esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Macroscale Optimization and Characterization
#### 3.1.1 Pre-Validation Study of CRANAD-2: Solvent Selection, Stability, and Fluorescence Signal  
The optimization of CRANAD-2 for fluorescence detection began with identifying the most suitable solvent. CRANAD-2's emission properties were evaluated in both MeOH and PBS. As shown in Figure 3a, MeOH emerged as the most suitable solvent, enhancing fluorescence intensity and stability, while PBS resulted in negligible fluorescence. Based on these results, MeOH was selected for all subsequent experiments, and higher-concentration samples were also examined, with the goal of maximizing fluorescence output to mitigate potential signal loss and ensure robust detection, especially considering the expected noise increase in microfluidic measurements.  
To establish a primary relationship between probe concentration and fluorescence intensity, a series of experiments were conducted at concentrations ranging from 0.5 uM to 25 uM, in MeOH. Figure 3b illustrates the direct relationship observed between CRANAD-2 concentration and fluorescence intensity, confirming a concentration-dependent trend; higher concentrations of CRANAD-2 yield stronger fluorescence signals essential for its use in quantitative biomarker detection. Additionally, the concentration of 25 uM was selected for subsequent experiments based on its ability to enhance fluorescence signal intensity, optimizing sensitivity while maintaining cost-efficiency. These findings establish the foundational fluorescence characteristics of CRANAD-2, providing a baseline for its use in subsequent binding studies with A\(\beta\) samples.  
Next, the temporal stability of CRANAD-2 fluorescence was assessed for a period of 24 h. The probe, prepared in MeOH, was tested immediately after preparation and after 24 h of storage. The results presented in Figure 3c demonstrate that CRANAD-2 fluorescence remained stable over the 24 h period, which is beneficial for real-time and continuous monitoring in assays that need several-hour analysis.
## Appendix B Derivations  
In this Appendix, we derive several representations that were used in the main text. We conduct these derivations in the \((p,z,\omega)\)-domain (which is indicated by a hat), where \(\omega\) denotes the angular frequency. We define the Fourier transform of an arbitrary (down- or upgoing) wavefield \(\mathbf{P}^{\pm}(p,z,\tau)\) as  
\[\mathbf{\hat{P}}^{\pm}(p,z,\omega)=\int_{-\infty}^{\infty}\mathbf{P}^{\pm}(p, z,\tau)\exp(-j\omega\tau)\mathrm{d}\tau, \tag{10}\]  
whereas the associated inverse Fourier transform is given by  
\[\mathbf{P}^{\pm}(p,z,\tau)=\frac{1}{\pi}\Re\bigg{[}\int_{0}^{\infty}\mathbf{ \hat{P}}^{\pm}(p,z,\omega)\exp(j\omega\tau)\mathrm{d}\omega\bigg{]}. \tag{11}\]  
Here, it is assumed that the signals are real-valued in the \((p,z,\tau)\)-domain and \(\Re\) denotes the real part. Our derivations are based on two reciprocity theorems for flux-normalized wave fields, which we present for a source-free volume that is enclosed by depth levels \(z_{m}\) (at the top) and \(z_{n}\) (at the bottom). First, we have the reciprocity theorem of the convolution type [15]  
\[\begin{split}&\{\hat{\mathbf{P}}_{A}^{+}(-p,z_{m},\omega)\}^{ \dagger}\hat{\mathbf{P}}_{B}^{-}(p,z_{m},\omega)-\{\hat{\mathbf{P}}_{A}^{-}(-p, z_{m},\omega)\}^{\dagger}\hat{\mathbf{P}}_{B}^{+}(p,z_{m},\omega)\\ &=\{\hat{\mathbf{P}}_{A}^{+}(-p,z_{m},\omega)\}^{\dagger}\hat{ \mathbf{P}}_{B}^{-}(p,z_{m},\omega)-\{\hat{\mathbf{P}}_{A}^{-}(-p,z_{m})\}^{ \dagger}\hat{\mathbf{P}}_{B}^{+}(p,z_{m},\omega).\end{split} \tag{12}\]  
Here, \(\hat{\mathbf{P}}_{A}^{+}\) and \(\hat{\mathbf{P}}_{A}^{-}\) are down- and upgoing wavefields in state \(A\), whereas \(\hat{\mathbf{P}}_{B}^{+}\) and \(\hat{\mathbf{P}}_{B}^{-}\) are equivalent wavefields in state \(B\). Furthermore, \({}^{\dagger}\) denotes matrix transposition. We have an equivalent reciprocity theorem of the correlation type [15]  
\[\begin{split}&\{\hat{\mathbf{P}}_{A}^{+}(p,z_{m},\omega)\}^{ \dagger}\hat{\mathbf{P}}_{B}^{+}(p,z_{m},\omega)-\{\hat{\mathbf{P}}_{A}^{-}(p, z_{m},\omega)\}^{\dagger}\hat{\mathbf{P}}_{B}^{-}(p,z_{m},\omega)\\ &=\{\hat{\mathbf{P}}_{A}^{+}(p,z_{m},\omega)\}^{\dagger}\hat{ \mathbf{P}}_{B}^{+}(p,z_{m},\omega)-\{\hat{\mathbf{P}}_{A}^{-}(p,z_{m},\omega) \}^{\dagger}\hat{\mathbf{P}}_{B}^{-}(p,z_{m},\omega),\end{split} \tag{13}\]  
where \({}^{\dagger}\) denotes the adjoint. In the following, we use Equations (12) and (13) to derive representations that are based on reflection and transmission data.
## 5 Discussion of Results  
From the results in Figures 2-4 and 6, we can find that the FLAC results of normally incident P and Sv waves' transmission across a single joint, normal incident P wave transmission through a joint set, and obliquely incident P wave transmission through a single joint are comparable with the analytical solutions. The FLAC results agree well with the analytical solutions. Therefore, FLAC is applicable to study the dependence of wave transmission on the incident angle and the two-dimensional effect.  
Then parametric studies on Sv wave transmission through the horizontal layered-structure rock slope were conducted. From Figure 8, \(\eta_{\rm{crest}}\) decreases with the increase of \(K\), which may be caused by the increase of \(k_{s}\). From Figure 3, it can be seen that with the increasing \(k_{s}\) the gradient of transmission coefficient \(T_{\rm{1S}}\) decreases. According to the definition of transmission coefficient \(T_{\rm{1S}}\), the gradient is the acceleration. This can explain why increasing \(K\) could lead to \(\eta_{\rm{crest}}\) decreasing. There is a clear change of trend at \(K\) = 2, which may be caused by the interaction of joints and stress wave; more studies need to be done. From Figure 9, \(\eta\) presents strong wavy increase. Because of the attenuation effects of joints on wave propagation, once waves transmit across the joint, the value of \(\eta\) will drop to a valley value, and then the topography effects make the values of \(\eta\) increase again. Moreover, the fluctuation weakens as \(K\) increases, which is the same as \(\eta_{\rm{crest}}\) versus \(K\). In addition, when \(H\) increases  
Figure 13: The distribution regularity of acceleration amplification coefficient of the slope on spatial domain (where \(K\) = 0.5); (**a**) \(\xi\) = 0.05; (**b**) \(\xi\) = 0.1; (**c**) \(\xi\) = 0.2; (**d**) \(\xi\) = 0.3; (**e**) \(\xi\) = 0.4.  
to the slope crest, \(\eta\) reaches a maximum, which is consistent with the dynamic response regularities of homogeneous slopes acquired by [PERSON] et al. [10]. There is also a sudden increase at about \(H=27.5\) m, caused by the strong amplification effect at the slope crest. From Figure 10, it can be found that the effects of \(K\) are not apparent from the distribution regularity of acceleration amplification coefficient of the slope, and the contour of \(\eta\) takes on rhythm, which is similar to the dynamic response of the homogenous slope made by [PERSON] et al. [27]. From Figure 11, we can see that \(\eta_{\text{crest}}\) is more sensitive to \(\xi\), compared to \(K\). This phenomenon may be caused by wave frequency, as can be seen in Table 3, but further study is needed. From Figure 12, it can also be seen that the fluctuation enhances as \(\xi\) increases, which is contrary to Figure 9. The enhancement of fluctuation may be caused by the same factors described in Section 4.1, which is a result of the combined action from attenuation effects of joints and topography effects.
## 3 Results/Validation
### Methodology Generalization  
The first part of the methodology, \"_Model_\", defined a set of primitive geometries to model spatial elements in the SITG [23] database for all elements present in the taxonomy (Appendix A, Figure A1). Geometries employed were, e.g., cylinders, extruded polygons and truncated cones. Secondary objects were represented by the same geometric model as that used for the primary ones, with exception of utility networks (cuboid instead of a tube) and geothermal drills (truncated cones instead of a cylinder).  
The completion stage \"_Complete_\" required the identification of meaningful completion rules to correct missing spatial and geometric attributes needed for 3D representation. It was possible to identify and formulate such rules for all attributes. A visual check shows that the description of geometries is consistent (Figure 14).  
Calibrations to set the level of confidence for the \"_Bound_\" stage have either been identified in collaboration with construction professionals or apply local empirical values and represent results correctly on a visual and a numerical level. Figure 13 shows a typical example of variations in the level of confidence for the case of the secondary water network.  
The integration strategy \"_Integrate_\" is also validated, since primary and secondary objects are correctly visualized by the GIS frontend. The use of completion rules has an impact on the level of confidence (as see in Table 2).
## 4 Discussion
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Affected** & **0–20 min After** & **20–30 min After** & **30–50 min After** & **50 min** \\
**Point Name** & **Dam Break** & **Dam Break** & **Dam Break** & **and After** \\ \hline Affected Point 1 & Pathway 1 & Pathway 2 & Pathway 2 & Pathway 2 \\ Affected Point 2 & Pathway 3 & Pathway 3 & Pathway 3 & Pathway 3 \\ Affected Point 3 & Pathway 4 & Pathway 4 & Pathway 4 & Pathway 4 \\ Affected Point 4 & Pathway 5 & Pathway 5 & Pathway 5 \\ Affected Point 5 & Pathway 6 & Pathway 6 & Pathway 7 & Pathway 8 \\ Affected Point 6 & Pathway 9 & Pathway 9 & Pathway 9 & Pathway 9 \\ Affected Point 7 & Pathway 10 & Pathway 10 & Pathway 10 & Pathway 10 \\ Affected Point 8 & Pathway 11 & Pathway 11 & Pathway 12 & Pathway 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Personnel evacuation plans for different time periods at each disaster site. ulations in large-scale scenarios requires improvement. We plan to optimize the efficiency of these simulations using high-performance computing (HPC) to reduce computational time and handle larger-scale scenarios. Additionally, the impact of heterogeneous human behavior (e.g., variations in age and mobility) on evacuation efficiency has not been adequately addressed. In future work, we will integrate an agent-based modeling (ABM) framework to simulate human behavior heterogeneity. This will allow us to account for differences in individual movement speeds and decision-making during evacuation. Future research could also focus on integrating multi-source data, such as satellite remote sensing and drone monitoring, to enhance the real-time capabilities of the model. By incorporating these data, we can improve the accuracy and timeliness of evacuation route planning during dam failure events. Furthermore, developing multi-hazard coupling models could increase the general applicability of emergency planning. This would enable the model to address various disaster scenarios, not just dam failures. Incorporating multi-objective optimization techniques, such as Pareto front analysis, would allow for a comparison of evacuation strategies across different scenarios. This approach would provide decision-makers with more flexible and effective solutions.
## 5 Numerical Results and Discussion
### Visualization Results  
#### 5.3.1 Object-Detection Results Of YOLO  
Figure 9 shows a section of the site data. After testing the data in Figure 9 with the input values, the corresponding results were obtained, as shown in Figures 13 and 14. The obtained AP values for each class range from 0.37 to 1.00. As the threshold value was not specified, the focus was on whether the recognition functioned properly. Thus, although the recognition was deemed successful, its accuracy was considered inadequate.
# COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
## 3 Description of Time Series Models
### DeepAR: Probabilistic Forecasting with Auto-Regressive Recurrent Networks  
DeepAR is a forecasting method based on autoregressive recurrent neural networks for probabilistic forecasting. The method approaches the forecasting problem by incorporating appropriate likelihoods and combining them with nonlinear data transformation techniques, as learned by a (deep) neural network [18].  
The method utilizes a long short-term memory based recurrent neural network architecture, as described in [55], while also building on top of previous work on deep learning for time series data [56; 57; 58] to address the probabilistic forecasting problem. Deeper networks, as described in [59], allow for more abstract data representations through more complex transformations and therefore are often preferable to shallow and wide neural networks.  
DeepAR offers three distinct benefits: Firstly, it produces probabilistic predictions in the form of Monte Carlo samples that can be utilized to calculate consistent quantile estimates for all sub-ranges in the forecasting scope. Secondly, by not assuming Gaussian noise, the method can support a broad range of likelihood functions, thus allowing the user to pick the one that best fits the statistical properties of the data. Lastly, as a deep-learning based method, it is capable of learning seasonal behaviors and complex dependencies with minimal manual intervention [18].
## 3 Results and Discussion
### Raw Data Alignment, Pre-Processing and Exploration  
First, a global alignment of the whole sample set was carried out, but since each analytical batch consisted of a reduced number of samples with smaller retention time shifting among them,  
Figure 2: Principal component analysis (scaled to unit variance and mean centred) of pooled quality control samples analysed for repeatability (intra- and inter-day) assessment along with the single control samples: (**a**) Score scatter plot and (**b**) residual standard deviation plot. LR: lampante olive oil with rancid defect; LF: lampante olive oil with fusty-muddy defect; LFH: lampante olive oil with fusty-muddy and musty-humid-earathy defect.  
the pre-alignment per batch was more straightforward and more convenient in terms of computational time. Given the difficulty of finding a parameter to compare the two alignment approaches, we compared the results from PCA and PLS-DA obtained with the data of the full sample set aligned by both approaches. Even though the results were highly similar, which is a good indicator of the weak influence exerted by the alignment approach on the model, the \"per batch\" approach was slightly more efficient in terms of explained variance in PCA and in better classification results of cross-validation with less LVs in PLS-DA (Supplementary Material, Table S2).  
Among the pre-processing techniques applied to the volatile fingerprinting data, unit variance scaling and mean centring offered the highest model's performance. Through PCA (four principal components, 39.4% of explained variance), four LOO samples were identified as outliers according to Hotelling's \(T^{2}\) and \(Q\)-residuals parameters, leaving a sample set of 301 virgin olive oils to apply the classification models.
# Research on Networking Protocols for Large-Scale Mobile Ultraviolet Communication Networks
## 4 Simulations
### Protocol Performance Comparison  
The proposed protocol is based on the UV lossless competition protocol in multi-hop networks (UVLLC). The lossless competition mechanism and bit simultaneous transmission mechanism have been experimentally validated in [15; 16; 17]. However, the lossless competition protocol in multi-hop networks essentially ensures that \"one node successfully transmits data per time slot\" across the network, regardless of its scale. In networks with a small number of nodes, this protocol demonstrates superior network performance. Nevertheless, as the network scale expands, the protocol still maintains the constraint of \"only one node transmitting data successfully per time slot\", failing to fully utilize node resources. This section analyzes and compares the performance of the two protocols under different network scales, where \(\rho\) is set to 0.00012, \(v\) is 10 mps, \(T_{\text{ref}}=10T_{\text{trans}}\), and other simulation conditions remain unchanged.  
As shown in Figure 12, the mechanism of \"only one node can transmit data per time slot\" helps UVLLC achieve lossless contention, but it fails to further improve throughput as the network scale expands. Only when the network is partitioned into multiple disjoint subnets due to node mobility can it passively allow multiple nodes to transmit data simultaneously. By contrast, LSM-UVCN actively utilizes nodes in the silent state to set up \"isolation zones\", which fully leverages node resources and enhances throughput by establishing more transmission paths concurrently.  
Figure 12: Throughput comparison of UVLLC and LSM-UVCN.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods
### Overall Methodology
#### 2.2.7 Digital Terrain Model Production  
In this study, the DSM was used to produce the digital terrain model (DTM) following three steps. The first step consisted of partially eliminating the trees (pseudo DTM) by resampling the DSM at 10 m resolution while keeping the minimum elevation values (ground elevation). The second step consisted of a circular convolutional filter of minimum type whose size 13 was chosen after various tests. This filter size corresponds exactly to 6 pixels on either side of the central pixel (i.e., a filter with a radius of 60 m). This second step allows us to eliminate all the trees while keeping the ground elevations. At this stage, the result is a DTM with a spatial resolution of 10 m. Finally, the last step consisted of resampling the generated DTM at a 20 cm resolution.
# Smart Healthcare Network Management: A Comprehensive Review
## 4 Smart Healthcare Network Management Protocols
### 6 LowPAN-SNMP: Lightweight Management for Resource-Constrained Networks  
6 LowPAN-SNMP [78] adapts the Simple Network Management Protocol (SNMP) for IPv6 Low-Power Wireless Personal Area Networks (6 LowPANs). It introduces mechanisms such as SNMP header compression, as described in Algorithm 3, to reduce message overhead, making it suitable for resource-constrained networks. Compatibility with standard SNMP is maintained through a proxy forwarder that translates between SNMP and 6 LowPAN-SNMP messages. The header compression in 6 LowPAN-SNMP can be mathematically modeled as  
\[C_{SNMP}=\frac{H_{SNMP}}{R_{6 LowPAN}} \tag{17}\]  
where  
* \(C_{SNMP}\): compression ratio of the SNMP header.
* \(H_{SNMP}\): original header size.
* \(R_{6 LowPAN}\): reduced header size after compression.  
Applications of 6 LoWPAN-SNMP include enabling the lightweight monitoring and management of sensor nodes, facilitating efficient data collection, and detecting sensor failures, transmission errors, and battery levels to ensure reliable operation.  
```
1:SNMP message \(M_{SNMP}\), header size \(H_{SNMP}\)
2:Compressed message \(M_{6 LowPAN}\)
3:Input SNMP message \(M_{SNMP}\) with header size \(H_{SNMP}\)
4:Apply header compression: \[R_{6 LowPAN}\gets H_{SNMP}\times\text{compression factor}\] (18)
5:Calculate compression ratio using Equation (15): \[C_{SNMP}\leftarrow\frac{H_{SNMP}}{R_{6 LowPAN}}\]
6:Generate compressed message \(M_{6 LowPAN}\) with reduced header size \(R_{6 LowPAN}\)
7:Send \(M_{6 LowPAN}\) to the destination over the 6 LowPAN network.
8:if proxy translation is required then
9: Forward \(M_{6 LowPAN}\) to the SNMP proxy.
10: Translate \(M_{6 LowPAN}\) back to \(M_{SNMP}\) for compatibility with standard SNMP devices.
11:endif
12:return Compressed message \(M_{6 LowPAN}\)
```  
**Algorithm 3** 6 LowPAN-SNMP
## 5 Numerical Simulation
The bandwidth of the fiber link from ONUs to OLT is 5 Gbps. The link from OLT to the Internet is the core network. Its bandwidth is 100 Gbps. The required bandwidth of each tactile internet service varies from 100 Mbps to 500 Mbps, with a step of 100 Mbps. Figure 6: The total latency with MEC-2 offloading. Figure 3.0 The latency with different tactile services. In Figure 8, our work with 15 hops showed the greatest capacity compared with our work (1 hop), our work (7 hops), and the control group. A greater hop means that the wireless mesh network has a more redundant path. By offloading the computing task for tactile internet services from the Internet, or OLT to the ONUs closer to users, the capacity of the entire network can grow. When the bandwidth of wireless links increases, the capacities of different situations all decrease. The total bandwidth of the entire network is limited; the bandwidth of a tactile internet service leads to small capacity. For a fixed bandwidth in the wireless link, such as 400 Mbps, our work with one hop still has a higher capacity compared with that of the control group. The capacity with different fiber bandwidths from ONUs to OLT is shown in Figure 3.1 In the WDM-based PON network, the bandwidth of the ONU-OLT link is [1.25, 2.50, 3.75, 5.00, 6.25, 7.50, 8.75, 10.00] Gbps. The bandwidth of each tactile internet service is 300 Mbps. The control group has the lowest capacity and a relatively stable increase rate. The reason for this phenomenon is that the wireless network of the control group only has a single hop. This means that the tactile internet service distribution has no choice of candidates. By contrast, our works with different hops all have more fluctuating curves. As the hop number grows, the candidate paths in the wireless mesh network increase. Our work with the largest hop has the most fluctuating curve. These fluctuations in our work are caused by the variety of offloading schemes. Figure 3.2 The capacity vs. the bandwidth of wireless link. Figure 3.3 The capacity vs. the bandwidth of ONU-OLT link.