esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Machine Learning
### Lstm  
The LSTM algorithm is one type of Recurrent Neural Network that deals mostly with sequential input data. Cell state is the key to LSTMs; it is the direct steps from \(C_{t-1}\) to \(C_{t}\) as shown in the upper part of Figure 1. The cell state is similar to a production chain; the parameter flows straight forward, but some linear processes, such as addition and multiplication, will interact. The state depends on these interactions, and if there are no interactions, it will flow along without changes. The LSTM block will remove or add information to the cell state through gates; gates are structures that allow optional information to cross. These gates can be implemented by sigmoid functions. The sigmoid function produces two decisions: either '0' or '1'. Assume that '0' will block information flow and '1' will let it go through. With this, a control will be done on how the information should flow through. Three of these gates are available in a LSTM cell, where these gates will determine the final cell state.  
The neuron we show in Figure 1 is described by the following functions  
\[f_{t}=\sigma(W_{f_{*}}[h_{t-1},x_{t}]+b_{f}) \tag{6}\]  
\[i_{t}=\sigma(W_{i_{t}}[h_{t-1},x_{t}]+b_{i}) \tag{7}\]  
\[\overset{\sim}{C}_{t}=tanh(W_{C}.[h_{t-1},x_{t}]+b_{C}) \tag{8}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\overset{\sim}{C}_{t} \tag{9}\]  
\[Q_{t}=\sigma(W_{Q}.[h_{t-1},x_{t}]+b_{Q}) \tag{10}\]  
\[h_{t}=Q_{t}*tanh(C_{t}) \tag{11}\]  
where  
\(f_{t}\) is the activation vector of the forget gate,  
\(\sigma\) is the sigmoid function,  
\(W\) is weight matrices to be learned during training,  
\(x_{t}\) is input vector to the LSTM unit,  
\(b\) is bias vector parameters to be learned during training,  
\(i_{t}\) is activation vector of the input gate,  
\(C_{t}\) is cell state vector,  
\(Q_{t}\) is activation vector of the output gate, and  
\(h_{t}\) is output vector of the LSTM unit.  
Figure 1: One Long Short-Term Memory (LSTM) block structure.
# Feasibility of LoRa for Smart Home Indoor Localization
## 2 Literature Review
### Long Range (LoRa)
Since LoRa signals can penetrate objects at long distance, the indoor localization system based on LoRa can assure continuous and robust location tracking of objects in a building. For example, the location of a tag in a small and confined space covered with thick walls in a building can be tracked continuously without additional anchors in the confined space. Despite the great potential of LoRa for indoor localization, not much is known about how LoRa-based localization would perform in a home with LOS and NLOS situations. Even though a couple of studies attempted to evaluate LoRa in indoor localization [71], the knowledge gained from these studies is highly limited because the configurations of the test environments are mostly unknown, and the results of localization were analyzed without providing the description on the LOS and NLOS situations impacting the performances. For instance, [25] reported 0.76 m-3.72 m combined an accuracy without thoroughly explaining the configurations of the test environment. This study did not describe what LOS situations and NLOS situations were created in the environment using what type of building objects and electronic devices and how the situations impacted the performance of localization. Rather than rough descriptions of the test conditions such as \"NLOS caused by varying walls\", the performance of LoRa should evaluated revealing the relations between the configuration of the test environment and their impact on the performances.
# The Feature Selection Effect on Missing Value Imputation of Medical Datasets
## 3 Research Methodology  
### Combination of Feature Selection and Missing Value Imputation  
The process combining feature selection and missing value imputation is illustrated in Figure 1. The incomplete \(M\) dimension dataset \(D\) is composed of training and test sets, denoted by \(D\_tr\) and \(D\_te\), respectively. For feature selection, \(D\_tr\) contains a number of complete (i.e., \(D\_complete\)) and incomplete (i.e., \(D\_incomplete\)) data samples. The feature selection step is performed on the \(D\_complete\) subset, leading to a new subset that contains \(N\) dimensions (where \(N<M\)), denoted as \(D\_complete^{\prime}\). It should be noted that the feature selection process only considers the data in \(D\_complete\), since each of these data contains no missing attribute values, which allow feature selection algorithms to successfully select a subset of representative features. However, the issue of whether \(D\_complete\) represents the population is beyond the scope of this paper. Next, the \(D\_incomplete\) subset is also reduced to the same \(N\) dimensional subset, denoted as \(D\_incomplete^{\prime}\).  
For missing value imputation, the \(D\_complete^{\prime}\) subset is used to construct a learning model. For the example of imputing the missing value of the \(i\)-th feature (\(i=1,2,\ldots,N\)) in \(D\_incomplete^{\prime}\), the learning model is trained by the data samples of \(D\_incomplete^{\prime}\), where the \(i\)-th feature of \(D\_incomplete^{\prime}\) is used as the output feature and the rest of the features are the input features. Then, estimations are produced by the model to replace the missing values in the \(D\_incomplete^{\prime}\) subset.  
The same process is performed over the testing set \(D\_te\). That is, the \(M\) dimensional testing set \(D\_te\) is reduced to the \(N\) dimensional testing set, denoted by \(D\_te^{\prime}\). Next, missing value imputation  
Figure 1: The combined feature selection and missing value imputation process. \(D\_complete\) and \(D\_incomplete\) mean the compete and incomplete data samples in training dataset \(D\). \(D\_incomplete^{\prime}\) and \(D\_incomplete^{\prime}\) mean the reduced feature set of the original \(D\_incomplete\) and \(D\_incomplete\), respectively. \(D^{\prime}\) represents the reduced feature set of the original \(D\) without any missing value.  
is performed by the learned model trained by \(D\_\)complete\({}^{\prime}\). Finally, the imputed dataset, denoted as reduced data \(D\_tr\)', is used to train a classifier, and its classification performance is examined by the reduced testing set (i.e., \(D\_te\)).  
The baseline imputation process, without feature selection being performed, uses \(D\_\)complete directly with the model to produce estimations for the missing values of \(D\_\)incomplete. The aim of this study is to examine differences in performance between the combined feature selection and imputation method and the baseline imputation method.
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 4 Analysis of Current Antenna Solutions Operating in 5G Systems at a 28 GHz Frequency  
Current literature contains many proposed solutions of microstrip antennas operating in 5G systems at a frequency of 28 GHz [14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. These solutions are characterized by their compactness, small geometric dimensions, and antenna bandwidth of about 2 GHz. Many of them are based on a RT Duroid 5880 laminate with a permittivity of 2.2. In the analyzed antennas, the radiator is mostly rectangular, and is powered by a microstrip feed line. The proposed antenna is different, due to the modifications made to the shape of the patch, which were made on the basis of solutions originally used in fractal antennas. These modifications can be used to create a proposed antenna capable of supporting different bandwidths.  
The antenna, proposed in this article, is capable of operating in systems intended for 5G communication, is designed for a resonance frequency equal to 28 GHz, and is based on a RT Duroid 5880 laminate with a rectangular radiator. The proposed antenna is designed to achieve a greater bandwidth than the antennas analyzed in the literature mentioned above. Antennas with a wider operating band can only be created by using complex spatial radiating structures, which makes them difficult to implement in 5G mobile devices.  
The developed antenna, despite the use of the well-known inset feed in its design, has achieved a much wider bandwidth than similar constructions of this type found in the literature [14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. This effect was achieved through optimization with a large number of variables, which was aimed at achieving the lowest possible reflectance and voltage standing wave ratio (VSWR) in the widest possible frequency range. The results significantly exceed the previously published parameter values for this type of antenna in terms of bandwidth, especially in cases of strict matching conditions (VSWR \(\leq\) 2, VSWR \(\leq\) 1.5, and VSWR \(\leq\) 1.25).
## 4 Results and Analysis
### Experimental Data  
Dynamic responses of the Breccia and Andesite to impact loads recorded by the SHPB system are shown in Figures 3 and 4, respectively. Among them, (a) is the raw signal recorded by the incident and transmission bars. Raw electric potential signals contain considerable noise. After removing the high-frequency noise by FFT filter with 0.02 Hz cutoff frequency, the available electric potential-time curves were obtained. Figures 3b and 4b respectively show the raw and filtered signals of Breccia at 4.86 m/s striking velocity and Andesite at 4.88 m/s striking velocity. The rock specimen in the state of stress equilibrium before fracture occurs was a basic assumption of the SHPB experiment. Figures 3c and 4c confirm the stress equilibrium by the three-wave equilibrium theory, i.e., In. + Re. = Tr. The forces acting on the contact ends between rock specimen and bars, including both the incident and transmitted bars, were calculated by Equations (1) and (2) in Section 2.2. To further validate the stress equilibrium, the curve of stress equilibrium factor, \(\eta=2(P_{1}-P_{2})/(P_{1}+P_{2})\)[38],against time was used to reflect the loading state of the rock specimen in the process of resisting impact load. As shown in Figure 3d, the stress equilibrium factor at 4.86 m/s striking velocity was still approximately zero until reaching the extreme values of \(P_{1}\) and \(P_{2}\) at 92 us, then the Breccia specimen fracture caused a fluctuation of the curve. Similar phenomena also occurred in the Andesite specimen at 4.88 m/s striking velocity, but the Andesite specimen lost its stress equilibrium at 225 us, which was almost 79 us after the extreme values of \(P_{1}\) and \(P_{2}\). Although the igneous rock specimens lost their stress equilibrium at different times of the \(P_{1}\) and \(P_{2}\) curves, Breccia at the time of extreme value and Andesite after the time of extreme value, they all satisfied the prerequisite of the SHPB experiment. Therefore, the reliability of the experimental results was verifiable. The tensile stress at the center of the disc was calculated by Equation (3), as shown in Figures 3e and 4e. A constant loading rate was obtained by linearly approximating the rising stage of the temporal tensile stress waveform. As shown in Figure 3e, the dynamic tensile strength of the Breccia was 7.856 MPa, corresponding to a fracture time of 85.8 us at a loading rate of 134.6 GPa:s\({}^{-1}\). The dynamic strength of the Andesite was 24.64 MPa, corresponding to a time of 145 us at a loading rate of 278.5 GPa:s\({}^{-1}\), as shown in Figure 4e.  
Figure 3: Experimental results for the Breccia specimen. (**a**) Raw signals of the Brazilian test. (**b**) Raw and filtered signal at a striking velocity of 4.86 m/s. (**c**) The incident (In.), reflected (Re.), transmitted (Tr.), and superposed (In. + Re.) waves. (**d**) Verified stress equilibrium. (**e**) Determination of dynamic tensile strength and loading rate.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 4 Discussion
### Challenges
#### 4.1.3 Field-Level and Deployment Challenges  
Deploying AIoT-based solar energy monitoring and control systems in real-world environments involves numerous field-level and infrastructural challenges. One critical issue is communication stability, especially in remote or decentralized solar farms where intermittent network coverage can lead to delays or data loss during real-time transmission and control. Sensor accuracy and reliability also pose significant challenges--malfunctioning or miscalibrated sensors can provide erroneous input to AI models, compromising system performance and decision-making. In addition to these field-level concerns, computational limitations remain a barrier. Many AI models require high processing power, making deployment on resource-constrained edge devices difficult. Integration with existing PV infrastructure is often complicated by heterogeneity in hardware standards, communication protocols, and control systems, leading to compatibility issues. Real-time responsiveness is another key concern, particularly under fluctuating solar irradiance, unpredictable load demands, or actuator latency. Ensuring reliable and adaptive system behavior under these dynamic conditions requires robust AI algorithms and control strategies. Furthermore, cybersecurity threats become more prominent as these systems rely heavily on distributed and cloud-based architectures. Unauthorized access or data breaches can lead to serious disruptions in operations. Lastly, economic factors, including the cost of hardware, AI integration, and maintenance, can hinder widespread adoption, particularly in small-scale or underfunded solar projects. To address these challenges, future research should focus on developing lightweight and energy-efficient AI models, improving communication resilience, enhancing sensor calibration and diagnostics, and promoting standardized frameworks that support seamless integration and scalability in diverse PV environments.
## 5 Experimental Results
### Efficiency Comparison Curve  
Figure 24 shows the efficiency comparison between the DAB converter using the dual-mode control strategy proposed in this paper and a DAB converter using the traditional SPS control strategy. It can be seen from the figure that the light load efficiency of the DAB converter using the dual-mode control strategy is better than that of the DAB converter using the traditional SPS strategy. In particular, in the forward power conversion mode, the maximum efficiency of the proposed control strategy is 10% higher than that of the traditional SPS strategy, as shown in Figure 24a. In the backward power conversion mode, the maximum efficiency of the proposed control strategy is 5% higher than that of the traditional SPS control strategy, as shown in Figure 24b.  
The characteristic of the SPS control is that high backflow power when operating at light loads reduces the converter efficiency. The light-load efficiency of the proposed dual-mode converter is enhanced because the converter operates in PWM mode to minimize backflow power. Experimental results verify that the dual-mode control strategy proposed in this paper improves the light load efficiency of the DAB converter compared with the traditional SPS control strategy. Table 4 shows a comparison of the related studies with the proposed dual-mode control strategy in terms of efficiency. Table 5 also shows the calculation for the power loss of the proposed dual-mode DAB converter at a load of 100 W.  
Figure 23: Waveforms of mode switching (in backward power conversion): (**a**) load change from 10 W to 200 W; (**b**) load change from 200 W to 10 W.  
Figure 22: Waveforms of mode switching (in forward power conversion): (**a**) load change from 150 W to 300 W; (**b**) load change from 300 W to 150 W.
# Beyond Mitigation: New Metrics for Space Sustainability Assessment +
###### Abstract  
The escalating volume of space operations and the proliferation of satellites underscore the urgent need for a pivotal shift towards sustainable space use. This paper highlights the importance of addressing space sustainability through a holistic framework. Robust international commitments, aligned with the United Nations Sustainable Development Goals (SDGs) 12 (Responsible Consumption and Production) and 13 (Climate Action), alongside the implementation of legislative measures, are essential for fostering responsible and sustainable practices in space activities. Furthermore, innovative technology advancements can potentially convert this space sustainability problem into an opportunity for the space sector. Mitigating the risk of debris is no longer sufficient. This article advocates for prioritizing sustainability in the design phase of the new missions and constellations. To achieve this objective, it is crucial to develop a comprehensive understanding of how various design parameters--such as orbital altitude, the number of satellites, the inclusion or exclusion of inter-satellite links, system interoperability, and reconfigurability--impact the sustainability of space systems. Hence, the investigation suggests creating innovative Key Performance Indicators (KPIs) that specifically target space sustainability. These KPIs would enable the evaluation of specific missions/constellations and the comparison of different design alternatives. The absence of current research on these KPIs requires the creation of new ones. This research introduces a preliminary framework for establishing these novel metrics, which can be vital for governments and companies to develop and oversee a sustainable future in space. By implementing a holistic strategy combining robust policy frameworks with cutting-edge technology solutions, we can guarantee the ongoing, secure, and environmentally responsible space utilization for future generations.  
space sustainability; mitigation; key performance indicators (KPIs); Sustainable Development Goals; debris risk mitigation; satellite constellations; sustainability metrics; sustainable space exploration
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
## 3 Proposed Reputation-Based Scheme
### Design and Modules  
Because of the broadcast medium of MANETs, when a node sends or forwards a packet, it is overheard by all its neighbors [22]. All neighbors in our proposed approach monitor packet sending and forwarding operations and calculate the sender's or forwarder's reputation--based on send and forward events. Our scheme will run on the top of DSR. The monitor observes the packet sending and forwarding activity to compute the C2C. The monitor forwards C2C to the reputation module and path manager ensures avoiding the route through selfish nodes. Finally, selfish node isolation is used to isolate nodes that are below the reputation threshold.
# Recent Advances in Research from Nanoparticle to Nano-Assembly: A Review
## 3 Stimuli Dependent Techniques
### Physical Stimuli-Based Self-Assembly of Nanomaterials
#### 3.2.1 Light-Induced Self-Assembly
For instance, NRs show two plasmonic resonances that correspond to the oscillation of electrons in the longitudinal and transverse modes. Therefore, by controlling the polarization of the trapping laser, the orientation of NRs can direct the assembly of anisotropic NPs [97]. The precision and SA of metallic plasmonic NPs via the optical tweezers technique capitalizes on the significant advancements made in various applications; in particular, it creates hot-spots for light energy-harvesting applications, bioimaging, fabrication of data storage devices, and plasmonic meta-materials [10]. However, phototoxicity, surface adhesion, non-uniform force distribution, and the photothermal effect limits its application [98]. Optical tweezers generate significant heat when high-intensity laser lights were used during SA, resulting in damage or denaturation of sensitive biological samples or plasmonic materials [99]. A design for the fine-tuning and manipulation of the SA approach with reduced photo-thermal effect is beneficial when dealing with the SA of sensitive plasmonic metallic NPs and biological samples. Recently, C-shaped nano engraving has been used to reduce the photothermal effect and manipulate the NPs [100]. By controlling the polarization of the incident light, using C-structure engravings modulates plasmonic resonance and that helps to dynamically control the electric field, trapping more forces and plasmonic heating compared to the traditional method.
## 5 Simulation Results and Analysis
### Simulation Design  
We simulate a simple network consisting of two ICN edge nodes (one for the source and one for the destination), a name resolution system, and multiple multipath service nodes. They are connected by links, where the network metrics can be arbitrarily set according to different simulation requirements. Adjustable network indicators include delay, bandwidth, packet loss rate, etc. The topology we use is a multipath topology, as shown in Figure 4. The topology of Figure 4 represents the infrastructure of the future Internet, and there are multiple completely disjoint paths between Internet service providers (ISPs). To further evaluate the performance of our proposed node selection algorithm, we use the topology shown in Figure 5, which contains three different AS domains. By default, the link capacity of the two topologies is 10 Mbps, and the link delay is 10 ms.Then the corresponding network indicators are changed as needed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Meaning** & **Symbol** & **Meaning** \\ \hline M & List of multipath service node sets & \(p_{0}\) & Average packet loss rate \\ K & List of optimal candidate node sets & \(\gamma\) & Influence factors of packet loss rate on path state \\  & Influence factors of bandwidth on path state & KTag & Tag value list of nodes in K \\  & Influence factor of round-trip delay on path state & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The meaning of symbols.