esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Model Training Results
The confusion matrix of Model 2 shows that the model's crack recognition ability is further enhanced (the accuracy rate rises to 0.71), and the performance of damage and missing classes is also relatively stable (both 0.71 and 0.79), indicating that the model maintains excellent stability for structural damage. At the same time, the recognition effect of the plant class has been significantly improved, reaching 0.77, indicating that the model has learned more boundary features of vegetation interference in the middle and late stages. However, the accuracy of the vandalism category dropped from 1.00 to 0.75, and some were misjudged as missing, which may be due to the strong similarity between local damage and the overall absence of bricks in the edge features of the image. In addition, there is still serious confusion between stain and intact. For example, 0.28% of the \"stain\" class is misjudged as background, indicating that the model is still not robust to fine-grained color differences and weak texture targets. The confusion matrix of Model 3 presents a clearer and more stable trend. Its recognition of the missing category is further improved to 0.86, becoming one of the categories with the highest recognition accuracy in this study. At the same time, crack and moss also maintained a high level, 0.79 and 0.89, respectively. However, it is worth noting that although the overall accuracy of the damage category is 0.71, its confusion rate is relatively high, especially the proportion of being misidentified as intact and background is 0.61 and 0.48, respectively, indicating that the model's recognition ability for edge wear and mild surface erosion still has bottlenecks. Also, the model's ability to recognize plant categories dropped to 0.62, the same as Model 1. There is still a clear confusion between stains and intact items, with 0.28% of stains being seen as background and many intact items being wrongly identified as damage or stains, showing that the model is not yet strong enough to recognize non-structural and low-contrast features. Combining the evolution trends of the three confusion matrices, we can observe that the YOLOv8 model maintains excellent recognition stability in categories with prominent features such as vandalism, missing, and moss. However, in categories such as stain, plant, and intact, which are like the background texture and have blurred visual boundaries, there are always different degrees of confusion. Although the overall accuracy of Model 2 is slightly lower than that of Model 1, it performs better in plant recognition and misjudgment control, while Model 3 has enhanced the final recognition ability of missing and crack categories, but there is also an accumulation of misjudgments between structural damage and background information.
## 7 Experimental Test--[PERSON] et al. [11]  
[PERSON] et al. [11] performed experimental tests on 21 RC column type cantilever specimens. These specimens simulate typical cross sections of bridge pillars. All specimens had a circular ring cross section, except one with solid circular. The parameters examined concerned geometric characteristics (cross section's outer and inner diameter and specimen's height), reinforcement ratios (longitudinal and transverse), and different values of the axial load.  
Experimental and analytical results of one specimen with the circular solid cross section are presented below. This specimen is named P1 and served as a reference specimen for the rest 20. The circular cross section of the specimen has a diameter of 1000 mm and height to diameter ratio equal to 3.5.  
Figure 25 shows geometry and reinforcement data of specimen P1. Table 5 summarizes the material data of this specimen and the concrete and steel model properties used in the analysis.  
Figure 23: [PERSON] et al. [13]—Specimen PC1. Displacement loading history \(\delta_{x}\) vs. load step number.  
Figure 24: [PERSON] et al. [13]—Specimen PC1. Force-displacement response \(P_{x}\)-\(\delta_{x}\).  
The cyclic uniaxial loading path of Figure 26 was applied to specimen P1 along with a normalized constant compressive axial load \(\upsilon\) = \(-\)0.065. The path consists of eight successive displacement patterns with their maximum values in increasing order. Each pattern consists of two cycles with equal positive and negative displacement values.  
\begin{table}
\begin{tabular}{c c} \hline Reinforcement data of specimen & **Value** \\ \hline
**Parameter** & **Value** \\ \hline Section shape \& dimension (mm) & \(\bullet\) 1000 \\ Cover \(c\) (mm) & 20 \\ Clear length \(L_{\text{clear}}\) (mm) & 3500 \\ Longitudinal bars (mm) & 24019.1 \\ Transverse reinf. (mm) & \(\mathcal{O}\)12.7 / 40 \\ \hline Concrete properties of specimen & **Value** \\ \hline
**Concrete Property** & **Value** \\ \hline \(f_{\text{c}}\) (MPa) & 32.50 \\ \(E_{cm}\) (GPa) & 28.50 \\ \hline Steel properties of specimen & **Steel Property** & **Value** \\ \hline \(f_{y}\) (MPa) & 499 \\ \(f_{u}\) (MPa) & 600 \\ \(E_{s}\) (GPa) & 200 \\ \hline Material strains used in analysis. & **Strain Property** & **Value** \\ \hline Concrete & \(\varepsilon_{c0}\) (\%\({}_{\omega}\)) & \(-2\) \\ \(\varepsilon_{cu}\) (\%\({}_{\omega}\)) & \(-4\) \\ Steel & \(\varepsilon_{y}\) (\%\({}_{\omega}\)) & \(\pm 2.00\) \\ \(\varepsilon_{au}\) (\%\({}_{\omega}\)) & \(\pm 100\) \\ \hline \end{tabular}
\end{table}
Table 5: Experimental data of specimen P1. [PERSON] et al. [11].  
Figure 25: [PERSON] et al. [11]—Geometry and reinforcement data of specimen P1.  
Figure 27 shows the force-displacement response \(P_{x}\)-\(\delta_{x}\). The analytical response is in almost excellent agreement with the experimental.
# Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks
## 3 Methods
### Tasks
#### 3.6.2 Delayed XOR  
We optimized ESNs and DDNs for the delayed XOR task. This highly non-linear Boolean task was first introduced in [28], and is commonly used as a benchmark for (physical) RC systems [36; 37; 38]. For this task, a random bitstream is presented to the reservoir. The task objective is to perform the bitwise XOR operation \(y(t)=u(t)\oplus u(t-k)\) between the current input, \(u(t)\), and the input of \(k\) timesteps ago, \(u(t-k)\). During hyperparameter optimization, both the training bitstream and the validation bitstream consisted of 2000 samples each. During evolution, the NRMSE of the readout predictions was used as the CMA-ESfitness measure. However, in Section 4, we report the bit error rate (BER), defined as the number of misclassified bits over the total number of bits in the presented bitstream.  
Besides being highly non-linear, the delayed XOR task requires maintaining input information for exactly \(k\) timesteps. For higher values of \(k\), the IPC is needed at larger lags. Thus, good performance on a delayed XOR with a large \(k\) implies a better memory and non-linearity trade-off. In this paper, we consider the \(k\)-delay bitwise XOR task for delays 1 through 19.
## 3 Methodology
### Clustering Features  
All clustering features were computed by applying a variation of the OPTICS algorithm [22] we call dynamicOPTICS. The advantage of OPTICS over k-means is that k-means requires the prior definition of the number of clusters to compute, while OPTICS determines said amount heuristically. In dynamicOPTICS, a unique hyperparameter that defines the minimum number of samples to create a cluster (_min_samples_) must be set. To set this value we performed a linear search considering _min_samples_ values between 2 and 50. For each instance, we stored the best _min_samples_ value, optimizing the _clusteringQuality_ measurement in Equation (1).  
\[Q=\alpha\times D_{out}-\beta\times D_{in}-\gamma\times OR \tag{1}\]  
where \(D_{out}\) is the average inter-cluster distance and is computed by Equation (2), \(D_{in}\) is the average intra cluster distance as shown in Equation (3), \(OR\) is the Outlier Ratio computed by Equation (4), \(\alpha,\beta,\gamma\in\mathbb{R}^{+}\), \(N_{\text{C}}\) is the number of clients in a cluster \(C\), \(C_{neighbour}\) is the closest cluster to \(C\), and \(centroid_{\text{C}}\) is the centroid of a cluster \(C\) computed by Equation (5).  
\[D_{out}=\frac{\sum_{c}^{|Clusters|}dist(centroid_{\text{C}},centroid_{\text{ C}_{neighbour}})}{|Clusters|} \tag{2}\]  
\[D_{in}=\frac{\sum_{i=0}^{N_{\text{C}}}(dist(ClusterClients[i],centroid_{ \text{C}}))}{N_{\text{C}}} \tag{3}\]  
\[OR=\frac{|Outliers|}{|Clients|}x| \tag{4}\]  
\[centroid_{\text{C}}=\frac{\sum_{i=0}^{N_{\text{C}}}(ClusterClients[i].x,ClusterClients[i].y)}{N_{ \text{C}}} \tag{5}\]  
The idea is to maximize clustering quality by minimizing the number of outliers, the distance between clients in the same cluster, and the distance between clients in different clusters. \(\alpha,\ \beta\), and \(\gamma\) were manually set from preliminary experiments to 1, 2, and 1, respectively. Figure 2 shows the clusters obtained in Homberger instances.  
The clustering features considered for this work are:  
* Optimal min_samples value: Measures clusterability.
* Cluster Ratio: Number of clusters.
* Outlier Ratio: Number of non-clustered clients.
* Avg. clients per cluster: Measures cluster size (number of clients).
* CV clients per cluster: Cluster size heterogeneity.
* Avg. of intra-cluster distances: Cluster density.  
Time Window Features  
Additionally, we propose the study of four features related to the time windows of the CVRPTW instances:  
* Ratio of the highest number of overlapping windows to total: Measures the tightest time interval.
* Ratio of average number of overlapping windows to total: Measures the average time tightness.
* Ratio of average window length to the longest window: Measures the normalized time window length.
* Ratio of the standard deviation of window length to the longest time window: Measures normalized time window heterogeneity.  
Considering that time-window constraints greatly reduce the solution space and complicate the search for feasible and good-quality solutions, we expect instances with narrow time-windows to be more computationally expensive and produce more unfeasible solutions.
## 3 Results and Discussion
### Reusability of MNPs  
Facile regeneration and fine reusability of MNPs are extremely significant for their practical usage in the view of economic cost. Previous report showed that the thiol-coated Fe\({}_{3}\)O\({}_{4}\) MNPs showed superior adsorbing performance toward Pb(II) ions in aqueous media [21]. Nevertheless, the MNPs were not easy to regenerate due to the extremely high affinity between thiol and heavy metals; for example, the amino/thiol bifunctional MNPs should be eluted with HNO\({}_{3}\) and ethylene diamine tetraacetic acid (EDTA) sequentially to desorb the Pb(II) ions [30]. In this study, the adsorption capacity of PEI-functionalized MNPs decreased significantly with pH declining as mentioned above. In other words, the Pb(II) ions would be desorbed from MNPs at low pH level, and therefore the spent MNPs could be easily regenerated via rinsing with aqueous hydrochloric acid solution. Accordingly, repetitive sorption and regeneration tests were performed five cycles to examine the recycling performance of PEI-functionalized Fe\({}_{3}\)O\({}_{4}\) MNPs. Results showed that the synthesized MNPs still maintained high adsorbing capacity after five cycles (Figure 4d), indicating the fine recyclability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Langmuir Parameters**} & \multicolumn{4}{c}{**Freundlich Parameters**} \\ \hline \(q_{m}\) **(mg/g)** & \(b\)**(L/mg)** & \(R^{2}\) & \(n\) & \(k\) **(L/mg)** & \(R^{2}\) \\ \hline
60.976 & 0.036 & 0.992 & 1.802 & 4.254 & 0.980 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Langmuir and Freundlich parameters for Pb(II) adsorption at 25 \({}^{\circ}\)C.  
Figure 3: Pb(II) adsorbing isotherm curve at 25 \({}^{\circ}\)C (**a**). [PERSON] isotherm simulation (**b**). [PERSON] isotherm simulation (**c**).
# Quantification of the Benefits for Power System of Resilience Boosting Measures
## 4 Modeling Framework for the Measures
### Modeling of Threat-Specific Passive Measures
#### 4.3.2 Superhydrophobic Coatings  
Hydrophobic or superhydrophobic materials can be used to build the anti-icing coatings. Although research is still ongoing, it is possible to model the coating action as a delay in the formation (sticking) of the first snow layer on conductor surface. Moreover, the ambient temperature is of paramount importance to quantify ice and snow adhesion forces to a metallic substrate [34; 35]. The study of complex temperature dependent phenomena at conductor/snow interface [36] suggests adopting a time variant collision factor \(\alpha_{1}(t)\), which depends on ambient temperature \(T_{amb}(t)\), as in Equation (6), in the Makkonen model [26].  
\[\alpha_{1}(t)=\left\{\begin{array}{ccc}0&if&T_{amb}(t)<-0.5^{\circ}C&AND&t-t_{ 0}\leq TLIM\\ 1&&otherwise\end{array}\right. \tag{6}\]  
In Equation (6), \(TLIM\) is the delay assured by the coatings (in hours) and it depends on the specific action mechanism of the coating and on environmental conditions where it operates, while \(t_{0}\) is the starting time of the wet snow accretion phenomenon.
## 3 A Taxonomy of Measuring Methods, Techniques, and Sensors in Physiological-Based DMSs
### Electrocardiography (ECG)  
ECG is a well-known method for measuring and recording the electrical activity of the heart with repeated cardiac contractions. Typically, the values of physiological parameters HR and HRV are determined from an electrocardiographic signal obtained non-invasively.  
Except for these two parameters, in driver monitoring systems, the ECG signal can be used for monitoring the RR parameter, since it is known that the amplitude of the QRS complex is modulated by respiration. So, the respiration signal can also be extracted from the ECG signal. This technique is called ECG-derived respiration. In addition, the ECG signal, in combination with the photoplethysmography signal, can be used for non-invasive cuffless measurement of BP. In DMS, ECG recording can be performed using contact, non-contact, or hybrid sensor techniques.
## 3 Results and Discussion
### Triplet Electronic States
For DFT, some approaches have also been proposed based on unrestricted methods [50]. Figure 5: Structures along the pathway from \(T_{1}\) towards pyrimidine. In the current study, a simpler qualitative approach has been carried out by analysing the populations and free-valence that arise from the calculation. For the M1 intermediary, N(4) has a free valence value of 0.2 and N(1) 0.6. These values show that such atoms will have a biradical character, in particular for N(1) atom. In the case of M2, the biradical character can be found located in atom N(4) (free valence value of 0.7) and C(8) (with 0.8). The biradical character may be also seen in the two semi-occupied molecular orbitals in Figure 6 as the M1 structure has a similar behaviour for all functions. The transition states TS1 and TS2 have been located 354.5-458.9 kJ mol\({}^{-1}\) and 223.2-340.6 kJ mol\({}^{-1}\) above \(S_{0}\) according to the functional used. As previously shown, the B3 LYP reveals the lower value and the M06-HF the higher, but considering the reaction path, \(\Delta E^{\ddagger}=E_{\text{TS1}}-E_{T_{1}}\) and \(\Delta E^{\ddagger}=E_{\text{TS2}}-E_{M0}\), the TS1 in B3 LYP has the larger barrier-height (72.1 kcal mol\({}^{-1}\)) and the lower relative energy barrier value (3.8 kcal mol\({}^{-1}\)) for the M0 \(\rightarrow\) M1 reaction. The vibrational analysis of the orthogonal normal mode relative to the reaction path is 398\(i\) and 879\(i\) for TS1 and TS2, respectively, associated to a stretching of the NN bond. The transition-state distances have been determined as \(R_{12-13}=1.7214\) A for TS1 and \(R_{15-16}=1.61575\) A. The energy profile of this mechanism can be better seen on Figure 7.  
After the extrusion of N\({}_{2}\), the system undergoes a conformational rearrangement through the allylic group. The transition-state presents a barrier of 52.8 kJ mol\({}^{-1}\), 56.2 kJ mol\({}^{-1}\), and 65.5 kJ mol\({}^{-1}\), for M06 HF, PBE0, and B3 LYP, respectively. Although the barrier height is larger than the extrusion of N\({}_{2}\), the shape of reaction coordinate at the critical point, associated to the imaginary frequency 471\(i\) cm\({}^{-1}\), shows a wider acceptance cone. Note, like steps 1a and 1b, the relative energy of the B3 LYP and larger energy barrier suggests a more stable M2 species. Figure 6: Semi-occupied orbitals of the M1 structure for the three functionals.
## 3 Results
As can be seen from the comparative diagram, all points are located below the diagonal, which indicates that all the values of the bearing capacities  
Figure 11: Comparative diagram of the bearing capacity of piles with protective shell, after and without wetting. of the piles after wetting are less than the values of the bearing capacities of the piles without wetting. From the obtained linear dependencies, linear correlation coefficients were determined, reflecting the degree of closeness of the relationship between two variables. In our case, the correlation coefficient is determined by the following formula:  
\[R^{2}=\frac{F_{u}F_{u}^{\prime}-\tilde{F}_{u}\tilde{F}_{u}^{\prime}}{\sigma_{F_{ u}}\sigma_{F_{u}}}=\frac{\sum\bigl{(}F_{u}-\tilde{F}_{u}\bigr{)}\Bigl{(}F_{u}- \tilde{F}_{u}^{\prime}\Bigr{)}}{n\sigma_{F_{u}}\sigma_{F_{u}^{\prime}}}, \tag{9}\]  
The correlation coefficient values change in the range: \(-1.0\leq R^{2}\leq+1.0\). If the value of the coefficient satisfies the condition \(R^{2}\leq 0.3\), then the relationship between the two variables is weak; if \(0.3\leq R^{2}\leq 0.7\), the relationship is average; if \(0.7\leq R^{2}\leq 1.0\), the relationship is close. In the case of piles with a protective shell, the obtained correlation coefficient for a straight line not crossing the origin is equal to 0.039, which indicates a weak connection between the function and the variables. For a straight line passing through the origin, the correlation coefficient is 0.61, which indicates the average relationship of the function with the variables. In the case of the ordinary pile, a similar pattern appears. Therefore, to determine the average value of the decrease in the bearing capacity because of wetting the near-pile soils, in both cases, we used linear functions that do not intersect the origin. Thus, after wetting an ordinary pile, the bearing capacity on average decreased by (1-0.723) \(\cdot\) 100 = 27.7% (19-38%, see Table 4), while a pile with a protective and load-bearing shell decreased by (1-0.911) \(\cdot\) 100 = 8.9% (4-14%, see Table 3).
# Technical Performance of an Inflatable Solar Dryer for Drying Amaranth Leaves in Kenya
## 4 Conclusions  
The drying performance of an inflatable solar dryer for drying amaranth leaves was compared to conventional sun drying. The ISD showed potential as a mobile solar dryer for small and medium enterprises (SMEs) to accelerate the drying process of amaranth leaves and to protect the drying product from dust and insects. The temperature inside the ISD was considerably higher than the ambient temperature, and the drying rate of leaves was thus faster in the ISD than with sun drying. The air deflector integrated in the preheating zone showed the desired effect, i.e., to avoid blowing away dried leaves from the trays. As a further hardware improvement, it is recommended that the width of the black bottom layer of the ISD be reduced to avoid lateral shading of the drying product. To improve the drying process of amaranth leaves, the stalks should be separated from the leaves, chopped, and put on different trays for longer drying.  
Further research should focus on a fan operation of the ISD also during the night for periods with favorable ambient conditions by increasing the battery capacity. Furthermore, advanced CFD modeling with an integration of mass and heat transfer should be used to further understand the influence of airflow distribution on the drying performance.  
Conceptualization, S.R., M.N., and J.M.; data curation, S.R.; formal analysis, S.R.; funding acquisition, C.G.K.C. and J.M.; investigation, S.R.; methodology, S.R.; project administration, M.N., S.S., C.G.K.C., and J.M.; resources, S.S., C.G.K.C., and J.M.; supervision, M.N. and J.M.; validation, S.R.; visualization, S.R.; writing--original draft preparation, S.R.; writing--review and editing, M.N., S.S., C.G.K.C., and J.M.  
This research was financially supported by the German Federal Ministry for Economic Cooperation and Development (BMZ) through the project 15.7860.8-001.00 \"Making Value Chains Work for Food and Nutrition Security of Vulnerable Populations in East Africa\", led by the International Center for Tropical Agriculture (CIAT).  
The authors are grateful to [PERSON], [PERSON], and [PERSON] from the International Center for Tropical Agriculture (CIAT) for all the facilities and support. The authors also express gratitude to [PERSON] as operational manager and [PERSON] as CEO of Azuri Health in Kenya for their support during field experiments. The authors would also like to thank [PERSON] for language editing.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### C-V Response to Dose  
There was a high linear correlation between all the LED strip C-V signals and dose. The C-V signals were collected during varied mAs whilst maintaining constant kVp and SID values. This setting was in correspondence with the standard linearity quality control test protocol. C-V signal data points highly conformed to the linear fits based on the R-square values. The cold white, warm white, red, green, and blue LED strip determination coefficients (R-squared values) were 0.9693, 0.6132, 0.9773, 0.9470, and 0.8234 respectively. Sensitivity coefficients for the cold white, warm white, red, green, and blue LED strips were 0.0123, 0.0038, 0.0181, 0.0133 and 0.0095 mV/mGy respectively. Therefore, as in Figure 6, the red LED strip had the highest sensitivity to dose. On the other hand, warm white had the least sensitivity to dose.  
Figure 5: The C–V responses of cold white, warm white, red, green, and blue LED strips to the SID parameter alteration.  
Figure 6: The C–V responses of cold white, warm white, red, green, blue LED strips to x-ray doses during varied mAs, and fixed kVp and SID.  
Similar to [PERSON]'s study [5], where the photodiode and phototransistor dosimetric parameter (collected charge) was evaluated for its linearity to dose, the C-V response linearity to absorbed dose was assessed. Absorbed dose is the energy deposited in a unit of mass by ionizing radiation (J/kg-Gy). LED strip C-V signal linearity to absorbed dose thus implies x-ray beam photons imparted their energy in the active area region of the LED strips. Consequently, energy linearly lost by the x-ray photons to the PN junction electrons induced charge drifts and eventually C-V signals.  
LED strips are a promising dosimetric tool because their signal increases in accordance with the absorbed dose of ionizing radiation measured by a standard dosimeter. Standard electronic dosimeters similarly produce electronic signals that are converted (calibrated) and displayed to inform of radiation dose in the dosimeter reader unit.