esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature
### Monitoring and Controlling Real-Life Buildings
\begin{table}
\begin{tabular}{l l} \hline \hline
**Protocol** & **Characteristics** \\ \hline \hline \multirow{6}{*}{BACnet} & Wired \\  & * very common, especially in large commercial buildings \\  & * initiated and maintained by ASHRAE 1 \\  & * open protocol, standardized according to ISO16484-5 \\  & * can be used from field to management layer \\  & * master-slave architecture \\  & * can implement security features (e.g., encryption) \\ \hline \multirow{6}{*}{KNX/EIB} & * used in both the residential and commercial building sector \\  & * open protocol, based on ISO/IEC14543 \\  & * tree network topology \\  & * implements control applications by interconnecting devices through addresses \\  & * wired or wireless (radio-based) communication (KNX-TP/KNX-RF) \\ \hline \multirow{6}{*}{Modbus} & * widely used in industrial automation, less frequently in building automation \\  & * open-source protocol \\  & * maser-slave architecture \\  & * available via TCP, RTU, or in ASCII mode (Ethernet-, RS-232-, or RS-422-based) \\ \hline \multirow{6}{*}{M-Bus} & * common bus, dedicated to metering devices \\  & * used, e.g., for gas, water, heating, and electricity meters \\  & * standardized according to EN13757-2 and EN 13757-3 \\  & * recently being extended by Wireless M-Bus \\ \hline \multirow{6}{*}{EnOcean} & * frequently used in both residential and non-residential buildings \\  & * developed by EnOcean alliance, standardized according to ISO/IEC 1453-3-1X \\  & * uses radio frequency bands below 1 GHz. with a range up to 30 m \\  & * uses energy harvesting (e.g., solar) and backup batteries \\  & * supports several topologies. including mesh and point-to-point \\  & * wide range of sensor/actuator types available \\ \hline \multirow{6}{*}{ZigBee} & * widely used in residential buildings \\  & * seven-layer protocol (incl. security layer) based on IEEE 802.15.4 \\  & * uses radio frequency bands between 900 MHz and 2.4 GHz \\  & * low power consumption \textless{}0.05 W, low speed 40 Gbps to 250 Gbps \\  & * range between 10 m to 100 m \\  & * mesh, tree, and point-to-point topology based on master-slave architecture \\ \hline \multirow{6}{*}{LoRaWAN} & * used in several test-beds and research contexts \\  & * uses radio frequency bands below 1 GHz \\  & * low power consumption, low speed 27 kbps to 50 kbps \\  & * range between 2 km to 15 km (urban and rural areas, respectively) \\  & * star-of-stars topology \\ \hline \multirow{6}{*}{WiFi} & * mostly used on higher automation levels and not in the field \\  & * based on the IEEs 802.11 standards family \\  & * high transfer speeds of around 1 Gbps, high power consumption of 0.1 W to 0.8 W \\ \hline \multirow{6}{*}{GPRS/GSM} & * used, e.g., for mobile (phone) communication and short message service (SMS) \\  & * decreased reliability and availability during periods of high external load \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Communication protocols of common wired and wireless devices used in sensor and control networks, based on [38; 40; 50].
# Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming
## 7 Future Work  
Although our simulation study proves that LQE successfully fulfills the design goal while exhibiting better performance compared to the existing ABRs, it is important to investigate its performance in the real world by using an implementation in an off-the-shelf mobile device. For the experiments in the real world, we have implemented an Android DASH client players based on Google Exoplayer [20]. Our implementation includes LQE as well as the other ABR schemes such as Tian and BBA using the reference V1 of the ExoPlayer, in which FormatEvaluator class takes charge of adaptive video streaming. We are currently porting our implementation to the latest version (V2) of the ExoPlayer. In future work, we will construct a test environment that consists of a testing video streaming server, our latest DASH client player, and UI to collect feedback from users. We will examine the performance metrics in real experiments together with the additional quality of service metrics such as Mean Opinion Score (MOS), which is a measure representing the overall perceived quality of streaming based on user feedback.  
Figure 7: Examples of Bit Rate Changes according to Throughput.
## 3 Results and Discussion
### CFD Analysis
#### 3.1.2 Effect of Hub Geometry Treatment on Performance for Off-Design Conditions  
The effect of hub configuration is assessed for the design volume coefficient of 0.051 and the optimum hub geometry configuration is identified as described in the previous section. Encouraged by the results obtained for the designed volume coefficient, the effect of off-design volume coefficients on the performance indicators relating to the various hub geometry configurations is also studied numerically. The volume coefficients considered in the analysis have values between 0.004 and 0.051. The static pressure rise coefficient constantly declines as the volume coefficient increases, as shown in Figures 18 and 19. This holds for both the hemispherical and elliptical hub shape combinations used in the study as well as hub-less base configurations.  
Figure 19: Coefficient for static pressure rise for ellipsoidal hub geometry arrangements for the range of volume coefficients chosen for the study.  
Figure 18: Coefficient for static pressure rise for hemispherical hub geometry arrangements for the range of volume coefficients chosen for the study.  
The hemispherical hub configuration S3 and the ellipsoidal hub configuration E5 are found to be the best design configurations, offering comparatively higher static pressure rise coefficients for any of the volume coefficients used in the analysis, when compared to other configurations, which also include the hub-less base model. In comparison with the hub-less base configuration model, the enhanced hemispherical hub geometry configuration S3 exhibits a significant increase in the static pressure-rise coefficient of around 3.27%, and the optimized ellipsoidal hub geometry E5 exhibits an improvement in the static pressure-rise coefficient of about 6.07%. Therefore, it can be deduced that for increased fan performance, it is beneficial to opt for either a hemispherical hub geometry that corresponds to a spheroidal hub ratio of 0.50, or an ellipsoidal hub geometry that corresponds to an ellipsoidal hub ratio of 1.10.
# A Method for Constructing a Synthesis Health Index for Metro Vehicle Wheelsets
## 3 The Construction Process of the Synthesis Health Index for Metro Vehicle Wheelsets  
The construction process of the SHI for metro vehicle wheelsets primarily includes the following steps:  
Step 1: Training and Testing Set Division. The dynamic detection data of metro vehicle wheelset profiles are randomly divided into training and testing sets, which are used for model construction and the validation of model efficacy, respectively.  
Step 2: Dimension Reduction. This step includes feature selection, normalization, and dimensionality reduction. First, feature selection is employed to identify parameters that reflect changes in the wheelset health status. Since the selected features still exhibit high dimensionality, standardization is applied to unify the units and limit the range of values. Subsequently, further dimensionality reduction is achieved by projecting the high-dimensional data into a lower-dimensional space using KPCA.  
Step 3: Outlier Removal. An isolation forest is utilized to eliminate outliers from the reduced-dimensional data, thereby minimizing their impact on health status classification.  
Step 4: Health Status Classification. The reduced-dimensional data are classified using hierarchical clustering, with health statuses defined according to the resulting categories. Further subdivisions of these categories are made based on the advantages of hierarchical clustering.  
Step 5: SHI Construction Using the Geometric Center of Healthy Categories as a Baseline. The geometric center points of the subdivided healthy categories are used as the baseline to calculate a one-dimensional SHI for the metro vehicle wheelset based on Euclidean distance.  
Step 6: SHI Smoothing and Performance Evaluation. The constructed SHI is smoothed and evaluated based on its monotonicity, trend, robustness, and recognizability.  
The construction process of the synthesis health index for metro vehicle wheelsets is shown in Figure 1.
## 4 Estimator Definitions
### Oversampled Reference Bank  
The fundamental resolution of the matched filter may be increased by oversampling the signals at a common multiple of the original signal bandwidth, but resampling is a computationally expensive operation and increases the number of multiplications \(M\) per test point and the total number of test points \(K\). This quickly becomes prohibitively expensive.  
We may limit this computational complexity by oversampling the reference signal \(x\) by a large factor (order 200), building a reference bank of shifted versions of \(x\) at this oversampled frequency, then downsampling the entries in this reference bank back to \(f_{s}\). This bank contains copies of \(x\) at the original sampling frequency \(f_{s}\), but each adjacent shift lies on the much more precise sampling lattice defined by the large oversampling frequency [14]. This allows us to test ToA hypotheses with much higher temporal resolution while still performing computations at the sampling frequency \(f_{s}\).  
We further limit the computational complexity by limiting the number of test points evaluated in the cost function. Instead of searching the same range of \(k\) that the matched filter evaluates, we instead search only in the immediate vicinity of the \(\hat{k}\) from (9). Label a coarse test point \(p\) some small number of samples before \(\hat{k}\), index the \"micro-shifts\" in the reference bank by \(q\), define the total number of test points as \(Q\) and construct the reference bank \(\mathbf{B}\) such that each row corresponds to a micro-shifted version of \(\mathbf{x}\), which builds a \(Q\times M\) matrix. The micro-resolution and the narrow-window cost function may be written [14] as  
\[\mathbf{g}^{\prime}[q]=\left|\sum_{m=0}^{M-1}\mathbf{z}[m+p]\mathbf{x}^{*}[m-q] \right|^{2},q\in[0,1,...,Q-1]. \tag{10}\]  
Given the carefully structured reference bank \(\mathbf{B}\), the sum may be evaluated as a simple matrix multiplication,  
\[\mathbf{g}^{\prime}[q]=|\mathbf{z}\mathbf{B}^{\dagger}|^{2}\, \tag{11}\]  
where \({}^{\dagger}\) denotes the Hermitian conjugate. The time domain ToA estimate \(\hat{v}\) then becomes  
\[\hat{v}=pf_{s}^{-1}+\hat{d}f_{s,bank}^{-1}\ ;\ \hat{q}=\arg\max_{q}\mathbf{g}^{ \prime}[q], \tag{12}\]  
where \(f_{s,bank}\) is the oversampling rate of the filter bank. \(\mathbf{B}\) may be evaluated offline and stored on the hardware, limiting the additional complexity of adding this second stage to the ToA estimation process [14].
## 3 Framework Design and Deployment
### Proposed Framework
#### 3.4.2 Local Model Training on Edge Devices  
In the next phase, each edge device engages in local model training using its own data to update the model. This process is defined as \(M_{i+1}^{local}=Train(M_{i},data_{local})\), where \(M_{i}\) represents the global model parameters at iteration \(i\), and \(data_{local}\) refers to the data held locally on each device. This design preserves user privacy by eliminating the need for raw data exchange, allowing devices to train the model independently.  
Local training enables each edge device to leverage unique data characteristics relevant to its environment, enhancing model adaptability. By performing this step locally, the framework benefits from diverse data sources without compromising privacy, reinforcing FL's core objective of decentralized model training with data security.
# Performance Analysis of Two-Hop mmWave Relay Nodes over the 5G NR Uplink Signal
## 4 Simulation Results and Discussion
### Bit Error Rate Performance
In this context, it can be seen that the (2 \(\times\) 2 \(\times\) 2) NR-D&F strategy with the LS algorithm reached a BER level of 5.9 \(\times\) 10\({}^{-3}\) at around SNR = 20 dB, which implies that 7.0 dB and 6.0 dB performance gains were successfully achieved, respectively, when compared with SISO NR-D&F and (2 \(\times\) 2 \(\times\) 2) NR-A&F protocols. However, the obtained result with an uplink mmWave (2 \(\times\) 4 \(\times\) 4) D&F relay node with the PCE algorithm overcame the relaying scheme described above. For example, it can be seen in Figure 11 that this algorithm generally exhibited a better  
Figure 11: BER comparison for different uplink mmWave NR-RN co-operative schemes implemented with the 64-QAM signal. BER performance of the proposed strategies and, more specifically, obtained 12 dB of BER gain when compared with the SISO D&F PCE algorithm and 5 dB considering the (2 \(\times\) 4 \(\times\) 4) A&F strategy. Nevertheless, when the (2 \(\times\) 4 \(\times\) 4) D&F LS technique was considered, a loss of approximately 0.9 dB was achieved in comparison with the (2 \(\times\) 4 \(\times\) 4) D&F PCE protocol. Therefore, it should be noted that the practical estimation is very close to the perfect estimation. Figure 12 illustrates the achievable BER performance of the proposed uplink mmWave NR-NNs, in which the 256-QAM modulation scheme is taken into consideration. It is indicated from Figure 12 that the implemented uplink mmWave MIMO NR-RN co-operative network achieves a lower BER level than the proposed protocols with the SISO technique. More explicitly, the error of the (2 \(\times\) 2 \(\times\) 2) A&F protocol decreases, the BER level of this scheme improves and approaches the 2.5 \(\times\) 10\({}^{-2}\) BER level at around SNR = 20, which suggests a gain of the 3 dB of the uplink mmWave SISO A&F algorithm. Additionally, when the number of antennas increases, i.e., (2 \(\times\) 4 \(\times\) 4) configuration, the BER behaviour may reach values of the (2 \(\times\) 2 \(\times\) 2) D&F PCE performance. On the other hand, when the uplink mmWave (2 \(\times\) 2 \(\times\) 2) D&F PCE strategy was considered, a 5 dB performance gain was attained and overcame, with SNR = 15 dB, the (2 \(\times\) 2 \(\times\) 2) A&F protocol. Furthermore, the results clearly show that the uplink mmWave (2 \(\times\) 4 \(\times\) 4) NR-D&F PCE strategy has the best BER performance among the proposed schemes. In this sense, the same performance as the (2 \(\times\) 4 \(\times\) 4) A&F protocol has been obtained at SNR = 13.5 dB, such that a 6.5 dB performance gain was achieved. However, it should be noted that the BER performance of the 64-QAM is lower than 256-QAM, which was due to the robustness that changes the 64-QAM modulation scheme to 256-QAM.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Exploiting Path Diversity
#### 4.2.2 Multipath Opportunistic RPL Algorithms
EDC of a node represents its proximity to the root in an opportunistic routing environment and is defined as already shown in Equation (15). Note that \(p_{ij}\) in Equation (15) is the packet reception ratio (PRR) when node \(i\) sends a packet to node \(j\). The first term in Equation (15) is called the single hop EDC, which indicates the expected time to deliver a packet from the node to any of its forwarders. The authors are focused on how to measure the single hop EDC. Although the definition of the single hop EDC requires that each node collect PRR for all of its potential upward forwarders, ORPL practically updates its single hop EDC simply by measuring latency from transmitting an upward anycast packet to receiving an ACK from any upward forward, without measuring individual PRR values. However, each node cannot update EDC in the same way by exploiting downward transmission. The reason is that EDC represents the opportunistic route quality between the node and the sink, whereas the downward transmission results represent the route quality between the node and its subtree nodes. To address the problem, in ORPL-DT, each node maintains both its EDC metric and PRR for each potential upward forwarder. For each upward transmission, a node calculates PRR by measuring the number of retransmissions until receiving an ACK from the upward forwarder. Using the updated PRR, the node calculates the current single hop EDC. Then, using the updated single hop EDC, it updates PRR for each of the other candidate upward forwarders. For each downward transmission, each node enables the downward forwarder receiving the packet to update the PRR information by piggybacking the number of retransmissions in the downward packet. Since the node sending the downward packet is a potential upward forwarder of the receiver, the receiver can update the single-hop EDC metric for its upward forwarder. Performance of ORPL-DT was evaluated through experiments on a testbed consisting of a sink node and 30 telosB motes with the ContikiOS. They used ContikiMAC with the default wakeup interval of 500 ms. They compared ORPL-DT and ORPL for diverse scenarios varying the interpacket intervals of downward and upward traffic. The results show that in all scenarios, ORPL-DT outperforms ORPL in terms of downlink PRR, uplink PRR, duty cycle, and routing control overhead. However, for a severely asymmetric link, the PRR information updated via downward transmissions may not accurately reflect PRR for the potential upward forwarder.
## 4 Discussion
### Hypothesis Test for Damage Detection  
The simulation of the hypothesis test for damage detection shows that minimal changes in the frequencies' mean can be detected. However, the discussed values for the minimum difference should only be interpreted as a qualitative comparison between the sensors and methods. Biases in temperature or skewed distributions could significantly influence the test leading to much higher required differences. Since the test mainly depends on the standard deviations of the distributions, the strain sensors' result requires a higher difference than the GBR's result. However, the test also leads to a higher difference if the number of elements in the test distribution decreases. The few elements in the FDD's test distribution result in a much higher value, indicating that longer measurement times are required for this method.
# Multi-Domain Modeling and Analysis of Marine Steam Power System Based on Digital Twin
## 6 Discussion  
Using the digital twin technology system based on the Modelica language, combined with multi-domain physical modeling, modular modeling, etc., this paper conducts in  
Figure 31: Variation curve of condenser pressure.  
Figure 32: Variation curve of cooling water temperature rise.  
depth research on the multi-domain modeling, dynamic characteristic analysis and fault simulation of the marine steam power system.
1. Using the digital twin technology system, the construction process of the digital model of the marine steam power system is given, including five steps: mechanism analysis, model development, model testing, model verification and model analysis. Based on its operational principle, a mathematical model of the equipment is established.
2. Based on the multi-domain modeling language Modelica and the system simulation platform of MWorks, combined with the modular modeling, the equipment and subsystem model library is developed, the digital model of the marine steam power system is integrated, and the model characteristic parameters are debugged. Finally, the correctness and high accuracy of the model are verified using experimental data for a certain type of steam turbine, ensuring the accuracy of the digital twin model.
3. Based on the digital model, the dynamic characteristics of the marine steam power system are analyzed. Through comparison with the test data, the error for the key performance parameters is established to be within \(\pm\)5%. The system's dynamic change laws under different cooling conditions and load conditions are analyzed. The results show that the dynamic operation trend is consistent with the actual situation. The operation characteristics of the unit under variable working conditions are mastered, and virtual reality mapping between the digital model and the physical system is realized.
4. Based on the digital model, the fault simulation of the marine steam power system is carried out, and a fault diagnosis process is proposed. Later, based on this digital model, a complete fault database for the marine steam power system will be established, and fault prediction for the marine steam power system based on digital twins will be carried out in subsequent research.
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 5 Results and Analysis  
### Experimental Environment  
The Cosmos Hub Client (Gaia Client) and the Osmosis Client were the foundation for deploying the measurement nodes used in this study. The device on which the measurement nodes are installed meets the minimal hardware requirements of each client. This study's computer has the following specifications: an Intel Xeon Silver 4114 \(\times\) 2 processor, 128 GB of RAM, and Linux 18.04. It also has adequate capacity to fulfill the synchronization demands of Osmosis and Cosmos Hub, with 5 T.B. HDDs.  
The Cosmos Hub Client (version 7.0.2), Osmosis Client (version 8.0.0), and Go-Language (version 1.18.2) are all part of the measurement nodes' software environment. The Cosmos Hub installed Version 3.0.0 of the IBC-GO module, which facilitates communication across blockchains, whereas Osmosis has Version 2.0.2 loaded.  
The measurements were collected during 24 h, from 1 June to 2 June 2022. The fungible tokens were moved from the mainnet of Osmosis to the mainnet of Cosmos Hub. Each token transfer necessitated the transmission of 1000 bits plus a 260-bit transaction fee. Token transfers were performed every 86.4 s for 1000 transactions every day.
# An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
## 2 Feature Engineering Based on Circle Model
### Process of the Proposed Method  
The parameter simulation of analog circuit is completed by PSPICE software and the output voltages are extracted from the output file by MATLAB. The process of the method is presented as follows:  
Step 1: Build a circle model through three points;  
Step 2: Determine whether the 3D feature construction has been completed. If not, go to the next step. If it is completed, go to step 4;  
Step 3: Judge the indiscernibility of fuzzy group;  
Step 4: Find out the second frequency and construct three-dimensional features by the complex voltage domain features of twice frequency;  
Step 5: Normalization processing is carried out and the feature preprocessing is completed;  
Step 6: Train ELM;  
Step 7: Test the model and realize the fault diagnosis of an analog circuit.