esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrofitting a Building's Envelope: Sustainability Performance of ETICS with ICB or EPS
## 6 Discussion and Conclusions
Moreover, the conclusions reached show thermal retrofitting has environmental benefits and that environmentally sustainable materials need to be more frequently used in construction. In fact, the environmental advantage of the latter is already known and proved, but they are still expensive. Therefore, a scale effect on their use can promote a decrease in price, making them more competitive in the construction market, or the public financing or the thermal retrofit interventions could include environmental sustainability requirements to reach similar benefits. Data curation, J.D.S. ; Methodology, J.D.S. ; Validation, A.M.P.C. and J.J.B.C.S. ; Writing--original draft, J.D.S. ; Writing--review & editing, A.M.P.C., J.J.B.C.S., J.M.C.L.d.B. and M.D.P. This research work was completed in the scope of the project MARIE--Mediterranean Building Rethinking for Energy Efficiency Improvement (2011-2014), cofunded by the European Regional Development Fund (ERDF) and the MED Programme. The authors are also grateful for the support from FCT (Foundation for Science and Technology) and from CERIS-ICIST Research Institute from Instituto Superior Tecnico, Universidade de Lisboa. Special thanks are due to the Portuguese manufacturers of construction materials, for providing the necessary data to complete this research work. The authors declare no conflict of interest. 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. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **C2C Life-Cyde** & **Performance** & **Heating and** & **Best** & **Difference to the Best** \\ (**LCA**) & **Stages Considered** & **Aspects** & **Coiling Needs** & **Performance/Design** & **Alternative Using the Other Insulation Material** \\ \hline
**GWP** & A1–A3; At; A5; B2–B4; C2–C4 & & & & 22\% (WE9) \\  & and D, without energy use for heating and cooling & Environmental & - & W9 & 30\% (WE9) \\ \hline
**FE-NRe** & & & & 10 & W14 & 2\% (WE14) \\ \hline
**GWP** & & & & 30 & \\  & A1–A3; At; A5; B2–B4; B6; C2– & & 50 & & W14/WE14 \\  & A1–A3; At; A5; B2–B4; B6; C2– & & 10 & & 4\% (WE14) \\  & C4 and D & & 30 & W14 & 1\% (WE14) \\  & & & 50 & & \\ \hline
**WLC** & A1–A3; At; A5; B2–B4; B6; C2– & & 10 & WE10 & 8\% (W11) \\  & A1–A3; At; A5; B2–B4; B6; C2– & & 25 & & 6\% (W14) \\  & C4 and D & & 35 & W14 & 5\% (W14) \\  & & & 50 & & 4\% (W14) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Cavity wall solution with the best performance, without considering the reference wall (the colour of each row depends on the best performing solution for each indicator: Brown for ICB and blue for EPS).
###### Abstract  
This study aims to investigate the joint strength of longitudinal plate-to-high-strength steel circular hollow-section X-type joints under plate axial load. The material properties of high-strength steel with nominal yield strengths of 460, 650, 900, and 1100 MPa were used for parametric analysis. The variables for analysis were ratios of chord diameter to thickness, plate width to chord diameter, and utilization. To determine the capacity of connections, the joint strengths using a deformation limit and a strength limit were considered and compared with American Institute of Steel Construction (AISC), Eurocode 3, and ISO 14346. The joint strength determined by the ultimate deformation limit is approximately equal to the joint strength determined by the strength limit state at the yield strength of 460 MPa. The difference between both the joint strengths, however, becomes higher with increasing yield strength. The design equations estimate the joint strength based on the ultimate deformation limit approximately until the limitation of the nominal yield strength in each design code. As the nominal yield strength increases, the joint strengths are overestimated. In using high-strength steel in circular hollow-section X-type joints, the reduction factors of 0.75 and 0.62 for AISC and ISO 14346 are suggested for the nominal yield strengths of 900 and 1100 MPa, respectively. In Eurocode 3, the reduction factor of 0.67 is also suggested for a yield strength of 1100 MPa.  
high-strength steel; circular hollow section (CHS); plate axial load; deformation limit state; strength limit state +
Footnote †: journal: Applied
# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 5 Fifth-Generation Performance with Multiservice
### Modeling Assumptions  
The following modeling assumptions were considered in the analysis:  
1. We assume that a single 5G cell has the maximum capacity. The capacity is affected by the 5G modulation schemes and the maximum number of users in the cell.
2. As per the modulation scheme in 5G, which is based on OFDMA (Orthogonal Frequency Division Multiple Access), the cell is divided into three virtual zones, as shown in Figure 4. According to 3 GPP Release 15 [36; 37], the physical layer of 5G downlinks uses different modulation schemes. Three modulation methods were assumed in this analysis, namely 128 Quadrature Amplitude Modulation (QAM) for zone 1 with 7 bits of transmitted information per symbol, 64 Quadrature Amplitude Modulation (QAM) for zone 2 with 6 bits of transmitted information per symbol, and 16 QAM for zone 3 with 4 bits.  
Figure 3: Maximum distance among different service classes.  
3. According to [37; 38], we consider a cyclic prefix-based OFDM (CP-OFDM) waveform, which offers improved spectrum separation compared to LTE, to meet the diverse applications expected in 5G and reduce inter-cell interference (ICI) and inter-symbol interference (ISI).
4. We assume a Poisson inter-arrival time \(\lambda\) within the cell. The traffic load is determined based on the zone areas (\(\alpha i\ast\lambda\), \(i=1\ldots\) zone \(i\)) and user density in the cell.
5. The call admission control algorithm (CACA) relies on a minimum bit rate threshold that users must meet to be admitted to the system.
6. The call admission control algorithm proposed in [12] was used in the proposed analysis.
## 3 Case Study
### DBN Establishment
#### 3.1.2 Network Structure and Parameter Determination
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Node** & **Description** & **States** & **Node** & **Description** & **States** \\ \hline X2 & Current & S0: safety (0–10 A) & & & S0: low \\  & Current & S1: mild overload (10–18 A) & Y2 & Temperature rise degree & S1: general \\  & & S2: severe overload (>18 A) & & & S2: high \\  & & S0: normal (0–40 \({}^{\circ}\)C) & & & S0: false \\ X3 & Ambient temperature & S1: relatively high (40–70 \({}^{\circ}\)C) & Y3 & Loose contact & S1: true \\  & & S2: very high (>70 \({}^{\circ}\)C) & & & S0: normal (30–65\%) \\ X4 & Ambient humidity & S1: relatively high (65–85\%) & Y4 & Poor heat dissipation & S0: false \\  & & S2: very high (>85\%) & & S1: true \\  & & S0: slight (0–2) & & & S0: low \\ X5 & Damage degree & S1: medium (2–5) & Y5 & Insulation breakdown & S0: medium \\  & & S2: severe (5–10) & & risk & S1: medium \\  & & S0: low (0–2) & & & S2: high \\ X6 & Aging degree & S1: medium (2–5) & Y6 & Overheating risk & S0: low \\  & & S2: severe (5–10) & & S2: high \\  & & S0: good (8–10) & & & S0: low \\ X7 & Connection quality & S1: general (5–8) & Y7 & Pyrolysis risk & S1: medium \\  & & S2: bad (0–5) & & & S2: high \\  & & S0: sufficient (8–10) & & & S0: false \\ X8 & Laying spacing & S1: relatively small (5–8) & Z & Fire & S1: true \\  & & S2: very small (0–5) & & & S2: very \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Y5** & **Y6** & **Y7** & \multicolumn{2}{c}{**Z**} \\ \cline{3-5}  & & & **False** & **True** \\ \hline S0 & S0 & S0 & 1.000 & 0.000 \\ S0 & S0 & S1 & 1.000 & 0.000 \\ S0 & S0 & S2 & 1.000 & 0.000 \\ S0 & S1 & S0 & 0.795 & 0.205 \\ S0 & S1 & S1 & 0.653
## 4 Discussion
### Language Selection Limitations and Their Impact  
Our study acknowledges several important limitations in language selection that may affect the generalizability of our findings. The six low-resource languages evaluated--Thai, Gujarati, Marathi, Pashto, Burmese, and Sinhala--exhibit notable geographical and linguistic concentration. Four of these languages (Gujarati, Marathi, Pashto, and Sinhala) belong to the Indo-European language family, while Thai and Burmese represent the Tai-Kadai and Sino-Tibetan families, respectively. This linguistic distribution creates potential biases in our evaluation.  
The geographical concentration primarily spans South and Southeast Asia, which may limit the applicability of our findings to languages from other regions such as Africa, indigenous languages of the Americas, or Pacific languages. Different linguistic regions may present unique challenges for cross-lingual transfer that are not captured in our current evaluation. For instance, languages with different writing systems (such as Arabic script, various African scripts, or logographic systems) might exhibit different patterns of semantic similarity encoding and retrieval effectiveness.  
Furthermore, the predominance of Indo-European languages in our dataset may overrepresent the effectiveness of our approach for languages that share certain structural similarities with English. The strong performance improvements observed in our X\(\rightarrow\)English direction might be partially attributed to underlying linguistic relatedness that facilitates cross-lingual transfer, rather than purely representing the universal effectiveness of retrieval-based example selection.  
Future work should evaluate MuRXLS across a more diverse set of languages, including representatives from major language families such as Niger-Congo (e.g., Swahili, Yoruba), Afro-Asiatic (e.g., Arabic, Amharic), and indigenous language families. Such evaluation would provide stronger evidence for the universal applicability of our approach and reveal potential language-family-specific patterns in retrieval effectiveness.
# Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion
## 4 Load-Carrying Capacity of RC Beams with ASR Deterioration
### Modeling of ASR Expansion  
This section describes a method for depicting ASR expansion in the analytical model and a coupled moisture-transfer-cracking method for precisely resolving the effect of moisture as a reaction factor in ASR degradation.
## 2 Materials and Methods
### System Design
#### 2.1.1 System Architecture  
In the application environment of fires occurring in primitive forest areas without a public network, we built a BeiDou satellite communication network based on Radio Determination Satellite Service (RDSS) protocol to establish communication with a remote command center [25; 26; 27]. Considering the urgency of firefighting operations and the need for firefighters to act immediately upon entering the scene, a local self-organized network is established by using Zigbee self-organized network technology based on the IEEE 802.15.4 [28] protocol [29; 30]. By utilizing this method, a reliable emergency communication network can be constructed at the fire scene quickly.  
In this study, the local self-organized network terminal and satellite communication network terminal are designed based on BeiDou RDSS technology. The local self-organizing network terminal can automatically collect various kinds of parameter information of the fire field and summarize this information and the user's freely edited information for the local equipment through the Zigbee wireless self-organizing network, and then wait for the next transmission from the satellite communication network terminal. The satellite communication network terminal then packages and sends the aggregated data to the BeiDou GEO orbiting satellite, thus establishing two-way communication with the remote command center when there is no public network [31; 32]. Figure 1 shows the system architecture.
## 3 System Model Design
### Calculating Mobile Similarity of Node and Narrowing the Node Range by a Single Threshold
#### 3.2.1 Computation of the Mobile between Nodes
For example, when people travel from home to the company, they should be given more weight. At the same time, when people move in a space for a long time, such as working status, the weight should be set smaller. According to the formula 8, we can calculate the mobile similarity of the nodes:  
\[M\_similarity(node_{u},node_{v})=\frac{\sum\limits_{i=1}^{n(u)}\sum\limits_{j= 1}^{n(v)}\lambda(\Delta t-\big{|}f_{ti}(u)-f_{tj}(v)\big{|})(\mu(s_{i}(u),s_{j} (v)))}{\sum\limits_{i=1}^{n(u)}\sum\limits_{j=1}^{n(v)}\lambda(\Delta t-\big{|} f_{ti}(u)-f_{tj}(v)\big{|})} \tag{8}\]  
where \(\lambda\) is the weight of different periods, \(i\), \(j\) represents the sequence of nodes, \(s\) represents the communication area, \(f_{ti}(u)\) represents the first communication time of node \(u\), \(f_{ti}(u)-f_{tj}(v)\) represents the time interval between nodes, and \(\mu(s_{i}(u),s_{j}(v))\) represents the position similarity between node \(u\) and node \(v\).
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 4 Design Issues in Gait, Balance and RoM Wearable Systems
### Soft Tissue Artifact  
A challenge in human motion analysis is posed by soft tissue artifact (STA). STA occurs from unequal movement of soft tissue layers (muscle, tendon, and dermis) between the bone and the skin surface [90]. Typically, relative translation and relative rotation are assumed to show the majority of STA, in which yields to be the components targeted for mitigation [91]. Another way to mitigate STA is by processing the translational acceleration and rotational velocity measured by an IMU [92]. Few studies included in this review handled soft tissue artifact in different ways. A common wayis to place the devices over the bones and not over the muscles to reduce soft tissue artifact [58]. Additionally, using bundles and straps and having care in positioning the bundles can minimize soft tissue artifact [57].  
Additionally, STA also occurs in optoelectronic systems when placing markers to the subject's segments. This needs to be taken into consideration since optoelectronic systems are often used to validate wearable sensor measurements [57; 58; 61; 62]. Few ways to minimize this issue is by having the marker within the field of view of at least two cameras, markers attached to the same segment should be distributed to minimize position error propagation to bone orientation, and the movement between underlying bones and the markers should be minimal [93; 94; 95; 96; 97].
# A New Approach to Characterize Superplastic Materials from Free-Forming Test and Inverse Analysis
###### Abstract  
For about 60 years, the aerospace industry has been strongly interested in superplastic forming processes to produce extremely light and complex-shaped components. Superplastic characteristics are found in lightweight metallic materials such as titanium-based, aluminum-based, and, more recently, magnesium-based alloys. Since the high ductility exhibited by superplastic materials is two orders of magnitude higher than that of conventional materials, complex-shaped components can be obtained. If made with conventional materials, they require expensive assembly operations. The behaviour of superplastic materials is summarized by a constitutive equation commonly obtained via tensile testing that subjects the tested material to a one-dimensional stress state. On the contrary, free-forming tests allows us to test the material by subjecting it to a stress state similar to that determined during a real superplastic-forming process. The aim of this work is to define the characteristic parameters of superplastic materials by free-forming tests. The behaviour of superplastic materials is commonly modelled using a power law which puts the material into a stress-to-strain-rate relationship. This law needs to identify two parameters characterizing superplastic materials: the strain rate sensitivity index and the strength coefficient. In this work, a new procedure is presented that implies the two material parameters vary with strain. It allows for a reduction in the number of constants needed to determine the material constitutive equation, thus requiring low simulation time compared to models that adopt the multiple-objective optimization based on genetic algorithms (GAs). It is more suitable to be used in the industrial field. Furthermore, the proposed procedure is compared with a conventional procedure which is also based on the inverse analysis carried out through the use of a finite element analysis. The results of the conventional procedure, based on the inverse analysis, which is conducted through the use of a finite element analysis, are used to calculate the material constants, and are compared with those coming from the procedure proposed in this work. The proposed procedure appears equally simple and gives more accurate results compared to the conventional procedure. In fact, the maximum percentage error, regarding the prediction of the forming times of a free-forming process, was reduced from 20% to 8%. The development of the proposed procedure, as well as the comparison of the results with a conventional procedure, required the development of an experimental activity. This activity consists of free-forming tests conducted at a constant pressure (the pressures employed vary from 0.2 to 0.4 MPa), at a temperature of 753 K, and on circular sheets (thickness 1.0 mm and radius 40 mm) in superplastic magnesium alloy AZ31.  
Keywords:magnesium alloy; superplastic forming; finite element analysis; inverse analysis; free-forming test +
Footnote †: journal: applied sciences
## 3 Instance Segmentation Framework Based on Improved YOLACT  
YOLACT (You Only Look At CoefficientTs) is a simple and fast real-time instance segmentation model. The overall architecture design is lightweight. Its segmentation accuracy and speed are well-balanced, which makes it convenient for deployment on edge devices. The YOLACT network consists of two parallel processing branches: the mask prediction branch, Protonet (prototype mask branch), and the object detection branch prediction head. The Protonet branch learns the feature representation of the target instance in the image and generates prototype masks for a single instance using a fully convolutional network structure, which contains the semantic and shape information of the target instance. The prediction head predicts the corresponding mask coefficients for each candidate box to obtain the position of instances in the image. The two branches perform parallel calculations, greatly improving the running speed of the entire model. Finally, the mask coefficients are fused with the prototype mask through matrix multiplication to obtain the final prediction result, shortening the inference time of the model and meeting real-time requirements. Despite its balanced performance in real-time instance segmentation, YOLACT has a lower accuracy compared to two-stage instance segmentation algorithms, while YOLACT's detection speed can still be improved, as it does not use a lightweight backbone network [30]. Therefore, this paper will mainly improve the network from the perspective of accuracy, while also improving the inference speed of the network. The overall network structure of the improved YOLACT is shown in Figure 1.  
The improved YOLACT model consists of five parts: (a) a backbone network, (b) a feature fusion network, (c) a head network, (d) a prototype mask network, and (e) a hybrid module. The backbone used in (a) is MobileNetV2, and the modified FPN module will be introduced in the next section. In the outputs of three branches of (c) the head network, a is the number of anchor boxes, c is the number of categories, and k is the number of prototype masks. For the quick and accurate segmentation of insulator images, we improve the backbone network, FPN module, and bounding box loss function, respectively, taking the characteristics of insulator defect segmentation into consideration, so as to improve the segmentation performance of the model.
## 4 Simulations Results
### Sensitivity and Robustness Testing
#### 4.4.2 Models with Uncertainty  
This section tests whether the control system is sufficiently robust to uncertainties in its mathematical model. Further examples concern cases when the models do not fully reflect the dynamic properties of the control process, and therefore, this assumption will not be met. It is clear that when the model uncertainty is large, it will not be possible to meet the requirements set for the control system. On the other hand, such studies are expedient because if it were to turn out that the developed design method is too sensitive to model uncertainty, it should not be recommended.  
Figure 5: Time series of controlled value 1—by model accuracy; 2—with the time constant \(T\) 20% larger; 3—with the time constant \(T\) 20% lower.  
Example 4  
If the plant, despite being a higher-order object, has a dominant time constant, then its model can be fairly accurately represented by a first-order transfer function with a time delay (12).  
These conditions are met by the process  
\[G_{5}(s)=\frac{0.5\,e^{-2.8s}}{0.128\,s^{4}+1.616\,s^{3}+6.6\,s^{2}+8.8\,s+1}\]  
whose model  
\[G_{5m}(s)=\frac{0.5\,e^{-3s}}{8.15s+1}\]  
can be used to determine the PI controller settings. Based on relationships (39) and (45), it was calculated that \(T_{i}=8.15\) and \(K_{p}=2\). A comparison of the system response with the reference response is shown in Figure 6.  
Example 5  
We consider an example taken from [19]:  
\[G_{6}(s)=\frac{3(1.5s+1)\,e^{-3s}}{(10s+1)(8s+1)(5s+2)(3s+1)(0.1s+1)}\]  
The FOPDT model of the plant  
\[G_{6m1}(s)=\frac{1.486e^{-8.3s}}{17.8s+1}\]  
is unable to adequately cover the dynamics of the process accurately. The controller settings calculated from this model are \(K_{p}=0.5309\) and \(T_{i}=8.3\) s. As a result of the high model uncertainty, the response of the system (Figure 7, (1)) already shows a slight overshoot, which also increased, and referred to (42), the settling time is \(t_{s}=94.3\) s. In this case, however, the SOPDT model has much less model uncertainty:  
\[G_{6m2}(s)=\frac{1.486e^{-6.1s}}{95.68s^{2}+18.52s+1}\]  
Using the described method on the basis of the adequate relationships for such models, (49), (50), and (53), the PID controller settings \(K_{p}=0.7516\), \(T_{i}=8.15\), and \(T_{d}=5.1663\) were obtained. The controller designed in this way allowed us to achieve the expected control performance, as indicated by the comparison of the system response (Figure 7, (3)) with the reference response (2).  
Figure 6: Response of the system described by the transfer function \(G_{5}(s)\). 2—Response of the reference signal (desired response) calculated for its model \(G_{5m}(s)\).