esa-sceva/satcom-chunk-collection huggingface dataset:
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 7 Discussion and Conclusions  
This paper highlights some of the inherent complexities and challenges associated with service and platform deployment in cloud-based environments, as required for the successful adoption of 5G technologies. This complexity can be mainly attributed to the broad spectrum of available infrastructure and platform technologies, each of which brings their own interfaces and communication protocols. As a possible solution to this problem, this paper introduced a novel workflow for the composition and deployment of platforms and services in multi-cloud environments. The proposed workflow follows the NGPaaS concepts of build-to-order and Build-Ship-Run, by utilising the RFB model and the RDCL 3D tool. Build-to-order implies the possibility to define custom platforms and services agnostically to the underlying technologies, based on customer demand. Build-Ship-Run implies the possibility to deploy this platform or service compositions on a wide range of execution environments,  
Figure 16: The 5G Public Safety Use Case.  
through a centralised environment (RDCL 3D tool). By following the NGPaaS workflow, platform and service deployment is greatly simplified and facilitated, since most of the underlying technologies are abstracted through generic blueprints and APIs. Finally, this paper successfully validated the proposed workflow by presenting a proof of concept scenario (Telco PaaS) that includes the composition and deployment of both a platform and a set of diverse services. The Telco PaaS use case is based on the CORD platform and represents a VNFaaS scenario, in which a VSP offers VNF services to its end customers, together with many value-added services (monitoring, healing, policy-based network management). Finally, to showcase the flexibility of the NGPaaS workflow, a second use case is also presented herein.  
Conceptualisation, P.V. and B.S.; Investigation, A.M.-K., J.S., P.V., A.B., M.M., S.V.R. and B.S.; Methodology, A.M.-K., J.S., P.V. and A.B.; Project administration, J.S. and B.S.; Resources, J.S., P.V., O.R., W.T. and B.S.; Software, A.M.-K., A.B., M.M. and S.V.R.; Supervision, J.S., P.V., O.R., W.T. and B.S.; Validation, A.M.-K., P.V., A.B., M.M. and S.V.R.; Visualisation, A.M.-K.; Writing--original draft, A.M.-K., P.V. and S.V.R.; Writing--review & editing, A.M.-K., J.S., P.V., A.B., M.M., O.R., S.V.R., W.T. and B.S. FundingThis work has been performed in the framework of the NGPaaS project, funded by the European Commission under the Horizon 2020 and 5G-PP Phase2 programs, under Grant Agreement No. 761 557 ([[http://ngpaaS.eu](http://ngpaaS.eu)]([http://ngpaaS.eu](http://ngpaaS.eu))). The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Re-Optimized Design for the ADR-Based Dual-Loop Controller of 100 m Class Aperture Radio Telescope
## 5 Conclusions  
This paper established an antenna (azimuth) servo system's multiple-motor-driven 4 DOF model and re-optimized the design of an active disturbance rejection-based dual-loop controller used for a 100 m class aperture radio telescope. The re-optimized design mainly centers on enhancing the anti-disturbance performance of the velocity-loop LADRC by adding disturbance-derivative compensation (DLADRC) and simplifying the position-loop controller to a proportional controller. The optimal scheme has good dynamic performance and strong attenuation to external disturbances. The research also shows that a superior velocity-loop controller is essential for a high-precision antenna servo control system.  
Future work will validate the proposed control strategy via experiments and further optimize it for higher-accuracy pointing of a radio telescope.  
Conceptualization, N.L. and N.W.; methodology, N.L.; software, N.L.; validation, N.L., N.W. and Z.L.; formal analysis, N.L.; investigation, Z.L.; resources, N.L., Q.X. and N.W.; data curation, N.L.; writing--original draft preparation, N.L.; writing--review and editing, N.L.; visualization, N.L.; supervision, Q.X.; project administration, N.W.; funding acquisition, N.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Young Scientists Fund of the National Natural Science Foundation of China (No. 12303099), the Scientific Instrument Developing Project of the Chinese Academy of Sciences (No. PTYQ202 ZYZZD01), and the Open Fund of Shaanxi Key Laboratory of Antenna and Control Technology.  
Data are contained within the article.  
The authors declare no conflicts of interest.
## 3 Field and Lab Ballast Data Preparation
### Methodology
#### 3.2.2 Laboratory Data Collection  
To comprehensively examine how various field conditions impact image segmentation performance, five pivotal influencing factors are considered: particle size, color, arrangement (i.e., the packing and overlapping of particles), lighting conditions, and degradation levels. Analyzing ballast images with diverse field conditions proves challenging due to the difficulty in precisely identifying the dominant factor(s) affecting segmentation performance. In uncontrolled field environments, isolating these factors for study is impractical. Consequently, capturing images of ballast particles in a well-controlled environment in the laboratory is important for giving clear guidance in training the ballast particle segmentation models. The approach facilitates the examination of different permutations of the five key variables and enhances the dataset's comprehensiveness.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sources** & **Fouling Conditions** & **Number of Images** \\ \hline \multirow{2}{*}{Railroad field sites} & Clean & 51 \\ \cline{2-3}  & Fouled & 93 \\ \hline Lab-engineered samples & Clean & 142 \\ \hline Aggregate quarries & Clean & 137 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ballast image dataset collected from railroad field and laboratory setups [23].  
For laboratory images, three types of ballast materials were prepared: (i) white limestone, (ii) dark red quartzite, and (iii) dark gray igneous rock (basalt or granite). Each material was divided into four distinct particle size groups based on AREMA ballast gradation specifications: (i) particles passing the 3/8 in. (9.53 mm) sieve, indicating fouled materials and fines; (ii) particles retained on the 3/8 in. (9.53 mm) sieve; (iii) particles retained on the 1 in. (25.4 mm) sieve; and (iv) particles retained on the 2 in. (51 mm) sieve. To ensure diverse coverage, various ballast particle configurations were designed to account for different combinations of the critical influencing factors.  
The complexity of particle configurations ranged from single-size groups in a non-contacting setting, to mixed-size groups/mixed colors, and eventually full gradations. Different lighting conditions were introduced, spanning from direct natural sunlight to shaded or diffused lighting, mirroring potential field lighting conditions. All images were captured perpendicular to the stockpile surface/ballast particles using a tripod with a cantilever arm and a smartphone camera. For each configuration, two images were taken at two heights (30 in. (762 mm) and 45 in. (1143 mm)). Additionally, a 1.5 in. (38.1 mm) diameter white ball was employed for scale calibration.
# Stress-Strain Model for Freezing Silty Clay under Frost Heave Based on Modified [PERSON]'s Equation
## 7 Conclusions  
A stress-strain model for freezing silty clay under frost heave based on a modified [PERSON]'s equation was proposed. The conclusions of the study and recommendations for future studies are as follows:  
1. Surface heaving significantly increases with freezing time in each freezing stage. Furthermore, frost heaving decreases with the increase of \(\sigma_{\
u}\), which inhibits frost heave. Therefore, the influence of \(\sigma_{\
u}\) should be considered when estimating the stress-strain behavior and mechanical properties of freezing soil. In addition, frost heave increases slightly with the decrease in the short-duration cooling temperature from one stage to the other;
2. By using the frost heave data, the correlation between the freezing rate (\(U_{i}\)) and temperature gradient (\(\
abla T_{i}\)) within each freeing soil layer was investigated. Then, [PERSON]'s equation was modified by substituting \(U_{i}\) with \(\
abla T_{i}\) and evaluating the frost heave strain within each freezing subsoil layer;
3. A nonlinear mechanical elasticity model with the compressive modulus as a function of the variables of temperature gradient, overburden pressure, and cooling temperature was proposed to characterize the stress-strain relationship of freezing soil resulting from the effect of frost heave;
4. Future studies should address other important factors and introduce them into this stress-strain model to achieve a more general understanding of the realistic stress-strain behavior of freezing soil resulting from the effects of frost heave. Meanwhile, researchers can use the proposed model with the finite element method to simulate more complex practical tests.  
**Author Contributions:** Conceptualization and methodology, L.G.; validation, and formal analysis, S.C.; writing--original draft preparation, J.L.; writing--review and editing, X.L. and X.D.; visualization, Y.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the National Key R&D Program of China (Grant No. 2018 YFC1505305), the National Scientific Foundation of China (Grant No. 41702382), the National Major Scientific Instruments Development Project of China (Grant Nos. 41627801), the Open Research Fund Program of the State Key Laboratory for Geomechanics and Deep Underground Engineering of China (Grant No. SKLGDUEK1807), the Open Research Fund Program of the State Key Laboratory of Permafrost Engineering of China (Grant No. SKLFSE201709), and the Applied Technology Research and Development Plan Program of Heilongjiang Province (Grant No. GA19A501).  
**Acknowledgments:** The author wishes to thank the editor and the reviewers for their contributions on the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges
## 2 Background and Overview of WSNs
### LEACH-Based Routing Protocols in WSNs
#### 2.3.3 Proposed Taxonomy of LEACH-based Routing Protocols in WSNs  
In the current survey, the focus is on CH selection and cluster formation techniques as designated criteria to present the classification of LEACH-based protocols in WSNs. In Figure 4, LEACH-based routing protocols are categorized into two categories: CH selection-based and cluster formation-based protocols. Each main category is further classified into different classifications based on the main factor in the proposed approaches.  
The next subsections describe each main category in detail. Then, existing familiar protocols that belong to each main category are presented in detail.  
Figure 4: Proposed taxonomy of LEACH–based routing protocols in WSNs.  
\begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**Previous Survey** & **Contributions** & **Limitations** \\ \hline
[30] & Sixty LEACH variants protocols are presented and compared using eight parameters. & Only communication–related protocols are presented based on single-hop and multi-hop communication. \\ \hline
[31] & Many LEACH–based protocols are discussed and compared using three parameters. & Protocols are discussed without any classification, and the authors used only three parameters for comparison. \\ \hline
[32] & Six protocols are discussed to overcome the non–uniform distribution of CH in LEACH. & Only six LEACH variants are presented without any classification. \\ \hline
[33] & Sixteen LEACH variants are discussed and compared using seven parameters. & Only 16 LEACH variants are discussed briefly without any classification. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of previous surveys on leach-based routing protocols.
## 2 Materials
### MODIS 17 GPP/NPP Algorithm and Estimated Products  
The GPP is one of the parameters representing the material circulation of the terrestrial ecosystem and is used for detecting and measuring the change in carbon circulation [10, 11, 18, 22, 28]. Primary production can be divided into the GPP, which represents the total amount of organic matter produced by vegetation through photosynthesis, and the NPP, which excludes the breathing quantity of the vegetation from total organic matter [19]. These two indices are expressed as the amount of carbon per unit area. In this study, the 8-day GPP output with spatial resolution of 1 km from the MODIS primary product provided by the NASA was used. The MODIS 17 GPP algorithm is associated with carbon circulation. The GPP is a function of sunlight and active radiation-related parameters. The MODIS GPP algorithm is based on the radiation use efficiency of vegetation [10, 19] as expressed in the following equations:  
\[GPP=\varepsilon\times FPAR\times PAR \tag{3}\]  
\[\varepsilon=\varepsilon_{\mathrm{max}}\times f(T_{\mathrm{min}})\times f(VPD) \tag{4}\]  
\[PAR=0.45\times R_{s} \tag{5}\]  
where \(\varepsilon\) is the radiation use efficiency of vegetation, PAR is the photosynthetically available radiation (MJ\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)), \(\varepsilon_{\mathrm{max}}\) is the maximum radiation use efficiency, \(T_{\mathrm{min}}\) is the daily minimum temperature (\({}^{\circ}\)C), VPD is the vapor pressure deficit (the difference between the vapor pressure and actual pressure) (Pa), \(R_{s}\) is the short wave radiant energy (MJ m\({}^{-2}\) d\({}^{-1}\)), and FPAR is the fraction of the PAR absorbed by the plant with a value ranging from 0 to 1. When the PAR absorbed by the plant is large or the value of \(\varepsilon\) is high, then the GPP value increases. Equation (3) shows that the MODIS GPPalgorithm assumes that the value of \(\varepsilon\) is determined according to the vegetation type [34]. The PAR is assumed to be 45% of R\({}_{\rm s}\) (Equation (3)), which expresses the change in use efficiency determined by the opening and closing of the stomata according to \(T_{\rm min}\) and VPD which have upper and lower limits ranging from \(\varepsilon_{\rm max}\) to 0. The MODIS 15 FPAR product is used due to its similarity with normalized difference vegetation index (NDVI) [10]. The meteorological data of the MODIS GPP algorithm used the Data Assimilation Office (DAO) provided by the NASA, having a spatial resolution of 1.00\({}^{\circ}\)\(\times\) 1.25\({}^{\circ}\) as input data. The upper and lower thresholds of \(\varepsilon_{\rm max}\), \(T_{\rm min}\) and VPD determined the opening and closing of the stomata use constant, which were determined according to the vegetation type [34].
# Evading Logits-Based Detections to Audio Adversarial Examples by Logits-Traction Attack
## 3 Proposed Method
### Detection Based on Logits Dispersion by Other Three Features
#### 3.4.3 Detection Based on the Number of Delineation Statistics  
This method is still based on the assumptions introduced in Section 3.4.2 but is not concerned with the fluctuation of values. The following Figure 6 shows the distribution interval statistics of logits values of a speech and its adversarial examples in the Lingvo model. The original transcription of the speech is \"THE MORE SHE IS ENGAGED IN HER PROPER DUTIES THE LESS LEISSURE WILL SHE HAVE FOR IT EVEN AS AN ACCOMPLISHMENT AND A RECREATION\", and the target sentence to generate the antagonistic example is \"[PERSON] IS A FINE FELLOW BUT POOR AND HELPLESS SINCE [PERSON] HAD HER ACCIDENT\".  
It has a similar pattern to Figure 3. That is, the magnitude of logits value of adversarial examples is generally less than that of normal speech. This method just counts the number of values in the decision frames that are bigger than the filtered value \(p\): \(c^{+}=r\).  
The speech is judged as an adversarial example if \(c^{+}\) is less than a threshold value. However, this method is easily affected by the number of decision frames and can only be used in adversarial example detection with similar text lengths.
# A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments
## 3 Design of Cooperative Search Strategy
### Design of Reward and Punishment Function  
In this paper, we consider that multiple UAVs perform search tasks in the framework of reinforcement learning and obtain a higher overall efficiency \(J(\mathbf{s}(k),\mathbf{u}(k))\). If a UAV gets a higher efficiency when it executes a search task, it is rewarded immediately. If it gets a lower efficiency, it is punished immediately. Therefore, the reward and punishment functions are designed as follows:  
\[r(k)=\left\{\begin{array}{l}R\qquad\qquad\qquad,\,\,d\geq D\&\,\,\text{{{ discover}}}\quad\text{{{target}}}\\ a\times J(\mathbf{s}(k),\mathbf{u}(k)),\,\,d\geq D\&\,\,\text{{{no}}}\quad\text{{{ target}}}\\ -R\qquad\qquad\qquad,\,\,\,d<D\end{array}\right., \tag{20}\]  
where \(a\) is a constant which influences the generalization ability of the learning process, and \(a\times J(\mathbf{s}(k),\mathbf{u}(k))\in(-R,R)\), \(R\), and \(-R\) are the maximum reward and punishment, respectively. \(J(\mathbf{s}(k),\mathbf{u}(k))\) is determined by Equation (16). In order to ensure the safe flight of each UAV, \(d\geq D\) needs to be satisfied. If the no-fly zone is considered, \(B\) should be greater than the radius of the no-fly zone \(D^{\star}\) where \(B\) is the distance between the UAV and the center of the no-fly zone. At this time, the reward and punishment functions are rewritten as follows:  
\[r(k)=\left\{\begin{array}{l}R,\quad d\geq D\&B\geq D^{\star}\&\,\text{{{ discover}}}\quad\text{{{target}}}\\ a\times J(\mathbf{s}(k),\mathbf{u}(k)),\,\,\,d\geq D\&B\geq D^{\star}\&\,\,\text{{{ no}}}\quad\text{{{target}}}\\ -R,\qquad d<D\|B<D^{\star}\end{array}\right.. \tag{21}\]  
That is, if a UAV may collide or fly into the no-fly zone, the maximum punishment is employed.  
The complexity of an iterative Algorithm 1 is about \(O(\|L\|)+O(N_{p}\times m\times k)\), where \(\|L\|\) is the scale of the problem space. Thus, the time complexity of the completion of all the iterations is \(O(M\times\|L\|)+O(M\times N_{p}\times m\times k)\), where the number of iterations of the algorithm is \(M\). The space complexity of the algorithm is \(O(\|L\|)+O(N_{p}\times m)\). For a given multi-UAV, the overall computational cost is determined by \(M\), \(m\), and \(k\).
# Semantic Communication on Digital Wireless Communication Systems
## 3 IER--A Novel Physical Layer Semantic Metric
### Manhattan distance and IER (Integer Error Rate)  
For non-binary coding, Hamming distance is not suitable to define the error distance. Here, we suggest the adoption of Manhattan distance [26] to measure the error distance between the transmitted/received messages. The Manhattan distance is defined below.  
Let \(\text{x}=(\text{x}_{1}\text{x}_{2,\ldots,}\text{x}_{n})\) and \(\text{y}=(\text{y}_{1}\text{y}_{2,\ldots,}\text{y}_{n})\) be the integer-valued vectors at the transmitter and receiver, respectively,
## 3 Method
### Criteria for Comparison of TCDs  
The performance of TCDs is compared using the energy concentration measure and their ability to accurately estimate the instantaneous frequency.  
**Energy Concentration (EC):** A good TF representation should be able to concentrate the signal energy around the instantaneous frequency while suppressing the cross-terms. Therefore, to compare different TF methods, we use the following energy concentration measure, [45],  
\[\text{EC}=\left|\sum_{t=1}^{N}\sum_{f=1}^{M}|p[t,f]|^{\frac{1}{t}}\right|^{q}, \qquad q>1, \tag{25}\]  
where \(t\) and \(f\) are discrete values, \(N\) is the number of time-samples and \(M\) is the number of frequency-samples of the discrete Fourier transform. The performance measure is used for both measured and simulated HRV signals [46]. In order to examine if the results obtained by the best method are really statistically significant, the student's \(t\)-test is performed as  
\[t=\frac{\widehat{X}_{1}-\widehat{X}_{2}}{s}, \tag{26}\]  
where \(\widehat{X}_{1}\), \(\widehat{X}_{2}\) are the sample means. The standard deviation is calculated as  
\[s=\sqrt{\frac{s_{1}^{2}}{n}+\frac{s_{2}^{2}}{n}}, \tag{27}\]  
where \(s_{1}^{2}\), \(s_{2}^{2}\) are the corresponding variances, and \(n\) represents number of samples of a given population. Once the test-statistic is obtained, the \(p\)-value is obtained from the \(t\)-distribution. If the \(p\)-value is less than 0.05, the difference is considered statistically significant.  
**Performance comparison using the accuracy of the IF estimates:** In order to estimate the accuracy of the estimated instantaneous frequency, the ground truth respiratory frequency (GTRF) is estimated from the spectrogram of the measured respiratory data, where a highpass FIR filter of length 100 with cut-off 0.1 Hz is applied initially. The spectrogram is performed using a Hanning window of length 32 s. For respiratory data of non-stationary characteristics, usually the spectrogram of window length around 1 min is applied, [11; 19]. However, we have shown in [9] that a window length of 32 s is a better choice in time-varying conditions.  
The estimated GTRF of the \(i\)th TP is the maximum value of the spectrogram, \(GTRF_{t}^{i}\), at each time instant, \(t=1\ldots N_{i}\). The estimated sequence is smoothed using a filter of duration 32 s. Figure 1 shows the GTRFs for all 35 participants.  
Figure 1: The ground truth respiratory frequencies (GTRF), estimated from the spectrogram maximum peak values of the corresponding respiratory frequencies of test participants (TP) 1-35.  
From each HRV TF-image, the instantaneous respiratory frequency \(IRF_{t}^{i}\) is estimated as the maximum value at each time instant \(t=1\ldots N_{i}\). The grand average over all TPs of all mean squared errors (MSE) is calculated as:  
\[\text{gr.av.MSE}=\frac{1}{35}\sum_{i=1}^{35}MSE_{i}=\frac{1}{35}\sum_{i=1}^{35} \frac{1}{N_{i}}\sum_{t=1}^{N_{i}}(IRF_{t}^{i}-GTRF_{t}^{i})^{2}. \tag{28}\]