esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Aroma Active Compounds in Fresh and Frozen PJs Through GC-O/AI and OAV
Seven volatiles perceived by GC-O were not identified through OAV analysis, possibly attributed to their high odor thresholds. Among the potential odorants, as the crucial characteristic contributors to PJs, hexanol and 1-octen-3-ol accounted for the highest OAVs in all samples, ranging from 158.725 to 231.231 and from 375.550 to 450.044, respectively. Similar results were found by [PERSON] et al. [19]. The OAVs of hexanal and (\(Z\))-3-hexen-1-ol, which are responsible for grassy and fruity notes, also had OAVs greater than 1. Of note, linalool, endowing a floral aroma, was dominant in the fresh control sample FPJ0, which is consistent with previous reports [19]. However, the OAVs of this volatile were decreased in frozen PJs (FPJ\({}_{--}20\)\({}^{\circ}\)C, FPJ\({}_{--}40\)\({}^{\circ}\)C, FPJ\({}_{--}80\)\({}^{\circ}\)C). This was consistent with the GC-O/AI analysis, and may explain the attenuated floral intensity in frozen samples. Similarly, \(\beta\)-myrcene was also undetected or sharply decreased during the freezing process.
## 3 Proposed Fault Diagnosis Scheme
### Hybrid Deep Recognition Scheme  
The proposed hybrid deep recognition scheme based on DCNN and AdaBoost tree-based ensemble classifier (called hybrid Ada-DCNN) is presented as Figure 3.  
In the diagnosis process, the raw signal is divided into segments for processing. Each segment is primarily processed in the time domain, frequency domain time-frequency domain using corresponding tools introduced previously. Based on the obtained results, various types of 2D samples are generated by image processing. Note that the sizes of different types of sample are independent and determined by the dimension of corresponding features. The rest of the proposed scheme is divided into two steps, namely, (1) underlying feature extraction and (2) multiclass classification.
## 2 Materials and Methods
### Analysis and Utilization of Data
#### 2.4.2 Construction of Spectral Indices  
The spectral index is a measure that mathematically combines the reflectance values from distinct bands [24]. By successfully integrating data from many bands, this approach reduces the impact of environmental background interference on the canopy spectrum. Furthermore, it offers superior sensitivity compared to using a single band. When comparing hyperspectral imaging to traditional multispectral imaging, one advantage of hyperspectral imaging is its ability to capture a larger number of spectral bands due to its narrower band width [25; 26]. This allows for more flexibility in selecting specific spectra within each spectral range. By combining different bands, hyperspectral imaging can uncover new sensitive bands and hyperspectral indices. This work examines the normalized difference spectral index (NDSI), ratio spectral index (RSI), and difference spectral index (DSI) for all possible combinations of two bands within the hyperspectral band range. The analysis includes both the original spectral reflectance and the first-order derivative spectra. The purpose of this study is to use the DSI to investigate the most effective band combinations for detecting nitrogen concentration in winter wheat plants. Additionally, we aim to develop a model for monitoring the nitrogen nutrition index in winter wheat plants using the optimal band combinations. The spectral indices used in the calculation are shown in Table 1.
## 1 Introduction
Then, recent investigations have demonstrated the relation between SK, squared envelope and L2/L1 norm [48, 49], providing new information for characterizing bearing faults, whereas other works have proposed alternatives to FK [50, 51]. Moreover, various preprocessing techniques were proposed to remove the effect of speed variability [22, 52, 53] and to separate the stationary part of the machine vibration signature from the nonstationary part strictly linked to the bearings contribution. Although many works pointed out the problem of damage detection from vibration data, few studies have investigated, from the diagnostic content standpoint, the relation between the measured dynamic response and the forcing terms that act in the dynamic equilibrium of the damaged bearing. In particular, the authors of this work strongly believe that the contact pressure between the rolling elements and the defected race is one of the main sources of excitation. As a result, the vibration signal is supposed to be somehow linked to the contact pressure signal through a transfer function related to the system elasticity. Next, for what concerns the system diagnostic, it is not straightforward to identify a correlation between the damage level and some health indicators. The main aim of this study finds its roots in the conjunction between the aforementioned aspects. On one side, the contact pressure signal is, at first, simulated by means of a non-Hertzian contact model [54] able to describe nonconstant curvatures typical of defected races. Then, the resulting signal is analyzed through the diagnostic techniques discussed before. Therefore, in this paper, a proposal of health indicators is formulated so that two conditions can be satisfied. The first is that they are intimately related to the content of the signal power purely imputable to the damage, thus expecting to be sensitive to the damage level. Secondly, their physical units enable a direct comparison with the vibration data in order to evaluate a correlation between the diagnostic content of the two signals. Given such considerations, the damage detection analysis is conducted on vibration data coming from the Case Western Reserve University (CWRU) Bearing Data Center [55], which has become a benchmark dataset for bearing diagnostics [12; 38; 56; 57; 58; 59; 60]. In this paper, it is proven that the energetic health indicators not only give insight into damage detectability and damage severity estimation, but they also relate the diagnostic contents of two different signals. Further, this seems to corroborate the hypothesis that the two signals are, to some extent, connected and that the energetic indicator is an evaluable reading key of such a link. Nevertheless, this preliminary study needs to be validated by a more refined statistical analysis considering a significant amount of data.
# A Maximum-Entropy Fuzzy Clustering Approach for Cancer Detection When Data Are Uncertain
## 5 Empirical Applications
### Breast Cancer Wisconsin Data
Notice that, in this application, the optimal entropy parameter \(p\) has been fixed to 0.5. From the performance table, we can see that EFC-ID showed better performance than FKM in terms of global performance, Sensibility, Specificity, and Negative Predictive Value. However, the Specificity and the Positive Predictive Value obtained by FKM are better but, unfortunately, also the false negative rate increases. In this case, it seems that FKM unbalances the weights in favor of _centers_ (\(w_{c}=0.97\)), providing a result similar to the simple FCM for point data (i.e., when \(w_{c}=1\)), and showing a high rate of false negative events. Conversely, the entropy regularization of EFC-ID guarantees a better balancing of weights (\(w_{c}=0.65\)). Moreover, we think that the high rate of false negative events obtained by FKM is a serious problem in the cancer detection field. In particular, false negative event tests at diagnosis of early disease and of relapse resulted in diagnostic and therapeutic delays. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**EFC-ID**} & \multicolumn{4}{c}{**FKM**} \\ \hline  & Estimate & Lower * & Upper * & Estimate & Lower * & Upper * \\ Sensitivity & 0.896 & 0.847 & 0.934 & 0.643 & 0.596 & 0.738 \\ Specificity & 0.934 & 0.895 & 0.955 & 0.916 & 0.903 & 0.977 \\ Pos.Pred.Val. & 0.864 & 0.811 & 0.906 & 0.898 & 0.821 & 0.963 \\ Neg.Pred.Val. & 0.937 & 0.906 & 0.960 & 0.666 & 0.614 & 0.743 \\ Youden index & 0.812 & 0.762 & 0.862 & 0.546 & 0.411 & 0.658 \\ Accuracy & 0.909 & 0.882 & 0.931 & 0.845 & 0.800 & 0.877 \\ Error rate & 0.091 & 0.069 & 0.118 & 0.177 & 0.132 & 0.188 \\ \hline \hline \end{tabular}
* 95% exact confidence interval.  
\end{table} 
Table 6: Performance indexes obtained by EFC-ID and FKM for malignant breast cancer diagnosis. Figure 3: ROC curves obtained by the EFC-ID and FKM approaches. (a) ROC curve obtained by EFC-ID approach; (b) ROC curve obtained by FKM approach.
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
###### Abstract  
Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario.  
remote sensing; WSN; UAV; LPWAN; LoRa; marine monitoring; low power electronics +
Footnote †: journal: Sensors
# DFT-Spread Spectrally Overlapped Hybrid OFDM-Digital Filter Multiple Access IMDD PONs
## 3 Upstream Optimum ONU Operating Conditions
### Optimum Clipping Ratio and DAC/ADC Resolution Bits  
In order to numerically explore the feasibility of utilizing the proposed technique to improve the upstream transmission performance of the PON, in this section, numerical simulations are first undertaken to identify the optimal operating conditions for achieving the best possible performance. Figure 3 presents the simulated bit error rate (BER) contours as a function of quantization bit and clipping ratio (CR) for an optical back-to-back (B2B) configuration. In obtaining these figures, the ROP at the OLT is fixed at \(-7\) dBm. Since the two ONUs are independent and have the same signal characteristics, without loss of generality, only channel-2 is plotted in Figure 3.  
It can be seen in Figure 3 that when the CR is low, the overall channel BER performance is high because the signal waveform is significantly clipped. In these figures, for a fixed bit resolution within a dynamic range from 7 to 8 bits, to maintain BERs below the FEC limit (\(1\times 10^{-3}\)), the CR should be \(\geq\)11 dB for the DFT-spread case. On the other hand, for the case without DFT-spread over the same dynamic region, the CR should be \(\geq\)12 dB, which shows an increase in CR by 1 dB. From the same figures, it can also be observed that the case excluding DFT-spread has a CR dynamic range of 12 dB \(\leq\) CR \(\leq\) 14 dB, while the case including DFT-spread achieves a CR dynamic range of 11 dB \(\leq\) CR \(\leq\) 14 dB. Outside these regions, the BER increases with increasing CR because of the rise in the quantization noise effect, and decreasing the CR causes the creation of in-band/out-band noise due to clipping distortion. This result indicates that the proposed PON can reduce the DAC/ADC bit resolution by 1 bit--i.e., from 7 bits to 6 bits--and reduce the CR by \(\geq\)1 dB to achieve BERs within the FEC limit. This behaviour clearly demonstrates that the proposed PON with the application of the DFT-spread technique allows the transceiver to adopt low CRs, without  
Figure 3: BER contour versus quantization bit resolution and clipping ratio for (**a**) excluding the DFT-spread, and (**b**) including the DFT-spread.  
greatly compromising the BER performance. Based on the above analysis, the identified optimal CR values and a DAC/ADC resolution of 7 bits were chosen to enable the ONUs to operate at their optimal conditions. The obtained results for the optimal parameters are presented in Table 1.
# Evaluation of BDS/GPS Multi-Frequency RTK Positioning Performance under Different Baseline Lengths
###### Abstract
At the long baseline RTK positioning level, the difference between the interpolated ionospheric corrections and the real ionospheric delay in the user's data can be too large and may hamper a fast estimation of the integer ambiguities. [PERSON] [7] proposed that a way to improve this performance is to weight the ionospheric corrections in the adjustment of the observations, instead of treating them in a deterministic way over medium distances. [PERSON] et al. [8] developed a new strategy for such long baseline RTK applicable to up to 1000 km baselines. The strategy consists of an EKF-based parameter estimator without generating any linear combination of the measurements. The strategy also includes the integer ambiguity resolution in carrier-phase measurements with the partial fixing feature and tight constraints to once fixed integer ambiguities. [PERSON] et al. [9] analyzed the factors affecting the precision of BDS medium-long baseline RTK; thereafter, 15 baselines ranging from 38 km to 167 km in different troposphere conditions are processed to assess the performance of relative ZTD-constrained RTK. They found that the troposphere parameter is difficult to distinguish from the height component, even with long time filtering for BDS-only RTK. Since B1C/B2a has better signal strength, it is worth further analyzing the positioning performance of B1C/B2a dual-frequency RTK under different baselines compared with other GNSS signals. The evaluation of RTK positioning performance of the new BDS-3 frequencies under different baseline lengths can enhance our understanding of BDS positioning performance and provide a basis for multi-frequency and multi-system combination positioning, thereby improving the positioning performance. Therefore, in this research, we use BDS and GPS data to systematically compare the positioning performance of different systems at different frequencies under different baseline lengths. The rest of this paper is organized as follows: Section 2 introduces the RTK algorithm under different baseline lengths; Section 3 describes the data collection and processing strategies; Section 4 analyzes the performance of multi-GNSS multi-frequency RTK in terms of satellites number, PDOP values, positioning accuracy, ratio values, and the time to first fix (TTFF) of ambiguity. Finally, the results of this research are specifically summarized in the conclusion.
# A Novel Localization Technique Using Luminous Flux
## 2 System Configuration Method
### Working Principle of VLC Channel  
The working principle of a VLC channel system is relatively simple--the data to be transmitted is mapped onto an electrical signal. The transmitters (LEDs) are driven by pulse width modulation orthogonal frequency multiplexing (PWM-OFDM). PWM-OFDM is a suitable candidate for achieving better frame synchronization in leading and trailing edges. On the receiver side, the detector uses a down-converter, through which the photocurrent is converted to measurable energy.  
The channel model for indoor VLC systems is based on  
\[\mathrm{y(t)=Rx(t)\otimes h(t)+\eta(t)} \tag{1}\]  
Here, R denotes the responsivity of the photodetector and \(\eta(t)\) represents the Gaussian modeled noise; \(\otimes\) expresses the convolution operation and x(t) denotes the instantaneous input power, which cannot be negative (x(t) \(>\) 0). The impulse response acquired, h(t), is convolved with x(t).  
The average power received at the photodetector as the photons impinge is given as  
\[\mathrm{yP_{\tau}=H(0)\star P_{t}} \tag{2}\]  
where \(P_{\tau}\) denotes the average transmitted power that is an integral of x(t) within the range [-T,T] given as Equation (3), and \(P_{t}\) is the total source power:  
\[\lim\limits_{T\rightarrow\infty}\left(\frac{1}{2T}\int\limits_{-T}^{T}x(t)dt \right)^{n} \tag{3}\]  
Moreover, the direct current (DC) gain of the channel is defined as:  
\[H(0)=\int\limits_{-T}^{T}h(t)dt \tag{4}\]  
The channel modeling for indoor VLC systems diverges according to the orientations of the transmitter and receiver. Details are provided in the following sections for different models, namely LoS and NLoS channels.
## 4 Conclusions  
The variations in the magneto-optic properties of RF-sputtered thin garnet films of stoichiometry Bi\({}_{2.1}\)Dy\({}_{0.9}\)Fe\({}_{3.9}\)Ga\({}_{1.1}\)O\({}_{12}\) occurring in response to varying deposition process parameters (substrate temperature and rotation rate) have been studied and reported for the first time. In particular, unexpected effects of varying the substrate rotation rate on the MO quality factor have been found, yet have been confirmed across multiple deposition batches. Experiments have demonstrated that the variations in RF sputtering process parameters have affected the garnet films' stoichiometry and MO quality through variations in both the specific Faraday rotation and the optical absorption coefficients. We found that an optimum substrate stage rotation rate existed at near 16 revolutions per minute for our deposition system, leading to consistently obtaining ferrite garnet films of improved MO quality (up to 15.1\({}^{\circ}\) at 532 nm) compared with films grown at slower or faster rotation rates. These MO property variations are indicative of the higher volumetric garnet-phase content achieved at optimum process parameters, as confirmed by the stronger XRD reflections observed. The results obtained reveal that several functional properties of garnet thin films of this type can be tuned and optimized by varying the deposition process parameters, and this can offer a flexible and cost-effective approach for the design and development of new, high-quality, and application-specific MO materials for various photonics and integrated-optics applications.  
This work is supported by Edith Cowan University, Australia.  
[PERSON] designed and performed the experiments; [PERSON], [PERSON] and K.A. discussed and analyzed the data; [PERSON] wrote the manuscript; [PERSON] and K.A. reviewed and improved the manuscript.  
The authors declare no conflicts of interest.
# Concatenated Coding for GNSS Signals in Urban Environments
## 3 Concatenated Coding Scheme for GNSS Messages  
In this section, an RS-LDPC concatenation code is proposed for GNSS messages, and the computational complexity of the proposed code is provided.
## 3 Proposed System
### Sentinel Nodes  
The classification algorithm allows to associate each received signal to one of the possible WF classes that are labeled with the authorized nodes' ID. When a malicious node wants to access the network, supposing it is able to steal the ID to one of the nodes, it sends its message with the associated ID. The sink node classifies the node as stated before and then cross checks the classification result and the claimed ID. Being that the malicious user is classified as one of the authorized users, the spoofing detection fails when the WF class and ID match. Assuming, for example, that the unauthorized user randomly selects one of the possible IDs, this occurs with probability \(1/N\). This means that in dense WSNs (i.e, when \(N\) is large) the probability of selecting the ID of the class resulting from the classification algorithm is very low, but it increases in small networks. For this reason, we propose to use some simple cooperative nodes named _sentinel nodes_ that allow to increase the classification space, thus increasing the detection. Sentinel nodes periodically send a beaconing signal, and thus are classified as an additional authorized source. In this way, the number of WF classes increases, and the previous probability is reduced as \(1/(N+N_{S})\), where \(N_{S}\) is the number of sentinel nodes. Using cooperative nodes is already proposed in the literature, such as, for example in ref. [23], where the additional nodes estimate the RSSI of the authorized communication link and forward this information to the sink node for enhanced detection. Here, cooperative nodes are simpler and do not perform any action. These simply periodically send a beaconing signal. This is more suitable for a large deployment and for low-cost and low-complexity WSNs.
## 5 Inversion Test of an Ideal Model
### One-Dimensional Five-Layer Model Test  
After establishing the layering principle and regularized weights, a geoelectric model in the shape of HKH with five HKH-type layers was created. This model was used to compare the effects of nonlinear inversion using various methods for the initial model design. The parameter settings are as follows: the resistivity of the first to fifth layers is, in order, 100, 5, 100, 20, and 200 \(\Omega\)M, and the layer thickness of the first to fourth layers is 40, 5, 65, and 40 M.  
To assess the influence of different initial model design methods for nonlinear inversion, the initial model sets the range of inversion model parameters for the first three layers only, where \(m_{1}\) is 2\(\sim\)200 \(\Omega\)M, \(m_{2}\) is 3\(\sim\)100 \(\Omega\)M, \(m_{3}\) is 5\(\sim\)400 \(\Omega\)M, \(h_{1}\) is 40\(\sim\)200 M, and \(h_{2}\) is 20\(\sim\)100 M. Figure 9 presents a comparison of the inversion results using the segmented regularization method with different numbers of initialized layers.  
It can be observed that due to the parameter uncertainty of the prior information, directly designing the initial model leads to too few model parameters and low fitting degrees, as shown in Figure 9a. The inversion result is relatively good when the initial model parameters correspond to the actual model parameters, as shown in Figure 9b; however, determining the required initial model parameters when inverting massive actual data is often challenging.  
When considering the use of more layers to avoid the problem of having too few parameters, it partially mitigates the influence of parameter uncertainty; however, due to the excessive number of layers, false anomalies appear to varying degrees, as shown in Figure 9c,d,f, and the robustness and clarity of the inversion model suffer.  
The proposed method of segmental regularization adopts the J\({}_{1}\)-type layer thickness threshold and the regularization factor of \(\lambda_{3}\) (\(2\times 10^{-4}\)). The inversion results are presented in Figure 9e and accurately identify the low-resistivity anomaly near a depth of 50 M and demonstrate excellent performance in delineating the high-low resistivity boundaries near  
Figure 9: Comparison of inversion results of different methods of initialization. Initial number of layers: (**a**) 3 layers, (**b**) 5 layers, (**c**) 10 layers, (**d**) 15 layers, and (**e**) 20 layers. (**f**) Piecewise regularization model.  
depths of 100 M and 150 M. The establishment of regularization constraints in the inner layers significantly improves the smoothness of the model. Moreover, the requirement for prior information in the design of the initial model is relatively low, making it more practical and tolerant of errors. In comparison to the other initialization methods described earlier, this method can more reliably obtain a straightforward electrical interface.