esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Validation of the Modified Ratio Vegetation Index (MRVI)
The MRVI obtained the highest correlation  
Figure 6: The relationship between the canopy reflectance and leaf C\({}_{\text{ab}}\) of winter wheat in exp1 at Yucheng Station, as follows: (**a**) the reflectance at the red band and leaf C\({}_{\text{ab}}\); (**b**) the reflectance at the near-infrared (NIR) band and leaf C\({}_{\text{ab}}\); (**c**) the reflectance at the blue band and leaf C\({}_{\text{ab}}\); (**d**) the reflectance at the green band and leaf C\({}_{\text{ab}}\); (**e**) \(\rho_{\text{blue}}/\left(\rho_{\text{green}}-\rho_{\text{blue}}\right)^{2}\) and the leaf C\({}_{\text{ab}}\); (**f**) \(\rho_{\  
coefficient of 0.73 in the pure pixels (Figure 7a), whereas the others were about 0.30-0.39 (Figure 7b-e). Furthermore, as shown in Figure 8a, the MRVI also obtained the highest correlation coefficient of 0.59 in all of the pixels (including the pure and mixed pixels), whereas the others were about 0.23-0.31, which are shown in Figure 8b-e. There were outliers in the mixed pixels, and the reason for that was mainly that the reflectance of the MODIS pixel was susceptible to the mixed-pixel effect with different land cover types. Although the mixed-pixel reduced the estimation accuracy of VIs, the MRVI obtained the better estimation results of the leaf C\({}_{\text{ab}}\) in Hengshui City. It was thus acceptable that the MRVI represented the effect of N on the winter wheat growth, instead of the GNDVI in the GPP1 and GPP2 models. Figure 8: Relationship between VIs and leaf C\({}_{\text{ab}}\) in all winter wheat pixels (pure and mixed pixels) of exp2 in Hengshui City: (**a**) MRVI and C\({}_{\text{ab}}\); (**b**) \(\rho_{\text{NIR}}/\rho_{\text{blue}}\) and C\({}_{\text{ab}}\); (**c**) RVI2 and C\({}_{\text{ab}}\); (**d**) SIPI and C\({}_{\text{ab}}\); (**e**) MSR and C\({}_{\text{ab}}\).
## 3 Results and Discussions
### Comparison of Fall Detection with Different Sensor Positions  
The purpose of this experiment is to demonstrate the capability of the proposed LSTM-based model for predicting the optimal position of the sensors for fall detection based on the observation window generated with the help of multi-window sliding. Therefore, we train a transformer encoder model using fall datasets (accelerometers and gyroscopes) from all 16 sensor positions and evaluate it using the test set. The average performance of the left and right sensor positions of the forearm, upper arm, hand, shank, hip, and foot is used to calculate the performance of the forearm, upper arm, hand, shank, hip, and foot, respectively. Furthermore, the thoracic result is the average of the upper and lower thoracic locations. We measure the model's balanced accuracy and F1 score to evaluate its performance. Based on the LSTM encoder-based model, we present the results for various sensor positions in Table 3. For the shank position, the model achieves a remarkable F1 score of 0.97. In general, the F1 score of the model is higher than 0.90, except for the foot and hand, where the F1 score is 0.88 and 0.85, respectively. The results indicate that different sensor locations give us various performances and that the shank is the optimal location for the sensors.
## 4 Comparison between Theoretical and Simulation Results of \(C_{2\varphi}\)
### Setting Threshold with Actual Data  
In order to set reasonable threshold of FLD, we can get the relationship curve about FLD output and \(C/N_{0}\) based on (6) and thermal jitter of FLL [24]:  
\[\sigma_{\text{t}FLL}=\frac{1}{2\pi T_{coh}}\sqrt{\frac{4 FB_{L}}{C/N_{0}}(1+\frac {1}{T_{coh}*C/N_{0}})} \tag{29}\]  
where \(\sigma_{\text{t}FLL}\) is the frequency jitter caused by thermal noise, \(B_{L}\) is equivalent noise bandwidth of tracking loop, \(F\) is equal to 1 and 2 for low, and high \(C/N_{0}\) individually. Normally, \(3\sigma_{\text{t}FLL}\) is used to estimate tracking performance of FLL. The relationship between threshold of FLD and \(C/N_{0}\) based on (6) and (29) is  
\[Th_{C_{2\varphi}}=\frac{1}{M}\sum_{i=1}^{M}\cos(12\pi T_{coh}\sigma_{\text{t} FLL}) \tag{30}\]  
Assuming that \(M=20\), coherent integration length \(T_{coh}=1\) ms, \(B_{L}=25\) Hz, we can figure (30) in Figure 13.  
Then, we recorded actual real-time data samples from the intermediate frequency (IF) signal of GPS receiver. The data were recorded on building roof for a duration of 100 s at 62 million samples  
Figure 12: MTLL comparison between FLD (\(M=1\)) and PLD.  
Figure 13: Relation between threshold of FLD and \(C/N_{0}\) (\(M=20\)).  
per second. The \(C/N_{0}\) of the GPS satellites were 37 dB-Hz (SVN 9), 40 dB-Hz (SVN 6), 43 dB-Hz (SVN 29), and 47 dB-Hz (SVN 5). From Figure 11, we can know that the theoretical threshold of FLD is 0.2 (37 dB-Hz), 0.6 (40 dB-Hz), 0.8 (43 dB-Hz), and 0.9 (47 dB-Hz). Figure 14 shows the comparison of FLD outputs and theoretical thresholds with different satellites. The results show that the theoretical thresholds fit well with FLD outputs of real signals, which provide a solid support for FLD threshold setting criteria.
# Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
###### Abstract
However, there are many other phenomena, e.g., weather and surface reflectance, that introduce effects into the signal that can be most effectively modelled by noise, rather than searching for their respective governing equations. We develop mathematical models for the observed noise and suggest how these models can be used for improved positioning and uncertainty quantification in future devices.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 2 Related Work
If a node is congested, the value of congestion metric will increase. In this case, it is not preferable for neighboring nodes to select this link as a parent node. To improve the network lifetime, the authors of [9] propose an energy-aware parent node selection algorithm. With this scheme, each node builds a hierarchical structure based on the hop count to the sink node. If the energy level of a node is lower than half of the original battery capacity, the node is moved one level lower in the tree model. By lowering the hierarchical level, a node with a low battery level cannot be selected as a parent node. The authors of [11] construct a binary tree based on the number of hops to the sink node. To balance the energy consumption, the parent node chooses the link with the smaller number of packets it receives from the child on the left or right. In [10; 12], each node calculates the link cost using the distance vector and remaining energy. In [13], each node selects a parent node from the neighboring nodes with predefined cognitive parameters, which is called a fitness function using the flower pollination algorithm. The fitness function considers the energy consumption and distance ratios of the average distance. Several tree-based routing protocols take multiple cognitive metrics into consideration concurrently and aim to jointly achieve multiple objectives by considering several decision criteria. For example, the authors of [14] take three cognitive metrics, i.e., the channel error rate, residual energy, and buffer capacity, and integrate the proposed metrics using heuristic coefficients. These coefficients denote the significance of each metric, and the sum of the coefficients is equal to 1. Each node then selects the neighboring node with the highest weighted sum as a parent node. Similarly, the authors of [15] take the link quality, residual energy, and relay node frequency and integrate these metrics using the weighted-sum method. The authors of [16] integrate multiple cognitive metrics, namely, the distance, number of associated nodes, and residual energy using the weighted-sum method. The authors of [17] integrate three cognitive metrics, such as the hop count, residual energy, and link quality using the heuristic coefficients. The authors of [18] consider four attributes, i.e., the link expiration time, trip time, node speed, and lifetime, and integrate them using an \"if-else\" statement. However, these approaches adopt subjective weights, which are often less objective and reduce their acceptance by the scientific community. To provide a flexible trade-off between the conflicting objectives, in our previous work [19], we propose a MCDM-based parent selection scheme in which each node finds the best parent node by determining the relative importance of multiple decision criteria using the AHP and then derives the weighted cost using the SAW method.
# Edge Computing Architecture for the Management of Underwater Cultural Heritage
## 3 Conceptual Framework
### Sensor Layer
#### 3.1.1 Continuous Measurement Systems  
An underwater sensor network is deployed in the area of interest at different heights on the seabed. It records environmental parameters using acoustic communication technology to transmit the information to the surface, where a network of buoys receives the data. They are equipped with a battery system to provide the device with sufficient autonomy.  
The node buoy network consists of a series of buoys deployed over the area of interest to act as a data gateway. They are responsible for receiving the data from both the sensor network and the support elements, such as the ROV or divers performing field inspections. By using acoustic transceivers, such as the H2 Observe solution [46], that measure the time it takes for a pulse to travel from the transmitter to the receiver, each buoy can calculate the distance that separates it from the element with which it is communicating. In turn, the absolute position of the buoy is calculated by a GPS receiver built into the buoy. This device is responsible for redirecting the information received acoustically and transmitting it to a buoy that acts as a central unit using LoRa communication technology. It periodically sends data on its status (battery level, status of internal systems, etc.) and absolute position. It has a power supply system composed of batteries and solar panels to give the device total autonomy.
## 2 Description of the Systems Under Investigation and Experimental Setups
### Harmonic Vibration Testing  
After the completion of random vibration testing, a quantitative investigation into the first mode took place to further understand the possible presence of nonlinear behaviors. This investigation was completed using harmonic testing, which gives a deeper and clearer picture of what is going on with each system at a set frequency range. The setup for this test can be found in Figure 6. The focus of harmonic testing will be on the first mode of vibration for all BARC systems under consideration. This is because the first mode gives the clearest results in all tests performed so far and is the easiest to activate with the setup shown in Figure 6. Similar quantitative experimental testing can be conducted for higher modes of vibration. It should be mentioned that the first vibratory mode for all systems is primarily in the Y-direction, so they are easier to activate and study.  
The central-cut width systems were studied in a range of 92.5 Hz to 107.5 Hz, for a total range of 15 Hz. Each assembly was excited at four different input excitation levels, including 50 mV, 100 mV, 125 mV, and 150 mV, using step-sine frequency analysis, with each step being 0.1 Hz and with the frequency being held at that step for 15 s to achieve a steady-state response. This means that each test would take approximately 37.5 min. Once  
Figure 15: A comparison of BARC central-cut width configurations using random vibration testing from the (**a**) side Y, (**b**) RC Y, and (**c**) RC Z accelerometers at \(1\times 10^{-4}\) V\({}^{2}\)/Hz excitation.  
again, as previously established, the side and RC accelerometers were investigated using the experimental data collected from all directions to find the presence of nonlinear trends, including nonlinear hardening, softening, and damping, as well as to establish a coupling trend between the box assembly and the removable component. Something else to look for is the nonlinear softening cut trend that has been seen previously, especially as discussed for random vibration testing.
# Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs +
###### Abstract
At the same time, the blockchain consensus mechanism was simplified to implement block recording and authentication, and finally, the effectiveness of the scheme was verified through simulation experiments. The contributions of this paper are as follows:  
1. Using the uniqueness and non-repudiation of RF fingerprints, the physical layer features are used as the identity of the vehicle node, so that it cannot be tampered with or forged. 2. We Simplify the critical technologies of the blockchain and propose a hash-chain-based cross-regional security authentication scheme for space-air-ground integrated VANETs. We replace the traditional consensus mechanism with Kafka distributed message processing to implement the verification and backup of the records on the block. 3. Combined with SAGIN to meet the needs of highly intelligent assisted driving, the hash-chain template is redefined according to the characteristics of network transmission. 4. Using OMNet ++, SUMO, and Veins joint simulation platforms to generate transactions for cross-regional traffic flow, Kafka [11], and Practical Byzantine Fault Tolerance Algorithm (PBFT) [12] consensus algorithms are compared. The experimental results show that the average delay of a single transaction generation block is about 0.9 ms, which meets the needs of efficient and low-latency authentication. The rest of this paper is organized as follows: Section 2 discusses related work. In Section 3, the redefined hash-chain template is introduced. Section 4 describes the model of cross-regional identity authentication system based on the hash chain. Section 5 illustrates the experiment process. Finally, Section 6 concludes this work.
###### Abstract  
To solve the problem in target acoustic signal processing and recognition when the target flies at a low altitude based on the acoustic positioning system, which is often affected by external interference and brings false information, this paper proposes a target signal processing and recognition algorithm for low-altitude target acoustic positioning based on variational modal decomposition and the test method of multiple five-element acoustic arrays. This algorithm uses VMD to decompose the target signal into modal components with different central frequencies and then performs wavelet threshold processing on the low-frequency part of the signal. After determining the remaining signal components and the low-frequency part's threshold, the residual component is reconstructed. Based on the test principle and calculation model of the five-element acoustic positioning system, following processing of the low-altitude target acoustic positioning signal using variational modal decomposition, the cross-correlation function method is introduced to perform correlation operations on the basic array of five acoustic sensors and then obtain the time value and time difference of the target acoustic information in each acoustic sensor, ultimately determining the spatial position of the target. Finally, we used the data fusion processing method for target coordinates in multi-acoustic basic arrays to determine the actual target position. By comparing the results obtained using the high-speed camera method with those of the proposed approach, it was found that the average error in the test area of 100 \(\times\) 100 m was less than 1 m.  
variational modal decomposition (VMD); target; cross correlation function; recognition; five-element acoustic array positioning Article  
[PERSON]; [PERSON] An Acoustic Array Sensor Signal Recognition Algorithm for Low-Altitude Targets Using Multiple Five-Element Acoustic Positioning Systems with VMD  
Applid  
Applid  
==WARNING: Truncated because of repetitions==
{B})\) and \(P(x_{C},y_{C},z_{C})\) are obtained from basic arrays B and C in the \(Oxyz\) coordinate system. \(S_{0}^{A}-S_{4}^{A}\) are five acoustic sensors within the basic array A, and the coordinate positions of the five acoustic sensors are defined as \(S_{0}^{A}(0,0,H)\), \(S_{1}^{A}(D,0,0)\), \(S_{2}^{A}(0,D,0)\), \(S_{3}^{A}(-D,0,0)\), and \(S_{4}^{A}(0,-D,0)\), respectively. The target is located at any position \(P_{A}(x_{A}\ ^{\prime},y_{A}\ ^{\prime},z_{A}\ ^{\prime})\) in the test system of basic array A, and the distance between the sound information generated by the target itself and each sensor is \(r_{0}\), \(r_{1}\), \(r_{2}\), \(r_{3}\), and \(r_{4}\), respectively. The corresponding time when the five acoustic sensors perceive the target sound information is \(t_{0}\), \(t_{1}\), \(t_{2}\), \(t_{3}\), and \(t_{4}\), respectively, then the relative delay between the arrival of \(S_{0}^{A}\) and \(S_{1}^{A}\) is \(\tau_{0i}=t_{i}-t_{0}\), \(i=1,2,3,4\), so, \(P_{A}(x_{A}\ ^{\prime},y_{A}\ ^{\prime},z_{A}\ ^{\prime})\) can be found by Equation (2).
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
###### Abstract  
Non-orthogonal multiple access (NOMA) system can meet the demands of ultra-high data rate, ultra-low latency, ultra-high reliability and massive connectivity of user devices (UE). However, the performance of the NOMA system may be deteriorated by the hardware impairments. In this paper, the joint effects of in-phase and quadrature-phase imbalance (IQI) and imperfect successive interference cancellation (ipSIC) on the performance of two-way relay cooperative NOMA (TWR C-NOMA) networks over the Rician fading channels are studied, where two users exchange information via a decode-and-forward (DF) relay. In order to evaluate the performance of the considered network, analytical expressions for the outage probability of the two users, as well as the overall system throughput are derived. To obtain more insights, the asymptotic outage performance in the high signal-to-noise ratio (SNR) region and the diversity order are analysed and discussed. Throughout the paper, Monte Carlo simulations are provided to verify the accuracy of our analysis. The results show that IQI and ipSIC have significant deleterious effects on the outage performance. It is also demonstrated that the outage behaviours of the conventional OMA approach are worse than those of NOMA. In addition, it is found that residual interference signals (IS) can result in error floors for the outage probability and zero diversity orders. Finally, the system throughput can be limited by IQI and ipSIC, and the system throughput converges to a fixed constant in the high SNR region.  
two-way relay; non-orthogonal multiple access; in-phase and quadrature-phase imbalance; imperfect successive interference cancellation; orthogonal multiple access 2020
## 4 Proposed Work  
The main objective of this paper is to propose a decentralized blockchain-based authentication and IPFS-based data management protocol for smart systems that is suitable for resource-constrained smart devices. Thus, the time consumed for message transmission and the issue of network latency are not discussed in this study as they depend on the blockchain network, specifically the consensus protocol.  
The proposed protocol leverages the following emerging technologies: the Ethereum blockchain and smart contracts for secure data management; IPFS (interplanetary file system) for decentralized storage; ECC (elliptic curve cryptography) for lightweight key management; SAS (Secure Authentication System) for robust mutual authentication; and an ASCON cipher for advanced IoT data encryption. This combination ensures the confidentiality, integrity, and availability of critical IoT application data, providing an efficient and robust infrastructure. Figure 1 illustrates the overall architecture of the proposed protocol, showcasing its essential elements.  
The secure IoT protocol leverages blockchain and IPFS technologies, and consists of two phases: mutual authentication and secure data sharing. Each device possesses the following:  
* Personal account;
* Corresponding device account;
* Shared ID.  
**Phase 1: Mutual Authentication**  
* Key Generation: IoT devices (IoT1 and IoT2) generate asymmetric key pairs (public and private).
* Public Key Registration: Public keys are stored on the blockchain for authentication.
* Shared Key Calculation: Devices calculate a shared key using the Diffie-Hellman protocol.
* Secure Authentication System (SAS) Generation: Devices generate a SAS by combining their shared key and shared ID.
* SAS Storage: The SAS is stored on the blockchain.
* SAS Comparison: Devices compare SASs for mutual authentication validation.  
**Phase 2: Secure Data Sharing**  
* Data Storage: IoT1 stores data (images, etc.) in IPFS.
* Content ID Retrieval: IoT1 retrieves data CID.
* CID Encryption: CID is encrypted using ASCON algorithm and shared key.
* Encrypted CID Storage: Encrypted CID is stored on blockchain.
* Encrypted CID Retrieval: IoT2 retrieves encrypted CID.
* CID Decryption: IoT2 decrypts CID using shared key.
* Data Retrieval: IoT2 retrieves data from IPFS using decrypted CID.  
**Tools Used**  
* Blockchain (authentication, key storage, and CID encrypted storage);
* IPFS (data storage);
* Asymmetric encryption algorithms (key generation);
* ASCON algorithm (CID encryption and decryption);
* Diffie-Hellman protocol (shared key generation).