esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mobile Communications Background
### IMT 2020 Capabilities  
It is critical to remember that each of the use cases addressed by the IMT-2020 has its own set of technical performance requirements. The fundamental design concepts of the IMT-2020 include flexibility and diversity, making it suitable for established and newly emerging UCs. The IMT-2020 is intended to provide better services than IMT-advanced (4G) in terms of peak data speeds, latency, mobility, connection density, and even network energy usage. For instance, IMT-2020 should provide a maximum attainable data rate of not less than 10 Gbit/s for an eMBB scenario that is more concerned with meeting users' needs for high data rates, and this may even climb to 20 Gbit/s in extreme cases, unlike the LTE that guarantees just 1 Gbps. In contrast to LTE systems, which offer 10 ms of latency, 5G systems will offer a latency of no more than 1 ms to be able to meet applications with extremely low latency needs, such as remote surgeries. In the work of [PERSON] [53], the key capabilities of the 5G NR and LTE systems were carefully considered on the basis of the relevant technical parameters. The work also suggested that these technical parameters may be updated further in later ITU-R recommendations to meet future targets, as they are based on research for IMT-2020 [53; 54].
# Data Privacy in the Internet of Things: A Perspective of Personal Data Store-Based Approaches
## 3 Data Privacy in the Internet of Things
### Information Privacy Protection
#### 3.3.1 Privacy Awareness  
In the literature, we find studies indicating that individuals are unaware of issues related to their privacy and carelessly disclose their personal data without noting the potential risks. They do not perceive that third parties can collect and share data with others for unauthorized and unknown purposes [37]. [PERSON]'s [38] vision of privacy awareness relates to enabling individuals to make informed decisions. It involves an awareness of who can access their data and how they might be processed and shared. He understands it to improve users' awareness and balance the privacy paradox [13] by helping people evaluate the benefits of sharing their personal data.  
Users' privacy awareness has been the focus of proposed improvements for years. Based on a literature review, [PERSON] and [PERSON] [37] proposed a Profile for Privacy-Aware Users encompassing a set of attributes that define a user who is effectively aware of and responsive to privacy concerns. They suggest that this profile could guide the development of technological initiatives to increase privacy awareness. For this, they developed their profile outlining five primary attributes: (i) awareness of privacy risks and consequences, (ii) knowledge of privacy policies and laws, (iii) empowerment to protect privacy (including privacy control and practical skills), (iv) informed decision-making, and (v) situational awareness of privacy.  
[PERSON] et al. [39] conducted online surveys and found moderate user awareness levels. They noted that younger individuals recognize the need for formal education on information privacy. Thus, they proposed a teaching tool to improve participants' knowledge of privacy protection methods, such as end-to-end encryption and two-factor authentication (2 FA).  
Other researchers have proposed tools and frameworks to increase users' privacy awareness, such as the YourPrivacyProtector [40] tool, which uses machine learning to categorize users based on their privacy awareness level and provides personalized privacy protection recommendations. The AppAware [41] allows users to visualize the permissions of installed mobile applications, making potential privacy risks more understandable. [PERSON] et al. [42] proposed a framework to address privacy concerns through an educational game to increase user privacy awareness related to excessive data sharing and encourage more cautious online behavior.
## 3 Analysis of the Selected Case Studies
### Case Study Selection  
The research was based on the deductive analysis method, through analyzing previous studies and research to determine the case study related to traditional natural climate adaptation. Four residential districts were selected from traditional cities, considering their climatic and spatial variation. The case study's functional dimension was restricted to the old residential district, in order to achieve the research objectives. The spatial dimension was considered to include cities that are far apart, such as the east, west, north, and south of West Asia, and within hot-dry and semi-arid regions.  
The case study areas were selected as follows:  
Figure 1: Methodology steps.  
1. A residential district, old Yazd; ii. A residential district, Al-Kadhimiya region, Baghdad; iii. A residential district, Old Damascus; and iv. A residential district, Sana'a old city.  
The rationale for this choice lies in covering the various patterns of the urban fabric of traditional cities in West Asia, under the same natural conditions, and given the fact that the old districts in the four cities still retain most of the characteristics of the physical and traditional form. They have been continuously inhabited for several centuries, emphasizing the long-term fulfillment of people's social requirements, even achieving the required adaptation, despite the climatic and social differences throughout this long period.
# THz Rectennas and Their Design Rules
## 5 Power Level
### Polarization  
Contrary to the widespread theoretical framework that deals with the electromagnetic radiation as a vectorized quantity, it is assumed that radiation of a black body is randomly polarized. Therefore, the formation of the EMCF function studied above is based on the assumption of unpolarized sources, which may hold true for solar radiation. At the same time, many recent experiments on rectennas show a polarization-dependent response. This has two major outputs: (a) this supports the hypothesis that their response is due to AC rectification, not a bolometric effect, as will be shown later, and (b) it can be said that the output assuming an unpolarized source will be different from that from a fully- or partially-polarized source [94]. The experiments in support of the polarization-dependent response of the rectenna are many, such as those of [45; 95; 96]. [PERSON] et al. [96] have experimentally ensured the polarization-dependent response of the rectenna system reaching a polarization ratio of seven times in their experiment. [PERSON] et al. [28] have also concluded a polarization-dependent response of the rectenna system at 30 THz, but it should be noted that it was assumed to be composite, i.e., resulting from both tunneling and thermal effects.
# Bandwidth Density Analysis of Coded Free-Space Optical Interconnects
## 6 Conclusions  
In this paper, we have analyzed the bandwidth density (BD) of an FSOI system employing arrays of laser sources and detectors with and without coding. Moreover, the effect of reducing the SNR for a given bit error rate via coding to improve the BD has been demonstrated. Particularly, we have studied the impact of utilizing Reed-Solomon error correction codes to improve the BD of the FSOI system model under the operation of the fundamental- and higher-order modes of the laser beam. The BD performance has been analyzed in the presence of both crosstalk and thermal noise sources. The provided numerical results have shown that the BD was significantly improved due to coding at the optimum code rate of each different investigated codeword length. The ideas presented in this paper can be expanded to comprise microlenses in the FSOI system's design in order to mitigate the crosstalk noise. Moreover, other kinds of coding schemes can be investigated.  
**Author Contributions:** Conceptualization, H.A.A. and N.K.A.-A.; Formal analysis, H.A.A., N.K.A.-A. and A.A.A.; Investigation, H.A.A., N.K.A.-A. and A.A.A.; Methodology, H.A.A., N.K.A.-A. and A.A.A.; Project administration, H.A.A. and N.K.A.-A.; Software, H.A.A., N.K.A.-A. and A.A.A.; Supervision, N.K.A.-A.; Validation, H.A.A., N.K.A.-A. and A.A.A.; Writing--original draft, H.A.A.; Writing--review and editing, H.A.A., N.K.A.-A. and A.A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: BD versus channel spacing without and with using RS-ECC of codeword lengths (n) 15, 31, 63, 127, and 255 for (**a**) TEM\({}_{00}\), (**b**) TEM\({}_{01}\), (**c**) TEM\({}_{10}\), and (**d**) TEM\({}_{20}\) modes.
# Transfer Reinforcement Learning for Combinatorial Optimization Problems
## 3 Methodology
### Reinforcement Learning Model  
The RL system developed was designed to apply the SARSA algorithm to experiments with combinatorial optimization problems: the ATSP and SOP. For this purpose, the model structure (actions, states and reinforcements) was adopted based on works in the literature [29; 30; 31; 37; 76]:  
* **States** are the locations that must be visited to form the route. Thus, the number of states varies according to the number of nodes (\(N\)) in the instance.
* **Actions** represent the possible movements between locations (states). The initial number of actions is equivalent to the number of states in the model. However, the actions available for execution vary according to the cities already visited when developing the route.
* **Reinforcements** are the cost of travel (\(c_{ij}\)) between the departure city (\(i\)) and the destination city (\(j\)), given as a function of the cost of travel (\(c_{ij}\)). The greater the distance between the nodes, the more negative the reinforcement will be, according to Equation (9): \[R=-c_{ij}\] (9) Also, the RLSOP algorithm [37] was adopted to deal with the precedence restrictions of the SOP. The RLSOP (Algorithm 2) checks whether the actions (arrival location) selected by the \(\epsilon\)_-greedy_ method have precedence restrictions. If this is the case, then another destination is selected and checked again.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
0 & 3 & 5 & 48 & 48 & 8 & 8 & 5 & 5 & 3 & 3 & 0 & 3 & 5 & 8 & 8 & 5 & 1,000,0  
```
1a_t = e-greedy();
2 cont = 0;
3 whilecont == 0 do
4 ifthere are precedence restrictions for the selected actionthen
5 ifat least one action corresponding to the precedence constraints of a_t has not yet been selectedthen
6 cont = 0;
7 else
8 cont = 1;
9
10 end if
11 ifcont == 0 then
12 remove the action a_t from the list of available actions at time t;
13a_t = e-greedy();
14
15 end if
16
17 end if
18
19 Return to_t;
```  
**Algorithm 2**Algorithm for analyzing precedence restrictions in the selection of actions from RL to SOP (RLSOP) [37]
###### Abstract  
The present study uses a method to address microvibrations effects on an optical satellite by combining simulations and experiments based on high-precision acceleration sensors. The displacement and angular displacement of each optical component can be obtained by introducing flywheel perturbation data from a six-component test bench to the finite element model of the optical satellite. Combined with an optical amplification factor inferred from the linear optical model, the pixel offset of the whole optical system is calculated. A high accuracy and broad frequency range for a new microvibration measurement experimental system is established to validate the simulation. The pixel offset of the whole optical system can be measured by testing the acceleration signals of each optical component and calculating optical amplification factors. The results are consistent with optical imaging test results, indicating correctness of the experimental scheme and the effectiveness of the simulation. The results suggest that the effect of microvibrations on a camera can be verified by using mechanical simulators instead of a whole optical camera for the experiment scheme, which is demonstrated to be an effective way for increasing efficiency in jitter measurements.  
microvibration; optical amplification factor; acceleration sensor; jitter measurement; pixel offset +
Footnote †: journal: Sensors
# Fiducial Lower Confidence Limit of Reliability for a Power Distribution System
## Materials and Methods
### LCL Analysis of Reliability for a Typical PDS
The lifetime of the DC/DC was assumed to follow an exponential distribution with a failure rate \(\lambda_{D}=96.3\times 10^{-9}\). The reliability of this subsystem with a hot standby subsystem at time \(t\) is  
\[R_{DD}(t)=1-\Big{(}1-e^{-\lambda_{D}t}\Big{)}^{2}. \tag{16}\]  
In general, there are a DC/DC, TM/TC and at least one SSPC in an intelligent PDS. First, one SSPC was considered for simplicity. Therefore, the reliability of the PDS with one SSPC subsystem at time \(t\) is  
\[ \begin{split} R(t)&=R_{DD}(t)R_{TT}(t)R_{SS}(t)\\ &=\Big{[}1-\big{(}1-e^{-\lambda_{D}t}\big{)}^{2}\Big{]}e^{- \lambda_{T}t}(1+\lambda_{T}t)\\ &\cdot\Bigg{\{}1-\Bigg{[}1-\exp\Bigg{\{}-\Big{(}t\exp\Big{\{} \frac{E_{1}}{E_{2}}S-\overline{X}\Big{\}}\Big{)}^{\frac{E_{2}}{S}}-\frac{t}{2 T}E-\sum\limits_{i=3}^{6}\lambda_{Si}t\Bigg{\}}\Bigg{]}^{2}\Bigg{\}}.\end{split}  \tag{17}\]  
The reliability of the entire PDS with N SSPC subsystems at time \(t\) is  
\[ \begin{split} R_{N}(t)&=R_{DD}(t)R_{TT}(t)[R_{SS}( t)]^{N}\\ &=\Big{[}1-\big{(}1-e^{-\lambda_{D}t}\big{)}^{2}\Big{]}e^{- \lambda_{T}t}(1+\lambda_{T}t)\\ &\cdot\Bigg{\{}1-\Bigg{[}1-\exp\Bigg{\{}-\Big{(}t\exp\Big{\{} \frac{E_{1}}{E_{2}}S-\overline{X}\Big{\}}\Big{)}\Big{\}}^{\frac{E_{2}}{S}}- \frac{t}{2T}E-\sum\limits_{i=3}^{6}\lambda_{Si}t\Bigg{\}}\Bigg{]}^{2}\Bigg{\}} ^{N}.\end{split}  \tag{18}\]  
In this subsection, the LCLs of reliability for the SSPC and PDS were formulized. The detailed calculation process is given in the next section. Figure 5: The reliability block diagram of the SSPC.
## 3 General Opportunities, Challenges, and Applications
### Main Challenges
#### 3.3.2 Threats and Adversarial Attacks  
Most digital systems have vulnerabilities, and FL, despite its advanced architecture, is not exempt. FL faces risks from adversarial attacks [52; 53]. These attacks are sophisticated attempts to deceive or manipulate the learning process. The decentralized nature of FL makes it imperative to have robust defense mechanisms in place. Techniques such as secure function evaluation, homomorphic encryption, and differential privacy can be applied in federated settings to provide privacy and combat threats. These methods offer potential defenses [2].
## 3 Determination of Input Parameters and Neuron Number of the Hidden Layer
### Determination of Neuro Number \(K\) in the Hidden Layer  
For neural networks with a single hidden layer, the optimal prediction model can be easily determined based on the method proposed above for a given number of neurons of the hidden layer. Therefore, the number of neurons \(K\) in the hidden layer is an important parameter to be determined. When the number of neurons in the hidden layer is equal to the number of samples in the training set, the neural network can approach the output value of the training sample with zero residual errors for any weight and threshold of the input and hidden layers [29]. However, the generalization performance is the worst in this case. To reduce the computation cost and increase the generalization performance of the model, the number of neurons in the hidden layer is generally smaller than the number of training samples.  
To determine the proper neuron number \(K\) in the hidden layer, the cross-validation (CV) method is adopted. CV is an effective method for evaluating model performance, which can avoid the occurrence of overlearning and under-learning in neural network models. In this method, the training dataset is divided into \(\mathbf{L}\) groups, and each subset of data will be used as a verification dataset. With the rest of the subset data adopted as a training set, the neural network model is obtained, and the mean squared residual errors of the predicted flight loads of the verification set will be used as an indicator. The number of neurons in the hidden layer that corresponds to the minimum mean squared residual errors will be adopted. The flowchart of this process is shown in Figure 5.  
Figure 5: Flowchart of determining proper neuron number \(K\) in the hidden layer.
# Experimental/Numerical Evaluation of Steel Trapezoidal Corrugated Infill Panels with an Opening
## 3 Results
### Ductility Ratio and Energy Dissipation Capacity  
Table 2 presents the ductility ratio values of the studied specimens. These values were determined using the envelope of the force-displacement curves due to cyclic loading. The real force-displacement curve was idealized to a bilinear curve using the Lung method [35] (see Figure 16), and the ductility ratio value was calculated (\(\mu=\Delta_{\text{max}}/\Delta_{\text{y}}\)). As specified in Table 2, the ductility ratio of both specimens with an opening is almost equal to 61% of the ductility ratio of the specimen without an opening. The amount of energy dissipated during cycles with displacements of 70 and 80 mm was determined and is presented in Table 2. Regarding Table 2, the amount of energy dissipated in these cycles for the specimen without an opening and the specimen with two openings, respectively, is 21% and 8% larger compared to the specimen with one opening.  
Figure 17 shows the cumulative dissipated energy at each drift angle for both tested specimens. The cumulative dissipated energies are determined by the summation of surrounding areas in cyclic loops. Referring to Figure 17, the amount of energy absorption is the same for both specimens up to the drift angle of 5%. Furthermore, the amount of energy absorption of the second specimen is almost 6% larger than the first specimen for the drift angles larger than 5%.
## 1 Introduction
### Proposed Improvements  
To prepare an optimal, vocabulary-based training dataset distribution for multilingual OCR training, there are several aspects and approaches that need to be taken into consideration, as follows:  
1. _Dynamic Dataset Augmentation_[24; 25]: The training dataset should be continually updated and expanded to include new text samples (using the Sobel filter [26]), variations in language, and emerging writing styles. This can be achieved through techniques such as synthetic data generation [27], web scraping, and crowd sourcing. Dynamic dataset augmentation ensures that the OCR system remains accurate and up-to-date with evolving language trends.
2. _Adaptive Vocabulary Selection_[28]: To cater to the diverse nature of languages and scripts, an adaptive vocabulary selection method is crucial. This approach enables the identification and selection of the most relevant and frequently occurring words, characters, or n-grams in each language, providing an optimal basis for training the OCR system.
3. _Domain-specific Adaptation_[29]: To enhance the OCR system's performance in specific industries or use cases, the training dataset should be enriched with domain-specific data. For example, incorporating legal, medical, or technical jargon can help improve the system's performance in those respective fields.
4. _Cross-lingual Pretraining_[30]: Leveraging transfer learning from a pretrained multilingual model [31] can aid in raising OCR system's efficiency for low-resource languages. By training on a large, diverse, and high-quality dataset, the OCR model learns to identify common patterns and structures across different languages, leading to better generalization.
5. _Data Imbalance Mitigation_[32]: A balanced dataset (Figure 2) is essential in training a robust multilingual OCR system. This involves addressing class imbalances by oversampling underrepresented classes or undersampling over-represented classes. Additionally, incorporating data augmentation techniques [33] such as rotation, scaling, and noise addition can help increase the variability of the training data. In the case of multilanguage training involving Japanese, English, and Hungarian at the same time, the character occurrence frequency must be evaluated per domain and per targeted solution.
6. _Evaluation Metrics and Benchmarks_[34]: Establishing appropriate evaluation metrics and benchmarks for multilingual OCR systems [35] is vital for assessing performance, identifying areas for improvement, and facilitating model comparison. These metrics should include character- and word-level recognition rates, language identification accuracy, and domain-specific evaluation measures.
7. _Multimodal Learning_[36]: Incorporating additional contextual information such as domain-specific metadata can improve the OCR system's ability to recognize text within different contexts. Multimodal learning enables the model to leverage multiple sources of information to enhance its understanding of the text.
## 6 Performance Evaluations
### Simulation Results
#### 6.3.5 Energy Balance Evaluation  
A further series of experiments are carried out in this part with the purpose of evaluating the suggested technique in terms of energy balance, EIF. The proposed approach is compared to SMRP [15] and EERP [23] for the scenarios mentioned earlier. During running time, The EIF was calculated to demonstrate the effectiveness of the proposed algorithm in achieving energy balance. The average traffic rate is set to three packets per second, and the deadline is set at 700 ms. In addition, the number of sink nodes in the network is fixed at three nodes.  
1. Energy balance evaluation under the first scenario  
This simulation aims to study the first scenario's EIF over time. Figure 20 shows the first scenario's EIF over time. EIF grows with runtime, as shown in the figure. In a random topology, some sink nodes are deployed in regions with several sensor nodes. Some sensor nodes are only bound to certain sink nodes, resulting in an unbalanced distribution of sensor nodes. This problem affects the network's energy variance. It explains why EIF grows over time.  
Figure 20 shows that the proposed algorithm has the lowest EIF. The proposed algorithm's node energy is closer to the average than existing methods. The EIF shows that the proposed approach can effectively balance residual energy among sensor nodes. This also confirms the proposed nodes' load metric and sink load metric. SMRP and EERP routing algorithms relay data packets to balance energy consumption. According to the EIF, residual energy cannot balance energy consumption. This further confirms the proposed nodes' energy consumption and sink load metrics for improved energy balance. The proposed technique is superior to SMRP and EERP. Hence, SMRP and EERP have the same EIF performance.  
Figure 19: Influence of increasing number of sink nodes on average end-to-end delay for the second scenario.  
Energy balance evaluation under the second scenario  
This experiment studies the EIF variation over runtime for the second scenario. Figure 21 shows the variation of EIF over simulation time for the second scenario. As shown in the figure, the EIF increases with increasing the simulation time for the same reasons described before. As in this scenario, the environmental data are assumed to be outside the normal range, and a number of routing paths are much more likely to be cut off due to environmental reasons. Therefore, the data transmission becomes more and more concentrated, further aggravating the network's energy imbalance. This explains why the resulting EIF for the second scenario is worth more than that under the first scenario. However, it is clear that the proposed algorithm achieves the minimum EIF compared with the others. This is evidence that the proposed algorithm provides a more effective energy balance than SMRP and EERP routing algorithms.