esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
The Shapiro-Wilk test was used to assess the normality of the data, and a significance level of \(p\leq 0.05\) was employed to determine whether the data followed a normal distribution. To investigate potential group differences in the acute effects of a specific training session on metabolic demands, perceived exhaustion, and jumping performance, a two-factor (time \(\times\) intervention) repeated-measures analysis of variance (rANOVA) was carried out, employing a significance level of \(p\leq 0.05\). Similarly, two-factor (time \(\times\) intervention) rANOVAs were conducted to assess the effects of the intervention on the jump, sprint, and CoD performance with the same significance level. The \(\alpha\)-level was adjusted using [PERSON]'s correction, statistics of significant post-hoc tests are provided as a range of the mean difference (MD), and the smallest lower and highest upper 95% confidence limits (CLs) are displayed in square brackets as [smallest lower; highest upper].  
Moreover, the data were assessed by analyzing practical relevance using magnitude-based inferences (MBIs), calculated starting from the 90% CL using a published spreadsheet [41] to assess the likelihood that a change in average is practically relevant [42]. To determine the threshold values, the smallest worthwhile change (SWC) was calculated for a small effect (0.2). To calculate this, the between-subject standard deviation of each performance variable was multiplied by 0.2 (\(SWC=0.2\times SD\)). For instance, if an athlete jumps 45 cm in the CMJ with a standard deviation of 4 for this test in their population, the athlete would have to jump 0.8 m higher to achieve a meaningful change. A CoD was categorized as follows based on the probability that the true value of the standardized mean difference (SMD) will be greater than the SWC: (1) \"Possibly\" was assigned when the true value of the SMD had a probability ranging from 25% to 75% of being greater than the SWC; (2) \"Likely\" was assigned when the true value of the SMD had a probability ranging from 75% to 95% of being greater than the SWC; and (3) \"Very likely\" was assigned when the true value of the SMD had a probability ranging from 95% to 99.9% of being greater than the SWC [42].  
In addition, a Pearson correlation was calculated with the total sample in order to examine correlations between the performance developments. For this purpose, the differences between pre and post values were calculated, and a significance level of \(p\leq 0.05\) was applied. Statistical analyses of the data were performed using the SPSS analysis software (version 27.0; SPSS Inc., Chicago, IL, USA), Microsoft Excel (version 16.16.5; Microsoft Corp., Redmond, WA, USA), and Jamovi (version 2.2.5.0).
# An Improved Dynamic Joint Resource Allocation Algorithm Based on SFR
## 5 Conclusions  
In this paper, the system model of soft frequency reuse is first analyzed. Then we put forward an improved dynamic joint resource allocation algorithm. The proposed scheme maximizes the system capacity and satisfies the minimum throughput requirements for each center zone and edge zone, simultaneously. DJRA obtains the optimal single cell resource allocation scheme by dynamically adjusting RB-power allocation according to the real-time distribution of users per cell, and then uses an iteration method to acquire the optimum resource allocation scheme. Finally, DJRA utilizes the interior sector partition method and proportional fair algorithm in each cell. Our scheme outperforms previous approaches for system capacity and cell edge user performance. Furthermore, DJRA guarantees that all the users have available resources. Despite the huge potential benefits of DJRA, several challenges must be overcome, for example, decreasing computational complexity. How to solve these problems is our next step along this research path.  
This paper is partially supported by National Natural Science Foundation of China (No. 61301095, No. 51509049), the Heilongjiang Province Natural Science Foundation(No. F201345) and the Fundamental Research Funds for the Central Universities of China (No. HEUCF160812/160810). The idea of this work was proposed by [PERSON] and [PERSON]. [PERSON] and [PERSON] performed the experiments and analyzed the simulation results. [PERSON] and [PERSON] assisted in the research work and wrote the paper.  
Figure 6: (a) Average throughput for a single cell (b) Average data rate for a edge-user.
## 3 Results
### Logistic Regression Analysis on Musculoskeletal Pain  
#### 3.3.1 Logistic Regression Analysis on Back Pain  
The logistic regression model for back pain had a Nagelkerke value of 0.130. It demonstrated that the model's explanatory power was satisfactory. Additionally, the model fit was considered adequate, as reflected by a chi-square value of 3.473 and a significance level of 0.901. The prediction accuracy of the model was reported at 69.0%.  
Table 6 shows the key factors contributing to back pain in food manufacturing workers based on logistic regression analysis, considering the general characteristics of workers and physical, biochemical, and ergonomic hazards. These contributors include age (\(p\) = 0.012), work experience (\(p\) = 0.007), industry type (\(p\) = 0.003), and handling heavy loads (\(p\) < 0.001). Research shows that individuals in their 60s are 2.152 times more likely to report back pain compared to those in their 40s or younger (\(p\) = 0.003). Moreover, workers with work experience of \(\geq\)10 years are 2.064 times more likely than those with <3 years (\(p\) = 0.006). Workers who handle heavy loads for 2-4 h are 2.041 times more likely to  
Figure 4: Mean exposure times (hours per day) for lower limb pain complaints. * Differences between groups exist at significance level of 0.05 (red letters); error bars = standard deviation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Hazard**} & \multicolumn{2}{c}{**No Pain**} & \multicolumn{2}{c}{**Lower Limb Pain**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{3-3} \cline{5-6}  & & **Mean** & & & **SD** & & **Mean** \\ \hline \multirow{3}{*}{Physical hazards} & Vibration & 2.294 & 2.227 & 2.910 & 2.546 & 0.016 * \\  & Noise & 1.643 & 1.791 & 2.244 & 2.283 & 0.008 * \\  & High temperature & 1.378 & 1.435 & 1.619 & 1.585 & 0.108 \\  & Low temperature & 1.343 & 1.547 & 2.125 & 2.321 & 0.001 * \\ \hline \multirow{3}{*}{Biochemical hazards} & Vapor & 0.869 & 0.359 & 0.917 & 0.421 & 0.207 \\  & Chemical contact & 0.854 & 0.337 & 0.907 & 0.411 & 0.191 \\  & Tobacco smoke & 0.841 & 0.213 & 0.867 & 0.235 & 0.255 \\ \hline \multirow{3}{*}{Ergonomic hazards} & Awkward posture & 2.485 & 2.146 & 3.732 & 2.582 & \textless{}0.001 * \\  & Handling heavy loads & 2.310 & 1.990 & 3.210 & 2.323 & \textless{}0.001 * \\ \cline{1-1}  & Standing posture & 4.301 & 2.351 & 4.757 & 2.549 & 0.063 \\ \cline{1-1}  & Receptive motion & 4.223 & 2.465 & 4.861 & 2.401 & 0.011 * \\ \hline \hline \end{tabular} Note: * significant at 0.05, ** hours per day, SD = standard deviation.  
\end{table}
Table 5: \(T\)-test results comparing daily hazard exposure between groups regarding lower limb pain.  
experience back pain than those under 2 h (\(p\) = 0.001), while workers handling loads for 4 h or more are 3.529 times more likely than those under 2 h (\(p\) < 0.001).
## 4 Discussion
### Implications for Mangrove Conservation  
Keeping in mind our results, in order to achieve sustainable mangrove management, practices that cause ecological degradation should be replaced by more sustainable ones [57]. The public and national governments increasingly appreciate mangrove forests, even though they have historically been regarded as unproductive environments [58]. In South America, for instance, all mangrove forests in Ecuador are now protected in some way [59], and a recent study of Brazilian mangrove forests found that approximately three-quarters of all mangrove forests are protected [60]. From the point of view of environmental conservation in Ecuador, governmental policies should be implemented to continue protecting mangrove ecosystems. Mangrove forests provide habitat for many species, reduce the impact of tides, and stabilize the coastline to mitigate erosion, in addition to forming a natural barrier that reduces environmental impact through mud deposition processes, providing better places for living organisms to settle, and improving the lives of many species. Conversation groups and environmental nongovernment organizations (NGOs) are leading a new wave of global efforts to preserve, restore, and even expand mangrove forests [61]. To outline realistic and attainable national and international goals, such as the Aichi Biodiversity Targets or those recently established by the Global Mangrove Alliance, accurate figures on the historical and current extent and losses of mangrove forests are required. For an initial overview of previous and more recent efforts to map mangrove forests using various methodologies, the FAO provides synopses of mangrove coverage at the national level. Mangrove forests are now valued as one of the most productive and biodiverse natural resources on the planet, providing humans with valuable ecosystem services [62; 63; 64].  
Land use change in Ecuador has resulted in forest loss and mangrove fragmentation, although conservation efforts are in place. Mangrove forests can recover on their own if natural regeneration is promoted by preserving undisturbed areas and protection initiatives. Remote sensing and GIS have improved large-scale mapping and identification of conservation issues by incorporating changes in mangrove area and measurements of fragmentation and connectivity into monitoring efforts. Through this, we can better understand the ecological impact of fragmentation and guide future research on mangrove restoration and reconnection [65].
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
## 2 Theory of MIMO System
### Receiver Design
Therefore, as long as the received signal is projected to the signal direction, it will be fully matched to reach the maximum value. The linear minimum mean square error algorithm combines the ideas of the first two algorithms to realize the maximum signal to interference-noise ratio. It can be found in Equation (14) that \(\mathbf{X},\mathbf{Z}\) and \(\mathbf{W}\) are random variables, and \(\mathbf{Y}\) is a function of the three random variables. Based on \(\mathbf{Y}\), the random variable \(\hat{x}_{i}\) should make sure that the average distance between the random variables and the real \(x_{i}\) long-term statistics have the minimum average error. That is to say, in the moment of observed \(\mathbf{Y_{1}}\), find a \(\hat{x}_{i}\) to calculate the distance observed between \(x_{i}\) at moment one. Then, in the moment of observed \(\mathbf{Y_{2}}\), find a \(\hat{x}_{i}\) to calculate the distance observed between \(x_{i}\) at moment two. By this way, the observed sequence \(\hat{x}_{i}\) will have the smallest average distance with the real-sent sequence \(x_{i}\). A simpler and commonly used method is to use the linear representation of the components \(y_{i}\) of \(\mathbf{Y}\), and the \(x_{i}\) will have the following format:  
\[\hat{x}_{i}=\sum_{j=1}^{r}g_{ij}y_{j} \tag{20}\]  
Thus we need to find a group of constants \(g_{ij}\) to minimize (21):  
\[\left|\hat{x}_{i}-x_{i}\right|^{2}=E\big{[}(\hat{x}_{i}-x_{i})(\hat{x}_{i}-x_ {i})^{*}\big{]} \tag{21}\]  
According to the orthogonal principle, we can get:  
\[\left\langle\hat{x}_{i}-x_{i},y_{j}\right\rangle=0 \tag{22}\]  
By the expansion of inner product of random variables, the value of all \(g_{ij}\) can be obtained according to (23):  
\[E\big{[}\hat{x}_{i}{y_{j}}^{*}\big{]}-E\big{[}x_{i}{y_{j}}^{*}\big{]}=0 \tag{23}\]  
For the above three algorithms, the zero forcing algorithm is most easily implemented if there is no correlation between the interference and the useful signal in the system. To a certain extent, it will amplify the system noise, but it can effectively eliminate the system interference. The combined ratio algorithm mainly focuses on useful information and the noise information of all sequences. The minimum mean square error algorithm lies on the effective transmission sequence and channel impulse response after the convolution, and will find the sequence that has the minimum distance from the received signal. The third algorithm performs better than the previous two, but the complexity also increases. In this paper, the zero forcing algorithm and the minimum mean square error algorithm are adopted to achieve channel equalization and reduce the system error rate.
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
## 1 Introduction
After receiving the data from gateways or cluster heads, the BS uploads the data related to smart cities on the cloud server via the Internet. The proposed architecture integrates the deployment of IoT-based WSN with cloud infrastructure, therefore, the technologies of smart cities communicate, transfer, and analyze important data to maintain the urban operations. Additionally, due to the limited constraints of IoT sensors, they may be disposed to failure that may slow down the data processing in smart cities. Moreover, malicious or compromised entities can lead to disruption in the data sensing and transmission, which results in critical information that may be lost. Such a situation degrades various operations and services for both the community and city infrastructure. Hence, the deployment of IoT-based WSN in the context of smart cities requires a more robust and secure communication solution to improve the functionalities of an urban area network. The proposed SASC architecture provides significant improvements over existing solutions in terms of higher level of data privacy, integrity, and robust authentication. Firstly, the SASC divides network nodes into various clusters based on dynamic pivotal positioning. The clustering of nodes allows a resourceful load distribution inside each cluster and improves network lifetime. Secondly, to cope with efficient data storage and processing capabilities under restricted constraint nodes, integrating WSNs with cloud infrastructure is adopted. In the end, the proposed SASC gives a secure and authentic algorithm for data protection and node validation in an insecure and unreliable communication environment. The proposed security algorithm requires the least computational time and space capabilities with ease to implement. The aforesaid contributions of the proposed SASC offer a remarkable impact on constraint devices under untrustworthiness and dynamic environment. Additionally, the proposed algorithm provides the node level authentication process to identify malicious nodes and to generate networks more consistent with efficient data delivery performance. The rest of the paper is organized as follows. Section 2 presents the related technology and problem finding of this work. Section 3 introduces SASC with its algorithms and design. The simulation model and numerical results of SASC in comparison with other solutions are discussed in Section 4. Finally, Section 5 concludes the paper.
## 6 Results and Discussions
### Simulation of the Monitoring and Control of Parking Lots in the North of a City  
First, we presented the verification of the computerized M/M/c/N submodel on which the simulation processes of the parking lot system model were based. Next, we carried out a sensitivity analysis of the M/M/c/N submodel parameters. Then, the use of the M/M/c/N submodel for the selection of the level of service in a parking lot system was introduced based on desirable performance levels. Finally, based on the use case of monitoring and controlling parking lots in a city, experiments were carried out to analyze the scalability of the system, study the performance of the reservation request service, and estimate the system recovery time in case of failures.
## 3 Technological Platform for the Wireless Sensor Node
### Lightweight PaRTiKle Operating System Design
#### 3.2.2 Execution Environments  
PaRTiKle has been designed to run under several different execution environments. There are presently three different execution environments are available, all of them for the ARM and x86 architecture: (1) on a bare machine, (2) as a Linux regular process and (3) as a domain of XtratuM [30,31]. This last alternative provides the possibility of executing PaRTiKle jointly with another general purpose operating system (Linux so far).  
* On a bare machine: PaRTiKle is the only system executed and is in charge of managing all of the hardware. This environment is the best option for applications with real-time constraints.
* As a Linux regular process: This environment is intended for testing purposes and the generated code is executed as a regular Linux process. PaRTiKle still has direct access to the hardware; however, real-time constraints are not guaranteed.
* As an XtratuM domain: XtratuM is a hypervisor that provides hardware virtualization and enables the execution of several kernels (or run-times) concurrently. PaRTiKle can be built to be XtratuM aware and then loaded using the XtratuM.
# Communication Performance Assessment for Advanced Metering Infrastructure
###### Abstract  
Advanced Metering Infrastructure (AMI), the foundation of smart grids, can be used to provide numerous intelligent power applications and services based on the data acquired from AMI. Effective and efficient communication performance between widely-spread smart meters and Data Concentrator Units (DCUs) is one of the most important issues for the successful deployment and operation of AMI and needs to be further investigated. This paper proposes an effective Communication Performance Index (CPI) to assess and supervise the communication performance of each smart meter. Some communication quality measurements that can be easily acquired from a smart meter such as reading success rate and response time are used to design the proposed CPI. Fuzzy logic is adopted to combine these measurements to calculate the proposed CPI. The CPIs for communication paths, DCUs and whole AMI can then be derived from meter CPIs. Simulation and experimental results for small-scale AMIs demonstrate the validity of the proposed CPI. Through the calculated CPIs, the communication performance and stability for AMI can be effectively assessed and supervised.  
Advanced Metering Infrastructure; Data Concentrator Unit; Communication Performance Index; reading success rate; response time +
Footnote †: journal:  
_Article_
# Multi-Tier Heterogeneous Beam Management for Future Indoor FSO Networks
## 3 MTH Beam Management Protocol
### Zone Configuration  
Optimal system deployment should account for zone configuration--including the number of elements per tier and the coverage associated with each element. The obvious motivation for multiple elements is to provide an opportunity for multiple simultaneous links; however, multiple elements also impact coverage and redundancy. Providing Tier 3 access throughout the Tier 2 zone requires a single element to steer to any location within the zone. To relax this requirement, the indoor FSO network may be deployed to only cover a portion of the zone or, alternatively, be configured with sectors or subzones assigned to different elements (Figure 5). The challenge with sectors is that the protocols must account for additional transitions as UDs move between sectors (i.e., from subzone to subzone). Distributed elements also add redundancy and mitigate outage concerns. When elements are distributed, potential coverage regions can overlap without leading to contention due to the small instantaneous coverage. This provides redundancy in the potential links for a UD to connect to, as we demonstrated in [19].
# A Real Case of Implementation of the Future 5G City
## 2 5G Network Architecture
### 5G Physical Infrastructure  
#### 2.1.1 Set-Up Phase  
During the set-up phase, enhanced-studies are devoted to investigating possible algorithms and strategies to efficiently and effectively implement new technologies and paradigms that are considered key enablers of 5G network capabilities. Simultaneously, lab tests are carried out to test basic RAN functions. RAN is deployed following the Cloud-RAN approach, which is based on the separation of the radio frequency (RF) elements of the BS (called the active antenna unit (AAU)) from the processing of the baseband signal (called the base band unit, (BBU)), which is centralized in a single point or virtualized in the cloud. AAU can be deployed on multimode networks while working on multiple frequencies. A cloud RAN allows easier operations using a simpler RF equipment at the network edge, while the RAN intelligence is centralized in the BBU. AAUs and BBUs must be connected with high-speed and low-latency links called _fronthaul_. Toward this goal, the decision is to adopt the Common Public Radio Interface (CPRI) specifications [21] for transmitting digital radio over fiber CPRI is a serial line interface transmitting constant bit rate data over a dedicated channel. During initial tests the physical architecture is made by a single BBU with a single AAU connected to a channel emulator and to basic user equipment. The 5G NR under investigation is based on an improved air interface that does the following:  
* allows operation in multiple frequency bands (3 GPP-Release 15). The breakthrough in this context is to allow a device being simultaneously connected to a high frequency layer that carries only user plane traffic and to a lower-frequency layer that provides ubiquitous connectivity to both user and control planes;
* uses massive MIMO techniques (3 GPP-Release 15). The BBU and AAUs enable computationally intensive operations to support an unprecedented number of antenna elements. Moreover, efficient beamforming schemes are considered to fully exploit massive MIMO capabilities;
* uses non-orthogonal multiple access (NOMA) (3 GPP-Release 16). The overhead due to orthogonal transmissions becomes very large when the number of connected devices increases and can  
Figure 2: Network slicing conceptual architecture.  
introduce unacceptable latency. For this reason, NOMA approach is considered for massive machines access;
* relies on a dense network deployment (3 GPP-Release 15), a multi-tier heterogeneous architecture where high-power macrocells are overlaid by a large number of low power small cells that provide high data rates and dedicated capacities to homes, enterprises, and urban hot-spots and to offload excess traffic from the macrocell.
## 3 System Model  
### Network Model  
In this paper, the network is composed of numerous sensors as well as a sink node. \(N\) sensor nodes are randomly deployed in a \(M\times M\) monitoring area, and the sink is placed in the center. The deployed sensor nodes are homogenous, and all have the same capabilities and limited power source, of the same initial energy, that cannot be recharged or replaced. Assume that the sink has no energy constraints. We make some basic assumptions as follows [25,26]:  
1. All the sensor nodes are static and do not change their location once deployed.
2. Each node can be identified by its unique ID that differs from other nodes.
3. All the sensors have knowledge of their location according to an equipped GPS.
4. The collisions during transmission are not considered in the network, and the radio channels are symmetric.  
The sensing radius and communication radius of each node are \(r_{s}\) and \(r_{c}\), respectively. Set \(r_{c}~{}\geq~{}2~{}r_{s}\) to ensure that all nodes that sense the target can communicate with each other.
6. The sensing model of the node adopts the Boolean omnidirectional model, which is given by [27]: \[P(s_{j},~{}tar)=\left\{\begin{array}{ll}1,&d_{s_{j},tar}\leq r_{S}\\ 0,&d_{s_{j},tar}>r_{S}\end{array}\right.,\] (1) where \(P(s_{j},~{}tar)\) is the probability that sensor \(s_{j}\) can sense the target, and \(d_{s_{j},tar}\) is the distance from sensor \(s_{j}\) to target. \(r_{s}\) is the sensing radius.