esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
In this paper, we have investigated how to relieve the effects of interference between D2D pairs and CUs in the uplink of the D2D underlaid network to improve the system throughput. The weighted efficiency interference-aware algorithm is proposed as the mechanism to allocate the spectrum resources for D2D communication. The weight of each D2D pair in each intended user set is calculated, to provide precise details of the proposed allocation algorithm. Simulation results demonstrate that the SINR of cellular uplink users perform well when D2D pairs reuse the RBs even in the case of the poor uplink state. Meanwhile, the system throughput goes up with as the number of D2D pairs increases, since the throughput of D2D communication rises proportionally as that of CUs decreases. Furthermore, the proposed allocation algorithm is stable over different parameters of users and channel states, provides high utility and performs superiorly to interference-aware and random allocation.  
As future work, the investigation on the situation that one D2D pair reuses multiple RBs of CUs to improve the efficiency of spectrum resource is aimed further. Also, to apply and adapt the proposed algorithm in industrial production and real cases, such as smart agriculture and smart mine, where the communication conditions and environments are highly yet dynamically varied.  
Methodology and investigation, J.L.; Validation and data curation, Y.F.; investigation, Writing--original draft preparation, X.Z.; Writing--supervision, review, and editing, K.L.  
This research was funded in part by National Natural Science Foundation of China (no. 51874010 and 61170060) and the Major Innovation Platform and High Efficiency Innovation Talent Team (no. 2017A053).  
The authors declare no conflict of interest.
## 5 Conclusions  
This report presents a novel type of 2048-element dual-polarization receiving phased array for \(Ku\)-band SATCOM applications fabricated using a novel copper paste sintering interconnection technology. The equivalent circuit model was first used to analyze the challenges inherent to fabricating complex multilayer PCBs using traditional processes--particularly when the number of layers needed is high and the interconnections are complex. Using the novel PCB approach, it is possible to reduce the presence of copper stubs in vertical interconnections between PCB layers, as well as avoid deteriorations in impedance matching within multilayer PCBs caused by parasitic parameters. The 128-element subarray with 14-layer PCB was formed by sintering the two laminated parts together with copper paste. The 16 subarrays were then assembled to form a 2048-element receiving phased array, and its RF performance was tested. The G/T values of the array exceeded 12 dB/K under uniform illumination in the operating band, and it was able to achieve a scanning range of \(\pm\)70\({}^{\circ}\) in all planes over a variety of polarization modes. This novel PCB process may improve the flexibility of multilayer PCB layouts and reduce the costs to manufacturing them, making it very suitable for the design of planar phased arrays.  
Conceptualization, S.S. and Y.Z.; methodology, S.S. and Y.B.; validation, S.S., S.M. and N.Z.; formal analysis, S.M.; investigation, N.Z.; resources, Y.Z.; data curation, S.M.; writing--original draft preparation, S.S.; writing--review and editing, Y.Z. and Y.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Sichuan Province (2024 NSFSC0469), and the Fundamental Research Funds for the Central Universities (ZYGX2020 ZB001).  
The data supporting the reported results cannot be shared due to privacy or ethical restrictions.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **This Work** & **[10]** & **[16]** \\ \hline Frequency & Ku & K & Ku \\ No. of Elements & 2048 & 64 & 1024 \\ Polarization & Dual-Linear & Dual-Linear & Dual-Linear \\ Antenna Type & Patch on PCB & Patch on PCB & Patch on PCB \\ PCB Process & Copper paste sintering & Normal & HDI \\ Kinds of Vias & 3 & 4 & 4 \\ Array Grid & Rectangular & Rectangular & Triangular \\ Elevation Scan & \(\pm\)70\({}^{\circ}\) & \(\pm\)50\({}^{\circ}\) & \(\pm\)70\({}^{\circ}\) \\ Azimuth Scan & \(\pm\)70\({}^{\circ}\) & \(\pm\)50\({}^{\circ}\) & \(\pm\)70\({}^{\circ}\) \\ G/T (dB/K) & 12 & \(-\)2.94 & 10.5 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison with Recently Reported SATCOM RX Arrays.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 4 Proposed Methodology
### Quality of Service Measures
#### 4.2.2 Throughput  
Throughput refers to the total data transmitted by a node over the total time. It measures the data handling capacity of the node, which provides insights into its capability to process and transmit large volumes of data efficiently. Higher throughput values denote a node's robust data management and transfer abilities. It is computed using the following equation:  
\[Throughput_{i}=\frac{\textit{Total data transmitted}}{\textit{Total time}} \tag{5}\]In the above equation, throughput is the rate at which data are transmitted, total data transmitted is the amount of data sent during the measurement period, and total time is the duration of the measurement period.
# Improving Water Efficiency in a Municipal Indoor Swimming-Pool Complex: A Case Study
## 1 Introduction  
It is generally known that the ecological services provided by aquatic ecosystems and the water industry are facing increasingly complex challenges: the demand for water is continuously rising, causing the rapid exhaustion of existing water resources. Urban population growth and changing lifestyles, implying more intensive water use [1] and increasing water pollution [2], are the leading causes of this phenomenon. Besides, future climate changes will involve higher temperatures and changes in the intensity and patterns of precipitation, leading to more frequent droughs, as well as reducing water quantity and quality, in southern Europe and the Iberian Peninsula [3, 4]. Thus, the urban water cycle is affected by climate change, but it is also contributing to climate change. Indeed, water potabilization processes, the delivery of water to consumers, and the treatment of wastewater use significant amounts of energy, contributing to increasing CO2 and other greenhouse gas emissions [1, 5, 6]. Therefore, in the face of this scenario, it is importantto implement water sustainability, promoting water use efficiency by reducing water consumption through the adoption of efficient products or devices (e.g., taps and showers), reducing waste and losses, and by reusing and recycling water [7]. Public indoor swimming-pool facilities use large amounts of water and, consequently, energy, because of their particular characteristics: (1) the relatively high temperature and humidity levels in the pool room; (2) the evaporation caused by pool usage; (3) the use of warm water for pools and showers; and (4) the requirement of a water treatment system [8; 9]. Furthermore, users' number and behavior, the variety of services provided, the operating patterns [8; 9], and the quality of indoor water and air [10; 11] also influence the aforementioned characteristics. Nevertheless, to our best knowledge, research concerning the impact of these facilities on urban water demand is scarce. Some research has focused on residential swimming pools, revealing that pools have a very high impact on urban water demand and consequently on ecological integrity and the services provided by freshwater ecosystems [12; 13; 14; 15]. However, approaches concerning municipal and public indoor swimming pools are even scarcer and, in some cases, very preliminary [16; 17; 18; 19]. Similarly, research concerning the recycling and reuse feasibility of pool filter backwashing is also very scarce [20; 21; 22; 23; 24]. Therefore, to contribute to filling this information gap, the aim of this research is: (a) to determinate the water demand of a municipal indoor swimming pool complex; (b) to propose water use efficiency measures, analyzing their feasibility and costs; (c) to evaluate the possibility of recycling and reusing the water from swimming-pool filter backwashing.  
Municipalities may use the results of this study to implement and improve measures to achieve greater water use efficiency in their infrastructure.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### Security
#### 2.6.1 Over-the-Air Activation (OTAA)  
End-devices require to join the network before the application specific data transmission stage. End nodes initiate the activation process with an unencrypted Join Request, with AppEUI (8 octets), DevUI(8 octets) and a DevNonce (2 octets). DevNonce is a randomly generated and GW keeps tracks of these values for each end-node.  
If the network server replies to the Join Request with a Join Accept, end-node is allowed to join the network. Unanswered messages yield that end-node cannot participate in the network.  
Join Accept message contains; a random AppNonce (3 octets), Network Identifier--NetID (3 octets), an end-node address--DevAddr (4 octets), a delay RxDelay between TX and RX(1 octet), DL configuration parameters DLSettings (1 octet) and optional channel frequency list CFList.  
Initial Join Request message sent from a node is unencrypted, however, the response message from Network Server (Join Accept message) is encrypted with AppKey. NwkSKey and AppSKey are calculated as follows:  
\[NwkSKey=\mathit{ases128\_encrypt}(AppKey,0x01|AppNonce|NetID|DevNonce|pad_{16})\]  
\[AppSKey=\mathit{ases128\_encrypt}(AppKey,0x02|AppNonce|NetID|DevNonce|pad_{16})\]
## 2 Integrated Loads Analysis
### Ultimate Load Analysis Condition  
The ultimate loads of the offshore wind turbine were analyzed under steady state wind condition at each wind speed. This is because marine environmental loads such as turbulence, irregular waves, and currents generate complex vibrations depending on the substructure, and eventually affect the ultimate load of the superstructure [17]. In addition, steady state wind conditions can reduce analytical errors with regards to the complexity of dynamic response loads of the offshore wind turbine. Table 2 shows the environmental conditions used in this study. For steady state wind conditions, wind speeds of 11 m/s, 14 m/s, 17 m/s, 20 m/s 22 m/s, and 24 m/s without turbulence were selected; they are above the rated wind speed. The wind shear exponent employed was 0.1, the tower shadow effect coefficient was 1 and yaw error was \(8^{\circ}\), respectively. Using these data, the integrated load analysis for ultimate loads was performed under the individual pitch movement and CPC condition, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline Rated power & 5 MW \\ Class & IB \\ No. of blade & 3 \\ Blade length & 61.5 m \\ Huh height & 90.55 m \\ Tower height & 88.15 m \\ Cut-in, Rated, Cut-out wind speed & 3 m/s, 11.4 m/s, 25 m/s \\ Cut-in, Rated rotational speed & 6.9 rpm, 12.1 rpm \\ Rotor overhang & 5 m \\ Rotor position & Upwind \\ Transmission & Gearbox \\ Power control & Pitch/Permanently stuck \\ Fixed/Variable & Variable \\ Gear ratio & 97 \\ Substructure type & Jacket \\ \hline \hline \end{tabular}
\end{table}
Table 1: General specification.  
Figure 1: Schematic of jacket type substructure.
# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
###### Abstract
Three different measures were alternatively used to compute the adjacency matrices for the graph generation, namely the phase locking value (PLV), the magnitude squared coherence (MSC), and the spatial distance. [PERSON] et al. [20] proposed a neonatal seizure identification method using spectral and spatial graph neural networks (SSGNNs). Spectral and spatial GNNs were combined and optimized with the Aquila algorithm to enhance seizure detection accuracy in affected children. Despite the good results obtained with these graph-based methods, scant consideration has been paid to the variances of multi-channel interdependencies over different frequency bands and the effectiveness of graph embedding before final classification. Given the aforementioned properties of existing methods, this paper proposes a novel graph-based method for neonatal seizure detection, focusing on the thorough representation and the adaptive classification of multi-channel EEG signals. In the graph generation section, the raw EEG is decomposed into a range of sub-band signals using wavelet packet decomposition (WPD), from which the temporal features within each channel and the synchronicity between different channels are extracted. Thus, for each EEG epoch, a set of band-wise graphs are constructed based on these narrowband features, which can provide more detailed information for classification. In the graph classification section, a novel network framework, namely the multi-band graph neural network (MBGNN), is developed to deal with the graph sets. In the MBGNN, the graph attention network (GAT) and the DIFFPOOL are combined to exploit the graph features and produce an effective graph embedding that preserves the graphical structure information. In addition, in order to take full advantage of the detailed information from different frequency bands, an attention mechanism is introduced in the proposed MBGNN. The multi-band graph embeddings are adaptively integrated through the band-wise attention mechanism, owing to which the classification performance of the MBGNN can be optimized for each object through the training process. The rest of the paper is organized as follows: Section 2 introduces the EEG database used in this paper. The details of the proposed method will be described in Section 3, including the preprocessing step, the graph generation method, and the MBGNN model. Section 4 presents the experimental scheme and results. Section 5 discusses the advantages and disadvantages of the proposed study with previous studies, as well as the potential for future work. Section 6 concludes the study.
## 4 Results
### Characterization of Lahore Pollution  
In Figure 3, it can be seen that there was a drastic increase in the AQI value from an average of 175 \(\upmu\)g/m\({}^{3}\), reaching a maximum of 478 \(\upmu\)g/m\({}^{3}\) and 466 \(\upmu\)g/m\({}^{3}\) in 2019 and 2020, respectively, during the smog season (October to December, every year). The variation in CO, SO\({}_{2}\), and NO\({}_{2}\) primary pollutant concentrations coincided considerably with the respective AQI plot.  
It can be observed that when the AQI value began to increase, the accumulation of primary pollutants showed lower values, compared with AQI values. On the contrary, when AQI values started to decrease, the actual accumulation of pollutants did not fall very fast. This showed that the AQI value calculation method must be improved, if not changed, to accommodate this difference. Neither HCHO, CH\({}_{4}\), nor any secondary pollutant had a similar impact on AQI values. Conversely, although AOD values were higher during the smog season than the rest of the year, they were maximum during the monsoon season when AQI values were comparatively low [3].  
Emission reduction due the implementation of the lockdown has been documented by several studies. Three comprehensive assessments of impacts of COVID-19-induced lockdowns on pollutant concentrations have been published [10, 38, 39]. The studies observed an average of 37% reduction in NO\({}_{2}\) in mega-city centers, and an average of 25% decline in AOD concentration for industrial sectors. In Lahore, a 43% decline in the NO\({}_{2}\) concentration was observed. However, the aim of this study was to characterize the pollutants in the three sectors, and we assumed that the reduction in the emission did not change the overall composition of the pollutants emitted from the sites.
# Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors
## 3 Proposed Multi-Criteria Learning Algorithm Using Secured Sensors
### Secured Data Transmission Using a Secured Session-Oriented Scheme
In the beginning, the gateway device transmits the route request packet along with its _id_ towards the sink node. Upon successful verification, the sink node acknowledges it, and later the gateway device requests the session key. If the time expires, the gateway device resends the request for the session key. Once the sink node receives the request for the session key, it generates the key and sends it towards the gateway device in encrypted form. The gateway device decrypts it and sends an acknowledgment message to the sink node that it has received the session key. The sink node confirms the acknowledgment message and afterward, both devices use the same session key for data encryption and decryption. Figure 4: Flowchart of the proposed algorithm. Figure 5: Message flow between the gateway and the sink node. Algorithm 1 explains the pseudocode for the proposed work. It has two main components: one for the authentication of mobile devices with the reinforcement learning technique to assign the rewards, and the other for session-oriented data encryption from mobile sensors towards the sink node. After the successful verification of mobile sensors, the proposed algorithm evaluates the route rank for the neighbors using multiple parameters, along with the link cost. Accordingly, the neighbor with the highest route rank is assigned a reward value and selected as a forwarder. Moreover, the proposed algorithm also established a secure session from mobile sensors towards the sink node using gateway services. In this case, only that node is allowed to send the route request to the sink node that has a valid session key. The secure session key is utilized by both the mobile sensor and the sink node for data encryption and decryption, respectively. ```
Input: SN: Sensor nodes RREQ: Route request ID: Identity K: Session key S: Sink node G: Gateway nodes Output: Authentic devices, Dynamic routes, R, Secure transmission, Sec
1.for i = 1:N
2 initiate Authen_service
3 if Authen_service = True
4 call keys_gen_process
5 else
6 node is declared as faulty
7.endfor
8 for j = 1:G
9 if dist(j) closest to S
10 mutual_authen service
11 encrypt(data, K)
12 else
13 execute keys_gen_process
14 mutual_authen service
15 e = encrypt(data, K)
16 endif
17.endfor
18.If destination == S
19 Recover K
20 decrypt(e, K)
21.endif
```  
**Algorithm 1**Multi-criteria learning algorithm with secured devices.
## 2 Principle and Model of TAN
### State Space Model of TAN  
The state space model of TAN can be described by Equations (1) and (2):  
\[X_{t+1}=X_{t}+S_{t}+v_{t} \tag{1}\]  
\[Y_{t}=H_{t}(x_{t})+E_{t} \tag{2}\]  
where \(X_{t}\) represents the indicated position coordinates of INS at time \(t\); \(S_{t}\) represents the indicated position change between two sampling intervals of INS; \(v_{t}\) represents the positioning error of INS; \(Y_{t}\) represents the real-time measurement of water depth series; \(H_{t}\) represents the water depth series corresponding to digital reference map; and \(E_{t}\) represents the error sequence of water depth measurement. Equation (1) can be regarded as the state equation in the TAN matching system. Equation (2) is the measurement equation. TAN finds the optimal position of inertial navigation by continuously measuring the depth of the AUV in real-time. The state equation is used to correct the errors of the INS.  
Figure 1: The principle of TAN.  
Influenced by the INS course and velocity errors, the INS trajectory must be brought closer to the real trajectory during terrain matching by changing course angle and position. The transformation relationship between INS trajectory and real trajectory can be obtained through the curve affine transformation model, as shown in Equation (3). The final matching position of AUVs is obtained by constantly searching for the four optimal transformation factors \(\left[k,\;\beta,\;\delta_{x},\;\delta_{y}\right]\)[17]:  
\[\begin{pmatrix}x_{i}^{u}\\ y_{i}^{u}\end{pmatrix}=k\begin{pmatrix}\cos\beta&\sin\beta\\ -\sin\beta&\cos\beta\end{pmatrix}\begin{pmatrix}\hat{x}_{j}^{p}\\ \hat{y}_{i}^{p}\end{pmatrix}+\begin{pmatrix}x_{1}^{p}\\ y_{1}^{p}\end{pmatrix}+\begin{pmatrix}\delta_{x}\\ \delta_{y}\end{pmatrix} \tag{3}\]  
where \([x_{i}^{u},y_{i}^{u}]\) represent horizontal coordinates of the \(i\)th trajectory point; \([\hat{x}_{i}^{p},\hat{y}_{i}^{p}]\) represents the position of the \(i\)th point relative to the first trajectory point; \(k\) represents the scaling factor; \(\beta\) represents the course angle deviation; and \(\delta_{x}\) and \(\delta_{y}\) represent the translation factor. PSO keeps updating the parameters of the matching model, optimizes the initial trajectory to be closer to the actual trajectory, and finally obtains the optimal matching trajectory. Each particle of the optimization process represents a specific transformation on the initial trajectory, which mainly contains three attributes: position, velocity, and fitness.
## 3 Discussion
### A. domesticus Metagenome  
Of the 16,290 original scaffolds in the _A. domesticus_ assembly, 6246 scaffolds were removed and submitted to NCBI as metagenome data. Most of these metagenome scaffolds had similarity to IIV6, but we were unable to assemble these scaffolds into a complete IIV6 sequence. Closer examination of the predicted proteins from the virus scaffolds indicated that only 32 scaffolds were high to medium quality viral genome sequences. Therefore, scaffolds with similarity to virus sequences at the nucleotide level may be sequencing artifacts, or some may represent novel viruses that have not been described in current databases. Importantly, our analysis did not identify common foodborne pathogens, such as _Salmonella_ sp., _Listeria_ sp., _E. coli_, or _Staphylococcus_ sp.  
The question of crickets harboring covert virus infections was recently addressed [77]. In that study, Densovirus sequences were found in sick and healthy insects, and the authors speculated that difference in immunity may prevent active diseases in healthy colonies. Our insects came from an insect farm with no reports of insect disease. We did find scaffolds similar to _Wolbachia_ symbionts in other insects. _Wolbachia_ has been reported to have a protective effect against viruses in some insects [78]. Further, _Wolbachia_ was reported in a previous study of microbial communities in _A. domesticus_[79]. More research is needed to understand the prevalence of iridoviruses in farmed insects, as well as the mechanisms that prevent active disease in infected but healthy individuals. Our goal is to engineer virus resistant crickets through CRISPR genetic engineering technology to improve the health of farmed insects. This is an important reason we seek to understand genes related to immunity in these insects.
# Types and Performances of Polymer Gels for Oil-Gas Drilling and Production: A Review
## 2 Types of Polymer Gels Used for Oil-Gas Drilling and Production  
Polymer gel materials are highly flexible, viscoelastic materials that can adapt their shape to enter fractures and pore channels of different sizes and morphologies. Therefore, they have been widely used in profile control, water shutoff, lost circulation control, and fracturing in formations. Commonly used polymer gels can be categorized according to their gelation method and mainly include in situ crosslinked gelation and ground pre-crosslinked gelation. In situ crosslinked gel is sensitive to pH, temperature, and salinity variations and reduces the permeability of fractures or channels. Pre-crosslinked gelation can only be used for those reservoirs with extreme high-permeability contrast.