esa-sceva/satcom-chunk-collection huggingface dataset:
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 3 Methodology
### Review of the Physical and Visual Condition of Components at Different Ages
#### 3.2.3 Failure Condition Performance (FCP)  
Failure condition performance (FCP) expresses the partial impact of failure factors on the component deterioration processes. It distinguishes the performance level of a partition in standard conditions from the level of the same type of partition in failure conditions, in which each failure has an exclusive deterioration pattern. The failure mechanisms likely to affect the overall performance level of various types of partitions are characterized in advance, based on professional judgment or data from relevant literature. Factors influencing the occurrence of failures are typically rooted in faulty construction and/or design, although a minority are caused by poor material quality. Failures are characterized by surface cracking, cracking at the junctions of partition-exterior wall and partition-ceiling, cracking around door frames, signs of corrosion on the surface, and instability of electrical fixtures within the partition.
# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 4 Experimental Results and Discussion
### Heatmaps for Object Recognition  
In the field of computer vision, heatmaps are a visualization tool used to display the intensity or importance of specific information, usually shown through changes in color. These maps typically use a color gradient from cold to warm colors, such as blue to red, where red generally represents higher values or larger focus points, and blue represents lower values or less focus.  
In neural networks utilizing attention mechanisms, heatmaps aid in visualizing which regions of an image the model focuses on during processing. Heatmap generation also reveals the spatial support regions used for image classification decisions. This study utilized heatmaps generated by Grad-CAM (gradient-weighted class activation mapping) to identify which regions of an image contribute most significantly to classification results. These heatmaps highlight the regions activated in the network when performing specific tasks. One reason for choosing Grad-CAM is its capability to produce heatmap visualizations overlaid on the original image, enabling researchers and practitioners to intuitively interpret the model's behavior. This interpretability is crucial for understanding why the model makes certain predictions, particularly in critical domains like object detection. Moreover, compared to the original CAM, Grad-CAM is a gradient-based technique that does not require architectural changes or retraining of the model. It can be applied to any CNN-based architecture, making it versatile and applicable across various domains and tasks without significant overhead. A comparative analysis of the heatmaps generated using the original YOLOv8 model and the enhanced model is showcased in Figure 14.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]-0.95/\%** & **FPS** \\ \hline YOLOv8n+CIOU & 96.7 & 84.6 & 202 \\ YOLOv8n+DIOU & 96.8 & 85.7 & 206 \\ YOLOv8n+GIOU & 96.5 & 84.2 & 208 \\ YOLOv8n+SIOU & 96.9 & 86.3 & 209 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different loss functions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AFN** & **SODL** & **SIOU** & **AP50(\%)** & **FPS** \\ \hline \(\times\) & \(\times\) & \(\times\) & 96.7 & 210 \\ \(\surd\) & \(\times\) & \(\times\) & 96.8 & 215 \\ \(\times\) & \(\surd\) & \(\times\) & 96.6 & 156 \\ \(\times\) & \(\times\) & \(\surd\) & 96.9 & 201 \\ \(\surd\) & \(\surd\) & \(\surd\) & 97.0 & 212 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison results of different models for ablation experiments.
# Detecting and Controlling Slip through Estimation and Control of the Sliding Velocity
## 7 Discussion  
The experimental results presented so far give evidence of the ability of the robot to handle different types of objects made of different materials (resin, plastic, and metal) and with large differences in their weight and friction properties. The robot is able to grasp and lift the objects without any knowledge of their weight by applying the minimum grasping force to hold them without any translational or rotational slippage. This is shown especially in the lifting of the empty aluminium can, which requires a grasping force as low as 0.2 N, while lifting the full can requires a 40 times larger force. The manipulation ability of the robot is not limited to safely grasping any object with no slipping; the grasp force can be suitably controlled to allow an intentional slip, which might be useful to change object orientation in hand even with a simple parallel gripper. The selection of a model-based manipulation method is motivated by the objective of allowing the robot to manipulate different types of objects without requiring an excessive learning and training burden. Indeed, the off-line learning phase is limited to the experimental identification of a few parameters of a friction dynamic model that is the basis of the manipulation control strategy. The few remaining parameters that depend on the specific object to be handled are estimated at run time by a simple and quick exploration phase (see the accompanying video). The main limitations of the method, at the present development stage, are related to the limited accuracy of the force/tactile sensor when it interacts with largely deformable objects and with rigid objects characterized by a curvature radius smaller than the one of the soft pad. Moreover, the model learning phase, even though robotized, is still quite lengthy.
# Complexity of Hamiltonian Cycle Reconfiguration
## 2 PSPACE-Completeness
### Strongly Chordal Split Graphs  
A graph is _chordal_ if each cycle in the graph of length greater than 3 has a chord. A _clique_ of \(G=(V,E)\) is a subset \(S\subseteq V\) such that \(uv\in E\) for any two vertices \(u,v\in S\). A graph is a _split graph_ if its vertex set can be partitioned into a clique and an independent set. A chordal graph is _strongly chordal_[19] if each cycle of even length at least 6 has an odd chord, that is, an edge joining two vertices having odd distance on the cycle. Strongly chordal graphs are closely related to chordal bipartite graphs. Let \(G=(U,W,E)\) be a bipartite graph. We define a split graph \(S(G)=(U\cup W,E\cup E_{U})\), where \(E_{U}=\{uu^{\prime}:\ u,u^{\prime}\in U\}\). It is known that a bipartite graph \(G\) is a chordal bipartite graph if and only if \(S(G)\) is strongly chordal. See ([20; 21], Lemma 12.4).  
Let \(G=(U,W,E)\) be a bipartite graph with \(|U|=|W|\). Obviously, any Hamiltonian cycle of \(S(G)\) does not pass through the edges in \(E_{U}\). Thus, there is a reconfiguration sequence from a Hamiltonian cycle \(C_{0}\) of \(G\) to another Hamiltonian cycle \(C_{t}\) of \(G\) if and only if there is a reconfiguration sequence from \(C_{0}\) to \(C_{t}\) in \(S(G)\). Now, we have the following from Theorem 2.2.  
Theorem 2.3: _The Hamiltonian cycle reconfiguration problem is PSPACE-complete for strongly chordal split graphs._
## 2 Edge Computing and Related Computing Paradigms
### Cloud Computing  
Using cloud computing, the access and abilities of computing, storage, and network infrastructure have been developed for applications [31]. According to the definition of the national institute of standards and technology (NIST), cloud computing is a pattern to further omnipresent and on-demand network access to shared resources [1]. Cloud data centers (provided by cloud providers, such as Google, IBM, Microsoft, and Amazon) offer virtualized resources that are highly accessible, scalable, and can be reconfigured dynamically; this reconfigurability helps cloud computing to offer services with a pay-as-you-go cost model [36]. With the pay-as-you-go cost model, users are able to conveniently access remote computing resources and data management services, and they will only be charged for the number of resources that they use. Cloud offers services such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) [31]. A variety of these services can be used by application developers based on their requirements for the applications that they develop.  
The initial aim of cloud computing was to allow users to have access to computing resources for omnipresent computing. Although cloud computing has assisted in achieving this aim, it may take a long time to access cloud-based applications, which is not feasible for some critical applications that require very low latency. The growing number of connected devices and the amount of data generated at the network edge require cloud resources to be in close proximity to the data generated place. Due to the high demand for high-bandwidth, low-latency, geographically distributed, and privacy-sensitive data, there is an essential need for computing models that can take place in close proximity to connected devices to be able to support the mentioned demands. For this purpose, edge computing has been proposed [37; 38]. In the following, we explain various computing paradigms and compare them.
# Drought and Carbon Cycling of Grassland Ecosystems under Global Change: A Review
## 2 Drought and Grassland \(C\) Cycling under Global Change
### Global Warming  
Global warming may accelerate the turnover of water [68]. Warming is often accompanied by drought and could reduce the primary production in many temperate grasslands, which is not necessarily mitigated by efforts to maintain or increase species richness [90]. In grassland communities, mild droughts and warming do not lead to enhanced resistance or recovery from an extreme recurrent drought. Also, grasslands experiencing recurrent drought demonstrate a larger decrease in green vegetation cover [91]. For some drought-affected areas, however, annual NPP and NEE are not reduced, likely because higher temperatures enhance photosynthesis, counteracting the drought effects on NPP in those grasslands [91; 92]. Meanwhile, it is expected that climate warming may directly accelerate soil respiration by stimulating the activities of soil fauna, microbes, and plant roots, and may indirectly accelerate soil respiration by stimulating \(N\) mineralization, litter production, and substrate [93; 94]. Warming and prolonged droughts may strongly alter SOM decomposition, but also the quantity and quality of litter input [95; 96].  
In general, under drought scenarios, warming can cause further aridity of the ecosystem and hence act to further reduce soil respiration. In contrast, many experiments have shown that rising temperatures increase the rate of soil respiration [97; 98]. In addition, warming can cause droughts that increase the carbon in roots and weaken the overall acclimation of plants to drought by regulating \(C\) allocation between source and sink organs [41]. It is expected that rising temperatures will affect decomposition more than primary productivity [99], the consequence being a net loss of soil \(C\) and a positive feedback to the climate system in the long term. However, there is still no agreement on how temperature sensitivity varies with the labiality of SOC [100]. Instead, warming and drought have a direct impact on soil \(C\) storage, mainly by altering the mineralization rates of SOM [101]. Also, warming likely affects ANPP in grasslands, and warming effect is moderated by shifts in the C3/C4 ratios of plant communities [102]. Forecasting \(C\) cycling between droughs will interact with elevated temperatures in the future has been a significant subject of many models. However, to date, a clear mechanism to cope with this interaction has not been developed.
# An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications
## 2 Thresholding Methods
Another single-scene approach is Sen2 Cor. This is an ESA processor consisting of five modules [35] designed to correct Sentinel-2 Top-Of-Atmosphere products for the effects of the atmosphere, and to output various second-level products, including scene classification maps with cloud and snow probabilities. Labels are assigned to each pixel as a combination of the confidence level of a set of threshold tests on spectral reflectance, ratios, and indices. MAJA (Maccs-Atcor Joint Method) [36; 37] was proposed by the National Centre for Space Studies (CNES) in collaboration with Center d'Etudes Spatiales de la BIOSphere (CESBIO). This is an example of a multi-temporal method that uses image time series to detect cloud-covered pixels. The frequent satellite revisit of an area allows a local correlation of pixel features. The algorithm is mainly based on a threshold of the temporal variation of the reflectance in the selected bands, as the position and characteristics of clouds vary  
Figure 1: **(Left)** Thick cloud and thin cirrus real image from the Sentinel 3-SLSTR data archive (S1 band). (center cirrus) and (right cumulus) generated through advanced artificial intelligence models (developed by OpenAI, specifically DALLE), illustrate idealized representations of these cloud types to enhance specific features of cumulus and cirrus clouds, providing clearer insights into their properties. considerably over time in images over the same areas compared to surfaces with cloud-free pixels. Some updates of the method are described in [38] where the major changes regard cloud shadow detection. Cloud shadows are usually more evident in the IR band, the variation of the vegetation cause also greater variations over time of the surface reflectances in the NIR and SWIR bands than in the shorter wavelengths. Consequently, the authors decided to use the red band that provided better results. To exploit temporal information, addressing the inherent complexity and the need for clear-sky pixel reference, long-term missions with open access to the satellite image archive and adequate computing power are required. Although the physical properties on which the rules are based allow the methods to be easily applied to instruments with similar characteristics [27], all these methods generally suffer from limitations as they rely on a priori knowledge to determine the test sets and thresholds, and on the spectral difference between cloudy and clear sky.
## 2 Materials and Methods  
### Fluids and Chemicals  
#### 2.1.1 Brine Composition  
Polymer, provided as a powder form, was dissolved in three different brines: (i) typical seawater, here named B1; (ii) one synthetic brine, here named reservoir brine German Brine/B2 (German brine refers to the composition of \(\sim\)186 g/L from the Bramberge Oilfield, reservoir, in Germany, as reported by [PERSON] et al. [72]), subsequently named B2, without further meaning, to avoid confusion (defined after References [72; 73]); and (iii) seawater with quadruple sulfates (B3).  
A detailed summary of the brine compositions is presented in Table 2. The parameter \(R^{+1}\) defines the brine hardness and is calculated by using the weight proportion of divalent cations in each brine, with the following formula (Equation (2)), as explained by [PERSON] et al. [72] and [PERSON] et al. [73].  
\[R^{+1}=\frac{\sum(\textit{Divalent cations})}{\sum(\textit{Total cations})} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Components**} & \multicolumn{4}{c}{**g/L**} \\ \cline{2-4}  & **Seawater (B1)** & **Reservoir Brine (B2)** & **Seawater + 4 SO4 (B3)** \\ \hline NaCl & 27.4 & 34.3 & 27.4 \\ KCl & 0.77 & 0.29 & 0.77 \\ CaCl\({}_{2}\).2H\({}_{2}\)O & 1.5 & 13.7 & 1.5 \\ MgCl\({}_{2}\).6H\({}_{2}\)O & 10.8 & 10.9 & 10.8 \\ Na\({}_{2}\)SO\({}_{4}\) & - & - & 15.73 \\ TDS & \(34.4\pm 10\) & \(50\pm 10\) & \(50\pm 10\) \\ \(R^{+1}\) & 0.13 & 0.27 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of the brines used to dissolve the polymer solutions. Brine composition used as solvent for polymer solutions.
# Theoretical Modeling of Piezoelectric Cantilever MEMS Loudspeakers
## 2 Theoretical Modeling of Piezoelectric Cantilever MEMS loudspeaker Using ECM
### Equivalent Circuit of Piezoelectric Cantilever MEMS Loudspeaker  
Figure 4 depicts the ECM model of the piezoelectric cantilever MEMS loudspeaker. In the mechanical domain, only the direction of vibration, i.e., the z-direction, needs to be considered. Therefore, the middle coupling element is modeled by a mass \(M_{m1}\) and a mechanical compliance \(C_{m1}\) in the z-direction:  
\[C_{m1}=\frac{h_{m}}{YS_{xy}}, \tag{17}\]  
where \(h_{m}\) denotes the height of the middle coupling element in the direction of vibration, \(S_{xy}\) is the cross-sectional area perpendicular to the direction of vibration, and \(Y\) is the Young's modulus of the middle coupling element in the direction of vibration. The diaphragm is modeled by a mass \(M_{m2}\). The surround is modeled by a mechanical compliance \(C_{m2}\) and a mechanical resistance \(R_{m2}\). The closed back cavity of the acoustic domain can be directly modeled by the compliance \(C_{ma}\) in the mechanical domain, and is expressed as follows:  
\[C_{ma}=\frac{V}{\rho_{0}c^{2}S_{d}^{2}}, \tag{18}\]  
where \(\rho_{0}\) denotes the air density, \(c\) is the speed of sound in air, \(V\) is the volume of the closed back cavity, and \(S_{d}\) is the area of the diaphragm.  
According to the equivalent circuit depicted in Figure 4, the velocity of the diaphragm \(v_{p}\) can be described as  
\[v_{p}=\frac{F_{p}}{Z_{m}}=\frac{\frac{rU}{j\omega C_{m0}}+j\omega M_{m1}+\frac {1}{j\omega C_{m1}+\frac{1}{j\omega C_{m2}}+j\omega M_{m2}+R_{m2}}+\frac{1}{j \omega C_{ma}}}, \tag{19}\]  
where \(F_{p}\) denotes the total force acting on the mechanical domain and \(Z_{m}\) is the total mechanical impedance. Then, the sound pressure \(p_{r}\) in the half-space radiation environment of a piezoelectric cantilever MEMS loudspeaker can be calculated using the following equation:  
\[|p_{r}|=\frac{\omega\rho_{0}}{2\pi r}|v_{p}S_{d}|, \tag{20}\]  
where \(r\) denotes the distance between the loudspeaker and the measuring point. According to Equation (19), the displacement of the diaphragm can also be described.  
Figure 3: Transformer for multiple pairs of MPCAs.
# Parametric Analysis of Outrigger Systems for High-Rise Buildings with Different Geometric Shapes
## 1 Introduction
To the author's best knowledge, [PERSON] [37; 38] made a major effort towards the understanding of outrigger performance in twisted, tilted, and tapered tall buildings. His study analyzed a 60-story building with a steel-breaced core, mega-columns, and outrigger systems. Parametric analyses considered outriggers placed at the top and mid-height of the twisted tower, and at one-third and two-thirds of the height of the tilted and tapered towers. Comparisons in terms of lateral displacement revealed that lateral stiffness decreases with increasing twist, while it increases with taper and tilt. The incomplete investigation of complex geometries represents a notable deficiency in the literature, especially in light of the contemporary architectural preference for such forms. To actively contribute in addressing such limitations, this study investigates the outrigger behavior in regular, twisted, and tilted high-rise composite buildings under lateral loading scenarios. The interaction of outrigger systems with RC core shear walls is explored via the parametric variation of key geometric and mechanical parameters to assess the outrigger's effectiveness. Specifically, the number, placement, and relative stiffness of the outrigger system with respect to the inner core are systematically altered, along with twist and tilt angles. Comparative analysis of idealized analytical and refined models elucidates the accuracy and limitations of simplified methodologies while providing insights into the complex structural behavior of these systems. The paper is structured as follows. Section 2 details the simplified modeling approach and discusses the parametric analysis results. Section 3 analyzes three refined models and compares the results with those obtained from the simplified models. Finally, Section 4 summarizes the key findings of this study.
## 4 Sensitivity Analyses: Results and Discussion
### Influence of the Frictional Component of Strength on the Mud Weight Prediction  
We investigated the mud pressure sensitivity to the frictional component of strength. We increased the friction angle of the weakness planes from \(\phi_{w}^{\prime}=32^{\circ}\) (Table 2) to \(\phi_{w}^{\prime}=38^{\circ}\). The percentage increase is \(\Delta\phi_{w}^{\prime}=+19\%\). Figure 13 shows the percentage decrease in SG at different \(R\) and pore fluid pressures \(p_{f}\). The figure indicates that the maximum change occurs close to \(\delta=50^{\circ}\)-\(60^{\circ}\) for all cases. The minimum change occurs at \(\delta=0^{\circ}\). The maximum SG change occurs when \(R=1.5\) and \(p_{f}=11\) MPa. The SG change increases with \(R\) and is mitigated by high pore fluid pressure.  
The instantaneous dimensionless constant \(m_{\beta\omega}\) of HBm can be considered as a frictional component of the rock strength [62]. Figure 6d shows the percentage increase/decrease in frictional strength calculated with \(m_{\beta\omega}=5.16\) and the function of \(m_{\beta\omega}\). The figure indicates that the variation is \(\cong\pm 30\%\). The constant \(m_{\beta\omega}=5.16\) is higher than \(m_{\beta\omega}\) of the function, in the range \(\beta_{w}=0^{\circ}\)-\(55^{\circ}\). Figure 13b shows the correspondent change in SG and indicates that SG is lightly underestimated with the average value \(m_{\beta\omega}=5.16\), when \(R=1.1\), with a maximum decrease of \(-1.9\%\). Furthermore, an overestimation of SG (+2.5%) with the average value \(m_{\beta\omega}=5.16\) occurs in the range of inclinations \(\delta=0^{\circ}\)-\(30^{\circ}\) when \(R=1.5\). The underestimation of SG occurs in the range \(\delta=25^{\circ}\)-\(90^{\circ}\). The more remarkable underestimations occur at \(\delta=90^{\circ}\), in all cases. The more substantial underestimation of SG occurs at high far field stress anisotropy, but an increase in pore fluid pressure reduces the magnitude of the underestimation. This outcome indicates a mitigation effect of the pore fluid pressure on the overestimation of the frictional strength, as found with WPM. The previous discussion indicates that the change in frictional strength component lightly influences the peak of mud pressure. In fact, the influence of a high change in frictional strength produces a small underestimation of the mud weight (\(-3.6\%\)), in the worst case  
Figure 13: (**a**) Percentage decrease in SG related to the increase in friction angle of the weakness planes from \(\phi_{w}^{\prime}=32^{\circ}\) to \(\phi_{w}^{\prime}=38^{\circ}\) at different \(R\) and \(p_{f}\). (**b**) Percentage increase/decrease in SG calculated with \(m_{\beta\omega}=\)5.16 and the function of \(m_{\beta\omega}\).  
(\(\Delta m_{\beta uv}=+27\%\)). The mud weight calculated with WPM results in being more sensitive to the change in frictional strength.  
Finally, the assumed constant value \(m_{\beta uv}=5.16\) is in agreement with \(m=6\pm 2\) suggested by [61] for shale, and the majority of \(m_{\beta uv}\) calculated from lab tests by [52; 53; 54].