esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 2 Model and Data
### S1 SSM Data
As part of the assimilation process, it is necessary to rescale the S1 SSM products to match the ISBA model climatology and to avoid any bias in the system associated with model-dependent soil parameters, as described in [45; 46]. To achieve this, the S1 SSM product was rescaled using the method proposed by [47], which involves a linear transformation that aligns the modeled SSM mean and variance with the estimated mean and variance of the observations. This linear transformation was applied on a seasonal basis using a three-month sliding window, as seasonal variability has been shown to significantly affect the rescaling process, as reported in [30; 48; 49]. In addition, a filter was applied to exclude urban areas and soil freezing before the rescaling parameters were estimated. Figure 1: Salamanca, Toulouse, and Montpellier regions where S1 SSM products are available. In situ stations (REMEDHUS, Meteopole-Flux, and SMOSMANIA) are represented by dots.
## 4 Simulation Results  
In this section, a simulated implementation of the proposed ML-based observer for a sensorless PMSM-FOC is discussed. The goal of the simulation was to determine the validity and performance of the proposed observer when it is implemented in a full-control process environment. The simulation was performed with MATLAB Simulink and was based on the architecture displayed in Figure 1. Full PMSM and inverter models are available in the Motor Control Blockset library of Simulink.
## 3 Proposed Schemes
### Proposed Scheme 2: Re-Intar  
Re-Intar is proposed to improve the packet delivery ratio and reduces end-to-end delay of the network. It addresses the limitations of both WDFAD-DBR and Intar techniques. Similar to Intar,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**NeighID** & **NumNeighbor** & **HopSink** & **DistNeighbor** & **Depth** & **Timestamp** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Format of neighbor table.  
Figure 2: Hello packet format.  
Data Forwarding Phase  
The CF value of PFN in Re-Intar depends on four parameters:  
\[CF(j)=\frac{Dist(i,j)\times dth_{diff}(i,j)}{Hop(j)\times Neighbor(j)}. \tag{2}\]  
where \(dth_{diff}(i,j)\) is the depth difference between source node \(i\) and PFN \(j\). The remaining parameters are similar to Equation (1). In Re-Intar, when a source/sender node generates a data packet, it uses Equation (2) and selects PFN having maximum value of CF as next forwarder. This process continues until the data packet is delivered to sink.  
To improve packet delivery ratio of Intar, Re-Intar also uses one hop backward transmission in the case of void region, a source node looks for non-PFNs in its transmission range and chooses one-hop backward node for data forwarding. When a source node generates data packet, it looks for PFN in its transmission range. If PFN is not found, then it looks for non-PFN in its transmission range and selects the one with minimum distance to sink as next forwarder of data packet. Algorithm 1 shows the procedure for data forwarding in Re-Intar.  
```
1:procedureNext-forwarder selection
2:\(N\leftarrow\) Total number of node in network
3:\(Tx-range\leftarrow\) Transmission range
4:\(PFN\leftarrow\) Potential forwarder node having depth less than Source/sender node
5:\(Non-PFN\leftarrow\) Node having depth greater than Source/sender node
6:forEach node \(i\in N\)do
7:\(Node(i)\leftarrow\) Generate data packet
8:ifSink in \(Tx-Range\)then
9: Packet to sink
10: Goto step 6:
11:else
12:ifPFN existthen
13:\(Next\) forwarder \(\leftarrow\) Select PFN using Equation (2)
14: Go to step 8:
15:else
16:ifNon \(-PFN\) existthen
17:\(Next\) forwarder \(\leftarrow\) Select non-PFN with minimum distance to sink
18: Go to step 8:
19:else
20: Packet dropped
21: Go to step 6:
22:endif
23:endif
24:endif
25:endif
26:endfor
27:endprocedure
```  
**Algorithm 1** Data forwarding algorithm.  
When a node sends data packet to next forwarder node, the data packet may not reach the next forwarder node due to bad channel condition [30]. To handle such situation, the sender node buffers the data packet and waits for certain time to overhear it from the receiver node. On overhearing the data packet from receiver node, the sender node removes the packet from its buffer. If the data packet is not heard within the waiting time, the sender node selects next forwarder from its PFNs with second highest value of CF, if other PFN exists. Otherwise, it selects the first one and rebroadcasts the packet. The waiting time depends on the propagation distance between the sender and receiver. The flow chart of Re-intar protocol is shown in Figure 3.
## 5 Multi-Replication in Data Centric Storage
### Resilient Data-Centric Storage  
In this work [33], a two-level replication strategy is proposed for control and data. The sensornet is divided into \(Z\) zones. Each zone could contain 3 types of nodes:  
* **Monitor Mode**: Each zone has one monitor node for each event type. The monitor nodes communicate to each other by exchanging information of monitor maps that includes control and summary information. The information contained in this monitor maps are: list of zones containing replica nodes, list of zones containing monitor nodes, event summaries and bloom filters that are used for attribute-based queries (i.e. get temperatures over 30 Celsius degrees).  
Figure 2: Multi-replication grid in GHT.  
* **Replica Mode**: Each zone has at most one replica node for each event type. In addition, if the replica node is present, it is the same node than the monitor node. The extra function is to store event data for the given event type.
* **Normal Mode**: These are nodes that are neither monitor nor replica nodes.  
The storage model is quite similar to the one proposed in GHT with multiple replicas: a node that generates an event forwards it to the monitor node in its zone. If the monitor node is a replica of that event, it stores the data, otherwise, since it has the list of zones containing replica nodes, forwards the data to the closest replica.  
The main contribution of this paper is the different query types that proposed, which are more sophisticated than the one proposed in [30, 31]. There are three types of queries:  
* **List**: This is the one described in GHT [30, 31]. A consumer sends a query requesting the data related to an event type to the monitor node in its area. This one forwards the query to all the replication nodes for that event type. Then, they reply directly to the consumer node with all the requested data.
* **Summary**: The consumer requests a summary of the event type information. It contacts its local monitor node that replies directly with the summary for that event type (e.g., average, max and min value of that event type).  
Figure 3: GHT multi-replication scenario.  
* **Attribute-based**: It is a query requesting data for different events which match certain constraints in the attribute values. For that purpose bloom filters are used since they provide a compact summary of data without false negative. A consumer sends a query to its local monitor node for a given event type. Then the monitor node duplicates the query and sends it to all the replication nodes with bloom filters matches. All the replicas reply directly to the querying node.  
This work extends and complements the top level of a multi-replication DCS system like GHT with multiple replicas, but it also adds complexity since an extra role is included in the system, the monitor node, which implies more energy expenditures for that task. As previous proposals, this work does not provide any discussion about the appropriate number of zones, \(Z\), to be used.
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## 3 Results
### Mapping Results from Sentinel-1A  
Results from the use of only the temporal VH images of Sentinel-1A are presented in Figure 6.  
In Figure 6, it can be observed that there are wide misclassifications especially with Rice, Water and Trees. Some pixels within deep water surfaces have been classified as Rice. Even on land, certain pixels have been misclassified as Rice, such as in lawns and asphalt pavements. These areas typically show low microwave backscatter. As the prevalence of surface winds could increase the roughness and, hence, the backscatter signals at a subsequent acquisition, some of these pixels could be misclassified as Rice. Water pixels can also be found in dryland surfaces such as asphalt pavements and lawns. Additionally, in a highly urbanized landscape such as Shanghai, it is very unlikely to find such a large area covered by trees, as can be observed in Figure 6. The class \"Trees\" has virtually taken all the areas that are likely to be occupied by the class \"Others\" (as defined in this study). The accuracy assessment report for the land-cover map presented in Figure 6 is given in Table 5.  
In Table 5, even though almost all validation points of Rice have been correctly classified, there are some commission errors in this class. Much of the commission errors in Water are derived from the misclassification of Rice. In addition, much of the omission errors in the class \"Others\" are derived from the misclassification of Trees. Built shows a relatively good agreement with ground reference data.  
Figure 6: Simplified land-cover map of study area derived only from VH images of Sentinel-1A.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 6 Conclusions  
We present a systems engineering methodology that is useful for conducting high level energy resilience analysis of military base energy infrastructure to determine if implementing nanogrids within an existing microgrid helps to protect electrical loads critical to national security from power outages. The methodology takes into account threats that are of specific interest to the military such as adversary actions and terrorist attacks. Rather than focusing on reducing energy cost during nominal conditions (as most civilian energy analyses attempt to achieve), our method focuses on improving resilience to threats.  
A case study is presented of a fictionalized Naval Support Activity Monterey military base where the Naval Postgraduate School is located. Several threats of interest to all military bases and of specific interest to military bases located on coastal California are analyzed against a proposed microgrid with nanogrid augmentation using the method. This case study demonstrates how the methodology can help a base energy manager to select an appropriate nanogrid and show the feasibility of the nanogrid in improving energy resilience of specific critical loads from a national security perspective as compared to existing microgrid infrastructure.  
Conceptualization, A.K., D.L.V.B., and A.P.; methodology, A.K., D.L.V.B., and A.P.; software, A.K.; validation, A.K., D.L.V.B., and A.P.; formal analysis, A.K., D.L.V.B., and A.P.; investigation, A.K., D.L.V.B., and A.P.; resources, D.L.V.B. and A.P.; data curation, D.L.V.B. and A.P.; original draft preparation, A.K.; writing--review and editing, A.K., D.L.V.B., and A.P.; visualization, A.K.; supervision, D.L.V.B. and A.P.; project administration, D.L.V.B.; funding acquisition, D.L.V.B. and A.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research is partially supported by the Naval Postgraduate School. Any opinions or findings of this work are the responsibility of the authors, and do not necessarily reflect the views of the sponsors or collaborators. Approved for Public Release; distribution is unlimited. The authors declare no conflict of interest.
## 3 Method
### Irradiance Model  
Multi-date satellite images are collected at different times due to environmental variations such as illumination, resulting in significant appearance variations between images, as shown in Figure 4, which violates the NeRF's assumption that the world is photometrically static. Therefore, we follow the irradiance model used in [11,16] to explicitly model the photometric differences between images. The irradiance model explicitly models the albedo \(\mathbf{c}_{\mathrm{a}}(\mathbf{x})\) as the output of the network rather than modeling the radiance \(\mathbf{c}(\mathbf{x})\) directly. Scene irradiance is expressed as the product of the static surface \(\mathbf{c}_{\mathrm{a}}(\mathbf{x})\) with the albedo color and the ambient illumination radiance \(\ell(\mathbf{x},\mathbf{d}_{\mathrm{s}})\):  
\[\mathbf{c}(\mathbf{x},\mathbf{d}_{\mathrm{s}})=\ell(\mathbf{x},\mathbf{d}_{ \mathrm{s}})\cdot\mathbf{c}_{\mathrm{a}}(\mathbf{x}) \tag{9}\]  
where  
\[\ell(\mathbf{x},\mathbf{d}_{\mathrm{s}})=t(\mathbf{x})+(1-t(\mathbf{x}))\! \cdot\!\mathbf{a}(\mathbf{d}_{\mathrm{s}}) \tag{10}\]  
\(\ell(\mathbf{x},\mathbf{d}_{\mathrm{s}})\) represents the ambient light radiance and \(t(\mathbf{x})\) is a transient scalar with a value between 0 and 1; \(\mathbf{a}(\mathbf{d}_{\mathrm{s}})\) is the ambient color; and the global ambient color is determined according to the sun direction \(\mathbf{d}_{\mathrm{s}}\), which is independent of the scene geometry. Different from Sat-NeRF, we model \(t(\mathbf{x})\) as a transient scalar rather than just a shadow-aware scalar and eliminate the input of the sun direction vector.  
For dynamic changes such as the shadows and cars present in multi-date images, we follow Sat-NeRF [16] and NeRF-W [40] by embedding the image-specific latent vector \(\mathbf{t}_{j}\) into the input layer to capture transient phenomena observed in the input image and separate them from the static representation of the 3D world.  
In summary, according to the irradiance model, the color \(\mathbf{c}\) of each point \(\mathbf{x}\) along the ray is  
\[\mathbf{c}\big{(}\mathbf{t}_{j},\mathbf{x},\mathbf{d}_{\mathrm{s}_{j}}\big{)} =\mathbf{c}_{\mathrm{a}}(\mathbf{x})\!\cdot\!(t(\mathbf{x})+(1-t( \mathbf{x}))\!\cdot\!\mathbf{a}(\mathbf{d}_{\mathrm{s}})) \tag{11}\]  
Figure 3: Ray casting and ray marching. Connect the camera position to the image pixels in world coordinates system to generate a ray \(\mathbf{r}(t)=\mathbf{o}+t\mathbf{d}\). NeRF computes sample points along the rays and applies volume-rendering techniques to determine each ray’s color. The 3D coordinates \(\mathbf{x}=(x,y,z)\) and ray direction \(\mathbf{d}=(\theta,\phi)\) of each sampled point are processed through a positional encoding \(\gamma\) and subsequently fed into a fully connected network. This network is responsible for predicting the volume density \(\sigma\) and RGB value \(\mathbf{c}\).
# A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing
## 2 Convolutional Neural Networks  
A dense neural network can be applied for image classification and detection. However, even for small images, the number of input neurons is high, which requires a high number of weights from the input neurons to the first hidden layer. For example, considering a small image with a size of 32 \(\times\) 32, the network would have 1024 input nodes. Considering a second layer with the same number of nodes, the network would need 1024 \(\times\) 1024 weights in the first layer. The number of weights increases quadratically with the image size. The more weights there are, the harder it is to train the network with good results, the more memory is needed and the higher the computational requirement. Instead of a dense network model that applies generically to any input data type, a convolutional neural network (CNN) considers the spatial information between pixels, that is, the output of a neuron of the input layer is the result of the convolution between a small subset of the image and a kernel of weights. This approach was first considered in [7].  
Regular convolutional neural networks have different types of layers: Convolutional, pooling, and fully connected. CNNs may contain other particular layers that typically result from a combination of layers used in regular models, which are known as irregular CNN. In the following, we explain in more detail the regular layers.
## 2 Related Work
### SR Methods  
Since the seminal research of SRCNN [36], several explorations have been conducted to enhance the efficiency of image SR, such as interpolation-based approaches [37] and reconstruction-based methods [13; 14; 15; 38; 39]. In [13], an SRGAN based on GAN is proposed, which is the first model capable of deriving photorealistic images for 4\(\times\) upscaling factors. In [38], a back-projection network (DBPN) supported by up-sampling and down-sampling layers is proposed for image SR reconstruction. [PERSON] et al. [39] built a deep architecture called RCAN supported by the channel attention mechanism. In [14], a typical RefSR algorithm named SRNTT is proposed to complete the local texture matching within the feature space and further add the matched textures to the output of the model. In [15], [PERSON] et al. propose a residual dense network (RDN), which takes full advantage of the hierarchical features acquired from the low-resolution image supported by the residual dense block. However, all of these still suffer from the fact that the space of possible mapping functions remains large, resulting in difficulty modeling a superior framework.  
For video SR, temporal features show significant effects and have been extensively explored. In [19; 21], they use optical flow to evaluate the motion between frames and perform warping. However, it is difficult to acquire accurate flow under large motions. In [20], the proposed TOFlow demonstrates that the standard optical flow cannot represent first-rank motion in the video SR task. Moreover, the advancement of DL methods greatly promotes this property in video deblurring [40; 41]. In [41; 42], they combine sequential frames without precise temporal alignment due to the fact that the appearance of blur seriously affects motion estimation. Different from these approaches, we aim to acquire non-local information from each frame due to the fact that the reconstruction of a pixel in a particular frame may rely on global features that do not exist in the receptive field of the convolution kernel.  
In the recent research, an attention mechanism has been utilized in video restoration along with CNN. [PERSON] et al. [43] have categorized frames into various groups and created a temporal group attention module. [PERSON] et al. [44] have suggested a reinforcement approach, supported by a learning-based framework with factorized spatio-temporal information. Furthermore, [PERSON] et al. [45] recommend using self-attention within local patches of a video. Although these methods have significantly enhanced the performance of video SR, there remains a dearth of model universality. Our work pays more attention to constructing a generalized model that deals with video inpainting and frame-by-frame SR simultaneously, which is an urgent problem in the field of deep learning as no previous studies have succeeded in this field.
## 3 Results
### Cross-Stream Migration in Straight Circular Channel Flow  
In low Reynolds number flows, the migration of spherical particles transversally to the flow direction is not possible due to the linearity and time-reversal symmetry of [PERSON]'s equations [47]. Deformable particles such as RBCs in flow might, however, break the symmetry and experience such a transverse migration [48]. The migration velocity away from the wall for non-spherical tank-treading vesicles was theoretically predicted by [PERSON] in [49] to obey:  
\[\dot{z}=\frac{U\dot{\gamma}R_{eff}^{3}}{z^{2}}, \tag{3}\]  
where \(z\) is the distance from the wall, \(U\) is the dimensionless drift velocity which is dependent on the vesicle shape and the viscosity contrast with the external fluid, and \(R_{eff}\) is the reduced radius of the particle (that is, \(4/3R_{eff}^{3}\tau\) equals the RBC volume). From this, the scaling of the vesicle distance from the wall can be integrated as given by [PERSON] et al. [48]:  
\[z^{3}=3 UR_{eff}^{3}\dot{\gamma}t+z_{0}^{3}. \tag{4}\]  
We investigated the transverse migration of a RBC in Poiseuille flow, similar to the work of [PERSON] et al. [50], in a periodic channel with a diameter of \(70\)\(\upmu\)m and a Reynolds number of \(\text{Re}=1.12\). In this channel flow, a single RBC is placed with a distance \(z_{0}\) from the channel wall. The simulation is then performed for two \(z_{0}\) starting distances from the wall, with and without the internal viscosity contrast. As the velocity profile in this case is almost fully parabolic, the shear rate is given by [51]:  
\[\dot{\gamma}=\frac{2 Re\,\
u}{R^{2}}\Big{[}1-\frac{z}{R}\Big{]}, \tag{5}\]  
where \(\
u\) is the dynamic viscosity and \(R\) is the radius of the channel.  
The trajectories of the RBCs are shown in the left image of Figure 4. According to Equation (4), the dimensionless lift velocity \(U\) can be found as the slope of the line fitted to the data points in the right image of Figure 4. Table 1 shows the results of the fitting procedure.  
Figure 3: Wheeler test done with and without viscosity contrast and compared to experimental values reported by [PERSON] et al. [46]. The shaded regions represent the uncertainty in the simulation results arising from the mesh discretisation.  
The fits using Equation (4) all have \(R^{2}\geq 0.99\) and the resulting dimensionless lift velocities (\(U\)) are in the same order as the values reported by the experiments [48,49]. The resulting lift is lower on the RBCs with the increased viscosity contrast of \(\Lambda=5\), which can be explained by the longer deformation time-scale of these cells. Since all RBCs are immersed in the flow in their equilibrium shape, local deformation time-scales play a significant role in the magnitude of the appearing lift force. The curves corresponding to \(\Lambda=1\) overestimate the lift velocity, however, the \(\Lambda=5\) cases are close to the value reported by the experiments for RBC sized vesicles (\(UR_{eff}^{3}\approx 6.4\)\(\upmu\)m).