tester (Model RTS-9, GuangZhou 4 probes tech, China). During testing, the change in the resistance of the samples was determined under heat treatment by defining the variation in sheet resistance (\(R_{c}\)) as follows [33]:  
\[R_{c}=R_{a}-R_{d}/R_{d} \tag{2}\]  
where \(R_{a}\) and \(R_{d}\) are the sheet resistances of the annealed and as-deposited samples, respectively.
# Comparative Accuracy Analysis of Truck Weight Measurement Techniques
## 3 Results
### Measurement tolerances
#### 3.3.2 Axle Weight  
The portable scales' axle weight measurements were compared with B-WIM and WIM measurements. The weight station did not measure the weight of individual axles. The tolerances for individual axle weight for more than 95% results were expected to be in the range \(\pm\)20%. The spatial restriction and visual comparison were not presented, but all the results are described in the discussion section.
## 5 Simulation Results  
In this section, we show the performance of the proposed diversity-controlled MUST technique. In particular, we evaluate the average BER performances of both the MUST-based UE and the MUST-enhanced UE. We show the minimum required power at the BS for satisfying the BER requirement according to \(\alpha\). In all link-level simulations, we utilize QPSK modulation and Rayleigh fading channels.  
Figure 5 shows the BER performance of the proposed diversity-controlled MUST technique for varying SNR values when \(\alpha=0.05\), \(N=2,4,8,16\), and \(N_{r}=2\). In the figure, lines represent the analytical results obtained from Equations (8) and (22), while symbols represent link-level simulation results. The mathematical analysis derived in this paper matches well with the computer simulations over all SNR values. As \(N\) increases, the BER performance of the MUST-enhanced UE becomes improved due to the frequency diversity. Note that the lower bound of the average BER of the  
MUST-enhanced UE in Equation (22) is almost the same as the computer simulation especially when the SNR is high. The BER performance of the MUST-based UE is better than that of the MUST-enhanced UE in low SNR values due to the larger power allocation to the MST-based UE. On the other hand, it becomes worse than the BER of the MUST-enhanced UE for high SNR values due to the diversity gain of the MUST-enhanced UE.  
Figure 6 shows the BER performance of the proposed diversity-controlled MUST technique for varying SNR values when \(\alpha=0.05\), \(N=2\), and \(N_{r}=1,2,4\). Figure 5 shows the BER performance according to the number of frequencies used for the MUST-enhanced UE for a given number of receive antennas at UEs, but Figure 6 shows BER performance according to the number of receive antennas at UEs for a given number of frequencies used for the MUST-enhanced UE. The performance tendencies in both Figures 5 and 6 are quite similar. The diversity order of the MUST-based UE is approximately equal to \(N_{r}\), while that of the MUST-enhanced UE is approximately equal to \(N\cdot N_{r}\).  
Figure 7 shows the transmit SNR at the BS over varying \(\alpha\) where the transmit power of the BS is adapted to satisfy the BER requirements when \(N=2\) and \(N_{r}=2\). We assume that \(P_{b.B}^{req}=5\times 10^{-4}\) and \(P_{b.E}^{req}=10^{-4}\). As expected, the required SNR for the MUST-based UE decreases as \(\alpha\) increases, while that for the MUST-enhanced UE increases as \(\alpha\) increases. In this figure, the optimal \(\alpha\) is equal to 0.06 and the minimum required SNR is equal to 21 dB.  
Figure 5: BER performance of the proposed MUST technique for varying SNR values when \(\alpha=0.05\), \(N=2,4,8,16\), and \(N_{r}=2\).  
Figure 6: BER performance of the proposed MUST technique for varying SNR values when \(\alpha=0.05\), \(N=2\), and \(N_{r}=1,2,4\).
# Behaviour of Steel Tubular Knee Joint in Aluminium Frames with Tension-Tie Element
## 2 Experimental Program
### Geometrical Properties  
The geometry of the steel joint (insert in aluminium portal frame) was selected to simulate the realistic situation occurring in the prefabricated aluminium frame, which is usually built by using a rectangular aluminium profile 230 \(\times\) 90 \(\times\) 3 mm with slots. Thus, the geometry of steel insert was determined with the size of the aluminium cross section. The insert was formed using butt-welded hollow sections RHS 180 \(\times\) 80 \(\times\) 3 mm, forming an angle of 105\({}^{\circ}\). Additionally, a smaller tube RHS 30 \(\times\) 20 \(\times\) 2 mm was butt-welded on the inner side of the steel knee insert as reinforcement (Figure 2).  
For aluminium portal frames with spans greater than 10 m, a high-strength steel tension tie element (typically \(\mathcal{O}\) 16, 6 \(\times\) 37-FC, 1770 MPa (EN 12385-1 [17])) is used. However, due to the requirements of the strain measurement, a tie element with a circular hollow section (CHS) 100 \(\times\) 4.75 (S 235 JRH) was selected (Figure 2). The tie element is connected to the beam by a T-adapter with two M20, 8.8 quality grade bolts, which are attached to nuts that are welded to the previously drilled holes on the lower flange of steel insert beam part [3]. On the other side, the tie element was connected to the rigid supporting structure using a similar T-adapter and a pair of M20, 8.8 quality grade bolts. That kind of setup allowed the steel tube to rotate freely around two horizontal axes (pins), thus only axial forces could occur.  
Figure 1: Test setup—a general view.