esa-sceva/satcom-chunk-collection huggingface dataset:
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 1 Introduction
From this viewpoint, we encode local knowledge of sensors into three neat matrices: local connection matrix (C); local data distribution matrix (D); and utility matrix (U) to encode utility of adding data into a data set. With these matrices, a compact and light-weight algorithm is proposed, where each node makes a series of simple all-in-one matrix computation to evaluate the benefits and cost of actions. Comparing the benefits against cost allows serious decisions to be made whether a piece of sensor data should be broadcast to make a good tradeoff between data relay to get high quality information and prolonging the lifetime of the network. In addition, energy consumption in data relay is taken into consideration towards cautious decision-making processes. As sensors do not have global knowledge of the network, a heuristic algorithm is proposed to maintain the size and value of these matrices from incoming messages to adapt to sensor mobility. By introducing matrix computations, and as our matrices only store local knowledge and updated in time, our approach is lightweight and can easily be deployed in large mobile sensor networks. Further, to manifest feasibility of this approach, we discuss the simulations carried out, and the results show that our approach can perform well in dynamic environments to effectively balance aggregating data with minimizing energy consumption.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 2 Grid Planning
### Sustainability and Environmental Planning
#### 2.6.1 Renewable Energy Integration  
Integrating renewable energy into power distribution is essential for advancing sustainability and efficiency in grid operations, offering both economic benefits and posing challenges. A model in [49] illustrates the cost reductions associated with increased DG penetration, emphasizing economic incentives. Studies [109, 110] aim to optimize investment and operational expenses, minimizing integration challenges through sophisticated mathematical models.  
The necessity of robust modeling to manage the variability of wind and solar power is underscored in [84] and involves capacity credit assessments of RESs and stochastic methods for precise planning. Addressing the variability of wind and solar energy requires advanced stochastic and probabilistic modeling to maintain grid stability and efficiency.  
The transition to a renewable-focused energy system faces hurdles like RES intermittency. Solutions like energy storage and demand management become crucial [168], introducing algorithms for the strategic integration of RESs and battery energy storage systems (BESSs) based on the genetic algorithm (GA) to enhance grid reliability.  
Digitalization plays a vital role in renewable integration, with studies highlighting AI's and big data's impact on optimizing grid planning and operations [19], modernization strategies for handling renewable influx [111], and analyses for economic and environmental sustainability [169].  
Renewable integration, alongside predictive maintenance and analytics, prepares distribution systems for the changing energy landscape. Despite initial investment and intermittency challenges, the benefits include sustainability, a reduced environmental footprint, and potential cost savings, highlighting the need for strategic planning and digital technology deployment for a resilient and sustainable power system.
## 3 Control Structures
### Implementation of DVC in RSCAD
#### 3.1.2 Active Power Control  
The active power control is developed with the DC voltage, direct frequency control and Voltage Dependent Active Power Reduction (VDAPR) control, as shown in Figure 4 inspired from Reference [9]. The control of active power is provided using the q-axis component of GSC voltage as per the Equation (10). Theoretically, since the DC capacitor provides the active power control, the DC energy barrier should be made higher which is to be done by increasing the capacitor size and DC voltage when in comparison with the conventional PI based current control [9]. However, considering the practical aspect, such an increase in capacitance is not incorporated for this work and the DC voltage is not increased and is set at 4 kV. Another significant modification that was done is providing the minimum limit, \(V_{a\_lim\_min}\), for the voltage measurement. A small value needs to be set for this parameter in RSCAD software or else an error causing division by zero would be bound to occur during the start of the simulation.  
The active power is calculated as follows:  
\[p=V_{PCC}i_{d}=-V_{PCC}\frac{V_{GSC\_d}}{x}, \tag{10}\]  
where \(V_{PCC}\) is the voltage at PCC, \(i_{d}\) is the active current in the AC side of GSC and \(V_{GSC\_d}\) is the reactive component of voltage in the AC side of GSC.  
The direct frequency control provides control in case of under frequency and over frequency. The control is modelled to be activated when the frequency is above 50.2 Hz or below 49.8 Hz. The VDAPR control is provided to control the power injection capacity of the GSC. Since power cannot be injected to the network by the GSC during the time of faults, the VDAPR allows in reducing the set point of reference power and thereby refining the dynamic stability of the network. The active power control loop parameters are provided in Table 12.  
Figure 4: Active power control loop.
# Dual Quaternion Embeddings for Link Prediction
## 4 Our DualQuatE Model
### Variations  
We introduce extensions of DualQuatE. DualQuatE is a transformation based model, which combines rotation and translation. To compare the effects of interaction of rotation and translation, we compare DualQuatE with DualQuatE-1 which models relations as rotation in three-dimensional space. Furthermore, we propose DualQuatE-2 to explore the role of scaling in the rotation.  
DualQuatE-1: We devise DualQuatE-1 which embeds entities and relations to quaternion space. Specifically, we represent entity embeddings \(\mathbf{h},\mathbf{t}\) with pure quaternions and relation embeddings \(\mathbf{r}\) with quaternions. We design a score function as follows\(f_{r}(h,t)=-||\mathbf{rhr}^{*}-\mathbf{t}||\) to model the relation as rotation in three-dimensional space. Namely, for each fact satisfies: \(\mathbf{rhr}^{*}=\mathbf{t}\).  
DualQuatE-2: To explore the effect of scaling in knowledge graph embeddings, we present DualQuatE-2 to introduce scaling. DualQuatE-2 maps knowledge graph embeddings to four-dimensional space. Especially, we represent entities and relations with quaternions where relation embeddings are not unit quaternions. We define score function \(f_{r}(h,t)=-||\mathbf{h}\mathbf{r}-\mathbf{t}||\) meaning relation transform head entity to tail entity combining rotation and scaling.
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 4 Synthesis Methods and Applications of Flower-like Copper- and Iron-Based Nanostructures
### Iron-Based NFs

Table 4: Synthesized iron-based nanoflowers under diverse synthesis parameters and their main findings. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Type of** & & & & & \\
**Nanofluwer** & **Synthesis Method** & **Diameter (nm)** & **Magnetic Properties** & **Application** & **Reference** \\ \hline \multirow{4}{*}{Fe\({}_{0.6}\)Mn\({}_{0.4}\)O} & One-pot & & & & \\  & high-temperature & & & & \\  & thermal & & & & \\  & decomposition & & & & \\ Fe\({}_{3}\)O\({}_{4}\)@PEG & Solvothermal & 70–250 & superparamagnetic & MRI contrast agent, & \\  & & & & & photothermal agent \\ \(\gamma\)Fe\({}_{2}\)O\({}_{3}\)@Au & Co-precipitation & 179 & superparamagnetic & PA and MRI agent, & \\  & & & & & \\  & Modified polyol & 15.1 \(\pm\) 2.8 35 \(\pm\) 3.8 & superparamagnetic & Cellular imaging & [83] \\  & & & & & \\  & & & & & \\ Fe\({}_{3}\)O\({}_{4}\)@PS-b-PAA & Polyol & 49 & n.a. & imaging tracer in MPI, & [113] \\  & & & & & photothermal agent \\ Fe\({}_{2}\)O\({}_{3}\)@Au & Polyol & 25–30 & n.a. & photothermal agent & [114] \\  & & & & & \\  & & & & & \\ \(\gamma\)Fe\({}_{2}\)O\({}_{3}\)@CuS & Polyol & 120.4 \(\pm\) 7.3 & superparamagnetic & Agent (PTT, PDT, & [115] \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 3: Iron-based nanoflowers synthesized by different processes.
# Laser Phase Noise Compensation Method Based on Dual Reference Channels in Inverse Synthetic Aldar
## 2 Models and Methods
### Analysis of the CGP Method with Single Reference Channel (SRC)
In such conditions, accurately estimating the RPN within the mismatch region becomes essential for enhancing compensation accuracy. When the reference delay extends, the range of the mismatch region increases, necessitating higher accuracy in residual phase estimation. In our previous work, we used a linear approximation to estimate the RPN, approximating the ratio of the RPN to the reference differential phase (DP) based on the corresponding delay differences:  
\[\Phi(t)=\phi_{L}(t-N\tau_{1})-\phi_{L}(t-\tau_{0})\approx(\Delta\tau_{0}/\tau_{ 1})[\phi_{L}(t)-\phi_{L}(t-\tau_{1})]=(\Delta\tau_{0}/\tau_{1})\phi_{\tau_{1}}(t). \tag{10}\]  
This method has proven to be an efficient approach for RPN compensation. It does not require prior information about the target, making it adaptable for targets at unknown distances or in motion. However, its applicability varies across different scenarios. In terms of compensation accuracy, the effectiveness of the linear approximation may decline as the mismatch path delay increases. For compensation speed, the unknown length of the mismatch path delay necessitates establishing multiple prediction values, each of which must be substituted in the linear approximation process. We then use the resulting RPN estimate to eliminate residual errors in the target signal and to identify the optimal prediction value based on the compensation effect. This optimization process requires extensive searching and iteration, resulting in a considerable computational load and slower compensation speed.
# Application of Machine Learning Techniques to
## 3 Methodology
### Climate Zoning
#### 3.1.3 Agglomerative Hierarchical Clustering  
The purpose of this step is to obtain the groups of sites/ stations with similar climate change patterns depicted by the component scores of the leading principal components obtained in the previous step. This is achieved using different clustering algorithms. Different behaviors in the clustering algorithm can be expected based on the data features, dimensions and input variable values. Traditionally, different clustering algorithms are used in previous studies; there is extensive literature regarding these clustering techniques [51; 62; 63; 61]. Recently, the agglomerative method of hierarchical clustering [64; 61] has received increased attention in climate literature. In this method, smaller clusters are developed according to a bottom-up approach, followed by a sequential combination with larger clusters depending on the Euclidian distance [65] between the clusters.  
In the algorithm, each evaluation (climate change pattern) was allocated to its self-cluster. Subsequently, the iterations in the algorithm were identified and joined with the closest cluster. This agglomeration continued until the formation of one cluster. A tree-like structure (dendrogram) of similarity was formed because of the hierarchical clustering, which provides meaningful information regarding the correlations/distance among different clusters. The validity of the number of clusters (CN)was determined prior to the identification of the maximum valid Euclidian distance. The agglomerative clustering algorithm of scikit-learn [53] was utilized for grouping the sites for each season, in this study.
# Advances in Engineering and Application of Optogenetic Indicators for Neuroscience
## 5 Neurotransmitter Indicators
Further optimization of iGluSnFR by rational design resulted in variants with improved brightness, kinetics, and altered glutamate affinity. The substitution of cpEGFP with a circularly permutated version of superfolder GFP (cpsfGFP) significantly enhanced brightness and photostability giving rise to a new series of the SF-iGluSnFR glutamate sensors [130] (SF stands for superfolder GFP used as reporting moiety; Figure 3a). Subsequent mutagenesis of the Gltl domain in SF-iGluSnFR generated two variants, named SF-iGluSnFR-A184S and SF-iGluSnFR-S72A, which exhibit higher affinity and faster kinetics, respectively, compared to the original iGluSnFR [130] (Table 3). The SF-iGluSnFR-A184S variant significantly outperformed iGluSnFR in detecting stimulus-evoked glutamate release at individual dendritic spines imaged in neuropils of the visual cortex under two-photon excitation. In turn, the faster off rate of SF-iGluSnFR-S72A enabled almost an order of magnitude higher temporal resolution than was previously achievable with original iGluSnFR. For example, SF-iGluSnFR-S72A fluorescence was able to resolve synaptic responses in brain tissue during single action potentials evoked at frequencies up to 100 Hz. Even higher temporal resolution was achieved by the iGluSnFR-S72T point mutant, named iGluu (u stands for \"ultrafast\") [129], which was able to resolve responses at single synapses in hippocampal slices during single action potentials evoked even at frequencies of 100 Hz. However, it showed about 1.5-fold lower sensitivity than SF-iGluSnFR-S72A under similar imaging conditions (Table 3). Overall, the kinetics and sensitivity of SF-iGluSnFRs and iGluu make them superior to the fastest green calcium sensor GCaMP6f in terms of monitoring presynaptic activity, as was demonstrated for granule cell axons within mouse cerebellar brain slices. Mutagenesis of the fluorescence moiety of iGluSnFRs enabled diversification of the spectral properties of the glutamate sensor, highlighting the versatility of its molecular design. Introduction of point mutations in the chromophore forming tripeptide led to SF-iGluSnFR variants with blue (emission peak \(\sim\)450 nm), cyan (emission peak \(\sim\)475 nm), and yellow (emission peak \(\sim\)530 nm) fluorescence [130]. On the other hand, substitution of cpEGFP with cpmApple from the R-GECO1 calcium sensor resulted
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 3 Receiver Comb Technologies
### Effects of Dispersion and Noise  
When implementing comb regeneration for self-homodyne applications, there are several aspects that need to be taken into consideration. The OSNR of the pilots tones seeding the receiver comb is one aspect, as in all-optical comb regeneration the OSNR of the lines seeding the receiver comb will determine the quality of the regenerated comb. Amplified-spontaneous-emission (ASE) noise on the carriers seeding the comb imposes a phase noise on the carriers and will therefore impact the regenerated comb. Due to scaling of the phase noise with line number, very narrow filtering of the seeding carriers is desired; however, care must be taken that the filtering is still larger than the clock variations. Assuming that the OSNR of the pilot tones is similar to that of the data channels, and not to impact the comb regeneration, we would like that \(\text{BW}_{\text{filter}}/R_{s}\ll n^{2}\)[64] where \(\text{BW}_{\text{filter}}\) is the filter bandwidth, \(R_{s}\) is the symbol rate and \(n\) is the number of carriers that are transmitted. For this reason, obtaining and filtering the line spacing in the electrical domain (e.g., using a PLL as discussed above) is desirable since narrow-bandwidth filters are available. We should note that in this case, the carrier seeding the receiver comb should also be filtered to avoid penalties, but a filter with much lower bandwidth than the symbol rate is sufficient.  
Apart from the OSNR, the phase correlation among the unmodulated tones will be affected by, e.g., dispersion during the transmission. As has been studied [63], the quality of the regenerated comb can be affected by the dispersive walk-off. If we assume a link with electronic dispersion compensation, the dispersive walk-off between the carriers together with the phase-noise scaling in parametric optical frequency combs [97] can be used to find that the comb regeneration requires \(n^{2}T\ll T_{\text{c}}\)[63] where \(T_{\text{c}}\) is the coherence time of the laser and \(T\) is the time delay between the two unmodulated carriers induced by dispersion walk off. This quantifies how dual-pilot comb regeneration for self-homodyne detection is sensitive to dispersive walk-off. This effect can be compensated by either using inline dispersion compensation or by just using a delay line to compensate for the walk-off among the pilots. Another effect to account for is the dispersive walk off between the data channels and the pilot tones. If we assume that the delay between pilot tones is compensated for, the phase difference between the regenerated local oscillators and the data channel suffering from highest walk off is given by \(nT\). Therefore, in order not to be penalized, we require the walk-off to be much smaller than the laser coherence time, \(nT\ll T_{\text{c}}\), which is a less stringent condition than the above.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Environmental Monitoring and Smart Farming
#### 3.2.11 Drone-to-Sensor Wireless Ranging Platform  
The platform for unmanned aerial vehicles (UAVs) proposed by [30] is targeted for environmental applications requiring mobility and other applications that require drone-to-wireless sensor systems. Regarding the processor and memory specifications, the STM32 WB55 RG MCU was utilized by the authors. The MCU consists of the ARMCortex-M4, which is used for the central processing tasks, and the ARM Cortex-M0 to cater for the radio communication protocols. It comprises up to 1 MB of embedded flash memory available for storing programs and data and 256 kB of partitioned SRAMs.  
The IEEE 802.15.4-2011-compliant Decawave (DW1000) transceiver was utilized. It supports frequency bands with center frequencies ranging from 3.5 GHz to 6.5 GHz. It is capable of achieving communication ranges of up to 300 m. The platform incorporates on-board magnetometer, temperature, pressure and humidity sensors. External sensors are also supported. Supercapacitors/batteries are used to power the WSN platform. Energy harvesting using inductive power transfer is also proposed by the authors. The sensor node consumes 190 uW of power in the sleep state and 29 J of energy is consumed during the landing stage of the drone.
# Development of Automatic Berthing Support Program for Autonomous Ships
## 3 Results
### Automatic Berthing Support Program
#### 3.1.3 Development of the Automatic Berthing Support Program  
The automatic berthing support program displays the required thruster output values for berthing, real-time distance to the berthing pier, and lateral speed of the ship on the output tab when the input elements are entered in the input tab on the right (Figure 5a).  
The input tab is divided into sections for ship-related information (e.g., ship type, length, and draft) (Figure 5b), pier-related information (e.g., pier heading and location), external environmental conditions (e.g., wind direction and speed, current direction and speed, and water depth), and tugboat attachment points. In the future, the information in the input tab could be designed to be automatically received from various equipment installed on the ship.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ship’s Type**} & \multicolumn{4}{c}{**Laden**} & \multicolumn{4}{c}{**Ballast**} \\ \cline{2-9}  & **B** & **C\({}_{\textbf{b}}\)** & **A\({}_{\textbf{T}}\)** & **A\({}_{\textbf{L}}\)** & **B** & **C\({}_{\textbf{b}}\)** & **A\({}_{\textbf{T}}\)** & **A\({}_{\textbf{L}}\)** \\ \hline Container & LBP/7.22 & 0.68 & B*38.0 & LBP \(\times\) 40.0 & LBP/7.22 & 0.63 & B*38.0 & LBP \(\times\) 20.0 \\ \hline Bulk & LBP/6.01 & 0.85 & B*16.1 & LBP \(\times\) 15.0 & LBP/6.01 & 0.80 & B*29.1 & LBP \(\times\) 15.7 \\ \hline Tanker & LBP/5.41 & 0.80 & B*17.7 & LBP \(\times\) 10.0 & LBP/5.41 & 0.75 & B*30.0 & LBP \(\times\) 20.0 \\ \hline PCC & LBP/5.88 & 0.60 & B*29.2 & LBP \(\times\) 29.9 & LBP/5.88 & 0.52 & B*34.9 & LBP \(\times\) 31.9 \\ \hline LNG & LBP/6.12 & 0.75 & B*36.0 & LBP \(\times\) 30.0 & LBP/6.12 & 0.70 & B*36.9 & LBP \(\times\) 31.7 \\ \hline Passenger & LBP/5.93 & 0.55 & B*13.8 & LBP \(\times\) 12.7 & LBP/5.93 & 0.52 & B*15.0 & LBP \(\times\) 15.0 \\ \hline Others & LBP/6.00 & 0.70 & B*20.0 & LBP \(\times\) 20.0 & LBP/6.00 & 0.70 & B*20.0 & LBP \(\times\) 20.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Linear regression between input data.  
Figure 5: _Cont._The program was developed using Python v3.12 and JavaScript, with GUI implemented via PyQt5 v5.15.11 and map visualization using the Folium library. Folium utilizes map data from OpenStreetMap (OSM) [27] to display GPS data in real time on the map. All experiments were conducted in a Windows 11 environment.