esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Open Research Challenges and Research Directions
### Policies and Standardization Issues  
Policies and standardization help improve the quality of healthcare services by providing a framework for the consistent delivery of care. They also help to reduce costs and errors and increase patient safety by ensuring that all healthcare providers are following the same protocols. It ensures that healthcare providers deliver quality care in a consistent and cost-effective manner. Standardization helps to improve patient safety, reduce medical errors, streamline processes, and enhance overall quality. It has been estimated that approximately 51% of medical devices currently in production are governed by Food and Drug Administration (FDA) rules, which disrupt medical delivery because they affect IoMT efficiency [109].  
Various value-added services, such as electronic health records and more, also require standardization. To achieve this, organizations such as the Information Technology and Innovation Foundation (IETF), the Internet Protocol for Smart Objects (IPSO) Alliance, and the European Telecommunication Standards Institute (ETSI) collaborate through IoT technology working groups. These groups work alongside m-health and e-health organizations and IoT researchers to standardize IoT-based healthcare services. Additionally, the healthcare sector is fragmented, with different stakeholders having diverse interests and objectives, which makes implementing a unified policy challenging. Despite these challenges, policies and standardization in healthcare offer numerous benefits. They help ensure that patients receive consistent, high-quality care regardless of the treatment location. Furthermore, these policies enhance communication and coordination among healthcare providers, leading to improved patient outcomes.
# Spiral-Resonator-Based Frequency Reconfigurable Antenna Design for Sub-6 GHz Applications
## 2 Antenna Design
### Fabricated Designs
Based on these restrictions, the truth table and block diagram of SPDT switches, whose models are CG2415M6, are represented in Figure 6b [51]. SPDT switch is a type of electrical switch that consists of a single input or common terminal (COM) and two output terminals known as the normally open (NO) and normally closed (NC) terminals as shown in the top view. The working principle of an SPDT switch involves the actuation of a mechanical mechanism to change the connection between these terminals. In its resting or unactuated position, the COM terminal (pin 5) is electrically connected to the NC terminal (pin 1), whereas the NO terminal (pin 3) is disconnected. This means that when the switch is not pressed or flipped, the circuit is completed between the COM and NC terminals, allowing current to flow through that path. When the switch is actuated or flipped, the mechanical mechanism inside the switch moves, causing the connection to change. In this new position, the COM terminal becomes connected to the NO terminal, and the connection between COM and NC is broken. This means that when the switch is pressed or flipped, the circuit is completed between the COM and NO terminals instead, redirecting the current flow through that path. The SPDT switch is commonly used to select between two different circuits or to switch between two different paths within a circuit. The COM terminal is typically connected to the input or source, whereas the NO and NC terminals are connected to different outputs or destinations. By actuating the switch, the connection can be changed from one output to the other. The connection can be changed from one output to the other. another. In this study, the COM (Common) terminal of the SPDT switch is connected to one of the digital input pins (e.g., pin 2) on the Arduino Uno. The NO (Normally Open) terminal of the switch is connected to the Arduino's 3 V power supply. The NC (Normally Closed) terminal of the switch is connected to the ground (GND) on the Arduino. It is also crucial to take into consideration that the remaining terminal (NO or NC, depending on the one that is used in the previous step) should be connected to the GND (ground) pin on the Arduino Uno. After the connection of the terminals to the Arduino Uno, the coding is determined based on the connections. In other words, based on the switch state, the appropriate code is written to perform specific actions, control other components, or make decisions in your Arduino program.
## 5 Conclusions  
The LCA method can be used for the building whole life cycle carbon emission assessment, and the carbon emission factor method is widely applied in the phases of building life cycle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phases** & **Case Range** & **Refurbished** & **New Building** \\  & **(tCO2e/m2)** & **Building (tCO2e/m2)** & **(tCO2e/m2)** \\ \hline Materialized phase & 0.3–0.8 & 0.438 & 0.736 \\ Operational phase & 2–4.5 & 1.94 & 1.94 \\ Whole life cycle & 2–8 & 2.39 & 2.69 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Whole life cycle carbon emission level of the refurbished building and new building.  
Figure 11: Comparison between existing cases and this project.  
The main challenge of the calculation process is the boundary selection and inventory collection, especially when carbon emission assessment is needed in the early stage of design and construction, or when the calculation relates to the process of disposal and refurbishment. It is significant to make full and rational use of design documents, standard documents, and related software.  
This paper calculated the whole life cycle carbon emission of the Zero-Carbon Pavilion in Shanghai Yangpu Riverside based on the design documents and a simulation model, and then analyzed the potential of energy-saving measures. The results show that the whole life cycle carbon emission of the refurbished building is 2.39 tCO2e/m2, while the whole life cycle carbon emission of the assumed new building is 2.69 tCO2e/m2, which are both in a low carbon emission level among the existing cases. The refurbished building saves nearly one-third of the carbon dioxide emissions during the construction phase compared to new construction. The application of a solar photovoltaic system saves one-third of the energy consumption and carbon emission of the building operation.  
Conceptualization, Y.P.; methodology, B.L.; software, B.L.; validation, B.L.; formal analysis, B.L.; investigation, B.L.; resources, M.K.; data curation, B.L.; writing--original draft preparation, B.L.; writing--review and editing, Y.P.; visualization, B.L.; supervision, Y.P.; project administration, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology (Grant No. G2021133019L) and the National Science Foundation of China (Grant No. 51978481).  
The authors declare no conflict of interest.
## 3 Finite Element Simulation
### FEM Results
#### 3.2.4. FEM Results of Two Circular Flat Bottom Holes with Different Radius and the Same Depth  
Two circular flat bottom holes with different radii and the same depth are used in Case 8 and Case 9. It is important to note that these defects and the excitation source are in the same line, which means that the defects have the different positions relative to the excitation source. The specimen parameters are listed in Table 2. Simulation results are shown in Figure 13. It can be seen that the shape and location of two holes can be identified, which demonstrates that the local wavenumber approach has a high resolution even in distinguishing adjacent defects. Table 5 shows the depth evaluations of Case 8 and Case 9. The depth error of the big hole is within 1%, while the depth error of the small one is in the range of 4% to 9%. Thus, the depth estimation of the big hole is more accurate than that of the small one. Besides, the relative position is also taken into consideration. The depth error of the small hole is 8.17% when it is in front of the big one. Apparently, this level is equivalent to that of Case 3. However, the depth error of the small hole is reduced to 4.76% when it is behind the big one. This indicates that the impact which the big hole exerts on the small one is related to their positions when they and the excitation source are in the same line.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Specimen**} & \multicolumn{2}{c}{**Actual Depth (mm)**} & \multicolumn{2}{c}{**Reconstructed Depth (mm)**} & \multicolumn{2}{c}{**Estimation Error of Depth**} \\ \hline \multirow{2}{*}{Case 4} & No. 1 & 1 & 0.9917 & 0.83\% \\  & No. 2 & 1 & 0.9917 & 0.83\% \\ \multirow{2}{*}{Case 5} & No. 1 & 1 & 0.9887 & 1.13\% \\  & No. 2 & 1 & 0.9887 & 1.13\% \\ \multirow{2}{*}{Case 6} & No. 1 & 1 & 0.9946 & 0.54\% \\  & No. 2 & 1 & 0.9976 & 0.24\% \\ \multirow{2}{*}{Case 7} & No. 1 & 1 & 0.9887 & 1.13\% \\  & No. 2 & 1 & 0.9976 & 0.24\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The depth evaluations of Case 4 to Case 7.  
Figure 13: Simulation results of Case 8 and Case 9. (**a**) Wavenumber distribution of Case 8; (**b**) depth distribution of Case 8; (**c**) wavenumber distribution of Case 9; (**d**) depth distribution of Case 9. The white circle represents the actual location of the damage. The actual depth of the defect is 1 mm.
## 2 Results and Discussion
### Key Aroma Compounds Identified of Three Black Teas
#### 2.1.1 Aroma Compounds Identified by SAFE/HS-SPME  
For the three kinds of black tea, SAFE and HS-SPME were used as two methods with different extraction principles to recover volatile compounds and obtain relatively complete results [16]. In the experimental results, 65 aromatic compounds were identified by HS-SPME, and 59 aromatic compounds were identified by SAFE. Among them, 2-methylpyrazine, 2,6-dimethylpyrazine, dihydroactinidiolide, coumarin, and \(\gamma\)-butyrolactone were only recovered by SAFE; 2-methylbutanal, 3-methylbutanal, isophorone, \(\alpha\)-ionone, \(\beta\)-ionone, \(\beta\)-cyclocitral, 2-phenyl-2-butanal, myrtrenal, cis-jasmone, dimethyl disulfide, dimethyl sulfide, and 2-methylpropanal were only recovered by HS-SPME. The results of this experiment also verified the previous conclusions: SAFE is effective in extraction of pyrazine and coumarin derivatives, while HS-SPME has a better effect on aldehydes and ketones [17]. Because the TICs of HS-SPME and SAFE are quite different (Figure S1), better results can be obtained when they are used in combination.
# Worldwide Connectivity for the Internet of Things Through LoRaWAN
## 6 Conclusions  
In this paper, we presented the architecture of the back-end of the LoRaWAN(tm) system, which is often neglected in literature. The LoRaWAN(tm) network architecture allows for a peculiar feature among the systems for massive IoT in unlicensed frequency band: it allows global roaming of end nodes between different operators without requiring any SIM. Such a distinctive attribute was introduced in the latest version of LoRaWAN(tm) specification, and represents a remarkable achievement for this technology. The possibility of global roaming worldwide, alongside with other well-known characteristics such as very low power consumption, localization capability, and low cost, makes the LoRaWAN(tm) system a prominent candidate for a global interoperable system for the massive IoT.  
Conceptualization, L.V. and M.C.; methodology, L.V. and M.C.; writing--original draft preparation, L.V. and M.C.; supervision, L.V.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} \hline \hline ABP & Activation by personalization \\ AS & Application server \\ IEEE EU164 & IEEE extended unique identifier--64-bit address space \\ GW & Gateway \\ ID & Identifier \\ JS & Join server \\ LPWAN & Low-power wide-area network \\ MAC & Medium access control \\ NB-IoT & Narrowband internet of things \\ NS & Network server \\ OTAA & Over-the-air activation \\ URLLC & Ultra-reliable low-latency communications \\ \hline \hline \end{tabular}
## 7 Architectural Challenges in Designing Any Smart Healthcare Application
### IoT Connectivity in Smart Healthcare  
Smart healthcare needs the connectivity and integration of multiple IoT devices that collect patient's data. A generic architecture of IoT connectivity in smart health care is presented in Figure 15. The manufacturers of the sensors and wearables uses different communication protocols and standards in the healthcare sector. Thus, the connectivity hinders the flow of transmitting the medical data for effective and timely diagnoses [86]. Wireless connectivity technologies must support the massive connectivity of IoT devices, as there are billions of connected smart devices [87]. In this paper, we have reviewed the communication protocols, and the technologies were analyzed, along with technical challenges and possible solutions based on coverage range. Short-range wireless communication technologies such as Zigbee, Bluetooth, NFC, and Wi-Fi are used in many short coverage areas. Sigfox, LoRa, LTE-M, and NB-IoT are long-range wireless network technologies with low power consumption and low data rates, which are the desirable qualities of IoT device deployment. The existing wireless IoT connectivity technologies such as short-range, LTE, 5G, Unlicensed LPWAN technologies (LoRa and Sigfox), and Licensed LPWAN technologies (LTE-M, NB-IoT) are facing many challenges in supporting the massive connectivity of IoT devices. Some of these issues are high signaling overhead, wireless resource scarcity, and inefficient wireless resource usage. Random access protocols of these existing technologies are based on ALOHA and CSMA/CA. However, these schemes do not apply to IoT devices due to their high latency, signaling overhead, and access collisions.  
End IoT devices such as sensors, gateways, and RFID tags connect to gateways through protocols such as NFC, BLE, Zigbee, etc. LoRaWAN connects these gateways to the cloud network server, and this will be connected to application servers IPv6 and UDP. Application layer protocols such as MQTT and CoAP are responsible for IoT applications to get user interaction.  
Four emerging technologies have been reviewed in [3], apart from the existing connectivity technologies. They are Compressive Sensing Random Access (CSRA) [88], Non-Orthogonal Multiple Access (NOMA), massive Multiple Input Multiple Output (mMIMO), machine-learning-based technologies (MLRA). These are promising technologies that can solve the shortcomings of existing technologies and enhance the efficiency of IoT devices' spectrum usage.
## 5 Conclusions and Future Work  
Decision making is a very hard task, as it often requires the analysis of hundreds of potential alternatives defined on multiple and conflict criteria. Methods based on decision rules, utility functions or outranking methods have been proposed in the multi-criteria decision aid field. All of these methods use only the utility values of the so-called domain criteria. With the rapid growth of the Web, there is a myriad of online platforms on which users can express their opinions on the products or services offered by a company. This paper has presented SentiRank, a ranking system based on the well-known ELECTRE methodology that takes into account not only the standard domain attributes describing the alternatives but also the opinions of the users about them. A novel ABSA module has been incorporated to calculate the polarity of the users' comments about different aspects and to integrate this information as new \"social\" criteria. A use case with a set of restaurants has shown the important contribution of these new features in the ranking process. The degree of relevance of this contribution may be modulated with the ELECTRE weights associated with each domain and social criterion. Thus, SentiRank opens up a new line of research on the integration of the polarity of textual reviews on MCDA systems.  
Concerning the future work, from a practical perspective we plan to extend the implemented system by adding more domains, such as hotels, laptops and cameras. We also plan to make the system a Web-based application and implement RESTfull APIs to allow developers and users to use the system remotely. We also plan to perform a prof-of-concept (PoC) analysis with surveys on real customers using the system. On the theoretical side, we plan to make the system multi-lingual, so that it is able to analyse reviews in other languages. We are also planning to study the possibility of using deep learning techniques in the aspect detection and aspect polarity assessment modules, as it is already being done in recommender systems [44].  
Concepts, M.J., A.V. and A.M.; methods, M.J.; software, M.J. and N.M.; validation, M.J., A.V. and A.M.; formal analysis, M.J., A.V. and A.M.; research, M.J.; resources and data curation, M.J. and N.M.; writing--original draft preparation, M.J.; writing--review and editing, A.M.; visualisation, M.J. and N.M.; supervision, A.M. and A.V.; funding acquisition, A.M. and A.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors acknowledge the research support of URV (2018 PFR-URV-B2 and [PERSON] PhD grant).  
The authors declare no conflict of interest.
## 4 Simulation Results
### Simulation Setup  
In the simulations, we generate the matrices \(\hat{\mathbf{T}}\) and \(\hat{\mathbf{R}}\) according to the inverse Wishart distribution (some discussions on the use of the inverse Wishart distribution can be found in [35; 36]; the inverse Wishart distribution is the conjugate prior to the actual correlation matrices \(\mathbf{T}\) and \(\mathbf{R}\) when \(\mathbf{h}\) is Gaussian-distributed; nevertheless, our proposed scheme is applicable to any distributions or types of covariance matrices) as  
\[\hat{\mathbf{T}}\sim\mathcal{W}^{-1}\big{(}(\kappa_{T}-M_{t}-1)\mathbf{C}_{T},\kappa_{T}\big{)}\]  
and  
\[\hat{\mathbf{R}}\sim\mathcal{W}^{-1}\big{(}(\kappa_{R}-M_{r}-1)\mathbf{C}_{R},\kappa_{R}\big{)},\]  
respectively, where \(\kappa_{T}\) and \(\kappa_{R}\) denote the degrees-of-freedom parameters of the inverse Wishart distribution. The values of \(\kappa_{T}\) and \(\kappa_{R}\) are set to \(\kappa_{T}=M_{t}+2\) and \(\kappa_{R}=M_{r}+2\), respectively. The elements of \(\mathbf{C}_{T}\) and \(\mathbf{C}_{R}\) are generated by the one-ring model [37]:  
\[[\mathbf{C}_{T}]_{m,n}=\mathcal{J}_{0}(2\pi\Theta_{T}|m-n|\text{$\kappa_{T}$}/ \lambda),\quad 1\leq m,n\leq M_{t}\]  
and  
\[[\mathbf{C}_{R}]_{m,n}=\mathcal{J}_{0}(2\pi|m-n|\text{$\kappa_{R}$}/\lambda), \quad 1\leq m,n\leq M_{r},\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Computational Complexity** & **Processing Time (s)** \\ \hline Iterative algorithm in [20] & \(\mathcal{O}\big{(}M_{t}^{3}+L^{3}+(M_{t}^{6.5}+L^{6.5})N_{\text{iter}}\text {log}(1/\psi)\big{)}\) & \(8.8098\times 10^{5}\) \\ \hline Proposed optimal training scheme & Total \(\mathcal{O}\big{(}M_{t}^{3}+L^{3}+(M_{t}^{5.5}+L^{3.5})\text{log}(1/\psi) \big{)}\) & 206.4642 \\ \hline Proposed suboptimal training scheme & Total \(\mathcal{O}\big{(}M_{t}^{3}+L^{3}\big{)}\) & 68.8214 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity comparison.  
_\"Semi-robust (suboptimal)\"_ indicate the proposed scheme with the optimal power allocation, that with the suboptimal power allocation, the nonrobust scheme, the semi-robust scheme, and the semi-robust scheme with the proposed suboptimal power allocation, respectively.
# Human Action Recognition Based on Improved Two-Stream Convolution Network
## 3 Improved Network Structure  
### General Network Structure  
Due to the shortcomings of the classical two-stream convolution network in obtaining the coherence features of action appearance, an improved two-stream convolution network is proposed in this paper. The original spatial stream input changes from a single video frame to a multi-video frame sampled at equal intervals. In order to improve the performance of the network, the network used in the feature extraction process is replaced by a VGG16 network with deeper ResNet. The network used in the motion stream has the same change. Moreover, this paper adds an advanced SimAM attention mechanism to the ResNet network, which greatly improves its feature extraction ability. The overall structure of the improved two-stream convolution network proposed in this paper is shown in Figure 3.  
To make the network work better, for an input video, first carry out equal spacing sampling, optical flow calculation, and other preprocessing work. The calculation formula of optical flow has been given by Formulas (1) and (2).  
Figure 3: Improved two-stream convolution network structure.  
Figure 2: BiGRU network structure for mining contextual features.  
The number of spatial stream input frames can be adjusted according to the actual situation, but the input images need to be arranged in strict order and equidistant order. Figure 3 shows the network structure and the process of forwarding propagation by taking the input three frames as an example. These three frames are sampled at equal intervals from the input video. Therefore, for a video, the three frames represent the appearance of the action at different times in a continuous action.A frame of images first passes through a convolution layer, a normalization layer, an activation layer, and a max-pooling layer. Then enter a continuous number of ResNet network basic block layers (BasicBlock). Each of the three frames goes through the same network.The feature map generated by three consecutive frames of images passing through the above network is input into the BiGRU network in sequence, and finally, the recognition result is obtained through the SoftMax layer.  
The input of the motion stream is the stacked optical flow image. After passing through the same front-end network as the spatial stream, the optical flow image enters multiple continuous basic unit layers of ResNet (Buttleneck), and finally enters the SoftMax layer to obtain the recognition result. The motion stream does not use the bidirectional Gru network.  
The recognition results of spatial stream and motion stream are fused by the classical weighted fusion method to obtain the final action recognition result.