esa-sceva/satcom-chunk-collection huggingface dataset:
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
## 6 Conclusions  
A regression-based approach for tracking changes in the likelihood in time-varying MIMO systems using one-bit ADCs was introduced. The proposed method utilized channel statistics to derive a channel-based likelihood probability and the decoded output to derive a sample-based likelihood probability. The optimization problem for the weighted linear combination of the two probabilities was formulated by minimizing the expected MSE. A linear regression method was applied to solve the problem, with a regularization technique  
Figure 5: FER for the proposed regression based on different detection methods in time-varying channels.  
Figure 6: MSE for the proposed regression based on different detection methods at SNR = 0 dB.  
incorporated to optimize the calculation. In particular, simulation results demonstrated that the proposed method outperformed conventional methods by effectively capturing the changes in the likelihood.  
The future direction of this study is to explore other regression techniques such as polynomial and Bayesian regressions. These advanced regression methods can improve the fit to the true likelihood probability. In addition to exploring different regression techniques, better features should be extracted from the channel model and received symbols. Features that provide more relevant information about the channel can further improve the regression performance. Further improvement will address the power consumption and computational complexity challenges associated with mmWave and THz communications in time-varying MIMO systems using one-bit ADCs.  
Conceptualization, T.-K.K.; Methodology, T.-K.K.; Software, T.-K.K.; Validation, T.-K.K.; Formal analysis, T.-K.K.; Investigation, M.M.; Resources, M.M.; Data curation, M.M.; Writing--original draft, T.-K.K.; Writing--review & editing, M.M.; Project administration, M.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) under Grant 2023R1A2C1004034, and in part by the BK21 FOUR Project funded by the Ministry of Education, South Korea, under Grant 4199990113966.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Insights into the Issue of Deploying a Private LoRaWAN
## 4 Experimental and Analytical Results
### Security Evaluation
#### 4.2.5 Security Comparison of Private and Public Network  
Due to several security imperfections in the public networks and basic specifications (mainly in 1.0 series), we assume that employing a private network may provide higher security than a public one under certain conditions. The main security benefit of using the private network is that keys are controlled and created by the end-users themselves. There is no possible danger caused by exposing encrypted data to a public operator. The specification 1.1 fixes this issue, but the problem remains if the operator employs the 1.0 series network server. Further, the developers can improve the security in their own private networks by adding security features and procedures presented in the state-of-the-art works, such as device-to-device encryption, reputation approaches, or by solving security associations between servers.  
At the same time, the larger public networks have one benefit--they can offer higher availability benefiting from multiconnectivity and presence of multiple gateways, and thus are more resistant to some kinds of attack, such as replay and denial of service attacks. A private network topology with a low number of gateways can be overwhelmed by a large amount of malicious or repeated messages. In these situations, robust public networks could be more stable and reliable.
###### Abstract
Different from the diffusion of the grouting cement in the soil below the pile tip, the research on the grout diffusion mechanism along the pile shaft during the process of pile tip post-grouting is rarely reported. For the shape of the grout bulbs below the pile tip, when the grouting mode is compaction grouting, it is generally assumed to be either a spherical shape or columnar shape, and the rationality of this assumption has been confirmed through extensive research. At the same time, there is no clear definition for the dimension and shape of the grout bulbs that the grouting cement along the pile body returned. The determination of the returned height of the grouting cement along the pile body is mainly dependent on empirical methods; even the different standards give different suggestion values of the grout returned height for the same situation [17; 18]. The shape of grout bulbs and the corresponding grouting mode along the pile body are affected by various parameters such as the grouting conditions, including the grouting pressure and grout volume [19; 20], the stress history of soil, etc. In this paper, self-developed grouting devices and an improved large-scale multi-functional interface shear apparatus that can be used to simulate the diffusion process of grouting cement along the pile body are adopted to conduct a series of indoor model tests, with three types of grouting pressure and grout volume, two types of soil stress history taken into account. By comparing the characteristics of the grouted bulbs, including the returned height along the pile shaft, the radial expansion distance, and the shape of grout bulbs, the grouting mode with different conditions was revealed. Finally, combined with the test results, a simplified model to describe the grout diffusion pattern and the corresponding grouting mode during the process of grouting cement along the pile body was proposed. The research results will improve the understanding of the diffusion process, the shape of grout bulbs, and the grouting mode of cement along the pile body during the process of pile tip post-grouting in sand.
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
###### Abstract  
The low earth orbit (LEO) mega constellation for the internet of thing (IoT) has become one of the hot spots for B5G and 6G concerns. Information-centric networking (ICN) provides a new approach to the interconnection of everything in the LEO mega constellation. In ICN, data objects are independent of location, application, storage and transport methods. Therefore, data naming is one of the fundamental issues of ICN, and research on the data naming mechanism of the LEO mega constellation for the IoT is thus the focus of this study. Adopting a fusion of hierarchical, multicomponent, and hash flat as one structure, a data naming mechanism is proposed, which can meet the needs of the IoT multiservice attributes and high-performance transmission. Additionally, prefix tokens are used to describe hierarchical names with various embedded semantic functions to support multisource content retrieval for in-network functions. To verify the performance of the proposed data naming mechanism, an NS-3-based simulation platform for LEO mega constellations for the IoT is designed and developed. The test simulation results show that, compared with the IP address, the ICN-HMcH naming mechanism can increase throughput by as much as 54% and reduce the transmission delay of the LEO mega satellites for the IoT by 53.97%. The proposed data naming mechanism can provide high quality of service (QoS) transmission performance for the LEO mega constellation for IoT and performs better than IP-based transmission.  
LEO mega constellation; internet of thing (IoT); information-centric networking (ICN) 2022
# Continuous-Wave and Mode-Locked Operation of an In-Band Pumped Tm,Ho,LuCaGdAlO4 Laser
###### Abstract  
We investigate in-band pumping of a Tm,Ho,LuCaGdAlO4 laser using a Raman-shifted Er-fiber laser (1678 nm), in the continuous-wave (CW) and mode-locked (ML) regimes. A maximum output power of 524 mW is obtained in the CW regime with a 5% output coupler at an absorbed pump power of 2.04 W, corresponding to a slope efficiency of 27.9%. A maximum CW wavelength tuning range of 160 nm at the zero level, from 1984 to 2144 nm, is obtained with a 0.2% output coupler. In the ML regime, pumping with 5.5 W (unpolarized), the average output power (0.2% output coupler) reaches 148 mW at a repetition rate of ~96 MHz. The output spectrum is centered at 207.15 nm with a FWHM of 21.5 nm (\(\sigma\)-polarization). The pulse duration amounts to 218 fs (time-bandwidth product equal to 0.327).  
mode-locking; Ho laser; CaGdAlO4; inhomogeneous broadening; disordered crystal; mixed crystal; in-band pumping; femtosecond pulses; co-doped crystal +
Footnote †: journal: Physics Letters B
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 3 Fundamentals of Channel Estimation
### Classification of Channel Estimation Schemes
#### 3.1.1 Overview of Pilot Based (Non-Blind) CE
In decision directed CE (DDCE), training symbols are adopted for CE as well as the re-modulated detected message symbols [85; 90; 91]. In these CE schemes, CE of previous symbols is used for data detection of the current estimates after which the newly detected data are used for estimating the current channel. The detection of data in DDCE can be achieved either by hard or soft decision where bitwise detection is adopted for soft decision and a specific constellation is forced for hard decision [92]. An MSE expression for DFT based DDCE schemes in \(M\)-ary phase shift keying (MPSK) for LTE-A downlink transmission system is proposed in [93]. Simulation results from this scheme are compared with practical systems based on long term evolution-advanced (LTE-A) downlink standards. Moreover, in [94], a novel soft DDCE technique is proposed that estimates the channel of interfering signals based on demodulation reference signal (DM-RS) using virtual pilot signals (VPS) for multiuser MIMO-OFDM (MU MIMO-OFDM) based transmission scheme. In this algorithm, reliable data tones are chosen from the desired and interfering signals as VPS, after which the quality of CE is improved by using iterative detection and decoding (IDD) scheme. The proposed algorithm shows superior performance gain over the conventional approaches that adopt either single user detection (SUD) or multiuser detection (MUD) such as the conventional MMSE for both SUD and MUD. It is worth mentioning that several pilot-based (or non-blind) CE techniques with captivating results (apart from the above mentioned) have been set down in writing for SISO cyclic prefix based OFDM systems. Some of these techniques are compared and summarised in Table 3.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Aim of Research** & **CE Schemes Applied** & **Applicable Technique or Waveform** & **Approach** & **Obtained Results** \\ \hline
[86] & Time domain channel statistics pilot based CE over AWGN & LS and MMSE & Pilot-based CE for SISO-CP-OFDM & CE modifications assuming finite length impulse response & MMSE CE demonstrated superior performance over LS CE in terms of MSE and BER \\ \hline
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 6 Intrusion Detection Overview
### Providing Strong Intrusion Detection in Cloud Computing  
Due to the increasing volume and complexity of cyber-attacks happening in the cloud computing domain, there is a need for enhanced security measures to counter unwanted malicious activity from criminals intending to cause harm. Therefore, a proposed method is the adaptation of an already successful cybersecurity technology with a demonstrated history to be implemented into cloud computing architecture.  
Throughout literature [49], intrusion detection technology can be observed in the cloud computing environment. The authors integrate a network IDS into a cloud-based environment (open-source) in the paper. The solution works by designing and developing the IDS to be compatible in a cloud system that provides services according to the Platform-as-a-Service (PaaS) model and it is structured as an added service option of the clouds infrastructure. The research demonstrates the practical implementation of the different strategies of implementation. Many advantages of this solution have been highlighted throughout the research, including the ability to monitor all traffic flowing to and away from the cloud infrastructure when placing the IDS close to the cluster controller. By placing the IDS close to the physical machines, each one can focus on a smaller area of network traffic, meaning that there is a smaller likelihood of the solution being overloaded. However, it has also been highlighted in the research that the IDS has been overloaded in a given scenario, and this has left the network open to coordinated attacks that can ruin the IDS technological functionality by using specifically designed traffic before commencing the real cyber-attack.  
In addition, IDS can be noticed in the work in [50], where the authors address the current challenges and opportunities present in using IDS technology in a cloud-based environment which can be used to create successful security deployment measures. The paper highlights multiple intrusions that can affect the confidentiality, integrity, and availability of the cloud system. The authors then explore various IDS technologies deployed in a cloud environment, including network-based IDS, host-based IDS, VMM/Hypervisor-based IDS, distributed IDS. The researchers then discuss the detection techniques used, such as signature-based IDS, anomaly-based IDS, and finally, hybrid detection. They also discuss the key limitations of using IDS technology, highlighted in the lack of features, scalability, and autonomic self-adaption. IDS technology is also not deterministic, which ultimately makes them unsuitable for cloud-based environments, demonstrating a significant challenge in attempting to use the technology as a valid cybersecurity measure in the environment.
## 1 Introduction
### Literature Overview  
#### 1.2.1 Case When Forecast Errors Are Normal i.i.d  
Traditional forecasting approaches are based on points forecasts, such as simple exponential smoothing (SES), AutoRegressive Mobile Average (ARMA), AutoRegressive Integrated Mobile Average (ARIMA), etc. These methods are vital especially for short- and medium-term applications and are also known for their simplicity in practice. However, how well it approximates the demand process has often not been sufficiently tested, due to the limited repertoire of forecasting model alternatives in software.  
These methods mentioned above can be used if the demand process is perfectly identified, and therefore, the forecast errors are independent and identically distributed (i.i.d) in this case. These errors are usually assumed to be i.i.d as in [PERSON] [4], [PERSON] et al. [5], [PERSON] and [PERSON] [6], [PERSON] et al. [7], [PERSON] et al. [8], [PERSON] et al. [9], and [PERSON] et al. [10]. However, it is reasonable to ask whether a correct identification of the process is possible given the complex relationships that drive the demand. Thereby testing the i.i.d assumptions on the errors is necessary. However, the forecast in the supply chain does not take into account the deviation from these assumptions and focuses on comparing different forecast error metrics, such as the mean absolute percentage error or the mean squared error, without analyzing the residual autocorrelation and deviations from the assumed statistical distribution [11]. According to [12] and [13], the stock control metrics, such as the cycle service level, inventory investments, and backorders can be under performance if the residuals are autocorrelated and distributed differently.
# Optimization Techniques for a Distributed In-Memory Computing Platform by Leveraging SSD
## 2 Background and Related Research Work  
### Background  
Apache Hadoop has been the de facto standard \"big data\" storage and processing platform by effectively distributing and managing the data and computations across many nodes. However, Hadoop can not achieve competitive performance for some applications such as machine learning, especially those consisting of several iterative stages and a relatively large amount of intermediate data. This is because in every iterative stage, Hadoop has to read and write data from/to an HDFS generated by MapReduce.  
Apache Spark exploits _resilient distributed datasets_ (RDD) [10] that can effectively manage any intermediate/final data in the main memory such as caching that can be efficiently utilized in each stage of iterative applications. Since the RDD is immutable, Spark introduces a concept of _lineage_ that can keep track of the history of RDD creations, which can be used for failure recovery. Through this concept, Spark can reduce the number of I/O operations on the disk compared to the Hadoop. Because of this distributed in-memory computing capability, Spark typically shows better performance than Hadoop for a wide range of data analytics applications.  
However, the RAM used for the main memory to store Spark's data is relatively expensive in terms of unit price per byte, so that it would be very difficult to construct a large enough amount of RAM in the Spark cluster to support various workloads. Therefore, the limited capacity of RAM can restrict the overall speed of Spark processing. If Spark cannot cache RDD to RAM due to limited space during the application processing, Spark has to re-generate the missing RDDs which could not fit into the RAM in every stage, becoming similar to Hadoop's approach. In addition, because the Spark job is a Java process running on the JVM, GC (garbage collection) occurs whenever the available amount of memory is limited. Since RDD is typically cached on the old space of JVM, when a major GC occurs, it can substantially affect the whole job processing performance. Moreover, the lack of memory can cause \"Shuffle Spill\", which is the process of spilling the intermediate data generated during shuffle from memory to disk. shuffle spill involves many disk I/O operations and CPU overheads. Consequently, a new solution must be considered in order to cache all of the RDDs and to secure memory for shuffle.
# Mechanically Reconfigurable, Beam-Scanning Reflectarray and Transmitarray Antennas: A Review
## 7 Conclusions  
We presented a review of different techniques for designing mechanically reconfigurable reflectarray and transmitarray antennas. We compiled and categorized the methodologies into three major groups and an additional hybrid group that combines one or more of these techniques. The antennas in each category feature specific properties, thus, offering a certain set of favorable performance metrics. We compared the scanning range, resolution and speed; we also compared the simplicity of implementation and operational frequency bandwidth. The techniques discussed herein can be applied to future mechanically reconfigurable antennas with superior performance, opening the door for new application spaces such as radars and communication systems.  
M.M., Z.Z., M.G., J.Z., M.M.H., J.H.B., and N.B. contributed to conceptualization, articulation, original draft preparation, review and the editing of the document. All authors have read and agreed to the published version of the manuscript.  
This work relates to the Department of Navy awards N00014-19-1-2502 and N00014-19-1-2502 issued by the Office of Naval Research. The United States Government has royalty-free license throughout the world in all copyright-able material contained herein.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
## 5 Research Questions
### Large Transport Vehicles  
The transport sector can benefit significantly from the integration of advanced technologies for the assessment and management of occupational risks. These technologies provide the opportunity for a more proactive, data-driven, and adaptive approach to ensure worker safety in various roles related to transportation [240]. In the transport sector, several authors emphasize that workers, especially drivers and captains, should apply these techniques to avoid accidents and other hazards [241; 242].  
According to [PERSON] [225], research shows that 75% to 96% of accidents in the maritime sector are due, either entirely or to a large extent, to human errors. He claims that accidents do not result from a single mistake or failure but from a series of consecutive or chain errors [243].  
In 2011, due to the complexity of Bayesian Networks (BNs), a new BN model was proposed to reduce the degree of complexity by lowering the number of variables used during modeling without compromising the severity of injuries [244] or reducing model performance [173]. Researchers claim that the model, with the reduction in the number of variables, works well in accident analysis during transportation and has a positive impact on industrial applications [245; 246].  
Road accident analysis has always been necessary and will remain so. However, there are three key axes: the human factor, the vehicle, and the road, which affect the overall outcome of the accident. Using Artificial Neural Networks (ANNs), accidents can be modeled, and safe conclusions can be drawn, impacting results in unpredictable ways [247; 248; 249; 250; 251; 252].  
Beyond road and maritime transportation, rail transport is also significant. [PERSON] and colleagues [184] point out that high-speed trains can achieve, along with time savings and low energy levels, transportation cost reductions. In response to this, [PERSON] and his colleagues [204] highlight the large number of rail accidents caused by human error. To improve these accidents, they propose a method called HEART, which consists of three key techniques: RARA, FANP, and fuzzy algorithms. This trio manages to minimize the likelihood of human error in high-speed trains [203]. That same year, it was emphasized that, for industries to benefit from high-speed trains without accidents, intensive work must be conducted because, in Europe, 35% of railway bridges are over 100 years old and operate at the limit. Moreover, it is noted that this factor has not been taken into account by any existing models [20; 253]].  
Accidents are not only encountered in land transport but also in air transport. To address dangerous accidents caused by pilots, a combination of two complex tools is proposed. One tool is based on empirical data and pilots' perceptions, while the other conducts multi-criteria analysis and classifies risks using fuzzy logic. This combination is suggested as a counterbalance for the lack of data [204; 254; 255; 256; 257; 258; 259].