esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Cyanobacterial Studies in Portugal: Current Impacts and Research Needs
## 2 Cyanobacterial Studies
### Phylogeny Studies  
The first study to apply clustering analysis (Figure 2) belongs to [PERSON] et al. [31], where in their study, M13 PCR fingerprinting, ERIC PCR fingerprinting and amplification of the internal transcribed spacer (ITS) region were used to characterize cultured strains of _C. raciborskii_ obtained from several freshwater lakes and rivers of Portugal. Results showed that when including in this analysis other taxa such as _M. aeruginosa_, _Chrysosporum_ spp., _Planktonix agandhi_ and _Oscillatoria neglecta_ the potential of the fingerprinting method demonstrated only the ability to differentiate strains at an intra-specific level failing as an identification tool. Nonetheless, ITS amplification turned to be a good method for strain clustering [31]. In 2009 the same authors published a work on the applications of several molecular techniques (STRR and LTRR PCR fingerprinting, phylogenetic analysis with the 16S rRNA and _rpoC1_ genes and M13 and ERIC PCR fingerprints) to evaluate the level of genetic diversity of 118 cyanobacterial isolates for taxonomic purposes. After the application of these methodologies in the 118 strains revealed good congruence, indicating the potential for use in cyanobacterial monitoring, as a quality management control. Later, [PERSON] et al. [32] when applying isolates of Portuguese _C. raciborskii_ found that they cluster together with other European strains and were closely related to Asian and Australian strains. This study used for the first time the genetic information of four genetic markers that were concatenated in the analysis (16S rRNA, _rpoC1_, ITS-L and ITS-S) [32]. Later, the same authors studied the genetic diversity and population structure of Portuguese isolates of _M. aeruginosa_[33]. In their study, a phylogenetic tree was produced resulting from the concatenation of four distinct genetic markers (16S rRNA, 16S-23S ITS, DNAgyrase subunit \(\delta\) and cell division protein _ftsZ_) and showed that Portuguese _M. aeruginosa_ population possesses 15 distinct genotypes with the southern strains clustering together. In this study, toxic strains were also analyzed and showed that they carry no cyanotoxicity significance appearing intermixed in the phylogenetic tree [33].
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 3 Data Acquisition and Preprocessing
### Ground-Truth Data Acquisition and Preprocessing  
Ground-truth data are a critical component of grassland above-ground biomass inversion research, providing the foundational support for model construction and validation. In August 2024, our research team conducted field sampling for above-ground biomass (AGB) in the study area. To ensure the representativeness and comprehensiveness of the data, sampling points covered various grassland types in Xiwuzhumuqin Banner, including meadow steppe, typical steppe, and wetland steppe.  
At each sampling point, a circular plot with a 50 m radius was selected. Three 1 m \(\times\) 1 m standard plots were placed in the 0\({}^{\circ}\), 120\({}^{\circ}\), and 240\({}^{\circ}\) directions to form an evenly distributed observation design [32], thereby capturing microtopographic variations (e.g., slope, aspect) and vegetation heterogeneity within the 30 m spatial resolution of Landsat 9 imagery. This angular distribution ensures that the samples cover diverse microenvironments (e.g., sunlit vs. shaded areas, moist vs. dry soil patches) typical of grassland ecosystems. The average values of the three plots were calculated to mitigate local variability, aligning ground measurements with the scale of satellite pixels and reducing uncertainties caused by mixed-pixel effects. In each plot, the following information was recorded: vegetation community height, cover, species diversity, and terrain factors (including latitude and longitude, slope, aspect, and elevation).  
Subsequently, the green vegetation within each plot was clipped to ground level, and the samples were placed in envelopes for immediate fresh-weight measurement. The average fresh weight of the three plots was calculated as the fresh-weight data for the sampling point. The collected samples were then transported to the laboratory, dried at a constant temperature of 65 \({}^{\circ}\)C for 24 h to remove moisture, and reweighed to obtain the dry-weight data of the vegetation. This method not only ensured the accuracy and consistency of the data but also provided reliable and real-world reference data for assessing the precision of the remote sensing inversion model.
## 3 The Q-Holonic-Based ARchitecture (QHAR)  
The proposed Q-Holonic-based Architecture (QHAR) considers the physical entities of the production system, the actors of the value chain (e.g., customers, suppliers, sub-contractors), and the flows exchanged between entities and actors. To represent these entities, actors, and flows, the Q-Holon is used, which we defined in a previous work [10]. The Q-holon is a generic Holon that can represent any entity or actor, thanks to its four dimensions (physical, cyber, human, and energy). An entity or actor can be modelled by a Q-holon by specifying the attributes and operations of its physical, cyber, human, and energy dimensions. Attributes and operations may depend on one of its dimensions, or a combination of two or more of them. The interactions between Q-Holons are materialized by the exchanged flows. In the first version of the Q-holon, four types of flows were considered: (1) Space (needs for physical adjacency or orientation between two elements); (2) Energy (energy transfer); (3) Data (data/information exchange), and (4) Material (materials exchange). To facilitate understanding and avoid confusion, we propose to consider positioning constraints (Flow #1, Space) not as a flow, but as an attribute. To better describe the spatial requirements and orientation constraints of an entity, it is better to express them in terms of attributes than in terms of flows. Indeed, these aspects constitute intrinsic parameters of the considered entity. Another reason that convinced us of the necessity of this change is the fact that the meaning of the \"Space\" flow is not homogeneous with the meaning of the three other flows. Indeed, the other flows (Energy, Data, and Material) express the transfer/exchange between two or more entities/actors of an element. This \"Space\" attribute indicates, if necessary, the layout constraints (location, relative position), required space, load and unload access points, and safety distance to be respected. Therefore, a new version of the Q-holon (see Figure 1) is used to define the QHAR, composed of four dimensions and with the ability to exchange three flows.  
The energy flow corresponds to any form of energy that entities/actors can exchange. It can be electrical energy, mechanical energy, thermal energy, and so on. The data flow can concern raw data or information that are more elaborate. The material flow concerns any exchange of tangible elements (e.g., parts, products).  
The Q-Holon allows us to deal with the multidimensional character of the CPPS that constitute modern production systems, as well as to consider Humans as full members of the production system. Thanks to these four dimensions and the three exchanged flows, it is possible to model and specify all the necessary aspects, to ensure the implementation and the piloting of the CPPS.  
Firstly, we introduce the QHAR in general, then present its main constituents (i.e., entities and actors, along with their attributes and operations) in more detail. Finally, we explain how the different constituents of the system interact with each other.  
Figure 1: Q-Holon.
# Automatic Bug Triaging via Deep Reinforcement Learning
## 4 Bug Triaging via Deep Reinforcement (BT-RL) Model
### ODM Model
#### 4.3.2 Policy and Expectation Function  
For the entire process, the expected cumulative reward at time \(t\) can be formulated as shown in Equation (8):  
\[G_{t}=R_{t+1}+\gamma R_{t+2}+\cdots=\sum_{m=0}^{\infty}\gamma^{m}R_{t+m+1}, \tag{8}\]  
where the term \(0\leq\gamma\leq 1\) is the discount rate, depending on how one intends to prioritize early rewards over later ones, and \(m\) indicates the number of bug reports.  
A value function, \(v_{\pi}\), is considered, which is the expected cumulative reward that the agent can receive in the long term, starting from a specified state and following the policy \(\pi\) thereafter. Specifically, the value of a state can be measured using two parameters, i.e., the state value functions and action value functions, depending on whether it depends on the state alone or both the state and the action.  
State value function (\(v_{\pi}(s)\)) represents the expected cumulative return from using strategy \(\pi\) to assign bug \(s\), and this implies that the strategy uniquely determines the function distribution.  
\[v_{\pi}(s)=E_{\pi}\left[\sum_{n=0}^{\infty}\gamma^{m}R_{t+m+1}|S_{t}=s\right], \tag{9}\]  
where the state-action value function (\(q_{\pi}(s,a)\)) represents the expected cumulative return using strategy \(\pi\) to assign \(a\) for bug \(s\). It can be formulated as follows:  
\[q_{\pi}(s,a)=E_{\pi}\left[R_{t+1}+\gamma\sum_{m=0}^{\infty}\gamma^{m}R_{t+m+2} |S_{t}=s,A_{t}=a\right], \tag{10}\]  
During training, to avoid falling into a local optimum, we used the policy \(\pi\) to balance the exploration and utilization of models, as follows:  
\[\pi(s_{t})=\left\{\begin{array}{l}\operatorname*{argmax}_{a\in A}Q(s_{t},a),\;if\;\mu\leq\varepsilon\\ \widetilde{a},\;otherwise\end{array}\right., \tag{11}\]  
where \(\mu\) is a random value generated from \([0,1]\) per round, \(\varepsilon\) is the exploration rate, and \(\widetilde{a}\) is the random developer.  
Each of the state value pairs is assigned a \(Q\)-value, which quantifies the expected discounted return achieved by performing action \(a\) at state \(s\).  
\[Q_{i+1}(s,a)=E_{\sigma}\left[r+\lambda\text{max}Q_{i}(s^{\prime},a^{\prime}) \middle|s,a\right], \tag{12}\]  
where \(\gamma\) denotes the developer's instant return, the discount rate, and \(s^{\prime}\) is the next state after \(s\).
## 2 Materials and Methods
### Stacking Test
#### 2.3.1 Physical Stacking Angle  
For convenience of observation and the recording of the stacking angle of the Sesbania seeds, a box was fabricated, using PMMA plate, to function as a physical stacking angle measurement device. The box had internal dimensions of 100 mm \(\times\) 100 mm \(\times\) 150 mm. During the experiment, 0.3 kg of Sesbania seeds was placed into the box. One side of the box was slowly raised, allowing the seeds to slide toward the opening under the influence of gravity, forming a stable slope, as illustrated in Figure 6. The physical stacking angle was measured using a digital inclinometer. The experiment was repeated 10 times, yielding an average value of 34.5\({}^{\circ}\).
## 4 Experiment and Evaluation
### Evaluation of CAD Model Retrieval  
Table 4 presents the evaluation results of CAD model retrieval using 1562 RGB images. Originally, 2000 RGB images were used for CAD model retrieval; however, 438 images were rejected from the test by the rules described in Section 3.2. The matching CAD model with the RGB object is retrieved based on the image similarity between the RGB object and the CAD rendering images. Given an RGB object, renderings of the 10 subcategories of CAD models are ranked from 1 st to 10 th. In Table 4, the number of RGB images that find the matching CAD models among the top five rankings is shown.  
For the SM-AT method, the rate of retrieving the matching CAD model within the top three rankings is 72.087%, and within the top five rankings, it is 85.147%. For the SM-NI method, these rates are 75.416% and 87.708%, respectively. These results show that both similarity measurements are useful for evaluating the similarity between the RGB image and the CAD image domains.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Rank 1** & **Rank 2** & **Rank 3** & **Rank 4** & **Rank 5** \\ \hline SM-AT & 720 & 250 & 156 & 117 & 87 \\ SM-NI & 741 & 258 & 179 & 101 & 91 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of RGB images that retrieve the matching CAD model as the specific rank.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Tool** & **Misc** & **Bookcase** & **Wardobe** & **Desk** & **Bed** & **Table** & **Sofa** & **Chair** & **Average** \\  & **(46)** & **(61)** & **(130)** & **(166)** & **(297)** & **(394)** & **(738)** & **(1092)** & **(2894)** & **(S818)** \\ \hline \multicolumn{2}{c}{_Acc30_} & & & & & & & & & & \\ \hline
3 DPoseLight [17] & 9 & 10 & 62 & 57 & 66 & 58 & 40 & 94 & 50 & 58 \\ PoseContrast & **11** & **25** & 76 & 58 & **78** & **71** & **53** & **97** & **86** & 62 \\ Proposed & **11** & 21 & **85** & **80** & 76 & 68 & **53** & 96 & 85 & **64** \\ \hline \multicolumn{2}{c}{_Median Error_} & & & & & & & & & & \\ \hline PoseContrast & 128.75 & **58.36** & 17.29 & 22.76 & **12.49** & 13.27 & **23.79** & 14.49 & 11.7 & 33.66 \\ Proposed & **107.18** & 75.99 & **12.44** & **16.11** & 12.76 & **13.06** & 25.4 & **13.13** & **11.66** & **31.97** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pose estimation performance using Pix3D (number of images is in parenthesis and bold font indicates the best performance in each column).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Base Classes** & **Novel Classes** & **All Samples** \\ \hline _Acc30_ & & & & \\ \hline PoseContrast & 46 & 45 & 50 \\ Proposed & **47** & **48** & **52** \\ \hline _Median Error_ & & & & \\ \hline PoseContrast & 51.32 & 56.92 & 29.43 \\ Proposed & **50.14** & **50.16** & **27.57** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pose estimation performance using ObjectNet3D.
## 4 Discussion
### Analysis of the Lithology Discrimination Effectiveness of the Optimal Method Combination  
From the perspective of exploration and development applications, the excellent performance of machine learning models in lithology discrimination mainly depends on whether the model can accurately identify important reservoirs represented by various types of sandstone. A model with practical value cannot only maintain high accuracy in identifying lithology labels with low oil contents such as mudstone. According to Figure 11, after supplementing simulated samples with the ADASYN algorithm and removing erroneous samples with the ENN algorithm, the random forest model learned the characteristics of sandstone and coarse sandstone more comprehensively, resulting in more accurate predictions for these lithology classes. In summary, after balancing the data, the discrimination accuracy of labels with fewer original data samples significantly improved. Data balancing algorithms have a significant positive impact on these classes and can maintain high discrimination accuracy for classes with a larger number of samples in the original dataset.
# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 2 Related Work
### Multi-User Experiences
An element of visual and verbal information change was added as users could only see the cube colors of their collaborators. Each pair of users played four playthroughs of the game, each time with a different latency configuration (0 ms, 150 ms, 300 ms, 450 ms). The results showed that high end-to-end latency between two VR clients had adverse effects on user performance, mutual understanding between collaborators, and perceived workload. For feelings of co-presence, however, no significant correlation with the network latency was observed.
# A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential
###### Abstract  
The implementation of the energy transition and the building of energy communities are driving forward the exploitation of the potential for rooftop photovoltaic power generation. Estimating rooftop PV generation potential requires the processing of different types of data, such as the cadastral information of buildings, a detailed description of available rooftop areas, and solar irradiance data. High-resolution estimation based on GIS data is normally limited to small survey areas. Instead, by using an algorithm for the efficient calculation of shadows over rooftops, and the integration of solar irradiance over time, we developed a procedure that allows for the rapid full census assessment of rooftop photovoltaic potential with a spatial resolution of 1 m, applicable at the regional scale and requiring minimal computational resources. We applied this approach to the rooftops of buildings in Sardinia, an island and region of Italy of particular interest for the energy transition. In addition to estimating the geographic potential, we carried out a preliminary assessment of the technical and economic potential, yielding a maximal photovoltaic rooftop generation potential of 22 TWh for the entire region.  
rooftop solar photovoltaic (PV) potential; geographic information systems (GIS); LiDAR; distributed generation +
Footnote †: journal: _Applied Sciences_  
_Article_  
A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential _Appl. Sci._  
**2023**, 13.7. [[https://doi.org/10.3390/app13010007](https://doi.org/10.3390/app13010007)]([https://doi.org/10.3390/app13010007](https://doi.org/10.3390/app13010007))
## 2 Design of Antenna  
### Geometry of the Antenna  
The initial geometric structure of the Vivaldi, shown in Figure 1a,b, adopts the 0.18 \(\upmu\)m CMOS process, with dimensions of 1200 \(\upmu\)m \(\times\) 1200 \(\upmu\)m \(\times\) 533.4 \(\upmu\)m, detailed dimensions are shown in Table 1. It is made up of approximately 1/4 elliptical structures. For achieving excellent impedance matching over a very broad bandwidth, a 50-ohm microstrip line accompanied by a grounded plane serves as the feed for the antenna. To further improve the impedance matching at lower frequencies, small elliptical cuts are used beneath each individual Vivaldi antenna. Slots are incorporated within the antenna design to ensure compliance with the process area, in line with the manufacturing process. Due to the lower gain of chip antennas, this article designs a power divider to achieve high gain with a 1 \(\times\) 4 array antenna configuration. In response to the losses associated with the implementation of the antenna on a chip, the proposed Vivaldi antenna is manufactured using a 0.18 \(\upmu\)m CMOS process. The Vivaldi has six layers of silicon dioxide (M1-M6) as the top metal layers, Figure 1c, along with Metall to Metal6, integrated with a silicon substrate. The silicon substrate has a thickness of 500 \(\upmu\)m. The top metal layer Metal6 is designated as the layer, having a thickness of 2.34 \(\upmu\)m.  
Equation (1) below defines the curve of the slot index.  
\[\mathbf{y}\ =\ \mathbf{C_{1}e^{dx}+C_{2}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Length** & **Value** & **Width** & **Value** \\ \hline L\({}_{1}\) & 600 & W\({}_{1}\) & 210 \\ L\({}_{2}\) & 225 & W\({}_{2}\) & 125 \\ L\({}_{3}\) & 274 & W\({}_{3}\) & 10 \\ L\({}_{4}\) & 125 & W\({}_{4}\) & 14 \\ L\({}_{5}\) & 260 & W\({}_{5}\) & 14 \\ L\({}_{6}\) & 1200 & W\({}_{6}\) & 10 \\ L\({}_{7}\) & 200 & W\({}_{7}\) & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detail parameter. (Unit: \(\upmu\)m).  
Figure 1: Side and top view of the Vivaldi. (**a**) Vivaldi side view; (**b**) Vivaldi top view; and (**c**) CMOS 0.18 \(\upmu\)m process stack used.  
\(\text{C}_{1}=0.185\), d = 0.150, and C\({}_{2}=0.230\). (The slot factor curve of an antenna is a graph used to describe how the performance of the antenna changes with variations in its shape and size.) The antenna elliptical curve \(\text{E}_{3}\) is defined by the following Equation (2).  
\[\text{E}_{3}:\frac{\text{x}^{2}}{\text{a}_{2}}+\frac{\text{y}^{2}}{\text{b}_{2 }}\ =\ \text{1} \tag{2}\]  
From Figure 2, it is observed that when the frequency is 85.47 GHz, the reflection coefficient is below \(-10\) dB. The applicable frequency range is 85.47-102.36 GHz. At a frequency of 95.5 GHz, the deepest point of the return loss is \(-23.60\) dB. The overall bandwidth where the return loss is below \(-10\) dB is 16.89 GHz.
# Improving Lossless Image Compression with Contextual Memory
## 4 The Proposed Method-Contextual Memory
### Description of the Contextual Prediction  
#### 4.2.1 Model Prediction  
To make a prediction, we propose the following algorithm (simplified from the original proposed algorithm [28], which had a probability refinement phase):  
1. We obtain a value from the memory for each context. One way to do that is to index the hash of the \"context value\" in a table
2. We average all the obtained \"memory values\"
3. Convert the average into a probability using the sigmoid function \[p=\sigma\Bigg{(}\frac{k}{n}\sum_{i=0}^{n}v_{i}\Bigg{)},\ v_{i}=M[i][hash(c_{i})]\] (3) \(p\) is the output probability (that a bit is one), \(n\) is the number of input contexts, \(c_{i}\) is the _context value_ of the i-th context, \(v_{i}\) is the _memory value_ from the memory M for context i, \(k\) is some ad-hoc constant \(\sigma\) is the sigmoid function.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 4 Challenges in Indoor Localization Systems
### Environmental and Corrosion Factors  
In industrial applications, environmental conditions such as temperature, humidity, and corrosive agents can degrade the performance of localization systems. For example, RFID-sensitive tags designed to detect environmental corrosiveness need to be resistant to nanometer-scale metal degradation [23]. These tags are critical for asset tracking in harsh environments where equipment reliability is paramount.
## 4 Results and Discussion
### Characteristics and Magnitude of the In Situ Stress  
The distance between the five in situ stress measurement boreholes at the three locations ranges from a minimum of 20 km to a maximum of 50 km, all situated north of the East Kunlun Mountain active fault zone. Consequently, a comprehensive analysis is conducted on the in situ stress data from these five boreholes when assessing the in situ stress state of the region. By examining the degree distribution diagram of in situ stress with depth in this area, it is observed that both maximum and minimum horizontal principal stresses exhibit a linear trend that increases with depth. The strong linear relationship indicates stability in regional stress field characteristics and consistency in distribution patterns. Through linear regression analysis of the collected data, we derive a regression  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Borehole**} & \multirow{2}{*}{**No.**} & \multicolumn{4}{c}{**Test Interval**} & \multicolumn{4}{c}{**Fracturing Parameter (MPa)**} & \multicolumn{4}{c}{**Principal Stresses (MPa)**} & \multirow{2}{*}{\(\mu_{\text{m}}\)} & \multirow{2}{*}{**Direction of \(\sigma_{\text{H}}\)**} \\  & & **Barial Depth** & & & & & & & & & & & & \\  & & **(m)** & \(P_{\text{H}}\) & \(P  
curve representing in situ stress variation with depth. The regression equations obtained here between horizontal stress and depth (m) are essentially the same as the results for mainland China [27], expressed as follows:  
\[\sigma_{\rm H}=0.0222\times H+4.05\,R^{2}=0.7410, \tag{6}\]  
\[\sigma_{\rm h}=0.0127\times H+2.84\,R^{2}=0.7836, \tag{7}\]  
As illustrated in Figure 3, the state of in situ stress varies with increasing depth. In the interval from the surface to 206.00 m, the in situ stress condition is characterized by \(\sigma_{\rm H}>\sigma_{\rm h}>\sigma_{\rm v}\), indicative of a reverse faulting regime. Below a depth of 206 m, however, the in situ stress state transitions to \(\sigma_{\rm H}>\sigma_{\rm v}>\sigma_{\rm h}\), which corresponds to a strike-slip faulting regime.