esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
A group of heavy metal oxide glasses based on the nominal composition of (75-x)TeO\({}_{2}\)-15 Sb\({}_{2}\)O\({}_{3}\)-10 WO\({}_{3}\)-xSm\({}_{2}\)O\({}_{3}\) (where; x = 0.2, 0.5, 0.8, 1, and 1.5 mol%) have been selected from the literature [9], where the authors have performed a characterization study on those glasses in terms of their luminescence properties. Their research findings encouraged us to broaden the scope of the characterization by including certain critical glass characteristics, namely nuclear radiation shielding capabilities. The compositions of the studied glass samples can be listed as follows.  
* 74.8 TeO\({}_{2}\)-15 Sb\({}_{2}\)O\({}_{3}\)-10 WO\({}_{3}\)-0.2 Sm\({}_{2}\)O\({}_{3}\)
* 74.5 TeO\({}_{2}\)-15 Sb\({}_{2}\)O\({}_{3}\)-10 WO\({}_{3}\)-0.5 Sm\({}_{2}\)O\({}_{3}\)
* 74.2 TeO\({}_{2}\)-15 Sb\({}_{2}\)O\({}_{3}\)-10 WO\({}_{3}\)-0.8 Sm\({}_{2}\)O\({}_{3}\)* 74 TeO\({}_{2}\)-15 Sb\({}_{2}\)O\({}_{3}\)-10 WO\({}_{3}\)-1 Sm\({}_{2}\)O\({}_{3}\)
* 73.5 TeO\({}_{2}\)-15 Sb\({}_{2}\)O\({}_{3}\)-10 WO\({}_{3}\)-1.5 Sm\({}_{2}\)O\({}_{3}\)  
Accordingly, we used two different tools to determine the critical gamma-ray shielding properties along with some mathematical applications for determining the effective removal cross section (\(\Sigma\)R) values for fast neutrons. The linear attenuation coefficients of the glasses were derived using the general-purpose Monte Carlo software MCNPX (v.2.7.0) [10] and the online computation platform Phy-X/PSD [11]. The purpose of this dual tool method was to assess the consistency of the findings obtained from Monte Carlo simulations.
## 1 Introduction
To tackle the aforementioned issues, this paper proposes novel strategies adopted in the design of the PROactive Monitoring for indoor EnvironmentTal quality and cOmfort (PROMET&O) system. Such a system aims to assess IEQ and how it is perceived by occupants in office environments. Indeed, low-cost monitoring devices, placed on workers' desks, monitor IEQ conditions for comparison with their comfort perception feedback. This allows for end-users' engagement and establishes best practices for IEQ enhancement and energy saving through a more conscious management of building systems. With respect to previous works, this paper proposes a full-stack design of an IEQ monitoring system, in which several strategies have been implemented to overcome the limitations of existing systems in both monitoring devices and cloud platforms. More precisely, this work makes the following contributions, which allow one to step forward with respect to the state of the art. At the multi-sensor level, the hardware design prioritizes flexibility, while optimizing sensor placement to minimize cross-sensitivity. Data preprocessing is employed to extract significant statistical parameters, thus reducing the amount of data transferred to the server. At the cloud back-end server level, a reference database design for IEQ parameters collection is introduced, along with a solution for data aggregation challenges over extended time intervals, which were not discussed previously. In addition, preaggregations are introduced to compute the final aggregation faster, allowing the user to receive up-to-date statistics aligned to the instant of the request. For data visualization on the front-end server, charts from open-source dashboarding software are integrated into a custom web applicatio that allows for extensive customization during development to ensure optimal usability. Moreover, the system's flexibility facilitates easy graph modification to meet evolving requirements without extensive coding. By incorporating these strategies, the PROMET&O system can evaluate IEQ and occupants' perception in office environments, promoting users' engagement and definingIEQ improvement and energy-saving practices. Its hardware and software flexibility enable future adaptation to similar applications. The paper is organized as follows. The overall architecture of the PROMET&O system is discussed in Section 2, with particular focus on the data sampling and aggregation processes. The developed multi-sensor device and the related firmware are discussed in details in Section 3, in which a comprehensive cost analysis is reported as well. Then, the main blocks the cloud server is comprised of are analyzed in Section 4. Preliminary experimental results are reported in Section 5, including a comparison of the proposed system against existing ones. Concluding remarks are drawn in Section 6.
## 3 Results and Discussion
### Corridor Geometry  
The transmission line geometry used in this work consists of a single-circuit line and two overhead ground wires (OGWs). This configuration--typical of 500 kV AC systems used in the U.S.--was taken from the work of [PERSON] and colleagues in [49]. The line and the metallic pipeline buried in the soil are shown in Figure 3. As one can see, the electrical line and the pipeline are embedded into two semicircular regions with radius 5 km. The MVP \(A_{z}\) is assumed to be null at the outer edge of the domain (Dirichlet boundary condition). The transmission line conductor's sizes, not specified in [49], were taken from [50], where a similar configuration was considered. The radii of the phase conductors and the OGNs are 15.3 mm and 5.6 mm. The pipeline has a diameter of 1 m and a thickness of 1.5 cm. Regarding the electric properties of the materials, the three phase conductors are made of aluminum (\(\sigma_{\mathrm{phase}}=3.5\times 10^{7}\) S/m), while the OGNs and the pipeline are made of steel (\(\sigma_{\mathrm{OGW,pipe}}=5.5\times 10^{6}\) S/m). The electrical conductivity of the soil is \(\sigma_{\mathrm{soil}}=1\times 10^{-2}\) S/m.  
The domain in Figure 3 was discretized with the open-source Gmsh 4.11.1 software [51], which allows one to easily embed structured regions in a non-structured Delaunay triangulation. This is particularly useful for capturing the current density distribution in highly conductive (or magnetically permeable) regions subjected to the skin effect, especially at frequencies typical of transient phenomena. The mesh has 70,350 nodes and 140,568 triangles. The characteristic size of the elements ranges from \(\approx\)250 m at the outer edge to \(\approx\)1 \(\times\) 10\({}^{-6}\) m at the surface of metallic conductors (e.g., the buried pipeline). At the soil surface a coarser mesh size can be used, due to the soil electrical conductivity being several orders of magnitude smaller than typical values for metallic conductors; yielding a larger skin depth.
# Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
## 3 Implementation and Results
### Model Selection  
This data analysis focuses on addressing the regression problem using empirically collected data and does not pertain to the use of DL for topographical feature extraction. This is for two reasons. The first is that data analysis utilizes empirically collected data rather than ray tracing simulation. The second reason is that topographical features are pre-extracted using image processing techniques rather than DL due to the size of the dataset. It is evident from the review of the literature that DL-based models require large volumes of data for training. Several different AI algorithms are chosen to cover the entire spectrum of AI, and different algorithms that have appeared in the literature for regression modeling of LoRa's propagation are compared.  
Optimized FSPL and linear regression (LR) are included to establish a baseline for capturing linear relationships in the data. However, LR is further expanded by using polynomial regression (PR), which is particularly useful as it can reveal the non-linear relationships in the dataset and examine the importance of feature engineering. Care must be taken to avoid overfitting, especially with high-degree polynomials, as the model might become too complex and sensitive to noise in the data.  
In the ANN category, LSTM, MLP, and ANFIS were noted in the literature. MLPs are by far the most common model in use, either as a standalone model or as the last layer of DL-based algorithms. Simple architecture with three hidden layers can approximate any arbitrary continuous function, with the only downside being prone to overfitting, which can be avoided by the use of regularization and validating the training process. LSTM is beneficial for sequential data when dependencies between data points that are apart in time are of importance, such as time series, and they are irrelevant given this dataset. Adaptive neuro-fuzzy inference systems (ANFIS) can take advantage of an expert's knowledge prior to the training and, therefore, can converge with fewer data samples; they can be helpful when conducting field measurements is costly. Two other classes are SVM/RVM and KNN; these models have the advantage of being less prone to overfitting and, therefore, easier to train compared to ANNs. The use of RVM was also noted due to its better generalization, where it was identified to suit the analysis of smaller-sized datasets. Finally, decision trees are chosen for being effective and having excellent AI explainability (XAI) [37]. Decision trees (DT) are also used as base estimators (weak learners) in the category of ensemble learning algorithms, such as random forest (RF) and gradient boosting (GR). GR and RF differ based on their training and aggregating the base estimators. Generally, the diversity of weak learners in ensemble learning algorithms provides less susceptibility to overfitting and better generalization.
# Time-Dependent Performance of a Multi-Hop Software Defined Network
###### Abstract  
It has been recently observed that Software Defined Networks (SDN) can change the paths of different connections in the network at a relatively frequent pace to improve the overall network performance, including delay and packet loss, or to respond to other needs such as security. These changes mean that a network that SDN controls will seldom operate in steady state; rather, the network may often be in transient mode, especially when the network is heavily loaded and path changes are critically important. Hence, we propose a transient analysis of such networks to better understand how frequent changes in paths and the switches' workloads may affect multi-hop networks' performance. Since conventional queueing models are difficult to solve for transient behaviour and simulations take excessive computation time due to the need for statistical accuracy, we use a diffusion approximation to study a multi-hop network controlled by SDN. The results show that network optimization should consider the transient effects of SDN and that transients need to be included in the design of algorithms for SDN controllers that optimize network performance.  
SDN switch; internet traffic; quality of service (QoS); diffusion approximation Article
## 7 Discussion  
### System Effectiveness Comparison and Analysis  
In the previous section, the system's scalability and functionality were validated, yet the effectiveness of the simulated scenarios within the TS remains open to question. For application scenarios, the participation of key vehicles (HV, RV) is authentic and valid. However, for communication scenarios, the performance evaluation of the TS necessitates further verification.  
In our previous work [4], we conducted a comparison between the communication performance evaluation of the TS and that of the NS-3 simulation. In highway scenarios, the difference in Packet Delivery Delay (PDD) between the two was approximately 2.02%, while the difference in Packet Delivery Ratio (PDR) was about 0.19%. For urban road scenarios, the discrepancy in PDD was roughly 3.72%, and the discrepancy in PDR was approximately 1.89%.  
The large-scale LTE-V2X test platform [18], with 198 OBUs deployed, was also cross-validated for the intersection scenario. They found that the difference in Packet Error Rate (PER) was approximately 3%, and the difference in average communication delay was about 7.4%. Additionally, the trends for evaluation parameters (Inter-Transmission Time (ITT), Channel Busy Ratio (CBR), and PER) remained consistent.  
It can be observed that this large-scale field testing scheme is practical, yet the analytical parameters of the TS we proposed need to be revised.
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## Experimental Program
### Specimen Design  
The geometry of the specimens is depicted in Figure 1. Each specimen had a cross section of \(250\) mm \(\times\) 300 mm and a height of 1800 mm, and the thickness of the protective layer was 30 mm. The concrete design strength grade was C30, and the actual cube compressive strength was determined  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & **Reinforcement** & **Proof Strength at** & **Ultimate** & **Elastic Modulus** & **Elongation** \\  & **Diameter** & **No Proportional** & **Strength** & **(\(\times\)10\({}^{5}\) MPa)** & **(\%)** \\  & **(mm)** & **Extension (MPa)** & **(MPa)** & **(\(\times\)10\({}^{5}\) MPa)** & **(\%)** \\ \hline
2304 & 12 & 636.5 & 842.3 & 1.56 & 33.0 \\
2304 & 16 & 531.8 & 768.3 & 1.56 & 36.4 \\
2304 & 25 & 542.7 & 760.7 & 1.40 & 34.1 \\ HRB400 & 16 & 477.0 & 654.3 & 2.02 & 26.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of reinforcement specimens.  
to be 30.8 MPa by testing three concrete cubes with dimensions of 150 mm \(\times\) 150 mm \(\times\) 150 mm. Two bent steel bars were placed at the corbel, and the stirrup spacing was reduced to 50 mm.  
Nine specimens were designed: one ordinary reinforced concrete column (HRC9) and eight SSRC columns (SSRC1-8). The system variable parameters considered included four eccentricities (80, 150, 200, and 270 mm), three longitudinal diameters (12, 16, and 25 mm), and three reinforcement rates (0.60%, 1.07%, and 2.61%). The design parameters of the specimens are listed in Table 2.
## 3 Results
### Ranking with COPRAS Method
In the second cluster, which includes countries such as France, Spain, Sweden, and Switzerland, France ranked first in terms of performance. In the third cluster, it ranked first among countries such as the USA, Germany, and the United Kingdom. Figure 7: Ranking of countries using the COPRAS method—2018. For 2021, the ranking of country performance was realized as shown in Figure 10. In the first cluster, which includes countries such as Germany, Italy, and the United Kingdom, Japan ranked first in terms of renewable energy utilization performance. In the same cluster, Turkiye ranked ninth in terms of renewable energy resource utilization performance. In the second cluster, which includes countries such as Spain, Sweeden, and Switzerland, France ranked first in terms of performance, while in the third cluster, it ranked first among countries such as the USA, Norway, and Canada. Figure 8: Ranking of countries using the COPRAS method—2019. Figure 9: Ranking of countries using the COPRAS method—2020. The ranking of 2022 country performance was realized as shown in Figure 11. In the first cluster, which includes countries such as Germany, Italy, and the United Kingdom, Japan ranked first in terms of renewable energy utilization performance. In the same cluster, Turkiye ranked ninth in terms of renewable energy resource utilization performance. In the second cluster, which includes countries such as Spain, Sweeden, and Switzerland, France ranked first in terms of performance, while in the third cluster, it ranked first among countries such as the USA, Norway, and Canada. Figure 11: Ranking of countries using the COPRAS method—2022. Figure 7.0 Ranking of countries using the COPRAS method—2021. The variables selected by the CRITIC and COPRAS methods were evaluated by cluster analysis in OECD countries. Each variable was weighted using the CRITIC method, and countries were ranked using the COPRAS method. Thus, it was determined which country assigned how much weight to which variable, and a cluster analysis was conducted for all countries by year. Due to the fact that some countries assign more importance to the selected variables compared to other countries, variable weights were higher in some country groups as a result of the CRITIC method. Since the importance assigned by the countries to the variables also varies according to the year, the country rankings contain differences.
###### Abstract  
Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored.  
robot localisation; UAV localisation; 5G NR; localisation algorithms +
Footnote †: journal: Sensors
## 5 Grmc-Sm
### Data Forwarding and Routing in GRMC-SM  
In GRMC-SM, all nodes forward their packets to one-hop neighbors or in-range sinks placed at shorter the distance from the surface than the node itself. The deployment of mobile sinks is uniform in the field to cover maximum volume of the network. If, a node is unable to find sink(s) in its transmission range then nodes relay data packet via multi-hop mechanism towards the destination by following the greedy approach. Algorithm 3 presents the data forwarding (DFM) and routing mechanisms in GRMC-SM.  
In case, a node is trapped in a coverage hole and does not find a potential neighbor node or nearby sink. This node broadcasts a void-node-declaration message to its neighbors in the CC and to the NCs to avoid the data loss and transmission trap. This declaration saves node battery and allows the network nodes to operate for maximal time period. This information is further spread to the nearby mobile sink, which aid the void node to deliver its sensed and received information to the base station for further processing. Once \(MS_{n}\) receives the void-declaration message, the movement of the closest mobile sink is triggered to change its course to provide to the void node at top priority. When mobile sink \(S^{\prime}(x,\Delta y,z)\) disseminates the changed co-ordinates in its transmission range, the void node forwards its data to \(S^{\prime}\). From there onward, mobile sink relays composite data to the sinks placed at the surface. As a last step, a set of surface sinks transmits data via radio link to monitoring centre on the surface.  
Figure 4: Schematic diagram of GRMC-SM.  
```
1 begin
2 Procedure\(Directional\)\(forwarding(Node,Data)\)
3 Initially, \(F_{s}=\phi\)
4 For Node '\(a\)',
5 forNeighbors(a)do
6\(Greedy\)\(forwarding\)
7 if\(seNeighbors(a)\)then
8 if\(D_{a}^{rs}<D_{s}^{rs}||D_{s}^{a}\leq R_{c}\)
9 Send packet
10 endif
11 end if
12 if\(neNeighbors(a)\)then
13 if\(D_{n}^{rs}<D_{a}^{rs}\)then
14\(F_{s}\gets F_{s}\bigcup n\)
15 Compute ADV using Equation (3)
16 Arrange nodes based on ADV
17 Select first priority node from \(F_{s}\)
18
19 Endif
20 end if
21 endif
22 end for
23 Endfor


```  
**Algorithm 3**Data forwarding mechanism (DFM).  
```
1 begin
2 Procedure\(Directional\)\(forwarding(Node,Data)\)
3 Initially, \(F_{s}=\phi\)
4 For Node '\(a\)',
5 forNeighbors(a)do
6\(Greedy\)\(forwarding\)
7 if\(seNeighbors(a)\)then
8 if\(D_{a}^{rs}<D_{s}^{rs}||D_{s}^{a}\leq R_{c}\)
8 Send packet
9 Endif
10 end if
11
12 end for
13 if\(neNeighbors(a)\)then
14 if\(D_{n}^{rs}<D_{a}^{rs}\)then
15\(F_{s}\gets F_{s}\bigcup n\)
16 Compute ADV using Equation (3)
17 Arrange nodes based on ADV
18 Select first priority node from \(F_{s}\)
19 Endif
20 end if
21 Endif
22 end for
23 Endfor
24 end for
25
26 end for
27
28 end for
```  
**Algorithm 4**Data forwarding mechanism (DFM).
# Multi-Objective Optimization of Square Corrugation Multilayer Nested Structures
## 6 Conclusions  
This study conducts multi-objective optimization of multi-layer nested thin-walled corrugated pipes, namely SCOD, combing the DCNSGA-III algorithm and TMDSM. The DCNSGA-III algorithm is applied to solve the non-dominated Pareto alternative to the  
Figure 12: The parametric comparison between initial and optimized structures.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(\mathbf{t_{int}}\) & \(\mathbf{t_{mid}}\) & \(\mathbf{t_{out}}\) & \(\mathbf{t_{rib}}\) & \(\mathbf{D_{mid}}\) & \(\mathbf{IPCF}\) & \(\mathbf{SEA}\) & \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(km)** & **(kN)** & **(kJ)** & **CFE** \\ \hline Optimal solution & & & & & & & & \\ FE values & 1.990 & 0.523 & 0.500 & 0.500 & 25.000 & 29.678 & 17.238 & 0.781 \\ Error & — & — & — & — & — & 2.050 & 18.371 & 0.869 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of optimization results and FE model.  
design of optimization. The optimal design point is searched by utilizing TMDSM from the alternatives to determine the definitive combination of structural parameters.  
The FE model of the SCOD is established based on the basic parameters, and validation is completed. Referring to the error of experiment and simulation results, the accuracy of the FE model is proved as reasonable and effective. In the early stage of optimization work, the design of experimental points is completed using the optimal LHD method, and then variables and target values are fitted according to the obtained experimental point data by utilizing PRS. By computing the four measurements, the surrogate model is validated. An optimal with comprehensive consideration of multiple objectives is obtained by retrieving from the Pareto front. The ultimate optimization result indicates that the _IPCF_ reduces by 53.4% and the _CFE_ increases by 8.7% after optimization. It can be concluded that the optimization method combining the DCNSGA-III algorithm and TMDSM used is effective.  
In the future investigation, we will focus on three aspects: (1) developing further experiments and providing more adequate test data to support the validation; (2) applying fuzzy methods and other theories to solve the problem of weight uncertainty to find a more suitable Pareto solution set; (3) studying the impact of ripple amplitude and number on structural crashworthiness and optimize for them.  
Methodology, H.Z., T.L. and Z.H.; software, D.Y. and L.W.; validation, H.Z., Z.H. and Y.Z.; formal analysis, D.Y.; writing--review and editing, H.Z., Z.H. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was Supported by the Foundation of State Key Laboratory of Automotive Simulation and Control [No. 20210236].  
Not applicable.  
**Data Availability Statement:** The study did not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
Figure 17: Second-order interaction response surface of IPCF.