esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization and Verification  
As mentioned above, WBR, BFC and RC have different effects on working performance and mechanical properties of concrete. Based on the response surface model, multi-objective optimization design is carried out to determine the best combination of WBR, BFC and RC. Based on the previous research [26; 27], the fluidity of concrete had a significant impact on its construction and workability, and higher fluidity could play a good role in promoting the construction of physical projects. However, when designing the concrete mix ratio, the mechanical properties of concrete should be considered comprehensively on the basis of ensuring good fluidity. Therefore, the target values of the response values in this study were all taken to the maximum value. Then, based on RSM, the optimal combination of WBR, BFC and RC was obtained through Design-Expert 8.0 software. The best combination of WBR, BFC and RC and the model verification results are shown in Table 9.  
Figure 6: 3D response surface plot between FS and factors: (**a**) Factors: BFC and WBR at RC = 20%; (**b**) Factors: RC and WBR at BFC = 5 kg/m\({}^{3}\); (**c**) Factors: RC and BFC at WBR = 0.44.  
As shown in Table 9, the optimal design combination was selected as follows: WBR was 0.39, BFC was 4.56 kg/m\({}^{3}\), and RC was 10%. Three specimens of 100 mm \(\times\) 100 mm \(\times\) 100 mm and three specimens of 100 mm \(\times\) 100 mm \(\times\) 400 mm were fabricated and tested for CS and FS, respectively. Three SP tests were performed immediately after concrete mixture was mixed. The results are shown in Table 9. The relative error is less than 5%, indicating that the accuracy of the prediction results meets the requirements. This shows that RSM can be used to design and optimize the preparation parameters of RBFC.
## 4 Results and Discussion
### Effect of Leaching Time on Mineralization Degree  
Figure 5 illustrates the relationship between the concentration of alkaline ions in the reaction environment and the soaking time obtained from the experiment. It demonstrates an initial increase followed by a decrease. Initially, the leaching rate of alkaline ions in fly ash is higher than the rate of formation and consumption of calcium silicate. As time passes, the leaching ion concentration increases. However, in the later stage of the reaction, the leaching rate becomes lower than the rate of calcium silicate formation and consumption. Consequently, the ion concentration gradually decreases over time. Figure 6 presents the mineralization efficiency of the reaction under different soaking times. Among the experimental samples, the mineralization degree without soaking treatment was 28.35%. With a soaking time of 12 h, the mineralization degree increased to 30% approximately. The maximum mineralization degree of 43.85% was achieved with a soaking time of 24 h. Beyond 24 h, the degree of mineralization slightly decreased, reaching 42.88% and 38.98% after soaking for 48 h and 72 h, respectively. The overall trend of mineralization degree with soaking time exhibits an initial increase, followed by a slight decrease, and eventually reaching a plateau. This trend aligns with the analysis of changes in the ion concentration. After soaking the fly ash, the concentration of alkaline ions in the liquid reaction environment gradually increases over time. In the early stage of soaking, the alkaline ions in the fly ash rapidly dissolve and diffuse, leading to a rapid increase in the ion concentration and reaction with CO\({}_{2}\). In the later stage of soaking, the ionic concentration in the liquid environment stabilizes, and the number of generated products remains relatively constant.  
Figure 7 shows the mineralization efficiency of the mineralization reaction with supercritical CO\({}_{2}\) under different soaking times. Among the experimental samples, the trend is similar to that of non-supercritical CO\({}_{2}\) in Figure 6. Comparing the two figures, the degree of mineralization has increased by about 10% with supercritical CO\({}_{2}\).  
Figure 5: The relationship between ionic concentration and soaking time.
# ML-Based 5G Network Slicing Security:
## 5 Emerging Threats and Security Concerns in ML Based 5G Network Slicing
If there are any errors in the network slice template, they may lead to various attacks such as content exposure, data leakage, injected malware, etc. Such attacks can lead to leaked user information from the databases and access to unencrypted channels, which will cause loss of confidentiality, integrity, and authenticity of the network. Some security measures such as encryption and decryption of the slice template and real-time security analysis must be taken to prevent such attacks. **Installation, configuration, and activation phase. ** The second phase in the life cycle includes the installation of the slices to the network, configuration of the services as per the request, and activation of the slices, i.e., as a ready-to-use software or service. The primary threat in this phase is the creation of fake slices and re-configuration of the slices during or before the final activation. The target points of these attacks are the APIs, which can ultimately affect installation and configuration and give an activation error in a slice. Security measures must be taken to secure APIs by providing operational and accessibility rights to the authorized people and utilizing TLS or O-Auth for authentication and authorization purposes. **Run time phase. ** This phase signifies that the slice is in use and allows updates concerning the requirements, changes in configuration, allocation, deallocation of resources, and network functions. The targets of attacks in this phase are controllers, hypervisors, the overall cloud system, control channels, and centralized control elements. However, APIs remain the main target of attacks. The types of attacks include performance attacks, privacy breaks, and data exposure. The security measures that must be taken are authentication and integrity of the network slices to prevent fake requests, slice isolation to prevent DoS and DDoS attacks, and secure-5G modeling to prevent accessibility of unauthorized and  
Figure 9: Life cycle of slice and the associated threats. malicious requests. Dynamic NFV can also be used, which provides an on-demand security mechanism. **Deactivation phase. ** This is the last phase of the network slice's life cycle, in which the resources and network functions are relieved. The slice is no longer in use. The most common threats which can harm the network, even after the slice is decommissioned, are due to improper handling while deactivating the slice and improper usage of resources and network functions. The targets for these attacks are user information databases, cloud storage, and centralized control elements. The measures to avoid attacks in this phase are proper deallocation of the resources and network functions that are no longer in use, and deletion of sensitive data that is no longer required.
# Effectiveness of High-Intensity Therapy in Rehabilitation of Parkinson's Disease Patients
## 4 Results
### Correlations and Borg Scale Analysis  
Correlation tests were conducted to examine the relationships between key clinical variables. Pearson and Spearman correlation analyses revealed that age and Hoehn-Yahr disease stage were not significantly correlated, suggesting that disease severity progression was not directly linked to age-related factors. However, a notable correlation was identified between UPDRS-MEDL2 scores and disease duration in Group 3 (r = 0.3880, \(p\) = 0.055), indicating a potential trend in symptom progression over time. Additionally, heart rate regulation was significantly correlated with [PERSON]'s symptoms (r = 0.323, \(p\) = 0.001), suggesting a connection between autonomic function and disease manifestations. The Borg Scale was used to assess perceived exertion across therapy groups. The mean Borg RPE scores ranged between 14.99 and 15.09, reflecting moderate exertion levels during therapy sessions. ANOVA results for the Borg Scale revealed significant differences between groups, particularly in Borg1 (F = 8.66, \(p\) < 0.0001) and Borg20 (F = 4.01, \(p\) = 0.0098), suggesting that different therapy modalities induced varying degrees of exertion. A Tukey-HSD test further confirmed that Group 4 exhibited significantly higher exertion levels compared to Groups 2 and 3 (_p_ < 0.0001), indicating that certain interventions required greater physical effort.
# Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
## 3 An Overview of NB-Fi, Sigfox, and LoRaWAN
### Modulation
#### 3.1.3 LoRaWAN  
LoRaWAN sensors communicate with BSs using the LoRa technology, which is based on the Chirp-Spread Spectrum (CSS) modulation. In LoRa, the signal continuously varies in frequency within window \(BW\) around the central frequency \(f_{c}\). It means that the frequency starts with one of the \(2^{SF}\) values within \(\left[f_{c}-\frac{BW}{2};f_{c}+\frac{BW}{2}\right]\), where \(SF\) is the spreading factor, which defines the symbol duration and the number of bits per symbol. Then, the frequency increases until \(f_{c}+\frac{BW}{2}\), drops to the minimum \(f_{c}-\frac{BW}{2}\) and grows until reaching the initial value. The key feature of the LoRa modulation is that frames transmitted with different \(SF\)s can be received and decoded simultaneously [23].  
The total bitrate can be calculated as follows [28]:  
\[R_{b}=\frac{SF\times CR}{\left[\frac{5^{SF}}{BW}\right]}, \tag{2}\]  
where \(CR\) is the rate of Hamming code and can be \(\frac{4}{5},\frac{4}{6},\frac{4}{7},\frac{4}{8}\)[22].  
Therefore, the bitrate depends on the \(BW\), \(SF\), and \(CR\). Higher values of \(SF\) result in lower bitrates; however, signals can be received at higher receiver sensitivities. Table 1 lists bitrates, \(SF\), critical receiver sensitivities, and \(CR\) for \(BW=125\) kHz.
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 3 The Research Status of MEMS Switches in Different Frequencies
### Research Regarding Insertion Loss and Isolation of MEMS Switches in Different Bands
#### 3.1.5 Other Types of MEMS Switches in Different Frequency Bands
Excellent performances of insertion loss and isolation were showed in the W band based on the 0.25 um SiGe-C BiCMOS technology proposed by [PERSON] et al. [PERSON] et al. (2018).
## 5 Experimentation and Results
### Comparative Study
#### 5.4.1 Comparative Analysis between Different Representations
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{DDO} & \multicolumn{2}{c}{**c**} & \multicolumn{2}{c}{**c} & \multicolumn{2}{c}{**FFF**} & \multicolumn{2}{c}{**GGC**} \\ \cline{3-14}  & & **Traditional** & **SimpleMI** & **WupperWH** & **Traditional** & **SimpleMI** & **WupperWH** & **Traditional** & **SimpleMI** & **WupperWH** & **Traditional** & **SimpleMI** & **WupperWH** \\ \hline \multirow{3}{*}{Trees methods} & DeducingGroup & 0.667 & 0.775 & 0.724 & 0.646 & **0.930** & 0.718 & 0.641 & 0.906 & 0.797 & 0.607 & 0.807 & 0.717 \\  & Rand-Free & 0.544 & 0.883 & 0.706 & 0.506 & 0.882 & 0.507 & 0.889 & 0.800 & 0.800 & 0.800 & 0.800 & 0.800 & 0.717 \\  & Rand-Free & 0.584 & 0.885 & 0.707 & 0.603 & **0.888** & 0.729 & 0.609 & 0.829 & 0.828 & 0.507 & 0.800 & 0.800 & 0.717 \\ \hline \multirow{3}{*}{Rules methods} & Zarek & 0.584 & 0.528 & 0.528 & 0.562 & **0.718** & **0.718** & 0.530 & 0.580 & 0.800 & 0.998 & 0.717 & 0.727 \\  & DATE & 0.645 & 0.844 & 0.666 & 0.841 & 0.722 & 0.566 & 0.884 & 0.871 & 0.421 & 0.807 & 0.723 \\  & FMRT & 0.584 & 0.882 & 0.538 & 0.590 & 0.528 & 0.578 & 0.513 & 0.574 & 0.593 & 0.800 & 0.721 \\  & Radar & **0.578** & 0.583 & 0.618 & 0.807 & 0.592 & 0.729 & 0.841 & 0.915 & 0.670 & 0.805 & 0.800 & 0.697 \\ \hline \multirow{3}{*}{SYM methods} & Naivelyves & **0.837** & 0.763 & 0.724 & 0.602 & **0.930** & 0.773 & 0.799 & **0.930** & 0.820 & 0.858 & **0.902** & 0.854 \\  & Lequile & 0.833 & **0.832** & 0.780 & 0.802 & **0.839** & 0.794 & **0.817** & 0.815 & 0.826 & 0.800 & 0.807 & 0.740 \\  & Lequile & 0.814 & **0.843** & 0.724 & 0.884 & **0.886** & 0.721 & 0.845 & 0.890 & 0.802 & 0.854 & 0.846 & 0.723 \\  & SPHERE & 0.853 & **0.830** & 0.726 & 0.443 & 0.826 & 0.748 & 0.691 & 0.601 & 0.805 & 0.800 & 0.807 & 0.717 \\  & SAD & 0.706 & **0.847** & 0.668 & 0.667 & **0.855** & 0.722 & 0.779 & **0.851** & 0.594 & 0.604 & 0.807 & 0.717 \\  & SAD & 0.707 & **0.845** & 0.771 & 0.592 & **0.859** & 0.721 & 0.756 & 0.800 & 0.730 & 0.800 & 0.807 & 0.717 \\ \hline  & BISPNetwork & 0.758 & **0.799** & 0.766 & 0.850 & **0.902** & 0.790 & 0.882 & 0.999 & 0.857 & 0.694 & **0.904** & 0.750 \\ \hline \multirow{3}{*}{Assembles methods} & Nativelyves & **0.839** & 0.855 & 0.270 & **0.831** & 0.530 & 0.827 & 0.818 & 0.839 & 0.820 & 0.850 & 0.807 & 0.748 \\  & Ad
## 5 Energy Management System  
UAVs require a reliable and efficient energy management system to operate effectively. UAV energy management systems typically consist of a battery or a combination of batteries, a power converter, and a control system. One important aspect of UAV energy management is maximizing the energy efficiency of the system. This can be done through several techniques, such as optimizing the flight path, adjusting the power output of the motor, and using regenerative braking to recover energy during descent [60].  
The estimation of the battery's remaining capacity and the monitoring of its health are two important aspects of UAV energy management. Algorithms are used to predict the state of charge and remaining capacity of the battery by analyzing the temperature, voltage, and current data. Overall, the efficient and dependable operation of UAVs depends on a well-designed and effective energy management system. It has the potential to shorten flight times, improve safety, and lower operational costs [50].
## 4 Conclusions  
The tests conducted in trial 1 did not show significant differences in the effectiveness of tested solutions in reaching the obstacle time and cornering time, but confirmed that for driving without intensive maneuvering and precise obstacle avoidance, the best solution is a panoramic 180\({}^{\circ}\) view (configuration 2). Cameras in this case should be placed in such a way that they emulate the view from the driver's cockpit, capturing a fragment of the vehicle hood.  
When driving in a very limited space, the best solution is a main camera located in the driver's cockpit, which shows the front view with the vehicle hood and works in conjunction with additional side cameras (configuration 3). It is needed to precisely assess the position of obstacles about the UGV chassis and to analyze the UGV surrounding area. Such a solution provides the shortest time of operation and the lowest number of errors. The main camera's placement in front of the UGV (configuration 2) produces dramatic, worse results.  
The solution, which combines a main camera in the driver's cockpit with additional side cameras, offers the best balance between task completion time and the number of errors, as well as the detection and recognition of obstacles. This solution facilitated the nearly flawless execution of planned tasks, allowing a relatively large UGV equipped with [PERSON]'s steering system to maneuver in tight spaces. This creates the possibility of a relatively simple adaptation to teleoperations of many specialist vehicles used in rescue or military operations.  
The applied approach does not exclude the possibility of additional use of environment recognition systems based on camera images, which will be the subject of further research.  
Conceptualization, K.C., P.K. and R.K.T.; methodology, T.M. and R.K.T.; software, K.C. and P.K.; validation, K.C. and M.P.; formal analysis, P.K. and R.K.T.; investigation, K.C. and R.K.T.; resources, T.M. and M.P.; data curation, R.K.T. and A.R.; writing--original draft preparation, P.K. and M.P.; writing--review and editing, P.K. and T.M.; visualization, K.C. and M.P.; supervision, M.P. and R.K.T.; project administration, T.M. and R.K.T.; funding acquisition, T.M. and A.R. All authors have read and agreed to the published version of the manuscript.  
This work was financed/co-financed by the Military University of Technology under research project UGB 708/2024.  
Figure 12: Average of number of obstacles and lane violations per ride for 3 camera configurations— trial 2.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee operating at Warsaw University of Life Science, resolution 15 February 2022, number 7/2022.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
###### Abstract  
Collecting time-series receive signal strength (RSS) observations and averaging them is a common method for dealing with RSS fluctuation. However, outliers in the time-series observations affect the averaging process, making this method less efficient. The Z-score method based on the median absolute deviation (MAD) scale estimator has been used to detect outliers, but it is only efficient with symmetrically distributed observations. Experimental analysis has shown that time-series RSS observations can have a symmetric or asymmetric distribution depending on the nature of the environment in which the measurement was taken. Hence, the use of the Z-score method with the MAD scale estimator will not be efficient. In this paper, the \(S_{n}\) scale estimator is proposed as an alternative to MAD to be used with the Z-score method in detecting outliers in time-series RSS observations. Performance comparison using an online RSS dataset shows that the Z-score with MAD and \(S_{n}\) as scale estimators falsely detected about 50% and 13%, respectively, of the RSS observations as outliers. Furthermore, the average absolute RSS median deviations between raw and outlier-free observations are 3 dB and 0.25 dB, respectively, for the MAD and \(S_{n}\) scale estimators, corresponding to a range error of about 2 m and 0.5 m.  
RSS fluctuation; MAD; \(S_{n}\) scale-estimator; outlier +
Footnote †: journal: Medical Imaging  
1
Footnote 1: email: _Applied_
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 2 The Improved Model
### Spectrum Analysis Model  
Taking a quadratic polynomial model as a basic function, periodic terms are added to describe the clock offset, called spectrum analysis model. Previous experiments show that two significant periodic terms, which may be mainly caused by orbital effects, are applicable for BDS-2 satellite clocks, hence, the function with periodic terms is expressed as [12],  
\[\begin{array}{l}clk(t_{i})=a_{0}+a_{1}(t_{i}-t_{0})+a_{2}(t_{i}-t_{0})^{2} \\ +b_{1}\sin(2\pi f_{1}(t_{i}-t_{0}))+c_{1}\cos(2\pi f_{1}(t_{i}-t_{0}))+b_{2} \sin(2\pi f_{2}(t_{i}-t_{0}))+c_{2}\cos(2\pi f_{2}(t_{i}-t_{0}))+\varepsilon_{ i}\end{array} \tag{2}\]  
where \(a_{0}\) is the clock bias correction coefficient, \(a_{1}\) is the clock drift correction coefficient, and \(a_{2}\) is clock drift rate correction coefficient. \(t_{0}\) is the reference epoch of clock offset series. \(t_{i}\) and \(clk(t_{i})\) denote the i-th epoch and its corresponding clock offset. \(b_{1}\), \(c_{1}\) are the coefficients for the main period \(1/\!f_{1}\), of which amplitude is \(\sqrt{b_{1}^{2}+c_{1}^{2}}\). \(b_{2}\), \(c_{2}\) are the coefficients for the secondary period \(1/\!f_{2}\), of which amplitude is \(\sqrt{b_{2}^{2}+c_{2}^{2}}\). \(\varepsilon_{i}\) is the noise.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Mobile Cloud Computing (MCC)
#### 3.4.2 Cloud Security Lifecycle  
The cloud security lifecycle, as illustrated in Figure 5, includes the steps and processes to confirm the security of data, infrastructure and applications in a cloud-based environment. The steps involved in the cloud security lifecycle are as follows:  
1. Evaluation and Planning: This process begins by identifying organisational requirements, regulatory requirements, and possible hazards. It then examines the architecture of the cloud environment and defines security specifications and objectives.
2. Architecture and Implementation: This requires an effective safety architecture for the cloud. Security controls, encryption technologies, access management, and regulations must all be implemented. These ascertain that security is incorporated at all levels of the cloud architecture.
3. Installation and Configuration: The installation ensures that security features and configurations in cloud services and platforms are properly configured. It implements the design's access and identity management, network safety, and data protection features.
* Monitoring along with Detection: This step involves monitoring technologies to track and investigate cloud-related activity. To discover possible security problems, we use intrusion systems detection, log evaluation, and real-time notifications.
* Incident Response: This step involves developing and documenting an incident response plan to manage security incidents efficiently.