esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
However, the increased expression of A\(\beta\) was downregulated by oral administration of GGW73 (100 and 200 mg/kg) in hippocampal tissues (GGW73 100 mg/kg group: 23.17 \(\pm\) 1.74 pg/mL, GGW73 200 mg/kg group: 23.67 \(\pm\) 1.69 pg/mL) (Figure 5A). The experimental results indicate that GGW73 could regulate cognitive disorders by diminishing excessive A\(\beta\) release in hippocampal tissues. Loss of cognitive function causes ACh deficiency in the hippocampus and cortex [12]. For this reason, a strategy to inhibit AChE in the hippocampus improves cognitive function disorders [68]. The increased AChE expression in hippocampal tissue by scopollamine was reduced by oral administration of GGW73 100 mg/kg (non-treatment group: 0.593 \(\pm\) 0.012 ng/mL, scopolamine group: 0.683 \(\pm\) 0.080 ng/mL, GGW73 100 mg/kg group: 0.493 \(\pm\) 0.054 ng/mL, donepezil group:0.653 \(\pm\) 0.069 ng/mL) (Figure 5B). Among various neuronal enzymes, \(\beta\)- and \(\gamma\)-secretase sequentially lyse APP proteins, which generate A\(\beta\)[69]. Furthermore, over-expression of \(\beta\)-secretase (BACE-1) and A\(\beta\) deposition has been detected in degenerative neuronal diseases such as AD and cognitive disorders [70]. As a muscarinic antagonist, scopolamine influences A\(\beta\) peptide release [67] and BACE-1 expression in the hippocampus [71; 72]. GGW73 100 mg/kg oral administration ameliorated BACE-1 expression in scopolamine-injected mice hippocampus tissue (non-treatment group: 138.4 \(\pm\) 10.9 pg/mL, scopolamine group: 192.9 \(\pm\) 9.1 pg/mL, GGW73 100 mg/kg group: 143.9 \(\pm\) 8.8 pg/mL, GGW73 200 mg/kg group: 188.4 \(\pm\) 9.6 pg/mL, donepezil group: 141.6 \(\pm\) 14.2 pg/mL) (Figure 5D). These results indicate that GGW73 has a regulatory effect on scopolamine-induced cognitive disorders. Synaptic plasticity is modulated by BDNF in the human brain [73]. Scopolamine influences neuronal plasticity by reducing BDNF levels in the mouse cerebrum [74] and hippocampus [75]. The decreased BDNF expression by scopolamine injection was significantly recovered by GGW73 oral administration in the hippocampus (non-treatment group: 710.5 \(\pm\) 82.1 pg/mL, scopolamine group: 398.7 \(\pm\) 88.0 pg/mL, GER 100 mg/kg group: 1009.1 \(\pm\) 67.54 pg/mL, GGW73 100 mg/kg group: 895.3 \(\pm\) 44.68 pg/mL, donepezil group: 748.3 \(\pm\) 168.0 pg/mL) (Figure 5C). The results showed that GGW73 restored scopolamine-induced hippocampal injury. In Western blot analysis, GGW73 oral administration slightly regulated BDNF and p-APP, but not in a dose-dependent manner (Figure 5E). However, these results showed GGW73 could modulate the signaling pathway in scopolamine-induced cognitive disorder. In addition, neurons of the hippocampus were condensed by scopolamine injection compared to those in the non-treatment group, which had a distinctly round nucleus and a constant structure. However, condensed neurons in the hippocampus were slightly recovered by GGW73 (100 mg/kg) administration (Figure 6).
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 3 Cluster Analysis
### Density-Based Clustering  
A great disadvantage of many clustering algorithms is that they are limited in cluster shapes. For instance, the K-means algorithm is only capable of producing circular clusters as the assignment of data points is always based on the distance from the cluster center. Figure 1 shows an example of clusters formed irregularly. If we wish to analyze these two parallel streets separately, it is impossible to do so with distance-based forms of clustering. In the displayed example, points A1 and A2 as well as B1 and B2 are further apart from each other than A1 from B1 and A2 from B2. Therefore, applying algorithms such as K-means would cause the closest points to be assigned to the same clusters, which would not achieve this irregular shape. However, the analysis of such non-circular shapes is possible with the use of so-called density-based clustering.  
One of the most famous density-based algorithms is DBSCAN (density-based spatial clustering of applications with noise). This algorithm defines density as the number of data points in a specific radius \(\epsilon\) and the parameter \(MinPts\). DBSCAN defines three types of points. A core point is a point that contains at least \(MinPts\) points within its \(\epsilon\) radius. A border point is a point that has less than \(MinPts\) points within its radius but exists within the radius of at least one core point. All other data points are considered to be noise and they are not taken into consideration. Finally, connected core points are core points located within each other's radius. The algorithm goes as follows:  
1. Assign the correct point type to each data point.
2. Create a cluster for each core point or a group of connected core points.
3. Assign every border point to the cluster of its nearest core point.  
This implementation of the algorithm ensures that virtually any connected shape can be represented as its cluster. Therefore, the analysis of various shapes of data, such as two parallel streets, is possible. Hierarchical DBSCAN has been successfully implemented by [PERSON] et al. [29] for clustering of a canopy cover acquired by UAV. Very similar functionality is also provided by the OPTICS algorithm. As such, both of these algorithms can be utilized to achieve similar results.
## 3 Results
### FTIR Analysis  
FTIR spectra of raw samples (miscanthus, hops, SS, and their mixtures) and those torrefied at the most severe torrefaction conditions (350 \({}^{\circ}\)C, 60 min) are shown in Figure 7. Significant differences in chemical structure, i.e., functional groups, can be observed from the spectra of thermally treated biomass samples due to changes in the chemical structure of the lignin, hemicellulose, and cellulose [19]. The raw samples showed peaks, typical for lignocellulosic biomass: the area between 3600 and 3200 cm\({}^{-1}\) is associated with the O-H bonds of cellulose, peaks between 2900 and 2800 cm\({}^{-1}\) represent the aliphatic C-H bonds of cellulose and hemicellulose, peaks in the area of 1700-1500 cm\({}^{-1}\) are attributed to the stretching vibrations of the C=O group in carboxylic acids in hemicelluloses and C=C aromatic rings of lignin, and peaks between 1500 and 1100 cm\({}^{-1}\) indicate C-C, C-O, and C-H bonds. The broad band at \(\sim\)1030 cm\({}^{-1}\) was observed in the case of hops and miscanthus samples attributed to the stretching vibration of C-O and C-O-C bonds of the lignin, while in the case of SS, it can additionally represent P-O bond. Peaks associated with the amide group of proteins and nitrogen compounds of SS occurred in the area between 1550 and 1400 cm\({}^{-1}\). The FTIR spectra of raw materials, such as SS [4] and miscanthus [89], agree with the spectra of the same or similar materials presented in other studies.  
After torrefaction, the intensity of peaks reflecting the O-H and C-H bonds decreased due to the degradation of cellulose and hemicellulose because of dehydroxylation and condensation reactions. The peaks of C=C and C=O groups representing lignin also showed a noticeable change, indicating that decarboxylation and acetylation reactions occurred in lignin compounds during torrefaction [90]. Torrefied hops, in comparison to other torrefied samples, showed a sharp peak at 870 cm\({}^{-1}\), most likely indicating aromatic C-H or hetero-aromatic compounds. According to FTIR results, it can be concluded that the torrefaction promotes the decomposition of mainly hydrogen and oxygen-containing functional groups. Similar changes in the chemical structure of torrefied biomass were also reported for other  
Figure 6: TGA (**a**) and DTG (**b**) curves for all materials, torrefied at 350 \({}^{\circ}\)C for 60 min.  
Figure 7: FTIR spectra of (**a**) raw samples and (**b**) torrefied samples (350 \({}^{\circ}\)C, 60 min).  
types of biomasses, such as rubber and Gliricidia wood [62], spent coffee grounds [19], corncobs [90], and others.
## 3 Experiment Description and GNSS Data Processing  
### Experiment Description  
In order to validate the proposed method considering the weightings of different tomographic information, the data from six ground-based GNSS stations over the period of Doy (day of year) 303 to 317, 2015 was selected in Guiyang, which is the capital of Guizhou province. The range of the tomographic area is from 106.10\({}^{\circ}\)E to 107.30\({}^{\circ}\)E in longitude and 26.10\({}^{\circ}\)N to 27.30\({}^{\circ}\)N in latitude, respectively. The horizontal resolution are both 0.24\({}^{\circ}\) in longitude and latitude directions while the vertical resolution adopts a non-uniformed strategy with the steps of 0.5/0.5/0.6/0.6/0.6/0.8/0.8/0.8/0.8/1.0/1.0 km from the bottom to the top, which results in a tomography grid with 5 \(\times\) 5 \(\times\) 11 voxels, five longitudinally, 5 latitudinally, and 11 in vertical directions. Fortunately, there is a radiosonde station (57816) located at this area of interest, which is regarded as the reference to assess the accuracy of tomographic results later. Figure 1 shows the geographic distribution of the selected stations and radiosonde station as well, while the details of each ground-based multi-GNSS stations are given in Table 1. For tropospheric tomography modeling, two schemes, including the equal weighting strategy for six types of equations which is regarded as Scheme 1 and the optimal weighting strategy proposed in this paper which is considered as Scheme 2, are determined.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Station Name** & **Height (m)** & **Receiver Type** & **Observation Types** \\ \hline XIUW & 1175.11 & Trimble NetR9+CNSS & R+G+C \\ BATY & 1274.44 & Trimble NetR9+CNSS & R+G+C \\ YONG & 1122.00 & Trimble NetR9+CNSS & R+G+C \\ QINZ & 1262.15 & Trimble NetR9+CNSS & R+G+C \\ QINY & 1028.87 & Trimble NetR9+CNSS & R+G+C \\ KAY & 1211.27 & Trimble NetR9+CNSS & R+G+C \\ \hline \end{tabular}
\end{table}
Table 1: Description of the selected ground-based multi-GNSS stations in Guiyang.  
Figure 1: Geographic distribution of the selected ground-based GNSS stations in Guiyang and the radiosonde station located in the tomographic area.
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 6 Further Discussion and Lessons Learned
### New Business Relationships  
In traditional networks, the agreements to exchange traffic are usually between ASs that have some physical connection between them. Only in specific places of the Internet is this condition not applied (for example, IXPs). Thereby, through BGP peers, ASs apply the BGP configuration that reflectsthe business relationships previously established and that limits the types of business relationships between ASs.  
Thus, it is expected that ASs will gain more advantages of the next generation of the control plane for the Internet because this allows them to control and explore the full potential of the Internet-wide infrastructure. For example, the observation of CAIDA's AS relationship database [90] allows the characterization of multi-homed ASs. Considering the period from December of 2015 to April of 2018, Figure 4 presents the total number of public ASNs in CAIDA's AS relationship database, as well as the total number of stub ASs with public ASNs. Therefore, this graph indicates that the majority of ASs on the Internet are stub ASs, and as discussed, its control of routes is limited by the physical bound with its neighbor ASs.  
Outsourcing the AS routing logic is one way to establish new business relationships on the Internet [73]. Offloading the routing functions of a customer AS to an external trusted contractor (e.g., transit AS) can optimize inter-domain traffic engineering, evolve inter-domain routing and allow the implementation of collaborative security and troubleshooting schemes.  
Another idea is to transit ASs to provide mechanisms to stub ASs that manage its routes [10]. A type of relationship where any ASs can establish a relationship with other non-neighbor ASs is very difficult to achieve in the current Internet architecture, since many network operational tasks (such as manual configuration) are needed. Furthermore, the relationship must make sense from the business perspective, and security mechanisms have to be deployed to guarantee an acceptable level of trust between the domains.  
Thus, to establish new business relationships between multiple ASs, a reliable and secure mechanism has to emerge for inter-domain routing. The manual configuration of the BGP and the use of TCP [17] to exchange NLRI are not enough for the next generation of the control plane for inter-domain routing that has to incorporate new mechanisms to exchange and establish network relationships through any AS on the Internet to enable dynamic resources, bandwidth and routing.  
Previous works in the field indicate the values of collaboration among ASs with different business models [91; 92; 93]. For example, exploring a more intensified collaboration between ASs that produces (e.g., CDN) and consumes the content (e.g., users) can produce gains for all participants [94; 95]. Moreover, with new inter-domain communication mechanisms, it will be possible to deploy visionary solutions such as an economy plane for the Internet [96].
# Real-Time 3D PET Image with Pseudoinverse Reconstruction
### Theory
#### List-Mode Data and Event by Event Reconstruction  
As it has been shown in previous works [3], the pseudoinverse of the SRM can be used for list-mode data reconstruction. In a 3D problem, each column of the PINV of the SRM, \(A^{\dagger}(:,i)\), contains the region of the image that would produce a coincidence in the given LOR \(i\). Taking this into account, the image can be refreshed after every event. A similar approach is also feasible in the proposed scheme. Each coincidence with coordinates \((\rho,\theta,z_{1},z_{2})\) can be separated into transaxial and axial coordinates. Using \(A_{z}^{\ddagger}\), for every coincidence with \(z_{1}z_{2}\) axial coordinates, each z slice \((z)\) of the image is weighted by the corresponding element of the pseudoinverse of the axial SRM \(A_{z}^{\ddagger}(z_{1}z_{2},z)\). Then, for each axial slice, coordinates \((x,y)\) of the image are updated event by event using each row of the transaxial SRM pseudoinverse \(A_{xy}^{\ddagger}(xy,\rho\theta)\). Although this is fast and feasible for a 2D case as presented by [PERSON] and [PERSON] [3], it takes longer than reconstructing with the whole sinogram, except for when the number of coincidences is very small. In our case, reading the list-mode coincidences and histograming them in a sonogram is faster than event by event reconstruction, even for frames as small as 0.1 s for the preclinical SuperArgus PET-CT scanner introduced in Section 2.2.1.
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 2 Materials and Methods
### Nationwide Flood Mapping Framework
#### 2.2.2 Dynamic Floodwater Mapping Cycle: Scheme B  
Using multiple satellites, Scheme B, a dynamic floodwater mapping cycle, links to the component of risk information to provide rapid flood maps. Figure 1b shows the four main steps in the dynamic flood mapping cycle, i.e., data acquisition, preprocessing, multiple image processing of SAR and optical images, and validation. This cycle is proposed in order to achieve and improve rapid response operations in consideration of large and long duration floods.
# K2S Challenge: From Undersampled K-Space to Automatic Segmentation
## 2 Materials and Methods
### Image Space Post-Processing
#### 2.2.3 Ground Truth Segmentation Generation  
Ground truth knee cartilage and bone segmentations were generated by separate DL pipelines and post-processing techniques, each trained with a radiologist in the loop.  
480 3D fat-suppressed CUBE sequences were acquired across three sites (UCSF, San Francisco, CA, USA; Hospital for Special Surgery, New York, NY, USA; Mayo Clinic, Rochester, MN, USA) with similar acquisition parameters to the 3D fat-suppressed CUBE sequences ultimately used in K2S. These volumes were manually segmented by readers trained by a senior radiologist with over 25 years of experience, split 400/80 into training and validation, and used to train a 3D V-Net for multiclass cartilage segmentation [51; 52]. This initial pipeline was inferred on 20 3D fat-suppressed CUBE sequences from the UCSF Orthopedic Institute with K2S acquisition parameters, but on volumes acquired prior to the eligibility window for K2S inclusion. The 20 inferred segmentations were manually corrected and quality checked (QC) by an intern under radiologist supervision. 15 of the 20 cases were used to fine-tune the pipeline in a second training, seeing convergence reached after 5 epochs, and the remaining 5 cases were used to select final model parameters.  
After the second training, the V-Net was inferred on all 816 cases eligible for K2S. The following post-processing steps were selected and applied under radiologist supervision: 3D morphological opening, 3D connected components analysis (preserving the largest femoral and patellar and the 2 largest tibial cartilage components), and 2D sagittal connected components analysis (preserving all connecting components larger than 150 pixels).  
Bone Segmentation Pipeline  
40 3D fat-suppressed CUBE sequences acquired at the UCSF Orthopedic Institute prior to the eligibility window for K2S inclusion were manually segmented by a trained reader for bone, tibia, and patella. These cases were used to train a baseline 3D U-Net for a binary bone segmentation model. An additional 15 cases acquired using the K2S acquisition parameters were also manually segmented by three radiologists with three (J.L.), three (P.G.), and four (F.G.) years of experience. The trained baseline model was inferred from these cases, which were used for model fine-tuning.  
The fine-tuned U-Net was inferred on the 816 cases with the following post-processing steps, applied under radiologist supervision: filling holes, morphological opening, and connected components analysis (preserving all connecting components larger than 1000 voxels and with centroids in central 50% of slices). Finally, the sizes of connected components were used to extract bone labels (femur, tibia, and patella).
# Dynamic SDN Controller Load Balancing
## 4 DCC Problem Formulation
### Distances between Controllers within Same Cluster  
In this section, we focus on the second aspect of the high level load balancing (mentioned in Section 3.2). The rationale behind this distance optimization is as follows. Since in the initialization phase, switches are matched to the closest controller, we then perform load balancing inside the same cluster; we want the other controllers to also be close to each other, otherwise this would imply that the switch might now be matched to a new controller far from it. For that purpose we define the maximal distance between controllers within the same cluster (over all the clusters) as follows:  
\[\eta\left(t\right)=max_{1\leq c\leq K}\;\;max_{1\leq i,j\leq M}\;\;d_{ij}Y \left(t\right)_{ic}Y\left(t\right)_{jc} \tag{7}\]  
where \(c\) is the cluster number, and \(i,j\) are the controllers in cluster c.  
Obviously, the best result would be to reach the minimum \(n(t)\) possible. Because if the controllers are close to each other, then the overhead consisting of the message exchanged between them will be less significant whereas if they are far from each other, then a multihop path will be required which will clearly impact the traffic on the control plane. However, if the constraint on \(n(t)\) is too strict this might not allow us enough flexibility to perform load balancing. Therefore, we propose to define the minimum distance required to provide enough flexibility for the load balancing operation, denoted as \"\(minMaxDistance\)\". If the value of \(minMaxDistance\) is not large enough, it is possible to adjust it by adding an offset to it. Finally, we denote by \(Cnt\) the constraint on the maximal distance as follows:  
\[Cnt=maxDistance+\text{offset} \tag{8}\]
## 4 Network Model  
In QFHR, the network environment consists of various intersections, which are connected to one another through two-way roads. Furthermore, QFHR defines both communication links namely vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Network elements, like road sections, intersections, roadside units (RSUs), and vehicles have a unique ID. Figure 3 displays this network model. In the following, the task of vehicles and RSUs in the network is explained:  
* **Roadside units (RSUs):** These components are located at intersections, and their task is to monitor the network and control congestion in each road section. RSUs store a traffic table to record the traffic status of the four road sections connected to the corresponding intersection. This table is periodically updated. Moreover, each RSU holds a Q-table produced by the Q-learning-based routing algorithm to select the best routes between different intersections on the network.
* **Vehicles:** Each vehicle periodically sends a hello message to its neighboring nodes. These vehicles establish a neighbor table in their memory to store information about neighboring nodes. Additionally, they can achieve their position and speed at any time using a positioning system.
# Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space
## 6 Conclusions  
In this paper, we have analyzed ionospheric effects in GNSS-R at grazing angles. This study encompasses the characterization of slant total electron content, relative ionospheric delay, the influence of ionospheric correction model uncertainties on GNSS-R group delay altimetry retrievals, the Doppler effect, and peak electron density height changes. Various factors have been considered such as satellite geometry, latitude-dependent regions, temporal variations, and solar activity. When analyzing the results during LSA (low solar activity) and HSA (high solar activity), it becomes evident that as the elevation decreases into the grazing regime below 20\({}^{\circ}\), the median relative ionospheric delay decreases due to the compensation from the direct signal contribution. However, it is important to note that the standard deviation of the delay, especially in terms of the Doppler shift, undergoes a substantial increase. This behavior poses a significant challenge for the model-based correction of ionospheric delay in GNSS reflectometry altimetry at grazing elevation angles. While model uncertainties do affect group delay sea height estimates it is important to highlight that these effects are not uniform across all GNSS-R observations. Coherent phase observations, for instance, offer a remarkable level of precision, down to the centimeter scale. Along reflection tracks characterized by consistent ionospheric bias, relative altimetry at a centimeter precision level can be achieved. This means that even in the presence of ionospheric delay bias, LEO space-borne GNSS-R systems, as reported in [26], can still provide precise results in the altimetric inversion. Total electron content, a crucial ionospheric parameter, exhibits complex variations spanning diurnal, monthly, seasonal, and 11-year solar cycles. Extended temporal coverage is essential for deciphering these patterns, especially in dynamic regions allowing analysis of seasonal trends. This study highlights the importance of spatially extended data, particularly in tropical areas with substantial ionospheric variability. Such data is key to comprehending ionospheric parameter evolution across different time scales and regions, influenced by factors like solar activity and geomagnetic storms. GNSS-R (global navigation satellite system reflectometry) stands as a valuable and complementary remote sensing tool in ionospheric studies, effectively addressing areas not covered by alternative methods. This capacity offers significant contributions to the modeling, prediction, and comprehension of ionospheric effects.