esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
We demonstrated a hybrid CPV module that combines a TJ solar cell and low-cost silicon solar cells by comparing various SOE designs. When we varied the angle of the SOE between 0\({}^{\circ}\) and 50\({}^{\circ}\), our analysis showed an optimized irradiance at 30\({}^{\circ}\) on the TJ solar cell in the effective wavelength range. Higher SOE angles increased the irradiance within the lossy wavelength range more than the power within the effective wavelength range, which decreased the performance of the hybrid CPV module. Likewise, taller SOEs also caused larger ETJloss/ETJeff ratios. We concluded that an SOE with an angle of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
Figure 6: Photographs of experimental measurement setup: (**a**) the solar simulator (WACOM, WXS-450S-65), (**b**) the CPV module with a Fresnel lens and a heat sink, (**c**) the perpendicular SOE (\(\theta=0^{\circ}\), h = 15 mm), (**d**) the pyramidal secondary optical element (SOE) (\(\theta=30^{\circ}\), h = 15 mm), (**e**) the laminated triple-junction solar cell with bypass diode, and (**f**) the polycrystalline silicon solar cell. Also shown are I-V and P-V curves of the (**g**) triple-junction solar cell and (**h**) silicon solar cells for the three SOE configurations.  
30\({}^{\circ}\) and a height of 15 mm was the most desirable, with the irradiances of 626.2 W/m\({}^{2}\) and 127.4 W/m\({}^{2}\) in the effective wavelength range, and a E\({}_{\text{TJ,loss}}\)/E\({}_{\text{TJ,eff}}\) ratio of 5.7%. Next, we compared three SOE configurations for the hybrid CPV module by using simulations and experimental measurements. The perpendicular SOE and the no SOE case generated maximum output powers of 149.0 W/m\({}^{2}\) and 181.5 W/m\({}^{2}\), respectively, in the TJ solar cell of the fabricated hybrid CPV module. The optimized pyramidal SOE, however, showed a higher maximum output power of 212.8 W/m\({}^{2}\) in the TJ solar cell. The hybrid CPV module has potential applications in next-generation power production systems and, therefore, requires more research in the future.  
Conceptualization, W.-L.J.; software, W.-L.J.; writing - original draft, W.-L.J.; writing - review & editing, W.-L.J.; experiments, K.-P.K.; visualization, J.-H.M.; validation, J.-Y.L., S.-H.M., J.-H.P. and J.-H.H.; resources, W.-K.P. and S.Y.; supervision, D.-S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Korea Institute of Energy Technology Evaluation and Planning (KETEP), the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea, grant number 20183010014310; the GIST Research Institute (GRI), no grant number.  
The authors would like to thank [PERSON] at Hanul Solar Energy for his technical support. This work was supported by the Korea Institute of Energy Technology Evaluation and Planning the Ministry of Trade, Industry & Energy of the Republic of Korea (No. 20183010014310), and the GIST Research Institute (GRI) grant funded by the GIST in 2020.  
The authors declare no conflicts of interest.
# Failure Mechanism of Back-Break in Bench Blasting of Thin Terrane
## 4 Comparison of Numerical Simulations
### Result Analysis
#### 4.2.2 Influence of Rock Dip Condition on the Failure Range of the Remaining Rock  
To determine the influence of the rock dip condition on the failure range of the remaining rock, the Mohr-Coulomb model and the maximum tensile stress model were used to describe the tensile, compressive and shear failures of the element under the explosion load [39] and the damage coefficient in the model was used to reflect the failure degree of the element. As shown in Figure 10, the damage coefficient represented the degree of cracking failure and the damage of the element interface. The value of 1 represented a complete failure. The results shown in Figure 10 indicated that the failure law of the rock mass was obviously different under different inclination conditions. Under the condition of the dipping-out-of-face rock stratum, the failure depth of the remaining rock changed little along the height direction. With the increase of dip angle, the failure depth of the top rock mass increased obviously, as shown in Figure 10c. The reason was that the top free face had less constraint on the rock stratum and the rock mass had an obvious failure trend along the bedding plane. In dipping-in-face rock stratum, when dip angle was small, the change of rock failure depth was not obvious; the average failure depth was basically within 0.6 m as shown in Figure 10a. With the increase of the dip angle, the failure depth of the bottom rock mass was significantly greater than that of the top rock mass. Combined with the structural dynamic analysis results, the main reasons for the failure mode were that under the action of the explosion load, the dynamic effect of the top layer structure was obvious and the bending failure depth was large and the rock failure developed towards the bottom. The numerical simulation results were in good agreement with the theoretical calculation and the failure shape of the rock mass was similar.  
Figure 9: Fitting diagram of aperture of fracture along the bedding plane.
## 2 Composition of LEO Satellite Network and APINT Model
### APINT Model
#### 2.3.3 Reporting Process  
The set of distance from each satellite on the current service awareness path \(P_{z}\) to the nearest ground gateway station is \(D_{z}=\{D_{1},\ldots,D_{M}\}\). The propagation time for the SGL is as follows:  
\[t_{\text{re}}^{i}=\frac{D_{i}}{C}\,\forall D_{i}\in D_{z}, \tag{5}\]  
where \(C\) represents the wireless transmission rate. Unlike terrestrial network, few nodes in the network can establish stable SGLs at any given moment. Therefore, nodes can only be recognized as potential reporting nodes when stable SGLs exist. \(T_{z}\) represents the time when the current service starts transmitting until the ground gateway station receives the service. The starting transmission time can be defined as \(T_{0}\), and the time interval of the available link between node \(i\) and the gateway station is \(t_{r}^{i}=[t_{s}^{i},t_{e}^{i}],t_{e}^{i}<T_{0}+T_{z},i\in P_{z}\), where \(t_{s}^{i}\) and \(t_{e}^{i}\), respectively, represent the moments when the node starts establishing the link and when the link is disconnected, and data reporting can only occur within this interval.
# Shear Strength Analysis and Slope Stability Study of Straight Root Herbaceous Root Soil Composite
## 4 Discussion  
### Root-Soil Synergy Mechanism  
By investigating the changes in the USRS root system following shear deformation, we observed distinct friction marks on the primary root surfaces. Moreover, minor fractures were observed in the lateral roots. This implies that under shear forces, the roots and soil collaboratively endure the load. Due to the distinct material properties of roots and soil, evident interlocking arises during initial deformation. As the shear force progressively increases, the roots gradually slide or tend to slide in the soil, relying on frictional resistance to counteract the movement. The primary roots experience tensile deformation, with the frictional resistance transferring the force to the surrounding soil mass along the direction of the main roots. Furthermore, the frictional resistance distribution along the lateral roots can inhibit the lateral deformation. Consequently, plant roots effectively suppress the emergence and expansion of tensile cracks in the soil, thereby enhancing soil strength. In practical slope engineering, embedding a specific volume of roots beneath potential sliding surfaces notably mitigates shallow landslide risks.
## 4 Discussion  
### Array Patterns for CB, MRT and ZF  
The left column of Figure 4 shows the DOD of the time-averaged gain observed when applying codebook precoding. The maximum value of the time-averaged array pattern was always found within the cell boundary. This was expected, as any instantaneous single-user codebook pattern has its main lobe pointing approximately towards an active UE, which was always situated within the cell. As a result of the linearity of (5), (9), and (10) with respect to the steering vector \(\mathbf{b}\), the 6 min time-averaged pattern is expressed as an average of the instantaneous patterns towards the UEs served during the averaging interval. The maximum of such averaged pattern was most likely to be found at the intersection of the instantaneous array patterns, i.e., somewhere within the cell. In the scenario with a 2-by-2 BS array (top-left), the maximum tends to be located around the cell center in azimuth. The reason for that is the low directivity of a typical pattern produced by an array of only 4 elements. For \(N=36\) (center-left), then the maxima distribution follows the average UE density peaks (Figure 2), with two clusters that correspond to the lanes between the building blocks, parallel to the \(x\)-axis (Figure 1). With 100 BS antenna elements (bottom-left), finally, the gain maxima closely follow the regions of high UE density (Figure 2), nearly covering the full azimuth range of \(120^{\circ}\). The center column of Figure 4 shows the DODs of the time-averaged gain found using MRT precoding. Similarly to CB, when the antenna count is low (\(N=4\), top-center plot), the maxima tend to be concentrated around the cell center. As \(N\) increases, \(G_{MRT}\) tends to be directed towards the regions densely occupied with the UEs with higher probability. However, MRT shows an increased spread of the gain DOD compared to CB. This can be attributed to the fact that unlike CB, which assigns a single beam per active UE, MRT superimposes a set of multiple beams with powers proportional to the contributions of the corresponding propagation paths to the total signal received by the UE. If a UE has a direct propagation path to the BS (i.e., LOS), the instantaneous MRT pattern is likely to have a global maximum in that direction (second strongest path--the ground reflection, if present, being orders of magnitude weaker).
# Automated Diatom Classification (Part B): A Deep Learning Approach
## 3 Deep Learning
### Validation  
Once any image analysis method has been applied, it is important to quantify the performance to know how accurate it is or compare it with other methods. The most widely used technique to measure the behavior of a classifier is the _Confusion Matrix_. [PERSON] performs a comprehensive review of this topic in [14]. Here, the most important concepts are summarized.  
The _Confusion Matrix_ is a tabular representation which states the relationship between the predicted instances and the correct results. This way, there are four kinds of items:  
* _True Positive._ The instance belongs to the class and so is predicted.
* _False Positive._ The instance does not belong to the class but is predicted as positive. This is the so-called _Type I error_.
* _True Negative._ The instance does not belong to the class and so is predicted.
* _False Negative._ The instance does belong to the class but is predicted as negative. This is the so-called _Type II error_.  
From this matrix, some metrics can be calculated to summarize the performance in certain aspects. The most important ones are:  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Layer Type** & **Size** & **Number of Kernels** & **Number of Neurons** \\ \hline Image input & 224\(\times\)224\(\times\)3 & & 150,528 \\ Convolution & 11\(\times\)11\(\times\)3 & 96 & 253,440 \\ ReLU & & & \\ Channel normalization & & & \\ Pooling & & & \\ Convolution & 3\(\times\)3\(\times\)256 & 384 & 64,896 \\ ReLU & & & \\ Convolution & 3\(\times\)3\(\times\)192 & 384 & 64,896 \\ ReLU & & & \\ Pooling & & & \\ Fully connected & & & 4096 \\ ReLU & & & \\ Dropout & & & \\ Fully connected & & & 4096 \\ ReLU & & & \\ Dropout & & & \\ Fully connected & & & 80 \\ Softmax & & & \\ Classification & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: AlexNet architecture layers.  
* True Positive Rate (TPR) or Sensitivity. Defined in Equation (1), it measures the proportion of positive samples correctly classified. \[\frac{\sum\text{{True positive}}}{\sum\text{{True positive}}+\sum\text{{False negative}}}\] (1)
* True Negative Rate (TNR) or Specificity. Defined in Equation (2), it measures the proportion of negative samples correctly classified. \[\frac{\sum\text{{True negative}}}{\sum\text{{False positive}}+\sum\text{{True negative}}}\] (2)
* Accuracy. Defined in Equation (3), it measures the proportion of correctly classified samples (positives and negatives) against the total population (number of samples that have been classified). \[\frac{\sum\text{{True positive}}+\sum\text{{True negative}}}{\sum\text{{Total population}}}\] (3)
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 4 Application and Evaluation in a Real Robot System
### Discussion  
Our results show that the secure verification of workflows comes with an insignificant overhead. Our infrastructure for workflow verification fulfills most requirements we posed on their own. The integrity of workflows is ensured by hashing each workflow together with its parameters building a Merkle tree from the workflow hashes and signing the root of the hash tree using public key cryptography. This not only makes each workflow in the queue immutable, it also preserves their order. Authentication and authorization can be part of every infrastructure component by either integrating it into the certificate verification process or by using API tokens. Our requirement of traceability and non-repudiation will be achieved by the larger infrastructure for compliant development as described in Section 5 as well as by employing our robotic black box system presented in previous work [34]. This will be used as access logging device making it impossible to deny certain actions after the execution. The communication channels' confidentiality can be preserved since we use standard security mechanisms like secure socket layer communication in the REST and GRPC services. The low overhead of our solution has been demonstrated above. Our approach is scalable since it is suitable to be used on a single robot but can also be distributed for larger fleets. In addition, we showed how easily workflow queues can be supported in our software stack.  
For our approach to be feasible in practice, additional operational mechanisms are required. As already mentioned, the access to the CSP should be restricted to authorized personnel. This can make sure that only a responsible person can sign off certain workflows. This should happen after the final acceptance of a robot program. After that, our approach makes sure that a compliant workflow stays compliant throughout its lifetime. Since the overhead introduced is small, authorized changes to a program can easily be made afterwards.
# A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis
## 2 Materials and Methods
### The Evaluated Metrics  
Various performance quantities are computed in this study to evaluate the proposed system. These performance parameters are True Positive (_TP_), True Negative (_TN_), False Positive (_FP_), False Negative (_FN_), accuracy, precision, recall (sensitivity), specificity, F-score, Jaccard index, and DSC.  
Accuracy (Acu) is calculated as illustrated in Equation (1):  
\[\text{Acu}=\frac{(\textit{TP}+\textit{TN})}{(\textit{TP}+\textit{TN}+\textit{ FN}+\textit{FP})} \tag{1}\]  
Precision (Pcs) is computed using Equation (2):  
\[\text{Pcs}=\frac{\textit{TP}}{(\textit{TP}+\textit{FP})} \tag{2}\]  
Recall (Rca) is computed using Equation (3):  
\[\text{Rca}=\frac{\textit{TP}}{(\textit{TP}+\textit{FN})} \tag{3}\]  
Specificity (Sfc) is determined as in Equation (4):  
\[\text{Sfc}=\frac{\textit{TP}}{(\textit{TP}+\textit{FP})} \tag{4}\]  
F-score (Fs) is calculated using Equation (5):  
\[\text{Fs}=2\ \times\ \left[\frac{(\textit{PCS}\ \times\ \textit{RCA})}{(\textit{ PCS}+\textit{RCA})}\right] \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name of the Hyperparameter** & **Value** \\ \hline Learning rate: L & 0.001 \\ \hline Batch size & 16 \\ \hline Dropout & 0.25 \\ \hline Optimizer & Adam \\ \hline Regression weight & 0.001 \\ \hline Momentum & 0.8 \\ \hline Activation functions & ReLU and Sigmoid \\ \hline Number of iterations & 3000, 5000, 8000 \\ \hline Number of epochs & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The applied settings of hyperparameters.  
Jaccard Index (JI) is computed using Equation (6):  
\[\text{JI}=\frac{\left(TL\,\cap\,\,PL\right)}{\left(TL\,\cup\,\,PL\right)} \tag{6}\]  
\(TL\) denotes the true labels and \(PL\) represents the output labels by the proposed model. DSC is calculated as shown in Equation (7):  
\[\text{DSC}=\frac{2 TP}{\left(2 TP+FP+FN\right)} \tag{7}\]  
A higher value of DSC means a better prediction is obtained.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band
### Single Element Flexible Antennas for the Sub-6 GHz 5G Band
#### 4.1.5 Transparent Flexible Antenna  
In [29], another slot-based wideband and transparent flexible co-planar waveguide antenna is fabricated on a PET substrate with a bandwidth ranging from 3.89 GHz to 5.9 GHz for the WLAN and sub-6 GHz. The antenna consists of two circular rings that are embedded in a star-shaped structure. The CPW feed contributes to a good impedance matching and smooth movement of electromagnetic waves. The change in the inner radius of the circular ring changes the reflection coefficient at lower and higher resonances with a negligible variation in the impedance bandwidth of the antenna. The antenna gave a notable gain of 3 dBi and an efficiency of 80% for the entire frequency band. The bending analysis of the antenna is performed with Styrofoam (\(\varepsilon_{r}=1.03\)) having a radius of 55 mm along the x- and y-axis. The measured reflection coefficient shows a deflection from the simulated results. However, the antenna resonates at a specific band suitable for sub-6 GHz 5G applications.  
In [61], a transparent antenna that is dually polarized and fabricated on a polycarbonate material is proposed for sub-6 GHz applications. The antenna is a stacked structure that consists of a square parasitic patch on the impedance matching, whereas the cutting the corners of the driven patch and the etching of slots in the ground contribute to the port isolation and stable gain. The antenna shows a measured gain of 4.14 dBi for a frequency range of 2.8-3.8 GHz. The antenna provides an impedance bandwidth ranging from 2.76 to 4.13 GHz and a good isolation greater than 22 dB. The antenna has a directional radiation pattern at 2.9 and 3.65 GHz. The front-to-back ratio is found to be better than 15 dB in the operating band.  
In another design in [62], a tri-band transparent and flexible antenna for 5G applications is presented. The proposed antenna consists of a transmission line which consists of a tree-shaped structure, with two rectangular patches. Two periodic slots are cut from the patch to achieve an additional resonance at 5.7 GHz. The reflection coefficient of the antenna shows better results when integrating the ITO than the copper substrate. The antenna covers a bandwidth ranging from 2.35-2.55 GHz, 3.75-3.9 GHz, and 5.35-5.95 GHz. The peak gains of 3.27 dBi, 9.47 dBi, and 4.47 dBi for 2.45 GHz, 3.8 GHz, and 5.7 GHz are achieved. The bending performance of the antenna is performed for different bending degrees, and it is observed that there is a shift in resonance for bending at 30\({}^{\circ}\) due to the change in the surface current distributions.  
The summary of the single-element flexible antenna for the sub-6 GHz 5G band is given in Table 7.
###### Abstract  
This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: \(\mathrm{R}^{2}_{\mathrm{CV}}=0.980\); FSS: \(\mathrm{R}^{2}_{\mathrm{CV}}=0.939\)). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging.  
**Keywords:** short-wave infrared (SWIR) spectroscopy; multiple sclerosis (MS); reflectance spectra; chemometric analysis; in vivo analysis
## 3 Experiments and Analysis
### Qualitative Comparison
#### 3.4.4 LRSD-Based Methods (IPI, IMNNLWEC)  
In LRSD-based methods, IPI detects a relatively large number of targets, but it also erroneously identifies sparse components from other regions as targets. In contrast, the  
Figure 8: Detectionresults and corresponding 3D visualizations for Seq4 to Seq6 using different algorithms. The red rectangle indicates the target location, with an enlarged view in the corner. A missing rectangle indicates a missed target in the frame. Rows 1, 3, and 5 (**a1–j1, a–j3, a5–j5**) correspond to detection results for Seq4, Seq5, and Seq6, respectively, while rows 2, 4, and 6 (**a2–j2, a4–j4, 4–j6**) show the 3D mesh visualizations for Seq4, Seq5, and Seq6.  
IMNNLWC method accurately locates sparse targets in simple cloud backgrounds but, as shown in Figure 7(h1-h6), performs poorly in complex backgrounds.