esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 UT-Based CLAT
### Propagation  
The propagation process involves the local robots as well as the target. According to the dynamics of Equations (1) and (2), each robot propagates its own state estimates and the local estimate of the target. Let the augmented state vector and the corresponding augmented covariance matrix for each robot's local state at time \(k-1\) be denoted respectively as \(\hat{\mathbf{x}}_{i,k-1}^{a}\in\mathbb{R}^{n_{a}}\) and \(\mathbf{P}_{i,k-1}^{a}\in\mathbb{R}^{n_{a}\times n_{a}}\), where \(n_{a}=n_{v}+n_{u}\),  
\[\hat{\mathbf{x}}_{i,k-1}^{a}\triangleq \begin{bmatrix}\hat{\mathbf{x}}_{i,k-1 }\\ \hat{\mathbf{u}}_{i,k-1}\end{bmatrix} ,\text{ and }\mathbf{P}_{i,k-1}^{a} \triangleq \begin{bmatrix}\mathbf{P}_{i,k-1}&\mathbf{0}\\ \mathbf{0}&\mathbf{Q}_{i}\end{bmatrix} . \tag{7}\]A set of \(2n_{a}+1\) sigma points, denoted as \(\mathcal{X}^{a}\), is selected as follows:  
\[\mathcal{X}^{a,0}_{i,k-1} =\hat{\mathbf{x}}^{a}_{i,k-1},\] \[\mathcal{X}^{a,r}_{i,k-1} =\hat{\mathbf{x}}^{a}_{i,k-1}+\left\{\sqrt{\left(n_{a}+\gamma \right)\mathbf{P}^{a}_{i,k-1}}\right\}_{(;r)},\text{if}\ r\in\{1,\cdots,n_{a}\},\] \[\mathcal{X}^{a,r}_{i,k-1} =\hat{\mathbf{x}}^{a}_{i,k-1}-\left\{\sqrt{\left(n_{a}+\gamma \right)\mathbf{P}^{a}_{i,k-1}}\right\}_{(;r-n_{a})},\text{otherwise}.\]  
Here, \(\gamma=\alpha^{2}\left(n_{a}+\kappa\right)-n_{a}\) is a scaling parameter, with \(0<\alpha\leq 1\) and \(\kappa\in\mathbb{R}\) as tuning parameters to control the spread of the sigma points. The weights for propagating the mean and covariances, denoted respectively as \(W^{r}_{m}\) and \(W^{r}_{c}\), are computed as  
\[W^{0}_{m} =\gamma/\left(n_{a}+\gamma\right),\] \[W^{0}_{c} =\gamma/\left(n_{a}+\gamma\right)+\left(1-\alpha^{2}+\beta\right),\] \[W^{r}_{m} =W^{r}_{c}=1/2\left(n_{a}+\gamma\right),\quad r=1,\ldots,2n_{a},\]  
where \(\beta\) is used to incorporate extra higher-order effects. Note that the definition of the sigma points directly implies that  
\[\sum\
olimits_{r=0}^{2n_{a}}(W^{r}_{m})\mathcal{X}^{a,r}_{i,k-1}=\mathcal{X}^ {a,0}_{i,k-1}=\hat{\mathbf{x}}^{a}_{i,k-1},\]  
or equivalently,  
\[\sum\
olimits_{r=0}^{2n_{a}}W^{r}_{m}\mathcal{X}^{r}_{i,k-1}=\hat{\mathbf{x}} _{i,k-1},\quad\sum\
olimits_{r=0}^{2n_{a}}W^{r}_{m}\mathcal{U}^{r}_{k-1}= \bar{\mathbf{u}}_{i,k-1},\]  
where \(\mathcal{X}^{r}_{i,k-1}\) and \(\mathcal{U}^{r}_{i,k-1}\) collect the components of \(\mathcal{X}^{a,r}_{i,k-1}\) corresponding to, respectively, \(\mathbf{x}_{i,k-1}\) and \(\mathbf{u}_{i,k-1}\).
# Kerr-Lens Mode-Locking: Numerical Simulation of the Spatio-Temporal Dynamics on All Time Scales
## Appendix A Detailed Algorithm and Pseudo-Code
\)\(\triangleright\) propagation matrix within the medium \(M\gets M_{prop}(1/2),\ \ q[t]\leftarrow\frac{(A_{d}[t]+B)}{(C_{d}[t]+D)}\)\(\triangleright\) Propagate the first half-step in the Kerr medium (we start at the entrance to the nonlinear medium) for\(j\gets 1\ \ \text{to}\ \ L_{c}/l\)do\(\triangleright\) propagation through the thin Kerr lenses separated by free propagation \(f_{K}^{j}[t],\phi_{NL}^{j}[t]\leftarrow\) CalculateThinkLMFocusAndPhase[\(P(t),q(t),l\)]\(\triangleright\) Calculation of the nonlinear focus and phase for each thin Kerr lens \(M[t]\gets M_{prop}(l)\cdot M_{kl}^{j}(f_{K}^{j}[t])\cdot M[t],\ \ q[t]\leftarrow\frac{(A_{d}[t]+B)}{(C_{d}[t]+D)}\) \(\phi_{NL}[t]\leftarrow\phi_{NL}[t]+\phi_{NL}^{j}[t]\)\(\triangleright\) Calculate total nonlinear phase endfor \(M\gets M_{prop}(1/2),\ \ q[t]\leftarrow\frac{(A_{d}[t]+B)}{(C_{d}[t]+D)}\)\(\triangleright\) propagating the final half-step in the crystal return\(q[t],\phi_{NL}[t]\) endprocedure
```  
**Algorithm A2** Procedures for cavity propagation with spatial Kerr-lens. ```
procedureCalculateThinkKLMFocusAndPhase(\(P(t),q(t),l\)) \(\triangleright\) Calculate the nonlinear focus \(f_{K}(t)\) and phase \(\phi_{NL}(t)\) based on the temporal power and beam waist \(w[t]\leftarrow\sqrt{\frac{\pi}{4}\text{Im}\frac{1}{q[t]}}\)\(\triangleright\) Calculate the instantaneous beam waist \(f_{K}[t]\leftarrow\frac{\pi}{8m_{l}^{4}[t]}\)\(\triangleright\) Non-linear focus \(\phi_{NL}[t]\leftarrow\frac{4m_{2}l}{\pi\lambda}\frac{P[t]}{w^{2}[t]}\)\(\triangleright\) Nonlinear self-phase modulation return\(f_{K}[t],\phi_{NL}[t]\) endprocedureprocedureSaturateGain(\(P(t),w(t),w_{p},U_{sat}\)) \(U\leftarrow\) Sum(\(P[t]\)) \(\triangleright\) Total pulse energy of the round trip (for gain saturation) for\(i\gets 1\) to \(M\)do if\(w[i]<w_{p}\)then\(\triangleright\) Apply gain penalty for small beams (relative to pump) \(g[i]=\frac{\delta_{0}}{1+\frac{U}{u_{sat}^{2}}\frac{w_{p}^{2}}{w^{2}[i]}}\)\(\triangleright\) Scale saturation energy according to the area overlap with the pump else \(g[i]=\frac{\delta_{0}}{1+\frac{U}{u_{sat}}}\) endif endfor return\(g[t]\) endprocedureprocedureDiffLoss(\(w(t),w_{p}\)) \(\triangleright\) Diffraction loss calculation for\(i\gets 1\) to \(M\)do if\(w[i]>w_{p}\)then\(\triangleright\) Apply diffraction losses for large beam (relative to pump) \(s_{D}[i]=\frac{1}{1+\frac{(w[i]-w_{p})^{2}}{2w_{p}^{2}}}\)\(\triangleright\) Assume Lorentzian aperture edge (can be chosen differently) else \(s_{D}[i]=1\) endif endfor return\(s_{D}[t]\) endprocedure
```  
**Algorithm A3** Procedures for non-linear loss and gain (diffraction and saturation).
## 2 Research Approach
### Control Charts
#### 2.2.4 EWMA Control Chart  
The detection of small-magnitude (sustained) shifts or drifts indicating early damage is important. However, the individual control chart and moving range control chart cannot always identify these changes due to their reflection of sample information at a specific time. Hence, EWMA control charts with memory characteristics were implemented [24]. The EWMA control chart monitors the exponentially weighted moving-average value EWMA\({}_{t}\) of the monitoring sequence. The EWMA\({}_{t}\) is defined as  
\[\text{EWMA}_{t}=\lambda\cdot x_{t}+(1-\lambda)\cdot\text{EWMA}_{t-1} \tag{10}\]where \(\lambda\) is an adjustable parameter to control the sensitivity of the EWMA control chart and its value is between 0 and 1. \(x_{t}\) is the measurement. EWMA\({}_{0}\) is set to \(\overline{x}\).  
The reference levels of the EWMA control chart are given by  
\[\text{CL}=\overline{x} \tag{11}\]  
\[\text{UCL}_{t}=\overline{x}+3\sigma\cdot\sqrt{\frac{\lambda}{2-\lambda}\Big{[}1 -(1-\lambda)^{2t}\Big{]}} \tag{12}\]  
\[\text{LCL}_{t}=\overline{x}-3\sigma\cdot\sqrt{\frac{\lambda}{2-\lambda}\Big{[}1 -(1-\lambda)^{2t}\Big{]}} \tag{13}\]  
where an unbiased estimate of \(\sigma\) is as shown in Equation (4). Moreover, since \(0<\lambda\leq 1\), \((1-\lambda)^{2t}\) converges quickly to 0 as it increases. Therefore, the control limits converge to  
\[\text{UCL}=\overline{x}+3\cdot\frac{\overline{MR}}{d_{2}}\cdot\sqrt{\frac{ \lambda}{2-\lambda}} \tag{14}\]  
\[\text{LCL}=\overline{x}-3\cdot\frac{\overline{MR}}{d_{2}}\cdot\sqrt{\frac{ \lambda}{2-\lambda}} \tag{15}\]  
Since the monitoring data of a track system are discontinuous, the exponentially weighted moving-average process is performed for each data segment and the EWMA\({}_{0}\) of each data segment is set to the mean of whole monitoring data.
## 2 Materials and Methods
### Field Trials  
#### 2.2.1 SSCDS Spray Application System  
A pneumatic spray delivery based SSCDS consisting of an applicator and a canopy delivery system (Figure 2) was selected for field trials [21]. Pertinent details regarding the applicator sub-systems with on-board pump, air-compressor, and spray tank can be found in [PERSON] et al. [18]. The canopy delivery sub-system consists of spray lines (main and return; \(\phi\) = 2.54 cm), reservoir, and nozzle/micro-emitter assembly. Spray lines (main and return) were mounted on the existing orchard trellis wires at 1.4 m and 0.6 m above ground level, respectively, using poly hose trellis wire clips (\(\phi\) = 2.54 cm, model: A32H, Jain Irrigation Inc., Fresno, CA, USA). The spray lines were connected in a loop and had manual flow control valves installed at the end of the loop. The reservoirs were mounted on the return line at an interval of 1.8 m. Each reservoir consisted of an inlet port, a bleed valve, a liquid column, an outlet port, a float, a diaphragm check valve, a nozzle supply column, pair of nozzle feed line, and an auto drain valve (Figure 2). These micro-emitters/nozzles were connected with the nozzle feed line of the reservoir using PE tubing (\(\phi\) = 0.6 cm). The details of the micro-emitters/nozzles used in the two treatments are provided in Table 1.  
The pneumatic spray delivery system has 3 operational stages, namely charging, recovery, and spraying/cleaning. In the charging stage, the reservoirs are filled with the spray mix using a hydraulic pressure of around 100 kPa through the main line. During recovery, the excess spray mix from the spray lines are recovered back to the spray tank using compressed air at 100 kPa through the return line. At this point, only the reservoirs contained spray mix and the contained volume was equivalent to one third of the application rate (234 L ha\({}^{-1}\)). A diaphragm check valve (cracking pressure = 207 kPa) in the reservoir restricted any flow of spray mix through emitters during charging and recovery. After recovery, the spray mix contained in the reservoirs was sprayed under a pneumatic pressure of about 310 kPa. Once the spraying is complete, the auto drain valve in the reservoir opens to drain the residual volume onto the soil and cleaning is achieved.  
SSCDS return line  
Inlet port  
Bleed valve  
Liquid column  
Float  
Nozzle feed line  
Outlet port  
Diaphrag check valve  
Nozzle supply column  
Auto drain valve  
**Applicator**  
**Figure 2. The schematics of tested pneumatic spray delivery based SSCDS with an applicator and canopy delivery system configured with (**a**) modified irrigation micro-emitter or (**b**) hollow cone nozzle in 3-tier arrangement (diamond shape with solid blue fill represents micro-emitter/nozzle).
## Materials and Methods
### Reliability and Validity
#### Statistical Analysis  
In accordance with [PERSON]'s statistical recommendations, the intraclass correlation coefficients (ICCs) and 95% confidence intervals (CIs) were used for the total scores and item scores of the DIMS to evaluate the interrater- and test-retest reliability [20]. To interpret the ICCs, recommendations by [PERSON] et al. were considered, with ICCs higher than 0.90 as excellent, between 0.75 and 0.90 as good, between 0.60 and 0.74 as moderately high and between 0.40 and 0.59 as moderate. ICCs less than 0.40 were indicative of low reliability [15]. [PERSON]'s rank correlation coefficients (\(r_{s}\)) were used to determine concurrent validity where 0.00-0.19 was considered very weak/no correlation, 0.20-0.39 weak, 0.40-0.59 moderate, 0.60-0.79 strong and 0.80 to 1.00 very strong [21]. Internal consistency was calculated by [PERSON]'s alpha with \(\alpha=0.00\) meaning no internal consistency and \(\alpha=1.00\) perfect internal consistency [21]. All statistics were calculated using IBM SPSS Statistics 25 (SPSS Inc., Chicago, IL, USA).
## 4 An Interoperable IoT Architecture for Precision Agriculture (RQ4)
### Server and Client  
The final part of the system is structured around the central server, which manages the exchange of information between the database and the requesting clients.  
The central server primarily responds to two types of external event:  
**Data Request**: Regardless of the origin of the request, the server checks that the request is correctly formulated according to the specifications dictated by the standard. If it is valid, the server converts the HTTP request into an SQL query to submit to the database, extracting the requested data and forwarding them in the format specified by the standard to the origin of the request. If the request is incorrectly formulated or the requested data are not present in the database, the server terminates the communication, notifying of the error.  
**Data Publication**: The server checks that the request and data formatting are correct. If valid, the server results the database to verify that the submitted data are consistent with the data already present. The server communicates a successful outcome if all operations are completed successfully. Otherwise, as before, the server terminates the communication, notifying the error.  
The operation of the connector is shown in the activity flow chart in Figure 4. In particular, the diagram illustrates all the steps described so far and the points where the connector must make decisions regarding the incoming data. The connector also validates the data by checking its integrity upon receipt, ensuring that the payload is free of errors. If any issues or discrepancies are identified, the data are rejected and discarded. Furthermore, if added robustness in transmission is desired, redundancy in the payload can be implemented, as proposed in [86].  
A major advantage of the SensorThings API (STA) for clients is its powerful query and search functionality [51]. Discovering what type of data is served by the STA instance is straightforward. In most cases, a simple web browser is sufficient, as all entities can be accessed simply by following the navigation links provided by the API. Through the client, data requests can be made following the specifications of the APIs provided by the SensorThings standard. The purpose of the client is to provide a tool for data extraction andanalysis. For the visualization of geospatial data on a map, it is the client's responsibility to extract the coordinates from the received data and request the relevant map from the central server (if it provides this service by implementing geospatial data management software, such as GeoServer) [87].
# Fully Reconfigurable Photonic Filter for Flexible Payloads
## 4 Discussion
The goal is to meet all the requirements of a Tunable Bandpass Filter (BPF) and, thereby, create a fully flexible photonic telecommunication payload. Figure 4: (**a**) Photonic filter spectra vs. power (P) fed to both RRs; (**b**) photonic filter spectra at the power (P) range ends.
## 3 Inversion Results and Stability
### Robustness of Anomalies  
We performed numerical experiments to verify the anomalies. The resolution of the selected stations was tested by a series of GDS inversions with synthetic data generated from checkerboard models. Anomalies in the checkerboard model are on a scale of \(10^{\circ}\times 10^{\circ}\) (as shown in Figure 8a), and conductivity variations are of one order [log\({}_{10}(\sigma/\sigma_{0})=-1\) and \(+1\)] around the magnitude of the background model value. The checkerboard model is set in layers between 520-670 km (Figure 8b) and 670-900 km (Figure 8c), respectively, corresponding to our interested layers. Synthetic data for inversion was generated by adding 5% Gaussian noise to the forward responses at selected stations of the model, and the starting model, initial and changing strategy of regularization parameter, and the other settings of the inversion parameters were the same as those in the preferred inversion. Inversion of the synthetic data (Figure 8) demonstrates that, in regions well covered by stations, the checkerboard structures (\(10^{\circ}\times 10^{\circ}\)) at depths of 520-900 km are resolved relatively well. \(n\) particular, the resistive anomaly beneath the Mediterranean Sea is reconstructed both in the shape and amplitude. However, in regions covered by few stations, the conductivity is poorly constrained. The resolution tests demonstrate that our inverted model is in the distinguishable range of GDS with the selected dataset.  
Figure 8: The resolution kernel checkerboard model in a single layer **(a)**; (**b,c**) correspond to a single inverse model in which the checkerboard model is within the ranges 520–670 km and 670–900 km. White circles are the locations of geomagnetic observatories and are the same as in Figure 1.  
In order to determine whether the anomaly is strictly required by the inversion data, we replaced Anomalies A and B at depths of 520 km to 900 km with the conductivity of the background model and then calculated the responses at the selected stations. The curves of the re-forward responses, preferred model responses, and observed data are drawn in Figure 9. It shows that if the anomalies were eliminated, the model-response misfit would become significantly worse at the stations close to the anomaly, such as SFS, PAG, TAM, and QSB. The response shifts a lot from the inverted responses which fit the observed data well, especially for the real components of the C-response, meaning that the conductivity of the reconstructed model is inconsistent with the real situation of the mantle [40]. We, therefore, conclude that the anomaly is a required feature of the data.
## 2 Related Work  
Polarized 3D imaging technology has made notable advances in reconstructing the geometric morphology of target surfaces, particularly in scenarios where specular and diffuse reflections coexist. The combination of these reflections complicates 3D reconstruction, prompting researchers to develop separation methods.  
[PERSON] et al. introduced a method based on a dichromatic reflection model that effectively enhances the accuracy of 3D reconstruction by separating specular and diffuse reflections. This method is particularly suitable for 3D imaging of highly reflective surfaces and considerably reduces the interference of specular reflections in polarized imaging [12]. Similarly, [PERSON] et al. developed a 3D reconstruction method that utilizes multispectral polarization information, which effectively addresses the ambiguity in zenith angle calculations and improves reconstruction accuracy [13]. [PERSON] et al. enhanced the estimation accuracy of azimuth and zenith angles by utilizing a rough depth map from a Kinect sensor to correct normal vectors and eliminate azimuth ambiguity, thereby improving the target depth accuracy using polarization cues [14]. [PERSON] et al. focused on the 3D reconstruction of complex surface shapes and proposed a technique based on near-infrared polarized imaging that globally corrects uncertainties in surface normals, considerably enhancing 3D reconstruction accuracy [15]. [PERSON] et al. designed a polarized 3D imaging system optimized for complex environments, demonstrating its feasibility for 3D reconstruction under adverse weather and complex backgrounds [16].  
[PERSON] and [PERSON] proposed a novel method for estimating target surface geometry by integrating polarization and shading information, notably enhancing shape recovery accuracy using shape-from-shadow information [17]. Additionally, [PERSON] et al. investigated the use of polarized images to distinguish the reflections of transparent surfaces from the background, which is crucial for reconstructing complex 3D surfaces [18]. [PERSON] and [PERSON] explored the potential applications of polarization imaging in complex scenes. [PERSON] focused on reconstructing surface morphologies using polarized imaging in intricate scenarios [19], whereas [PERSON] introduced a new method for processing polarization information and reconstructing 3D features [20].  
In this study, we employed [PERSON]'s law and [PERSON]'s law to estimate the zenith angle of target reflected light from both specular and diffuse reflections. We developed a mixed reflection model for 3D surface reconstruction that demonstrates high precision under natural lighting conditions. Unlike traditional methods, the proposed approach is applicable not only in controlled laboratory environments but also in complex outdoor settings, offering notably improved 3D reconstruction accuracy for distant maritime targets.
## 5 Conclusions  
Electricity supply at an optimized cost through effective DSM is one of the core issues related to the smart grid. Note that the cost optimization relates to both utilities and consumers/prosumers. From the prosumer's perspective, this would mean minimum buying price and maximum selling price. This can lead to unfair pricing particularly in a time of severe energy shortage. There must be some mechanism of price control. The proposed scheme takes this objective into account and minimizes the price from buyers' point of view. In this paper, we have presented PESM scheme for cooperative DSM in smart grid. Prosumer-based social communities will help the utilities in solving the energy management issues and reducing the carbon prints. However the energy sharing optimization within a single community is the basic building block for inter-community optimal energy sharing. A test system with six users, having realistic profiles, has been presented in order to simulate cooperative DSM while a few realistic criteria have been employed for energy sharing priorities among prosumers. Although the number of users in this scenario is small, the proposed methodology is scalable; the obtained results indicate the effectiveness of the proposed algorithm for large communities as well. The scheme allows users with excessive energy to sell their surplus energy to users with an energy shortage in an optimized manner. The multiple criteria approach results in 44.13% reduction in shortage for a typical scenario. This is much higher than 30.16% reduction achieved by single criterion approach. In other words, the method and parameters used to prioritize users can affect the resulting shortage reduction quite significantly. Since the proposed scheme is automated, it is possible that price is very high in some instances. Along with any suitable price control mechanism imposed by a regulatory body, the scheme would be more useful in maintaining the overall prosumer satisfaction. The automated price control within the given regulations is one of the possible future enhancements in the scheme. As indicated earlier, it can be observed from the results that not all of the shortages can be met when there is not enough surplus energy available. It is also obvious that all surplus energy can not be used up when there is relatively less shortage. This problem can be addressed by storing surplus energy in preceding slots for use in the future according to requirements. Furthermore, the surplus energy available even after optimization may be allocated to other communities, as part of inter-community energy sharing optimization. These futuristic ideas along with experimentation with other optimization techniques are in the scope of our future work.  
All the authors have contributed significantly. [PERSON] and [PERSON] wrote major part of the paper and prepared the code for simulation purposes. [PERSON] and [PERSON] developed mathematical Model and also wrote some part of the paper. [PERSON] contributed significantly in analyzing the results and wrote part of a section.  
The authors declare no conflict of interest.