esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
In this study, we conducted experiments on two types of satellite videos to validate the effectiveness of the proposed method. One is a true-color (RGB) video from the Jilin-1 satellite, provided by Changguang Satellite Technology Co., Ltd, Changchun, China. The other is a gray-scale video from the Skysat satellite. The detailed technical parameters of the Jilin-1 and Skysat-1 satellites are listed in Table 1.  
The Jilin-1 video was taken on 3 May 2017, with a ground spatial resolution of 0.91 m. The scene is located in the Atlanta Airport area of the United States. The entire video has 296 frames, with a frame rate of 10 frames per second. The video has a width of 12,000 pixels and a height of 5000 pixels. We selected a region of 1024 \(\times\) 1024 pixels as our study area, shown in Figure 2a.  
The Skysat video used in this study was collected in April 2014, with a ground spatial resolution of 1.5 m. The scene is located at Burj Khalifa, the United Arab Emirates. The entire video has a total of 900 frames, with a frame rate of 30 frames per second. The video has a resolution of 1920 \(\times\) 1080 pixels. We selected a region of 1024 \(\times\) 1024 pixels as our study area, shown in Figure 2b.  
The experiments were conducted on a computer equipped with an Intel i7-9750H CPU @ 2.6 GHz, 32 GB of memory, and running Windows 10 (Intel, Santa Clara, CA, USA). We compared several other algorithms for detecting tiny moving vehicles, including GMM, improved frame difference (IFD), and ViBe. For fairness, all algorithms were tested on the images after pre-processing.  
In order to quantify the accuracy of the experimental results, we labeled the moving vehicles in the experimental satellite video. The annotation of satellite videos is arduous work, because vehicles are difficult to distinguish from a background with the naked eye due to the lack of prominent features. We observed several adjacent frames to determine whether the target was a moving vehicle. The moving objects labeled in the study area are shown in Figure 3a,b.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Jilin-1** & **SkySat-1** \\ \hline Imaging mode & Staring and push-broom & Staring and push-broom \\  & image & image \\ Continuous imaging time & 120 s & 120 s \\ Spatial resolution & 0.92 m & 1.1 m \\ Orbit types & Sun-synchronous & Sun-synchronous \\ Satellite altitude & 535 km & 587 km \\ Ground coverage & 11 km \(\times\) 4.5 km & 3.2 km \(\times\) 1.35 km \\ Maximum lateral swing angles & \(\pm\)45\({}^{\circ}\) & – \\ Frame frequency & 10 frames/s & 30 frames/s \\ \hline \end{tabular}
\end{table}
Table 1: The technical parameters of Jilin-1 and Skysat-1 satellite.  
Figure 2: The study area of Jilin-1 (**a**) and Skysat (**b**).
## 2 Materials and Methods
### Feature-Sensitive Analysis  
The \"black box\" problem means that the extent to which the input variable affects the learned NN model is not known. Feature-sensitivity analysis is a method for determining whether the input variables affect the ML classification task [28; 29; 30]. To measure the sensitivity of the input variable, the NN was trained using \"noisy\" data for the target variable. This method could calculate the extent to which the target variable affected the BCR prediction. The NN used backpropagation, and the Tangent function was used as the activation function. The feature-sensitivity analysis using noisy data is shown in Equation (1):  
\[NN_{k}=NN(X) \tag{1}\] \[NN_{k,x_{i}}=NN(X,x_{i\cdot\eta})\]  
Here, NN is a neural network learning algorithm, and X is the original dataset. The NN model that is learned through data X is defined as \(NN_{k}\), where \(\eta\) is the noise, and \(x_{i\eta}\) is the value that is obtained by applying noise to the input variable. We define the data with noise applied to the variable as \((\texttt{X},\ x_{i\cdot\eta})\). For the value of \(\eta\), a continuous variable used a random value within a specific range \([0,0.01]\), and for a categorical variable, all data were set to 0. The NN model in which the noise value is substituted is defined as \(NN_{k,x_{i}}\). The learning model was defined as described above, and the sensitivity of each variable was determined. Equation (2) presents the sensitivity calculation for each variable.  
\[\text{Sensitivity}(\texttt{X},x_{i})=\frac{1}{n}\sum\abs{NN_{k,x_{i}}(X)-NN_{ k}(X)}_{n} \tag{2}\]  
Here, \(NN_{k,x_{i}}(X)\) and \(NN_{k}(X)\) are the results of substituting X for each model. The Sensitivity \((\texttt{X},x_{i})\) in this formula is defined as the sensitivity value. A larger sensitivity value indicates a greater effect on the BCR prediction.
## 3 Cases Studies: Unlocking the Sociodemographic Knowledge with EO-Methods
### Gridded Systems for Collecting Sociodemographic Data and the Role of Data Cubes
When government officials participate in the modelling process, they gain understanding and the ability to certify outputs as official data, a current barrier to government use of open data. Figure 6: The IDEAMAPS data ecosystem and pilot gridded mapping system for Accra, Ghana. By meeting community, government, and other key stakeholder needs, IDEAMAPS fosters a living data ecosystem of continually contributed training and covariate data defined by local context experts, and the ability to incorporate an important parameter into deprivation models: level of agreement among local experts about what is, and is not, a deprived area in a given city. To protect vulnerable communities from eviction, harassment, fines, and other negative consequences, all IDEAMAPS data visualisations are aggregated to a 100 m\(\times\)100 m grid system to obfuscate the exact boundaries of vulnerable communities. This also serves to anonymise sensitive information and streamline data to match existing urban datasets (e.g., GHSL, WorldPop) for data scientists who contribute modelling innovations to the IDEAMAPS ecosystem. Both examples highlight the possibilities of the grid as an artefact for providing training data for taking advantage of EO via its predictive capacity in a data cube, as well as its importance as a useful technique for viewing and sharing the results. As such, it focuses on improving the interpretation, comparability, merging and exchanging of geo-spatial data that comes from different sources. Gridded systems protect vulnerable groups by not showing exact boundaries while still providing data access that is useful for interaction and cooperation with diverse citizen groups.
## 5 Conclusions  
Broadband SC was realized utilizing the cladding modes of an endlessly single-mode photonic crystal fiber. The modes had sufficient dispersion and, using a tapered-fiber optical coupling technique, enough power was coupled into the cladding to generate an SC with a higher spectral power density than could be achieved when only core-pumping the fiber. The cladding modes were shown, both interferometrically and numerically, to be near the optimum zero-dispersion wavelength for SC generation when pumped with a 1060 nm laser. Tapered-fiber preparation allowed for the efficient coupling of laser radiation into the cladding modes (up to 50.4% efficiency). This was sufficient to surpass the core-pumped SC generation in overall output power before the fiber was damaged. The conversion of laser radiation in a multimode waveguide with adapted dispersion was able to overcome the maximum power limit set by the damage threshold in core-pumped SC, but this came at a cost of reduced spatial coherence in the final spectrally-integrated SC output. The nonlinear dynamics of light in a multimode configuration were similar to, but different from, the previous experiments presented in an MM-GRIN fiber [20] and will provide an interesting platform for exploration owing to the fact that the light stays approximately in the same spatial superposition it was launched into or generated in. The modes can still be dispersed with relatively weak perturbations for the visible spectral range, such as when bending of the fiber occurs. Furthermore, there appear to be defects in the air-hole cladding which generate SC more readily than others. The core is one of these defects, but there are other spatial locations too. Lastly, the spectral power densities realized in the visible spectral range are among the highest achieved so far. Since no damage of the fiber ends occurred, even at maximum pumping power, further power scaling is possible in the future. It is believed this high-efficiency conversion into the visible is due to a large number of modes which are optimum for4-wave mixing with the pump, which made the conversion process much more efficient. However, the mechanisms supporting such an idea are yet to be confirmed.  
[PERSON] carried out the dispersion measurements of the fiber and the investigation of the spectral conversion of the laser radiation in the core and cladding. [PERSON] simulated the propagation properties of the fiber numerically. Together they developed the method and wrote the paper with the scientific support of A.F.L. and P.H.  
The authors would like to thank the Saxon State Ministry of Science and Art for financial support under grant number 4-7544.10/7/3.  
The authors would like to thank fiberware, Inc. for providing us with the endlessly single-mode fiber and members of the Optical Technologies working group at the West Saxon University of Applied Sciences and members of the Fraunhofer Institute for Material and Beam Technologies at the Application Center for Optical Metrology and Surface Technologies for their fruitful discussions and preparatory lab work.  
The authors declare no conflicts of interest.
## 4 Discussion  
### Capability of SIF Data in the Crop Growth Stage Estimation Framework  
The objective of this study was to assess the capability of SIF data within a crop growth stage estimation framework, particularly under the context of agronomic practices. A site on the North China Plain served as a case study to examine ground and satellite-based SIF data, comparing their performance with traditional vegetation indices.  
While prior research has explored the relationship between SIF and gross primary production, it predominantly focused on land surface phenology, typically limited to the start or end of the growing season [18; 45; 46]. In contrast, cropland systems involve complexities, demanding more precise and detailed information for crop growth monitoring. Some studies emphasized crop growth monitoring and the importance of aligning remote sensing transition dates with specific crop growth stages within an agronomic framework. [6; 7; 42; 66; 68; 77]. Notably, SIF data has not been rigorously evaluated within this framework. Additionally, previous works often treated distinct crop growth stages as equal events without considering their varying importance based on crop physiology and economic factors [6; 66]. However, the importance of different crop growth stages is not the same based on the physiological characters of crops and the economic factors of agricultural activities. Therefore, this study evaluates the potential of SIF data in estimating KGSs within this comprehensive framework.  
The findings reveal that, in the context of maize, SIF data outperforms EVI data for the identification of most KGSs, except for T&S (Figure 5). Conversely, EVI data excels in detecting T&S, suggesting the possibility of combining SIF and EVI data for more accurate maize growth stage estimation. In contrast, winter wheat exhibits different growth dynamics, with the ability to sustain greenness from regreen to maturity, minimizing EVI data fluctuations. SIF data, which reflects photosynthetic activity, remains unaffected and performs well in winter wheat growth stage estimation as Figure 5 illustrates [27; 78; 79]. Moreover, the comparison between ground-based and satellite data demonstrates that TROPOMI SIF provides superior or, at the very least, reasonably accurate estimations.  
In summary, considering both the alignment between remote sensing transition dates and specific crop growth stages and the importance of different crop growth stages, this study evaluated SIF data integrated into a remote sensing crop growth stage estimation framework. Compared with EVI data, which reflects the greenness of monitoring crops, SIF data have superior performance on winter wheat growth stages estimation with the ability to reflect photosynthetic activity. SIF data also offers more accurate estimations for maize. Thus, SIF data are capable for crop growth stage estimation. Notably, TROPOMI SIF data exhibits the potential to perform crop growth stage identification at a regional scale without compromising accuracy, highlighting its utility in agricultural monitoring applications.
## 5 Design of the PV System Dynamics
### Calculation of \(k_{1}\)  
The calculation of the SMC parameter \(k_{1}\) is performed using the closed-loop expression of \(v_{pv}\) given in (30) and the \(v_{mpt}\) expression given in (32):  
\[V_{pv}(s)=\frac{k_{1}}{C_{pv}\cdot s+k_{1}}\cdot\underbrace{ \frac{\Omega}{s+\Omega}\cdot\frac{\Delta v_{po}}{v_{mpt}}}_{v_{mpt}} \tag{36}\]  
Applying the inverse Laplace transformation to (36) results in the following time-domain waveform of \(v_{pv}\):  
\[v_{pv}(t)=\Delta v_{po}\cdot\left[1+\frac{\Omega}{\frac{k_{1}}{C _{pv}}-\Omega}\cdot e^{\left(-\frac{k_{1}}{C_{pv}}\cdot t\right)}-\frac{\frac {k_{1}}{C_{pv}}}{\frac{k_{1}}{C_{pv}}-\Omega}\cdot e^{\left(-\Omega\cdot t \right)}\right] \tag{37}\]  
The previous closed-loop waveform of the PV voltage, as given in (37), shows that \(\lim\limits_{t\rightarrow\infty}v_{pv}(t)=\Delta v_{po}\); thus, the steady-state error is zero. In addition, parameter \(k_{1}\) must be calculated to impose a desired settling time \(t_{s}\) to the \(v_{pv}\) waveform, which is measured when the PV voltage (37) reaches a given percentage of the final value \(\Delta v_{pv}\), where the most common limit in engineering is \(\epsilon=2\) %. Such a settling time must be less than the perturbation period \(T_{a}\) of the P&O; otherwise, the P&O algorithm could become unstable, as demonstrated in [26, 27]. Therefore, \(k_{1}\) is calculated by solving (37) for \(v_{pv}=\Delta v_{pv}\cdot(1-\epsilon)\) and \(t=t_{s}<T_{a}\) to obtain the value given in (38), where \(W(\cdot)\) is the Lambert-W function [28].  
\[k_{1}=\frac{1}{a_{1}}\cdot W\left(\frac{a_{1}\cdot e\cdot\left[ \frac{a_{1}\cdot e}{a_{2}}\right]}{a_{2}}\right)-\frac{\epsilon}{a_{2}}\quad; \quad a_{1}=\frac{t_{s}}{C_{pv}}\;,\;a_{2}=\frac{-e+e^{-\Omega\cdot t_{s}}}{ \Omega\cdot C_{pv}}\;\text{and}\;t_{s}<T_{a} \tag{38}\]
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 2 Model Description and Problem Formulation
### TEC State Transition  
Denote by \(\hat{\mathbf{x}}_{n,t-1}\) and \(\mathbf{P}_{n,t-1}\) the dynamic state estimation and TEC state of target \(n\) at the beginning of time slot \(t-1\). Starting from the initial dynamic state estimation \(\hat{\mathbf{x}}_{n,0}\) and \(\mathbf{P}_{n,0}\), \(\hat{\mathbf{x}}_{n,t}\), and \(\mathbf{P}_{n,t}\) are recursively updated over time slots \(t\geq 1\) being conditioned on the chosen actions, taking into account the dynamic models, as follows.  
If the target is tracked at time \(t-1\) (\(a_{n,t-1}=1\)), then the target will follow the dynamic model probability \(\mathbf{u}_{n}^{1}\). If it is detected with the detection probability \(p_{\text{d}}\), the TEC state update follows the non-linear filter as  
\[\mathbf{P}_{n,t}=\phi_{n}^{1}\Big{(}\mathbf{P}_{n,t-1},\hat{\mathbf{x}}_{n,t-1}, \mathbf{z}_{n,t},\mathbf{u}_{n}^{1}\Big{)}. \tag{7}\]  
For details, the reader is referred to [38].  
If the target is not detected, the TEC state of the target can be updated by  
\[\mathbf{P}_{n,t}=\phi_{n}^{0}\Big{(}\mathbf{P}_{n,t-1},\mathbf{u}_{n}^{1}\Big{)} =\sum_{m=1}^{M}u_{n}^{1,m}\bar{\mathbf{P}}_{n,t|t-1}^{m}, \tag{8}\]  
where  
\[\bar{\mathbf{P}}_{n,t|t-1}^{m}=\bar{\mathbf{P}}_{n}^{m}\mathbf{P}_{n,t-1}( \mathbf{F}_{n}^{m})^{\prime}+\mathbf{Q}_{n}^{m}. \tag{9}\]  
If the target is not tracked at time \(t-1\) (\(a_{n,t-1}=0\)), then  
\[\mathbf{P}_{n,t}=\phi_{n}^{0}\Big{(}\mathbf{P}_{n,t-1},\mathbf{u}_{n}^{0}\Big{)} =\sum_{m=1}^{M}u_{n}^{0,m}\bar{\mathbf{P}}_{n,t|t-1}^{m}. \tag{10}\]  
Meanwhile, the dynamic state estimation \(\hat{\mathbf{x}}_{n,t}\) can be updated under action 1 or predicted under action 0 based on the dynamic model probability and the non-linear filter.  
Note that since the one-step prediction is based on the dynamic model and no measurement is required, the TEC update recursion given by (8) and (10) is deterministic. It follows that action \(a_{n,t-1}\) predicts the TEC \(\mathbf{P}_{n,t}\) through sum-weighted method based on the model probability vector \(\mathbf{u}_{n}^{n_{n+1}-1}\). When \(a_{n,t-1}=1\) but not detected, the target \(n\) maneuvers with higher probability \(u_{n}^{1,m}\), \(m=2,3,\ldots,M\) than the maneuvering probability \(u_{n}^{0,m}\) under \(a_{n,t-1}=0\). Meanwhile, since \(q_{n}^{1}<q_{n}^{m}\), \(m=2,3,\ldots,M\), the trace of TEC state \(\mathbf{\mathrm{P}}_{n,t}\) in (8) will be larger than that of (10).
# Solving Multi-Document Summarization as an Orienteering Problem
###### Abstract  
With advances in information technology, people face the problem of dealing with tremendous amounts of information and need ways to save time and effort by summarizing the most important and relevant information. Thus, automatic text summarization has become necessary to reduce the information overload. This article proposes a novel extractive graph-based approach to solve the multi-document summarization (MDS) problem. To optimize the coverage of information in the output summary, the problem is formulated as an orienteering problem and heuristically solved by an ant colony system algorithm. The performance of the implemented system (MDS-OP) was evaluated on DUC 2004 (Task 2) and MultiLing 2015 (MMS task) benchmark corpora using several ROUGE metrics, as well as other methods. Its comparison with the performances of 26 systems shows that MDS-OP achieved the best F-measure scores on both tasks in terms of ROUGE-1 and ROUGE-L (DUC 2004), ROUGE-SU4, and three other evaluation methods (MultiLing 2015). Overall, MDS-OP ranked among the best 3 systems.  
Keywords:multi-document summarization; orienteering problem; ant colony system; graph-based
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Slotted-ALOHA (S-ALOHA)
Which was designed and analyzed by the authors in [122]. This protocol uses one buffer, to allow the communicating nodes to generate and send numerous data packets, while also altering the position of other buffers to allow data packets to be routed back to their intended destination if they collide. The ST-Slotted-CS_ALOHA protocol outperforms Slotted_CS_ALOHA in terms of improving the energy consumption of the network, increasing throughput, and lowering the average delay factor, but the number of communicating nodes drops drastically, when compared to the Slotted_CS_ALOHA protocol. * _Modified-Slotted-ALOHA_ The modified-slotted-ALOHA protocol is a recommended protocol for resolving issues, when an acknowledgement is not received [123]. This protocol employs a modulated buffer to help create many data packets and resend them to a communicating node in UWSNs. This modulation scheme will save energy that would otherwise be wasted by the sensor nodes. This protocol also overcomes the problems of low throughput, and high average delay factor by employing a buffer, which saves data packets before sending them. Therefore, they can be resent if a collision happens, or an acknowledgement is not received. This will increase the average data transfer rate and solve the power consumption issue. The proposed protocol relies on back-off technology, which uses random time to determine the best time to transmit the data packets in UWSNs [123].
# On the Capacity of Optical Backbone Networks
## 3 Channel Capacity  
### Capacity of a Communication Channel  
According with [PERSON]'s theory [6], the capacity of a band limited communication channel in the presence of AWGN is given by  
\[C_{ch}=B\text{log}_{2}\bigg{(}1+\frac{P}{N_{0}B}\bigg{)}, \tag{2}\]  
where \(C_{ch}\) is the channel capacity in bits per second (bit/s), \(B\) is the channel bandwidth in Hz, \(P\) is the average signal power in watt, and \(N_{0}\) is the noise power spectral density (PSD) in watt/Hz. The signal transmitted through the channel is assumed to be based on a sequence of symbols transmitted at the symbol rate of \(R_{s}\). Each symbol encodes multiple bits of information by modulating the amplitude and/or the phase of the signal.  
The minimum bandwidth that a modulated signal can have to allow transmission without inter-symbol interference is defined by the Nyquist criterion and is equal to \(R_{s}\)[4]. For such minimum bandwidth, the signal-to-noise ratio becomes  
\[SNR=\frac{P}{N_{0}R_{s}}, \tag{3}\]  
and the channel capacity can be written as  
\[C_{ch}=R_{s}\text{log}_{2}(1+SNR). \tag{4}\]
# Anomaly Detection for Hydraulic Power Units--A Case Study
## 2 Materials and Methods
### Data Acquisition and Communication
#### 2.1.2 Azure IoT Edge  
Azure IoT Edge [35] is a framework for developing and deploying edge solutions using Docker containers [36] that can communicate with each other. The core of IoT Edge architecture consists of two containers: Edge Agent and Edge Hub. The first container is responsible for handling and keeping an eye on all containers belonging to the solution. Whenever some container needs to be started, restarted, stopped, or updated, Edge Agent comes into play. Edge Hub is responsible for handling communication between containers with containers and containers with the cloud. Just like any other Docker container, containers within IoT Edge can fulfill desired functionalities by performing code inside the container. Containers are stored in a cloud container registry where they can be downloaded and uploaded. Deployment at scale is one of the most important features of Azure IoT Edge. Using this feature for purposes of anomaly detection for the HPUs project will be described in Section 5. A full description of Azure IoT Edge is presented on its official website [35].  
On the edge computer, the Azure IoT Edge ecosystem was installed. With the dedicated software mentioned above (points 1 and 2), IoTEdge was responsible for acquiring measurements and sending them to Azure Cloud. Functionality is encapsulated in the Docker container.  
A container can be thought of as a virtual machine but far more minimalist. It is an environment that provides all that is needed to run a program inside it. In the context of the described project, the Modbus Master container, visible in Figure 3, must have a Python interpreter with the PyModbus package installed and a minimal quasi-Linux operating system that is able to run the Python interpreter. It is important that the same container can be run on edge devices of different kinds, provided that they have the same CPU architecture and Docker environment installed.  
The process of deploying containers on edge devices is carried out as follows:  
1. Code development, including implementation of data acquisition presented in Algorithm 1;
2. Building Docker container;
3. Pushing Docker container to container registry;
4. Order container download to edge device via Azure CLI [37] or Portal Azure [38].  
Programming within step 1 is performed in the same way as for a code that is not intended for containerization. This means that there are no additional techniques to be learned by the developer to provide a code that will also work within the container. The synthesis of the Docker container in step 2 requires a couple of specific Docker commands [39] and configuration files. As a result, a new Docker container appears, which is ready to be uploaded in step 3, for example, to Docker Hub [40] or Azure Container Registry [41]. The last step 4 ends the process of the deployment container on the edge device. Thanks to Edge Agent core-container, the just downloaded custom container starts automatically.  
Figure 2: Communication scheme.