esa-sceva/satcom-chunk-collection huggingface dataset:
# Cable-Driven Mechanism Models for Sensitive and Actuated Minimally Invasive Robotic Instruments
## 2 Modeling of Sensitive Cable-Driven Mechanisms  
### Cable-Driven Sensitive Models  
The basic idea of our invention is to design the sensitive mechanism composed of two parts: the mobile part (A) and fixed part (B), shown in Figure 3. Part B is fixed to part D of the MIRI, and part A is fixed to part C (connected to the forceps). In order to simplify the discussion in this document, the general behavior of models is shown in a plane, but applications and implementations have been performed in a plane and space, as can be shown in [5].  
The motion of part A with respect to part B is performed by using cables. Figure 4 shows the general scheme used for two different cable-driven mechanism models (MM1 left and MM2 right), presented for the first time in [5]. In Figure 4, tensions on cables are shown from \(\mathbf{T}_{10}\) to \(\mathbf{T}_{80}\). Anchor points of cables with the mobile part A are shown with red points. In each model (MM1 or MM2), eight cables are used; however, the substantial difference between the two mechanism models can be shown by analyzing Figure 4. The formulation (conceived and presented in [5]) allows for the reconstruction of the motion of part A, measuring and synchronizing the elongation and shortening of a spring at the end of each cable. Thanks to the measure of the stiffness coefficient of each spring, it is also possible to measure the external load applied on part A.  
Figure 3: Minimally invasive robotic instrument (MIRI) (**left**) and sketch of the planar section (**right**).  
Figure 5 shows the planar section of the two sensitive cable-driven mechanism models (MM1 on left, MM2 on right), presented in [5]. The two models were conceived to measure the forces and torques on part C connected to the forceps (by using cables) during the interaction with the environment.  
Another simplified sensitive cable-driven mechanism model (MM3), with motion in a plane, and validated algorithms to measure forces and displacements were presented in [12]. Figure 6 shows a section of the planar model MM3. A revolute joint in \(O\) allows for the performance of the motion of the planar system with 1 degree of freedom and for the  
Figure 4: Sketches of two different sensitive cable-driven mechanism models (left: MM1; right: MM2), presented for the first time in [5]. Tensions on cables are shown from \(\mathbf{T}_{10}\) to \(\mathbf{T}_{80}\). Anchor points of cables with mobile part A are shown in red.  
Figure 5: Planar section of sensitive cable-driven mechanism models (MM1 on left and MM2 on right), introduced in [5]. CoMA and CoMB are the centers of mass, respectively, of mobile part A and fixed part B.  
measurement of external loads and displacements by using only two cables. More details on this mechanism model can be shown in [12].
## 1 Introduction  
Glaucoma is the leading cause of irreversible blindness worldwide [1]. Disease progression is usually insidious, eventually leading to a loss of peripheral vision and, when untreated, blindness [2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. The pathophysiology of glaucoma involves damage to the optic nerve that usually results from an increase in intraocular pressure (IOP) [1; 2]. In closed-angle glaucoma, the clinical presentation is typically acute, with pain and loss of vision from pupillary dilation or lens dislocation into the iris [12; 13; 14; 15]. Drainage of aqueous humor is impaired, leading to severely elevated IOP [1; 16], resulting in ganglion cell death and subsequent blindness. By contrast, open-angle glaucoma typically presents asymptomatically and progresses more slowly than closed-angle glaucoma. In this case, the aqueous humor outflow at [PERSON]'s canal is impeded by pathologic resistance in the trabecular meshwork or uveoscleral outflow [17; 18]. The subsequent accumulation of fluid increases IOP, leading to retinal ganglion cell (RGC)death, axonal thinning, optic nerve cupping, and loss of vision [19; 20; 21; 22; 23; 24; 25].  
Mouse models of glaucoma are useful for identifying factors involved in the elevation of IOP and testing potential therapeutic interventions. One such model involves the injection of microbeads into the anterior chamber (AC) of the murine eye. Microbeads impede outflow at the canal of Schlemm [26; 27], thereby replicating the pathophysiology of open-angle glaucoma. Increased IOP causes progressive loss of RGCs that plateaus approximately three weeks after bead injection [26; 27].  
In this study, we examined the behavior of Myo/Nog (M/N) cells in a modified version of this murine glaucoma model. M/N cells were named for their expression of Myo/D, a skeletal-muscle-specific transcription factor, and Noggin, an inhibitor of bone morphogenetic proteins [28; 29; 30; 31; 32; 33; 34]. M/N cells also express brain-specific angiogenesis inhibitor 1 (BAI1) [35]. They are typically present in low numbers within the eye under normal conditions [30; 36; 37; 38; 39; 40; 41]. Injury to the retina or lens activates M/N cells, stimulating their proliferation and migration to wounds, and, in some cases, promotes their differentiation into contractile myofibroblasts [37; 38; 39; 42; 43; 44; 45]. Depletion of M/N cells in a model of oxygen-induced retinopathy (OIR) led to increased photoreceptor cell death [38], whereas intravitreal injection of M/N cells isolated from murine brains into the light-damaged retina actually reduced photoreceptor cell loss and preserved function [39]. Similarly, cell death was decreased when M/N cells were injected into the site of focal brain injury [46].  
The following study was undertaken to examine the behavior of endogenous M/N cells in the anterior and posterior segments in response to elevated IOP. Also tested were the effects of adding brain-derived (donor) M/N cells to the AC on IOP, RGC loss, and thinning of the nerve fiber layer (NFL) of the retina.
# Abnormal Activity Detection Using Pyroelectric Infrared Sensors
## 5 Spectral Clustering
### Likelihood Matrix Construction  
Since the training sequences are generated by a hidden mechanism associated with human's underlying activities, it is reasonable to model these sequences using a generative model [27]. In this work, we adopt a set of HMMs to model the training sequences. HMMs are a type of non-deterministic stochastic finite state automata, which are widely employed in signal processing  
Figure 3: A block diagram illustrating our approach.  
Figure 2: Measurement matrix: seven pyroelectric infrared (PIR) sensors for 17 sampling cells.  
and pattern recognition [28]. The parameters of a continuous HMM with Gaussian mixture emissions can be represented in the following compact form:  
\[\lambda=\{\pi,A,\mu,\Sigma\} \tag{3}\]  
where \(\pi\) is the initial state probability distribution, \(A\) is the state transition probability distribution, \(\mu\) is the mean vector, and \(\Sigma\) is the covariance matrix.  
The \(i\)th training sequence \(Y_{i}\) can be presented as the output of \(M\) PIR sensors,  
\[Y_{i}=\left[\begin{array}{cccc}m_{1}(1)&m_{1}(2)&\ldots&m_{1}(T_{i})\\ \vdots&\vdots&\ddots&\vdots\\ m_{M}(1)&m_{M}(2)&\ldots&m_{M}(T_{i})\end{array}\right] \tag{4}\]  
where \(m_{j}(t)\) is the output of the \(j\)th PIR sensor at time \(t\), \(t=1...T_{i}\). By using the Baum-Welch algorithm [10], we fit \(N\) HMMs, one for each individual sequence \(Y_{i},1\leq i\leq N\).  
To calculate the distance between each pair of these sequences, a probabilistic model-based framework for sequence clustering is proposed in [29]. The likelihood matrix \(\mathbf{L}=\{l_{ij}\}\), whose \(ij\)th element is defined as  
\[l_{ij}=\text{log }p_{ij}=\frac{1}{\text{length}(Y_{j})}\text{log}P(Y_{j}; \lambda_{i}),1\leq i,j\leq N \tag{5}\]  
where \(Y_{j}\) is the \(j\)th sequence, \(\lambda_{i}\) is the model trained for the \(i\)th sequence, and \(P(Y_{j};\lambda_{i})\) is the likelihood of \(Y_{j}\) generated by model \(\lambda_{i}\).
# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
###### Abstract
Therefore, using intelligent algorithms to establish efficient blasting prediction and optimization models is of significant practical importance, as it helps in finding blasting design parameters that achieve the optimal balance between fragmentation and blasting cost. This study established an intelligent hybrid model (GA-LSSVM) to predict rock blast fragmentation (P\({}_{50}\)). Using the root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (r) as evaluation metrics, the GA-LSSVM was compared with RIME-LSSVM, PSO-LSSVM, LSSVM, and the Kuz-Ram model. Additionally, a multi-objective optimization model that integrates blast fragmentation and blasting cost was established, providing optimal blasting design parameters to ensure the efficient operation of blasting activities.
## 5 Conclusions  
In this study, the influence of the number of holes and the difference in using a calibration fuel for conventional diesel fuel was evaluated to analyse the rate injection behaviour. The main conclusions obtained were:  
* The nozzle with the highest fuel efficiency was the eight-hole nozzle with 124 microns, as it provided a better atomisation of the fuel with a lower injection rate value. In this way, it was possible to increase the injection pressure and decrease the pulse width or energisation time. In addition, by obtaining better atomisation, the air-fuel mixture would be more homogeneous, which would increase the combustion engine performance.
* If the cross-sectional area of the holes was smaller and the number of holes was greater, the fuel distribution in the engine would be much better and the injection rate would be adequate, as happened with nozzle number 2. On the other hand, nozzle number 1 (x number of holes and x diameter) was the least efficient in this case, since the values of flow rate, mass, and injection rate were the highest compared to the rest of the nozzles.
* When working nozzle 1 was used at a low engine speed and pressure (idle), injector 1 was not activated. This indicates that, to keep the vehicle running, the injection pressure, engine speed, and injection pulse would have to be increased, resulting in an even higher injection rate and fuel consumption.
* It is important to stress the importance of fuel viscosity characteristics, as these were shown to play important roles in the behaviour of the injection rate and, thus, fuel efficiency. This would extend engine life and reduce emissions to the environment.
* The two zero-dimensional models presented to predict the injection rate behaviour were close to the real behaviour of the 4 JJ1 engine, so this will be very useful for future studies concerning injection rate behaviour.
* The importance of the zero-dimensional model used in the study should be emphasised, as this model facilitates data collection and shortens processing time. This is because, in most cases, physical data are used, which are often not very accessible.  
Conceptualization, E.V.R.-R., K.M.-C., J.A.S., R.G.-C.; methodology, E.V.R.-R., J.L.-L., J.A.S., R.G.-C.; software, E.V.R.-R., J.A.S.; validation, E.V.R.-R.; formal analysis, E.V.R.[PERSON], [PERSON], [PERSON]; investigation, [PERSON], [PERSON]; resources, J.A.S.; data curation, E.V.R.-R. and J.A.S.; writing--original draft preparation, [PERSON]-R., J.A.S., [PERSON]; writing--review and editing, [PERSON]-R., J.A.S., [PERSON]; visualization, [PERSON]-R., J.A.S.; supervision, [PERSON]-R., J.A.S., [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by (i) the government of Castilla-La Mancha community to the project ASUAV, Ref. SBPLY/19/180501/000116, and (ii) Contribution from the national project (PID2020-118387 RB-C32).  
The author declare the availability of the data and authorship of the data.  
The authors declare no conflict of interest.
# Advancing Sustainable Textile Metrology: Reflectivity Measurement with Controlled Light Sources
## 3 Results and Discussion
### Summarizing Discussion  
The results of the performed study show the effect of lighting conditions on the reflectivity of samples with different knitted macrostructures. The analysis clearly indicates that certain knits perform better under specific types of lighting, with fluorescent lighting and daylight providing the highest reflectivity for most samples. For example, sample B demonstrates the highest reflectivity under all lighting conditions, indicating that its knit pattern is the most effective for light reflection, while sample F has the weakest reflectivity, which may be attributed to its knit structure with larger pores and fewer interlacings.  
The correlation analysis conducted across different lighting conditions also supports these observations. The graph in Figure 10 shows a strong positive correlation between reflectivity under daylight and fluorescent lighting (r = 0.978), which means that textiles with high reflectivity under daylight almost always exhibit high reflectivity under fluorescent lighting. These results emphasize that if we know the reflectivity under one of these lighting conditions, we can predict the reflectivity under the other with high accuracy. This is particularly useful for industrial and commercial applications, where lighting conditions typically include fluorescent lamps or daylight.  
Interestingly, the analysis shows a weaker correlation between reflectivity under daylight and home lighting (Figure 11). The confidence interval is wider, indicating that the differences between samples are greater--some textiles show very good reflectivity under daylight, but not as effectively under home lighting. For instance, sample A has average reflectivity under all conditions, but under home lighting, it does not reach the level of reflectivity it demonstrates under fluorescent or daylight conditions. This underscores that the type of lighting significantly impacts the reflective properties of textiles, and home lighting, with its warmer spectrum, can reduce the effectiveness of reflective threads. This conclusion is important, as some types of street lighting also fall within this warm spectrum.  
Using cluster analysis, it was determined that the samples could be grouped into three clusters based on their reflectivity. Samples with low reflectivity (Cluster 0), such as D, E, and F, likely contain fewer visible or less integrated reflective threads on the front side, explaining their lower efficiency. Samples with moderate reflectivity (Cluster 1), such as A and C, demonstrate medium effectiveness, while sample B, with the most pronounced reflectivity, falls into Cluster 2 and is most suitable for applications requiring strong light reflection.
## 1 Introduction  
Today it is well understood that exposure to air pollution, especially particulate matter (PM), has negative impacts on human health. Epidemiological and toxicological studies report a relation between exposure to elevated PM concentrations and respiratory and cardiovascular diseases [1]. To protect human health limit values for PM\({}_{2.5}\) and PM\({}_{10}\) have been included in the European Directive 2008/50/CE. Even lower threshold values are recommended by the World Health Organization [2]. In addition to direct limit values for PM\({}_{2.5}\) and PM\({}_{10}\), the European Directive 2004/107/CE sets target values for several toxic substances related to PM, including polycyclic aromatic hydrocarbons (PAHs). Moreover the Sustainable Development Goals (SDGs) defined by the United Nations to achieve a better and more sustainable future for the whole globe by 2030, list air quality within the targets and explicitly mention PM concentrations in urban environments as an indicator to evaluate the current situation [3].  
Despite reductions in the PM burden during recent decades, many regions within the European Union are still struggling to attain the PM\({}_{10}\) limit value and the violations of air quality regulations pose a challenge on both the local and regional scale. To reduce ambient air pollution, it is necessary to understand the emitter-receptor relationship and to have detailed knowledge about individual source contributions. PM is a complex mixture, and the ambient PM concentration at a specific location depends on both local emissions and the background pollution load [4; 5]. In January and February 2017 high PM levels were observed throughout mid-Europe [6]. Enhanced PM burdens were also observed in Austria. Within Austria the city of Graz is known as a 'PM hotspot', prone to enhanced PM levels due to the combination of orography, in-situ emissions, and long-range transport of air masses [7].  
During the first months of 2017, the European Union (EU) short time limit value for the PM\({}_{10}\) concentrations (50 \(\upmu\)g/m\({}^{3}\) as daily mean) was exceeded several times in Graz. Peak abundances even exceeded 100 \(\upmu\)g/m\({}^{3}\). This study evaluates whether the observed exceedances can be attributed mainly to local effects (i.e., an urban impact) or regional pollution transport leading to a high initial pollution load and how the chemical composition of PM and the contribution of main aerosol source to total PM mass are affected by this interplay, aiming at health related issues as well as possible reduction measures. All analyses are based on input data readily available from a state-of-the-art air quality monitoring network, covering both polluted and background sites, thus allowing a transfer of the analytical approach presented to other regions.
# Implementing and Testing a U-Space System: Lessons Learnt
## 1 Introduction
### Related Work
Some operations will require specific services and considerations. This is the case of UAM. As a result of the project CORUS-XUAM [9], the last version of the ConOps now includes a Vertiport Availability service. Such service should be based on an analysis of the capacity and demand for a forecast of the availability. This analysis can also support the design of the future vertiports or their expansion. All this and more was the task of the project HorizonUAM; find in [10] the long relation of publications which resulted from this project. The services mentioned are a small part of all those identified in the ConOps. Some of them are expected from a given implementation phase and some are required only in certain types of airspace volumes. In any case, once having the necessary services with an adequate performance, they have to be coordinated. A harmonized information and communication exchange is necessary to facilitate the compatibility and the competitiveness of the services provided by the U-space service providers (USSPs). Procedures for this coordination need to be described in detail. Criteria will have to be defined to assign the airspace to the users. Policies will have to be set to manage the traffic. Rules need to be established for both the system and the users to respond to contingencies. Defining most of this is the goal of the ongoing project AI4 HyDrop (An AI-based Holistic Dynamic Framework for a safe Drone's Operations in restricted and urban areas) [11]. This project will develop a framework to coordinate the services implied in the submission of a flight plan request and its acceptance. The artificial intelligence (AI) in the project will be found in services like the one creating wind and turbulence models to predict micro-scale effects in the urban architecture; also in the one that will use AI techniques to detect rogue drones; and finally, the Flight Authorization service will make use of AI, for instance, for the strategic deconfiction. The German Aerospace Center (DLR) contributes to this project by adapting and expanding the prototype developed for the project Labyrinth (introduced in the next subsection), which will be used for simulations to validate the solutions proposed.
## 5 Conclusions  
Using the Northeast Tiger and Leopard National Park as a study area, we systematically evaluated the terrain and canopy height inversion performance of the Carbon Sinks and Aerosol LiDAR (CASAL) system by comparing its L2 products with airborne LiDAR-derived data. Additionally, we validated the capabilities of next-generation satellite light detection and ranging (LiDAR) for detecting terrain elevation and forest canopy height changes. This study provides critical insights into the accuracy and potential of CASAL and satellite LiDAR technologies in ecological monitoring. Key findings include the following: (1) an exceptionally strong correlation (\(R^{2}=0.999\)) between CASAL-derived ground elevations and airborne laser scanning digital terrain model (DTM) products, with a root mean square error (RMSE) = 2.70 m and median DTM differences \(<\)0.7 m; (2) a significant linear relationship exists between canopy heights derived from terrestrial carbon monitoring satellite full-waveform data and airborne LiDAR measurements within footprint ranges. We further investigated relationships between multiple forest height metrics (RH80-RH100) and airborne LiDAR-derived forest heights. Our findings provide critical insights for applying terrestrial carbon monitoring satellite laser altimetry data to forest carbon stock estimation. Certain CASAL height estimation characteristics remain partially unexplained, potentially due to temporal data acquisition differences and forest vegetation growth dynamics. Future work should validate CASAL LiDAR performance across diverse environments and acquisition conditions, optimizing factors such as geolocation accuracy, reference data error reduction, and minimized data acquisition time differences.  
Conceptualization, Z.C., S.H. and A.F.; methodology, Z.C.; validation, Z.C.; formal analysis, S.H.; investigation, Z.C.; resources, A.F.; data curation, Z.C.; writing--original draft preparation, S.H.; writing--review and editing, S.H.; visualization, S.H.; supervision, A.F.; project administration, A.F.; funding acquisition, A.F. All authors have read and agreed to the published version of the manuscript.  
The National Key Research and Development Program of China, 2023 YFF1303902.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.