esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[14; 15] analysed the effect of an interior low emissivity coating (\(\epsilon=0.25\)) that was applied to the indoor surface of a small cabin in Sweden. The indoor temperature and the energy consumption for heating and cooling the building was monitored and then compared to a normal cabin without an interior surface coating. The results show a ~5% reduction in heating energy consumption. Moreover, a theoretical study was carried out simulating the energy consumption of a retail house under different climates. The results highlight that reducing the internal surface emissivity determines a significant reduction in the heating energy demand for all of the considered climates. On the contrary, a slight increase of the cooling energy demand was observed. These penalties can be considered negligible in cold climates, on the contrary in a warm climate the increase of the cooling demand in summer cannot be counterbalanced by the winter benefits. An energy-saving coherent with [15] was reported by [PERSON] et al. [16]. The study analyses through CFD simulations the interaction between the interior emissivity and the energy consumption in a simple room. The results for well-sealed room claims a ~5% of energy saving. Nevertheless, the benefits can significantly differ for high ventilation rate. More recent theoretical studies of low-emissivity coatings (\(\epsilon=0.1\)) applied to the indoor surfaces of a room, highlight possible savings of up to 22% if an operative temperature-based control at a position near a low-emissivity surface is implemented. Nevertheless, by using paints with ideal emissivity of 0.1 [15; 16; 17]), most of the low-emissivity paints commercially available present a higher thermal emissivity [7], even though several studies have theoretically demonstrated interesting improvement in the winter indoor comfort conditions and energy savings of between 5% and 22%. Moreover, all of these Low-E paints contain metal flakes that confer a final metalised effect (silver colour); this aspect could be less relevant for special environments (ice risk ceilings, indoor ski tracks) and for industrial buildings, while it could represent a strong limitation from the aesthetical point of view in residential and office buildings. In light of these considerations, the aims of this research study are:  
* At the material level, to experimentally assess the emissivity properties of different paint mixtures with limited metallised effect and, therefore, higher emissivity by changing the aluminium paint concentration. For this sake, a new procedure while using Heat Flow Meter apparatus was developed. * At the component/room level, to analyse whether some benefits (increase of wall thermal insulation and improvement of the indoor comfort) are still achievable, even by using paints with a more realistic emissivity and with limited metallised effect. In the present study, paints with different emissivity (from ~0.4 to ~0.9) were applied on one side of the partition wall of a double climatic chamber apparatus and their performance was compared.
## 2 FANET Architecture and Routing Protocol
### Routing Protocols
#### 2.2.1 Topology-Based
In addition, it is often necessary to expose network topology and node state information, which can lead to some security issues such as route forgery. In the next subsection, we will introduce security-based routing.
## 11 Application Examples
### Intermediate State  
Let a smooth wall of height \(H=3m\) retaining a soil with the following characteristics: \(c^{\prime}=\)20 kPa, \(\phi^{\prime}=\)30\({}^{\circ}\), \(\gamma=18\,kN/m^{3}\), \(E_{Young}=\)5000 kPa or 10,000 kPa and \(\mu=0.3\). Dynamic conditions are considered with pseudo-static coefficients: \(k_{h}=0.3\) and \(k_{v}=k_{h}/2\). The wall is allowed to slide horizontally away from the soil by 7.5 mm. For any depth \(z\), the required displacement \(\Delta x_{\text{max}}\) for the active state to fully develop can be obtained using Equation (30).  
Two \(\Delta x_{\text{max}}\) vs. \(z\) curves were drawn in Figure 15 (left), that is, for the \(E_{Young}=\)5000 kPa and 10,000 kPa soil. As shown, for the \(E_{Young}=\)10,000 kPa soil, the 7.5 mm of wall movement are more than enough for the active state to fully develop along the whole height of the wall. The earth pressures in this case are given by the continuous curve of Figure 15 (right).  
However, for the soil with \(E_{Young}=5000\) kPa, the 7.5 mm of wall movement are not enough for the active state to fully develop along the whole height of the wall. As shown in Figure 15 (left), a zone of intermediate earth pressures exists \((0.32m<z<2.08m)\) between two active zones (at \(z<0.32\) m and \(z>2.08\) m). The intermediate earth pressures for \(0.32m<z<2.08m\) are then calculated using Equation (10) (along with Equations (8), (9), and (12)). The mixed distribution of earth pressures in this case is given by the thick dashed curve of Figure 15 (right). The distribution of earth pressures at rest is also given for comparison purposes (dotted line in Figure 15 (right)).  
As shown in Figure 16, a different retaining wall movement mode--in this respect rotational as for its base--gives a completely different distribution of intermediate earth pressures; the maximum yield of the wall was considered again as 7.5 mm.  
It is also interesting how the mobilized shear strength (expressed by the factor \(1/f_{m}\)) is affected by the mode of failure of the retaining structure as well as the modulus of elasticity of soil; see Figure 17.  
Figure 14: Example chart showing the variation of \(\Delta x_{\text{max}}\) with depth \(z\) for \(E_{Young}=\) 5000 and 10,000 kPa.  
Figure 16: Example chart as described in Figure 15 but for the case of rotating wall.  
Figure 15: Example chart showing (left) the variation of \(\Delta x_{\max}\) with depth \(z\) for two different \(E_{Young}\) values (5000 and 10,000 kPa) and the allowable wall movement in sliding and (right) the distribution of earth pressures for the intermediate state, as well as for the state at rest and the active one.
# PAGE--Practical AES-GCM Encryption for Low-End Microcontrollers
## 3 Face-Light: Fast AES-CTR Mode Encryption for Low-End Microcontrollers  
In this section, we present an advanced implementation method for AES-CTR mode on low-end embedded processors. Compared to previous work (i.e., FACE), the proposed technique focused on the low-end microcontroller together with the masking operation. Nonce and counter values construct the packet of the CTR mode. For the universal setting, 96-bit nonce values and 32-bit counter values are assigned. Throughout the whole process, the nonce remains same while the counter value gets transformed in each processing. The previous work is based on the 8-bit unit, which requires the table update in every 256 counts during encryption procedures. The proposed implementation is targeting for the 32-bit counter, which only needs to set the pre-calculated table in the initialization stage, where the cache table is not updated throughout the whole process. The explanation are presented in Figure 1. The top and bottom of the Figure 1 indicate first block and \(n\)-th block, respectively. Each square holds 8-bit data. White and colored blocks portray identical and different parts, respectively.
# FREDA: Few-Shot Relation Extraction Based on Data Augmentation
## 3 Methodology
### Sentence Encoder  
We choose BERT as the encoder, as shown in Figure 3, given an instance \(x=\{w_{1},\ldots,w_{c}\}\), with \(x\) containing \(c\) tokens. When the instance is encoded by BERT, its corresponding embedding \(X=\{W_{1},\ldots,W_{c}\}\) is obtained. We will select the instances to go through the encoder to form the hidden state corresponding to the start token mentioned by the head entity and the hidden state corresponding to the start token mentioned by the tail entity as the output of the encoder.  
\[S_{h}=\left\{S_{hk}^{i}\in\mathbb{R}^{d};i=1,\ldots,N,k=1,\ldots,K\right\} \tag{1}\]  
\[Q_{h}=\left\{Q_{hj}\in\mathbb{R}^{d};j=1,\ldots,G\right\} \tag{2}\]  
\[S_{t}=\left\{S_{tk}^{i}\in\mathbb{R}^{d};i=1,\ldots,N,k=1,\ldots,K\right\} \tag{3}\]  
\[Q_{t}=\left\{Q_{tj}\in\mathbb{R}^{d};j=1,\ldots,G\right\} \tag{4}\]  
The \(S_{h}\) and \(S_{t}\) represent the hidden states corresponding to the start token of the head and tail entity mentions in the support set, respectively. The \(Q_{h}\) and \(Q_{t}\) represent the hidden states corresponding to the start token of the head and tail entity mentions in the query set, respectively. The \(S_{hk}^{i}\) and \(S_{tk}^{i}\) represent the hidden states corresponding to the start token of the head entity mentions and tail entity mentions, respectively, in the \(k\)th instance of the \(i\)th relation in the support set. The \(Q_{hj}\) and \(Q_{tj}\) represent the hidden states corresponding to the start token of the head entity mentions and tail entity mentions, respectively, in the \(j\)th instance of the query set.  
We only utilize the hidden states corresponding to the start tokens of the two entity mentions. By concatenating these two pieces of information, we obtain the required representation for the support set, denoted as \(S\), and the query set, denoted as \(Q\). This effectively reduces the introduction of excessive and unnecessary parameters:  
\[S=cat(S_{h},\ S_{t}), \tag{5}\]  
Figure 3: BERT model. An example from the support set; red represents the head entity and blue represents the tail entity.  
\[Q=cat(Q_{h},\ Q_{t}\,) \tag{6}\]  
where \(S\in\ R^{2d}\) and \(Q_{h}\in\ R^{2d}\). \(d\) is the size of the contextual representation of the sentence encoder.  
The relation triple information representation is obtained by encoding the relation triples, and we can obtain two types of embeddings: the [CLS] token embedding \(T_{c}\) and the average of all token embeddings \(\widetilde{T_{p}}\). These embeddings are then concatenated to form the desired relation triple information representation:  
\[T=cat(T_{c},\widetilde{T_{p}}) \tag{7}\]  
where \(T\in\ R^{2d}\) belongs to the spliced relation triple information representation. The average of all token embeddings \(\widetilde{T_{p}}\in\ R^{d}\) is computed as follows:  
\[\widetilde{T_{p}}=\frac{1}{C}\sum_{j=1}^{C}TP(j). \tag{8}\]  
The \(TP(j)\) in the equation denotes the embedding matrix of the \(j\)th token in a relation triple instance, with a fixed token length of \(C\) for each instance.
# Triangular Sierpinski Microwave Band-Stop Resonators for K-Band Filtering
## 7 Experimental Results and Discussion  
### Manufacturing of the Devices  
The technological solution used for manufacturing the devices was a standard photolithographic process on a high-resistivity silicon wafer. The substrate was 635-\(\upmu\)m thick, with a resistivity of \(\rho\geq 15,000\)\(\Omega\)cm, and was thermally oxidized at \(T=1050\)\({}^{\circ}\)C, to obtain a thickness of silicon oxide of approximately \(t_{SiO2}=1\)\(\upmu\)m. At high frequencies, this is normally enough to confine most of the electromagnetic field in the wafer's plane to the dielectric layer's level. Thermal oxidation helps to provide a dielectric layer in the planar region where the electromagnetic field must be confined to minimize the resistivity losses. The dielectric constant of the obtained thermal oxide was approximately \(\varepsilon_{SiO2}=4\pm 10\%\). To mitigate skin depth effects causing losses along the propagation path of the signal, the Au-made planar structures were electroplated with a maximum thickness of 5 \(\upmu\)m. The same approach was followed to manufacture all of the studied structures onto the same wafer, including the 20 GHz and 26 GHz resonators. A 200-\(\upmu\)m thick frame was left to surround the external figure and the internal triangles, in order to consider the internal  
Figure 13: Simulated performance of the hexagonal resonators for operation around 26 GHz. The configuration based on the C1 resonator is the best one in terms of the depth of the notch.  
complexity of a kind of perturbation with respect to the full triangle and to check the possibility of frequency tunability induced by this choice.
## 2 Data and Methods
### Eddy Covariance Measurements  
The measurements of carbon, water, energy fluxes, and meteorological data were provided by The FLUXNET2015 dataset ([[https://fluxnet.org/data/fluxnet2015-dataset/,accessed](https://fluxnet.org/data/fluxnet2015-dataset/,accessed)]([https://fluxnet.org/data/fluxnet2015-dataset/,accessed](https://fluxnet.org/data/fluxnet2015-dataset/,accessed)) on 29 Nov 2022) for more than 200 sites globally. These data were standardized by data processing, including quality control and gap-filling. The GPP data in this study (GPP_NT_VUT_REF) were partitioned from the net ecosystem exchange (NEE) dataset, following the nighttime partitioning approach. In the FLUXNET2015 dataset, sites were labeled with quality control flags ranging from 0 (poor quality) to 1 (good quality). We selected 78 Tier-1 sites with good quality for this study, encompassing ten terrestrial ecosystem vegetation types: deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), mixed forest (MF), grasslands (GRA), savannas (SAV), shrubland (SHR), wetlands (WET), and cropland (C3/C4) (Figure 1). More detailed information about the sites is shown in Table S1 in the supplementary material. For each site, the FLUXNET2015 dataset was split into two parts, with data from the odd-numbered years used for the calibration model, and data from even-numbered years used for the validation model. For each variable, we aggregated the daily data into a 16-day time step to match the time step of the Landsat data.
## 3 Modeling the Time Effect on Music Recommendations
### Problem Statement
This frequency is computed for each pair \((u_{i},\ s_{j})\):_  
\[dPlayF\left(u_{i},\ s_{j}\right)=\frac{\sum_{p=1}^{n_{p}\left(u_{i},\ s_{j} \right)}dPlay\left(u_{i},\ s_{j},\ p\right)}{\sum_{j}\sum_{p=1}^{n_{p}\left(u_ {i},\ s_{j}\right)}dPlay\left(u_{i},\ s_{j},\ p\right)} \tag{11}\]This information is represented in the decay play matrix \(\mathbf{D}:=d_{i,j}\) where \(\mathbf{D}\in M_{mcm}(\mathbb{N})\) and \(d_{i,j}:=dPlayF\big{(}u_{i},\ s_{j}\big{)}\). The songs played by each user \(u_{i}\) are ordered by their \(dPlayF\) values in a sequence defined as \(\big{[}dPlayF_{K^{\prime}}(u_{i})\big{]}\big{[}dPlayF_{k}(u_{i})\big{)}\ dPlayF_{k+1}(u_{i})\), where \(dPlayF_{k}(u_{i})\) denotes the decay frequency of a song \(s_{j}\) with rank \(k\). \(K^{\prime}=1\) is assigned to the song having the highest frequency. Then, the decay rating \(r_{i,j}\) for a song with rank \(k\) is computed as a linear function of the frequency percentile. These two definitions form the basis of the method proposed in this paper for dealing with the evolution of user preferences when no explicit ratings are available. Managing time context also presents some drawbacks in the music field, especially when the computation of implicit ratings is necessary. One of the main problems of context-aware methods is the sparsity, which is caused by the addition of contextual dimensions. Even in context-free scenarios, there is a certain degree of sparsity because many elements \(r_{ij}\) of the rating matrix are null. This is caused by the fact that the number of songs rated by the user is much lower than the number of songs in the system. When contextual pre-filtering is applied, the time dimension is restricted to a range of values \(S_{t}\) and only the ratings associated to that specific time period are considered to make recommendations for that temporal context. This accentuates the sparsity problem. For instance, to recommend a song to be listened to in the morning, only the ratings corresponding to songs played in the morning can be used if contextual pre-filtering is used. The problem becomes worse when, besides the contextual periods, we have to establish additional periods to calculate the ratings from the number of plays. Moreover, user listening habits regarding the time context are not clearly defined and can vary considerably from one user to another. Therefore, user time-dependent behaviors would be managed in a different way to improve recommendations. In our work, the similarity between users is not only obtained from the ratings they give to songs, but also from the time the songs are played.
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 5 Computational Validation
### Results for the First Test Feeder  
Table 4 presents the numerical results in the 33-bus grid when the proposed and comparative metaheuristics were implemented.  
The numerical results in Table 4 show the following:  
* The reduction concerning the benchmark case with the CBGA was about USD/year 981,318.19, which corresponds to 26.9294%. The reduction reached with the DCVSA was about USD/year 981,617.69, (26.9376%), and the best solution for the benchmark case was reached with the proposed GNDO approach, with USD/year 981,671.42, i.e., a reduction of 26.9391%. These results show that all the optimizers yield an  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Site (Node)/Size (kW)** & \(\mathbf{z_{\text{cost}}}\) (USS/Year)** \\ \hline Bench. case & – & 3,644,043.01 \\ \hline CBGA & \{11(1162.95), 14(943.48), 31(1482.75)\} & 2,662,724.82 \\ \hline DCVSA & \{9(580.31), 15(1291.37), 31(1715.59)\} & 2,662,425.32 \\ \hline GNDO & \{10(974.26), 16(920.22), 31(1692.51)\} & 2,662,371.59 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Application of the GNDO approach to the location and sizing of PV generation in the 33-bus grid and comparison of results with literature reports.  
Figure 4: Daily demand and generation curves of Medellin, Colombia.  
annual expected reduction of USD 981,300.00 per year of operation. Furthermore, comparing the best literature report (solution with the DCVSA in [22] with the proposed GNDO approach, an improvement of about USD/year 53.73 is achieved, which makes it the best result reported in the current literature for the 33-bus grid with a DC monopolar configuration. * The three algorithms detected that one of the best sites to locate a PV source is node 31, where injections of power higher than 1540 kW are listed. This location demonstrates that the 33-bus grid is one of the most sensitive nodes to install PV sources concerning the expected improvement of the objective function. In addition, the total PV capacity installed with the CBGA is 3589.18 kW, 3587.27 kW with the DCVSA, and 3586.99 kW with the proposed GNDO. These values show that, for the 33-bus grid, the proposed GNDO installed less power with better objective function values, which a better nodal selection can explain in comparison with the CBGA and the DCVSA. As for the processing times, it is worth mentioning that the proposed GNDO approach takes an average time of 159.99 s to solve the studied problem, which shows that for solving a nonlinear programming model with integer and continuous variables (solution space with infinite dimensions), the proposed GNDO approach takes less than 3 min. This implies that a utility company can use this approach to evaluate hundreds of nodal locations for PV generation units with lower processing times in order to find an adequate solution from both economic and technical perspectives.
## 4 Results & Discussion
### Mitigation of Random Over-Fitting Using Development Data
These calculations, displayed in Table 16, support the hypothesis that using a single fold for model validation and evaluation could lead to overoptimistic results, and that using separating folds for each stage is an important methodological consideration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **AVCA** & & **DARTH-VAT** \\ \hline
**Corpus** & **Vowel** & **Dev.–Base.** & **Test–Base.** & **Dev.–Base.** & **Test–Base.** \\ \hline Neurovoz & /a:// & \(-1.20\) & \(-9.70\) & \(-1.10\) & \(-5.10\) \\  & /e:// & \(-0.70\) & \(-10.3\) & \(-1.40\) & \(-6.90\) \\  & /i:// & \(+1.80\) & \(-5.80\) & \(0.00\) & \(-9.40\) \\  & /o:// & \(+0.60\) & \(-4.70\) & \(+2.90\) & \(-11.90\) \\  & /u:// & \(+1.90\) & \(-11.8\) & \(+1.90\) & \(-8.50\) \\ \hline \multicolumn{2}{c}{Mean difference} & \(+0.48\) & \(-8.46\) & \(+0.46\) & \(-8.36\) \\ \hline ItalianPVS & /a:// & \(0.00\) & \(-9.10\) & \(0.00\) & \(-2.00\) \\  & /e:// & \(-1.00\) & \(-5.10\) & \(0.00\) & \(-1.00\) \\  & /i:// & \(-1.00\) & \(-5.10\) & \(0.00\) & \(-4.00\) \\  & /o:// & \(-1.00\) & \(-5.10\) & \(0.00\) & \(-3.20\) \\  & /u:// & \(-1.00\) & \(-5.10\) & \(+1.00\) & \(-2.00\) \\ \hline \multicolumn{2}{c}{Mean difference} & \(-0.80\) & \(-5.90\) & \(+0.20\) & \(-2.44\) \\ \hline mPower & /a:// & \(-0.80\) & \(-2.30\) & \(-3.30\) & \(-4.90\) \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Difference between development/test and baseline classification accuracy. Average differences are listed. Figure 10: Accuracy of random forest classifiers using single folds for both development (purple) and testing (gray).
## 4 System Development and Experimental Verification
### System Development
#### 4.1.2 Software System Development  
The software system comprises four main levels: collection, perception, decision, and HMI interaction layers. The collection layer is responsible for base code, image distortion correction, sensor external parameter calibration, point cloud coordinate transformation, LiDAR PTP synchronization, data collection, and outputs registered images and point cloud data streams in real time. The perception layer is responsible for modules such as real-time point cloud registration densification, real-time drawing of the robotic arm's 3D passable area, abnormal point cloud cluster detection, AI recognition of hazardous objects based on images, hazardous object classification, and equipment status self-check. The decision  
Figure 10: Front-end Sensor. (**a**) Sensor appearance model. (**b**) Internal sensor layout. (**c**) Front-end sensor prototype.  
layer assesses the risk level, calculates the adjustment amount required by the robotic arm to avoid collisions, and generates and saves log information. The HMI interaction layer executes the commands of the decision layer, generates prompt videos and sound alerts, and sends avoidance action information via the communication interface, completing the risk warning process. The specific software process is shown in the Figure 11 below:
## 1 Introduction
### Comparison of Centralized and Distributed Solutions  
Among the aforementioned solutions, centralized approaches offer high-quality solutions but lack robustness in complex environments. They struggle to adapt to dynamic obstacles and exhibit an exponential growth in time complexity as the number of AGVs increases, making them unsuitable for large-scale AGV deployments.  
However, distributed solutions excel in adaptability to dynamic environments and scale linearly with the number of AGVs, making them highly scalable. However, they suffer from lower solution quality, reduced path feasibility, and slow convergence during training, leading to long training times.  
To reduce the complexity of the MAPF problem while combining the strengths of both centralized and distributed solutions, this paper decomposes the MAPF problem into the following two sub-problems: AGV pathfinding and AGV obstacle avoidance. The central system pre-plans paths using A* (ignoring other AGVs and unexpected obstacles), while each AGV leverages its onboard distributed navigation strategy to handle dynamic obstacle avoidance, achieving a hybrid approach of macro-level control and micro-level autonomy. By using a single agent A* for preliminary planning, the central system maintains linear scalability in solving time as the number of AGVs increases. Meanwhile, the distributed navigation strategy of AGVs enables them to decide whether to follow the centrally planned path or reroute around obstacles based on real-time environmental conditions. This hybrid strategy ensures robustness in complex manufacturing environments.