esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The history of heat pump and refrigeration is very long, and ever since mankind acquired mechanical refrigeration more than a century ago, heat pump and refrigeration technologies have made remarkable progress. However, the negative impacts of heat pumps and refrigeration on the global environment became visible over the last several decades. One of the major issues was ozone layer depletion by chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) that are used as refrigerants of heat pump and refrigeration systems. These materials were, or are going to be, phased out based on an international agreement, the Montreal Protocol. Another major issue is global warming; heat pump and refrigeration systems directly and indirectly release a large amount of greenhouse gases (GHGs). Although the current refrigerants, hydrofluorocarbons (HFCs), have no ozone depletion potential (ODP), they are strong GHGs. For example, the global warming potentials (GWPs) of refrigerants for split-type and packaged air conditioners, such as R410A and R407C, are around 2000 [1]. Some studies have already reported that the impact of HFCs on global warming is not negligible [2, 3], and recently, toward the phasing out of HFCs, the regulations in the production and use of them were accelerated by the Kigali Amendment to the Montreal Protocol. Indirect emissions of carbon dioxide by heat pump and refrigeration systems are also significant. The system itself is highly efficient, especially compared with conventional heating systems, but the application of heat pumps and refrigeration is wide. Unfortunately, most of the systems are driven by electricity; therefore, the amount of electricity consumed by heat pumps and refrigeration is huge and still increasing worldwide.  
In this context, a vast amount of research related to heat pumps and refrigeration is ongoing. Presently, intensive effort is being made on new artificial refrigerants with low GWP, such as the so-called hydrofluoroolefins (HFOs) [4]. As the design of heat pump and refrigeration systems should be optimized against refrigerants, the simple replacement of refrigerants will, in many cases, cause performance degradation. Therefore, it is essential to know thermophysical properties and heat transfer characteristics for the optimum design of systems. Natural refrigerants, such as water, ammonia, carbon dioxide, hydrocarbons, and air are ideal, and many systems using natural refrigerants are already commercial. Nevertheless, there are still many difficulties in replacing refrigerants for split or packaged air conditioners--which are one of the largest energy consumers in the building sector--with natural refrigerants, due to limitations in safety, size, and cost.  
Papers which contributed to this special issue include a variety of scientific and engineering approaches for improvement in system efficiency, as well as for the development of new systems driven by minimal electricity consumption. The studies were expediently categorized into three groups, given in the following.
## 3 System Functional Modules
### Sensor Node Module Hardware and Software Design
#### 3.2.2 Design of Node Power Management System  
The node power management system consists of some solar panels, a 4.2 V 2 AH Li-ion battery and a regulated power supply system (Figure 6). The power management system can provide a lasting and stable power supply for the system; it can regularly test the battery, and shut down the system or turn off the charging device in case of too-low or too-high power.  
Figure 5: An encapsulated node.
# Achievable Rate of NOMA-Based Cooperative Spectrum-Sharing CRN over Nakagami-\(m\) Channels
## 3 System Model
### Received Signals in Two Transmission Phases  
Each node (transmitter/receiver) consists of a single antenna, and half duplex transmission is assumed. Similarly to [7], the cooperative NOMA-based transmission has two phases. In the first phase, the PT sends a symbol, \(x_{0}\), to the PR with transmission power, \(P_{p}\), such that the PR receives  
\[y_{p}^{(1)}=\sqrt{P_{p}}h_{p}x_{0}+n_{p}^{(1)}, \tag{1}\]  
Figure 1: A depiction of a NOMA-based spectrum-sharing CRN with two transmission phases.  
where \(h_{p}\) denotes the channel between the PT and PR. \(n_{p}^{(1)}\) is the noise term in the first transmission phase at the PR, which is assumed to follow a Gaussian distribution with zero-mean and variance \(\sigma_{p}^{2}\). During the first transmission phase, the same symbol, \(x_{0}\), is also received at ST and SRs, where the received signals at ST and SRs can be expressed as  
\[y_{s}^{(1)}=\sqrt{P_{p}}h_{s}x_{0}+n_{s}^{(1)} \tag{2}\]  
and  
\[y_{s,k}^{(1)}=\sqrt{P_{p}}h_{s,k}x_{0}+n_{s,k}^{(1)} \tag{3}\]  
respectively, and \(h_{s}\) denotes the channel between the PT and ST; \(h_{s,k}\) represents the channel between the PT and \(k^{\text{th}}\) SR. \(n_{s}^{(1)}\) and \(n_{s,k}^{(1)}\) denote the noise terms at the ST and \(k^{\text{th}}\) SR, which are assumed to follow Gaussian distributions with zero means and variances \(\sigma_{s}^{2}\) and \(\sigma_{s,k}^{2}\), respectively. Here, we assume that \(\sigma_{p}^{2}=\sigma_{s}^{2}=\sigma_{s,k}^{2}\), \(\forall k\). The ST successfully decodes symbol \(x_{0}\), when \(\rho_{p}|h_{s}|^{2}\geq 2^{2R_{0}}-1\), where \(R_{0}\) is the target data rate of PR and \(\rho_{p}=P_{p}/\sigma_{s}^{2}\).  
Once symbol \(x_{0}\) is successfully decoded at the ST in the first transmission phase, the ST superimposes this symbol with the symbols of SRs for the next NOMA-based transmission phase. The superimposed signal generated at the ST is given by \(\sqrt{\alpha_{0}P_{s}}x_{0}+\sum_{k=1}^{K}\sqrt{\alpha_{k}P_{s}}x_{k}\), where \(x_{k}\) is the symbol of the \(k^{\text{th}}\) SR, and \(P_{s}\) represents the transmission power at the ST. This superimposed signal is transmitted from the ST to PR and SRs in the second transmission stage. Therefore, the received signals at the PR and \(k^{\text{th}}\) SR in the second transmission phase are given by  
\[y_{p}^{(2)}=g_{p}\sum_{i=0}^{K}\sqrt{\alpha_{i}P_{s}}x_{i}+n_{p}^{(2)} \tag{4}\]  
and  
\[y_{s,k}^{(2)}=g_{s,k}\sum_{i=0}^{K}\sqrt{\alpha_{i}P_{s}}x_{i}+n_{s,k}^{(2)} \tag{5}\]  
respectively, where \(g_{p}\) denotes the channel between ST and PR in the second transmission phase. Similarly, \(g_{s,k}\) represents the channel between the ST and \(k^{\text{th}}\) SR. \(n_{p}^{(2)}\) and \(n_{s,k}^{(2)}\) are noise terms at the PR and the \(k^{\text{th}}\) SR, respectively, in the second transmission phase, which are assumed to follow Gaussian distributions with zero mean and the same variances as in the first transmission stage.
## 2 Thematic Review and Analysis
### Heat Storage Systems  
In DH systems, heat storage is employed to shift excess production to periods of high demand. This includes medium-scale storage at the network level, small-scale storage at substations, and large-scale seasonal storage. This section focuses on medium-scale heat storage systems at the heat source and their impact on return temperature.  
[PERSON] [23] observed the storage requirements for two different temperature schemes:  
1. For an 80/60 \({}^{\circ}\)C temperature scheme, a peak flow rate of 2.4 L/s necessitates a storage volume of 2880 liters for a 20 min discharge duration.
2. For an 80/40 \({}^{\circ}\)C temperature scheme, a peak flow rate of 1.2 L/s requires 1440 liters of storage capacity for the same 20 min discharge duration.  
[PERSON] et al. [87] demonstrated that thermal storage capacity increases with decreasing DH return temperature. Specifically, storage capacity rises linearly from 290 MWh\({}_{\text{th}}\) at a return temperature of 55.4 \({}^{\circ}\)C to 500 MWh\({}_{\text{th}}\) at 40 \({}^{\circ}\)C.
# Improving Ship Maneuvering Safety with Augmented Virtuality Navigation Information Displays
## 2 Methodology
### Detailed Assumptions of Simulation Study (Simulation Scenarios)
#### 2.3.5 Scenario 5a  
Now Train--Unberthing  
The simulation test Scenario 5a included the experts' simulated series of passages consisting of safe unberthing of the sea barge Wagenborg 4 from Jachtowe Quay, using two tugs Java Victor, connected with the barge by towing lines. The passage consisted of unberthing and towing the sea barge possibly close to the Swinoujsce-Szczecin fairway center line, then directing the tug-barge train to proceed along a straight stretch of the Odra Zachodnia river, towards the port of Swinoujsce (Figure 7).  
The detailed assumptions of Scenario 5a were as follows:  
* Objective to be achieved: the lowest possible value of the maneuvering area width, determined by the barge's contour and the two towing vessels.
* Main tow train parameters: \(\circ\) & Barge LOA = 100 m, breadth = 33 m, draught = 6.05 m; \(\circ\) & Tug LOA = 41.8 m, breadth = 11.4 m, draught = 4 m; \(\circ\) & Displacement = 1036 t; \(\circ\) & Block coefficient = 0.555; \(\circ\) & Main engine: 2 \(\times\) diesel, total power = 2939 kW; \(\circ\) & Max speed = 13.6 kn; \(\circ\) & Type of propulsion: 2 \(\times\) fpp, outwards; \(\circ\) & Number of rudders: 2; \(\circ\) & Max rudder angle = 45.0 deg.
* Navigational-maneuvering conditions: \(\circ\) & the initial position of the tow: parallel to Jachtowe Quay; \(\circ\) & the final position of the tow: the center line of Szczecin-Swinoujsce fairway northward; \(\circ\) & initial SOG: longitudinal v\({}_{\rm w}\) = 0.0 kn, transverse v\({}_{\rm p}\) = 0.0 kn; \(\circ\) & heading: HDG = 025\({}^{\circ}\).
* The criteria used for assessment:  
\begin{table}
\begin{tabular}{c c c} \hline
**Displays** & **Series** & **Number of Trials** \\ \hline Visual display & 4\_Visual & 20 \\ ECDIS & 4\_ECDIS & 20 \\ Pilot\_max & 4\_Pmax & 20 \\ Pilot\_precise & 4\_Pprecise & 20 \\ AV & 4\_AV & 20 \\ \hline \end{tabular}
\end{table}
Table 5: Display types and number of trials for the cruise ship scenario.  
Figure 7: The situational-navigational plan of Scenario 5a for tow train unberthing. Red—initial position, black—destination.  
* Primary: the value of the average and maximum maneuvering area of the tow train, determined from maximum distances of the tow train's contour from the assumed reference point;
* Secondary: efficiency of maneuvering--time length of maneuvers.
* The moment of measuring significant parameters: maximum distances of the tow train's contour from the adopted reference point, calculated analytically after the passage was completed on the basis of registered coordinates of the center of gravity of the barge and tugs.  
The types of displays used, the name of the series and the number of trials in a given series of the Scenario 5a are presented in the Table 6.
## 2 Numerical Methodology
### Material Parameters  
The microwave power was 500 W with a frequency of 2.45 GHz and a total heating time of 15 s. The simulation data were stored at 0.1 s intervals. The microwave heating objects were pyrite and coal matrix. The type of coal matrix in this research was anthracite. As the damage condition of the coal matrix was the key research content of this study and its composition could influence the damage results, an industrial analysis was carried out, as detailed in Table 2. The electrothermal and mechanical parameters of the coal matrix and pyrite are presented in detail in Tables 3 and 4. The above parameters were based on the experimental data of team studies and authoritative papers [13]. Variations in moisture content within coal can affect its mechanical properties. However, the moisture of the anthracite selected for this study was only 1.3%, indicating an extremely low moisture content, which had a minimal impact. Therefore, to simplify the model, the influence of moisture on the mechanical properties of coal was neglected. The results under the assumption that the coal used was dry primarily aimed to explore the direct effects of microwaves on coal, providing a foundation for more complex simulation studies in the future. Subsequent research will incorporate moisture factors to achieve more accurate simulation results.
## 2 Related Works
The experiment findings show that the suggested BI-LSTM-GMM can obtain recall, accuracy, and precision up to 94 percent using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and validation. However, [PERSON]. [11] proposed a deep learning model for detecting DDoS attacks on a collection of frames collected from network activity in this research. Since it incorporates the extraction of features and classification methods in its structure, as well as layers that upgrade themselves as it is learned, the DNN model can perform rapidly and accurately even with tiny data. Experimentation using the CICDDoS2019 dataset, which contains the most recent DDoS attack types produced in 2019, revealed that attacks on network activity were identified with 99.99 percent accuracy and attack categories were characterized with a 94.57 percent accuracy rate. The authors of [28] suggested a hybrid deep generative model that efficiently detects malware variations by combining global and local data. While its virus is transformed into an image to effectively describe global features using a pre-defined implicit space, it retrieves local features utilizing machine code sequencing. The two features retrieved from the dataset are synthesized and passed to the intrusion detection systems. By combining all these features, the suggested model obtains a 97.47 percent accuracy, which is considered to be state-of-the-art efficiency. The CAM findings indicate that the created malware enhances the detection accuracy. Research gap: While machine learning/deep learning-based approaches have been successful, an important issue--choosing optimal features--has been unaddressed. The authors of [11] developed a feed-forward DL-based approach for forecasting DDoS attacks using benchmark data. It attempted to predict DDoS attacks by utilizing a DL technique known as a feed-forward DL classifier. However, the selection of effective predictors prior to applying deep learning to large data sets may produce encouraging outcomes. As a result, traditional deep learning classifiers may be ineffective in predicting DDoS attacks using benchmark data, if optimal sets of features are not selected. To address the limitations of the baseline study [11], we propose an efficient hybrid deep learning model (CNN+BiLSTM) augmented with feature selection. For the sake of being practically useful, this study addresses the problem of DDoS attack detection by adding optimum feature selection into the proposed hybrid DL-based architecture.
###### Abstract  
Background: The treatment of abdominal aortic aneurysm (AAA) is today commonly treated by inserting a stent-graft by the endovascular route, without resorting to open surgery. However, some clinical cases do not allow this less invasive approach, meaning that the stent-graft cannot be inserted and open surgery is used. Methods: In the study, we propose a fluid-structure interaction (FSI) analysis of an aneurysmatic aorta that could not be treated with Endovascular Aneurysm Repair (EVAR). The vessel is reconstructed through segmentation from CT scans and subsequently modeled on CAD software to create the surface and thickness of the vessel itself. Subsequently, we proceeded to carry out Computational Fluid Dynamics (CFD) and FSI simulation. We propose a computational study on a vessel geometry that is faithful to reality and customized. Results: Hemodynamic variable results of the carried out simulations indicate that low velocity and consequently very low WSS areas located in aneurysm site are no longer found when conventional or patient-specific grafts are inserted. The wall stress distribution of aorta FEM analysis enabled the identification of the area at risk of failure, that is, in the posterior part of the aneurysm (\(\sim\)10\({}^{7}\) Pa), while FSI analysis of the patient-specific graft led to a uniform von Mises stresses distribution (\(\sim\)10\({}^{5}\) Pa), except for the junctions where peak stress occurred. Conclusion: The importance of this study is to highlight the benefits of the personalized stent/graft. As the authors expected, the study shows the numerous benefits of the customized stent/graft in terms of blood flow trend and wall stress compared to a traditional stent/graft by supporting the tendency to want to shift the target towards customized stents/grafts, also in the vascular surgery sector.  
Keywords: stent-graft; patient specific; Computational Fluid Dynamics; finite element analysis +
Footnote †: journal: Applied Sciences
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 5 Power Profiling Model  
For power profiling, the consumer's load data provided by smart meters are used to extract the required features. In this model, the profiling process is divided into four stages, namely data extraction, load data analysis, load data clustering, and power profile assignment. Power consumption is profiled by comparing the time series of the consumer's load data, observing consumption patterns in the short term (one day), and analyzing the load factor in the long term. In this section, we explain each stage of this model in more detail.
# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
## 3 Heterogeneity-Aware Federated Optimization Framework
### Overall Framework of Heterogeneity-Aware Federated Optimization
#### 3.1.3 Simulation Procedure  
The simulation procedure is designed to comprehensively capture the process of training and aggregating federated models, with consideration of data heterogeneity. The following steps outline the procedure:  
1. Local Model Training:  
1. Each client trains a local model using its own dataset.
2. Heterogeneity Metrics: For each client, heterogeneity metrics are computed based on* Feature Statistics: Local feature embeddings are extracted, and statistical measures such as mean and variance are calculated.
* Manifold Structures: High-dimensional features are projected into a low-dimensional manifold space using techniques like t-SNE, capturing geometric discrepancies in the latent space.
* Model and Metric Sharing: At the end of each federated round, each client shares
* The updated model parameters.
* The computed heterogeneity metrics (based on feature statistics and manifold structures) with the server.
* Global Model Update:
* The server receives the model parameters and heterogeneity metrics from all the participating clients.
* Heterogeneity-Aware Aggregation step: The server uses the received heterogeneity metrics to compute the aggregation weights for each client. Clients with a higher heterogeneity are assigned lower weights to mitigate their negative impact on the global model.
* The server then aggregates the model parameters from all the clients based on the computed weights, updating the global model.
* Model Synchronization: After updating the global model, the server sends the updated model parameters back to the clients for the next round of local training.  
This simulation procedure ensures that the federated learning process takes into account the heterogeneity present across the clients and adjusts the aggregation strategy accordingly.
## 2 Materials and Methods
### Lidar Technology
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Lidar Technique** & **Physical Process** & **Remarks** \\ \hline \multirow{3}{*}{Elastic} & Based on the elastic scattering of aerosols and clouds (Mie theory). Scattering is sensitive to particles about the same size as the wavelength of light and is responsible for the white appearance of clouds. & To solve the lidar equation, the lidar ratio is needed. \\ \cline{2-3}  & Based on Raman inelastic scattering, this technology is used to retrieve the properties of aerosols and clouds. It also provides temperature profiles and can distinguish between liquid water and ice clouds. & Aerosol and molecule properties can be rediscovered independently, without particular assumptions. Nevertheless, the system needs periodic calibrations. \\ \hline \multirow{3}{*}{HSRL} & HRRL provides unambiguous separation of aerosol and cloud scattered signal, allowing for accurate aerosol optical depth measurements and better aerosol type discrimination. & The signals from molecules and aerosols are treated independently, with higher precision. However, this technology is quite expensive. \\ \cline{2-3}  & Based on Doppler shift, DWL analyzes the frequency shift between the transmitted and received light spectrum to retrieve the wind speed along the line of sight. & In addition to wind speed, DWL can also be used as an elastic lidar with the same limitations. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of different lidar techniques for atmospheric studies.
###### Abstract  
The tensile strength of a silicon nanowire (SiNW) that had been integrated into a silicon-on-insulator (SOI)-based microelectromechanical system (MEMS) device was measured using electrostatic actuation and sensing. SiNWs of about 150 nm diameter and 5 \(\upmu\)m length were batch-fabricated into a 5-\(\upmu\)m-thick SOI device layer. Since there was no interface between the SiNW and the MEMS device and the alignment was perfect, the SiNW integration into an SOI-MEMS was expected to be useful for developing highly sensitive biochemical sensors or highly reliable torsional mirror devices. The SiNW was tensile tested using the electrostatic MEMS testing device. The integration was achieved using a combination of anisotropic and an isotropic dry etching of silicon, with an inductively coupled plasma reactive ion etching. A fabricated silicon beam of 800 nm square was thinned by a sacrificial oxidation process. The tensile strength of the wire was 2.6-4.1 GPa, which was comparable to that of microscale silicon MEMS structures. The reliability of such a thin device was successfully verified for future applications of the device structures.  
silicon; nanowire; tensile testing; fracture; MEMS +
Footnote †: journal: Applied Physics Letters
# Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
## 3 Sleep Stage Detection
### Wake State Detection  
The wake interval \(W(t)\) was determined, as shown in (12), by reflecting the characteristics of the wake state, during which the subject's body motion became more active compared to the sleep state.  
\[W(t)=\begin{cases}1,&\text{if }Mov(t)>\frac{1}{T}\cdot\sum_{t=1}^{T}Mov(t)\\ 0,&\text{otherwise.}\end{cases} \tag{12}\]  
The wake interval represents the scenario in which the subject's movement, detected using the sensing radar, increases above the average of the motion estimation indicator for the entire duration. The detected wake intervals were compared with the PSG readings. Figure 5 shows examples of wake-stage detection obtained by applying (10) to the radar signals acquired from the four subjects. These results are in agreement with the PSG results.  
Figure 4: FMCW radar-based REM sleep-stage-detection results. (**a**) Case of normal detection. (**b–d**) Case with false detection.