esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 3 Results and Discussion  
### Plant Height Estimation in Maize Populations  
#### 3.1.1 Analysis of Regional Growth Segmentation in Point Cloud Populations of Maize Seedlings  
The segmentation of the maize population was achieved using the color-based region growth algorithm. Key parameters such as distance threshold, color threshold, and the minimum and maximum number of clustered points were established. Figure 6 provides a top-down view of the segmentation results for experiment f and experiment g. In trial f, the segmentation successfully isolated the single-plant point clouds, achieving clear boundaries. In contrast, trial g exhibited poor segmentation due to plant occlusion. Under certain circumstances, the segmentation effect can be improved by adjusting the threshold values. In Figure 6, different colors represent different plants or plant clusters during the segmentation process. In Experiment f, where the segmentation was successful, the point clouds of individual plants were clearly separated, with each plant being assigned a distinct color. In Experiment g, due to plant occlusion, the segmentation was suboptimal, leading to multiple plants or clusters being incorrectly grouped together under the same color, resulting in unclear boundaries.  
Figure 7. By adjusting key parameters, Figure 6(a) illustrates a scenario where single-plant clusters correspond to isolated monocultures with optimal segmentation. In Figure 6(b), multiple clusters needed to be combined to represent individual plants. In cases where single-plant clusters were not achieved, the point clouds were manually merged using CloudCompare software to enhance the segmentation quality. In Figure 7, distinct colors are used to represent different clusters of maize plants, where each color corresponds to an individual plant or a group of plants identified by the segmentation algorithm. In Figure 6(a), each individual maize plant is assigned a unique color, indicating successful segmentation where the plants are isolated, with clear boundaries between them. In Figure 6(b), multiple plant clusters have been incorrectly merged into the same color due to occlusion or similarity in point cloud data, resulting in blurred boundaries and suboptimal segmentation outcomes.  
Figure 6. Algorithm segmentation effect.  
Figure 7. Clustering of different experimental monocultures.
## 1 Introduction  
Lithium-ion batteries are key enablers for the sustainable use of energy. One important open question is to analyze the aging of batteries in real use. Numerous works are conducted in laboratory conditions based on isolated battery cells in order to understand the aging [1], and subsequently, models which are capable of predicting aging based on analytical [2], black box [3], or hybrid are proposed [4]. These might be valid for single cell applications such as cell phones in well-defined conditions. However, for most applications, battery packs with multiple cells in series and parallel supervised by a battery management system (BMS) are used. For these kinds of applications, the aging might be more difficult to track due to the influence of BMS, and therefore fewer studies are known [5]. Still, there are quite a few applications using small unsupervised battery modules of some cells in series without supervision by a BMS. This kind of application can be found not only in model aircrafts, as in the presented study, but also in small mobility solutions such as scooters [6].  
This project conducts a simplified study of the aging of lithium-ion batteries from a database taken from the real use of nine Hacker Topfuel Eco-x batteries packs of 5000 mAh and 10 lithium cells [7] used in aeromodeling of aircrafts during the years 2016 to 2021. The objective of the first part of the analysis is to discuss the relationships between the data obtained and to provide an explanation about the performance evolution of these batteries. The aim is to determine which of the parameters analyzed are most decisive in aging. A nondimensional representation is chosen to harmonize influence of parameters.  
Subsequently, different evolution algorithms used to make black box battery models are compared. The implementation of the chosen model is presented, as well as a battery life prediction model design, using a genetic algorithm (GA) in MATLAB. The purpose of the project is to explain a procedure for studying and modeling the performance of small unsupervised battery packs. Based on this kind of model, it would be possible to provide a robust prediction or remaining useful life (RUL) in real time using limited amount of memory, so that it might be integrated in a VCU (vehicle control unit). Although the data show trends conforming to the theory, the results still must be discussed carefully, as the measurements are performed in real use and not in laboratory conditions.  
In the next section, the experimental setup is presented. A thorough analysis of measurement results with the goal to identify parameters indicating cell aging is presented in Section 3. The black box model based on GA is presented in Section 4 and it is trained with various sets of data with the goal of predicting battery aging. The paper closes with conclusions in Section 5.
## 6 Directional Design Variables (Geometrical)  
Sensitivities were calculated for the case of directional variables. In the present problem, the only directional input design variables are the cross-section parameter \(d\) and \(b\), which are analogous. In this case, sensitivities were calculated with respect to \(d\). This variable only affects the eigenvalues associated with \(I_{y}\); thus, the sensitivities of some modes are zero, as seen in Table 8. Here, the variable \(d\) is perturbed to become \(d^{*}=0.01+\epsilon_{1}+\epsilon_{2}\). Table 8 shows the results for the sensitivity analysis. Good agreement was found between the numerical results and the analytical solution with a maximum relative error of \(2.310\times 10^{-7}\%\) for the eigenvalues.  
Figure 8: Eigenvector sensitivities with respect to the global geometric parameters. Solid black lines represent the analytical solution, red represents the first and second mode, magenta the third and fourth mode, and green the fifth and sixth mode.  
Figure 9 shows the results of the first- and second-order sensitivities of the eigenvectors reflecting good agreement when compared against the analytical solutions. The average RE of 0.496% was equal for both sensitivities. Although the sensitivities of the eigenvalues are distinct for each of the modes the sensitivities of the eigenvectors are equal for each pair of modes, and the methodology can recover this behavior.
## 3 Experimental Setup
When the ratio of the equivalent capacitance of two photodiodes was adjusted, the difference from the equivalent capacitance was compensated effectively. Resistors \(R_{1}\) and \(R_{2}\) were fixed to compensate coarsely the series resistance, which made the resistance approximately same above and below joint Z. The series resistance above and below joint Z were strictly equal by finely tuning an adjustable resistor \(R_{3}\). Consequently, the phase discrepancy of output current signal of both photodiodes induced by unequal equivalent resistance and capacitance was compensated. In comparison to the self-subtraction detector without the differential fine-tuning circuit and adjustable BV, the CMRR was strongly improved from the original 20 dB to 67 dB. Figure 3: Schematic of the circuit of photodetector: (**a**) the self-subtraction photodetector scheme; and (**b**) the self-subtraction photodetector scheme with the differential fine-tuning circuit and adjustable BV.
## 1 Introduction
### Contributions of This Review Paper  
This survey paper aims to close these research gaps by focusing on more recent papers and more recent technologies like the use of multimodal techniques, DRL, and LLMs, and exploring the integration of physiological data in HAR and its application in healthcare. It also specifically reviews the available datasets with physiological and inertial data that can be used for future work in this field.  
The main contributions of this review paper are described below:  
1. Analysis of human activities and sensors used for the recognition of human activities.
2. Review of recent vision and non-vision HAR publications.
3. Review of recent HAR algorithm with focus on multimodal techniques, DRL, and LLMs.
4. Review of multimodal datasets with physiological data.
5. Discussion on the applications of HAR in healthcare.
6. Discussion on the challenges and future directions for HAR.  
Table 1 shows a comparison of our review paper and other review papers. The comparison is based on six factors: the range of years that the papers reviewed cover, the datasets  
reviewed (if any), the review of recent methodologies (DRL, multimodality, and LLMs) in HAR, and whether HAR applications in healthcare were analysed.  
Our review paper provides a very recent review of new methodologies (DRL, multimodality, and LLMs) in HAR. It highlights the role these new methodologies can play in advancing the task of HAR. In the detection of complex human activities, multimodal HAR models are shown to demonstrate improved performance over the single-modal ones, and DRL can be used to improve the result of multimodal fusion. LLMs can be used in the annotation of large datasets and in developing of personalised HAR systems and models that better understand and predict human behaviour using contextual information and user preferences. DRL is also shown to improve feature extraction and human-robot interactions. It can also be used to adjust the movement of robots in homes for better HAR. In addition, our paper provides a review of multimodal datasets with physiological data which can be further utilised in the application of multimodal HAR in healthcare. We also look at applications of HAR in healthcare and directions for future research.
## 2 Materials and Methods
### Chlorophyll a Pulse Amplitude Modulated Fluorometry  
At the end of the experimental period, and before cell harvesting, 1 mL of each replicate culture was used for bio-optical assessment, using chlorophyll-a pulse amplitude modulated (PAM) fluorometry (FluorPen FP100, Photo System Instruments, Brno, Czech Republic). Cell culture subsamples for bio-optical assessment were acclimated for 15 min in the dark and chlorophyll transient light curves were generated using the preprogrammed OJIP (fluorescence rise through four phases called O, J, I and P) protocol [22]. Rapid light curves (RLC) were generated using the preprogrammed LC1. The parameters determined and calculated by the software from this analysis are shown in Table 1[23; 24].  
\begin{table}
\begin{tabular}{c l} \hline
**Variable** & **Description** \\ \hline \(\Phi\) PSII & Photosystem II (PSII) maximum quantum yield (F\({}_{\mathrm{v}}\)/F\({}_{\mathrm{m}}\), where F\({}_{\mathrm{v}}\) is the variable fluorescence and F\({}_{\mathrm{m}}\) is the maximum fluorescence). \\ \(\alpha\) & Photosynthetic efficiency, corresponding to the initial slope of the relative electron transport rate (rETR) versus photosynthetic photon flux density (PPFD) curve. \\ \(Q_{phur}\)\(\alpha\) & Photosynthetic efficiency, corresponding to the initial slope of the relative electron transport rate (rETR) versus pigment weighted light absorption. \\ \(R\)ETR & Relative electron transport rate, obtained from applying the equation: \(\Phi\) PSII \(\times\) PPFD \(\times\) 0.5 (factor for correcting for the energy generated only at the PSII side, assuming each photosystem absorbs 50\% of the incoming energy). \\ \(\mathrm{ETR_{max}}\) & Maximum ETR having as basis the PPFD. \\ \(\mathrm{Q_{phur}}\) & Maximum ETR calculated using the pigment weighted light absorption. \\ AOECS & Active oxygen evolving complexes at the PSII donor side. \\ ABS/CS & Absorbed energy flux per cross-section. \\ TR/CS & Trapped energy flux per cross-section. \\ ET/CS & Electron transport energy flux per cross-section. \\ DI/CS & Dissipated energy flux per cross-section. \\ RC/CS & Number of available reaction centers per cross-section. \\ \hline \end{tabular}
\end{table}
Table 1: Summary of fluorometric analysis parameters and their description.
###### Abstract  
The seismic performance of ordinary reinforced concrete shear walls, that are commonly used in high-rise residential buildings in Korea (h \(<\) 60 m), but are prohibited for tall buildings (h \(\geq\) 60 m), is evaluated in this research project within the framework of collapse probability. Three bidimensional analytical models comprised of both coupled and uncoupled shear walls exceeding 60 m in height were designed using nonlinear dynamic analysis in accordance with Korean performance-based seismic design guidelines. Seismic design based on nonlinear dynamic analysis was performed using different shear force amplification factors in order to determine an appropriate factor. Then, an incremental dynamic analysis was performed to evaluate collapse fragility in accordance with the (Federal Emergency Management Agency) FEMA P695 procedure. Four engineering demand parameters including inter-story drift, plastic hinge rotation angle, concrete compressive strain and shear force were introduced to investigate the collapse probability of the designed analytical models. For all analytical models, flexural failure was the primary failure mode but shear force amplification factors played an important role in order to meet the requirement on collapse probability. High-rise ordinary reinforced concrete shear walls designed using seven pairs of ground motion components and a shear force amplification factor \(\geq\) 1.2 were adequate to satisfy the criteria on collapse probability and the collapse margin ratio prescribed in FEMA P695.  
ordinary reinforced concrete shear walls; coupling beams; seismic fragility; incremental dynamic analysis; performance-based seismic design +
Footnote †: journal: Applied Soft. 2020, 10, 4075; doi:10.3390/app10124075
# Robustness of Artificial Neural Networks Based on Weight Alterations Used for Prediction Purposes
## 3 Methodology
### Simulations to Verify How the Prediction Accuracy of the ANNs Is Generally Affected  
Firstly, different random simulations were developed to verify whether weight alterations affect the percentage of correct predictions of the ANNs in general. One NoC  
Figure 1: Structure of the neural network.  
topology/partition was randomly chosen, a dedicated ANN is responsible for this partition, and random ANN weight alterations were injected and simulated. Different simulation cases were developed using one, two, three, and four different weight alterations in the case of 8 \(\times\) 8 NoC topologies with 4 \(\times\) 5 NoC partitions/ANN sizes. For each weight, one random bit was chosen to be altered. All the different NoC partitions and ANNs were verified in this work. As a starting point, we only used 8 \(\times\) 8 NoC topologies with 4 \(\times\) 5 partitions/ANN sizes and changed one bit for each weight in order to verify if the percentage of correct predictions is generally affected. In the following sections, we present more simulations of alterations for more than one bit of different ANN sizes.  
Figures 2 and 3 present the results for one, two, three, and four weight alterations in the developed ANNs. Figure 2 presents the results from the simulations in the case of checking all the weights of the ANN: one bit alteration. Figure 3 shows the results  
Based on the above graphs/results, we conclude that weight alterations impact the prediction accuracy of the ANNs. Since the accuracy whereby only the input weight alterations are checked decreased below 90% on average, and the accuracy whereby all the weight alterations are checked is above 90% on average, we can conclude that if the alteration is presented in the input weights, the impact on accuracy is larger than if the alteration is presented in the remaining weights. This allows us to assume that the input-hidden connections of the ANN are more influential in the prediction process than the hidden-output connections of the ANN.
# Experimental Study on Fluid Dissipation Effects in Core Samples by NMR Measurement
## 2 Principles of NMR Core Experiments  
NMR technology is a crucial method for acquiring core porosity and fluid type information. In a static magnetic field, the spin orientations of hydrogen nuclei are arranged randomly. When an external magnetic field is applied, the hydrogen nuclei undergo precession in the direction of the magnetic field, generating a magnetization vector. Applying a horizontal pulse sequence rotates the magnetization vector to a horizontal orientation. Once the external pulse sequence is removed, the magnetization vector gradually returns to its original orientation in a process referred to as relaxation. Relaxation time is categorized into longitudinal relaxation time T\({}_{1}\) and transverse relaxation time T\({}_{2}\)[14]. In NMR core analysis experiments, the transverse relaxation time is typically measured. The resulting one-dimensional T\({}_{2}\) distribution data can estimate the pore size distribution in the sample and identify the fluid types present within the pores [15]. As target reservoirs become increasingly complex and with advancements in NMR experimental technology, NMR core analysis has evolved from one-dimensional T\({}_{2}\) distribution measurements to two-dimensional distribution. Two-dimensional nuclear magnetic resonance leverages the coupling relationship between T\({}_{1}\) and T\({}_{2}\) to generate the T\({}_{1}\)-T\({}_{2}\) two-dimensional distribution [16], allowing fluid components that overlap at T\({}_{2}\) to be distinguished at T\({}_{1}\), thereby enhancing fluid identification capabilities compared to one-dimensional methods [17; 18].
## 2 Materials and Methods
### Cytotoxicity Assay  
Plates with 96 wells were seeded with HaCaT cells at 1 \(\times\) 10\({}^{4}\) cells/well. The microplates were placed inside a CO\({}_{2}\) 5% incubator (37 \({}^{\circ}\)C, humidified atmosphere). The medium was replaced after 24 h with 100 \(\mu\)L with the different concentrations of each compound, ranging from 0 to 10 \(\mu\)g/mL. To evaluate the toxicity of the compound in the cell line, the different experimental conditions were tested per sextuplicate (n = 6) for 12, and 24 h. The concentrations selected to calculate the EC\({}_{50}\) were 0.01, 0.05, 0.1, 0.5, 1, 5 and 10 \(\mu\)g/mL at 12 and 24 h. After an incubation period of 12 or 24 h with the compound, cytotoxicity was measured using the (3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium) (MTT) test, incubating the HaCaT cells with the reagent for 5 h and measuring the absorbance at 550 nm, after solubilized the formazan crystals with DMSO. A Varioskan microplate reader was used to it (ThermoScientific, Canada). The background color was subtracted from the final absorbance values by performing the same experiment in parallel but without adding the MTT reagent.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 5 Validation, Evaluation and Obtained Results
### DiffServ QoS  
Contrary to IntServ, DiffServ seeks to use methods in order to categorise traffic into distinct classes. The solution Northbound core OvS switch is not able to apply the tool QoS rules, which forces them to be implemented in each slice switch port instead and creating the so-called DiffServ domains. However, OpenFlow related parameters can still be applied in _br-int_, which allows our slice-based QoS manager to take advantage of it in order to mark DSCP values. Moreover, since IP headers contain the DSCP bit field, which is positioned in the ToS octet, it is possible to define OpenFlow rules that mark and redirect traffic flows based on their packet's DSCP value. Hence, the services' differentiation also allows OvS to define traffic prioritisation within a given slice, which causes important queues to receive all the excess bandwidth that they can use before less prioritised queues receive any.  
Our proposed slice-based QoS manager provides an OvS and OpenFlow level API to allow classification and to differentiate services within each slice domain. Therefore, in the case of prioritisation requirements, the relation between them and DSCP values has to be set by the gateway administrator. We followed the commonly utilised DSCP values' list, which is described by RFC 2475. DiffServ analyses the pre-defined traffic classes on each domain node. Thus, our model can differentiate flows based on matching parameters. The defined queue rules are shown in Table 2.  
Therefore, our solution in the core switch classified traffic, applying a DSCP value based on a match of the packet transport layer protocol. Traffic that took advantage of TCP was marked with 10, while in traffic that was using UDP, a value of 18 was applied instead. Additionally, each slice switch had QoS OvS rules, specifying the queue rules to where traffic should be redirected based on OpenFlow entries that would match the known DSCP values. This means that after the core switch, the following path OvS elements  
Figure 18: Two slices’ IntServ: total achieved bandwidth comparison.  
did not have to deal with flows any longer, merely inspecting the packets ToS octet in order to apply QoS. The total achieved bandwidth in each slice condition is illustrated in Figure 19, which represents the efficiency of our solution.