esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Segmented Images
#### 2.2.2 Training Modified UNet Architecture with Segmented Images  
[PERSON] et al. modified a UNet and trained it to predict an image showing path loss distribution within an area. The training was performed with 3D Ray Tracing data at 28 GHz over areas that are 512 (m\({}^{2}\)), with the transmitting antenna at the center. Inputs to the network are terrain information, building information, and foliage information for the area under study encoded as colored images. The inclusion of Line of Sight (LOS) data further improved prediction accuracy, which surpassed distance dependence prediction and conditional least squares. The model is named FadeNet, and it demonstrated faster prediction than 3D Ray Tracing [30].  
[PERSON] et al. trained a UNet model named PLNet. The inputs are images representing the distribution of parameters, clutter, terrain, building, frequency, antenna height, etc., within the region of interest, and the output is an image showing path loss distribution. Predictions by PLNet outperformed those of empirical models and 3D Ray Tracing [31]. The authors did not specify the range of parameter values for which the model is valid. The models in [30; 31] are based on UNet, and the output is a segmented image of the path loss distribution. Such a model can only be used for analysis rather than design as numeric path loss values are required.
## 1 Introduction
Furthermore, designing adequate server consolidation techniques can result in further power saving. Contribution: This paper attempts to address this challenge by using ideas from reinforcement learning techniques and designing a novel server consolidation technique. The resource management system performs allocations that optimize both profit (value) and energy using a combination of light-weight heuristics and historic run-time results. The system considers the scheduling problem as a Multi-Armed Bandit (MAB) model where each individual job allocation is a possible action. The approach uses a novel algorithm, inspired by the Upper-Confidence Bound technique [26], collects profiling information at run-time (exploration) to optimize future allocations of jobs (exploitation). We also propose a network-aware approach to server-consolidation called Bandwidth Constrained Consolidation (BCC) and study its impact on a data center. While consolidating tasks can reduce the power consumption of data centers, due to the arrival of new tasks and completion of existing tasks, the consolidated utilization profile of the servers may change adversely affecting the power consumption over time. Hence, the BCC consolidation algorithm should be repeated periodically. Hence we also propose a method to find the optimal inter-consolidation time for a data center and derive a mathematical formulation to estimate the optimal inter-consolidation time. This will enable optimally scheduling consolidation in a data center without the need for extensive simulations and measurements to achieve the optimality. Paper Organization: Section 2 presents work related to this paper. Section 3 introduces the problem, including the definition of a job and its value, the MAB model, and the model of HPC systems. The novel, reinforcement learning-based approach is introduced in Section 3.4. In Section 4 we introduce the network aware server consolidation technique, traffic model, our proposed algorithm, and mathematical model to estimate optimal inter-consolidation time. Experimental results are presented in Sections 5 and 6 concludes the paper.
## 5 Results and Analysis
### Impact of Training Algorithms  
Multi-layer DNN training algorithms, such as traingdx and traincgp, play a vital role in shaping the learning process and influencing the DNN's performance [20; 21].  
In one experiment, the neural network architecture comprised three hidden layers with 300, 200, and 100 neurons, trained for 500 epochs using the Polak-Ribiere Conjugate Gradient (traincgp) algorithm. The training and testing datasets remained the same (one million and ten thousand samples, respectively).  
Figure 11 presents the performance of the traincgp function. The SER values increased across all SNR levels, indicating a rise in errors. Conversely, the training state (Figure 10(b)) reached a very low gradient (2.2367 \(\times\) 10\({}^{-8}\)) and stopped early at epoch 78, suggesting potential underfitting. The validation performance (Figure 10(c)) also confirms this, with the best validation performance being relatively high (5.2811 \(\times\) 10\({}^{-8}\)). The error histogram (Figure 10(d)) depicts the distribution of errors across different ranges.  
Figure 11: Performance curve of DNN model illustrating the relationship between SER and SNR during the application of traincgp.  
In a separate experiment, the Variable Learning Rate Back-Propagation (trainingdx) algorithm was employed with the same network architecture and dataset size (three layers, 300-200-100 neurons, one million training samples, and ten thousand testing samples) and trained for 500 epochs.  
The results in Figure 12 show that the traingdx function achieved lower SER values compared to the traincgp function across all SNR levels. This indicates better performance in terms of symbol detection accuracy. The training state (Figure 12b) reached a slightly higher gradient (5.9796 \(\times\) 10\({}^{-5}\)) and stopped earlier at epoch 327, again suggesting potential underfitting. The validation performance (Figure 12c) achieved a slightly better value (5.2832 \(\times\) 10\({}^{-5}\)) compared to the traincgp function. The error histogram (Figure 12d) provides insights into the distribution of errors.  
Figure 12: Performance curve of DNN model illustrating the relationship between SER and SNR during the application of traingdx.
## 3 Program Design
### WD-Based Multiparameter Component Assessment Model
#### 3.1.1 WD-Based Single Parameter Health Calculation
The temporary increase in the similarity of current data around day 2050 causes the SOC results to be temporarily unavailable, which needs to be considered together with the HDDD based on the MMD to ensure that no erroneous results occur. Therefore, the method proposed in reference [15] needs to consider these two indicators at the same time. These two indicators complement each other in performing a reasonable and complete component health assessment. However, the HDDD based on the WD approach does not suffer from these two problems. Using only one HDDD based on the WD indicator, the health status of each component can be assessed in a timely, accurate, and comprehensive manner. Thus, the new method solves the problem of the difficulty in rationally integrating multiple indicators for decision making. The WD has several important advantages over traditional distance metrics. Firstly, the WD can handle nonoverlapping or partially overlapping distributions, which traditional methods often struggle with. This allows for more comprehensive comparisons in various scenarios. Secondly, the WD has better robustness to outliers and noise and can better accommodate anomalies in real data [39; 40]. Most importantly, the WD has better interpretability compared with the results of the MMD and can more accurately describe the degree of deviation from the component state.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 3 Coverage and Capacity Evaluation
### Communication Time between the Satellite and GST(\(\tau\)(i))  
The communication time \(\tau(i)\) between the satellite and the GST depends on the link budget, and the link budget depends on the sensitivity of the receiver, elevation angles, antenna gains and losses such as free space path loss (FSPL), polarization loss, and ionospheric loss. These parameters are first determined through the ground tests using the designed payload and GST, and the communication time is derived using the parameters.
## 3 Results
### Heart Rate Variability  
The HRV indices (LF, HF, and LF/HF) before and after the EF and sham treatments are presented in Table 1. For the LF component, the two-way repeated-measure ANOVA indicated that there were no significant effects of the Time factor (before vs. after), the EF factor, or their interaction (F(1, 11) = 1.970, \(p\) = 0.1881; F(1, 11) = 0.5987, \(p\) = 0.4554; F(1, 11) = 0.8219, \(p\) = 0.3841, respectively). For the HF component, no significant effects of the Time factor (before vs. after), the EF factor, or their interaction were found (F(1, 11) = 0.5172, \(p\) = 0.4870; F(1, 11) = 0.2256, \(p\) = 0.6441; F(1, 11) = 3.299, \(p\) = 0.0966, respectively). For LF/HF, no significant effects of the Time factor (before vs. after), the EF factor, or their interaction were found (F(1, 11) = 3.503, \(p\) = 0.0881; F(1, 11) = 0.003398; \(p\) = 0.9546; F(1, 11) = 3.680, \(p\) = 0.0814, respectively). No significant changes in the HRV indices were observed after the EF treatment in the present study.  
The correlation between the HRV indices (LF, HF, LF/HF) and SC indices (SC-SFs, SCL) was assessed before and after the EF and sham treatments. The Spearman correlation coefficients indicated no statistically significant correlations between these indices (\(p\) > 0.05).  
Figure 5: Heart rate before (filled column) and after (open column) the EF or sham treatments. The means and standard deviations (vertical bar) for the 12 subjects are presented. Two-way repeated-measure ANOVA indicated the effect of the Time factor (before vs. after, an asterisk). No significant effects of the EF factor (EF vs. sham) or the interaction of the Time and EF factors were observed.  
Figure 6: Relationship of the heart rate with skin conductance spontaneous fluctuation (**A**) and with the skin conductance level (**B**) before and after the EF and sham treatments. Filled circles indicate individual data. Linear regression lines are presented in this Figure to show data with statistical significance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**EF**} & \multicolumn{3}{c}{**Sham**} \\ \cline{3-10}  & & \multicolumn{3}{c}{**Before Treatment**} & \multicolumn{3}{c}{**After Treatment**} & \multicolumn{3}{c}{**Before Treatment**} & \multicolumn{3}{c}{**After Treatment**} \\ \cline{3-10}  & & **Mean** & **s.d.** & **Mean** & **s.d.** & **Mean** & **s.d.** & **Mean** & **s.d.** \\ \hline LF & ms\({}^{2}\) & 91.4 & 91.9 & 186.9 & 263.0 & 94.6 & 72.6 & 138.0 & 226.0 \\ \hline HF & ms\({}^{2}\) & 244.3 & 158.0 & 219.8 & 182.0 & 175.8 & 142.4 & 232.2 & 213.4 \\ \hline LF/HF & & 0.42 & 0.30 & 1.12 & 1.25 & 0.73 & 0.41 & 0.79 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Heart rate variability indices before and after the EF and sham treatments.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 4 Current Research Trends, Challenges, Solutions, and Future Research Directions
### Challenges, Solutions, and Future Research Directions
#### 4.2.1. Challenges in Hybrid Beamforming for Millimeter-Wave MIMO Communications  
* **Hardware Complexity and Cost:** Implementing hybrid beamforming requires a balance between digital and analog components which can increase the hardware complexity and associated costs. This could be a significant obstacle for widespread adoption.
* **Beam Alignment:** At millimeter-wave frequencies, the beam widths are very narrow, making accurate beam alignment critical and challenging, particularly in mobile scenarios.
* **Channel Estimation and Feedback:** Accurate channel information is crucial for effective beamforming. The high dimensionality of MIMO systems at millimeter-wave frequencies makes channel estimation and feedback more complex.
* **Interference Management:** In dense networks, managing interference between multiple beams and users without significant degradation of the signal quality is challenging.
* **Power Consumption:** Higher-frequency operations could lead to increased power consumption, posing challenges for battery-powered devices like UAVs and mobile handsets.
* **Integration with Existing Systems:** Ensuring compatibility and coexistence with legacy systems is important for a smooth transition and requires careful design and standardization.
* **Regulatory and Standardization Issues:** As millimeter-wave technologies are still developing, regulatory frameworks and standardization efforts have to keep pace to ensure interoperability and efficient spectrum use.
## 4 Methodology and Prototype Implementation
### Establishing a Transparent Data Tunnel  
In addition to the physical setup, the systems of the aEMP and the EMS must be implemented. Both systems are developed and implemented in different versions for each of HKS3-5. The applications are implemented in Python using the module pyOpenSSL for operating TLS. PyOpenSSL is a wrapper for using the popular OpenSSL library. The free software implements the TLS protocol and a variety of cryptographic functions. For the aEMP, in all three scenarios, HKS3-5, a TLS server must be present. The aEMP communicates with the SMGW via the protocol TLS1.2 using cipher suite \"TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256\". The certificates signed by the sub-CA, and the sub-CA certificate to authorize the certificate chain, must be secured in the system of the aEMP. Regarding the EMS, while HKS4 and HKS5 both also require a TLS server, HKS3 needs a client application in which the SOCKSv5 protocol is implemented. Within the SOCKSv5 protocol, the authentication method \"TLS\" must be used according to ([6], p. 61f). This method further requires a specific sub-negotiation process according to \"SSL for SOCKSv5\" protocol [37]. The TLS connection established within the SOCKSv5 protocol uses cipher suite \"TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384\". We did not find any SOCKSv5 implementation for Python incorporating this sub-negotiation, so both protocols are self-implemented(see Supplementary Materials). In this process, firstly, the CLS connects via SOCKSsv5 to the SMGW and requests authentication using \"SSL for SOCKSv5\". Secondly, a TLS tunnel is established, and therefore, the following communication is encrypted. Finally, the CLS requests a connection to the aEMP. The whole communication sequence is implemented following the requirements specified in ([6], p. 61).
## 5 IETF ROLL Working Group
### Research Challenges
#### 5.4.3 Mobility  
Currently RPL does not support node mobility. By supporting mobile nodes, the protocol can also be used for other application domains such as monitoring and safety systems for diverse traffic situations. In the field of mobile nodes in an RPL network, research activities are just starting. This means that lots of problems still have to be solved.  
In [80] a multipath routing protocol for mobile sensor networks is presented. The protocol, called DMR, is based on the RPL framework. The performance of the protocol was compared against AODV and AOMDV by simulations in the NS-2.34 simulator. An improvement of energy efficiency with 25% and 20%, compared to AODV and AOMDV is achieved, while maintaining a delivery ratio of more than 97%.  
Because a vehicular environment incorporates design elements of RPL, [81] provides a simulation performance study of adaptations of RPL for VANETs, by tuning different parameters of RPL. The test setup consists of nodes traversing in a line with an interdistance equal to the transmission range. The sink is positioned in the middle of the line. Information is transferred by multi-hopping with neighboring moving nodes.  
In [82] adaptations are presented to enable data collection of a mobile node traversing, in a random pattern, in an environment of fixed nodes, running standard RPL. This is achieved mainly by having the mobile node continuously monitor his parents and neighborhood.  
The selection and evaluation of the preferred parent, for mobile nodes, is still an open issue. For mobile nodes the link quality and neighboring nodes will vary due to the movement of the node. Therefore constant discovery of new neighbors and constant link analysis will be necessary. Because estimations are based on measurements, they also include an estimation of the past link quality. For fixed networks this estimation will be strongly correlated with the current link quality. For mobile nodes this estimation is typically outdated. An adaptive configuration for link estimation could help solve this problem, for instance by taking into account the movement (direction, speed,...) of the node, by reasoning on the succession of estimations (increasing values can indicate a node is approaching a parent or decrease of values a the opposite) or by adapting monitoring and/or discovery of neighbors.  
Choosing a parent is important for reliably sending and receiving information. For fixed networks the parent with the best (stable) link quality is indeed the best choice. For mobile nodes, switching too frequently to a different parent also influences the reliability and energy consumption (extra control traffic for downward traffic). Therefore a selection of a parent which will be a good parent for a longer time can be a better choice.
# A Survey on Shortest Unique Substring Queries
## 1 Introduction
They showed how to construct a data structure of size \(\mathcal{O}(m+\pi_{\mathrm{s}}(N,m))\) in \(\mathcal{O}(m\log m+\pi_{\mathrm{c}}(N,m))\) time that can answer interval-\(\mathsf{SUS}\) queries in \(\mathcal{O}(\pi_{q}(N,m)+occ)\) time, where \(occ\) is the output size, and \(\pi_{\mathrm{s}}(N,m)\), \(\pi_{\mathrm{c}}(N,m)\) and \(\pi_{q}(N,m)\) are the size, construction time and the query time for a predecessor/successor query on m elements from a universe of \([1,N]\). In 2017, [PERSON] et al. [3] proposed the _approximate_ version of \(\mathsf{SUS}\) queries, where mismatches are allowed. This version can be applied in computational biology, where factors such as genetic mutation and experimental error make approximate string matching necessary [9]. They presented an in place algorithm for both exact and approximate versions of the problem. Afterwards, different trade-offs have been presented for _k-mismatch_\(\mathsf{SUS}\) problem [9; 10; 15; 16]. In 2018, [PERSON] et al. [4] proposed a _palindromic_ variant of interval \(\mathsf{SUS}\) problem. A palindromic string or substring is an important structure in DNA, RNA or protein sequence analysis [22]. In biology data, palindromic structures show the ability of molecules to fold and form double-stranded stems [23]. Given a string \(\mathcal{S}\), a _shortest unique palindromic substring_ (\(\mathsf{SUPS}\)) for an interval \([s,t]\) of \(\mathcal{S}\) is the shortest substring that is palindromic and unique in \(\mathcal{S}\) which contains \([s,t]\). For solving the interval \(\mathsf{SUPS}\) problem, they preprocess \(\mathcal{S}\) in \(\mathcal{O}(n)\) time and space to output \(\mathsf{SUPS}\)s in \(\mathcal{O}(occ+1)\) time. There are other works on \(\mathsf{SUPS}\) queries by [PERSON] et al. [11; 12] based on the \(RLE\) which are space economical solutions. In 2019, [PERSON] et al. [5] focused on the _range_ version of \(\mathsf{SUS}\) queries (_Range-\(\mathsf{SUS}\)_) and generalized the problem. Range queries are a classic data structure topic, which has a great motivation in string processing problems [5; 24; 25; 26]. Given a range \([\alpha,\beta]\), the problem is to return a shortest substring with exactly one occurrence in \([\alpha,\beta]\). They presented an \(\mathcal{O}(n\log n)\)-word data structure which answers \(\mathsf{rSUS}\) queries in \(\mathcal{O}(\log_{w}n)\) time per query in the word RAM model, where \(w=\Omega(\log n)\) is the word size [5]. In this survey, we are going to discuss all approaches mentioned above on \(\mathsf{SUS}\) queries. The focus of this work is on techniques applicable to \(\mathsf{SUS}\) queries and is to compare all the main results in terms of complexities, restrictions, problem definitions, motivations, and applications. The main papers that we are going to focus on have been categorized in Table 1. In the last section, we discuss the related open questions on the variant topics related to \(\mathsf{SUS}\) queries.
# Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular
## 5 Conclusions  
In this contribution, we disclosed a piloted proof-of-concept (PoC) of an mmWave-integrated heterogeneous network (HetNet) utilizing mmWave technologies both for backhaul and access for integration into cellular networks. In the HetNet, CP/UP splitting was implemented to allow user plane access in the mmWave small cells, while retaining a reliable control plane connection to the macro cell. Passive high-gain antenna technology in mmWave was applied for wireless backhauling of the small cells. A lens antenna array with steerable beam was used for mmWave access. This PoC was conducted to demonstrate the practicality of the mmWave-integrated HetNet. Successful CP/UP splitting between the conventional LTE and mmWave WiGig was demonstrated, and seamless connectivity was achieved between the different technologies. The high-throughput performance of the long-range backhaul link using a passive reflectarray was also confirmed. Although the mmWave access targets high-speed data communication in the first phase of 5G, the proposed architecture may be applied to future ultra-real-time and low latency sensor networks as required in autonomous driving that will be realized in the later phase of 5G. The achieved results will be contributed respectively to 3 GPP, IEEE, ITU and ETSI.  
**Acknowledgments: Part of this work has been done under a project named \"Millimeter-Wave Evolution for Backhaul and Access (MiWEBA)\" supported by Seventh Framework Programme in European Union Japan (FP7-ICT-2013-EU-Japan, Project Number 608637) and the Ministry of Internal Affairs and Communications (MIC) in Japan. The authors would like to thank all participants who took part in the MiWEBA project.**  
Figure 17: Snapshot of small cell on/off (**left**) and its energy efficiency (**right**).  
**Author Contributions:** [PERSON] designed, implemented and tested the LTE/WiGig-integrated HetNet. [PERSON] designed and implemented the PoC application. [PERSON] and [PERSON] designed the passive reflectarray and backhaul prototype. [PERSON] provided literature surveys on the latest mmWave PoC. [PERSON] and [PERSON] designed and conducted field experiments of LAA prototype. [PERSON], [PERSON] and [PERSON] led the PoC in the MiWEBA project as coordinators.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analysis or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.